Ahead of Publication / Just Accepted

The current study introduces a novel approach to evaluating the short-circuit (SC) withstand capability of 3-phase, 400/220/33 kV, 500 MVA power transformer through dynamic short-circuit (DSC) tests conducted under actual grid conditions. Departing from conventional lab-based methods, it utilizes a 765 kV grid supply for high-voltage to medium-voltage (HV-MV) windings (i.e., 400kV–220kV) and a 400 kV grid supply for medium-voltage to low-voltage (MV-LV) windings (i.e., 220kV–33kV), replicating operational conditions with greater accuracy. The actual-grid energization technique highlights the feasibility of performing high-fidelity DSC testing without the need for specialized test facilities. The research establishes a detailed correlation between transformer design parameters and short-circuit performance, providing essential insights into mechanical and thermal robustness during fault events. Additionally, it identifies the equipment and setup required for field-based DSC tests across different supply configurations, presenting a viable testing framework for utilities and manufacturers. The results confirm the transformer’s ability to withstand SC stresses across varying dynamic conditions, including those involving auxiliary LV supplies. This grid-based testing methodology enhances the reliability assessment process and sets a new benchmark for realistic, cost-effective validation of transformer short-circuit performance in high-capacity transmission systems.

This paper presents a simplified multi-mode model predictive current control (MPCC) scheme for a novel NPC-OEWIM powered by dual three-phase four-leg inverters with a 2:1 DC-link voltage ratio. Conventional MPCC requires evaluating 91 effective voltage vectors (VVs), resulting in high computational complexity. To address this, a cascaded split-sequence strategy is proposed that projects the 3D VVs onto the αβ plane, selects candidate VVs for the first inverter, refines the candidate set for the second inverter, and finally chooses the optimal VVs based on zero-sequence components. This method reduces predictive iterations from 91 to 23 – a 75 % reduction – and eliminates the requirement for weight coefficients. Additionally, a multi-mode switching strategy is integrated to reduce the switching frequency over a wide speed range: under low-speed conditions, only the low-voltage side inverter is active; during medium-speed operation, the high-voltage side inverter is engaged; and at high speeds, both inverters operate concurrently. Experimental results demonstrate significant computational efficiency and robust performance, making the proposed MPCC be well suited for high-performance industrial applications.

Photovoltaic (PV) module performance is heavily influenced by temperature, requiring precise modelling to optimise rooftop systems that support the UN Sustainable Development Goals. This paper presents a simplified mathematical model for estimating the temperature of solar cells in rooftop installations under dynamic environmental conditions. The model considers primary convective and conductive heat transfer, with the heat transfer coefficient being adaptively adjusted to reflect the interaction between the rear surface of the PV panel and the ambient temperature at different wind speeds. Additionally, the thermal model is used to predict power generation. Validation through real-time experiments on a 100-W PV panel showed a deviation of only 1–2% in temperature predictions and around 4 % and 3 % in power output in unshaded and shaded conditions, respectively. These results confirm the model’s reliability and its practical usefulness for accurate performance evaluation of rooftop PV systems in real-world environments. Furthermore, power loss caused by leakage current in the bypass string under shading conditions was experimentally measured, opening a new path for future research.

To contribute to quality understanding of the multiple factors’ impact on load management, we provided the analysis of the concurrent impact of available multiple impact factors on load, examined correlations between the factors and load, and offered and validated the multifactor load model for the suburban field. The results show the concurrent impact of the factors under consideration on load in suburban field. This study continues to confirm that the application of load modeling using multiple impact factors is justified and reveals the importance of concurrent context and user factors effect already analyzed in urban region.

Due to the rapid development of modern smart grid and Internet of Things technology, the operational and maintenance demands on smart meters, the core metering equipment of the power system, are becoming increasingly significant. Traditional maintenance methods waste resources and are inefficient. Therefore, a new maintenance paradigm combining stable, efficient prediction technology and intelligent, decision-making optimization is urgently needed. This paper proposes a multi-objective predictive maintenance optimization framework based on a long short-term memory (LSTM)-extreme gradient boosting (XGBoost) hybrid model and an improved firefly algorithm (IFA). The framework addresses three core challenges in smart meter operation and maintenance: complex failure modes, limited resources, and conflicting objectives. The main innovations of this study are: 1. A spatiotemporal hybrid prediction model for meter failures is proposed for the first time. This model combines bidirectional LSTMs to capture long-term parameter dependencies, such as voltage and current, and introduces an enhanced XGBoost with a dynamic threshold Huber loss function (DTHL). This reduces error by 25 % in voltage mutation scenarios. 2. A maintenance decision optimization model is designed based on an improved firefly algorithm. The model suggests a maintenance-specific brightness index and a cosine annealing step strategy. The convergence speed is 3.2 times faster than NSGA-II. 3. Support edge-cloud collaborative computing with a prediction response time of less than 200 ms. Using real data from the Gansu Electric Power Company from 2020 to 2024 covering 9,823 devices, the LSTM-XGBoost model performs well in predicting faults. Under budget and manpower constraints, IFA’s cost savings and risk reduction rates are 8.2 % and 9.5 % higher than NSGA-II, respectively, and resource utilization is 89.7 %. SHAP analysis shows that temperature, voltage fluctuations and communication packet loss rate are key fault characteristics.

The objective of this study is to meticulously forecast wind power generation utilizing Wavelet Transform and neural network techniques. Leveraging extensive wind power generation data, we delve into the intricate characteristics of the dataset to formulate a prediction model that seamlessly integrates WRT and neural network principles. The aim is to attain a precise prediction of wind power generation, thereby furnishing invaluable decision-making insights for optimal dispatching and energy management in the realm of wind power generation. This endeavour holds significant implications for enhancing the efficiency and sustainability of wind energy utilization. Firstly, WRT is used to preprocess the wind power generation data. Utilizing wavelet decomposition and reconstruction techniques, we meticulously extract the pertinent information embedded within the data, thereby mitigating the detrimental effects of noise and outliers on prediction outcomes. Subsequently, a neural network is employed as the core prediction model, facilitating the realization of wind power generation prediction through the meticulous training of network parameters. To rigorously assess the predictive prowess of our model, we leverage actual wind power generation data for rigorous testing and benchmark it against other prediction methodologies. This comprehensive evaluation ensures the reliability and accuracy of our approach in forecasting wind power generation. The results show that the wind power generation prediction model based on WRT and neural network has high prediction accuracy and stability. Specifically, the prediction accuracy of the model on the test set is over 90 %, and the prediction error is reduced by over 20 % compared with the traditional prediction methods. This study also further analysed the source of forecasting error, found that the main reason is due to the uncertainty and volatility of wind power data. Therefore, the research proposed targeted improvement scheme, including optimizing WRT parameters, improving neural network structure and so on. The wind power generation forecasting model based on WRT and neural network proposed in this paper has high forecasting performance and practical value, and can provide strong support for optimal scheduling and energy management of wind power generation.

The increasing deployment of photovoltaic (PV) systems necessitates efficient fault detection methods to ensure optimal performance and reliability. The performance of the PV panels can be significantly affected by faults and anomalies. Various machine learning techniques can be used for this purpose, each with its strengths and weaknesses. Developing a proper fault diagnosis strategy to identify PV faults for the smooth and uninterrupted operation of PV panels plays a vital role. This paper presents a comparative assessment of different machine learning techniques for photovoltaic (PV) panel fault diagnosis. This study aims to evaluate and compare the performance of various machine learning algorithms including support vector machine (SVM), k-nearest neighbour (KNN), decision tree (DT), discriminant analysis (DA) and ensemble classifier (EC) in identifying and diagnosing faults in PV panels. Through extensive experimentation and analysis, the effectiveness of these machine learning techniques in fault detection and classification is assessed in terms of accuracy, precision and efficiency. Additionally, the robustness and scalability of the models are examined under different operating conditions and fault scenarios. This study used simulated data to train and test machine learning models and evaluated their performance based on accuracy and precision. The findings of this study provide valuable insights into the applicability and performance of data-driven machine learning techniques for fault identification in PV panels, thereby contributing to the development of more efficient and reliable PV systems for the renewable energy industry.

The most commonly used inverters for renewable energy applications are cascaded H-bridge (CHB) inverters. Fault tolerance is required for semi-conductor switches and DC supplies to safe guard the system under fault conditions for continuous operation. A novel seven-level hybrid CHB inverter with fault tolerance is introduced through the incorporation of a reduced switch seven-level boost (RSSLB) inverter module in conjunction with the H-bridge modules. This inverter configuration guarantees uninterrupted operation even in the presence of faults. Alongside its fault-tolerant features, the RSSLB module within the proposed system facilitates a voltage boost of three and ensures self-balancing of the switched capacitors with its implemented control scheme. Extensive simulation results under various operating scenarios validate the concept of the unique fault-tolerant inverter topology that has been developed. The proposed configuration is validated by the experimental findings by the prototype system developed in the lab.

This article introduces a novel single-phase triple boost inverter based on switched capacitor (SC) technology, designed for grid integration applications. The proposed topology utilizes a single DC source, 13 switches (including 5 bidirectional switches), and 3 capacitors to achieve a voltage gain of three. Notably, this topology can generate 13 output levels, resulting in reduced total harmonic distortion (THD) and improved efficiency compared to existing triple boost topologies. Additionally, the capacitors are self-balanced through the control scheme, eliminating the need for external balancing circuits. The proposed topology is compared with other existing topologies in the literature, and its performance is validated through MATLAB/Simulink simulations using level-shifted pulse width modulation (LSPWM) and experimental results.

This paper presents a novel optimization framework for determining optimal renewable energy and methanation system capacities in remote island power grids while ensuring frequency stability constraints. The proposed method combines hierarchical clustering with mixed-integer linear programming to solve large-scale optimization problems efficiently while maintaining grid frequency within ±0.3 Hz. When applied to Japan’s Tsushima Island power system (peak demand: 36 MW), our approach achieves reductions of over 10 % in operating costs and 15 % in CO 2 emissions compared to conventional diesel-only systems. The key innovation lies in simultaneously addressing frequency stability constraints, renewable energy integration, and methanation system sizing through a comprehensive optimization framework that effectively manages both the variability of renewable sources and decarbonization goals. The optimal facility sizing is determined through the ɛ -constraint method and Pareto optimization to minimize both costs and emissions, thereby establishing a systematic methodology for implementing sustainable energy systems in isolated power grids.

New data centers today demand more scalable and efficient solutions with the rapid advances in deep learning, distributed systems, and high-concurrency data processing. Despite the significant improvements in these areas, existing systems continue to suffer from latency, congestion control, and resource allocation, especially during high workloads and network failures. Two new frameworks for optimizing RDMA-based data flow in remote settings – ConVer and DynoFlow – are introduced in this research. ConVer employs user-space path management and multipath transfer to achieve low latency and high throughput, whereas DynoFlow provides a software-defined, modular approach that efficiently handles network failures and varying traffic loads. Per experimental results, ConVer significantly improves throughput and achieves a latency of 1.3 m s (99th percentile). DynoFlow fares better in coping with network failures and optimizing route utilization. We solve significant problems in modern data centers by demonstrating through extensive experimental evaluation that the frameworks significantly enhance throughput and resilience in distributed systems.

Wireless Power Transfer (WPT) technology has got wide range of applications in which Electric Vehicle (EV) wireless charging is gaining more popularity as there is a provision of dynamic charging in addition to static charging. The main objective of this paper is to design and analyze 5 kW rated Magnetic Resonant Coupling (MRC) wireless power transfer charging circuit for EV. The Series-Series MRC WPT circuit’s design formulae are derived in this paper. The circuit parameters like inductances and capacitances could be determined for any power rating and mutual coupling coefficient between transmitter and receiver coils by using the design formulae derived in this paper. The performance of SS topology based EV charging circuit has been studied analytically at different coupling coefficients by estimating the power transfer efficiency. The analytical design of Series-Series MRC WPT circuit has been verified by comparing the output power and load current with the results obtained from MATLAB and TINA (Toolkit for Interactive Network Analysis) software simulation. An experimental set-up is established for SS topology-based charging circuit rated for output power of 5 W with circuit elements designed by considering coupling coefficient of 0.2 between the coils. The measurements are conducted to verify the constant current characteristics under various load conditions. Finally, the effect on output power and load current of SS topology based EV charging circuit at different coupling coefficients between transmitter and receiver coils is discussed in the paper.

With the advancement of globalization, the rapid development of circulating energy has attracted the attention of all walks of life. Timely monitoring of the accuracy assessment in the Chinese and English transfer environment has an extremely important impact on understanding and promoting the development of circulating energy storage equipment. In the process of Chinese and English transfer, the circulation of energy storage equipment has become more convenient and quickly accepted by users because of its wide application. Based on this background, this study proposed an ICE-LDA model based on the LDA model for developing accuracy assessment in Chinese and English transfer. Through the method of transfer assessment in Chinese and English, the detection of transfer information in Chinese and English and the similarity of accuracy distribution are measured. This study will model the transfer data collected in Chinese and English. In this process, this study will use the TF-IDF algorithm to extract the transfer information in circulating energy storage devices to improve the accuracy of Chinese and English.

With the continuous progress of social civilization, electricity plays an indispensable role in human life. China is a vast country with wide grid coverage and frequent natural disasters, which cause certain losses to the national economy. Therefore, this paper fully considers the role of meteorological information in the emergency decision-making of smart grid disaster prevention, uses the Dijkstra algorithm to clarify the best path, and integrates many factors such as traffic coefficient, road level, road congestion and road quality to establish the optimal path model, which can effectively improve the management level, reduce the smart grid loss by more than 48 %, protect the residents and industrial electricity consumption, and provide safe operation of the smart grid. Powerful guarantee.

Cybersecurity is a critical component influencing the safe and stable operation of the smart grid, as the smart grid (SG) is an information-physical fusion system (also known as a Cyber-Physical System [CPS]) integrating sensing, communication, computation, decision-making, and control built on the foundation of the traditional grid. The most common cyberattack that compromises data integrity in the smart grid is the False Date Injection Attack (FDIA). If this type of attack is not discovered in time, it can take control of physical equipment or target a network transmission line to obstruct control decisions, which can result in power network failure or even a cascade failure in the grid. An integrated learning-based detection method is currently proposed to biclassify power grid data in order to address the issues of low accuracy, high false detection rate, and poor model differentiation ability when applying a single classifier in machine learning to detect false data. The integrated learning detection method is based on GBDT (Gradient Boosting Decision Tree), XGBoost, and Light GBM, with RF- Light GBM and Bagging classifier as the base classifiers, which are integrated by voting strategy after Bayesian tuning. Following simulation experiments, the algorithm is able to significantly outperform the checking rate and accuracy of traditional detection algorithms in detecting false data on the power grid by effectively addressing the issues of low checking rate and accuracy of single classifier detection as well as instability of single classifier detection.

Traditional monitoring systems rely on different types of sensors in the field of new energy grid connection monitoring. The collected data have inconsistent problems; the monitoring method is not real-time enough; the fault detection and anomaly recognition accuracy is low. This paper combines intelligent sensing technology and GNNs (Graph Neural Networks) to design a more efficient, smart, and precise new energy grid connection monitoring information verification method. High-precision intelligent sensors are used to collect multidimensional data of the new energy grid connection system in real-time, and data fusion technology is used to solve the data inconsistency problem between different sensors to ensure efficient and accurate data collection. The graph neural network algorithm framework is used to build the relationship diagram of nodes and edges, and the GNN model is used for information verification and fault detection. The advantages of graph structure are used to accurately obtain the information transmission of each node and improve the accuracy and real-time performance of fault detection. The collected data of the intelligent sensor and the graph neural network model are synergistically optimized to form a closed loop of data processing, model training, and fault prediction. The experimental results show that among the 10 different folds, the GNN model has a lower loss value, with an average loss value of 0.312, which can reduce the error in the information transmission process when processing the monitoring information of the new energy grid connection system; the fault recognition rates of the GNN model in abnormal voltage, current, frequency, and temperature scenarios are 0.92, 0.87, 0.9, and 0.85, respectively, which is suitable for complex fault detection tasks.

The increasing need for renewable energy and storage solutions, driven by the depletion of traditional energy sources, highlights the importance of integrating electric vehicles (EVs) as part of the energy storage system. The failure of distribution transformers during peak EV charging times in Kerala, India, highlights a significant issue related to judicious management of charging and discharging of Electric vehicles during peak time. The paper presents a novel control unit for EVs that supports both electric vehicle modes. The work focuses on designing and analyzing a Dual Active Bridge (DAB) based advanced charging system for G2V and V2G mode operation. A control scheme for managing power flow with bidirectional capability is developed based on the battery’s State-of-Charge (SoC) and the charging time slot during the day. The novelty of the work is the combined Time-of-Use (ToU) and State-of-Charge (SoC) based control algorithm for power flow management. The performance of the controller in both operating modes is thoroughly evaluated and validated, providing a robust foundation for its implementation. The system model is created in MATLAB/Simulink, and the effectiveness of the power flow control algorithm is tested through hardware-in-the-loop (HIL) experiments using OPAL-RT and TMS320F28379D microcontrollers. The proposed SoC and ToU-based power management strategy is economically efficient because it capitalizes on the pricing difference between off-peak and peak periods. With high charging/discharging efficiency, the combination of SoC and ToU-based tariffs proves profitable and sustainable, offering significant cost savings and financial returns in the long run. The intelligent power flow control unit will offer a practical solution to the peak demand challenges in utility due to uncoordinated EV charging.

Partial discharge (PD) describes localized electrical discharges that occur within insulating materials. Owing to its exceptional electrical and thermal properties, epoxy resin is widely utilized as an insulation medium in electrical systems. Insulation classes F and H are categorized based on their thermal endurance ratings, with insulation degradation remaining a primary cause of power equipment failures. PD-based diagnostic tools enable precise condition monitoring of insulation systems, critical for reliability. Voids inherent in solid dielectric materials often initiate insulation breakdown, particularly when PD activity originates within enclosed cavities. This paper investigates the relationship between void dimensions and insulation lifespan in dry-type transformers, focusing on PD-induced degradation within internal voids. Class-F and Class-H epoxy resin samples, incorporating artificial circular voids of 2 mm, 3.5 mm, and 4 mm diameters, were analyzed in collaboration with ESSENNAR R&D Center using IEC60270-compliant testing protocols. Key findings reveal that increased void size correlates with reduced partial discharge inception voltage (PDIV) and heightened discharge magnitudes and repetition rates. While observed discharges remained below the IEC60270 threshold of 140 pC, PDIV values approached the critical 10 pC limit in larger voids, underscoring the impact of void geometry on insulation performance. These insights emphasize the need for stringent quality control in epoxy resin applications to mitigate PD risks.

The incorporation of Hybrid Energy Storage Systems (HESS) into alternating current (AC) electrified railway systems has attracted considerable attention. Nevertheless, only a little focus has been placed on optimizing the dimensions and daily delivery of HESS throughout the entire project duration. This research introduces an innovative bi-level method for Railway Traction Substation Energy Management (RTSEM). At the master level, HESS dimensions are refined, whereas the slave level emphasizes daily allocation. The slave model utilizes Mixed-Integer Linear Programming (MILP) to control energy distribution, synchronizing HESS, renewable sources, and regenerative braking. An extensive cost evaluation takes into account battery deterioration and replacement expenses throughout the project duration. Using an integrated CPLEX solver, Ray optimisation is utilized to address the RTSEM challenge. The model is evaluated on an actual high-speed railway line scenario in China. Findings indicate that combining HESS with renewable energy yields considerable cost savings, as the suggested Ray Optimization-based model achieves a lowest fitness value of 0.016, in contrast to the highest fitness values of 0.033, 0.005, and 0.002 realized by BSO, GA, and GOA models, respectively.

Sub-synchronous oscillations (SSO) pose significant challenges to the stability and reliability of modern power systems, especially in grids with high penetration of renewable energy sources such as wind and solar power. This paper provides a comprehensive review of SSO phenomena, including their evolution, mechanisms, and impacts on power system stability. A detailed classification of SSO is presented, covering grid-level, device-level, and control strategy-dependent oscillations, along with renewable-energy-specific interactions. Notably, it is found that GFL inverter-based sources are highly prone to SSO due to their PLL-based synchronization, whereas GFM inverters are generally more stable, offering improved damping and inertia-like responses. This study highlights recent SSO incidents, such as the 2009 Texas PV case and the 2019 Hornsea wind event, to underscore practical implications. Additionally, a taxonomy of SSO types and inverter technologies is provided. For instance, GFL systems in weak grids with SCR < 3 showed higher oscillation risks. The insights gained from this review suggest that robust control strategies and coordination between GFL and GFM units are essential. These findings pave the way for enhanced grid resilience and provide numerical indicators to guide future renewable deployment in weak grid conditions. While GFM inverters are generally more stable than GFL inverters, it is important to note that they are not entirely immune to SSO. Under certain conditions – such as improper tuning of droop or virtual inertia parameters, lack of coordination with GFL inverters, or interaction with mechanical systems, GFM inverters themselves can become a source of SSOs. Therefore, robust and well-coordinated control design is essential even for GFM-based systems to prevent unintended dynamic instabilities.

In response to rising expectations for more efficient and better power generation, the switched-capacitor multilevel inverter (SCMLI) was developed. Most recently, SCMLIs were created to allow for step-up ac output via inversion and single-stage voltage boosting. This study presents a boost type single source nine-level (9-level) SCMLI that uses two capacitors and one diode to reduce the number of components. The capacitor’s voltage boost from a single source is achieved through the series-parallel connecting procedure. No more than twice the input voltage can be applied to the semiconductor devices under any given condition. To raise the voltage levels without adding more dc input, the suggested SCMLI can be expanded with a minimal number of components. After a thorough evaluation of the power losses and circuit performance, the design is confirmed to be superior by comparing it to newly designed single-phase 9-level MLIs. To confirm the key characteristics of the 9-level SCMLI in dynamic operational settings, we offer comprehensive simulation and experimental findings.

Substations are one of the key elements in electrical power system network. Efficient grounding in substation design forms the base for ensuring the safety of the person and equipments. Depending on the geometrical aspect of the substation, five different grid shapes are applicable. In this paper all 5-grid shape analysis is carried out to have comparison on performance analysis of the grid designing process. Also, in grid design calculations a concept of three different lengths is introduced which makes the grid designing simple and error free. Also, all the calculations are carried out owing to five different grid designing methods. Various softwares are used to make grid designing effective and lesser time consuming. Mathematically calculated results incorporating the length updation with grid shape applications are verified by ESGSD software. The methodology developed in this paper is applicable to any shape of the grid and ensures the safety and reliability of the grounding grid.

Power metering system electricity theft may jeopardize the security and stability of the power system in addition to reducing the financial gains of the power grid. This research suggests a power theft detection model based on the Pearson correlation coefficient and support vector machine (SVM) to increase the detection accuracy. The relationship between the user’s daily electricity consumption and the line loss of electricity of the day in the station region is first examined using the Pearson correlation coefficient to determine whether a user is committing power theft. The SMOTE technique is used to oversample a few classes of samples to address the issue of the limited amount of data on electricity theft users. This prevents sample imbalance from having an impact on the detection model. The SVM algorithm is then used to build the electricity theft detection model, and the Gaussian kernel function is used to translate the linearly indivisible power consumption data to the high-dimensional space to guarantee the dataset’s linear divisibility. With a classification accuracy of 93.8 % and a positive sample classification accuracy of 95.4 % for the detection of electricity theft users, the experimental portion uses the State Grid of China’s actual electricity consumption dataset. Following model training and cross-validation, the results demonstrate that the model can successfully identify electricity theft, demonstrating the model’s superiority and applicability in this area.

The increasing penetration of distributed energy resources (DERs) into Active Distribution Networks (ADNs) demands innovative control solutions to address critical challenges such as voltage stability and power quality. Existing decentralized control strategies often fail to handle the intricate interactions between control loops in highly coupled systems, leading to suboptimal performance, excessive reactive power requirements, and operational limitations. This paper introduces a novel multivariable control framework designed for voltage regulation in ADNs. The proposed framework incorporates advanced Control Allocation and Measurement Combination modules, developed through a systematic methodology that merges Process Systems Engineering and Power Systems principles. By formulating the control design as a bilevel mixed-integer nonlinear programming (BMINLP) optimization problem, the framework minimizes voltage deviations, reactive power usage, and the reliance on actuators and sensors, ensuring system stability even under extreme conditions such as significant active power generation drops. Dynamic simulations performed on the IEEE 33-bus system demonstrate that the proposed approach outperforms traditional decentralized controllers by effectively preventing reactive power saturation and delivering superior voltage regulation. The results indicate that the voltage at all nodes stays within the permissible limits, with a maximum deviation of 0.7 %. Moreover, it achieves these results with reduced hardware and fewer control loops, highlighting its resource efficiency. This work represents a significant step forward in the control of ADNs with high DER penetration. By addressing the limitations of conventional strategies, it offers a scalable and robust solution that optimizes control resources, enhances system stability, and supports the transition to decentralized, renewable-powered distribution networks.

A transformer is one of the most important high voltage (HV) power apparatus in any electrical system. In transformers, liquid insulation, primarily mineral oil, is used in bulk. It acts as an insulation and cooling medium in the transformer. Therefore, it plays an important role in the safe and reliable operation of the transformer. It is understood from several studies that because of the shortage of petroleum oil and its non-environment-friendly nature, scientists, engineers, and researchers are concentrating their studies on synthetic oil, natural ester oil, so that they can replace mineral oil successfully. Studies related to the characterization of different types of natural ester oil are made for their superiority over mineral oil. However, very few studies have been conducted on different combinations of mineral oil (MO) with natural ester oil, such as coconut oil (CO) and rice bran oil (RO), as an alternative insulation. In this paper, a study has been made on different combinations of MO with CO and RO for their performance characteristic studies like breakdown voltage (BDV), flash point, DC resistivity, and tan delta on the lines of International Electrotechnical Commission (IEC) and American Society for Testing Materials (ASTM) standards. It is observed from the studies that different combinations of MO with CO and RO enhanced its BDV, which is the prime electrical property for studying the dielectric strength of any insulation. However, blended RO with MO has balanced properties in terms of dielectric strength, flash point, and DC resistivity compared to all other virgin oil and blended oils. It is also less toxic compared to the mineral oil. Therefore, blended rice bran oil with mineral oil may be considered an alternative to traditional mineral oil as it is cost-effective and less toxic. Therefore, it may be an efficient alternative to insulating oil for the transformer industries.

Energy Engineering is a well-liked branch of engineering, wherein the graduates in this discipline are estimated for their English skills under several cases. The capability of listening broadly enhances the effectiveness of English listening teaching; thus, this makes college English listening lessons dynamic, engaging, information-rich, and facilitates an immersive learning experience for students. This is to cultivate students’ attention in learning, form learning inspiration, and finally, attain the objective of enhancing their English skills. This work explores the findings from experimental work associated with establishing an educational setting specifically designed for the foreign language instruction of upcoming energy engineering students. The aim of this work is to a) design a series of short interactive videos or reusable learning objects (RLOs) covering an extensive area of psychosocial and practical problems appropriate to the listening capability of Energy Engineering students; b) establish the ease of access, suitability, take-up, and adherence of the RLOs; and c) evaluate the advantages and cost-effectiveness of the RLOs. Ultimately, the experimentation analysis is performed for the student’s average score for basic level, middle level, and higher level students from five universities. The analysis states that regarding university 5, the basic level females attained the highest score of 92.9, while the middle-level female attained the average score of 86.27. Meanwhile, for higher level, both attained average score of 68.89. From the analysis, it is clear that the basic level student’s average score is better than the middle level and higher level.

A resilient distributed secondary control strategy is proposed for DC microgrids (MGs) with denial-of-service (DoS) attacks on communication channels to achieve accurate current sharing and voltage regulation. According to the designed resilient control strategy, when there is no DoS attacks, the quantization information is sent to neighboring distributed generators (DGs) only when the triggering condition is satisfied. When there is a DoS attack, the communication can not be carried out normally. Then, each DG would attempt to send messages to its neighbors at a preset period until the attack disappears. The maximum allowable communication attempt period is determined by theoretical analysis, which is one of sufficient conditions to ensure current sharing and voltage regulation. As a result, the control signals are updated by discrete-time quantized information, which greatly reduces the communication burden, and effectively defends against DoS attacks. The asymptotic stability of the system is analyzed to show the results that voltage regulation and accurate current sharing are achieved. The proposed control strategy is verified through simulations.

Accurate and timely wind power forecast is difficult because of the volatility and intermittency of wind energy, which are strongly influenced by meteorological conditions. In order to solve this problem, this research suggests a data-driven wind power prediction technique based on Elman neural networks and an enhanced generative adversarial network (GAN). First, outliers in the supervisory control and data acquisition (SCADA) power and wind speed data are found by combining the methods of density clustering and regression threshold truncation. To guarantee the temporal continuity of wind power data with the original characteristics, minority class samples are then generated using an enhanced GAN and added to the dataset. In order to obtain an accurate wind power prediction, the balanced data is then fed into a prediction model that combines the ABC algorithm and Elman neural networks. The fitness value is the mean squared error (MSE) of the training data, and the optimization objective is the connection weights of the Elman neural network. Results from experiments show that the suggested model outperforms earlier state-of-the-art techniques and significantly increases wind power prediction accuracy. It also has the advantages of high stability and quick convergence speed, and it can capture the long-term dependencies of wind power data.

In recent days, solar energy has achieved tremendous growth and technological developments that result in reduced cost requirements and increased power quality. Marketing such renewable products, branding and promotion is important because the customers are attracted through these branding names. Vocabulary selection of energy products for branding and promotion empowers the attention of customers and hence the selection of brand names is considered as the initial step in the marketing field. The visual attention of the data is extracted by the eye tracker. So, in this research work, vocabulary selection methods for solar panel branding and promotion in the Chinese language are analyzed. The awareness programs conducted by the Chinese government for the branding and promotion of solar panels are discussed. In addition, vocabulary selection methods with their requirements for branding are analyzed. Eye tracking-based vocabulary selection method is briefly discussed and the measures used for branding and promoting solar panels are also elucidated. After, vocabulary building is accomplished in the second stage through eye tracking-based vocabulary selection. The performance analysis is carried out in terms of correlation, Jaccard, cosine similarity and word error rate. The correlation between the eye tracking-based vocabulary selectionsis the same as that of the user feedback-based vocabulary selection. The selected vocabulary learning method could accomplish user satisfaction and perception in terms of various performance measures.

Power Quality issues are the major concern for the most of the industry and home appliances. The power quality (PQ) issues such as fast variation in voltages, under voltage, over voltage, harmonics, unbalance and flickering causes huge financial loss to the industries. Dynamic Voltage Restorer (DVR) is one of the solutions to maintain voltage quality on the point of connection of the load. A 10 kVA Dynamic Voltage Restorer (DVR) using Modified Synchronous Reference Frame-Phase Locked Loop (SRF-PLL) and Proportional Resonant (PR) controller were developed. The developed DVR employs three single-phase series voltage controllers to compensate for voltage sags in each individual phase and a three-phase shunt controller to maintain the DC bus voltage. The modified SRF-PLL has been used to extract the load references which are in phase with the positive sequence of the grid voltage. The drawbacks of the conventional Proportional Integral (PI) controller such as yielding steady state error has been addressed by the PR controller which can effectively track varying references by the virtue of high gain at the operating point. The performance of the developed DVR has been tested using MATLAB simulations and also validated with the experiments on hardware prototype with the programmable load to create the dynamics such as under voltage, over voltage, and unbalance in the load. Further, on field trials and performance evaluation of the developed DVR proto unit is carried out in a spinning mill unit of a textile industry in South India. This exercise was carried out to propose a solution to the degraded performance of 10 kW active front end rectifier of the motor drive in the spinning mill, due to poor power quality. The developed DVR proto-unit could maintain regulated AC voltage with good waveform quality during different grid side disturbances. The performance of proposed DVR shows that it’s implementation can address multiple PQ issues in industries.

Fault detection becomes especially crucial for maintaining the dependability and safety of power equipment as power systems become more complicated. This work aims to increase the accuracy and efficiency of fault signal identification in power equipment by proposing a method based on nonlinear stochastic resonance and a Duffing chaotic oscillator to detect weak signals in power systems. First, this study develops a weak signal detection model based on the Duffing chaotic oscillator approach. By modifying the system parameters to put the signal in a critical condition, the model greatly increases the sensitivity of the signal to the tiny periodic sinusoidal waveforms. In the meantime, the stochastic resonance method’s high resistance to noise interference improves the dependability of signal identification. The experimental results demonstrate that the approach is more accurate and stable than the conventional method and can extract fault signal features when working with weak signals in high noise conditions. Ultimately, this paper’s research offers a fresh, efficient method for identifying power system faults.

In the construction, operation and maintenance of modern power grids, the monitoring, acceptance testing, and operation and maintenance of distribution networks have always been key links. With the rapid development of technologies such as smart grids, the Internet of Things and artificial intelligence (AI), how to enhance the efficiency of distribution network acceptance testing and operation and maintenance management has become an urgent issue to be addressed. However, traditional distribution network acceptance testing and operation and maintenance management rely on manual operations, which are inefficient, error-prone, and lack data integration and intelligent support. In response to the above problems, this paper studies and designs an automatic verification platform for distribution network monitoring information based on AI. The platform first collects and integrates distribution network information, performs preliminary data processing, and then realizes automatic verification and anomaly detection of the monitored platform data through the long short-term memory (LSTM) network model. In addition, the particle swarm optimization (PSO) algorithm is utilized to optimize the task scheduling of the platform, and the greedy algorithm is used to optimize the platform’s load balancing, aiming to enhance the platform’s processing efficiency, response speed, and resource allocation capability. After acceptance testing, the outcomes demonstrate that the platform designed in this paper has good verification consistency for the distribution network, and the verification consistency of various major equipment remains above 90 %. It can detect abnormalities and give feedback for different monitoring points in different distribution network areas. The traditional operation and maintenance solution is compared with the platform designed in this paper regarding operation and maintenance management efficiency. The results show that in the seven key indicators of operation and maintenance management efficiency, the platform designed in this paper has shown obvious advantages, providing a practical solution for the intelligent management of the distribution network and contributing to the development of automation, intelligence, and efficiency of power grid operation and maintenance.

This paper explores electronic product design with a focus on embedded systems, highlighting the strategic approaches used by design engineers to balance innovation, efficiency, and competitiveness. It examines design practices across firms and introduces a framework that portrays design engineers as “administrative men,” adept at navigating project complexities by choosing among four distinct design paths: “Repeat Order, Variant Design, Innovative Design, and Strategic Design.” The “Repeat Order” path focuses on reproducing existing designs with minimal changes, while “Variant Design” adapts designs to specific requirements. “Innovative Design” aims for novel solutions, and “Strategic Design” integrates long-term innovation with business strategies. Each path reflects unique goals, resource demands, and methodologies. To support the selection of the most suitable design path, this study introduces a novel metric and a structured methodology for evaluating design yield and cost. This approach includes analyzing complexity, estimating yield, and calculating total costs, culminating in an objective function designed to enhance design efficiency and accuracy. By offering actionable insights and a comprehensive decision-making framework, the paper aims to optimize the embedded design process, addressing the evolving challenges and opportunities in electronic product development.

Transportation is vital for modern societies, facilitating daily commutes and the movement of goods, but it is also a major contributor to pollution, second only to industrial emissions. While electric vehicles (EVs) offer a greener alternative, their charging demands-affected by factors such as vehicle type, location, battery state of charge (SoC), and charging rate (C-Rate) can place additional strain on power distribution networks and introduce non-linear loads, impacting network stability. This study investigates the effects of EV charging on the distribution network, focusing on changes in voltage, current, and total harmonic distortion (THD) at the point of common coupling (PCC). Using MATLAB environment, a 10 kVA single-phase distribution network was designed with various realistic loads, and EVs were modeled as rectifier-converter systems to assess their impact. The integration of EVs into the network is considered by taking variation with respect to changes in its location, battery SoCs and C-Rate at the time of charging. The research also examines the real-time impact of two-wheeler EVs, such as Bajaj Chetak, OLA S1-Pro, Okinawa, and Tunwal Lithino Li, with a focus on harmonic distortion injected in the grid current. The harmonic distortion events were captured via HIOKI PW-3900 Power Quality Meter (PQM). The findings provide useful insights for future decision-making regarding EV integration into power distribution networks.

In transmission and distribution networks, transformers are subjected to numerous system faults over their operational life span, so testing of transformers is crucial. To ensure a robust design and optimal performance, comprehensive testing is required as per the international standard. The IEC60076-5 Standard specifies that a transformer’s short-circuit (SC) withstand capability can be validated through calculation, design analysis, or testing in accredited laboratories. This study takes an experimental approach to assess the short-circuit withstand capability of a 160 MVA, 230/110 kV, 3-phase transformer using a 400 kV grid supply as per the IEC standard. The focus is on the general requirements for testing the 230 kV transformer, and the equipment used to identify the most effective method for conducting dynamic short-circuit (DSC) tests under real grid conditions with the available 400 kV grid source. Additionally, the study verifies the test results, assessing the transformer’s ability to withstand short-circuit events. Results show that the high-capacity autotransformer successfully endured high-voltage to medium-voltage (HV-MV) short-circuit conditions, demonstrating its resilience against short circuits under various dynamic scenarios.

As the global energy transition accelerates, eco-friendly power equipment technology is expected to play a more significant role in emerging power systems. In eco-friendly gas-insulated switchgear (GIS), the interruption of high currents induces electrical wear on internal contacts, leading to surface erosion, oxidation, and the formation of microscopic asperities. These alterations contribute to localized heat accumulation, thereby degrading the thermal performance of the equipment. To address this, a novel temperature rise model is introduced for eco-friendly GIS insulated with dry air, integrating Baharami electrical contact theory with multi-field coupling finite element simulations. This model dynamically reflects the evolving morphological changes in contact surfaces under varying degrees of electrical wear. A detailed analysis of equipment under different operational conditions, with adjustments to parameters such as contact roughness and asperity distribution, simulates the real-time deterioration process of contacts due to electrical wear. Experimental validations indicate that the temperature simulation errors of the model remain within 5 % across most key points, with the model demonstrating favorable solving speed and convergence. Compared to the results obtained using the traditional model, the maximum temperature rise error decreased from 11.3 % to 7.5 %, representing a reduction of 33.6 %. The average temperature error at each measurement point also showed a significant decrease, from 3.48 K to 2.41 K, corresponding to a reduction of 30.7 %. Under severe electrical wear, temperature rises can exceed standard limits, with an accelerated rate of increase as wear progresses, posing risks to equipment reliability. Adjustments to the parameters of the moving and stationary contacts effectively reduce the maximum temperature rise across the three-phase busbar, mitigating localized temperature anomalies and enhancing the temperature rise characteristic of eco-friendly GIS. These findings offer insights for the design and maintenance of eco-friendly GIS, promoting reliable operation and advancing sustainable power systems.

Translation is the process of converting a text sequence from one language to other. Translation systems are often used to translate between various language texts, but they may also be used for speech or a mix of the two, such as text-to-speech or speech-to-text. Statistical machine translation (SMT) employs large target language models (LMs) to increase the fluency of produced texts, and it is often considered that “more data is better data” when developing language models. This work focuses on translating the Renewable Energy (RE) terms in English and Chinese languages. The developed work mainly focuses on two models namely; Translation language model and Context adaptability model. Initially, a model is developed that is capable of translating renewable energy terms from English to Chinese. Subsequently, context adaptability model is built, where, two processes are carried out. The two processes include, (a) building a word corpus related to RE and (b) building dictionaries related to RE. The dictionaries are built by generating sentiment lexicons using Pointwise Mutual Information (PMI) model. Finally, the language is trained using improved BERT model.

A vehicle that runs on electricity rather than conventional fossil fuels is known as an Electric Vehicle (EV). They have advantages for the environment making them more pollution-free and helping to reduce the cost. However, there are still certain obstacles to overcome, such as cost and a poor network for charging. Utilizing Hadoop helps maximize the effectiveness of charging, cut expenses, and enhance the EV experience in general. So, this paper presents the performance scoring model for new energy vehicles based on Hadoop for improving the charging efficiency of PHEVs and reducing fuel costs. By leveraging the power of Hadoop and implementing optimization algorithms, this model can determine the best charging hours for PHEVs, leading to more efficient and cost-effective charging. By utilizing Hadoop’s distributed computing framework, the model can process and analyze the data in parallel, enabling faster and more accurate. The developed Hadoop model can consider various parameters to determine the best charging hours for PHEVs. Moreover, the Enhanced Bird Swarm Optimization Algorithm (EBSO) integrated with the Hadoop model to minimize the operation cost of the Battery Swapping Stations (BSS) while EV charging. This can provide insights into the effect of PHEV fuel costs. By analyzing data on fuel consumption, electricity prices, and charging patterns; it can calculate the cost savings achieved by optimizing charging schedules. This information could be valuable for PHEV owners, helping them make informed decisions about when and how to charge their vehicles to minimize the operation cost of the BSS. With further advancements in technology, the developed model has the potential to significantly contribute to the widespread adoption of PHEVs and the transition to a more sustainable transportation system.

Electricity theft increases the line loss rate of the power grid, causing a greater burden to the power grid company. At the same time, in terms of social impact, electricity theft disrupts the normal order of electricity consumption and has a bad effect on society and electricity-using enterprises. Most of the methods of stealing electricity are by affecting the metering method of the electric energy meter or privately connecting the wires to make the electric energy less metered or not metered, which not only destroys the power metering device but also seriously affects the stable operation of the power grid. The main purpose of this paper is to research the design of an intelligent anti-stealing monitoring device based on ARM. In this paper, the overall scheme of the anti-stealing device involved is designed through the analysis of the characteristics of electricity stealing. At the same time, the main components of the system and the work to be completed by each part of the module are pointed out. Finally, the software flow of the system is designed, and the whole system is tested and analyzed. Experiments show that the on-site verification of each abnormal power meter is consistent with the software display results, indicating that the anti-power stealing monitoring device has a high accuracy rate in power stealing monitoring, has a good on-site power stealing monitoring effect, and accurately monitors abnormal tables and abnormal conditions at the same time, which can reduce the line loss rate, which proves the feasibility of the device.

This paper aims to solve the problem of insufficient accuracy of abnormal behaviour recognition in smart grid monitoring. By applying the CNN (Convolutional Neural Network)- BiLSTM (Bidirectional Long Short-Term Memory) model, combined with spatial feature extraction and time series analysis, the detection ability of complex abnormal patterns is improved. The safety and stability of power grid operation are guaranteed, and efficient management and accurate fault diagnosis of smart grid are realized. Multidimensional time series data, including voltage, current, power factor, and other information, are obtained from sensors and equipment of smart grids, and data cleaning, abnormal annotation, and standardization are performed. The sliding window method is used to divide the time series data to adapt to the input of the deep learning model. The model structure includes a CNN layer for extracting local features, a BiLSTM layer for capturing time series dependencies, and finally a fully connected layer for abnormal behaviour classification. In the test set, the average accuracy of epochs within 30–50 times reaches 97.6 %. Experimental findings demonstrate that the accuracy, precision, and recall of the CNN-BiLSTM model on the test set are better than those of the traditional CNN-LSTM, BiLSTM, and Transformer models. The CNN-BiLSTM model can effectively enhance the accuracy of abnormal behaviour detection in smart grids and provide a reliable solution for the safety monitoring of power grids.

Stator Slot Size Variation (SSSV) in the induction motor occurs due to magnetic stress,a force caused by stretching the magnetic flux lines across the surface of a conducting material. This is a serious issue that affects the performance of induction motors. The amount of magnetic stress produced is proportional to the average SSSV measured in the stator. Induction motor faults such as stator winding faults, rotor winding faults, voltage unbalance, phase reversal and overload are the causes of high magnetic stress. Variation in the stator slot causes greater leakage flux, which subsequently decreases the performance, and the harmonic level is increased in the induction motor. In this proposed work, multimodal sensors are used to acquire the flux, vibration, temperature and current from the induction motor. The multimodal sensor signals obtained from the induction motor is analysed using Hilbert-Huang Transform (HHT) to calculate Energy Band Values (EBV).The Microscopic Camera Images (MCI) values and the calculated EBV are used to predict the SSSV using Decision Tree Regression (DTR) algorithm. According to experimental findings, a stator slot that deviates more than 0.1 % from its typical size causes a large magnetic stress. Early prediction of high magnetic stress and faults in an induction motor may lead to avoid unnecessary motor faults by measuring SSSV. The efficiency of the proposed method for predicting SSSV in induction is about 95.8 %.

This paper introduces a novel integration of sliding mode control and digital twin technology to develop a sensorless control model for hub motors. The proposed approach overcomes the limitations of traditional methods, such as the inability to pre-evaluate parameter changes and the lack of multidimensional monitoring and interaction analysis. The constructed model enables real-time simulation that closely reflects actual motor behavior, allowing precise tuning and validation of control parameters. Simulation results show that the system achieves rapid response, robust performance under varying load and speed conditions, and superior adaptability compared to existing methods. These advancements highlight the potential of this approach for precise and robust motor control in robotics and autonomous vehicles.

Line-starting permanent magnet synchronous motor (LSPMSM) has the advantage of induction motor and permanent magnet motor. The double-slotted stator-rotor structure can lead to the complexity of the LSPMSM air-gap magnetic field. This further leads to the variation of tangential electromagnetic force (TEMF) and cogging torque of the LSPMSM, which affects the vibration characteristics of the motor. In this paper, the effect of stator-rotor slot combinations on the TEMF of High-voltage line-starting permanent magnet synchronous motor (HVLSPMSM) is analyzed by using a 10 kV, 630 kW HVLSPMSM as an example. Firstly, a mathematical model of the air-gap magnetic field considering double slots is established by using the conformal transformation, and the harmonic distribution characteristics of the radial and tangential air-gap magnetic flux density under different stator-rotor slot combinations are obtained. Then, the expression of the TEMF density is constructed based on the Maxwell tensor method, and the conversion relationship between the TEMF density and the local tangential force is analyzed. Further, the influence of cogging torque amplitude and frequency on different stator-rotor slot combinations is derived by analyzing the TEMF density. Finally, the electromagnetic-mechanical coupling model of the HVLSPMSM is established, and the influence of TEMF on the vibration spectrum of the motor under different stator-rotor slot combinations is resolved by numerical calculations. This paper provides a reliable theoretical guide to improve the performance of HVLSPMS.

This project explores the integration of battery energy storage systems (BESS) in residential settings to optimize energy management with a novel focus on standalone BESS configurations independent of solar photovoltaic (PV) systems. The objective is to analyze electricity price patterns, evaluate different BESS configurations, and develop strategies for efficient charging and discharging. This enables homeowners to store excess electricity during low-demand periods and discharge it during peak demand, reducing grid reliance and saving on electricity costs. The analysis reveals that a standalone BESS can reduce electricity costs by leveraging low-demand periods with electricity prices averaging 0.4 DKK/kWh, compared to peak prices exceeding 0.8 DKK/kWh. For June 2023, optimal charging (6:00–14:00, 18:00–22:00) and discharging (14:00–18:00, 23:00–6:00) periods were identified, leading to potential cost savings of up to 30 %. The findings highlight the potential of BESS to transform energy management and suggestions such as a net metering program are proposed to mitigate these challenges.

Distribution system networks (DSN) are presently gaining more concern in terms of security and stability due to the penetration of distributed energy resources (DERs) and the integration of microgrids. The active DSNs may have their operating point near the maximum power transfer capability due to the optimal asset utilization, which jeopardizes the stability of the network. The article presents a novel approach for optimizing the placement of solar PV, D-STATCOMs and energy storage in distribution networks. The method minimizes the L-Index for voltage stability, reduces voltage deviations, and lowers annual operating costs, ensuring enhanced performance and cost-efficiency for modern power systems. The article presents a novel approach of progressive L-Index to determine the suitable sites for optimal location of the DERs using Particle Swarm Optimization (PSO). It also utilizes the mixed-integer nonlinear programming using GAMS to compute the appropriate size of DERs, D-STATCOM and the energy storage. The studies have been performed on the standard IEEE-69 bus test DSN. The results exhibit that the proposed scheme enhances the overall voltage profile of the DSN, and the VSM is upgraded by 41.60 % with DER installation, as compared without DG.

Evaporative cooling technique is considered one of the effective methods for improving efficiency and power generation of a photovoltaic (PV) module by reducing the operating temperature of its surface. In this paper a theoretical study of heat transfer through a PV module was conducted to investigate how the calculated cell temperature and module efficiency are influenced by the ambient temperature, solar irradiation, and water flow rate, which affect the heating and cooling rates of the module surface. Experimental investigation was done to confirm the theoretical findings concerning the decrease of cell temperature and hence the increase of module efficiency with the increase of either the air flow on module cooling by using sprinkler for water misting or the mass flow of water on module cooling by using nozzles for making a water film over the module surface. The experimental results show a reduction of 26.94 % in cell temperature on using sprinkler against 28.32 % for nozzles with continuous cooling and 24.14 % using sprinkler against 26.75 % for nozzles with intermittent cooling. Experimental results show that evaporative cooling on using sprinkler and nozzles methods increase the electrical efficiency from 13.04 % without cooling to 14.5 % and 14.75 with continuous cooling against increase of the electrical efficiency to 14.29 and 14.7 with intermittent cooling. The maximum electrical efficiency in the datasheet at standard condition records 15.4 %. This means that the evaporative cooling over the PV module strongly improves the system performance to approach its efficiency at standard test condition STC. There is no significant difference between continuous and intermittent cooling in reducing the cell temperature and thus increasing efficiency. Moreover, intermittent cooling reduces the amount of water used for cooling.

The integration of shunt bypass diodes in photovoltaic (P-V) module to mitigate hot spots frequently leads to the emergence of multiple in the PV array characteristics. Researchers consistently strive to develop, integrate, and refine innovative techniques inspired by various natural processes to achieve a global optimum that enhances the overall efficiency of PV systems. However, these techniques face challenges in adapting parameters to strike a delicate balance between exploration and exploitation, which is essential for circumventing local optima, reducing computation times, and refining precision to optimize energy capture. In this context, this paper introduces a groundbreaking new adaptive Maximum Power Point Tracking (MPPT) controller inspired by the social behavior and reproductive tactics observed in bonobos (BO). This innovative approach is underpinned by two key strategies: fission and fusion, with dynamic parameter adjustment in real-time. this enables for efficient exploration and exploitation of the search space, following the positive and negative phases of the BO. This method was compared with three methods: PSO, DE, and ICS, and evaluated through six simulation scenarios, ranging from 1 to 6 peaks, as well as three experimental scenarios: one uniform and the other two involving partial shading, using an Arduino board and a buck converter. According to the comparative analysis, the new BO algorithm outperforms the three other approaches in all performance evaluation parameters. It shows an average improvement in convergence time of more than 39.18 % and an average precision exceeding 99 %, with minimal oscillation in steady-state operation. This translates into an average MPE efficiency of over 96.66 %. Additionally, the experimental results confirm the findings from the simulations.

This paper presents a novel reduced switch multilevel inverter based on switched capacitor technique for grid integration. It generates nine-level output voltage waveform with only 10 switches. It is suitable for high gain applications since it has a boosting ability of four times. Another feature is that the capacitors have built-in self-voltage balancing. The level shifted pulse width modulation (LSPWM) technique is used to explore the performance of the proposed topology. To determine its optimal performance, the suggested topology is compared to the state-of-the-art topologies in the literature. Simulation of the proposed topology is done under different parameter variations. Further, the thermal design of the proposed topology is done using PLECS software to find the efficiency. Finally, the hardware prototype is implemented using dSPACE 1104 controller to verify its performance.

In this era of Web World-3, usage of the Internet of Things (IoT) in power system networks has attained scalable altitude. By using this technology, data can be transferred and monitored from the far end. Further, the suggested corrective actions are also executed. However, recently, many cyber-attacks have been observed on the power system in which activities such as injection of false data, unwanted switching operations, formation of sub-system layers, and false indication of failure of cyber-physical components (CpC) are experienced. In the worst case, it leads to invite cascade tripping of the power system. Utmost care is taken for the selection of CpC for the power system. However, it is observed that the cyber attackers mostly take entry into the network using the CpC of the power system. Cyber attackers perform false switching operations and false data injection. This article suggests a cyber security concept to minimize the false switching operations and false data injection in the power system network. A hardware model is prepared and the working scheme is implemented using IoT technology. The hardware result suggests that the un-authentic attempt/cyber-attack has been identified and the alarm is generated. It also does not permit the un-authentic person to access the real-time data. A wide range of applicability of the suggested scheme has been verified by hardware results. In addition to this, the power factor correction algorithm also works satisfactorily in the hardware along with the cyber security constraints. In order to prove wide range of applicability of the suggested scheme, additional features such as controlling of multiple switching devices and interlock between CB and earthing switch has been successfully implemented in developed hardware.