Volume 25, Issue 4

The integration of wind turbines and wind farms into the power grid presents significant challenges, including the variable and uncertain nature of wind power, the need for efficient transmission and distribution of power, and the need for advanced control and monitoring systems. Recent advances in wind turbine and wind farm technology have greatly improved their ability to integrate into the power grid. This includes the use of variable speed generators and power electronics, such as inverters, which allow for more accurate control of the power output and better alignment with the grid’s frequency. Additionally, advanced control algorithms, such as model predictive control and feedback linearization, have been developed to improve the stability and reliability of wind power systems. Communication systems have also been integrated into wind power systems to allow for real-time monitoring and control of the wind farm’s performance. This improves the ability of the wind farm to provide ancillary services, such as frequency regulation and voltage control. In this work, the major interconnection requirements for the U.S. grid for the expected mass integration of wind farms were discussed focusing on sustainable and reliable control strategies. The paper explores various control schemes that are used for providing response for grid reliability and sustainability. The analysis also included the future steps that OEMs of wind turbines need to take and the regulatory framework that is needed for ancillary services to provide an equitable platform for all generating units – conventional and renewables.

Power Quality, Equipment and Personnel safety of any distributed generation (DG) system connected to utility Grid merely depends on accurate detection of Islanding and non-islanding Power quality disturbances. The main objective of the proposed research is to detect islanding events with very narrow non-detection zone (NDZ) and classification of power quality disturbances with higher accuracy using signal processing and intelligent method together. A noise robust down sampling empirical mode decomposition (DEMD) is used to extract signature of islanding and power quality (PQ) disturbance features from the collected voltage signals and multilayer perceptron neural network (MLNN) is proposed to classify islanding and non-islanding (PQ) events. The performance of the proposed (DEMD-MLNN) technique is verified with IEEE-9 bus distributed generation system dominated by solar &wind energy penetration. The simulation work is carried out in MATLAB/Simulink platform. The efficacy of the proposed DEMD-MLNN is verified by large number of numerical experimentations with and without noise and comparing with existing competitive well-known techniques.

Partial discharge in oil paper insulation is an important reason for insulation breakdown. The study of partial discharge in transformer oil is of great significance for ensuring the reliability of transformers. In this paper, a comprehensive observation platform of partial discharge with needle plate electrode and oil-paper insulation is built, and the original shape image set of partial discharge is collected and established. Through multi-channel convolution processing of the true color image, gray level equivalent distribution of 3D section, segmentation stretching of threshold interval and binary neighborhood search, a general technical method suitable for processing a series of low brightness discharge images with background interference is finally obtained.

In this manuscript, a new control approach is counselled for DSTATCOM to resolve power quality (PQ) related issues. The Non-dual Adaptive Controller (NDAC) is employed with DSTATCOM to enhance the performance in terms of stability. The DSTATCOM is the constituent of voltage source inverter shunt DC-capacitor. A hysteresis block is employed for switching pulse triggering. The counselled scheme is a new method of shunt compensation and offers superior performance, for the lower frequency band in comparision with traditional strategies. Now-a-days we are dealing with huge nonlinear loads which draws non sinusoidal currents and also they generate large amount of harmonics. There are various advantages of using this new modified adaptive controllers over the tradition controllers. We are going to compare the performance of this new counselled controllers performance with the existing one in this literature. This new controller is initially simulated over MATLAB 2016 and then the efficacy of the counselled control approach (NDAC) is investigated using Laboratory prototype. Hardware setup is then authenticated through prototype with the help of dSPACE 1104. The execution reflects wide variation in the performances of both the controllers and also shows the supremacy of Non-dual adaptive controllers in terms of various aspects like harmonic current reduction, reduction in Total harmonic distortions during load dynamics.

The basic philosophy of two-stage blast interrupter is combination of self-blast and puffer-interrupter principles. Gas circuit breaker (GCB) with two-stage blast interrupter has two gas chambers. High gas pressure in one chamber generates by using arc energy and in other chamber, by mechanical compression. In such type of breakers, interruption becomes quite difficult at low currents and hence optimization of gas chambers design is essential. In view of above, a numerical program has been developed to calculate SF6 gas pressure-rise in various interrupter volumes and gas flow rates across different passages of interrupter w.r.t. time. To validate model developed in the study, an experimental set-up is established and measured transient gas pressure-rise generated during 420 kV GCB operation. The effect of speed-time characteristics, stroke length and piston diameter on performance parameters of 420 kV GCB model is analyzed. Further, to understand gas flow pattern across insulated nozzle during GCB operation, authors developed a CFX model. By using this numerical model, effect of nozzle profile, gas flow passage areas and profile of gas discharge vents on transient gas pressure and Mach number distribution across inter-electrode gap and gas flow rates through various outlets has been discussed in detail in the paper.

This paper focuses on designing a simple yet efficient H ∞ controller for regulating the voltage/frequency, output current, and active/reactive power-sharing in a microgrid (MG). The purpose of designing the robust H ∞ controller is to regulate the frequency and voltage in face of the uncertainties in the microgrid and load variation. The studied DC MG test bed includes an SMPS (switching-mode power supply) and DC-DC converter, necessary sensor and data acquisition systems. The proposed control scheme is simulated using MATLAB/Simulink and is then verified in real-time using LabVIEW. The control scheme is developed in the LabVIEW software’s block diagram panel of the virtual instrumentation interface. From both simulation and LabVIEW implementation, load sharing among the DGs, and voltage and frequency regulation at the nominal values are achieved by employing the proposed H ∞ controller. The robustness of the proposed controller is evaluated by comparing it with a robust observer-based controller (Zarei A, Mousavi Y, Mosalanezhad R, Atazadegan MH. Robust voltage control in inverter-interfaced microgrids under plug-and-play functionalities. IEEE Syst J 2020;14:2813–24) under balanced, unbalanced, and non-linear loads. The comparison results envisage that the proposed controller provides improved voltage/frequency regulation and power-sharing during disturbances including the addition of non-linear load to the MG system.

Feedforward multilayer perceptron models (MLPs) have been applied to power distribution system state estimation (DSSE) in the past. Existing methods usually employ an ad-hoc or trial and error approach to MLP hyperparameter selection, and thus a systematic way of selecting the optimal hyperparameters including the number of neurons per hidden layer, learning rate, number of training epochs and training batch size is desirable and needed. This paper presents an approach based on Bayesian Optimization with Gaussian Processes for selecting MLP model hyperparameters for state estimation purposes. Results of the optimized MLP models are presented alongside the unoptimized models to compare performance of training, testing, and validation in terms of root-mean-squared-error (RMSE). Additionally, machine learning pipelines were employed and total execution time (seconds) for each trial is presented. The study shows that the MLP models obtained through the proposed optimization method outperform unoptimized models in terms of generalization capability for unseen, new cases.

Electric vehicles are in vogue nowadays and it has been gaining some ground in India after Tesla entered into the Indian automobile market. Electric vehicles do not require conventional fuel sources such as fuel, oil, etc., so they do not pollute the environment. BLDC motors are used as the motor of electric vehicles, as they provide a higher speed as well as higher efficiency as compared to other motors. Since the vehicles are powered electrically, it is imperative for us to charge the vehicles just like cell phones. Just like fuel cars have fuel stations for filling up fuel for the operation of vehicles, there must also be battery charging or swapping stations for charging the battery of electric vehicles. This paper would determine how we must charge the electric vehicle using solar along with the traditional grid charging architecture. These two must work in tandem to provide the necessary electrical power to assuring the sufficiently charged battery at the swapping station for uninterrupted use of electric vehicles even in emergency cases. Here, the solar PV along with the traditional grid (Renewable and Conventional both respectively) is used to charge the battery at the swapping station, so it is named a hybrid battery swapping station (HBSS). Also, the concept of charging the battery packs in batches with scheduling is used which reduces the installation cost and the area requirement for the swapping stations compared to other existing battery swapping stations (BSS) or battery charging stations (BCS).

With the development of science and technology, the energy issue has gradually become a topic of global common exploration. According to the relevant information, this paper designed a distributed new energy system based on the blockchain consensus algorithm. The system took the distributed new energy system as the main body, integrated it with blockchain technology, and finally formed a distributed new energy system based on blockchain consensus algorithm. The feasibility of the system was finally determined after the technical tests of voltage detection, power allocation, information collection, and algorithm security. In order to understand the differences between the performance of this system and the traditional system, this paper has made several performance data comparative analysis tests for this system and the traditional system. The test items included energy conversion utilization rate, energy storage, energy use price, energy cleanliness, energy access rate, energy use, etc. Finally, the relevant data of commercial power consumption and residential power consumption accounting for 30 % and 47 % of the total power consumption were obtained, and the data was used to analyze the source of power access. The proportion of green energy is 10 % and 17 %, respectively. According to a series of operation processes such as design, experiment, and analysis, it was finally determined that the system was feasible and effective.

With the development of society, non-renewable energy such as oil and natural gas are gradually consumed and exhausted, and the energy crisis has become the biggest resistance to the development. Therefore, the development of New Energy (NE) has become an important issue for the Sustainable Development (SD) of modern society. Distributed NE system is the new direction of modern energy development. Therefore, higher requirements are put forward to improve the performance of distributed NE systems. The traditional NE system is relatively scattered and cannot be used intensively. There is no consensus among various NE systems, and the energy utilization is insufficient. It leads to a great waste of energy in the process of developing and using NE. The introduction of Blockchain Technology (BT) has contributed to solving many practical problems. BT can not only ensure the authenticity of data but also meet the requirements of high quality by taking advantage of its own characteristics of data tamperability and traceability mechanism. The Consensus Algorithm (hereinafter indicated by CA) in the blockchain ensures that the final results received by all participants are consistent and error-free through data synchronization, and distributed, consistency cooperation. The access of distributed NE solves the disadvantage that the traditional NE system cannot be used in a centralized way, to access NE more efficiently, conveniently, and quickly. In this paper, the distributed NE system was accessed based on the blockchain CA. By adopting BT and CA, a distributed NE system that was easy to access was established. The system could systematically deploy the existing developed NE and then make centralized use of it. This paper compares the efficiency, stability, and loss of access of the distributed NE system using the blockchain CA with that of the traditional distributed NE system. The results showed that the use of blockchain CA to access distributed NE could effectively utilize NE, and the efficiency of distributed NE access after using blockchain CA increased by about 28.8 %. At the same time, the experimental results showed that the access stability was high and the access loss was low. With the continuous development of NE, the blockchain CA would gradually become an important technical support in the energy field, thus promoting the overall development of the energy industry.

In this article, a new control method is proposed for the stability of Electrical Vehicles (EVs) with 2-motor wheels. This control method provides an optimized limit for the torque levels that produced by the front and rear motors, proportional to the road surface condition, to prevent the vehicle from slipping. In addition, a fuzzy logic-based braking system is proposed to improve vehicle performance during deceleration. The vehicle is described by the model with three degrees of freedom, which provide good accuracy. The tires are modeled according to the magic formula. To evaluate the effectiveness of the proposed method, simulations were performed in the ©MATLAB. The conclusions show that the proposed control method can well maintain the steadiness of the EV on dry and slippery roads when driving straight, accelerating or decelerating, and turning. This will prevent the vehicle from slipping and locking the wheels.

This paper presents a recent bio-inspired optimization algorithm for the critical topic of load frequency control (LFC) in an isolated multi-source power generating system. A specific model that emerges from the scientific literature and consists of reheated thermal, hydro and gas-turbine power sources is examined. The main goal is to tune the controller of each generating source i.e. find their optimal gains through bio-inspired optimization algorithms. Namely, the algorithms applied are the Whale Optimization Algorithm (WOA), Grey Wolf Optimizer (GWO), Particle Swarm Optimization (PSO), and the newly proposed Harris Hawks Optimization (HHO). It was observed that all optimization algorithms succeeded in obtaining controllers’ gain values which ensure stable system operation. Also, the simulation results indicated the superiority of the proposed HHO algorithm over the other algorithms for all the considered scenarios. In this article, for the first time to the authors’ knowledge extend an attempt has been made to optimize an isolated multi-source system using the bio-inspired HHO algorithm. The present study considered the LFC problem with a variety of different scenarios, taking into account both sub-cases of nonlinearities (e.g., generation rate constraint, boiler dynamics, etc.) and their combinations, as well as combinations of different controllers and load disturbances, which are not found in the literature. Last but not least, the robustness of the selected controller was further evaluated. The obtained results clearly demonstrated that the controller’s gains established in normal conditions do not require retuning when critical system parameters undergo a significant variation.