Volume 21, Issue 3

In this paper, the status quo, analytical methods, and treatment technologies of steady-state power quality problem with the largest influential degree and range are reviewed. Typical harmonic sources in regional power grids and their characteristics are investigated, the influence of harmonics on a regional power grid is described, and the main harmonic control measures including active and passive types are introduced. The sources, status quo, and adverse effect of the three-phase imbalance problem of regional power grids are examined. Meanwhile, relevant standards and imbalance calculation methods are introduced. The operation measures and treatment equipment for solving the three-phase imbalance problem are also presented. The status quo, mechanism, and danger of low voltage (LV) in regional grids are analyzed as well as the LV monitoring methods, and the corresponding treatment technologies are summarized. Finally, the key to treating the steady-state power quality of regional power grids is proposed.

Renewable energy sources are widely installed across countries. In recent years, the capacity of the installed renewable network supports large percentage of the required electrical loads. The relying on renewable energy sources to support the required electrical loads could have a catastrophic impact on the network stability under sudden change in weather conditions. Also, the recent deployment of fast charging stations for electric vehicles adds additional load burden on the electrical work. The fast charging stations require large amount of power for short period. This major increase in power load with the presence of renewable energy generation, increases the risk of power failure/outage due to overload scenarios. To mitigate the issue, the paper introduces the machine learning roles to ensure network stability and reliability always maintained. The paper contains valuable information on the data collection devises within the power network, how these data can be used to ensure system stability. The paper introduces the architect for the machine learning algorithm to monitor and manage the installed renewable energy sources and fast charging stations for optimum power grid network stability. Case study is included.

This article presents a comparison between two different methods to perform model reduction of an Electrical Power System (EPS). The first is the well-known Kron Reduction Method (KRM) that is used to remove the interior nodes (also known as internal, passive, or load nodes) of an EPS. This method computes the Schur complement of the primitive admittance matrix of an EPS to obtain a reduced model that preserves the information of the system as seen from to the generation nodes. Since the primitive admittance matrix is equivalent to the Laplacian of a graph that represents the interconnections between the nodes of an EPS, this procedure is also significant from the perspective of graph theory. On the other hand, the second procedure based on Power Transfer Distribution Factors (PTDF) uses approximations of DC power flows to define regions to be reduced within the system. In this study, both techniques were applied to obtain reduced-order models of two test beds: a 14-node IEEE system and the Colombian power system (1116 buses), in order to test scalability. In analyzing the reduction of the test beds, the characteristics of each method were classified and compiled in order to know its advantages depending on the type of application. Finally, it was found that the PTDF technique is more robust in terms of the definition of power transfer in congestion zones, while the KRM method may be more accurate.

This paper proposes a new maximum power point tracking (MPPT) technique of photovoltaic system based on Kalman filter (KF) and associate to Artificial Neural Networks (ANN). The design process of photovoltaic (PV) modules can be greatly enhanced by using advanced and accurate models. Furthermore, the use of a neural model especially for accuracy improvement of the electrical equivalent circuit parameters, where the analytic equation of the model cannot be easily expressed, because the relationship between parameters is nonlinear. The proposed neural network is trained once by using some measured I-V and P-V curves and to keep in account the change of all the parameters at different operating conditions. For that reason, to get the fast tracking performance on this noisy conditions, and to maximize the power of photovoltaic system a KF method have been used. The performance analysis of perturb and observe (P&O) and KF MPPT techniques has been simulated in MATLAB/Simulink software and their model and control schemes has been analyzed and validated.

The advances in Wide Area Measurement Systems (WAMS) and deployment of a huge number of phasor measurement units (PMUs) in the grid are generating big data volume. This data can be used for a variety of applications related to grid monitoring, management, operation, protection, and control. With the increase in this data size, the respective storage capacity needs to be enhanced. Also, communication infrastructure readiness remains bottleneck to transfer this big data. One of the probable solutions could be transmitting compressed data. This paper presents techniques for data compression in the smart transmission system using singular values decomposition (SVD) and the eigenvalues decomposition (EVD). The SVD and EVD based principal component analysis (PCA) techniques are applied to the real-time PMU data collected from extra-high voltage (EHV) substations of transmission utility in the western regional grid of India. Adequacy of data is checked by Kaiser-Meyer-Olkin ( KMO ) test in order to have the satisfactory performance of these techniques towards achieving the objective of efficient data compression. Results are found satisfactory gives compression more than 80% using real time data.

As the main power source of the industrial production and mining industry, the health of high-voltage induction motor (HVIM) has attracted extensive attention. The loop current (LC) of the inter-turn short-circuit (ITSC) fault seriously endangers the stability of HVIM. A comparative analysis is made of HVIM in this paper when the motor works under fault conditions. The purpose of this study is to analyze the influence of ITSC fault on motor performance when the LC is considered and the LC is not considered. In this paper, a 10 kV, 1000 kW HVIM is taken as an example to establish finite element calculation model. By comparing finite element calculation data and test data, the model correctness is verified. Based on the correctness model, the fault models are established. The three-phase current unbalances, torque ripple and losses are studied when the motor works under the different fault conditions. By comparing the three-phase current unbalance, torque ripple and loss of the motor when the LC is considered and the LC is not considered. The influence mechanism of the ITSC fault on three-phase current unbalance, torque ripple and losses are revealed. Based on the above analysis, some references are given for ITSC fault diagnosis and fault degree identification.

Controlled islanding is an effective way of preventing the system from catastrophic blackouts. This is generally solved either as a constrained combinatorial optimization problem or a slow coherency based linearized approach. The combinatorial explosion of the solution space of an extensive power network increases the complexity of solving, while the linearized slow coherency approach cannot track the varying coherent generator groups with a change in system operating conditions. Offline coherent studies are utilized in wide area measurement system (WAMS) data-based approaches to determine islanding boundaries. So, the present study proposes a novel coherency based controlled islanding technique that clusters generators and load buses simultaneously from the measured signals, ensuring generator coherency. Therefore, identification of inter-area modes from bus voltage angle signals is necessary to determine coherent bus groups. So, Zolotarev polynomial based filter bank (ZPBFB) is adopted in the present work to determine inter-area modes. The dimensional reduction techniques are used to cluster the coherent buses. The bus clusters thus obtained with the proposed method are compared with bus clusters determined from small signal stability analysis. The proposed method is demonstrated on IEEE 39-bus, 68-bus and 118-bus test systems and compared with graph spectra based controlled islanding.

The optimal size and location of the compensator in the distribution system play a significant role in minimizing the energy loss and the cost of reactive power compensation. This article introduces an efficient heuristic-based approach to assign static shunt capacitors along radial distribution networks using multi-objective optimization method. A new objective function different from literature is adapted to enhance the overall system voltage stability index, minimize power loss, and to achieve maximum net yearly savings. However, the capacitor sizes are assumed as discrete known variables, which are to be placed on the buses such that it reduces the losses of the distribution system to a minimum. Load sensitive factor (LSF) has been used to predict the most effective buses as the best place for installing compensator devices. IEEE 34-bus and 118-bus test distribution systems are utilized to validate and demonstrate the applicability of the proposed method. The simulation results obtained are compared with previous methods reported in the literature and found to be encouraging.

Modern power systems are increasingly becoming more complex and thus become vulnerable to voltage collapse due to constant increase in load demand and introduction of new operation enhancement technologies. In this study, an approach which is based on network structural properties of a power system is proposed for the identification of critical nodes that are liable to voltage instability. The proposed Network Structurally Based Closeness Centrality (NSBCC) is formulated based on the admittance matrix between the interconnection of load to load nodes in a power system. The vertex (node) that has the highest value of NSBCC is taken as the critical node of the system. To demonstrate the significance of the concept formulated, the comparative analysis of the proposed NSBCC with the conventional techniques such as Electrical Closeness Centrality (ECC), Closeness Voltage Centrality (CVC) and Modal Analysis is performed. The effectiveness of all the approaches presented is tested on both IEEE 30 bus and the Southern Indian 10-bus power systems. Results of simulation obtained show that the proposed NSBCC could serve as valuable tool for rapid real time voltage stability assessment in a power system.

Eastern Regional (ER) grid of India is one of the five regional grids consisting of voltage level 765 kV, 400 kV, 220 kV and numerous buses at lower voltage range. ER grid is the backbone of the Indian Grid as it is one of the main power generating hubs. Stability of the Indian Grid depends a lot on ER grid as it is the core of the generation and transmission system. For performing stability studies and to determine the weak area of the grid generally L-Index method is used. For detailed analysis like computation of voltage recovery time, maximum frequency deviation and critical clearing time of the buses of the system L-Index method is not suitable. Hence dynamic simulation is performed as it provides options to compute various parameters of ER Grid. The system has been studied using 197 buses of voltage levels 220 kV and above and transmission lines running over a length of 23,693 km. In this paper the power flow modelling, simulation and analysis of the Eastern Grid of India is performed. L-Index algorithm along with dynamic simulation provides the scope of computation of voltage recovery time, maximum frequency deviation and critical clearing time (by introducing an initial transient disturbance) along with the prediction of weak area of the system which gives a complete picture of transient stability of ER Grid.

This paper presents the solution to mitigate the total harmonic distortion (THD) in multilevel inverters (MLIs) using novel improved whale optimization algorithm (IWOA). The IWOA falls under the category of swarm-based nature inspired optimization algorithms. It uses a novel diffusion process using a random walk technique and utilizes an additional ranking system to estimate the optimum solution to minimize THD. Moreover, THD minimization is further accomplished through nine various meta-heuristic algorithms for investigation and comparative analysis. The selected algorithms along with the proposed IWOA are rigorously tested on single phase 5 and 7 level cascaded H-Bridge MLIs for various performance parameters such as consistency, computational efficiency and speed of convergence. It is found that the proposed algorithm outperforms the nine algorithms and is efficient for THD minimization for modulation index (MI) in the range of 0–1. The results are analyzed and reported after thorough verification using MATLAB simulation. Highlights It presents a novel switching technique through improved whale optimization algorithm (IWOA) to optimize the switching angles of a single phase 5 and 7 level multilevel inverters (MLIs) in order to minimize the total harmonic distortion (THD). IWOA incorporates a novel diffusion process and two new position updating techniques The proposed IWOA provides better computational efficiency, improved consistency and faster convergence compared to the older whale optimization algorithm (WOA) with minimal tuning of the algorithm’s parameters. The proposed IWOA is compared alongside various meta-heuristics like genetic algorithm (GA), particle swarm optimization (PSO), grey wolf optimizer (GWO), krill herd (KH), artificial electric field algorithm (AEFA), sun flower optimizer (SFO), galactic swarm optimization (GSO), fruit fly optimization algorithm (FOA) and whale optimization algorithm (WOA) for optimal THD minimization.

An accumulator or battery is an energy storage cramped in an adaptable stockade. Lithium-ion batteries are commonly used in hybrid electric vehicles (HEV) and battery operated electric vehicles (BOEV) due to its eco-friendliness and increased efficiency. To maintain lithium batteries in the safe operating region and also to perform tasks like cell balancing, preventing thermal runaway, maintain the state of health, an effective battery management system (BMS) is required. The BMS should also communicate effectively between host devices and battery packs. This paper proposes a reliable, modular and cost-efficient BMS, which will emanate an alert when a fault occurs and thus preventing the battery from damage. An efficient control strategy has been proposed for charging and discharging of the battery pack. The thermal analysis of the lithium-ion battery used in this work is simulated using battery design studio (BDS) with the inclusion of a self-discharging effect. The proposed hardware setup also provides a provision for on-board diagnosis (OBD) and logging in the accumulator management system (AMS) to constantly monitor the cell parameters like voltage, current, and temperature. The live data display of AMS working is also shown during abnormal and normal conditions. Also, an attempt is made to use the design of proposed AMS for HEV.