ALO-optimized artificial neural network-controlled dynamic voltage restorer for compensation of voltage issues in distribution system

Ansal, V.

doi:10.1007/s00500-019-03952-1

Cited by 23 publications

(8 citation statements)

References 17 publications

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“…The ANN has adaptable and conscience capabilities, which improves precision through interpolation. To increase power quality and eliminate harmonic distortions in sensitive loads, the ANN controller is employed in the design and modelling of DVR [142]. In the event of a disruption, DVR is used to enhance the real power of the inverter.…”

Section: Artificial Neural Network Control (Ann)mentioning

confidence: 99%

“…But in this controller chattering effect produced by the switching and this effect, causes the heating loss, temperature loss and law control accuracy. For avoiding the Chattering effect, many algorithms can be used such as Super twisting [147], Real Twisting [148], Neural Network [142], Sub-Optimal [149], Smooth Super twisting [150] and Integral [151].…”

Section: Sliding Mode Controlmentioning

confidence: 99%

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A review on the state of the art of dynamic voltage restorer: topologies, operational modes, compensation methods, and control algorithms

Shah,

Ullah,

Nouman

et al. 2024

Eng. Res. Express

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The state-of-the-art dynamic voltage restorers (DVRs) have advanced significantly in recent years. Modern DVRs can detect voltage sags or dips on the power line in real-time and are able to rapidly inject a compensating voltage to maintain a constant voltage at the load. This helps to improve the power quality of the electrical distribution system and to protect sensitive electronic equipment from voltage fluctuations. One key development in the field of DVRs has been the use of advanced control algorithms that enable the DVR to track the voltage more accurately on the power line and to inject a compensating voltage more accurately. These algorithms can be implemented using digital signal processors (DSPs) or field-programmable gate arrays (FPGAs), which allow for fast and precise control of the DVR. Another important development has been the use of high-frequency inverters in DVRs. These inverters can operate at much higher frequencies than traditional inverters, which allows them to switch on and off more quickly and to inject a more precise compensating voltage. This can help to further improve the power quality of the electrical distribution system. Overall, the state-of-the-art DVR technology continues to evolve, and new advances are being made all the time to improve the performance and reliability of these devices.

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Section: Artificial Neural Network Control (Ann)mentioning

confidence: 99%

Section: Sliding Mode Controlmentioning

confidence: 99%

A review on the state of the art of dynamic voltage restorer: topologies, operational modes, compensation methods, and control algorithms

Shah,

Ullah,

Nouman

et al. 2024

Eng. Res. Express

View full text Add to dashboard Cite

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“…There are no dispensing devices, nonlinear loads, or utilities employed as compensation in the electrical power system. This complicated structure guides the power system toward an unsteady energy source [1]. complicated energy systems known as "smart grids" (SGs) utilized two-way communication between distributed generation (DG), control systems, and loads to improve voltage stability, create the good utilization of RES, self-repair systems when a problem arises, and provide customers with the chance to control their electricity usage and save funds on maintenance cost [2].…”

Section: Introductionmentioning

confidence: 99%

Mitigation of Voltage Sag and Swell in Smart Grid by using ANT-LION Intelligent Controller

2023

5th ISSE National Conference (INAC-05) on Systems Approach for Self-Reliance in Advanced Technologies

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In the operational condition of an electrical power system, the need for proper utilization with quality of utilization is primal. Where different types of quality measures are deployed such as the linear filters and adaptive filters to condition the current quality, a power flow controller is deployed to compensate for the dissipation losses or fault tolerance. Where efforts are made in enhancing power quality, efforts are also made in the utilization of it. With the rapid and ever-increasing demand for power supply and rapid increases in industrial and urbanization, the demand has exceeded the supply capacity of all generation systems. To compensate for the demanded power requirement, in addition to the existing power generation units, additional subunits are added to the power system to compensate for the demanded supply. Smart grids are designed as a cluster of various generation units and consumption units. The demanded power is processed in these smart grids and using a processing algorithm, these grids play a crucial part in adjusting the power supply allocation to compensate for it. Here, the grid systems are either designed for a concentric parameter or multi-objective monitoring in making a decision. The issue is with the complexity in the parameter validation, where multi-objective monitoring gives the benefit of accurate scheduling, the complexity in parameter monitoring is higher. The main objective of the proposed is weight-defined parameter monitoring of power scheduling in multiparameter monitoring, where the past approach of a preference-based scheduler is to be developed with different intelligent controller techniques like UNITED POWER FLOW CONTROLLER (UPFC) with ANT-LION optimization (ALO) algorithm is proposed and compared with ANFIS, Adaptive FLC, Reducedorder FL, FOPI, and FOFL. The PQ issue in the system is helped by the UPFC device. A shunt active power filter is used in series with an artificial neural network (ANN) with an ALO-based controller to improve UPFC performance by allaying current and voltage power quality (PQ) concerns. It proved that our proposed system is best in smart grid applications using MATLAB/Simulink.

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“…Regarding the classification aspect for PQD, the support vector machine (SVM), 8 artificial neural network, 9 decision tree (DT), 10 probabilistic neural network, 11 and several others are among the frequently employed as classifiers. Despite achieving relatively high classification accuracies, the efficiency of these classification techniques depends on the extraction of valid features 12 and the selection of appropriate classifiers.…”

Section: Introductionmentioning

confidence: 99%

Classification of multiple power quality disturbances based on continuous wavelet transform and lightweight convolutional neural network

Zhou

et al. 2023

Energy Science & Engineering

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Aiming at the problems of noise interference and too many network parameters for power quality disturbances' (PQDs') classification based on deep learning, the lightweight convolutional neural network combining maximum likelihood Kalman filter and continuous wavelet transform is proposed. In this proposed method, the disturbed PQD signals are denoised by maximum likelihood Kalman filter, and then the denoised PQDs are converted to time‐frequency diagrams, which can provide rich time and frequency domain information, and finally the lightweight convolution neural network is used for automatically extracting and classifying multiple PQDs. To verify the effectiveness and superiority of the proposed method, a variety of PQDs were tested under different noise levels, the experiment results indicate that the average classification accuracy can reach more than 99% even in the case of 10 dB noise. Compared with the existing classification methods, the accuracy and noise immunity ability are improved. Additionally, the proposed method has decided advantages, as evidenced by its low parameter count of 0.73M and short average test time with only 0.7 ms.

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ALO-optimized artificial neural network-controlled dynamic voltage restorer for compensation of voltage issues in distribution system

Cited by 23 publications

References 17 publications

A review on the state of the art of dynamic voltage restorer: topologies, operational modes, compensation methods, and control algorithms

A review on the state of the art of dynamic voltage restorer: topologies, operational modes, compensation methods, and control algorithms

Mitigation of Voltage Sag and Swell in Smart Grid by using ANT-LION Intelligent Controller

Classification of multiple power quality disturbances based on continuous wavelet transform and lightweight convolutional neural network

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