Power Quality Enhancement using Dynamic Voltage Restorer (DVR) by Artificial Neural Network and HysteresisVoltage Control Techniques

Arpitha, M J; Sowmyashree, N; Shashikala, M. S.

doi:10.1109/gcat47503.2019.8978333

Cited by 6 publications

(3 citation statements)

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“…70% of the data was used to train ANN, 15% to test the training data results, and the rest to validate the system network. Figure 7 shows an ANN controller circuit that has been trained and evaluated for performance based on mean square error (MSE), a standard metric used to calculate the average squared error between the actual value and the reference value [20], [26], [29].…”

Section: Ann Trainingmentioning

confidence: 99%

“…PSO is a reliable metaheuristic method that can optimize the controller's performance with few parameters, but the results are optimal. Convergence and optimum values are obtained faster than other methods and do not depend on parameters [20]- [22]. This study proposed to test DVR based on PSO and ANN to find a more suitable controller.…”

Section: Introductionmentioning

confidence: 99%

“…In contrast, comparing and improving the types of reliable control strategies on DVRs is one method of finding the most efficient controller to use [19]. Research by Arpitha et al [20] used a similar method, comparing types of strategy control, namely artificial neural network (ANN) and hysteresis voltage control. This study used photovoltaic as a direct current (DC) source on the DVR, then simulated the sag and swell due to a 3-phase short-circuit fault.…”

Section: Introductionmentioning

confidence: 99%

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Dynamic voltage restorer quality improvement analysis using particle swarm optimization and artificial neural networks for voltage sag mitigation

Siregar,

Muhammad,

Arief

et al. 2023

IJECE

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Power quality is one of the problems in power systems, caused by increased nonlinear loads and short circuit faults. Short circuits often occur in power systems and generally cause voltage sags that can damage sensitive loads. Dynamic voltage restorer (DVR) is an efficient and flexible solution for overcoming voltage sag problems. The control system on the DVR plays an important role in improving the quality of voltage injection applied to the network. DVR control systems based on particle swarm optimization (PSO) and artificial neural networks (ANN) were proposed in this study to assess better controllers applied to DVRs. In this study, a simulation of voltage sag due to a 3-phase short-circuit fault was carried out based on a load of 70% of the total load and a fault location point of 75% of the feeder’s length. The simulation was carried out on the SB 02 Sibolga feeder. Modeling and simulation results are carried out with MATLAB-Simulink. The simulation results show that DVR-PSO and DVR-ANN successfully recover voltage sag by supplying voltage at each phase. Based on the results of the analysis shows that DVR-ANN outperforms DVR-PSO in quality and voltage injection into the network.

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Section: Ann Trainingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%