Performance Analysis of DVR Using ANN Controller for Voltage Quality Enhancement

Babu, P. Sathish; Kamaraj, N.

doi:10.4028/www.scientific.net/amm.573.356

Cited by 3 publications

(2 citation statements)

References 7 publications

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“…One such approach involves the development of a Levenberg Marquardt back-propagation (LMBP) algorithm, which utilizes intelligent computational systems for supervised learning. By optimizing an Artificial Neural Network (ANN) model [38], the proposed algorithm facilitates the computation of load voltage components in the training process. However, a common challenge with ANN models is slow learning and the tendency to get trapped in local optima.…”

Section: Literature Reviewmentioning

confidence: 99%

Intelligent Voltage Sag Compensation Using an Artificial Neural Network (ANN)-Based Dynamic Voltage Restorer in MATLAB Simulink

Mohammed Uddin,

A Bakar

2023

IJIE

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An innovative Dynamic Voltage Restorer (DVR) system based on Artificial Neural Network (ANN) technology, implemented in MATLAB Simulink, accurately detects, and dynamically restores voltage sags, significantly improving power quality and ensuring a reliable supply to critical loads, contributing to the advancement of power quality enhancement techniques. Voltage sags are a prevalent power quality concern that can have a significant impact on sensitive electrical equipment. An innovative approach to address voltage sags through the operation of a Dynamic Voltage Restorer (DVR) based on Artificial Neural Network (ANN) technology. The proposed system, developed using MATLAB Simulink, leverages the ANN's capabilities to accurately detect voltage sags and dynamically restore the voltage to the affected load. The ANN is trained using a comprehensive dataset comprising voltage sag events, enabling it to learn the intricate relationships between sag characteristics and optimal compensation techniques. By integrating the trained ANN into the DVR control scheme, real-time compensation for voltage sags is achieved. The effectiveness of the proposed system is rigorously evaluated through extensive simulations and performance analysis. The results demonstrate the superior performance of the ANN-based DVR in terms of voltage sag detection accuracy and restoration precision. Consequently, the proposed system presents an intelligent and adaptive solution for voltage sag compensation, ensuring a reliable and high-quality power supply to critical loads. This research contributes to the advancement of power quality enhancement techniques, facilitating the implementation of intelligent power system.

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Section: Literature Reviewmentioning

confidence: 99%

Intelligent Voltage Sag Compensation Using an Artificial Neural Network (ANN)-Based Dynamic Voltage Restorer in MATLAB Simulink

Mohammed Uddin,

A Bakar

2023

IJIE

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“…End-users who are experiencing power quality problems may benefit from DVRs [33]. Figure 1 In restoring the load side voltage to the required level , a compensating voltage of the required magnitude and frequency must be injected [36][37][38][39]. The system can inject up to 50% of the rated voltage, but only for a brief period (up to 0.1 seconds).…”

Section: Philosophy Dynamic Voltage Restorer (Dvr)mentioning

confidence: 99%

Improved Instantaneous Reactive Power (PQ) Theory Based Control of DVR for Compensating Extreme Sag and Swell

et al. 2022

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A Novel Multiobjective Control of DVR to Enhance Power Quality of Sensitive Load

Pandu¹,

Kamaraj²

2015

The Scientific World Journal

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The Dynamic Voltage Restorer (DVR) is one of the fast, flexible, and cost effective solutions available in compensating the voltage-related power quality problems in power distribution systems. In this paper is discussed how power quality enhancement of sensitive load is achieved by applying three versions of Autonomous Group Particle Swarm Optimization like AGPSO1, AGPSO2, and AGPSO3 for tuning the Proportional-Integral DVR controller under balanced and nonlinear load conditions. A novel multiobjective function is formulated to express the control performance of the system, which is quantified using three power quality indices such as Total Harmonic Distortion (THD), voltage sag index, and RMS voltage variation. The obtained results are compared with the Proportional-Integral (PI) controller tuned by Ziegler-Nichols (ZN) method and also by Simple Particle Swarm Optimization based PI controlled DVR. The proposed methodology has improved the performance in terms of the considered power quality indices and the simulation has been carried out in MATLAB/Simulink environment.

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Performance Analysis of DVR Using ANN Controller for Voltage Quality Enhancement

Cited by 3 publications

References 7 publications

Intelligent Voltage Sag Compensation Using an Artificial Neural Network (ANN)-Based Dynamic Voltage Restorer in MATLAB Simulink

Intelligent Voltage Sag Compensation Using an Artificial Neural Network (ANN)-Based Dynamic Voltage Restorer in MATLAB Simulink

Improved Instantaneous Reactive Power (PQ) Theory Based Control of DVR for Compensating Extreme Sag and Swell

A Novel Multiobjective Control of DVR to Enhance Power Quality of Sensitive Load

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