An overview of various control techniques of DVR

Rovai, Chirag H.; Doorwar, Ashish

doi:10.1109/iccpct.2014.7054844

Cited by 7 publications

(6 citation statements)

References 22 publications

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“…Sag voltage, often a consequence of single-phase-to-ground short circuit faults, exerts a significant impact on the integrity of load distribution along the feeder line. To effectively address voltage sag issues, the utilization of a Dynamic Voltage Restorer (DVR) is recommended [39]. Within the domain of power quality assessment, voltage sags and swells are acknowledged as critical factors affecting sensitive loads in distributed systems.…”

Section: Literature Reviewmentioning

confidence: 99%

“…To address this issue, a controller system is developed, utilizing a combination of linear and non-linear fuzzy logic [27], particle swarm optimization (PSO) [12], ant lion optimizer-optimized artificial neural network (ALO-ANN) [24] and Grasshopper Optimization Algorithm models [29]. This literature review [11] [41] provides a concise overview [23] of the configurations and control strategies of the Dynamic Voltage Restorer (DVR) as described in previous research [34] [39]. The DVR provides a remedy for a range of power quality concerns, including addressing problems related to voltage harmonics as well as compensating for voltage sags and swells [4].…”

Section: Literature Reviewmentioning

confidence: 99%

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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%

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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“…A short circuit fault is an intentional or unintentional conduction connection through resistance or impedance between two or more points under normal circumstances with a potential difference. Therefore, additional power electronics devices are needed to perform voltage compensation, namely dynamic voltage restorer (DVR) [4]- [6], unified power quality control (UPQC) [7], and distributed static compensator (DSTATCOM) [8]. DVR is the most effective device in dealing with voltage sags.…”

Section: Introductionmentioning

confidence: 99%

Dynamic voltage restorer performance analysis using fuzzy logic controller and battery energy storage system for voltage sagging

Siregar,

Azhari Nasution,

Suan Tial

et al. 2024

IJECE

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Power quality is a major issue in the power transfer process. This is caused by disturbances such as voltage sags, voltage spikes, and harmonics. Voltage sag is the most common disturbance in the electric power system. However, the dynamic voltage restorer (DVR) is the most effective device for voltage sags. This research uses the DVR to overcome voltage sags using fuzzy logic controller (FLC) and battery energy storage system (BESS) to improve the performance of the DVR. The results showed that DVR using FLC improved the quality of voltage recovery compared to BESS because FLC injected a greater voltage of 0.0991 pu than BESS.

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“…This voltage sag occurs when the voltage changes with a range of 10%-90% at the rms value of the voltage for a duration of 0.5 cycles (0.01 seconds) to 1 minute. This is due to several factors, namely the use of nonlinear loads, supply to large loads, variations in loading, short circuit faults, and poor system design [2]- [5]. Various techniques, including power electronic devices, solve electrical power quality problems.…”

Section: Introductionmentioning

confidence: 99%

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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An overview of various control techniques of DVR

Cited by 7 publications

References 22 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

Dynamic voltage restorer performance analysis using fuzzy logic controller and battery energy storage system for voltage sagging

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

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