Power Quality Enhancement in Power Distribution system using Artificial intelligence based Dynamic Voltage Restorer

Sundarabalan, C.K.; Selvi, K.

doi:10.15676/ijeei.2013.5.4.4

Cited by 13 publications

(10 citation statements)

References 14 publications

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“…It should be noted that in addition to using fuzzy logic, conventional controller gains can be tuned using various techniques such as genetic algorithm, quadratic linear regulator, neural networks, and so on . The main drawback of using a genetic algorithm to tune the gains of conventional controllers is that it requires basic data and complex calculations and the disadvantages of using a quadratic linear regulator is the impossibility of eliminating overshoot and steady state error.…”

Section: Hybrid Dvr‐pvmentioning

confidence: 99%

Compensation of voltage sag and voltage interruption using DVR‐PV based on fuzzy‐adaptive controller

Karimi

Simab

Nafar

2020

Int Trans Electr Energ Syst

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Summary Dynamic voltage restorer (DVR) can preserve a sensitive load against electrical distribution system interruption. After connecting DVR to the system, voltage sag is quickly resolved and no voltage oscillation caused by malfunction of sensitive loads occur. When there is a high voltage sag in distribution or transmission, in order to compensate voltage to the value before fault, both reactive compensation and active power compensation should be performed which requires a DC source in DC link of power quality compensators; if there is only one capacitor in DC link, only reactive power compensation can be performed, as a result significant voltage drops cannot be modified. Distributed generation sources like photovoltaic (PV) can be used as a battery instead of DC link supply of D‐FACTS devices due to DC nature of their output voltage. Performance of DVR in modifying power quality indices depends on performance of its control system. In this paper, a fuzzy adaptive controller is used instead of the conventional PI controller in the control system of the DVR‐PV hybrid compensator to improve its performance in compensating voltage interruption and different types of voltage sag. DVR based on fuzzy controller performs better than DVR based on other controllers like PI. Simulation of electrical distribution system is done in the presence of DVR based on fuzzy‐adaptive control scheme and PV using Matlab/Simulink.

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Section: Hybrid Dvr‐pvmentioning

confidence: 99%

Compensation of voltage sag and voltage interruption using DVR‐PV based on fuzzy‐adaptive controller

Karimi

Simab

Nafar

2020

Int Trans Electr Energ Syst

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“…Reactive power control loop (control unit of q-coordinate) and active power control loop (control unit of d-coordinate) have a separate PI controller. As a same of feedback controllers, the PI controller operates by summing the error and integral of these values [16]. Structure of PI controller depicted in Figure 4.…”

Section: Pi Controllermentioning

confidence: 99%

“…As shown in Figure 5. The major object of the ANN controller is to get proper values for weights and biases which are the learnable parameters inside the controller structure that gives the required output [16].…”

Section: Ann Controllermentioning

confidence: 99%

Improvement of the performance of STATCOM in terms of voltage profile using ANN controller

Alatshan

Alhamrouni

Sutikno

et al. 2020

IJPEDS

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The electronic equipments are extremely sensitive to variation in electric supply. The increasing of a nonlinear system with several interconnected unpredicted and non-linear loads are causing some problems to the power system. The major problem facing the power system is power quality, controlling of reactive power and voltage drop. A static synchronous compensator (STATCOM) is an important device commonly used for compensation purposes, it can provide reactive support to a bus to compensate voltage level. In this paper, the Artificial Neural Network (ANN) controlled STATCOM has been designed to replace the conventional PI controller to enhance the STATCOM performance. The ANN controller is proposed due to its simple structure, adaptability, robustness, considering the power grid non linearities. The ANN is trained offline using data from the PI controller. The performance of STATCOM with case of Load increasing and three-phase faults case was analyzed using MATLAB/Simulink software on the IEEE 14-bus system. The comprehensive result of the PI and ANN controllers has demonstrated the effectiveness of the proposed ANN controller in enhancing the STATCOM performance for Voltage profile at different operating conditions. Furthermore, it has produced better results than the conventional PI controller.

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“…The input to the neural network is the error voltage at the PCC and the error voltage at the dc link. The error is determined and a portion of it is propagated backward through the network [24].…”

Section: Ann Controllermentioning

confidence: 99%

Enhancing the performance of cascaded three-level VSC STATCOM by ANN controller with SVPWM integegration

Almelian¹,

Mohd²,

Ahmad³

et al. 2019

IJECE

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This article presents a cascaded three-level voltage source converter (VSC) based STATCOM employing an artificial neuron network (ANN) controller with a new simple circuit of space vector pulse width modulation (SVPWM) technique. The main aim of utilizing ANN controller and SVPWM technique is to minimize response time (RT) of STATCOM and improve its performance regard to PF amplitude, and total harmonic distortion (THD) of VSC output current during the period of lagging/leading PF loads (inductive/capacitive loads). The performance of STATCOM is tested using MATLAB/SIMULINK in IEEE 3-bus system. The simulation results clearly proved that the STATCOM with intelligent controller is more efficient compared to a conventional controller (PI controller), where ANN enables the voltage and current to be in the same phase rapidly (during 1.5 cycles) with THD less than 5%.

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Power Quality Enhancement in Power Distribution system using Artificial intelligence based Dynamic Voltage Restorer

Cited by 13 publications

References 14 publications

Compensation of voltage sag and voltage interruption using DVR‐PV based on fuzzy‐adaptive controller

Compensation of voltage sag and voltage interruption using DVR‐PV based on fuzzy‐adaptive controller

Improvement of the performance of STATCOM in terms of voltage profile using ANN controller

Enhancing the performance of cascaded three-level VSC STATCOM by ANN controller with SVPWM integegration

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