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Article

An Intelligent Battery Energy Storage-Based Controller for Power Quality Improvement in Microgrids

1
Electrical Engineering Department, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia
2
Researcher at K.A.CARE Energy Research & Innovation Center at Dhahran, Dhahran 31261, Saudi Arabia
*
Author to whom correspondence should be addressed.
Energies 2019, 12(11), 2112; https://doi.org/10.3390/en12112112
Received: 25 April 2019 / Revised: 18 May 2019 / Accepted: 21 May 2019 / Published: 2 June 2019
(This article belongs to the Special Issue Advanced Control in Microgrid Systems)
Modern power systems rely on renewable energy sources and distributed generation systems more than ever before; the combination of those two along with advanced energy storage systems contributed widely to the development of microgrids (MGs). One of the significant technical challenges in MG applications is to improve the power quality of the system subjected to unknown disturbances. Hence innovative control strategies are vital to cope with the problem. In this paper, an innovative online intelligent energy storage-based controller is proposed to improve the power quality of a MG system; in particular, voltage and frequency regulation at steady state conditions are targeted. The MG system under consideration in this paper consists of two distributed generators, a diesel synchronous generator, and a photovoltaic power system integrated with a battery energy storage system. The proposed control approach is based on hybrid differential evolution optimization (DEO) and artificial neural networks (ANNs). The controller parameters have been optimized under several operating conditions. The obtained input and output patterns are consequently used to train the ANNs in order to perform an online tuning for the controller parameters. Finally, the proposed DEO-ANN methodology has been evaluated under random disturbances, and its performance is compared with a benchmark controller. View Full-Text
Keywords: artificial neural networks; battery energy storage systems; diesel synchronous generator; differential evolution optimization; microgrids; photovoltaic system; power quality improvement artificial neural networks; battery energy storage systems; diesel synchronous generator; differential evolution optimization; microgrids; photovoltaic system; power quality improvement
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MDPI and ACS Style

Alshehri, J.; Khalid, M.; Alzahrani, A. An Intelligent Battery Energy Storage-Based Controller for Power Quality Improvement in Microgrids. Energies 2019, 12, 2112. https://doi.org/10.3390/en12112112

AMA Style

Alshehri J, Khalid M, Alzahrani A. An Intelligent Battery Energy Storage-Based Controller for Power Quality Improvement in Microgrids. Energies. 2019; 12(11):2112. https://doi.org/10.3390/en12112112

Chicago/Turabian Style

Alshehri, Jaber, Muhammad Khalid, and Ahmed Alzahrani. 2019. "An Intelligent Battery Energy Storage-Based Controller for Power Quality Improvement in Microgrids" Energies 12, no. 11: 2112. https://doi.org/10.3390/en12112112

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