Power Quality Improvement in Microgrids Under Critical Disturbances Using an Intelligent Decoupled Control Strategy Based on Battery Energy Storage System

• Main Authors: Jaber Alshehri, Muhammad Khalid
• Format: Article
• Language: English
• Published: IEEE 2019-01-01
• Series: IEEE Access
• Subjects: Artificial neural networks; battery energy storage systems; differential evolution optimization; intelligent decoupled controllers; microgrids; photovoltaic system
• Online Access: https://ieeexplore.ieee.org/document/8862829/

The concept of microgrids (MGs) provides the flexibility to integrate renewables into the power network. Nevertheless, the transience of most renewable energy sources (RESs) exacerbates the power quality of the grid network. Furthermore, the unpredictability of RESs additionally becomes challenging in case of high magnitude disturbances. The deployment and optimal utilization of energy storage systems, to act as an energy buffer are hence pertinent. In this paper, a control strategy for a battery energy storage system (BESS) is formulated based on two intelligent decoupled controllers. The objective is the restoration of system voltage and frequency considering a wide range of disturbances and hence circumvent the power quality degradation. The proposed controller is based on hybrid differential evolution optimization and artificial neural network (DEO-ANN). The controller parameters are tuned online by training the ANN with the sets of input and output data obtained during the process of optimizing the two controllers under low and high disturbances using DEO. Finally, the effectiveness of the proposed controller is validated on a power network consisting of a synchronous generator, photovoltaic power system, and BESS. The simulation results prove the robustness of the proposed control approach as compared with a benchmark controller.