Operation and Control of the Distributed Energy Resources to Improve the Power Quality in Electrical Distribution System Using Hbmo -Ann - A Novel Control Algorithm

D Babu Rajendra Prasad; A D Kulkarni; T Ananthapadmanabha

doi:10.17485/IJST/v15i42.2439

Article

Operation and Control of the Distributed Energy Resources to Improve the Power Quality in Electrical Distribution System Using Hbmo -Ann - A Novel Control Algorithm

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Indian Journal of Science and Technology

DOI: 10.17485/IJST/v15i42.2439

Year: 2022, Volume: 15, Issue: 42, Pages: 2204-2218

Original Article

Operation and Control of the Distributed Energy Resources to Improve the Power Quality in Electrical Distribution System Using Hbmo -Ann - A Novel Control Algorithm

D Babu Rajendra Prasad^1*, A D Kulkarni², T Ananthapadmanabha³

¹Research Scholar, Department of EEE, NIE, Mysuru, Karnataka, India
²Rtd. Professor, Department of EEE, NIE, Mysuru, Karnataka, India
³Director, Mysuru University School of Engineering, Mysuru, Karnataka, India

*Corresponding Author
Email: [email protected]

Received Date:29 January 2022, Accepted Date:02 October 2022, Published Date:10 November 2022

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Objectives: To propose a novel hybrid control plan to enhance the power quality (PQ) of distributed energy sources (DERs) such as solar energy (SE), wind energy (WE), and battery energy storage system (BEES) technologies. Method: A BEES, a PV farm, and a wind farm are integrated into the grid to supply home loads. The ABMO-ANN control system is a novel method that combines the Hybrid Barnacles Mating Optimization Algorithm (HBMO) and the Artificial Neural Network (ANN) (ANN). The power is balanced by balancing the power consumed by BEES and the power provided by the VSI inverter in this new proposed algorithm. Findings: In this HBMO-ANN control model, the proportional integral (PI) gain parameter controller generates the signals based on the load variation to manage the hybrid RES energy sources optimally. The assumed variables as system parameters in this suggested prediction approach are direct current (DC) voltage and active with reactive power. Novelty: The new HBMO-ANN method creates optimal control, aiming to improve power system damping and sustain line voltage with the reactive power compensation provided. However, when compared to existing control methods, the performance of the HBMO-ANN approach with a PI controller is also justified. The suggested methodology is compared to existing methods in MATLAB/Simulink.

Keywords: Power quality improvement; Renewable energy sources; Hybrid Barnacles Mating Optimization Algorithm; Reactive power; Power system

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Copyright

© 2022 Prasad et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)