Title: Hybrid BBO-PSO-based extreme learning machine neural network model for mitigation of harmonic distortions in micro grids
Authors: S. Gunasekaran; R. Maheswar
Addresses: Department of Electrical and Electronics Engineering, Coimbatore Institute of Technology, Coimbatore, Tamilnadu, India ' Department of Electronics and Communication Engineering, Sri Krishna College of Technology, Coimbatore, Tamilnadu, India
Abstract: Microgrid tends to be the cluster of some of the renewable energy sources. The most important research area in the power distribution system side is the improvement in the quality of power delivered to the end users. This paper focuses on enhancing the power quality of the microgrid system; shunt active power filters (SAPF) is employed at the distribution side and to design an appropriate controller that achieves a better compensation for the considered SAPF. It is to be noted that the compensation methodology is dependent on the regulation process of the DC-link voltage. Traditionally, this regulation process is carried out employing a closed loop proportional-integral controller. In this paper, a hybrid biogeography-based optimisation – particle swarm optimization-based extreme learning machine neural network model is proposed to design the compensation for the SAPF and to mitigate the harmonics so that effective power gets delivered through the grid.
Keywords: microgrid; shunt active power filter; SAPF; power quality; harmonic mitigation; biogeography-based optimisation; BBO; particle swarm optimisation; PSO; extreme learning machine neural networks.
International Journal of Operational Research, 2020 Vol.38 No.4, pp.507 - 524
Received: 16 Jan 2017
Accepted: 29 Oct 2017
Published online: 21 Apr 2020 *