Open AccessImproving power quality and load profile using PV‐Battery‐SAPF system with metaheuristic tuning and its HIL validation
Author(s) -
Kumar Ravinder,
Bansal Hari Om,
Kumar Dinesh
Publication year - 2020
Publication title -
international transactions on electrical energy systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.428
H-Index - 42
ISSN - 2050-7038
DOI - 10.1002/2050-7038.12335
Subject(s) - photovoltaic system , particle swarm optimization , battery (electricity) , electric vehicle , total harmonic distortion , engineering , ant colony optimization algorithms , maximum power point tracking , automotive engineering , energy storage , computer science , power (physics) , control theory (sociology) , electrical engineering , voltage , algorithm , inverter , control (management) , artificial intelligence , physics , quantum mechanics
Summary Nonlinear load results in inferior power quality and a non‐uniform power demand curve. This gets aggravated due to the charging of electric vehicles. This paper presents a new control approach of integrating a photovoltaic (PV) cell with battery storage to a shunt active power filter (SAPF) for electric vehicle (EV) applications. The multifunctional PV‐Battery‐integrated SAPF (PV‐Battery‐SAPF) performs harmonic mitigation along with clean power generation, energy storage, uniform load demand curve, and battery swapping. It is achieved through a two‐stage topology. In the first stage, maximum power point (MPP) of a PV array is robustly tracked using metaheuristic algorithms like cuckoo search algorithm and particle swarm optimization. In the second stage, an ant colony optimization‐tuned controller is developed which adaptively controls the SAPF to improve the power quality. The suggested method provides efficient MPP tracking, lesser total harmonic distortion, better dynamic system performance, and appropriate charging/discharging of battery leading to increased system reliability. The proposed system is validated in real time on a hardware‐in‐the‐loop (HIL) testing platform.