Power Quality Enhancement in a Grid-Integrated Photovoltaic System Using Hybrid Techniques

Prasad Kumar Bandahalli Mallappa,Herminio Martinez Garcia,Guillermo Velasco Quesada

doi:10.3390/app112110120

Prasad Kumar Bandahalli Mallappa, Herminio Martinez Garcia + Show 1 more

Open Access

https://doi.org/10.3390/app112110120

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Journal: Applied sciences	Publication Date: Oct 28, 2021
Citations: 1	License type: CC BY 4.0

Affiliation: Universitat Politècnica de Catalunya

Abstract

In recent years, the photovoltaic (PV) system was designed to supply solar power through photovoltaic arrays. The PV generator exhibits nonlinear voltage–current characteristics and its maximum power point tracking (MPPT), which varies with temperature and radiation. In the event of non-uniform solar insolation, several multiple maximum power points (MPPs) appear in the power–voltage characteristic of the PV module. Thus, a hybrid combination of binary particle swarm optimization (BPSO) and grey wolf optimization (GWO) is proposed herein to handle multiple MPPs. This combination is nowhere found in the literature, so the author chose this hybrid technique; and the main advantage of the proposed method is its ability to predict the global MPP (GMPP) in a very short time and to maintain accurate performance, even under different environmental conditions. Moreover, a 31-level multilevel inverter (MLI) was designed with a lower blocking voltage process to reduce the complexity of the circuit design. The entire system was executed in the MATLAB platform to examine the performance of the PV system, which was shown to extract a maximum power of 92.930 kW. The simulation design clearly showed that the proposed method with a 31-level MLI achieved better results in terms of total harmonic distortion (THD) at 1.60%, which is less when compared to the existing genetic algorithm (GA) and artificial neural networks (ANNs).

Highlights

Solar energy is an inexhaustible and less-polluting energy resource that has received widespread attention in renewable energy production
The designed PV model was named Sun Power (SPR 305W). This portion demonstrates the outcomes of the simulation, connected through the PV module, which operates with the proposed binary particle swarm optimization (BPSO)–grey wolf optimization (GWO) algorithm to extract the maximum power
The hybrid combination of BPSO–GWO was proposed, and the convergence speed was shown to be a significant characteristic of this technique

Summary

Introduction

Solar energy is an inexhaustible and less-polluting energy resource that has received widespread attention in renewable energy production. Photovoltaic power generation is an effective method for using solar energy [1]. Most research works have concentrated on extracting more power, which can be achieved effectively from PV cells [2]. In comparison to other renewable energy sources, solar PV is a natural energy source that is more efficient because it is free, clean, and abundant [3]. The energy consumption of the PV system has been reduced by effectively designing MPPT [4]. This paper presents some efficient ideas to improve the performance of MPPT. In the past few years, several MPPT techniques have been implemented, but still, some enhancements are required in MPPT to improve the PQ features

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