An Improved Bat Algorithm for More Efficient and Faster Maximum Power Point Tracking for a Photovoltaic System Under Partial Shading Conditions

Chih Yu Liao,Ibrahim Saiful Millah,Wei-Tzer Huang,Kuo Lung Lian,Ramadhani Kurniawan Subroto

doi:10.1109/access.2020.2993361

Abstract

Solar modules under partial shading (PS) conditions will result in power and voltage characteristic curves (P-VCC) having multiple peaks. If the maximum power point cannot be obtained, the output power of the solar modules will be greatly reduced. Hence, there have been various maximum power point tracking (MPPT) control methods developed to address this problem. One alternative is to employ the meta-heuristic approach (MHA) to track the global maximum power point (GMPP). Recently, a new MHA called Bat Algorithm (BA) has performed well in the MPPT. Nevertheless, BA may fail to track the GMPP when there are some local maximum power points (LMPPs) close to the GMPP. Also, the tracking time needs to be further reduced to accommodate rapidly changing irradiance. Therefore, a combination of BA with the abandonment mechanism of Cuckoo Search (CS) is proposed to improve the tracking performance of the BA. Both simulation and experimental results show that the proposed method, as compared to BA, yields better accuracy and an improvement of convergence speed of about 35% for various P-VCCs can be achieved. Moreover, the MBA has also been tested against some of the state-of-the-art MPPT algorithms such as Particle Swarm Optimization and Grey Wolf Optimization (GWO), and the results showed the superiority of the proposed method.

Highlights

In recent years, the rise of environmental awareness and the greenhouse effect have led many countries to actively develop renewable energy sources such as solar, wind, tidal and geothermal energies
The proposed modified bat algorithm (MBA) combines the abandonment mechanism from Cuckoo Search (CS) with Bat Algorithm (BA). Both simulation and experimental results confirm that the proposed method is able to significantly improve the convergence time and accuracy compared to the original BA
All the initial voltage values of BA and MBA are set at 90%, 80%, 70%, 60% and 50% of the open circuit voltage (215 V), respectively

Summary

INTRODUCTION

The rise of environmental awareness and the greenhouse effect have led many countries to actively develop renewable energy sources such as solar, wind, tidal and geothermal energies. Liao et al.: Improved BA for More Efficient and Faster Maximum Power Point Tracking between each PV module [11], [12] This method will cause the overall P-V characteristic curve (P-VCC) to exhibit multiple peaks [7], [13]. The improved differential evolution for achieving GMPP under PS has been proposed by Tey et al [41] This method provides fast convergence and simple implementation due to the fewer control parameters. The proposed modified bat algorithm (MBA) combines the abandonment mechanism from CS with BA Both simulation and experimental results confirm that the proposed method is able to significantly improve the convergence time and accuracy compared to the original BA.

OVERVIEW OF BAT ALGORITHM

EXPERIMENTAL VALIDATION

DYNAMIC CASES

CONCLUSION