A Hybrid Maximum Power Point Tracking Method without Oscillations in Steady-State for Photovoltaic Energy Systems

Chih-Chiang Hua,Yu-Jun Zhan

doi:10.3390/en14185590

Abstract

This paper proposes a hybrid maximum power point tracking (MPPT) method with zero oscillation in steady-state by combining genetic algorithm (GA) and perturbation and observation (P&O) method. The proposed MPPT can track the global maximum power point (GMPP) fast for a photovoltaic (PV) system even under partial shaded conditions (PSC). The oscillations around the GMPP are eliminated and the power loss can be reduced significantly. In addition, the proposed MPPT can make the PV system operate at the highest efficiencies under various atmospheric conditions. During the MPP tracking, the system will oscillate around the MPPs, resulting in unnecessary power loss. To solve the problem, the artificial intelligence (AI) algorithms, such as PSO, Bee Colony optimization, GA, etc., were developed to deal with this issue. However, the problem with the AI algorithm is that the time for convergence may be too long if the range of the MPP search space is large. In addition, if the atmospheric conditions change fast, the PV system may operate at or close to the local maximum power points (LMPPs) for a long time. In this paper, a method combining the P&O’s fast tracking and GA’s GMPP tracking ability is proposed. The proposed system can stop the oscillations as soon as the GMPP is found, thus minimizing the power loss due to oscillations. The proposed MPPT can achieve superior performance while maintaining the simplicity of implementation. Finally, the simulation and experimental results are presented to demonstrate the feasibility of the proposed system.

Highlights

With the increasing growth of renewable energy, photovoltaic (PV) systems are increasingly used to generate electrical power from solar irradiation incident on PV modules
The complexity and cost of the whole system increases. The methods such as the hill-climbing (HC), perturbation and observation (P&O) [12,13,14,15,16,17] and the incremental conductance (INCs) can obtain high efficiency with a low computational cost, they have gained a primary position among the maximum power point tracking (MPPT) algorithms
This paper presents a hybrid global maximum power point tracking method that can track GMPP under partial shaded conditions (PSC) with nearly zero oscillations at steady-state

Summary

Introduction

With the increasing growth of renewable energy, photovoltaic (PV) systems are increasingly used to generate electrical power from solar irradiation incident on PV modules. In [5,6,7,8,9,10], several control schemes have been presented to track the MPPs for PV systems Some of these methods have simple configurations but low efficiencies due to the use of open-circuit voltage and short-circuit current method. The methods such as the hill-climbing (HC), perturbation and observation (P&O) [12,13,14,15,16,17] and the incremental conductance (INCs) can obtain high efficiency with a low computational cost, they have gained a primary position among the MPPT algorithms These algorithms suffer from: (1) Operating at local maximum power points (LMPPs) under partial shaded conditions (PSCs), (2) The steady-state oscillations cause large power loss. As soon as the GMPP is found, the perturbations are stopped, the oscillations and the power loss are reduced

Genetic Algorithm

Discussions

Conclusions