Document Type

Journal Article

Publication Title

Energy Reports

Volume

9

First Page

1535

Last Page

1548

Publisher

Elsevier

School

School of Engineering

RAS ID

55949

Funders

Edith Cowan University

Comments

Hassan, A., Bass, O., & Masoum, M. A. S. (2023). An improved genetic algorithm based fractional open circuit voltage MPPT for solar PV systems. Energy Reports, 9, 1535-1548. https://doi.org/10.1016/j.egyr.2022.12.088

Abstract

To extract the maximum power from solar PV, maximum power point tracking (MPPT) controllers are needed to operate the PV arrays at their maximum power point under varying environmental conditions. Fractional Open Circuit Voltage (FOCV) is a simple, cost-effective, and easy to implement MPPT technique. However, it suffers from the discontinuous power supply and low tracking efficiency. To overcome these drawbacks, a new hybrid MPPT technique based on the Genetic Algorithm (GA) and FOCV is proposed. The proposed technique is based on a single decision variable, reducing the complexity and convergence time of the algorithm. MATLAB/Simulink is used to test the robustness of the proposed technique under uniform and non-uniform irradiance conditions. The performance is compared to the Perturb & Observe, Incremental Conductance, and other hybrid MPPT techniques. Furthermore, the efficacy of the proposed technique is also assessed against a commercial PV system's power output over one day. The results demonstrate that the proposed GA-FOCV technique improves the efficiency of the conventional FOCV method by almost 3%, exhibiting an average tracking efficiency of 99.96% and tracking speed of around 0.07 s with minimal steady-state oscillations. Additionally, the proposed technique can also efficiently track the global MPP under partial shading conditions and offers faster tracking speed, higher efficiency, and fewer oscillations than other hybrid MPPT techniques.

DOI

10.1016/j.egyr.2022.12.088

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

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