Modified normative fish swarm algorithm for optimizing power extraction in photovoltaic systems

• Published in: Evolutionary intelligence 2023-08, Vol.16 (4), p.1135-1154
• Main Authors: Tan, Weng-Hooi, Mohamad-Saleh, Junita
• Format: Article
• Language: English
• Subjects: Algorithms; Applications of Mathematics; Artificial Intelligence; Bioinformatics; Control; Electric rates; Electricity pricing; Engineering; Evolutionary algorithms; Mathematical and Computational Engineering; Maximum power tracking; Mechatronics; Optimization; Parameter modification; Photovoltaic cells; Research Paper; Robotics; Solar power generation; Statistical Physics and Dynamical Systems
• ISSN: 1864-5909; 1864-5917
• DOI: 10.1007/s12065-022-00724-z

Background : According to ASEAN Centre of Energy 2020 report, electrification rates in certain ASEAN countries are considerably low. The energy sector faces issues of unreliable supply, undersupply and high electricity rates. As power supply is an important source of daily requirement, this research work aims to improve the efficiency of solar power generation efficiency. In line with the aim, we proposed a modified Normative Fish Swarm Algorithm (mNFSA) and applied it to Maximum Power Point Tracking (MPPT) process in Photovoltaic (PV) application systems. Significant modifications have been made to the original NFSA, including the removal of unnecessary features, the formulation of modified behaviors, the refinement of adaptive parameters as well as settings, and the implementation of the MPPT architecture. Results : A complete PV system model is constructed for simulation, in which 10 PV arrays with different shading levels are connected in series in the PV panel. For a consistent evaluation, 10 sets of data on maximum extracted power were collected during MPPT simulation process. The statistical data were recorded for comprehensive performance checks. Overall, mNFSA was compared with 6 other optimization algorithms, including 5 state-of-the-art algorithms and 1 related evolutionary algorithm. Conclusions : The results demonstrate that mNFSA outperforms other compared algorithms in terms of maximum extracted power and relative percentage error, demonstrating higher compatibility and effectiveness of mNFSA for MPPT. The statistical results support that mNFSA is one of the most robust algorithms and best suited for MPPT applications.