Abstract
This paper presents a novel modified Levy flight optimization for a photovoltaic PV solar energy system. Conventionally, the Perturb and Observe (P&O) algorithm has been widely deployed in most applications due to its simplicity and ease of implementation. However, P&O suffers from steady-state oscillation and stability, besides its failure in tracking the optimum power under partial shading conditions and fast irradiance changes. Therefore, a modified Levy flight optimization is proposed by incorporating a global search of beta parameters, which can significantly improve the tracking capability in local and global searches compared to the conventional methods. The proposed modified Levy flight optimization is verified with simulations and experiments under uniform, non-uniform, and dynamic conditions. All results prove the advantages of the proposed modified Levy flight optimization in extracting the optimal power with a fast response and high efficiency from the PV arrays.
Highlights
Greater awareness of clean energy has undoubtedly forced many governments and private sectors to adopt renewable energy resources [1,2]
Metaheuristics optimization algorithms are implemented in maximum power point tracker (MPPT) such as Ant Colony Optimization, Artificial Bee Colony [2], Cuckoo search [40], and the Fireflies Algorithm [42]
The research reveals that Levy-based Particle Swarm Optimization (PSO) outperforms the conventional PSO with a fast response time due to the characteristics of the Levy flight distribution with fat-tailed characteristics [43]
Summary
Greater awareness of clean energy has undoubtedly forced many governments and private sectors to adopt renewable energy resources [1,2]. Fuzzy Logic Control (FL) [29] and Particle Swarm Optimization (PSO) [34,35] are implemented by researchers in tracking the global maximum power point (GMPP). Metaheuristics optimization algorithms are implemented in MPPT such as Ant Colony Optimization, Artificial Bee Colony [2], Cuckoo search [40], and the Fireflies Algorithm [42] The performance of these algorithms in MPPT application shows good results in terms of tracking speed, steady-state oscillation, and stability. The research reveals that Levy-based PSO outperforms the conventional PSO with a fast response time due to the characteristics of the Levy flight distribution with fat-tailed characteristics [43]. This significantly inspired the authors to apply this technique in MPP.
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