Purpose – The purpose of this paper is to solve the problem that the standard particle swarm optimization (PSO) algorithm has a low success rate when applied to the optimization of multi-dimensional and multi-extreme value functions, the authors would introduce the extended memory factor to the PSO algorithm. Furthermore, the paper aims to improve the convergence rate and precision of basic artificial fish swarm algorithm (FSA), a novel FSA optimized by PSO algorithm with extended memory (PSOEM-FSA) is proposed. Design/methodology/approach – In PSOEM-FSA, the extended memory for PSO is introduced to store each particle’ historical information comprising of recent places, personal best positions and global best positions, and a parameter called extended memory effective factor is employed to describe the importance of extended memory. Then, stability region of its deterministic version in a dynamic environment is analyzed by means of the classic discrete control theory. Furthermore, the extended memory factor is applied to five kinds of behavior pattern for FSA, including swarming, following, remembering, communicating and searching. Findings – The paper proposes a new intelligent algorithm. On the one hand, this algorithm makes the fish swimming have the characteristics of the speed of inertia; on the other hand, it expands behavior patterns for the fish to choose in the search process and achieves higher accuracy and convergence rate than PSO-FSA, owning to extended memory beneficial to direction and purpose during search. Simulation results verify that these improvements can reduce the blindness of fish search process, improve optimization performance of the algorithm. Research limitations/implications – Because of the chosen research approach, the research results may lack persuasion. In the future study, the authors will conduct more experiments to understand the behavior of PSOEM-FSA. In addition, there are mainly two aspects that the performance of this algorithm could be further improved. Practical implications – The proposed algorithm can be used to many practical engineering problems such as tracking problems. Social implications – The authors hope that the PSOEM-FSA can increase a branch of FSA algorithm, and enrich the content of the intelligent algorithms to some extent. Originality/value – The novel optimized FSA algorithm proposed in this paper improves the convergence speed and searching precision of the ordinary FSA to some degree.
Due to the non-linear characteristics I–V of the photovoltaic (PV) curve, the tracking of the maximum power point (MPP) under partial shading (PS) conditions can sometimes be a challenging task. This paper presents a modified artificial fish swarm algorithm (AFSA) for MPP tracking (MPPT) in PV modules under PS. In this algorithm, the AFSA optimized by particle swarm optimization (PSO) algorithm with extended memory (PSOEM-FSA) is improved by hybridizing it with adaptive visual and step, and the resulting algorithm is a comprehensive improvement on the AFSA (abbreviated as CIAFSA). Combining the searching capabilities of the PSOEM-FSA and the self-learning ability of adaptive visual and step for AFSA, CIAFSA is developed. To validate the effectiveness of this novel MPPT technique, the PV system along with the proposed MPPT algorithm is simulated using the Matlab/Simulink Simscape toolbox. Results show that the proposed approach is more effective in MPPT in PV systems under PS conditions when compared with other methods in searching precision.
Maximum power extraction for PV systems with multiple panels under partial shading conditions (PSCs) relies on the configuration of the system and the optimal searching algorithms used. This paper described a PV system with multiple PV panels in series. Each panel has a dc-dc step-down converter, hence allowing independent control of load and source power ratio corresponding to the irradiation levels. An H-bridge terminal inverter is also used for grid connection. An advanced searching algorithm (TSPSOEM) is proposed in the paper for the distributed maximum power point tracking (DMPPT). This applies the basic particle swarm optimization (PSO) procedure but with an extended memory and incorporating the grouping concept from shuffled frog leaping algorithm (SFLA). The new algorithm is applied simultaneously to all PV-converter modules in the chain. The system can exploit the variable converter ratios and reduces the effect of differential shading, both between panels and across panels. The paper presents the system and the proposed new algorithm and demonstrating superior results obtained when compared with other conventional methods.
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