Locomotion-based Hybrid Salp Swarm Algorithm for Parameter Estimation of Fuzzy Representation-based Photovoltaic Modules

Abstract

Identifying the parameters of photovoltaic (PV) modules is significant for their design and simulation. Because of the instabilities in the weather action and land surface of the earth, which cause errors in measuring, a novel fuzzy represen-tation-based PV module is formulated and developed. In this paper, a novel locomotion-based hybrid salp swarm algorithm (LHSSA) is presented to identify the parameters of PV modules accurately and reliably. In the LHSSA, better leader salps based on particle swarm optimization (PSO) are incorporated to the traditional salp swarm algorithm (SSA) in a serialized scheme with the aim of providing more valuable information for the leader salps of the SSA. By this integration, the proposed LHSSA can escape the local optima as well as guide the seeking process to attain the promising region. The proposed LHSSA is investigated on different PV models, i. e., single-diode (SD), double-diode (DD), and PV module in crisp and fuzzy aspects. By comparing with different algorithms, the comprehensive results affirm that the LHSSA can achieve a highly competitive performance, especially on quality and reliability.

Keywords

• Salp swarm algorithm (SSA)
• particle swarm optimization (PSO)
• photovoltaic (PV) model
• hybridization