Solar photo voltaic module parameter extraction using a novel Hybrid Chimp-Sine Cosine Algorithm

Abstract

ABSTRACT The research on deriving accurate equivalent circuit of solar photovoltaic (PV) modules is increasing due to the necessity of constructing efficient energy conversion devices. The PV panel manufacturers provide data on three essential points on current–voltage (I–V) characteristics for standard temperature conditions (STC). Hence, the research on PV modules for different environmental/operational conditions with equivalent mathematical models are quite complex. Therefore, there is a necessity to derive accurate PV equivalent circuit parameters using novel AI-based approaches. This work proposes a novel hybrid meta-heuristic algorithm, hybrid Chimp-Sine cosine algorithm (HCSCA), for PV panel equivalent circuit parameter extraction. A well-known single- and double-diode PV models have been investigated with the proposed algorithm for different categories of PV modules, namely monocrystalline, polycrystalline, and thin film. The parameters derived from the proposed approach result in minimum error over different executions in the order of less than 10−10, which recommends better implementation in the present scenario. The nature of extracted parameters and I–V characteristics of considered PV panels are examined over different runs, which provided satisfactory performance characteristics with the proposed algorithm and recommended for its practical implementation.