Emended heap-based optimizer for characterizing performance of industrial solar generating units using triple-diode model

Abstract This paper presents an emended Heap-based optimizer (EHBO) for characterizing the accurate-performance of three-diode-based model (3DbM) of solar generating units (SGUs). The accurate extraction of the 3DbM parameters is an important issue to meet the actual characteristics of SGUs. Therefore, a better-quality version of HBO, named EHBO, is proposed to attain the same. As the complicated natures accompanied with the 3DbM such as multi-variable, multi-modal as well as its sensitivity to tiny changes, an EHBO is introduced through two improvements viz Hill-Climbing strategy (HCS), and informed searching-based learning strategy (ISLS). The HCS assists the algorithm to attain the promising areas of the search space and then enriches the diversity as well as the exploration capability while the ISLS aims to bolster the quality of the best individual and thus, can enable the exploitation ability. Therefore, EHBO is faster and more accurate than the classical HBO in achieving the global optimum as well as balancing exploration and exploitation abilities. The proposed EHBO is investigated on three commercial SGUs, namely, PWP-201, Kyocera polycrystalline KC200GT, and Ultra 85-P with maximum cropped errors of 2.0507 mA, 0.2211 mA, and 2.417 mA for these modules; respectively. Comprehensive experiments with comparisons are conducted to show the effectiveness and efficacy of the EHBO based model versus other competing optimizers. Based on the conducted investigations, it can be confirmed that the EHBO is a promising optimization method to deal with uncertain parameters of SGUs with different technologies.