Constraint estimation in three-diode solar photovoltaic model using Gaussian and Cauchy mutation-based hunger games search optimizer and enhanced Newton–Raphson method

Manoharan Premkumar, Pradeep Jangir, Chandrasekaran Kumar, Somasundaram David Thanasingh Sundarsingh Jebaseelan, Hassan Haes Alhelou, Rajvikram Madurai Elavarasan, Huiling Chen

Research output: Contribution to journalArticleResearchpeer-review

21 Citations (Scopus)

Abstract

The reliability of the photovoltaic models is strongly reliant on their parameters, which are primarily determined by the optimization algorithm and the objective function. As a result, obtaining the parameters under different environmental conditions is critical for increasing their performance, reliability and significantly lowering cost. Many optimization techniques are reported to address this problem based on the complexity. As a result, an enhanced version of the recently reported Hunger Games Search Optimizer (HGSO) method called Gaussian and Cauchy Mutation-based HGSO (GCMHGSO) algorithm for defining the requirements of the Three-Diode equivalent Model (TDeM) by utilizing multiple representations in the algorithm along with an efficient objective function. The Cauchy mutation increases the exploration ability, and Gaussian mutation increases the exploitation ability of the basic HGSO. Furthermore, an Enhanced Newton–Raphson Method (ENRM) is presented to effectively solve the behaviour of the current–voltage relation of the TDeM. The robust optimization is also considered to demonstrate the impact of the measurement error. Comparing the GCMHGSO-ENRM to other competitors reveals that the proposed GCMHGSO-ENRM can accurately find the best solution, and its effectiveness is verified in many statistical parameters. It is found that the GCMHGSO-ENRM algorithm is stable and robust compared to other competitors.

Original languageEnglish
Pages (from-to)1733-1772
Number of pages40
JournalIET Renewable Power Generation
Volume16
Issue number8
DOIs
Publication statusPublished - 8 Jun 2022
Externally publishedYes

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