Boosted backtracking search optimization with information exchange for photovoltaic system evaluation

Weng, Xuemeng; Liu, Yun; Heidari, Ali Asghar; Cai, Zhennao; Lin, Haiping; Chen, Huiling; Liang, Guoxi; Alsufyani, Abdulmajeed; Bourouis, Sami

doi:10.1002/ese3.1329

Cited by 8 publications

(2 citation statements)

References 128 publications

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“…This study compares ERINMRIME with other advanced algorithms, including IJAYA 86 , GOTLBO 87 , OLGBO 88 , TLBOBSA 64 , GOFPANM 89 , MLBSA 90 , EHHO 91 , Dwarf Mongoose Optimizer (DMO) 92 , Artificial Hummingbird Algorithm (AHA) 93 , Social Network Search (SNS) 94 , Mantis Search Algorithm (MSA) 95 , BSA 96 , RIME 44 . Particular experimental outcomes are displayed in Table 9 .…”

Section: Experimental and Analysis For Pv Modelsmentioning

confidence: 99%

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Environment random interaction of rime optimization with Nelder-Mead simplex for parameter estimation of photovoltaic models

Shi,

Chen,

Heidari

et al. 2024

Sci Rep

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As countries attach importance to environmental protection, clean energy has become a hot topic. Among them, solar energy, as one of the efficient and easily accessible clean energy sources, has received widespread attention. An essential component in converting solar energy into electricity are solar cells. However, a major optimization difficulty remains in precisely and effectively calculating the parameters of photovoltaic (PV) models. In this regard, this study introduces an improved rime optimization algorithm (RIME), namely ERINMRIME, which integrates the Nelder-Mead simplex (NMs) with the environment random interaction (ERI) strategy. In the later phases of ERINMRIME, the ERI strategy serves as a complementary mechanism for augmenting the solution space exploration ability of the agent. By facilitating external interactions, this method improves the algorithm’s efficacy in conducting a global search by keeping it from becoming stuck in local optima. Moreover, by incorporating NMs, ERINMRIME enhances its ability to do local searches, leading to improved space exploration. To evaluate ERINMRIME's optimization performance on PV models, this study conducted experiments on four different models: the single diode model (SDM), the double diode model (DDM), the three-diode model (TDM), and the photovoltaic (PV) module model. The experimental results show that ERINMRIME reduces root mean square error for SDM, DDM, TDM, and PV module models by 46.23%, 59.32%, 61.49%, and 23.95%, respectively, compared with the original RIME. Furthermore, this study compared ERINMRIME with nine improved classical algorithms. The results show that ERINMRIME is a remarkable competitor. Ultimately, this study evaluated the performance of ERINMRIME across three distinct commercial PV models, while considering varying irradiation and temperature conditions. The performance of ERINMRIME is superior to existing similar algorithms in different irradiation and temperature conditions. Therefore, ERINMRIME is an algorithm with great potential in identifying and recognizing unknown parameters of PV models.

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Section: Experimental and Analysis For Pv Modelsmentioning

confidence: 99%

“…Weng et al . proposed the integration of the Backtracking Search Optimization Algorithm (BSA) and TLBO 64 . It has been used in the energy PV models.…”

Section: Introductionmentioning

confidence: 99%