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Extracting parameters and constructing high-precision models of photovoltaic modules through actual current-voltage data is required for simulation, control, and optimization of a photovoltaic system. Because of the application of such problems, the identification of unknown parameters accurately and reliably remains a challenging task. In this paper, we propose an enhanced Harris Hawks Optimization (EHHO), which combines orthogonal learning (OL) and general opposition-based learning (GOBL), to estimate the parameters of solar cells and photovoltaic modules effectively and accurately. In EHHO, OL helps to improve the speed of the HHO method and the accuracy of the solution. At the same time, the GOBL mechanism can increase both diversity of the population and the HHO’s exploitation performance. In addition, these two mechanisms defend the equilibrium between the exploitation and exploration rates. The results show that accuracy, reliability, and other aspects of this method are better than most existing methods. Thus, we observed that EHHO can be used as an effective method for parameter estimation of solar cells and photovoltaic modules.
The population-based HHO was the most successful and popular optimization method currently. The HHO focuses on performance and provides a variety of search patterns based on random switching statements. It is a gradient-free optimization algorithm with several energetic and time-varying stages of exploration and exploitation tendencies. In spite of previous methods published in lower impact journals, the HHO was published in the Journal of Future Generation Computer Systems (FGCS) with an impact factor of 6.125 in 2019, and from the first day of publication, it has gotten growing consideration among researchers owing to its flexible structure, high performance, and first-rate results. The leading logic of the HHO technique is created according to some successful life patterns of Harris' hawks in nature called "surprise pounce". Due to the HHO technique's efficacy, there are many variants of HHO now in the best leading Elsevier and IEEE transaction journals.
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The HHO algorithm is a high performance and easy to code, and straightforward to understand optimizer, while it has some time-varying components. The primary method was published in a top prestigious computer science journal. In 2020, it turned into the most used method for solving any problem. This method's source codes are widely available in almost all programming languages, and it has both a latex template and word office file for the pleasure of users. This method is backed up with a 24-h online service for reacting to users' questions on the code..
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Jiao, S., Chong, G., Huang, C., Hu, H., Wang, M., Heidari, A. A., ... & Zhao, X. (2020). Orthogonally adapted Harris Hawk Optimization for parameter estimation of photovoltaic models. Energy, 117804. https://doi.org/10.1016/j.energy.2020.117804
"I propose to consider the question, 'Can machines think?"
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