A review on modeling of solar photovoltaic systems using artificial neural networks, fuzzy logic, genetic algorithm and hybrid models,International Journal of Energy Research

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A review on modeling of solar photovoltaic systems using artificial neural networks, fuzzy logic, genetic algorithm and hybrid models
International Journal of Energy Research ( IF 4.3 ) Pub Date : 2020-06-25 , DOI: 10.1002/er.5608
Kunal Sandip Garud ₁ , Simon Jayaraj ₂ , Moo‐Yeon Lee ₁

Affiliation

The uncertainty associated with modeling and performance prediction of solar photovoltaic systems could be easily and efficiently solved by artificial intelligence techniques. During the past decade of 2009 to 2019, artificial neural network (ANN), fuzzy logic (FL), genetic algorithm (GA) and their hybrid models are found potential artificial intelligence tools for performance prediction and modeling of solar photovoltaic systems. In addition, during this decade there is no extensive review on applicability of ANN, FL, GA and their hybrid models for performance prediction and modeling of solar photovoltaic systems. Therefore, this article focuses on extensive review on design, modeling, maximum power point tracking, fault detection and output power/efficiency prediction of solar photovoltaic systems using artificial intelligence techniques of the ANN, FL, GA and their hybrid models. In addition, the selected articles on the solar radiation prediction using ANN, FL, GA and their hybrid models are also summarized. Total of 122 articles are reviewed and summarized in the present review for the period of 2009 to 2019 with 90 articles in the field of {ANN, FL, GA and their hybrid models} + solar photovoltaic systems and 32 articles in the field of {ANN, FL, GA and their hybrid models} + solar radiation. The review shows the suitability and reliability of ANN, FL, GA and hybrid models for accurate prediction of the solar radiation and the performance characteristics of solar photovoltaic systems. In addition, this review presents the guidance for the researchers and engineers in the field of solar photovoltaic systems to select the suitable prediction tool for enhancement of the performance characteristics of the solar photovoltaic systems and the utilization of the available solar radiation.

中文翻译：

基于人工神经网络，模糊逻辑，遗传算法和混合模型的太阳能光伏系统建模综述

与太阳能光伏系统的建模和性能预测相关的不确定性可以通过人工智能技术轻松有效地解决。在2009年至2019年的过去十年中，人们发现了人工神经网络（ANN），模糊逻辑（FL），遗传算法（GA）及其混合模型，这些潜在的人工智能工具可用于太阳能光伏系统的性能预测和建模。另外，在这十年中，没有对ANN，FL，GA及其混合模型在太阳能光伏系统的性能预测和建模中的适用性进行广泛的审查。因此，本文着重于对设计，建模，最大功率点跟踪，使用ANN，FL，GA及其混合模型的人工智能技术对太阳能光伏系统进行故障检测和输出功率/效率预测。此外，还总结了有关使用ANN，FL，GA及其混合模型进行太阳辐射预测的部分文章。在本综述中，对2009年至2019年期间的122篇文章进行了总结和总结，其中{ANN，FL，GA及其混合模型} +太阳能光伏系统领域有90篇文章，{ANN领域中有32篇文章，FL，GA及其混合模型} +太阳辐射。审查显示了ANN，FL，GA和混合模型的适用性和可靠性，可准确预测太阳辐射和太阳能光伏系统的性能。此外，

更新日期：2020-06-25

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