Precise model parameters being the prerequisite for realizing accurate PV models, parameter identification techniques have gained immense interest over the years among the researchers specializing in PV systems. The application of various promising metaheuristic algorithms to optimize the model parameters have lightened up the scope of further enhancements in this field. Ever since, numerous metaheuristic algorithms have deployed for this purpose. With handful of techniques available in this regard, this paper takes up an initiative to review the existing metaheuristic algorithms based parameter extraction techniques with an emphasis on their compatibility, accuracy, convergence speed, range of parameters set and their validating environment. Based on the analysis conducted, accurate models available for 17 different industrial solar cells/modules are identified. Inspired by this review, an unidentified gateway between parameter extraction and fault detection in PV systems have been identified; and has further extended this review to differentiate some models that can help the researchers to achieve accurate, efficient and rapid fault detection. This review is a valuable gathering of statistics from the various researches carried out in PV parameter extraction which can assist enhanced researches for fault detection in PV systems as well.

精确的模型参数是实现准确的光伏模型的前提，多年来，参数识别技术在光伏系统领域的研究人员中引起了极大的兴趣。各种有希望的元启发式算法在优化模型参数方面的应用，减轻了该领域进一步改进的范围。从那时起，为此目的已经部署了许多元启发式算法。在这方面可以使用的技术很少的情况下，本文提出了一项基于现有的基于元启发式算法的参数提取技术的倡议，重点是它们的兼容性，准确性，收敛速度，参数集的范围及其验证环境。根据进行的分析，确定了可用于17种不同工业太阳能电池/模块的精确模型。受到这次审查的启发，已经确定了光伏系统中参数提取和故障检测之间的未知网关。并且进一步扩展了本文的审查范围，以区分一些模型，这些模型可以帮助研究人员实现准确，高效和快速的故障检测。这篇综述是从光伏参数提取中进行的各种研究中收集到的有价值的统计信息，这些数据也可以帮助增强光伏系统故障检测的研究。高效，快速的故障检测。这篇综述是从光伏参数提取中进行的各种研究中收集到的有价值的统计信息，这些数据也可以帮助增强光伏系统故障检测的研究。高效，快速的故障检测。这篇综述是从光伏参数提取中进行的各种研究中收集到的有价值的统计信息，这些数据也可以帮助增强光伏系统故障检测的研究。