Abstract：
Power generation systems using photovoltaic (PV) technology have become increasingly popular due to their high production efficiency. A partial shading defect is the most common defect in this system under the process of production, diminishing both the amount and quality of energy produced. This paper proposes an Artificial Neural Network and Golden Eagle Optimization based prediction of the fault and its detection in a standalone PV system to recover the optimum performance and diagnosis of the PV system. The proposed technique combines the Artificial Neural Network (ANN) and Golden Eagle Optimization (GEO) algorithm. The major contribution of this work is to raise PV systems\' performance. The result is a defect in the classification and identification of an ANN is used. The use of GEO provides an efficient optimization technique for ANN training, which reduces the training time and improves the accuracy of the model. The proposed technique is executed on the MATLAB site and contrasted with different present techniques, like genetic algorithm (GA),Elephant Herding Optimization (EHO) and Particle Swarm Optimization (PSO). The findings displays that the proposed technique is more accurate and effective than the existing methodologies for detecting and diagnosing defects in PV systems.
摘要：
使用光伏（PV）技术的发电系统由于其高生产效率而变得越来越流行。局部着色缺陷是本系统在生产过程中最常见的缺陷,减少产生的能量的数量和质量。本文提出了一种基于人工神经网络和金鹰优化的故障预测及其在独立光伏系统中的检测，以恢复光伏系统的最佳性能和诊断。所提出的技术结合了人工神经网络（ANN）和金鹰优化（GEO）算法。这项工作的主要贡献是提高光伏系统的性能。结果是在使用ANN的分类和识别中存在缺陷。GEO的使用为神经网络训练提供了一种有效的优化技术，缩短了训练时间,提高了模型的准确性。所提出的技术在MATLAB网站上执行，并与不同的现有技术进行对比，像遗传算法(GA)，大象群优化(EHO)和粒子群优化(PSO).研究结果表明，所提出的技术比现有的检测和诊断光伏系统缺陷的方法更准确和有效。
