Effective fault diagnosis in a PV system requires understanding the behavior of the current/voltage (I/V) parameters in different environmental conditions. Especially during the winter season, I/V characters of certain faulty states in a PV system closely resemble that of a normal state. Therefore, a normal fault detection model can falsely predict a well-operating PV system as a faulty state and vice versa. In this paper, an intelligent fault diagnosis model is proposed for the fault detection and classification in PV systems. For the experimental verification, various fault state and normal state datasets are collected during the winter season under wide environmental conditions. The collected datasets are normalized and preprocessed using several data-mining techniques and then fed into a probabilistic neural network (PNN). The PNN model will be trained with the historical data to predict and classify faults when new data is fetched in it. The trained model showed better performance in prediction accuracy when compared with other classification methods in machine learning.

中文翻译：

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用于光伏系统故障检测的智能故障检测模型

在光伏系统中进行有效的故障诊断需要了解不同环境条件下电流/电压（I / V）参数的行为。特别是在冬季，PV系统中某些故障状态的I / V特性与正常状态非常相似。因此，正常的故障检测模型可以错误地将运行良好的光伏系统预测为故障状态，反之亦然。本文提出了一种智能的故障诊断模型，用于光伏系统的故障检测与分类。为了进行实验验证，冬季在宽广的环境条件下收集了各种断层状态和正常状态数据集。使用多种数据挖掘技术对收集的数据集进行标准化和预处理，然后将其输入到概率神经网络（PNN）中。PNN模型将使用历史数据进行训练，以便在提取新数据时对故障进行预测和分类。与机器学习中的其他分类方法相比，训练后的模型在预测准确性上表现出更好的性能。