基于XGBoost的光伏阵列故障诊断方法研究

针对光伏阵列输出具有非线性并受最大功率点跟踪影响,从而导致传统的诊断方法精度低、模型性能差等问题,提出一种基于极端梯度提升的光伏阵列故障诊断方法。首先,在光伏电池单二极管模型的基础上,建立光伏阵列仿真模型,利用PVsyst软件对光伏阵列的输出特性和故障成因进行系统的模拟分析,得到了故障特征参数,并通过特征重要度排序验证了所选择故障特征参数的有效性;其次,提取光伏阵列不同故障状态下的故障特征,构建基于XGBoost的故障诊断模型;最后,利用网格搜索和交叉验证对诊断模型的超参数进行寻优,通过混淆矩阵计算评价指标来评估诊断模型的性能。并将该方法与决策树、随机森林以及梯度提升树相比,结果表明,该方法不仅能准确检测所有的故障种类,并且模型的泛化能力更好,诊断准确率更高。

Research on fault diagnosis method of photovoltaic array based on extreme gradient boosting

An extreme gradient boosting (XGBoost) method for photovoltaic array fault diagnosis is proposed to solve the problems of low precision and poor model performance due to the nonlinear output of photovoltaic array and the influence of maximum power point tracking. Firstly, based on the single diode model of photovoltaic cell, a simulation model of photovoltaic array was established, and the output characteristics and fault causes of photovoltaic array were systematically simulated and analyzed by using PVsyst software, and the fault characteristic parameters were obtained and the validity of the selected fault feature parameters is verified by the feature importance ranking. Secondly, the fault characteristics of photovoltaic array under different fault states are extracted, and the fault diagnosis model based on XGBoost is constructed. Finally, grid search and cross validation were used to optimize the hyperparameters of the diagnostic model, and the performance of the diagnostic model was evaluated by confounding matrix calculation. Compared with decision tree, random forest and gradient lifting tree, the results show that the proposed method not only can accurately detect all kinds of faults, but also has better generalization ability and higher diagnosis accuracy.