基于半监督学习标签传播-极端随机树算法的光伏阵列故障诊断及定位

Fault Diagnosis and Localization of Photovoltaic Arrays Based on Semi-supervised Learning Label Propagation-extra Tree Algorithm

对光伏阵列故障进行精确诊断和定位有助于提升光伏发电系统的可靠性。针对现有的诊断方法过度依赖大量有标签样本,难以同时兼顾故障类型诊断、故障定位及低成本等问题,将多传感器法与半监督学习算法相结合,构建了一种融合标签传播算法(label propagation,LP)和极端随机树(extra-trees,ET)的半监督学习算法LP-ET。为克服工程实际故障样本较少且往往缺失故障标签的问题,搭建了光伏阵列故障仿真模型获取样本,引入LP算法,基于少量含故障类型及定位信息的有标签故障样本,实现原始故障样本集全标注;继而引入ET模型,持续构建大量决策树形成极端随机树,采用多数投票机制(Bagging)获得故障类型及定位结果。实验结果表明,所提出的LP-ET模型可以在含有大比例未标注样本数据集情况下实现短路、断路、退化及遮阴故障的较高精度诊断,兼顾单组件及多组件故障,有效解决光伏阵列故障诊断及定位问题。

Accurate diagnosis and localisation of the faults in the PV arrays help improve the reliability of the PV power generation systems. In order to solve the problems that the existing diagnostic methods rely too much on a large number of labeled samples and cannot take into account the fault type diagnosis, the fault location and the costs at the same time, and combining the multi-sensor method with a semi-supervised learning algorithm, a semi-supervised learning algorithm(LP-ET) integrating the Label Propagation(LP) with the Extra-Trees(ET) is built.In order to overcome the less engineering fault samples and the lack of the fault labels,a PV array fault simulation model is built to obtain the samples.The LP algorithm is introduced to achieve the full labelling of the samples in the original fault sample set based on a small number of the labelled fault samples containing the fault type and location information.Then, the ET model is applied to continuously build a large number of decision trees to form an extreme random tree.A majority voting mechanism is used(Bagging) to obtain the fault type and location results. Experimental results showthat theproposed LP-ET model realizes high precisiondiagnoses under the short circuit, the open circuit, the degradation and the shading faults in the case of a large proportion of unlabeledsample data sets.It takes both the single component and multi-component faults into consideration, effectively solving the problem of PV array fault diagnosis and location.