模糊C均值聚类在光伏阵列故障样本数据识别中的应用

陆灵骍; 朱红路; 连魏魏; 戴松元; 姚建曦

doi:10.12096/j.2096-4528.pgt.2018.029

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模糊C均值聚类在光伏阵列故障样本数据识别中的应用

新能源 | 更新时间：2025-07-04

- 模糊C均值聚类在光伏阵列故障样本数据识别中的应用
- Application of FCM Method in Data Division of Photovoltaic Array Fault Samples
- 发电技术 2018年39卷第2期页码：188-194
- 作者机构：
  
  华北电力大学可再生能源学院, 北京市昌平区 102206
- 作者简介：
  
  [ "陆灵骍(1995), 男, 硕士, 研究方向为光伏电站的智能运维及故障诊断, lingxinglu@ncepu.edu.cn" ]
  [ "朱红路(1982), 男, 讲师, 研究方向包括光伏电站功率预测、智能运维及能量管理, hongluzhu@126.com" ]
- 基金信息：
- DOI：10.12096/j.2096-4528.pgt.2018.029
  中图分类号：
- 收稿：2017-12-20，
  
  纸质出版：2018-04-28
- 稿件说明：
移动端阅览
陆灵骍, 朱红路, 连魏魏, 等. 模糊C均值聚类在光伏阵列故障样本数据识别中的应用[J]. 发电技术, 2018,39(2):188-194.

Lingxing LU, Honglu ZHU, Weiwei LIAN, et al. Application of FCM Method in Data Division of Photovoltaic Array Fault Samples[J]. Power Generation Technology, 2018, 39(2): 188-194.
陆灵骍, 朱红路, 连魏魏, 等. 模糊C均值聚类在光伏阵列故障样本数据识别中的应用[J]. 发电技术, 2018,39(2):188-194. DOI： 10.12096/j.2096-4528.pgt.2018.029.

Lingxing LU, Honglu ZHU, Weiwei LIAN, et al. Application of FCM Method in Data Division of Photovoltaic Array Fault Samples[J]. Power Generation Technology, 2018, 39(2): 188-194. DOI： 10.12096/j.2096-4528.pgt.2018.029.

摘要

光伏电站由数量庞大的光伏组件构成，因复杂的生产工艺及艰苦的工作环境，光伏系统直流侧故障频发，直接影响到光伏系统的发电效益。如何从光伏阵列的运行数据中提取有效的故障样本，并对其进行识别，是建立故障模型的重要步骤。因此提出一种基于模糊C均值(fuzzy C-means，FCM)聚类算法对故障样本进行划分及标识的方法。首先对故障条件下光伏阵列的输出特性进行分析，提取出故障特征向量(开路电压

，短路电流

，最大工作点电压

，最大工作点电流

)。为排除外部激励条件对电气参数的影响，将故障特征向量统一转换至标准测试条件(standard test condition，STC)。最后根据FCM算法良好的模糊信息处理功能，对开路故障、短路故障、阴影故障、异常老化故障的样本进行聚类划分。实际运行数据证明，该方法可以精确、可靠地对光伏系统直流侧典型故障的样本进行智能聚类，并有效地描述不同故障下光伏阵列电气参数的分布特征。

Abstract

The photovoltaic (PV) power station is composed of a large number of photovoltaic modules.Due to the complicated production technology and hard working environment

photovoltaic system DC-side faults occur frequently

directly affecting the photovoltaic system's power generation efficiency.How to extract valid fault sample from the PV array's operating data and identify the fault is an important step to establish a fault model.Therefore

a method based on fuzzy C-means (FCM) clustering to divide and identify the fault samples was proposed.Firstly

the output features of PV array under fault conditions were analyzed and the fault eigenvectors were put forward (open circuit voltage

short circuit current

maximum power point voltage and current).In order to exclude the influence of external excitation conditions on the electrical parameters

the fault eigenvectors were uniformly converted to the standard test condition (STC).Finally

according to the good fuzzy information processing function of FCM

the fault samples of open fault

short fault

shadow fault and abnormal aging fault were clustered.By using the actual operation data

it was proved that this method could accurately and reliably classify the fault samples of the typical fault on the DC side of the PV system and could effectively describe the distribution characteristics of the PV array's electrical parameters in different faults.

关键词

Keywords

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