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Failure Diagnosis Method of Photovoltaic Generator Using Support Vector Machine
Journal of Electrical Engineering & Technology ( IF 1.9 ) Pub Date : 2020-05-08 , DOI: 10.1007/s42835-020-00430-9
Kyeong-Hee Cho , Hyung-Chul Jo , Eung-sang Kim , Hyang-A. Park , June Ho Park

The capacity of photovoltaic (PV) generators can increase owing to the 4030 policy of the Government of South Korea.. In addition, there has been significant interest in developing a technology for the maintenance of PV generators owing to an increase in the number of outdated PV generators. This paper describes a failure diagnosis method that uses operational data for power generation and solar radiation of PV generators. The measured data stored since four years in an operational 50-kW PV generator that was installed in 2014, were analyzed. The proposed failure diagnosis logic uses support vector machine classification as a failure diagnosis method that can classify normal and failure data. The failure data were processed to be used as the fault diagnosis logic for solar power generators. A new 50-kW PV generator, which contained no fault data, was used for a case study in this paper. Fault data were generated and the operation data of the PV generators were diagnosed by applying the proposed method. In addition, the accuracy was calculated and the results were analyzed.

中文翻译:

基于支持向量机的光伏发电机故障诊断方法

由于韩国政府的 4030 政策,光伏 (PV) 发电机的容量可以增加。此外,由于过时的数量增加,人们对开发用于维护光伏发电机的技术产生了浓厚的兴趣。光伏发电机。本文介绍了一种故障诊断方法,该方法使用光伏发电机发电和太阳辐射的运行数据。对 2014 年安装的运行中的 50 千瓦光伏发电机四年以来存储的测量数据进行了分析。所提出的故障诊断逻辑使用支持向量机分类作为故障诊断方法,可以对正常数据和故障数据进行分类。对故障数据进行处理,作为太阳能发电机组的故障诊断逻辑。一台没有故障数据的新型 50 千瓦光伏发电机,在本文中用于案例研究。应用所提出的方法生成故障数据并诊断光伏发电机的运行数据。此外,还计算了准确度并分析了结果。
更新日期:2020-05-08
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