Anomaly data detection algorithm of small photovoltaic power station based on machine learning - Han, Bitong; Xie, Hongbin; Sui, Jiayin; Shan, Yu; Chen, Mingdong; Li, Yanhao (SPIE, 2022)
With the intensification of the global energy crisis and the increasing shortage of fossil fuels, more and more countries have begun to strengthen the development and utilization of new energy. In recent years, with the help of policies and capital, the photovoltaic industry has achieved rapid development and occupies a leading position in new energy. The output power of a photovoltaic power station is affected by various factors such as solar radiation intensity, temperature, and installation method. The aging of photovoltaic modules, surface dust and component damage will also affect the power generation efficiency of photovoltaic power plants. The identification of abnormal data can not only help power station owners and operation and maintenance manufacturers to find potential equipment failures and other problems at the first time, but also can effectively avoid safety risks and economic losses. This paper proposes an abnormal data detection algorithm for small photovoltaic power plants based on machine learning. First, the operating data of photovoltaic power plants are normalized, and then the abnormal scores of the data samples are calculated by the iForest method, and then the classification center is calculated by the K-means method. This method can realize abnormal data detection in small photovoltaic power plants without irradiators, effectively avoid potential failures and risks of photovoltaic power plants, and ensure the operation safety of power plants and power grids to a certain extent.