基于多站支路功率联合学习的分布式光伏支路异常检测方法

In recent years, the number of distributed Photovoltaics (PV) stations has increased rapidly. Frequent PV array anomalies have caused a great loss of power generation efficiency, which brings the demand for detecting multi-station PV array anomalies accurately and efficiently. To solve this problem, an anomaly detection method based on the joint learning of multi-station PV array power was proposed. In this method, the similarity and difference representations of PV array anomaly identification features were firstly extracted with the joint learning of array anomaly detection tasks of multiple PV stations. The multi-scale convolution neural network was constructed to capture the differential anomaly identification features of multi-station PV array power. Then, the auxiliary task was used to fully learn the similar representation of PV array anomaly identification features. A multi-stage training strategy was adopted to reduce the negative impact of auxiliary tasks on the accuracy of PV array anomaly detection. In the comparison of multiple experiments, the proposed method had a great performance in improving the accuracy of array anomaly detection on multiple distributed PV stations. In addition, this method also had the superiority in modeling convenience, because only one model needed to be built to realize the anomaly detection of distributed PV multi-station arrays.