Abnormal Detection Scheme of Substation Equipment based on Intelligent Fusion Terminal

In recent years, with the rapid development of science and technology, complex power services have made it more difficult to early warning of abnormal operating status of smart grid equipment. Aiming at the operation status information of substation equipment involved in complex power business, an abnormal detection scheme for substation equipment based on intelligent fusion terminal is proposed. The scheme is divided into three phases to strengthen anomaly detection. First, by calculating the Spearman correlation coefficient, the correlation between different label indicators is screened out from the operation status information of substation equipment. Then, considering the timeliness of state information data, in order to analyze the future operation state of substation equipment, the time series of different indicators are predicted by using the long short-term memory neural network. Finally, in order to further intuitively and accurately discover the abnormal conditions of the future operation status of the substation equipment, the clustering algorithm based on the density space is used to cluster the time series output by the long short-term memory neural network, and the abnormal conditions are marked. The experimental results show that by deploying the scheme, the smart fusion terminal can timely and effectively give early warning of equipment abnormalities and ensure the stable operation of the smart grid.