IOT and Deep Learning Based Method for Detecting Anomalies in Power System Operation Status

The current conventional power system operation status anomaly detection method is mainly realized by mining anomaly data, which leads to poor monitoring accuracy due to the lack of effective fusion of anomaly data features. In this regard, an abnormal detection method of power system operation status based on IOT(Internet of Things technology) and deep learning is proposed. By cleaning and normalizing the operation status data to improve the data purity, and performing heterogeneous data fusion processing, the steps of anomaly detection are optimized by combining IOT technology and deep learning algorithm. The checkout results indicate that the algorithm constructed by proposed method has a high accuracy of anomaly component detection and a high detection accuracy.