Power grid fault diagnosis model based on the time series density distribution of warning information

Fast and intelligent power grid fault diagnosis is a key means of improving the intelligence level of power grid dispatching. Currently, data-driven power grid fault diagnosis lacks a method to quantify the time series characteristics of alarm information, and it is difficult to build a deep learning model. This paper proposes a power grid fault diagnosis method based on the time series density distribution of alarm information. First, the alarm information is classified; quantized time sequence feature extraction is performed for the device, and the obtained quantized time sequence features are superimposed. After graphical coding, a time series density distribution map of the alarm information of each device and a mosaic map reflecting the overall fault event are generated, providing a unified data representation for the deep learning model. Then, a faulty equipment type classification model and equipment state judgment model are constructed based on a residual network. The faulty equipment and fault type are determined using a sequential evaluation strategy that first determines the type of faulty equipment and then determines the state of the equipment. The proposed method can meet the requirements of on-line fault diagnosis of a power grid and provide emergency decision support for dispatchers.