Study on the Transient Temperature Evolution Characteristics of Three-Phase Co-Box Type GIS and Inversion Method for Busbar Temperature

The online diagnosis technology used to determine the internal thermal status and defects of GIS equipment is important. In the existing GIS bus thermal defect fault diagnosis methods, sensors are usually installed on the highest and lowest temperature areas of the enclosure surface, and then an artificial neural network is established to obtain the highest temperature inside the GIS. These methods only consider the temperature under steady-state conditions, and the temperature signals collected by sensors are different, which leads to low accuracy and weak generality. This paper investigated the transient temperature evolution characteristics defined as a sequence of temperature values over time, and adopted them as new features. The steady and transient enclosure and environment temperature data were used to train the Generalized Regression Neural Network (GRNN) for the inside busbar temperature inversion. Experimental tests proved that the proposed method has a higher accuracy compared to traditional characteristic parameters, especially for the less significant temperature rise. This article provides a technical means for determining the internal temperature rise status of GIS equipment through external temperature monitoring in practical applications.