GIS partial discharge defect diagnosis system and method based on Extreme Learning Machine

Abstract

Partial discharge type identification is of great significance to the diagnosis of insulation faults in high-voltage power equipment. The partial discharge type recognition method based on deep learning map diagnosis has the disadvantages of large memory space and high hardware environment requirements. This paper presents a GIS discharge defect diagnosis system and method based on Extreme Learning Machine (ELM), which can be deployed on the edge layer equipment. The principal component analysis method is used to obtain the principal parameters that characterize the defects and form a sample feature data set. The sample feature data set is used to train the neural network model and continuously optimize it. Finally, the trained neural network model is used to identify the types of partial discharge defects. This method can effectively solve the problems that the partial discharge type identification method based on atlas diagnosis occupies large memory space and requires high hardware environment, which is convenient for the wide application of engineering.

Partial discharge location method in GIS coaxial bus structure based on multi-normal photoelectric array

“…The direction of the light is the intersection of the two planes γ1 and γ2, as shown in Figure 1(d). The final ray direction can be solved by equation (2).…”

“…Most of the faults were insulation faults. The insulation faults caused by abnormal surface of basin insulator, metal particles, loose shielding cover and burr of high voltage guide rod are particularly prominent [2]. In order to monitor discharge faults in GIS, various detection methods based on the characters of current, electromagnetic and acoustic have been proposed, which have become an important means of fault prevention at present [3].…”