Diagnosis of contamination discharge state of porcelain insulators based on GA-CNN

ABSTRACT

Porcelain insulators play an important role in power transmission lines. It is of great significance to improve the accuracy of diagnosis of porcelain insulators’ discharge state and ensure the reliability of power supply. Therefore, this paper presents a diagnosis method of polluted discharge state of porcelain insulator based on GA-optimised CNN network structure. Firstly, the artificial pollution discharge test of porcelain insulator is carried out. According to the characteristics of leakage current, the discharge development process is divided into five stages: normal state, initial discharge, through discharge, flashover and flashover completion. GA algorithm is used to optimise the parameters of CNN model, and several single models are established simultaneously to compare the progress with the proposed model. The results show that GA has the advantages of global optimisation, less adjustment parameters, etc. It can automatically select the best structure of CNN network, avoid the problem of poor performance of artificial selection of CNN network structure, reduce the time required for parameter selection, and improve the accuracy of diagnosis of polluted discharge state of porcelain insulators, with the diagnosis accuracy as high as 99.2%. The results show that the discharge state of porcelain insulator surface can be judged by leakage current.

KEYWORDS:

• Genetic algorithm
• convolutional neural network
• porcelain insulator
• pollution discharge
• state diagnosis

Disclosure statement

No potential conflict of interest was reported by the author(s).