Research on expert diagnosis system for mechanical fault of high voltage circuit breaker based on fuzzy matrix and neural network technology

2016 ◽  
Author(s):  
Pingyin Hou ◽  
Shijun Bai ◽  
Yun Ge ◽  
Yongqiang Zhang ◽  
Haojun Zhang
Keyword(s):  
Neural Network ◽  
High Voltage ◽  
Circuit Breaker ◽  
Network Technology ◽  
Diagnosis System ◽  
Fuzzy Matrix ◽  
High Voltage Circuit Breaker ◽  
Neural Network Technology

scholarly journals Development and Fault Prediction of a New Operating Mechanism of HTPPM

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Hongkui Yan ◽  
Xin Lin ◽  
Jianyuan Xu
Keyword(s):  
Neural Network ◽  
High Voltage ◽  
Ant Colony Algorithm ◽  
Circuit Breaker ◽  
High Energy ◽  
Least Square ◽  
Fault Prediction ◽  
High Energy Density ◽  
Circuit Breakers ◽  
High Voltage Circuit Breaker

In this article, we take a 126 kV single-break vacuum circuit breaker as the research object and study the application of high-energy-density PM motor in the high-voltage circuit breaker for the first time. The PM motor maintains maximum power density and torque density during the start-up phase. Note that most of the faults of high-voltage circuit breakers are mechanical faults. We designed a set of mechanical fault prediction systems for high-voltage circuit breakers. We present the prediction method of the opening and closing action curve of the high-voltage circuit breaker. It is inspired by Chaos Ant Colony Algorithm (CAS) and an optimized Long- and Short-Term Memory (LSTM) cycle neural network. We constructed the main structure of the neural network expert system and established the fault prediction model of the high-voltage circuit breaker, based on the LSTM cycle neural network, optimized by CAS. We used the improved least-square method to achieve the operation accuracy of the phase control switch. Finally, we completed the development and experiment of the prototype.

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