Electric Power ›› 2022, Vol. 55 ›› Issue (5): 111-121.DOI: 10.11930/j.issn.1004-9649.202201039

• Power System • Previous Articles     Next Articles

Fault Diagnosis Method for Circuit Breaker Opening and Closing Coil Based on IEMD and GA-WNN

LI Tianhui1, PANG Xianhai1, FAN Hui2, ZHEN Li2, GU Chaomin1, DONG Chi1   

  1. 1. State Grid Hebei Electric Power Research Institute, Shijiazhuang 050021, China;
    2. State Grid Hebei Electric Power Co., Ltd., Shijiazhuang 050021, China
  • Received:2022-01-13 Revised:2022-02-25 Online:2022-05-28 Published:2022-05-18
  • Supported by:
    This work is supported by Science and Technology Project of SGCC (No.kjcd2020-003), Science and Technology Project of State Grid Hebei Electric Power Co., Ltd. (No.kj2019-067).

Abstract: The running state of the secondary circuit or operating mechanism of vacuum circuit breakers can be reflected by the characteristics of current curves. Firstly, three kinds of common faults, including core blockage, abnormal voltage (too high or too low) and breakdown, are simulated in laboratory, and a fault current curve characteristic library is established. Secondly, based on the property that the product of energy density in the inherent mode function of the fault current signals after ensemble mode decomposition and its corresponding average period is a constant, an improved empirical mode decomposition method(IEMD) is proposed to extract the current eigenvalues of the opening and closing coils, which are used as the input sample set of the neural network. On this basis, a circuit breaker fault diagnosis method is proposed by combining the improved genetic algorithm(GA) and wavelet neural network(WNN). This method uses the improved genetic algorithm to optimize the parameters of the neural network in order to solve the problem of parameter sensitivity of the wavelet neural network, thus improving the convergence speed of the diagnosis algorithm and the accuracy of fault diagnosis. Simulation results show that compared with the traditional neural network diagnosis method, the proposed fault diagnosis method has a diagnostic accuracy of 91%, increasing by 10 percentage point.

Key words: circuit breaker, opening and closing coil, improved set modal decomposition, improved wavelet neural network, fault diagnosis