Based on vibration signal of high voltage circuit breaker,a new method of intelligent fault diagnosis that wavelet packet extracts energy entropy which are used as characteristic vector of the support vector machine(S...Based on vibration signal of high voltage circuit breaker,a new method of intelligent fault diagnosis that wavelet packet extracts energy entropy which are used as characteristic vector of the support vector machine(SVM)to construct classifier for fault diagnosis is presented.The acceleration sensors are applied to collecting the vibration data of different states of high voltage circuit breakers based on self-made experimental platform in this method.The wavelet packet are fully applied to analyze the vibration signal and decompose vibration signal into three layers,and wavelet packet energy entropy of each frequency band are as the characteristic vector of circuit breaker failure mode.Then the intelligent diagnosis network is established on the basis of the support vector machine theory.It is verified that the method has a better capability of classification and a higher accuracy compared with the traditional neural network diagnosis method through distinguishing the three fault modes which are tripping device stuck,the vacuum arcing chamber fixed bolt looseness and too much friction force of the transmission mechanism of circuit breaker in this paper.展开更多
To promote the accuracy and application of arcing time measurement for SF_6 circuit breaker in substation,five measurement methods are investigated by two cases experimentally. First,the test results of the five metho...To promote the accuracy and application of arcing time measurement for SF_6 circuit breaker in substation,five measurement methods are investigated by two cases experimentally. First,the test results of the five methods for a circuit breaker in different stages of wear and a circuit breaker with a component failure were presented. Then,the time error is analyzed by simulation.Finally,the advantage and disadvantage of these methods are discussed.展开更多
基金Project Supported by National Natural Science Foundation of China(51177104)Liaoning Province Natural Science Foundation of China(201102169)
文摘Based on vibration signal of high voltage circuit breaker,a new method of intelligent fault diagnosis that wavelet packet extracts energy entropy which are used as characteristic vector of the support vector machine(SVM)to construct classifier for fault diagnosis is presented.The acceleration sensors are applied to collecting the vibration data of different states of high voltage circuit breakers based on self-made experimental platform in this method.The wavelet packet are fully applied to analyze the vibration signal and decompose vibration signal into three layers,and wavelet packet energy entropy of each frequency band are as the characteristic vector of circuit breaker failure mode.Then the intelligent diagnosis network is established on the basis of the support vector machine theory.It is verified that the method has a better capability of classification and a higher accuracy compared with the traditional neural network diagnosis method through distinguishing the three fault modes which are tripping device stuck,the vacuum arcing chamber fixed bolt looseness and too much friction force of the transmission mechanism of circuit breaker in this paper.
基金Project Supported by the Technique Project of China Southern Power Grid Co.,Ltd.(20142001342)
文摘To promote the accuracy and application of arcing time measurement for SF_6 circuit breaker in substation,five measurement methods are investigated by two cases experimentally. First,the test results of the five methods for a circuit breaker in different stages of wear and a circuit breaker with a component failure were presented. Then,the time error is analyzed by simulation.Finally,the advantage and disadvantage of these methods are discussed.
