Fault Diagnosis Of Circuit Breaker Research Articles

Mechanical Fault Diagnosis of Circuit Breaker Based on Improved BREMD and ELM

In order to accurately and quickly obtain the fault information from the circuit breaker vibration signal, a novel feature extraction method based on improved bandwidth restricted empirical mode decomposition (IBREMD) is proposed, and then the extreme learning machine (ELM) is employed for fault diagnosis. Since it is difficult to determine the optimal bandwidth frequency in traditional bandwidth restricted empirical mode decomposition, the introduced IBREMD approach adopts an optimization function to select the bandwidth restricted signal frequency, which is used during the process of empirical mode decomposition. In this way, the frequency resolving ability is dramatically improved, resulting in excellent feature extraction performance. The experimental results show that the diagnosis accuracy is 98.3%, which promise to be effective in terms of fault diagnosis of circuit breaker.

Application of Multiscale Entropy in Mechanical Fault Diagnosis of High Voltage Circuit Breaker.

Mechanical fault diagnosis of a circuit breaker can help improve the reliability of power systems. Therefore, a new method based on multiscale entropy (MSE) and the support vector machine (SVM) is proposed to diagnose the fault in high voltage circuit breakers. First, Variational Mode Decomposition (VMD) is used to process the high voltage circuit breaker’s vibration signals, and the reconstructed signal can eliminate the effect of noise. Second, the multiscale entropy of the reconstructed signal is calculated and selected as a feature vector. Finally, based on the feature vector, the fault identification and classification are realized by SVM. The feature vector constructed by multiscale entropy is compared with other feature vectors to illustrate the superiority of the proposed method. Through experimentation on a 35 kV SF6 circuit breaker, the feasibility and applicability of the proposed method for fault diagnosis are verified.

Extreme Learning Machine in the Breaker Fault Diagnosis

In order to improve the diagnosis method of the existing high-voltage circuit breaker fault, demonstrated a new diagnosis methord of mechanical failure of high voltage circuit breaker based on vibration signal. According to the factors of high voltage circuit breaker failure and the features of Single-hidden Layer Feedforward Neural Network, SLFN, a method of high voltage circuit breaker fault diagnosis proposed based on Extreme Learning Machine (ELM). Finally, the experiment proves the effectiveness of this method for breaker fault diagnosis based on vibration signal analysis and ELM.

Based on EEMD and Multiclass Relevance Vector for High Voltage Circuit Breaker Mechanical Fault Diagnosis

Mechanical failure of high voltage circuit breaker accounted for the largest percentage of, it is necessary to diagnosis the mechanical fault .The acoustic signal of high voltage circuit breaker contains a large number of mechanical state information, can put the acoustic signal characteristics as a basis for high voltage circuit breaker fault diagnosis. M - RVM expanding traditional RVM to multiple categories, very suitable for fault diagnosis of high voltage circuit breaker.In this paper, the M-RVM combined with EEMD method for high voltage circuit breaker mechanical fault diagnosis, the experimental results show that the method has a good diagnosis effect.

The Study of Fault Diagnosis the High-Voltage Circuit Breaker Based on Neural Network and Expert System

An intelligent fault diagnosis system based on neural network and expert system integration is studied. The system uses neural networks to acquire fault codes and the corresponding type of fault, which are stored the knowledge base. In fault diagnosis, the expert system uses fault codes that can be found the corresponding fault type from the knowledge base through forward reasoning, and gives a reasonable solution. Meanwhile, this system also overcomes some shortcomings that the neural network doesn’t have functional and expert system has difficult to acquire knowledge. Used neural network and expert system in high-voltage circuit breaker fault diagnosis, the simulation indicates that this method is effective and feasible.

New fault diagnosis of circuit breakers

Wavelet packets and neural networks have been used to analyze the vibration data of circuit breakers (CBs) for the detection of incipient CB faults. Wavelet packets are used to convert measured vibration data from healthy and defective CBs into wavelet features. Selected features highlighting the differences between healthy and faulty condition are processed by a backpropagation neural network for classification. Testing has been done for three 66-kV CBs with simulated faults. Detection accuracy is shown to be far better than other classical techniques such as the windowed Fourier transform, stand alone artificial neural networks or expert system. The accuracy of detection for some faults can be as high as 100%.

New Fault Diagnosis of Circuit Breakers

Wavelet packets and neural networks have been used to analyze the vibration data of circuit breakers for the detection of incipient circuit breaker faults. Wavelet packets are used to convert measured vibration data from healthy and defective circuit breakers into wavelet features. Selected features highlighting the differences between healthy and faulty condition are processed by a back-propagation neural network for classification. Testing has been done for three 66 kV circuit breakers with simulated faults. Detection accuracy is shown to be far better than other classical techniques such as the windowed Fourier transform, stand alone artificial neural networks or expert system. The accuracy of detection for some faults can be as high as 100%.