Fault Diagnosis Of Circuit Breaker Research Articles

Research on Circuit Breaker Operating Mechanism Fault Diagnosis Method Combining Global-Local Feature Extraction and KELM.

In response to the lack of generality in feature extraction using modal decomposition methods and the susceptibility of diagnostic performance to parameter selection in traditional mechanical fault diagnosis of high-voltage circuit breaker operating mechanisms, this paper proposes a Global-Local feature extraction method based on Generalized S-Transform (S-Translate) combined with Gray Level Co-Occurrence Matrix (GLCM) and complemented by Maximum Relevance and Minimum Redundancy (mRMR) feature selection. The GL (Global-Local)-mRMR-KELM fault diagnosis model is proposed, which employs the Kernel Extreme Learning Machine (KELM). In this model, the original time-frequency domain features and the time-frequency features of the Generalized S-Transform matrix of vibration signals under different states of the circuit breaker are first extracted as global features. Then, the GLCM is obtained to extract texture features as local features. Finally, the mRMR and KELM are comprehensively applied to perform feature selection and classification on the dataset, thereby accomplishing the fault diagnosis of the circuit breaker's operating mechanism. In this study, the 72.5 kV SF6 circuit breaker operating mechanism is taken as the research object, and three types of mechanical faults are simulated to obtain a vibration signal. Experimental results verify the effectiveness of the proposed GL-mRMR-KELM model, achieving a diagnostic accuracy of 96%. This research provides a feasible approach for the fault diagnosis of circuit breaker operating mechanisms.

A novel circuit breaker fault diagnosis method based on dense residual and attention mechanism

AbstractIn recent years, deep learning‐based fault diagnosis technology for high‐voltage circuit breakers (HVCB) has advanced significantly, but the working environment of HVCBs is complex, resulting in unsatisfactory fault diagnosis results of HVCBs in noisy environment and existing deep learning methods are difficult to solve this problem. This paper proposes a multi‐channel convolutional neural network combines dense residual structure and attention mechanism to achieve high‐precision and high‐robust diagnosis of HVCBs in noisy backgrounds. A dense residual network is introduced into the convolutional neural network to prevent feature loss during network propagation to preserve the difference information between the network layers as much as possible, Simultaneously, a channel attention mechanism is introduced to adaptively adjust the weights of different convolution channels. The model can extract multi‐scale features from the original signal and fully exploit the intrinsic relationship between the vibration signal and the HVCB's operating state. The experimental results show that the diagnostic method can still meet the requirements of fault diagnosis in the presence of noise, with an average diagnostic accuracy rate of 85.92% when the signal‐to‐noise ratio is −4. The model outperforms the traditional single‐channel model in terms of diagnostic accuracy and stability.

Fault diagnosis of WOA-SVM high voltage circuit breaker based on PCA Principal Component Analysis

High voltage circuit breakers (HVCB) are significant protection and control devices for electric systems, and its operating state directly influences the stability and reliability of electric systems. In order to solve the problem of low precision of the mechanical diagnostic accuracy of HVCB, this paper propose a means of optimization based on the principal component analysis (PCA), and the diagnosis of the HVCB is supplemented by the support vector machine (SVM) of the whale algorithm (WOA) optimization. Firstly, the present machine fault state of the circuit breaker is experimentally simulated, the LabVIEW software is used to obtain the vibration signal of different states, then the Hilbert Huang conversion is made to the collected vibration signal to obtain an appropriate envelope spectrum, and the entropy of the envelope spectrum is calculated. Then, we use the PCA method to calculate the contribution rate of each characteristic value,and the feature values with a cumulative contribution rate higher than 90% are used as the final feature vector . Finally , use the WOA algorithm to optimize the penalty factors and kernel parameters of SVM. Through parameter optimization and feature optimization,it can be found that the WOA-SVM was superior to GA-SVM for optimized time and accuracy, providing an effective method for fault diagnosis of the circuit breaker.

Circuit Breaker Fault Diagnosis Method Based on Coil Current Time Sequence Phase Trajectory Characteristics

The traditional circuit breaker fault diagnosis method suffers from insufficient feature information extraction and is easily affected by abnormal signal acquisition. To address this, this paper introduces the phase space reconstruction algorithm to reconstruct the current signal for fault diagnosis based on phase trajectory features. The proposed method uses a first-order forward differencing method and mutual information method to process abnormal data and select the parameters of the reconstruction, then extract overall and local inflection point features to construct a fault feature set. The support vector machine algorithm-based model is trained and tested using actual samples, and the results show that the proposed method can adaptively sample anomalous signals, exhibit strong robustness, and significantly improve the accuracy of fault classification.

A Novel Hybrid Transfer Learning Approach for Small-Sample High-Voltage Circuit Breaker Fault Diagnosis On-site

Although the data-driven fault diagnosis method has achieved perfect diagnosis of high-voltage circuit breakers (HVCBs) mechanical fault under the massive data built in the laboratory, it is still a challenge to train a high-precision and robust diagnosis model under the condition of small samples on-site at this stage. To solve the above issues, this paper proposes a novel hybrid transfer learning to realize small-sample HVCB fault diagnosis on-site. To fully learn domain discriminative features and domain matching, this paper simultaneously introduces domain adaptation transfer learning and domain adversarial training into small-sample HVCB diagnosis on-site. At the same time, the two kinds of feature transfer learning are combined through ensemble learning to get the final diagnosis result. To extract discriminative features that characterize HVCB faults, this paper constructs a one-dimensional attention residual convolutional neural network, which can ensure that the network pays attention to key features while fully extracting temporal fine-grained information. The experimental results show that the hybrid transfer learning proposed in this paper achieves 94.69% accuracy of small-sample HVCB fault diagnosis on-site, which is significantly higher than other methods. It has laid a solid foundation for small-sample HVCB fault diagnosis on-site.

Fault Diagnosis Algorithm of Transformer and Circuit Breaker in Traction Power Supply System Based on IoT

Transformers and circuit breakers are essential equipment in traction power supply systems. Once a fault occurs, it will affect the train’s regular operation and even threaten passengers’ personal safety. Therefore, it is essential to diagnose the faults of the transformers and circuit breakers of the traction power supply system. At present, power companies have made many achievements in fault diagnosis of power equipment, but there are still problems with real-time and accuracy. The Internet of Things (IoT) is a technology that connects different types of terminal devices for information exchange and communication to achieve intelligence. It includes data acquisition and transmission, information interaction, processing, and decision-making from bottom to top. It uses sensor terminals to obtain real-time status information on electrical equipment. Moreover, it conducts real-time monitoring and intelligent processing of the equipment status of the traction power supply system. In this paper, the multi-data fusion technology of the IoT combines the real-time information of electrical equipment with fault diagnosis to realize the fault diagnosis of transformers and circuit breakers. First, we built an equipment fault diagnosis system based on the multi-terminal data fusion technology of the IoT. Secondly, the transformer fault diagnosis model is established. We adopt the BP neural network algorithm based on particle swarm optimization (PSO) to realize transformer fault diagnosis and use PSO to optimize the feature subset to improve the diagnosis performance. Finally, the fault diagnosis model of the vacuum circuit breaker is established. We select the current change and time node as typical fault feature quantities and use the PSO–BP neural network algorithm to realize the fault diagnosis of the circuit breaker.

Data Mining in the Vibration Signal of the Trip Mechanism in Circuit Breakers Based on VMD-PSR

To address the difficulty in characterizing early mechanical faults in the trip mechanism of circuit breakers, a data mining method based on variational mode decomposition (VMD) and phase space reconstruction (PSR) method was proposed. First, the vibration signal in the trip stage was separated from the whole according to the current features. Then, it was decomposed using the VMD algorithm to obtain the intrinsic mode functions (IMFs) and these sub signals were mapped to high-dimensional phase space based on the PSR algorithm. Then, the features of the attractor trail shape and the recurrence plot matrixes were extracted. In order to judge the fault in the trip mechanism, a fault simulation test was carried out and the characteristic under different faults was analyzed. Based on these samples, a fault identification model is established by support vector machine (SVM) and the effectiveness is verified by other test samples. The accuracy of the SVM model is 98%, which is higher than that of the BPNN and KNN clustering models. This research supplements the existing method for condition evaluation of the trip mechanism and can provide a reference for circuit breaker fault diagnosis.

Circuit Breaker Fault Diagnosis Method Based on Improved One-Dimensional Convolutional Neural Network

Circuit Breaker Fault Diagnosis Method Based on Improved One-Dimensional Convolutional Neural Network

Few-Shot Transfer Learning With Attention Mechanism for High-Voltage Circuit Breaker Fault Diagnosis

Data-driven artificial intelligence methods, especially convolutional neural networks (CNNs), have achieved excellent performance in high-voltage circuit breaker (HVCB) fault diagnosis. However, CNN relies heavily on massive data. When the amount of data decreases, the fault diagnosis performance drops severely. To settle these problems, a few-shot transfer learning (FSTL) with attention mechanism (AM) to realize the mechanical fault diagnosis of HVCBs is proposed. First, a one-dimensional CNN with AM is used to extract the fault features of HVCBs. The introduction of the AM makes CNN pay more attention to the interesting part of the fault signal to extract discriminative features. Then, domain adaptive transfer learning is used to realize a reliable diagnosis of HVCBs in small samples.The subdomain adaptation is adopted to adjust the distribution of related subdomains under the same category. The proposed subdomain adaptation can not only align the global distribution well but also effectively align the distribution of the same category of subdomains. Experimental results show that the FSTL proposed can achieve highly accurate and robust fault diagnosis of HVCBs with few-shot on-site. Compared with the traditional methods, the FSTL is obvious and provides a reliable reference for the diagnosis of HVCBs.

A Novel Capsule Convolutional Neural Network with Attention Mechanism for High-Voltage Circuit Breaker Fault Diagnosis

The traditional fault diagnosis method of high-voltage circuit breakers (HVCBs) based on deep learning largely depends on the huge data size. When the data sample size is insufficient, it is difficult to meet the needs of diagnosis performance. To improve the diagnostic performance, a one-dimensional attention convolutional capsule neural network is proposed in this paper. First, to take advantage of the time fine-grained information of the vibration signals, a one-dimensional convolutional neural network with a wide convolution kernel is designed, thus avoiding the tedious manual feature extraction of the original data. Then, an attention mechanism is added in the pooling layer to change the weight allocation of vibration signal fragments and increase its feature extraction capability. Finally, a capsule layer is introduced between the convolution layer and the fully connected layer to represent feature vectorization, which ensures the integrity of fault feature extraction. The classification structure digital capsule is obtained by the feature transfer of dynamic routing. The experiment results show that the combination of the attention mechanism and capsule layer is feasible and the accuracy can reach 94.7% when the number of samples is small. Compared with the traditional method, the proposed method still has high diagnostic accuracy and anti-noise ability in the case of small sample.

An Intelligent Fault Diagnosis Method for CNN-SVM Circuit Breaker Based on Quantum Particle Swarm Optimization

In order to make use of fewer fault data samples to diagnose the main fault types of circuit breakers accurately in real time, an intelligent fault diagnosis method for circuit breakers based on convolutional neural network (CNN) and quantum particle swarm optimization (QPSO) is proposed. Firstly, the key features of the circuit breaker operational signal are extracted through the CNN model, and the extracted feature vectors are input into the support vector machine (SVM) for fault diagnosis. In order to improve the diagnostic performance, this paper uses QPSO algorithm to optimize the parameters of the classifier, it effectively solves the local optimal problem. The experimental results show that the method presented in this paper has achieved good results in fault diagnosis of circuit breakers, and the accuracy of diagnosis is up to 100%, which highlights the superiority of this method.

Mechanical Fault Diagnosis of Circuit Breakers Based on XGBoost and Time-domain Features

In order to improve the efficiency of feature extraction of mechanical vibration signal of circuit breaker and the reliability of state recognition of circuit breakers, a mechanical fault diagnosis method of high voltage circuit breaker based on XGBoost is adopted. Firstly, 17 time-domain features are extracted from the measured vibration signals of circuit breakers, constructing feature vector and the separability of eigenvectors is analyzed. Then the feature vector is input into XGBoost, the depth and size of the tree are optimized to realize the high reliability discriminant analysis of the mechanical state of circuit breaker. Experiments on vibration data of circuit breakers prove that, this method has high efficiency in feature extraction and the overall recognition accuracy is high.

Fault diagnosis of circuit breakers based on time–frequency and chaotic vibration analysis

Mechanical malfunction is a main failure mode for circuit breakers (CBs). Vibrations generated from CB switching operations contain rich information of its mechanical condition. However, the vibrations are highly time-varying and non-stationary, which makes it very difficult to precisely extract effective features for machinery fault diagnosis. This study presents a methodology to obtain the CB vibration characteristics based on time–frequency and chaotic analysis. A new method, called adaptive chirp mode decomposition (ACMD), is introduced to extract the fast fluctuating instantaneous frequency and catch each signal component individually from the CB's vibration signal. A high resolution adaptive time–frequency spectrum which can clearly represent the mechanical condition alteration in CB is obtained by the ACMD. The component with the most significant time–frequency fluctuation is reconstructed into a high-dimensional phase space to recover and extract the dynamic variation characteristics of the CB. Based on the reconstructed phase space, a new set of features, namely RST (ratio of major–minor axis, shape complexity and trajectory compactness), is proposed for realising the stability and accurate diagnosis of CB faults. Experimental study and practical application cases are presented showing the efficiency of the methodology proposed here.

A novel fault diagnosis method for circuit breakers based on optimized affinity propagation clustering

Online condition monitoring and fault diagnosis of circuit breakers (CBs) is a significant method to effectively improve the stability and reliability of the power system. However, the currently used fault diagnosis method still have certain defects including the inability to identify unknown faults for training samples. Therefore, this paper proposes an evolving method for fast and accurate online fault diagnosis of CBs. On the basis of collecting samples of CB trip/close coil current (CC) features, an optimized affinity propagation (AP) clustering algorithm to accurately extract the sample clustering exemplars is presented. Additionally, operating state identification and fault diagnosis of CBs is carried out by calculating the similarity coefficient between the new sample and exemplars online. Diagnosis of unknown faults is also achieved by introducing the threshold and comparing it with similarity coefficient results. Simulation results prove that the proposed method can precisely identify various known CBs faults and has the ability to recognizes unknown CBs fault samples even when the number of training samples is small, providing a foundation for CB fault location and condition-based maintenance.

Fault diagnosis method for energy storage mechanism of high voltage circuit breaker based on CNN characteristic matrix constructed by sound-vibration signal

Aiming at the problem that some traditional high voltage circuit breaker fault diagnosis methods were over-dependent on subjective experience, the accuracy was not very high and the generalization ability was poor, a fault diagnosis method for energy storage mechanism of high voltage circuit breaker, which based on Convolutional Neural Network (CNN) characteristic matrix constructed by sound-vibration signal ,was proposed. In this paper, firstly, the morphological filtering was used for background noise cancellation of sound signal, and the time scale alignment method based on kurtosis and envelope similarity were proposed to ensure the synchronism of the sound-vibration signal. Secondly, the Pearson correlation coefficient was used to construct two-dimensional image characteristic matrix for the expanded sound-vibration signal. Finally, the characteristic matrix was trained by utilizing CNN. Local Response Normalization (LRN) and core function decorrelation were utilized to improve the structure of CNN model, which reduced the bad impact of large data fluctuation of energy storage process on the diagnostic accuracy of circuit breaker energy storage mechanism. Compared with the traditional method, the proposed method has obvious advantages, whose total accurate rate up to 98.2 % and generalization performance is excellent.

Fault diagnosis of vacuum circuit breaker based on coil current of release

The release is the key component of operation mechanism of circuit breaker. Its current waveform contains many information. The fault diagnosis of circuit breaker can be realized by analyzing the current waveform of coil. Firstly, the combination method of wavelet analysis and seeking extreme points in region extremum is proposed to obtain the feature values of coil current of circuit breakers. Then, a novel intelligent particle swarm optimization (PSO) algorithm was adopted to search the optimal parameters of support vector machine (SVM). Finally, a high-performance SVM with optimal parameters is constructed for fault diagnosis. The experimental results show that the method can achieve a high fault diagnosis accuracy.

High Voltage Circuit Breaker Fault Diagnosis Based on Reasoning chain and Bayes

In order to find the reason of the high voltage circuit breaker rejection quickly, an fault diagnosis method based on reasoning chain and Bayesian network (BN) is presented in the paper. Firstly, the concept of event set is introduced. A fault symptom set is constructed based on the measured circuit breaker characteristic parameters and warning signals. And the direct cause of circuit breaker failure, such as a part of the circuit breaker components or mechanism failure constitute the fault cause set. After that, when the high voltage circuit breaker fails, a subset of fault symptoms and fault causes is obtained according to the information. Then an reasoning chain is constructed according to the causal relationship between the two. Finally, a corresponding Bayesian network model for reasoning chain is built. The probability of fault causes is obtained by Bayesian backward reasoning, so as to realize the rapid analysis of circuit breaker faults. And the effectiveness of the method is verified by the result of practical fault diagnosis examples.

Fault Diagnosis of Circuit Breaker Energy Storage Mechanism Based on Current-Vibration Entropy Weight Characteristic and Grey Wolf Optimization–Support Vector Machine

The reliable storage of spring potential energy is a prerequisite for ensuring the correct closing and opening operations of a circuit breaker. A fault identification method for circuit breaker energy storage mechanism, combined with the current–vibration signal entropy weight characteristic and grey wolf optimization-support vector machine (GWO-SVM), is proposed by analyzing the energy conversion and transmission relationship between control loop, motor, transmission component, and spring. First, the current envelope is denoised in the modulus maxima wavelet domain, the time-domain feature is extracted via a Hilbert transform (HT), and the kurtosis is calculated. Second, the energy method is used to select $K$ parameters, the objective function optimization method is used to select $\alpha $ for variational mode decomposition (VMD), the intrinsic mode function (IMF) is obtained by decomposing the vibration signal with VMD and the permutation entropy characteristics of IMFs are extracted. Third, the characteristic of current and vibration signals for classification are edited by the entropy weight method, and the corresponding weights are provided in accordance with the sample information amount and importance. Finally, the current–vibration entropy weight characteristics are constructed and sent to the SVM model for learning and training. The GWO algorithm is used to optimize the parameters of the SVM model based on the mixed kernel function, which reduces adverse effects from the model parameters’ choice of circuit breaker fault diagnosis. The results show that the overall diagnostic accuracy for the experimental samples reaches 100% and has good generalization capability based on the current–vibration entropy weight characteristic and the GWO-SVM method.

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.