High Voltage Circuit Breaker Fault Research Articles

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.

Principal component analysis (PCA) based sparrow search algorithm (SSA) for optimal learning vector quantized (LVQ) neural network for mechanical fault diagnosis of high voltage circuit breakers

An essential piece of equipment for the protection and management of the electricity grid is a high voltage(HV) circuit breaker. The reliability and stability of the electricity grid are directly affected by the condition of operation of a high voltage circuit breaker. This study proposes a PCA-SSA-LVQ-based high-voltage circuit breaker diagnosis method to identify mechanical faults of high-voltage circuit breakers more quickly and effectively. Laboratory simulation of vibration signals for different fault states of high voltage circuit breakers. Finite intrinsic mode functions(IMF) are obtained by empirical modal decomposition(EMD) of the simulated vibration signal. The envelope spectrum of the IMF is extracted using the Hilbert envelope transform, and the concept of information entropy is used to determine the entropy value of the envelope as the characteristic quantity of the signal. Using principal component analysis, one can decrease the eigenvectors’ dimension. The high voltage circuit breaker LVQ neural network fault diagnosis model is made, and the initial weight of the LVQ neural network is optimized using the SSA optimization algorithm. The accuracy was improved in the end. The optimized and non-optimized LVQ neural networks were compared. The results show that the PCA-SSA-LVQ neural network has a higher diagnostic rate and faster training convergence. The main contribution of this paper is to reduce the redundancy of feature vectors using the PCA algorithm, optimize LVQ neural network by the SSA algorithm, and creatively use these three algorithms together. The training speed and the accuracy of state recognition are improved.

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.

Development and Fault Prediction of a New Operating Mechanism of HTPPM

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.

A Newly Designed Diagnostic Method for Mechanical Faults of High-Voltage Circuit Breakers via SSAE and IELM

In general, vibration signals generated by the switching operation of a high-voltage circuit breaker (HVCB) contains important information to reflect its mechanical status. A method for mechanical fault diagnoses of an HVCB based on a semisupervised stacked autoencoder (SSAE) and an integrated extreme learning machine (IELM) is proposed in this study. First, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is used to decompose the vibration signal to obtain the time–frequency energy matrix. Then, an SSAE model is applied to automatically extract the characteristic information from the energy matrix. As a result, two-level classifiers can be constructed. The first level is utilized to identify normal or abnormal states, and the second level is selected to identify various types of faults in the abnormal state. The classifiers of these levels are composed of binary IELM. The advantages of the proposed method are that it not only can automatically extract the high-recognition features from the time–frequency energy matrix of high dimension to complete the identification of the existing fault types in the training set but also can accurately identify the samples of unknown types of faults. Experimental results show that the proposed method can effectively diagnose mechanical faults of an HVCB, and the classification accuracy reaches 99.5%.

Mechanical fault diagnosis of high-voltage circuit breakers using multi-segment permutation entropy and a density-weighted one-class extreme learning machine

Condition monitoring for high-voltage circuit breakers (HVCBs) is of great significance for the safety of power grids. Based on machine-learning methods, most relevant studies have contributed significantly to improving the classification accuracy of known states. However, these studies have neglected the detection of unknown faults. In this study, a new one-class classifier, called a density-weighted one-class extreme learning machine (DW-OCELM), was proposed to detect unknown faults of HVCBs. The DW-OCELM determines the classification boundary considering data distribution by introducing the notion of density weight, such that samples located in low-density regions are more likely to be separated, improving detection performance. On this basis, a multi-class classifier was developed based on the homogeneous combination of multiple DW-OCELMs to classify known states. In addition, the proposed classifiers were trained based on multi-segment permutation entropy calculated from vibration signals. Experiments on a 35 kV HVCB demonstrated that the proposed methods outperformed other state-of-the-art techniques.

Multisensor Decision Approach for HVCB Fault Detection Based on the Vibration Information

Mechanical faults of high-voltage circuit breakers (HVCBs) always occur during long-term operation; thus, the recognition of such fault types has become a key issue. Now, most related research involves improvements to the classification method for higher precision based on a single sensor, ignoring the multi-sensor information fusion of the vibration signal for the fault diagnosis of HVCB. Classical evidence theory is a common approach for solving multi-sensor fusion. However, it fails upon encountering conflicting evidence. Therefore, this study proposes an improved multi-sensor evidence combined rule to optimize the evidence gathered from different sensors, especially for the highly conflicting and invalid evidence. A numerical simulation example indicates that the proposed method achieves a better performance. On this basis, the proposed method is applied to diagnose HVCB faults with multi-sensor. Further, the superiority of the multi-sensor fusion is proved via a comparison with the diagnostic results of five typical classifiers under a single sensor; specifically, by comparing it with traditional Dempster's combination rules, it is verified that the proposed method is better than other multi-sensor fusion methods, which can be used in the field of HVCB fault diagnosis.

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.

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.

Research on Fault Diagnosis of High-Voltage Circuit Breaker Based on Support Vector Machine

In order to realize the diagnosis of the state of the high-voltage circuit breaker in the smart grid, the wavelet-packet technique is used to extract the characteristic value of the signal of the dynamic contact of the high-voltage circuit breaker. The characteristic value of the obtained signal is processed by fuzzy clustering, which inputs the processed feature values into the Support Vector Machine (SVM) to implement fault diagnosis. The high-voltage circuit breakers that need to be identified have the following faults: contact spring failure, trip spring shaft pinout, and trip spring failure. After the above series of processes, the paper reached the conclusion that it is feasible to use SVM to diagnose the high voltage circuit breaker fault system, which has a good diagnostic effect.

Fault Diagnosis of High-Voltage Circuit Breakers Using Mechanism Action Time and Hybrid Classifier

Accurate fault diagnosis of high-voltage circuit breakers is crucial for the safety of power grids. Mechanism action time, though can reflect the state of high-voltage circuit breakers, is usually difficult to obtain, as multiple sensors are required for acquisition. To solve this problem, a novel method that can extract mechanism action time from vibration signals was proposed in this paper. The method involved enhancement of vibration signals using short-time zero-crossing rate and determination of mechanism action time by the double threshold method. Then, time parameters were utilized to calculate singular spectrum entropy as the input vector for a classifier. Finally, a hybrid classifier that involved support vector data description and extreme learning machine was developed to identify faults. The hybrid classifier can not only classify known mechanical states but also detect unknown mechanical faults of high-voltage circuit breakers. The effectiveness of the proposed method was verified using a 35-kV high-voltage circuit breaker.

Multi-mapping fault diagnosis of high voltage circuit breaker based on mathematical morphology and wavelet entropy

Mechanical faults of high voltage circuit breakers (CBs) seriously affect the reliability of their operation, which may cause severe damage to power systems. In order to monitor operational conditions and detect mechanical faults of CBs, a multi-parameter monitoring system is designed and a fault diagnosis method based on multi-mapping is proposed. The paper focuses on the trip/close circuits, the spring-charging mechanism and the transmission mechanism, and obtains four current signals and a vibration signal that can reflect CB conditions. For the current signals, a morphological filter is used to remove noise and then characteristics of the waveforms' shape information are extracted. For vibration signals, the wavelet packet transform is used to decompose the signal into various frequency bands, and the sample entropy of the low frequency bands and the wavelet energy of the high frequency bands are calculated, respectively. Based on these feature parameters, a multi-mapping strategy is proposed for CB fault diagnosis. Laboratory experiments have been conducted to obtain on-site signals under various conditions, and experiment results have verified that monitoring the aforementioned signals and using the corresponding feature extraction and fault diagnosis methods, the mechanical faults of high voltage CBs can be effectively diagnosed.

Intelligent Fault Diagnosis of HVCB with Feature Space Optimization-Based Random Forest

Mechanical faults of high-voltage circuit breakers (HVCBs) always happen over long-term operation, so extracting the fault features and identifying the fault type have become a key issue for ensuring the security and reliability of power supply. Based on wavelet packet decomposition technology and random forest algorithm, an effective identification system was developed in this paper. First, compared with the incomplete description of Shannon entropy, the wavelet packet time-frequency energy rate (WTFER) was adopted as the input vector for the classifier model in the feature selection procedure. Then, a random forest classifier was used to diagnose the HVCB fault, assess the importance of the feature variable and optimize the feature space. Finally, the approach was verified based on actual HVCB vibration signals by considering six typical fault classes. The comparative experiment results show that the classification accuracy of the proposed method with the origin feature space reached 93.33% and reached up to 95.56% with optimized input feature vector of classifier. This indicates that feature optimization procedure is successful, and the proposed diagnosis algorithm has higher efficiency and robustness than traditional methods.

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.