Circuit Breaker Fault Research Articles

A potential fault of circuit breaker found using SF6 decomposition products detection

A 126kV circuit breaker was found to have latent fault by SF6 decomposition product detection method. It was found that the sulfur hexafluoride gas decomposition products of the circuit breaker contained a large amount of CO gas, a certain amount of SO2 and H2S, and a small amount of HF gas. The characteristic components and their contents of the decomposition products were analyzed, it is preliminarily concluded that there is insulation material fault in the circuit breaker, and the potential fault is further confirmed by the disassembly inspection that the MoS2 lithium base grease is overcoated at the metal tie rod joint.

Study on Online Dispatching Defensive Strategy for Power Grid Considering Expected Circuit Breaker Fault Set

In order to prevent serious blackouts caused by cascading faults, a defense strategy which considers the expected circuit breaker fault set is proposed for online despatching that is applied to power grid protection. In this paper, the multi-stage game model has been established to study the online defense strategy of circuit breaker failure from the perspective of attacker and defender. From the perspective of attacker in the power grid, the initial expected fault set based on the circuit breaker defect prediction is taken as the initial action. Considering the two factors that the removal of the power flow transfer branch and the incorrect operation of the zone III distance protection because of hidden fault. Then, a strategic space for the attacker is formed by using the risk theory predict the operation of power grid. From the perspective of the defender, a method for adjusting the power system using sensitivity control and adaptive adjustment of the backup protection setting is proposed. The paper finds the optimal strategy for both parties through defining the profit function. Finally, the IEEE 39-bus system is taken as an example to analyze the model, and the simulation results verify the feasibility of the article model.

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 simple and effective detection strategy using double exponential scheme for photovoltaic systems monitoring

Effective and efficient monitoring of a photovoltaic plant are indispensable to maintain the generated power at the desired specifications. In this work, a simple and effective monitoring method based on parametric models and the double exponentially smoothing scheme is designed to monitor photovoltaic systems. This method merges the simplicity and flexibility of empirical models and the sensitivity of the double exponentially smoothing strategy to uncover small deviations. Essentially, the empirical models are adopted to obtain residuals to detect and identify occurred faults. Here, a double exponentially smoothing scheme is used to sense faults by examining the generated residuals. Moreover, to extend the flexibility of the double exponentially smoothing approach, a nonparametric detection threshold has been computed via kernel density estimation. Several different scenarios of faults were considered to assess the developed method, including PV string fault, inverter disconnection, circuit breaker faults, partial shading, PV modules short-circuited, and soiling on the PV array. It is showed using real data from a 9.54 kWp photovoltaic system that the considered faults were successfully traced using the developed approach.

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%.

Fault diagnosis for circuit-breakers using adaptive chirp mode decomposition and attractor’s morphological characteristics

In order to minimize operation and maintenance costs and enhance the reliability of circuit-breaker (CB), faults should be detected simply and diagnosed effectively. To enable this, monitoring or diagnostic-test techniques should provide critical information of the CB and make the decision-making process simple. In recent years, vibration-based analysis has gained attention for the said purpose. However, the diagnostic performance is limited by the highly time-varying and non-stationary vibration signals. Hence, within this paper a new diagnostic framework is proposed for CB fault diagnosis. The recently proposed adaptive chirp mode decomposition (ACMD) is introduced for extracting the fast fluctuating instantaneous frequency in the CB’s vibration signal. A high-resolution adaptive time-frequency spectrum which can clearly represent the time-frequency (TF) characteristics of the vibration signal is obtained by combining the wavelet transform and ACMD. The component with the most significant TF fluctuation is reconstructed into phase space to study the dynamical characteristics of CB. Based on the reconstructed phase space, a new set of dynamical features, namely RST (i.e., ratio of major to minor axis, shape complexity and trajectory compactness), is proposed for achieving a stable and accurate diagnosis of CB faults. The proposed diagnostic framework is evaluated on two experimental scenarios with different types of CB. The reasonable diagnostic performances confirm the ability of the proposed technique in diagnosing CB faults.

Study on Closing Spring Fatigue Characteristics of High Voltage Circuit Breaker

One of the most causing closing fault of high voltage circuit breaker is closing spring failure. In order to avoid such closing fault, this paper analyzed the relationship between energy of closing spring and its load, as well as the experiment carried out to get the minimum energy when closing. The time-varying static stress relaxation curve of closing spring force was obtained by analogy the real working condition of high voltage circuit breaker which closing spring is compressed for a long time, the operation time-varying dynamic stress relaxation curve of closing spring force was also obtained by carrying mechanical endurance test. In the end, established the model of closing spring storage life and fatigue life, closing spring life prediction and fault early warning of high voltage circuit breaker was then realized.

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 model of adaptive miniature circuit breaker based on fractal theory and probabilistic neural network

In recent years, with the development of national economy and the continuous improvement of the electrical level of daily life, various household appliances have gradually entered the field of life. Small circuit breakers have become the most widely used terminal protection equipment in electrical terminal distribution equipment of buildings, its performance plays a key role in the safety of circuits and electrical appliances. Although the size of the small circuit breaker is small, its working principle is not simple. Especially the short circuit breaking of miniature circuit breaker is very complicated. However, most of the faults of small circuit breakers are mechanical ones. The vibration signal of the circuit breaker during closing and closing contains its state characteristic information, the vibration signals of different faults will have certain differences. This paper studies the fault characteristics of small circuit breaker engine, combining fractal technology and probabilistic neural network, a research method of fault diagnosis model for small circuit breaker is proposed. In this paper, the characteristics of probabilistic neural network (PNN) are analyzed in detail, and the fault diagnosis method of probabilistic neural network is used to train samples and classify the faults accurately. Probabilistic neural network is an effective method for fault identification of small circuit breakers. A sample training library was established to facilitate fault classification. In order to classify and identify faults, a fault diagnosis method for miniature circuit breaker based on fractal technique and probabilistic neural network is adopted. The feasibility of this method is verified by data analysis.

Study of Ablation of Arc Contacts and Dynamic Contact Resistance in High Current Breaker

The fault of power circuit breaker (PCB) can lead very serious problems, especially in high voltage–power grid. The structure of contacts may be composed of main contacts and arc contacts, and this structure can avoid ablation of main contacts efficiently. It is necessary to study the eroded state of the arc contacts and the dynamic contact resistance of arc contacts to estimate the operating state of the PCB. The wear of contacts contains arc ablation and mechanical wear through the characteristics of contacts. The arc ablation is caused by the high temperature of the arc and the mechanical wear is caused by the mechanical friction of static and movable contacts. Some experiments have been done under different size of contacts and different current through them. The results show that the mechanical wear increases, sometimes heavier, with the increase of the size of fixed contacts and the decrease of the size of movable ones because the force between fixed and movable contacts becomes larger. The results also show that the ablation of arc contacts increases with the increase of current, especially when the current is up to 20 kA. With the high temperature of arc reaching the hardness point of copper, the arc ablation and mechanical erosion are increasing obviously since the contacts become soft and easy to wear. Another result of the study shows it is important to choose the material of contacts and the force between movable and fixed contacts. The harder and less resistance the material of contacts, the better the operating situation. To monitor the operating situation of the breaker, the dynamic contact resistance was measured 25 times. As a result, the dynamic contact resistance is sensitive to the current and increasing with the increase of continuous experiment time because of the increasing of metal ions. Furthermore, if the SF6 in the PCB is changes, the dynamic contact resistance would be changed. The dynamic contact resistance in this experiment varies substantially within the range of 80–250 μΩ at the current of 20 kA.

Assessment of Unintentional Islanding Operations in Distribution Networks with Large Induction Motors

This paper is aimed at assessing the impact of unintentional islanding operations (IOs) in the presence of large induction motors (IMs) within distribution networks (DNs). When a fault occurs, following the circuit breaker (CB) fault clearing, the IMs act transiently as generators, due to its inertia, until the CB reclosing takes place. The present work is the outcome of a project carried out in a small DN, where field measurements were recorded over two years. This paper provides a detailed description of the test system, a selected list of field measurements, and a discussion on modeling guidelines used to create the model of the actual power system. The main goal is to validate the system model by comparing field measurements with simulation results. The comparison of simulations and field measurements prove the appropriateness of the modeling guidelines used in this work and highlight the high accuracy achieved in the implemented three-phase Matlab/Simulink model.

The study on material defects of structural parts of circuit breaker

The fault of circuit breaker is caused by the defect of metal material. A series of tests including design drawings, physical and chemical tests, composition tests and metallographic observation, have been carried out for the failed springs. The experimental results showed that the failure of energy storage springs was caused by combination of design flaws and improper heat treatment process. At the same time, the corresponding noteworthy key points in the process of material production were put forward.

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.

Performance Prediction Method Considering Shock Wave in Nozzle for High-Voltage Circuit Breaker

Interrupting-performance indexes that reflect the physical properties of gas flow are proposed for a breaker terminal fault of a high-voltage circuit breaker. A method of evaluating the shock wave and flow velocity was developed that uses a theoretical equation of supersonic flow which takes into account the nozzle’s shape and stagnation pressure. The predicted flow velocities and positions at which the shock wave forms have strong correlations with the test results.

Power Quality Disturbances Assessment during Unintentional Islanding Scenarios. A Contribution to Voltage Sag Studies

This paper presents a novel voltage sag topology that occurs during an unintentional islanding operation (IO) within a distribution network (DN) due to large induction motors (IMs). When a fault occurs, following the circuit breaker (CB) fault clearing, transiently, the IMs act as generators due to their remanent kinetic energy until the CB reclosing takes place. This paper primarily contributes to voltage sag characterization. Therefore, this novel topology is presented, analytically modelled and further validated. It is worth mentioning that this voltage sag has been identified in a real DN in which events have been recorded for two years. The model validation of the proposed voltage sag is done via digital simulations with a model of the real DN implemented in Matlab considering a wide range of scenarios. Both simulations and field measurements confirm the voltage sag analytical expression presented in this paper as well as exhibiting the high accuracy achieved in the three-phase model adopted.

Fault Diagnosis Method of Circuit Breakers Based on Weighted Fuzzy Petri Nets

The maintenance and self-healing of circuit breakers are very difficult when the fault diagnosis results of circuit breakers cannot cover all kinds of fault causes. This paper proposes a method for comprehensively diagnosing circuit breaker faults. The method simulates the action process of circuit breakers based on weighted fuzzy Petri nets and makes it feasible to infer circuit breaker fault causes from different aspects. The model shows the action sequence of the circuit breaker by associating the control mechanism, the operating mechanism, the transmission mechanism and the contact part of the circuit breaker, and each part of it is described with the corresponding abnormal signals of the circuit breaker stored in the substation end. Specific fault reasons of the circuit breaker can be inferred by considering the action sequence and the relationship between the abnormal information and fault causes. The results for some test cases show that this method can obtain the diagnosis results covering various possible fault causes.

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.

Characteristic parameter change of circuit breaker under closing spring fatigue

Closing spring fatigue faults of high voltage circuit breakers affect the timing parameters in closing operations and reduce the closing performance of the circuit breaker. Traditional tests of timing parameter based on travel curve cannot be applied online, and sensor installation is complicated. In this paper, a new method to extract key circuit breaker timing parameters from the vibration signal under closing spring fatigue fault is proposed. First, the travel curve of the circuit breaker under closing spring fatigue is simulated in Automatic Dynamic Analysis of Mechanical Systems (ADAMS). Results indicate that the time intervals between key points of the travel curve can be used as fault features. Then, according to the working principle of the circuit breaker’s spring operating mechanism, the vibration event caused by component impact in the closing operation is analyzed. The corresponding timing parameters are extracted from the vibration signal using the double threshold method based on the short-time energy to entropy ratio. Finally, comparison of experimental measurements with ADAMS simulation results and vibration extraction provides the change law of the fault feature. The correctness of the proposed method is verified. This paper presents a new method for online monitoring of circuit breaker closing spring fatigue.

Fault Diagnosis of High Voltage Circuit Breaker Based on Multi-classification Relevance Vector Machine

The high voltage circuit breaker’s fault as an important form of electrical contact fault in the power system, which is extremely difficult to diagnose under the condition of small fault dataset. This paper proposes a fault diagnosis method based on multi-classification relevance vector machine for high voltage circuit breakers. To make up with the scarcity of the sample fault data in classifying the features of the high voltage circuit breakers, a multi-classification relevance vector machine algorithm is designed on the basis of “One-Against-One” multi-classification model, and tested by public data-sets to verify the good generating performance of this algorithm on small sample data-sets. Then, the time and the currents features are extracted from the closing coil current information of the high voltage circuit breaker to form fault eigenvector. Consequently, numerous two-classification relevance vector machine models were trained and then tested for the optimality of the acquired parameters. The results show that the proposed algorithm can effectively identify many faults of circuit breaker and has better classification accuracy than BP neural network and Support Vector Machine under conditions of small sample data.

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.