Search Results (9)

To enhance the precision of evaluating the operational status of SF6 high-voltage circuit breakers (HVCBs) and devise judicious maintenance strategies, this study introduces an operational state assessment method for SF6 HVCBs grounded in the integrated data-driven analysis (IDDA) model. The relative degradation weight (RDW) is introduced as a metric for quantifying the relative significance of distinct indicators concerning the operational condition of SF6 HVCBs. A data-driven model, founded on critical factor stability (CFS), is formulated to convert environmental indicators into quantitative computations. Furthermore, an optimized fuzzy inference (OFI) system is devised to streamline the system architecture and enhance the processing speed of continuous indicators. Ultimately, the efficacy of the proposed model is substantiated through validation, and results from instance analyses underscore that the presented approach not only attains heightened accuracy in assessment compared to extant analytical methodologies but also furnishes a dependable foundation for prioritizing maintenance sequences across diverse components. Full article

Online monitoring of outdoor high voltage SF6 circuit breakers (HVCBs) is essential to detecting potential damages. To this end, the study of accurate and non-invasive monitoring methods has been significantly investigated in recent decades. Considering that HVCB vibration patterns carry important information about mechanical and electrical integrity and that vibration analysis requires a low level of invasiveness, this article presents methods of obtaining mechanism and reaction times using interference signals of outdoor high voltage SF6 circuit breakers (HVCBs). Compared to traditional methods of monitoring outdoor SF6 circuit breakers that are based on encoders, the proposed method presented ease of installation, as it only requires the insertion of accelerometers. The method of obtaining the mechanism time is based on the use of interference and auxiliary contact signals, in the time domain, where the accelerometer is installed, in the structure of the HVCB, makes it possible to guarantee the moment of the trip command. To obtain the reaction time of each HVCB pole, the envelope technique was applied with the Hilbert transform module of the hearing signal, filtered in a certain resonance band. The proof of the technique of analyzing the vibration of the signal in time was developed with laboratory tests of an HVCB, instrumented with accelerometers and angular encoders. The results obtained via vibration analysis were compared with those obtained via angular encoders and it was concluded that with the acceleration signals, in time, it is possible to obtain performance parameters of an HVCB from its displacement curve. Finally, the online monitoring of the circuit breaker applied in the field is presented, where the acquisition of trip current signals, the condition of the SF6 gas and extinguishing current signals were added to the instrumentation. Full article

In this paper, a new D-distance factor is proposed to determine the failure probability and to prioritize maintenance actions of power circuit breakers in high-voltage substations. The D-distance factor is calculated by using the condition index and renovation index of a high-voltage circuit breaker (HVCB). To facilitate effective decision-making on maintenance with a simple method and less computational effort, the proposed model incorporates the weighting–scoring method (WSM) and analytical hierarchy process (AHP) with the various diagnostic methods for condition index assessments as well as the operation requirements of HVCBs for renovation index assessments. Many significant parameters from circuit breaker testing, such as insulation resistance, contact resistance, contact timing, SF6 gas measurements, gas leakage rate, visual inspection, etc., have been considered for condition index determination. In addition to these, other significant parameters, such as age of the circuit breaker, age of the interrupter and mechanism, number of fault current interruptions, actual load current to rated current ratio, actual short circuit current to rated interrupting current ratio, maintenance ability, spare parts availability, maintenance expertise level, etc., are also considered for renovation index determination. To validate the proposed model, the practical test data of twenty 115 kV HVCBs in various substations of a distribution utility in Thailand were utilized and tested. By analyzing the actual condition and operation requirement of the circuit breaker, the output, as the condition index and renovation index using the proposed method, is discussed with HVCB experts in the utility to adjust the scores and weights of all criteria to obtain the most accurate and reliable model. The results show that the D-distance technique measured from the risk matrix, which is defined as the failure probability, can be used to rank the maintenance schedule from urgent to normal maintenance tasks. In addition, various failure probabilities in the risk matrix of the circuit breaker can be used to determine the appropriate maintenance strategies for the power circuit breaker in each group. Finally, the proposed method could help the utility managers and maintenance engineers manage the maintenance planning effectively and easily for thousands of HVCBs in the grid, and it can be further applied with other high-voltage equipment in both transmission and distribution systems to facilitate the maintenance activities according to available costs and human resources. Full article

Currently, the online non-destructive testing (NDT) methods to measure the contact states of high-voltage circuit breakers (HVCBs) with SF₆ gas as a quenching medium are lacking. This paper aims to put forward a novel method to detect the contact state of an HVCB based on the vibrational signal. First, for a 40.5-kV SF₆ HVCB prototype, a mechanical vibration detection system along with a high-current generator to provide the test current is designed. Given this, vibration test experiments are carried out, and the vibration signal data under various currents and corresponding contact states are obtained. Afterward, a feature extraction method based on the frequency is designed. The state of the HVCB contacts is then determined using optimized deep neural networks (DNNs) along with the method of adaptive moment estimation (Adam) on the obtained experimental data. Finally, the hyperparameters for the DNNs are tuned using the Bayesian optimization (BO) technique, and a global HVCB contact state recognition model at various currents is proposed. The obtained results clearly depict that the proposed recognition model can accurately identify five various contact states of HVCBs for the currents between 1000 A and 3500 A, and the recognition accuracy rate is above 96%. The designed experimental and theoretical analysis in our study will provide the references for future monitoring and diagnosis of faults in HVCBs. Full article

The reliability and performance of high-voltage circuit breakers (HVCBs) will directly affect the safety and stability of the power system itself, and mechanical failures of HVCBs are one of the important factors affecting the reliability of circuit breakers. Moreover, the existing fault diagnosis methods for circuit breakers are complex and inefficient in feature extraction. To improve the efficiency of feature extraction, a novel mechanical fault feature selection and diagnosis approach for high-voltage circuit breakers, using features extracted without signal processing is proposed. Firstly, the vibration signal of the HVCBs’ operating system, which collects the amplitudes of signals from normal vibration signals, is segmented by a time scale, and obviously changed. Adopting the ensemble learning method, features were extracted from each part of the divided signal, and used for constructing a vector. The Gini importance of features is obtained by random forest (RF), and the feature is ranked by the features’ importance index. After that, sequential forward selection (SFS) is applied to determine the optimal subset, while the regularized Fisher’s criterion (RFC) is used to analyze the classification ability. Then, the optimal subset is input to the hierarchical hybrid classifier, and based on a one-class support vector machine (OCSVM) and RF for fault diagnosis, the state is accurately recognized by OCSVM. The known fault types are identified using RF, and the identification results are calibrated with OCSVM of a particular fault type. The experimental proves that the new method has high feature extraction efficiency and recognition accuracy by the measured HVCBs vibration signal, while the unknown fault type data of the untrained samples is effectively identified. Full article

As high-voltage circuit breakers (HVCBs) are directly related to the safety and the stability of a power grid, it is of great significance to carry out fault diagnoses of HVCBs. To accurately identify operating states of HVCBs, a novel mechanical fault diagnosis method of HVCBs based on multi-feature entropy fusion (MFEF) and a hybrid classifier is proposed. MFEF involves the decomposition of vibration signals of HVCBs into several intrinsic mode functions using variational mode decomposition (VMD) and the calculation of multi-feature entropy by the integration of three Shannon entropies. Principle component analysis (PCA) is then used to reduce the dimension of the multi-feature entropy to achieve an effective fusion of features for selecting the feature vector. The detection of an unknown fault in HVCBs is achieved using support vector data description (SVDD) trained by normal-state samples and specific fault samples. On this basis, the identification and classification of the known states are realized by the support vector machine (SVM). Three faults (i.e., closing spring force decrease fault, buffer spring invalid fault, opening spring force decrease fault) are simulated on a real SF6 HVCB to test the feasibility of the proposed method. The detection accuracies of the unknown fault are 100%, 87.5%, and 100% respectively when each of the three faults is assumed to be the unknown fault. The comparative experiments show that SVM has no ability to detect the unknown fault, and that one-class support vector machine (OCSVM) has a weaker ability to detect the unknown fault than SVDD. For known-state classification, the adoption of the MFEF method achieved an accuracy of 100%, while the use of a single-feature method only achieved an accuracy of 75%. These results indicate that the proposed method combining MFEF with hybrid classifier is thus more efficient and robust than traditional methods. Full article

Mechanical fault diagnosis of high-voltage circuit breakers (HVCBs) based on vibration signal analysis is one of the most significant issues in improving the reliability and reducing the outage cost for power systems. The limitation of training samples and types of machine faults in HVCBs causes the existing mechanical fault diagnostic methods to recognize new types of machine faults easily without training samples as either a normal condition or a wrong fault type. A new mechanical fault diagnosis method for HVCBs based on variational mode decomposition (VMD) and multi-layer classifier (MLC) is proposed to improve the accuracy of fault diagnosis. First, HVCB vibration signals during operation are measured using an acceleration sensor. Second, a VMD algorithm is used to decompose the vibration signals into several intrinsic mode functions (IMFs). The IMF matrix is divided into submatrices to compute the local singular values (LSV). The maximum singular values of each submatrix are selected as the feature vectors for fault diagnosis. Finally, a MLC composed of two one-class support vector machines (OCSVMs) and a support vector machine (SVM) is constructed to identify the fault type. Two layers of independent OCSVM are adopted to distinguish normal or fault conditions with known or unknown fault types, respectively. On this basis, SVM recognizes the specific fault type. Real diagnostic experiments are conducted with a real SF₆ HVCB with normal and fault states. Three different faults (i.e., jam fault of the iron core, looseness of the base screw, and poor lubrication of the connecting lever) are simulated in a field experiment on a real HVCB to test the feasibility of the proposed method. Results show that the classification accuracy of the new method is superior to other traditional methods. Full article

In order to improve the identification accuracy of the high voltage circuit breakers’ (HVCBs) mechanical fault types without training samples, a novel mechanical fault diagnosis method of HVCBs using a hybrid classifier constructed with Support Vector Data Description (SVDD) and fuzzy c-means (FCM) clustering method based on Local Mean Decomposition (LMD) and time segmentation energy entropy (TSEE) is proposed. Firstly, LMD is used to decompose nonlinear and non-stationary vibration signals of HVCBs into a series of product functions (PFs). Secondly, TSEE is chosen as feature vectors with the superiority of energy entropy and characteristics of time-delay faults of HVCBs. Then, SVDD trained with normal samples is applied to judge mechanical faults of HVCBs. If the mechanical fault is confirmed, the new fault sample and all known fault samples are clustered by FCM with the cluster number of known fault types. Finally, another SVDD trained by the specific fault samples is used to judge whether the fault sample belongs to an unknown type or not. The results of experiments carried on a real SF₆ HVCB validate that the proposed fault-detection method is effective for the known faults with training samples and unknown faults without training samples. Full article

Mechanical faults of high voltage circuit breakers (HVCBs) are one of the most important factors that affect the reliability of power system operation. Because of the limitation of a lack of samples of each fault type; some fault conditions can be recognized as a normal condition. The fault diagnosis results of HVCBs seriously affect the operation reliability of the entire power system. In order to improve the fault diagnosis accuracy of HVCBs; a method for mechanical fault diagnosis of HVCBs based on wavelet time-frequency entropy (WTFE) and one-class support vector machine (OCSVM) is proposed. In this method; the S-transform (ST) is proposed to analyze the energy time-frequency distribution of HVCBs’ vibration signals. Then; WTFE is selected as the feature vector that reflects the information characteristics of vibration signals in the time and frequency domains. OCSVM is used for judging whether a mechanical fault of HVCBs has occurred or not. In order to improve the fault detection accuracy; a particle swarm optimization (PSO) algorithm is employed to optimize the parameters of OCSVM; including the window width of the kernel function and error limit. If the mechanical fault is confirmed; a support vector machine (SVM)-based classifier will be used to recognize the fault type. The experiments carried on a real SF₆ HVCB demonstrated the improved effectiveness of the new approach. Full article