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

Ma, Suliang; Chen, Mingxuan; Wu, Jianwen; Wang, Yuhao; Jia, Bin; Jiang, Yuan

doi:10.3390/s18041221

Cited by 39 publications

(27 citation statements)

References 36 publications

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“…In the diagnose of the circuit breaker, contact displacement [1], electromagnet coil current [2], and vibration characteristics [3][4][5] are widely used as diagnosis parameters.…”

Section: A Previous Work 1) Fault Diagnose Of Circuit Breakermentioning

confidence: 99%

Mechanical Condition Identification and Prediction of Spring Operating Mechanism of High Voltage Circuit Breaker

et al. 2020

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Spring operation mechanism is widely used in high voltage circuit breakers, and its reliability is related to the ability of the circuit breaker breaking fault current. During the life cycle of spring operating mechanism, stress relaxation, metal fatigue, and any other mechanical defects are easily occurring. And the mechanical performance of the circuit breaker will be influenced by the above defects. Therefore, identifying and predicting the mechanical conditions of the spring operation mechanism can improve the reliability of the circuit breaker. In the present paper, the 252 kV circuit breakers are used as test objects. Firstly, the spring stress relaxation test, the life-cycle test, and the failure simulated test of 252 kV circuit breakers are carried out. Secondly, a multi-body dynamics simulation model of the experimental prototype is established. Thirdly, support vector machine, random forest, and deep neural network are used in the condition identification of the circuit breaker to compare their performances. Then, the prediction model of spring in stress relaxation test is built, however, the model is not suitable for the life-cycle test of repeat close-open operation. Finally, the remaining useful life prediction model is proposed by using Wiener Process.

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“…In the diagnose of the circuit breaker, contact displacement [1], electromagnet coil current [2], and vibration characteristics [3][4][5] are widely used as diagnosis parameters.…”

Section: A Previous Work 1) Fault Diagnose Of Circuit Breakermentioning

confidence: 99%

Mechanical Condition Identification and Prediction of Spring Operating Mechanism of High Voltage Circuit Breaker

et al. 2020

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“…The value of L is set to 10, and T is the dimension of the feature subset. RF integrates the characteristics of bagging and random selection feature splitting; its advantages are: (1) out-of-bag (OOB) data generated by the bagging method, and it can be used to measure the importance of a single variable and estimate the generalization error of the combined classifier models; (2) Due to the large number theorem, with the increase of decision tree in RF, it is not easy to be over-fitted; (3) The algorithm can tolerate abnormal values and noises properly, and it has high classification accuracy [22].…”

Section: Feature Importance and Fault Classifiersmentioning

confidence: 99%

A Novel Mechanical Fault Feature Selection and Diagnosis Approach for High-Voltage Circuit Breakers Using Features Extracted without Signal Processing

Lin

Wang

et al. 2019

Sensors

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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.

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“…With the development of artificial intelligence technology, intelligent mechanical fault-detection methods have been successfully applied to various kinds of mechanical equipment [3][4][5], such as gear boxes and wind turbines among others. At the same time, related technical methods have been extended to the field of high-voltage circuit-breaker state detection, and have achieved phased results [6][7][8]. Therefore, we believe the establishment of intelligent, information-based, automated GIS mechanical defect-detection systems will become an inevitable trend in the development of high-voltage switchgear.…”

Section: Introductionmentioning

confidence: 99%

Vibration Feature Analysis for Gas-Insulated Switchgear Mechanical Fault Detection under Varying Current

Feng

2020

Applied Sciences

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As a key component to ensure the safe operation of the power grid, mechanical defect diagnosis technology of gas-insulated switchgear (GIS) during operation is often neglected. At present, GIS mechanical fault detection based on vibration information has not been developed. The main reason is that the excitation current is considerable but uncontrollable in the actual operation of GIS. It is difficult to eliminate the influence of excitation on the vibration amplitude and form an effective vibration feature description technology. Therefore, this paper proposes a unified feature-extraction method for GIS vibration information that reduces the influence of current amplitude for mechanical fault diagnosis. Starting from the GIS mechanical analysis, the periodicity of vibration excitation and the influence of amplitude are discussed. Then, combined with the non-linear characteristics of GIS systems and non-linear vibration theory, the multiplier frequency energy ratio (MFER) is designed to extract vibration-unified features of GIS for diagnosing the mechanical fault under different current levels. The diagnosis results of the experimental data with different feature-extraction methods show the applicability and superiority of the proposed method in the GIS’s mechanical fault-detection field based on vibration information.

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

Cited by 39 publications

References 36 publications

Mechanical Condition Identification and Prediction of Spring Operating Mechanism of High Voltage Circuit Breaker

Mechanical Condition Identification and Prediction of Spring Operating Mechanism of High Voltage Circuit Breaker

A Novel Mechanical Fault Feature Selection and Diagnosis Approach for High-Voltage Circuit Breakers Using Features Extracted without Signal Processing

Vibration Feature Analysis for Gas-Insulated Switchgear Mechanical Fault Detection under Varying Current

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