High-Voltage Circuit Breaker Fault Diagnosis Using a Hybrid Feature Transformation Approach Based on Random Forest and Stacked Autoencoder

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

doi:10.1109/tie.2018.2879308

Cited by 107 publications

(36 citation statements)

References 32 publications

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“…happens, the vibration signal changes in compassion with the healthy signal. To give an illustration, deformation of shaft, close coil, insulation pull rod, and damper of CB have been investigated based on the vibration signals in [51]. Similarly, in [53], the malfunction in damper and insulation push/pull rod have been identified using vibration.…”

Section: E Vibrationmentioning

confidence: 99%

“…Energy entropy of Hilbert marginal spectrum (HMS), variational mode decomposition (VMD), [53] (2019) Wavelet, stacked auto encoder neural network [46] (2018) Current vibration signal entropy weight characteristic, grey wolf optimization-support vector machine (GWO-SVM), [49] (2019) Phase space reconstruction, Lyapunov exponent (LLE), correlation dimension (CD),Kolmogorov entropy (KE) [51] (2019) Empirical mode decomposition (EMD) energy entropy, multi-class support vector machine (MSVM) [ Neural network and support vector machine (SVM) [18] (2019) empirical mode decomposition (EMD), empirical wavelet transform (EWT), wavelet packet transform (WPT), variational mode decomposition (VMD) for extraction of features. Subsequently, this signal can be transformed into several intrinsic mode functions.…”

Section: Vibrationmentioning

confidence: 99%

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Condition Monitoring of High Voltage Circuit Breakers: Past to Future

Razi-Kazemi

Niayesh

2021

IEEE Trans. Power Delivery

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High voltage circuit breakers (HVCBs) play a critical role on providing the desired reliability, and resiliency in power systems. In order to extend their lifetime and predict the failures, various maintenance policies could be applied on these critical components. Amongst these strategies, condition-based maintenance (CBM) provides a satisfactory agreement with future smart environment. This paper aims to provide an insight into the relevant developments in this subject and to explore the viable visions compatible with future research stream. Accordingly, three directions, i.e. diagnostic signals, intelligent modelling and using monitoring data in asset management have been addressed in this paper. It presents challenges dealing with real-time assessment of the diagnostic signals relating to measurements, and analyses. Subsequently, the issues associated with using artificial intelligent (AI) and Machine learning for providing intelligent algorithms have been discussed. Finally, the connection between the monitoring data and the asset management approach is investigated. The latter is looking for the subjects including remaining lifetime estimation, prioritization, and health index definitions. This paper has attempted to make a bridge from past to future research trends in the failure diagnosis of HVCBs.

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Section: E Vibrationmentioning

confidence: 99%

Section: Vibrationmentioning

confidence: 99%

Condition Monitoring of High Voltage Circuit Breakers: Past to Future

Razi-Kazemi

Niayesh

2021

IEEE Trans. Power Delivery

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“…In PSO, acceleration coefficients c 1 , c 2 , and weight factor w were assigned to 2 and 0.5, respectively. The hyperparameters of BPNN 39 , RBFNN 40 , ELM 41 , DRNN 13 , and RF 42 (2020) 10:4687 | https://doi.org/10.1038/s41598-020-61464-7…”

Section: Experiments and Data Development A Test Bench Model Was Estmentioning

confidence: 99%

Toward Enhanced State of Charge Estimation of Lithium-ion Batteries Using Optimized Machine Learning Techniques

Hannan

Lipu

Hussain

et al. 2020

Sci Rep

182

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State of charge (Soc) is a crucial index used in the assessment of electric vehicle (eV) battery storage systems. thus, Soc estimation of lithium-ion batteries has been widely investigated because of their fast charging, long-life cycle, and high energy density characteristics. However, precise Soc assessment of lithium-ion batteries remains challenging because of their varying characteristics under different working environments. Machine learning techniques have been widely used to design an advanced Soc estimation method without the information of battery chemical reactions, battery models, internal properties, and additional filters. Here, the capacity of optimized machine learning techniques are presented toward enhanced Soc estimation in terms of learning capability, accuracy, generalization performance, and convergence speed. We validate the proposed method through lithium-ion battery experiments, EV drive cycles, temperature, noise, and aging effects. We show that the proposed method outperforms several state-of-the-art approaches in terms of accuracy, adaptability, and robustness under diverse operating conditions.

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“…Therefore, the analysis of vibration signals generated by HVCBs action can find a variety of mechanical faults, such as insufficient spring energy storage, loose screws, etc. [ 6 , 7 ].…”

Section: Introductionmentioning

confidence: 99%

Mechanical Fault Diagnosis of a High Voltage Circuit Breaker Based on High-Efficiency Time-Domain Feature Extraction with Entropy Features

Huang

2020

Entropy

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The fault samples of high voltage circuit breakers are few, the vibration signals are complex, the existing research methods cannot extract the effective information in the features, and it is easy to overfit, slow training, and other problems. To improve the efficiency of feature extraction of a circuit breaker vibration signal and the accuracy of circuit breaker state recognition, a Light Gradient Boosting Machine (LightGBM) method based on time-domain feature extraction with multi-type entropy features for mechanical fault diagnosis of the high voltage circuit breaker is proposed. First, the original vibration signal of the high voltage circuit breaker is segmented in the time domain; then, 16 features including 5 kinds of entropy features are extracted directly from each part of the original signal after time-domain segmentation, and the original feature set is constructed. Second, the Split importance value of each feature is calculated, and the optimal feature subset is determined by the forward feature selection, taking the classification accuracy of LightGBM as the decision variable. After that, the LightGBM classifier is constructed based on the feature vector of the optimal feature subset, which can accurately distinguish the mechanical fault state of the high voltage circuit breaker. The experimental results show that the new method has the advantages of high efficiency of feature extraction and high accuracy of fault identification.

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High-Voltage Circuit Breaker Fault Diagnosis Using a Hybrid Feature Transformation Approach Based on Random Forest and Stacked Autoencoder

Cited by 107 publications

References 32 publications

Condition Monitoring of High Voltage Circuit Breakers: Past to Future

Condition Monitoring of High Voltage Circuit Breakers: Past to Future

Toward Enhanced State of Charge Estimation of Lithium-ion Batteries Using Optimized Machine Learning Techniques

Mechanical Fault Diagnosis of a High Voltage Circuit Breaker Based on High-Efficiency Time-Domain Feature Extraction with Entropy Features

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