Quantitative Evaluation of Electrical Life of AC Contactor Based on Initial Characteristic Parameters

Sun, Shuguang; Wang, Qiufu; Du, Taihang; Wang, Jingqin; Li, Senjuan; Zong, Junji

doi:10.1109/tim.2020.3031160

Cited by 14 publications

(4 citation statements)

References 23 publications

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“…The methods of the first category extract physical characteristics from the acquired data, and construct models that map the characteristics to the electrical endurance or contact erosion. Sun et al [5] presented a method that represents the comprehensive evaluation indexes of the electrical life from the probability weighted means of the closing voltage and the breaking arc energy of every 1000 operations by Spearman ran correlation and the weighted Euclidean distance, and finally mapped the comprehensive evaluation index to the electrical life via unitary regression modelling. Mohammadhosein et al [6] investigated the relationship between eroded mass and characteristics such as the transferred electrical charge, the arc current, the arc energy, etc., for SF 6 circuit breakers and presented an online monitoring scheme.…”

Section: A Review Of the State Of The Artmentioning

confidence: 99%

A CNN-Regression-Based Contact Erosion Measurement Method for AC Contactors

Fang²,

Wu³

et al. 2022

IEEE Trans. Instrum. Meas.

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This paper proposes a method that indirectly measures the contact erosion of alternating current (AC) contactors via mapping electrical signals to the contacting alloy erosion condition which is represented by the accumulated contact mass loss (ACML). Electrical signal waveforms and their corresponding ACMLs of every make-and-break operation are acquired in endurance tests. A supervised convolutional neural network regression (CNNR) architecture containing six onedimensional convolution layers is proposed to model the relation between waveforms and ACMLs. We compare different CNNR architectures as well as different training schemes by the test precision to obtain the optimal solution. Experiments prove that the proposed CNNR architecture with an optimized training scheme can achieve a precise ACML measurement when only voltage waveforms of make operations are used. The best results reach mean absolute errors of 3.29% and 1.59% corresponding to two datasets respectively, which are superior to the results of other regression methods in the comparison and prove the theoretical significance and applicational values.

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Section: A Review Of the State Of The Artmentioning

confidence: 99%

A CNN-Regression-Based Contact Erosion Measurement Method for AC Contactors

Fang²,

Wu³

et al. 2022

IEEE Trans. Instrum. Meas.

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“…The arc occurrence signal and contact cumulative mass loss during the AC contactor disconnection were extracted in the literature [8], and a convolutional neural network (CNN) was used to construct classification and regression models to predict the electrical endurance of AC contactors. The contact voltage and arc energy of the contactor were extracted as characteristic parameters in the literature [9], and the electrical life evaluation indexes corresponding to the closing and opening stages are determined according to the weighted Euclidean distance calculation. A reliability assessment method linking contact material loss to electrical load stresses was proposed in the literature [10], which describes the degradation process of AC contactors.…”

Section: Introductionmentioning

confidence: 99%

Remaining electrical life prediction of AC contactor based on CAE-BiGRU-Attention

Xing,

Liu,

Peng

et al. 2023

Meas. Sci. Technol.

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To tackle the challenges of low prediction accuracy caused by single-feature modeling, and the hidden state of the neural network easily loses some information of the l ong time series, a method for predicting the remaining electrical life of AC contactor using a Convolutional Autoencoder-Bidirectional Gated Recurrent Unit-Attention (CAE-BiGRU-Attention) was proposed in this work. Firstly, the feature parameters were extracted from the AC contactor full-life test, and an optimal feature subset was selected using Neighborhood Component Analysis (NCA) and Spearman rank correlation coefficient to characterize the degradation state of electrical life effectively. Then, the deep information of the optimal feature subset was extracted using CAE. Finally, the remaining electrical life of the AC contactor was treated as a long time series problem and predicted in time series by BiGRU-Attention accurately. The case analysis demonstrates that the model has better prediction accuracy than RNN (Recurrent Neural Network), LSTM (Long Short-Term Memory), GRU, BiGRU and CAE-BiGRU models, with an average effective accuracy of 97.12 %. This effectively demonstrates the model’s feasibility to accurately predict temporal sequences in the remaining electrical life prediction of electrical equipment.

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“…Li K et al fitted cumulative arc erosion frequency empirical distribution functions and histograms with gamma cumulative curves and probability density curves, demonstrating that the degradation process of AC contactors follows a gamma process, and performed remaining lifespan prediction using the gamma process model [ 10 ]. S. Sun et al selected contact voltage during closing and arc energy during opening as feature parameters, calculated their correlation coefficients with electrical lifespan using Spearman rank correlation, and then combined the contact voltage and arc energy proportionally to establish a univariate regression lifespan prediction model [ 11 ]. W Zeng conducted in-depth research on the degradation process of relays and established a relay performance degradation model based on linear regression theory, using the model to predict the lifespan of aerospace relays [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

The Prediction of Residual Electrical Life in Alternating Current Circuit Breakers Based on Savitzky-Golay-Long Short-Term

Ouyang

Chi

2023

Sensors

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In order to improve the accuracy of predicting the remaining electrical life of AC circuit breakers, ensure the safe operation of electrical equipment, and reduce economic losses caused by equipment failures, this paper studies a method based on the Savitzky–Golay convolution smoothing long short-term memory neural network for predicting the electrical life of AC circuit breakers. First, a full lifespan test is conducted to obtain degradation data throughout the entire life cycle of the AC circuit breaker, from which feature parameters that effectively reflect its operational state are extracted. Next, principal component analysis and the maximum information coefficient are used to remove redundancy in the feature parameters and choose the best subset of features. Subsequently, the Savitzky–Golay convolutional smoothing algorithm is employed to smooth the feature sequence, reducing the impact of noise and outliers on the feature sequence while preserving its main trends. Then, a secondary feature extraction is performed on the smoothed feature subset to obtain the optimal secondary feature subset. Finally, the remaining electrical lifespan of the AC circuit breaker is treated as a long-term sequence problem and the long short-term memory neural network method is used for precise time-series forecasting. The proposed model outperforms backpropagation neural networks and the gate recurrent unit in terms of prediction precision, achieving an impressive 97.4% accuracy. This demonstrates the feasibility of using time-series forecasting for predicting the residual electrical lifespan of electrical equipment and provides a reference for optimizing the method of predicting remaining electrical life.

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Quantitative Evaluation of Electrical Life of AC Contactor Based on Initial Characteristic Parameters

Cited by 14 publications

References 23 publications

A CNN-Regression-Based Contact Erosion Measurement Method for AC Contactors

A CNN-Regression-Based Contact Erosion Measurement Method for AC Contactors

Remaining electrical life prediction of AC contactor based on CAE-BiGRU-Attention

The Prediction of Residual Electrical Life in Alternating Current Circuit Breakers Based on Savitzky-Golay-Long Short-Term

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