The test data verifying &amp; prediction model of electrical contact

Li, Wenhua; Li, Kui; Sun, Lijun; Zhao, Sheng; Ji, Lijuan

doi:10.1109/holm.2004.1353152

Cited by 3 publications

(1 citation statement)

References 7 publications

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“…The performance of relays is crucial to the stable operation of power systems, which has led many scholars to conduct extensive research on relay performance evaluation and life prediction [7,8]. Reference [9] constructs a relay failure prediction model based on contact resistance as a key parameter, but the accuracy of models built on a single parameter is often unsatisfactory. Reference [6] considers both contact resistance and operation time and establishes relevant prediction models.…”

Section: Introductionmentioning

confidence: 99%

Research on the Reliability Test and Life Assessment Methods of Relays Used in Circuit Breaker Operating Mechanism

Zhou

Gao

et al. 2023

Energies

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As one of the key components of the circuit breaker operating mechanism, the relay can experience performance degradation due to harsh environmental factors, such as salt spray, during operation, which can ultimately affect the normal operation of the circuit breaker and threaten the safe and stable operation of the power system. To effectively evaluate the condition of the relay operating in salt spray environments over the long term, this study conducted accelerated aging experiments on a certain model of auxiliary switch under salt spray conditions using an existing test platform. The relay’s pull-in voltage, release voltage, pull-in time, and release time were analyzed as characteristic parameters affected by salt spray. The results showed that the intrusion of salt spray caused a decline in the coil performance of the relay, which required higher voltage to provide the electromagnetic force needed for operation, leading to an increase in operating voltage. On the other hand, coil degradation also caused a decrease in the electromagnetic force generated by the step voltage, resulting in a slower operating time. Subsequently, a Genetic Algorithm_Back Propagation Neural Network GA_BP) algorithm model suitable for identifying the relay status in this study was established. After optimization by a Genetic Algorithm Neural Network (GA), the recognition accuracy increased from 78.6% to 91.8%, which showed significant improvement. By clarifying the changes in the characteristic parameters of the relay in a salt spray environment and relying on experimental data, this study established a relevant state recognition model. The research results have important engineering application value for understanding the relay operating status of the circuit breaker operating mechanism in salt spray environments, conducting circuit breaker operating mechanism relay life prediction, and preventing circuit breaker operating mechanism failures.

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Section: Introductionmentioning

confidence: 99%

Research on the Reliability Test and Life Assessment Methods of Relays Used in Circuit Breaker Operating Mechanism

Zhou

Gao

et al. 2023

Energies

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Prognostics for Electromagnetic Relays Using Deep Learning

et al. 2022

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Electromagnetic Relays (Electromagnetic Relay (EMR)s) are omnipresent in electrical systems, ranging from mass-produced consumer products to highly specialised, safety-critical industrial systems. Our detailed literature review focused on EMR reliability highlighting the methods used to estimate the State of Health or the Remaining Useful Life emphasises the limited analysis and understanding of expressive EMR degradation indicators, as well as accessibility and use of EMR life cycle data sets. Prioritising these open challenges, a deep learning pipeline is presented in a prognostic context termed Electromagnetic Relay Useful Actuation Pipeline (EMRUA). Leveraging the attributes of causal convolution, a Temporal Convolutional Network (TCN) based architecture integrates an arbitrary long sequence of multiple features to produce a remaining useful switching actuations forecast. These features are extracted from raw, high volume life cycle data sets, namely EMR switching data (Contact-Voltage, Contact-Current). Monte-Carlo Dropout is utilised to estimate uncertainty during inference. The TCN hyperparameter space, as well as various methods to select and analyse long sequences of multivariate time series data are investigated. Subsequently, our results demonstrate improvements using the developed statistical feature-set over traditional, time-based features, commonly found in literature. EMRUA achieves an average forecasting mean absolute percentage error of ±12 % over the course of the entire EMR life. INDEX TERMSElectromagnetic relay, prognostics, prognostics and health management, predictive maintenance, remaining useful life, artificial intelligence, deep learning, temporal convolutional networks, Monte-Carlo dropout. ABBREVIATIONS AT Arcing time. BT Bounce time. CAE Convolutional auto encoder. CC Coil current. CI Contact current. CNN Convolutional neural network. CR Contact resistance. CT Closing time. CV Contact voltage. DCR Dynamic contact resistance. DI Degradation indicator. EI Exponential indexing. The associate editor coordinating the review of this manuscript and approving it for publication was Sajid Ali . EMR Electromagnetic relay. EMRUA Electromagnetic relay useful actuation pipeline. EOL End of life. FC Fully connected layer. GI Growing-sequence indexing. LI Linear indexing. LSTM Long-short-term-memory network. MAE Mean absolute error. MAPE Mean absolute percentage error. MCD Monte-carlo dropout. MVTD Multivariate time series data. NN Neural network. OT Over-travel time.

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Research on life prediction based on wavelet transform and ARMA model for space relay

Wang

et al. 2009

2009 4th IEEE Conference on Industrial Electronics and Applications

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The electrical life is an important index to evaluate the reliability of relay, and it is closely related with many characteristic parameters of relay such as contact resistance, pick-up time, over-travel time, etc. By using the time series analysis and by taking some characteristic parameters as predicted variables, the life of relay can be obtained by the life prediction which is a nondestructive and short-cycling life test method. However, the predicted accuracy is greatly influenced by the complex variations of characteristic parameters, and as a result it sometimes becomes too low to be accepted. For the purpose of improving the predicted accuracy, a life prediction method for space relays based on wavelet transform and ARMA (auto-regression moving average) time series is proposed in this paper. Taking the pick-up time of a relay (which can be regarded as a nonstationary time series) as an example, we divide it into three parts-trend term, cyclic term and stochastic term by adopting wavelet decomposition and reconstruction. Afterwards, models of each term are built with different methods and then they are employed to predict the future value, respectively. Finally, the predicted results of the original time series are obtained by superimposing the respective prediction of each term. The comparison between the method used in this paper and the conventional regression analysis method indicates that the former has higher predicted accuracy.

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The test data verifying & prediction model of electrical contact

Cited by 3 publications

References 7 publications

Research on the Reliability Test and Life Assessment Methods of Relays Used in Circuit Breaker Operating Mechanism

Research on the Reliability Test and Life Assessment Methods of Relays Used in Circuit Breaker Operating Mechanism

Prognostics for Electromagnetic Relays Using Deep Learning

Research on life prediction based on wavelet transform and ARMA model for space relay

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