Electrical Modelling of Switching Arcs in a Low Voltage Relay at Low Currents

Najam, Ammar; P, Pieterse; Uhrlandt, Dirk

doi:10.3390/en13236377

Cited by 9 publications

(10 citation statements)

References 25 publications

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“…In addition, the discharge voltage decreases with the increase of FEF (figure 5(c)). FEF much larger than 100 must be considered in order to obtain the typical discharge voltage values of less than 60 V as in experimental observations [44] on arcs in air with a length of 2 mm and electric current values between 0.8 and 16 A. In the further considerations, we fix the value FEF = 200 also for the following reason.…”

Section: Results From Modelling and Discussionmentioning

confidence: 99%

Unified modelling of low-current short-length arcs between copper electrodes

Baeva¹,

Boretskij²,

González³

et al. 2020

J. Phys. D: Appl. Phys.

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In this work, we present for the first time a unified model of a low-current short-length arc between copper electrodes. The model employs one-dimensional fluid description of the plasma in argon and copper vapour at atmospheric pressure and the heat transfer in the electrodes made of copper. The solution of the particle and energy conservation of electrons and heavy particles is coupled with the solution of the Poisson equation, from which the self-consistent electric field is obtained. The operation of the non-refractory cathode is based on thermo-field emission. Heat fluxes from the plasma to the electrodes are considered so that a phase change and evaporation from the cathode and a release of copper atoms into the plasma are taken into account. The influence of the copper atoms and ions on the plasma properties is analysed and discussed. The model’s predictions are compared with experimental data and a qualitative agreement is obtained besides the restrictions of the one-dimensional fluid model.

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Section: Results From Modelling and Discussionmentioning

confidence: 99%

Unified modelling of low-current short-length arcs between copper electrodes

Baeva¹,

Boretskij²,

González³

et al. 2020

J. Phys. D: Appl. Phys.

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“…Further analysis of contact surface at different stages using Energy Dispersive X-Ray (EDX) and Scanning Electron Microscopy (SEM) have been used [54], [92]. In [93] the arc discharge is optically monitored. Lastly, measurements of static and dynamic contact pressure enable an assessment of the contact health [94].…”

Section: B: Intrusive Dismentioning

confidence: 99%

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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“…Determining the values of the parameters of the arc model is considered to be the most difficult problem in the application of the black-box arc model. Furthermore, most of the existing arc model research has focused on arcs discharged in a vacuum or in open air [26][27][28]. The arc in the pyro-breaker presented here, however, is discharged in deionized water.…”

Section: Literature Reviewmentioning

confidence: 99%

Application of an Improved Mayr-Type Arc Model in Pyro-Breakers Utilized in Superconducting Fusion Facilities

Wang

2021

Energies

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Pyro-breaker, a fast-responding, highly reliable and explosive-driven circuit breaker, is utilized in several Quench Protection Systems (QPS). The commutation process and its parameters are the main technical considerations in the process of designing a new pyro-breaker. The commutation parameters, such as the commutation time and the current change rate, are not only determined by the electrical parameters of the commutation circuit but also the arc behavior during the operation. The arc behavior is greatly affected by the structure and the driving mechanism of the Commutation Section (CS) in the pyro-breaker. The arc model was developed decades ago and the black-box arc model is considered a valid method to study arc behavior. In this paper, the Schavemaker black-box arc model, an improved Mayr-type arc model, is applied to study the commutation process of a newly designed pyro-breaker. Unlike normal circuit breakers, the arc discussed in this paper is discharged in deionized water. A parameter selection method is proposed. The practicability of the method is verified by numerical calculation in Power Systems Computer Aided Design (PSCAD) and experimentally.

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Electrical Modelling of Switching Arcs in a Low Voltage Relay at Low Currents

Cited by 9 publications

References 25 publications

Unified modelling of low-current short-length arcs between copper electrodes

Unified modelling of low-current short-length arcs between copper electrodes

Prognostics for Electromagnetic Relays Using Deep Learning

Application of an Improved Mayr-Type Arc Model in Pyro-Breakers Utilized in Superconducting Fusion Facilities

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