Unscented-Kalman-filter-based single-phase adaptive reclosing of shunt-compensated extra-high-voltage transmission lines

Saad, Muhammad; Kim, Chul‐Hwan

doi:10.1016/j.aej.2022.01.024

Cited by 3 publications

(2 citation statements)

References 41 publications

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“…e radius, length, and area of the secondary arc were analyzed by processing the edge coordinates thus obtained [6]. e variations in voltage harmonics generated during singlephase AC fault clearance were used to estimate the secondary arc extinction [7] e secondary arc model has been developed in ATP, which brings together the strengths of the main existing models and implements a pseudorandom variation of the secondary arc length, to provide accurate results for the determination of extinction times of secondary arc in studies of monopolar reclosures [8]. e stochastic model of secondary arc with di erent initial positions was applied into the arc chain model to calculate the arcing time with dispersion [9].…”

Section: Introductionmentioning

confidence: 99%

Investigation of Arcing Time Prediction for Secondary Arc Based on Statistical Theory and Multivariable Regression Algorithms

Liu

Zhang

et al. 2022

Mathematical Problems in Engineering

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Research on the secondary arc has long been important in the development of ultrahigh-voltage (UHV) transmission technology. Predicting the arcing time of secondary arc is important for single-phase automatic reclosing of the circuit breaker to protect the power system. In response to this problem, this study uses a multifactor variance model to select factors that significantly influence the arcing time and verifies its validity through residual analysis. The results show that the parameters of the circuit, such as wind direction, recovery voltage, secondary current, and arc length, significantly influence the arcing time. The density-based clustering algorithm (DBSCAN) is then used to remove outliers form the data, and the influence of each factor on the arcing time is analyzed through scatterplots. Finally, linear multivariate regression and nonlinear Gaussian process regression are used to fit the results. The results show that the linear regression has a good imitative effect. The proposed method is accurate and provides novel means of predicting the arcing time.

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Section: Introductionmentioning

confidence: 99%

Investigation of Arcing Time Prediction for Secondary Arc Based on Statistical Theory and Multivariable Regression Algorithms

Liu

Zhang

et al. 2022

Mathematical Problems in Engineering

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“…Then energy coefficients are developed from those extracted harmonics which are further compared with an adaptive threshold to recognize fault nature. The authors of [9], used an unscented Kalman filter (UKF) in SPM to estimate voltage harmonics. These harmonics are used to develop two indices to monitor the extinction time of secondary arc and fault type.…”

Section: Introductionmentioning

confidence: 99%

Pattern Recognition Based Auto-Reclosing Scheme Using Bi-Directional Long Short-Term Memory Network

2022

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Precise knowledge of secondary arc extinction instant and fault nature (temporary or permanent) is necessary for auto-reclosing after a single line-to-ground fault. Existing intelligent reclosing schemes rely on the extraction of appropriate features using a signal processing module (SPM) during online data monitoring. The value of features varied greatly under different operating scenarios as well as the computational burden is greatly enhanced owing to SPM which significantly impacts the performance of the auto-reclosing scheme. Hence, in this study bi-directional long short-term memory (Bi-LSTM) network is designed which integrates feature extraction and classification process. Thus, the proposed scheme is directly incorporated into the incoming voltage data without using any SPM/ filtering technique. The open-source test system provided by the developers of Hydro-Quebec, Canada is used for training and testing. Around 4860 different signals are collected by varying power system parameters and secondary arc conditions to develop dataset A. The Bi-LSTM model is tested under no noise, low noise of SNR 30, and high noise of SNR 10. To ensure the efficacy of the proposed scheme, uni-directional long short-term memory (U-LSTM), gated recurrent unit (GRU), and machine learning models are also trained on the same dataset. Later, for validation, a second dataset B is developed by varying surge impedance loading, frequency-dependent transmission lines, and arc resistance. Then the efficiency of pre-trained artificial neural networks (ANNs) is validated on this unseen dataset. The testing and validation on both datasets confirm superior efficiency of Bi-LSTM in comparison to U-LSTM, GRU, and other models.INDEX TERMS Artificial neural network (ANN) based reclosing, bi-directional long short-term memory (Bi-LSTM), patterns based reclosing, secondary arc, single-phase auto-reclosing.

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Leveraging existing relays to improve single phase auto-reclosing

Dantas

Lopes

Silva

et al. 2022

Electric Power Systems Research

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Unscented-Kalman-filter-based single-phase adaptive reclosing of shunt-compensated extra-high-voltage transmission lines

Cited by 3 publications

References 41 publications

Investigation of Arcing Time Prediction for Secondary Arc Based on Statistical Theory and Multivariable Regression Algorithms

Investigation of Arcing Time Prediction for Secondary Arc Based on Statistical Theory and Multivariable Regression Algorithms

Pattern Recognition Based Auto-Reclosing Scheme Using Bi-Directional Long Short-Term Memory Network

Leveraging existing relays to improve single phase auto-reclosing

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