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Deep learning-based fault location using PMU in tapped four-circuit transmission lines

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Abstract

The use of four-circuit transmission lines in power systems is increasing due to their advantages. On the other hand, quick and accurate fault location is of particular importance to speed up the repair of the fault in four circuit lines. Due to the mutual induction between the circuits, the usual fault location methods are not exploitable. This problem will be more complicated when four circuit lines are tapped lines. In this paper, in order to overcome the existing complexities and shortcomings of the previous methods, a fault location algorithm has been proposed based on deep learning (CNN-LSTM) and phasor measurement units. In order to increase the accuracy of the proposed algorithm, the input data are obtained from both terminals' PMUs. The features of the proposed method include no need to know the parameters of the line, low sensitivity to fault conditions (fault inception, fault resistance, etc.), the ability to implement on transposed, untransposed, and tapped lines, and reduction in computational complexity. The algorithm evaluation indicates a high accuracy so that the maximum average error of the proposed method is 3%.

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Abbreviations

\(W_{{{\text{cnn}}}}\) :

Weight coefficient of the filter

\(x_{t}\) :

tTh input sample

\(f_{{\text{sub }}}\) :

Maximum function

\(b_{{{\text{cnn}}}}\) :

Bias coefficient

\(\sigma_{{{\text{cnn}}}}\) :

Activation function

\(t_{nq}^{{{\text{in}} }}\) :

Output of the qth neuron of the \(n\)th input feature surface

\(\sigma\) :

Sigmoid function

tanh:

Tanh function

\(f_{t} ,i_{t}\) :

Combination weights of the inputs

\(x_{t} ,h_{{\left( {t - 1} \right)}}\) :

The weighted combination tanh

\(W_{0} ,U_{0}\) :

Weights

\(b_{0}\) :

Bias

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Authors and Affiliations

  1. College of Electrical Engineering, Yellow River Conservancy Technical Institute, Kaifeng, 475004, Henan, China

    Zhiyu Yan & Yanan Yang

Authors
  1. Zhiyu Yan
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  2. Yanan Yang
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Contributions

ZY contributed to writing—original draft preparation, conceptualization, supervision, project administration. YY contributed to methodology, software, validation, language review.

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Correspondence to Zhiyu Yan.

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Yan, Z., Yang, Y. Deep learning-based fault location using PMU in tapped four-circuit transmission lines. Int. J. Dynam. Control 12, 1270–1278 (2024). https://doi.org/10.1007/s40435-023-01296-1

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  • DOI: https://doi.org/10.1007/s40435-023-01296-1

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