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Fault Detection, Classification, and Location in Underground Cables

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Power Quality in Microgrids: Issues, Challenges and Mitigation Techniques

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 1039))

Abstract

Underground (UG) cables have become widespread in the present scenario as these cables' durability is high and there are fewer environmental issues. With time, the insulation of the UG cables begins to deteriorate. As a result, their conduction efficiency decreases. To achieve the integrity of an underground cable, it is vital to accurately identify a faulty segment to limit the amount of time that the system is unavailable during a fault. Therefore, a fast and accurate fault detection approach is necessary to expedite system restoration, reduce outage duration, minimize economic losses, and considerably increase system reliability. There are several traditional techniques used for fault classification and localization and those are arduous, sluggish, and computationally intensive, they are reliant on mathematical modeling and necessitate specialized skill. To overcome such problems, several artificial intelligence approaches such as machine learning techniques as well as deep learning methods have been deliberated in this chapter. The above-mentioned approaches are considered for fault location, classification, and detection in the UG cable. All the methods are taken from some conference papers, journals, and e-books from 2002 to 2022. The benefits and drawbacks of all fault diagnosis approaches have also been explored.

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Abbreviations

ML:

Machine learning

DL:

Deep learning

ANFIS:

Adaptive neuro-fuzzy interference system

ANN:

Artificial neural network

FLS:

Fuzzy logic system

SVM:

Support vector machine

CNN:

Convolutional neural network

RNN:

Recurrent neural network

LSTM:

Long short-term memory

SUGPD:

Smart underground power distribution system

ADAGRAD:

Adaptive gradient descent

RMSPROP:

Root mean square prop

ADAM:

Adaptive moment estimation

DBN:

Deep belief network

GRNN:

General Regression neural network

PT:

Potential transformer

CT:

Current transformer

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Acknowledgements

This research work was supported by “Woosong University’s Academic Research Funding—2023.”

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Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, IIIT Bhubaneswar, Bhubaneswar, Odisha, India

    Smrutisikha Jena & Debani Prasad Mishra

  2. Department of Railroad and Electrical Engineering, Woosong University, Daejeon, Republic of Korea

    Surender Reddy Salkuti

Authors
  1. Smrutisikha Jena
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  2. Debani Prasad Mishra
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  3. Surender Reddy Salkuti
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Corresponding author

Correspondence to Surender Reddy Salkuti .

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Editors and Affiliations

  1. Department of Railroad and Electrical Engineering, Woosong University, Daejeon, Korea (Republic of)

    Surender Reddy Salkuti

  2. Department of Electrical Engineering, Veer Surendra Sai University of Technology (VSSUT), Burla, Odisha, India

    Papia Ray

  3. Department of EECE, University of South Africa, Pretoria, Gauteng, South Africa

    Arvind R. Singh

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Jena, S., Mishra, D.P., Salkuti, S.R. (2023). Fault Detection, Classification, and Location in Underground Cables. In: Salkuti, S.R., Ray, P., Singh, A.R. (eds) Power Quality in Microgrids: Issues, Challenges and Mitigation Techniques. Lecture Notes in Electrical Engineering, vol 1039. Springer, Singapore. https://doi.org/10.1007/978-981-99-2066-2_10

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  • DOI: https://doi.org/10.1007/978-981-99-2066-2_10

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