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Online fault detection and classification of 3-phase long transmission line using machine learning model

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Abstract

The rapid growth of power systems around the world has improved the complexity of power networks. It has become a challenging task to ensure a reliable and uninterruptible electrical power supply to the utility/end user. The good state of the power line can be restored by accurate fault detection. Moreover, accurate fault prediction plays an important role in network planning and operation. A novel approach for long transmission line fault detection, classification and prediction is presented in this paper. Different types of transmission line faults like line-to-ground (LG), line-to-line (LL), Line-line-ground (LLG), Line-line-line (LLL), Line-line-line-ground (LLLG), etc. have been introduced. The fault detection and classification are done online using a deep neural network (DNN) for better accuracy. This approach can deal with non-linear problems with very high accuracy. In addition to this, the proposed uses the instantaneous measure of the fault parameters like current, voltage, etc. online. In the MATLAB Simulink environment, the long transmission line is simulated. Designing a fault free and 100% reliable power system is practically impossible. Hence a perfect and most accurate fault prediction method can definitely improve the reliability and hence power quality of the system. The data extracted from the MATLAB workspace is analyzed in detail using seven different types of machine learning methods like logistic regression, SVM, Naïve Bayes, etc. in the Python platform individually. A comparative analysis is made to identify the most suitable predictive method for the proposed system.

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Authors and Affiliations

  1. College of Engineering Bhubaneswar, Bhubaneswar, Odisha, India

    Anjan Kumar Sahoo

  2. Centurion University of Technology and Management, Bhubaneswar, Odisha, India

    Sudhansu Kumar Samal

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  1. Anjan Kumar Sahoo
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  2. Sudhansu Kumar Samal
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Contributions

AKS agreed on the content of the study. AKS and SKS collected all the data for analysis. AKS agreed on the methodology. AKS and SKS completed the analysis based on the agreed steps. Results and conclusions are discussed and written together. Both authors read and approved the final manuscript.

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Correspondence to Anjan Kumar Sahoo.

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Sahoo, A.K., Samal, S.K. Online fault detection and classification of 3-phase long transmission line using machine learning model. Multiscale and Multidiscip. Model. Exp. and Des. 6, 135–146 (2023). https://doi.org/10.1007/s41939-022-00132-x

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