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<p>Accurate fault location in transmission lines remains a critical challenge for modern power systems, particularly as networks become increasingly complex with the integration of renewable energy sources and smart grid technologies. Traditional fault location methods often need help with high-impedance faults, non-homogeneous line parameters, and dynamic system conditions, leading to extended outage durations and reduced system reliability. This study addresses these challenges by developing an enhanced fault location method that combines conventional electromagnetic principles with advanced machine learning techniques. The methodology employs an approach that integrates modified impedance-based calculations with convolutional neural networks and machine learning regression. The method was validated using a modified IEEE 39-bus test system through simulations, incorporating several fault scenarios and system conditions. Testing utilised synchronised measurements from both transmission line ends, with data captured at 4096 samples per second. Results demonstrate significant improvements over existing techniques, achieving a 99.1% fault detection rate, 98.2% classification accuracy, and 1.2% mean absolute percentage error in location estimation. The method showed particular strength in challenging scenarios, reducing errors by 79.4% for high-impedance faults and maintaining accuracy under variable renewable generation conditions. The proposed method advances power system protection by providing a robust, adaptive solution suitable for modern grid requirements. Its conventional instrumentation implementation facilitates practical adoption, offering improved reliability and reduced outage durations in real-world applications.</p>

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Advanced method for precise fault location in transmission networks

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年6月12日

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  • Osita U. Omeje,
  • Olanrewaju M. Bankole,
  • Daniel E. Okojie,
  • Candidus U. Eya

摘要

Accurate fault location in transmission lines remains a critical challenge for modern power systems, particularly as networks become increasingly complex with the integration of renewable energy sources and smart grid technologies. Traditional fault location methods often need help with high-impedance faults, non-homogeneous line parameters, and dynamic system conditions, leading to extended outage durations and reduced system reliability. This study addresses these challenges by developing an enhanced fault location method that combines conventional electromagnetic principles with advanced machine learning techniques. The methodology employs an approach that integrates modified impedance-based calculations with convolutional neural networks and machine learning regression. The method was validated using a modified IEEE 39-bus test system through simulations, incorporating several fault scenarios and system conditions. Testing utilised synchronised measurements from both transmission line ends, with data captured at 4096 samples per second. Results demonstrate significant improvements over existing techniques, achieving a 99.1% fault detection rate, 98.2% classification accuracy, and 1.2% mean absolute percentage error in location estimation. The method showed particular strength in challenging scenarios, reducing errors by 79.4% for high-impedance faults and maintaining accuracy under variable renewable generation conditions. The proposed method advances power system protection by providing a robust, adaptive solution suitable for modern grid requirements. Its conventional instrumentation implementation facilitates practical adoption, offering improved reliability and reduced outage durations in real-world applications.