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Distribution reliability in electrical power systems is critical for ensuring an uninterrupted power supply to consumers at minimal cost. According to IEEE Standard Terms, reliability is the probability that a device will function without failure over a specified time period or amount of usage. For electric power distribution, this translates to maintaining continuous power supply and addressing customer concerns over power outages. Several indices, as defined by IEEE Standard 1366-2012, are...
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Machine Learning-Based Fault Location for Smart Distribution Networks Equipped with Micro-PMU.

Hamid Mirshekali1, Rahman Dashti1, Ahmad Keshavarz2

  • 1Clinical-Laboratory Center of Power System & Protection, Faculty of Intelligent Systems Engineering and Data Science, Persian Gulf University, Bushehr 7516913817, Iran.

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Summary

This study introduces a machine learning method for precise fault location in smart grids, using micro-phasor measurement units (micro-PMUs) to overcome challenges posed by distributed generations and unpredictable fault characteristics.

Keywords:
fault section locationmachine learningmicro-phasor measurement unitsneighborhood component analysissupport vector machine

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Area of Science:

  • Electrical Engineering
  • Power Systems
  • Machine Learning Applications

Background:

  • Unpredictable faults in distribution networks threaten public safety and cause power outages.
  • Distributed generations (DGs) in smart grids introduce variable current levels and impedances, complicating fault detection.
  • Accurate fault location is crucial for rapid network restoration and minimizing financial losses.

Purpose of the Study:

  • To develop a novel machine learning-based fault location method for smart distribution networks.
  • To ensure the method's effectiveness regardless of fault characteristics and DG performance.
  • To utilize micro-phasor measurement units (micro-PMUs) for enhanced fault detection accuracy.

Main Methods:

  • Employed machine learning algorithms using voltage data recorded by micro-PMUs at substations and DGs.
  • Utilized frequency components of voltage signals as feature vectors.
  • Applied Neighborhood Component Feature Selection (NCFS) for feature extraction and dimensionality reduction, followed by a Support Vector Machine (SVM) classifier.

Main Results:

  • The proposed method demonstrated notable accuracy in identifying faulty sections across various fault types.
  • Simulations on an 11-node IEEE standard feeder with three DGs validated the algorithm's performance.
  • The approach effectively reduced uncertainties in protection systems, even with complex DG interactions.

Conclusions:

  • The developed machine learning approach offers a robust solution for fault location in smart distribution networks.
  • Micro-PMU data, processed via NCFS and SVM, provides a reliable basis for accurate fault identification.
  • This method enhances the resilience and efficiency of power distribution systems.