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March 14, 2026Sivas Cumhuriyet Üniversitesi Mühendislik Fakültesi Dergisi0 citations

Biological Interactions in Distribution Networks and Analysis of Weather-Related Failures Using Artificial Neural Networks

KDKübra DağgezVSVekil Sarı

Key Points

  • This research aims to analyze the impact of biological factors and weather conditions on electrical distribution line failures.
  • Utilized a data-driven approach with artificial neural networks (ANN) to analyze outage data.
  • Employed real outage data from 2021-2023 alongside meteorological information.
  • Developed and tested the ANN model using MATLAB software.
  • Achieved an accuracy rate of 97% in predicting outages using the ANN model.
  • Demonstrated effective generalization ability of the model on test data.
  • Provided insights for electricity distribution companies regarding biologically induced outages.

Abstract

Overhead line faults have a critical impact on the reliability of electrical distribution systems. Literature reviews show that overhead lines are highly susceptible to environmental factors such as weather conditions, vegetation and wildlife. This study presents a data-driven approach to analysing biologically induced outages in overhead distribution lines using Artificial Neural Network (ANN) techniques. The study aims to predict regions where outages are likely to occur using meteorological data. For the analysis, real outage data from an electricity distribution company for the period 2021-2023, together with meteorological data for the same period, were used to model the ANN structure in MATLAB software. The model achieved an accuracy rate of 97% on the test data, demonstrating a high generalisation ability. The results of this study provide valuable insights for electricity distribution companies to better understand biologically induced outage problems, and to develop effective models for predicting such outages.

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Cite This Study

Dağgez et al. (2025) studied this question.

synapsesocial.com/papers/69b4fb9db39f7826a300bf9ehttps://doi.org/10.66248/cumfad.1600116
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