Real-Time Prediction of Electricity Distribution Network Status Using Artificial Neural Network Model: A Case Study in Salihli (Manisa, Turkey)

Year 2020, Volume: 16 Issue: 3, 307 - 321, 29.09.2020

Mahmut Sayar , Hilmi Yüksel

https://doi.org/10.18466/cbayarfbe.740343

Cited By: 1

Abstract

Electricity distribution networks are critical to the delivery of energy and the continuity of the economy. The healthy and efficient operation of these networks depends on the prediction of failures, their early detection and the rapid recovery of the resulting failures. The causes of failure are internal and external factors. Many studies in different sectors that use different techniques for failure prediction in the literature. The use of artificial intelligence techniques, which are becoming increasingly important today, in failure estimates; in terms of estimation success and effectiveness, it brings many privileges compared to other techniques. In this study, a status prediction model has been developed by using artificial neural network (ANN) technique for power outages and healthy working conditions of the electricity distribution network installed in Salihli district of Manisa province. In previous studies, using artificial intelligence techniques in the energy sector generally focused on one component of network, lifetime, energy demand estimation, battery life and goods failures. The effect of meteorological factors has not been studied on the distribution network situation using artificial intelligence techniques. In this study we use hourly power outages and hourly meteorological factors that cause failures or healthy conditions. It is aimed to effective risk management and make anticipation of power outage occurring in electricity transmission network, to make preventive maintenance for failures, to make suggestions for early intervention and shortening downtime and maintenance.

Keywords

Production and Service Systems, Operations Management, Artificial Intelligence, Electric Power Distribution Network, Fault Diagnosis, Risk Management, Reliability

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