Proposing a new optimized forecasting model for the failure rate of power distribution network thermal equipment for educational centers

Aidin Shaghaghi, Reza Omidifar, Rahim Zahedi, Ali Asghar Pourezzat, Mansour Keshavarzzadeh

Article ID: 2087
Vol 6, Issue 2, 2023

VIEWS - 217 (Abstract) 100 (PDF)

Abstract


To gain a deep understanding of maintenance and repair planning, investigate the weak points of the distribution network, and discover unusual events, it is necessary to trace the shutdowns that occurred in the network. Many incidents happened due to the failure of thermal equipment in schools. On the other hand, the most important task of electricity distribution companies is to provide reliable and stable electricity, which minimal blackouts and standard voltage should accompany. This research uses seasonal time series and artificial neural network approaches to provide models to predict the failure rate of one of the equipment used in two areas covered by the greater Tehran electricity distribution company. These data were extracted weekly from April 2019 to March 2021 from ENOX incident registration software. For this purpose, after pre-processing the data, the appropriate final model was presented with the help of Minitab and MATLAB software. Also, average air temperature, rainfall, and wind speed were selected as input variables to the neural network. The mean square error has been used to evaluate the proposed models’ error rate. The results show that the time series models performed better than the multi-layer perceptron neural network in predicting the failure rate of the target equipment and can be used to predict future periods.


Keywords


failure rate prediction; seasonal time series models; artificial neural network; electricity distribution company

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DOI: https://doi.org/10.24294/tse.v6i2.2087

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