Analysis of Technological Damage at 110 kV Substations in JSC IDGC of the North Caucasus- «Sevkavkazenergo»

Plieva, Madina; Гаврина, О.А.; Kabisov, A A

doi:10.1109/fareastcon.2019.8934076

Cited by 7 publications

(1 citation statement)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the study of Plieva et al [10] , a statistical analysis is presented regarding the leading causes of damage in the electricity distribution network. Based on the monitoring information of a distribution company located in Spain between 2014 and 2019, they discovered the leading causes of accidents and malfunctions in electricity distribution networks.…”

Section: Introductionmentioning

confidence: 99%

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

Shaghaghi,

Omidifar,

Zahedi

et al. 2023

Therm. Sci. Eng.

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%