Journal of Siberian Federal University. Engineering & Technologies / Analytical Assessment of Power Transmission Through the Networks of the PJSC “ROSSETI Volga – Mordovenergo” Branch with the Possibility of Creating Predictive Failure Models

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Issue
Journal of Siberian Federal University. Engineering & Technologies. 2025 18 (4)
Authors
Naumov, Igor V.; Polkovskaya, Marina N.; Yakupova, Marina A.; Fedorinova, Elvira S.
Contact information
Naumov, Igor V.: Irkutsk National Research Technical University; Irkutsk State Agrarian University named after A. A. Ezhevsky Irkutsk, Russian Federation; ; Polkovskaya, Marina N.: Irkutsk State Agrarian University named after A. A. Ezhevsky Irkutsk, Russian Federation; Yakupova, Marina A.: Irkutsk State Agrarian University named after A. A. Ezhevsky Irkutsk, Russian Federation; Fedorinova, Elvira S.: Irkutsk State Agrarian University named after A. A. Ezhevsky Irkutsk, Russian Federation
Keywords
emergency situation; failure events; under-discharge of electricity; autoregressive equation; probability of failure
Abstract

The purpose of the scientific article is to consider the possibilities of creating predictive models for assessing accidents in the electrical networks of PJSC ROSSETI Volga – Mordovenergo based on an analysis of the dynamics of failures for 2018–2023. Based on the proposed classification, the failure rate was analyzed and the percentage of failures of varying intensity relative to the total number of failures was determined. It has been established that the largest number of emergency shutdowns is accounted for by failures, as a result of which the under-output of EE does not exceed 1 thousand kWh. Such failures accounted for 93.47 % (7130 ed.) of the total number of failures during the study period, while the number of undelivered EE as a result of these failures amounted to 445,497 thousand kWh. High-intensity failures, which resulted in a power outage of more than 10 thousand kWh, account for only 0.079 % of the total number of failures. Nevertheless, they account for 7.9 % of the total under-output of EE (52.09 thousand kWh). The main causes of emergency situations are considered and a probabilistic and statistical analysis of the possibility of failure events before 2026 for the main reasons is performed. It is determined that trend models for March, May, June, and September can be used to predict the number of equipment failures due to non–compliance with maintenance deadlines; autoregressive models for February, October, and December. General scientific methods of numerical, probabilistic and statistical analysis and forecasting methods were used as methods. The results of the study may be of interest to the management of electric grid companies when developing emergency response measures for the future

Pages
452–471
EDN
LPCOFL
Paper at repository of SibFU
https://elib.sfu-kras.ru/handle/2311/156181

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