Observational analysis reveals peak summer emergency outages across 23 regional electric grid associations, highlighting maintenance quality and line length as primary drivers of network reliability.
The paper presents an analysis of emergency situation dynamics in the electrical networks of the Unified Energy System (UES) of Russia for the period between 2014 and 2021. The software algorithms developed by the authors were used for approximating a series of emergency outages. These algorithms were used to calculate and visualise the examined indicators. Methods of correlation and regression analysis were used to calculate the autoregressive parameters and trend equations used to predict emergency outages. The paper discusses the emergency dynamics modes in the largest Russian grid companies with voltages of 110 kV and above. The authors analyse data on the occurrence of emergency situations in 23 electric grid associations that are part of the UES of Russia for the period between 2014 and 2021. The percentages of accidents at the largest electrical network facilities were determined, taking into account their length, as well as the seasonal characteristics of the territories that the power lines run through. In addition, data on the deterioration of the key network elements, as well as federal investments in the development of the examined company were taken into account. According to the findings, a specific indicator that depends on the length of the networks and the quality of maintenance organization should be regarded as the most reliable assessment of the actual state of faulty electrical networks. An assessment of the deterioration level of the examined networks showed that the greatest deterioration is observed in PAO Rosseti Ural (Yekaterinburg, Russia) (more than 60%), while the lowest deterioration is observed in JSC Rosseti Yantar (25%) (Kaliningrad, Russia). When assessing the seasonal component as one of the accident criteria, it was established that the greatest damage occurs in the summer period, i.e., in June, July, and August. It was revealed that the autoregression and trend equations can be used to predict the examined indicators in the short term.
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Наумов et al. (2024) studied this question.
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