Optimal decision-making method for power grid maintenance scheduling during multiple power outage events based on large language models

Abstract

Introduction When several outage maintenance events take place at the same time, the topology and operating mode of the power system change considerably, and experience-based maintenance plans designed for single outage events can no longer capture the combined risk of these events. Methods This paper proposes an optimal decision-making method for maintenance scheduling during multiple outage events based on a large language model. First, an adaptive load forecasting model is built, in which frequency-domain features extracted by the Fourier transform and convolutional networks are fed into a pre-trained large language model; the forecasts then support a weak-link evaluation based on electrical betweenness. Second, a risk overlapping degree model quantifies the cumulative risk of simultaneous outages. On this basis, a joint optimization model coordinates the monthly maintenance schedule and the day-ahead risk-prevention measures. Results Case studies on a 35-node test system show that the method identifies weak links in each maintenance window and achieves a risk overlapping degree of 2.6. Compared with the baseline models, it reduces the level of risk. Tests on IEEE standard systems up to 118 buses confirm the applicability of the method to larger grids. Discussion The proposed method provides a practical decision-making tool for maintenance scheduling under multiple power outage events and can be extended to integrated energy systems.