Intelligent Recommendation Method of Blackout Window Period based on FP-growth Algorithm

With the continuous expansion of power grid scale, the rationality of blackout window period has great significance to ensure the safe operation of power grid. At present, the preparation of blackout window period is mostly based on manual experience, which is inefficient. Considering the increasing digital level of power system, based on massive historical data, this paper proposes an intelligent recommendation method for power equipment in the same blackout window period based on FP-growth and strong association rule mining. It has been applied to the actual production and operation to provide auxiliary reference for the staff in the blackout window period and improve the work efficiency.