融合卷积-双向长短期记忆注意力机制净负荷预测的配电网故障恢复策略OA

[Objectives]In response to the significantly increased complexity of fault recovery in distribution networks caused by high-penetration distributed generation(DG)and the insufficient stability of islanded operation,a fault recovery method incorporating net load prediction is proposed for DG-integrated distribution networks.[Methods]A hybrid prediction model of convolutional neural networks and bi-directional long short-term memory networks(CNN-BiLSTM)incorporating a meteorological feature attention mechanism is designed to achieve high-precision net load prediction during fault periods.Then,a multi-objective power supply recovery model is developed,and an optimized genetic algorithm and Broyden-Fletcher-Goldfarb-Shanno is adopted to solve island partitioning and reconfiguration scheme based on net load prediction results.[Results]Simulation results based on the PG&E 69-node system show that the prediction accuracy of the proposed method is relatively improved by 21.8%,the continuous power supply time of one of the islands is increased by 150%,and the increased power supply reaches 198.464 kW·h.[Conclusions]The proposed strategy effectively solves the island instability problem caused by DG randomness,providing a new method for rapid self-healing of high-proportion new energy distribution networks.