发电技术2026，Vol.47Issue(3)：494-503,10.DOI:10.12096/j.2096-4528.pgt.260303
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
Distribution Networks Fault Recovery Strategy Fused With Convolutional Neural Networks-Bi-Directional Long Short-Term Memory-Attention Net Load Forecasting
摘要
Abstract
[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.关键词
分布式电源/有源配电网/遗传算法/卷积神经网络/孤岛划分/故障恢复/净负荷预测Key words
distributed generation/active distribution network/genetic algorithm/convolutional neural networks/island partitioning/fault recovery/net load prediction分类
信息技术与安全科学引用本文复制引用
储云迪,丁泽楷,林政宇,吕湛,侯世玺,史朋飞..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[J].发电技术,2026,47(3):494-503,10.基金项目
国家自然科学基金项目(62476080) （62476080）
江苏省自然科学基金项目(BK20241779).Project Supported by National Natural Science Foundation of China(62476080) （BK20241779）
Natural Science Foundation of Jiangsu Province(BK20241779). （BK20241779）
