通过不确定性感知联邦学习提升微电网抗攻击能力，保障能源管理安全与经济性。
Uncertainty-Aware Federated Learning for Cyber-Resilient Microgrid Energy Management
- 融合自编码器与预测不确定性量化，实现隐私保护下的攻击检测
- 在极端攻击下恢复93.7%的预测性能，降低5%运营成本
- 适合关注微电网安全与分布式优化的研究者
在网络攻击条件下，维持微电网能源管理系统经济效率与运行可靠性仍具挑战。现有方法通常假设测量数据正常，预测不确定性未量化，且无法缓解对可再生能源预测的恶意攻击。本文提出一个综合性的网络弹性框架，集成基于联邦长短期记忆的光伏预测，以及新型两级级联虚假数据注入攻击检测与能源管理优化系统。该方法结合自编码器重构误差与预测不确定性量化，在保护数据隐私的同时实现抗攻击储能调度。研究了极端虚假数据攻击条件，导致预测性能下降58%，运营成本上升16.9%。所提框架将误报率降低70%，恢复93.7%的预测性能损失，实现5%运营成本节约，缓解34.7%的攻击引发经济损失。结果表明，以精度为导向的多信号融合级联检测优于单信号方法，验证了去中心化微电网中安全与性能的协同优势。
原文摘要 · Abstract (English)
Maintaining economic efficiency and operational reliability in microgrid energy management systems under cyberattack conditions remains challenging. Most approaches assume non-anomalous measurements, make predictions with unquantified uncertainties, and do not mitigate malicious attacks on renewable forecasts for energy management optimization. This paper presents a comprehensive cyber-resilient framework integrating federated Long Short-Term Memory-based photovoltaic forecasting with a novel two-stage cascade false data injection attack detection and energy management system optimization. The approach combines autoencoder reconstruction error with prediction uncertainty quantification to enable attack-resilient energy storage scheduling while preserving data privacy. Extreme false data attack conditions were studied that caused 58% forecast degradation and 16.9\% operational cost increases. The proposed integrated framework reduced false positive detections by 70%, recovered 93.7% of forecasting performance losses, and achieved 5\% operational cost savings, mitigating 34.7% of attack-induced economic losses. Results demonstrate that precision-focused cascade detection with multi-signal fusion outperforms single-signal approaches, validating security-performance synergy for decentralized microgrids.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。