论文标题
在短期负载预测中探索基于物理的约束：针对网络攻击的防御机制
Exploring Physical-Based Constraints in Short-Term Load Forecasting: A Defense Mechanism Against Cyberattack
论文作者
论文摘要
短期负载预测是一项必不可少的任务，它支持公用事业安排产生足够的能力以平衡供求，并可以成为网络攻击的有吸引力的目标。已经表明，电力系统状态估计容易受到虚假数据注入攻击的影响。同样，输入变量上的错误数据注入可能会导致较大的预测错误。负载预测系统应具有缓解此类攻击的保护机制。一种方法是建模可以识别异常的物理系统约束。这项研究调查了与负载预测应用相关的可能约束。查看区域预测的载荷，我们通过时间序列中使用的相似性度量分析了每个区域之间的关系，以识别约束。历史ERCOT负载数据的综合结果表明，识别恶意作用的措施的差异。尽管如此，这些静态测量不能被视为在不同情况下的有效指数。
Short-term load forecasting is an essential task that supports utilities to schedule generating sufficient power for balancing supply and demand, and can become an attractive target for cyber attacks. It has been shown that the power system state estimation is vulnerable to false data injection attacks. Similarly, false data injection on input variables can result in large forecast errors. The load forecasting system should have a protective mechanism to mitigate such attacks. One approach is to model physical system constraints that would identify anomalies. This study investigates possible constraints associated with a load forecasting application. Looking at regional forecasted loads, we analyze the relation between each zone through similarity measures used in time series in order to identify constraints. Comprehensive results for historical ERCOT load data indicate variation in the measures recognizing the existence of malicious action. Still, these static measures can not be considered an efficient index across different scenarios.
