While inverter-based distributed energy resources (DERs) play a crucial role in integrating renewable energy into the power system, they concurrently diminish the grid's system inertia, elevating the risk of frequency instabilities. Furthermore, smart inverters, interfaced via communication networks, pose a potential vulnerability to cyber threats if not diligently managed. To proactively fortify the power grid against sophisticated cyber attacks, we propose to employ reinforcement learning (RL) to identify potential threats and system vulnerabilities. This study concentrates on analyzing adversarial strategies for false data injection, specifically targeting smart inverters involved in primary frequency control. Our findings demonstrate that an RL agent can adeptly discern optimal false data injection methods to manipulate inverter settings, potentially causing catastrophic consequences.

本研究针对由分布式能源资源引发的电力系统频率不稳定问题，聚焦智能逆变器在面临网络攻击时的脆弱性。通过强化学习方法，本文揭示了攻击者可能采用的虚假数据注入策略，研究表明这种方法能够有效识别并利用逆变器设置，带来严重后果。

基于强化学习的频率控制器虚假数据注入方案的发现