在可再生能源高渗透率的背景下，电力系统的负荷频率控制（load frequency control，LFC）面临虚假数据注入攻击（false data injection attack，FDIA）的安全威胁。现有检测方法难以有效区分控制输入攻击和测量数据攻击，影响系统的稳定性和安全性。为此建立了包含可再生能源及储能系统的LFC状态空间模型，并分析了FDIA对系统动态特性的影响。通过状态空间分解方法将攻击信号解耦为控制输入攻击和测量攻击，提高检测精度。基于滑模观测器设计攻击估计方法，实现对攻击信号的实时检测。进一步结合H_{\mathrm{\infty }}控制理论，提出了抗攻击控制（attack⁃resilient control，ARC）策略，以增强系统在攻击环境下的鲁棒性。仿真算例表明：与传统方法相比攻击估计均方误差降低约30 %，系统频率响应稳定性显著提升。结果表明，该方法能够有效检测FDIA并提高电力系统的安全性和抗干扰能力。

With the growing integration of renewable energy, load frequency control （LFC） in power systems faces security risks from false data injection attack （FDIA）. Existing detection methods struggle to differentiate control input attacks from measurement attacks, compromising system stability and security. This paper develops a state-space model for LFC incorporating renewable energy and energy storage systems and analyzes the impact of FDIA on system dynamics. A state-space decomposition method is employed to decouple attack signals into control input and measurement attacks, improving detection accuracy. A sliding mode observer-based attack estimation method is proposed for real-time detection. Additionally, an attack-resilient control （ARC） strategy is designed using H_{\mathrm{\infty }} control theory to enhance system robustness. Simulations show that the proposed method reduces the attack estimation mean squared error by nearly 30% and significantly improves frequency response stability compared to traditional methods. These results demonstrate the method′s effectiveness in detecting FDIA and enhancing power system security.