融合故障检测的模型预测控制方法用于弹性负荷频率控制
Contingency Detection Integrated Model Predictive Control for Resilient Load Frequency Control
浏览论文内容
中文总结 AI 辅助
针对电力系统故障引发的MPC-LFC预测不匹配问题，提出CDI-MPC框架，通过融合故障检测与预测调节提升闭环LFC性能，仿真验证其检测准确性与性能提升效果。
中文摘要 AI 辅助
故障会改变电力系统动态特性，并在基于模型预测控制（MPC）的负荷频率控制（LFC）中引入预测不匹配问题。尽管保护系统可检测或清除此类故障，但LFC时间尺度下的MPC控制器可能无法获取故障后对应的动态模型。本文提出一种融合故障检测的MPC（CDI-MPC）框架，将感知扰动的故障检测与预测频率调节相结合。故障被建模为随机混合系统（SHS）的随机离散事件，并开发了感知扰动的残差公式以联合识别活跃模式并估计未知扰动。检测到的模式随后用于更新MPC预测模型，从而在运行条件变化时减少故障引发的预测不匹配。仿真结果表明，该方法在多种故障场景和未知扰动下可实现准确的故障检测，并显著提升闭环LFC性能。
英文摘要
Contingencies can alter power-system dynamics and introduce prediction mismatch in model predictive control (MPC)-based load frequency control (LFC). Although such events may be detected or cleared by protection systems, the corresponding post-contingency dynamic model may not be available to the MPC controller on the LFC time scale. This paper proposes a contingency detection-integrated MPC (CDI-MPC) framework that combines disturbance-aware contingency detection with predictive frequency regulation. Contingencies are modeled as stochastic discrete events of a stochastic hybrid system (SHS), and a disturbance-aware residual formulation is developed to jointly identify the active mode and estimate unknown disturbances. The detected mode is then used to update the MPC prediction model, reducing contingency-induced prediction mismatch under changing operating conditions. Simulation results demonstrate accurate contingency detection and substantial improvements in closed-loop LFC performance under multiple contingency scenarios and unknown disturbances.
