Evolutionary Fractional-Order Extended Kalman Filter of Cyber-Physical Power Systems
针对信息物理电力系统的状态估计问题，提出一种基于进化算法和深度集成学习的分数阶扩展卡尔曼滤波器，通过分数阶建模和参数优化提升估计精度，优于传统方法。
State estimation of cyber-physical power systems (CPPSs) is of great significance for power system optimization, control, and security analysis. Additionally, fractional differential calculus is based on differentiation and integration of arbitrary fractional order, which can more accurately describe the physical phenomenon model than the traditional integer calculus. Thus, this article proposes a novel fractional-order extended Kalman filter (FOEKF) based on the evolutionary algorithm and deep ensemble learning techniques for the state estimation problem of CPPSs from the fractional-order theory perspective. First, the power system is modeled as a fractional version to describe the physical phenomenon better according to the fractional differential calculus theory. Then, considering the difficulties in determining fractional orders in the fractional-order power system, a deep ensemble learning-based approach is used to design the fitness function and a genetic algorithm is developed to determine these parameters by optimizing the designed objective function. Furthermore, to solve the difficulties in estimating for fractional-order power system by integral extended Kalman filter (EKF), the evolutionary FOEKF (EFOEKF) is presented as the estimator for the designed fractional-order power system. Finally, to improve the performance of EFOEKF under bad datum scenarios caused by cyber-attacks or sudden loads, an enhanced EFOEKF method is developed by using an adapted exponential weighting function. The numerical simulation results show that the proposed EFOEKF is better than EKF and FOEKF on four different IEEE bus systems in terms of the mean absolute error.