基于改进粒子滤波的综合能源系统预测辅助状态估计

高效准确的状态估计是综合能源系统安全稳定的基础。粒子滤波具有精度高且对于非线性系统适应性更强的优点，已应用于电力系统的状态估计。为了提高综合能源系统的状态估计精度，研究粒子滤波在综合能源系统中的应用，提出了一种基于改进粒子滤波的综合能源系统预测辅助状态估计方法。首先，本文构建了包含电-气-热网络的区域综合能源系统模型；其次，将粒子滤波算法拓展到电-气-热网络，在详细分析粒子滤波相关理论的基础上，针对粒子滤波算法存在的跟踪误差问题对粒子滤波的预测步进行改进；最后，利用经典的综合能源系统算例对文中提出的改进粒子滤波算法进行验证。结果证明该方法能够有效解决传统粒子滤波算法的跟踪误差问题，提高系统的估计精度。

Forecasting-aided state estimation of integrated energy systems based on improved particle filter

Efficient and accurate state estimation is the basis for the safety and stability of the integrated energy system(IES). Particle filter has high precision and strong adaptability to nonlinear systems, and has been applied to state estimation of power systems. To improve the precision of state estimation in IES, the application of particle filter in the IES is studied, and a forecasting-aided state estimation method based on improved particle filter is proposed. Firstly, a regional IES model including an electricity-gas-heat network is constructed. Secondly, the particle filter algorithm is applied to the electricity-gas-heat network. The prediction step of the particle filter is improved because of the tracking error problem, which is based on the detailed analysis of particle filter theory. Finally, the improved particle filter algorithm is verified by using the classical IES example. The results show that this method can effectively solve the tracking error problem of the traditional particle filter, which can improve the precision of state estimation in IES.