基于网络结构的电力系统分布式动态状态估计研究

With the growing size and the increasingly strengthened interconnection of power systems, and the widespread use of Phasor Measurement Units, the centralized state estimation and distributed static state estimation methods are difficult to satisfy the real-time and accuracy demand. Therefore, it promotes the development of distributed dynamic state estimation algorithms for power systems, which have become a hot subject in the field of systems engineering in recent years. This project will devote to the following work. (1) By using the nonlinear filters, hybrid measurements and the information transmitted from neighbors, a distributed dynamic state estimation algorithm for power systems will be designed, and the stability of the proposed algorithm will be analyzed. Furthermore, some new methods are used to identify model parameters on-line, improve the accuracy of state estimation and enhance the robustness of the proposed algorithm. (2) For power systems with cross-correlated noises and weakly coupled states among the neighboring subsystems, a distributed dynamic state estimator and a suboptimal state predictor for each subsystem will be investigated. (3) The distributed state estimation problem for power systems with delays, communication packet loss and asynchronism between the neighboring subsystems will be further studied. At the same time, we would investigate the case that the considered system can be partitioned as a cyclic network, and its effect on the estimation precision of the proposed algorithm. This research will not only promote the development of distributed dynamic state estimation theory for power systems, but also play a guiding role in the design and the implementation of algorithms for the electric engineering practice.

随着电网规模的不断扩大，互联程度的日益加强以及同步相量测量装置的广泛应用，集中式状态估计和分布式静态状态估计方法很难满足实时性和准确性的需求，从而促进了电力系统分布式动态状态估计算法的发展，目前已成为系统工程领域的热门研究专题。本项目拟致力于如下方面的研究：(1) 利用非线性滤波技术、混和量测和邻居信息，设计电力系统分布式动态状态估计算法，并分析算法的稳定性；进一步利用新的方法在线辨识系统参数，提高状态估计精度，并加强算法的鲁棒性。(2) 针对存在关联噪声和邻居子系统之间状态关联的电力系统，设计分布式动态状态估计器和各子系统的次优状态预测器。(3) 进一步研究电力系统存在时滞、丢包和时钟不同步的分布式状态估计问题，并分析分区后的网络含有环的情况对算法估计精度的影响。预期结果不仅可以推动电力系统分布式动态状态估计理论的发展，而且将对电力工程实践中算法的设计和实施产生重要的指导意义。

随着系统规模的不断扩大，互联程度的日益加强以及同步相量测量装置的广泛应用，集中式状态估计和分布式静态状态估计方法很难满足实时性和准确性的需求，从而促进了分布式动态状态估计算法的发展，目前已成为系统工程领域的热门研究专题。本项目主要研究了大规模电力系统的分布式容积卡尔曼滤波，存在关联噪声的网络化动态系统的分布式状态估计，和微分方程及分数阶微分方程解的定性分析。本项目所取得的重要结果如下：1) 利用容积卡尔曼滤波技术，为大规模电力系统设计了一种分布式动态状态估计算法，并利用边界量测和邻居传递的信息修正各子系统的局部状态估计，改进了已有的某些结果；2) 研究存在关联噪声和邻居子系统之间状态关联的网络化系统。通过重新构造各子系统的量测信息，使得量测噪声相互独立，设计一种分布式状态估计算法，并证明了该算法得到的状态估计和预测的误差协方差矩阵是有界的；3) 利用广义的Riccati变换，积分平均技术和比较原则，讨论了多类具有分布偏差变元的中立型微分方程和分数阶微分方程解的振动性和渐近性；4) 利用Banach代数上的Dhage不动点定理，建立了时间尺度上带线性扰动和混合扰动的微分方程的基本理论。本项目的实施既推动了网络化系统分布式动态状态估计理论的发展，也对工程实践中算法的设计和实施产生重要的指导意义。