基于一致性理论的多区域电力系统分布式状态估计

随着电网规模不断扩大,传统集中式状态估计方法的数据通信与存储任务重、计算量大,难以满足现代电力系统状态估计需求。在计及系统状态估计非线性的基础上,将电力系统划分为若干个不重叠的子区域,并利用拉格朗日乘子法对状态估计方程进行解耦,建立电力系统多区域非线性状态估计模型。基于一致性理论建立全分布式状态估计方法对模型进行求解,该方法无需状态估计控制中心,只需各子区域交换一致性变量和边界节点的状态变量信息,各子区域便可平行独立地计算本地状态变量估计值,较集中式状态估计均衡了通信及计算负担。IEEE 14节点系统仿真结果验证了所提分布式状态估计方法的有效性。

Distributed state estimation of multi-area power systems based on consensus algorithm

Along with the continuous increasing of power grid scale, the traditional centralized state estimation methods face heavy task of data communication and large computation amount, which is difficult to satisfy the requirements of current power system state estimation. Power system is divided into several non-overlapping subsystems considering the nonlinearity of power system state estimation, and Lagrange multiplier method is used for decoupling the state estimation equation into a multi-area nonlinear state estimation model of power system. A fully distributed state estimation method is proposed based on consensus theory. The method does not need state estimation control center but only needs each subsystem to exchange the consensus variables and the state variables of boundary nodes, and each subsystem can independently calculate the estimation values of local state variables in parallel, thus the communication and computational burden is balanced compared with the centralized state estimation. Simulative results of IEEE 14-bus system verify the effectiveness of the proposed method.