基于拉格朗日松弛技术的复杂有源配电网分布式状态估计

为了提高复杂有源配电网状态估计问题的计算速度，提出了一种基于拉格朗日松弛技术的复杂有源配电网分布式状态估计方法。建立了复杂有源配电网分区模型，该模型从复杂有源配电网的量测配置情况和分布式并行计算效率角度出发，综合考虑了分区后各子区域的计算速度与精度，利用拉格朗日松弛技术将分区问题中的约束条件吸收到目标函数中，降低了分区模型的求解难度。通过网络解耦技术使各子区域相对独立，可在分布式并行环境下求解该网络的状态估计问题。各子区域选取指数型目标函数状态估计模型，该模型能够自动排除不良数据影响，在保证结果精度的同时有效减小了所求问题的系统规模和雅可比矩阵阶数，提高了状态估计算法效率。仿真算例结果表明，所提方法可实现对复杂有源配电网的合理分区，有效提高了状态估计的计算速度。

Distributed state estimation of complex active distribution network based on Lagrange relaxation technique

Due to the slow calculation speed of state estimation in complex active distribution network, a distributed state estimation of complex active distribution network based on Lagrange relaxation technique is proposed. A partition model of complex active distribution network is established, in which the measurement configuration of complex active distribution network and distributed parallel computing efficiency are analyzed. The calculation speed and accuracy of each sub-region after partition are also considered. By using the Lagrange relaxation technique, the constraint conditions are absorbed into the objective function, which can reduce the difficulty of solving the partition model. The state estimation problem of the network can be solved in a distributed parallel environment by using the network decoupling method. The state estimation of each sub-region is carried out by using the state estimation method with exponential objective function which can automatically exclude bad data effects, which effectively reduces the system size and Jacobi matrix order of the problem, and improves the efficiency of the state estimation algorithm while ensuring the accuracy of the results. Simulation results show that the proposed method can achieve reasonable partition in a complex active distribution network, and the calculation speed of the state estimation is largely improved. This work is supported by Science and Technology Project of State Grid Corporation of China (No. PD71-14-032).