配电网状态估计是配电管理系统(DMS)的一项重要的高级应用功能,是通过一些可以获得的量测数据估计另一些未量测的信息，从而将配电系统的信息补充完整，为分析和决策服务的过程。 首先实现了一种适用于配电网状态估计的基于节点电压法的潮流计算方法，既可以进行辐射状配电网潮流计算，又可以进行环状和网格状配电网潮流计算。 将配电网分解成若干区域，在各个区域端点的量测数据和区域内反映各个用户负荷的统计规律伪量测数据基础上，提出了三种配电网状态估计算法，具体工作包括： 提出了一种以各个负荷的整体可信率最高为目标的配电网状态估计方法； 探索了一种基于增量最小二乘算法的配电网状态估计方法，改进了目标函数并采用迭代的措施解决非线性问题； 提出了一种基于遗传算法的配电网状态估计方法，恰当选取了目标函数和约束条件，并采用归一化浮点数编码的基因反映负荷的分布，采用了多交叉变异协同处理的措施； 在MATLAB平台上实现了上述三种状态估计算法。 利用典型算例，对上述三种状态估计方法以及直接均值估计法进行了比较，得出下列结论： 最大可信度算法较直接均值估计法的状态估计质量有所改善，增量最小二乘算法和遗传算法较最大可信度算法的状态估计质量有明显改善，遗传算法较增量最小二乘算法的状态估计质量有较好改善；最大可信度算法、增量最小二乘算法和遗传算法的状态估计时间依次增长。基于遗传算法的配电网状态估计虽然可以取得最好的估计效果，但是所需时间显著长于其他状态估计方法。

Distribution Network State Estimation (DNSE) is one of the most important advanced applications in Distribution Management System (DMS). As a great help for analysis and decision making, DNSE can complete the information of a distribution system by estimating some un-gathered parameters from the obtained datum. As a foundation, the node voltage based power flow approach for distribution estimation is realized, which can not only used in radial networks, but also suitable for looped and grid ones. A distribution network can be divided into several parts. Based on the real information collected from the FTUs on the terminals of the part and the pseudo measurement datum embodying the statistic behavior of customers, three DNSE algorithms are investigated. A new DNSE algorithm which guaranteed the total belief level to be the highest is put forward. An increment least square algorithm based DNSE methodology is proposed, the index of which is improved, and an iterative approach is introduced to solve the non-linear problem. A genetic algorithm based DNSE method is presented. The appropriate index and constraint conditions are adopted. The profile of loads is represented by genes of standardized float coding. The multi-crossover and multi-mutation coordinately measure is utilized in the procedure. The proposed three DNSE algorithms are programmed on MATLAB 7.0. Comparisons are made on the described three DNSE approaches using typical examples. It is concluded that: The estimation results of maximum belief level DNSE is better than that of direct average value estimation. The estimation results of maximum belief level DNSE can be remarkably improved by increment least square algorithm based DNSE and genetic algorithm based DNSE. The estimation results of genetic algorithm based DNSE is the best. The estimation time of maximum belief level algorithm, increment least square algorithm and genetic algorithm increases in turn. Although genetic algorithm based DNSE can obtain the best estimation result, it needs a rather long estimation time.