基于决策树自标识的主动配电网状态估计算法

本文为解决目前配电网前端数据数量大、缺省多、分析复杂等问题，提出一种适用于主动配电网的状态估计算法来管理分析前端数据。本文提出了基于决策树自标识的主动配电网状态估计算法，通过估计前预处理数据，对数据进行分类以及修正，使输入状态估计模型中的数据有更好的相容性。同时，本文针对分布式能源配套量测装置少的问题，建立了考虑分布式电源的状态估计模型，对分布式能源缺省数据进行补全修正，提高输入数据的质量。该方法运用到实际算例中可以看出，对比传统的状态估计，基于决策树自标识的主动配电网状态估计算法有更好的估计效果以及更快的迭代速度。因此本文提出的算法能有效的运用到当前大规模分布式能源接入的配电网状态估计中。

Active Distribution Network State Estimation Algorithm Based on Decision Tree of Self-identification

In order to solve the problems of large number of front-end data, data-missing and complex analysis, this paper proposes a state estimation algorithm suitable for active distribution network to manage the analysis data. This paper proposes an active distribution network state estimation algorithm based on decision tree self-identification. By estimating the pre-processed data, classifying and correcting the data, the data in the input state estimation model is better compatible. At the same time, this paper establishes a state estimation model considering distributed power supply for the problem of less distributed energy measurement equipment, and supplements the distributed energy default data to improve the quality of input data. The method can be verified in the actual example. Compared with the traditional state estimation, the active distribution network state estimation algorithm based on decision tree self-identification has better estimation effect and faster iteration speed. Therefore, the proposed algorithm can be effectively applied to the current state estimation of large-scale distributed energy access.