基于遗传优化的调控系统缺失数据填补算法

A missing data filling algorithm for dispatching and control system based on genetic optimization

数据缺失问题是电网调度控制系统中重要的研究课题。为保证数据的完整性和准确性,提出一种基于遗传优化的调度控制系统缺失数据填补算法。该算法利用遗传优化方法估计不完整数据的参数,获得最优数据参数,在最优参数基础上利用马尔科夫链蒙特卡罗算法对缺失数据进行估计、填补。对电力调度控制系统中缺失数据的填补结果分析,发现所提出的缺失数据填补算法能快速准确地填补缺失数据,保证了电网调度控制数据的完整性和准确性。

The problem of data loss is an important research topic in the grid dispatching and control system. A new method based on genetic optimization for dealing with missing data is proposed to ensure the data integrity and accuracy. The proposed method can estimate incomplete data parameters by genetic optimization algorithm. According to the optimal parameters, the Markov Chain Monte Carlo algorithm is used to estimate the missing data. Through filling the incomplete data in the grid dispatching and control system, it is discovered that the proposed method can find more missing data within the same time duration and improve the accuracy of estimated values which guarantee the data integrity and accuracy.