优化GSA算法在电力调度系统不良数据检测中的应用

Application of Optimized GSA Algorithm in Bad Data Detection of Power Dispatching System

为了克服传统聚类方法的初始值随机性较大对GSA算法的影响,本文提出了一种基于区域密度统计方法的优化GSA算法。该算法通过计算每个聚类对象的区域密度来选择最远的点,并以最高的区域密度作为初始聚类中心。实验结果表明,优化后的GSA算法提高了聚类的色散和不良数据辨识精度的准确性。同时,该算法大大降低了迭代计算的计算复杂度,提高了计算速度,节省了大量的计算时间。在系统庞大、数据量大的情况下,该算法是一种快速有效的算法,具有良好的应用前景。

In order to overcome negative effects of random selection of clustering initial values of traditional GSA bad data identification algorithm on identification precision and computation rate, this paper proposes an optimized GSA algorithm based on area density statistics method. This algorithm by computing the area density of each cluster object to select k points that are farthest from each other and are at the highest area density as the initial cluster center. The experimental results show that the optimized GSA algorithm improves the accuracy of the degree of clustering dispersion and the recognition accuracy of the bad data. At the same time, the algorithm greatly reduces the computational complexity of iterative computation,improves the computing speed and saves a lot of computing time. In the case of huge system and large amount of data,this method is a rapid and efficient algorithm, and has potential of good application.