自动电压控制（AVC）系统由于缺乏对发电厂遥测量数据真实性的有效和准确辨识,容易引起装置误动.支持向量机（SVM）是一种具有优良模式识别性能的数据挖掘方法.文中利用SVM建立发电厂遥测量不良数据的辨识模型：首先应用SVM非线性回归对各种运行情况下发电厂的遥测量数据进行曲线拟合,然后应用SVM训练分类网络.将实时遥测量数据输入到训练好的曲线拟合网络和分类网络中,就能够迅速判断该遥测量数据是否为不良数据.仿真算例验证了SVM模型的有效性和准确性.

Automatic voltage control （ AVC） system may cause misoperation due to its incorrect and inefficient identification for the remote measurement data of power plants. In order to solve this problem,this paper adopts the support vector machine （ SVM） ,a data mining method with excellent pattern recognition ability,to establish a bad data identification model. In this method,first,SVM nonlinear regression is used to perform a curve fitting for the remote measurement data. Then,the classification network is trained with SVM. Finally,the real-time data are input in the curve fitting network and the classification network to judge whether the data are correct. Simulated results show that the proposed model is effective and accurate.