基于遗传算法与支持向量回归的发电机运行参数趋势预测

针对支持向量回归(SVR)模型在设备运行参数趋势预测中。根据人为经验选取模型参数导致预测精度不高的问题,提出了一种使用遗传算法(GA)优化SVR模型参数的方法(GA-SVR)。将该方法应用于发电机定子线圈出水温度的实时趋势预测中。结果表明,相较于SVR模型,GA-SVR具有更高的预测精度,能够满足电厂对发电机运行参数变化的趋势预测精度要求。

Trend Prediction of Generator Operating Parameters Based on Genetic Algorithm and Support Vector Regression

In the trend prediction of equipment operating parameters, the prediction accuracy will descend with the empirical parameters selection in support vector regression (SVR). A method that genetic algorithm (GA) uses to optimize the parameters of SVR (GA-SVR) is proposed in this paper. This method was used for the real time trend prediction of stator coil outlet temperature of the generator, results show that the prediction accuracy of GA-SVR was higher than SVR, and it can meet the requirement of generator operating parameters trend prediction accuracy in power plant.