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为了准确地进行燃煤机组负荷预测,引入支持向量机(SVM)方法建立了锅炉炉膛多层火焰图像与机组负荷之间的复杂关系模型.将该方法应用于某660 MW燃煤锅炉机组中,用训练后的SVM模型进行负荷预测,并与BP神经网络模型预测结果进行比较.结果表明:采用SVM方法预测机组负荷,模型能够辨识出火焰辐射图像与机组负荷之间的复杂关系,实现对负荷的准确预测;SVM模型预测精度比BP网络模型高,SVM模型具有预测精度高、泛化能力强等优点,且模型训练时间较短.
In order to accurately predict the load of coal-fired units, a complex relationship model between multi-layer flame image and unit load is established by using support vector machine (SVM) method. This method is applied to a 660 MW coal-fired boiler unit, The SVM model was used to predict the load and the result was compared with the BP neural network model.The results showed that the SVM method could predict the unit load and the model could identify the complex relationship between the flame radiation image and the unit load, The prediction accuracy of SVM model is higher than that of BP network model. The SVM model has the advantages of high prediction accuracy and generalization ability, and the model training time is short.