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Forecasting Method of Microprocessor Protective Device State Trend Based On LS-SVM

机译:基于LS-SVM的微处理器保护装置状态趋势预测方法

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A new method is presented to exactly forecast trend of running state trend for the microprocessor protective device based on LS-SVM to realize condition maintenance. LS-SVM is introduced to forecast state trend of the microprocessor protective device. The real-time current, history overhauling data of microprocessor protection deceive and fault corresponding running state are chosen as input value, and running state of the microprocessor protective device is chosen as output value. The experimental results indicate that accurate and generalized performance is better by LS-SVM to forecast state trend of the microprocessor protective device with the small training set of sample, and LS-SVM is higher forecast accuracy than the BP neural network. The comparison of different kernel functions of LS-SVM shows that RBF kernel function is most suitable for state trend forecasting of microprocessor protective device.
机译:提出了一种基于LS-SVM的微机保护装置精确预测运行状态趋势的新方法,以实现状态维护。引入LS-SVM来预测微处理器保护装置的状态趋势。选择微处理器保护欺骗的实时电流,历史大修数据和故障对应的运行状态作为输入值,并选择微处理器保护装置的运行状态作为输出值。实验结果表明,采用LS-SVM预测样本量较少的微处理器保护装置的状态趋势时,LS-SVM的精度和广义性能更好,并且LS-SVM的预测精度高于BP神经网络。通过对LS-SVM不同内核功能的比较,可以看出RBF内核功能最适合微处理器保护装置的状态趋势预测。

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