基于混沌时间序列的水电机组状态短期预测

基于混沌时间序列短期可以预测的特点,构建水电机组状态短期预测。用采样周期确定相空间时延τ,G-P算法确定关联维数从而确定相空间的嵌入维数m,小数据量法证明水电机组振动状态的混沌特性。在重构相空间中,运用加权一阶局域法构建水电机组状态短期预测模型。结果表明:混沌特性指数λ=0.2605的水电机组振动状态具有混沌特性,可以在最佳嵌入维数m=4的情况下进行预测,实例结果表明采用混沌理论进行水电机组状态短期预测是可行的。

Short-term Prediction of Hydroturbine Generating Unit Condition Based on Chaotic Time Series

Based on the characteristic of chaotic time series,a model was built to predict hydroturbine generating unit condition.The time delayτ was determined by sampling period,and the embedding dimension m was chosen according to correlation dimension,which was calculated by G-P algorithm.Chaotic characteristic of vibration signal series of hydroturbine generating unit was proved by small data sets arithmetic.The prediction model of hydroturbine generating unit condition was constructed by an adding-weight one-rank local-region method after the phase space was reconstructed.The results show that vibration signal series has a chaotic characteristic while the chaotic property exponent λ=0.2605.Therefore,a prediction model can be carried out while the best embedding dimension m is 4.The results demonstrate that the prediction method is feasible.