基于时间序列组合模型的水电机组状态趋势预测

水电机组状态参量具有小样本、非线性和非平稳性等特点,传统预测理论很难对其实现状态趋势预测,考虑从多角度优化预测算法,建立了基于时间序列的组合预测模型。本研究利用小波变换理论提取信号的细节特征,将机组状态参量分解为非线性的趋势项和平稳性的波动项,分别利用最小二乘支持向量机(LSSVM)理论和自回归(AR)模型进行趋势预测,利用加法原则重构信号实现水电机组状态参量的趋势预测。取某电站振动状态序列进行实例计算,结果表明预测值与实测值基本一致,具有较高的预测精度。研究结果将对水电机组的状态预警起到一定的推动作用。

State trend prediction of hydropower generating units using time series combination model

It is difficult to achieve trend forecasting by the traditional prediction theory, because the state parameters of hydropower generating units are nonlinear, non-stationary, and with a small sample size available. Therefore, a time series combination model has been developed in this study. Wavelet transform can focus into any details of the signal and decompose a state sequence into a non-linear trend part and stationary fluctuating parts. By applying such decomposition to the vibration state sequences of hydropower generating units, a Least Square Support Vector Machine (LSSVM) prediction model was used for the trend part, and an Auto Regressive (AR) model for the fluctuating parts. The forecasting outcomes of these models were integrated by the principle of superimposition to achieve a final prediction. A case study of vibration state sequences shows that the forecasted and measured values agree well. Thus, the combination model presented in this paper achieves a high accuracy and the results would be useful for motivating the status early-warning for hydropower generating units.