Xueli An

Using fuzzy theory and information entropy for water quality assessment in Three Gorges region, China

Considering that the water quality assessment is a fuzzy concept with multiple indicators and classes, and there are still some limits of fuzzy comprehensive evaluation method, the fuzzy mathematics method and the information entropy theory are combined to establish an improved fuzzy comprehensive evaluation method for water quality assessment. In this method, the exponential membership function has been adopted to solve the zero-weight problem, and the information entropy has been used to modify the coefficients of weight in order to exploit the useful information of data to a maximum extent. In addition, the weighted average principle has been taken to replace the maximum membership principle for reserving the information in the assessment coefficients as much as possible. The water quality of Three Gorges region is taken as an example and the results show that the improved fuzzy comprehensive evaluation method is superior to the traditional model and worth to be recommended.

Wind farm power prediction based on wavelet decomposition and chaotic time series

In this paper, a prediction model is proposed for wind farm power forecasting by combining the wavelet transform, chaotic time series and GM(1,1) method. The wavelet transform is used to decompose wind farm power into several detail parts associated with high frequencies and an approximate part associated with low frequencies. The characteristic of each high frequencies signal is identified, if it is chaotic time series then use weighted one-rank local-region method to predict it. If not, use GM(1,1) model to predict it. And the GM(1,1) model is also used to predict the approximate part of the low frequencies. In the end, the final forecasted result for wind farm power is obtained by summing the predicted results of all extracted high frequencies and the approximate part. According to the predicted results, the proposed method can improve the prediction accuracy of the wind farm power.

Application of the intrinsic time-scale decomposition method to fault diagnosis of wind turbine bearing

A fault diagnosis method of wind turbine bearing based on intrinsic time-scale decomposition (ITD) is put forward. In the proposed method, the vibration signal of the main bearing is decomposed into several proper rotation components by the ITD method. The frequency centers of the proper rotation components that contain predominant energy are computed and considered as fault feature vectors. The nearest neighbor algorithm is applied to identify the fault types of the wind turbine bearing. The experimental data of the wind turbine spherical roller bearing in four conditions (normal, outer race fault, inner race fault and roller fault) are applied to evaluate the performance of the proposed method. The results demonstrate the feasibility and accuracy of this approach for the diagnosis of the wind turbine bearing faults under uncertain conditions.

Correlation Analysis of Oil Temperature Trend for Wind Turbine Gearbox

Applying spatial correlation filtering method to de-noise the noise of wind turbine running parameters, the stochastic volatility is eliminated. On this basis, the correlation analysis between gearbox oil temperature trend and four parameters include rotor average speed, rotor temperature, gearbox driving side bearing temperature, outside air temperature is performed. The results indicate that the correlation between gearbox oil temperature trend and rotor temperature is the highest, with gearbox driving side bearing temperature takes second place, with rotor average speed takes third place but is still large. The relationship between gearbox oil temperature trend and outside air temperature is poor.Copyright