Study on Obtaining Real Power Curve of Wind Turbines Using SCADA Data

Abstract

The key problem to be solved in the process of wind turbine (WT) operation and maintenance is to obtain the wind turbine performance accurately. The power curve is an important indicator to evaluate the performance of wind turbines. How to model and obtain the power curve of wind turbines has always been one of the hot topics in research. This paper proposes a novel idea to get the actual power curve of wind turbines. Firstly, the basic data preprocessing algorithm is designed to process the zero value and null value in the original supervisory control and data acquisition (SCADA) data. The moving average filtering (MAF) method is employed to deal with the wind speed, the purpose of which is to consider the comprehensive result of wind on the wind turbine power in a certain period. According to the momentum theory of the ideal wind turbine and combined with the characteristics of the anemometer installation position, the deviation between the measured wind speed and the actual wind speed is approximately corrected. Here, the influence of dynamic changes in air density is also considered. Then, the Gaussian fitting algorithm is used to fit the wind-power curve. The characteristics of the power curve before and after wind speed correction are compared and analyzed. At the same time, the influence of the parameter uncertainty on the reliability of the power curve is considered and investigated. Finally, the characteristics of the power curves of four wind turbines are compared and analyzed. The research results show that among these power curves, WT3 and WT4 are the closest, WT2 is the next, and WT1 has the farthest deviation from the others. The research work provides a valuable basis for on-site performance evaluation, overhaul, and maintenance of wind turbines.

Early Fault Warning Method of Wind Turbine Main Transmission System Based on SCADA and CMS Data

“…Pandit et al [8] proposed a nonparametric modeling method based on SCADA data for wind turbine power curve estimation, which better meets the condition monitoring needs of later generation units. Dai et al [9] processed the wind speed correction and used Gaussian fitting of the corrected wind power curve to obtain a more accurate wind turbine performance index. Dao et al [10] conducted two-stage cointegration analysis on SCADA data and successfully monitored gearbox faults.…”