A New Wind Turbine Power Performance Assessment Approach: SCADA to Power Model Based with Regression-Kriging

Pengfei Zhang, Zuoxia Xing, Shanshan Guo, Mingyang Chen and Qingqi Zhao
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Pengfei Zhang: School of Electrical Engineering, Shenyang University of Technology, Shenyang 110870, China
Zuoxia Xing: School of Electrical Engineering, Shenyang University of Technology, Shenyang 110870, China
Shanshan Guo: School of Electrical Engineering, Shenyang University of Technology, Shenyang 110870, China
Mingyang Chen: School of Electrical Engineering, Shenyang University of Technology, Shenyang 110870, China
Qingqi Zhao: Skills Training Center, State Grid Liaoning Electric Power Co. Ltd., Jinzhou 121001, China

Energies, 2022, vol. 15, issue 13, 1-15

Abstract: Assessment of the wind turbine output power (WTG OP) during the operation and maintenance is one of the key indicators of operation quality evaluation. It is often carried out in the form of the wind speed-power curve. This form only considers the wind speed, and it is usually measured according to relevant IEC standards, e.g., IEC 61400-12, which has problems such as long measurement duration and harsh conditions. This study proposes a WTG OP assessment method based on SCADA data by using the regression-kriging algorithm. The influences of wind shear, turbulence intensity, and air density on the WTG OP were analyzed. Two regression-kriging output power models were built based on SCADA data (i.e., SCADA2power model) and wind resource parameters from met mast (i.e., wind2power model). According to the evaluation of the simulation result, it was found that the results of the two models are basically consistent. Based on the evaluation of historical data under normal operating conditions, the goodness of fitting output power of the two models is 99.9%. This shows that the regression-kriging-based wind turbine power performance assessment method based on SCADA data has an accurate prediction and the potential of general application in WTG OP evaluation.

Keywords: wind turbine; output power; regression-kriging; SCADA; wind resource; met mast (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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