Assessment of power curve performance of wind turbines in Adama-II Wind Farm

Abstract

The power curve of a manufacturer must be compared with the actual power curve after commissioning because various factors lead to deviations. The main purpose of this study was to assess and compare the performance of the N72, N73, and N74 wind turbines of the Adama-II wind farm against the manufacturer's guaranteed power curve. The methods employed were high-correlation-based nacelle anemometry and Artificial Neural Network (ANN). The high-correlation-based nacelle anemometry method used here differs from the conventional IEC61400–12–2 in that it was based on a strong correlation between the mast and nacelle speed of a turbine from which the nacelle transfer function (NTF) was computed. The NTF was obtained by power interpolation from 10-minute wind mast data and the nacelle speed of N72. The measured power curve was then obtained by applying NTF to the nacelle speeds. The turbines were modeled using ANN, illustrating manufacturer's power curve was higher than the modeled values above 9 m/s. According to the analysis, the measured power curves of N73 and N72 were within the manufacturer's AEP uncertainty range at a mean wind speed of 4–9 m/s but showed a deviation of 10–18 % at higher wind speeds. The power production of the N74 turbine was within the manufacturer’s uncertainty limit only at a mean wind speed of 4–7 m/s, whereas at a wind speed above 7 m/s, it deviated by 22–40 %. Therefore, performances of N72 and N73 in terms of the AEP were better, except at wind speeds of 10 and 11 m/s. However, the performance of the N74 turbine was significantly lower than the manufacturer. ANN also gave similar results except at higher wind speeds where high-correlation-based nacelle anemometry performed better. Overall, the proposed methods demonstrated the performance of the turbines compared with the guaranteed power curve.