Modeling Wind-Turbine Power Curves: Effects of Environmental Temperature on Wind Energy Generation
Abstract
:1. Introduction
- Nonlinearity: the nonlinear behavior of the functions of activation of the neurons will enable the neural networks to act as universal proxies of nonlinear functions.
- They can be adapted to the development of any type of data representation by just retraining them. In contrast to physical models [16], they are less dependent on the knowledge of the “experts” for model development.
2. Materials and Methods
2.1. A Description of Modeling Methods
2.1.1. Artificial Neural Networks (ANN)
2.1.2. Fuzzy Logic
2.2. Wind Turbine Power Curve and Performance Dependency with Environmental Temperature
2.3. Wind Turbine Power Curve Model
2.3.1. Data Pre-Processing
- Air Temperature [°C].
- Barometric Pressure [bar].
- Wind speed [m/s].
- Power [kW].
- −10 < T (°C) < 50
- ≤ ws (m/s) ≤ 13 (when modeling zone 2)
- < P (bar) < 1050
- 0 < Pow (% of rated power) < 96% (1450 kW) (when modeling zone 2)
- ws (m/s) ≥ 5 & Pow (% of rated power) < 2.5 (If the wind speed is higher than 5 m/s but the Pow is lower than 2.5, the turbine is damaged or stopped and, thus, the corresponding data should be removed.)
- ws (m/s) < 4 & Pow (% of rated power) > 7.5 (Likewise, if the wind speed is lower than 4 m/s but the Pow is higher than 7.5, the anemometer is either damaged or broken. Therefore, the corresponding data should be removed.)
- The equipment is available during the 10-min period of the data.
2.3.2. Power Curve Modeling Methodology
- Zone (1), where the wind energy is insufficient to start-up the wind turbine movement. In that case, the expert system would apply the model Pow = 0.
- Zone (2) is called the ascending ramp, where the wind turbine is accelerated until its nominal rotation speed rises. In that case, the expert system will apply the neural network model later described.
- Zone (3), where the wind turbine rises to nominal power. In that case, the expert system will apply the model Pow = Pownominal.
- Zone (4), where the wind energy is excessive to operate under safe structural conditions. In that case, the expert system will apply the model Pow = 0.
3. Results
3.1. Estimating Wind Production with the Model
3.2. An Application of the Methodology
4. Discussions
Author Contributions
Funding
Conflicts of Interest
References
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ΔT (°C) (from 1990) | Performance (%) | Lost Energy MWh | Lost Incomes (kUSD) ’18 * | Additional ktCO2 ** |
---|---|---|---|---|
0.5 | 100 | 0 | 0 | 0 |
1.0 | 99.9992 | 17.4 | 3.9 | 5.7 |
1.5 | 99.9984 | 34.7 | 7.8 | 11.5 |
2.0 | 99.9976 | 52.1 | 11.7 | 17.2 |
2.5 | 99.9968 | 69.4 | 15.59 | 22.9 |
3.0 | 99.9960 | 86.8 | 19.49 | 28.6 |
3.5 | 99.9952 | 104.2 | 23.39 | 34.4 |
4.0 | 99.9944 | 121.5 | 27.29 | 40.1 |
4.5 | 99.9936 | 138.9 | 31.18 | 45.8 |
5.0 | 99.9928 | 156.3 | 35.08 | 51.6 |
5.5 | 99.9920 | 173.6 | 39.98 | 57.3 |
6.0 | 99.9912 | 191 | 42.9 | 63.0 |
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Rodríguez-López, M.Á.; Cerdá, E.; Rio, P.d. Modeling Wind-Turbine Power Curves: Effects of Environmental Temperature on Wind Energy Generation. Energies 2020, 13, 4941. https://doi.org/10.3390/en13184941
Rodríguez-López MÁ, Cerdá E, Rio Pd. Modeling Wind-Turbine Power Curves: Effects of Environmental Temperature on Wind Energy Generation. Energies. 2020; 13(18):4941. https://doi.org/10.3390/en13184941
Chicago/Turabian StyleRodríguez-López, Miguel Á., Emilio Cerdá, and Pablo del Rio. 2020. "Modeling Wind-Turbine Power Curves: Effects of Environmental Temperature on Wind Energy Generation" Energies 13, no. 18: 4941. https://doi.org/10.3390/en13184941