Modeling of a Robust Confidence Band for the Power Curve of a Wind Turbine

Hernández, Wilmar; Méndez, Alfredo; Maldonado, Jorge Luis; Balleteros, Francisco

doi:10.3390/s16122080

Cited by 9 publications

(5 citation statements)

References 28 publications

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“…However, the practical engineering operating environment of wind turbines installed in wind farms is much more complex than the “standard environment”, i.e., the output of wind turbines is affected by many factors, resulting in the fluctuation of output in a large range ( Figure 2 ) rather than strictly in accordance with the MPC [ 12 , 13 , 14 ]. Considering the open and complex operating environment, the wind turbine has a relatively high outage probability.…”

Section: Introductionmentioning

confidence: 99%

Modified Approach of Manufacturer’s Power Curve Based on Improved Bins and K-Means++ Clustering

Fang

Wang

Liu

et al. 2022

Sensors

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The ideal wind turbine power curve provided by the manufacturer cannot monitor the practical performance of wind turbines accurately in the engineering stage; in this paper, a modified approach of the wind turbine power curve is proposed based on improved Bins and K-means++ clustering. By analyzing the wind speed-power data collected by the supervisory control and data acquisition system (SCADA), the relationship between wind speed and output is compared and elaborated on. On the basis of data preprocessing, an improved Bins method for equal frequency division of data is proposed, and the results are clustered through K-means++. Then, the wind turbine power curve correction is realized by data weighting and regression analysis. Finally, an example is given to show that the power curve of the same type of wind turbines, which, installed in different locations, are discrepant and different from the MPC, and the wind turbine power curve obtained by using this method can reflect the output characteristics of the wind turbine operating more effectively in a complex environment.

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Section: Introductionmentioning

confidence: 99%

Modified Approach of Manufacturer’s Power Curve Based on Improved Bins and K-Means++ Clustering

Fang

Wang

Liu

et al. 2022

Sensors

View full text Add to dashboard Cite

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“…Wind speed has a great impact on the amount of energy that is generated by wind turbines. In fact, the amount of energy that can be theoretically obtained from the wind is proportional to the cube of wind speed [ 5 ], although, in practice, the power output of a specific wind turbine and the wind speed are usually related following a sigmoid power curve that is given by the manufacturer [ 6 ]. Certainly, wind speed is highly dynamic, so the energy that is available to a wind-powered device fluctuates significantly, even within short periods of time.…”

Section: Introductionmentioning

confidence: 99%

Efficient Wind Speed Forecasting for Resource-Constrained Sensor Devices

Herrería-Alonso

Suárez-González

Rodríguez-Pérez

et al. 2021

Sensors

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Wind energy harvesting technology is one of the most popular power sources for wireless sensor networks. However, given its irregular nature, wind energy availability experiences significant variations and, therefore, wind-powered devices need reliable forecasting models to effectively adjust their energy consumption to the dynamics of energy harvesting. On the other hand, resource-constrained devices with limited hardware capacities (such as sensor nodes) must resort to forecasting schemes of low complexity for their predictions in order to avoid squandering their scarce power and computing capabilities. In this paper, we present a new efficient ARIMA-based forecasting model for predicting wind speed at short-term horizons. The performance results obtained using real data sets show that the proposed ARIMA model can be an excellent choice for wind-powered sensor nodes due to its potential for achieving accurate enough predictions with very low computational burden and memory overhead. In addition, it is very simple to setup, since it can dynamically adapt to varying wind conditions and locations without requiring any particular reconfiguration or previous data training phase for each different scenario.

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“…VWF consists of 11 wind turbines (WTs) GOLDWIND GW70 class “S,” with direct drive technology and permanent magnet synchronous generator of 1.5 MW nominal power (see Figure 1(b)). The location has an NNW-SSE orientation on the top of the Villonaco hill, in an approximate length of 2900 m. The farm was constructed at an approximate cost of USD 41.8 million, and has been operating since 2013 (Hernandez et al, 2016). This wind farm works under special conditions, with high annual average wind speed over 10 m/s, low air density of 0.89 kg/m 3 , and turbulence intensity of 0.15 (Reyes et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Wind power forecasting for the Villonaco wind farm

et al. 2020

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Wind energy is a non-programmable form of generation, hence, accurate and reliable wind energy prediction is of great importance for the efficient operation of wind farms. This article presents a study for the prediction of active power for the Villonaco Wind Farm (VWF), located in southern Ecuador at approximately 2700 m above sea level. Through the use of artificial neural networks, experimental tests are developed based on the models of Multi-Layer Perceptron (MLP), Long Short-Term Memory (LSTM), and Convolutional Neural Network (CNN) to obtain a hybrid model that fits the best characteristics of the individual models. Data from the active power SCADA (Supervisory Control and Data Acquisition) system for the years 2014 to 2018 are used to train and validate the models. Hybrid model is presented as the most appropriate option by the values obtained, viz., the mean absolute error (MAE), the mean squared error (MSE), and mean absolute percentage error (MAPE) that were 0.1365, 0.0974, and 144.26, respectively, outperforming to the others wind power forecast models.

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Modeling of a Robust Confidence Band for the Power Curve of a Wind Turbine

Cited by 9 publications

References 28 publications

Modified Approach of Manufacturer’s Power Curve Based on Improved Bins and K-Means++ Clustering

Modified Approach of Manufacturer’s Power Curve Based on Improved Bins and K-Means++ Clustering

Efficient Wind Speed Forecasting for Resource-Constrained Sensor Devices

Wind power forecasting for the Villonaco wind farm

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