Very short-term probabilistic forecasting of wind power based on dual-Doppler radar measurements in the North Sea

Valldecabres, Laura; Nygaard, NG; Bremen, Lueder von; Kühn, Martin

doi:10.1088/1742-6596/1037/5/052010

Cited by 5 publications

(7 citation statements)

References 12 publications

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“…To optimize the probabilistic forecasting methodology, we conducted a sensitivity analysis on the area of influence encompassing the target wind turbine, as introduced in [29]. The area of influence A i is defined as a circle centred at the wind turbine ( Figure 5c).…”

Section: Optimization Of the Wind Turbine Area Of Influencementioning

confidence: 99%

“…To optimize the area of influence, we use wind speed densities predicted one-minute ahead (k = 1), as we want to optimize the area of influence with predictions close to the real distribution of wind speeds in front of the rotor. Here, we consider samples with a minimum number of wind field vectors N min = 20 to estimate the predictive densities, following [29]. A total of T = 10,447 predictions are considered for the sensitivity analysis.…”

Section: Optimization Of the Wind Turbine Area Of Influencementioning

confidence: 99%

“…Dual-Doppler (DD) radar measurements of the wake behind an offshore wind farm were also reported in [26]. Power performance studies [27] and ramp events detection [28] were also documented using DD radar measurements.In [29], the authors introduced a methodology that uses DD radar measurements as input for predicting the average aggregated power of seven offshore wind turbines in a probabilistic framework. This paper extends the methodology presented in [29] to predict the power generated by individual wind turbines and analyses the characteristics of DD radar measurements for very short-term forecasts of wind power.…”

mentioning

confidence: 99%

“…Power performance studies [27] and ramp events detection [28] were also documented using DD radar measurements.In [29], the authors introduced a methodology that uses DD radar measurements as input for predicting the average aggregated power of seven offshore wind turbines in a probabilistic framework. This paper extends the methodology presented in [29] to predict the power generated by individual wind turbines and analyses the characteristics of DD radar measurements for very short-term forecasts of wind power. Thus, the main goals of this paper are (i) the description of a methodology that uses fully resolved DD radar measurements to create probabilistic forecasts of wind power, (ii) its evaluation on seven wind turbines in an offshore wind farm with a very short-term horizon of five minutes and (iii) the assessment of the measurement characteristics and quality aspects of DD radar observations for very short-term forecast of wind power.Our paper is organised as follows: first, we introduce the DD radar system along with the measurement campaign in Section 2.…”

mentioning

confidence: 99%

“…In [29], the authors introduced a methodology that uses DD radar measurements as input for predicting the average aggregated power of seven offshore wind turbines in a probabilistic framework. This paper extends the methodology presented in [29] to predict the power generated by individual wind turbines and analyses the characteristics of DD radar measurements for very short-term forecasts of wind power.…”

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confidence: 99%

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On the Use of Dual-Doppler Radar Measurements for Very Short-Term Wind Power Forecasts

et al. 2018

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Very short-term forecasts of wind power provide electricity market participants with extremely valuable information, especially in power systems with high penetration of wind energy. In very short-term horizons, statistical methods based on historical data are frequently used. This paper explores the use of dual-Doppler radar observations of wind speed and direction to derive five-minute ahead deterministic and probabilistic forecasts of wind power. An advection-based technique is introduced, which estimates the predictive densities of wind speed at the target wind turbine. In a case study, the proposed methodology is used to forecast the power generated by seven turbines in the North Sea with a temporal resolution of one minute. The radar-based forecast outperforms the persistence and climatology benchmarks in terms of overall forecasting skill. Results indicate that when a large spatial coverage of the inflow of the wind turbine is available, the proposed methodology is also able to generate reliable density forecasts. Future perspectives on the application of Doppler radar observations for very short-term wind power forecasting are discussed in this paper. methods can be found in [6][7][8]. Very short-term forecasts of wind speed and power are often based on machine learning methods such as artificial neural networks [9] or Markov chain models [10,11], which are trained with historical data. Alternatively, combinations of different models (known as hybrid models) have been proposed to overcome the deficiencies of single models [12]. Parallel to the development of single point or deterministic forecasting models, the use of probabilistic forecasts, which include information about the uncertainty associated with the predicted events, has increased. Examples of very short-term probabilistic forecasts of wind power using statistical methods can be found in [13,14]. These probabilistic forecasts are known as predictive densities or probability distributions and provide important information for making risk-based decisions [15].Over the last two decades, the use of remote sensing measurements such as long-range lidars [16] has been extended in the wind industry. These systems are capable of measuring wind speed and direction (under certain assumptions) up to 30 km [17]. Unlike conventional wind measurements from met-mast or satellites, they present an adequate trade-off between temporal and spatial resolution for wind farm applications. However, the prediction horizon of a remote sensing-based forecasting model is limited by the maximum range of the remote sensing measurements and also influenced by meteorological conditions [18]. Indeed, publications on the use of long-range lidar measurements for wind energy applications have reported measurements with a maximum range of less than ten kilometers [19][20][21]. Despite those limitations, a recent contribution showed that a lidar-based forecasting technique can provide better results than conventional statistical benchmarks when forecasting near-coastal winds with le...

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Section: Optimization Of the Wind Turbine Area Of Influencementioning

confidence: 99%

Section: Optimization Of the Wind Turbine Area Of Influencementioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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On the Use of Dual-Doppler Radar Measurements for Very Short-Term Wind Power Forecasts

et al. 2018

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Active power dispatch strategy of the wind farm based on improved multi‐agent consistency algorithm

Yao

Chen

et al. 2019

IET Renewable Power Generation

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With the increase of wind power penetration in the power system, wind farm (WF) needs to limit active power and accurately track the instructions from the dispatch centre. Since a WF has many distributed wind turbines (WTs), it is a crucial issue to reasonably distribute power reference values to WTs. In this study, a novel active power dispatch (APD) strategy based on dynamic grouping of WTs is proposed. This strategy considers the characteristics and operating conditions of WTs, which can smoothen the power reference values to WTs and reduce fluctuations of key parameters of WTs. Then, a distributed dispatch strategy based on multi-agent system consistency algorithm (MASCA) is applied for APD, which provides a dispatch strategy for WTs that does not require a centralised control centre. And the segmental virtual consistency algorithm is presented as an improvement of MASCA, which innovatively allows MASCA to support the grouping strategy for APD. Finally, the simulations show that the proposed strategy can enable WTs to obtain smoother reference power to track the dispatching instruction while reducing fluctuations of rotor speed and pitch angle, which is helpful to alleviate the fatigue of WTs. The dispatch strategy also shows good robustness when some communication is interrupted.

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Wind farm flow reconstruction and prediction from high frequency SCADA Data

Rott

Petrović

Kühn

2020

J. Phys.: Conf. Ser.

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The flow inside a typical large wind from propagates through the array from turbine to turbine. We present an algorithm that intuitively processes measured values from as much as possible turbines in real-time and uses them to determine a flow reconstruction and minute-scale forecast for the downstream turbines. For the validation we used full-field measurements from the offshore wind farm Global Tech I. The flow reconstruction is compared to the measurements of one turbine, which was excluded from the algorithm and achieved a root mean square error of 0.55 ms−1 for the wind speed estimation. The flow forecasting is tested for three prediction horizons 30s, 60s and 120s. Together with automated error correction to account for calibration errors and wake effects, the flow prediction achieves a root mean square error of 0.52 ms−1 for the 120s-forecast of the wind speed, which beats the persistence forecasting method. The reconstruction allows to analyse the flow in the wind farm, to detect abnormal turbine behaviour and to estimate fatigue loads, and the minute-scale forecasting is a useful tool for predictive wind farm control and estimating the available power of a wind farm, which becomes more and more necessary for grid stability.

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Very short-term probabilistic forecasting of wind power based on dual-Doppler radar measurements in the North Sea

Cited by 5 publications

References 12 publications

On the Use of Dual-Doppler Radar Measurements for Very Short-Term Wind Power Forecasts

On the Use of Dual-Doppler Radar Measurements for Very Short-Term Wind Power Forecasts

Active power dispatch strategy of the wind farm based on improved multi‐agent consistency algorithm

Wind farm flow reconstruction and prediction from high frequency SCADA Data

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