scholarly journals Improved Evaluation of The Wind Power Potential of a Large Offshore Wind Farm Using Four Analytical Wake Models

2021 ◽  
Vol 11 (1) ◽  
pp. 35-48
Author(s):  
Mohammed Amine Hassoine ◽  
Fouad Lahlou ◽  
Adnane Addaim ◽  
Abdessalam Ait Madi
Keyword(s):  
Wind Direction ◽  
Power Output ◽  
Power Loss ◽  
Wind Farm ◽  
Offshore Wind ◽  
Total Power ◽  
Offshore Wind Farm ◽  
Wake Effect ◽  
Power Deficit ◽  
Total Power Output

The objective of this paper is to investigate the ability of analytical wake models to estimate the wake effects between wind turbines (WTs). The interaction of multiple wakes reduces the total power output produced by a large offshore wind farm (LOFWF). This power loss is due to the effect of turbine spacing (WTS), if the WTs are too close, the power loss is very significant. Therefore, the optimization of turbine positions within the offshore wind farm requires an understanding of the interaction of wakes inside the wind farm. To better understand the wake effect, the Horns Rev 1 offshore wind farm has been studied with four wake models, Jensen, Larsen, Ishihara, and Frandsen. A comparative study of the wake models has been performed in several situations and configurations, single and multiple wakes are taken into consideration. Results from the Horns Rev1 offshore wind farm case have  been evaluated and compared to observational data, and also  with the previous studies. The power output of a row of WTs is sensitive to the wind direction. For example, if a row of ten turbines is aligned with the 270° wind direction, the full wake condition of WTs is reached and the power deficit limit predicted by Jensen model exceeds 70%. When a wind direction changes only of  10° (260° and 280°), the deficit limit reduces to 30%. The obtained results show that a significant power deficit occurs when the turbines are arranged in an aligned manner. The findings also showed that all four models gave acceptable predictions of the total power output. The comparison between the calculated and reported power output of Horns Revs 1 showed that the differences ranged from - 8.27 MW (12.49%) to 15.27 MW (23.06%) for the Larsen and Frandsen models, respectively.

scholarly journals Analysing uncertainties in offshore wind farm power output using measure–correlate–predict methodologies

2020 ◽  
Vol 5 (2) ◽  
pp. 601-621
Author(s):  
Michael Denis Mifsud ◽  
Tonio Sant ◽  
Robert Nicholas Farrugia
Keyword(s):  
Wind Speed ◽  
Wind Direction ◽  
Power Output ◽  
Wind Farm ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Offshore Wind ◽  
Energy Yield ◽  
Offshore Wind Farm ◽  
Power And Energy

Abstract. This paper investigates the uncertainties resulting from different measure–correlate–predict (MCP) methods to project the power and energy yield from a wind farm. The analysis is based on a case study that utilises short-term data acquired from a lidar wind measurement system deployed at a coastal site in the northern part of the island of Malta and long-term measurements from the island's international airport. The wind speed at the candidate site is measured by means of a lidar system. The predicted power output for a hypothetical offshore wind farm from the various MCP methodologies is compared to the actual power output obtained directly from the input of lidar data to establish which MCP methodology best predicts the power generated. The power output from the wind farm is predicted by inputting wind speed and direction derived from the different MCP methods into windPRO® (https://www.emd.dk/windpro, last access: 8 May 2020). The predicted power is compared to the power output generated from the actual wind and direction data by using the normalised mean absolute error (NMAE) and the normalised mean-squared error (NMSE). This methodology will establish which combination of MCP methodology and wind farm configuration will have the least prediction error. The best MCP methodology which combines prediction of wind speed and wind direction, together with the topology of the wind farm, is that using multiple linear regression (MLR). However, the study concludes that the other MCP methodologies cannot be discarded as it is always best to compare different combinations of MCP methodologies for wind speed and wind direction, together with different wake models and wind farm topologies.

scholarly journals Analysing Uncertainties in Offshore Wind Farm Power Output using Measure Correlate Predict Methodologies

2019 ◽  
Author(s):  
Michael Denis Mifsud ◽  
Tonio Sant ◽  
Robert Nicholas Farrugia
Keyword(s):  
Wind Speed ◽  
Wind Direction ◽  
Power Output ◽  
Wind Farm ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Offshore Wind ◽  
Energy Yield ◽  
Offshore Wind Farm ◽  
The Mean

Abstract. This paper investigates the uncertainties resulting from different Measure-Correlate-Predict methods to project the power and energy yield from a wind farm. The analysis is based on a case study that utilizes short-term data acquired from a LiDAR wind measurement system deployed at a coastal site in the northern part of the island of Malta and long-term measurements from the island’s international airport. The wind speed at the candidate site is measured by means of a LiDAR system. The predicted power output for a hypothetical offshore wind farm from the various MCP methodologies is compared to the actual power output obtained directly from the input of LiDAR data to establish which MCP methodology best predicts the power generated. The power output from the wind farm is predicted by inputting wind speed and direction derived from the different MCP methods into windPRO® (https://www.emd.dk/windpro). The predicted power is compared to the power output generated from the actual wind and direction data by using the Mean Squared Error (MSE) and the Mean Absolute Error (MAE) measures. This methodology will establish which combination of MCP methodology and wind farm configuration will have the least prediction error. The best MCP methodology which combines prediction of wind speed and wind direction, together with the topology of the wind farm, is that using Artificial Neural Networks. However, the study concludes that the other MCP methodologies cannot be discarded as it is always best to compare different combinations of MCP methodologies for wind speed and wind direction, together with different wake models and wind farm topologies.

scholarly journals Correlations of power output fluctuations in an offshore wind farm using high-resolution SCADA data

2021 ◽  
Vol 6 (4) ◽  
pp. 997-1014
Author(s):  
Janna Kristina Seifert ◽  
Martin Kraft ◽  
Martin Kühn ◽  
Laura J. Lukassen
Keyword(s):  
Wind Turbine ◽  
Wind Turbines ◽  
Power Output ◽  
Wind Farm ◽  
Offshore Wind ◽  
Offshore Wind Farm ◽  
Flow Conditions ◽  
Output Fluctuations ◽  
Power Difference ◽  
Time Correlations

Abstract. Space–time correlations of power output fluctuations of wind turbine pairs provide information on the flow conditions within a wind farm and the interactions of wind turbines. Such information can play an essential role in controlling wind turbines and short-term load or power forecasting. However, the challenges of analysing correlations of power output fluctuations in a wind farm are the highly varying flow conditions. Here, we present an approach to investigate space–time correlations of power output fluctuations of streamwise-aligned wind turbine pairs based on high-resolution supervisory control and data acquisition (SCADA) data. The proposed approach overcomes the challenge of spatially variable and temporally variable flow conditions within the wind farm. We analyse the influences of the different statistics of the power output of wind turbines on the correlations of power output fluctuations based on 8 months of measurements from an offshore wind farm with 80 wind turbines. First, we assess the effect of the wind direction on the correlations of power output fluctuations of wind turbine pairs. We show that the correlations are highest for the streamwise-aligned wind turbine pairs and decrease when the mean wind direction changes its angle to be more perpendicular to the pair. Further, we show that the correlations for streamwise-aligned wind turbine pairs depend on the location of the wind turbines within the wind farm and on their inflow conditions (free stream or wake). Our primary result is that the standard deviations of the power output fluctuations and the normalised power difference of the wind turbines in a pair can characterise the correlations of power output fluctuations of streamwise-aligned wind turbine pairs. Further, we show that clustering can be used to identify different correlation curves. For this, we employ the data-driven k-means clustering algorithm to cluster the standard deviations of the power output fluctuations of the wind turbines and the normalised power difference of the wind turbines in a pair. Thereby, wind turbine pairs with similar power output fluctuation correlations are clustered independently from their location. With this, we account for the highly variable flow conditions inside a wind farm, which unpredictably influence the correlations.

scholarly journals Wind Farm Power Production Assessment: Introduction of a New Actuator Disc Method and Comparison with Existing Models in the Context of a Case Study

2019 ◽  
Vol 9 (3) ◽  
pp. 431 ◽  
Author(s):  
Nikolaos Simisiroglou ◽  
Heracles Polatidis ◽  
Stefan Ivanell
Keyword(s):  
Wind Farm ◽  
Power Production ◽  
Offshore Wind ◽  
Offshore Wind Farm ◽  
Actuator Disc ◽  
Power Deficit ◽  
The Given ◽  
Method Show ◽  
Do So

The aim of the present study is to perform a comparative analysis of two actuator disc methods (ACD) and two analytical wake models for wind farm power production assessment. To do so, wind turbine power production data from the Lillgrund offshore wind farm in Sweden is used. The measured power production for individual wind turbines is compared with results from simulations, done in the WindSim software, using two ACD methods (ACD (2008) and ACD (2016)) and two analytical wake models widely used within the wind industry (Jensen and Larsen wake models). It was found that the ACD (2016) method and the Larsen model outperform the other method and model in most cases. Furthermore, results from the ACD (2016) method show a clear improvement in the estimated power production in comparison to the ACD (2008) method. The Jensen method seems to overestimate the power deficit for all cases. The ACD (2016) method, despite its simplicity, can capture the power production within the given error margin although it tends to underestimate the power deficit.

scholarly journals Monitoring offshore wind farm power performance with SCADA data and an advanced wake model

2017 ◽  
Vol 2 (1) ◽  
pp. 175-187 ◽  
Author(s):  
Niko Mittelmeier ◽  
Tomas Blodau ◽  
Martin Kühn
Keyword(s):  
Power Output ◽  
Wind Farm ◽  
Offshore Wind ◽  
Offshore Wind Farm ◽  
Power Performance ◽  
Power Normalization ◽  
Efficient Detection ◽  
Asset Value ◽  
Output Ratio ◽  
Wake Model

Abstract. Wind farm underperformance can lead to significant losses in revenues. The efficient detection of wind turbines operating below their expected power output and immediate corrections help maximize asset value. The method, presented in this paper, estimates the environmental conditions from turbine states and uses pre-calculated lookup tables from a numeric wake model to predict the expected power output. Deviations between the expected and the measured power output ratio between two turbines are an indication of underperformance. The confidence of detected underperformance is estimated by a detailed analysis of the uncertainties of the method. Power normalization with reference turbines and averaging several measures performed by devices of the same type can reduce uncertainties for estimating the expected power. A demonstration of the method's ability to detect underperformance in the form of degradation and curtailment is given. An underperformance of 8 % could be detected in a triple-wake condition.

scholarly journals Monitoring offshore wind farm power performance with SCADA data and advanced wake model

2016 ◽  
Author(s):  
Niko Mittelmeier ◽  
Tomas Blodau ◽  
Martin Kühn
Keyword(s):  
Power Output ◽  
Wind Farm ◽  
Offshore Wind ◽  
Offshore Wind Farm ◽  
Power Performance ◽  
Efficient Detection ◽  
Asset Value ◽  
Measurement Devices ◽  
Wake Model ◽  
Measured Power

Abstract. Wind farm underperformance can lead to significant losses in revenues. Efficient detection of wind turbines operating below their expected power output and immediate corrections help maximise asset value. The presented method estimates the environmental conditions from turbine states and uses pre-calculated power matrices from a numeric wake model to predict the expected power output. Deviations between the expected and the measured power output are an indication of underperformance. The confidence of detected underperformance is estimated by detailed analysis of uncertainties of the method. Power normalisation with reference turbines and averaging several measurement devices can reduce uncertainties for estimating the expected power. A demonstration of the method’s ability to detect underperformance in the form of degradation and curtailment is given. Underperformance of 8 % could be detected in a triple wake condition.

Sign in / Sign up

Export Citation Format

Share Document