scholarly journals Determination of optimal wind turbine alignment into the wind and detection of alignment changes with SCADA data

2018 ◽  
Vol 3 (1) ◽  
pp. 395-408 ◽  
Author(s):  
Niko Mittelmeier ◽  
Martin Kühn
Keyword(s):  
Wind Direction ◽  
Energy Yield ◽  
Measurement Equipment ◽  
Power Losses ◽  
Multilinear Regression ◽  
Flow Distortion ◽  
Operational Lifetime ◽  
Flow Equilibrium ◽  
Ground Based Lidar ◽  
Wind Vane

Abstract. Upwind horizontal axis wind turbines need to be aligned with the main wind direction to maximize energy yield. Attempts have been made to improve the yaw alignment with advanced measurement equipment but most of these techniques introduce additional costs and rely on alignment tolerances with the rotor axis or the true north. Turbines that are well aligned after commissioning may suffer an alignment degradation during their operational lifetime. Such changes need to be detected as soon as possible to minimize power losses. The objective of this paper is to propose a three-step methodology to improve turbine alignment and detect changes during operational lifetime with standard nacelle metrology (met) mast instruments (here: two cup anemometer and one wind vane). In step one, a reference turbine and an external undisturbed reference wind signal, e.g., met mast or lidar are used to determine flow corrections for the nacelle wind direction instruments to obtain a turbine alignment with optimal power production. Secondly a nacelle wind speed correction enables the application of the previous step without additional external measurement equipment. Step three is a monitoring application and allows the detection of alignment changes on the wind direction measurement device by means of a flow equilibrium between the two anemometers behind the rotor. The three steps are demonstrated at two 2 MW turbines together with a ground-based lidar. A first-order multilinear regression model gives sufficient correction of the flow distortion behind the rotor for our purposes and two wind vane alignment changes are detected with an accuracy of ±1.4∘ within 3 days of operation after the change is introduced. We show that standard turbine equipment is able to align a turbine with sufficient accuracy and changes to its alignment can be detected in a reasonably short time, which helps to minimize power losses.

scholarly journals Determination of optimal wind turbine alignment into the wind and detection of alignment changes with SCADA data

2018 ◽  
Author(s):  
Niko Mittelmeier ◽  
Martin Kühn
Keyword(s):  
Wind Direction ◽  
Energy Yield ◽  
Measurement Equipment ◽  
Power Losses ◽  
Flow Distortion ◽  
Operational Lifetime ◽  
Horizontal Axis Wind Turbines ◽  
Flow Equilibrium ◽  
Ground Based Lidar ◽  
Wind Vane

Abstract. Upwind horizontal axis wind turbines need to be aligned with the main wind direction to maximize energy yield. Attempts have been made to improve the yaw alignment with advanced measurement equipment but most of these techniques introduce additional costs and rely on alignment tolerances with the rotor axis or the true north. Turbines that are well aligned after commissioning, may suffer an alignment degradation during their operational lifetime. Such changes need to be detected as soon as possible to minimize power losses. The objective of this paper is to propose a three-step methodology to improve turbine alignment and detect changes during operational lifetime with standard nacelle metrology (met) mast instruments (here: two cup anemometer and one wind vane). In step one, a reference turbine and an external undisturbed reference wind signal, e.g. met mast or lidar are used to determine flow corrections for the nacelle wind direction instruments to obtain a turbine alignment with optimal power production. Secondly a nacelle wind speed correction is enabling the application of the previous step without additional external measurement equipment. Step three is a monitoring application and allows to detect alignment changes on the wind direction measurement device by means of a flow equilibrium between the two anemometers behind the rotor. The three steps are demonstrated at two 2 MW turbines together with a ground based lidar. A first order multi linear regression model gives sufficient correction of the flow distortion behind the rotor for our purposes and two wind vane alignment changes are detected with an accuracy of ±1.4 ° within three days of operation after the change is introduced. We could show, that standard turbine equipment is able to align a turbine with sufficient accuracy and changes to its alignment can be detected in a reasonable short time which helps to minimize power losses.

Design of Small Wind Turbine

2008 ◽  
Author(s):  
R. S. Amano ◽  
Ryan Malloy
Keyword(s):  
Wind Speed ◽  
Wind Turbine ◽  
Wind Direction ◽  
Pitch Angle ◽  
Bridge Circuit ◽  
Small Wind Turbine ◽  
Swash Plate ◽  
Wind Vane ◽  
Blade Pitch Angle

The project has been completed, and all of the aforementioned objectives have been achieved. An anemometer has been constructed to measure wind speed, and a wind vane has been built to sense wind direction. An LCD module has been acquired and has been programmed to display the wind speed and its direction. An H-Bridge circuit was used to drive a gear motor that rotated the nacelle toward the windward direction. Finally, the blade pitch angle was controlled by a swash plate mechanism and servo motors installed on the generator itself. A microcontroller has been programmed to optimally control the servo motors and gear motor based on input from the wind vane and anemometer sensors.

scholarly journals Accuracy of Wind Observations from Open-Ocean Buoys: Correction for Flow Distortion

2020 ◽  
Vol 37 (4) ◽  
pp. 687-703 ◽  
Author(s):  
Michael Schlundt ◽  
J. Thomas Farrar ◽  
Sebastien P. Bigorre ◽  
Albert J. Plueddemann ◽  
Robert A. Weller
Keyword(s):  
Flow Distortion ◽  
Error Sources ◽  
Neutral Winds ◽  
Flow Simulations ◽  
Temporal Sampling ◽  
Empirical Correction ◽  
The Individual ◽  
Wind Vane ◽  
Subsequent Comparison ◽  
Good Agreement

AbstractThe comparison of equivalent neutral winds obtained from (i) four WHOI buoys in the subtropics and (ii) scatterometer estimates at those locations reveals a root-mean-square (RMS) difference of 0.56–0.76 m s−1. To investigate this RMS difference, different buoy wind error sources were examined. These buoys are particularly well suited to examine two important sources of buoy wind errors because 1) redundant anemometers and a comparison with numerical flow simulations allow us to quantitatively assess flow distortion errors, and 2) 1-min sampling at the buoys allows us to examine the sensitivity of buoy temporal sampling/averaging in the buoy–scatterometer comparisons. The interanemometer difference varies as a function of wind direction relative to the buoy wind vane and is consistent with the effects of flow distortion expected based on numerical flow simulations. Comparison between the anemometers and scatterometer winds supports the interpretation that the interanemometer disagreement, which can be up to 5% of the wind speed, is due to flow distortion. These insights motivate an empirical correction to the individual anemometer records and subsequent comparison with scatterometer estimates show good agreement.

scholarly journals Application of Gene Expression Programming, Artificial Neural Network and Multilinear Regression in Predicting Hydrochar Physicochemical Properties

2020 ◽  
Author(s):  
Jibril Abdulsalam ◽  
Abiodun Ismail Lawal ◽  
Ramadimetja Lizah Setsepu ◽  
Moshood Onifade ◽  
Samson Bada
Keyword(s):  
Neural Network ◽  
Gene Expression ◽  
Artificial Neural Network ◽  
Performance Metrics ◽  
Gene Expression Programming ◽  
Mean Bias Error ◽  
Energy Yield ◽  
Multilinear Regression ◽  
Soft Computing Techniques ◽  
Artificial Neural

Abstract Globally, the provision of energy is becoming an absolute necessity. Biomass resources are abundant and have been described as a potential alternative source of energy. However, it is important to assess the fuel characteristics of the various available biomass sources. Soft computing techniques are presented in this study to predict the mass yield (MY), energy yield (EY), and higher heating value (HHV) of hydrothermally carbonized biomass by using Gene Expression Programming (GEP), multiple-input single output-artificial neural network (MISO-ANN), and Multilinear regression (MLR). The three techniques were compared using statistical performance metrics. The coefficient of determination (R2), mean absolute error (MAE), and mean bias error (MBE) were used to evaluate the performance of the models. The MISO-ANN with 5-10-10-1 and 5-15-15-1 network architectures provided the most satisfactory performance of the three proposed models (R2 = 0.976, 0.955, 0.996; MAE = 2.24, 2.11, 0.93; MBE = 0.16, 0.37, 0.12) for MY, EY and HHV respectively. The GEP technique’s ability to predict hydrochar properties based on the input parameters was found to be satisfactory, while MLR provided an unsatisfactory predictive model. Sensitivity analysis was conducted, and the analysis revealed that volatile matter (VM) and temperature (Temp) have more influence on the MY, EY, and HHV.

scholarly journals Wind direction estimation using SCADA data with consensus-based optimization

2019 ◽  
Vol 4 (2) ◽  
pp. 355-368 ◽  
Author(s):  
Jennifer Annoni ◽  
Christopher Bay ◽  
Kathryn Johnson ◽  
Emiliano Dall'Anese ◽  
Eliot Quon ◽  
...  
Keyword(s):  
Real Time ◽  
Wind Turbines ◽  
Wind Direction ◽  
Wind Farm ◽  
Control Strategies ◽  
Reliable Estimate ◽  
Local Conditions ◽  
Efficient Operation ◽  
Wind Vane ◽  
Inflow Conditions

Abstract. Wind turbines in a wind farm typically operate individually to maximize their own performance and do not take into account information from nearby turbines. To enable cooperation to achieve farm-level objectives, turbines will need to use information from nearby turbines to optimize performance, ensure resiliency when other sensors fail, and adapt to changing local conditions. A key element of achieving a more efficient wind farm is to develop algorithms that ensure reliable, robust, real-time, and efficient operation of wind turbines in a wind farm using local sensor information that is already being collected, such as supervisory control and data acquisition (SCADA) data, local meteorological stations, and nearby radars/sodars/lidars. This article presents a framework for developing a cooperative wind farm that incorporates information from nearby turbines in real time to better align turbines in a wind farm. SCADA data from multiple turbines can be used to make better estimates of the local inflow conditions at each individual turbine. By incorporating measurements from multiple nearby turbines, a more reliable estimate of the wind direction can be obtained at an individual turbine. The consensus-based approach presented in this paper uses information from nearby turbines to estimate wind direction in an iterative way rather than aggregating all the data in a wind farm at once. Results indicate that this estimate of the wind direction can be used to improve the turbine's knowledge of the wind direction. This estimated wind direction signal has implications for potentially decreasing dynamic yaw misalignment, decreasing the amount of time a turbine spends yawing due to a more reliable input to the yaw controller, increasing resiliency to faulty wind-vane measurements, and increasing the potential for wind farm control strategies such as wake steering.

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 Sensitivity analysis of mesoscale simulations to physics parameterizations: a case study of storm Ciara over the Belgian North Sea using WRF-ARW

2021 ◽  
Author(s):  
Adithya Vemuri ◽  
Sophia Buckingham ◽  
Wim Munters ◽  
Jan Helsen ◽  
Jeroen van Beeck
Keyword(s):  
Sensitivity Analysis ◽  
Temporal Resolution ◽  
North Sea ◽  
Wind Direction ◽  
Offshore Wind ◽  
Wave Radiation ◽  
Energy Yield ◽  
Physics Parameterizations ◽  
Belgian North Sea

Abstract. The Weather, Research and Forecasting (WRF) model includes a multitude of physics parameterizations to account for atmospheric dynamics and interactions such as turbulent fluxes within the planetary boundary layer (PBL), long and short wave radiation, hydrometeor representation in microphysics, cloud ensemble representation in cumulus, amongst others. A sensitivity analysis is conducted in order to identify the optimal WRF-physics set-up and impact of temporal resolution of re-analysis dataset for the event of sudden changes in wind direction that can become challenging for reliable wind energy operations. In this context, Storm Ciara has been selected as a case study to investigate the influence of a broad combination of different interacting physics-schemes on quantities of interest that are relevant for energy yield assessment. Of particular relevance to fast transient weather events, two different temporal resolutions (1-hourly and 3-hourly) of the lateral boundary condition's re-analysis dataset, ERA5, are considered. Physics parameterizations considered in this study include: two PBL schemes (MYNN2.5 and scale-aware Shin Hong PBL), four cumulus schemes (Kain-Fritsch, Grell-Devenyi, and scale-aware Grell-Freitas and multi-scale Kain-Fritsch,) and three microphysics schemes (WSM5, Thompson and Morrison) coupled with two geospatial configurations for WRF simulation domains. The resulting WRF predictions are assessed by comparison to observational RADAR reflectivity data on precipitation. In addition, SCADA data on wind direction and wind speed from an offshore wind farm located in the Belgian North Sea is considered to assess modeling capabilities for local wind behavior at farm level. For precipitation, results are shown to be very sensitive to model setup, but no clear trends can be observed. For wind-related variables on the other hand, results show a definite improvement in accuracy when both scale-aware cumulus and PBL parameterizations are used in combination with 1-hourly temporal resolution reanalysis data and extended domain sizes.

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