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 A Framework for Autonomous Wind Farms: Wind Direction Consensus

2018 ◽  
Author(s):  
Jennifer Annoni ◽  
Christopher Bay ◽  
Kathryn Johnson ◽  
Emiliano Dall'Anese ◽  
Eliot Quon ◽  
...  
Keyword(s):  
Wind Turbines ◽  
Wind Direction ◽  
Wind Farm ◽  
Wind Farms ◽  
Robust Estimate ◽  
Local Conditions ◽  
Efficient Operation ◽  
Account Information ◽  
Data Acquistion ◽  
Wind Vane

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. In an autonomous wind farm, enabling 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 an autonomous wind farm is to develop algorithms that provide necessary information to ensure reliable, robust, and efficient operation of wind turbines in a wind plant using local sensor information that is already being collected, such as supervisory control and data acquistion (SCADA) data, local meteorological stations, and nearby radars/sodars/lidars. This article presents a framework for implementing an autonomous wind farm that incorporates information from local sensors in real time to better align turbines in a wind farm. Oftentimes, measurements made at an individual turbine are noisy and unreliable. By incorporating measurements from multiple nearby turbines, a more robust estimate of the wind direction can be obtained at an individual turbine. Results indicate that this estimate of the wind direction can be used to improve the turbine's knowledge of the wind direction and could decrease dynamic yaw misalignment, decrease the amount of time a turbine spends yawing due to a more robust input to the yaw controller, and increase resiliency to faulty wind-vane measurements.

scholarly journals Sensitivity Analysis of Turbine Fatigue and Ultimate Loads to Wind and Wake Characteristics in a Small Wind Farm

2021 ◽  
Author(s):  
Kelsey Shaler ◽  
Amy N. Robertson ◽  
Jason Jonkman
Keyword(s):  
Sensitivity Analysis ◽  
Wind Turbine ◽  
Wind Direction ◽  
Wind Farm ◽  
Vertical Wind ◽  
Normal Operation ◽  
Reynolds Stresses ◽  
Significant Events ◽  
Inflow Conditions ◽  
Calibration Parameters

Abstract. Wind turbines are designed using a set of simulations to determine the fatigue and ultimate loads, typically focused solely on unwaked wind turbine operation. These structural loads can be significantly influenced by the wind inflow conditions. When placed in the wake of upstream turbines, turbines experience altered inflow conditions, which can additionally influence the fatigue and ultimate loads. Although significant research and effort has been put into measuring and defining such parameters, limited work has been done to quantify the sensitivity of structural loads to the inevitable uncertainty in these inflow conditions, especially in a wind farm setting with waked conditions. It is therefore important to understand the impact such uncertainties have on the resulting loads of both non-waked and waked turbines. The goal of this work is to assess which wind-inflow- and wake-related parameters have the greatest influence on fatigue and ultimate loads during normal operation for turbines in a three-turbine wind farm. Twenty-eight wind inflow and wake parameters were screened using an elementary effects sensitivity analysis approach to identify the parameters that lead to the largest variation in the fatigue and ultimate loads of each turbine. This study was performed using the National Renewable Energy Laboratory 5 MW baseline wind turbine with synthetically generated inflow based on the International Electrotechnical Commission (IEC) Kaimal turbulence spectrum with IEC exponential coherence model. The focus was on sensitivity to individual parameters, though interactions between parameters were considered, and how sensitivity differs between waked and non-waked turbines. The results of this work show that for both waked and non-waked turbines, ambient turbulence in the primary wind direction and shear were the most sensitive parameters for turbine fatigue and ultimate loads. Secondary parameters of importance for all turbines were identified as yaw misalignment, u-direction integral length, and the exponent and u components of the IEC coherence model. The tertiary parameters of importance differ between waked and non-waked turbines. Tertiary effects account for up to 9.0 % of the significant events for waked turbine ultimate loads and include veer; non-streamwise components of the IEC coherence model; Reynolds stresses; wind direction; air density; and several wake calibration parameters. For fatigue loads, tertiary effects account for up to 5.4 % of the significant events and include vertical turbulence standard deviation; lateral and vertical wind integral lengths; lateral and vertical wind components of the IEC coherence model; Reynolds stresses; wind direction; and all wake calibration parameters. This information shows the increased importance of non-streamwise wind components and wake parameters in fatigue and ultimate load sensitivity of downstream turbines.

scholarly journals Results from a wake-steering experiment at a commercial wind plant: investigating the wind speed dependence of wake-steering performance

2021 ◽  
Vol 6 (6) ◽  
pp. 1427-1453
Author(s):  
Eric Simley ◽  
Paul Fleming ◽  
Nicolas Girard ◽  
Lucas Alloin ◽  
Emma Godefroy ◽  
...  
Keyword(s):  
Wind Speed ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Wind Direction ◽  
Wind Farm ◽  
Poor Performance ◽  
Power Production ◽  
Wind Rose ◽  
Wind Speeds ◽  
Variability Model

Abstract. Wake steering is a wind farm control strategy in which upstream wind turbines are misaligned with the wind to redirect their wakes away from downstream turbines, thereby increasing the net wind plant power production and reducing fatigue loads generated by wake turbulence. In this paper, we present results from a wake-steering experiment at a commercial wind plant involving two wind turbines spaced 3.7 rotor diameters apart. During the 3-month experiment period, we estimate that wake steering reduced wake losses by 5.6 % for the wind direction sector investigated. After applying a long-term correction based on the site wind rose, the reduction in wake losses increases to 9.3 %. As a function of wind speed, we find large energy improvements near cut-in wind speed, where wake steering can prevent the downstream wind turbine from shutting down. Yet for wind speeds between 6–8 m/s, we observe little change in performance with wake steering. However, wake steering was found to improve energy production significantly for below-rated wind speeds from 8–12 m/s. By measuring the relationship between yaw misalignment and power production using a nacelle lidar, we attribute much of the improvement in wake-steering performance at higher wind speeds to a significant reduction in the power loss of the upstream turbine as wind speed increases. Additionally, we find higher wind direction variability at lower wind speeds, which contributes to poor performance in the 6–8 m/s wind speed bin because of slow yaw controller dynamics. Further, we compare the measured performance of wake steering to predictions using the FLORIS (FLOw Redirection and Induction in Steady State) wind farm control tool coupled with a wind direction variability model. Although the achieved yaw offsets at the upstream wind turbine fall short of the intended yaw offsets, we find that they are predicted well by the wind direction variability model. When incorporating the expected yaw offsets, estimates of the energy improvement from wake steering using FLORIS closely match the experimental results.

scholarly journals Results from a Wake Steering Experiment at a Commercial Wind Plant: Investigating the Wind Speed Dependence of Wake Steering Performance

2021 ◽  
Author(s):  
Eric Simley ◽  
Paul Fleming ◽  
Nicolas Girard ◽  
Lucas Alloin ◽  
Emma Godefroy ◽  
...  
Keyword(s):  
Wind Speed ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Wind Direction ◽  
Wind Farm ◽  
Poor Performance ◽  
Power Production ◽  
Wind Rose ◽  
Wind Speeds ◽  
Variability Model

Abstract. Wake steering is a wind farm control strategy in which upstream wind turbines are misaligned with the wind to redirect their wakes away from downstream turbines, thereby increasing the net wind plant power production and reducing fatigue loads generated by wake turbulence. In this paper, we present results from a wake steering experiment at a commercial wind plant involving two wind turbines spaced 3.7 rotor diameters apart. During the three-month experiment period, we estimate that wake steering reduced wake losses by 5.7 % for the wind direction sector investigated. After applying a long-term correction based on the site wind rose, the reduction in wake losses increases to 9.8 %. As a function of wind speed, we find large energy improvements near cut-in wind speed, where wake steering can prevent the downstream wind turbine from shutting down. Yet for wind speeds between 6–8 m/s, we observe little change in performance with wake steering. However, wake steering was found to improve energy production significantly for below-rated wind speeds from 8–12 m/s. By measuring the relationship between yaw misalignment and power production using a nacelle lidar, we attribute much of the improvement in wake steering performance at higher wind speeds to a significant reduction in the power loss of the upstream turbine as wind speed increases. Additionally, we find higher wind direction variability at lower wind speeds, which contributes to poor performance in the 6–8 m/s wind speed bin because of slow yaw controller dynamics. Further, we compare the measured performance of wake steering to predictions using the FLORIS (FLOw Redirection and Induction in Steady State) wind farm control tool coupled with a wind direction variability model. Although the achieved yaw offsets at the upstream wind turbine fall short of the intended yaw offsets, we find that they are predicted well by the wind direction variability model. When incorporating the predicted achieved yaw offsets, estimates of the energy improvement from wake steering using FLORIS closely match the experimental results.

scholarly journals An offshore wind farm energy injection mastering using aerodynamic and kinetic control strategies

2018 ◽  
Vol 61 ◽  
pp. 00002 ◽  
Author(s):  
Djamel Ikni ◽  
Ahmed Ousmane Bagre ◽  
Mamadou Bailo Camara ◽  
Brayima Dakyo
Keyword(s):  
Power Quality ◽  
Wind Turbines ◽  
Wind Farm ◽  
Control Strategies ◽  
Wind Farms ◽  
Optimal Strategies ◽  
Offshore Wind ◽  
Offshore Wind Farm ◽  
Energy Storage Devices ◽  
Storage Devices

The injection of wind farm production into a grid, needs optimal strategies for energy transfer management. Usually, the power produced by the wind farms does not fulfil all the grid code requirements. The main problem is generally based on the way to reduce the impacts of power production fluctuations on the grid voltage and its frequency. To solve this problem, some authors suggest the use of an interface such as energy storage devices in order to compensate the wind power fluctuations. In fact, the storage devices installed between the wind farm and the grid can improve the power quality in terms of stability but in other hand the size and the cost of the system can be increased. In this paper, two solutions have been proposed in case the power quality produced by the wind farm is out of the grid code requirements. The improvement of the energy quality of an offshore wind farm without storage and connected to the grid is discussed. The proposed solution is to operate the wind turbines with a reserve of power. To distribute this reserve equitably among wind turbines, a proportional distribution algorithms has been developed. The results obtained show clearly the effectiveness of the strategy.

Optimization of Large Wind Farms Using a Genetic Algorithm

2012 ◽  
Author(s):  
Anshul Mittal ◽  
Lafayette K. Taylor
Keyword(s):  
Genetic Algorithm ◽  
Wind Speed ◽  
Wind Turbines ◽  
Wind Direction ◽  
Wind Farm ◽  
Wind Farms ◽  
Unit Power ◽  
Wake Model ◽  
Variable Wind ◽  
Large Wind

Optimizing the placement of the wind turbines in a wind farm to achieve optimal performance is an active area of research, with numerous research studies being published every year. Typically, the area available for the wind farm is divided into cells (a cell may/may not contain a wind turbine) and an optimization algorithm is used. In this study, the effect of the cell size on the optimal layout is being investigated by reducing it from five rotor diameter (previous studies) to 1/40 rotor diameter (present study). A code is developed for optimizing the placement of wind turbines in large wind farms. The objective is to minimize the cost per unit power produced from the wind farm. A genetic algorithm is employed for the optimization. The velocity deficits in the wake of the wind turbines are estimated using a simple wake model. The code is verified using the results from the previous studies. Results are obtained for three different wind regimes: (1) Constant wind speed and fixed wind direction, (2) constant wind speed and variable wind direction, and (3) variable wind speed and variable wind direction. Cost per unit power is reduced by 11.7% for Case 1, 11.8% for Case 2, and 15.9% for Case 3 for results obtained in this study. The advantages/benefits of a refined grid spacing of 1/40 rotor diameter (1 m) are evident and are discussed. To get an understanding of the sensitivity of the power produced to the wake model, optimized layout is obtained for the Case 1 using a different wake model.

scholarly journals The Application of Water Cycle Optimization Algorithm for Optimal Placement of Wind Turbines in Wind Farms

Energies ◽  
2019 ◽  
Vol 12 (22) ◽  
pp. 4335 ◽  
Author(s):  
Hegazy Rezk ◽  
Ahmed Fathy ◽  
Ahmed A. Zaki Diab ◽  
Mujahed Al-Dhaifallah
Keyword(s):  
Wind Speed ◽  
Wind Turbines ◽  
Wind Direction ◽  
Wind Farm ◽  
Water Cycle ◽  
Reliability And Validity ◽  
Wind Farms ◽  
Optimal Placement ◽  
Grey Wolf Optimizer ◽  
Variable Wind

Wind farms (WFs) include an enormous number of wind turbines (WTs) in order to achieve high capacity. The interaction among WTs reduces the extracted amount of wind energy because wind speed decreases in the wake region. The optimal placement of WTs within a WF is therefore vital for achieving high performance. This permits as many WTs as possible to be installed inside a narrow region. In this work, the water cycle algorithm (WCA), a recently developed optimizer, was employed to identify the optimal distribution of WTs. Minimization of the total cost per kilowatt was the objective of the optimization process. Two different cases were considered: the first assumed constant wind speed with variable wind direction, while the second applied variable wind speed with variable wind direction. The results obtained through the WCA optimizer were compared with other algorithms, namely, salp swarm algorithm (SSA), satin bowerbird optimization (SBO), grey wolf optimizer (GWO), and differential evolution (DE), as well as other reported works. WCA gave the best solution compared to other reported and programmed algorithms, thus confirming the reliability and validity of WCA in optimally configuring turbines in a wind farm for both the studied cases.

scholarly journals Rancangan Monitoring Wind Direction Indicator Berbasis Arduino di Bandar Udara Indonesia

2021 ◽  
Vol 4 (2) ◽  
pp. 70-73
Author(s):  
Ahmad Rossydi ◽  
Andi Yuyun Irmayanti ◽  
Sugiyanto
Keyword(s):  
Real Time ◽  
Personal Computer ◽  
Wind Direction ◽  
Wind Vane

Windsock adalah sebagai penanda angin dan relatif kecepatan angin. Alat ini sangat berfungsi di dunia penerbangan. Setiap bandara wajib memiliki windsock sebagai penunjang penentu arah angin. Alat ini dipasang di suatu bandara udara yang dapat dilihat dengan jelas oleh petugas lalu lintas udara (ATC). Tetapi di bandara A.P.T. Pranoto-Samarinda masih menggunakan sistem manual untuk melihat windsock menggunakan teropong jarak jauh. Pengembangan alat ini dilatar belakangi dikarenakan kurang efektif dan efisiennya saat penggunaan windsock yang sudah tersedia di Bandar Udara A.P.T. Pranoto Samarinda. Maka di sini penulis tertarik untuk mengangkat sebuah judul dalam tugas akhir ini yaitu  “Rancangan Monitoring Wind Direction Indicator berbasis Arduino di Bandar Udara Aji Pangeran Tumenggung  Pranoto Samarinda ”. Hasil akhir yang dicapai dari pengembangan windsock ini adalah dengan tujuan agar tidak mencari arah angin secara manual, dengan dikembangkannya alat ini dapat mencari arah angin secara otomatis. Windsock ini juga dilengkapi dengan wind vane dan wind cone sebagai pengukur arah mata angin dan kecepatan angin disekitar alat tersebut. Perancangan sistem monitoring Wind Direction Indicator berbasis Arduino, diharapkan dapat mendeteksi kecepatan dan arah angin yang mampu memberikan data secara real time. Agar data yang diperoleh dapat tersampaikan kepada masyarakat dengan cepat dan akurat, maka dibutuhkan suatu sistem yang memadai. Sistem yang sekarang ini sedang berkembang pesat yaitu Personal Computer (PC) dan jaringan internet. Perancangan alat Wind Direction Indicator ini terdiri dari hardware yang berupa Arduino, sensor arah dan sensor kecepatan angin. Alat Wind Direction Indicator dapat membantu memudahkan ATC dalam memantau arah dan kecepatan angin yang sangat penting bagi penerbangan, dimana sebelumnya proses ini menggunakan teropong untuk memantau arah angin. Proses pemantauan arah angin menjadi lebih mudah dengan menggunakan alat ini, karena hasil pembacaan arah dan kecepatan angin akan langsung muncul pada monitor.

scholarly journals Unmanned Aerial Drones for Inspection of Offshore Wind Turbines: A Mission-Critical Failure Analysis

Robotics ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 26
Author(s):  
Mahmood Shafiee ◽  
Zeyu Zhou ◽  
Luyao Mei ◽  
Fateme Dinmohammadi ◽  
Jackson Karama ◽  
...  
Keyword(s):  
Wind Energy ◽  
Failure Analysis ◽  
Wind Turbines ◽  
Wind Farm ◽  
Failure Modes ◽  
Early Stage ◽  
Offshore Wind ◽  
Efficient Operation ◽  
Offshore Wind Turbines ◽  
Stage Of Development

With increasing global investment in offshore wind energy and rapid deployment of wind power technologies in deep water hazardous environments, the in-service inspection of wind turbines and their related infrastructure plays an important role in the safe and efficient operation of wind farm fleets. The use of unmanned aerial vehicle (UAV) and remotely piloted aircraft (RPA)—commonly known as “drones”—for remote inspection of wind energy infrastructure has received a great deal of attention in recent years. Drones have significant potential to reduce not only the number of times that personnel will need to travel to and climb up the wind turbines, but also the amount of heavy lifting equipment required to carry out the dangerous inspection works. Drones can also shorten the duration of downtime needed to detect defects and collect diagnostic information from the entire wind farm. Despite all these potential benefits, the drone-based inspection technology in the offshore wind industry is still at an early stage of development and its reliability has yet to be proven. Any unforeseen failure of the drone system during its mission may cause an interruption in inspection operations, and thereby, significant reduction in the electricity generated by wind turbines. In this paper, we propose a semiquantitative reliability analysis framework to identify and evaluate the criticality of mission failures—at both system and component levels—in inspection drones, with the goal of lowering the operation and maintenance (O&M) costs as well as improving personnel safety in offshore wind farms. Our framework is built based upon two well-established failure analysis methodologies, namely, fault tree analysis (FTA) and failure mode and effects analysis (FMEA). It is then tested and verified on a drone prototype, which was developed in the laboratory for taking aerial photography and video of both onshore and offshore wind turbines. The most significant failure modes and underlying root causes within the drone system are identified, and the effects of the failures on the system’s operation are analysed. Finally, some innovative solutions are proposed on how to minimize the risks associated with mission failures in inspection drones.

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