scholarly journals Integrated wind farm layout and control optimization

2020 ◽  
Author(s):  
Mads M. Pedersen ◽  
Gunner C. Larsen
Keyword(s):  
Wind Speed ◽  
Wind Turbine ◽  
Wind Farm ◽  
Design Space ◽  
Wind Farm Layout ◽  
Control Optimization ◽  
Mean Wind Speed ◽  
The Individual ◽  
And Control ◽  
Selection Of

Abstract. Design of an optimal wind farm topology and wind farm control scheduling depends on the chosen metric. The objective of this paper is to investigate the influence of optimal wind farm control on the optimal wind farm layout in terms of power production. A successful fulfilment of this goal requires: 1) an accurate and fast flow model; 2) selection of the minimum set of design parameters that rules the problem; and 3) selection of an optimization algorithm with good scaling properties. For control of the individual wind farm turbines, the two most obvious strategies are wake steering based on active wind turbine yaw control and wind turbine derating. The present investigation is a priori limited to wind turbine derating. A high-speed linearized CFD RANS solver models the flow field and the crucial wind turbine wake interactions inside the wind farm. The actuator disk method is used to model the wind turbines, and utilizing an aerodynamic model, the design space of the optimization problem is reduced to only three variables per turbine – two geometric and one carefully selected variable specifying the individual wind turbine derating setting for each mean wind speed and direction. The full design space spanned by these (2N + Nd Ns N) parameters, where N is the number of wind farm turbines, Nd is the number of direction bins, and Ns is the number of mean wind speed bins. This design space is decomposed in two subsets, which in turn define a nested set of optimization problems to achieve the fastest possible optimization procedure. Following a simplistic sanity check of the platform functionality regarding wind farm layout and control optimization, the capabilities of the developed optimization platform is demonstrated on the Swedish offshore wind farm. For this particular wind farm, the analysis demonstrates that the expected annual energy production can be increased by 4 % by integrating the wind farm control in the design of the wind farm layout, which is 1.2 % higher than what is achieved by optimizing the layout only.

scholarly journals Integrated wind farm layout and control optimization

2020 ◽  
Vol 5 (4) ◽  
pp. 1551-1566
Author(s):  
Mads M. Pedersen ◽  
Gunner C. Larsen
Keyword(s):  
Wind Turbine ◽  
Wind Farm ◽  
Design Space ◽  
Wind Farm Layout ◽  
Control Optimization ◽  
Mean Wind Speed ◽  
The Individual ◽  
Full Design ◽  
And Control ◽  
Selection Of

Abstract. The objective of this paper is to investigate the joint optimization of wind farm layout and wind farm control in terms of power production. A successful fulfilment of this goal requires the following: (1) an accurate and fast flow model, (2) selection of the minimum set of design parameters that rules or governs the problem, and (3) selection of an optimization algorithm with good scaling properties. For control of the individual wind farm turbines with the aim of wind farm production optimization, the two most obvious strategies are wake steering based on active wind turbine yaw control and wind turbine derating. The present investigation is limited to wind turbine derating. A high-speed linearized computational fluid dynamics (CFD) Reynolds-averaged Navier–Stokes (RANS) solver models the flow field and the crucial wind turbine wake interactions inside the wind farm. The actuator disc method is used to model the wind turbines, and utilizing an aerodynamic model, the design space of the optimization problem is reduced to only three variables per turbine – two geometric and one carefully selected variable specifying the individual wind turbine derating setting for each mean wind speed and direction. The full design space is spanned by these (2N+NdNsN) parameters, where N is the number of wind farm turbines, Nd is the number of direction bins, and Ns is the number of mean wind speed bins. This design space is decomposed into two subsets, which in turn define a nested set of optimization problems to achieve a significantly faster optimization procedure compared to a direct optimization based on the full design space. Following a simplistic sanity check of the platform functionality regarding wind farm layout and control optimization, the capability of the developed optimization platform is demonstrated on a Swedish offshore wind farm. For this particular wind farm, the analysis demonstrates that the expected annual energy production can be increased by 4 % by integrating the wind farm control into the design of the wind farm layout, which is 1.2 % higher than what is achieved by optimizing the layout only.

scholarly journals Uncertainty Quantification of Wind Turbine Wakes Under Random Wind Conditions

2019 ◽  
Author(s):  
Tássia Penha Pereira ◽  
Stephen Ekwaro-Osire ◽  
João Paulo Dias ◽  
Nicholas J. Ward ◽  
Americo Cunha
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Wind Turbine ◽  
Uncertainty Quantification ◽  
Wind Farm ◽  
Uncertainty Propagation ◽  
Financial Risks ◽  
Wake Effect ◽  
Energy Field ◽  
Mean Wind Speed

Abstract Understanding and minimizing the uncertainties in the wind energy field is of high importance to reduce the reliability risks and financial risks of wind farm projects. The present work aims to observe the levels of uncertainty in modeling the wake effect by attempting to perform statistical inference of a wake parameter, the wind speed deficit. For this purpose, an uncertainty propagation framework is presented. The framework starts by randomly sampling mean wind speed data from its probability density function (PDF), that is fed an inflow model (TurbSim), resulting in random full-flow fields that are integrated into an aeroelastic model (FAST), which results in the variability of the power and thrust coefficients of a wind turbine. Such coefficients and wind data, finally, fed the wake engineering model (FLORIS). The framework ends with the determination of the 95% coefficient intervals of the time-averaged wind speed deficit. The results obtained for the near and far wake regions introduce fundamentals in estimate the uncertainty in wind speed deficit of a single wind turbine wake and concludes that a systematic uncertainty quantification (UQ) framework for wind turbine wakes may be a useful tool to wind energy projects.

scholarly journals Evaluation of the impact of active wake control techniques on ultimate loads for a 10 MW wind turbine

2022 ◽  
Vol 7 (1) ◽  
pp. 1-17
Author(s):  
Alessandro Croce ◽  
Stefano Cacciola ◽  
Luca Sartori
Keyword(s):  
Wind Turbine ◽  
Energy Production ◽  
Wind Farm ◽  
Control Techniques ◽  
Control Optimization ◽  
Cost Of Energy ◽  
The Individual ◽  
The Cost ◽  
Wind Statistics ◽  
The Impact

Abstract. Wind farm control is one of the solutions recently proposed to increase the overall energy production of a wind power plant. A generic wind farm control is typically synthesized so as to optimize the energy production of the entire wind farm by reducing the detrimental effects due to wake–turbine interactions. As a matter of fact, the performance of a farm control is typically measured by looking at the increase in the power production, properly weighted through the wind statistics. Sometimes, fatigue loads are also considered in the control optimization problem. However, an aspect which is rather overlooked in the literature on this subject is the evaluation of the impact that a farm control law has on the individual wind turbine in terms of maximum loads and dynamic response under extreme conditions. In this work, two promising wind farm controls, based on wake redirection (WR) and dynamic induction control (DIC) strategy, are evaluated at the level of a single front-row wind turbine. To do so, a two-pronged analysis is performed. Firstly, the control techniques are evaluated in terms of the related impact on some specific key performance indicators, with special emphasis on ultimate loads and maximum blade deflection. Secondarily, an optimal blade redesign process is performed with the goal of quantifying the modification in the structure of the blade entailed by a possible increase in ultimate values due to the presence of wind farm control. Such an analysis provides for an important piece of information for assessing the impact of the farm control on the cost-of-energy model.

scholarly journals A Comparison of Edge Detectors in the Framework of Wake Pattern Modeling for Wind Turbines

2020 ◽  
Vol 6 (2) ◽  
Author(s):  
Yanjun Yan ◽  
James Z. Zhang ◽  
Hayrettin Bora Karayaka
Keyword(s):  
Power Generation ◽  
Wind Speed ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Wind Farm ◽  
Linear Prediction ◽  
Edge Detector ◽  
Wake Effect ◽  
Edge Detectors ◽  
The Individual

To monitor wind turbine health, wind farm operators can take advantage of the historical SCADA (supervisory control and data acquisition) data to generate the wake pattern beforehandfor each wind turbine, and then decide in real time whether observed reduction in power generation is due to wake or true faults. In our earlier efforts, we proposed an effective wakepattern modeling approach based on edge detector using Linear Prediction (LP) with entropy-thresholding, and smoothing using Empirical Mode Decomposition (EMD) on the windspeed difference plots. In this paper, we compare the LP based edge detector with two other predominant edge detectors, Sobel and Canny edge detectors, to quantitatively justifythe appropriateness and effectiveness of the LP based edge detector in wind turbine wake pattern analysis. We generate a fused wake model for the turbine of interest with multiple neighboring turbines, and then analyze the wake effect on turbine power generation. With a fused wake pattern, we do not need to identify the individual source of wake any more. Weexpect that wakes cause reduced wind speed and hence reduced power generation, but we have also observed from the SCADA data that the wind turbines in wake zones tend to overreact when the wind speed is not yet close to the highwind- shut-down threshold, which causes further power generation loss.

scholarly journals Analysis of Wind Turbine Aging through Operation Data Calibrated by LiDAR Measurement

Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2319
Author(s):  
Hyun-Goo Kim ◽  
Jin-Young Kim
Keyword(s):  
Power Generation ◽  
Wind Speed ◽  
Wind Turbine ◽  
Wind Farm ◽  
Free Stream ◽  
Turbine Blades ◽  
Wind Farms ◽  
Lidar Measurement ◽  
Wind Turbine Blades ◽  
Wake Effect

This study analyzed the performance decline of wind turbine with age using the SCADA (Supervisory Control And Data Acquisition) data and the short-term in situ LiDAR (Light Detection and Ranging) measurements taken at the Shinan wind farm located on the coast of Bigeumdo Island in the southwestern sea of South Korea. Existing methods have generally attempted to estimate performance aging through long-term trend analysis of a normalized capacity factor in which wind speed variability is calibrated. However, this study proposes a new method using SCADA data for wind farms whose total operation period is short (less than a decade). That is, the trend of power output deficit between predicted and actual power generation was analyzed in order to estimate performance aging, wherein a theoretically predicted level of power generation was calculated by substituting a free stream wind speed projecting to a wind turbine into its power curve. To calibrate a distorted wind speed measurement in a nacelle anemometer caused by the wake effect resulting from the rotation of wind-turbine blades and the shape of the nacelle, the free stream wind speed was measured using LiDAR remote sensing as the reference data; and the nacelle transfer function, which converts nacelle wind speed into free stream wind speed, was derived. A four-year analysis of the Shinan wind farm showed that the rate of performance aging of the wind turbines was estimated to be −0.52%p/year.

Control Proxy Functions for Sequential Design and Control Optimization

2011 ◽  
Vol 133 (9) ◽  
Author(s):  
Diane L. Peters ◽  
P. Y. Papalambros ◽  
A. G. Ulsoy
Keyword(s):  
Simultaneous Optimization ◽  
Sequential Optimization ◽  
Control Optimization ◽  
Smart Products ◽  
System Solution ◽  
Optimal System Design ◽  
Problem Formulations ◽  
And Control ◽  
Sequential Strategy ◽  
Selection Of

Optimal system design of “smart” products requires optimization of both the artifact and its controller. When the artifact and the controller designs are independent, the system solution is straightforward through sequential optimization. When the designs are coupled, combined simultaneous optimization can produce system-optimal results, but presents significant computational and organizational complexity. This paper presents a method that produces results comparable with those found with a simultaneous solution strategy, but with the simplicity of the sequential strategy. The artifact objective function is augmented by a control proxy function (CPF), representing the artifact’s ease of control. The key to successful use of this method is the selection of an appropriate CPF. Four theorems that govern the choice and evaluation of a CPF are given. Each theorem is illustrated using a simple mathematical example. Specific CPFs are then presented for particular problem formulations, and the method is applied to the optimal design and control of a micro-electrical mechanical system actuator.

Study on a global control strategy for rotation speed with different work states in variable-speed constant-frequency wind turbines

2011 ◽  
Vol 225 (7) ◽  
pp. 968-976 ◽  
Author(s):  
G Zheng ◽  
H Xu ◽  
X Wang ◽  
J Zou
Keyword(s):  
Wind Turbine ◽  
Wind Turbines ◽  
Control Strategy ◽  
Control Strategies ◽  
Stable Operation ◽  
Constant Frequency ◽  
Global Control ◽  
Control Rules ◽  
The Individual ◽  
And Control

This paper studies the operation of wind turbines in terms of three phases: start-up phase, power-generation phase, and shutdown phase. Relationships between the operational phase and control rules for the speed of rotation are derived for each of these phases. Taking into account the characteristics of the control strategies in the different operational phases, a global control strategy is designed to ensure the stable operation of the wind turbine in all phases. The results of simulations are presented that indicate that the proposed algorithm can control the individual phases when considered in isolation and also when they are considered in combination. Thus, a global control strategy for a wind turbine that is based on a single algorithm is presented which could have significant implications on the control and use of wind turbines.

Multi-Criteria Selection of Wind Turbine Using TOPSIS Approach

2020 ◽  
Author(s):  
Shafiqur Rehman ◽  
Salman A. Khan ◽  
Luai M. Alhems
Keyword(s):  
Wind Turbine ◽  
Wind Farm ◽  
Selection Process ◽  
Geographical Location ◽  
Real Data ◽  
Maximum Energy ◽  
Decision Criteria ◽  
Topsis Approach ◽  
Selection Of

Abstract The recent revolution in the use of renewable energy worldwide has opened many dimensions of research and development for sustainable energy. In this context, the use of wind energy has received notable attention. One critical decision in the development of a wind farm is the selection of the most appropriate turbine compatible with the characteristics of the geographical location under consideration in order to harness maximum energy. This selection process considers multiple decision criteria which are often in conflict with each other, as improving one criterion negatively affects one or more other criteria. Therefore, it is desired to find a tradeoff solution where all selection criteria are simultaneously optimized to the best possible level. This paper proposes a TOPSIS (The Technique for Order of Preference by Similarity to Ideal Solution) based approach for multi-criteria selection of wind turbine. Three decision criteria, namely, hub height, wind speed, and net capacity factor are used in the decision process. A case study is shown on real data collected from the Aljouf region located at an altitude of 753 meters above sea level in the northern part of Saudi Arabia. Seventeen turbines with rated capacities ranging from 1.5 GW to 3 GW from various manufacturers are evaluated. Results indicate that Vestas V110 turned out to be the most appropriate turbine for the underlying site.

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