Design and Control Framework for Selecting Wind Turbine Gear Ratios Based on Optimal Power Generation and Blade Stress

2016 ◽  
Author(s):  
Amrita Lall ◽  
Hamid Khakpour Nejadkhaki ◽  
John Hall
Keyword(s):  
Wind Turbine ◽  
Energy Production ◽  
Control Algorithm ◽  
Power Production ◽  
Wind Data ◽  
Data Set ◽  
Blade Root ◽  
Partial Load ◽  
And Control ◽  
Root Stress

A variable ratio gearbox (VRG) can enable small wind turbines to operate at discrete variable rotor speeds. This reliable, low-cost, alternative does not require power conversion equipment, as is the case with conventional variable speed. Previous work conducted by the author has demonstrated that a VRG can increase the power production for a fixed-speed system with passive blades. The current study characterizes the performance of a wind turbine equipped with a VRG and active blades. The contribution of this work is an integrative framework that optimizes power production with blade root stress. It works by defining a set of control rules that specify the VRG gear ratio and pitch angle that will be used in relation to wind speed. Three ratios are selected through the proposed procedure. A case study based on the simulation of a 300-kW wind turbine model is performed to demonstrate the proposed technique. The model is constructed with aerodynamic, mechanical, and electrical submodels. These drivetrain components work together to simulate the conversion of moving air to electrical power. The blade element momentum (BEM) technique is used here to compute the blade loading. The resulting torque and rotor speed are reduced and increased, respectively, through the mechanical system gearbox. The output from this is then applied to the electrical generator. The BEM technique is also used here to determine the bending and thrust and loads that are applied to the blade. The stress in the root of the blade is then determined based on these loads, and that caused by centrifugal force and gravity. The proposed method devises a VRG design and control algorithm based on the unique wind conditions at a given installation site. Two case studies are conducted using wind data sets provided by the National Renewable Energy Laboratory (NREL). Low and high-speed data set are selected as inputs to demonstrate the versatility of the proposed method. Dynamic programming is used to reduce the computational expense. This enables the simulation of an exhaustive set of potential VRG combinations over each set of recorded wind data. Each possible combination is evaluated in terms of the total energy production and blade-root stress produced over the simulation period. A set of weights is applied to a multi-objective function that computes the cost associated with each combination. A Pareto analysis is then used to identify the VRG combination and establish the control algorithm for both systems. The results suggest that the VRG can improve energy production in the partial-load region by roughly 10% in both cases. Although stress increases in Region 2, it decreases in Region 3, and overall, through the optimal selection of gear combinations.

An Integrative Framework for Design and Control Optimization of a Variable-Ratio Gearbox in a Wind Turbine With Active Blades

2016 ◽  
Author(s):  
Amrita Lall ◽  
Hamid Khakpour Nejadkhaki ◽  
John Hall
Keyword(s):  
Cost Function ◽  
Wind Turbine ◽  
Low Cost ◽  
Electrical Power ◽  
Induction Machine ◽  
Power Production ◽  
Wind Data ◽  
Variable Ratio ◽  
Machine Model ◽  
Blade Root

A variable ratio gearbox (VRG) provides discrete variable rotor speed operation, and thus increases wind capture, for small fixed-speed wind turbines. It is a low-cost, reliable alternative to conventional variable speed operation, which requires special power-conditioning equipment. The authors’ previous work has demonstrated the benefit of using a VRG in a fixed-speed system with passive blades. This work characterizes the performance of the VRG when used with active blades. The main contribution of the study is an integrative design framework that maximizes power production while mitigating stress in the blade root. As part of the procedure, three gear ratios are selected for the VRG. It establishes the control rules by defining the gear ratio and pitch angle used in relation to wind speed and mechanical torque. A 300 kW wind turbine model is used for a case study that demonstrates how the framework is implemented. The model consists of aerodynamic, mechanical, and electrical submodels, which work collaboratively to convert kinetic air to electrical power. Blade element momentum theory is used in the aerodynamic model to compute the blade loads. The resulting torque is passed through a mechanical system and subsequently to the induction machine model to generate power. The BEM method also provides the thrust and bending loads that contribute to blade-root stress. The stress in the root of the blade is also computed in response to these loads, as well as those caused by gravity and centrifugal force. Two case studies are performed using wind data that was obtained from the National Renewable Energy Laboratory (NREL). Each of these represents an installation site with a unique set of wind conditions that are used to customize the wind turbine design. The framework uses dynamic programming to simulate the performance of an exhaustive set of combinations. Each combination is evaluated over each set of recorded wind data. The combinations are evaluated in terms of the total energy and stress that is produced over the simulation period. Weights are applied to a multi-objective cost function that identifies the optimal design configurations with respect to the design objectives. As a final design step, a VRG combination is selected, and a control algorithm is established for each set of wind data. During operation, the cost function can also be used to bias the system towards higher power production or lower stress. The results suggest a VRG can improve wind energy production in Region 2 by roughly 10% in both the low and high wind regions. In both cases, stress is also increased in Region 2, as the power increases. However, the stress in Region 3 may be reduced for some wind speeds through the optimal selection of gear combinations.

A Methodology to Synthesize Gearbox and Control Design for Increased Power Production and Blade Root Stress Mitigation in a Small Wind Turbine1

2017 ◽  
Vol 139 (8) ◽  
Author(s):  
Hamid Khakpour Nejadkhaki ◽  
Amrita Lall ◽  
John F. Hall
Keyword(s):  
Wind Speed ◽  
Low Cost ◽  
Control Design ◽  
Power Production ◽  
Wind Speeds ◽  
Blade Root ◽  
Squirrel Cage Induction Generator ◽  
And Control ◽  
Root Stress ◽  
Selection Of

Large wind turbines typically have variable rotor speed capability that increases power production. However, the cost of this technology is more significant for small turbines, which have the highest cost-per-watt of energy produced. This work presents a low-cost system for applications where cost and reliability are of concern. The configuration utilizes the fixed-speed squirrel cage induction generator. It is combined with a variable ratio gearbox (VRG) that is based on the automated-manual automotive transmission. The design is simple, low cost and implements reliable components. The VRG increases efficiency in lower wind speeds through three discrete rotor speeds. In this study, it is implemented with active blades. The contribution of this work is a methodology that synthesizes the selection of the gearbox ratios with the control design. The design objectives increase the power production while mitigating the blade stress. Top-down dynamic programming reduces the computational expense of evaluating the performance of multiple gearbox combinations. The procedure is customizable to the wind conditions at an installation site. A case study is presented to demonstrate the ability of the strategy. It employs a 300 kW wind turbine drivetrain model that simulates power production. Two sets of wind data representing low and high wind speed installation sites were used as the input. The results suggest a VRG can improve energy production by up to 10% when the system operates below the rated wind speed. This is also accompanied by a slight increase in the blade-root stress. When operating above the rated speed, the stress decreases through the optimal selection of gear combinations.

Integrative Modeling Platform for Design and Control of an Adaptive Wind Turbine Blade

2018 ◽  
Author(s):  
Hamid Khakpour Nejadkhaki ◽  
John F. Hall ◽  
Minghui Zheng ◽  
Teng Wu
Keyword(s):  
Simulation Model ◽  
Wind Turbine ◽  
Real Time ◽  
Turbine Blade ◽  
Design Tool ◽  
Real Time Control ◽  
Time Control ◽  
Partial Load ◽  
And Control

A platform for the engineering design, performance, and control of an adaptive wind turbine blade is presented. This environment includes a simulation model, integrative design tool, and control framework. The authors are currently developing a novel blade with an adaptive twist angle distribution (TAD). The TAD influences the aerodynamic loads and thus, system dynamics. The modeling platform facilitates the use of an integrative design tool that establishes the TAD in relation to wind speed. The outcome of this design enables the transformation of the TAD during operation. Still, a robust control method is required to realize the benefits of the adaptive TAD. Moreover, simulation of the TAD is computationally expensive. It also requires a unique approach for both partial and full-load operation. A framework is currently being developed to relate the TAD to the wind turbine and its components. Understanding the relationship between the TAD and the dynamic system is crucial in the establishment of real-time control. This capability is necessary to improve wind capture and reduce system loads. In the current state of development, the platform is capable of maximizing wind capture during partial-load operation. However, the control tasks related to Region 3 and load mitigation are more complex. Our framework will require high-fidelity modeling and reduced-order models that support real-time control. The paper outlines the components of this framework that is being developed. The proposed platform will facilitate expansion and the use of these required modeling techniques. A case study of a 20 kW system is presented based upon the partial-load operation. The study demonstrates how the platform is used to design and control the blade. A low-dimensional aerodynamic model characterizes the blade performance. This interacts with the simulation model to predict the power production. The design tool establishes actuator locations and stiffness properties required for the blade shape to achieve a range of TAD configurations. A supervisory control model is implemented and used to demonstrate how the simulation model blade performs in the case study.

scholarly journals The Power Curve Working Group's Assessment of Wind Turbine Power Performance Prediction Methods

2019 ◽  
Author(s):  
Joseph C. Y. Lee ◽  
Peter Stuart ◽  
Andrew Clifton ◽  
M. Jason Fields ◽  
Jordan Perr-Sauer ◽  
...  
Keyword(s):  
Wind Turbine ◽  
Statistical Tests ◽  
Power Production ◽  
Meteorological Conditions ◽  
Power Curve ◽  
Power Performance ◽  
Power Prediction ◽  
Baseline Method ◽  
Data Set ◽  
Wide Range

Abstract. Wind turbine power production deviates from the reference power curve in real-world atmospheric conditions. Correctly predicting turbine power performance requires models to be validated for a wide range of wind turbines using inflow in different locations. The Share-3 exercise is the most recent intelligence-sharing exercise of the Power Curve Working Group, which aims to advance the modeling of turbine performance. The goal of the exercise is to search for modeling methods that reduce error and uncertainty in power prediction when wind shear and turbulence digress from design conditions. Herein, we analyze the data of 55 wind turbine power performance tests from 9 contributing organizations with statistical tests to quantify the skills of the prediction-correction methods. We assess the accuracy and precision of four proposed trial methods against the Baseline method, which uses the conventional definition of power curve with wind speed and air density at hub height. The trial methods reduce power-production prediction errors compared to the Baseline method at high wind speeds, which contribute heavily to power production; however, the trial methods fail to significantly reduce prediction uncertainty in most meteorological conditions. For the meteorological conditions when a wind turbine produces less than the power its reference power curve suggests, using power deviation matrices leads to more accurate power prediction. We also identify that for more than half of the submissions, the data set has a large influence on the effectiveness of a trial method. Overall, this work affirms the value of data-sharing efforts in advancing power-curve modeling and establishes the groundwork for future collaborations.

scholarly journals A method for preliminary rotor design – Part 2: Wind turbine Optimization with Radial Independence

2021 ◽  
Vol 6 (3) ◽  
pp. 917-933 ◽  
Author(s):  
Kenneth Loenbaek ◽  
Christian Bak ◽  
Michael McWilliam
Keyword(s):  
Cost Function ◽  
Wind Turbine ◽  
Energy Production ◽  
Optimization Problems ◽  
Turbine Rotor ◽  
Global Optimum ◽  
Blade Root ◽  
Rotor Design ◽  
Global Power ◽  
Optimization Methodology

Abstract. A novel wind turbine rotor optimization methodology is presented. Using an assumption of radial independence it is possible to obtain an optimal relationship between the global power (CP) and load coefficient (CT, CFM) through the use of Karush–Kuhn–Tucker (KKT) multipliers, leaving an optimization problem that can be solved at each radial station independently. It allows solving load constraint power and annual energy production (AEP) optimization problems where the optimization variables are only the KKT multipliers (scalars), one for each of the constraints. For the paper, two constraints, namely the thrust and blade root flap moment, are used, leading to two optimization variables. Applying the optimization methodology to maximize power (P) or annual energy production (AEP) for a given thrust and blade root flap moment, but without a cost function, leads to the same overall result with the global optimum being unbounded in terms of rotor radius (R̃) with a global optimum being at R̃→∞. The increase in power and AEP is in this case ΔP=50 % and ΔAEP=70 %, with a baseline being the Betz optimum rotor. With a simple cost function and with the same setup of the problem, a power-per-cost (PpC) optimization resulted in a power-per-cost increase of ΔPpC=4.2 % with a radius increase of ΔR=7.9 % as well as a power increase of ΔP=9.1 %. This was obtained while keeping the same flap moment and reaching a lower thrust of ΔT=-3.8 %. The equivalent for AEP-per-cost (AEPpC) optimization leads to increased cost efficiency of ΔAEPpC=2.9 % with a radius increase of ΔR=17 % and an AEP increase of ΔAEP=13 %, again with the same, maximum flap moment, while the maximum thrust is −9.0 % lower than the baseline.

Power Optimization of Model-Scale Floating Wind Turbines Using Real-Time Hybrid Testing With Autonomous Actuation and Control

2017 ◽  
Author(s):  
Samuel Kanner ◽  
Elena Koukina ◽  
Ronald W. Yeung
Keyword(s):  
Wind Turbine ◽  
Real Time ◽  
Wind Turbines ◽  
Control Algorithm ◽  
Aerodynamic Force ◽  
Tangential Force ◽  
Turbine Blades ◽  
Power Production ◽  
Hybrid Testing ◽  
Model Scale

In this research, real-time hybrid testing of floating wind turbines is carried out at model-scale. The semi-submersible, triangular platform is built to support two vertical-axis wind turbines (VAWTs). On account of incongruous scaling issues between the aerodynamics and the hydrodynamics, the wind turbines are not constructed at the same scale. Instead, remote-controlled (RC) plane motors and propellers are used as actuators to mimic only the tangential forces on the wind turbine blades, which are attached to the physical model. On a VAWT, the tangential force is proportional to the torque on the turbine, which contributes to the power production. A control algorithm is implemented using the wind turbine generators to optimize the platform heading and hence, the theoretical power absorbed by the wind turbines. The computer fluid dynamics simulations of two-dimensional counter-rotating turbines is briefly discussed. The results from the simulations are discussed in the context of existing onshore experimental data. This experimental approach only seeks to recreate the aerodynamic force which contributes to the power production. In doing so, the generator control algorithm can be validated. The advantages and drawbacks of this technique are discussed, including the need for low inertia actuators, which can quickly respond to control signals.

Power Optimization of Model-Scale Floating Wind Turbines Using Real-Time Hybrid Testing With Autonomous Actuation and Control1

2019 ◽  
Vol 141 (3) ◽  
Author(s):  
Samuel Kanner ◽  
Elena Koukina ◽  
Ronald W. Yeung
Keyword(s):  
Wind Turbine ◽  
Real Time ◽  
Wind Turbines ◽  
Control Algorithm ◽  
Aerodynamic Force ◽  
Turbine Blades ◽  
Power Production ◽  
Hybrid Testing ◽  
Model Scale ◽  
Tangential Forces

Real-time hybrid testing of floating wind turbines is conducted at model scale. The semisubmersible, triangular platform, similar to the WindFloat platform, is built instead to support two, counter-rotating vertical-axis wind turbines (VAWTs). On account of incongruous scaling issues between the aerodynamic and the hydrodynamic loading, the wind turbines are not constructed at the same scale as the floater support. Instead, remote-controlled plane motors and propellers are used as actuators to mimic only the tangential forces on the wind-turbine blades, which are attached to the physical (floater-support) model. The application of tangential forces on the VAWTs is used to mimic the power production stage of the turbine. A control algorithm is implemented using the wind-turbine generators to optimize the platform heading and hence, the theoretical power absorbed by the wind turbines. This experimental approach only seeks to recreate the aerodynamic force, which contributes to the power production. In doing so, the generator control algorithm can thus be validated. The advantages and drawbacks of this hybrid simulation technique are discussed, including the need for low inertia actuators, which can quickly respond to control signals.

scholarly journals Predicting the Extreme Loads in Power Production of Large Wind Turbines Using an Improved PSO Algorithm

2019 ◽  
Vol 9 (3) ◽  
pp. 521 ◽  
Author(s):  
Caicai Liao ◽  
Kezhong Shi ◽  
XiaoLu Zhao
Keyword(s):  
Control System ◽  
Wind Turbine ◽  
Pitch Angle ◽  
Bending Moment ◽  
Pso Algorithm ◽  
Power Production ◽  
Bending Moments ◽  
Rotor Speed ◽  
Blade Root ◽  
Extreme Loads

Predicting the extreme loads in power production for the preliminary-design of large-scale wind turbine blade is both important and time consuming. In this paper, a simplified method, called Particle Swarm Optimization-Extreme Load Prediction Model (PSO-ELPM), is developed to quickly assess the extreme loads. This method considers the extreme loads solution as an optimal problem. The rotor speed, wind speed, pitch angle, yaw angle, and azimuth angle are selected as design variables. The constraint conditions are obtained by considering the influence of the aeroelastic property and control system of the wind turbine. An improved PSO algorithm is applied. A 1.5 MW and a 2.0 MW wind turbine are chosen to validate the method. The results show that the extreme root load errors between PSO-ELPM and FOCUS are less than 10%, while PSO-ELPM needs much less computational cost than FOCUS. The distribution of flapwise bending moments are close to the results of FOCUS. By analyzing the loads, we find that the extreme flapwise bending moment of the blade root in chord coordinate (CMF_ROOT) is largely reduced because of the control system, with the extreme edgewise bending moment of the blade root in chord coordinate (CME_ROOT) almost unchanged. Furthermore, higher rotor speed and smaller pitch angle will generate larger extreme bending moments at the blade root.

scholarly journals The Power Curve Working Group's assessment of wind turbine power performance prediction methods

2020 ◽  
Vol 5 (1) ◽  
pp. 199-223 ◽  
Author(s):  
Joseph C. Y. Lee ◽  
Peter Stuart ◽  
Andrew Clifton ◽  
M. Jason Fields ◽  
Jordan Perr-Sauer ◽  
...  
Keyword(s):  
Wind Turbine ◽  
Statistical Tests ◽  
Power Production ◽  
Meteorological Conditions ◽  
Power Curve ◽  
Power Performance ◽  
Power Prediction ◽  
Baseline Method ◽  
Data Set ◽  
Wide Range

Abstract. Wind turbine power production deviates from the reference power curve in real-world atmospheric conditions. Correctly predicting turbine power performance requires models to be validated for a wide range of wind turbines using inflow in different locations. The Share-3 exercise is the most recent intelligence-sharing exercise of the Power Curve Working Group, which aims to advance the modeling of turbine performance. The goal of the exercise is to search for modeling methods that reduce error and uncertainty in power prediction when wind shear and turbulence digress from design conditions. Herein, we analyze data from 55 wind turbine power performance tests from nine contributing organizations with statistical tests to quantify the skills of the prediction-correction methods. We assess the accuracy and precision of four proposed trial methods against the baseline method, which uses the conventional definition of a power curve with wind speed and air density at hub height. The trial methods reduce power-production prediction errors compared to the baseline method at high wind speeds, which contribute heavily to power production; however, the trial methods fail to significantly reduce prediction uncertainty in most meteorological conditions. For the meteorological conditions when a wind turbine produces less than the power its reference power curve suggests, using power deviation matrices leads to more accurate power prediction. We also determine that for more than half of the submissions, the data set has a large influence on the effectiveness of a trial method. Overall, this work affirms the value of data-sharing efforts in advancing power curve modeling and establishes the groundwork for future collaborations.

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