Partial Load Region Research Articles

Wind farm control and power curve optimization using induction-based wake model

This paper proposes a control strategy to achieve minimum wake-induced power losses in a wind farm. At first, the axial-induction-based wake model is developed to consider the aerodynamic wake interactions among wind turbines. To optimize the generated power of the whole wind farm, the axial induction factor of each wind turbine is calculated by the genetic algorithm. As a supervisory controller, each wind turbine’s optimal axial induction factor calculated by the genetic algorithm is implemented as a setpoint of each wind turbine’s internal controller. In the internal control loop, a comprehensive controller is designed to track the commanded axial induction factor. In the partial load region, the commanded axial induction factor was attained by tuning the generator torque. In the transient and full load regions, the blade pitch angle is tuned to keep the generator speed and torque at the rated values. The performance of the proposed control strategy is investigated through case studies, including three different wind speeds and a time-varying wind speed case in a 3 × 3 wind-farm layout. The simulation results show the satisfactory performance of the proposed approach.

Sensor fault-tolerant control for wind turbines: an iterative learning method

The combined wind speed estimator and tip speed ratio (WSE-TSR) tracking control scheme is widely used to regulate power production for large-scale modern wind turbines. Although very effective, such an advanced control scheme, based on the prior model information, is highly dependent on external measurements. For partial-load region control, the only external information involved is commonly the measured rotor or generator speed. Inaccuracy in such sole measurement results in an unintended turbine operation and might lead to sub-optimal power production and instability. This paper presents a fault-tolerant control (FTC) method, which aims to eliminate the sensor fault effects for modern wind turbine systems. To fulfil this goal, an iterative learning scheme is proposed to detect and estimate the multiplicative sensor fault, on which an adaptive FTC law is formulated such that the effects of the sensor fault are eliminated. Case studies show that the proposed iterative learning FTC method performs well in detecting, estimating, and accommodating the sensor fault under realistic turbulent wind conditions. The advanced wind turbine controller can maintain its control performance even under faulty conditions, preventing further damage to other turbine components and allowing for continuous power production.

An iterative data-driven learning algorithm for calibration of the internal model in advanced wind turbine controllers

Modern industrial wind turbine controllers for partial-load region control are becoming increasingly complex by progressively relying on modeled aerodynamic characteristics. These advanced turbine controllers generally consist of a combined wind speed estimator and tracking controller, allowing for a granular trade-off between energy capture maximization and (fatigue) load minimization. Because of the limited measurements available to the controller, the control scheme's internal model quality is of utmost importance in satisfying performance and stability requirements. Therefore, the calibration thereof is of particular interest. To date, little work has been performed on the direct calibration of the model information. This work proposes a data-driven iterative learning algorithm for calibrating the internal physical model parameters. The learning algorithm uses generally available closed-loop turbine measurements, complemented with an external measurement of the rotor effective wind speed (REWS), and is thereby largely nondisruptive. The algorithm is based on steady-state assumptions and performs iterative batch-wise updates of the internal control model toward convergence. As the algorithm corrects at the actual turbine operating point, short-term relocations of the turbine's operating point can be used to calibrate in a broader operational domain. Results show outstanding learning capabilities for an aerodynamically degraded wind turbine under realistic turbulent wind conditions. Moreover, a sensitivity study is performed to expose the algorithm's susceptibility to measurement errors, algorithm tuning, and the size of the data set.

Online data‐driven control of variable speed wind turbines using the simultaneous perturbation stochastic approximation approach

AbstractThis article presents a data‐driven control algorithm for a variable‐speed wind turbine in the partial load region. The controller is designed and optimized online to capture maximum energy from the wind. The online optimization is based on the simultaneous perturbation stochastic approximation method (SPSA) that is data‐driven, has a low computational load, and converges rapidly. In the control structure, the generator torque control is activated, and the aerodynamical models and the wind speed value are not required. A suitable cost function is defined, and a time window optimization problem is solved via the SPSA algorithm. The efficiency of the proposed method is evaluated on a 4.8 MW wind turbine benchmark.

Nonlinear Control Strategies for Enhancing the Performance of DFIG-Based WECS under a Real Wind Profile

Wind speed variations affect the performance of the wind energy conversion systems (WECSs) negatively. This paper addressed an advanced law of the backstepping controller (ABC) for enhancing the integration of doubly fed induction generator (DFIG)-based grid-connected WECS under wind range of wind speed. This enhancement was achieved through three control schemes, which were blade pitch control, rotor-side control, and grid-side control. The blade pitch control was presented to adjust the wind turbine speed when the wind speed exceeds its rated value. In addition, the rotor and grid-side converter controllers were presented for improving the direct current link voltage profile and achieving maximum power point tracking (MPPT) under speed variations, respectively. To evaluate the effectiveness of the proposed ABC control, a comparison between PI and sliding-mode control (SMC) was presented, considering the parameters of a 1.5 MW DFIG wind turbine in the Assilah zone in Morocco. Moreover, some changes in the DFIG parameters were introduced to investigate the robustness of the proposed controller under parameter uncertainties. Simulation results showed the capability of the proposed ABC controller to enhance the performance of the DFIG-WECS based on variable speed and variable pitch turbine, at both below and above-rated speed, leading to an error around 10−3 (p.u), with an ATE = 0.4194 in the partial load region; in terms of blade pitch control, an error of 2.10−4 (p.u) was obtained, and the DC-link voltage profile showed a measured performance of 5 V and remarkable THD value reduction compared to other techniques, with a measured THD value of 2.03%, 1.67%, and 1.46% respectively, in hyposynchronous, hypersynchronous, and pitch activation modes of operation. All simulations were performed using MATLAB/SIMULINK based on real wind profiles in order to make an exhaustive analysis with realistic operating conditions and parameters.

Sensitivity analysis for evaluation of the effect of sensors error on the wind turbine variables using Monte Carlo simulation

The dynamics of the wind turbine behaviour and identifying the factors that change turbine performance are very complex and challenging. Quantifying the impact of these factors on improving the wind turbine performance is invaluable. Many attempts have been made to describe the behaviour of the wind turbine variables. Sensitivity analysis and Monte Carlo simulation are methods to identify important parameters affecting model behavior. Here, with these methods, the authors investigate the effect of error in sensors on output power and measured variables of the wind turbine in partial load region and full load region. Then, using the importance factor, the effect of error in the different sensors on the output power of the wind turbine is ranked in the two operating regions, so that, in practice, the sensors that have the greatest impact on the output power are designed as redundant.

The effect of a speed exclusion zone and active tower dampers on an upwind fixed-hub two-bladed 20 MW wind turbine

Large two-bladed offshore wind turbines possess cost-saving potentials in their complete life-cycle, together with general structural blade advantages, compared to similar three-bladed machines. However, the general dynamics and tower-eigenfrequency interactions are challenging. Therefore, a speed exclusion zone for the partial load region and two straightforward pitch- and generator-driven tower dampers for the whole power production region are introduced. They enable the incorporation of a reasonable controller for a two- and a three-bladed baseline reference to facilitate a fair and objective turbine comparison. The outlined control features significantly reduced tower fatigue loads by 18% and 70% in the case of a three-bladed 20 MW and a two-bladed 20 MW turbine, respectively.

Wind Farm Yield and Lifetime Optimization by Smart Steering of Wakes

Wake steering has an impact on both the wind farm energy yield and turbine loads. We evaluate these effects based on yaw-corrected power and thrust curves and tabulated turbine load simulation results. In a first step, wind direction and wind speed dependent yaw angles were determined by maximization of the wind farm power for an example wind farm. Secondly, the optimizations were repeated for the objective of minimal flapwise blade fatigue loads. The results were then combined such that the power optimized yaw angles dominate the partial load region while the load optimized results were chosen for higher wind speeds. We find that this combination increases the annual energy production in an example wind rose while simultaneously reducing the lifetime damage equivalent loads. The analysis was repeated including wind direction uncertainty into the optimization. This significantly reduced the benefit of wake steering on AEP but caused only a mild decrease of the overall load reduction.

Full-scale field test of a\xa0model predictive control system for a\xa03\u202fMW wind turbine

Model predictive control (MPC) is a strong candidate for modern wind turbine control. While the design of model predictive wind turbine controllers in simulations has been extensively investigated in academic studies, the application of these controllers to real wind turbines reveals open research challenges. In this work, we focus on the validation of a linear time-variant MPC system for a 3 MW wind turbine in a full-scale field test. First, the study proves the MPC’s capability to control the real wind turbine in the partial load region. Compared to the turbine’s baseline PID controller, the MPC system offers similar results for the electrical power output and for the occurring mechanical loads. Second, the study validates a previously proposed, simulation-based rapid control prototyping process for a systematic MPC development. The systematic development process allows to completely design and parameterize the MPC system in a simulative environment independent of the real wind turbine. Through the rapid control prototyping process, the MPC commissioning in the wind turbine’s programmable logic controller can be realized within a few hours without any modifications required in the field. Thus, this study establishes the proof of concept for a linear time-variant MPC system for a 3 MW wind turbine in a full-scale field test and bridges the gap between the control design and field testing of MPC systems for wind turbines in the multi-megawatt range.

Wind power smoothing in partial load region with advanced fuzzy-logic based pitch-angle controller

The wind energy system (WES) has an undesirable characteristic in which its power output varies with wind speed, resulting in fluctuations in the grid frequency and voltage. Especially, in partial load region where the wind speed is below rated, there is a concern in regards to WES output power. This part of the work initially employed exponential moving average (EMA) concept to generate reference power. Later on, an interval type-2 fuzzy logic (advanced fuzzy logic) based pitch-angle controller is implemented and designed for good reference tracking and therefore, it can smoothen out the WES output power more effectively. Real time simulations are also developed to show the applicability of the proposed controller using the OPAL-RT digital simulator. Below rated wind speed pattern has considered for real time simulations and results are obtained with different control techniques. The results show that the proposed interval type-2 fuzzy logic (advanced fuzzy logic) based pitch-angle controller offers better performance in tracking reference power and hence, it offers good smoothing of output power fluctuations than conventional proportional-integral (PI) and traditional fuzzy logic (Type-1) controllers. The performance of the proposed controller is also estimated using power smoothing and energy loss functions in terms of performance indices.

Efficiency improvement of a variable speed wind turbine: Effect of controller design and generator speed range

In this paper, a theory and practice-based research is conducted using real data, in order to increase the efficiency of a Vestas 660 kW wind turbine. The data obtained from Binalood’s wind farm, shows the degraded performance of these turbines. This is mainly because of the weak performance of the classical controller in tracking to reach the maximum power in the partial load region. Another factor with a negative effect on efficiency of the turbines, is the low range of generator speed variation. In this research, two solutions are proposed to solve these problems. First, a nonlinear state feedback controller with wind speed calculator is designed which improves the turbine performance. Second, it is shown by simulation results that the turbine efficiency is improved by increasing the range of generator speed variation. Also, the model is validated using actual data and FAST model.

Dependable power extraction in wind turbines using model predictive fault tolerant control

In this paper, a fault tolerant model predictive control scheme is proposed for wind turbines in the partial-load region to meet the control objectives in the presence of disturbances, uncertainties, sensor and actuator faults. The aim of wind turbine control systems in the partial-load region is to capture maximum power by tracking the optimal generator speed. But any fault in the sensors and actuators can take away the closed-loop system from the main objectives and maybe make the system unstable in some cases. At first, an online model predictive controller (MPC) is designed as a nominal controller to track the maximum power and guarantee all constraints satisfaction without considering any fault. In the next step, an adaptive sliding mode observer (SMO) is designed to estimate the actual states and sensor faults, simultaneously. Finally, an additive control law is represented and shown that it is able to tolerate the actuator faults effectively. Using extensive simulation results it is shown that the proposed strategy is able to handle the uncertainties, sensor and actuator faults in the control system, simultaneously.

Switching Performance Improvement Based on Model-Predictive Control for Wind Turbine Covering the Whole Wind Speed Range

A wind energy conversion system is mainly operated in a partial-load region and a full-load region. In the partial-load region, the primary objective is to maximize energy conversion efficiency, whereas the main goal is maintaining the output power at the rated level in the full-load region. The two control regions are switched in accordance with prevailing wind speed. However, steep and large changes of generator power, as well as mechanical loads, may be experienced during switching transient, which bring about negative impacts on power system reliability and turbine lifetime. This paper presented an advanced model-predictive control considering switching performance improvement. The proposed method not only achieved the control objectives in both two control regions, but also mitigated uncertain power fluctuations and mechanical loads during control mode switching. This high switching performance benefited from the presented dynamic switching transient process driven by the defined wind speed crossing events. During switching transient, system operating states were transferred along with the proposed dynamic vector. Small-signal analysis was employed to prove the good stability and dynamic performance of the system in switching transients. Simulation and hardware-in-the-loop experimental studies verified the effectiveness of the proposed method.

Integration of multiple passive load mitigation technologies by automated design optimization—The case study of a medium‐size onshore wind turbine

AbstractThe present work considers the application to a medium‐size onshore wind turbine of passive load mitigation technologies, first individually and then integrated together. The study is conducted with the help of a comprehensive automated design optimization procedure, which eases the generation and comparison of consistent solutions, each satisfying the same overall requirements. Passive load mitigation is here obtained by inducing bend‐twist coupling to the blades. The coupling is generated by rotating the fibers of anisotropic laminates, by the aerodynamic sweeping of the blade and by offsetting the spar caps in opposite directions on the pressure and suction sides. The first two solutions yield significant benefits, while the third, for this particular wind turbine, is ineffective. In addition, the typical power losses associated with bend‐twist coupled blades are reduced by a novel regulation strategy that varies the fine pitch setting in the partial load region. After having considered each load mitigation technology by itself, fiber rotation and sweeping are combined together and used to design a rotor with a larger swept area. The final design generates cost of energy savings thanks to a large‐diameter, highly coned, soft‐in‐bending rotor that results in lower turbine costs and a higher energy capture compared with the baseline design.

Ultimate and fatigue load mitigation by an inertial-driven passive flap, using a geometrically exact multibody formulation

The paper characterizes the performance of a passive flap concept when applied to a modern very large conceptual wind turbine. The passive flap responds automatically to blade and/or tower vibrations, inducing a change of camber that opposes dynamic loads on the wind turbine. This is obtained in a purely passive manner, without the need for actuators or sensors.The present study is based on a detailed, geometrically exact multibody formulation of the device, which is able to capture all kinematic and structural dynamic effects of this inertia-driven device. The present modeling of the passive device improves on previous studies conducted with simplified models.Results show a significant ability in the reduction of both fatigue and ultimate loads, including the case of flap-specific fault scenarios. Solutions for limiting losses in energy yield caused by non-null average flap rotations in the partial load region are also investigated. The present analysis motivates further studies aimed at reaping the benefits of load alleviation enabled by the passive flap, for example by designing a new enlarged rotor at similar key loads on the rest of the machine.

Output Power Smoothing and Voltage Regulation of a Fixed Speed Wind Generator in the Partial Load Region Using STATCOM and a Pitch Angle Controller

The output power and terminal voltage of the fixed speed induction generator fluctuate in the partial load region where the wind speed is below the rated vale, resulting in fluctuations in the grid frequency and voltage. In this paper, a novel pitch angle control strategy has developed by introducing an exponential moving average (EMA) concept from which the controller reference power (signal) can be set for below-rated wind speeds. Therefore, the employed pitch angle controller together with static synchronous compensator (STATCOM), named the unified voltage and pitch angle controller (UVPC), addresses the objective of smoothing the output power and terminal voltage regulation of a wind generator, subjected to below-rated wind speed variations. Moreover, an interval type-2 fuzzy logic technique has incorporated in the pitch angle controller design, since it is more efficient in handling the uncertainties in membership functions and rules than its traditional fuzzy logic counterparts. Simulation results clearly show that the proposed UVPC effectively smoothens out the generator output power and also regulates the terminal voltage at its constant magnitude.

Cascading Control Based on Intelligent Algorithms for a Wind Turbine Equipped with a Doubly-Fed Induction Generator

This paper presents an intelligent cascaded nonlinear control of a Doubly-Fed Induction Generator, based on variable speed wind turbine. The whole system is presented in d-q synchronous reference frame. The main objectives of the controller defined in the partial load region, are optimizing wind energy captured, improving the quality of the power generated and minimizing mechanical stress in the drive train. The energy conversion is based on the proposed dual loop control structure using two introduced algorithms: the extreme learning machine, which is used to improve the system knowledge and the adaptive particle swarm optimization used to search the optimal gains of the conventional proportional integral controller, widely used in control of electrical part. The global controller is first tested for a velocity profile of the high wind turbulence. Secondly, it is compared to the conventional PI for showing its performances in terms of power maximization, sensitivity to perturbations and robustness against changes in parameters of the machine. The proposed control strategy is approved by simulation using software Matlab/Simulink.

Fault Tolerant and Optimal Control of Wind Turbines with Distributed High-Speed Generators

In this paper, the control scheme of a distributed high-speed generator system with a total amount of 12 generators and nominal generator speed of 7000 min − 1 is studied. Specifically, a fault tolerant control (FTC) scheme is proposed to keep the turbine in operation in the presence of up to four simultaneous generator faults. The proposed controller structure consists of two layers: The upper layer is the baseline controller, which is separated into a partial load region with the generator torque as an actuating signal and the full-load operation region with the collective pitch angle as the other actuating signal. In addition, the lower layer is responsible for the fault diagnosis and FTC characteristics of the distributed generator drive train. The fault reconstruction and fault tolerant control strategy are tested in simulations with several actuator faults of different types.

Maximizing Wind Energy Capture for Speed-Constrained Wind Turbines During Partial Load Operation

With the development of wind turbine technology, more wind turbines operate in the partial load region, where one of the main objectives is to maximize captured wind energy. This paper presents the development of an optimal control framework to maximize wind energy capture for wind turbines with limited rotor speed ranges. Numerical optimal control (NOC) techniques were applied to search for the achievable maximum power coefficient, thus maximum wind energy capture. Augmentations of these optimal techniques significantly reduced the computational cost. Simulation results show that, in comparison with the traditional torque feedback and conventional optimal control algorithms, the proposed augmented optimal control algorithm increases the harvested energy while minimizing the computational expense for speed-constrained wind turbines during partial load operation.

Second-Order Cone Programming-Based Optimal Control Strategy for Wind Energy Conversion Systems Over Complete Operating Regions

An efficient control strategy of a wind energy conversion system (WECS) plays a crucial role in wind power utilization. In this paper, a novel multivariable control strategy for a variable-speed variable-pitch WECS is proposed. It is designed for the complete operating regions of a wind turbine, including both of the partial load region and the full load region, with the objectives of maximizing energy capture, smoothing power output, alleviating drive train transient loads, and reducing pitch actuator activities. In particular, this paper considers the wind speed disturbance as norm-bounded, instead of deterministic or chance constraints in the widely used quadratic control methods such as linear quadratic Gaussian control and model predictive control. Moreover, the WECS control with the norm-bounded disturbance is formulated as a second-order cone programming (SOCP) problem that has not been previously employed for wind power control. Finally, simulation results are provided to demonstrate the validity of the proposed method.