scholarly journals Design of a Robust Adaptive Controller for the Pitch and Torque Control of Wind Turbines

Energies ◽  
2020 ◽  
Vol 13 (5) ◽  
pp. 1195 ◽  
Author(s):  
Srikanth Bashetty ◽  
Joaquin I. Guillamon ◽  
Shanmukha S. Mutnuri ◽  
Selahattin Ozcelik

In this paper, robust adaptive control is designed for pitch and torque control of the wind turbines operating under turbulent wind conditions. The dynamics of the wind turbine are formulated by considering the five degrees of freedom system (rotor angle, gearbox angle, generator angle, flap-wise deflection of the rotor blade, and axial displacement of the nacelle). The controller is designed to maintain the rotor speed, maximize the aerodynamic efficiency of the wind turbine, and reduce the loads due to high wind speeds. Gaussian probability distribution function is used for approximating the wind speed, which is given as the disturbance input to the plant. The adaptive control algorithm is implemented to 2 MW and 5 MW wind turbines to test the robustness of the controller for varying parameters. The simulation is carried out using MATLAB/Simulink for three cases, namely pitch control, torque control, and the combined case. A case study is done to validate the proposed adaptive control using real wind speed data. In all the cases, the results indicate that the rotor speed follows the reference speed and show that the designed controller gives a satisfactory performance under varying operating conditions and parameter variations.

2020 ◽  
Vol 190 ◽  
pp. 00019
Author(s):  
Katherin Indriawati ◽  
Choirul Mufit ◽  
Andi Rahmadiansah

The variation of wind speed causes the electric power generated by the turbine also varies. To obtain maximum power, the rotor speed of wind turbines must be optimally rated. The rotor speed can be controlled by manipulating the torque from the generator; this method is called Torque Control. In that case, a DC-DC converter is needed as the control actuator. In this study, a buck converter-based supervisory control design was performed on the Horizontal-axis wind turbines (HAWT). Supervisory control is composed of two control loops arranged in cascade, and there is a formula algorithm as the supervisory level. The primary loop uses proportional control mode with a proportional gain of 0.3, whereas in the secondary loop using proportional-integral control mode with a proportional gain of 5.2 and an integral gain of 0.1. The Supervisory control has been implemented successfully and resulted in an average increase in turbine power of 4.1 % at 5 m s–1 and 10.58 % at 6 m s–1 and 11.65 % at 7 m s–1, compared to wind turbine systems without speed control.


2021 ◽  
Author(s):  
Daniel Escobar-Naranjo ◽  
Biswaranjan Mohanty ◽  
Kim A. Stelson

Abstract Adaptive control strategies are commonly used for systems that change over time, such as wind turbines. Extremum Seeking Control (ESC) is a model-free real-time adaptive control strategy commonly used in conventional gearbox wind turbines for Maximum Power Point Tracking (MPPT). ESC optimizes the rotor power by constantly tuning the torque control gain (k) when operating below rated power. The same concept can be applied for hydrostatic wind turbines. This paper studies the use of ESC for a 60-kW hydrostatic wind turbine. First, a systematic approach to establish the ideal ESC is shown. Second, a comparison of the power capture performance of ESC versus the conventional torque control law (the kω2 law) is shown. The simulations include a timesharing power capture coefficient (Cp) to clearly show the advantages of using ESC. Studies under steady and realistic wind conditions show the main advantages of using ESC for a hydrostatic wind turbine.


Author(s):  
Sina Ameli ◽  
Olugbenga Anubi

Abstract This paper solves the problem of regulating the rotor speed tracking error for wind turbines in the full-load region by an effective robust-adaptive control strategy. The developed controller compensates for the uncertainty in the control input effectiveness caused by a pitch actuator fault, unmeasurable wind disturbance, and nonlinearity in the model. Wind turbines have multi-layer structures such that the high-level structure is nonlinearly coupled through an aggregation of the low-level control authorities. Hence, the control design is divided into two stages. First, an ℒ2 controller is designed to attenuate the influence of wind disturbance fluctuations on the rotor speed. Then, in the low-level layer, a controller is designed using a proposed adaptation mechanism to compensate for actuator faults. The theoretical results show that the closed-loop equilibrium point of the regulated rotor speed tracking error dynamics in the high level is finite-gain ℒ2 stable, and the closed-loop error dynamics in the low level is globally asymptotically stable. Simulation results show that the developed controller significantly reduces the root-mean- square of the rotor speed error compared to some well-known works, despite the largely fluctuating wind disturbance, and the time-varying uncertainty in the control input effectiveness.


Energies ◽  
2021 ◽  
Vol 14 (20) ◽  
pp. 6807
Author(s):  
Henok Ayele Behabtu ◽  
Thierry Coosemans ◽  
Maitane Berecibar ◽  
Kinde Anlay Fante ◽  
Abraham Alem Kebede ◽  
...  

The risk of oscillation of grid-connected wind turbine generators (WTGs) is well known, making it all the more important to understand the characteristics of different WTGs and analyze their performance so that the problems’ causes are identified and resolved. While many studies have evaluated the performance of grid-connected WTGs, most lack clarity and precision in the modeling and simulation techniques used. Moreover, most of the literature focuses on a single mode of operation of WTGs to analyze their performances. Therefore, this paper updates the literature by considering the different operating conditions for WTGs. Using MATLAB/SIMULINK it expands the evaluation to the full range of vulnerabilities of WTGs: from the wind turbine to grid connection. A network representing grid-connected squirrel-cage induction generator (SCIG) and doubly-fed induction generator (DFIG) wind turbines are selected for simulation. The performances of SCIG and DFIG wind turbines are evaluated in terms of their energy generation capacity during constant rated wind speed, variable wind speed, and ability of fault-ride through during dynamic system transient operating conditions. The simulation results show the performance of DFIG is better than SCIG in terms of its energy generation capacity during variable wind speed conditions and active and reactive power control capability during steady-state and transient operating conditions. As a result, DFIG wind turbine is more suitable for large-scale wind power plants connected to weak utility grid applications than SCIG.


2020 ◽  
pp. 0309524X2096941
Author(s):  
Khaled Taha Abd-Elwahab ◽  
Ali Ahmed Hassan

The different operating conditions of wind turbines pose great challenges for efficient and reliable fault detection. Therefore, a good analysis of wind turbine data is essential in assessing the state of the wind turbines, since the traditional threshold cannot provide a timely warning as it indicates that the malfunction has already occurred. This paper presents a new method for analyzing the actual data of the turbines, using aggregated model consisting of the neighborhood comparison method, K-means clustering and decision tree model to diagnose faults. The wind speed of the adjacent turbines is compared with each other, then other parameters of the same wind speed are also compared with each other. The purpose of comparison is that, the wind turbines which are similar in wind speed are similar in performance as well. This approach helps us to discover the abnormal data for turbine performance with in the normal operating range. The abnormal performance of any turbine destroys the similarity relationship between its data and the neighboring unit’s data. The main advantage of this approach is the possibility to detect the beginning of abnormal performance in real time, a case study using real SCADA data is used to validate this approach, which demonstrates its effectiveness and advantages.


2020 ◽  
pp. 0309524X1990100
Author(s):  
Cherif Khelifi ◽  
Fateh Ferroudji

The output wind power curve versus wind speed is the most important characterization parameter of wind turbines. It allows quantifying and analyzing the design performances of wind turbines, monitoring its database, and controlling the operation modes and manufacturing products. Wind power curve can be used to select the proper rotor size to estimate the potential of wind energy at candidate wind sites and to assess the control device of the operating conditions. Developing model strategies for wind farms has the basic objectives such as the optimization of wind power produced and the minimization of dynamic loads to provide the best quality of output wind power at reasonable cost. Optimal design of wind turbines requires maximum-closing to the cubical output wind power curve despite technical and economic considerations. This study aims to determine the design wind speed of a wind turbine based on modeling-optimization of the output wind power curve under certain working conditions. The procedure is applied to a unit wind turbine in Gamesa wind farm (G52/850, 10.2 MW, http://www.thewindpower.net ) connected to an electrical grid located in south-west Algeria and extrapolated for other windy sites in Algeria. From simulation results, the design wind speed to inlet wind speed ratio [Formula: see text] increased from 0.35 to 7.68 once [Formula: see text] increased from 0.001 to 2.9999. Consequently, the output wind power predicted an increase of about 17.7% and an annual specific wind energy factor of about 2.55%–4% than nominal value given by the manufacturer, reducing the unit average cost of the electricity, generated by wind farms, by about 18.75%.


Author(s):  
S. G. Ignatiev ◽  
S. V. Kiseleva

Optimization of the autonomous wind-diesel plants composition and of their power for guaranteed energy supply, despite the long history of research, the diversity of approaches and methods, is an urgent problem. In this paper, a detailed analysis of the wind energy characteristics is proposed to shape an autonomous power system for a guaranteed power supply with predominance wind energy. The analysis was carried out on the basis of wind speed measurements in the south of the European part of Russia during 8 months at different heights with a discreteness of 10 minutes. As a result, we have obtained a sequence of average daily wind speeds and the sequences constructed by arbitrary variations in the distribution of average daily wind speeds in this interval. These sequences have been used to calculate energy balances in systems (wind turbines + diesel generator + consumer with constant and limited daily energy demand) and (wind turbines + diesel generator + consumer with constant and limited daily energy demand + energy storage). In order to maximize the use of wind energy, the wind turbine integrally for the period in question is assumed to produce the required amount of energy. For the generality of consideration, we have introduced the relative values of the required energy, relative energy produced by the wind turbine and the diesel generator and relative storage capacity by normalizing them to the swept area of the wind wheel. The paper shows the effect of the average wind speed over the period on the energy characteristics of the system (wind turbine + diesel generator + consumer). It was found that the wind turbine energy produced, wind turbine energy used by the consumer, fuel consumption, and fuel economy depend (close to cubic dependence) upon the specified average wind speed. It was found that, for the same system with a limited amount of required energy and high average wind speed over the period, the wind turbines with lower generator power and smaller wind wheel radius use wind energy more efficiently than the wind turbines with higher generator power and larger wind wheel radius at less average wind speed. For the system (wind turbine + diesel generator + energy storage + consumer) with increasing average speed for a given amount of energy required, which in general is covered by the energy production of wind turbines for the period, the maximum size capacity of the storage device decreases. With decreasing the energy storage capacity, the influence of the random nature of the change in wind speed decreases, and at some values of the relative capacity, it can be neglected.


Energies ◽  
2019 ◽  
Vol 12 (6) ◽  
pp. 982 ◽  
Author(s):  
Xin Wu ◽  
Hong Wang ◽  
Guoqian Jiang ◽  
Ping Xie ◽  
Xiaoli Li

Health monitoring of wind turbine gearboxes has gained considerable attention as wind turbines become larger in size and move to more inaccessible locations. To improve the reliability, extend the lifetime of the turbines, and reduce the operation and maintenance cost caused by the gearbox faults, data-driven condition motoring techniques have been widely investigated, where various sensor monitoring data (such as power, temperature, and pressure, etc.) have been modeled and analyzed. However, wind turbines often work in complex and dynamic operating conditions, such as variable speeds and loads, thus the traditional static monitoring method relying on a certain fixed threshold will lead to unsatisfactory monitoring performance, typically high false alarms and missed detections. To address this issue, this paper proposes a reliable monitoring model for wind turbine gearboxes based on echo state network (ESN) modeling and the dynamic threshold scheme, with a focus on supervisory control and data acquisition (SCADA) vibration data. The aim of the proposed approach is to build the turbine normal behavior model only using normal SCADA vibration data, and then to analyze the unseen SCADA vibration data to detect potential faults based on the model residual evaluation and the dynamic threshold setting. To better capture temporal information inherent in monitored sensor data, the echo state network (ESN) is used to model the complex vibration data due to its simple and fast training ability and powerful learning capability. Additionally, a dynamic threshold monitoring scheme with a sliding window technique is designed to determine dynamic control limits to address the issue of the low detection accuracy and poor adaptability caused by the traditional static monitoring methods. The effectiveness of the proposed monitoring method is verified using the collected SCADA vibration data from a wind farm located at Inner Mongolia in China. The results demonstrated that the proposed method can achieve improved detection accuracy and reliability compared with the traditional static threshold monitoring method.


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