New structure of sliding mode control for variable speed wind turbine

With the rapid development of wind power generation technology, how to enhance the capture efficiency of wind turbines is still a challenging problem due to the stochastic characteristics of wind speed. In this paper, an effective wind speed estimation (EWSE) based Maximum Power Point Tracking (MPPT) controller for variable-speed wind turbines (VSWT) is presented. A wind speed estimation model is firstly developed via support vector regression (SVR), the training of which is conducted by utilizing data acquired through the existing supervisory control and data acquisition (SCADA) systems. After training, the estimate of effective wind speed is obtained from the SVR model in an online manner. A low-pass filter is utilized to remove the high-frequency components of the estimate signal, which can highly mitigate the incurred mechanical loads. Thereafter, a simple proportional-integral (PI) controller is designed to maneuver the rotor speed to its desired value by combining the EWSE module. Finally, the feasibility and performances of the proposed controller are validated and demonstrated with WP 1.5MW wind turbines on the platform of FAST (Fatigue, Aerodynamics, Structures, and Turbulence) code and Simulink.

Analysis and synthesis of sliding mode control for large scale variable speed wind turbine for power optimization

This work discusses the design of a nonlinear controller for a single mass model of a variable speed wind turbine without measurement of wind speed. Dynamic load on the drive train shaft plays a vital role in turbine lifespan. So the design of the nonlinear controller should pay more care in mitigation of transient load on the drive train shaft. The objective of the controller is to extract the optimal power with limited dynamic loads on the shaft. The above objectives are satisfied by tracking the reference rotor speed which is done by a nonlinear controller. Effective wind speed is obtained by Modified Newton Raphson (MNR), which is used for deriving the reference rotor speed. In this paper, a nonlinear controller, i.e. double integral sliding mode controller (DISMC) is proposed for the single mass model of a wind turbine at partial load region (below rated wind speed). Lyapunov candidate function is used for analyzing the stability of the proposed controller. The performance of the proposed controller is compared with conventional integral sliding mode controller (ISMC) in terms of performance parameters. The robustness of the controllers are analyzed in the presence of different input disturbances and different wind speed profiles. From results, it is found that DISMC can able to accommodate various input disturbance level up to 5kNm.

DAC is a linear control technique used to mitigate the effect of disturbance on the plant. It is a superposition of full state feedback and disturbance feedback. This paper presents a control technique based on Disturbance Accommodation Control (DAC) to reduce fatigue on drive train generated by wind which is combination of step and periodic disturbances (generated by wind shear and tower shadow) of the turbine for above rated wind speed (Region III). We have presented a wind modeling for wind shear and tower shadow effect for the wind turbine control simulation and also developed a control scheme to mitigate the effect of 3p flicker on drive train. 5MW wind turbine of the National Renewable Laboratories (NREL) is used as research object and results are simulated in MATLAB/Simulink. We designed the controller based on linearized model of the wind turbine generated for above rated wind speed and then tested its performance on the nonlinear model of wind turbine. We have shown a comparison of the results for proportional-integral(PI) and proposed DAC controller tested on nonlinear model of wind turbine. Result shows that our proposed controller shows better mitigation of flicker generated due to 3p effect by using collective pitch control as compared to PI with tower and actuator dynamics.

Optimization of wind energy extraction for variable speed wind turbines using fuzzy backstepping sliding mode control based on multi objective PSO

With increasing of the capacity and flexibility of the wind turbine generator system (WTGS), it is more and more important to make the operation even better and to reduce the load while the wind speed is above the rated speed of WTGS. The individual pitch control can solve the above two problem efficiently. But the control of WTGS is very complicated because of the fluctuation and intermittence of wind speed and the complexity of power electronics. In classical control, the controller design needs accurate mathematical model, but this kind of model of the wind turbine generator system is very difficult to get because of the nonlinear characteristics. Using fuzzy controller, we can simplify and avoid the sophisticated mathematical model. Combining with the studied method and results of other scholars about pitch control, we put forward the individual pitch control based on fuzzy PI control and used to control the wind turbine so that it can be more stable and meanwhile its operation load can be reduced, and the fluctuation of the power is minimized. Firstly, we use oscillation acceleration of one bladed as input and PI controller to get one pitch angle; secondly, we use rotation speed as input and use fuzzy PI controller to get a pitch angle; and thirdly, we use a distributed controller to get a pitch angle; finally, combine the three pitch angles and used as the real control pitch angles of the three blades. The simulation results are given in this paper.

This paper presents a new optimal controller design for the sensorless variable speed wind turbine (SVSWT) based on the extreme learning machine (ELM) and the adaptive particle swarm optimisation (APSO) algorithms. The two main objectives of this command are to maximize the conversion of wind energy below the rated wind speed and to maintain the safety of the wind turbine system (WT) by minimizing stress on the drive train shafts. The proposed technique is based on the efficiency of the ELM for single hidden layer feed forward neural networks (SLFN) combined to sliding mode control (SMC) to respectively, improve the used model and stabilize the operation of the WT. ELM algorithm with high learning speed is used to approximate the nonlinear unmodelled dynamics while SMC is used to compensate the external disturbances and modelling errors. APSO algorithm is introduced to adapt and optimize the gain of the SMC. The efficiency of the proposed method is illustrated in simulations by the comparison with traditional SMC.

This paper presents a robust intelligent proportional-integral sliding mode controller for a variable speed wind turbine (VSWT). The main objective of the controller is to optimise the energy captured from the wind, and minimise the mechanical stress in the system. In order to guarantee the wind power capture optimisation without any chattering problems, this study propose to combine the sliding mode control (SMC), proportional integral (PI) control and particle swarm optimisation (PSO) algorithm. The PSO technique with efficient global search is used to optimise the PI and SMC parameters simultaneously to control the system trajectories to a sliding manifold that determines the system performance. The stability of the system using this controller is shown by the Lyapunov theory. The simulation results of the proposed PSO-PI based SMC (PSO-PISMC) method are compared with the PSO-I based SMC (PSO-ISMC) and the conventional PSO based SMC (PSO-SMC). The comparison results reveal that the proposed controller is more effective in reducing the tracking error and chattering. In addition, the controller shows more robustness against uncertainties and faster transient response of the system with reduced steady state error.

The battery charging control within wind turbine applications is the most important challenge in standalone exploitation mode because of the random nature of wind energy and irregular use of electricity. Within this problematic, this paper deals with monitoring and control of a permanent magnet synchronous generator voltage for battery charging. In the proposed topology, the output direct drive PMSG is linked to the battery through a vector-controlled pulse modulated (PWM) rectifier. Most wind energy conversion systems are based on precise knowledge of the wind speed, which makes the adjustment of the charge voltage relatively complicated, and degrades the powers captured with erroneous measurements of this wind speed. In this context, all the efforts of this work are meant to simplify and improve the dynamic performance of the system. The technique used in this work is devoid of wind measurement. By neglecting different losses, the approach adopted leads us to the reference electromagnetic torque, assuming that the wind speed and consequently the rotational speed of the turbine vary slowly in steady state. Using the load current and voltage measures, the proposed controllers calculate permanently the power reference that corresponds to the fixed battery voltage. A vector control approach is adopted to achieve decoupled power control on the supply side power converter by generating the direct and quadrature control voltages. These voltages are necessary to generate the corresponding duty cycles of the PWM active switching devices. Hence, the PWM rectifier maintains the battery voltage at a fixed value by balancing both the rectifier output current and the load input current. To activate the vector control, two dynamic controllers are designed, simulated, and compared. The first controller is based on simple PI regulators. Meanwhile, the second one, which is a sliding mode technique, is applied to control the battery voltage. In order to assess the effectiveness of the used techniques, simulation models have been subjected to critical conditions of changing in speed and load. Simulation results revealed a poor accuracy of the first technique during transients and its close correlation under load disturbance. Whereas the sliding mode controller has high precision and strong robustness in steady state and during transient phases. The system simulation is performed using MATLAB/SIMULINK.

This article considers an adaptive control scheme previously developed for region 2 control of a variable speed wind turbine. In this paper, the question of theoretical stability of the torque controller is addressed, showing that the rotor speed is asymptotically stable under the torque control law in the constant wind speed input case and L/sub 2/ stable with respect to time-varying wind input. Further, a method is derived for selecting /spl gamma//sub /spl Delta/M/ in the gain adaptation law to guarantee convergence of the adaptive gain M to its optimal value M*.

Considering the below rated power region of a variable speed wind turbine approximated by a single-degree-of-freedom system, a hybrid adaptive controller based on sliding mode control and radial basis functions neural networks was proposed. Stability of this controller was assessed by using Lyapunov approach. The control algorithm was implemented by means of Matlab/Simulink software package. Comparison with other reference controllers has proven that the proposed controller is relevant as it improves performance in terms of produced electrical energy by the wind turbine system while reducing the amplitude and fluctuations of mechanical loads.

Recently, an increasing number of larger wind turbines (1 MW and up) are being developed with a variable speed-variable pitch control mechanism. The main objective of using a variable speed-variable pitch control strategy is to improve the fast response speed and get maximum energy. But the power generated by wind turbine changes rapidly because of the continuous fluctuation of wind speed and direction. At the same time, wind energy conversion systems are of strong nonlinear characteristics because of many uncertain factors. Based on combined nonlinear sliding-mode control and fuzzy set theory, this paper presents a new kind of state controller to eliminate the steady state error, compensate the nonlinearity and have excellent robustness. (5 pages)

Combined hydro-wind frequency control scheme: Modal analysis and isolated power system case example

A nonlinear model of wind turbine based on a neural network (NN) is described for the estimation of wind turbine output power. The proposed nonlinear model uses the wind speed average, the standard deviation and the past output power as input data. An anemometer with a sampling rate of one second provides the wind speed data. The NN identification process uses a 10-min average speed with its standard deviation. The typical local data collected in September 2000 is used for the training, while those of October 2000 are used to validate the model. The optimal NN configuration is found to be 8-5-1 (8 inputs, 5 neurons on the hidden layer, one neuron on the output layer). The estimated mean square errors for the wind turbine output power are less than 1%. A comparison between the NN model and the stochastic model mostly used in the wind power prediction is done. This work is a basic tool to estimate wind turbine energy production from the average wind speed.

This paper presents a feedback/feedforward nonlinear controller for variable-speed wind turbines with doubly fed induction generators. By appropriately adjusting the rotor voltages and the blade pitch angle, the controller simultaneously enables: (a) control of the active power in both the maximum power tracking and power regulation modes, (b) seamless switching between the two modes, and (c) control of the reactive power so that a desirable power factor is maintained. Unlike many existing designs, the controller is developed based on original, nonlinear, electromechanically-coupled models of wind turbines, without attempting approximate linearization. Its development consists of three steps: (i) employ feedback linearization to exactly cancel some of the nonlinearities and perform arbitrary pole placement, (ii) design a speed controller that makes the rotor angular velocity track a desired reference whenever possible, and (iii) introduce a Lyapunov-like function and present a gradient-based approach for minimizing this function. The effectiveness of the controller is demonstrated through simulation of a wind turbine operating under several scenarios.