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

The Switched Reluctance Motors (SRMs), with many outstanding advantages, are gradually being widely applied in industries, households, and recommended in many works. However, most studies in the field of SRM control only concentrate on the mathematical model of the motor itself, neglecting the nonlinearity introduced by the inverter, which is responsible for switching between phases to drive the motor. This paper proposes an adaptive backstepping sliding mode control algorithm based on the SRM nonlinear model that combines both the motor and the inverter. Firstly, a backstepping sliding mode controller is used to track the desired value and ensure the stability of the system according to the Lyapunov criterion. Secondly, a fuzzy logic system is added to adjust the controller parameters to account for uncertainty and external disturbance, as well as to minimize the chattering phenomenon. Finally, a few simulation scenarios are performed to assess the effectiveness of the proposed controller. The simulation results clearly demonstrate that the proposed controller surpasses the previously published H infinity controller in terms of speed control quality for the combined nonlinear model of SRM. The proposed controller exhibits zero steady-state error, zero overshoot, and a short settling time of approximately 0.5 seconds. Moreover, the system's output quickly stabilizes when affected by disturbance noise.

Multi-objective PSO applied to PI control of DFIG wind turbine under electrical fault conditions

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

Variable-speed fixed-pitch (VSFP) wind turbine is widely used in small-to-medium scale wind turbine markets due to its simple structure, low cost and high reliability features. However, power control, especially maximum power limitation control at high wind velocities, of such turbine concept is hard to realize and it has not been studied much in existing researches so far. To overcome this difficulty, we propose a simple power control strategy for VSFP wind turbines, covering the entire range of wind velocity. The proposed strategy contains a novel maximum power point tracking (MPPT) control method, a constant-speed (CS) soft-stalling control method and a constant-power (CP) soft-stalling control method. These three sub-control methods are appropriately combined in a way to realize seamless transitions among different operational modes (MPPT, CS and CP). The system structure and operational principle of the proposed strategy are thoroughly analyzed. Theoretical analysis is verified by simulation and experimental results performed by a 1.2kW fixed-pitch variable-speed wind turbine prototype.

Advanced control of large-scale wind turbines: Structural load reduction and lifetime control

In this chapter an adaptive sliding mode controller for the Furuta pendulum is proposed. The Furuta pendulum is a class of underactuated mechanical systems commonly used in many control systems laboratories due to its complex stabilization which allows the analysis and design of different nonlinear and multivariable controllers that are useful in some fields such as aerospace and robotics. Sliding mode control has been extensively used in the control of mechanical systems as an alternative to other nonlinear control strategies such as backstepping, passivity based control etc. The design and implementation of an adaptive sliding mode controller for this kind of system is explained in this chapter, along with other sliding mode controller variations such as second order sliding mode (SOSMC) and PD plus sliding mode control (PD \(+\) SMC) in order to compare their performance under different system conditions. These control techniques are developed using the Lyapunov stability theorem and the variable structure design procedure to obtain asymptotically stable system trajectories. In this chapter the adaptive sliding mode consist of a sliding mode control law with an adaptive gain that makes the controller more flexible and reliable than other sliding mode control (SMC) algorithms and nonlinear control strategies. The adaptive sliding mode control (ASMC) of the Furuta pendulum, and the other SMC strategies shown in this chapter, are derived according to the Furuta’s pendulum dynamic equations making the sliding variables, position errors and velocity errors reach the zero value in a specified reaching time. The main reason of deriving two well known sliding mode control strategy apart from the proposed control strategy of this chapter (adaptive sliding mode control) is for comparison purposes and to evince the advantages and disadvantages of adaptive sliding mode control over other sliding mode control strategies for the stabilization of the Furuta pendulum. A chattering analysis of the three SMC variations is done, to examine the response of the system, and to test the performance of the ASMC in comparison with the other control strategies explained in this chapter.

The efficiency and reliability of wind power has been shown to be depending on the applied control strategy of the wind turbine. In this paper, an adaptive control strategy is proposed for variable speed wind turbine (VSWT), producing energy limitation above rated wind speed. In the proposed control strategy, the process is modeled using a neural networks based identifier, providing the sensitivity information of the process to the control input. Another neural networks is employed as an inverse model controller established via inverse system method. These two neural networks are off-line learned firstly and are on-line updated using the back propagation algorithm. Simulation results have shown the effectiveness of the proposed adaptive control strategy.

In view of the reduction of wind turbine operating efficiency caused by wind speed measurement errors, this paper proposes a rated power control strategy for wind turbines for practical applications; in order to solve the problem that the pitch PI controller parameters of wind turbines are not easy to calculate and tune in the process of design and optimization, this paper proposes a complete set of methods for parameter tuning of pitch control PI controllers for wind turbines. This paper establishes a Bladed-Matlab co-simulation platform, uses Bladed software to build a complete model of a 2MW wind turbine, and designs a rated power control strategy for wind turbines. After the wind turbine model is linearized at different wind speed points by applying Bladed software, the linearized model for PI parameter tuning is obtained, and then the optimal PI parameters for each wind speed point are set by using particle swarm algorithm. According to the variation law between the PI parameters and the pitch angle obtained before, the PI parameters are adjusted adaptively by variable gain PI control. The simulation results show the superiority of the control strategy and parameter tuning method used in this paper.

Because of the wind power generation has good economic and social benefits, which has been aroused more and more people's attention. The variable pitch control has an important part in the wind turbine. The indexes of the variable pitch has great influence on the dynamic performance with the unit, which can become an important index to measure the performance of the wind turbine. Due to the non-linearity and randomness of the variable pitch control system, the selection of the control strategy becomes very important. By establishing the function of the PID controller and the input deviation, which the PID controller has the nonlinear gain variable of the three parameters. Adopting the nonlinear PID control strategy according to the change of the different wind speed. The simulation results show that the dynamic performance of the nonlinear PID control is better than the traditional PID control, and it also can improve the speed and accuracy of variable pitch control system.

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.

In this paper, a multi-objective hybrid firefly and particle swarm optimization (MOHFPSO) was proposed for different multi-objective optimal power flow (MOOPF) problems. Optimal power flow (OPF) was formulated as a non-linear problem with various objectives and constraints. Pareto optimal front was obtained by using non-dominated sorting and crowding distance methods. Finally, an optimal compromised solution was selected from the Pareto optimal set by applying an ideal distance minimization method. The efficiency of the proposed MOHFPSO technique was tested on standard IEEE 30-bus and IEEE 57-bus test systems with various conflicting objectives. Simulation results were also compared with non-dominated sorting based multi-objective particle swarm optimization (MOPSO) and different optimization algorithms reported in the current literature. The achieved results revealed the potential of the proposed algorithm for MOOPF problems.

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 addresses the problem of three-dimensional (3D) path following control for underactuated autonomous underwater vehicles in the presence of parameter uncertainties and external disturbances. Firstly, 3D path following error model was established based on virtual guidance method. Then, an adaptive robust control system was proposed using backstepping and sliding mode control, and we adopt fuzzy logic theory to approximate unknown nonlinear function to solve the problems of nonlinearity, uncertainties and external disturbances in the path following. System stability was proved by Lyapunov stable theory. Finally, simulations were conducted and the results showed that the controller is of excellent adaptability and robustness in the presence of parameter uncertainties and external disturbances.

This study focuses on an advanced second-order sliding mode control strategy for a variable speed wind turbine based on a permanent magnet synchronous generator to maximize wind power extraction while simultaneously reducing the mechanical stress effect. The control design based on a modified version of the super-twisting algorithm with variable gains can be applied to the cascaded system scheme comprising the current control loop and speed control loop. The proposed control inheriting the well-known robustness of the sliding technique successfully deals with the problems of essential nonlinearity of wind turbine systems, the effects of disturbance regarding variation on the parameters, and the random nature of wind speed. In addition, the advantages of the adaptive gains and the smoothness of the control action strongly reduce the chatter signals of wind turbine systems. Finally, with comparison with the traditional super-twisting algorithm, the performance of the system is verified through simulation results under wind speed turbulence and parameter variations.

This paper explains a control strategy for variable speed wind turbine coupled to a permanent magnet synchronous generator (PMSG) in stand-alone mode. The control structure for a variable speed wind turbine is explained with a maximum power extraction. The wind generating system consists of variable speed wind turbine (VSWT) with three types of wind speed data (i) Step wind speed (ii) Staircase wind speed and (iii) random wind speed, Permanent magnet synchronous generator, uncontrolled rectifier, voltage source inverter and Bidirectional DC-DC converter along with a Battery storage system has been presented. The control strategy is so adapted to supply the load with a constant voltage and frequency under load change and wind speed change. The control strategy involves control of the bi-directional DC-DC converter of a battery system in order to maintain the constant DC-link voltage due to the variation in wind speed to the constant DC-link voltage. The control strategy used for Inverter control consists of a DC-link control, voltage and current control which is used to control the load voltage and frequency under load variation. The wind energy system is modelled in MATLAB/ SIMULINK.