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

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.

In this work, a torque controller for a variable rotational speed wind turbine has been modelled using Reinforcement Learning and considering the Optimal Torque - Maximum Power Point Tracking problem as one of optimization. The reward optimization function is designed as a non-linear function depending mainly on the rotor power variation. Based on this, an optimal action (electromagnetic torque variation) regulates the turbine rotational speed. A simulated 1.5 MW three bladed wind turbine operation is managed by the torque controller. It keeps the turbine working at optimal operational conditions after a successful training process, which is carried out using the Proximal Policy Optimization algorithm. For the controller training, the turbine confronts constant and then randomly staggered wind speed behaviour. Time series of rotor angular speed, torque and power are presented. Our results show that the modelled controller is able to reach and maintain the wind turbine operation at its optimal power generation conditions. This methodology avoids using some empirical parameter characteristic of the Optimal Torque - Maximum Power Point Tracking algorithm widely used in wind turbine control systems.

Under the trends to using renewable energy sources as alternatives to the traditional ones, it is important to contribute to the fast growing development of these sources by using powerful soft computing methods. In this context, this paper introduces a novel structure to optimize and control the energy produced from a variable speed wind turbine which is based on a squirrel cage induction generator (SCIG) and connected to the grid. The optimization strategy of the harvested power from the wind is realized by a maximum power point tracking (MPPT) algorithm based on fuzzy logic, and the control strategy of the generator is implemented by means of an internal model (IM) controller. Three IM controllers are incorporated in the vector control technique, as an alternative to the proportional integral (PI) controller, to implement the proposed optimization strategy. The MPPT in conjunction with the IM controller is proposed as an alternative to the traditional tip speed ratio (TSR) technique, to avoid any disturbance such as wind speed measurement and wind turbine (WT) characteristic uncertainties. Based on the simulation results of a six KW-WECS model in Matlab/Simulink, the presented control system topology is reliable and keeps the system operation around the desired response.

Due to the Randomness and fluctuation of the photovoltaic (PV) system, Maximum Power Point Tracking (MPPT) algorithm is employed to ensure the maximum power extraction from PV arrays, when it is connected to the grid. The previous studies have indicated that the MPPT algorithm is one of the main causes of interharmonics emission in PV systems. The MPPT algorithm parameters such as perturbation step size inevitably have a strong impact on the interharmonics in the current injected to the grid. In general, a smaller MPPT perturbation step size will reduce the interharmonics emission level, but it will result in poor tracking performance of the MPPT algorithm. Thus, there is a trade-off between the MPPT efficiency and the interharmonics emission in PV system. In this paper, an interharmonics suppression scheme with variable step size MPPT algorithm was proposed. In the suppression scheme, two voltage values near by the MPPT voltage and two tracking step size are preset. When the dc-link voltage of PV is between the two preset voltage values, it means that the PV power near its MPP and the smaller perturbation step size implemented in the MPPT algorithm. Otherwise, the bigger perturbation step size is employed. It can greatly reduce interharmonics emission level and maintain the MPPT efficiency. Simulations have been carried out to verify the effectiveness of the suppression scheme.

Optimal tracking and robust power control of the DFIG wind turbine

Due to the nature of unpredicted wind speed, determining the optimal generator speed to extract the maximum available wind power at any wind speed is essential. Therefore, it is significant to include an intelligent controller that can track the maximum peak regardless of wind speed. This paper describes the design and development of particle swarm optimization (PSO)-based maximum power point tracking (MPPT) algorithm to variable-speed fixed-pitch wind turbines. Other than the electrical power subjected to maximization, the proposed algorithm does not need any additional sensor. In addition, the MPPT algorithm does not require any prior knowledge of the wind energy system. Unlike the conventional search optimization method, PSO-based MPPT algorithm produces almost negligible oscillations at the maximum power once the true peak is located. In short, the proposed MPPT is simple, flexible, accurate and efficient in maximum wind power tracking. In this work, MATLAB/Simulink simulation package is used to simulate the performance of the proposed MPPT algorithm.

By detection of input signal, Maximum Power Point Tracking (MPPT) algorithms are used to maximize the potential output power. Since Perturbation and Observe (P&O) method was first applied in a Photovoltaic (PV) system, a myriad of MPPT algorithms has been proposed. With the pros and cons of various MPPT algorithms, a detailed analysis of several typical direct search and module-integrated MPPT algorithms is presented in this paper. Directions of future work for implementing new MPPT algorithms are also outlined.

In the present paper, an optimal operation of a grid-connected variable speed wind turbine equipped with a Doubly Fed Induction Generator (DFIG) is presented. The proposed cascaded nonlinear controller is designed to perform two main objectives. In the outer loop, a maximum power point tracking (MPPT) algorithm based on fuzzy logic theory is designed to permanently extract the optimal aerodynamic energy, whereas in the inner loop, a second order sliding mode control (2-SM) is applied to achieve smooth regulation of both stator active and reactive powers quantities. The obtained simulation results show a permanent track of the MPP point regardless of the turbine power-speed slope moreover the proposed sliding mode control strategy presents attractive features such as chattering-free, compared to the conventional first order sliding technique (1-SM).

In this study, a single sensor‐based maximum power point tracking (MPPT) algorithm is presented for permanent magnet synchronous generator‐based wind energy conversion system (WECS). The MPPT method used in this study uses only the wind speed sensor to generate the reference point required for the maximum power. A discrete‐time controller is proposed to bring the system to the reference point determined by the MPPT algorithm. The designed MPPT controller consisting of a new discrete‐time reaching‐law approach and a discrete‐time integral sliding mode control (SMC). The designed discrete‐time SMC‐based controller is implemented by using an Acorn RISC Machine (ARM)‐based LM4F120 microcontroller, and its performance is examined on a WECS emulator prepared in the laboratory. The results got from the real‐time studies prove that the proposed controller has the satisfactory performance to bring the system to the MPP. The performance of the proposed controller is also compared with the conventional sliding mode controller and perturb and observe method. The obtained results proved that the proposed controller has a better performance than the other controllers in terms of the settling time and output voltage's ripple.

This paper presents a sensor-less control strategy based on the Fuzzy Logic Controller (FLC) Maximum Power Point Tracking Algorithm (MPPT) for stand-alone hydrokinetic energy harnessing in the river. The FLC MPPT algorithm was proposed due to the variation of the water velocity and the weakness of the conventional MPPT algorithm, such as the Perturb and Observe (P&O) MPPT algorithm under highly nonlinear water velocity. The hydrokinetic comprises a water turbine, permanent magnet synchronous generator (PMSG), passive rectifier, and DC boost converter. In this work, MATLAB/Simulink has been implemented to design the circuit topology for hydrokinetic technology. Then, the modeling of the complete system has been simulated under the different speeds of water velocities. The proposed FLC MPPT has been compared to the conventional P&O MPPT algorithm regarding power efficiency and tracking response time. The results show that the proposed FLC MPPT algorithm achieved an average 93 % efficiency in extracting the output power and a 35.49 % faster response time than the P&O MPPT algorithm.

In this paper, the implementation of the Maximum Power Point Tracking (MPPT) technique is developed using buck-boost converter. Perturb and observe (P&O) MPPT algorithm is used to searching maximum power from the wind power plant for charging of the battery. The model used in this study is the Variable Speed Wind Turbine (VSWT) with a Permanent Magnet Synchronous Generator (PMSG). Analysis, design, and modeling of wind energy conversion system has done using MATLAB/simulink. The simulation results show that the proposed MPPT produce a higher output power than the system without MPPT. The average efficiency that can be achieved by the proposed system to transfer the maximum power into battery is 90.56%.

A comparative study between frequently used maximum power point tracking (MPPT) algorithms and a proposed Least Squares Support Vector Machine (LS-SVM) based MPPT algorithm under rapidly changing insolation condition (RCIC) is presented in this paper. The comparison shows that the proposed LS-SVM based MPPT algorithm offers some advantages over existing MPPT algorithms under RCIC, namely elimination of ripples in the output voltage of a PV solar system as well as improved tracking accuracy. The algorithms have been tested through simulation studies carried out on real data recorded by the Australia Bureau of Meteorology. The study compares the performances of LS-SVM, P&O and IncCond.

Wind energy systems have become a major commodity in the use of renewable energy resources, but its implementation requires great technical challenges in power extraction. Giligenting Island, Madura, East Java, is a prospective area for wind energy development. An algorithm applied to obtain optimal power from wind power generation. This paper presents a performance evaluation of the methods that can be applied as the MPPT (Maximum Power Point Tracking) algorithms. P&O (Perturbation and Observation), IC (Incremental Conductance), and PSO (Particle Swarm Optimization) are studied and discussed as the MPPT techniques in this research. All three methods are implemented on systems that have the same characteristics, they are compared to the simulation results. At the base wind turbine speed of 12 m/s, the P&O method obtained MPPT power of 295.82 W with settling time at 1.02 s. The IC method obtained MPPT power of 297.08 W with settling time at 1 s. Then, The PSO method achieved an MPPT power of 297.45 W with time settling at 0.88 s. According to the simulation results, the PSO is the algorithm that provided the best performance among all MPPT algorithms studied. Beside good response to wind speed variations, PSO also has a simple structure and easy to implement.

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.

PV irrigation systems have begun to be used intensively today, as energy needs increase. In Partially Shaded Conditions (PSC), the efficiency of the PV system decreases significantly, and traditional Maximum Power Point Tracking (MPPT) algorithms become insufficient. On the other hand, traditional MPPT algorithms require sensors to measure the current and voltage of the PV system. In this study, a sensorless hybrid MPPT algorithm is proposed to reduce system costs and enable operation without the need for PV system data. A simulation study was conducted in the MATLAB/Simulink environment to examine the PV system. The proposed algorithm has been tested under four different PSC scenarios. PV system power, motor speed, and currents were examined under each condition. The high maximum power tracking performance of the proposed algorithm is demonstrated through simulation results. In the steady state, the lowest MPPT efficiency was 95.66%, whereas the highest MPPT efficiency was 99.9%. The MPPT algorithm completed in less than 2 seconds, with the first stage taking 1.3 seconds to reach most of the maximum PV system power. The second stage of the MPPT algorithm was used to achieve maximum power in a narrower area.