Analysis of Control Strategy Development: Backstepping and Classical Regulators for Power Regulation in a Wind Turbine System

In the present paper, the flower pollination algorithm (FPA) is employed for tuning the controller parameters of a doubly fed induction generator (DFIG) in a wind energy system. These parameters are then compared with those generated by the genetic algorithm (GA) and the proportional-integral (PI) (initial design) controllers. Performance analysis of the DFIG is carried out in dynamic mode in two case studies. The first case study is carried out with no failure, the second one is subject to a short circuit in the electrical network. In this latter case study, a break occurs in the rotor circuit and disconnects the DFIG from the power grid. This gives rise to an excessive current in the rotor circuit which in turn influences the converters AC/DC/AC and makes the IGBT very sensitive. The GA and the FPA are used to tune the PI controllers with the purpose of improving the quality of a power supply should electrical disturbances occur. The results show that by applying an optimal PI controller design to a DFIG using the FPA the performance of the DFIG system can be improved in the event of disturbances. When the PI controller tuning using the GA and the initial control system design is compared with the DFIG using the optimized design, a significant decrease in the overshoot of the rotor current and the DC-link voltage is observed.

A conventional sliding mode (SMC) and second-order sliding mode (SOSMC) control schemes based on pulse width modulation (PWM) for the rotor side converter (RSC) and grid side converter (GSC) feeding a doubly fed induction generator (DFIG) are presented in this paper. The proportional integral (PI) controllers for wind turbine system (WTS) driven-DFIG have shown many limitations such as parameter adjustment difficulties, sensibility to DFIG parameter variations and poor robustness. Therefore, nonlinear controllers based on SMC and SOSMC are proposed to improve the wind energy conversion efficiency and the system robustness by controlling the RSC and GSC at a constant switching frequency. The proposed controllers turn-out to be more robust against parameter variations of the DFIG and external disturbances. In addition, the SMC and SOSMC approaches can optimize the WTS production, i.e. improve quality of energy produced and energy efficiency. Detailed simulation studies of WTS under various conditions are carried out in MATLAB/Simulink software, and the results confirm the robustness of the proposed control techniques.

The increasing penetration of wind power presents many technical challenges to power system operations. An important challenge is the need of voltage control to maintain the terminal voltage of a wind plant to make it a PV bus like conventional generators with excitation control. In the previous work for controlling wind plant, especially the Doubly Fed Induction Generator (DFIG) system, the proportional-integral (PI) controllers are popularly applied. These approaches usually need to tune the PI controllers to obtain control gains as a tradeoff or compromise among various operating conditions. In this paper, a new voltage control approach based on a different philosophy is presented. In the proposed approach, the PI control gains for the DFIG system are dynamically adjusted based on the dynamic, continuous sensitivity which essentially indicates the dynamic relationship between the change of control gains and the desired output voltage. Hence, this control approach does not require any good estimation of fixed control gains because it has the self-learning mechanism via the dynamic sensitivity. This also gives the plug-and-play feature of DFIG controllers to make it promising in utility practices. Simulation results verify that the proposed approach performs as expected under various operating conditions.

This paper presents dynamic modeling and control of Doubly Fed Induction Generator (DFIG) based on wind turbine systems, where the stator of DFIG is directly connected to the grid and the rotor was fed by a three level PWM NPC inverter. The active and reactive power control of the DFIG is based on the feedback technique by vector control method by using a classical regulator of Proportional-Integral (PI) type which allows us, in association with the looping of powers, to obtain an efficient and robust system. This approach is a very attractive solution for devices using DFIG as wind energy conversion systems; because, it is a simple, practical implementation, commonly applied in the wind turbine industry and it presents very acceptable performance, However, this control approach has certain limitations and has several causes, vector command with NPC three-level inverter pulse width modulation (PWM) is used to control the reactive power and active power of the generator. Then, use the neural network design to replace the traditional proportional-integral (PI) controller. Finally, the Matlab/Simulink software is used for simulation to prove the effectiveness of the command strategy.

For the recent expansion of renewable energy applications, Wind Energy System (WES) is receiving much interest all over the world. However, area load change and abnormal conditions lead to mismatches in frequency and scheduled power interchanges between areas. These mismatches have to be corrected by the LFC system. This paper, therefore, proposes a new robust frequency control technique involving the combination of conventional Proportional-Integral (PI) and Model Predictive Control (MPC) controllers in the presence of wind turbines (WT). The PI-MPC technique has been designed such that the effect of the uncertainty due to governor and turbine parameters variation and load disturbance is reduced. A frequency response dynamic model of a single-area power system with an aggregated generator unit is introduced, and physical constraints of the governors and turbines are considered. The proposed technique is tested on the single-area power system, for enhancement of the network frequency quality. The validity of the proposed method is evaluated by computer simulation analyses using Matlab Simulink. The results show that, with the proposed PI-MPC combination technique, the overall closed loop system performance demonstrated robustness regardless of the presence of uncertainties due to variations of the parameters of governors and turbines, and loads disturbances. A performance comparison between the proposed control scheme, the classical PI control scheme and the MPC is carried out confirming the superiority of the proposed technique in presence of doubly fed induction generator (DFIG) WT.

This paper represents a novel sensorless method for the vector control of doubly fed induction generator (DFIG) in a wind turbine system. The proposed method is based on the fuzzy fractional order adaptive disturbance rejection control (FFOADRC) estimating the rotor velocity. In this new method, there is no need to calculate the coupling terms and eliminate them by feed-forward compensation. In addition, all disturbances (internal and external) are estimated by a fractional order extended state observer (FESO). The effects of these disturbances are then neutralized by generating a suitable control command. The operation of the proposed system has been simulated in Matlab/Simulink environment. The comparisons were made between FFOADRC, adaptive disturbance rejection control (ADRC), fuzzy ADRC (FADRC), and proportional-integral (PI) controller under different operating conditions. The results show that: (1) After DFIG starts and under similar conditions, using FFOADRC, FADRC, and ADRC, the velocity reaches the steady state with the overshoot values of 0%, 3.64%, and 8.03%, respectively. (2) In the steady state after wind velocity variation, the %THD values of the stator current using FFOADRC, FADRC, and ADRC are, respectively, 1.47, 1.54, and 2.79. In this case, utilizing the PI controller, the control circuit has a slower performance than three other controllers. (3) The comparison between the aforementioned controllers during DFIG velocity control shows that using FFOADRC, the values of settling time, rise time, peak time, and delay time are smaller, and we have better performance that indicates the superiority of FFOADRC over ADRC, FADRC, and PI controller. Therefore, FFOADRC improves the wind turbine performance in different conditions

Hitherto this paper presents around Active power along with Reactive power control of a grid allied doubly fed Induction Generator (DFIG) with wind energy system (WES) employing PI & ANFIS controller and ALO (Ant lion Optimization) FOPI (Fraction Order PI) Controller. DFIG is adapted in d-q revolving allusion cage circuit with stator flux oriented, field oriented control approach. By using a coterminous converter of Variable speed constant Frequency (VSCF) along with active the reactive power and DC tie voltage are controlled at sub and super synchronous speeds. A PI controller has been combined with an ANFIS controller in order to enhance the power controlling capability at steady state and voltage dip conditions and then the results are combined with FOPI controller tuned by a Heuristic Ant Lion Controller to strengthen the work of conventional PI controller. The Active and Reactive Powers can intensify further by casting with an MPPT arrangement depleted by using Cuckoo Search.

In this study, an optimisation method, based on bacteria foraging, is investigated to tune the parameters of the proportional–integral (PI) controllers in a doubly‐fed induction generator (DFIG) wind energy system connected to the grid. The generator is connected to the grid directly at the stator and through the back‐to‐back converter at the rotor. The control system includes PI controllers, at the rotor side, to regulate the rotor currents and PI controller to regulate the dc‐link voltage for efficient power transfer. The control parameters, of three PI controllers, are optimised offline using the bacteria foraging optimisation algorithm and a modelled DFIG wind energy system. Various performance criteria, based on the tracking errors, are used to assess the efficiency of the optimisation method. Furthermore, the conventional tuning method and genetic algorithm optimisation method are conducted and compared to the bacteria foraging optimisation method to demonstrate its advantages. The optimised control parameters are evaluated on a DFIG wind energy experimental setup. Experimental and simulation results are provided to validate the effectiveness of each optimisation method.

Unbalanced stator conditions cause rotor current harmonics and torque pulsations in Doubly-Fed Induction Generators (DFIG) which are used widely in wind energy systems. From a hardware perspective, current control techniques to minimize the rotor current harmonics include rotor-side converter injected voltage compensation and grid-side converter compensation. From a software perspective, the current controllers either adopt synchronous reference frame for controller design or adopt positive synchronous (qd <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> ) negative synchronous reference frames (qd <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-</sup> ) to decompose the harmonics in rotor currents and control them separately. This paper develops a proportional resonance (PR) control strategy in the stationary reference frame (alphabeta) to minimize rotor current harmonics and torque pulsations. The main advantages of the proposed method are (i) only one transformation (abc/alphabeta) is required and (ii) harmonic filters are not required. The proposed control strategy is compared with the proportional integral (PI) control strategyin qd <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> and qd <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-</sup> and the proportional integral and resonant (PIR) control strategy in qd <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> . Simulations performed in Matlab/Simulink are presented to illustrate the effectiveness of the proposed control strategy.

Nowadays, the challenging errand is enhancing the wind energy system (WES) performance to be more competitive and economically viable. Most researchers believe that one of the best ways to enhance the performance of the doubly-fed induction generator (DFIG)-based-WES is the optimization of the proportional-integral (PI) controllers for the variable frequency converter system. Many objectives with different optimization techniques have been used in literature to achieve optimal performance. Each choice has its advantages and disadvantages; thus, the research area is still open for getting optimal performance. This paper presents a new design approach for better performance of PI controllers and, hence DFIG over a wide range of operating conditions through two main themes. The first is by introducing a new multi-objective formulation, while the second is utilizing recent optimization techniques like Grey Wolf Optimizer and Whale Optimization Algorithm. The proposed approach is applied to get the best parameters of the related PI controllers for rotor and grid side converters of the simulation model of a 6 MW wind farm located in Jabal Alzayt along the Red Sea Coast in Egypt and directly connected to the grid. The results confirmed the effectiveness of the proposed approach to help the DFIG-based-WES to agree with the Egyptian Grid Code during disturbances compared with the traditional objective formulation.

Renewable energy systems, especially wind energy have attracted interest as a result of the increasing concern about CO2 emissions. Wind energy systems using a doubly fed induction generator (DFIG) have some advantages due to variable speed operation and four quadrants active and reactive power capabilities compared with fixed speed squirrel cage induction generators (Simoes & Farret, 2004). The stator of DFIG is directly connected to the grid and the rotor is connected to the grid by a bi-directional converter as shown in Figure 1. The converter connected to the rotor controls the active and the reactive power between the stator of the DFIG and ac supply or a stand-alone grid (Jain & Ranganathan, 2008). The control of the wind turbine systems is traditionally based on either stator-flux-oriented (Chowdhury & Chellapilla, 2006) or stator-voltage-oriented (Hopfensperger et al, 2000) vector control. The scheme decouples the rotor current into active and reactive power components. The control of the active and reactive power is achieved with a rotor current controller. Some investigations using PI controllers and stator-flux-oriented have been reported by Pena et al (2008). The problem with the use of a PI controller is the tuning of gains and the cross-coupling on DFIG terms in the whole operating range. Some investigations using predictive functional controller (Morren et al, 2005) and internal mode controller (Guo et al, 2008) have presented a satisfactory power response when compared with the power response of PI, but it is hard to implement one of them due to the predictive functional controller and internal mode controller formulation. Another way to achieve the DFIG power control is using fuzzy logic (Yao et al, 2007). The controllers calculate at each sample interval the voltage rotor to be supplied to the DFIG to guarantee that the active and the reactive power reach their desired reference values. These strategies have satisfactory power response, although the errors in parameters estimation and the fuzzy rules can degrade the system response. The aim of this chapter is to provide the designing and the modeling of a deadbeat power control scheme for DFIG in accordance with the present state of the art. In this way, the deadbeat power control aims the stator active and reactive power control using the discretized DFIG equations in synchronous coordinate system and stator flux orientation. The deadbeat controller calculates the rotor voltages required to guarantee that the stator active and reactive power reach their desired references values at each sample period using a rotor current space vector loop. Experimental results using a TMS320F2812 plataform are presented to validate the proposed controller.

Using the proportional dual integral strategy to improve the characteristics of the indirect field-oriented control of DFIG-based wind turbine systems

Introduction. Nowadays, the most widely used wind energy conversion system in wind farms is based on a doubly fed induction generator (DFIG); it has a large speed range and can function in multiple modes. Problem. Harmonic distortion in wind energy conversion system can degrade output waveform quality, reduce power conversion efficiency. Goal. This study investigates the dynamic performance of a wind energy conversion system comprising a grid-connected load, a 13-level hybrid multilevel converter and a doubly fed induction generator (DFIG), using a PI controller. The study aims to evaluate the dynamic performance and power quality of wind energy conversion systems, and to develop a novel hybrid metaheuristic method combining differential evolution (DE) and grey wolf optimization (GWO)-based selective harmonic elimination pulse-width modulation (SHEPWM) control strategies. This method reduces total harmonic distortion (THD) and ensures compliance with IEEE 519 standards, while increasing the power transferred to the grid. Methodology. The system, which includes a grid-connected load, a 13-level converter, and a DFIG, is modeled and simulated in MATLAB/Simulink under steady-state wind conditions. Vector control via stator flow orientation was used to modify the energy quality provided by the DFIG, making the system comparable to the DC machine. Our approach was to use a PI controller in order to directly control the active and reactive DFIG power through multi-level converter then a hybrid metaheuristic algorithm combining DE and GWO is implemented to solve the SHEPWM nonlinear transcendental equations. The proposed algorithm is evaluated based on its ability to suppress lower-order harmonics and improve THD performance, these converters increase the power transmitted to the power grid by reducing harmonic content of the output voltages. Results. By using the DE-GWO hybrid method and a PI controller, lower-order harmonics were effectively removed and THD was reduced to meet IEEE 519 standards. Simulations showed an improvement in output wave quality and better energy conversion efficiency compared to conventional optimization methods. Scientific novelty of the proposed work lies in the fact that the study introduces a novel DE-GWO hybrid optimization method for PWM (SHEPWM) in 13-level hybrid multilevel converter applied to wind energy systems. Practical value. The novel method demonstrates that constant high performance in wind energy systems may be achieved by combining intelligent optimization algorithms with complex multilevel converter designs This means it can be effectively integrated into contemporary wind farms where meeting grid standards, adjusting to varying sizes, and ensuring long-term reliability are crucial. References 26, table 1, figures 19.

The increase in electricity demand places its focus on renewable energies as sustainable energy resources. Wind energy is one of the most important green energy sources. The doubly fed induction generator (DFIG)‐based wind farm has now gained prominence due to its many advantages, such as variable speed operation and autonomous control of active and reactive power. When the DFIG stator windings are directly connected to the power grid, when a grid fault occurs, some unwanted high current may be produced in the rotor windings, and the protection system will prevent the rotor side converter (RSC) from operating. Therefore, voltage stability is a significant factor in maintaining the DFIG‐based wind farm in operation during grid faults and disturbances. This paper applies a static synchronous compensator (STATCOM) to restore the voltage levels of the Egyptian power grid connected to Al Zafarana‐5th stage wind farm, which is made of 100 Gamesa G52/850 kW DFIG machines. In this paper, the STATCOM is controlled by a proportional integral (PI) and is compared with a STATCOM controlled by fuzzy logic control (FLC). For simulation, the MATLAB/SIMULINK environment is used. Moreover, the simulation results show that STATCOM devices with fuzzy logic controllers improve the effects of grid faults and disturbances such as a single line to ground fault, a line to line fault, voltage sag, and voltage swell as compared with STATCOM with PI controllers. Also, STATCOM devices based on FLC improve the stability and power quality of the system and the power system restoration procedures for the existing and future‐planned wind farms.

Wind Energy Conversion Systems (WECS) are mostly installed in remote locations; therefore, these systems are prone to various fault conditions. The control of active and reactive power control is very important when WECS are succumbed to faults. In this research paper, the Doubly Fed Induction Generator (DFIG) based WECS operation is analyzed for single and three phase faults near and far away from the DFIG. In this paper, a new hybrid controller, which is the combination of Artificial Neural Network (ANN) and Proportional Integral (PI) controller is developed to mitigate the above said problem. The performance of the hybrid controller is compared with the individual control performances of ANN and PI applied to DFIG. The results are presented for various fault conditions for these three different controllers. Results clearly show the uniqueness of the hybrid controller compared to individual performances of ANN and PI controllers. Also the results are examined with the Integral Square Error (ISE) values of active and reactive powers for hybrid, ANN and PI controllers for various fault conditions.