DFIG-based Wind Energy System Research Articles

A Comparative Analysis on Proportional-Integral and Fuzzy-Logic Control Strategies for Doubly-Fed Induction Generators

This research investigates superior control methods for optimize the performance of Doubly-Fed Induction Generators in wind energy conversion systems. This proposed work, compares the well-established Proportional-Integral controller, with a Fuzzy Logic controller, known for its effectiveness in managing non-linear systems. To achieve a thorough analysis, this research develops a detailed mathematical model, specifically adopted to the DFIG system. It employs a PI controller for both the rotor-side and grid-side converters of the DFIG system. To enhance performance, a Fuzzy Logic controller is introduced to replace the PI controller, based on real-time operating conditions and gain values. Extensive simulations evaluate rigorously various performance metrics of the DFIG system under different control strategies. This analysis provides valuable insights to guide the selection of optimal control techniques, for wind energy systems using DFIGs. The analysis contributes to advancements in reliability, and efficiency for DFIG-based wind-energy systems, furthering the development of sustainable energy solutions.

Implementation of third-order sliding mode for power control and maximum power point tracking in DFIG-based wind energy systems

In the field of generation systems, system performance, and power quality are greatly affected by traditional control techniques based on proportional-integral (PI) controllers, which are affected by internal and external factors. Wind power conversion systems based on doubly-fed induction generators are exposed to certain nonlinearities, uncertainties, and external disturbances. From another side, the extraction of the mechanical wind energy required a high-performance control system. This study proposes a robust nonlinear chattering-free third-order sliding mode control (TOSMC) based power control applied to fix its primary disadvantages which are the chattering phenomena and active and reactive power ripples. To show the potential of the proposed TOSMC control, a comparison study considering the TOSMC control, traditional sliding mode control, and the other existing controls is done in terms of power ripples, harmonic distortion of stator current, robustness, and response time. To ensure maximum power extractions and to protect the system, the maximum point power tracking technique based on TOSMC and the proposed control are also implemented. In addition, These techniques are used to control the stator powers. Finally, simulations (using Matlab software) are performed using different wind speeds with durability testing. The simulated results show that the proposed TOSMC control with the MPPT-TOSMC technique in this paper achieves satisfactory results in terms of reducing power ripples, response time, and harmonic distortion of stator current compared to the sliding mode control, where the harmonic distortion reduction ratio of the current was (50.24%) and the active power ripple reduction ratio (47.50%) and the reactive power (46.68%).

Neural Network Predictive Control for Improved Reliability of Grid-Tied DFIG-Based Wind Energy System under the Three-Phase Fault Condition

This research explores a distinctive control methodology based on using an artificial neural predictive control network to augment the electrical power quality of the injection from a wind-driven turbine energy system, engaging a Doubly Fed Induction Generator (DFIG) into the grid. Because of this, the article focuses primarily on the grid-integrated wind turbine generation’s dependability and capacity to withstand disruptions brought on by three-phase circuit grid failures without disconnecting from the grid. The loading of the grid-integrated power inverter causes torque and power ripples in the DFIG, which feeds poor power quality into the power system. Additionally, the DC bus connection of the DFIG’s back-to-back converters transmits these ripples, which causes heat loss and distortion of the DFIG’s phase current. The authors developed a torque and power content ripple suppression mechanism based on an NNPC to improve the performance of a wind-driven turbine system under uncertainty. Through the DC bus linkage, it prevented ripples from being transmitted. The collected results are evaluated and compared to the existing control system to show the advancement made by the suggested control approach. The efficacy of the recommended control methodology for the under-investigation DFIG system is demonstrated through modelling and simulation using the MATLAB Simulink tool. The most effective control technique employed in this study’s simulations to check the accuracy of the suggested control methodology was the NNPC.

Optimized Convolutional Neural Network-Based Adaptive Controller for 6-Phase DFIG-Based Wind Energy Systems

This article proposes an optimized convolutional neural network-based adaptive control scheme (OCAC) for DFIG-based wind energy conversion systems. While linear systems can function successfully with the help of a PI controller, the behavior of the system becomes unstable when physical variations are present, rendering the PI controller ineffective. The purpose of this research is to guarantee that the proposed OCAC acquires self-adaptation under all conditions. By accounting for crucial circumstances such as changes in wind speed, fluctuations in generator parameters, and asymmetrical grid faults, the efficiency of OCAC control is proven. The hyperparameters of the deep convolutional neural network are optimized using the flower pollination algorithm, which boosts the network’s speed and precision. In comparison to the non-optimized technique, an overall improvement of 6.3% in training accuracy was attained through the use of the optimized method. Furthermore, the suggested OCAC would forecast the next systemic state and update control strategies of DFIG-based wind energy systems in real-time. The effectiveness of the OCAC is assessed with five different state-of-the-art algorithms under two distinct test scenarios. The simulation was conducted using MATLAB software. A comparison with a PID controller showed that the total harmonic distortion of the grid current decreased by 16.57% and that of the generator current decreased by 12.07%.

Multilevel Converter and Fuzzy Logic Solutions for Improving Direct Control Accuracy of DFIG-based Wind Energy System

The purpose of this study is to enhance the accuracy of direct power/torque control (DPC/DTC) applied to back-to-back converters supplying a doubly fed induction generator (DFIG) based wind power system. Two solutions are proposed. The first one is to increase the degree of freedom of the DTC and DPC control by implementing three-level back-to-back converters. Fuzzy logic control is the second solution to enhance the performances of both conventional direct power/torque control, leading in a decrease of the DFIG's torque/flux ripples and the active/reactive powers ripples supplied by the grid side converter, consequently, reduce the grid currents' total harmonic distortion (THD). The MATLAB/Simulink environment is used to evaluate the wind power generation system performances. The collected findings show that the fuzzy direct control (FDC) technique outperforms conventional direct control (CDC) when used for two-level back-to-back converters.

Improvement of Robustness of MPC Adding Repetitive Behavior for the DFIG Current Control

This paper shows an increase in the robustness of a DFIG-based wind energy system when a predictive-repetitive controller in the dq reference frame is used for the control of the rotor current. This designing approach proposes a model predictive control with a receding horizon technique that uses the frequency decomposition of the reference signal as a filter. Furthermore, an augmented space-state model from the original plant model is obtained, and its implementation is addressed from the perspective of normal operating conditions of DFIG in wind energy systems. Additionally, this repetitive controller increases the robustness of the predictive control when a mismatch in machine parameters is considered. Experimental results presented in this paper endorse the advantages of this controller.

Design and Simulation of DFIG based Wind Energy System using AI EMS

Abstract: Renewable energy sources are considered to be an unpredictable resource especially in wind turbines; rapid variation in wind speeds cause power fluctuations leads to interrupt the power system stability during the high-level power operation. To overcome these issue energy storage systems such as Battery storage is added to balance the grid and helps to balance the generated power and the power demand. The battery system is also used to mitigate the power fluctuations and helps to give more power to balance the load requirement. The proposed (EMS) Energy Management System integrates with the Artificial Neural Network for operation of both battery and wind system depending upon the demand need. In the case of the battery management system, the intelligent controllers help to control the State of Charge (SoC) limits of the battery to make sure the required time of charging and discharging. The proposed Artificial Intelligent Controller acts as an Energy Management System and Battery Management System as well as it is used for power balance generation and load also efficiently mitigating the voltage fluctuation during variation in the load. MATLAB is used for the simulation process since it contains the features of Artificial Intelligence which can in turn function as switching device. Keywords: Renewable energy sources, Battery Storage, Energy management system, State of charge, Artificial neural network etc.

Voltage Sag Mitigation with DVR optimized in DFIG based Wind Energy System

One of the most serious power quality problems in electrical networks is voltage drops. The DVR is known as the most common special power devices used in solving these power quality problems. In this study, optimization is made with WCA algorithm in DVR’S control circuit under wind energy system for fast and reliable detection of mains voltage disruptions, voltage drops and voltage spikes, Its performances were evaluated according to conventional PI controller. These control methods can instantly extract the amplitude and phase information of the mains voltage to detect voltage changes quickly and accurately and with the help of this information, The voltage that needs to be injected into the connection point can be obtained. In the simulation study, the tensions produced by DVR in DFIG based wind energy system and injected into the system were also presented using these two different control methods. The superiority of WCA-based PI controller is clearly seen in the results.

An Improved Fault Ride Through Scheme and Control Strategy for DFIG-Based Wind Energy Systems

This paper proposes a fault ride through scheme for doubly-fed induction generator (DFIG)-based wind energy systems. The proposed framework combines the robustness properties of fractional order sliding mode control (FOSMC) with the active/reactive power control capability of dynamic voltage restorer (DVR) and high-power density of superconducting magnetic energy storage (SMES) devices. The FOSMC is formulated using a novel sliding manifold to control the series active filter and generate appropriate voltage for injection into the stator terminal so as to maintain the dynamic stability of the DFIG in the presence of grid faults. The DVR is used as a cost-effective solution to compensate for grid-induced voltage sags. The SMES is used as a grid-enabling device that instantaneously stores and discharges high levels of power to mitigate the sudden drop in the power resulting from the grid fault. The effectiveness of the proposed approach is demonstrated using a DFIG-based wind energy system subjected to both symmetrical and unsymmetrical faults.

A fractional order sliding mode control-based topology to improve the transient stability of wind energy systems

This paper proposes a control topology that integrates the robust performance of fractional order sliding mode control with the high charging/discharging rate and low maintenance requirements of super-capacitors to improve the transient stability of wind energy systems. The observer-based fractional-order sliding mode control (FOSMC) approach is designed to regulate the dc-link voltage and mitigate dynamic instabilities resulting from grid faults, matched and mismatched uncertainties and parameter variations. The super-capacitor, on the other hand, serves as an additional energy storage element that prevents dangerous current surges thereby stabilizing the power delivered to the grid. The proposed approach was validated using a DFIG-based wind energy system installed in a small-scale standalone power supply network. The obtained results confirmed the approach’s ability to effectively reduce current fault induced ripples and properly regulate the dc link voltage. They also revealed a remarkable reduction in the thermal stress of semiconductor junctions in the presence of grid faults and disturbances. A further comparison with a standard sliding mode control (SSMC) approach showed that the use of fractional calculus to formulate the FOSMC resulted in smoother control actions and reduced chattering effects compared to SSMC.

Artificial Intelligence Integrated Fractional Order Control of Doubly Fed Induction Generator-Based Wind Energy System

This paper proposes an artificial intelligence integrated (AI) fractional order robust control for a DFIG based wind energy conversion system. To reduce the chattering phenomena in the excitation signal, fuzzy system is employed for the adaptive adjustment of the discontinuous control gain while preserving the robustness of the closed-loop system. The stability of the closed loop system with fuzzy fractional order robust control (FFORC) is ensured using fractional order Lyapunov system. The proposed FFORC control scheme is tested using processor in the loop (PIL) experiment.MATLAB/Simulink environment is used to emulate DFIG based wind energy system and a Texas Instrument (TI) DSP320F37D processor is used for interfacing the proposed control scheme with the emulated DFIG model in Simulink environment. System performance under the proposed FFORC scheme is compared with classical sliding mode control(SMC).The experimental results justifies the superiority of the proposed FFORC control scheme under all test conditions.Under ideal condition and with the proposed FFORC control scheme, the speed tracking error is approximately zero while with SMC method the peak tracking error is 0.4 radian/s. Similarly the active and reactive powers tracking is smooth with the proposed control system, while with SMC method the reactive power oscillates on both sides of the reference and it reaches 0.01 kVAR on positive side and −0.01kVAR on the negative side of the plot.Under parameters variation, system with FFORC control scheme offers minimum steady state error which is about 0.01 radian/s, while in case of SMC with saturation function a peak value of 0.6 radian/s is recorded. In case of SMC with sgn function, the speed tracking error is around 0.1 radian/s.Moreover the proposed FFORC scheme exhibits minimum chattering.

A Fractional Order SMC approach to Improve the Reliability of Wind Energy Systems During Grid Faults

This paper proposes a fractional order sliding mode approach for the converters of a DFIG-based wind energy system. It aims at mitigating grid faults and improving power reliability in the presence of uncertainties and sudden load variations. Its reliance on fractional calculus results in smoother control actions and reduced chattering compared to standard SMC. It also implements a super-capacitor as energy storage for its faster response compared to a conventional battery. The proposed approach was validated using a DFIG-based wind energy system installed in a small-scale standalone power supply network. Its performance was further compared to that of a standard PI approach. The obtained results showed significant improvement in the performance of the wind energy system during grid faults.

Development of improved direct current based saturated core fault current limiter in DFIG system for enhancing the low voltage ride‐through capability

With the increase of electrical energy consumption and associated power grid expansion the complexity in the grid network increases which tends to increase the fault occurrence possibilities. Low voltage ride-through capability helps the system to achieve the standard of grid code by remaining connected with the grid during fault occurrence. Direct current-based saturated core fault current limiter (DC-SCFCL) is a type of fault current limiter with superior influence in fault ride-through (FRT) capability. It has an arrangement of embedded alternating current coil and direct current (DC) coil across the side and centre of the core limb. However, being more effective in FRT, the ideal DC-SCFCL has some of its drawback such as development of high voltage across the DC coil and more current difference during fault occurrence. To limit these drawbacks a modified DC-SCFCL with shorted ring and also working with the coil below its critical temperature has been proposed in this study. The voltage profile, as well as the current profile, has been analysed which comprises its behaviour in all conditions. The proposed technique is applied to DFIG-based wind energy system to restrict the fault current and enhance the FRT capability.

A long-range generalized predictive control algorithm for a DFIG based wind energy system

This paper presents a new Long-range generalized predictive controller in the synchronous reference frame for a wind energy system doubly-fed induction generator based. This controller uses the state space equations that consider the rotor current and voltage as state and control variables, to execute the predictive control action. Therefore, the model of the plant must be transformed into two discrete transference functions, by means of an auto-regressive moving average model, in order to attain a discrete and decoupled controller, which makes it possible to treat it as two independent single-input single-output systems instead of a magnetic coupled multiple-input multiple-output system. For achieving that, a direct power control strategy is used, based on the past and future rotor currents and voltages estimation. The algorithm evaluates the rotor current predictors for a defined prediction horizon and computes the new rotor voltages that must be injected to controlling the stator active and reactive powers. To evaluate the controller performance, some simulations were made using Matlab/Simulink. Experimental tests were carried out with a small-scale prototype assuming normal operating conditions with constant and variable wind speed profiles. Finally, some conclusions respect to the dynamic performance of this new controller are summarized.

Optimal Tuning of PI Controllers for DFIG-Based Wind Energy System using Self-adaptive Differential Evolution Algorithm

Optimal Tuning of PI Controllers for DFIG-Based Wind Energy System using Self-adaptive Differential Evolution Algorithm

Experimental validation of SMC-based DTC-VC for DFIG-WECS

This paper proposes the decoupling control of direct torque control (DTC) and vector control (VC) for doubly fed induction generator (DFIG)-based wind energy system (WES). DTC controls the generator speed, torque and rotor flux; similarly VC regulates the capacitor link voltage and power quality of grid. Furthermore, proposed technique analyzed with sliding mode control (SMC) and conventional controllers independently. SMC shows better improvements of both stator and rotor currents, capacitor link voltage, torque and generator speed in contrast to proportional and integral controller. The proposed DTC-VC aims to improve performance of transient and dynamic behavior of DFIG-based WES during sine nature of torque and random as well as step change variation of generator speed. It is also tested in real-time environment at 7.5 hp as well as MATLAB/Simulink of 5.5 kw rating.

A Single Input Variable FLC for DFIG-Based WPGS in Standalone Mode

This paper proposes a novel single input variable fuzzy logic controller (FLC) strategy for a wind turbine driven doubly fed induction generator (DFIG) with a battery energy storage in autonomous mode. The proposed control scheme has multi-functionalities such as harmonic elimination, compensation of unbalanced load currents, and extraction of maximum power from the wind. The control of rotor side converter and load side converter is based on the field-oriented approach. The proposed single input variable FLC-based control has good dynamic response as compared to conventional control algorithms. The tip speed ratio-based maximum power point tracking technique is used to extract maximum power from the wind. The simulation of DFIG-based wind power generation system is carried out under various conditions, such as constant wind speed, variable wind speeds, and load currents unbalancing, using Simpower Systems toolbox of MATLAB. The DFIG stator voltages and currents are found balanced and sinusoidal by maintaining constant frequency and voltage at PCC. Finally, a prototype of DFIG-based wind energy system is developed in the laboratory to verify the performance of the proposed control scheme at variable wind speeds and linear, nonlinear loads. The total harmonic distortions of the DFIG stator voltages and currents are obtained within the limits of the IEEE 519 standard.

Instantaneous direct voltage and frequency control in DC grid tied DFIG based wind energy system

The paper presents a new configuration of DFIG based wind energy system which belongs to the connection with the DC grid/bus. The presented configuration of DFIG based wind energy systems is kind of stand-alone DC-DFIG based system where: (i) The load replaces the utility grid; (ii) the rotor is connected to the DC grid/bus/micro-grid via rotor side converter. Being a stand-alone DC-DFIG based system, only voltage and frequency regulation is considered here in this research. The rotor side converter provides the controlled amplitude & frequency of the rotor current for regulating the stator side voltage magnitude and frequency. Also, a simple and easy to implement in hardware as well as in software control technique for the rotor side converter, based on root mean square detection of stator/load voltage, is proposed in this study. The proposed configuration is found suitable in the areas having low-to-medium wind speed. The MATLAB Simulink model of 2 MVA DFIG has been considered to evaluate the performance of the system. Also, hardware set-ups comprise with (i) 3.2 kW DFIG (ii) DC shunt motor, and (iii) FPGA Altium NanoBoard3000 controller, are considered to reveal the effectiveness of the proposed configuration and controller in real time environment.

Control for grid-connected DFIG-based wind energy system using adaptive neuro-fuzzy technique

Control for grid-connected DFIG-based wind energy system using adaptive neuro-fuzzy technique

Mathematical modeling and control of DFIG-based wind energy system by using optimized linear quadratic regulator weight matrices

Summary This paper deals with a genetic algorithm (GA)–based linear quadratic regulator (LQR) controller to improve the dynamic response, stability, and robustness of the doubly fed induction generator (DFIG) system at various stator voltage disturbances. The complete model has been represented by a state-space model. This helps to optimally control all the states through the full-state feedback LQR controller. GA is employed in the LQR algorithm for optimal tuning of the Q and R matrices. For finding out the effectiveness of the proposed controller, its dynamic response has been compared with proportional integral (PI) and LQR controllers. The simulation results indicate that the proposed controller has better performance in comparison to the PI and LQR controllers in terms of peak value, settling time, rise time, and steady-state values of the DFIG outputs. The stability and robustness of the study system have also been investigated by the eigenvalue and participation factor analyses.