Maximum power point tracking of DFIG-WT using feedback linearization control based current regulators

Nonlinear maximum power point tracking control and modal analysis of DFIG based wind turbine

Transient simulation model of doubly-fed induction generator-based wind turbine has a complex model structure and considerable control parameters. An accurate simulation model is especially important for studying the static and transient characteristics of doubly-fed induction generator-based wind turbine and the grid-connected system. Based on the measured data of low voltage ride-through testing, the integrated parameter identification method is proposed here. Combined with the time-domain simulation analysis, curve fitting is used to determine the control strategy of doubly-fed induction generator-based wind turbine. The improved genetic algorithm is used to identify the generator parameters, power control parameters, and resistance of Crowbar of doubly-fed induction generator-based wind turbine. Combined with the electrical parameters provided by the wind turbine manufacturer, electromagnetic transient model on full operation condition and rapid starting period, corresponding to a measured doubly-fed induction generator-based wind turbine in West Inner Mongolia Power Grid, is then established, which can operate in the full wind speed range and quickly enter the stable state within 1 s after start-up. Comparison of simulation results and measured data shows that the established electromagnetic transient simulation model using parameters identified by the proposed method can accurately reflect the key characteristics of doubly-fed induction generator-based wind turbine.

This paper presents a sensor fault-tolerant control (SFTC) strategy, combining perturbation observer-based direct power control (PODPC) and two-stage Kalman filter (TSKF) to enhance the fault tolerance of a doubly fed induction generator-based wind turbine (DFIG-WT) subject to rotor and stator current sensor faults. In the PODPC scheme, the interactions between active and reactive power control loops are represented by newly introduced perturbation states, and the feedback linearization control is realized with the state estimations derived from perturbation observers to achieve the decoupled power control. No rotor current sensors nor parameters of DFIG-WT are required in the implementation of PODPC. Stator current TSKFs are designed to generate residuals for fault detection and isolation, and provide current estimations to replace the faulty current measurements for system reconfiguration under stator current sensor faults. Simulation studies undertaken on a grid-connected DFIG-WT system reveal that the proposed SFTC strategy is immune to rotor current sensor faults, and it provides strong fault tolerance to stator current sensor faults.

Proposing and evaluation of MPPT algorithms for high-performance stabilized WIND turbine driven DFIG

Effective power regulation is critical for ensuring the stability and performance of Doubly Fed Induction Generator (DFIG)-based wind turbines. This study conducts a comparative study between two control methodologies: fuzzy logic control (FLC) and sliding mode control (SMC), to regulate power in DFIG-based wind turbines. The paper emphasizes the importance of power regulation in wind turbines and its impact on grid stability, presents mathematical equations governing DFIG-based wind turbine systems, encompassing machine equations, power equations, and control objectives related to power regulation. A comprehensive model of the DFIG-based wind turbine system is developed, accounting for dynamic machine behavior and grid interface considerations. The modeli ng process integrates relevant electrical and mechanical components, along with power regulation control strategies. Subsequently, FLC and SMC control methodologies are developed and implemented to regulate desired power. Detailed discussions on controller design and tuning are provided, aligning with specific power control requirements. The results highlight SMC’s effectiveness in achieving precise power regulation within DFIG-based wind turbines, showcasing its superior response characteristics. The study offers valuable insights into the advantages and trade-offs associated with each control approach, aiding researchers and practitioners in selecting the most suitable control methodology for their operational needs. In summary, this paper contributes to the field of wind turbine control systems through a comparative study of FLC and SMC controllers, focusing on regulating power in DFIG-based wind turbines. The findings demonstrate SMC’s superior performance in response, enhancing our understanding of control methodologies for optimizing wind turbine performance and grid integration in sustainable energy systems.

This paper presents an optimal Direct Torque Control (DTC) strategy for Doubly Fed Induction Generator based wind turbine. The proposed strategy is considered based on Particle Swarm Optimization (PSO) Algorithm. The PSO is found to be robust and fast in solving nonlinear problems. Motivation for application of PSO approach is to overcome the limitation of the conventional controllers design, which cannot guarantee satisfactory control performance when designed by trial and error. In this work PSO algorithm was used to adjust the hysteresis torque and flux comparators bandwidths and to tune the parameters of Variable Gain PI (VGPI) controller designed for Maximum Power Point Tracking (MPPT) speed control of wind turbine to ensure high performance torque control. The optimal control strategy is considered in detail and it is shown that the use of the optimized controllers reduces rotor flux and electromagnetic torque ripples and improves the system dynamic performances. The effectiveness of the proposed direct torque control based on PSO algorithm (optimal DTC) method is illustrated through simulations on 1.5 MW DFIG based wind turbine. Simulation results illustrate the improved performances of optimal DTC compared with the conventional DTC.

This paper presents a new FRT control strategy to improve the dynamic response of doubly fed induction generator based wind turbine (DFIG-WT) during grid fault. The proposed control scheme is based on hybrid protection circuits, which comprises an active crowbar circuit, a series dynamic resistance (SDR) circuit, and a DC-link Chopper circuit. A coordinated control strategy is been developed to enable the circuit. This strategy enables the crowbar to disable the rotor side converter (RSC), allowing the rotor overcurrent to path through SDR resistors, and the DC-Chopper is engaged to suppress the overvoltage in DC-Link. Simulation analysis by using MATLAB/Simulink shows that the proposed strategy enhanced FRT capabilities for DFIG in symmetrical voltage dip, protected the power converter, limited the rotor overcurrent, and suppressed the DC-link overvoltage.

This paper proposes a perturbation estimation based nonlinear adaptive power decoupling controller for doubly fed induction generator based wind turbines (DFIG-WTs). Perturbation states are defined to include the nonlinearities, uncertainties of the system model, the cross-coupling between control loops, and external disturbances. Perturbation observers are designed to estimate the fast time-varying perturbation states. With perturbation estimation, the DFIG-WT system is fully decoupled, and an output feedback control can be designed for the control of rotor currents. Rotor current references are calculated based on the steady-state relation between active/reactive power and rotor current, and stator dynamic is ignored. The performance of the proposed controller is evaluated and verified via both simulation and experimental tests.

This paper discusses modeling of a micro-grid with PV-Wind-Diesel generator hybrid system and its operations. The PV system is modeled with a DC/AC inverter with a pre-defined Maximum Power Point Tracking (MPPT) control. Fixed speed induction generator based wind turbines are used to model the wind farm. The diesel generator controls are modeled with droop governor and automatic voltage regulator (AVR). The PV inverter controls, including MPPT and voltage droop, are modeled in the DC/AC inverter itself. The proposed MPPT control is more stable with additional input of PV cell temperature. The complete system is modeled in PSCAD/EMTDC software. Performance of this system is studied on (i) fault tripping of the main grid and (ii) islanded operation of micro-grid with intermittent power from the wind and PV plants. The simulation results confirm the smooth operations of the proposed concept for disturbances from main grid as well as due to the intermittent wind and PV renewable power plants.

This study proposes the speed control of an asynchronous motor (AM) using the Antiwindup design. First, the conventional vector control based on proportional-integral (PI) controllers is developed for a constant speed set point. Then, a driving cycle is based on measurements on the Safi/Rabat motorway in Morocco using a microcontroller equipped with a GPS device. The collected practical speed is used as a speed reference for conventional vector control. The /Antiwindup controller of the direct rotor flow-oriented control is used to improve the performance of conventional vector control and optimize the energy consumption of the drive train. The effectiveness of the proposed control scheme is verified by numerical simulation. The results of the numerical validation of the proposed scheme showed good performance compared to conventional vector control. The speed control systems are analyzed for different operating conditions. These control strategies are simulated in the MATLAB/SIMULINK environment. The simulation results of the improved vector control of the Asynchronous Machine (AM) are used to validate this optimization approach in the dynamic regime, followed by a comparative analysis to evaluate the performance and effectiveness of the proposed approach. A practical model based on a TMS320F28379D embedded board and its reduced voltage inverter (24V) is used to implement the proposed method and verify the simulation results. Doi: 10.28991/ESJ-2022-06-04-012 Full Text: PDF

This paper proposes an advanced control strategy for DFIG based Wind Turbine. The designed method is based on a combination of Particle Swarm Optimization (PSO) and Artificial Neural Network (ANN). PSO combined ANN is suggested to track the available maximum power point (MPPT) at varying wind speeds. Thus, PSO is used to improve the dynamic performance of Doubly Fed Induction Generator (DFIG) by optimizing its Proportional Integral (PI) controller gains. This work highlights the performance of the PSO optimized PI controller against classical one. The proposed control strategy is verified via 2 MW DFIG-WT using the MATLAB/Simulink environment. The obtained results validate the proposed PSO-PI as an effective tool for improving the dynamic behavior of DFIGWT, it reveals that the overshoots are reduced by 50 % compared to the classical PI. In addition, a faster transient response is achieved using the proposed control strategy.

In this work, comparative analysis between various maximum power point tracking (MPPT) control schemes and pitch angle control methodologies are presented. In first place, proportional integral (PI), Sliding mode (SMC), Backstepping (BS) and Fuzzy logic (FLC) control strategies applied for MPPT to maximize the power during the wind energy conversion are developed. Then, the blades pitch angle control using PI and FLC controllers in order to limit the output power of the wind turbine (WT) are presented. The systems performances were simulated and compared using Matlab/Simulink environment. Simulation results show the effectiveness of the proposed control strategies compared to the PI one, regarding reference tracking, inaccessibility of turbine parameters and sensibility to high variations of wind speed.

Solar energy is one of the principal renewable energy sources for electric power generation. However, maximizing the power extraction from solar Photo-Voltaic (PV) systems remains a challenge due to their inherent low conversion efficiency. To address this issue, a Maximum Power Point Tracking (MPPT) controller is necessary to optimize power extraction in a PV system. This paper aims to conduct a comparative study between two distinct MPPT control schemes. The first is a simple single-loop system employing the Perturb and Observe (P&O) algorithm. The second is an advanced dual-loop system integrating the P&O algorithm with a Proportional-Integral (PI) controller. The comparison evaluates the systems' performance in terms of steady-state accuracy. For this purpose, a MATLAB/Simulink model of a stand-alone PV panel was developed. The proposed MPPT schemes were then implemented on this PV system under varying environmental conditions to assess their ability to track the MPPT. The simulation results indicate that the dual-loop control scheme outperforms the single-loop scheme in terms of steady-state performance, particularly during abrupt environmental changes.

The self-excited induction generator (SEIG) is widely applied in the wind energy conversion system (WECS) to enhance power generation. The power generation from WECS is varied in terms of varying wind speed. Hence, to improve the working of SEIG-based WECS, the multi-stage coati optimized proportional integral (CPI) fractional order proportional integral derivative (FOPID) controller is proposed in this work. The proposed coati optimized proportional integral - fractional order proportional integral derivative controls the SEIG’s grid side converter and generator side converter. The coati optimization algorithm optimizes the controller parameters of a proposed multi-state controller. The proposed CPI-FOPID controls both the voltage at the grid side converter and the current at a generator side converter. Moreover, the pitch angle of the wind turbine (WT) is controlled by the fuzzy-based tilt integral derivative (F-TID) controller. The proposed work will be implemented on the Matlab/Simulink platform, and the THD of 0.63% has proven the efficacy of the proposed methodology.

A hybrid feedback linearizing-Kalman filtering control algorithm for a distillation column