Metaheuristic-Based Control Parameter Optimization of DFIG-Based Wind Energy Conversion Systems Using the Opposition-Based Search Optimization Algorithm

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.

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.

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 is a promising source of electricity in the world and fastest-growing. Doubly-Fed Induction Generator (DFIG) systems dominate and widely used in wind power system because of their advantages over other types of generators, such as working at different speeds and not needing continuous maintenance. In this paper used the PI controller and Flexible AC Transmission System (FACTS) device specifically static compensator (STATCOM) to investigate the effect of the controller and FACTS device on the system. PI controller tuning by Particle Swarm Optimization technique (PSO) to limit or reduced the fault current in (DFIG) system. The responses of different kinds of faults have been presented like; two lines to ground faults and three lines to ground faults at different operating conditions. Faults are applied to three proposed controllers; the first controller is the Proportional-Integral (PI), the second controller is PI-controller based on Particle Swarm Optimization (PI-PSO) technique and STATCOM. A reactive power static synchronous compensator (STATCOM) is used, the main aim for the use of STATCOM is to improve the stability of a wind turbine system in addition to this is improving voltages profile, reduce power losses, treatment of power flow in overloaded transmission lines. The simulation programming is implemented using MATLAB program.

Doubly Fed Induction Generator (DFIG)-based Wind Energy Systems (WESs) have become increasingly prominent in the global energy sector, owing to their superior efficiency and operational flexibility. Nevertheless, DFIGs are notably vulnerable to fluctuations in the grid, which can result in power quality issues—including voltage swells, sags, harmonic distortion, and flicker—while also posing difficulties in complying with Fault Ride-Through (FRT) standards established by grid regulations. To address the previously mentioned challenges, this paper develops an integrated approach utilizing a Dynamic Voltage Restorer (DVR) in conjunction with a Lithium-ion storage system. The DVR is coupled in series with the WES terminal, while the storage system is coupled in parallel with the DC link of the DFIG through a DC/DC converter, enabling rapid voltage compensation and bidirectional energy exchange. Simulation results for a 2 MW WES employing DFIG modeled in MATLAB/Simulink demonstrate the efficacy of the proposed system. The approach maintains terminal voltage stability, reduces Total Harmonic Distortion (THD) to below 0.73% during voltage sags and below 0.42% during swells, and limits DC-link voltage oscillations within permissible limits. The system also successfully mitigates voltage flicker (THD reduced to 0.41%) and harmonics (THD reduced to 0.4%), ensuring compliance with IEEE Standard 519. These results highlight the proposed system’s ability to enhance both PQ and FRT capabilities, ensuring uninterrupted wind power generation under various grid disturbances.

Production of rated power and protection of generator and power converter from an overload are necessary. Therefore, measuring the accurate value of the pitch angle of the doubly fed induction generator (DFIG) wind turbine is essential. An assessment study between the adaptive particle swarm optimisation (PSO) technique and conventional proportional integral (PI) controller of the pitch control system in limiting the electrical output power at the rated value of DFIG is introduced in this study. Pitch control with PSO is designed by solving the nonlinear equation of pitch angle at each wind speed higher than rated wind speed. The PI controller gains of the pitch system are evaluated to keep the power limited at rated value. Accuracy in measuring pitch angle is essential because a small difference in pitch angle value results in an overload on the generator. The performance of each parameter of DFIG with a detailed analysis is studied. The simulation shows that the pitch control system with PSO technique gives better results in regulating the DFIG output power compared to PI controller method under wind speed variation.

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.

Variations in wind speed cause different transient responses in a Doubly Fed Induction Generator (DFIG)-based wind farm. Transients during grid disturbances, depending on controller parameters, can lead to a system collapse, especially when significant fluctuations exist in wind speed despite the presence of SmartParks (energy storage). When a fault is introduced, the variable frequency converter (VFC) is the most susceptible part in a DFIG. The VFC is controlled by a set of Proportional Integral (PI) controllers. Parameters of PI controllers are very difficult to tune using traditional methods due to nonlinearity in DFIGs and the increasing complexity of smart grids. This paper presents an implementation of a new efficient heuristic approach, the Mean Variance Optimization (MVO) algorithm, on a digital signal processor, for online tuning of PI controllers on the rotor-side converter of a DFIG. With the MVO-optimized PI controllers on the wind farm, the entire smart grid remains stable under grid disturbances for a wide range of wind speeds. The results demonstrate that intelligent controller tuning is critical for optimal operation of DFIG-based wind farms in a smart grid despite the presence of energy storage.

Small-signal stability analysis of DFIG based wind power system using teaching learning based optimization

Wind energy conversion systems (WECSs) operating in stand‐alone mode are increasingly recognized as a promising solution for delivering reliable renewable electricity in remote areas. However, their performance is often hindered by sudden wind speed fluctuations and parameter variations, which can compromise both stability and power quality. To address these challenges, this article presents a hardware implementation study of a direct power control (DPC) strategy applied to a doubly fed induction generator (DFIG) in variable‐speed WECSs. The proposed approach introduces a robust integral–proportional (IP) regulator for the rotor‐side converter (RSC), with its gains optimally tuned using the particle swarm optimization (PSO) algorithm. Control is performed through the direct and quadrature axis components of the rotor current using a back‐to‐back alternating current–direct current–alternating current converter. The methodology involves both simulation and real‐time implementation using the dSPACE dS1103 board, with system validation under subsynchronous and supersynchronous operating conditions. A Bode‐plot analysis is employed to compare the stability margins and response times of conventional and PSO‐optimized control schemes. Both simulation and experimental results demonstrate that the proposed strategy ensures high stability, accurate reference tracking, and effective decoupling of active and reactive power, even under sudden wind speed variations. Moreover, it achieves superior dynamic responses, shorter settling times, and significantly reduced power errors, thereby confirming its suitability for enhancing the performance and reliability of stand‐alone wind energy systems.

This paper addresses the use of resonant control to improve the grid ride-through capability of Doubly Fed Induction Generators (DFIG) under unbalanced grid conditions. DFIG is very sensitive to grid disturbances as its stator circuit is directly connected to the grid. Voltage sags can be harmful to the wind energy conversion systems (WECS) as they induce high voltages and high currents in the rotor circuit. It is important that wind energy systems continue operating during grid disturbances in order to guarantee grid stability. Electric system operators worldwide are introducing minimum requirements for the connection of wind energy systems to the grid. In this regard, this paper presents the insertion of resonant control to the classical PI control strategy in order to improve the ride-through capability during unbalanced voltage dips. The study was carried out in a 30kW WECS laboratory bench. Simulation results using MATLAB/Simulink and experimental results show the control performance.

A voltage dip is a sudden drop of voltages - generally between 10 and 90 % of the rated RMS value - during a period lasting from half a cycle to a few seconds on the phases of the power lines. It is one of the most important power quality problems affecting the stability of the Doubly Fed Induction Generator (DFIG) in Wind Energy Conversion System (WECS) and hence needs to be analyzed for a given machine for its performance analysis under grid disturbances. When a voltage dip problem happens in the given power as of faults, the magnitude of the rotor and stator currents of DFIG get increased, and hence disturbs steady state operation of the system. Therefore, in this paper, the worst voltage dip of 90 % is tested for grid faults on a 5 kW DFIG to validate its performance under this phenomenon. Based on the results of the simulation - along with its experimental validation - the machine is found to be robust for faults staying a shorter period without disconnecting it from the grid. On the other hand, operating a machine for a longer period while keeping it connected to a grid during a heavy dip, may result in its degradation as the rated limits are violated. To rectify and mitigate the aforesaid problems, a control system - with and without a crowbar - is developed for symmetrical faults to tackle such a problematic situation, and to discuss its fulfillment of the current grid code requirements. Finally, a complete model of the DFIG coupled with the grid is developed, modeled, analyzed and simulated using MATLAB/Simulink user-defined function toolbox block.

Integration of doubly fed induction generator (DFIG)-based wind energy system (WES) introduces small-signal angular stability issues in synchronous generator dominated power systems. It is essential to understand the effect of system parameters on damping contribution by DFIG to the rest of the power system. This article presents a simplified mathematical formulation for analyzing system parameter's impact on the damping of interarea oscillations. A simple power system model consisting of two synchronous machines and a DFIG has been considered for mathematical derivations. For deriving the damping torque contribution to the rest of the power systems, DFIG has been used as a feedback system. The derived mathematical formulation has been validated for a higher order power system integrated with WES. The derived expression of damping contribution to synchronous machines supports the concept that the damping is highly dependent on transmission line reactance, which further assists the idea that WES placement in the power system has a crucial role in improving damping contribution to synchronous machines.

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 proposes an innovative algorithm-based control model for improving the doubly fed induction generator (DFIG) system’s low-voltage ride-through (LVRT) capabilities. The Mountain Gazelle optimization (MGO) algorithm is combined with an adaptive neuro-fuzzy inference system (ANFIS)-based proportional-integral (PI) regulator, called the MGOANFIS-PI approach. In the proposed method, the data set of an MGO-based PI regulator is used after rectification to train the MGOANFIS controller, so updating the ANFIS technique improves MGO’s searching behavior. The implemented control method ensures LVRT capability on grid-tied wind conversion plants based on DFIG in case of a malfunction or a voltage drop. Multiple metrics relating to LVRT are monitored in the proposed system, comprising current, active, and reactive power and voltages. The objective to be achieved, which is resolved through the cascaded MGOANFIS-based PI scheme, is defined. Based on the collected data, ANFIS performs and anticipates the optimal control signal from converters on the rotor and network sides. The MGOANFIS-PI methodology effectively handles system performance and voltage instability during four fault circumstances. The LVRT functionality of the DFIG system and the power quality are enhanced with the assistance of the proposed methodology. Simulations are carried out using MATLAB/Simulink framework. Two test systems are examined: the IEEE 39 bus and the 9 MW wind power plant (WPP) test systems. The WPP’s ability to withstand grid voltage sags is analyzed as part of an effectiveness assessment. The relevance and efficacy of the proposed MGOANFIS-PI method are evaluated, and proved the better performance of the cascaded MGO-based ANFIS-PI controller when compared to PI controllers optimized by comparing it with other well-known optimization techniques, i.e., Gorilla troop optimizer, particle swarm optimization, and genetic algorithm. In the three-phase IEEE 39 bus system and the 9 MW test scheme, detailed wind power plants could persevere in the face of every fault condition.