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

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.

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.

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.

Load frequency control is one of the most important issues in multi area power systems. In this context, due to growing development of wind turbines in power systems, this type of renewable power sources can be used efficiently in frequency control. This paper applies doubly-fed induction generators (DFIGs) along with other conventional generation units for frequency performance enhancement after disturbances in multi-area power systems. Proportional integral (PI) controller is the conventional controller which is used in DFIGs to contribute in frequency control. The parameters of PI controller for DFIG are optimized by improved particle swarm optimization (IPSO) method. Simulation results in a three-area power system illustrate the efficiency of the DFIGs for frequency and tie-line power oscillations improvement.

In this chapter, a hybrid Artificial Neural Network (ANN) with Proportional Integral (PI) control technique for a Doubly Fed Induction Generator (DFIG) based wind energy generation system is developed and its performance is compared with NN and PI control techniques. With the increased utilisation of wind power generation, a dynamic performance analysis of the Doubly Fed Induction Generator under varied operating situations is necessary. Three control strategies are proposed in this Chapter: the first uses a PI controller, the second uses an ANN controller, and the third uses a mix of ANN and PI. The findings obtained using MATLab/Simulink indicate the performance of the proposed control techniques. The dynamic performance of the DFIG is improved with the Hybrid control technique, according to the findings. The Hybrid ANN-based system that estimates the control parameters of the generator showed satisfactory characteristics as was verified in the presented results.  

This paper presents a load frequency control (LFC) of five areas connected to same tie-line and it consist of non-conventional main power grids (Thermal-Hydro) and micro-grids (Wind-Diesel). Performance of conventional PI controllers is compared with PSO (Particle Swarm Optimization) tuned PI controller in a regulated and deregulated environment. A five area power system model has been made using Matlab/simulink to analyse load frequency control measures under different conditions based on participation factors, load disturbances and variations in local load demands. Simulation results shows that PSO(Particle Swarm Optimization) tuned PI(Proportional & Integral) controller's gains give much better performance than the conventional PI controller parameters which are more difficult to determine and time-consuming. The effect of DFIG (Doubly fed Induction Generator) in the power system which helps to smooth the tie line power is also been described.

The Vector controlled doubly fed induction generator (DFIG) wind energy system in a multi-machine power system is very sensitive to faults on transmission lines and thus affects the stability of the system. The performance of vector controlled DFIG highly depends on PI controller parameters. The objective of this paper is to optimize the performance of vector controlled DFIG in multi-machine power systems under faulty conditions by tuning the parameters using advanced differential evolution algorithm. An objective function is formulated, which drives the system with good voltage profile, reduced power oscillations under faulty conditions. The parameters of PI controller are tuned using Composite Differential Evolution algorithm. Simulations are performed on a two area benchmark problem in Matlab/Simulink. The simulation results of the optimized DFIG wind energy system are compared with the actual existed system. The simulation results show that the overall performance and stability of the optimized system are enhanced.

Wind energy systems using doubly fed induction generators (DFIGs) rely on vector-oriented control (VOC) to achieve decoupled regulation of active and reactive power. Conventional VOC strategies typically employ proportional-integral (PI) controllers for rotor current control; however, these controllers struggle under nonlinear and variable operating conditions, such as wind speed fluctuations, leading to degraded dynamic performance and power quality. To overcome these limitations, this paper proposes an intelligent VOC approach based on the M5-Pruned model tree (M5P) algorithm, complemented by a comparative study with fuzzy logic controllers (FLC). The M5P-based controller introduces a data-driven mechanism that adapts to system dynamics, ensuring improved accuracy and robustness compared to traditional PI and FLC methods. A detailed MATLAB/Simulink simulation of a grid-connected wind turbine with DFIG evaluates the controllers under varying wind profiles. Results demonstrate that the proposed M5P strategy significantly reduces overshoot and settling time, enhances power regulation, and improves overall system stability. These findings highlight the advantages of machine learning-based control in addressing the shortcomings of conventional VOC techniques for renewable energy applications.

Adaptive control universality of STATCOM for efficient wind energy conversion systems

This article employs two decomposition techniques and the residue principles to determine the order of the converter controls of a doubly fed induction generator (DFIG) in terms of providing system damping. The dynamic model of the various components of the DFIG system—the wind turbine, induction generator, converter system, the load and transmission network are derived and 4 converter controls identified. The hierarchy analysis demonstrates that out of 4 converter controls the magnitude of the rotor applied voltage is the most effective one. The finding was verified by simulating the system through both lead-lag as well as proportional-integral (PI) controller designs, the controller parameters being found through the residue principle. Simulation studies on the doubly fed generator system show that both the controller designs provide good damping to the system, the PI control being slightly superior. The controlled rotor voltage was observed to be able to avert voltage collapse in the system arising in the event of severe disturbance conditions.

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.

Recently, the Gravitational-Search Algorithm (GSA) has been presented as a promising physics-inspired stochastic global optimization technique. It takes its derivation and features from laws of gravitation. This paper applies the GSA to design optimal controllers of a nonlinear system consisting of a doubly-fed induction generator (DFIG) driven by a wind turbine. Both the active and the reactive power are controlled and processed through a back-to-back converter. The active power control loop consists of two cascaded proportional integral (PI) controllers. Another PI controller is used to set the q-component of the rotor voltage by compensating the generated reactive power. The GSA is used to simultaneously tune the parameters of the three PI controllers. A time-weighted absolute error (ITAE) is used in the objective function to stabilize the system and increase its damping when subjected to different disturbances. Simulation results will demonstrate that the optimal GSA-based coordinated controllers can efficiently damp system oscillations under severe disturbances. Moreover, simulation results will show that the designed optimal controllers obtained using the GSA perform better than the optimal controllers obtained using two commonly used global optimization techniques, which are the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO).

Traditional centralized power stations face challenges such as high greenhouse gas emissions, reliance on non-renewable resources, and inefficiencies in meeting peak power demand. Additionally, Renewable Energy Sources (RESs) like solar and wind complicate efforts to maintain a consistent power supply and grid stability. This study proposes a Hybrid Renewable Energy System (HRES) that integrates a Photovoltaic (PV) system with a Doubly Fed Induction Generator (DFIG) based Wind Energy Conversion System (WECS) to address these challenges. The PV system incorporates a High Gain Cubic Boost Converter (HG-CBC), regulated by a Raccoon Optimized Algorithm (ROA) based Proportional Integral (PI) controller, to enhance voltage levels. The DFIG based WECS stabilizes power flow through a Pulse Width Modulation (PWM) rectifier and a PI-controlled PWM generator. Moreover, a 3-phase Voltage Source Inverter (3Φ VSI) converts DC power into AC for grid integration. Experimental verification using MATLAB/Simulink demonstrates a high efficiency of 98%, showcasing the model's ability to significantly boost system reliability and suitability for HRES-powered grids.

Conventional control techniques such as proportional integral (PI) control and proportional integral derivative (PID) control are widely used as the load frequency controller in power system control applications; however, the increase in complexity of the power system is degrading the performance of classical control. Therefore, in this paper a new control approach using fractional calculus has been proposed for the load frequency control problem of two-area hydro-thermal power system. The paper presents an interconnected hydro-thermal power system working under different operating conditions introduced in the form of various nonlinearities. Four case studies have been presented in this paper considering transient analysis with (i) different loading conditions, (ii) parameter variations, (iii) different nonlinearities (governor dead band nonlinearity, time delay and generation rate constraints), and (iv) superconducting magnetic energy storage (SMES) device. A tilt integral derivative (TID) control has been presented as a secondary controller to stabilize the frequency deviations occurring in the two-area power system with the above-mentioned different operating conditions. The parameters of TID controller have been optimized using a differential evolution algorithm which solves an optimization problem formulated using the integral of time-weighted absolute error (ITAE) performance index. In addition, a comparison of dynamic system response obtained using TID control and PID control has been presented, which focusses on the better performance of TID control over PID control in an interconnected power system.

In this study, an integral sliding mode control approach for controlling the power electronics converters of a doubly-fed induction generator (DFIG) wind turbine system is presented. The power electronics interface consists of back-to-back converters. The rotor side converter regulates the active and reactive powers at the DFIG stator through controlling the stator currents. The stator current dynamics, with respect to the rotor voltages, is developed from the conventional equations of the DFIG model. In this control configuration, the knowledge about the rotor currents is not required, which reduces the use of the current measurement sensors. The grid side converter ensures constant dc-link voltage while transferring the power from the DFIG rotor to the grid. The proposed control approach uses a composition of sliding mode and integral parts to improve the overall performance and robustness against parametric variations and uncertainties. A lab-scale DFIG wind turbine system is used to investigate the proposed control approach efficiency under various operating conditions. The experimental results show the effectiveness of the proposed control approach in achieving control objectives to operate the DFIG wind turbine system.