DFIG-based Wind Energy Conversion System Research Articles

A Three-Level Neutral-Point-Clamped Converter Based Standalone Wind Energy Conversion System Controlled with a New Simplified Line-to-Line Space Vector Modulation

Wind power systems, which are currently being constructed for the electricity worldwide market, are mostly based on Doubly Fed Induction Generators (DFIGs). To control such systems, multilevel converters are increasingly preferred due to the well-known benefits they provide. This paper deals with the control of a standalone DFIG-based Wind Energy Conversion System (WECS) by using a three-level Neutral-Point-Clamped (NPC) converter. The frequency and magnitude of the stator output voltage of the DFIG are controlled and fixed at nominal values despite the variable rotor speed, ensuring a continuous AC supply for three-phase loads. This task is achieved by controlling the DFIG rotor currents via a PI controller combined with a new Simplified Direct Space Vector Modulation strategy (SDSVM), which is applied to the three-level NPC converter. This strategy is based on the use of a line-to-line three-level converter space vector diagram without using Park transformation and then simplifying it to that of a two-level converter. The performance of the proposed SDSVM technique in terms of controlling the three-level NPC-converter-based standalone WECS is demonstrated through simulation results. The whole WECS control and the SDSVM strategy are implemented on a dSPACE DS 1104 board that drives a DFIG-based wind system test bench. The obtained experimental results confirm the validity and performance in terms of control.

LVRT Enhancement of DFIG-based WECS using SVPWM-based Inverter Control

Abstract: Across many countries, wind turbine generation systems (WTGS) have been established over the past few decades. In this paper, we augment the low voltage ride-through (LVRT) enrichment facility of driving a DFIG-based wind energy conversion system (WECS) using space vector pulse width modulation (SVPWM)-based inverter control. The proposed technique employs an SVPWM-based control algorithm to regulate the voltage and frequency of the output power during grid faults, thereby enhancing the WECS's ability to remain connected to the grid and provide power. The study focuses on decreasing transient current throughout the instant of fault. Modeling and control approaches were also discussed in this study. The performance of the proposed technique is evaluated using MATLAB/Simulink simulations, and the results demonstrate that the technique effectively improves the LVRT capability of the DFIG-based WECS. Background: Due to the variation in wind speed, the power generated by wind turbines is inconsistent. The power generated and the losses in wind turbines change correspondingly with changes in wind speed. The only type of machine that can generate power at speeds below the fixed speed is the doubly-fed induction generator (DFIG). But DFIG is oversensitive to network faults, which makes the bidirectional converters and DC link capacitor fail due to high inrush current and over-voltage. Methods: The converters connected to DFIG consist of an AC-to-DC converter, a boost converter, and a space vector pulse width modulation (SVPWM)-based DC-AC converter. The performance of the SVPWM controller is analyzed during symmetrical and unsymmetrical fault conditions. Results: The anticipated control provides adequate reactive power support to the network through the time of the fault and improves voltage and current waveform. The reactive power flow is also analyzed, and the effectiveness of the proposed controller is verified using MATLAB and Simulink. Conclusion: SVPWM (Space Vector Pulse Width Modulation)-based inverter control is an effective technique for wind energy conversion systems (WECS). The use of SVPWM can provide accurate and precise control of the AC voltage generated from the DC voltage source, resulting in improved system efficiency and reduced harmonic distortion in the output waveform. : The comparative analysis of THD suggests that SVPWM is a superior technique compared to other inverter control techniques such as sine-triangle pulse width modulation (SPWM) and carrier-based pulse width modulation (CPWM). SVPWM can help to reduce the distortion in the output waveform, leading to improved system efficiency, reduced wear on the system components, and overall better performance of the WECS. Furthermore, SVPWM offers several advantages over other inverter control techniques, including better utilization of DC voltage, improved voltage control, and better utilization of switching devices. These advantages make SVPWM a valuable tool for optimizing the operation of WECS and improving the reliability and performance of renewable energy systems. The value of THD for SVPWM inverter control in WECS is 1.53 under symmetrical fault and 1.34 for unsymmetrical fault, respectively. In summary, the use of SVPWM-based inverter control for WECS is an effective way to improve the efficiency and performance of the system while reducing the distortion in the output waveform and providing adequate reactive power support. The advantages of SVPWM over other inverter control techniques make it a valuable tool for the development and optimization of renewable energy systems.

Comparative study of controllers in battery energy storage system integrated with doubly fed induction generator-based wind energy conversion system for power quality improvement

This study presents a comparative analysis of controllers in a Battery Energy Storage System (BESS) integrated with a Doubly Fed Induction Generator (DFIG)-based Wind Energy Conversion System (WECS) aimed at improving power quality. The research background introduces the challenges associated with integrating renewable energy sources into the grid, emphasizing the intermittent nature of wind energy and its impact on power quality. Existing detailed problems, including voltage and frequency fluctuations, and grid instability, are outlined, highlighting the need for effective control strategies to mitigate these issues. The paper's contribution lies in evaluating and comparing different control techniques for BESS integration, including Proportional-Integral (PI) control, Fuzzy Logic (FL), and Artificial Neural Network (ANN) control. Through simulation studies conducted in MATLAB/Simulink, the effectiveness of each controller in regulating rotor current, rotor and stator power, voltage stability, state of charge (SoC) and grid synchronization is assessed. The findings provide valuable insights into the optimal controller selection for enhancing power quality in DFIG-based WECS with integrated energy storage systems, contributing to the advancement of renewable energy integration and grid stability.

Stability analysis and application of a synchronverter-based control approach to DFIG-based wind energy conversion systems

This work proposes an enhanced control approach relying on the synchronverter concept for wind energy conversion systems (WECSs) based on the doubly-fed induction generator (DFIG). It consists of incorporating a virtual filter resistor into the control loop of the synchronverter, which is associated with the grid-side converter (GSC) aiming at emulating the behavior of a synchronous generator when providing ancillary services such as frequency support. A comprehensive state-space representation is derived to demonstrate that the virtual filter resistor is essential to ensure stability. After the proper adjustment of the model, it is possible to verify the effectiveness of the synchronverter-based control applied in the IEEE 14-bus modified system employing parameters obtained from a real power plant. The results show that the application of the introduced method results in less severe frequency dips and faster transient response during frequency support even when compared with the case in which only synchronous machines are connected to the grid.

Control of DFIG-based microgrid and seamless transition from stator connected and disconnected modes

This study aims to ameliorate the contribution capability of doubly-fed induction generator (DFIG) to participate in standalone microgrid operation. The islanded microgrid consists of a solar photovoltaic array for solar energy conversion and battery energy storage in addition to DFIG-based wind energy conversion system. Using a simplified control approach, the study describes multi-mode operation of a DFIG-based AC/DC microgrid using a stator-side solid-state transition switch (SSTS). Using SSTS operation, the DFIG stator can be seamlessly disconnected and reconnected from the point of common coupling without interrupting power to the loads in the microgrid. Additionally, non-ideal AC loads can be handled efficiently without the need for computationally exhaustive approaches to enhance stator voltages and currents.

Development of constant switching frequency model predictive control for DFIG-based wind energy conversion system in standalone and grid connected operation

This paper presents a constant switching frequency (CSF) model predictive control (MPC) for a doubly-fed induction generator (DFIG)-based wind energy conversion system (WECS) in standalone and grid-connected operations. The system comprises a cascade connection of a rotor side converter (RSC) and a grid side converter (GSC) with a common DC-link. A battery energy storage system (BESS) is also connected to the common DC-link via a bi-directional dc–dc converter (BDDC). The RSC is controlled through CSF-model predictive current control (CSF-MPCC) scheme in both standalone mode (SAM) and grid-connected mode (GCM) of operations. The GSC control employs the CSF-MPCC scheme in grid-connected operation, whereas CSF-model predictive voltage control (CSF-MPVC) scheme is implemented in standalone operation. Moreover, the power balance is achieved by controlling the BDDC using MPCC to discharge or charge the BESS in response to surplus or deficient power generation. The effectiveness of the proposed control approach for DFIG-based WECS in both SAM and GCM is verified through MATLAB-based simulation and experimental results under different dynamic conditions like variable wind velocity changing from 12 m/s to 8 m/s, different grid demands from 3 kW to 1.5 kW and load change of 4 kW to 6.5 kW. Moreover, the robustness of the proposed control is corroborated by parametric sensitivity, time delay, and grid unbalance, along with its low-voltage ride-through (LVRT) capability during an LLLG fault.

Jellyfish search algorithm-based optimum tuning of PI controller for a front-end converter in a DFIG-based wind energy conversion system

Jellyfish search algorithm-based optimum tuning of PI controller for a front-end converter in a DFIG-based wind energy conversion system

Refined vector control structure and indirect MPPT for grid connected DFIG-based wind energy conversion system, and appraisal on matrix converter interface

Refined vector control structure and indirect MPPT for grid connected DFIG-based wind energy conversion system, and appraisal on matrix converter interface

Optimizing direct power control of DFIG-based WECS using super-twisting algorithm under real wind profile

In this study, we address the optimization of the direct power control of a doubly fed induction generator within a wind conversion system under actual wind conditions. The primary objective is to enhance the dynamic response of the wind energy conversion system (WECS) while minimizing the impact of wind fluctuations on power generation. To achieve this goal, we introduce a novel control methodology based on the super-twisting algorithm (STA). This approach allows for effective regulation of both reactive and active power output in the WECS. We employ comprehensive simulations using a detailed model of the WECS and real wind profiles to evaluate the efficacy of the STA-based control strategy. Our simulations demonstrate that the adopted STA-based control strategy successfully tracks the desired power set-point and effectively mitigates the adverse effects of wind power fluctuations and uncertainties on the WECS power output. Specifically, it exhibits superior performance in managing transients and rejecting disturbances compared to a conventional approach employing a switching table and hysteresis controller. These results suggest the practical viability and potential applications of the STA-based control strategy in real-world wind energy systems.

DC Link Voltage Enhancement in DC Microgrid Using PV Based High Gain Converter with Cascaded Fuzzy Logic Controller

Renewable-based sources can be interconnected through power electronic converters and connected with local loads and energy storage devices to form a microgrid. Nowadays, DC microgrids are gaining more popularity due to their higher efficiency and reliability as compared to AC microgrid systems. The DC Microgrid has power electronics converters between the DC loads and renewable-based energy sources. The power converters controlled with an efficient control algorithm for maintaining stable DC bus voltage in DC microgrids under various operating modes is a challenging task for researchers. With an aim to address the above-mentioned issues, this study focuses on the DC link voltage enhancement of a DC Microgrid system consisting of PV, DFIG-based wind energy conversion system (WECS), and battery Energy Storage System (ESS). To elevate PV output voltage and minimize the oscillations in DC link voltage, a high-gain Luo converter with Cascaded Fuzzy Logic Controller (CFLC) is proposed. Droop control with virtual inertia and damping control is proposed for DFIG-based WECS to provide inertia support. Artificial Neural Network (ANN) based droop control is utilised to regulate the ESS’s State of Charge (SOC). The effectiveness of the proposed converter and its control algorithms for maintaining stable DC bus link voltage has been analysed using MATLAB/Simulink and experimentally validated using a prototype model and FPGA Spartan 6E controllers.

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%.

Performance Evaluation of Grid-Connected DFIG-Based WECS with Battery Energy Storage System under Wind Alterations Using FOPID Controller for RSC

In the present energy scenario, wind energy is the fastest-growing renewable energy resource on the globe. However, wind-energy-based generation systems are also associated with increasing demands for power quality and active power control in the power network. With the advancements in power-electronics-based technology and its use in non-conventional energy conversion systems, it has witnessed tremendous growth in wind energy conversion systems (WECSs). At the same time, integrating wind farms into the grid system also results in many power quality issues in the power system that involve these renewable energy sources feeding power networks. This paper reports the effectiveness of grid-connected doubly fed induction generator (DFIG)-based WECS with a battery energy storage system (BESS) under variable wind conditions. In this study, a rotor side converter (RSC) is controlled to achieve the optimal torque for a given maximal wind power. The control scheme is simulated using MATLAB for a 2 MW-rated DFIG used in a WECS. Additionally, in this paper, a new fraction order proportional integral derivative (FOPID) controller is introduced into the system’s RSC, and its performance is also observed. The BESS technique is used with a DC link to improve the overall performance of the DFIG-based WECS under different wind conditions. To control the BESS, a proportional integral (PI) controller is introduced to increase the charging and discharging rates. Two models are developed in MATLAB/Simulink: one model is a basic model, and other model is equipped with a BESS and a PI controller in the BESS. The results validate the effectiveness of the proposed PI-controller-equipped BESS at improving the overall performance of the WECS system under study.

A novel application of adaptive filtering algorithm for LVRT capability enhancement of grid-connected DFIG-based wind energy conversion systems (WECS)

A novel application of adaptive filtering algorithm for LVRT capability enhancement of grid-connected DFIG-based wind energy conversion systems (WECS)

Computation of steady-state operating conditions of a DFIG-based wind energy conversion system considering losses

Computation of steady-state operating conditions of a DFIG-based wind energy conversion system considering losses

An adaptive fuzzy logic control technique for LVRT enhancement of a grid-integrated DFIG-based wind energy conversion system

The fast improvement of wind energy conversion technology with optimization algorithms has recently received a lot of attention. However, their slow convergence speed, huge computational load, and low efficiency are the main drawbacks and to improve these disadvantages, a new adaptive fuzzy logic controller strategy is proposed to enhance the low voltage ride-through of the grid-connected doubly fed induction generator during severe grid faults. The rotor side converter and the grid side converter are controlled using an adaptive fuzzy logic controller topology under cascaded structure to improve the performance. The novel application of the ‘generalized variable step-size diffusion continuous mixed p-norm’ adaptive filtering algorithm is proposed to modify the calibrating factors of the fuzzy logic controllers at a rapid convergence speed with low normalized misalignment error. The proposed adaptive algorithm-based fuzzy logic controller has a better low voltage ride-through improvement capability than that of using the particle swarm optimization-based proportional-integral controller during severe grid disturbances. The convergence speed of the proposed adaptive filtering algorithm is compared to that of existing algorithms such as least mean fourth, least mean square, and continue mixed p-norm algorithms. In addition, a comparison is also made with different common optimization methods. The field-programmable gate array-based real-time experimental results are presented to validate the proposed adaptive control topology.

Event-Triggered robust control scheme for sub-synchronous resonance mitigation in DFIG-based WECS

Series compensation is an economically feasible solution for facilitating the integration of high levels of wind energy sources into the grid. However, high levels of series compensation in transmission lines typically results in sub-synchronous resonance (SSR) in doubly-fed-induction-generator (DFIG)-based wind energy conversion systems (WECS). The introduction of negative equivalent resistance due to negative SSR slip results in self-sustaining oscillating instability and potentially loss of stability of the whole system. This paper proposes an event-triggered sliding mode control (ETSMC)-based control approach for the effective damping of sub-synchronous resonance. The ETSMC control is designed for the rotor side converter of the DFIG, and a phase compensator unit is augmented with the ETSMC control to enhance its efficacy in mitigating specific unstable mode. The proposed control approach is validated using a DFIG-based WECS in IEEE first benchmark model. The results prove the high efficacy of the control approach in mitigating SSR and ensuring system’s fast recovery. In addition to the attributes of the SMC approach, reliance on an event triggering mechanism leads to only seldom control updates, thereby solving the problem of requirement of high communication bandwidth and computational power.

Robust Finite Control-Set Model Predictive Control for Power Quality Enhancement of a Wind System Based on the DFIG Generator

For many academics, it has proven difficult to operate a wind energy conversion system (WECS) under changeable wind speed while also enhancing the quality of the electricity delivered to the grid. In order to increase the effectiveness and performance of the DFIG-based Wind Energy Conversion System, this research suggests an updated model predictive control technique. This study intends to regulate the generator in two ways: first, to follow the reference wind speed with high precision using the rotor side and grid side converters; second, to reduce system error. The suggested approach optimizes a value function with current magnitude errors based on the discrete mathematical model to forecast the converter’s switching state. In this system, the converter switching states are used directly as control inputs. Thus, the converter may be immediately subjected to improved control action. The key advantage of the suggested strategy over current FCS-MPC methods is error reduction. The originality of this research is in the proposal of a cost function that allows for both successful results and computation time minimization. To achieve this, the system is first presented, followed by a description of the predictive control, and then this method is applied to the rotor side control and grid side control. To demonstrate the efficacy and robustness of the suggested technique, a random wind profile was used to examine the system’s performance with a unitary power factor. This was done in order to compare the results with other controls that have been reported in the literature. The simulation results, which were conducted using a 1.5 kW DFIG in the MATLAB/Simulink environment, demonstrate that the FCS-MPC technique is highly effective in terms of speed, accuracy, stability, and output current ripple.

Fixed-time observer-based control of DFIG-based wind energy conversion systems for maximum power extraction

To extract maximum power from the available wind energy, it is necessary that the doubly-fed induction generator (DFIG) based wind energy conversion system (WECS) outputs that maximum power at each instant of time for different wind speeds. This can be achieved by controlling the rotor voltage of the DFIG. To make it possible, a novel maximum power point tracking (MPPT) controller in combination with state and disturbance observers and fixed-time stability notion is proposed in this paper. The sliding mode control (SMC) method is used to design the observer-based controller and ensure the robustness. The sliding mode state observer is designed while only the measurement of the stator reactive power and rotor speed is required. The modelling uncertainties and external disturbances are estimated by the sliding mode disturbance observer without information about their upper bounds in advance. Then, the combinatorial effect of uncertainties and disturbances are fully compensated by the designed controller. The fixed-time convergence issue is addressed where the bound on the settling time is user-defined using design parameters regardless of initial conditions. The elimination of chattering issue is considered in the design of the SMC laws. The stability analysis of the closed-loop system is obtained via the Lyapunov stability theory. The validity and robustness of the proposed method is tested in Simulink/MATLAB for a DFIG under two different scenarios of wind speed modelling. The proposed fixed-time method is also compared with a classical finite-time method. Comparing the results demonstrate that the proposed method outperforms the other method.

Optimal MPPT and BES Control for Grid-Tied DFIG-Based Wind Energy Conversion System

As the wind energy is progressively pursued to supplement conventional power generation, the stochastic wind behavior considerably deteriorates power quality (PQ) and stability of the connected grid. This article is attributed to the comprehensive control design of the grid-integrated doubly fed induction generator (DFIG) wind energy system that aims to address encountered wind variability and PQ intricacies. Battery energy storage with bidirectional dc–dc converter control is assimilated at the dc-link of the DFIG that regulates the dc-link voltage to mitigate the wind power fluctuations in specified grid power modes. A higher order adaptive control for grid-interfaced converter accurately computes desired weights of various components to exercise accurate power balance and PQ control. Moreover, a novel Jaya particle swarm optimization algorithm is adopted for the optimal tuning of wind maximum power point tracking and the dc-link voltage PI controller parameters that significantly improves steady-state and dynamic responses of both the controllers. The rotor speed converges faster with precise reference tracking that leads to almost zero steady-state error using optimized PI controller gains. Along with the attenuated initial overshoot and fast settling time during sudden large step wind speed change, the dc-link voltage also characterizes superior response with optimal tuning during load unbalance and abnormal grid voltage conditions. The presented control strategy analyzes the capability of DFIG system under variable wind-speed changes, specified grid power modes, load changes, and other grid disturbances. The satisfactory system response is endorsed through experimental results conducted on a laboratory prototype.

A Robust Multiloop Disturbance Rejection Controller for a Doubly Fed Induction Generator-Based Wind Energy Conversion System

The doubly fed induction generator (DFIG) is a nonlinear system with various uncertainties, including parameter changes, wind speed variations, and rapid dynamic fluctuations, making model-based control techniques challenging to implement. Hence, the multi-loop active disturbance rejection controller (MADRC) is proposed as an enhanced active disturbance rejection control to stabilize and reject external disturbances and system uncertainties. In this article, two active disturbance rejection controllers (ADRCs) are connected in series to achieve the rotor side converter (RSC) controller. The performance of the proposed control strategy is first evaluated using simulation on a 3.7-kW DFIG setup, and subsequently on a 1-kW DFIG-based wind energy conversion system (WECS) developed in our laboratory. The proposed controller is compared to a proportional-integral (PI) controller, single-loop active disturbance rejection controller (SADRC), and sliding mode controller (SMC) in order to demonstrate its effectiveness. The obtained outcomes are evaluated for reference tracking, robustness to parameter fluctuation, and disturbance rejection in DFIG. External disturbances and system uncertainties are effectively mitigated by the proposed MADRC scheme.