Doubly Fed Induction Generator Research Articles

Adaptive inertia control strategy of asynchronous sending system based on coordination between DFIG and HVDC frequency limit controller

In a highly proportional hydropower asynchronous sending grid, the system is in a low damping state at all times because of the dual influence of the highly proportional hydropower water hammer effect and highly proportional DC outgoing rigid load, making frequency stability control difficult. In this study, an adaptive inertia control strategy for an asynchronous sending system based on the coordination of the wind turbine and high-voltage direct current (HVDC) frequency limiter controller (FLC) is proposed for a highly proportional hydropower asynchronous sending grid with a double-fed induction generator as the research object. The wind turbine and HVDC system provide virtual inertia support inside and outside the dead zone of FLC, respectively, and the additional virtual inertia-controlled FLC provides a fast response to the instantaneous power dip caused by wind turbine speed recovery. In addition, fuzzy control is introduced in the inertia control link of the wind turbine and FLC to dynamically adjust the virtual inertia adjustment coefficients of the wind turbine and HVDC system in accordance with the system frequency state to further enhance the coordination of frequency control. The effectiveness of the strategy is verified in a three-machine, single-return, DC outgoing system model.

Intelligent Type-2 Fuzzy Logic Controller for Hybrid Microgrid Energy Management with Different Modes of EVs Integration

The rapid integration of renewable energy sources (RES) and the electrification of transportation have significantly transformed modern energy infrastructures, emphasizing the need for efficient and flexible energy management systems. This study presents an intelligent, variable-fed, Type-2 Fuzzy Logic Controller (IT2FLC) designed for optimal management of Hybrid Microgrid (HMG) energy systems, specifically considering different modes of Electric Vehicles (EVs) integration. The necessity of this study arises from the challenges posed by fluctuating renewable energy outputs and the uncoordinated charging practices of EVs, which can lead to grid instability and increased operational costs. The proposed IT2FLC is based on comprehensive mathematical modeling that captures complex interactions among HMG components, including Doubly Fed Induction Generator (DFIG) units, photovoltaic (PV) systems, utility AC power, and EV batteries. Utilizing a yearly dataset for simulation, this work examines the HMG’s flexibility and adaptability under dynamic conditions managed by the proposed intelligent controller. A Simulink-based model is built for this study to replicate the dynamical operation of the HMG and test the precise and real-time decision-making capability of the proposed IT2FLC. The results demonstrate the IT2FLC’s superior performance, achieving a substantial cost avoidance of nearly $3,750,000 and efficient energy balance, affirming its potential to sustain optimal energy utilization under stochastic conditions. Additionally, the results attest that the proposed IT2FLC significantly enhances the resilience and economic feasibility of hybrid microgrids, achieving a balanced energy exchange with the utility grid and efficient utilization of EV batteries, proving to be a superior solution for optimal operation of hybrid grids.

Variable speed wind turbine based on doubly fed induction generator using genetic algorithms

Optimization of the control of doubly fed induction generators (DFIGs) is essential for many applications, such as renewable energy systems, industrial automation, and electric cars. Unfortunately, the dynamic and non-linear character of DFIG frequently makes it difficult for conventional control techniques to adjust, which results in less-than-ideal performance. To overcome these obstacles, this paper presents an optimized fuzzy speed control of a doubly fed induction wind generator using a genetic algorithm, which has more advantages than its counterpart PI speed controller. In this study, the modeling of generator in the Park's frame was presented, as well as its indirect vector control applied to the stator flux. Then, to guarantee tracking of the ideal operating point in real-time and to produce the most electricity possible for varying wind speeds, we used a fuzzy PI speed controller. To improve the sizing operation of this controller, we opted for the genetic algorithm technique combined with one of the local search methods, which facilitated the search and reduced the effort compared to the trial-and-error sizing method. Furthermore, this made it possible for the wind system to track the optimal power point maximum with good performance. The simulation results of the suggested control displayed by MATLAB-Simulink illustrate the effectiveness and adaptability of the proposed control scheme across different operating conditions. The analysis of the results showed good performance for speed, small voltage and current ripple when using the fuzzy PI speed controller with genetic algorithm technique. offering promising prospects for practical implementation in variable speed wind turbine applications.

Optimal Artificial Intelligence Technique for LVRT Capability Improvement of a Grid-tied Wind Energy Conversion System: A MGOANFIS-PI Methodology

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.

Artificial Intelligence with Metaheuristic Algorithm Based Optimization for a Hybrid Energy Storage System

This paper presents an optimization framework for a hybrid energy storage system (HESS) integrating photovoltaic (PV) and wind energy conversion systems (WECS). The PV system voltage is stepped up using a Cuk converter, controlled by a Particle Swarm Optimization (PSO) tuned Artificial Neural Network (ANN) controller to enhance dynamic performance and ensure maximum power point tracking (MPPT). The Doubly Fed Induction Generator (DFIG) based WECS is interfaced through a Pulse Width Modulation (PWM) rectifier regulated by a Proportional Integral (PI) controller, maintaining stable and efficient power conversion. Both converter outputs converge at a common DC link, whose voltage is subsequently applied to a single phase Voltage Source Inverter (VSI). This VSI is connected to the grid through an LC filter, ensuring smooth power delivery with minimal harmonics. The proposed system combines AI and metaheuristic algorithms for optimized control, enhancing overall efficiency, reliability and stability of the renewable energy integration into the grid. Simulation results demonstrate the effectiveness of the PSO ANN and PI control strategies in managing the dynamic interactions and power quality of the hybrid system. The proposed hybrid controller shows 92.47% tracking efficiency and the proposed converter shows 89.6% efficiency.

Noise-like-Signal-Based Sub-Synchronous Oscillation Prediction for a Wind Farm with Doubly-Fed Induction Generators

The DFIG-based wind farm faces sub-synchronous oscillation (SSO) when it is integrated with a series-compensated transmission system. The equivalent SSO damping is influenced by both wind speed and compensation level. However, it is hard for the wind farm to obtain a compensation level in time to predict the SSO risk. In this paper, an SSO risk prediction method for a DFIG wind farm is proposed based on the characteristics identified from noise-like signals. First, SSO-related parameters are analyzed. Then, the potential SSO frequency and damping are identified from signals at normal working points by integration using variational mode decomposition and Prony analysis. Finally, a fuzzy inference system is established to predict the SSO risk of a DFIG wind farm. The effectiveness of the proposed method is verified by simulation. The proposed prediction method can predict SSO risks caused by the variation in wind speed, while the transmission line parameters are undetectable for the wind farm.

Blade imbalance fault identification in doubly fed induction generator through current signature analysis using wavelet transform

Using wind turbines (WTs) equipped with doubly fed induction generators (DFIG) is a popular technology for generating renewable energy. To ensure safe operation, prompt maintenance, and better operational reliability, the induction generator used in wind energy must be monitored. In this paper, an analysis is carried out on stator currents of the DFIG machine in a wind farm to identify any blade imbalances in the wind farm. A fault characteristics extraction analysis is carried out on the machine stator currents to detect the fault in the system. Firstly, the mathematical equation of the DFIG blade unbalanced stator current is generated using the DFIG model. Secondly, Park's Transformation is used to modify the stator's 3-phase current. Further, by evaluating the feature frequency amplitude variation in the squared signal by doing a spectral analysis on the stator current vector's squared signal. Lastly, a Simulink model for the DFIG is developed. The suggested approach analyses the fault signal of the imbalanced blade fault at various wind velocities. The outcomes show that the suggested method for diagnosing impeller imbalance faults can successfully locate the fault.

The wind turbine's direct power control of the doubly-fed induction generator

The study suggests a comprehensive approach to modeling and controlling variable-speed wind turbine systems that utilize doubly fed induction generators (DFIGs). To make sure that energy is transferred efficiently between the DFIG rotor and the grid, a two-level inverter with perfect bidirectional switches is used. Using the tip speed ratio algorithm and taking into consideration the randomness in wind speed, the maximum power at the wind turbine is optimized. Then, the control strategy utilizes direct power control (DPC) due to its various advantages. The advantages of employing this control technique are manifold. Firstly, it eliminates the necessity for rotor current control loops. Secondly, it obviates the need for controllers such as PI controllers to manage torque and flux. Furthermore, it has yielded exceptional simulation results when implementing direct power control (DPC) within the MATLAB/Simulink environment, specifically in the context of a doubly fed induction generator (DFIG) wind power system.

A modeling method for the control circuit of a double-fed induction generator in a wind power system based on multimodel fusion

Abstract The current modeling techniques of a Double-fed induction generator (DFIG) for wind power systems have shortcomings in the idealization of equipment internal parameters and unitization of system coupling. In this paper, a multi-model fusion method of doubly-fed induction wind turbine modeling for wind power systems is proposed. Firstly, the 1.5 MW DFIG body is constructed according to the parameters of the physical prototype. Then, to realize the system’s control of DFIG, the DIFG external circuit and control circuit of the wind power system are built to realize multi-system coupling simulation modeling. Through simulation analysis, the effectiveness of the multi-model coupling simulation system and the availability of the method are proved.

Advanced control scheme of doubly fed induction generator for wind turbine using second sliding mode control

This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator(DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC).Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.

Multiple faults detection in doubly-fed induction generator wind turbine using artificial neural network

The development of fault detection methods in wind turbine (WT), especially for single fault detection, is continuously increasing. However, the rapid growth of fault detection in WT leads to another challenge where multiple faults can occur. The single fault detection method in WT is no longer reliable, especially when multiple faults occur simultaneously. Therefore, multiple faults detection in doubly-fed induction generators (DFIG) WT was proposed using an artificial neural networks (ANN) model. These multiple faults include internal and external stator faults happening simultaneously. Internal stator faults cover inter-turn short circuit faults and open circuit faults, while external stator faults cover loss of excitation and external short circuit faults. The performance of the developed multiple faults detection model was measured using accuracy and the root mean square error (RMSE) value. The results show that the developed model performs well with high accuracy and a low RMSE value. Thus, the developed model can accurately detect the coexistence of multiple faults in DFIG WT.

Combined Rotor-Side and Grid-Side Suppression of Sub-Synchronous Oscillation in Doubly-Fed Wind Turbines

Abstract In the field of wind power generation, the significant geographical separation between wind farms and load centers often necessitated the long-distance and large-scale transmission of wind energy, which was typically achieved through transmission lines equipped with series compensation capacitors. However, this setup led to interactions between the series compensation capacitors and the control systems of doubly-fed induction generators (DFIGs), potentially causing sub-synchronous oscillations in grid-connected systems. To address this issue, our study focused on a DFIG-based wind farm interconnected via a series-compensated line. We proposed an innovative suppression strategy that incorporated additional virtual damping on the grid side. This strategy was effectively combined with an ancillary approach of adding virtual inductance on the rotor side, offering a robust solution to mitigate sub-synchronous oscillations. The simulation results showed that the joint application of these two strategies was more effective than implementing them individually. Notably, the strategy involving additional virtual damping on the grid side outperformed the approach of adding virtual inductance on the rotor side.

Analytical Model and Topology Optimization of Doubly-Fed Induction Generator

Analytical Model and Topology Optimization of Doubly-Fed Induction Generator

Experimental Characterization of Common-Mode Voltages and Currents in Gearbox Bearings of a Doubly-Fed Induction Generator Wind Turbine on a Test Bench

Wind turbine (WT) drivetrain failures can lead to expensive repairs and downtime, significantly impacting the Levelized Costs of Electricity (LCOE). Gearbox failures are a major contributor to maintenance costs, with High-Speed-Shaft (HSS) bearings having the highest failure rate. Frequently occurring premature HSS bearing damage indicates design inadequacies. Currently, bearing design standards (DIN/ISO 281) are primarily based on fatigue. Other damage mechanisms like adhesion or damages due to electrical currents passing through the bearings are not considered. WT gearbox bearings are exposed to electrical currents, mainly caused by common-mode (CM) voltages. Bearing currents lead to surface damages and also may promote the formation of white etching areas (WEA) or cracks (WEC) and thus need to be investigated further. The CM voltage is an inherent feature of the power converter, which generates CM currents in the electrical machine. The resulting high voltages at the HSS bearings can lead to electrical discharge currents in the rolling contact, which damage the raceways of the bearing. This paper presents an experimental method for characterization of the currents and voltages that pass from the generator to the HSS of the gearbox in a multi-megawatt WT drivetrain on a test bench. CM currents and voltages measured at the converter are correlated with the shaft voltages at the HSS bearing. The results indicate, that the CM currents directly pass the HSS and its bearings into the gearbox housing. The CM currents show peaks indicating discharge events with over 100 A. Furthermore, at the discharge events, high-frequency oscillations with over 20 MHz are observed in CM current and bearing voltage.

An optimal regulation of grid-connected doubly-fed induction generator angular speed and the DC-link voltage

In this work, a linear quadratic regulator controller is utilized to optimize the gains of the machine-side converter and grid-side converter controllers. The DC-link voltage and the rotational speed of the doubly-fed induction generator are optimally regulated by these controls. Additionally, the small-signal stability of the proposed system has been evaluated using the linearized model of the system. The small-signal stability analysis of the designed system has been accomplished through the application of the eigenvalues and participation factors technique. The suggested controller has been tested in a variety of operating conditions under small disturbances. MATLAB/SIMULINK simulation simulations have been used to evaluate the efficacy of the suggested optimal control system. Additionally, the generator-angular speed and DC-link voltage dynamic performances demonstrate the efficacy of the proposed controller. Finally, the robustness of the proposed controller has also been tested and validated using OPAL-RT technology under large perturbations at stator terminal voltage.

Investigation of the Impact of SSSC-Based FLC on the Stability of Power Systems Connected to Wind Farms

The integration of renewable energy sources into power systems has increased significantly in recent years. Among various types of renewable energy, the use of wind energy is growing rapidly due to its low operating cost, wide distribution worldwide, and no greenhouse gas emissions. However, power systems integrated with wind energy may face stability and reliability issues due to the intermittent nature of wind power. Therefore, in power systems connected to wind farms, it is usually required to use some compensators such as static synchronous series compensator (SSSC) to increase the system performance under abnormal conditions. On the other hand, for an SSSC to be effective in improving the system performance, it must be equipped with a suitable controller. In this paper, a fuzzy logic controller (FLC) is used for the SSSC because of its advantages over conventional controllers. Extensive research has been conducted in power systems with wind turbines in which SSSC or FLC has been used; however, their simultaneous application in such systems has received less attention. Therefore, this article aims to fill this gap. The proposed method is implemented on two power systems and the simulation results are analyzed. In both systems, the dynamic behavior of three different wind farms is examined. In the first and second wind farms, either a squirrel cage induction generator (SCIG) or doubly-fed induction generator (DFIG) are used, whereas in the third one which is a combined wind farm (CWF), an equal number of SCIG and DFIG are employed. In wind farms with SCIG or DFIG, an SSSC is also utilized. Furthermore, an FLC is employed for the SSSC to improve its efficacy. A proportional integral (PI) controller is also considered for the SSSC, and its results are compared with FLC results. The simulation results confirm the superiority of FLC over PI controller.

Nonlinear robust sliding mode - Backstepping hybrid control for WECS -theoretical design and experimental evaluation

This paper proposes a new contribution in the field of optimizing control techniques for wind systems to enhance the quality of the energy produced in the grid. Although the Sliding Mode control technique, whether classical or involving the use of artificial intelligence, has shown interesting results, its main drawback lies in the oscillation phenomenon commonly referred to as “chattering.” This phenomenon affects the accuracy and robustness of the system, as well as the parametric variation of the system. In this work, we propose a solution that combines two nonlinear techniques based on the Lyapunov theorem to eliminate the chattering phenomenon. It is a hybrid approach between the Backstepping strategy and the Sliding Mode, aiming to control the active and reactive powers of the doubly fed induction generator (DFIG) connected to the electrical grid by two converters (grid side and machine side). This hybrid technique aims to improve the performance of the wind system in terms of precision errors, stability, as well as active and reactive power. The proposed solution has been validated in the Matlab & Simulink environment to assess the performance and robustness of the proposed model, as well as experimentally validated on a test bench using the DSPACE 1104 card. The obtained results are then compared with other techniques, demonstrating a significant improvement in performance.

Current and flux correlation analysis for detection, location, and phase identification of stator inter-turn short-circuit fault in doubly-fed induction generator

This study proposes an innovative method for identifying and locating inter-turn short-circuit faults in doubly-fed induction generators (DFIGs), commonly used in wind-energy production. Our approach uniquely integrates data from both current and magnetic-flux monitoring, offering a complete framework for fault diagnostics. We effectively distinguish between normal and abnormal conditions in the generators by observing the patterns in current and flux waveforms. Normal conditions are marked by uniform waveforms, whereas faults create noticeable imbalances. A key feature of our research is the development of a technique to accurately determine which phase of the generator is affected by the fault, facilitating quicker and more effective repairs. To pinpoint the exact location of the fault, we use a set of four magnetic-flux sensors strategically placed around the generator. These sensors detect variations in magnetic flux, enabling the precise location of the fault. By focusing on these three essential elements—fault detection, phase identification, and exact fault localisation—our proposed algorithm significantly improves the dependability of DFIGs in wind-power applications.

Characteristic analysis and mitigation strategy for SSCI in series-compensated DFIG-based wind farm controlled by a virtual synchronous generator

The proliferation of virtual synchronous generator (VSG) technology within series-compensated double-fed induction generator (DFIG)-based wind farms is substantially hampered by the attendant risk of subsynchronous control interaction (SSCI), resulting in a significant research deficiency on systematic control interaction analysis and the development of mitigation strategies. The paper proposes an advanced active disturbance rejection control (ADRC) framework, incorporating real-time compensation mechanisms to mitigate the inadequate suppression efficacy attributable to the VSG's diminished output impedance. Initially, the mathematical expression for the VSG output impedance is rigorously deduced, and the positive damping attributes of the VSG in relation to SSCI are elucidated from the perspective of underlying mechanistic principles. Subsequently, the suppressive mechanism of SSCI by the ADRC is revealed in the context of VSG involvement, and the consequent augmentation of SSCI attributed to PI control is systematically derived. In immediate succession, the quanta of oscillation and inductive cross-coupling are encapsulated as the system's aggregate disturbance, thereby streamlining the ADRC to its primary order configuration, permitting the utilization of an extended state observer (ESO) for the dynamic estimation of said disturbance. Furthermore, a fractional-order filter function is instituted to engineer an augmented ESO, which refines the output voltage of the grid-side converter. Concurrently, a meticulous discourse on the rectification strategy for the proposed ESO parameters and its stability ensues. Ultimately, the efficacy of the mechanism analysis, alongside the robustness of the proffered control strategy for SSCI mitigation under diverse perturbation conditions, is corroborated via impedance evaluation and time-domain simulation.

Multi-agent system-assisted fault section location scheme for distribution networks with DFIG-based wind DGs

This paper proposes a multi-agent system (MAS)-assisted relaying scheme to identify the fault section in a distribution network (DN) that incorporates doubly-fed induction generators (DFIG)-based wind distributed generations (DGs). Firstly, to mitigate the adverse effect of the uncertain fault behavior of DFIGs on conventional directional elements, the fault direction identifiers (FDIs) are assigned at the I-type and II-type buses in the DN using the high-frequency component principle. Furthermore, a MAS-assisted relaying structure is devised to collect and integrate these identifiers and then accomplish the fault section location. On the one hand, since the setting of FDIs only requires local current information and the logical signals are solely exchanged within each predetermined associated region, the proposed fault section location scheme can be implemented in an immature DN with a limited number of voltage transformers or under weak synchronization conditions. On the other hand, the autonomy and cooperation of intelligent Agents also enable the proposed scheme to determine the fault section in a decentralized decision-making (DDM) mode, eliminating the dependence on strong central control and reducing redundant data transmission in a multi-bus distribution system. Simulation results under representative faulty scenarios validate the expected performance of the protection scheme.