Wind Turbine Generator System Research Articles

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.

A Modified Reduced-Order Generalized Integrator–Frequency-Locked Loop-Based Sensorless Vector Control Scheme Including the Maximum Power Point Tracking Algorithm for Grid-Connected Squirrel-Cage Induction Generator Wind Turbine Systems

A Modified Reduced-Order Generalized Integrator–Frequency-Locked Loop-Based Sensorless Vector Control Scheme Including the Maximum Power Point Tracking Algorithm for Grid-Connected Squirrel-Cage Induction Generator Wind Turbine Systems

Multiple-node model of wind turbine generating system for unbalanced distribution system load flow analysis

This paper discusses a method to integrate a wind turbine generating system (WTGS) into a three-phase unbalanced distribution system load flow (DSLF) analysis. The proposed method is based on the single-phase multiple-node model. In the present work, the single-phase multiple-node model is extended to a three-phase multiple-node model to facilitate the load flow analysis of a three-phase unbalanced power system network. The multiple-node model (i.e., three-node model) will only modify the load flow analysis by introducing two lines and two load buses to the distribution system network where the WTGS is installed. Thus, a standard three-phase load flow program can be employed to compute the unknown quantities in the DSLF problem formulation. The proposed method is verified by incorporating the model into the load flow analysis of three-phase distribution networks. The investigation uses two representative distribution networks (i.e., 19-bus and 25-bus networks). The results of the study confirm the validity of the proposed method.

Cyber-Resilient Converter Control System for Doubly Fed Induction Generator-Based Wind Turbine Generators

As wind turbine generator systems become more common in the modern power grid, the question of how to adequately protect them from cyber criminals has become a major theme in the development of new control systems. As such, artificial intelligence (AI) and machine learning (ML) algorithms have become major contributors to preventing, detecting, and mitigating cyber-attacks in the power system. In their current state, wind turbine generator systems are woefully unprepared for a coordinated and sophisticated cyber attack. With the implementation of the internet-of-things (IoT) devices in the power control network, cyber risks have increased exponentially. The literature shows the impact analysis and exploring detection techniques for cyber attacks on the wind turbine generator systems; however, almost no work on the mitigation of the adverse effects of cyber attacks on the wind turbine control systems has been reported. To overcome these limitations, this paper proposes implementing an AI-based converter controller, i.e., a multi-agent deep deterministic policy gradient (DDPG) method that can mitigate any adverse effects that communication delays or bad data could have on a grid-connected doubly fed induction generator (DFIG)-based wind turbine generator or wind farm. The performance of the proposed DDPG controller has been compared with that of a variable proportional–integral (VPI) control-based mitigation method. The proposed technique has been simulated and validated utilizing the MATLAB/Simulink software, version R2023A, to demonstrate the effectiveness of the proposed method. Also, the performance of the proposed DDPG method is better than that of the VPI method in mitigating the adverse impacts of cyber attacks on wind generator systems, which is validated by the plots and the root mean square error table found in the results section.

Real-time current sensor fault detection and localization in DFIG wind turbine systems

<span lang="EN-US">The degree of reliability is one of the main issues in having the uninterrupted operation of fault-tolerant measurement and control systems. This paper presents a technique for detecting and localizing current sensor fault in doubly fed induction generator wind turbine systems (DFIG-WT). For this purpose, a methodology divided into three steps has been carried out and is presented as follows: the first step is the application of the zero-sequence component as an indicator of the presence of the sensor fault. The second step is based on the application of Concordia transformation on the current signals to generate different criteria for localizing the faulty sensor. Finally, an artificial neural network model is developed to analyze the localization criteria and identify the faulty sensor. All simulations are carried out in MATLAB/Simulink environment. Results show that the proposed technique is effective and can be used in real-time fault detection and localization in the DFIG-WT.</span>

Power maximization control of small wind system using permanent magnet synchronous generator

This article describes modeling and simulations to determine a method for the power performance evaluation of autonomous wind turbine system with this simple load scheme. The method applies to small systems equipped with a permanent magnet generator in the wind turbine a diode rectifier, boost converter and batteries. In this work, the aerodynamic characteristics of wind turbines and the power conversion system topology are explained. The maximum power tracking of the wind turbine generator system using the Matlab software is presented and its results show, at least in principle, that the maximum power tracking algorithm developed is suitable for wind turbine generation systems.

Mechanical–electrical‐grid model for the doubly fed induction generator wind turbine system considering oscillation frequency coupling characteristics

AbstractWith the evolution of renewable energies, many doubly fed induction generators (DFIGs) are being connected to the power grid, whose operation and grid‐connection stability have a major impact on the power grid. Currently, most studies focus on either modeling the mechanical–electrical section or the electrical‐grid section, and discussions have been limited to shaft oscillation or frequency coupling problems. In this study, a mechanical–electrical‐grid model of a DFIG was established to examine the impacts of wind speed and system control parameters on electrical damping and grid‐connection stability. The accuracy of the proposed model and validity of the analyses were verified using simulations. The following were observed: (1) In the case of changing wind speeds, the wind speed and the applied control model determine the shaft oscillation of DFIG, whereas the grid‐connected impedance on the rotor side is dependent on the wind speed. (2) At a constant wind speed, changes in control parameters under different control modes affect the dynamic characteristics of the drive train differently, whereas the grid‐connected impedance on the rotor side is primarily determined by the proportional gain of the inner/outer loop of the control system. The conclusions drawn from this study can further improve the safe and stable operation of DFIG wind power generation systems as well as their connection to the power grid.

Permanent Magnet with Direct Drive Synchronous Wind Turbine Generator System

This postulation illustrated a control framework for an immediate drive permanent magnet coordinated generator wind turbine system, with the goal of maximising the efficiency of this framework and capturing the maximum amount of wind energy. Additionally, in order to decrease the electric speed sensor installed on the rotor shaft of the PMSG (Permanent Magnet), as well as to reduce the complexity of the framework equipment and increase the stability of the framework, a sliding mode eyewitness-based PM rotor position and speed sensor less control calculation is shown here.The wind turbine and the permanent magnet combination machine with PMSG simulation models are first built in this proposal, and then largest force control calculations for this framework are presented. The best tip speed percentage based on the highest force point after control is used to achieve the strongest catch for the framework.

YSA ve Bukalemun Optimizasyon Algoritmalarına Dayalı DER'lerde Tekno Ekonomik için Pratik Radyal Dağıtım Besleyicisi

Distributed energy resources (DERs) are a better choice to meet load demand close to load centers. Optimal DER placement and DER ratings lead to power loss reduction, voltage profile improvement, environmental friendliness, dependability, and postponement of system changes. This study uses artificial neural networks and the Chameleon Optimization Algorithm to analyze the best integration of renewable energy sources and electric vehicles in distribution feeders to reduce power loss, regulate voltage levels, and decrease the cost and emissions under unpredictable load demand. In this study, the generated output power of the models is compared to solar photovoltaic generation systems and wind turbine generation systems. As a result, a fitness function with several objectives has been developed to reduce total active power loss while also reducing total cost and emissions generation. The study took into account the influence of EV charging/discharging behavior on the distribution system. The 28-bus rural distribution network in feeders is used to test the suggested methodology. Final analysis of the numerical results showed that the Artificial Neural Network and Chameleon Optimization Algorithms outperformed in terms of power loss (440.94 kw) and average purchase of real power (2224 kw), but these parameters do not favor the other optimization algorithms. This showed that the proposed strategy is both viable and effective.

Reduced Order DFIG Models for PLL-Based Grid Synchronization Stability Assessment

This paper presents a reduced-order modeling approach to study the grid synchronization stability of type-3 wind turbine generator (WTG) by considering the dynamics of the phase-locked loop (PLL) alone. The proposed model is validated by comparing its time-domain response and small-signal results with the full-order dynamical model of a type-3 WTG system. Extension of the proposed reduced-order model is shown in a wind farm with N numbers of the WTGs, and time-domain simulations are performed under stable and unstable scenarios. The developed model is further utilized for directly assessing the transient stability of a doubly-fed induction generator-based wind turbine generator (DFIG-WTG) system leveraging Lyapunov's direct method. From the direct transient stability assessment methodology, an approximate estimate of the critical clearing time (CCT) is obtained for the DFIG-WTG system to ensure large-signal stability following severe grid fault. Finally, the parametric conditions are derived theoretically, which can determine the stable and unstable region of operation of the DFIG-WTG system with respect to the PLL parameters, grid impedance, and input wind speed.

Frequency Support Control of Multi-Terminal Direct Current System Integrated Offshore Wind Farms Considering Direct Current Side Stability

The frequency stability of modern power systems is challenged due to widespread application of large-scale renewable energy systems, of which the offshore wind farm (OWF) is one of the dominant resources. The OWFs are usually integrated into the grid by multi-terminal direct current (MTDC) transmission systems, which makes the energy flow complicated and the frequency control design challenging. A frequency support control method of MTDC system integrated OWFs (referred to as the OWF-MTDC system) is proposed in this paper. First, the wind turbine generation system (WTGS) is controlled to reserve a certain amount of available power according to the real-time wind speed for more comprehensive frequency regulation. Then, the frequency support control of OWFs is designed, and they can release the rotor kinetic energy and reserved power to support the onshore grid frequency. In addition, the virtual inertia control of a modular multi-level converter (MMC) is designed, which can also provide frequency support in an emergency by use of the DC capacitor. To ensure that the frequency control of the OWF-MTDC system does not degrade the stability of the system, a detailed DC impedance model of the MMC-based MTDC systems is developed, considering the constant power control and DC voltage control. Based on the impedance model, the impact of the frequency control coefficients on the DC side stability of the MTDC system is analyzed. Simulation results validate the stability analysis and verify the proposed frequency control method, which can effectively provide frequency support to the onshore power grid.

Control method of an AC-AC MMC for open-end stator winding wind turbine generation system

With the increasing of power rating and voltage level of wind turbine, the traditaional low voltage converter is being replaced by multilevel converter. This paper proposes a new ac-ac modular multi-level converter (MMC) connecting the open-end stator winding permanent magnet synchronous generator (OW-PMSG) to AC power grid. The working principle of the proposed topology is analyzed. Under different grid conditions, a unified capacitor voltage balance control method and a current control method are proposed. Aiming at the low grid voltage ride through problem, a distributed braking chopper circuit and the corresponding control strategy is given. Simulation is conducted for verification. Compared with the back to back (BTB) MMC for OW-PMSG, the number of capacitors and arm inductors of the proposed topology are reduced by 43% and 33%. Moreover, the number of the state variables of the average capacitor voltages and currents of the proposed topology are reduced by 33% and 47%, reducing the control complexity. The proposed topology also has higher efficiency than the BTB MMC for OW-PMSG. Compared with the other ac-ac MMC topologies for OW-PMSG, the combined power rating of the power devices of the proposed topology is much lower and thus the proposed topology has higher power density.

Numerical simulation of turbulent natural convection heat transfer in an MW-class offshore wind turbine nacelle based on a multi-feature acquisition meshing technique

In order to improve the computational accuracy of numerical simulations of complex thermal environments in MW-class offshore wind turbine nacelles, this paper improves the numerical simulation method for 10 MW offshore wind turbine nacelles. Firstly, the model of the wind turbine generator system (WTGS) inside the nacelle is designed and the model is simplified. Secondly, a multi-featured acquisition meshing is performed for the turbulent thermal behavior in the nacelle, using a combination of mesh geometry parameters, mesh accuracy parameters, mesh irrelevance parameters and detection verification parameters to construct a structural mesh topology. The modified SST k-ω turbulence model is used to solve the momentum and energy equations of the nacelle model. The complex physical phenomena of air recirculation, boundary layer separation and thermal stratification in the nacelle are verified based on the calculated results and compared with the experimental data, which are in good agreement with the experimental results. This paper has certain application value and guidance significance in the field of engineering such as the design of offshore wind turbine nacelle structure and cooling system design.

Protocol-based control for wind turbine generator systems with semi-Markov jump parameters

This paper is concerned with the protocol-based control design problem for wind turbine generator systems (WTGSs) via a proportional-integral observer. Considering the variable actual wind speed, the operation points of WTGSs between different subareas are described by a semi-Markov jump process. To efficiently modulate transmission frequency, an adaptive-memory event-triggered protocol (AMETP) is constructed using a sequence of previously broadcasted packets and adaptive triggering thresholds, resulting in better dynamic performance. Benefitting from the parameter-dependent Lyapunov stable theory, some sufficient criteria are formulated for the stochastic stability of the closed-loop systems. At last, the feasibility of the presented approach is tested via a numerical example.

Solution to the deterministic and stochastic Optimal Reactive Power Dispatch by integration of solar, wind-hydro powers using Modified Artificial Hummingbird Algorithm

In electrical power networks, the optimal reactive power dispatch (ORPD) problem is essential to the system studies to perform reliable and secure operations by maintaining the control variable within their permissible limits. An electric network consisting of thermal generators has been studied widely for optimal power dispatch problems. Increasing renewable energy resources (RERs) penetration into the electric power grid required power flow studies while integrating these resources. It is a strenuous task to incorporate renewable energy resources into the ORPD problem due to the stochastic nature of RERs. This paper solved the stochastic optimal reactive power dispatch (ORPD) problem considering the uncertainties of renewable resources such as; solar PV, wind turbine, and hydropower generation systems. The time-varying load demand and the power generated from the renewable energy resources are represented using the normal, the lognormal, the Weibull, and the Gumbel probability density function (PDFs). Then, the Monte Carlo simulations reduction of scenarios-based technique is applied to generate a suitable number of scenarios. The second contribution presents an efficient version of the Artificial Hummingbird Algorithm (MAHA) for solving the Stochastic and Non-Stochastic ORPD. The proposed MAHA is based on the levy flight motion and the distance bandwidth motion around the best solution to enhance the exploration and exploitation behavior of the traditional AHA to avoid trapping into the local minima. The proposed algorithm is validated and tested on the IEEE 30-bus system for active power loss reduction, voltage profile improvement, and voltage stability enhancement. The results showed the effectiveness and superiority of the proposed MAHA for solving the ORPD problem compared to the well-known conventional algorithms such as AHA, GWO, SCA, DO, BWO and other state-of-the-art algorithms.

Stability Analysis of Microgrid with Load Shedding Scheme using RTDS

A microgrid system was simulated using the powerful RTDS simulator, the modelling was done on the RSCAD software, and the microgrid comprised of a diesel generator, a photovoltaic (PV) system and a Doubly-Fed Induction Generator (DFIG) wind turbine system as DERs. The paper comprises the study on stability analysis of the microgrid in grid-connected and islanded modes of operation, along with a successful load shedding scheme which was implemented based on the loads distinguished as low priority and high priority loads in order to keep the system balanced during faults.

Metaheuristic Method for a Wind-Integrated Distribution Network to Support Voltage Stabilisation Employing Electric Vehicle Loads

Distributed generation (DG) has been incorporated into the distribution networks and, despite the rising prevalence of electric vehicle (EV) loads that are uncertain and cause substantial challenges in their operation, it is necessary to enhance the voltage profile, reduce power losses, and consequently improve the stability of whole networks. The recently proposed beluga whale optimisation algorithm is explored in the optimisation framework to determine the most suitable size of wind turbine generating systems (WTGS), while the optimum placements are determined by minimising the placement index (P-Index) using the distribution load flow (DLF) method. The voltage stability factor (VSF) is employed to formulate the P-Index to enhance voltage sensitivity in distribution systems. The main purpose of this article is to assess the influence of voltage-dependent, uncertain ZIP-form EV loads in order to analyse their potential in the active and reactive power operations of the distribution network while retaining the system voltage within a specified limit by significantly reducing system losses and taking distribution network-level constraints into account. The efficacy of the methodology is validated on the standard IEEE-33 test system by formulating two performance indices to determine a significant enhancement in convergence characteristics and a reduction in system losses.

Soft-masks guided faster region-based convolutional neural network for domain adaptation in wind turbine detection

Wind turbine generator system plays a fundamental role in electricity generation in industry 4.0, and wind turbines are usually distributed separately and in poor locations. Unmanned Aerial Vehicles (UAV) which could overcome the above challenges are deployed to collect photographs of wind turbines, could be used for predictive maintenance of wind turbines and energy management. However, identifying meaningful information from huge amounts of photographs taken by drones is a challenging task due to various scales, different viewpoints, and tedious manual annotation. Besides, deep neural networks (DNN) are dominant in object detection, and training DNN requires large numbers of accurately labeled training data, and manual data annotation is tedious, inefficient, and error-prone. Considering these issues, we generate a synthetic UAV-taken dataset of wind turbines, which provides RGB images, target bounding boxes, and precise pixel annotations as well. But directly transferring the model trained on the synthetic dataset to the real dataset may lead to poor performance due to domain shifts (or domain gaps). The predominant approaches to alleviate the domain discrepancy are adversarial feature learning strategies, which focus on feature alignment for style (e.g., color, texture, illumination, etc.) gaps without considering the content (e.g., densities, backgrounds, and layout scenes) gaps. In this study, we scrutinize the real UAV-taken imagery of wind turbines and develop a synthetic generation method trying to simulate the real ones from the aspects of style and content. Besides, we propose a novel soft-masks guided faster region-based convolutional neural network (SMG Faster R-CNN) for domain adaptation in wind turbine detection, where the soft masks help to extract highly object-related features and suppress domain-specific features. We evaluate the accuracy of SMG Faster R-CNN on the wind turbine dataset and demonstrate the effectiveness of our approach compared with some prevalent object detection models and some adversarial DA models.

Decentralized Robust Control of a Coupled Wind Turbine and Diesel Engine Generator System

This paper presents the design of a robust decentralized controller for the frequency and active power control of a single stand-alone (off-grid) mechanically-coupled generator system including wind turbine and diesel engine generator subsystems. Two robust decentralized controllers are designed and optimized for the two subsystems by using the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula> -synthesis technique. The decentralized controllers address the mechanical coupling between the two subsystems without communicating with each other. In addition, both controllers are designed in such a way that they are robust against the concurrent variations of all the system parameters, which in turn improves the frequency and power control performances significantly. Simulation results indicate that the proposed decentralized controllers have a better performance as compared to the benchmark decentralized model predictive controllers and decentralized linear-quadratic Gaussian controllers. It is also demonstrated that the proposed control scheme is able to damp out mild and large disturbances originating from the load and wind power changes, and has an acceptable robust performance against the concurrent variations in all parameters of the system.

Cost evaluation of wind turbine generator system with magnetic continuously variable transmission

Cost evaluation of wind turbine generator system with magnetic continuously variable transmission