Induction Generator Research Articles

Energy management in gyms microgrid: Nonlinear control of stationary bikes and treadmills with parallel rectifiers and AC/DC conversion

This article presents the complete design of a nonlinear control system for multiple stationary bikes connected to a mechanical energy conversion system equipped with squirrel cage induction generators (SIGs) linked to AC/DC converters. The primary objective of this study is to investigate the feasibility of powering multiple treadmills using the DC bus via DC/AC converters. The novelty of our approach lies in the integration of a new strategy of control system to achieve multiple control objectives within a gym microgrid environment, and an energy management algorithm to ensure the energy flow between intermittent generation and the considered, somewhat random, demand. The system is composed of several subsystems: i) stationary bikes connected to the DC bus via inverters acting as intermittent power sources; ii) a Li-ion battery-based energy storage system interfaced through a Buck-Boost converter; iii) electromechanical loads, including treadmills, powered by DC/AC converters, in addition to other DC loads. The main control objectives are as follows: a) each stationary bike extracts energy from the DC network by regulating the torque applied by the athlete, ensuring that the torque follows the reference torque; b) the treadmill’s speed must follow the reference generated based on the athlete’s condition; c) ensuring the protection of the energy storage system by monitoring its current and voltage; d) all mentioned objectives are achieved while maintaining the DC bus voltage at a reference value. To achieve these objectives, a nonlinear control approach based on Lyapunov theory is used to design the controllers. A formal analysis is conducted to assess the performance of the studied system. The system’s performance is demonstrated using the MATLAB/Simulink environment. Numerous simulations demonstrate that every control objective is achieved. Specifically, the system maintained the DC bus voltage at 500 V with a maximum deviation of 1 V, and the treadmill speed followed the reference within a margin of 0.1 m/s.

Frequency security-constrained unit commitment with fast frequency support of DFIG-based wind power plants

The integration of inverter-based renewable energy sources into power grids presents a unique challenge: a reduction in grid inertia, leading to potential frequency stability issues. This paper introduces an innovative approach to address this challenge, focusing on the dynamic role of wind power plants equipped with dual-fed induction generators (DFIG). Unlike traditional methods that rely on fixed inertia control strategies and wind power droop coefficients, our method offers a dynamic solution tailored to the fluctuating nature of grid frequency dynamics. We propose a novel variable inertia support mechanism, coupled with an optimized power point tracking control and strategic reserve, to maximize the effective use of wind power reserves without waste. Additionally, we account for the inherent uncertainties in wind power generation, enhancing the frequency support capabilities of wind turbines through a fast frequency response strategy. A key feature of our approach is the application of an advanced piecewise linearization fitting method to establish a robust frequency nadir constraint. This is integrated into a two-stage robust frequency security-constrained unit commitment model, enabling optimal real-time decision making for frequency stability in wind power plants. The efficacy and practicality of our proposed method are verified through a detailed case study on a modified IEEE 39-bus system and a hardware-in-the-loop experiment. Compared with the traditional fixed parameter control method, the cost is reduced by approximately 0.42%, while the linearization error at frequency nadirs remains within the range of -4% to 1.5%.

Control of dual star induction generator based on multi-level inverter used in wind energy conversion system

This paper presents vector control of dual star induction generator (DSIG) integrated into variable speed wind energy and supplied by three-level NPC converters. Now the DSIG is the most common among multiphase machines when used in high power generation systems, which is associated with two converters. Maximum power point tracking (MPPT) for extracting a maximum of power fluctuating wind speed is illustrated. In order to decrease the fluctuations that appear in the currents generated by DSIG to the electrical network, we propose the NPC structure three-level inverter. Simulation results of a 1.5 MW Wind turbine are presented to illustrate the validity of this application.

Investigation of the Impact of Fault Characteristics on the Cost-Effectiveness of Doubly Fed Induction Generator-Based Wind Systems in Withstanding Low-Voltage Ride-Through

Wind farms utilizing doubly fed induction generators (DFIGs) can have a significant impact on the stability of power networks as both the stator and rotor of the DFIG are linked to the grid, which can result in excessive overcurrent and overvoltage in the event of a grid fault and can activate the protective mechanism, leading to the disconnection of the WF and generating instability in the system. One term that is often mentioned in the literature is low-voltage ride-through (LVRT) capability, which is crucial to the stability of microgrids (MGs). To handle LVRT, advanced protection schemes or supporting devices are required. In addition, MGs must comply with the operational limits imposed by different countries for LVRT. Therefore, numerous solutions for improving LVRT have been proposed, including external approaches that are expensive to adopt and internal procedures that provide economic gains but are more difficult to apply. Consequently, to help lower the cost of installing WFs, the study investigates how fault characteristics affect MGs’ ability to meet grid LVRT code requirements or even choose the right code to be used. It also aims to give a clear understanding of how fault characteristics affect the grid’s behavior during different types of faults, which will be helpful in choosing the best LVRT-enhancing method or device and for determining the optimal ratings for these devices, and thus reduce the cost of installation. The study offers case studies and simulations using Matlab 2024/Simulink, which could help engineers to ensure reliable grid integration of renewable energy sources in a cost-effective manner.

Optimizing DFIG‐DC system performance via model predictive control: Torque ripple, DC voltage drop, and THD reduction

AbstractIn this study, two novel control methods are theoretically introduced as direct slip angle control (DSAC) and model predictive direct slip angle control (MPDSAC) for rotor side converter (RSC) of the standalone Doubly‐Fed Induction Generator (DFIG)‐DC system (S‐DFIG‐DC). The numerical analysis of the proposed methods demonstrated that the proposed DSAC method could reduce torque ripple and current harmonics, decrease the output DC voltage drop in fast changes of load condition, and have faster dynamic response. Also, the MPDSAC method could further diminish torque and flux ripple, current harmonics, and output DC voltage drop in sharp changes of load condition compared with the presented DSAC and direct torque control (DTC) methods. The sensitivity analysis of the proposed control methods is investigated at different operating conditions. The performance and usefulness of the DSAC and MPDSAC schemes are verified by various experiments and compared with the conventional DTC method.

SUIVI DE LA QUALITÉ DE PUISSANCE MAXIMALE D'UN GÉNÉRATEUR D'INDUCTION À DOUBLE ALIMENTATION BASÉ SUR UN CONTRÔLEUR DE RÉSEAU NEURONAL ARTIFICIEL POUR SYSTÈME DE CONVERSION D'ÉNERGIE ÉOLIENNE

Renewable energy sources are playing a crucial role in satisfying the upcoming energy source needs for the world. The current power system is power electronics converter based, with several non-linear loads and spotted generations of renewable energy sources, resulting in many power quality issues. Most existing research analyzed MATLAB simulations and presented a high Total Harmonics Distortion (THD) level. This research work proposed an artificial neural network (ANN) control technique for a doubly fed induction generator (DFIG) based wind energy conversion systems (WECSs). To decrease chattering phenomena during the excitation arrangement, an advanced controller works for adaptive modification of the irregular power gain, even preserving the strength of the closed-loop scheme. The proposed PI controller one step forward improves reliability and tracks maximum voltage from the pulse width modulation rectifier. At primary, the modeling of the DFIG was presented. Then, using the proposed ANN controller, the rotor magnitude was tuned to recognize vector control of active and reactive power. The converter intends to activate at unity power factor and provide input currents with adequate harmonic content. The interface between the power’s electronic converter and the DFIG pulls out the most real power potential. The proposed prototype hardware model and simulation results are verified.

Wideband oscillation analysis of VSC-HVDC connected DFIG wind farm

This paper investigates the wideband oscillations of a doubly fed induction generator (DFIG)-based wind farm connected via a voltage source converter-based HVDC (VSC-HVDC) system across the full frequency band. A small signal model of the system is developed to facilitate this analysis. Oscillation mode characteristics are then examined through eigenvalue analysis and participation factors. The study further explores the impact of strongly correlated factors on typical wideband oscillation modes and their inter-mode interactions. Findings reveal that interactions between the DFIG wind farms and the VSC-HVDC system can induce new super-synchronous oscillations. Additionally, a damping coupling is observed between the medium-frequency oscillation interactive transformation mode and the stator-line mode.

Enhancing doubly-fed induction generator performance in wind energy systems using intelligent control

Enhancing doubly-fed induction generator performance in wind energy systems using intelligent control

Inertia Estimation for Microgrid Considering the Impact of Wind Conditions on Doubly-Fed Induction Generators

Inertia Estimation for Microgrid Considering the Impact of Wind Conditions on Doubly-Fed Induction Generators

Research on Mathematical Model of Energy Storage Assisted Wind Turbine Frequency Regulation Control Strategy

Abstract This paper proposes an improved frequency regulation strategy for the Doubly-Fed Induction Generator (DFIG) to mitigate the impact of wind power integration on system frequency. An energy storage system is used on the wind side to participate in the frequency regulation. The influence of wind power integration on the power system frequency is analyzed and an improved frequency control strategy for the DFIG is proposed. A control strategy for energy storage devices to support wind turbine frequency control is proposed. An energy storage device model is established that considers power correction based on the state of charge to avoid excessive charging and discharging. According to the operating state of the DFIG, a control strategy for the frequency regulation of the energy storage device is proposed. According to different wind speeds, a control strategy is proposed for the participation of the joint wind storage system in the frequency regulation. This control strategy can provide frequency support to the system when the wind turbine cannot participate in frequency regulation according to the different operating conditions of the wind turbine. After the wind turbine uses the improved frequency control strategy, it accelerates the recovery of the DFIG rotor speed while avoiding a secondary drop in the system frequency. In addition, when the energy storage device has a poor state of charge and the system frequency is good, it charges to restore the state of charge.

Effect of rotation on achieving constant voltage in three-phase self-excited induction generator for small scale wind turbines application

Three-phase Self-Excited Induction Generators (SEIGs) are commonly employed for electricity generation in remote or isolated areas. SEIGs are preferred in such regions due to their ability to create a magnetic field by adding a capacitor to one of their terminals. Nevertheless, a significant challenge in utilizing SEIGs is maintaining a consistent output voltage in the presence of load fluctuations. This study aims to investigate the impact of generator rotation on the SEIG's output voltage and determine the optimal rotation speed required for achieving a stable output voltage. Ensuring stable voltage regulation is crucial to guarantee the proper functioning of all loads connected to the SEIG. Furthermore, operating the SEIG in parallel with other generators is advantageous. The methodology employed in this study involves varying the load supplied by the SEIG at different capacitor values. Unwanted voltage variations occur due to load fluctuations within a generating system or SEIG. Adjustments to the generator's rotation speed are made to uphold a uniform voltage level. The variables considered in this study include the generator's rotation speed, capacitor size, and load fluctuations. Simulation results demonstrate that the SEIG's output voltage is affected by the generator's rotation speed, and maintaining a consistent voltage necessitates appropriate adjustments to capacitor values and generator speed. This research enhances understanding of SEIG characteristics and offers guidance on effective settings for maintaining a stable output voltage at various generator rotation speeds. Future research can focus on practically implementing these findings to enhance the performance of SEIGs in real-world applications

Impact of ABT based control strategies on ALFC and AVR in DFIG-integrated interconnected power systems

To maintain a stable electricity supply in interconnected power grids, effective frequency and voltage regulation are required. Interconnected power systems use Automatic Load Frequency Control (ALFC) and Automatic Voltage Regulator (AVR) for this purpose. The integration of renewable sources, such as Doubly Fed Induction Generators (DFIGs), creates challenges for such control loops. This study investigates the impact of using various Frequency-Linked Pricing (FLP)-based operating strategies under Availability-Based Tariff (ABT) on combined ALFC and AVR in a two-area interconnected power system with DFIGs. FLP-based operating strategies under ABT react to frequency changes through price signals, which can act faster compared to traditional AGC methods that rely on communication between control centers. This quicker response helps maintain frequency and voltage during load disturbances. Most of the earlier research focused mostly on using only a single type of FLP-based operating strategy under ABT within the ALFC loop, neglecting the combined ALFC-AVR interaction. This work addresses this gap by evaluating three FLP-based operating strategies under ABT along with the traditional approach for their impact on frequency and voltage under step load disturbance. Simulation studies conducted in MATLAB 2022a demonstrate the effectiveness of the proposed strategies, providing valuable insights for improving frequency and voltage control in modern power systems. The investigation reveals that the strategy that considers the difference between the actual and reference values of the unscheduled interchange (UI) charge and the marginal cost of generation to produce the control command outperforms the other strategies and restores the frequency and voltage to their nominal values.

Model predictive direct torque algorithm for coordinated electrical grid operation of wind energy conversion system-based doubly fed induction generator

ABSTRACT In this paper, robust predictive control (RPC) based on direct torque control space vector modulation (DTC-SVM) of the doubly fed induction generator wind turbine system (DFIG-WTS) is investigated to improve energy capture effectiveness. A novel cost function is introduced for the predictive speed regulator where the aerodynamic torque is considered as an unknown perturbation. This technique is designed to improve dynamic performance such as reference tracking, disturbance rejection, and robustness to parameter variations. The use of the DTC-SVM technique enhances performance in terms of constant switching frequency and reduced torque-flux ripples. The simulation results using real-time MATLAB/Simulink demonstrate the feasibility and validity of the proposed strategy, where very satisfactory performance has been achieved.

Design of Battery Energy Storage System Torsional Damper for a Microgrid with Wind Generators Using Artificial Neural Network

Ancillary frequency controllers such as droop controllers are beneficial for frequency regulation of a microgrid with high penetration of wind generators. However, the use of such ancillary frequency controllers may cause torsional oscillation in the doubly fed induction generator (DFIG). In this paper, a supplementary torsional damper in a battery energy storage system (BESS) is designed to improve the damping ratio for the DFIG torsional mode. Since the optimal damper gain depends on system variables such as the number of diesel generators, the number of wind generators, and BESS droop gain, an artificial neural network (ANN) is trained using these system variables as inputs and the desired BESS damper gain as the output. After the ANN has been trained with the training patterns, it can provide the desired BESS damper gain in an accurate and efficient manner. The effectiveness of the proposed ANN approach for BESS damper design is demonstrated by MATLAB/SIMULINK R2022b simulations.

A new relaying approach for protecting TCSC compensated transmission line connected to DFIG based wind farm

In modern power systems, Thyristor Controlled Series Capacitor (TCSC)-compensated transmission lines are crucial in transporting bulk power from large wind farms. However, the performance of conventional distance relays is adversely impacted by the typical fault characteristics of the Doubly Fed Induction Generator (DFIG) and TCSC. To address this challenge, this paper presents a new relaying algorithm such as Complete Ensemble Empirical Mode Decomposition with Adaptive Noise-based Dominant Mode Algorithm-Hilbert Transform (CEEMDAN-DMA-HT) for fault detection and CEEMDAN assisted Enhanced Jaya Optimization-based Random Vector Functional Link Network (EJAYA-RVFL) for fault classification. In proposed fault detection algorithm, the differential current from both ends of the line is subjected to Complete Ensemble Empirical Mode Decomposition with Adaptive Noise, leading to the extraction of distinct intrinsic mode functions (IMFs). Employing the Dominant Mode Algorithm, the IMF with the highest Pearson correlation coefficient, referred to as the dominant IMF, is identified. Subsequently, a comparison between this dominant mode IMF and the original input signal is conducted after passing the obtained signal through the Hilbert Transform (HT), enabling effective fault detection. The proposed classifier leverages a straightforward feature set that quantifies the energy of each phase and the ground. These features serve as inputs for the proposed classifier, facilitating the categorization of different fault types. To evaluate the effectiveness of the proposed relaying algorithm, several fault and non-fault scenarios are simulated on a test power system using MATLAB/Simulink. The results demonstrate that; the proposed fault detection and classification algorithm are able to detect and classify faults in less than a half-cycle. The proposed method gives 100% fault accuracy for fault detection and 99.97 % accuracy for fault classification. Furthermore, the proposed classifier achieves the performance metrics like Precision (0.998), Recall (0.997) and F1-Score (0.996), providing quantitative insights into its accuracy and dependability. Finally, a comparative analysis is carried out with existing approaches.

Optimization Pump as Turbine Coupled to a Self-Excited Induction Generator Using Multi-Objective Genetic Algorithm

Optimization Pump as Turbine Coupled to a Self-Excited Induction Generator Using Multi-Objective Genetic Algorithm

Coordinated Frequency Modulation Control Strategy of Wind Power and Energy Storage Considering Mechanical Load Optimization

When a doubly fed induction generator (DFIG) participates in primary frequency modulation by rotor kinetic energy control, the torque of the generator is changed sharply and the mechanical load pressure of the shaft increases rapidly, which aggravates the fatigue damage of shafting. In order to alleviate the fatigue load of shafting, energy storage was added in the primary frequency modulation of a wind turbine, and a coordinated frequency modulation control strategy of wind power and energy storage based on fuzzy control was proposed. The wind-storage frequency modulation power command was allocated to reduce the response speed of the wind turbine to alleviate the load pressure on the shafting by the fuzzy controller considering the rotor speed range and the state of energy storage charge, and the remaining demand power was supplemented by energy storage. Finally, the joint simulation model based on GH Bladed–Matlab was used to verify the effectiveness of the proposed control strategy. Compared with the traditional integrated control of virtual inertia, the proposed method can reduce the load pressure and fatigue damage of the shafting while satisfying the requirement of frequency modulation.

Amplification of electromagnetic fields by a rotating body

In 1971, Zel’dovich predicted the amplification of electromagnetic (EM) waves scattered by a rotating metallic cylinder, gaining mechanical rotational energy from the body. This phenomenon was believed to be unobservable with electromagnetic fields due to technological difficulties in meeting the condition of amplification that is, the cylinder must rotate faster than the frequency of the incoming radiation. Here, we measure the amplification of an electromagnetic field, generated by a toroid LC-circuit, scattered by an aluminium cylinder spinning in the toroid gap. We show that when the Zel’dovich condition is met, the resistance induced by the cylinder becomes negative implying amplification of the incoming EM fields. These results reveal the connection between the concept of induction generators and the physics of this fundamental physics effect and open new prospects towards testing the Zel’dovich mechanism in the quantum regime, as well as related quantum friction effects.

Mitigating sub-synchronous oscillation using intelligent damping control of DFIG based on improved TD3 algorithm with knowledge fusion

The occurrence of sub-synchronous oscillation (SSO) phenomenon in doubly-fed induction generators (DFIGs)-based wind turbines threatens the secure and stable operation of the power grid. Conventional sub-synchronous damping controllers encounter challenges in adapting to the dynamic operating conditions of power systems. This paper introduces an Intelligent Sub-Synchronous Damping Controller (I-SSDC) for DFIGs that integrates deep reinforcement learning (DRL) and knowledge to address the limitations of conventional methods for SSO mitigation. The initial step involves formulating a framework for I-SSDC using the improved twin delayed deep deterministic policy gradient (TD3) algorithm incorporating Softmax. Following this, a surrogate model is constructed, employing Weighted Linear Regression and regularization. This model is designed to identify the predominant influencing factors of SSO, focusing on the selection of the output signal (installation position) to optimize decision-making in I-SSDC. The objective is to enhance the controller’s environmental adaptability and interpretability. Moreover, knowledge and experience related to SSOs are integrated into agent training to improve the exploration efficiency of the agent. Case studies under various operating conditions of the test power system validate the efficacy of the proposed I-SSDC in suppressing SSOs.

Enhancing stability via coordinated control of generators, wind farms, and energy storage: Optimizing system parameters

This study delves into the intricacies of power system stability, specifically addressing the challenges posed by integrating renewable energy sources, primarily focusing on wind power. The intricate dynamic interactions between Synchronous Generators (SGs), Doubly Fed Induction Generators (DFIGs), and Battery Energy Storage Systems (BESS) under varying operating conditions necessitate advanced control strategies for effective stabilization. Conventional control methods, such as Power System Stabilizers (PSS) and damping controllers, are scrutinized for their inherent sensitivity to system changes and limitations in mitigating the dynamic complexities arising from integrating Wind Farms (WFs) and BESS. This research accentuates the critical need for synchronized control mechanisms to preempt dynamic instability in power systems, considering factors like wind speed fluctuations, WF integration intricacies, and responses to severe disturbances. The introduction of DFIG-based WFs is particularly examined for its impact on altering system inertia, leading to Low-Frequency Oscillations (LFOs) and diminished damping, thereby heightening stability concerns. The proposed control strategies encompass the integration of BESS, PSS, and coordinated damping controllers meticulously tailored to counteract systemic instability. Recognizing the potential inadequacy of individual control methods during severe disturbances, the study underscores the imperative of synchronized operation between PSS and BESS damping controllers. Furthermore, the investigation sheds light on the pivotal role of uncertainties in power system stability, advocating for a paradigm shift from deterministic methods to more nuanced probabilistic approaches. The proposed coordinated probabilistic control designs, leveraging participation factors for SGs, DFIGs, and BESS, offer a sophisticated solution to mitigate uncertainties and fortify system stability across diverse conditions. A rigorous evaluation employing Real-Time Digital Simulator (RTDS) simulations on the IEEE 9-bus test system substantiates the practical efficacy of the proposed coordinated control approach. The discerned outcomes underscore discernible enhancements in both small signal and transient stability, affirming the practical viability and reliability of the advanced control strategies delineated in this study.