Grid-side Control Research Articles

Optimization Control of Sub-Synchronous Oscillations in Doubly Fed Generators with Wind Turbines Using the Genetic Algorithm

The sub-synchronous oscillation accident of large-scale doubly fed wind turbines connected to a grid through series compensation has caused a serious impact on the power system. By optimizing the parameters of the doubly fed wind turbines control system, the system impedance can be effectively improved to solve the problem of sub-synchronous oscillation. However, owing to the complexity of a grid-connected system of doubly fed generators connected to wind turbines and the influence of the time-varying oscillation characteristics of the system, it is often difficult to achieve a successful suppression. To solve this problem, this paper proposes an optimized additional damping method for the rotor- and grid-side controllers, which can achieve efficient suppression of the sub-synchronous oscillation. The parameters of the proposed additional damping method are optimized for all variable operation conditions using a genetic algorithm under the established frequency–domain impedance model. The detailed time–domain simulation model was constructed with the RTLAB platform to verify the proposed method. The experimental results show that the optimized control strategy can effectively and quickly suppress the sub-synchronous oscillation under different operating conditions, and the amplitude suppression rate reached 85.99%, which effectively improved the grid-connected stability of the wind turbines.

Fuzzy Logic Control of Two-Stage Grid- Connected Photovoltaic System

In this paper, a two-stage grid-connected photovoltaic system is presented. The optimal performance of solar panels at varying temperatures and irradiance levels is achieved by the application of a fuzzy logic-based maximum power point tracking method. Space vector modulation is used to control the inverter; to achieve capacitor voltage balance, redundant switching states of a three level inverter are used. The grid side control regulates the power and the current injected to the grid, the conventional PI controllers are replaced by fuzzy PI. Simulations have been made using Simulink environment on MATLAB to validatethe control of the proposed system.

Inertial Energy Storage Integration with Wind Power Generation Using Transgenerator–Flywheel Technology

A new type of generator, a transgenerator, is introduced, which integrates the wind turbine and flywheel into one system, aiming to make flywheel-distributed energy storage (FDES) more modular and scalable than the conventional FDES. The transgenerator is a three-member dual-mechanical-port (DMP) machine with two rotating members (inner and outer rotors) and one stationary member (stator). The transgenerator–flywheel system is introduced with its configuration, transgenerator overview, flywheel operation principle and power management strategies, and control system. Simulations are performed in MATLAB 2023b/Simulink to verify the system viability, including control system verification and flywheel storage performance evaluation. The results show that the inner and outer rotors can be controlled independently with an accurate and fast control response, and the grid-side control works properly. The flywheel performs well, with considerable charging power and storage capacity.

Design and development of PI controller for DFIG grid integration using neural tuning method ensembled with dense plexus terminals

In a DFIG grid interconnected system, the control of real and reactive power relies on various factors. This paper presents an approach to regulate the flow of real and reactive power using a Neural Tuning Machine (NTM) based on a recurrent neural network. The focus is on controlling the flow of reactive power from the rotor-side converter, which is proportional to the grid-side controller through a coupling voltage. The proposed NTM method leverages neural networks to fine-tune the parameters of the PI controller, optimizing performance for DFIG grid integration. By integrating dense plexus terminals, also known as dense connections, within the neural network, the control system achieves enhanced adaptability, robustness, and nonlinear dynamics, addressing the challenges of the grid. Grid control actions are based on the voltage profile at different bus locations, thereby regulating voltage. This article meticulously examines the analysis in terms of DFIG configuration and highlights the advantages of the neural tuning machine in controlling inner current loop parameters compared to conventional PI controllers. To demonstrate the robustness of the control algorithm, a MATLAB Simulink model is designed, and validation is conducted with three different benchmarking models. All calculations and results presented in the article strictly adhere to IEEE and IEC standards. This research contributes to advancing control methodologies for DFIG grid integration and lays the groundwork for further exploration of neural tuning methods in power system control.

Study of produced harmonics in DFIG powered by wind turbines over linear and nonlinear loads

Due to the growing interest in electricity, both conventional and non-conventional energy sources are growing. As the number of powertrains increases, just as the interconnectivity of these components grows terrifyingly, so does the power response along with the variable overcurrent. Which acquires the majority of power shares in an environmentally responsible manner. One thing that sets wind power apart is how little it costs. With a proven capacity of around 21262 MW, India's wind farm ranks fifth in the world with an exceptionally high annual expansion rate [1]. Management New Harmless to Ecosystem Strength (MNRE) and association of Tamil Nadu, Maharashtra and Gujarat movements. Considering the 7.6 Gw of wind cap that has been produced in the last 15 months, the target is to produce an additional 10 Gw in FY 2019[2]. The Twofold Dealt with Acknowledgement Generator (DFIG) structure was found to be affected by the wind in uneven stacking. The electricity produced makes endless sounds available [3]. The proposed study depicts the evaluation of noise produced by wind-related electricity. The combined dynamic channel uses the DFIG system for both direct and indirect loads. This work recreates the sounds produced by direct and indirect loads using mutt dynamic channel reduction for Grid Side Control (GSC) and Rotor Side Control (RSC). The end product of this effort shows that using the unequal weight system, the value of sounds dropped to 4.96% from 29.66%. Additionally, it shows that in cases where the nonlinear weight has no effect on the structure, the consonant value is exactly 0.14%. Various research projects are now being carried out in an effort to develop channels that can suppress consonant levels higher than the dedicated hybrid feed channel. MATLAB R2018a variant is used to generate the model for the proposed work. The model is shown in the Simulink section of MATLAB under Programming.

A Study on Power Flow Analysis in Power Systems with the Participation of Double-Fed Induction Generators based Wind Turbine System

This paper focuses on double-fed induction generator (DFIG) based wind turbine system (WTS) and evaluate its effect to the distribution of power flow in power system. The structure of DFIG based WTS will be make detailed, including a rotor, stator and back-to-back converters. Power flows in itself are analyzed in both over and under synchronous operating modes. Control structure of DFIG is shown corresponding to rotor-side controller and grid-side controller to harness wind power and synchronize to the power grid. Operating scenarios that can happen in a proposed grid with the participation of DFIG based WTS are also shown to analyze the distribution of power flows in whole system whenever there are variations of power consumption and power generation. Simulation process will be carried out by MATLAB software to demonstrate proposed operating scenarios. Simulation results will show the power flows in whole system, the power adaptability corresponding to variation of load steps, the decrease of power from DFIG based WTS and the variation of currents at buses. Moreover, power exchange between power source and considered grid and current going through main units also can be determined and provided to dispatch center to ensure system stability.

Neuro-Fuzzy Based High-Voltage DC Model to Optimize Frequency Stability of an Offshore Wind Farm

Lack of synchronization between high voltage DC systems linking offshore wind farms and the onshore grid is a natural consequence owing to the stochastic nature of wind energy. The poor synchronization results in increased system disturbances, grid contingencies, power loss, and frequency instability. Emphasizing frequency stability analysis, this research investigates a dynamic coordination control technique for a Double Fed Induction Generator (DFIG) consisting of OWFs integrated with a hybrid multi-terminal HVDC (MTDC) system. Line commutated converters (LCC) and voltage source converters (VSC) are used in the suggested control method in order to ensure frequency stability. The adaptive neuro-fuzzy inference approach is used to accurately predict wind speed in order to further improve frequency stability. The proposed HVDC system can integrate multiple distributed OWFs with the onshore grid system, and the control strategy is designed based on this concept. In order to ensure the transient stability of the HVDC system, the DFIG-based OWF is regulated by a rotor side controller (RSC) and a grid side controller (GSC) at the grid side using a STATCOM. The devised HVDC (MTDC) is simulated in MATLAB/SIMULINK, and the performance is evaluated in terms of different parameters, such as frequency, wind power, rotor and stator side current, torque, speed, and power. Experimental results are compared to a conventional optimal power flow (OPF) model to validate the performance.

Analysis and Implementation of a Three-Phase Grid-Connected PV/Wind Hybrid System

Abstract: The increasing energy demand and depletion of fossil fuels has risen in awareness of searching for alternative energy source thus the inexhaustible solar and wind energy is becoming an interesting topic which has grabbed the attention of researchers to make it sustainable power. The objective of this paper is to provide sustainable power for rural areas and remote places. This paper gives the architecture of hybrid system. The proposed system consists of solar PV and Doubly Fed Induction Generator (DFIG) based wind turbine. In Solar PV MPPT technique is used to maximize the power. The DFIG has two controllers Rotor side control and Grid side control.

Impact of an Unsymmetrical Fault on the Controller of the DFIG Wind Farm

Objectives: DFIG-based wind farms are predominant in their performance when compared to other types of generators. In the field study, due to transient events, converters at the machine side and grid side are affected. To protect the converters from an abnormal operation, overcurrent protective relays have to be designed and incorporated. Unsymmetrical fault analysis are help in the design of protective devise and speed sensors. Methods: DFIG-based wind systems are developed using the DIgSILENT power factory software and EMT simulations are executed during the unsymmetrical faults applied near the Machine Side Converter (MSC) and Grid Side Converter(GSC). Findings: During the simulation studies, various parameters are transient time, maximum current, steady-state, and voltage drop identified near the MSC and GSC. The short circuit parameters are attenuated during the unsymmetrical faults and the rotor maximum current increases by 2 to 3 times the rated current per unit, 40% to 50% voltage dip, and the current reach the steady state value after 1.213 sec during the fault. The parameters are affected more when a fault occurs near the GSC when compared to a fault near MSC. Novelty: These research findings will contribute to a better understanding and improvement of sensor sensitivity, as well as the development of control mechanisms, and protection tactics in designing more accurate and appropriate for DFIG-WT. Keywords: DoublyFed Induction Generator; Machine Side Controller; Grid Side Controller; Unsymmetrical Fault; Sensor

An Integrated Energy Control System to Provide Optimum Demand Side Management of a Grid-Interactive Microgrid

The use of renewables can make it more challenging to maintain a balance between supply and demand in microgrids due to their variable generation profiles. To address this issue, microgrids can be designed to be grid-interactive or to include energy storage units, or both. Additionally, demand-side management (DSM) strategies can be implemented to facilitate control during peak periods. This paper presents a control system for a grid-interactive microgrid with photovoltaic (PV) panels and energy storage units. The proposed system uses a fuzzy-based algorithm to control the energy storage units and provides DSM through the use of a hybrid daily pricing model that combines multi-time rate and inclining block rate pricing methods. To optimize the global maximum power point of the PV panels under partial shading conditions, the microgrid model is tested with several metaheuristic optimization algorithms, including particle swarm optimization (PSO), gray wolf optimization (GWO), and dragonfly algorithm (DA). A hybrid algorithm that combines PSO and DA is also proposed. The resulting integrated control system includes both demand-side and grid-side control operations.

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.

Equivalent modeling and multi-parameter coupling optimization for DFIG-based wind farms considering SSO mode

As a low-carbon and environmentally friendly renewable energy source, wind power has been globally recognized as the best solution to achieve energy saving and emission reduction and promote low-carbon economic growth. With the increase of wind power penetration, wind power has a great impact on sub-synchronous state stability and dynamic characteristics of the grid-connected system. Aiming at the fact that the correlation between clustering indexes and sub-synchronous oscillation (SSO) mode and the difference of the contribution to the clustering results are seldom considered in the current equivalent modeling of doubly-fed induction generator (DFIG)-based wind farm, this paper proposes a clustering method based on the index dimension reduction and weighted fuzzy C-means (WFCM) clustering algorithm. Besides, for the SSO study of the grid-connected system without sufficiently considering the coupling effects between controller parameters, a multi-parameter coupling optimization design strategy combining orthogonal experiment method (OEM) and response surface method is proposed. Firstly, the dominant variables of SSO mode of the DFIG-based wind farm connected to weak grid by series compensation system are taken as the initial clustering indexes. After dimension reduction by principal component analysis, the WFCM algorithm is utilized to cluster the wind farm. Then, the proportional and integral coefficients of the grid-side controller, rotor-side controller and phase-locked loop are optimized to achieve the simultaneous optimization of the SSO characteristics and dynamic characteristics of the system. Finally, the interaction between control parameters and the influence degree and trend on the system performance are quantitatively evaluated, and the optimal parameter combination is obtained. The proposed strategy can mitigate SSO more effectively while improving anti-interference than the particle swarm optimization based on OEM.

An Innovative Converterless Solar PV Control Strategy for a Grid Connected Hybrid PV/Wind/Fuel-Cell System Coupled With Battery Energy Storage

The proposed work addresses the modeling, control, energy management and operation of hybrid grid connected system with wind-PV-Battery Energy Storage System (BESS) integrated with Fuel Cell (FC) and Electrolyzer. A hybrid PV-Wind-FC with electrolyzer consisting of BESS with the least number of control loops and converters has been proposed. The proposed hybrid system presents a cost efficient solution for integrating PV into a hybrid system by eliminating the PV converter. This includes the design of controllers for grid-connected hybrid systems with a renewable distributed generator (Wind and PV) as a primary source, BESS as a secondary source and FC with Electrolyzer as a tertiary source. In addition, the lead compensator along with integrator is used for obtaining enough phase margin and removing steady state error completely. It increases the stability of the controller and adds phase shift φ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>s</i></sub> at a cross gain frequency (ω <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>cut</i></sub> ). The Grid Side Controller (GSC) is capable of providing frequency support to the utility grid, when it is linked to the grid. In the proposed configuration, PV power is maximized and injected into grid through GSC. Rotor Side Converter (RSC) and GSC ensure the support for sharing the burden of the grid station. Moreover, the proposed controller of BESS with coordination of FC eliminates the effect of intermittency of power generated from wind and PV. Excess power production by renewable distribution generation is used by Electrolyzer to generate hydrogen. This hydrogen is further used by FC when there is not enough power generation due to unfavorable weather conditions. The energy management has been presented to fulfill the load profile, avoid BESS overcharging and to minimize the intermittency and fluctuation of Wind and PV sources. This method guarantees steady power flow and service continuity. The Simulink model of the proposed system results validate the efficiency of the proposed hybrid system as compared to the conventional hybrid system reported in the literature. The modeling of the proposed system and analysis has been demonstrated using the MATLAB Simulink model. Lastly, the energy management of the system has also been examined and compared with the conventional power system.

Integration of Wind and PV Systems Using Genetic-Assisted Artificial Neural Network

The prominence of Renewable Energy Sources (RES) in the process of power generation is exponentially increased in the recent days since these sources assist in minimizing the environmental contamination. A grid-tied DFIG (Doubly Fed Induction Generator) based WECS (Wind Energy Conversion System) is introduced in this work, in which a Landsman converter is implemented to improvise the output voltage of PV without any fluctuations. A novel GA (Genetic Algorithm) assisted ANN (Artificial Neural Network) is employed for tracking the Maximum power from PV. Among the rotor and grid side controllers, the former is implemented by combining the stator flux with d-q reference frame and the latter is realized by the PI controller. The proposed approach delivers better performance in the compensation of real and reactive power along with the DC link voltage control. The controlling mechanism is verified in both MATLAB and experimental bench setupby using an emulated wind turbine for the concurrent control of DC link potential, active and reactive powers.The source current THD is observed as 1.93% and 2.4% for simulation and hardware implementation respectively.

A Review of Control Techniques for Wind Energy Conversion System

Wind energy is the most efficient and advanced form of renewable energy (RE) in recent decades, and an effective controller is required to regulate the power generated by wind energy. This study provides an overview of state-of-the-art control strategies for wind energy conversion systems (WECS). Studies on the pitch angle controller, the maximum power point tracking (MPPT) controller, the machine side controller (MSC), and the grid side controller (GSC) are reviewed and discussed. Related works are analyzed, including evolution, software used, input and output parameters, specifications, merits, and limitations of different control techniques. The analysis shows that better performance can be obtained by the adaptive and soft-computing based pitch angle controller and MPPT controller, the field-oriented control for MSC, and the voltage-oriented control for GSC. This study provides an appropriate benchmark for further wind energy research.

Review of the Modern Maximum Power Tracking Algorithms for Permanent Magnet Synchronous Generator of Wind Power Conversion Systems

Wind energy conversion systems (WECSs) are considered green generators, environmentally friendly, and fully suitable energy sources to replace fossil energy sources. WECS’s output power is hugely dependent on the random nature of the wind. There are many solutions to improve the output power for WECSs, such as adjusting the profile of turbine blades, locating installation places, improving generators, etc. Nevertheless, maximum power point tracking (MPPT) algorithms for WECSs are optimal and the most effective because they are flexible in controlling different variable wind speeds and match all types of WECS. The parameters on the generator side control or the grid side control will be adjusted when MPPT algorithms are used, allowing the output power of WECSs to be maximized while maintaining stability in variable-speed wind. There are various MPPT algorithms, but the current problem is their efficiency and whether it requires deep knowledge to select the best MPPT solutions because each method has different advantages and disadvantages. This study has implemented an overview of modern maximum power tracking algorithms applied to permanent magnet synchronous generators in WECS with MPP methods based on speed convergence, efficiency, self-training, complexity, and measurement of wind parameters.

Integration of wind and solar farms in a doubly fed induction generator using hybrid GA-ANN controllers

• An emulated wind turbine (WT) drive system is used. • The THD values for source current are 3.03% for PI and 1.93% for GA-ANN. • Landsman converter has proved to be more efficient. • The gain of 4 and an output voltage of 1200 v which is more when compared to Zeta and Flyback converters. Because of global warming concerns and the aim of decreasing nonrenewable energy sources, the direction of power production is being steered towards sustainable power sources rather than conventional ones. Hence, a n emulated wind turbine (WT) drive system is used in this study to assess the behavior of a grid-connected Doubly Fed Induction Generator (DFIG) based wind energy system. In order to ensure uniform and continuous power supply from renewable sources to the grid, a hybrid microgrid system has been created by taking into account a PV fed Landsman converter as part of the design. The control of wind generators is divided into two parts: rotor control and grid control. The grid side control is achieved via the use of a PI controller, and the rotor side control is achieved through the combination of the stator flux and the D -q reference frame. The suggested system manages actual and reactive power, as well as DC link voltages, in an efficient manner, and this has been shown at a variety of various speeds (sub and super synchronous). This paper presents a new Artificial Neural Network (ANN)-based tracking of the Maximum Power Point (MPP) for PV-fed Landsman converters that is based on the Genetic Algorithm (GA). The controlling mechanism is tested on an emulated wind turbine that is driven for concurrent control of the DC tie potential, active and reactive powers using MATLAB and an experimental bench configuration. By using this methodology, the THD values for source current utilizing the PI and GA-ANN based systems, are obtained as 3.03% and 1.93%, respectively. Also, the employment of Landsman converter has proved to be more efficient with the gain of 4 and an output voltage of 1200 V which is more when compared to Zeta and Flyback converters.

Torque Control for PMSG-Based Wind-Power System Using Stationary abc-Reference Frame

The power system of wind farms is generally characterized by a weak grid, in which voltages may be heavily distorted and imbalanced, challenging the control scheme of wind-power converters that must be impervious to such disturbances. The control scheme in the stationary natural abc-frame has shown good performance under non-ideal voltage conditions, and then this paper proposes to analyze the operational performance of a wind-power system based on a permanent magnet synchronous generator subject to non-ideal conditions of the grid voltage, with its control scheme devised in the abc-reference frame. The proposed control scheme considers the torque control decoupling the flux and torque for the generator-side, showing the possibility to implement the machine torque control, without any coordinates transformation using a closed loop dot-product approach, between the field flux and stator currents. For the grid-side converter, the load current compensation is proposed, using the load current decomposition and conservative power theory (CPT), improving the grid power quality. The simulation results estimate the performance of the grid-side control under distorted and asymmetrical voltages, and the generator-side control against torque disturbances due to wind speed variation. Finally, experimental results in a small-scale test bench validate the proposed control scheme in injecting active and reactive power into the grid, and the torque control under wind speed variation.

Nonlinear Control Strategies for Enhancing the Performance of DFIG-Based WECS under a Real Wind Profile

Wind speed variations affect the performance of the wind energy conversion systems (WECSs) negatively. This paper addressed an advanced law of the backstepping controller (ABC) for enhancing the integration of doubly fed induction generator (DFIG)-based grid-connected WECS under wind range of wind speed. This enhancement was achieved through three control schemes, which were blade pitch control, rotor-side control, and grid-side control. The blade pitch control was presented to adjust the wind turbine speed when the wind speed exceeds its rated value. In addition, the rotor and grid-side converter controllers were presented for improving the direct current link voltage profile and achieving maximum power point tracking (MPPT) under speed variations, respectively. To evaluate the effectiveness of the proposed ABC control, a comparison between PI and sliding-mode control (SMC) was presented, considering the parameters of a 1.5 MW DFIG wind turbine in the Assilah zone in Morocco. Moreover, some changes in the DFIG parameters were introduced to investigate the robustness of the proposed controller under parameter uncertainties. Simulation results showed the capability of the proposed ABC controller to enhance the performance of the DFIG-WECS based on variable speed and variable pitch turbine, at both below and above-rated speed, leading to an error around 10−3 (p.u), with an ATE = 0.4194 in the partial load region; in terms of blade pitch control, an error of 2.10−4 (p.u) was obtained, and the DC-link voltage profile showed a measured performance of 5 V and remarkable THD value reduction compared to other techniques, with a measured THD value of 2.03%, 1.67%, and 1.46% respectively, in hyposynchronous, hypersynchronous, and pitch activation modes of operation. All simulations were performed using MATLAB/SIMULINK based on real wind profiles in order to make an exhaustive analysis with realistic operating conditions and parameters.

Operation of Grid-Connected PV-Battery-Wind Driven DFIG Based System

This article presents a photovoltaic (PV)-battery and wind driven doubly fed induction generator (DFIG) based grid-connected system with an improved multifunctional control scheme for grid-side converter (GSC). A three-stage improved reduced-order multiple integrator control is used to maintain the reactive power into the grid as well as it regulates the dc-link voltage across the GSC. The grid side control improves power quality in different abnormal conditions. Moreover, it behaves in such a way that it reduces the rise time, the maximum peak overshoot, as well as the settling time during the transients. The rotor side converter is used to provide the required amount of reactive power using the field-oriented control, for the wind power generator (WPG). A DFIG is used as a WPG. The single-stage PV array and a battery with bidirectional converter are connected to the common dc link of the GSC. The battery helps to extract the maximum wind power in light load conditions. The charging and discharging of the battery depend on the renewable energy generation and load demand. The dynamic behavior is improved by adding a PV feedforward term with the total active load current component. Here, the stator current total harmonic distortion (THD) and grid current THD are maintained as per the IEEE standard. Simulated and test results show the performance of the developed system in different dynamic conditions, such as load unbalancing, changes in PV insolation, and change in speed from the cut-in to cut-out speeds of the wind turbine. Moreover, these results show the battery behavior during different dynamic conditions.