Wind Energy Conversion System Research Articles

A hybrid active fault-tolerant control scheme for wind energy conversion system based on permanent magnet synchronous generator

A hybrid active fault-tolerant control scheme for wind energy conversion system based on permanent magnet synchronous generator

Efficiency Optimization and Power Flow Control in a Wind Energy Conversion System with Storage via Vienna rectifier

The study addresses efficiency enhancement and power flow control in a wind energy conversion system. This system comprises a Permanant Magnet Synchronous Generator (PMSM) driven by a wind turbine, Vienna rectifier, Li-ion battery, and DC load. Fluctuations in wind and energy demand impact battery life and state of health (SOH). To mitigate this, a nonlinear controller regulates current for optimal battery charging. Three operating modes, including MPPT, Constant Current (CC) charging, and Constant Voltage (CV) charging, are considered. An energy management algorithm facilitates smooth switching between controllers. The perfomances are evaluated using MATLAB/SIMULINK environment.

Sensorless nonlinear control based on integral backstepping technique for PMSG- based wind energy conversion system

This article addresses the control of the wind energy conversion system (WECS) based on a permanent magnet synchronous generator (PMSG) connected to the grid through a three-phase rectifier and an inverter followed by an L filter. Two observers have been developed to estimate the mechanical torque of the PMSG and the rotor flux. Integral backstepping control (IBC) has been employed to regulate the wind energy conversion system. Its objective is to achieve maximum power point tracking while maintaining the bus voltage at its reference and regulating the injected power into the grid to ensure a unity power factor. The developed control approach has been tested using Matlab/Simulink and compared with the linear PI controller. Simulation results demonstrate, on one hand, the robustness, efficiency, and superiority of the nonlinear technique employed, and on the other hand, the dynamic performance of the developed observers.

Improving the Performance of Hybrid System-Based Renewable Energy by Artificial Intelligence

Abstract Artificial intelligence (AI) has emerged as a critical indicator of technological progress in recent years. The present study uses AI to enhance the efficiency of a hybrid system that operates on renewable energy sources. The hybrid system we propose consists of a wind energy conversion system (WECS), a photovoltaic system (PVS), a battery storage system (BSS) and electronic power converters. AI manages these converters cleverly. We use the maximum power point tracking (MPPT)-based fuzzy logic controller (FLC) to regulate the boost converter in the PVS and the WECS. We propose an adaptive neuro fuzzy inference system (ANFIS)-based controller to control the bidirectional converter of the storage system. The design of this module intends to maintain voltage stability on the direct current (DC) bus and improve energy quality. We study and simulate this system using MATLAB/SIMULINK. The results of this research show that the FLC-MPPT technique outperforms the Perturb and Observe (P&O) algorithm in terms of efficiency in power production. The console we propose also shows good results in maintaining the voltage stability in the DC bus in comparison with the proportional integral (PI) controller. This paper has the potential to contribute to the development of environmentally friendly resource performance.

Fractional-Order Extremum Seeking Based MPPT for Wind Energy Conversion System with PMSG and ANPC Inverter Using Model Predictive Control

In this paper, a real-time optimization-based extremum seeking (ES) approach is applied to a grid-connected PMSG-based wind energy conversion system (WECS) for maximum power point tracking (MPPT). The proposed model-free approach aims harness the output power of the WECS by controlling the system from the DC side, eliminating the need for speed sensor. Moreover, using fractional order calculus in ES improves the convergence speed and robustness compared with the conventional integer-order ES methods. Additionally, an MPC controller for the inner loop is designed to maintains fast transient response through ANPC inverter. Finally, the effectiveness of the proposed method is verified by simulating the WECS in MATLAB/Simulink environment. It was shown that the proposed method is feasible and robust under different weather conditions.

Design and control of utility grid-tied bipolar DC microgrid

Abstract This paper explains in detail the design and control of a utility grid-connected bipolar DC microgrid, which consists of a solar photovoltaic system (SPV), a wind energy conversion system (WECS), a battery energy storage system (BESS) at the DC bus, and a three-level neutral point clamped (NPC) converter to connect the DC microgrid to the utility grid. A three-level bidirectional DC–DC converter at the battery energy storage system (BESS) is controlled with model predictive current control (MPCC), and a three-level NPC converter is controlled with a voltage-oriented control based proportional resonant (VOC-PR) controller. The proposed MPCC scheme for the three-level bidirectional DC–DC converter and the VOC-PR control scheme for the three-level NPC converter coordinate with each other to balance and regulate the voltage at the bipolar DC microgrid, control the power flow to the loads at the DC microgrid and the load at the point of common connection (PCC), and also inject or draw power from the utility grid and BESS based on the availability of Renewable Energy Sources (RES). Simulation and experimental results are presented to demonstrate the effectiveness of the proposed MPCC control scheme.

Harnessing the Potential of High-Efficiency Synchronous Generators in Wind Energy Conversion Systems

Harnessing the Potential of High-Efficiency Synchronous Generators in Wind Energy Conversion Systems

Advanced Control Strategy of Switched Reluctance Generator-Based Wind Energy Conversion Systems Using Backstepping and Extremum Seeking Techniques

Switched reluctance generators (SRG) become increasingly competitive with other electrical machines due to their design, which includes only a stator winding. The SRG are known for their reliability and capacity to operate over a wide speed range, these generators find significant use in variable wind power plants. The main limitations of these machines are torque fluctuation and relatively low efficiency. In this respect, the aim of this work is to mitigate these drawbacks. Presently, an extremum seeking online method has been developed, and a backstepping controller is designed for SRG-based wind energy conversion system (WESC) connected to the grid via a VSC converter. One of the main objectives of the proposed controller is to achieve the optimum rotation speed of the turbine and to regulate the SRG for tracking the maximum power point (MPPT) while minimizing torque ripple via Direct Instantaneous Torque Control (DITC). To show the efficiency and performance of the proposed controllers, simulation examples of the proposed controllers are established using MATLAB/SIMULINK tools. The simulation results show that the efficiency and performance of the system using the proposed controller are significantly improved.

Optimizing renewable polygeneration: A synergetic approach harnessing solar and wind energy systems

Transitioning from fossil fuels to renewable energy ensures a sustainable and green future by limiting greenhouse gas emissions and their nuisance effects. This study proposes a polygeneration system that harnesses renewable energy to produce multiple energy vectors simultaneously and equipped with storage to mitigate the impact of intermittent weather. To perform dynamic analysis, proposed system has been modeled, optimized, and simulated using the transient simulation software TRNSYS®. The designed model has been optimized in GenOpt, linked with TrnOpt, under the weather conditions of Gujrat, Pakistan. The power system is energized by solar photovoltaic, thermal and wind power to produce cooling, heating, electricity, hydrogen, and oxygen as energy vectors, and also provides electric, hydrogen, and thermal storage. Prime movers utilized for energy conversion consists of evacuated glass tube collector, photovoltaic panels, wind energy conversion system, fuel cell, electrolyzer, and absorption chiller, which facilitate air-conditioning, space and water heating, electric power supply for electric vehicle, building load & national grid, hydrogen for internal combustion engines, fuel cell electric vehicle and industrial applications, as well as oxygen for hospitals. The study has shown that thermal collector has 68% efficiency, a solar fraction 0.78, peak outlet temperature 143.77 °C, and generates a total 1399656.25 kJ thermal energy. The wind and photovoltaic systems have maximum efficiencies values 52.24 % and 10.90 % respectively, resulting in the production of 44.8 MWh electric energy. Embracing the synergy of solar and wind power in a polygeneration system holds the key to a sustainable and eco-friendly future.

Enhancing the control of doubly fed induction generators using artificial neural networks in the presence of real wind profiles.

This study tackles the complex task of integrating wind energy systems into the electric grid, facing challenges such as power oscillations and unreliable energy generation due to fluctuating wind speeds. Focused on wind energy conversion systems, particularly those utilizing double-fed induction generators (DFIGs), the research introduces a novel approach to enhance Direct Power Control (DPC) effectiveness. Traditional DPC, while simple, encounters issues like torque ripples and reduced power quality due to a hysteresis controller. In response, the study proposes an innovative DPC method for DFIGs using artificial neural networks (ANNs). Experimental verification shows ANNs effectively addressing issues with the hysteresis controller and switching table. Additionally, the study addresses wind speed variability by employing an artificial neural network to directly control reactive and active power of DFIG, aiming to minimize challenges with varying wind speeds. Results highlight the effectiveness and reliability of the developed intelligent strategy, outperforming traditional methods by reducing current harmonics and improving dynamic response. This research contributes valuable insights into enhancing the performance and reliability of renewable energy systems, advancing solutions for wind energy integration complexities.

A control strategy incorporating multiple EV charging stations for an islanded micro‐grid energy management and voltage regulation

AbstractAn electric vehicle (EV) charging station control strategy (CSCS) is proposed for proper power‐sharing among various components of an islanded microgrid (MG), consisting of the photovoltaic (PV) system, the wind energy conversion system (WECS), variable load (critical and non‐critical), multiple charging stations (CSs) and the dump load (LD). The proposed control strategy is examined in three distinct cases with varying operating conditions owing to solar irradiance, wind speed and load variations. For proper energy management (EM) and voltage regulation (VR), the diverse EVs have varying battery capacities parked in the multiple CSs to either absorb or deliver power through a two‐way power flow between the islanded MG system and the EV‐based CSs, which is reliant on the supply and demand (S&D) energy difference. The simulation is executed via the MATLAB software and two different modes—excess power mode (EPM) and deficient power mode (DPM)—are employed to validate the proposed CSCS's competency. For a realistic assessment of the proposition, the results are also verified by using a hardware‐in‐loop (HIL) real‐time (RT) OPAL‐RT simulator (OP5700). Furthermore, the findings from MATLAB and the OPAL‐RT simulator are compared to establish the robustness of the proposed strategy.

Enhancing Wind Power Plant Integration Using a T-NPC Converter with DR-HVDC Transmission Systems

This paper introduces an innovative control technique tailored for the seamless integration of wind power plants (WPP) into onshore grids. Our approach utilizes Diode-Rectifier-based High Voltage Direct Current (HVDC) system and incorporates a T-type Neutral Point Clamped (T-NPC) converter on the HVDC back, providing solutions for current challenges in wind power integration. While the use of Diode Rectifier (DR) as the Offshore converter for HVDC transmission has proven advantageous in terms of cost and loss reduction, it also introduces new challenges, such as maintaining AC grid voltage and frequency on the Offshore side and achieving the synchronization of the wind energy conversion system (WECS). This paper proposes a comprehensive control strategy to adresse these challenges by developing frequency and AC voltage control loops, ensuring the synchronization of WECS with the DR AC side. A notable distinctive feature of our proposed technique is to ensure the regulation of the DC voltage of the HVDC cable, accomplished through the onshore inverter. This crucial aspect ensures stable and efficient power transmission from wind power plants to the onshore grid.

Theoretical Development and Experimental Evaluation of a Nonlinear Observer for sensorless WECS

This research paper introduces a novel sensorless wind energy conversion system (WECS) with a Vienna rectifiers-based high-gain observer. The main emphasis is on crafting a high-gain observer capable of providing real-time estimates for challenging-to-measure signals like rotor speed and load torque. Addressing the complexities arising from system nonlinearity and high dimensionality, the paper showcases the effectiveness of the proposed observer through simulations and theoretical analysis. Furthermore, an experimental validation on the RealTime Digital Simulator (RTDS) platform validates the observer’s performance and reliability, highlighting its potential for enhancing sensorless WECS technology.

Virtual Synchronous Machine Control for Doubly Fed Induction Machine-Based Wind Energy Conversion Systems

Virtual Synchronous Machine Control for Doubly Fed Induction Machine-Based Wind Energy Conversion Systems

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

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

A New Multilevel Converter Configuration for Medium-Voltage Open-Winding PMSG-Based Wind Energy Conversion Systems

A New Multilevel Converter Configuration for Medium-Voltage Open-Winding PMSG-Based Wind Energy Conversion Systems

A Novel Intelligent Power Control Technique for a Type-3 Wind Energy Conversion System with LVRT Capability and Improved Dynamic Performance

A Novel Intelligent Power Control Technique for a Type-3 Wind Energy Conversion System with LVRT Capability and Improved Dynamic Performance

Comparative study between PI, FLC, SMC and Fuzzy sliding mode controllers of DFIG wind turbine

In speed control, double-fed-induction generator (DFIG) has attracted attention in wind energy conversion systems (WECs). These systems can offer higher performances by controlling converters that connect the generator windings to the grid network. Therefore, different control strategies have been used. We can find the proportional integral (PI) and sliding mode control (SMC) which offer better performances. However, PI has constant gains that can’t change with the external variation and SMC has a chattering problem. Therefore, a hybrid control system that combines fuzzy logic control (FLC) and SMC to perform fuzzy sliding mode control (FSMC) is proposed. The hybrid system can improve the FLC and SMC robustness. The DFIG modeling and each of the control strategies have been detailed. To demonstrate the effectiveness of the control strategies, a comparative study of different control strategies (PI, FLC, SMC and FSMC) is described and performed using MATLAB/Simulink software. The results obtained from the present study show that FSMC is more robust and efficient than the other controllers (PI, FLC and SMC). It has high performances (low settling time, high steady state accuracy) assuring a perfect power decoupling with minimal ripples and errors.

Backstepping control of a wind energy conversion systems based on a DFIG connected to the grid

Backstepping control of a wind energy conversion systems based on a DFIG connected to the grid

An Effective SST-FLC for Mitigation of Reactive Power Compensation of DFIG Based Wind Energy Conversion System

As wind-derived energy is increasingly incorporated into interconnected energy networks, this research attempts to control reactive power and regulates the voltages of wind energies. Since wind energy is volatile, it will generate unstable power and voltages. The reactive powers control of wind energy and faults on the grid is becoming more essential. One of a power system’s most crucial characteristics, it has a big impact on the stability of the whole thing. In terms of high-switching-frequency energy electronics generators, solid-state transformers (SST) may perform reactive power correction in WFs. The solid-state transformers suggested here connect wind farms powered by double-fed induction generators to the grids in place of energy transformers with real controllers in a conventional arrangement. The dual-active bridge converters and fuzzy logic controllers are both built into the solid-state transformers architecture. Power transmission between both sources of power can be accomplished using bidirectional DC–DC converters in either way. Modernized grid applications are the most promising ones for solid state transformers and increase the efficiency up to 96.61% with 0.91 Power factor of the entire power generation system with bit error rate of 0.2%. This will reduce the cost, improves the system’s efficiency, and also improves the system’s performance.