Wind Energy Conversion System Research Articles

Application of DPC to improve the integration of DFIG into wind energy conversion systems using FOPI controller

While the direct power control (DPC) approach has proven effective in improving the efficiency of wind energy conversion systems (WECS) using doubly fed induction generators (DFIG), its applicability is currently confined to a single usage and has not been extended to meet numerous applications. This work aimed to modify the implementation of DPC in WECS-DFIG for several objectives. This is accomplished by updating the reference power of the conventional DPC method into an adapted one to achieve two goals independently. The first objective is to track the maximum power during wind speed variations. This tracking is performed by updating the reference power to match the maximum available power at the current wind speed. The second purpose is to ensure that the WECS remains connected to the grid and continues to operate smoothly even in the event of faults; supporting fault-ride through (FRT) capability. That is achieved by reducing the reference power during these faults. The discrimination between these two objectives is based on the voltage level at the point of connecting WECS to the grid. The controller provided is an improved fractional order PI controller developed using arithmetic optimization technique (AOA). A comparison between the AOA and cuckoo search is presented. The results demonstrate the efficacy of the suggested configuration and regulator in enhancing the performance of integrating DFIG into the WECS in the presence of wind fluctuations and short circuit faults occurring. It is worth noting that AOA is better than cuckoo search in fine-tuning the settings of the FOPI controller.

Optimal predictive voltage control of a wind driven five phase PMSG system feeding an isolated load

Wind energy conversion systems (WECS) are considered to be a promising alternative to traditional power generation systems. The principal purpose of this study is to enhance the performance of a stand-alone wind-driven five-phase PMSG system. The initial step involves providing a clear explanation of the system model, which includes the wind turbine, five-phase PMSG, and a battery storage system. Then, the maximum power point tracking (MPPT) and pitch angle control (PAC) technique is employed to optimize the power delivered to an isolated load. Furthermore, the system incorporates the machine-side converter (MSC) and battery converter, which require control to efficiently regulate the power delivered from them to an isolated load. In addition, the MSC is regulated by two predictive control algorithms: predictive torque control (PTC) and a newly formulated predictive voltage control (PVC). The PTC faces limitations such as considerable ripple, significant load commutation, and the weighting factor of its cost functions. The proposed prediction technique aims to overcome these constraints by using a simple cost function and eliminating the need for weighting factors to address stability issues also another study's objective revolves around proposing a PVC controller that achieves an optimal design. The results demonstrate that the newly proposed predictive controller outperforms the conventional control method for the wind generator. The developed controller produces consistently distributed sinusoidal currents with significantly fewer ripples and current harmonics. This fact is verified mathematically as the total harmonic distortion (THD) is reduced to 1.168 % as an average value.

Robust Fuzzy-PID Technique for the Automatic Generation Control of Interconnected Power System with Integrated Renewable Energy Sources

This paper proposes a robust hybrid fuzzy PID controller, with the PID gain values tuned using the particle swarm optimization (PSO) technique for the automatic generation control (AGC) of the two-area hybrid power system. PSO Algorithm is applied to reduce the integral of time-weighted absolute error (ITAE) of the frequency and tie-line power variations of the two-area interconnected power system. The suggested method’s efficacy is examined on an interconnected two-area power system with Area 1 containing thermal reheat plants, hydropower plants, and diesel generating units, and Area 2 comprising thermal reheat plants and renewable energy sources (RES): wind energy conversion system, solar photovoltaic system, and electric vehicles. The thermal reheat power plants are modelled taking into consideration the governor dead band (GDB) and the generation rate constraints (GRC). The performance of the presented controller is compared with the genetic algorithm-optimized PID AGC and the PSO-optimised PID AGC. The simulation outcomes show superior results of the proposed controller against other controllers. Further, sensitivity analysis reflects that the proposed technique provides better dynamic response and robustness than the other techniques.

Stochastic Model Predictive Control Based on Polynomial Chaos Expansion With Application to Wind Energy Conversion Systems

The wind energy conversion system (WECS) has a complex structure, and its state space model is highly nonlinear. Due to the random uncertainty of wind speed, it poses a huge challenge to achieve optimal control tasks and ensure the safe and stable operation of the system. Therefore, this article proposes a stochastic model predictive control strategy based on Polynomial Chaotic Expansion (PCE), which achieves the control tasks of MPPT and constant power regions in wind energy conversion systems. Firstly, a simple algorithm is proposed to obtain a set of basis functions that are suitable for the stochastic variable wind speed. Then, the obtained basis functions are used to propagate the uncertainty of the original uncertain differential equation of the wind energy conversion system through polynomial chaotic expansion. Combining the operating region and constraint conditions of the wind energy conversion system, the original stochastic uncertainty problem is transformed into a deterministic convex optimization problem. Using NREL 5MW wind turbine as the research object for simulation, the task of capturing maximum wind energy in MPPT area and tracking rated power points in constant power area was achieved. The experimental results show that the proposed control method can effectively improve the wind energy capture capability and achieve accurate tracking of output power to rated power.

Augmented Two-Stage Hierarchical Controller for Distributed Power Generation System Powered by Renewable Energy: Development and Performance Analysis

The sustainable development of an area is highly reliant on a reliable electrical energy supply. Microgrids are important in integrating distributed energy resources (DERs) using power electronic converters. However, microgrid control becomes challenging with the increasing number of distributed generators and loads. With the conventional droop control method, power contributions from DER converters cannot be accurately shared due to a mismatch of line impedances. In this paper, an augmented hierarchical control mechanism is proposed to solve the issues mentioned above. This hierarchical control mechanism consists of primary and secondary controllers. The primary stage utilized the droop controller to improve optimal power flow, mainly for the resistive network. The secondary stage is based on an improved methodology to compensate for the voltage and frequency variations during small and large signal disturbances. Moreover, the modelling and analysis for PMSG-based wind energy conversion systems are also presented. The response of the primary controller for active and reactive power sharing is investigated. The analysis emphasizes the demonstration of optimal power-sharing under normal and abnormal conditions for the considered load. Finally, the suggested robust controller’s performance is evaluated in a MATLAB environment, and simulation results show the proposed scheme’s superiority under different operating conditions. The frequency is stable at 50 Hz after a 50 KW load is added.

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.

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.

Power quality enhancement in the wind energy distribution system using HHO algorithm based UPFC

ABSTRACT This study proposes using a Wind Energy Conversion System (WECS) to power a Unified Power Flow Controller (UPFC) Direct Current (DC) capacitor, maintaining appropriate voltage levels. This arrangement allows UPFC adjustment to improve Power Quality (PQ). The main issue rapid growth of nonlinear loads causes sudden variations in source voltage, resulting in a reduction in power quality within seconds. UPFC is used to address these concerns. This work uses the Harris Hawks Optimizer (HHO) to improve the performance of the UPFC. To begin, the aim function parameters are defined, including voltage, current, active power, and reactive power. Thus, these characteristics are used to generate control pulses for the series and shunt active power filters. HHO is used to find the best solution and predict the ideal UPFC control signal. Additionally, voltage instability and power dissipation decrease under different load conditions. Thus, system power supply quality improves. To evaluate the effectiveness of the proposed technique, two scenarios were identified: fault circumstances and engine speed changes. MATLAB/Simulink was used to implement the suggested strategy and compare it to established methodologies like Grey Wolf Optimization (GWO), Modified Particle Swarm Optimization (MPSO), and Whale Optimization Algorithm (WOA).

Intelligent vector control for a novel mechatronic modeling of a 1.5MW variable speed WECS using the bicausality concept under a real wind flow: A Bond Graph Approach

Enhancing the dynamic behaviors of Wind Energy conversion Systems (WECS) based on a doubly fed induction machine (DFIG) is one of the most attractive challenging in the field of maximizing renewable energy's output power, improving its quality and ensuring its efficient grid integration. For this purpose, a novel mechatronic modelling of a 1.5 MW variable-speed WECS based on a doubly fed induction machine using the Bond graph Approach is proposed, and three robust control strategies are displayed to regulate: the DFIG's active and reactive powers for the networks side converter (NSC), maximizing the extracted power and controlling the pitch angle for the load area. The flux oriented vector control laws presented in this contribution are based upon the independent control of magnetic flux and the electromagnetic torque inside the DFIG machine. They are derived from the integrated model's Inverse Bond Graph (IBG). This robust control strategy based on the bicausality concept enabled us to elaborate the Vector Control model that relies on a Radial Basis Function RBF-VC to be trained online with an optimal dataset under some real wind profiles. The robustness and effectiveness of the proposed model and RBF-VC controller are verified. The simulation results were conducted to confirm the validity of the proposed technique. Hands-on experience is carried out by means of the 20-sim software package.

Control of wind energy conversion systems with permanent magnet synchronous generator for isolated green hydrogen production

This paper addresses the design and analysis of the control system for a Wind Energy Conversion System (WECS) with a Permanent Magnet Synchronous Generator (PMSG) and its application for isolated green hydrogen production. The designed control maximizes the wind turbine (WT) power generation by regulating the electrolyzer current consumption, where the electrolyzer operates as a controlled load, ensuring power balance in the system and enabling the generation of maximum power from the WECS without the use of any energy storage system. The system involves directly integrating a WECS with an alkaline electrolyzer (AEL), and its configuration includes a WT with a PMSG, an AC/DC voltage source converter (VSC) acting as the generator side converter (GSC), and a DC/DC converter acting as the electrolyzer side converter (ESC) with Maximum Power Point Tracking (MPPT) control for the WT. The proposed control system has been fully modeled and tested through simulations in Matlab/Simulink, demonstrating its reliable performance under variable wind speeds. Simulation results illustrate how, over rated wind speeds, the pitch control limits the rotational speed and power to their maximum values, and at under rated wind speeds, the ESC control regulates the AEL current following the MPPT of the WT, while the GSC control maintains the DC bus voltage at its designated nominal value. Overall, the proposed control system shows robust and effective performance across the entire range of possible operational points, ensuring no wind generation power losses by following the maximum power point.

Sampled-data observer design for sensorless control of wind energy conversion system with PMSG

This paper presents a nonlinear observer for a variable-speed wind energy conversion system (WECS) utilizing a permanent magnet synchronous generator (PMSG). The study addresses the design of high-gain sampled-data observers based on the nonlinear WECS model, supported by formal convergence analysis. An essential aspect of this observer design is the incorporation of a time-varying gain, significantly enhancing system performance. Convergence of estimation errors is demonstrated using the input-to-state stability method. Simulation of the proposed observer is conducted using the MATLAB-Simulink tool. The obtained results are presented and analyzed to showcase the overall effectiveness of the proposed system.

Enhanced grid integration through advanced predictive control of a permanent magnet synchronous generator - Superconducting magnetic energy storage wind energy system

In this study, the use of an Unscented Kalman Filter as an indicator in predictive current control (PCC) for a wind energy conversion system (WECS) that employs a permanent magnetic synchronous generator (PMSG) and a superconducting magnetic energy storage (SMES) system connected to the main power grid is presented. The suggested UKF indication in the hybrid WECS-SMES arrangement is in charge of estimating vital metrics such as stator currents, electromagnetic torque, rotor angle, and rotor angular speed. To optimize control strategies, PCCs use these projected properties rather than direct observations. To control the unpredictable wind energy's nature, SMES must be regulated to minimize fluctuations in the DC-link voltage and power output to the main grid. Fractional order-PI (FOPI) controllers are used in a novel control structure for the SMES system to regulate the output power and DC-link voltage. An artificial bee colony optimization approach is employed to optimize the FOPI controllers. Three commonly utilized indicators, including sliding-mode, EKF, and Luenberger, were evaluated using “MATLAB" to evaluate the performance of the UKF estimate. Assessment criteria such as mean absolute percentage error and root mean squared error were used to gauge the accuracy of the estimates. Simulation findings showed the efficiency of fractional order-PI controllers for SMES and the proposed UKF indication for predictive current control, especially in the presence of measurement noise and over a variety of wind speeds. An improvement in estimation accuracy of up to 99.9 % was demonstrated by the UKF indicator. Moreover, the stability of the suggested UKF-based PCC control for the hybrid WECS-SMES combination was confirmed using Lyapunov stability criteria."

Optimization of wind energy conversion systems

Optimization of wind energy conversion systems

Model-free control for variable-speed wind energy conversion systems based on power tracking

Model-free control for variable-speed wind energy conversion systems based on power tracking

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

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

Maximum power optimization of a direct-drive wind turbine connected to PMSG using multi-objective genetic algorithm

This work aimed to develop and evaluate a maximum power point tracking (MPPT) control system for a wind energy conversion system (WECS) based on a permanent magnet synchronous generator (PMSG). PMSG is commonly used to generate direct-drive and variable-speed wind energy. Initially, the generator and converter on the DC load side are controlled to follow the wind speed reference set by the MPPT algorithm. The paper presents the optimization problem formulation, including the optimization space, constraints, and objectives. The genetic algorithm (GA) is used to extract the maximum power from the WECS in this design improvement. In this study, to control and stabilize the maximum power point (MPP) of the wind turbine, a proportional integral (PI) controller and a GA heuristic approach were utilized. The GA approach was employed to determine the best settings (Kp, Ki) using MATLAB/Simulink with a 12.3 kW PMSG to model and simulate the proposed system. Based on four performance indicators-integrated squared error (ISE), integrated absolute error (IAE), integrated time absolute error (ITAE), and integrated time squared error (ITSE), the GA approach was used to optimize the controller settings. The results of the simulation show that the wind turbine (WT) can effectively track the necessary MPP. The simulation's output also includes generated power, DC bus voltage, electromagnetic torque, and currents.

Analysis of magnetic saturation effects in the squirrel cage induction generators

This work concerns an investigation on the analysis of the magnetic saturation effects in the three-phase squirrel cage induction generator which is considered as a main device in wind energy conversion systems. The magnetic saturation is considered as an important factor causing destructive effects on power qualities such as harmonics and distortion. Several approaches have been presented in the literature for the modeling of the electric machines considering magnetic saturation. The widely used and precise approach is the finite elements method which is particularly characterized by its high computational time. The novelty in this paper is that a state model has been developed for the healthy conditions of the three-phase squirrel cage induction generators considering the experimental variation of the magnetizing inductance in terms of the magnetizing current. Simulation and experimental tests are provided to extract some important signatures on stator voltages and currents. It will be deduced that the spectrum analysis of stator currents contains useful information on magnetic saturation. Experimental and theoretical results illustrate the consistency of this approach for the modeling and analysis of the squirrel cage induction generators considering the magnetic saturation.

Using a Stand-Alone Wind Energy Conversion System as a Sustainable Technology to Produce Electricity in Iraq

This work looks into Iraq's best places for wind turbines, with an emphasis on economic factors. It studies wind energy conversion systems using mathematical models from Matlab and Simulink. This research aims to pinpoint areas where wind energy can produce power on its own, separate from the national grid, for a range of applications, including energy storage, water pumps, heating, and cooling. In addition, the project will use PMSG to investigate wind turbine efficiency and create vector control technique technology for permanent magnet synchronous generators. Two Iraqi cities were taken to make the calculations of wind speed and its effect on electrical generations (Nasiriyah and Basra), where wind speeds of a height of 20 and 50 m were taken at the hub level of the turbine. The mechanical, electrical, and performance characteristics of the turbine were computed during the test to achieve optimal operation cases. The development of the system was done by MATLAB SIMULINK, where the overall efficiency was enhanced to 88.22%.

Investigation of self-excited induction generator for supporting domestic loads and its extension to a microgrid

This study provides a technical and financial analysis for the incorporation of a microgrid structure with a wind energy conversion system for producing electricity. This study’s primary aim is to provide solutions for issues that arise when isolated induction generators are employed with microgrids. A closed-loop smart electronic load controller is used to regulate the loads in the system that are supplied by the generator. A switched variable capacitor bank is used to supply reactive power initially during a voltage dip at varying winds and loads to sustain the voltage profile. Additionally, a simple voltage control loop-based controller for the generator-side converter maintains the voltage at a steady level. Using HOMER software, an economic study of the suggested wind-based microgrid structure is also presented. A laboratory experimental setup is used to support the MATLAB/Simulink study of the proposed method and its control. The findings support the feasibility of implementing the suggested plan in grid-isolated regions for supplying critical loads.

Analysis of Model Predictive Control-Based Energy Management System Performance to Enhance Energy Transmission

A supervisory control system using Model Predictive Control (MPC) has been designed to evaluate the efficiency of wind and solar power and is consistent with the cost function in the supervisory MPC optimization problem. A two-layer Economic Model Predictive Control (EMPC) framework has been developed and has improved results such as cost reductions compared to recent advanced methods. A speed Generalized Predictive Control (GPC) scheme intended for wind energy conversion systems was developed last year, with simulation results indicating superior performance over previous models. A Hierarchical Distributed Model Predictive Control (HDMPC) can work under different weather conditions with improved economic performance and keep a good balance between power delivery and load demand. An energy management system (EMS), built on the basis of MPC, can be quite lucrative for the sphere in the present climate scenario, with the selection and testing of suitable algorithms, controlled processes, cost functions, and a set of constraints as well as with proper optimizations carried out. Previous research indicates that an MPC-based EMS has the potential to be a good solution to manage energy well and also introduced it to the world experimentally. The key intention of this research study is to explore the existing advances that have been introduced and to analyze their performance in terms of cost function, different sets of constraints, variant conversion processes, and scalability to achieve more optimized operation of MPC-based EMS.