Grid-connected Wind Generators Research Articles

Investigating safety precautions for linking photovoltaic and wind power generation to the grid

The grid integration of renewable energy sources such as solar and wind power has received significant research attention, however, there remains a vast scope for exploration and improvement. Specifically, the discourse around relay protection and safety automatic devices in these power generation methods has been somewhat limited during the grid connection process. This article endeavors to investigate the technical obstacles encountered by photovoltaic and wind power grid protection mechanisms and crucial equipment, amidst the steady growth of renewable energys share in the power grid. This exploration is considered advantageous. The aim is to study and summarize existing research on protective measures and automation equipment for photovoltaic and wind power generation during grid connection, to promote technological innovation and development in this field. This paper critically assesses studies conducted so far on the topic of relay protection and automation hardware used in grid-connected solar and wind power generation, underscoring the significance of different protection techniques and their pivotal roles in ensuring system safety during grid connectivity. It then dives into the present obstacles and constraints that these technologies contend with. In conclusion, it suggests a few potential areas and anticipations for prospective research.

An improved uncertainty and disturbance estimator-based speed control for grid-connected pumping kite wind generator

Pumping kite generator systems (KGS) represent a novel and exciting concept for future wind energy harvesting at higher altitudes where the wind is more reliable, stable, and highly predictable. The configuration of such systems generally employs a flying tethered kite attached to a generator/motor and connected to the grid using a power electronic interface. This work aims to enhance control system efficiency and increase the generated power by improving the speed control of the generator. To this end, an uncertainty and disturbance estimator (UDE) based speed controller is developed to deal with the challenges presented by this new technology, particularly periodical variation of the rotational speed reference in maximum power point tracking. The practicality and effectiveness of the proposed UDE-based control method are demonstrated through simulation and experimental results. It is shown that the proposed control strategy offers improved tracking performance and robustness against model uncertainties and external disturbances.

Hybrid matrix converter for grid connected multiphase wind generation

ABSTRACT This paper presents a novel multiphase hybrid matrix converter topology dedicated to grid-connected five-phase wind generation. The proposed converter, named Five-phase Ultra-Sparse Series Z-source Matrix Converter (FUSSZMC), aims to facilitate the integration of the five-phase wind turbine generators. This is done by taking into account the improvement of the injected power quality and allowing the fixed output converter voltage level to that of the grid, thanks to the use of the series Z-source, under the variable voltage magnitude of the wind turbine generator, which depends on the wind speed. The proposed space vector modulation as a control strategy for FUSSZMC was detailed, where it was demonstrated that a suitable use of both large and small current vectors improves the input current waveforms. The global model of the proposed system was tested by simulation under Matlab/Simpowersys environment. The obtained results demonstrate clearly the advantages of using FUSSZMC and its modulation strategie. These benefits includea good input and output current waveforms, as reflected by the obtained total harmonic distortion (THD) values, that fall within the range prescribed by international standards. Additionally, FUSSZMC is capable of operating with unity input power factor, regulating input voltages and improving the quality of injected power into the grid.

PERFORMANCE ANALYSIS ON PMSG BASED WIND GENERATION SYSTEM INTERFACED MULTILEVEL CONVERTER WITH ARTIFICIAL INTELLIGENCE TECHNIQUE

This paper focuses on the development of ANN based MPPT interfaced Permanent magnet synchronous generator (PMSG) for wind energy conversion system (WECS). There are many drawbacks to conventional energy sources, such as higher fossil fuel prices, damaging to the environment, a shortage of resources, and increased pollution. Due to the unpredictable and arbitrary nature of wind energy, it is crucial to introduce several control strategies in order to maximize its efficiency. In this work, Levenberg algorithms are developed for Artificial Intelligence Technique using MPPT algorithms for PMSG-based wind energy conversion systems. The proposed system consists of 3 kW PMSG integrated WECS, DC to DC boost converter, multilevel inverter and different loads. The DC to DC converter is connected to the multilevel inverter through a DC link capacitor. The inverter power is fed to the grid through a step-down transformer. MPPT controllers are used in the standalone wind energy conversion system. The goal of this research is to develop an MPPT controller using Levenberg-based Artificial Intelligence Technique for wind energy conversion systems. With the proposed Levenberg-based Artificial Intelligence Technique, MPPT uses voltage and current as input variables for DC to DC boost converter duty ratios. The Artificial Intelligence Technique of MPPT controller for standalone wind energy conversion systems with various different input/output conditions is modeled and simulated in MATLAB/ Simulink environment. The performance of the Artificial Intelligence Technique-based MPPT controller versus the conventional incremental conductance MPPT controller. Simulation results show that for a wide range of input wind speed, Artificial Intelligence Technique based MPPT controller shows improved performance than the conventional MPPT at various operating conditions. The response of the developed Levenberg based Artificial Intelligence Technique based MPPT algorithms for PMSG based wind energy conversion system in MATLAB/ Simulink environment to investigate the robustness of the developed control strategy and validate the reliability and stability under different input/output conditions for grid connected wind generation system.

Robust Speed Controller for PMSG Wind System Based on Harris Hawks Optimization via Wind Speed Estimation: A Real Case Study

Modern wind power systems have recently tended to focus on achieving fast-tracking wind speeds (WSs), high maximum power point tracking (MPPT) efficacy without mechanical sensors, and high performance under uncertain WS together with an effective control system. Therefore, a sensorless MPPT method is introduced, which calculates the actual WS to save system installation costs and boost performance levels. The implemented MPPT method is based on the approximating of the 3-order polynomial to the aerodynamics torque power coefficient. In this study, three-speed control strategies (SCSs) for a grid-connected permanent magnet synchronous wind generator (PMSWG) are examined and assessed. Harris Hawks’ algorithm (HHA)-based PI controller (HHA-PIC) is used in place of (the conventional proportional-integral controller (CPIC), and adaptive fuzzy logic controller (AFLC)) as a speed controller to overcome their drawbacks. To track the generator speed to the desired speed, the HHA-PIC is used. All the CPIC, AFLC, and HHA-PIC have been carefully thought out and constructed to satisfy the speed control loop’s responsive performance. Additionally, a comparison of SCSs amongst the categories under investigation is done. The effect of HHA on the functionality of SCS is verified using MATLAB/SIMULINK. To ensure the efficacy and supremacy of the HHA-PIC over the CPIC and AFLC, a wide variety of WSs (step change, ramp, and real fluctuations) are applied. The HHA-PIC boosts system efficiency over AFLC and CPIC by 0.81% and 8.48%, respectively. Finally, it can be said that HHA is a crucial remedy for the problems with CPIC and is superior to AFLC.

Improving the performance of grid connected wind generator with a PI control scheme based on the metaheuristic golden eagle optimization algorithm

• A novel Golden Eagle optimization algorithm (GEOA) is proposed. • GEOA is used to improve the Transient and Dynamics stability of wind farm. • Transient and Dynamics stability of grid-tied PMSG-VSWT is investigated during symmetrical, unsymmetrical faults and Stochastics wind variation at that site. • The results of GEOA are compared with the results of NR, PSO and GOA. • The results shown that the GEOA is robust and competitive. Wind power installations are globally established thoroughly, resulting in significant electrical power penetration into power grids. Consequently, tremendous efforts are expended to improvise the performance of the grid-connected Permanent Magnet Synchronous Generator driven by a Variable-Speed Wind Turbine (PMSG-VSWT). In this work, eight cascaded Proportional Integral (PI) controllers in the machine side and grid side converters are optimally tuned by the golden eagle optimization algorithm with the purpose of enhancing the dynamics and transient stability performances of the grid connected PMSG-VSWT.The integration of the square error criterion was utilised as a fitness function in the optimization process, which was processed using the simulation-based optimization technique. The proposed control technique's efficacy was compared to various optimal PI controllers under symmetrical and unsymmetrical fault conditions. This study also employed the measured real-time events collected at the Coimbatore wind farm in Tamil Nadu (India) to produce realistic results and aid in the pursuit of higher performance. The wind farm modelling and simulation were processed using the DIgSILENT power factory/MATLAB Simulink environment and was utilized to test the proposed control approach's achievability. Finally, the study demonstrates that the golden eagle optimization technique improves the wind farm's performance when compared to observed events and other techniques.

Improving the Performance of Grid Connected Wind Generator with a Pi Control Scheme Based on the Metaheuristic Golden Eagle Optimization Algorithm

Improving the Performance of Grid Connected Wind Generator with a Pi Control Scheme Based on the Metaheuristic Golden Eagle Optimization Algorithm

Optimization and Performance Evaluation of Non-Overlap Wound-Field Converter-Fed and Direct-Grid Wind Generators

Non-overlap winding technology continues to remain relevant in the design of wound-field machines as an alternative for high torque density permanent magnet machines. In this paper, the finite element analyses-optimisation of two variants of the non-overlap wound-field machines viz., wound-field flux switching machine (WF-FSM) and phase shifting wound rotor synchronous machine (WRSM), are compared, first in terms of their performance for large-scale converter-fed wind generator drives, then experimentally as sub-scaled converter-fed versus direct grid-connected wind generators, respectively. This study is unique because there are no prior attempts to design, optimize and analyse on these machines for medium-speed wind power generation, as well as experiment on sub-scale prototypes for direct-grid and converter-fed operation. All investigations are contemplated in the medium-speed wind generator drivetrain which provides a tradeoff for generator efficiency and size. From the global optimisation of both machines at large-scale power levels, the torque per mass of the WF-FSM is found to be 50 % lesser compared to the WRSM. This is due to approximate volume, with closely matched optimal split and aspect ratios. In terms of the sub-scaled experimentation, both generators can easily vary their generated output power to match with varying wind resource, but direct grid-connected WRSM generator yields better efficiency performance compared to the WF-FSM converter-fed operating mode, given that the generator terminal voltage of the former is highly regulated.

Wind-driven permanent magnet synchronous generators connected to a power grid: Existing perspective and future aspects

Scholars are motivated to work in the field of renewable energy systems (RESs) especially on grid-connected wind generators because of the exciting and noticeable developments going on in this area. This progress is utilized to obtain the maximal, efficient, and stable electric power from the RESs and integrating it into existing systems to improve its efficiency, stability, reliability, and overall power quality. Recently, permanent magnet synchronous generators (PMSGs) have become the main pillar of advanced wind systems thanks to their fascinating pluses over other types of wind generators. This paper presents the up-to-date trends in converter topologies, control approaches, maximal power production methods, and grid integration issues for PMSG-based wind systems. The performed statistical analyses assure the dominance of the two-level back-to-back converter among the studied power converter topologies, field-oriented control method for the machine side converter, voltage oriented control method for the grid side converter control, perturb and observe algorithm amongst the studied maximum power point techniques, and fault ride-through capability out of grid integration issues. Further, recent general trends in technological advancements for PMSG wind system components are illustrated as a pie chart in terms of percentage figures. It is expected that the researchers working in this field would benefit from this article in terms of the presented state-of-the-art statistical analyses and its related literature given in this study.

Hybrid PSO-GSA algorithm-based optimal control strategy for performance enhancement of a grid-connected wind generator

<span>Due to the great level of wind energy penetration in the existing network, huge efforts have been directed to enhance the grid-connected wind generator performance. This paper shows an application of a hybrid algorithm of the particle swarm optimization and the gravitational search algorithm (PSO-GSA) to enhance the transient stability of the grid-tied wind energy conversion system. The variable-speed wind turbine (VSWT) direct-drive permanent-magnet synchronous generator is connected to the network through a full-scale converter. The generator- and grid-side converters are controlled by utilizing an optimum proportional-integral (PI) controller. The criterion of the integral squared error is utilized as an objective function. The PSO-GSA based-PI controller efficacy is validated by comparing its results with that are obtained by utilizing the genetic algorithm (GA)-based-PI controller. The performance of the suggested control scheme is checked during various fault conditions. The control scheme quality is legalized by the simulation results that are obtained using MATLAB/Simulink program</span>

Hybrid PSO-GSA algorithm-based optimal control strategy for performance enhancement of a grid-connected wind generator

Hybrid PSO-GSA algorithm-based optimal control strategy for performance enhancement of a grid-connected wind generator

Performance Improvement of Wind Generator Using Hybrid Particle Swarm Algorithm and Grey Wolf Optimizer

The past few years have witnessed a massive increase in energy demand and severe environmental deterioration. As a result, renewable energy’s, specifically wind energy’s contribution, to the electric power network has increased. This paper introduces a new technique combining two optimization methods, which are Grey Wolf Optimization (GWO) and Particle Swarm Optimization (PSO) in order to increase the efficiency of variable-speed wind turbine connected to a permanent-magnet synchronous generator. This system is connected to the grid by a frequency converter using a conventional proportional-integral controller. PSO is an optimization technique that aims at ascertaining the optimal solution by changing the location of particles in a predetermined searching field. GWO is also an optimization technique whose idea depends on the behavior of grey wolves. This article shows that the performance of the GWO is improved by hybridizing GWO with PSO. Nowadays, controller designers are facing a great challenge for tuning proportional-integral controllers, principally in non-linear systems like Wind Energy Conversion Systems. For assuring the efficiency of the introduced hybrid control scheme, a comparison between the simulation outcomes of the GWO with PSO and another optimization technique, which is the Genetic Algorithm is implemented. The simulation outcomes depict the potency of the GWO with the PSO method in order to improve the grid-connected wind generator response under different operating conditions. In order to model realistic performance, data from the Zaafarana wind farm in Egypt has been used as a guide to provide realistic details on wind speed in this article. The MATLAB/Simulink program is utilized to demonstrate the simulation process.

Fractional Order Pi Controller for the Highly Efficient Power Regulation of DFIG Based Wind Power Conversion System

In an electric power grid connected wind generation system, dynamic control strategy is essential to use the wind energy efficiently as well as for an energy optimization. The present study has focused on decoupled power regulation of doubly fed induction generator, operating in wind turbine, in accordance with the vector control approach by applying fractional order proportional integral (FOPI) controller. The FOPI controller is designed based on a simple method; up such that the response of closed loop process is similar to the response of a specified fractional model whose transfer function is Bode’s ideal function. In this tuning operation, the parameters of the proposed fractional controller are established analytically using the impulse closed-loop response of the controlled process. To show the superior action of the developed FOPI controller in comparison with standard PI controller in different function conditions, the study is validated through simulation using the software MATLAB/Simulink

Voltage Deviation Minimization of Grid Connected Wind Generation System Using Hybrid PI-Fuzzy Logic Controller Based Static Var Compensator

Voltage Deviation Minimization of Grid Connected Wind Generation System Using Hybrid PI-Fuzzy Logic Controller Based Static Var Compensator

Grasshopper Optimization Algorithm-Based PI Controller Scheme for Performance Enhancement of a Grid-Connected Wind Generator

Wind power installation has been rapidly increasing around the globe, which resulted in a massive penetration into the electric grids. So, tremendous attempts are exerted to improve the behaviour of the grid-tied permanent magnet synchronous generator driven by a variable-speed wind turbine (PMSG-VSWT). This study exhibits the application of the grasshopper optimization algorithm-based proportional-integral (PI) controller with the purpose of enhancing the dynamic and transient stability performances of the grid-tied PMSG-VSWT. The simulation-based optimization method is utilized in the optimization process, where the integration of square error criterion is considered as a fitness function. The effectiveness of the proposed control technique is compared with Newton–Raphson algorithm-based PI controllers, taking into account subjecting the system to symmetrical and unsymmetrical fault conditions. For achieving practical responses, real wind speed data that extracted from Zafarana wind farm in Egypt are utilized in this study. The feasibility of the proposed control approach is validated by the simulation studies, which are accomplished by using MATLAB/Simulink environment.

A Novel Adaptive Control Method for Performance Enhancement of Grid-Connected Variable-Speed Wind Generators

Huge penetration of grid-tied wind generators into the existing electricity networks increased various challenges in modern power grids. Tremendous attempts are accomplished to properly enhance the behavior of the wind generation systems. This article exhibits a new self-tuned control approach for enhancing the performance of a permanent-magnet synchronous generator-based wind turbine, which is interlinked to the electricity network. The self-tuned technique relies on an improved multiband-structured subband adaptive filter (IMSAF) algorithm, which achieves less computational intricacy over the least-mean-square approach. The IMSAF algorithm-based self-tuned proportional-integral (PI) controller is employed to adjust the interface voltage source converters through a cascaded control structure. The IMSAF algorithm updates the multiple PI controllers' gains on-line without the necessity to optimize or fine-tune. To achieve realistic responses, practical wind speed data measured in Zaafarana wind farm, Egypt, are implemented in this study. The efficacy of self-tuned control approach is compared with that realized using an optimized PI control approach by the water cycle and the genetic algorithms, considering symmetrical and unsymmetrical faults, as the network disturbances. The validity of self-tuned control approach is widely confirmed by performing simulation analyses using MATLAB/Simulink software, and satisfactory responses are achieved. Notably, the IMSAF-based self-tuned control approach is realized to be an accurate means for improving the characteristic of grid-tied wind generators.

A grid-connected variable-speed wind generator driving a fuzzy-controlled PMSG and associated to a flywheel energy storage system

In this paper, we propose a variable speed-wind energy conversion system based on a permanent magnetic synchronous generator (PMSG). A complete system model with multiple controllers is proposed and simulated. Two controllers are proposed and examined for regulating the current from the PMSG, the first is a simple PI controller and the second uses a fuzzy component to improve the tracking performance. A flywheel energy storage system (FESS) is associated to the proposed variable speed wind generator (VSWG). The FESS is linked at the DC bus stage in order to regulate the power supplied to the grid. In simple terms, if the generated power exceeds the demand, the excess is stored by the FESS for use when a shortage occurs. In this scenario, the VSWG is controlled to capture the maximum wind energy by means of the maximum power point tracking (MPPT) method. To maintain this operation as the turbine speed increases above the rated speed, we implement a pitch angle controller. The FESS is based on a squirrel-cage induction machine (IM) coupled to a flywheel. A controlled grid converter allows for the exchange of active and reactive powers with the grid. A proportional integral (PI) based grid connection is proposed that controls the current and voltage amplitudes as well as the frequency passed onto the grid. The aim of the converter control is to achieve a decoupled active and reactive power control suitable for operation at a unitary power factor. The dynamic model of the proposed system is simulated using the Matlab–Simulink package. The obtained results are presented and discussed to illustrate the performance of the overall proposed system.

Grasshopper Optimization Algorithm-Based Fuzzy Logic Controller for Performance Enhancement of a Grid-Connected Wind Generator

Due to the direction of the world to diminish pollution production and to enhance the environmental conditions, the penetration of wind energy in the electric grid is appreciably increased nowadays. The variable-speed wind turbine direct-drive permanent-magnet synchronous generator is connected to the electrical network through a frequency converter-based an optimum controller, such as a conventional proportional-integral controller (PI) or the fuzzy logic controller (FLC). The PI has a constant gain but has some drawbacks, such as affected by variable variation. This paper exhibits an implementation of a grasshopper optimization algorithm (GOA) to design FLC. GOA-based FLC is utilized to control the grid- and generator-side converters to enhance the transient stability of the grid tied-wind energy. To ensure the efficacy of the proposed control scheme, a comparison among the results achieved by the GOA-based FLC and Newton Raphson based-PI has occurred. The simulation results describe the capability of the GOA-based FLC to enhance the response of the grid-tied wind energy under various operation cases. Simulation is carried out by MATLAB/Simulink program.

High-gain observer-based sensorless control of a flywheel energy storage system for integration with a grid-connected variable-speed wind generator

This paper introduces an induction machine-based flywheel energy storage system (FESS) for direct integration with a variable-speed wind generator (VSWG). The aim is to connect the FESS at the DC bus level of a permanent magnet synchronous generator-based VSWG in order to stabilize the DC bus voltage as well as the power flowing into the grid. A rotor flux-oriented control strategy is proposed for the FESS converter based on the actual speed of the flywheel rotor. Since mechanical speed sensors are prone to failure and increase the maintenance cost of the system, a sensorless technique is proposed to estimate the rotor speed through a specially synthesized high-gain observer (HGO). The proposed observer achieves accurate tracking of the flywheel speed and flux and reduces the adverse effects of variations in the rated rotor resistance. Simulation results obtained using the MATLAB-Simulink environment are presented to illustrate the theoretical synthesis and analysis of the proposed FOC and sensorless HGO control strategies.

Whale optimization algorithm-based Sugeno fuzzy logic controller for fault ride-through improvement of grid-connected variable speed wind generators

Due to the extensive penetration of wind power plants (WPPs) into the grid, grid codes have been imposed such that the WPPs stay linked to the grid during faults for a period to maintain the grid stability. This paper designs optimal Sugeno fuzzy logic controllers (FLCs) to improve the fault ride-through (FRT) ability of grid-connected WPPs. The meta-heuristic algorithm, whale optimization algorithm (WOA), is utilized to design the control rules and the Gaussian memberships of eight Sugeno FLCs, simultaneously, by minimizing the high dimensional multi-objective fitness function. The WOA-FLCs and the grid-connected gearless permanent magnet synchronous generator driven by a variable-speed wind turbine (VSWT-PMSG) are modeled using PSCAD/EMTDC environment. The effectiveness of the FRT ability of grid-connected VSWT-PMSG is investigated during balanced and unbalanced grid fault conditions. The simulation results of using WOA-FLCs revealed fast time response, less overshoot, and small steady-state error compared with those achieved by using a genetic algorithm (GA) and grey wolf optimizer (GWO).