Wind Generation System Research Articles

Single Phase Grid Connected PV-Wind-Diesel-Battery System Operated Three Phase Induction Motor for Aqua-Farms

Sustainable Aqua Farms requires continuous power supply to drive three phase induction motors for maintaining good oxygen levels. Unfortunately many places are suffering from frequent power cuts as well as availability of only single phase supply. Hence, 1-Φ to 3-Φ conversion is essential to operate 3-Φ motors as well as required operation of diesel generators during no supply from grid. However, consumption of heavy diesel causes many problems in aqua culture which is not sustainable. Hence, in order to reduce diesel consumption, and increase eco-friendly environment, photovoltaic (PV) and wind generator system can be integrated to system. A proper energy management system is required among all these sources to achieve cost effective system with best performance. The battery is used for responding in transient time periods. The proposed energy management system can reduce consumption of diesel by operating it at maximum efficiency. In case of any excess power available from wind and PV, the surplus power can be transmitted to single phase grid to reduce electricity bill. In order to maintain stable dc-link voltage, Sliding Mode Controller (SMC) based DC to DC converters are incorporated. The proposed controller reduces the ripples in motor torque to improve the life time of components used in system as well as can reduce power consumption with the addition these renewable energy resources. For reduction of ripples in torque, a TS-Fuzzy based speed sensor less direct torque controller is implemented to run induction motor through three phase inverter.

Economic dispatch in micro-grids with alternative energy sources and batteries

The problem of economic dispatch in a micro-grid interconnected to the conventional electrical system consists of optimizing the operating cost to satisfy the different operating requirements and the demand for electrical power required by the users. In the development of this proposal, there are challenges caused by the random nature of electricity generation systems based on renewable energy sources, for an adequate operation of the micro-grid, which can be solved through systems Energy Storage System (BESS). This paper addresses the problem of optimal economic dispatch in a microgrid that includes an energy storage system based on a battery bank and interconnected to the main grid through a mathematical programming approach, considering the benefits for customers by reducing their demand of electrical power and taking into account the costs of treatment pollutants produced by conventional energy sources. The generation systems used to satisfy the energy demand of the customers within the interconnected micro-grid are: a) conventional diesel generators, b) wind generation systems, c) photovoltaic systems, d) a hydroelectric turbine, and e) a storage system based on electric batteries. The results of the effectiveness of the proposed mathematical model show that the demand for electrical power to the main grid can be reduced and customers obtain economic benefits by integrating the incentive-based demand response program into the energy management problem when environmental contingency events occur. The results show that when there are peaks in energy demand, the operating costs of the micro-grid increase by up to 380%. In the same way, when there are failures in renewable energy sources, storage systems (BESS) and diesel generators work at 100% of their capacities. The proposal is implemented in Lingo 17.0 software and simulation results show the feasibility of the proposal through the optimization of the operation of the micro-grid.

Evaluation of drag coefficient for a quadrotor model

This paper focuses on the quadrotor drag coefficient model and its estimation from flight tests. Precise assessment of such a model permits the use of a quadrotor as a sensor for wind estimation purposes without the need for additional onboard sensors. Firstly, the drag coefficient has been estimated in a controlled environment via wind generator and motion capture system. Later, the evolution of the coefficient is observed for various mass and fuselage shapes. Finally, an estimation method is proposed, based on the least-squares optimization, that evaluates the drag of the quadrotor directly from outdoor flight data. The latter leads the methodology towards easier adoption in other researchers’ systems without the need for complex and expensive flight testing facilities. The accuracy of the proposed method is presented both in simulation, based on a realistic flight dynamics model, and also for real outdoor flights.

LMI-based Decentralized Load Frequency Control of a Hybrid Power System with a Virtual Synchronous Generator and Battery Storage

This paper proposes the participation of wind generation in the decentralized control of load frequency of a hybrid power system consisting of, in addition to wind generation, conventional generation and battery storage. The wind generation is modelled as a ‘Virtual Synchronous Generator (VSG)’ in a separate control area with its own virtual frequency. It has also been proposed to operate the wind generation system in a ‘de-loaded’ mode, thereby allowing it to take part in frequency regulation services. For the purposes of a decentralized control design, the overall system model is decomposed into three subsystems. Static state-feedback control gains are computed by posing the decentralized control problem as a set of linear matrix inequalities (LMIs) subject to structural and stabilizing constraints.

Smart Grid and wind generators: an overview of cyber threats and vulnerabilities of power supply networks

The subject of this study is the cyber vulnerability of wind generators, as part of the cyberphysical system of intelligent power supply networks, Smart Grid. Wind generators produce electricity for further distribution in the network between «smart» electricity consumers, which often include autonomous power systems in medical institutions, autonomous power supply of homes, charging stations for cars, etc. Wind generators operate in two aspects: in the physical and information space. Thus, a violation of the security of the information flow of a wind generator can affect the physical performance of electricity generation, and disable equipment. The study aims to identify types of cyber threats in the wind generator network based on the analysis of known attack incidents, analysis of the Smart Grid network structure, network devices, protocols, and control mechanisms of a wind generator. The tasks of the work are: review and analyze known cyberattack incidents; review the classification of cyber threats to wind farms; consider the most common methods of attacks on the cyberphysical system of wind farms; consider ways of intrusions into the information flow of the cyberphysical system wind generator; consider resilience mechanisms of wind generators in case of a cyberattack, consider the directions of further research. The methods are a systematic approach that provides a comprehensive study of the problem, quantitative and qualitative analysis of incidents of cyber attacks on wind generators, and methods of attacks. The following results were obtained: 11 large-scale known incidents of cyber attacks on the cyberphysical systems of the energy sector and smart power supply networks were analyzed, and information flow features and structure of the wind generators were considered. Main communication interfaces of the Smart Grid network were reviewed, control mechanisms for the physical parts of the wind generator system such as automatic voltage regulator, and automatic generation control were observed, vulnerable data transmission protocols, DNP3 in particular, were analyzed, possible consequences in the case of a cyber-intrusion into the network were considered. Conclusions: wind farms, as part of the Smart Grid system, are a convenient target for cyberattacks, as the number of potential ways to interfere with the information flow of the cyberphysical system is growing due to an increase in the number of sensors, communication channels in the network. This is especially important for the further development of wind farm security systems, which at the time, are not able to provide high accuracy of intrusion detection into the information flow.

Particle Swarm Optimization for Optimal Frequency Response with High Penetration of Photovoltaic and Wind Generation

As the installation of solar-photovoltaic and wind-generation systems continue to grow, the location must be strategically selected to maintain a reliable grid. However, such strategies are commonly subject to system adequacy constraints, while system security constraints (e.g., frequency stability, voltage limits) are vaguely explored. This may lead to inaccuracies in the optimal placement of the renewables, and thus maximum benefits may not be achieved. In this context, this paper proposes an optimization-based mathematical framework to design a robust distributed generation system, able to keep system stability in a desired range under system perturbance. The optimum placement of wind and solar renewable energies that minimizes the impact on system stability in terms of the standard frequency deviation is obtained through particle swarm optimization, which is developed in Python and executed in PowerFactory-DIgSILENT. The results reveal that the proposed approach has the potential to reduce the influence of disturbances, enhancing critical clearance time before frequency collapse and supporting secure power system operation.

Improved current control loops in wind side converter with the support of wild horse optimizer for enhancing the dynamic performance of PMSG-based wind generation system

ABSTRACT The creation of optimization methods is relatively common and popular today for solving issues in engineering areas. In this paper, a new wild horse optimizer (WHO) method is adopted to design an optimal controller for a permanent-magnet synchronous generator (PMSG)-based wind turbine system (WTS). A control method consisting of two current control loops of the optimized controller is proposed for the control of the machine-side converter. The dynamic performance of PMSG can be improved during the different normal and abnormal conditions through effective tuning of the PI controller gains which enhances the overall response of the system parameters. This enhancement consequently results in the realization of the maximal power tracking during wind speed changes and the fault ride-through capability during faults and large-signal events. Furthermore, the power fluctuations due to the incoming wind speed variants can be smoothed by using the suggested controller. In addition, the proposed controller system employs an optimal torque control method and braking chopper system to help in achieving such tasks. MATLAB simulations of a 1.5 MW PMSG-WTS have been carried out and compared with the Ziegler Nichols-based PI controller in order to validate the feasibility and efficacy of the proposed controller.

Modelling the effect of distributed battery energy storage in an isolated power system

Wind generation is often curtailed in isolated power systems to ensure system operational security. This happens during periods of high wind generation and low system demand. This research investigates the effect of battery storage deployed in an isolated power system with a high share of renewables, on the total cost of generation, emissions levels, ramping time, and excess energy. A commercial unit commitment software package is used to model the Northern Ireland power system at various levels of battery storage using actual load, wind, and generator data. The results from the study confirm that for a high load month, deployment of battery energy storage can reduce the total cost of generation by 2.5%, reduce the emissions by 11%, reduce ramping time by 52%, and dump energy by 0.5% of total demand. The conclusion reached is that battery storage will become a key piece of infrastructure for low carbon power systems.

Supervisory Control Scheme of a Wind Farm Connected to a Hybrid Microgrid

Nowadays, the generation of wind power combined with a battery energy storage system offers a viable solution for electricity supply to isolated areas with no access to the grid. However, the speed of the wind is unpredictable and the inherent nonlinearities of the wind turbine and generator system cause fluctuations in the voltage, power, and frequency. This paper proposes a new three-layered control technique applied to a wind farm that uses doubly-fed induction generators (DFIGs) connected to a remote hybrid microgrid. The first layer provides local control to each DFIG wind turbine based on a nonlinear and robust control strategy. The second layer coordinates and controls the flow of power between the turbine and the storage system to make sure that load demand is being satisfied and the desired limits of the state of charge are respected. Finally, and in the third layer, we implement a supervisory controller that will be responsible for the generation of the references for the active power as per the load demand. The proposed off-grid wind power system and battery storage and its three-layer hierarchical control scheme are simulated under MATLAB/Simulink environment and tested under various load and speed profiles. Through the numerous test data obtained, it has been shown that the control strategy under study is robust and effective.

Fuzzy Control Strategy Applied to an Electromagnetic Frequency Regulator in Wind Generation Systems

This paper presents the implementation of a fuzzy control strategy for speed regulation of an electromagnetic frequency regulator (EFR) prototype, aiming to eliminate the dependence on knowledge of physical parameters in the most diverse operating conditions. Speed multiplication is one of the most important steps in wind power generation. Gearboxes are generally used for this purpose. However, they have a reduced lifespan and a high failure rate, and are still noise sources. The search for new ways to match the speed (and torque) between the turbine and the generator is an important research area to increase the energy, financial, and environmental efficiency of wind systems. The EFR device is an example of an alternative technology that this team of researchers has proposed. It considers the main advantages of an induction machine with the rotor in a squirrel cage positively. In the first studies, the EFR control strategy consisted of the conventional PID controllers, which have several limitations that are widely discussed in the literature. This strategy also limits the EFR’s performance, considering its entire operating range. The simulation program was developed using the Matlab/Simulink platform, while the experimental results were obtained in the laboratory emulating the EFR-based system. The EFR prototype has 2 poles, a nominal power of 2.2 kW, and a nominal frequency of 60 Hz. Experimental results were presented to validate the efficiency of the proposed control strategy.

An Effective Load Management for Grid Connected Hybrid Energy Sources

This paper introduces to control a standalone hybrid renewable system, involving PV cell and wind turbines as the primary energy sources along with fuel cell and battery energy source as an emotionally supportive system. While trying to work on the solidness and security of the hybrid renewable system sustainable framework, a battery bank, is incorporated as supporting units, due to the discontinuous and fluctuation in primary energy sources commitment. In this paper we model an independent sustainable source micro grid with various sources, which are wind energy with PMSG, PV panel, Fuel cell and battery storage system. Analysis of each source is done under variation conditions and variation of source parameters, such as wind speed of wind turbine, illumination, temperature of PV cell and state of charge of the battery. If main power sources of PV panel and wind turbines is not available, the battery storage device act as backup supply for load. This storage source (battery) can be charged when abundance power is produced from the PV panel and wind generation system. Investigation on each sources with dynamic changes of boundaries are studied with siumulation results analysis is studied by utilizing MATLAB/ SIMULINK software.

A Review on Popular Control Applications in Wind Energy Conversion System Based on Permanent Magnet Generator PMSG

There has always been a high expectation that wind generation systems would capture maximum power and integrate properly with the grid. Utilizing a wind generation system with increased management to meet the growing electricity demand is a clever way of accomplishing this. However, wind power generation systems require a sophisticated, unique, and dependable control mechanism in order to achieve stability and efficiency. To improve the operation of the wind energy conversion method, researchers are continually addressing the obstacles that presently exist. Therefore, it is necessary to know which control can improve the whole system’s performance and ensure its successful integration into the network, despite the variable conductions. This article examines wind turbine control system techniques and controller trends related to the permanent magnet synchronous generator. It presents an overview of the most popular control strategies that have been used to control the PMSG wind power conversion system. Among others, we mention nonlinear sliding mode, direct power, backstepping and predictive currents control. First, a description of each control is presented, followed by a simulation performed in the Matlab/Simulink environment to evaluate the performance of each control in terms of reference tracking, response time, stability and the quality of the signal delivered to the network under variable wind conditions. Finally, to get a clear idea of the effect of each control, this work was concluded with a comparative study of the four controls.

Idealized analysis of relative values of bidirectional versus unidirectional electric vehicle charging in deeply decarbonized electricity systems

SummaryWe employed an idealized macro-energy system model to examine how the value of unidirectionally- and bidirectionally-charging electric vehicles (EVs) varies with EV penetration and mix of electricity generators. We find that EVs can help wind and solar-based electricity generation systems to be less costly by making better use of power that would otherwise be curtailed and, potentially, by giving electricity back to the grid at times of peak net load. At low levels of EV penetration, bidirectional EVs are valuable because they can provide electricity at times of main load peak. At today’s low levels of EV penetration, bidirectional EVs stimulate investments in solar and wind generation and substantially reduce the need for grid-battery storage compared to unidirectional EVs. At high levels of EV penetration, generation capacity must be increased, and most peaks in main net load demand can be met by reductions in charging by unidirectional EVs.

Experimental and numerical analysis of wind field effects on the dynamic responses of the 10 MW SPIC floating wind turbine concept

Experimental methods are useful to study global responses of floating wind turbines under wind excitations. One experimental technique is based on the regeneration of wind fields in open space in a hydrodynamic laboratory and the use of the performance-matched rotor to produce the equivalent aerodynamic thrust force according to a target wind field. However, for wind generation systems, due to small variations in the output of each fan and differences among the fans, the obtained wind speed varies in space and time, making it difficult to generate the target ideal uniform, constant or time-varying wind fields. For investigating the quality of the wind field in the experiment and their effects on the global responses, numerical simulations under wind only conditions for the full-scale 10 MW SPIC wind turbine were compared with the upscaled responses from model tests. Because of the limitation in the generation and measurement of the target wind field, and the model scaling issue for wind turbine aerodynamics considering the TSR variation, there are uncertainties in interpreting the obtained responses. Numerical simulations were then performed to investigate the effects of spatial and temporal variations in wind fields. Wind shear influences 1P and 3P responses. Below the rated wind speed, when the turbulence intensity increases, the average responses decrease and the response spectra are dominated by low-frequency and 3P components. In Kaimal and Mann wind fields with coherent structures, the generated power and pitch motion show fewer fluctuations than in uniform time-varying wind field, while the nonuniform wind field excites greater 1P and 3P responses.

Experimental investigation on load mitigation of a 5 MW wind turbine in digital fluid power pitch system test rig based on predictive load mitigation controller

ABSTRACT The pitch control system plays a vital role in generating rated power and mitigates mechanical loads during strong wind gusts. Generally, the hydraulic pitch system deployed in a large-scale wind turbine uses a proportional/servo valve to adjust the pitch angle. However, these valves suffer from low operating efficiency which leads to poor pitching performance. Thus, a high-speed digital hydraulic valve-based Digital Fluid Power Pitch System (DFPPS) test rig was proposed in this paper. Moreover, the conventional controller was not able to produce adequate pitching performance during high wind flux. Therefore, a machine learning-based pitch controller was implemented in the developed test rig. The developed DFPPS test rig comprises simulation loop (Wind Generator System (WGS) model, pitch gear model, Predictive Load Mitigation Controller (PLMC), and pitch load model), and DFPPS-hardware interfaced through an I/O interface. Here, PLMC was developed to improve the load mitigation performance without compromising the generator performance at high wind speeds. The proposed PLMC is a cascade arrangement of Recurrent Elman Neural Network (RENN) tuned Fractional-Order Proportional Integral Derivative (FOPID). Further, for collecting the optimal datasets to train the RENNs in PLMC, a Customized Particle Swarm Optimization (CPSO) algorithm was utilized. Two experiments are performed considering the developed WGS model and benchmark Fatigue Aero-elastic Structure Turbulence (FAST) simulator deployed in the DFPPS test rig. The experimental results of PLMC (experiment) were compared to simulation results PLMC (simulation). Further, the experimental output of the PLMC was compared to established controllers in the literature (Radial Basis Function tuned Fractional-Order Proportional Integral Derivative (RBF-FOPID) and baseline Proportional Integral (PI)). In the experiment using FAST, relative to RBF-FOPID, the PLMC (experiment) decreased the Standard Deviation (STD) of blade and tower moment by 81.5% and 81.4%, respectively. Consequently, compared to baseline PI, the PLMC (experiment) reduced the STD of blade and tower moment by 85.6%, and 84.4%, respectively. The proposed controller effectively mitigated the load and also the generator performance was close to the rated value than its counterparts.

A hybrid control topology for cascaded multilevel inverter with hybrid renewable energy generation subsystem

In this paper, a hybrid control topology is proposed for cascaded multilevel inverter (CMLI) with a grid-connected hybrid system involves wind and photovoltaic generation subsystem. The proposed hybrid control technique is the joint execution of Reptile Search Algorithm (RSA) and Gradient Boosting Decision Tree (GBDT) algorithm thus it is called RSA-GBDT approach. The purpose of proposed approach is to achieve load power demand and manage power regulation. Initially, the Cascade Multilevel Inverter (CMLI) is designed to obtain optimal control signal through proposed controller. The proposed control technique is utilized in two phases of the proposed system. In the first phase, RSA is used to evaluate the optimal gain parameters considering the range of source currents is also utilized to generate the optimal offline control signal data set. According to data set, in the second phase, the GBDT works and forecasts the most optimal control signals from the online CMLI. Finally, the resulting control signal is used to control CMLI's Insulated Gate Bipolar Transistor (IGBT). The proposed RSA-GBDT technique develops the operation mode for solar and wind generation system discovers the switching states of converter. The proposed control system minimizes the variation of system parameters and external disturbances. Also, the load demand is achieved and the proposed RSA-GBDT control approach is executed on MATLAB/Simulink platform and performance is analyzed.

Advances in Fault Condition Monitoring for Solar Photovoltaic and Wind Turbine Energy Generation: A Review

Renewable energy-based power generation technologies are becoming more and more popular since they represent alternative solutions to the recent economic and environmental problems that modern society is facing. In this sense, the most widely spread applications for renewable energy generation are the solar photovoltaic and wind generation. Once installed, typically outside, the wind generators and photovoltaic panels suffer the environmental effects due to the weather conditions in the geographical location where they are placed. This situation, along with the normal operation of the systems, cause failures in their components, and on some occasions such problems could be difficult to identify and hence to fix. Thus, there are generated energy production stops bringing as consequence economical losses for investors. Therefore, it is important to develop strategies, schemes, and techniques that allow to perform a proper identification of faults in systems that introduce renewable generation, keeping energy production. In this work, an analysis of the most common faults that appear in wind and photovoltaic generation systems is presented. Moreover, the main techniques and strategies developed for the identification of such faults are discussed in order to address the advantages, drawbacks, and trends in the field of detection and classification of specific and combined faults. Due to the role played by wind and photovoltaic generation, this work aims to serve as a guide to properly select a monitoring strategy for a more reliable and efficient power grid. Additionally, this work will propose some prospective with views toward the existing areas of opportunity, e.g., system improvements, lacks in the fault detection, and tendency techniques that could be useful in solving them.

Optimal sizing of an isolated microgrid with electric vehicles using stochastic programming

An isolated microgrid system with renewable sources and energy storage systems ensures sustainable access to electricity and is especially suitable to handle inadequate electrical infrastructure in rural areas. The capacity configuration of the system depends on the uncertainties of renewable energy sources and home user behaviour, and the stochastic behaviour of electric vehicle (EV) users. This study determines the optimal configuration of the isolated microgrid that comprises wind and photovoltaic generation systems as well as batteries and EVs by proposing a two-stage stochastic programming-based multi-objective optimization problem where the objective functions are minimization of the life cycle cost and reliability. The problem is solved using the deterministic equivalent of the stochastic programming model to find the exact solution. The scenarios in the model are generated for one year period to consider the effects of uncertainties throughout seasons. Cases for different roles and configurations of electric vehicles are simulated and analysed to compare their impacts on the sizing of the microgrid. Also, the effects of charger size on the microgrid configuration are investigated through simulations. The simulation results show that a slight increase in LPSP values causes a drop in cost values of around 10%–20% in each EV scenario. The minimum cost is obtained when more efficient charge & discharge plugs for EVs are used. Furthermore, the robustness of the model results is observed through repeated experiments with different random parameters.

Toward an intelligent community microgrid energy management system based on optimal control schemes

This article investigates a modelization of the energy management strategy of a grid-tied photovoltaic (PV), wind generator, and battery system. The model uses a smart metering system to collect data from various electrical components of a microgrid. The power optimization strategy is modeled under demand response, including time-of-use pricing to create a home's energy flow more efficiently. Two optimal control strategies are developed to coordinate energy expenses, expressed as the cost of minimizing electricity supplied by the utility grid and maximizing the opportunity for energy to sell to the grid. A nonlinear open-loop control and quadratic programming using model predictive control (MPC) are compared to assess the performance of the developed model. The design model takes into consideration the operational cost of batteries and degradation. The importance of the energy storage system is demonstrated in the context of energy conservation and gain on the demand side. MPC's capacity to specify the proper control and forecast future system reaction enables the proposed method to ensure significant daily energy and cost savings from 70.44 to 24.74 $ day−1 in the first scenario (without disturbances) and from 70.44 to 18.24 $ day−1 in the second case (with disturbances). In both methods, the net savings of greenhouse gas are approximately 288.10 (kgCO2-eq) and 338.87 (kgCO2-eq). These show better performance of the MPC compared to the linear model. Highlights Investigate the dynamic performance of an intelligent home energy management scheme. Implement a grid-tied photovoltaic, wind turbine, and energy storage system. Use intelligent metering to collect data from various electrical components of a smart home. Compare the energy saving from an open-loop algorithm and a model predictive control. Assess the topmost benefits of the optimal control method of grid-tied distributed energy resources.

Design and Performance Evaluation of PM Vernier Generator Technology for a Small-Scale Uncontrolled Passive Wind Generator System

In this article it is proposed that permanent magnet Vernier generators be used for its new and unknown application in a small-scale uncontrolled passive wind generator system. Various aspects, such as the machine selection, conventional versus split-tooth topology, power matching, torque quality, and the overall feasibility of the proposal are investigated by evaluating optimized generator designs. It is found that the conventional permanent magnet Vernier generator with a magnetic gearing ratio of eight is the superior generator choice for this application. The performance of the optimum permanent magnet Vernier generator is validated through test results of a manufactured prototype. Other designs in the article are confirmed by comparing 2-D and 3-D finite element results.