Wind Energy Systems Research Articles

Optimizing Induction Wind Energy System with EDLC and PMSG for Efficient Power Control

This paper represents a comprehensive study on the optimization of an Induction Wind Energy System (IWEC) integrated with Electric Double Layer Capacitors (EDLCs) and a Permanent Magnet Synchronous Generator (PMSG) to enhance power control in the quest for sustainable and efficient energy conversion systems. As wind energy plays a pivotal role in the renewable energy landscape, maximizing its conversion efficiency is crucial for a sustainable future. Advanced control strategies and optimization techniques are applied to delve into the intricate details of the IWEC-EDLC-PMSG system, resulting in improved power quality, enhanced transient response, and superior grid integration capabilities. The research demonstrates that this integrated configuration outperforms conventional wind energy systems in terms of efficiency, grid compatibility, and dynamic response. Moreover, the study addresses key aspects like system stability, fault tolerance, and reliability, ensuring the robustness of this innovative solution. In addition to advancing wind energy technology, this research offers valuable insights into integrating energy storage systems and advanced control techniques for efficient power control in renewable energy applications, contributing to a sustainable energy future.

From Patents to Progress: Genetic Algorithms in Harmonic Distortion Monitoring Technology

Sustainable energy sources, such as wind energy, are pivotal in driving our energy landscape towards a more environmentally conscious and responsible future. Wind power, as an exemplar of clean and renewable energy solutions, adeptly harnesses the kinetic energy of the wind to generate electricity. While wind energy significantly contributes to our sustainability objectives, the quality of the energy it produces is equally essential. A critical challenge in this context is harmonic distortion, which manifests as unwanted fluctuations in the frequency and amplitude of electrical waveforms. Effectively mitigating these distortions within wind energy systems is vital to maintaining the stability and reliability of power grids, guaranteeing that the electricity supplied adheres to high-quality standards. The objective of this study was to conduct a technological prospection focused on the contemporary scenario of genetic algorithm applications in addressing harmonic variations. This investigation unearthed a total of 634 relevant documents. The findings suggest that the utilization of genetic algorithms for enhancing energy quality is a relatively recent but promising field. The State Grid Corp of China emerged as the principal contributor, with ten noteworthy inventors identified. Remarkably, both China and the United States lead in patent filings. The insights gleaned from these documents underscore the potential for further exploration and the synergistic application of these techniques. These collaborative efforts have the potential to yield processes and devices that offer significant economic and environmental advantages for the energy industry, solidifying our commitment to a cleaner and more sustainable energy future.

An Enhanced Voltage Control Strategy with DC Microgrid for EV Stations

Growing energy demand and fossil fuel scarcity have led to significant advancements in electric vehicles (EVs) and renewable energy generation. The grid’s ability to function may be severely impacted by the massive amount of electricity that electric vehicles use when grouped together at a charging station. On the charging station side, installing renewable energy is therefore thought to be a promising win-win solution. Using a photovoltaic (PV) system and a wind energy system based on distributed flexible inverter generators (DFIG), this project aims to develop a novel control mechanism for a micro grid configuration. Building a reliable and effective energy ecosystem is the main objective. Initially, the GWO-PI controller is used to optimize the power produced by photovoltaic sources. The energy that has been harvested is then mixed with the DFIG’s output. However, the incorporation of renewable energy sources poses issues with power quality and harmonics in the micro grid. Initially, they offer a dependable power source to numerous electric vehicle (EV) charging stations. Variations in frequency or voltage can put stress on the parts of the charging station, which could result in wear and tear. In addition, the consistent DC power is employed to support diverse DC loads, guaranteeing effective energy management throughout the micro grid. A sophisticated PI control system is put in place to deal with these problems. Simulink and MATLAB 2021a are used to simulate and validate the proposed method.

Grid Connected Wind Energy System Power Quality Improvement Using STATCOM

The wind energy generation, utilization and its grid penetration in electrical grid are increasing worldwide. Injection of the wind power into an electric grid affects the power quality. The wind generated power is always fluctuating due to its time varying nature and causing stability problems. This weak interconnection of wind generating source in the electrical network affects the power quality and reliability. This paper demonstrates the power quality problem due to installation of wind turbine with the grid. In this proposed scheme Static Compensator (STATCOM) is connected at a point of common coupling with a battery energy storage system to mitigate the power quality issues. The STATCOM gives reactive power support to wind generator and also load. The battery energy storage is integrated to sustain the real power source under fluctuating wind power. The STATCOM control scheme is simulated using MATLAB/SIMULINK in power system block set.

Two-body dynamic simulations of a combined semi-submersible floating offshore wind turbine and torus wave energy converter

Dynamic responses of a combined wind and wave energy system which consist of a 5MW semi-submersible floating offshore wind turbine (FOWT) and a torus-shaped wave energy converter (WEC) are investigated. In the investigated configuration, the torus WEC is constrained to move only in the heave direction with respect to the FOWT. This results in a two-body dynamic system that allows for the extraction of wave energy through the relative heave motion. A Modelica-based multi-body system (MBS) code is used to perform fully coupled dynamic analyses of the combined system. The wind turbine loads are calculated using the state-of-the-art Blade Element Momentum (BEM) method, while the wave power take-off (PTO) system is modelled as a linear spring and a viscous damper. A case study is presented where the mass of the torus is varied to investigate the impact on the two-body dynamics of the combined system. The system performance is assessed in terms of the maximum wave power absorption and the quality of wind power production.

Energy Storage Operation Analysis of High-proportion Wind Power System Based on Optimization Model

Energy storage is a valid way to ensure the actual-time power equilibrium of renewable energy systems. However, owing to the comparatively high cost of accumulation energy, the use of energy storage to equilibrium the system power will add to the system run cost, and the economy and dependability of the power system will be influenced. Therefore, in this paper, a wind-thermal-storage joint optimization model considering load-side demand response and carbon capture integrated cost is established for different wind power installed capacities, and the PSO algorithm and CPLEX solver are selected to solve the model. The power balance change and energy storage configuration of the system are compared and analyzed under the condition that the lowest cost of power generation operation is the goal function, which provides some guidance for energy storage collocation of the wind energy system and reliable power supply of the system. The results of the instance show that the improvement model introduced in this paper can validly solve the power balance issue of the high ratio wind power system with energy storage, and ensure the safe and reliable functioning of the power system while reducing the power generation cost of the system.

Influence of the load frequency and moisture content on fatigue deterioration of high‐strength concrete

AbstractWith the expansion of offshore wind energy systems, the number of fatigue‐loaded concrete structures exposed to high moisture contents has significantly increased in the last decades. Investigations in the literature show a strong reduction of fatigue resistance with increasing moisture content of the concrete. However, a detailed quantified understanding of the influence of the moisture content and the underlying damage mechanisms is still lacking. In a conjoint project as part of the priority program SPP 2020 funded by the German Research Foundation (DFG), the water‐induced fatigue damage are investigated. In this paper, the results of compressive fatigue testes on high‐strength specimens with different moisture contents are presented. The cyclical loading was carried out at three different load frequencies and maximum stress levels, respectively. The results show that the damaging effect due to fatigue loading at high moisture content is more pronounced with decreasing stress levels and decreasing frequencies. Considering the strain development and acoustic emissions the results indicate that different damage mechanisms are acting with increasing moisture content of concrete. These mechanisms are elaborated in this paper.

Wake characteristics of a balloon wind turbine and aerodynamic analysis of its balloon using a large eddy simulation and actuator disk model

Abstract. In the realm of novel technologies for generating electricity from renewable resources, an emerging category of wind energy converters called airborne wind energy systems (AWESs) has gained prominence. These pioneering systems employ tethered wings or aircraft that operate at higher atmospheric layers, enabling them to harness wind speeds surpassing conventional wind turbines' capabilities. The balloon wind turbine is one type of AWESs that utilizes the buoyancy effect to elevate the turbine to altitudes typically ranging from 400 to 1000 m. In this paper, the wake characteristics and aerodynamics of a balloon wind turbine were numerically investigated for different wind scenarios. Large eddy simulation, along with the actuator disk model, was employed to predict the wake behavior of the turbine. To improve the accuracy of the simulation results, a structured grid was generated and refined by using an algorithm to resolve about 80 % of the local turbulent kinetic energy in the wake. Results contributed to designing an optimized layout of wind farms and stability analysis of such systems. The capabilities of the hybrid large eddy simulation and actuator disk model (LES–ADM) when using the mesh generation algorithm were evaluated against the experimental data on a smaller wind turbine. The assessment revealed a good agreement between numerical and experimental results. While a weakened rotor wake was observed at the distance of 22.5 diameters downstream of the balloon turbine, the balloon wake disappeared at about 0.6 of that distance in all the wind scenarios. Vortices generated by the rotor and balloon started to merge at the tilt angle of 10∘, which intensified the turbulence intensity at 10 diameters downstream of the turbine for the wind speeds of 7 and 10 m s−1. By increasing the tilt angle, the lift force on the wings experienced a sharper increase with respect to that of the whole balloon, which signified a controlling system requirement for balancing such an extra lift force.

A multivariate statistical method for risk parameter scenario generation and renewable energy bidding in electricity markets

To maximize the expected profits and manage the risks of renewable energy system under electricity market environment, scenario-based- stochastic optimization model can be established to generate energy bidding strategies, in which the probabilistic scenarios of risk parameters are usually obtained by using statistical or machine learning methods. This paper proposes a practical multivariate statistical method for risk parameter scenario generation, which is used by a wind energy system faced with uncertain electricity prices and wind power productions, and it considers the correlation between dependent risk parameters by using historical data directly. The probabilities of scenarios containing correlated risk parameters are calculated by using multivariate histograms, in which the asymmetric correlation between different parameters existing in the historical data are preserved. Additionally, in order to make the stochastic optimization problem with large numbers of scenarios tractable, a multivariate scenario reduction method is used to trim down the scenario number. By solving the stochastic optimization problem, optimal day-ahead bidding curves for the wind energy system are generated, and Douglas–Peucker algorithm is used to fit the bidding curves according to market requirements. Case studies based on real world data in electricity markets are performed to prove the effectiveness of the proposed risk parameter scenario generation method and energy bidding strategies. Finally, conclusions and practical suggestions on future research works are provided.

Innovative maximum power point tracking technique for wind energy conversion system

In this work, an optimal control scheme based on the Maximum Power Point tracking for the Wind Energy Conversion System using Permanent Magnet Synchronous Generator (PMSG) is proposed and also modeled. The system studied in this paper consists of wind energy system powering a battery using a buck-boost converter as an interface. By modifying the buck-boost duty cycle, we vary the reflected voltage at PMSG, and accordingly its speed. It is controlled with Perturb and Observe (P&O) Maximum Power Point Tracking (MPPT) approach. The overall system is simulated with MATLAB/SIMULINK.

Carbon footprint and energy life cycle assessment of wind energy industry in Libya

The recent investigation has demonstrated that wind energy holds great potential as a viable and environmentally friendly energy source in Libya. The study employed a Life Cycle Assessment (LCA) methodology to evaluate various energy, economic, and environmental indicators for potential wind farm installations at multiple suitable locations across Libya. The assessment encompassed estimations of energy requirements and greenhouse gas (GHG) emissions associated with the conversion of wind energy into electricity throughout the entire life cycle of the proposed wind farms. In light of Libya not being a producer of wind energy converters, a novel approach was developed to define the system's boundaries. These boundaries included distinct subsystems, each corresponding to various stages within the life cycle of a wind energy system, encompassing factors such as shipping emissions from the manufacturer's location to Tripoli's marine ports, land transportation to the wind farm sites, energy and emissions associated with installation, operation, maintenance, and eventual disposal of wind turbines.Hourly climate data spanning a 25-year period from 1995 to 2020 were gathered from the SolarGis climate information site. The System Advisor Model (SAM) program was utilized to predict the energy yields of 100 MW capacity wind farms at 12 sites in Libya. Additionally, a novel eco-environmental indicator known as the Life Cycle Levelized Cost of Energy (LCLCOE) was introduced, which factors in all environmental damage costs throughout the entire lifespan of wind energy projects. The study's findings revealed that the Gamesa turbine, with a capital cost of $146,916,400 for a 100 MW capacity wind energy farm, exhibited the most favorable economic and environmental performance. GHG emission factors across all examined cities ranged from 32 to 70 gGHG/kWh, with carbon payback durations spanning from 4.5 to 12.3 months. The estimated energy payback period varied from 13 to 22 months, while the LCLCOE ranged from 4.8 to 8.4 ¢/kWh.

Experimental investigation of advanced turbine control strategies and load-mitigation measures with a model-scale floating offshore wind turbine system

To advance the control co-design of offshore wind energy systems, the authors perform basin-scale experiments with a fully instrumented and actuated floating offshore wind turbine model. The model consists of a 1:70 scale performance-matched model of the International Energy Agency Wind Technology Collaboration Programme 15-MW reference turbine atop the VolturnUS-S semisubmersible platform. The Reference OpenSource Controller provides real-time blade pitch and generator torque control. We aim to develop an open data set for the validation of numerical models in predicting the influence of turbine control and load-mitigation measures. For this purpose, we measure the effects of advanced turbine control features, including peak shaving and floating feedback as well as hull-based control using tuned mass dampers on the system. Overall, the results demonstrate measurable and consistent influences from the control and load-mitigation measures, thus confirming the usefulness as a validation data set. Peak shaving attenuates the response to wind turbulence at near-rated wind speed. Floating feedback reduces the load and platform pitch motion associated with the negative damping induced by blade pitch control. The tuned mass dampers also attenuate the system response near the targeted frequencies under suitable conditions. We also identify detrimental side effects of each load-mitigation measure.

Mixformer: Mixture transformer with hierarchical context for spatio-temporal wind speed forecasting

Wind energy has attracted more and more attention due to its sustainability and pollution-free nature. As wind energy is highly dependent on wind speed, wind speed forecasting is of great importance for fully harvesting wind energy and ensuring the stability and reliability of wind energy systems. By accurately predicting the future wind speed, we can make efficient power schedules and consequently improve the utilization of wind power. Although existing forecasting methods have achieved satisfactory wind forecasting performance, they may still fail to discover the intricate spatio-temporal dependencies obscured in entangled patterns. To resolve this issue and further improve the prediction accuracy, we propose Mixformer, a novel mixture Transformer with hierarchical context for spatio-temporal wind speed forecasting. Mixformer first applies seasonal-trend decomposition, and then employs an MLP to predict the trend part and an attention model to predict the seasonal part. Besides, Mixformer innovatively proposes a spatio-temporal Gaussian mixture attention (ST-GMA) layer, which fuses the periodic temporal and long-term spatial context. To capture intricate long-term spatial characteristics, Mixformer extracts global information by the Dynamic Time Warping algorithm. Moreover, with the ST-GMA layer, Mixformer builds a hierarchical encoder–decoder architecture to fully utilize the context at different scales for seasonal forecasting and further enhance the spatio-temporal modeling. Empirical experiments on four real-world benchmark datasets show that Mixformer yields the lowest prediction errors with the MAE scores of 1.66, 1.68, 3.14 and 0.70 and the RMSE scores of 2.07, 2.01, 3.99 and 0.98, and improves the prediction accuracy by 8.43%, 5.36%, 4.14%, 6.57% in term of MAE scores and 9.18%, 6.92%, 4.01%, 5.75% in term of RMSE scores compared to state-of-the-art methods, which demonstrates the superiority of Mixformer. In a nutshell, Mixformer presents a promising method for spatio-temporal wind speed forecasting and may achieve desirable performance in real-world applications of wind power systems.

From wind conditions to operational strategy: optimal planning of wind turbine damage progression over its lifetime

Abstract. Renewable energies have an entirely different cost structure than fossil-fuel-based electricity generation. This is mainly due to the operation at zero marginal cost, whereas for fossil fuel plants the fuel itself is a major driver of the entire cost of energy. For a wind turbine, most of the materials and resources are spent up front. Over its lifetime, this initial capital and material investment is converted into usable energy. Therefore, it is desirable to gain the maximum benefit from the utilized materials for each individual turbine over its entire operating lifetime. Material usage is closely linked to individual damage progression of various turbine components and their respective failure modes. In this work, we present a novel approach for an optimal long-term planning of the operation of wind energy systems over their entire lifetime. It is based on a process for setting up a mathematical optimization problem that optimally distributes the available damage budget of a given failure mode over the entire lifetime. The complete process ranges from an adaptation of real-time wind turbine control to the evaluation of long-term goals and requirements. During this process, relevant deterministic external conditions and real-time controller setpoints influence the damage progression with equal importance. Finally, the selection of optimal planning strategies is based on an economic evaluation. The method is applied to an example for demonstration. It shows the high potential of the approach for an effective damage reduction in different use cases. The focus of the example is to effectively reduce power of a turbine under conditions where high loads are induced from wake-induced turbulence of neighbouring turbines. Through the optimization approach, the damage budget can be saved or spent under conditions where it pays off most in the long term. This way, it is possible to gain more energy from a given system and thus to reduce cost and ecological impact by a better usage of materials.

Robust finite-time integral terminal sliding mode control design for maximum power extraction of PMSG-based standalone wind energy system

This paper introduces a novel control strategy called Finite-time Integral Terminal Sliding Mode Control (FITSMC), explicitly designed for a permanent-magnet synchronous generator (PMSG)-based standalone Wind Energy Conversion System (WECS). The primary objective of the FITSMC strategy is to regulate the operation of the wind turbine efficiently and maximize power extraction from the WECS. To achieve this, the system is driven onto a sliding surface within a predefined terminal time, ensuring rapid convergence and overall stability. An important advantage of the FITSMC strategy is its ability to maintain a standalone wind power system close to the maximum power point, even under varying wind conditions and load changes. In addition, the controller demonstrates robustness against uncertainties and disturbances, making it highly suitable for real-world applications. Extensive simulations and analyses have been conducted to validate the effectiveness of the proposed FITSMC. The results show a superior control performance compared to traditional methods. Consequently, the FITSMC strategy represents a promising advancement in control techniques for standalone wind power systems, providing an efficient and reliable approach for harnessing power from wind energy.

Wind energy systems adapted to the seawater greenhouse desalination unit designed for arid coastal countries

The wind energy can be used to power the seawater greenhouse. The aim of this study is to present the feasibility of wind energy in the seawater greenhouse desalination unit. The seawater greenhouse combines fresh water production with growth of crops in a greenhouse system. The technique is adapted for farms in arid coastal regions that are suffering from salt infected soils and shortages of potable groundwater. The dimension of the greenhouse has the greatest effect on the water production and energy consumption. A wide shallow greenhouse (200 m wide by 50 m deep) has been found to give 297 litres per day of fresh water in only eight hours (between 09.00 and 17.00). The greenhouse produces 98 % of total fresh water. This interval corresponds to the higher winds period.

Reliable Accuracy of On-line Permanent Magnet Synchronous Machine Parameters’ Identification Process by Using a Meta-heuristic Optimization Algorithm

In wind energy systems, it is necessary to have a good knowledge of the used generator and its parameters. Despite the motor manufacturer’s specifications, accurately estimating the machine parameter reference values is critical for the perfect control system. In this context, off-line identification tests of a 3 kW permanent magnet synchronous machine were conducted to determine the mechanical and electrical parameters. In addition, this research exploits a meta-heuristic optimization technic dubbed grey wolf optimizer in the on-line parameter’s identification field. The estimate method is focused on the parameter’s estimation linked to a reference model. The rule is based on the cancelation of the current error among the measured and the associated estimated current. For each measurement, the grey wolf estimator adapts the reference model parameters until the model response and the accurate response be similar. All were implemented using the real-time interface of the DSpace DS1104 Controller card while using the ControlDesk graphical user interface and the Simulink/MATLAB environments. Results from hardware simulation confirm the efficacy and benefits of the suggested strategy.

Refining the airborne wind energy system power equations with a vortex wake model

Abstract. The power equations of crosswind Ground-Gen and Fly-Gen airborne wind energy systems (AWESs) flying in circular trajectories are refined to include the contribution from the aerodynamic wake, modeled with vortex methods. This reveals the effect of changing the turning radius, the wing geometry and the aerodynamic coefficients on aerodynamic performances and power production. A novel power coefficient is defined by normalizing the aerodynamic power with the wind power passing through a disk with a radius equal to the AWES wingspan, enabling the comparison of different designs for a given wingspan. The aspect ratio which maximizes this power coefficient is finite, and its analytical expression for an infinite turning radius is derived. By considering the optimal wing aspect ratio, the maximum power coefficient is found, and its analytical expression for an infinite turning radius is derived. Ground-Gen and Fly-Gen AWESs, with the same idealized characteristics, are compared in terms of power production, and later three AWESs from the literature are analyzed. With this modeling framework, Ground-Gen systems are found to have a lower power potential than Fly-Gen AWESs with the same geometry because the reel-out velocity makes Ground-Gen AWESs fly closer to their own wake.

Onshore versus offshore wind power trends and recent study practices in modeling of wind turbines’ life-cycle impact assessments

Wind energy has gradually become one of the most efficient and reliable forms of sustainable energy, and it is being globally utilized to accelerate the expansion of green power production. However, recent projections of onshore & offshore wind energy systems indicate that further improvements are continuously required in terms of the deployments, capacity factors, costs, etc. to achieve an optimal wind power generation. According to these projections, the most critical current issue is the offshore systems' high levelized cost of energy, which demands the enhancements of the technologies to ensure the cost reduction in the future. Moreover, a number of reports are underscoring that offshore power systems can also leave a larger carbon footprints through their life-cycles compared to the onshore counterpart. On the other hand, the modelling methods of the power plants' life-cycle impact assessment (LCIA) have nowadays become among the most compelling research problems in the field of wind power systems engineering. In addition to comparatively assessing the characteristics of onshore & offshore energy systems based on some reports & studies, this work is therefore aimed at rigorously exploring the existing study practices in modelling the wind power plants' LCIA. A number of these practices were based on the implementation of various conventional modelling methods that may not help to make accurate assessments of the wind power plants’ life-cycle impacts. Hence, one of the main contributions of this study is to summarize a novel LCIA modelling approach based on Internet of Things (IoT) and predictive digital twin technologies. Unlike the conventional methods, real-time data extraction from the power plants makes these technologies possible to conduct the most accurate assessments of the life-cycle impacts.

Stator current signal crossing for fault diagnosis of self-excited induction generators

This paper presents a novel method for modelling and diagnosis of electrical and mechanical faults in fixed-Speed Self-Excited Induction Generators (SEIGs) operating in autonomous mode in a small-scale wind energy system. The proposed method is validated using the finite element method. After the selection of the magnetising capacitors, the self-excitation process is performed under no-load conditions. Once the stator voltage is established, a symmetrical three-phase load is connected. The fault detection method introduced here is called Stator Current Signal Crossing (SCSC). The SCSC extracts a new signal from the stator currents, that enables the detection of stator inter turn shortcircuits, broken rotor bars, and dynamic eccentricity faults in SEIGs. A spectral analysis of SCSC using the Fast Fourier Transform (FFT) algorithm is used to precisely locate the induced fault components. What sets this fault-tracking method apart from its predecessors is its exceptional ability to detect faults of any magnitude by analysing the modulation of the SCSC signal. These faults are directly identified by the presence of distinct harmonics, each indicative of a specific type of fault. This study also focuses on the SEIG in a wind energy system, whereas previous works have mainly addressed the induction machine in motor mode. In contrast, previous methods involved analysing a single current signal and isolating specific harmonics from a wide frequency range. The effectiveness of the proposed fault detection method and the self-excitation process are illustrated by simulation results and spectral analysis.