Wind Power Research Articles

A control strategy of DC transmission system for offshore wind power based on hybrid converters

Abstract As the DC transmission system of modular multilevel converters for offshore wind power suffers problems like large volume, heavy weight, difficulty in platform transportation, and high cost, a DC transmission scheme for offshore wind power based on diode rectifier units and modular multilevel converters is put forth in the present work; Meanwhile, the features of active-power coordination, the DC harmonic current, and the AC harmonic current of the hybrid converter in this proposed scheme are analyzed to propose control strategies for active power coordination control, DC harmonic current suppression, and AC harmonic current suppression of the hybrid converter, respectively. The performance and feasibility of the proposed strategies are verified by testing the hardware in the loop real-time simulation system for transmission of offshore wind power through hybrid converters based on RTDS. It is found that the proposed strategies could improve the stability, flexibility, and reliability of the system.

Hybrid wind speed optimisation forecasting system based on linear and nonlinear deep neural network structure and data preprocessing fusion

Wind speed time series forecasting has been widely used in wind power generation. However, the nonlinear and non-stationary characteristics of wind speed make accurate wind speed forecasting a difficult task. In recent years, the rapid development of artificial intelligence and machine learning technology provides a new solution to the problem of wind speed forecasting. Combining the advance of artificial intelligence and data analysis strategy, this paper proposes a wind speed forecasting system based on multi-model fusion and integrated learning. Considering the differences in data observation and training principles of various algorithms and exploiting the advantages of each model, a wind speed forecasting system with multiple machine learning algorithms embedded in integrated learning is constructed, which includes three modules: data preprocessing, optimization and forecasting. The data preprocessing module can conduct quantitative analysis through input data decomposition and feature extraction, and the combination of multi-objective intelligent optimization algorithm and combined forecasting method can effectively forecast the wind speed time series. The validity of the algorithm is verified using the data of Shandong wind farm in China. The forecasting results show that compared with the traditional single model forecasting, the proposed integrated wind speed forecasting system based on the fusion of multiple models has higher forecasting accuracy.

Connectedness between international oil and China's new energy industry chain: A time-frequency analysis based on TVP-VAR model

With the growing prominence of global environmental concerns, the interplay between the oil and new energy industries has become increasingly vital. We employ a connectedness approach based on the TVP-VAR model to explore the dynamic connectedness in both time and frequency domains between the oil and various industries within the new energy industry chains. Empirical findings reveal total connectedness of approximately 70 %, primarily manifested as inter-industry associations within the new energy industry and total connectedness predominantly emerges in short term and is sensitive to extreme events. Additionally, the oil and wind power industries have consistently played roles as net recipients of risk. Conversely, the photovoltaic, energy storage, and new energy battery industries have consistently acted as net risk propagators. The roles of the hydroelectric, nuclear power, and new energy vehicle sectors in risk propagation vary with different frequency components. Thirdly, we identify six pairs of industry combinations exhibiting significant two-way spillover effects. Finally, after two robustness tests, the above conclusions remain valid. These research findings offer valuable insights for policymakers and investors.

A review of the harvesting methods for offshore renewable energy – advances and challenges

The present work offers general information about present-day and developments in offshore renewable energy, specially of wind, wave and the solar energy harnessing structures. In the introduction, the authors raise awareness of the despite more untapped potentials that exist in the oceans and the offshore Wind Energy Market that is experiencing a steep rise in the recent past due to technological advancement and the great policies that support this energy source. This mainly focuses on the very huge additionality of offshore wind power and the improvements in floating solar PV systems. Section three outlines an assortment of studies that were done by different writers on the strengths and weaknesses of offshore renewable energy technologies through a meta-analysis of the literature. The method of data extraction revolved around several factors, namely technological admissibility, resource identification, location and environmental and economic factors. The sources used comprised of scholarly articles published within the peer reviewed journals in the period of 2013 to 2024, to ensure that the study offered a comprehensive analysis on the technological advancements. The results and discussion section gives an analysis of the results achieved and demonstrates the developments in Offshore Wind Energy, Wave Energy Conversion, and Floating Solar PV Systems. Thus, offshore wind energy is described as the rapidly evolving segment and particular focus is made on the floating wind farms and their advancements in the aspects of power density and structural stability. Wave energy converters are discussed regarding their applicability to complement hybrid electricity production despite limited data on the devices’ performance. Another key feature is that the FFPV systems are reported to be more efficient and cheaper than the land mounted systems. Thus, the article emphasizes the need to produce the further literature on offshore renewable energy, political support, and cooperation between different sectors. It concludes that an offshore renewable energy has the potential for the future development due to advancement in the technology as well as due to environmental factors.

Framework for strategic deployment of hybrid offshore solar and wind power plants: A case study of India

Renewable energy sources are gaining prominence as eco-friendly and sustainable alternatives to fossil fuels due to their availability and minimal greenhouse gas emissions. Nonetheless, the critical challenge is the availability of renewable resources, which fluctuates with changes in climatic conditions. This limitation poses a consistent challenge to generating base load power if it relies solely on a single type of renewable resource. Addressing this, integrating multiple renewable sources into hybrid systems has emerged as a viable solution. This study presents a framework, integrating Geographic Information Systems (GIS) and Hybrid Multi-Criteria Decision Making (MCDM) techniques to identify plausible locations for the deployment of Hybrid Offshore Solar and Wind Power Plants (HOSWPP) and the developed framework is demonstrated considering Indian Exclusive Economic Zone (EEZ) as a study area. Using the proposed approach, Indian EEZ region is classified into five suitability classes. The effectiveness of regions within each class is further assessed in terms of complementarity measured using Kendall's coefficient. Findings suggested that Kendall's coefficient for highly suitable class is −0.41 indicating the regions identified in this study are the prime locations for installing HOSWPP. A total of twenty optimal sites for HOSWPP deployment, predominantly in the offshore regions of Tamil Nadu and Gujarat. Eighteen sites are located along Kanyakumari to Thisayanvilai in Tamil Nadu, including areas in the Gulf of Mannar and near Valinokkam are found plausible. The rest of the two sites are in the offshore regions of Gujarat. This study provides a strategic roadmap to increase the renewable footprint, contributing to the global transition towards cleaner energy sources.

Fault Diagnosis of Wind Turbine Blades with Continuous Wavelet Transform based Deep Learning Model Using Vibration Signal

Wind Turbines are the most crucial devices in wind energy conversion systems to increase energy generation efficiency from wind sources. Blade failure in wind turbines is due to the induced vibrations of change in ecological variables. It is necessary to periodically inspect the state of the wind turbine blades to enhance safety by decreasing downtime, lessening the likelihood of unplanned failures requiring extensive repair, and progressively increasing power generation with reduced logistical costs. The primary goal of the proposed research is to adopt a hybrid strategy that combines convolutional neural network models and continuous wavelet transform. The bump wavelet-based continuous wavelet transform with a convolutional neural network model is employed to classify the faulty wind turbine blades based on the extracted vibration signals of turbine blades. This approach distinguishes between different states of blade faults affecting wind turbine blades during operational phases. The research considers blade faults in the horizontal axis wind turbine, including blade bending, erosion, and the connection looseness between the hub and blade. The cross-validation and hold-out methods are used to validate the classification accuracy and are compared with other existing popular methods. The hold-out method has a better classification accuracy of 97.916%.

Less power when more is needed. Climate-related current and possible future problems of the wind energy sector in Poland.

Heat waves, combined with an increasing share of weather-dependent renewable energy sources, lead to disruptions in the operation of energy systems. This paper estimates how energy consumption in Poland changed during four heat waves (2010-2022). It also analyzes how often wind conditions may occur in the summer, that interrupt energy production by land-based wind farms. Moreover, authors are looking at future changes of climate indices responsible for increased energy consumption and possible reduction of energy production in wind farms. During the 2010 heatwave, the increase in energy consumption during the hottest days did not exceed 4%. Later summer demand increased; in 2019, it was 8% higher than during the 2015 heatwave. In 2016-2022, energy consumption at the average daily air temperature above 26°C was over 8% higher on working days than at 18°C. Recently, even in regions with favorable wind conditions, the number of summer days with low wind speed (2 ms-1) exceeds 14. For Poland, the number of days with low wind speeds will increase by about ten. Therefore, the increasing demand for energy on hot days, combined with their more frequent occurrence, may have a negative impact on the stability of the energy system in the future.

Improving the surface durability of wind gears via shot peening and superfinishing

Gearbox power density plays a key role in wind energy competitiveness. As wind turbines have become increasingly larger, gear surface durability and flank fracture load carrying capacity have become key elements in gearbox sizing. While shot peening and superfinishing have been studied independently as potential techniques to improve surface durability, few reported studies have combined both of these processes to increase the gear flank load-carrying capacity. In this work, spot pitting tests were carried out to evaluate the expected load-carrying capacity increase for wind gears through this combination of finishing processes. The results are consistent with previous research's conclusions and highlight the need for further investigations to evaluate potential increased flank fracture risk related to the near-surface residual stress distribution.

Reducing neural network complexity via optimization algorithms for fault diagnosis in renewable energy systems

This article discusses the challenges involved in diagnosing faults in renewable energy systems. A significant challenge is the high computational demands of the artificial intelligence algorithms needed for grid-connected photovoltaic (GCPV) and wind energy conversion (WEC) systems in fault diagnosis. To address this issue, several methods are proposed to reduce computation time, minimize memory requirements, and improve efficiency. First, optimization algorithms such as the Salp Swarm Algorithm (SSA), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO), combined with machine learning classifiers for feature selection, are suggested to reduce computational time and memory space requirements. This approach notably decreases computation time but has a limited impact on memory space. Secondly, further reduction in memory space requirements is recommended by using the variogram method for data reduction. This method can leverage the outputs of preceding algorithms to optimize renewable energy systems, making their operations cost-effective and efficient. Finally, the outputs of the variogram are used to train neural network (NN), recurrent neural network (RNN), and long short-term memory (LSTM) classifiers to differentiate between various modes of operation in GCPV and WEC systems. Experimental results demonstrate the effectiveness and robustness of the proposed methods, showing that memory space can be reduced by 1.3 to 5.6 times and CPU time by 1.1 to 11 times while maintaining accuracy and improving efficiency compared to conventional algorithms.

Optimized Hybrid Osprey with PSO Control for Improved VSC-HVDC-Wind Power Integration

Innovative power systems include offshore wind farms (OWFs) to generate electricity from renewable sources. So, reliable control systems are also necessary to make sure these power systems run smoothly. The purpose of this study is to combine the Osprey Optimization with Particle Swarm Optimization (OOPSO) to fine-tune the gains of Proportional-Integral (PI) controllers within the investigated wind energy conversion system (WECS) to enhance its performance. The study confirms the effectiveness of using the Hybrid OOPSO algorithm to optimize PI controllers in a WECS. The WECS includes two voltage source converter (VSC) stations at each end of a high voltage direct current (HVDC) transmission system. The transmission system links OWFs to the grid. The research demonstrates that the Hybrid OOPSO algorithm is superior to the genetic algorithm (GA) and PSO by enhancing setup stability and allowing fast voltage regaining following different fault cases. The introduced method performs more remarkably than traditional methods such as GA and PSO. The results of this study have shown that the proposed OOPSO enhances the overshoot in onshore voltage transient response under a symmetrical fault condition by 26% over PSO, 24% over GA, and 16% over OOA. The study is conducted using MATLAB/Simulink. The study underscores the importance of advanced optimization methods to guarantee offshore wind energy systems' efficient and dependable functioning.

High performance hybrid omnidirectional wind energy harvester based on flutter for wireless sensing and hydrogen production applications

High performance hybrid omnidirectional wind energy harvester based on flutter for wireless sensing and hydrogen production applications

Evaluating vector winds over eastern China in 2022 predicted by the CMA-MESO model and ECMWF forecast

Vector winds play a crucial role in weather and climate, as well as the effective utilization of wind energy resources. However, limited research has been conducted on treating the wind field as a vector field in the evaluation of numerical weather prediction models. In this study, we treat vector winds as a whole by employing a vector field evaluation method, and evaluate the mesoscale model of the China Meteorological Administration (CMA-MESO) and ECMWF forecast, with reference to ERA5 reanalysis, in terms of multiple aspects of vector winds over eastern China in 2022. The results show that the ECMWF forecast is superior to CMA-MESO in predicting the spatial distribution and intensity of 10-m vector winds. Both models overestimate the wind speed in East China, and CMA-MESO overestimates the wind speed to a greater extent. The forecasting skill of the vector wind field in both models decreases with increasing lead time. The forecasting skill of CMA-MESO fluctuates more and decreases faster than that of the ECMWF forecast. There is a significant negative correlation between the model vector wind forecasting skill and terrain height. This study provides a scientific evaluation of the local application of vector wind forecasts of the CMA-MESO model and ECMWF forecast.

On the use of fractal geometry to boost galloping-based wind energy harvesting

In this study, we introduce fractal bluff bodies to galloping energy harvesters (GEH) to improve the performance of wind energy harvesting. Three basic sectional shapes are proposed for GEH, from which four fractal bluff bodies are developed. An aero-electro-mechanical model with distributed parameters is established to elucidate the underlying mechanism. Then numerical simulations and wind tunnel tests are conducted based on a fabricated prototype. Compared with the conventional galloping energy harvester with a cuboid bluff body (GEH-CB), the GEH with a fractal bluff body (GEH-FB) owns a lower cut-in wind speed and can generate higher voltages. The galloping critical wind speed for GEH-FB is reduced by 0.4 m/s. Furthermore, the output voltage can increase by 51.97% at the wind speed of 5 m/s. Especially, compared to the GEH-CB, the GEH-FB demonstrates a great advantage in terms of power density, being approximately 164.1% higher. Three-dimensional computational fluid dynamics (3D-CFD) shows that the wake vortex area and the space between the adjacent vortices both increase, which explains the aerodynamic mechanism of a large aerodynamic force and a performance enhancement in wind galloping energy harvesting. Finally, some practical application tests exhibit the effectiveness of the proposed fractal galloping harvesters.

An OSL-dated, stacked Holocene dust mass accumulation rate record on the Chinese Loess Plateau and its implications for Northern Hemisphere dust activity

Accurate reconstruction of geological dust activity is crucial for understanding past climate change and its interaction with dust cycle. Loess on the Chinese Loess Plateau (CLP) is an ideal and sensitive material for revisiting past dust activity. Previously, the changes in the dust mass accumulation rate (MAR) over various time scales were widely established on the CLP. However, few absolutely dated dust records have been obtained and synthesized for the Holocene. This study addresses this gap by compiling 23 optically stimulated luminescence-dated Holocene loess sections across the CLP. We developed a high-quality chronology via Bayesian age-depth modeling and derived a MAR record for each section. Then, we obtained a mean MAR record for the CLP by stacking individual records. We propose that, in contrast to site-specific MARs, stacked MARs represent the mean dust accumulation conditions for the entire CLP and can be used to track relatively large-scale dust activity and climate change. The stacked MAR record suggests a moderate weakening trend of dust accumulation from ∼11.5 to 7.5 thousand years ago (ka BP), followed by a pronounced strengthening trend from ∼7.5 to 3.0 ka BP. A comparison with other regional dust records reveals a shift in dust activity at ∼8–6 ka BP in northern China and western Mongolia. We argue that the East Asian winter and summer monsoons jointly contributed to Holocene mean dust MAR variations on the CLP by changing dust transport wind energy and dust source aridity, respectively. A comparison of Holocene dust records in Asia with those in the northwestern Pacific Ocean and Greenland suggests asynchronous variations in dust activity between proximal and distal Asia-sourced dust deposition. This is because, unlike proximal deposition, distal deposition can be controlled not only by the dust source conditions but also by the intensity and the position of the Westerlies.

Aerodynamic characteristics of tall building with wind turbines at corners

This study experimentally investigates the impact of a pair of vertical-axis wind turbines at the leading corners of a tall building on its aerodynamic characteristics. These wind turbines have the potential to serve dual purposes: harnessing wind energy under normal wind conditions and mitigating wind loading of the building under strong wind conditions. The wind tunnel testing results in this study indicate when the tip speed ratio of the turbines is 0.34, with the wind turbines rotating toward downstream, the standard deviation of lift coefficient of the building decreases by 30.9%. Meanwhile, the mean pressure coefficient and the standard deviation of pressure coefficient on both the side face and leeward face of the building also exhibit a certain degree of reduction. The peak value of the power spectral density of lift coefficient of the building is also significantly decreased. This study clearly demonstrates that the wind turbines at the leading corners of tall buildings have the potential to effectively reduce wind loading of the buildings.

Feather-inspired triboelectric nanogenerator with lift and drag modulation for wind energy harvesting

Feather-inspired triboelectric nanogenerator with lift and drag modulation for wind energy harvesting

Blade Airfoil Optimization and Its Impact on Vertical Axis Wind Turbine Performance

Abstract Amidst the emergence of crises in petroleum prices and supply, the imperative for the development of clean energy has become increasingly apparent. Vertical axis wind turbines (VAWT), as a type of wind turbine, still grapple with challenges such as low wind energy utilization and sensitivity to dynamic wind speeds. This study focuses on optimizing the airfoil of a two-dimensional vertical axis wind turbine and analyzes the performance variations in a three-dimensional model. Following the optimization, the average power coefficients of the dual-blade two-dimensional VAWT increased by 7.9% and 4%, respectively, while those of the dual-blade three-dimensional model rose by 7.9% and 1.2%. However, the performance of the three-dimensional model was compromised due to the influence of tip vortices, resulting in inferior performance compared to the two-dimensional model. Both three-dimensional optimized blade designs exhibited a substantial reduction in the flow separation region downstream and improved pressure distribution fluctuations caused by the shedding of tip vortices, leading to decreased pressure differentials. Along the spanwise section, the power coefficient gradually decreased from the central segment to the tip segment. Notably, the power coefficient of the optimized blades at the tip segment increased significantly, accompanied by an augmented pressure differential between the upper and lower surfaces. This augmentation contributed to a significant enhancement in lift, thereby facilitating the rotation of the vertical axis wind turbine.

High-performance and ultra-robust triboelectric nanogenerator based on hBN nanosheets/PVDF composite membranes for wind energy harvesting

Triboelectric nanogenerator (TENG) is a novel energy technology that converts high-entropy mechanical energy from the environment into electrical energy using triboelectrification and electrostatic induction effects. However, the low surface charge density and easy wear of traditional triboelectric materials are bottlenecks for their practical applications. Here, a remarkable triboelectric properties, superior mechanical properties, exceptional wear resistance, and high thermal conductivity hexagonal boron nitride nanosheets (hBNNS)/ polyvinylidene difluoride (PVDF) composite negative triboelectric material is developed. The inclusion of hBNNS as an electron acceptor and nucleating agent not only effectively boosted the surface charge density (711 μC/m2) and mechanical characteristics of the composite membrane, but also the friction coefficient (COF) and thermal conductivity of the 2 wt% hBNNS/PVDF composite membrane decreased by 28.6 % and increased by 22.2 % compared to the PVDF matrix, respectively. The optimized TENG delivers a peak voltage of 434 V, a current of 53 μA, and a power of 4.84 mW. Furthermore, it exhibits exceptional ultra-robust, enabling continuous operation for 72 h at a wind speed of 17.5 m/s while maintaining performance above 90.6 %. Additionally, the TENG is capable of effectively powering a smart hygrothermograph and realizing wireless real-time monitoring, or directly lit up 102 white LEDs in a serial connection. This work addresses the challenges of low output performance and limited lifespan in TENG from a material perspective, providing a valuable reference method for the design of negative triboelectric materials with high surface charge density, good mechanical properties, low COF, and high thermal conductivity.

Exploring The Potentials and Drawbacks of Hysteresis Current Controller For SRM: A Case Study Of Wind Energy Conversion System

Switched Reluctance Machines (SRMs) have drawn interest in renewable energy systems because of their special qualities and applicability. This study examines the idea of hysteresis current regulation in a wind energy conversion system with a Switched Reluctance Generator (SRG). The system is modeled in MATLAB/SIMULINK. The model is simulated and analyzed to ascertain the system’s benefits and shortcomings, focusing on reducing torque ripples in SRG. Considering most classical controllers are complicated to construct, the hysteresis current control is straightforward and effective. However, this controller's two primary shortcomings are that it produces a variable switching frequency and increases current ripples at a steady state, both enhancing acoustic noise in SRM. Lastly, the report suggests that improving controller performance should be the primary emphasis of future research in this field.

A wind farm control strategy for frequency regulation reserve: Optimize wake loss and frequency support capability

—With a high proportion of renewable energy, the issue of grid frequency fluctuations is becoming increasingly prominent. To tackle this challenge, wind farms can enhance frequency regulation response by increasing rotational kinetic energy through overspeed control. However, this approach may result in energy losses due to the wake effect. This paper proposes an optimized configuration strategy for wind farm frequency regulation reserve (FRR), utilizing pitch angle cooperative control to reduce wake losses. The objective is to explore the optimal configuration for maximizing wind energy utilization under FRR conditions. A genetic algorithm is used to find the optimal control parameters in the established FLORIS-based wind farm power reserve wake model, which involves rotor speed and pitch angle control, to maximize output power and rotational kinetic energy. Based on this, a lookup table is constructed to map wind conditions to the optimal control parameters for different FRR level. According to the reserve power schedule, optimization control is applied to the wind farm. Simulation results show that, compared to the traditional equal distribution strategy for FRR power, the proposed control strategy can enhance the total kinetic energy reserve of the wind farm by optimizing wake loss, thereby improving frequency support capability.