Wind Power Industry Research Articles

Offshore Wind Power Transmission Decision-making Based on Grey Correlation and Topsis

With the profound transformation of the global energy structure, the development and utilization of wind energy have become the focus of global attention. The offshore wind power industry has ushered in a historic development opportunity and China’s offshore wind power industry has entered the fast track of development. However, due to the variety of transmission schemes available for offshore wind power, there are many differences in the economy, reliability, environment, risk, and other aspects of different transmission schemes, so it is difficult to make the optimal decision. To comprehensively compare the advantages and disadvantages of different transmission schemes in actual scenarios, this paper first identifies their influencing factors to obtain an evaluation index system and uses the entropy weight method to determine the index weight based on the index evaluation data of typical offshore wind power transmission projects at home and abroad, and the grey correlation method and TOPSIS method are integrated to build a comprehensive evaluation model suitable for offshore power transmission. Finally, the scientific and rigorous nature of the research method and model is verified by example analysis. The optimal decision comprehensive evaluation model constructed in this paper can realize scientific and comprehensive evaluation of offshore wind power transmission schemes and has important decision-making significance for the selection of transmission schemes in different scenarios.

An evolutionary analysis on the effect of tax incentives on R & D diffusion of wind power industry in complex network

The innovation performance of Chinese wind power enterprises has not been effectively improved, which may be caused by the behavior of enterprises using the information asymmetry between government and enterprises to camouflage R & D and obtain policies incentives. Therefore, this paper attempts to construct an evolutionary game model of tax incentives in complex network, introduces technology spillover and reputation cost, and discusses the diffusion of R & D in Chinese wind power industry. The results show that: (1) Reducing technology spillover not only contributes to the R & D diffusion, but also reduces the peak in the proportion of enterprises with camouflaging R & D. (2) Increasing reputation cost of violations can accelerate enterprises to reach the equilibrium and reduce the time that the proportion of the behavior with camouflaging R & D decreases. (3) The increase in incentives of lower tax rate for high-tech enterprises will create “lowland effect” for high-tech enterprises. And the increase in incentives of extra deduction for R & D expenses will increase the R & D diffusion of non-high-tech enterprises, while also suppressing the peak of the proportion in non-high-tech enterprises with camouflaging R & D.

Examining and prioritizing the effect of sustainable energy on the job market to advance China's green workforce

China, the world's greatest emitter of greenhouse gases, has committed to reaching peak carbon dioxide emissions by 2030 and carbon neutrality by 2060. One strategy to accomplish this aim is switching to a low-carbon economy via advancing renewable energy (RE) projects. Therefore, this study focuses on solar, wind, biomass, and hydropower and examines the beneficial employment effects of RE projects in China. The study uses fuzzy-based Multi-Criteria Decision Making (MCDM) methodologies, such as the Analytical Hierarchy Process (AHP) and Weighted Aggregated Sum Product Assessment (WASPAS), to evaluate numerous employment effect criteria and sub-criteria. Based on these evaluations, the report prioritizes four main types of RE projects. According to the fuzzy AHP technique results, direct employment, skill requirements, and local employment are the most crucial employment effect factors. The study then employed the fuzzy WASPAS approach to assessing various RE initiatives' employment prospects. According to the findings, hydropower is the best choice for creating jobs, followed by wind and solar power initiatives. In recent years, the hydro, wind, and solar power industries have experienced rapid expansion, creating numerous job possibilities in production, installation, operation, and maintenance. Additionally, creating RE projects can boost regional economic growth and lessen poverty.

Numerical and experimental analysis of blade-tower clearance for large-scale wind turbines

With the increase of the rotor diameter and the flexibility of the blade, the efficient blade-tower clearance control becomes increasingly important. As a widely used strategy in the wind power industry to increase the blade-tower clearance, the fine pitch strategy still has the problem of power de-rating under typical operating conditions. Therefore, a novel coupled blade-tower clearance control strategy is proposed, combining the fine pitch strategy with a revised LiDAR monitoring system. Incorporating the LiDAR's characteristics and the operation modes of wind turbines, the LiDAR is designed to be installed at the front of the nacelle to monitor the real-time blade-tower clearance and feed back to adjust the pitch angle, allowing for safe operation of the wind turbine and maximizing output power. The reliability of the proposed strategy and the influence of weather on the strategy are verified through 22-day all-weather field tests on numerous wind turbines in a wind farm in China, nearly 100% of the efficiency can be achieved if conditions permit. Additionally, the strategy’s two trigger modes—the clearance threshold and data validity protection mechanism—are exemplified, demonstrating the efficiency of the proposed coupled blade-tower clearance control strategy.

A comprehensive wind speed prediction system based on intelligent optimized deep neural network and error analysis

At present, wind power is one of the most promising clean energy sources. Due to its high variability, accurate wind speed forecasting is an essential part of the wind power industry. This paper proposes a comprehensive forecasting system consisting of three parts: Firstly, the novel SC-EWT wind speed signal reconstruction system proposed in this article can effectively extract information from wind speed signals. Secondly, the QBGA-GRU model is used for signal reconstruction prediction, where QBGA is an improved genetic algorithm proposed in this article that can improve prediction accuracy. Finally, the IPSO-BLS algorithm is used to quickly analyze and predict prediction errors and further correct the predicted results. The system can provide users with adaptive signal decomposition, high-precision deep learning prediction and error analysis functions to achieve efficient and fast short-term wind speed forecasting, which can be used in the fields of wind power integration deployment and wind turbine early warning. The experiments show that the proposed prediction strategy has good predictive ability for wind speed samples in two regions, and its error is smaller than that of commonly used intelligent algorithms. Compared with other models, the proposed comprehensive prediction strategy has better predictive performance and smaller prediction errors than other combination models.

Impact analysis of mergers and acquisitions on the performance of China's new energy industries

The development of new energy industries is crucial to a nation's energy transition to carbon neutrality, while photovoltaic, wind power, lithium battery, and new energy vehicles industries have been active in the capital markets. We choose mergers and acquisitions (M&A) activities in such industries in China to investigate how they affect firms' short-run market performance and long-run innovation. Using data gathered from 2012 to 2020, we evaluate the degree of stock price volatility with the Carhart four-factor model combined with event study analysis. Changes in innovation after M&A activity has occurred are evaluated using the PSM-DID model. Empirical analysis shows that M&A could have a positive, short-run impact on stock prices of new energy industries, while the impact of technology M&A on sub-industries is not consistent. The cumulative average abnormal returns (CAAR) of the wind power and photovoltaic industries turn from negative to positive, and the correlative new energy vehicles and lithium battery industries reach the highest returns in a window period with similar trend lines. Furthermore, technology M&A positively affects the innovation performance of new energy firms, and this effect lasts three to four years. In contrast to prior industry-wide and conclusion-inconsistent M&A studies, we concentrate on new energy industries and prove the definite positive effect of technology M&A for the firms.

Ice-phobic properties of MoS2-loaded rice straw biogas residue biochar-based photothermal and anti-corrosion coating with low oxygen to carbon ratio

Icing of wind turbine blades will seriously hinder the development of the wind power industry, and the use of biomass resources to solve the icing problem is conducive to promoting the synergistic development of biomass and wind energy. In this study, ice-phobic coatings with photothermal and anti-corrosion properties were prepared by surface modification pyrolysis and hydrothermal reaction with rice straw biogas residue as raw material. The erosion of KOH and the surface modification of MoS2 produced a rough structure of the material, and the high-temperature pyrolysis and hydrothermal reaction promoted the dehydrogenation and decarboxylation reactions, which reduced the number of oxygen-containing functional groups and decreased the surface energy of the material. The ice-phobic coating has superhydrophobic properties with a contact angle of 158.32°. Due to the small surface area in contact with water, the coating was able to significantly reduce the icing adhesion strength to 53.23 kPa. The icing wind tunnel test results showed that the icing area and mass were reduced by 10.54% and 30.08%, respectively, when the wind speed was 10 m s−1 and the temperature was − 10 °C. Photothermal performance tests showed that the MoS2-loaded material had light absorption properties, and the coating could rapidly warm up to 58.3 ℃ under xenon lamp irradiation with photothermal cycle stability. The loading of MoS2 acts as a physical barrier, reducing the contact of corrosive media with the substrate, thus improving the anti-corrosion of the coating. This study has practical application value and significance for the development of the anti-icing field under complex environmental conditions.Graphical

Mechanical properties prediction of ferrite ductile iron via machine learning

The rapid development of the wind power industry requires ductile iron to maintain high toughness while improving strength. Machine learning offers the possibility to accelerate various aspects of material development and performance optimization. In this paper, we established a composition-property dataset for ferritic ductile iron, and a variety of machine learning algorithms are compared to construct a composition-property model for ferritic ductile iron finally. The composition is chosen as the model input, and the outputs are four properties: tensile strength, Brinell hardness, yield strength and elongation. The correlation coefficients of the models constructed are above 0.96, and the mean absolute percentage errors are below 5%. The mean relative error between the model predictions and the experimental values is 4.43%, which effectively verifies the reliability of the composition-properties model of ductile iron constructed. This paper also uses the machine learning model to predict the effect law of each element content on the mechanical properties of ductile iron, and uses thermodynamic prediction of phase composition to verify the reliability of the machine learning method to predict the mechanical properties of ductile iron, which provides strong guidance for the design of new ductile iron with high strength and high plasticity at room temperature.

Analysis of the Threshold Effect of Renewable Energy Industry Subsidies Based on the Perspective of Industry Life Cycle

Based on the industry life cycle theory, this study investigates the influence mechanism of renewable energy industry subsidies on industry development. Using data from 78 public Chinese electric power enterprises in the A-share market from 2011 through 2020, the threshold effect of government subsidies on the development of the renewable energy industry is empirically tested, and the heterogeneity of subsidy efficiency in the renewable energy industry is explored. The results reveal an inverted U-shaped nonlinear relationship between government subsidies and the enterprise output of the sample electric power enterprises. In addition, market investment has a positive and limited effect on enterprise output, and there is heterogeneity in subsidy efficiency and the signalling effect of subsidies on investment in the wind and photovoltaic power industries. Accordingly, we recommend that government departments use rational and flexible subsidy policy tools according to different industries, set reasonable subsidy ranges, and proactively design top-level policies that will actively broaden the application of renewable energy consumption to help industry development. By examining the overall threshold effect of electric power enterprises, this study fills a gap in the literature, as there has been scant research on the specific relationship between renewable energy industry subsidies and industry development.

Understanding Taiwan’s offshore wind industry

Understanding taiwan’s offshore wind industry Here, Open Access Government speaks to Gwo-shyh Song, Chairman of MMA Global Aqua, and explores the challenges, successes and long term plans for the offshore wind power projects in Taiwan. Can you provide an overview of the current state of the offshore wind industry in Taiwan and its significance in the country’s energy transformation? Taiwan had no experience in offshore wind power development in the past. The offshore wind power industry includes wind farm developers, who conduct environmental assessments of wind farms, as well as wind turbine system suppliers, marine engineering companies, cable manufacturers, and wind farm operators. Therefore, the Ministry of Economic Affairs allows 100% foreign ownership to attract foreign investment and gradually increases domestic demand to cultivate local related companies in order to achieve the goal of offshore wind power localization.

Design and Development of Wind Power Refined Simulation Software

A challenge to the steady operation of the system as well as the design, operation, and maintenance of wind power is posed by the wind power industry's rapid development and the rate at which wind power is incorporated into the power system. This work creates high-quality simulation software for wind power to address the complicated modeling requirements brought on by significant wind power penetration. The software implements the functions of fine modeling, offline simulation, real-time simulation, and data analysis and uses Python, Shell, and C++ language programming as its primary programming languages. Additionally, to confirm the software's functionality, this study employs OpenFAST single-unit and three-unit wind field samples. The reality demonstrates how easily real-time simulation software can be implemented and how well it does so. Software also has a cheap time and economic cost, and its application value is considerable.

Load-bearing characteristics of concrete-filled steel tubular combined piles (CFSTCPs) in sand-rock composite ground

Novel concrete-filled steel tubular combined piles (CFSTCPs) have been widely used in offshore wind power projects in Fujian, China. The bearing capacity of their foundations has become a particular concern in the offshore wind power industry. However, experiments to understand the bearing performance of CFSTCPs in sand-rock composite ground remain few. In this study, a series of model experiments were conducted to investigate the load-bearing characteristics of CFSTCPs in sand-rock composite ground, and evaluate the effects of the rock-socketed length and uniaxial compressive strength (UCS) of the bedrock. Moreover, the accuracy and regularity of the test results were verified through numerical testing. The results showed that the load-settlement curve of the CFSTCP exhibited the failure pattern of “continuous and gradual, without obvious inflection point.” When the ultimate load was reached, interface sliding occurred between the steel pipe and sand. The pile tip penetrated the bedrock, resulting in penetration failure. The dimensionless relationship between the bearing ratio of the pile-tip resistance, rock-socketed length, and bedrock UCS was obtained to determine the critical rock-socketed length. Finally, an empirical method for calculating the ultimate bearing capacity of CFSTCPs was proposed. These findings promote the design of CFSTCPs for offshore wind turbine foundations.

Synergistic effects in the co-pyrolysis of thermoset epoxy resin and biomass on kinetics and product distribution

With the rapid growth of wind power, batteries, and aerospace industries, sustainable disposal of thermoset plastics has become a long-term concern. However, pyrolysis, a highly promising solution for this issue, has seen limited studied on thermoset plastics. Drawing inspiration from the synergistic interactions observed between thermoplastics and biomass, this study explored the co-pyrolysis of a thermoset epoxy resin, diglycidyl ether of bisphenol A, cured with 4,4′-Diaminodiphenylmethane, with ash-free biomass pseudo-components (cellulose, lignin, and xylan) using thermogravimetry and Py-GC/MS. The results revealed distinctive synergistic effects in Epoxy-Cellulose and Epoxy-Lignin mixtures, delaying the onset and hastening the conclusion of weight loss. This was accompanied by increased activation energies, assessed through the Friedman and Kissinger-Akahira-Sunose methods, as well as higher char yields. Conversely, in Epoxy-Xylan, synergistic effects led to reduced activation energy across most α values, resulting in a lower onset temperature and a heightened maximum rate of mixture degradation. Regarding product distribution, all three pseudo-components, particularly lignin, enhanced the selectivity of bisphenol A. Additionally, cellulose and xylan altered the kinetics of N–C and C(N)–C(OH) bond cleavage, reducing the amount and altering the distribution of 2-B-N compounds (compounds featuring two benzene rings and nitrogen). Conversely, the presence of resin reduced the volatiles from cellulose and xylan, promoting phenol-type products and –H and alkylene side chains while diminishing guaiacol-type products and alkyl side chains. Three mechanisms were proposed to elucidate these observed synergistic effects: catalytic effects of –OH groups from biomass pseudo-components, hydrogen exchange or hydrogen bonding between resin-derived nitrogen and biomass-derived oxygen-containing functional groups, and radical reactions. These insights into the co-pyrolysis of biomass and cured epoxy resin can inform the selection of suitable feedstocks for various applications.

Interference effects on the upstream wave generated by the catamaran moving across a depth change

With the development of the offshore wind power industry, there is an urgent need to improve the energy efficiency of the operation and maintenance(O&M) catamaran. When the catamaran accesses the offshore wind farm, the vessel could sail on an uneven bottom. Based on our previous study, the effect of the step bottom on the wave-making resistance and upstream wave had been quantified. However, wave interference plays a major role in the hydrodynamic characteristics of the catamaran hull, thus we extended our programme to calculate the hydrodynamic performance of the demi-hulls at the variable water depth. An upstream tsunami could be initiated by the hydrodynamic interaction between the catamaran and the uneven bottom shown from the simulation results, there is an abrupt change in the unsteady hydrodynamic forces as well. The oscillating amplitude and period of the unsteady forces acting on the catamaran are highly dependent on the separation distance between the demi-hulls and speed. The maximum amplitude of the upstream wave and hydrodynamic forces forecast are implemented after the main dimension parameters of the catamaran are determined in the preliminary design. The present simulation programme can supply the reference for the design evaluation of the O&M catamaran in the future.

Missing interpolation model for wind power data based on the improved CEEMDAN method and generative adversarial interpolation network

Randomness and fluctuations in wind power output may cause changes in important parameters (e.g., grid frequency and voltage), which in turn affect the stable operation of a power system. However, owing to external factors (such as weather), there are often various anomalies in wind power data, such as missing numerical values and unreasonable data. This significantly affects the accuracy of wind power generation predictions and operational decisions. Therefore, developing and applying reliable wind power interpolation methods is important for promoting the sustainable development of the wind power industry. In this study, the causes of abnormal data in wind power generation were first analyzed from a practical perspective. Second, an improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) method with a generative adversarial interpolation network (GAIN) network was proposed to preprocess wind power generation and interpolate missing wind power generation sub-components. Finally, a complete wind power generation time series was reconstructed. Compared to traditional methods, the proposed ICEEMDAN-GAIN combination interpolation model has a higher interpolation accuracy and can effectively reduce the error impact caused by wind power generation sequence fluctuations

Simulation and calculation of maximum transmission power for offshore wind plants accounting for the Electro-Magnetic transient process

The offshore wind power industry is witnessing a shift towards larger scale and longer offshore distances, pushing the transmission capacity and transmission distance of submarine cables to their maximum limits. Consequently, it is essential to evaluate the maximum transmission limit of offshore wind power. At the same time, the larger scale of the wind farm will make the Electro-Magnetic transient (EMT) processes have a greater impact on the system stability. To address these issues, this paper firstly established a wind generator (WG) model through a permanent magnet synchronous generator (PMSG) model with averaging parameters, as well as a cross-linked polyethylene (XLPE) submarine cable model to construct an offshore wind power system via AC transmission. Subsequently, a continuous EMT simulation method is proposed to calculate the maximum transmission capacity and transmission distance limit. Real-time transient simulations were conducted to check the transient stability constraints during the calculation process. Finally, through several case studies on different types of cables, the simulation and calculation results are presented to provide useful references for engineering applications.

Decarbonization of the wind power sector in China: Evolving trend and driving factors

There is a worldwide consensus that renewable energy sources, such as wind power, will play an essential role in the global low-carbon energy transition. However, most quantifications of the CO2 emissions of wind power systems ignored the indirect footprints induced by supply chains. Thus, this study developed input–output (IO)-based hybrid life-cycle assessment (LCA) models based on China's 2007, 2012, and 2017 economic benchmark data to quantify the economy-wide greenhouse gas (GHG) footprint of China's wind power sector. Moreover, the key sectors driving the footprint variation were identified, and the footprint structure was analyzed via the production layer decomposition method. The study results confirm the decarbonization of the wind power sector in China, with a decrease in the GHG footprint from 52.27 g CO2e/kWh in 2007 to 21.23 g CO2e/kWh in 2017. This 59.38% GHG footprint reduction was mainly driven by the electricity and heat sector. Most of wind power's GHG footprint (more than 84%) was derived from upstream industries, highlighting the necessity of supply chain analysis in the governance of the wind power industry.

A Comprehensive Review on Machine Learning Techniques for Forecasting Wind Flow Pattern

The wind is a crucial factor in various domains such as weather forecasting, the wind power industry, agriculture, structural health monitoring, and so on. The variability and unpredictable nature of the wind is a challenge faced by most wind-energy-based sectors. Several atmospheric and geographical factors influence wind characteristics. Many wind forecasting methods and tools have been introduced since early times. Wind forecasting can be carried out short-, medium-, and long-term. The uncertainty factors of the wind challenge the accuracy of techniques. This article brings the general background of physical, statistical, and intelligent approaches and their methods used to predict wind characteristics and their challenges—this work’s objective is to improve effective data-driven models for forecasting wind-power production. The investigation and listing of the effectiveness of improved machine learning models to estimate univariate wind-energy time-based data is crucially the prominent focus of this work. The performance of various ML predicting models was examined using ensemble learning (ES) models, such as boosted trees and bagged trees, Support Vector Regression (SVR) with distinctive kernels etc. Numerous neural networks have recently been constructed for forecasting wind speed and power due to artificial intelligence (AI) advancement. Based on the model summary, further directions for research and application developments can be planned.

Risk assessment and management in the offshore wind power industry: A focus on component handling operations in ports

The global shift towards energy transition has spurred significant interest in offshore wind power generation. This has resulted in a heightened demand for handling offshore wind power components in ports. However, these components pose challenges due to their large-scale, non-standard, and vulnerable characteristics, necessitating meticulous pre-planning and complex interface synchronization operations. Therefore, effective safety management and comprehensive risk management plans are crucial to prevent accidents.Given the limited literature on the risks associated with offshore wind power component handling in ports, this study provides a risk analysis framework and valuable insights for risk assessment and management in the industry. The study is based on the HOLES model and employs a mixed-methods design that includes in-depth interviews, on-site observations, and the Decision-Making Trial and Evaluation Laboratory (DEMATEL) technique, enabling a comprehensive understanding of the phenomenon. The proposed risk analysis framework serves as a checklist to enhance safety in the handling operations. The critical risk factors identified in the study are primarily attributed to the dimensions of “organization”, “liveware”, and “operating procedures”, with the impact of COVID-19 on the operational environment considered as a short-term factor. These critical risk factors provide guidance to operators and stakeholders in resource allocation, risk mitigation, and the development of effective management strategies. As the empirical data was collected from Taiwan, this contribution is particularly valuable for emerging offshore wind markets facing similar challenges. The study addresses a knowledge gap and establishes a foundation for future research and advancements in the field.

Development of a 2 μm Solid-State Laser for Lidar in the Past Decade.

The 2 μm wavelength belongs to the eye-safe band and has a wide range of applications in the fields of lidar, biomedicine, and materials processing. With the rapid development of military, wind power, sensing, and other industries, new requirements for 2 μm solid-state laser light sources have emerged, especially in the field of lidar. This paper focuses on the research progress of 2 μm solid-state lasers for lidar over the past decade. The technology and performance of 2 μm pulsed single longitudinal mode solid-state lasers, 2 μm seed solid-state lasers, and 2 μm high power solid-state lasers are, respectively, summarized and analyzed. This paper also introduces the properties of gain media commonly used in the 2 μm band, the construction method of new bonded crystals, and the fabrication method of saturable absorbers. Finally, the future prospects of 2 μm solid-state lasers for lidar are presented.