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Large-scale constructions of offshore wind power are carried out in southeast coast of China, where the seabed is covered in weathered granite. However, the bearing capacity and failure mechanism of monopile foundation in weathered-granite seabed are rarely researched. In this paper, the stress-strain characteristics and strength parameters of granite with different weathering degrees were obtained through laboratory tests, and the pre-strain test method was used to study mechanical parameter variation of moderately weathered granite in the crushing process. Experiment found that the strength parameters decrease mainly after the strain reaching 50% of the ultimate strain of moderately weathered granite. In addition, the bearing capacity and failure mode of rock-socketed monopile under unidirectional horizontal loading was investigated numerically. Analysis showed that the failure mode of rock-socketed monopile under unidirectional horizontal loading is progressive and presents two obvious failure zones. More critical, the existence of vertical loading effectively improves the horizontal bearing capacity of monopile foundation in the weathered rock seabed, and the failure envelope of the monopile foundation in H-V loading space is elliptic-shaped. This provides further understanding of the mechanical properties of submarine weathered granite, and the helpful reference for stability evaluation of rock-socketed monopile foundation in weathered granite seabed. [ABSTRACT FROM AUTHOR]

Wind power generation does not emit greenhouse gases or pollutants, but there are some carbon emissions from the manufacturing, transportation, operation, and waste disposal of wind turbines. Directly driven permanent magnet and doubly fed asynchronous wind turbines currently have the largest market share in China, but few Chinese studies have compared their differences in carbon reduction potential. This paper uses life cycle assessment (LCA) to quantitatively analyze the full life cycle carbon emissions of the two wind turbines to determine which type of wind turbine has greater carbon reduction potential, obtaining the following results. (1) The full life cycle greenhouse gas emissions of 2.5 MW directly driven permanent magnet and doubly fed asynchronous wind turbines are 8.48 and 10.43 g CO2/kWh, respectively. The direct‐driven permanent magnet wind turbine is superior in terms of carbon reduction. (2) The stage with the greatest impact and the greatest difference between the two wind turbines in the full life cycle is the production stage, during which the carbon emissions of the directly driven permanent magnet and doubly fed asynchronous wind turbines are 1.045 × 106 and 1.210 × 106 kg, respectively. (3) According to sensitivity analysis, proper waste disposal and transportation can reduce carbon emissions from wind turbines. These research findings can be used to help achieve carbon peaking and neutrality goals, as well as the technological development of wind power enterprises. [ABSTRACT FROM AUTHOR]

This paper discusses how the incorporation of high-resolution ground coverage dataset ESA WorldCover into a wind flow field and wake simulation calculation, as well as the use of the coupled wake model for wind farm output simulation, can improve the accuracy of wind resource assessment using engineering examples. In the actual case of grid-connected wind farms in central China, SCADA wind speed data is reconstructed to the free flow wind speed in front of the wind turbine impeller using the transfer function of the nacelle, and the wind farm is modeled using OpenWind software, simulating the wind speed at the height of each wind turbine hub and each wind turbine output. The results show that when other initial data are consistent, using ESA's high-precision land cover dataset WorldCover 10 m to make roughness lengths which improves the wind farm output simulation accuracy by 8.91%, showing that it is worth trying to apply WorldCover 10 m to the wind farm simulation design. At the same time, this case is used to compare and analyze the application of the Eddy-Viscosity wake model and the two coupled wake models based on the Eddy-Viscosity wake model. The results show that the coupled wake model will have higher accuracy than the Deep Array Eddy Viscosity wake model and it is 1.24% more accurate than the Eddy Viscosity wake model, and the ASM Eddy Viscosity wake model is 5.21% more accurate than the Eddy Viscosity wake model. [ABSTRACT FROM AUTHOR]

Wind power curve describes the relationship between wind speed and output power of wind turbine. In this paper, we propose a novel image-based abnormal data cleaning algorithm aiming to provide high quality data for wind power curve modeling and relevant applications. For image binarization, a multiscale space is built to determine the optimal scale at which the most stable as well as informative shape representations are extracted. The active shape model is utilized to model the power curve shape and generate a set of segmentation candidates for filtering abnormal data. An energy space is constructed based on class uncertainty map generated from dual feature images and eventually the optimal segmentation boundary is determined by an optimization process. Comparative experiments are conducted over the real-world datasets consisted of 37 wind turbines from two wind farms located in West and East China respectively. The accuracy reaches as high as 0.95, the precision rate P is 0.98, recall rate R is 0.99 and F1-score is 0.96, which demonstrates the superior performance of the proposed method. Besides, the reproducibility and sensitivity analyses are also carried out to assess the algorithm's performance. • A novel image-based abnormal WPC data cleaning algorithm is presented in this paper. • A multi-scale image space is built to determine the optimal scale for WPC data binarization. • Dual feature images are constructed to generate the class uncertainty map. • The Active Shape Model is used to model WPC shapes and filter abnormal data. • Accuracy, reproducibility and sensitive analysis are conducted on real-world datasets. [ABSTRACT FROM AUTHOR]

Public subsidies for offshore wind power will be eliminated in 2022. At present, offshore wind power still depends on subsidies. In this new situation, it is necessary to study the influence on the stakeholders and how the offshore wind power industry achieves healthy and sustainable development. Firstly, this paper qualitatively analyzes the impact of the cancellation of state subsidies on offshore wind turbine manufacturers and developers and finds that if there is no subsidy to relay offshore wind power, the development of offshore wind power will stagnate, and the wind turbine manufacturers will face great challenges. Then, by constructing the subsidy demand calculation model and the transmission and distribution price cross subsidy calculation model according to China's electricity price-related policies, the results show that the local subsidy is basically feasible to continue the national subsidy, and the burden is decreasing year by year, so the relay subsidy should be continued. Based on the cloud model, an evaluation model for the development of offshore wind power benefits from the cancellation of national subsidies is constructed to quantify the impact of the cancellation of overseas subsidies on local governments and power grid enterprises. According to the example analysis, under the background of the cancellation of national subsidies, Guangdong has the best development of offshore wind power benefits, which verifies the feasibility of the proposed model. [ABSTRACT FROM AUTHOR]

Estimating the total wind power output from the meteorological information at a province level (called Provincial Regional Wind Power Conversion Model, PRWPCM) plays vital and fundamental roles in energy modeling community and regional wind power forecasting. How to construct a reliable PRWPCM is a real challenge, since PRWPCM involves a large number of widely distributed wind turbines, massive meteorological data across the whole province, and complex nonlinear correlations. This paper proposes a lightweight PRWPCM by integrating Geographic Information System (GIS) analysis technology and Convolutional Neural Network (CNN). First, we conduct the land suitability analysis for wind turbine sites through the multi-criteria GIS layer overlay method to make the provincial wind turbine land suitability map (WTLSM) with scored divisions from the least suitable to the most suitable areas. On this basis, a new fusion mechanism for geographic and meteorological information is proposed, through which the raw meteorological data matrix can be reconstructed to filter and amplify the meteorological information that is more relevant to the total wind power output of the province, and avoid the time-consuming and labor-intensive data collection and processing, large-size model construction and validation. Second, a CNN-based regression architecture is designed to further capture the mapping relationship between the reconstructed meteorological data and total wind power output of the province; each type of meteorological factor is considered as an input channel and the attention modules are introduced to adaptively enhance useful channels and suppress less useful ones. Numerical experiments based on the wind power operation data of Yunnan Province, China, are conducted to validate the superiority of the proposed PRWPCM via benchmarking against 13 classical methods. • Mapping relationship between wind power output and meteorological information at a province level is recognized by integrating GIS and CNN. • A new fusion mechanism for geographic and meteorological information is proposed to achieve lightweight modeling. • Comparative experimental studies are conducted in Yunnan province, China. [ABSTRACT FROM AUTHOR]

The rotational inertia and primary frequency response capabilities of power systems are declining as synchronous generators (SGs) are replaced by inverter-based variable renewable energy (VRE) resources. Therefore, when a high VRE proportion is present, it becomes essential to include frequency security constraints in power system planning. Under this circumstance, the decline in SGs would deteriorate the system frequency response performance; thus, inverter-based resources are supposed to provide frequency response support. This paper presents a frequency-constrained stochastic planning method for high VRE share systems. Frequency response support from wind farms is considered in the planning process for the first time, and the unique characteristics of wind power support are addressed. Specifically, the uncertainty of and variations in wind power support capabilities are modeled from the field-measured data, and the optimal combination of various support schemes can be determined by introducing binary variables with novel linear constraints into the planning process. Moreover, the nonlinear frequency constraints are transformed and embedded in the planning model by the proposed computationally efficient adaptive piecewise linearization method. The planning is formulated as a stochastic optimization model to consider the VRE power uncertainty. Finally, case studies on the IEEE 30-bus system validate the proposed method, with results indicating time savings of 22%. Further, compared with the case that only SGs provide frequency support, the results based on the East China system demonstrate that considering wind power support can improve the VRE share from 49.5% to 79.7%. [ABSTRACT FROM AUTHOR]

Offshore wind power is an important renewable energy source and plays an essential role in optimizing the energy structure worldwide. Simultaneously, offshore wind turbine (OWT) selection is a complicated process since it concerning various variables and optimization scenarios. In this paper, a novel fuzzy Bayesian network-based model for multiple-attribute decision-making (MADM) is proposed. First of all, a three-layer decision-making framework for OWT selection is established through systematically combing previous studies, expert knowledge, and the principal component analysis (PCA) results by treating the wind turbine parameters, wind turbine economy, wind turbine reliability, and navigation safety as the attributes, and the corresponding 11 influencing factors are identified and quantified. Moreover, a triangular fuzzy number is introduced to fuzzify each influencing factor, and the belief degree for different linguistic variables corresponding to the specific influencing factor is employed in the fuzzy IF-THEN rule system. Then, the belief rule base is transformed into the Bayesian network as the conditional probability tables (CPTs), which can directly express the influence relationship of various factors and realize the integration of various influence factors to obtain the optimal scheme. Finally, the proposed model is validated by taking a case study in busy waterways in the Eastern China Sea as an example. This research provides an intuitive, feasible, and practical way for OWT selection. • A novel fuzzy Bayesian network-based MADM model for OTW selection in busy waterways is proposed. • A three-layer decision-making framework is established for the OWT selection. • Comprehensively consider each influencing factor's qualitative and quantitative characteristics. • Model can not only conduct the uncertainty caused by ambiguity but also intuitively achieve optimal reasoning and evaluation. [ABSTRACT FROM AUTHOR]

For offshore wind development in Southern China, typhoon represents the extreme environmental loading condition to be considered in the design of the turbine structures. For the geotechnical design of the foundations, it is specified by the design code that the effect of cyclic loading on the soil strength and stiffness must be considered. This necessitates the complete time history of loads on the foundation experienced during a design typhoon. Idealised storm profiles with staged mean wind speeds and significant wave heights are commonly used as input to time-domain simulations to generate the foundation load history. However, no such design typhoon profile is provided in the Chinese design standard which creates considerable difficulty and uncertainty in the design process. In this paper, the methodology and the rationale that were applied to derive the design typhoon profiles for an offshore wind project in Guangdong province are presented. The wind and wave characteristics generated by typhoon are described in detail and the implications for the foundation design are discussed. To demonstrate application of the proposed typhoon profile, a case study for the ULS design of a suction bucket jacket is presented. • Design typhoon profiles are developed for an offshore wind farm in Southern China. • The typhoon wind and wave characteristics are described and implications for geotechnical foundation design are discussed. • The methodology and the rationale for developing the design typhoon profiles are presented. [ABSTRACT FROM AUTHOR]

As a promising renewable solution for sustainable power generation worldwide, wind energy is receiving continuing attention from both industry and the academic community. However, the randomness and intermittency of wind energy will affect the stable operation and stability of the power system and further affect the economic benefits of the power grid. What makes the matter worse is the inevitable coupling between each pair of wind turbines in the large-scale wind farm. Besides, the resolution of prediction is severely limited by the spatial scale of wind farms. These problems bring great difficulties for the control and scheduling of wind farms. To this end, this paper proposes a novel wind speed prediction method for wind farms by borrowing some wisdom from machine learning methods. First, density peak clustering (DPC) is employed to separate the tremendous number of scattered wind turbines into a much significantly reduced number of groups, the wind turbines in each of which are treated as a unity. Based on the priority setting of each indicator in clustering, the data are preprocessed with different weightings. Principal component analysis is utilized to avoid DPC's poor clustering effects in case the dataset is high-dimensional. Finally, by considering simultaneous effects from historical and present data, long short-term memory based deep learning neural networks are trained and used to iteratively predict the potential of the wind energy in each unit for each time slot. The effectiveness of the proposed algorithm is verified by taking an in-service wind farm in China as an example. [ABSTRACT FROM AUTHOR]

As a renewable energy source, wind power has received growing attentions, but mostly been utilized in wind power farms or high-rise buildings where the wind with high speed is available. Few studies focus on the micro-wind utilization in low-rise buildings due to the bottleneck that the wind speed cannot meet the minimal speed requirement of small wind turbines. This paper aims to identify the optimal building design which could enable the harvesting of the maximal micro-wind power around low-rise residential buildings. Based on the comparison among different building shapes and the computational fluid dynamics (CFD) analysis via software Phoenics , this paper identified that the building shape of “composite prism” could enable the harvesting of the most micro-wind power. The identified building shape was then tested in a simulated environment of a residential community in Pingtan Island, China. The local wind conditions, long-term community planning, and the requirement of comfort level were all considered in the simulation model. The result shows that the potential of utilizing wind energy in low-rise residential buildings is huge by adopting the proposed building shape of “composite prism”. The finding has significant implications for renewable energy utilization in built environment. [ABSTRACT FROM AUTHOR]

In response to the unbalanced sample categories and complex sample distribution of the operating data of the pitch system of the wind turbine generator system, this paper proposes a method for fault detection of the pitch system of the wind turbine generator system based on the multiclass optimal margin distribution machine. In this method, the power output of the wind turbine generator system is used as the main status parameter, and the operating data history of the wind turbine generator system in the wind power supervisory control and data acquisition (SCADA) system is subject to correlation analysis with the Pearson correlation coefficient, to eliminate the features that have low correlation with the power output status parameter. Secondary analysis is performed to the remaining features, thus reducing the number and complexity of samples. Datasets are divided into the training set for training of the multiclass optimal margin distribution machine fault detection model and test set for testing. Experimental verification was carried out with the operating data of one wind farm in China. Experimental results show that, compared with other support vector machines, the proposed method has higher fault detection accuracy and precision and lower false-negative rate and false-positive rate. [ABSTRACT FROM AUTHOR]

With development of grid-connected wind power, data-driven wind turbine power curve (WTPC) becomes vital for many valuable applications whereas it is hugely affected by the potential outliers in supervisory control and data acquisition (SCADA) system. In this paper, high-fidelity WTPC modeling and its application on condition monitoring are deeply studied. Firstly, through irregular space-division and nonlinear space-mapping, stepwise data cleaning procedure is proposed. On this basis, the cleaned data are used for high-fidelity modeling where optimized least square support vector machine (LSSVM) is chosen for deterministic WTPC modeling and conditional kernel density estimation (CKDE) is selected for uncertainty modeling after comparison where the models are updated via the sliding time window mechanism. Using the consistency of historical data-driven models in a certain future time window, a daily condition monitoring system, including daily multi-index system and improved fuzzy comprehensive evaluation method (FCEM), is established to monitor future wind turbine condition. Finally, measured data from a wind farm in north China are acquired for validation. The results show that the outliers are effectively cleaned for high-fidelity WTPC modeling. The daily condition monitoring system including daily raw data preprocessing, multi-index system and FCEM algorithm can provide efficient daily rating score. • A versatile way for outlier identification in two-dimensional space is proposed. • Periodically updated deterministic and uncertainty WTPC modeling are presented. • A daily multi-index system is provided to capture different features of WTPC. • Daily comprehensive rating system of wind turbine power generation is realized. [ABSTRACT FROM AUTHOR]

The environment-friendly development of wind power is complicated as the atmospheric impacts of wind farms are difficult to measure. Wind power has made contributions to emission reduction, but it is undeniable that the substantial wind farms (SWFs) have had a non-negligible impact on the local ABL. This paper aims to provide a reliable methodological scheme and practical data for human beings to objectively understand the development of wind power. Based on the data from one of the Chinese gigawatt-scale wind power bases in Zhangbei County of Hebei Province, the mesoscale simulations were conducted to explore the impacts of substantial wind farms on the local and regional atmosphere. The results show that: (i) the impact of substantial wind farms on the local area is significant and sustained, while this impact on the regional area is slight and occasional; (ii) it can bring about the seasonal and diurnal differences in the local ABL property; (iii) it can not only induce the horizontal turbulence directly caused by wind turbine wake flow, but also produce the vertical mixing in the near-surface ABL. A systematic analysis of wind power potential and its atmospheric impact is urgent to ensure sustainable development of wind power. • Wind power brings significant and sustained local atmospheric impacts. • The WRF model coupled with WTDP is capable to explore such impacts. • Filling the gaps in the practical assessment of gigawatt-scale wind power. • The seasonal and diurnal impacts of SWFs are quantitatively assessed. [ABSTRACT FROM AUTHOR]

Wind power is just in place for a major improvement to the power grid in security and economy. More accurate forecast methods are emerged to benefit grid operators and their customers. A wind speed behavior prediction method based on multi-feature and multi-scale integrated learning (MFMS) is developed in this paper. The behavior characteristics are proposed to solve the problem of the temporal and spatial feature extraction distinctly of wind speed. Multi-scale feature fusion and spatial pyramid pooling algorithms are proposed to reduce the loss of micro-space-scale and short-time-scale information. Meanwhile, environmental features and background features are applied to consider the influence of the overall wind farm environment on the wind speed of local wind turbines. The wind speed data of sixteen wind turbines in a wind farm at Zhangjiakou in North China are used to verify the method. Results show that MAPE, RMSE and R2 of 4-h ultra-short-term wind speed prediction are 6.164%, 0.275 and 0.966, respectively, which are improved by 1.786%–6.757%, 0.051–0.216 and 0.014–0.074 compared with other methods. The method proposed in this paper can be applied to wind speed prediction and provide a guideline for wind farm power prediction. • A multi-step wind speed prediction model of wind turbine is proposed. • Meteorological factors and wind behavior were used as background/foreground feature. • Features are deeply excavated with the shallow feature loss been overcome. • The model efficiency and accuracy is validated by wind data from a wind farm. • The model shows high accuracy in ultra-short-term wind speed prediction. [ABSTRACT FROM AUTHOR]

A novel pumped hydro combined with compressed air energy storage (PHCA) system is proposed in this paper to resolve the problems of bulk energy storage in the wind power generation industry over an area in China, which is characterised by drought and water shortages. Thermodynamic analysis of the energy storage system, which focuses on the pre-set pressure, storage volume capacity, water air volume ratio, pump performance, and water turbine performance of the storage system, is also presented. This paper discovers how such parameters affect the performance of the whole system. The ideal performance of this novel system has the following advantages: a simple, highly effective and low cost structure, which is comparable to the efficiency of a traditional pumped hydro storage system. Research results show a great solution to the current storage constraints encountered in the development of the wind power industry in China, which have been widely recognised as a bottleneck in the wind energy storage industry. [ABSTRACT FROM AUTHOR]

As the increasing hub height of modern wind turbine, wind shear model is generally adopted as a useful tool to extrapolate wind speed available to higher levels for wind power assessment. This paper conducts a data-driven study to examine the power law model in assessing the wind power in forested regions featured with seasonally-varied roughness. Specifically, wind speeds are observed using a laser lidar mounted in forested regions in the northeast of China, ranging from 80 m to 200 m with a constant interval of 20 m. Subsequently, comparisons are made between the wind profiles established based on the power law model and field observations. The exponents used include the empirical exponents and the fitted exponents, the former are derived from the standard codes, and the latter are fitted based on the seasonal and annual data collections. Results indicate that the power law model is unable to fully capture the vertical distribution of wind speed in forested regions, it is the exponent that plays a decisive role influencing the reliability representing the real wind profile. Compared with the empirical exponents, the seasonally-fitted exponents exhibits much better suitability for wind speed extrapolation in forested regions, as well as for the wind power assessment. • Wind profiles are observed using lidar in forested regions. • Power law model is investigated for wind power assessment over forested region. • Seasonal and annual exponents are fitted for power law model in forested regions. [ABSTRACT FROM AUTHOR]

The government of China has committed to bring carbon dioxide emissions to a peak before 2030 and to achieve carbon neutral before 2060 to tackle climate change. Renewable energy plays a key role in the energy strategy of China in order to achieve the targets for reducing greenhouse gas emissions while ensuring energy security. China has abundant offshore wind resource, distributed along its 18,000 km long coastline and 6000 islands. The first offshore wind farm of China was built in 2008 and put into operation in 2010. It provided green energy for 2010 Shanghai World Expo and then launched an era of offshore wind power development in the coastal areas adjacent to highly populated regions. Over the last decades, many thousands wind turbines have been installed, with an accumulated installed capacity of over 13 GW. This paper reviews the development of offshore wind power and foundation technology used for offshore wind turbines in China using published information, data, and web sources. An ongoing offshore wind farm project is taken as an example to describe the foundation technologies involved. • The development of offshore wind power in China is reviewed. • The foundation technology for offshore wind in China is reviewed. • Foundation technologies of an ongoing offshore wind farm project is described. [ABSTRACT FROM AUTHOR]

Abstract: Compared to the conventional fossil fuel energy, wind power provides clean energy which can mitigate the impacts of greenhouse gas emission and optimize the electric power source structure. During the last decade, the strong support of the Chinese government has contributed to the rapid development of the Chinese wind power sector which has in turn resulted in a significant growth of the Chinese wind turbine manufacturing industry. This growth went through several phases including the initial approval of several leading global Wind Turbine Manufacturers (WTMs) to enter the Chinese market through various methods which included the establishment of wholly foreign-owned enterprises from 2005. Similarly, several government policies have contributed to the significant expansion in terms of both productivity and quantities by local Chinese WTMs. The entrance of foreign WTMs into the Chinese market coupled with the rapid growth of local WTMs has contributed to intense competition in China’s wind turbine manufacture market. This paper analyzes the characteristics of the wind turbine manufacturing industry in China and establishes a hierarchical structure of the WTMs’ competitive priority system. This system consists of 5 indicators and 10 sub-indicators. By comparing the different performances of each indicator, the competitive advantages and disadvantages of the foreign and local WTMs in the Chinese market are identified. The findings provide a valuable reference for the WTMs to improve their competitive priorities and to formulate their competitive strategy in the Chinese wind turbine market. This paper provides inputs for the sustainable development of wind power industry in other countries. [Copyright &y& Elsevier]

Wind and solar energy are paid more attention as clean and renewable resources. However, due to the intermittence and fluctuation of renewable energy, the problem of abandoning wind and photovoltaic power is serious in China. Hydrogen production by water electrolysis is the effective way to solve the problem of renewable energy absorption. However, the multi-energy system has several optimization objectives for the capacity configuration, which are generally conflicting. The "impossible triangle" problem in the system is difficult to solve. Furthermore, the system capacity configuration is greatly affected by factors such as operating mode and energy storage form, etc. Therefore, the three different application scenarios are proposed both in the off-grid and grid-connected system, in which the energy storage system consists of only battery, only hydrogen, both hydrogen and battery, respectively. The system operation strategy is based on that the main purpose of hydrogen energy is storage, transportation and utilization alone. The multi-objective capacity configuration optimization based on the improved NSGA-Ⅱalgorithm is proposed, which is verified to be superior to NSGA-Ⅱand MOPSO. The capacity configurations of off-grid and grid-connected multi-energy systems are compared and analyzed. The economy of grid-connected system is better than that of off-grid system. The sensitivity analysis of important parameters is carried out such as wind/solar resources, load level and equipment price. The average wind speed has the significant impact on the net present value of the system. The capacity configuration and operation strategy proposed in this paper are effectively feasible to increase the renewable energy accommodation and meet comprehensive performance requirements of multi-energy complementary system. • Improved NSGAⅡalgorithm for capacity configuration can solve "impossible triangle" problem. • Different energy storage forms are analyzed in off-grid and grid-connected systems. • Hydrogen comprehensive utilization is superior to its electrical power regulation. • Relationship between net present value and sensitive factors is analyzed. [ABSTRACT FROM AUTHOR]

Due to atmospheric pollution from fossil fuels, the reduction of wind turbine costs, and the rise of the low-carbon economy, wind energy conversion systems have become one of the most significant forms of new energy in China. Therefore, to reduce investment risk and maximize profits, it is necessary to assess wind resources before building large wind farms. This paper develops a comprehensive system containing four steps to evaluate the potential of wind resources at two sites in Xilin Gol League and at additional two sites in Alxa League of Inner Mongolia, China: (1) By calculating the total scores of three indexes, including the effective wind power density (EWPD), wind available time (WAT) and population density (PD), an indexes method is applied to assess the theoretical wind energy potential from 2001 to 2010. (2) To judge the fluctuations in the wind speed, the Fisher optimal partition method and the Jonckheere–Terpstra test are used to analyze the changes in the average monthly and yearly wind speeds from 2001 to 2010. (3) Three probability density functions, i.e., Weibull, Gamma and Lognormal, are used to assess the wind speed frequency distribution in 2010. To enhance the evaluation accuracy, three intelligent optimization parameter estimation algorithms, i.e., the particle swarm optimization algorithm (PSO), differential evolution algorithm (DE) and ant colony algorithm (ACO), are used to estimate the parameters of these distributions. (4) It is helpful to analyze the wind characteristics when assessing wind resources and selecting wind turbines. Therefore, the optimal frequency distribution based on the best parameter estimation method can be chosen to calculate the wind power density, the most probable wind speed and the wind speed carrying the maximum energy. The experimental results show that Site 1 and Site 4 are more suitable for large wind farms than Site 2 or Site 3. [ABSTRACT FROM AUTHOR]

In 2009, the implementation of feed-in tariff (FIT) and attractive public subsidies for onshore wind farms aroused great investment enthusiasm and spurred remarkable development of wind power in China. Meanwhile, rapid learning-by-doing has significantly cut down the cost of wind turbines and the capital cost of wind farms as well. Therefore, it is the right time to examine the appropriateness of the existing FIT policy for wind power in China. In this paper, we employ the analytical framework for levelized cost of electricity (LCOE) to model the generation cost of wind power. Results show that the existing FIT policy is attractive to investors, but serious curtailment and turbine quality issues could make wind power unprofitable. Meanwhile, rapid substantial decreases in the cost of wind power have made it competitive to coal power in 2013, implying that it is possible and necessary to reform the FIT policy for new wind farms. In the future, energy policies for onshore wind power in China could be concentrated on reducing the integration cost, so as to reduce the overall system cost. [ABSTRACT FROM AUTHOR]

This paper studies the 'catching-up' process of China's emerging wind turbine manufacturing ecosystem, and analyses the dynamics of the shifting collaborative innovation network for the period 1980-2011. We have focused on the leading wind turbine manufacturers [i.e., manufacturers of entire wind turbine system integrators (EWSIs)], as well as manufacturers of the turbines' sub-ecosystems (i.e., four key component provider manufacturers: blade, gearbox, generator and control system). A network analysis method was adopted to depict the evolutionary process of the wind turbine manufacturing ecosystem. In summary, this research explores: 1) the different technology accumulation/localisation patterns in four different phases of the manufacturing ecosystem, and the collaborative innovation network; 2) the different roles and their interactions in the wind turbine manufacturing ecosystem in China; 3) the driving forces of the evolution of the wind turbine manufacturing ecosystem. The findings aim to demonstrate the evolutionary trajectory of China in terms of the 'catching-up' of this industry, and contribute to theories of the business ecosystem lifecycle and complementary assets. [ABSTRACT FROM AUTHOR]

China is the largest power producer and consumer and has the largest installed capacity of wind turbines (WTs) worldwide. In the last two decades, China's installed capacity of WTs has exploded, significantly impacting its wind power (WP) industry and promoting the development of the industry globally. This paper examines China as one part of the world and elucidates its role, status, and impact on global WP development from a global perspective. Research indicates that China has become the global leader in WP, its WP development has greatly promoted the growth rate of global WP, and it has become an indispensable force in global WP development. However, the comprehensive capability of China's WP generation is not only lower than that of other major WP countries but also lower than the global average. China's WTs have low global recognition and are difficult to export. In addition, the proportion of WP in China's power structure is lower than that of most other major WP countries. Fortunately, these gaps between China and other major WP countries are gradually narrowing. The goal of this paper is to help researchers and investors better understand China's situation and to provide a reference for the rational investment in and development of China's WP. • China has become a global wind power leader, but only in the installed capacity. • China's wind turbines have low recognition in global, and difficult to export. • Comprehensive capability of China's wind power is lower than that of global average. • Currently, China's wind power mainly wins in terms of quantity rather than quality. [ABSTRACT FROM AUTHOR]

• A non-singleton FIS fault detection method is proposed to detect early wind turbine faults. • A non-singleton fuzzy input generation method is proposed. • A mechanism of expanding linguistic terms and rules in FIS is put forward. • Based on the defuzzified result, fault factor is designed to measure fault severities. • Real wind turbine SCADA data are used in the experiments of this paper. Wind power generation efficiency has been negatively affected by wind turbine (WT) faults, which makes fault detection a very important task in WT maintenance. In fault detection studies, fuzzy inference is a commonly-used method. However, it can hardly detect early faults or measure fault severities due to the singleton input and the limited linguistic terms and rules. To solve this problem, this paper proposes a WT fault detection method based on expanded linguistic terms and rules using non-singleton fuzzy logic. Firstly, a generation method of non-singleton fuzzy input is proposed. Using the generated fuzzy inputs, non-singleton fuzzy inference system (FIS) can be applied in WT fault detection. Secondly, a mechanism of expanding linguistic terms and rules is presented, so that the expanded terms and rules can provide more fault information and help to detect early faults. Thirdly, the consequent of FIS is designed by the expanded consequent terms. The defuzzified result, which is defined as the fault factor, can measure fault severities. Finally, four groups of experiments were conducted using the real WT data collected from a wind farm in northern China. Experiment results show that the proposed method is effective in detecting WT faults. [ABSTRACT FROM AUTHOR]

Abstract: Climate change and limited primary energy resources require indigenous renewable electricity generation options to change the current coal-dominated power source matrix in China. The wind power is such a solution for the above challenges, and it still has large space for improvement in China. In this paper several critical factors related to Chinese wind power were studied in details, including the wind resources, the wind turbine industry and the policies from the Chinese government. Based on the study, the perspective of wind power in China was discussed. With outstanding advantages, the offshore wind power has a bright future in China, so its main characteristics are discussed. Based on the discussions, suggestions were given to improve the development of Chinese wind power, and the government''s further measures are also recommended. [Copyright &y& Elsevier]

Abstract: This paper examines the current situation of wind industry development, evaluates the potentials of GHG mitigation and identifies the key determinants of scaling up wind power deployment in China. China has doubled its wind capacity every year for the past 4 years, the total installed capacity reached 12 Gigawatts (GW) and surpassed the 10-GW target 2 years ahead of schedule in the national plan for renewable energy development ,and would reach 100–120GW by 2020 according to the government’s new energy plan. It may become the biggest wind power generation and wind turbines manufacturing country of the world in the next years if the abundant wind resources and enormous domestic market can be harnessed with appropriate policies and efficient technology. The recent positive move in vigorous development of wind power in China implies that the total installed capacity will far exceed the targets of the government’s 2007 renewable energy plan. However, the prosperous Chinese wind market has also revealed some worrisome signals and weakness , such as low capacity factor and frequent outage of wind farms, inadequate grid infrastructure, long distance transmission, low quality of turbines, adverse price bidding, nepotism in wind farm developer selection process and regulatory uncertainty and policy inconsistency which all conspire to hinder effective power generation in the massively new installed wind capacities. A coherent policy framework is required for creating enabling environment for accelerating wind energy penetration and state-of-art technology deployment in the country. It is argued that institutional, financial and technical capacity will need to be cemented to exploit the huge potentials of wind resources to meet the rapidly growing demand for electricity in China in the coming decades with minimised environmental implications. [Copyright &y& Elsevier]

Abstract: A reliable supply of energy is essential to maintain and to improve human being''s living conditions. Compared to the conventional coal-fired approach, renewable energy (RE) helps to mitigate the impacts of greenhouse gas emissions to a large extent. As a newly developed sector with large resource potential and good commercial prospects, China''s wind power industry is exceeding expectations. By 2008, installed capacity of wind power in China totaled over 12GW, making China the fourth largest wind market in the world. However, China has to admit the gap with foreign leading countries, mainly in technical R&D for large-scale wind turbine generation units. This paper attempts to formulate an analytical model for studying and assessing factors that have significant impacts on the local industry. An improved dynamic Diamond Model is developed to help the international community to understand the status quo of the Chinese wind power industry. In order to build a stronger wind power industry, the elements identified in the model need to be strengthened. [Copyright &y& Elsevier]

In order to mitigate global climate change and air pollution, the Chinese government has assigned high priority to expanding low-carbon power generation in China. Recent studies have shown that wind power is one of the most promising renewable energy option in China. Although many studies have estimated the generation potential of onshore wind power, their results vary widely from 1783 TWh to 39,000 TWh. Therefore, we examine the different assumptions in these papers and identify three main factors influencing the results. The three influencing factors are: weather data set, land utilisation factor, and wind turbine configuration. For our model-based analysis, we define a reference scenario which is used to compare the results. Our analysis shows using a different weather data set increases the generation potential to roughly 35,000 TWh. This is 54% higher than the generation potential of the reference scenario. The land utilisation factor also has a large influence, ranging between −10% and −51%. The studies' assumptions and data should be subjected to careful scrutiny, as the calculated wind power potentials are widely used to develop decarbonisation strategies for the energy system. • Study results on wind power potentials in China vary from 1783 TWh to 39,000 TWh • Influencing factors: weather data set, land utilisation, and wind turbine configuration. • Model-based sensitivity analysis with nine scenario settings. • Chosen weather data set has the highest impact on the calculated results with a variation of 54%. [ABSTRACT FROM AUTHOR]

• Frequent principal fault detection and localization with imbalanced data is studied. • We design a novel synthetic oversampling algorithm to balance the wind buffer data. • We modify a data preprocessing strategy for wind buffer data with missing values. • We build a 1D-convolutional neuron network model to extract features automatically. • We propose a frequent principal fault detection and localization approach. Frequent principal fault detection and localization (FPFDL), as a new problem of fault diagnosis of the wind turbine system in practice, has gained a growing concern in wind power industries. The knowledge-based fault diagnosis method with historical wind buffer data is a feasible way to solve this problem. However, due to the uncertainty of the principal fault and the incompletion of the practical data, the wind buffer data used is imbalanced, inadequate, and unfixed-length, which leads to higher misclassification rates for the minority classes by traditional machine learning methods. To overcome these challenges, a novel FPFDL approach is proposed in this paper. Firstly, we design an improved oversampling algorithm to generate and develop the balanced dataset based on the imbalanced dataset of unfixed-length. This algorithm combines the dependent wild bootstrap oversampling and the modified synthetic minority oversampling technique. So, it can consider the temporal dependence and the relationship between the samples during data oversampling. Secondly, we introduce the one-dimensional convolutional neural networks to achieve automatic high-level local feature extraction and fault identification. Finally, the experimental results of seven cases using the datasets collected from two real wind farms in China validate our proposed approach's effectiveness and robustness with imbalanced wind buffer data of unfixed-length. [ABSTRACT FROM AUTHOR]

Caused by the expansion construction of large-scale wind farm, wind turbines of different rotor-diameters always exist in typical area with abundant wind resources. This paper investigates the wake characteristics of turbines with different rotor-diameters and their wake interactions as well as performance through LiDAR measurement and SCADA data. The experiment wind farm is located in North China with a flat terrain and three different types of wind turbines are installed. Results show that the wake recovery is related to the rotor diameter, the larger of the turbine, the slower of its wake recovery. The wake of downwind turbine is aggravated by the upstream turbine's wake, reflected in broader wake width, larger velocity deficit and depth. Meanwhile, the upstream wind turbine's wake that close to the downstream wind turbine can also be affected, reflected in the velocity deficit and the wake width suddenly increases when the wake approaching the downwind turbine. The power loss caused by wake effect increased sharply when wind speed decreases and the wake of a 1.5 MW wind turbine makes a great power loss even after a long distance, thus, the wake effect of large wind turbine should be paid more attentions. • Wake interaction and turbines' performance in adjacent area of large-scale wind farm is studied. • The experimental wind field has three types of wind turbines with different rotor diameters. • LiDAR measurements and SCADA data are both adopted. • Wake characteristics and interactions are analyzed in different scenarios. • The wind turbine power generation under different conditions is analyzed. [ABSTRACT FROM AUTHOR]

• Use modified emergy ecological footprint method to assess sustainability of the wind farm during its whole life cycle. • Uncertainty analysis was conducted to judge the influence on the ecological footprint when UEVs of materials in the input data perturbed. • Provide recommendations concerning management for wind farms. Wind power generation has always been considered as clean energy, according with national ecological civilization construction and responding to climate change. However, from the perspective of resource conservation, wind farms directly or indirectly occupy a large amount of land resources along the entire life cycle. Based on the emergy analysis, this paper estimates the ecological footprint of a wind farm in Dalian, evaluates its sustainability, and analyzes the ecological footprint of the four phases which include wind turbines production and transportation, construction, operation and maintenance, and demolition during the life cycle of the wind farm. It was concluded that the emergy carrying capacity of the wind farm was 3879.57 hm2/a, and the emergy ecological footprint was 5117.59 hm2/a. The wind farm was in an ecological deficit. Among the four phases, the ecological footprint of construction was the largest (60.93%), wind turbines production and transportation phase (33.77%) took the second, followed by operation and maintenance (4.59%) and demolition (0.71%). The main contribution of the materials was steel, followed by concrete, ecological protection investment, fiber glass, land occupation and epoxy resin. Finally, in order to illustrate the way to achieve sustainable development of the wind farm, uncertainty analysis and scenario analysis were carried out. It was found that when 62% of the recycled materials were used for wind turbines production, the wind farm realized ecological balance. Reducing the solar transformity (UEV) of steel and concrete can also decrease the ecological footprint of the wind farm. [ABSTRACT FROM AUTHOR]

• Focus on low wind sites, a promising area for wind energy utilization in China. • Low mean wind speed decreases the acceleration or deceleration of wind turbine. • High turbulence makes it hard to track maximum power points. • The optimal torque gain decreases with increasing turbulence intensity. • The proposed MPPT control avoids the searching problem caused by wind speed changes. With the rapid development of wind power in China, high wind sites have become scarce and are often located far away from major load centers. Due to technological improvement and close proximity to load centers, low wind sites (wind speeds around 3–7 m/s) have become economically viable in China. However, the characteristics of low mean wind speed and high turbulence intensity at low wind sites make maximum power point tracking (MPPT) control challenging. Therefore, an improved maximum power point tracking (IMPPT) control approach is proposed in this paper. This approach is based on decreasing torque gain (DTG) control algorithm, and is designed to dynamically adjust the gain coefficient aimed for better energy capture at low wind sites. The proposed IMPPT algorithm is compared with traditional MPPT algorithms by simulation using actual wind speeds, including DTG control, adaptive torque gain (ATG) control, the improved MPPT control based on the reduction of tracking range (RTR-MPPT control), and an improved ATG control based on curve fit method (CF-ATG control). Simulation results verify that the proposed approach (IMPPT algorithm) is more effective. [ABSTRACT FROM AUTHOR]

China's wind power capacity is increasing so rapidly that some areas are not able to consume all the produced wind power. Therefore, it has already become the inevitable trend in wind power integration to achieve the controlled operation of wind turbine. This paper first offers a method to predict the maximum generation capacity of every type of wind turbine, including both under-controlled wind turbine and uncontrolled wind turbine. And then a strategy for active power control of wind farm based on the predicting results is proposed. Considering thoroughly the active power fluctuation of wind turbine and the error of the prediction, the strategy adopts both long and short controlling periods to carry out coordinated optimization control. The simulation results show that the strategy can keep the active power output of wind farm in step with the control order, and consequently the generating efficiency is improved. In addition, the strategy is also applicable to the wind farm with some uncontrollable wind turbines, and the controlling precision can be satisfied. [ABSTRACT FROM AUTHOR]