The development of wind power clusters has scaled in terms of both scale and coverage,and the impact of weather fluctuations on cluster output changes has become increasingly complex.Accurately identifying the forward...The development of wind power clusters has scaled in terms of both scale and coverage,and the impact of weather fluctuations on cluster output changes has become increasingly complex.Accurately identifying the forward-looking information of key wind farms in a cluster under different weather conditions is an effective method to improve the accuracy of ultrashort-term cluster power forecasting.To this end,this paper proposes a refined modeling method for ultrashort-term wind power cluster forecasting based on a convergent cross-mapping algorithm.From the perspective of causality,key meteorological forecasting factors under different cluster power fluctuation processes were screened,and refined training modeling was performed for different fluctuation processes.First,a wind process description index system and classification model at the wind power cluster level are established to realize the classification of typical fluctuation processes.A meteorological-cluster power causal relationship evaluation model based on the convergent cross-mapping algorithm is pro-posed to screen meteorological forecasting factors under multiple types of typical fluctuation processes.Finally,a refined modeling meth-od for a variety of different typical fluctuation processes is proposed,and the strong causal meteorological forecasting factors of each scenario are used as inputs to realize high-precision modeling and forecasting of ultra-short-term wind cluster power.An example anal-ysis shows that the short-term wind power cluster power forecasting accuracy of the proposed method can reach 88.55%,which is 1.57-7.32%higher than that of traditional methods.展开更多
Power quality is a crucial area of research in contemporary power systems,particularly given the rapid proliferation of intermittent renewable energy sources such as wind power.This study investigated the relationship...Power quality is a crucial area of research in contemporary power systems,particularly given the rapid proliferation of intermittent renewable energy sources such as wind power.This study investigated the relationships between power quality indices of system output and PSD by utilizing theories related to spectra,PSD,and random signal power spectra.The relationship was derived,validated through experiments and simulations,and subsequently applied to multi-objective optimization.Various optimization algorithms were compared to achieve optimal system power quality.The findings revealed that the relationships between power quality indices and PSD were influenced by variations in the order of the power spectral estimation model.An increase in the order of the AR model resulted in a 36%improvement in the number of optimal solutions.Regarding optimal solution distribution,NSGA-II demonstrated superior diversity,while MOEA/D exhibited better convergence.However,practical applications showed that while MOEA/D had higher convergence,NSGA-II produced superior optimal solutions,achieving the best power quality indices(THDi at 4.62%,d%at 3.51%,and cosφat 96%).These results suggest that the proposed method holds significant potential for optimizing power quality in practical applications.展开更多
Spiral pile foundations,as a promising type of foundation,are of significant importance for the development of offshore wind energy,particularly as it moves toward deeper waters.This study conducted a physical experim...Spiral pile foundations,as a promising type of foundation,are of significant importance for the development of offshore wind energy,particularly as it moves toward deeper waters.This study conducted a physical experiment on a three-spiral-pile jacket foundation under deep-buried sandy soil conditions.During the experiment,horizontal displacement was applied to the structure to thoroughly investigate the bearing characteristics of the three-spiral-pile jacket foundation.This study also focused on analyzing the bearing mechanisms of conventional piles compared with spiral piles with different numbers of blades.Three different working conditions were set up and compared,and key data,such as the horizontal bearing capacity,pile shaft axial force,and spiral blade soil pressure,were measured and analyzed.The results show the distinct impacts of the spiral blades on the compressed and tensioned sides of the foundation.Specifically,on the compressed side,the spiral blades effectively enhance the restraint of the soil on the pile foundation,whereas on the tensioned side,an excessive number of spiral blades can negatively affect the structural tensile performance to some extent.This study also emphasizes that the addition of blades to the side of a single pile is the most effective method for increasing the bearing capacity of the foundation.This research aims to provide design insights into improving the bearing capacity of the foundation.展开更多
Wind power generation is subjected to complex and variable meteorological conditions,resulting in intermittent and volatile power generation.Accurate wind power prediction plays a crucial role in enabling the power gr...Wind power generation is subjected to complex and variable meteorological conditions,resulting in intermittent and volatile power generation.Accurate wind power prediction plays a crucial role in enabling the power grid dispatching departments to rationally plan power transmission and energy storage operations.This enhances the efficiency of wind power integration into the grid.It allows grid operators to anticipate and mitigate the impact of wind power fluctuations,significantly improving the resilience of wind farms and the overall power grid.Furthermore,it assists wind farm operators in optimizing the management of power generation facilities and reducing maintenance costs.Despite these benefits,accurate wind power prediction especially in extreme scenarios remains a significant challenge.To address this issue,a novel wind power prediction model based on learning approach is proposed by integrating wavelet transform and Transformer.First,a conditional generative adversarial network(CGAN)generates dynamic extreme scenarios guided by physical constraints and expert rules to ensure realism and capture critical features of wind power fluctuations under extremeconditions.Next,thewavelet transformconvolutional layer is applied to enhance sensitivity to frequency domain characteristics,enabling effective feature extraction fromextreme scenarios for a deeper understanding of input data.The model then leverages the Transformer’s self-attention mechanism to capture global dependencies between features,strengthening its sequence modelling capabilities.Case analyses verify themodel’s superior performance in extreme scenario prediction by effectively capturing local fluctuation featureswhile maintaining a grasp of global trends.Compared to other models,it achieves R-squared(R^(2))as high as 0.95,and the mean absolute error(MAE)and rootmean square error(RMSE)are also significantly lower than those of othermodels,proving its high accuracy and effectiveness in managing complex wind power generation conditions.展开更多
In this study,a machine learning-based predictive model was developed for the Musa petti Wind Farm in Sri Lanka to address the need for localized forecasting solutions.Using data on wind speed,air temperature,nacelle ...In this study,a machine learning-based predictive model was developed for the Musa petti Wind Farm in Sri Lanka to address the need for localized forecasting solutions.Using data on wind speed,air temperature,nacelle position,and actual power,lagged features were generated to capture temporal dependencies.Among 24 evaluated models,the ensemble bagging approach achieved the best performance,with R^(2) values of 0.89 at 0 min and 0.75 at 60 min.Shapley Additive exPlanations(SHAP)analysis revealed that while wind speed is the primary driver for short-term predictions,air temperature and nacelle position become more influential at longer forecasting horizons.These findings underscore the reliability of short-term predictions and the potential benefits of integrating hybrid AI and probabilistic models for extended forecasts.Our work contributes a robust and explainable framework to support Sri Lanka’s renewable energy transition,and future research will focus on real-time deployment and uncertainty quantification.展开更多
A series of high-strength wind power steels with various microstructural morphologies was produced by hot-rolled and thermo-mechanical controlled processes.The microstructure,microhardness,and tensile behavior observe...A series of high-strength wind power steels with various microstructural morphologies was produced by hot-rolled and thermo-mechanical controlled processes.The microstructure,microhardness,and tensile behavior observed using in-situ techniques in various types of steels were investigated.The experimental results demonstrated that the 3 microstructural morphologies(band-,net-,and fiber-structures)can be clarified and categorized;each type possesses different tensile strengths,yield behaviors,and strain hardening behaviors.This can be attributed to different strain distribution caused by the structural morphology;band-structure steels exhibit a yield plateau primarily attributed to the relatively weak constraint effect of pearlite on ferrite;net-structure steels display 3 strain hardening stages due to the staged plastic deformation;fiber-structure steels achieve superior strength through their uniform stress distribution.Furthermore,the initial strain hardening rate,transition strain,and uniform elongation were influenced by the features of the constituent phases.Based on these findings,methods for estimating the yield strength and tensile strength of the steels with two phases were discussed and experimentally validated.展开更多
Wind power forecasting plays a crucial role in optimizing the integration of wind energy into the grid by predicting wind patterns and energy output.This enhances the efficiency and reliability of renewable energy sys...Wind power forecasting plays a crucial role in optimizing the integration of wind energy into the grid by predicting wind patterns and energy output.This enhances the efficiency and reliability of renewable energy systems.Forecasting approaches inform energy management strategies,reduce reliance on fossil fuels,and support the broader transition to sustainable energy solutions.The primary goal of this study is to introduce an effective methodology for estimating wind power through temporal data analysis.This research advances an optimized Multilayer Perceptron(MLP)model using recently proposedmetaheuristic optimization algorithms,namely the FireHawk Optimizer(FHO)and the Non-Monopolize Search(NO).A modified version of FHO,termed FHONO,is developed by integrating NO as a local search mechanism to enhance the exploration capability and address the shortcomings of the original FHO.The developed FHONO is then employed to optimize the MLP for enhanced wind power prediction.The effectiveness of the proposed FHONO-MLP model is validated using renowned datasets from wind turbines in France.The results of the comparative analysis between FHONO-MLP,conventionalMLP,and other optimized versions of MLP show that FHONO-MLP outperforms the others,achieving an average RootMean Square Error(RMSE)of 0.105,Mean Absolute Error(MAE)of 0.082,and Coefficient of Determination(R^(2))of 0.967 across all datasets.These findings underscore the significant enhancement in predictive accuracy provided by FHONO and demonstrate its effectiveness in improving wind power forecasting.展开更多
Currently,renewable energy has been broadly implemented across diverse sectors,particularly evidenced by its substantially higher integration levels in power systems.Thelarge-scale integration of renewable energy reso...Currently,renewable energy has been broadly implemented across diverse sectors,particularly evidenced by its substantially higher integration levels in power systems.Thelarge-scale integration of renewable energy resources introduces distinct fault characteristics into power grids,potentially rendering traditional line protection schemes inadequate for transmission lines interfacing with these sources.Therefore,it is imperative to study line protection methods unaffected by the integration of renewable energy resources.After analyzing the propagation process of the fault traveling wave along the transmission line,a non-unit protection based on traveling wave distancemeasurement is proposed.The core principle of the proposedmethod relies onmeasuring the temporal interval ΔT between detections of the first and second fault backward traveling waves at the measurement point.The Teager energy operator(TEO),which demonstrates advantages in highlighting stepmutation characteristics,was employed to extract ΔT by processing the fault backward traveling wave signal detected at the measurement point.To evaluate the efficacy of the proposed protection method,various fault scenarios were simulated in the established PSCAD/EMTDC simulation system.Validation results confirm that the proposed non-unit protection can provide full-line protection coverage,enabling fast and reliable discrimination of internal and external faults with inherent immunity to renewable energy penetration.展开更多
Improving the accuracy of the evaluation of the performance of wind farms in large wind power bases located in complex terrain under the actual atmosphere is crucial to the sustainable development of wind power.To thi...Improving the accuracy of the evaluation of the performance of wind farms in large wind power bases located in complex terrain under the actual atmosphere is crucial to the sustainable development of wind power.To this end,this study combined the Weather Research and Forecasting(WRF)model with the Wind Farm Parameterization(WFP)method to investigate the wake characteristics and operational performance of large onshore wind farms in the complex terrain of Jiuquan City,Gansu Province,China.The research results showed that after verification,the systematic error of the WRF simulations was less than 3%.The WRF model and the WFP scheme simulated a significant warming phenomenon within the wind power base area,while a cooling effect was observed outside.The analysis of the wake effects indicated that the impact of PhaseⅠconstruction on PhaseⅡconstruction of the wind power base was minimal.During the operation of the entire wind power base,the wind speed within the wind farm decreased by approximately 10%,and the influence range of the predominant wind direction extended over a hundred kilometers downwind.The research conclusions provide a powerful scientific basis for optimizing design and operation,improving efficiency,minimizing the negative impacts on adjacent wind turbines,and ensuring the sustainable development of wind energy through dynamic planning and scientific assessment.展开更多
As the core facility of offshore wind power systems,the structural safety of offshore booster stations directly impacts the stable operation of entire wind farms.With the global energy transition toward green and lowc...As the core facility of offshore wind power systems,the structural safety of offshore booster stations directly impacts the stable operation of entire wind farms.With the global energy transition toward green and lowcarbon goals,offshore wind power has emerged as a key renewable energy source,yet its booster stations face harsh marine environments,including persistent wave impacts,salt spray corrosion,and equipment-induced vibrations.Traditional monitoring methods relying on manual inspections and single-dimensional sensors suffer from critical limitations:low efficiency,poor real-time performance,and inability to capture millinewton-level stress fluctuations that signal early structural fatigue.To address these challenges,this study proposes a biomechanics-driven structural safety monitoring system integrated with deep learning.Inspired by biological stress-sensing mechanisms,the system deploys a distributedmulti-dimensional force sensor network to capture real-time stress distributions in key structural components.A hybrid convolutional neural network-radial basis function(CNN-RBF)model is developed:the CNN branch extracts spatiotemporal features from multi-source sensing data,while the RBF branch reconstructs the nonlinear stress field for accurate anomaly diagnosis.The three-tier architectural design—data layer(distributed sensor array),function layer(CNN-RBF modeling),and application layer(edge computing terminal)—enables a closedloop process from high-resolution data collection to real-time early warning,with data processing delay controlled within 200 ms.Experimental validation against traditional SOM-based systems demonstrates significant performance improvements:monitoring accuracy increased by 19.8%,efficiency by 23.4%,recall rate by 20.5%,and F1 score by 21.6%.Under extreme weather(e.g.,typhoons and winter storms),the system’s stability is 40% higher,with user satisfaction improving by 17.2%.The biomechanics-inspired sensor design enhances survival rates in salt fog(85.7%improvement)and dynamic loads,highlighting its robust engineering applicability for intelligent offshore wind farm maintenance.展开更多
The wind power potential in Interior Alaska is evaluated from a micrometeorological perspective. Based on the local balance equation of momentum and the equation of continuity we derive the local balance equation of k...The wind power potential in Interior Alaska is evaluated from a micrometeorological perspective. Based on the local balance equation of momentum and the equation of continuity we derive the local balance equation of kinetic energy for macroscopic and turbulent systems, and in a further step, Bernoulli’s equation and integral equations that customarily serve as the key equations in momentum theory and blade-element analysis, where the Lanchester-Betz-Joukowsky limit, Glauert’s optimum actuator disk, and the results of the blade-element analysis by Okulov and Sorensen are exemplarily illustrated. The wind power potential at three different sites in Interior Alaska (Delta Junction, Eva Creek, and Poker Flat) is assessed by considering the results of wind field predictions for the winter period from October 1, 2008, to April 1, 2009 provided by the Weather Research and Forecasting (WRF) model to avoid time-consuming and expensive tall-tower observations in Interior Alaska which is characterized by a relatively low degree of infrastructure outside of the city of Fairbanks. To predict the average power output we use the Weibull distributions derived from the predicted wind fields for these three different sites and the power curves of five different propeller-type wind turbines with rated powers ranging from 2 MW to 2.5 MW. These power curves are represented by general logistic functions. The predicted power capacity for the Eva Creek site is compared with that of the Eva Creek wind farm established in 2012. The results of our predictions for the winter period 2008/2009 are nearly 20 percent lower than those of the Eva Creek wind farm for the period from January to September 2013.展开更多
In this paper the author describes the current development of tbe 10-GW wind power base in Jiuquan, Gansu Province. The.future planning of the wind power base and the relevant power grid are also introduced based on t...In this paper the author describes the current development of tbe 10-GW wind power base in Jiuquan, Gansu Province. The.future planning of the wind power base and the relevant power grid are also introduced based on the assessment on the wind resources and the wind power operation with the grid.展开更多
With abundant wind resources and high pressure imposed on en-vironmental protection, wind power development has a promising future. Butdue to intermittent nature, wind power can bring into full play only if being con-...With abundant wind resources and high pressure imposed on en-vironmental protection, wind power development has a promising future. Butdue to intermittent nature, wind power can bring into full play only if being con-nected into power grid to ensure its supply reliability and continuity, aswell as operational economy. However, technical and market barriers haveprevented wind power from integrating into power grid. To foster wind powerdevelopment, these barriers should be removed by both government incentivepolicies and sophisticated technologies.展开更多
Base on the full and accurate statistical informa tion, the status and existing technical problems of wind power in China are analyzed in terms of yearly installed capacity, single unit capacity, regional distribution...Base on the full and accurate statistical informa tion, the status and existing technical problems of wind power in China are analyzed in terms of yearly installed capacity, single unit capacity, regional distribution, investment as well as the market shares and the local ization of wind power units etc. The paper believes that wind power industry in China will grow by leaps and bounds in near future.展开更多
To solve the problem of residual wind power in offshore wind farms,a hydrogen production system with a reasonable capacity was configured to enhance the local load of wind farms and promote the local consumption of re...To solve the problem of residual wind power in offshore wind farms,a hydrogen production system with a reasonable capacity was configured to enhance the local load of wind farms and promote the local consumption of residual wind power.By studying the mathematical model of wind power output and calculating surplus wind power,as well as considering the hydrogen production/storage characteristics of the electrolyzer and hydrogen storage tank,an innovative capacity optimization allocation model was established.The objective of the model was to achieve the lowest total net present value over the entire life cycle.The model took into account the cost-benefit breakdown of equipment end-of-life cost,replacement cost,residual value gain,wind abandonment penalty,hydrogen transportation,and environmental value.The MATLAB-based platform invoked the CPLEX commercial solver to solve the model.Combined with the analysis of the annual average wind speed data from an offshore wind farm in Guangdong Province,the optimal capacity configuration results and the actual operation of the hydrogen production system were obtained.Under the calculation scenario,this hydrogen production system could consume 3,800 MWh of residual electricity from offshore wind power each year.It could achieve complete consumption of residual electricity from wind power without incurring the penalty cost of wind power.Additionally,it could produce 66,500 kg of green hydrogen from wind power,resulting in hydrogen sales revenue of 3.63 million RMB.It would also reduce pollutant emissions from coal-based hydrogen production by 1.5 tons and realize an environmental value of 4.83 million RMB.The annual net operating income exceeded 6 million RMB and the whole life cycle NPV income exceeded 50 million RMB.These results verified the feasibility and rationality of the established capacity optimization allocation model.The model could help advance power system planning and operation research and assist offshore wind farm operators in improving economic and environmental benefits.展开更多
This paper constructs the index system of the regional division of the development stage of China's wind power resources,including the index of energy,the index of wind energy endowments and other indices.Based on...This paper constructs the index system of the regional division of the development stage of China's wind power resources,including the index of energy,the index of wind energy endowments and other indices.Based on principal component analysis and layered clustering analysis of these indices,and combined with the conceptual function of the development and utilization stage of the wind power,this paper divides the development and utilization stage of the wind power into four stages taking province as the basic yardstick:optimization growth stage,the rapid growth stage,the slow growth stage and the initial growth stage.In addition,this paper briefly discusses the basic strategy that should be adopted in each development stage of wind power resources.展开更多
To solve the severe problem of wind power curtailment in the winter heating period caused by ＂power determined by heat＂ operation constraint of cogeneration units, this paper analyzes thermoelectric load, wind power...To solve the severe problem of wind power curtailment in the winter heating period caused by ＂power determined by heat＂ operation constraint of cogeneration units, this paper analyzes thermoelectric load, wind power output distribution and fluctuation characteristics at different time scales, and finally proposes a two level coordinated control strategy based on electric heat storage and pumped storage. The optimization target of the first level coordinated control is the lowest operation cost and the largest wind power utilization rate. Based on prediction of thermoelectric load and wind power, the operation economy of the system and wind power accommodation level are improved with the cooperation of electric heat storage and pumped storage in regulation capacity. The second level coordinated control stabilizes wind power real time fluctuations by cooperating electric heat storage and pumped storage in control speed. The example results of actual wind farms in Jiuquan, Gansu verifies the feasibility and effectiveness of the proposed coordinated control strategy.展开更多
In the second half of 2021, electricity rationing suddenly appeared all over the country, so that many enterprises can not take precautions, especially the production enterprises, causing a great impact. The main reas...In the second half of 2021, electricity rationing suddenly appeared all over the country, so that many enterprises can not take precautions, especially the production enterprises, causing a great impact. The main reasons are the national energy consumption double control policy, coal price rise, economic transformation and other reasons, and the national power rationing is to ensure the completion of the 14th five-year energy conservation binding target and promote the realization of carbon peak neutrality target task. The development of clean energy is one of the important measures to achieve carbon neutrality. Therefore, wind power generation, especially offshore power generation, is a development direction in the future. Offshore wind power generation technology started relatively late, there are still many problems to be solved, there is still a long way to go, the following is to analyze the content of offshore wind power generation.展开更多
Three- and four-bucket offshore wind power foundations with a new form of force-transferring structure are proposed in this paper, and the integrated finite element model of foundation-soil-transition structure is est...Three- and four-bucket offshore wind power foundations with a new form of force-transferring structure are proposed in this paper, and the integrated finite element model of foundation-soil-transition structure is established by using ABAQUS. The carrying capacity of the proposed foundations is studied under vertical load, horizontal load and bending moment. It can be seen that the vertical bearing capacity of multi-bucket foundation can be roughly estimated by the vertical bearing capacity of single-bucket; the horizontal bearing capacity of the three-bucket foundation scheme is controlled by displacement, while that of the four-bucket foundation scheme is controlled by the internal forces of soils. Moreover, the carrying capacity is provided by the overall structure formed by multi-bucket before soil failure. Compared with the conventional single-bucket foundation, there are mainly tension and pressure that are applied to the multi-bucket foundation, so that the carrying capacity of the foundation can be fully utilized. The probability of soil failure can be well reduced with the proposed multi-bucket foundation, and the stress transmission of force-transferring structure is more consistent through steel beams with variable cross-section.展开更多
Accurate wind power forecasting in wind farm can effectively reduce the enormous impact on grid operation safety when high permeability intermittent power supply is connected to the power grid.Aiming to provide refere...Accurate wind power forecasting in wind farm can effectively reduce the enormous impact on grid operation safety when high permeability intermittent power supply is connected to the power grid.Aiming to provide reference strategies for relevant researchers as well as practical applications,this paper attempts to provide the literature investigation and methods analysis of deep learning,enforcement learning and transfer learning in wind speed and wind power forecasting modeling.Usually,wind speed and wind power forecasting around a wind farm requires the calculation of the next moment of the definite state,which is usually achieved based on the state of the atmosphere that encompasses nearby atmospheric pressure,temperature,roughness,and obstacles.As an effective method of high-dimensional feature extraction,deep neural network can theoretically deal with arbitrary nonlinear transformation through proper structural design,such as adding noise to outputs,evolutionary learning used to optimize hidden layer weights,optimize the objective function so as to save information that can improve the output accuracy while filter out the irrelevant or less affected information for forecasting.The establishment of high-precision wind speed and wind power forecasting models is always a challenge due to the randomness,instantaneity and seasonal characteristics.展开更多
基金funded by the State Grid Science and Technology Project“Research on Key Technologies for Prediction and Early Warning of Large-Scale Offshore Wind Power Ramp Events Based on Meteorological Data Enhancement”(4000-202318098A-1-1-ZN).
文摘The development of wind power clusters has scaled in terms of both scale and coverage,and the impact of weather fluctuations on cluster output changes has become increasingly complex.Accurately identifying the forward-looking information of key wind farms in a cluster under different weather conditions is an effective method to improve the accuracy of ultrashort-term cluster power forecasting.To this end,this paper proposes a refined modeling method for ultrashort-term wind power cluster forecasting based on a convergent cross-mapping algorithm.From the perspective of causality,key meteorological forecasting factors under different cluster power fluctuation processes were screened,and refined training modeling was performed for different fluctuation processes.First,a wind process description index system and classification model at the wind power cluster level are established to realize the classification of typical fluctuation processes.A meteorological-cluster power causal relationship evaluation model based on the convergent cross-mapping algorithm is pro-posed to screen meteorological forecasting factors under multiple types of typical fluctuation processes.Finally,a refined modeling meth-od for a variety of different typical fluctuation processes is proposed,and the strong causal meteorological forecasting factors of each scenario are used as inputs to realize high-precision modeling and forecasting of ultra-short-term wind cluster power.An example anal-ysis shows that the short-term wind power cluster power forecasting accuracy of the proposed method can reach 88.55%,which is 1.57-7.32%higher than that of traditional methods.
基金funded by the Inner Mongolia Nature Foundation Project,Project number:2023JQ04.
文摘Power quality is a crucial area of research in contemporary power systems,particularly given the rapid proliferation of intermittent renewable energy sources such as wind power.This study investigated the relationships between power quality indices of system output and PSD by utilizing theories related to spectra,PSD,and random signal power spectra.The relationship was derived,validated through experiments and simulations,and subsequently applied to multi-objective optimization.Various optimization algorithms were compared to achieve optimal system power quality.The findings revealed that the relationships between power quality indices and PSD were influenced by variations in the order of the power spectral estimation model.An increase in the order of the AR model resulted in a 36%improvement in the number of optimal solutions.Regarding optimal solution distribution,NSGA-II demonstrated superior diversity,while MOEA/D exhibited better convergence.However,practical applications showed that while MOEA/D had higher convergence,NSGA-II produced superior optimal solutions,achieving the best power quality indices(THDi at 4.62%,d%at 3.51%,and cosφat 96%).These results suggest that the proposed method holds significant potential for optimizing power quality in practical applications.
基金The National Natural Science Foundation of China(No.52171274).
文摘Spiral pile foundations,as a promising type of foundation,are of significant importance for the development of offshore wind energy,particularly as it moves toward deeper waters.This study conducted a physical experiment on a three-spiral-pile jacket foundation under deep-buried sandy soil conditions.During the experiment,horizontal displacement was applied to the structure to thoroughly investigate the bearing characteristics of the three-spiral-pile jacket foundation.This study also focused on analyzing the bearing mechanisms of conventional piles compared with spiral piles with different numbers of blades.Three different working conditions were set up and compared,and key data,such as the horizontal bearing capacity,pile shaft axial force,and spiral blade soil pressure,were measured and analyzed.The results show the distinct impacts of the spiral blades on the compressed and tensioned sides of the foundation.Specifically,on the compressed side,the spiral blades effectively enhance the restraint of the soil on the pile foundation,whereas on the tensioned side,an excessive number of spiral blades can negatively affect the structural tensile performance to some extent.This study also emphasizes that the addition of blades to the side of a single pile is the most effective method for increasing the bearing capacity of the foundation.This research aims to provide design insights into improving the bearing capacity of the foundation.
基金funded by the Science and Technology Project of State Grid Corporation of China under Grant No.5108-202218280A-2-299-XG.
文摘Wind power generation is subjected to complex and variable meteorological conditions,resulting in intermittent and volatile power generation.Accurate wind power prediction plays a crucial role in enabling the power grid dispatching departments to rationally plan power transmission and energy storage operations.This enhances the efficiency of wind power integration into the grid.It allows grid operators to anticipate and mitigate the impact of wind power fluctuations,significantly improving the resilience of wind farms and the overall power grid.Furthermore,it assists wind farm operators in optimizing the management of power generation facilities and reducing maintenance costs.Despite these benefits,accurate wind power prediction especially in extreme scenarios remains a significant challenge.To address this issue,a novel wind power prediction model based on learning approach is proposed by integrating wavelet transform and Transformer.First,a conditional generative adversarial network(CGAN)generates dynamic extreme scenarios guided by physical constraints and expert rules to ensure realism and capture critical features of wind power fluctuations under extremeconditions.Next,thewavelet transformconvolutional layer is applied to enhance sensitivity to frequency domain characteristics,enabling effective feature extraction fromextreme scenarios for a deeper understanding of input data.The model then leverages the Transformer’s self-attention mechanism to capture global dependencies between features,strengthening its sequence modelling capabilities.Case analyses verify themodel’s superior performance in extreme scenario prediction by effectively capturing local fluctuation featureswhile maintaining a grasp of global trends.Compared to other models,it achieves R-squared(R^(2))as high as 0.95,and the mean absolute error(MAE)and rootmean square error(RMSE)are also significantly lower than those of othermodels,proving its high accuracy and effectiveness in managing complex wind power generation conditions.
文摘In this study,a machine learning-based predictive model was developed for the Musa petti Wind Farm in Sri Lanka to address the need for localized forecasting solutions.Using data on wind speed,air temperature,nacelle position,and actual power,lagged features were generated to capture temporal dependencies.Among 24 evaluated models,the ensemble bagging approach achieved the best performance,with R^(2) values of 0.89 at 0 min and 0.75 at 60 min.Shapley Additive exPlanations(SHAP)analysis revealed that while wind speed is the primary driver for short-term predictions,air temperature and nacelle position become more influential at longer forecasting horizons.These findings underscore the reliability of short-term predictions and the potential benefits of integrating hybrid AI and probabilistic models for extended forecasts.Our work contributes a robust and explainable framework to support Sri Lanka’s renewable energy transition,and future research will focus on real-time deployment and uncertainty quantification.
基金funded by the National Key Research and Development Program of China(No.2022YFB3708200)。
文摘A series of high-strength wind power steels with various microstructural morphologies was produced by hot-rolled and thermo-mechanical controlled processes.The microstructure,microhardness,and tensile behavior observed using in-situ techniques in various types of steels were investigated.The experimental results demonstrated that the 3 microstructural morphologies(band-,net-,and fiber-structures)can be clarified and categorized;each type possesses different tensile strengths,yield behaviors,and strain hardening behaviors.This can be attributed to different strain distribution caused by the structural morphology;band-structure steels exhibit a yield plateau primarily attributed to the relatively weak constraint effect of pearlite on ferrite;net-structure steels display 3 strain hardening stages due to the staged plastic deformation;fiber-structure steels achieve superior strength through their uniform stress distribution.Furthermore,the initial strain hardening rate,transition strain,and uniform elongation were influenced by the features of the constituent phases.Based on these findings,methods for estimating the yield strength and tensile strength of the steels with two phases were discussed and experimentally validated.
基金the Deanship of Graduate Studies and Scientific Research at University of Bisha,Saudi Arabia for funding this research work through the Promising Program under Grant Number(UB-Promising-42-1445).
文摘Wind power forecasting plays a crucial role in optimizing the integration of wind energy into the grid by predicting wind patterns and energy output.This enhances the efficiency and reliability of renewable energy systems.Forecasting approaches inform energy management strategies,reduce reliance on fossil fuels,and support the broader transition to sustainable energy solutions.The primary goal of this study is to introduce an effective methodology for estimating wind power through temporal data analysis.This research advances an optimized Multilayer Perceptron(MLP)model using recently proposedmetaheuristic optimization algorithms,namely the FireHawk Optimizer(FHO)and the Non-Monopolize Search(NO).A modified version of FHO,termed FHONO,is developed by integrating NO as a local search mechanism to enhance the exploration capability and address the shortcomings of the original FHO.The developed FHONO is then employed to optimize the MLP for enhanced wind power prediction.The effectiveness of the proposed FHONO-MLP model is validated using renowned datasets from wind turbines in France.The results of the comparative analysis between FHONO-MLP,conventionalMLP,and other optimized versions of MLP show that FHONO-MLP outperforms the others,achieving an average RootMean Square Error(RMSE)of 0.105,Mean Absolute Error(MAE)of 0.082,and Coefficient of Determination(R^(2))of 0.967 across all datasets.These findings underscore the significant enhancement in predictive accuracy provided by FHONO and demonstrate its effectiveness in improving wind power forecasting.
基金funded in part by Science and Technology Project of State Grid Sichuan Electric Power Company under Grant 521997230003.
文摘Currently,renewable energy has been broadly implemented across diverse sectors,particularly evidenced by its substantially higher integration levels in power systems.Thelarge-scale integration of renewable energy resources introduces distinct fault characteristics into power grids,potentially rendering traditional line protection schemes inadequate for transmission lines interfacing with these sources.Therefore,it is imperative to study line protection methods unaffected by the integration of renewable energy resources.After analyzing the propagation process of the fault traveling wave along the transmission line,a non-unit protection based on traveling wave distancemeasurement is proposed.The core principle of the proposedmethod relies onmeasuring the temporal interval ΔT between detections of the first and second fault backward traveling waves at the measurement point.The Teager energy operator(TEO),which demonstrates advantages in highlighting stepmutation characteristics,was employed to extract ΔT by processing the fault backward traveling wave signal detected at the measurement point.To evaluate the efficacy of the proposed protection method,various fault scenarios were simulated in the established PSCAD/EMTDC simulation system.Validation results confirm that the proposed non-unit protection can provide full-line protection coverage,enabling fast and reliable discrimination of internal and external faults with inherent immunity to renewable energy penetration.
基金funded by“The Factors Affecting the Accuracy of Wind Resource Assessment and Comprehensive Post-Evaluation Techniques for Operating Wind Power Projects,”grant number YJ24.002“The Research and Application of Future Medium to Long Term Wind Resource Assessment for Wind Farms Based on Artificial Intelligence Project,”grant number 2023021。
文摘Improving the accuracy of the evaluation of the performance of wind farms in large wind power bases located in complex terrain under the actual atmosphere is crucial to the sustainable development of wind power.To this end,this study combined the Weather Research and Forecasting(WRF)model with the Wind Farm Parameterization(WFP)method to investigate the wake characteristics and operational performance of large onshore wind farms in the complex terrain of Jiuquan City,Gansu Province,China.The research results showed that after verification,the systematic error of the WRF simulations was less than 3%.The WRF model and the WFP scheme simulated a significant warming phenomenon within the wind power base area,while a cooling effect was observed outside.The analysis of the wake effects indicated that the impact of PhaseⅠconstruction on PhaseⅡconstruction of the wind power base was minimal.During the operation of the entire wind power base,the wind speed within the wind farm decreased by approximately 10%,and the influence range of the predominant wind direction extended over a hundred kilometers downwind.The research conclusions provide a powerful scientific basis for optimizing design and operation,improving efficiency,minimizing the negative impacts on adjacent wind turbines,and ensuring the sustainable development of wind energy through dynamic planning and scientific assessment.
基金supported by the Science and Technology Project of China Huaneng Group Co.,Ltd.Research on Key Technologies for Monitoring and Protection of Offshore Wind Power Underwater Equipment(HNKJ21-H40).
文摘As the core facility of offshore wind power systems,the structural safety of offshore booster stations directly impacts the stable operation of entire wind farms.With the global energy transition toward green and lowcarbon goals,offshore wind power has emerged as a key renewable energy source,yet its booster stations face harsh marine environments,including persistent wave impacts,salt spray corrosion,and equipment-induced vibrations.Traditional monitoring methods relying on manual inspections and single-dimensional sensors suffer from critical limitations:low efficiency,poor real-time performance,and inability to capture millinewton-level stress fluctuations that signal early structural fatigue.To address these challenges,this study proposes a biomechanics-driven structural safety monitoring system integrated with deep learning.Inspired by biological stress-sensing mechanisms,the system deploys a distributedmulti-dimensional force sensor network to capture real-time stress distributions in key structural components.A hybrid convolutional neural network-radial basis function(CNN-RBF)model is developed:the CNN branch extracts spatiotemporal features from multi-source sensing data,while the RBF branch reconstructs the nonlinear stress field for accurate anomaly diagnosis.The three-tier architectural design—data layer(distributed sensor array),function layer(CNN-RBF modeling),and application layer(edge computing terminal)—enables a closedloop process from high-resolution data collection to real-time early warning,with data processing delay controlled within 200 ms.Experimental validation against traditional SOM-based systems demonstrates significant performance improvements:monitoring accuracy increased by 19.8%,efficiency by 23.4%,recall rate by 20.5%,and F1 score by 21.6%.Under extreme weather(e.g.,typhoons and winter storms),the system’s stability is 40% higher,with user satisfaction improving by 17.2%.The biomechanics-inspired sensor design enhances survival rates in salt fog(85.7%improvement)and dynamic loads,highlighting its robust engineering applicability for intelligent offshore wind farm maintenance.
基金the National Science Foundation for funding the project work of Megan Hinzman and Samuel Smock in summer 2011Hannah K.Ross and John Cooney in summer 2012 through the Research Experience for Undergraduates(REU)Program,grant number AGS1005265the Alaska Department of Labor for funding Dr.Gary Sellhorst’s project work
文摘The wind power potential in Interior Alaska is evaluated from a micrometeorological perspective. Based on the local balance equation of momentum and the equation of continuity we derive the local balance equation of kinetic energy for macroscopic and turbulent systems, and in a further step, Bernoulli’s equation and integral equations that customarily serve as the key equations in momentum theory and blade-element analysis, where the Lanchester-Betz-Joukowsky limit, Glauert’s optimum actuator disk, and the results of the blade-element analysis by Okulov and Sorensen are exemplarily illustrated. The wind power potential at three different sites in Interior Alaska (Delta Junction, Eva Creek, and Poker Flat) is assessed by considering the results of wind field predictions for the winter period from October 1, 2008, to April 1, 2009 provided by the Weather Research and Forecasting (WRF) model to avoid time-consuming and expensive tall-tower observations in Interior Alaska which is characterized by a relatively low degree of infrastructure outside of the city of Fairbanks. To predict the average power output we use the Weibull distributions derived from the predicted wind fields for these three different sites and the power curves of five different propeller-type wind turbines with rated powers ranging from 2 MW to 2.5 MW. These power curves are represented by general logistic functions. The predicted power capacity for the Eva Creek site is compared with that of the Eva Creek wind farm established in 2012. The results of our predictions for the winter period 2008/2009 are nearly 20 percent lower than those of the Eva Creek wind farm for the period from January to September 2013.
文摘In this paper the author describes the current development of tbe 10-GW wind power base in Jiuquan, Gansu Province. The.future planning of the wind power base and the relevant power grid are also introduced based on the assessment on the wind resources and the wind power operation with the grid.
文摘With abundant wind resources and high pressure imposed on en-vironmental protection, wind power development has a promising future. Butdue to intermittent nature, wind power can bring into full play only if being con-nected into power grid to ensure its supply reliability and continuity, aswell as operational economy. However, technical and market barriers haveprevented wind power from integrating into power grid. To foster wind powerdevelopment, these barriers should be removed by both government incentivepolicies and sophisticated technologies.
文摘Base on the full and accurate statistical informa tion, the status and existing technical problems of wind power in China are analyzed in terms of yearly installed capacity, single unit capacity, regional distribution, investment as well as the market shares and the local ization of wind power units etc. The paper believes that wind power industry in China will grow by leaps and bounds in near future.
基金supported by Manage Innovation Project of China Southern Power Grid Co.,Ltd.(No.GZHKJXM20210232).
文摘To solve the problem of residual wind power in offshore wind farms,a hydrogen production system with a reasonable capacity was configured to enhance the local load of wind farms and promote the local consumption of residual wind power.By studying the mathematical model of wind power output and calculating surplus wind power,as well as considering the hydrogen production/storage characteristics of the electrolyzer and hydrogen storage tank,an innovative capacity optimization allocation model was established.The objective of the model was to achieve the lowest total net present value over the entire life cycle.The model took into account the cost-benefit breakdown of equipment end-of-life cost,replacement cost,residual value gain,wind abandonment penalty,hydrogen transportation,and environmental value.The MATLAB-based platform invoked the CPLEX commercial solver to solve the model.Combined with the analysis of the annual average wind speed data from an offshore wind farm in Guangdong Province,the optimal capacity configuration results and the actual operation of the hydrogen production system were obtained.Under the calculation scenario,this hydrogen production system could consume 3,800 MWh of residual electricity from offshore wind power each year.It could achieve complete consumption of residual electricity from wind power without incurring the penalty cost of wind power.Additionally,it could produce 66,500 kg of green hydrogen from wind power,resulting in hydrogen sales revenue of 3.63 million RMB.It would also reduce pollutant emissions from coal-based hydrogen production by 1.5 tons and realize an environmental value of 4.83 million RMB.The annual net operating income exceeded 6 million RMB and the whole life cycle NPV income exceeded 50 million RMB.These results verified the feasibility and rationality of the established capacity optimization allocation model.The model could help advance power system planning and operation research and assist offshore wind farm operators in improving economic and environmental benefits.
基金National Basic Research and Development Program(973)(Grant no.2007CB210306)
文摘This paper constructs the index system of the regional division of the development stage of China's wind power resources,including the index of energy,the index of wind energy endowments and other indices.Based on principal component analysis and layered clustering analysis of these indices,and combined with the conceptual function of the development and utilization stage of the wind power,this paper divides the development and utilization stage of the wind power into four stages taking province as the basic yardstick:optimization growth stage,the rapid growth stage,the slow growth stage and the initial growth stage.In addition,this paper briefly discusses the basic strategy that should be adopted in each development stage of wind power resources.
基金National Natural Science Foundation of China(No.61663019)
文摘To solve the severe problem of wind power curtailment in the winter heating period caused by ＂power determined by heat＂ operation constraint of cogeneration units, this paper analyzes thermoelectric load, wind power output distribution and fluctuation characteristics at different time scales, and finally proposes a two level coordinated control strategy based on electric heat storage and pumped storage. The optimization target of the first level coordinated control is the lowest operation cost and the largest wind power utilization rate. Based on prediction of thermoelectric load and wind power, the operation economy of the system and wind power accommodation level are improved with the cooperation of electric heat storage and pumped storage in regulation capacity. The second level coordinated control stabilizes wind power real time fluctuations by cooperating electric heat storage and pumped storage in control speed. The example results of actual wind farms in Jiuquan, Gansu verifies the feasibility and effectiveness of the proposed coordinated control strategy.
文摘In the second half of 2021, electricity rationing suddenly appeared all over the country, so that many enterprises can not take precautions, especially the production enterprises, causing a great impact. The main reasons are the national energy consumption double control policy, coal price rise, economic transformation and other reasons, and the national power rationing is to ensure the completion of the 14th five-year energy conservation binding target and promote the realization of carbon peak neutrality target task. The development of clean energy is one of the important measures to achieve carbon neutrality. Therefore, wind power generation, especially offshore power generation, is a development direction in the future. Offshore wind power generation technology started relatively late, there are still many problems to be solved, there is still a long way to go, the following is to analyze the content of offshore wind power generation.
基金Supported by the National Natural Science Foundation of China(No.51309179)the Tianjin Municipal Natural Science Foundation(No.14JCQNJC07000)
文摘Three- and four-bucket offshore wind power foundations with a new form of force-transferring structure are proposed in this paper, and the integrated finite element model of foundation-soil-transition structure is established by using ABAQUS. The carrying capacity of the proposed foundations is studied under vertical load, horizontal load and bending moment. It can be seen that the vertical bearing capacity of multi-bucket foundation can be roughly estimated by the vertical bearing capacity of single-bucket; the horizontal bearing capacity of the three-bucket foundation scheme is controlled by displacement, while that of the four-bucket foundation scheme is controlled by the internal forces of soils. Moreover, the carrying capacity is provided by the overall structure formed by multi-bucket before soil failure. Compared with the conventional single-bucket foundation, there are mainly tension and pressure that are applied to the multi-bucket foundation, so that the carrying capacity of the foundation can be fully utilized. The probability of soil failure can be well reduced with the proposed multi-bucket foundation, and the stress transmission of force-transferring structure is more consistent through steel beams with variable cross-section.
基金the National Natural Science Foundation of China(NSFC)(Nos.61806087,61902158)Jiangsu Province Natural Science Research Projects(No.17KJB470002)+1 种基金Natural science youth fund of Jiangsu province(No.BK20150471)Jiangsu University of Science and Technology Youth Science and Technology Polytechnic Innovation Project(No.1132931804)。
文摘Accurate wind power forecasting in wind farm can effectively reduce the enormous impact on grid operation safety when high permeability intermittent power supply is connected to the power grid.Aiming to provide reference strategies for relevant researchers as well as practical applications,this paper attempts to provide the literature investigation and methods analysis of deep learning,enforcement learning and transfer learning in wind speed and wind power forecasting modeling.Usually,wind speed and wind power forecasting around a wind farm requires the calculation of the next moment of the definite state,which is usually achieved based on the state of the atmosphere that encompasses nearby atmospheric pressure,temperature,roughness,and obstacles.As an effective method of high-dimensional feature extraction,deep neural network can theoretically deal with arbitrary nonlinear transformation through proper structural design,such as adding noise to outputs,evolutionary learning used to optimize hidden layer weights,optimize the objective function so as to save information that can improve the output accuracy while filter out the irrelevant or less affected information for forecasting.The establishment of high-precision wind speed and wind power forecasting models is always a challenge due to the randomness,instantaneity and seasonal characteristics.
