The world’s most powerful offshore wind turbine has begun feeding electricity into the grid off the coast of southeast China,marking a major technological leap in the country’s wind power industry.The colossal turbi...The world’s most powerful offshore wind turbine has begun feeding electricity into the grid off the coast of southeast China,marking a major technological leap in the country’s wind power industry.The colossal turbine,developed and installed by China Three Gorges Corp.(CTG),is located in the Phase II Liuao offshore wind farm,more than 30 km off the coast of Fujian in waters deeper than 40 metres.The 20-mw unit successfully completed commissioning and started operation on 5 February,CTG announced.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
Establishing power systems with a high share of renewable energy sources is a pivotal step toward achieving a globally sustainable transition to green and low-carbon energy.This study focuses on low-output wind power ...Establishing power systems with a high share of renewable energy sources is a pivotal step toward achieving a globally sustainable transition to green and low-carbon energy.This study focuses on low-output wind power that affects the generation capacity of power systems with a high share of renewable energy sources.Utilizing the Coupled Model Intercomparison Project Phase 6 datasets,a predictive model for low-output wind power was employed to investigate regional trends worldwide.The frequency and duration of low-output wind-power events exhibited increasing trends globally,particularly in East Asia and South America,but not in North America.By 2060,the annual total days with low-output wind power in East Asia and South America could rise to 13 and 5 d,and the maximum continuous duration of low-output wind power could reach 5 and 2 d,respectively.As wind power becomes a primary elec-tricity source,such low output could lead to shortages in energy supply within the power system,trig-gering large-scale power outages.This issue calls for critical attention when establishing power systems with a high share of renewable energy sources.The conclusions provide a basis for analyzing power supply risks and configuring flexible power sources for scenarios with a high share of renewable energy.展开更多
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
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
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
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
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
Stratospheric airships are lighter-than-air vehicles capable of continuous flying for months.The energy balance of the airship is the key to long-duration flights.The stratospheric airship is entirely powered by the s...Stratospheric airships are lighter-than-air vehicles capable of continuous flying for months.The energy balance of the airship is the key to long-duration flights.The stratospheric airship is entirely powered by the solar array.It is necessary to accurately predict the output power of the array for any flight state.Because of the uneven solar radiation received by the solar array,the traditional model based on components has a slow simulation speed.In this study,a data-driven surrogate modeling approach for prediction the output power of the solar array is proposed.The surrogate model is trained using the samples obtained from the high-accuracy simulation model.By using the input parameter preprocessor,the accuracy of the surrogate model in predicting the output power of the solar array is improved to 98.65%.In addition,the predictive speed of the surrogate model is ten million times faster than the traditional simulation model.Finally,the surrogate model is used to predict the energy balance of stratospheric airships flying throughout the year under actual global wind fields.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
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
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
The Sine and Wormhole Energy Whale Optimization Algorithm(SWEWOA)represents an advanced solution method for resolving Optimal Power Flow(OPF)problems in power systems equipped with Flexible AC Transmission System(FACT...The Sine and Wormhole Energy Whale Optimization Algorithm(SWEWOA)represents an advanced solution method for resolving Optimal Power Flow(OPF)problems in power systems equipped with Flexible AC Transmission System(FACTS)devices which include Thyristor-Controlled Series Compensator(TCSC),Thyristor-Controlled Phase Shifter(TCPS),and Static Var Compensator(SVC).SWEWOA expands Whale Optimization Algorithm(WOA)through the integration of sine and wormhole energy features thus improving exploration and exploitation capabilities for efficient convergence in complex non-linear OPF problems.A performance evaluation of SWEWOA takes place on the IEEE-30 bus test system through static and dynamic loading scenarios where it demonstrates better results than five contemporary algorithms:Adaptive Chaotic WOA(ACWOA),WOA,Chaotic WOA(CWOA),Sine Cosine Algorithm Differential Evolution(SCADE),and Hybrid Grey Wolf Optimization(HGWO).The research shows that SWEWOA delivers superior generation cost reduction than other algorithms by reaching a minimum of 0.9%better performance.SWEWOA demonstrates superior power loss performance by achieving(P_(loss,min))at the lowest level compared to all other tested algorithms which leads to better system energy efficiency.The dynamic loading performance of SWEWOA leads to a 4.38%reduction in gross costs which proves its capability to handle different operating conditions.The algorithm achieves top performance in Friedman Rank Test(FRT)assessments through multiple performance metrics which verifies its consistent reliability and strong stability during changing power demands.The repeated simulations show that SWEWOA generates mean costs(C_(gen,min))and mean power loss values(P_(loss,min))with small deviations which indicate its capability to maintain cost-effective solutions in each simulation run.SWEWOA demonstrates great potential as an advanced optimization solution for power system operations through the results presented in this study.展开更多
This study investigates the Maximum Power Point Tracking(MPPT)control method of offshore windphotovoltaic hybrid power generation system with offshore crane-assisted.A new algorithm of Global Fast Integral Sliding Mod...This study investigates the Maximum Power Point Tracking(MPPT)control method of offshore windphotovoltaic hybrid power generation system with offshore crane-assisted.A new algorithm of Global Fast Integral Sliding Mode Control(GFISMC)is proposed based on the tip speed ratio method and sliding mode control.The algorithm uses fast integral sliding mode surface and fuzzy fast switching control items to ensure that the offshore wind power generation system can track the maximum power point quickly and with low jitter.An offshore wind power generation system model is presented to verify the algorithm effect.An offshore off-grid wind-solar hybrid power generation systemis built in MATLAB/Simulink.Compared with other MPPT algorithms,this study has specific quantitative improvements in terms of convergence speed,tracking accuracy or computational efficiency.Finally,the improved algorithm is further analyzed and carried out by using Yuankuan Energy’s ModelingTech semi-physical simulation platform.The results verify the feasibility and effectiveness of the improved algorithm in the offshore wind-solar hybrid power generation system.展开更多
Although wind energy is volatile,the output of a wind-storage plant is partially dispatchable,making it a promising paradigm on the generation side.A grid-friendly wind-storage plant ought to be able to continuously o...Although wind energy is volatile,the output of a wind-storage plant is partially dispatchable,making it a promising paradigm on the generation side.A grid-friendly wind-storage plant ought to be able to continuously output the desired power over a certain period of time.This paper proposes a dependable dynamic capacity provision scheme of a wind-storage plant over a daily horizon.It stipulates a minimum number of periods during which the committed capacity must be fulfilled and a maximum mismatch during the remaining periods when the desired power output is not achievable.In the general case,the day-ahead piecewise constant capacity provision results in a two-stage stochastic program formulated as a mixed-integer linear program.Specifically,for constant capacity provision,a decomposition algorithm is developed to determine the global optimal solution,and the complexity grows linearly with the number of scenarios.Given the committed capacity trajectory,the real-time operation problem is modeled as a four-state stochastic dynamic program.The discrete state-action values are derived recursively via the principle of optimality.Real-time dispatch actions are generated by using the action-value tabular leveraging inexact ultra-short-term forecasts.Numerical tests over one year demonstrate that the proposed method successfully fulfills reliable operation on 355 days and achieve an optimality gap of 9.47%compared with the ex-post optimum,which is comparable to model predictive control using exact 2–3-hour-ahead wind power forecasts.展开更多
Monocolumn composite bucket foundation is a new type of offshore wind energy foundation.Its bearing characteristics under shallow bedrock conditions and complex geological conditions have not been extensively studied....Monocolumn composite bucket foundation is a new type of offshore wind energy foundation.Its bearing characteristics under shallow bedrock conditions and complex geological conditions have not been extensively studied.Therefore,to analyze its bearing characteristics under complex conditions-such as silty soil,chalky soil,and shallow bedrock-this paper employs finite element software to establish various soil combination scenarios.The load-displacement curves of the foundations under these scenarios are calculated to subsequently evaluate the horizontal ultimate bearing capacity.This study investigates the effects of shallow bedrock depth,the type of soil above the bedrock,the thickness of layered soil,and the quality of layered soil on the bearing characteristics of the monocolumn composite bucket foundation.Based on the principle of single-variable control,the ultimate bearing capacity characteristics of the foundation under different conditions are compared.The distribution of soil pressure inside and outside the bucket wall on the compressed side of the foundation,along with the plastic strain of the soil at the base of the foundation,is also analyzed.In conclusion,shallow bedrock somewhat reduces foundation bearing capacity.Under shallow bedrock conditions,the degree of influence on foundation bearing capacity characteristics can considerably vary on different upper soils.The thickness of each soil layer and the depth to bedrock in stratified soils also affect the bearing capacity of the foundation.The findings of this paper provide a theoretical reference for related foundation design and construction.In practice,the bearing performance of the foundation can be enhanced by improvingthe soil quality in the bucket,adjusting the penetration depth,adjusting the percentage of different types of soil layers in the bucket,and applying other technical construction methods.展开更多
An optimized volt-ampere reactive(VAR)control framework is proposed for transmission-level power systems to simultaneously mitigate voltage deviations and active-power losses through coordinated control of large-scale...An optimized volt-ampere reactive(VAR)control framework is proposed for transmission-level power systems to simultaneously mitigate voltage deviations and active-power losses through coordinated control of large-scale wind/solar farms with shunt static var generators(SVGs).The model explicitly represents reactive-power regulation characteristics of doubly-fed wind turbines and PV inverters under real-time meteorological conditions,and quantifies SVG high-speed compensation capability,enabling seamless transition from localized VAR management to a globally coordinated strategy.An enhanced adaptive gain-sharing knowledge optimizer(AGSK-SD)integrates simulated annealing and diversity maintenance to autonomously tune voltage-control actions,renewable source reactive-power set-points,and SVG output.The algorithm adaptively modulates knowledge factors and ratios across search phases,performs SA-based fine-grained local exploitation,and periodically re-injects population diversity to prevent premature convergence.Comprehensive tests on IEEE 9-bus and 39-bus systems demonstrate AGSK-SD’s superiority over NSGA-II and MOPSO in hypervolume(HV),inverse generative distance(IGD),and spread metrics while maintaining acceptable computational burden.The method reduces network losses from 2.7191 to 2.15 MW(20.79%reduction)and from 15.1891 to 11.22 MW(26.16%reduction)in the 9-bus and 39-bus systems respectively.Simultaneously,the cumulative voltage-deviation index decreases from 0.0277 to 3.42×10^(−4) p.u.(98.77%reduction)in the 9-bus system,and from 0.0556 to 0.0107 p.u.(80.76%reduction)in the 39-bus system.These improvements demonstrate significant suppression of line losses and voltage fluctuations.Comparative analysis with traditional heuristic optimization algorithms confirms the superior performance of the proposed approach.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
文摘The world’s most powerful offshore wind turbine has begun feeding electricity into the grid off the coast of southeast China,marking a major technological leap in the country’s wind power industry.The colossal turbine,developed and installed by China Three Gorges Corp.(CTG),is located in the Phase II Liuao offshore wind farm,more than 30 km off the coast of Fujian in waters deeper than 40 metres.The 20-mw unit successfully completed commissioning and started operation on 5 February,CTG announced.
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supported by the Joint Research Fund in Smart Grid(U1966601)under cooperative agreement between the National Natural Science Foundation of China(NSFC)and the State Grid Cor-poration of China(SGCC).
文摘Establishing power systems with a high share of renewable energy sources is a pivotal step toward achieving a globally sustainable transition to green and low-carbon energy.This study focuses on low-output wind power that affects the generation capacity of power systems with a high share of renewable energy sources.Utilizing the Coupled Model Intercomparison Project Phase 6 datasets,a predictive model for low-output wind power was employed to investigate regional trends worldwide.The frequency and duration of low-output wind-power events exhibited increasing trends globally,particularly in East Asia and South America,but not in North America.By 2060,the annual total days with low-output wind power in East Asia and South America could rise to 13 and 5 d,and the maximum continuous duration of low-output wind power could reach 5 and 2 d,respectively.As wind power becomes a primary elec-tricity source,such low output could lead to shortages in energy supply within the power system,trig-gering large-scale power outages.This issue calls for critical attention when establishing power systems with a high share of renewable energy sources.The conclusions provide a basis for analyzing power supply risks and configuring flexible power sources for scenarios with a high share of renewable energy.
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 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.
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.
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supported by the National Natural Science Foundation of China(Nos.51775021,52302511)the Fundamental Research Funds for the Central Universities,China(Nos.YWF-23-JC-01,YWF-23-JC-04,YWF-23-JC-09)。
文摘Stratospheric airships are lighter-than-air vehicles capable of continuous flying for months.The energy balance of the airship is the key to long-duration flights.The stratospheric airship is entirely powered by the solar array.It is necessary to accurately predict the output power of the array for any flight state.Because of the uneven solar radiation received by the solar array,the traditional model based on components has a slow simulation speed.In this study,a data-driven surrogate modeling approach for prediction the output power of the solar array is proposed.The surrogate model is trained using the samples obtained from the high-accuracy simulation model.By using the input parameter preprocessor,the accuracy of the surrogate model in predicting the output power of the solar array is improved to 98.65%.In addition,the predictive speed of the surrogate model is ten million times faster than the traditional simulation model.Finally,the surrogate model is used to predict the energy balance of stratospheric airships flying throughout the year under actual global wind fields.
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 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.
文摘The Sine and Wormhole Energy Whale Optimization Algorithm(SWEWOA)represents an advanced solution method for resolving Optimal Power Flow(OPF)problems in power systems equipped with Flexible AC Transmission System(FACTS)devices which include Thyristor-Controlled Series Compensator(TCSC),Thyristor-Controlled Phase Shifter(TCPS),and Static Var Compensator(SVC).SWEWOA expands Whale Optimization Algorithm(WOA)through the integration of sine and wormhole energy features thus improving exploration and exploitation capabilities for efficient convergence in complex non-linear OPF problems.A performance evaluation of SWEWOA takes place on the IEEE-30 bus test system through static and dynamic loading scenarios where it demonstrates better results than five contemporary algorithms:Adaptive Chaotic WOA(ACWOA),WOA,Chaotic WOA(CWOA),Sine Cosine Algorithm Differential Evolution(SCADE),and Hybrid Grey Wolf Optimization(HGWO).The research shows that SWEWOA delivers superior generation cost reduction than other algorithms by reaching a minimum of 0.9%better performance.SWEWOA demonstrates superior power loss performance by achieving(P_(loss,min))at the lowest level compared to all other tested algorithms which leads to better system energy efficiency.The dynamic loading performance of SWEWOA leads to a 4.38%reduction in gross costs which proves its capability to handle different operating conditions.The algorithm achieves top performance in Friedman Rank Test(FRT)assessments through multiple performance metrics which verifies its consistent reliability and strong stability during changing power demands.The repeated simulations show that SWEWOA generates mean costs(C_(gen,min))and mean power loss values(P_(loss,min))with small deviations which indicate its capability to maintain cost-effective solutions in each simulation run.SWEWOA demonstrates great potential as an advanced optimization solution for power system operations through the results presented in this study.
基金supported by the 2022 Sanya Science and Technology Innovation Project,China(No.2022KJCX03)the Sanya Science and Education Innovation Park,Wuhan University of Technology,China(Grant No.2022KF0028)the Hainan Provincial Joint Project of Sanya Yazhou Bay Science and Technology City,China(Grant No.2021JJLH0036).
文摘This study investigates the Maximum Power Point Tracking(MPPT)control method of offshore windphotovoltaic hybrid power generation system with offshore crane-assisted.A new algorithm of Global Fast Integral Sliding Mode Control(GFISMC)is proposed based on the tip speed ratio method and sliding mode control.The algorithm uses fast integral sliding mode surface and fuzzy fast switching control items to ensure that the offshore wind power generation system can track the maximum power point quickly and with low jitter.An offshore wind power generation system model is presented to verify the algorithm effect.An offshore off-grid wind-solar hybrid power generation systemis built in MATLAB/Simulink.Compared with other MPPT algorithms,this study has specific quantitative improvements in terms of convergence speed,tracking accuracy or computational efficiency.Finally,the improved algorithm is further analyzed and carried out by using Yuankuan Energy’s ModelingTech semi-physical simulation platform.The results verify the feasibility and effectiveness of the improved algorithm in the offshore wind-solar hybrid power generation system.
基金supported by the Smart Grid-National Science and Technology Major Project of China(2024ZD0802000).
文摘Although wind energy is volatile,the output of a wind-storage plant is partially dispatchable,making it a promising paradigm on the generation side.A grid-friendly wind-storage plant ought to be able to continuously output the desired power over a certain period of time.This paper proposes a dependable dynamic capacity provision scheme of a wind-storage plant over a daily horizon.It stipulates a minimum number of periods during which the committed capacity must be fulfilled and a maximum mismatch during the remaining periods when the desired power output is not achievable.In the general case,the day-ahead piecewise constant capacity provision results in a two-stage stochastic program formulated as a mixed-integer linear program.Specifically,for constant capacity provision,a decomposition algorithm is developed to determine the global optimal solution,and the complexity grows linearly with the number of scenarios.Given the committed capacity trajectory,the real-time operation problem is modeled as a four-state stochastic dynamic program.The discrete state-action values are derived recursively via the principle of optimality.Real-time dispatch actions are generated by using the action-value tabular leveraging inexact ultra-short-term forecasts.Numerical tests over one year demonstrate that the proposed method successfully fulfills reliable operation on 355 days and achieve an optimality gap of 9.47%compared with the ex-post optimum,which is comparable to model predictive control using exact 2–3-hour-ahead wind power forecasts.
文摘Monocolumn composite bucket foundation is a new type of offshore wind energy foundation.Its bearing characteristics under shallow bedrock conditions and complex geological conditions have not been extensively studied.Therefore,to analyze its bearing characteristics under complex conditions-such as silty soil,chalky soil,and shallow bedrock-this paper employs finite element software to establish various soil combination scenarios.The load-displacement curves of the foundations under these scenarios are calculated to subsequently evaluate the horizontal ultimate bearing capacity.This study investigates the effects of shallow bedrock depth,the type of soil above the bedrock,the thickness of layered soil,and the quality of layered soil on the bearing characteristics of the monocolumn composite bucket foundation.Based on the principle of single-variable control,the ultimate bearing capacity characteristics of the foundation under different conditions are compared.The distribution of soil pressure inside and outside the bucket wall on the compressed side of the foundation,along with the plastic strain of the soil at the base of the foundation,is also analyzed.In conclusion,shallow bedrock somewhat reduces foundation bearing capacity.Under shallow bedrock conditions,the degree of influence on foundation bearing capacity characteristics can considerably vary on different upper soils.The thickness of each soil layer and the depth to bedrock in stratified soils also affect the bearing capacity of the foundation.The findings of this paper provide a theoretical reference for related foundation design and construction.In practice,the bearing performance of the foundation can be enhanced by improvingthe soil quality in the bucket,adjusting the penetration depth,adjusting the percentage of different types of soil layers in the bucket,and applying other technical construction methods.
基金supported by Yunnan Power Grid Co.,Ltd.Science and Technology Project:Research and application of key technologies for graphical-based power grid accident reconstruction and simulation(YNKJXM20240333).
文摘An optimized volt-ampere reactive(VAR)control framework is proposed for transmission-level power systems to simultaneously mitigate voltage deviations and active-power losses through coordinated control of large-scale wind/solar farms with shunt static var generators(SVGs).The model explicitly represents reactive-power regulation characteristics of doubly-fed wind turbines and PV inverters under real-time meteorological conditions,and quantifies SVG high-speed compensation capability,enabling seamless transition from localized VAR management to a globally coordinated strategy.An enhanced adaptive gain-sharing knowledge optimizer(AGSK-SD)integrates simulated annealing and diversity maintenance to autonomously tune voltage-control actions,renewable source reactive-power set-points,and SVG output.The algorithm adaptively modulates knowledge factors and ratios across search phases,performs SA-based fine-grained local exploitation,and periodically re-injects population diversity to prevent premature convergence.Comprehensive tests on IEEE 9-bus and 39-bus systems demonstrate AGSK-SD’s superiority over NSGA-II and MOPSO in hypervolume(HV),inverse generative distance(IGD),and spread metrics while maintaining acceptable computational burden.The method reduces network losses from 2.7191 to 2.15 MW(20.79%reduction)and from 15.1891 to 11.22 MW(26.16%reduction)in the 9-bus and 39-bus systems respectively.Simultaneously,the cumulative voltage-deviation index decreases from 0.0277 to 3.42×10^(−4) p.u.(98.77%reduction)in the 9-bus system,and from 0.0556 to 0.0107 p.u.(80.76%reduction)in the 39-bus system.These improvements demonstrate significant suppression of line losses and voltage fluctuations.Comparative analysis with traditional heuristic optimization algorithms confirms the superior performance of the proposed approach.
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.
