Offshore wind farms are becoming increasingly distant from onshore centralized control centers,and the communication delays between them inevitably introduce time delays in the measurement signal of the primary freque...Offshore wind farms are becoming increasingly distant from onshore centralized control centers,and the communication delays between them inevitably introduce time delays in the measurement signal of the primary frequency control.This causes a deterioration in the performance of the primary frequency control and,in some cases,may even result in frequency instability within the power system.Therefore,a frequency response model that incorporates communication delays was established for power systems that integrate offshore wind power.The Padéapproximation was used to model the time delays,and a linearized frequency response model of the power system was derived to investigate the frequency stability under different time delays.The influences of the wind power proportion and frequency control parameters on the system frequency stability were explored.In addition,a Smith delay compensation control strategy was devised to mitigate the effects of communication delays on the system frequency dynamics.Finally,a power system incorporating offshore wind power was constructed using the MATLAB/Simulink platform.The simulation results demonstrate the effectiveness and robustness of the proposed delay compensation control strategy.展开更多
This paper deals with control method related to a wind system operating in stand-alone applications. The stand-alone wind system is composed of three energy transfer subsystems： wind generator subsystem （wind turbin...This paper deals with control method related to a wind system operating in stand-alone applications. The stand-alone wind system is composed of three energy transfer subsystems： wind generator subsystem （wind turbine and electrical generator）, energy storage subsystem, respectively, specific local network subsystem （controlled loads）. This wind power system performs in the same time the maximization of wind energy conversion and the power balance between produced and required power. Three structures of the energy storage subsystem, based on buffer battery operation and/or capacitor voltage control, are discussed. The simulation results show that the proposed stand-alone wind power system ensures a good management of the local energy request. The design of the structures is analyzed in Matlab/Simulink environment, using PowerSim toolbox.展开更多
For the output of wind power system has the characteristics of randomness, volatility and intermittence, the voltage of wind power system fluctuates frequently and voltage sag is one of the most common voltage fluctua...For the output of wind power system has the characteristics of randomness, volatility and intermittence, the voltage of wind power system fluctuates frequently and voltage sag is one of the most common voltage fluctuations in wind power system. For the problem of voltage sag of wind power system, the limitations of the detection methods such as the square detection method, the half-wave RMS detection method and wavelet transform are summed up, and a new detecting method named Hilbert-huang Transform(HHT) is put forward in this paper, which can detect the voltage sag accurately and timely. In order to solve the problem of end effect in the process of empirical mode decompostion (EMD), a self-adaptive method named improved waveform matching is applied in dealing with the end issue. Voltage fluctuations are reflected by two parameters named voltage amplitude and frequency of each intrinsic mode function (IMF) in HHT. The practicality of the method is verified by Matlab simulation.展开更多
This paper focuses on the small-signal stability of power system integrated with DFIG-based wind farm. The model of DFIG for small-signal stability analysis has built;the 3-generator 9-bus WECC test system is modified...This paper focuses on the small-signal stability of power system integrated with DFIG-based wind farm. The model of DFIG for small-signal stability analysis has built;the 3-generator 9-bus WECC test system is modified to investigate the impacts of large scale integration of wind power on power system small-signal stability. Different oscillatory modes are obtained with their eigenvalue, frequency and damping ratio, the results from eigenvalue analysis are presented to demonstrate the small-signal stability of power system is enhanced with the increasing output of the wind farm.展开更多
This paper describes the performance, generated power flow distribution and redistribution for each power plant on the grid based on adapting load and weather forecasting data. Both load forecasting and weather foreca...This paper describes the performance, generated power flow distribution and redistribution for each power plant on the grid based on adapting load and weather forecasting data. Both load forecasting and weather forecasting are used for collecting predicting data which are required for optimizing the performance of the grid. The stability of each power systems on the grid highly affected by load varying, and with the presence of the wind power systems on the grid, the grid will be more exposed to lowering its performance and increase the instability to other power systems on the gird. This is because of the intermittence behavior of the generated power from wind turbines as they depend on the wind speed which is varying all the time. However, with a good prediction of the wind speed, a close to the actual power of the wind can be determined. Furthermore, with knowing the load characteristics in advance, the new load curve can be determined after being subtracted from the wind power. Thus, with having the knowledge of the new load curve, and data that collected from SACADA system of the status of all power plants, the power optimization, load distribution and redistribution of the power flows between power plants can be successfully achieved. That is, the improvement of performance, more reliable, and more stable power grid.展开更多
An improved discontinuous space vector modulation(IDSVM)approach is proposed through analysis of key factors to impact fault tolerance control performance for the AC/DC converter in an all-DC offshore wind power syste...An improved discontinuous space vector modulation(IDSVM)approach is proposed through analysis of key factors to impact fault tolerance control performance for the AC/DC converter in an all-DC offshore wind power system.The three main factors being studied are compensation ratios of active vectors,type of vectors used to resynthesize reference voltage vector and zero vectors distribution in time domain.Their effects are discussed from perspective of energy input,transmission and loss in the AC/DC converter.Three schemes using different compensation ratios for active vectors are compared,and scheme C using projections on current axis achieves better fault tolerance performance.In this paper,the distorted voltage vector is used to synthesize reference voltage vector and the healthy zero vector is redistributed in the twelve sectors.The former is beneficial to reduce three-phase current spikes and the latter helps to stabilize DC-link voltage.Performances of the improved modulation approach are verified on an experimental platform simulating the offshore wind turbine with single switch and multiple switch open-circuit faults.Its robustness and high performance are thoroughly evaluated.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
The rapid expansion of offshore wind power plays a crucial role in China’s pursuit of its‘dual carbon goals’.Fractional frequency transmission,an emerging technology for delivering large-scale offshore wind power,c...The rapid expansion of offshore wind power plays a crucial role in China’s pursuit of its‘dual carbon goals’.Fractional frequency transmission,an emerging technology for delivering large-scale offshore wind power,currently lacks extensive research attention.This paper addresses this gap by proposing a reliability assessment model for fractional frequency systems,encompassing generation,boosting,transmission,and conversion processes.Additionally,the study conducts a quantitative analysis of severe weather impacts on offshore component maintenance.With a focus on China’s offshore wind power development,the research includes comparative analyses of various offshore regions,system topologies,and transmission methods to evaluate system reliability.This comprehensive analysis serves as a valuable reference for the strategic planning and large-scale deployment of grid-connected offshore wind power systems.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
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
Power quality improvements help guide and solve the problems of inefficient energy production and unstable power output in wind power systems.The purpose of this paper is mainly to explore the influence of different e...Power quality improvements help guide and solve the problems of inefficient energy production and unstable power output in wind power systems.The purpose of this paper is mainly to explore the influence of different energy storage batteries on various power quality indicators by adding different energy storage devices to the simulated wind power system,and to explore the correlation between systementropy generation and various indicators,so as to provide a theoretical basis for directly improving power quality by reducing loss.A steady-state experiment was performed by replacing the wind wheel with an electric motor,and the output power qualities of the wind power systemwith andwithout energy storagewere compared and analyzed.Moreover,the improvement effect of different energy storage devices on various indicatorswas obtained.Then,based on the entropy theory,the loss of the internal components of the wind power system generator is simulated and explored by Ansys software.Through the analysis of power quality evaluation indicators,such as current harmonic distortion rate,frequency deviation rate,and voltage fluctuation,the correlation between entropy production and each evaluation indicator was explored to investigate effective methods to improve power quality by reducing entropy production.The results showed that the current harmonic distortion rate,voltage fluctuation,voltage deviation,and system entropy production are positively correlated in the tests and that the power factor is negatively correlated with system entropy production.In the frequency range,the frequency deviationwas not significantly correlated with the systementropy production.展开更多
High-frequency oscillation(HFO)of gridconnected wind power generation systems(WPGS)is one of the most critical issues in recent years that threaten the safe access of WPGS to the grid.Ensuring the WPGS can damp HFO is...High-frequency oscillation(HFO)of gridconnected wind power generation systems(WPGS)is one of the most critical issues in recent years that threaten the safe access of WPGS to the grid.Ensuring the WPGS can damp HFO is becoming more and more vital for the development of wind power.The HFO phenomenon of wind turbines under different scenarios usually has different mechanisms.Hence,engineers need to acquire the working mechanisms of the different HFO damping technologies and select the appropriate one to ensure the effective implementation of oscillation damping in practical engineering.This paper introduces the general assumptions of WPGS when analyzing HFO,systematically summarizes the reasons for the occurrence of HFO in different scenarios,deeply analyses the key points and difficulties of HFO damping under different scenarios,and then compares the technical performances of various types of HFO suppression methods to provide adequate references for engineers in the application of technology.Finally,this paper discusses possible future research difficulties in the problem of HFO,as well as the possible future trends in the demand for HFO damping.展开更多
There is a growing need to explore the potential of coal-fired power plants(CFPPs)to enhance the utilization rate of wind power(wind)and photovoltaic power(PV)in the green energy field.This study developed a load regu...There is a growing need to explore the potential of coal-fired power plants(CFPPs)to enhance the utilization rate of wind power(wind)and photovoltaic power(PV)in the green energy field.This study developed a load regulation model for a multi-power generation system comprising wind,PV,and coal energy storage using realworld data.The power supply process was divided into eight fundamental load regulation scenarios,elucidating the influence of each scenario on load regulation.Within the framework of the multi-power generation system with the wind(50 MW)and PV(50 MW)alongside a CFPP(330 MW),a lithium-iron phosphate energy storage system(LIPBESS)was integrated to improve the system’s load regulation flexibility.The energy storage operation strategy was formulated based on the charging and discharging priority of the LIPBESS for each basic scenario and the charging and discharging load calculation method of LIPBESS auxiliary regulation.Through optimization using the particle swarm algorithm,the optimal capacity of LIPBESS was determined to be within the 5.24-4.88 MWh range.From an economic perspective,the LIPBESS operating with CFPP as the regulating power source was 49.1% lower in capacity compared to the renewable energy-based storage mode.展开更多
To mitigate the impact of wind power volatility on power system scheduling,this paper adopts the wind-storage combined unit to improve the dispatchability of wind energy.And a three-level optimal scheduling and power ...To mitigate the impact of wind power volatility on power system scheduling,this paper adopts the wind-storage combined unit to improve the dispatchability of wind energy.And a three-level optimal scheduling and power allocation strategy is proposed for the system containing the wind-storage combined unit.The strategy takes smoothing power output as themain objectives.The first level is the wind-storage joint scheduling,and the second and third levels carry out the unit combination optimization of thermal power and the power allocation of wind power cluster(WPC),respectively,according to the scheduling power of WPC and ESS obtained from the first level.This can ensure the stability,economy and environmental friendliness of the whole power system.Based on the roles of peak shaving-valley filling and fluctuation smoothing of the energy storage system(ESS),this paper decides the charging and discharging intervals of ESS,so that the energy storage and wind power output can be further coordinated.Considering the prediction error and the output uncertainty of wind power,the planned scheduling output of wind farms(WFs)is first optimized on a long timescale,and then the rolling correction optimization of the scheduling output of WFs is carried out on a short timescale.Finally,the effectiveness of the proposed optimal scheduling and power allocation strategy is verified through case analysis.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 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
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
基金the support of the National Natural Science Foundation of China(52077061)Fundamental Research Funds for the Central Universities(B240201121).
文摘Offshore wind farms are becoming increasingly distant from onshore centralized control centers,and the communication delays between them inevitably introduce time delays in the measurement signal of the primary frequency control.This causes a deterioration in the performance of the primary frequency control and,in some cases,may even result in frequency instability within the power system.Therefore,a frequency response model that incorporates communication delays was established for power systems that integrate offshore wind power.The Padéapproximation was used to model the time delays,and a linearized frequency response model of the power system was derived to investigate the frequency stability under different time delays.The influences of the wind power proportion and frequency control parameters on the system frequency stability were explored.In addition,a Smith delay compensation control strategy was devised to mitigate the effects of communication delays on the system frequency dynamics.Finally,a power system incorporating offshore wind power was constructed using the MATLAB/Simulink platform.The simulation results demonstrate the effectiveness and robustness of the proposed delay compensation control strategy.
文摘This paper deals with control method related to a wind system operating in stand-alone applications. The stand-alone wind system is composed of three energy transfer subsystems： wind generator subsystem （wind turbine and electrical generator）, energy storage subsystem, respectively, specific local network subsystem （controlled loads）. This wind power system performs in the same time the maximization of wind energy conversion and the power balance between produced and required power. Three structures of the energy storage subsystem, based on buffer battery operation and/or capacitor voltage control, are discussed. The simulation results show that the proposed stand-alone wind power system ensures a good management of the local energy request. The design of the structures is analyzed in Matlab/Simulink environment, using PowerSim toolbox.
文摘For the output of wind power system has the characteristics of randomness, volatility and intermittence, the voltage of wind power system fluctuates frequently and voltage sag is one of the most common voltage fluctuations in wind power system. For the problem of voltage sag of wind power system, the limitations of the detection methods such as the square detection method, the half-wave RMS detection method and wavelet transform are summed up, and a new detecting method named Hilbert-huang Transform(HHT) is put forward in this paper, which can detect the voltage sag accurately and timely. In order to solve the problem of end effect in the process of empirical mode decompostion (EMD), a self-adaptive method named improved waveform matching is applied in dealing with the end issue. Voltage fluctuations are reflected by two parameters named voltage amplitude and frequency of each intrinsic mode function (IMF) in HHT. The practicality of the method is verified by Matlab simulation.
文摘This paper focuses on the small-signal stability of power system integrated with DFIG-based wind farm. The model of DFIG for small-signal stability analysis has built;the 3-generator 9-bus WECC test system is modified to investigate the impacts of large scale integration of wind power on power system small-signal stability. Different oscillatory modes are obtained with their eigenvalue, frequency and damping ratio, the results from eigenvalue analysis are presented to demonstrate the small-signal stability of power system is enhanced with the increasing output of the wind farm.
文摘This paper describes the performance, generated power flow distribution and redistribution for each power plant on the grid based on adapting load and weather forecasting data. Both load forecasting and weather forecasting are used for collecting predicting data which are required for optimizing the performance of the grid. The stability of each power systems on the grid highly affected by load varying, and with the presence of the wind power systems on the grid, the grid will be more exposed to lowering its performance and increase the instability to other power systems on the gird. This is because of the intermittence behavior of the generated power from wind turbines as they depend on the wind speed which is varying all the time. However, with a good prediction of the wind speed, a close to the actual power of the wind can be determined. Furthermore, with knowing the load characteristics in advance, the new load curve can be determined after being subtracted from the wind power. Thus, with having the knowledge of the new load curve, and data that collected from SACADA system of the status of all power plants, the power optimization, load distribution and redistribution of the power flows between power plants can be successfully achieved. That is, the improvement of performance, more reliable, and more stable power grid.
基金funded by National Key Research&Development Program of China under Grant No.2019YFE0104800,Major Research Plan of the National Natural Science Foundation of China under Grant No.92270101.
文摘An improved discontinuous space vector modulation(IDSVM)approach is proposed through analysis of key factors to impact fault tolerance control performance for the AC/DC converter in an all-DC offshore wind power system.The three main factors being studied are compensation ratios of active vectors,type of vectors used to resynthesize reference voltage vector and zero vectors distribution in time domain.Their effects are discussed from perspective of energy input,transmission and loss in the AC/DC converter.Three schemes using different compensation ratios for active vectors are compared,and scheme C using projections on current axis achieves better fault tolerance performance.In this paper,the distorted voltage vector is used to synthesize reference voltage vector and the healthy zero vector is redistributed in the twelve sectors.The former is beneficial to reduce three-phase current spikes and the latter helps to stabilize DC-link voltage.Performances of the improved modulation approach are verified on an experimental platform simulating the offshore wind turbine with single switch and multiple switch open-circuit faults.Its robustness and high performance are thoroughly evaluated.
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supported by the Project“Research on Power Frequency System Planning Methods Considering the Interactive Influence of Fractional Frequency Transmission System”(No.030400KK52220025).
文摘The rapid expansion of offshore wind power plays a crucial role in China’s pursuit of its‘dual carbon goals’.Fractional frequency transmission,an emerging technology for delivering large-scale offshore wind power,currently lacks extensive research attention.This paper addresses this gap by proposing a reliability assessment model for fractional frequency systems,encompassing generation,boosting,transmission,and conversion processes.Additionally,the study conducts a quantitative analysis of severe weather impacts on offshore component maintenance.With a focus on China’s offshore wind power development,the research includes comparative analyses of various offshore regions,system topologies,and transmission methods to evaluate system reliability.This comprehensive analysis serves as a valuable reference for the strategic planning and large-scale deployment of grid-connected offshore wind power systems.
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 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Supported by the National Natural Science Foundation of China(No.51966013)Inner Mongolia Natural Science Foundation Jieqing Project(No.2023JQ04)+1 种基金the National Natural Science Foundation of China(No.51966018)the Natural Science Foundation of Inner Mongolia Autonomous Region(No.STZC202230).
文摘Power quality improvements help guide and solve the problems of inefficient energy production and unstable power output in wind power systems.The purpose of this paper is mainly to explore the influence of different energy storage batteries on various power quality indicators by adding different energy storage devices to the simulated wind power system,and to explore the correlation between systementropy generation and various indicators,so as to provide a theoretical basis for directly improving power quality by reducing loss.A steady-state experiment was performed by replacing the wind wheel with an electric motor,and the output power qualities of the wind power systemwith andwithout energy storagewere compared and analyzed.Moreover,the improvement effect of different energy storage devices on various indicatorswas obtained.Then,based on the entropy theory,the loss of the internal components of the wind power system generator is simulated and explored by Ansys software.Through the analysis of power quality evaluation indicators,such as current harmonic distortion rate,frequency deviation rate,and voltage fluctuation,the correlation between entropy production and each evaluation indicator was explored to investigate effective methods to improve power quality by reducing entropy production.The results showed that the current harmonic distortion rate,voltage fluctuation,voltage deviation,and system entropy production are positively correlated in the tests and that the power factor is negatively correlated with system entropy production.In the frequency range,the frequency deviationwas not significantly correlated with the systementropy production.
基金supported in part by the Fundamental Research Funds for the Central Universities under Grant 2682023CX019National Natural Science Foundation of China under Grant U23B6007 and Grant 52307141Sichuan Science and Technology Program under Grant 2024NSFSC0115。
文摘High-frequency oscillation(HFO)of gridconnected wind power generation systems(WPGS)is one of the most critical issues in recent years that threaten the safe access of WPGS to the grid.Ensuring the WPGS can damp HFO is becoming more and more vital for the development of wind power.The HFO phenomenon of wind turbines under different scenarios usually has different mechanisms.Hence,engineers need to acquire the working mechanisms of the different HFO damping technologies and select the appropriate one to ensure the effective implementation of oscillation damping in practical engineering.This paper introduces the general assumptions of WPGS when analyzing HFO,systematically summarizes the reasons for the occurrence of HFO in different scenarios,deeply analyses the key points and difficulties of HFO damping under different scenarios,and then compares the technical performances of various types of HFO suppression methods to provide adequate references for engineers in the application of technology.Finally,this paper discusses possible future research difficulties in the problem of HFO,as well as the possible future trends in the demand for HFO damping.
基金supported by the Natural Science Foundation of China(Grant Nos.52076079,52206010)Natural Science Foundation of Hebei Province,China(Grant No.E2020502013)the Fundamental Research Funds for the Central Universities(2021MS076,2021MS079).
文摘There is a growing need to explore the potential of coal-fired power plants(CFPPs)to enhance the utilization rate of wind power(wind)and photovoltaic power(PV)in the green energy field.This study developed a load regulation model for a multi-power generation system comprising wind,PV,and coal energy storage using realworld data.The power supply process was divided into eight fundamental load regulation scenarios,elucidating the influence of each scenario on load regulation.Within the framework of the multi-power generation system with the wind(50 MW)and PV(50 MW)alongside a CFPP(330 MW),a lithium-iron phosphate energy storage system(LIPBESS)was integrated to improve the system’s load regulation flexibility.The energy storage operation strategy was formulated based on the charging and discharging priority of the LIPBESS for each basic scenario and the charging and discharging load calculation method of LIPBESS auxiliary regulation.Through optimization using the particle swarm algorithm,the optimal capacity of LIPBESS was determined to be within the 5.24-4.88 MWh range.From an economic perspective,the LIPBESS operating with CFPP as the regulating power source was 49.1% lower in capacity compared to the renewable energy-based storage mode.
基金supported by the State Grid Jiangsu Electric Power Co.,Ltd.Technology Project(J2023035).
文摘To mitigate the impact of wind power volatility on power system scheduling,this paper adopts the wind-storage combined unit to improve the dispatchability of wind energy.And a three-level optimal scheduling and power allocation strategy is proposed for the system containing the wind-storage combined unit.The strategy takes smoothing power output as themain objectives.The first level is the wind-storage joint scheduling,and the second and third levels carry out the unit combination optimization of thermal power and the power allocation of wind power cluster(WPC),respectively,according to the scheduling power of WPC and ESS obtained from the first level.This can ensure the stability,economy and environmental friendliness of the whole power system.Based on the roles of peak shaving-valley filling and fluctuation smoothing of the energy storage system(ESS),this paper decides the charging and discharging intervals of ESS,so that the energy storage and wind power output can be further coordinated.Considering the prediction error and the output uncertainty of wind power,the planned scheduling output of wind farms(WFs)is first optimized on a long timescale,and then the rolling correction optimization of the scheduling output of WFs is carried out on a short timescale.Finally,the effectiveness of the proposed optimal scheduling and power allocation strategy is verified through case analysis.
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 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.
