Randomness and fluctuations in wind power output may cause changes in important parameters(e.g.,grid frequency and voltage),which in turn affect the stable operation of a power system.However,owing to external factors...Randomness and fluctuations in wind power output may cause changes in important parameters(e.g.,grid frequency and voltage),which in turn affect the stable operation of a power system.However,owing to external factors(such as weather),there are often various anomalies in wind power data,such as missing numerical values and unreasonable data.This significantly affects the accuracy of wind power generation predictions and operational decisions.Therefore,developing and applying reliable wind power interpolation methods is important for promoting the sustainable development of the wind power industry.In this study,the causes of abnormal data in wind power generation were first analyzed from a practical perspective.Second,an improved complete ensemble empirical mode decomposition with adaptive noise(ICEEMDAN)method with a generative adversarial interpolation network(GAIN)network was proposed to preprocess wind power generation and interpolate missing wind power generation sub-components.Finally,a complete wind power generation time series was reconstructed.Compared to traditional methods,the proposed ICEEMDAN-GAIN combination interpolation model has a higher interpolation accuracy and can effectively reduce the error impact caused by wind power generation sequence fluctuations.展开更多
According to the Chinese 'Twelfth Five-Year Plan', two large scale wind farms are planned to be built in the shore of Shandong province and Guangdong province to meet the increasing electricity demand with eco...According to the Chinese 'Twelfth Five-Year Plan', two large scale wind farms are planned to be built in the shore of Shandong province and Guangdong province to meet the increasing electricity demand with economic development. Before the construction of wind farm, it is necessary to evaluate the wind potential and its temporal variation along the coast of Shandong province, Guangdong province and Zhejiang province that have been studied in this paper. The data used were obtained from Goddard Earth Observing System (GEOS) Data Assimilation System. The results showed that there is rich wind supply in Zhejiang province with small annual variation. Further away from shore, the wind energy will increase fastest in Guangdong area. The yield of wind energy in Shandong province is not as rich as in the other two provinces as predicted in the study. Furthermore, the layout of wind turbines in wind farm was also investigated to absorb wind energy at the highest efficiency by wind farm. Our results provide a reference for the future construction of wind farms.展开更多
The author’s research group has been conducting research on applications of various meteorological Grid Point Value (GPV) data offered by the Japan Meteorological Agency (JMA) to the field of wind power generation. I...The author’s research group has been conducting research on applications of various meteorological Grid Point Value (GPV) data offered by the Japan Meteorological Agency (JMA) to the field of wind power generation. In particular, the group’s research has been focusing on the following areas: 1) the use of GPV data from the JMA Meso-Scale Model (MSM-S;horizontal resolution: 5 km) and the JMA Local Forecast Model (LFM-S;horizontal resolution: 2 km), and 2) examinations of the prediction accuracy of local wind assessment with the use of these data. In both the MSM-S and the LFM-S, grid points are fixed at 10 m above the sea (ground) surface. The purpose of the present study is to establish a method in which the values of the MSM-S and LFM-S wind speed data from the 10 m height are used as the reference wind speed and are, using a power law, vertically extrapolated to 80 to 90 m heights, typical hub-heights of offshore wind turbines. For this purpose, the present study examined time-averaged vertical profiles of wind speed over the ocean based on the MSM-S data and in-situ data in the Hibikinada area, Kitakyushu City, Fukuoka Prefecture, Japan. As a result, it was revealed that a strong wind shear existed close to the sea surface, between the 10 and 30 m heights. In order to address the above-mentioned wind shear, a two-step vertical extrapolation method was proposed in the present study. In this method, two values of N, specifically for low and high altitudes (below and above approximately 30 m, respectively), were calculated and used. The data were created for the five years between 2012 and 2016. Similarly to previous analyses, the analysis of the created data set indicated that the potential of offshore wind power generation in the Hibikinada area, Kitakyushu City is quite high.展开更多
在中小型永磁同步风力发电机中,桥式整流+DC/DC变换器拓扑具有结构简单的优点,其中DC/DC电路有Boost和Buck两种拓扑,探究两者的最大功率点跟踪(Maximum Power Point Tracking,MPPT)控制性能区别是对其优化设计的关键,测控系统是性能分...在中小型永磁同步风力发电机中,桥式整流+DC/DC变换器拓扑具有结构简单的优点,其中DC/DC电路有Boost和Buck两种拓扑,探究两者的最大功率点跟踪(Maximum Power Point Tracking,MPPT)控制性能区别是对其优化设计的关键,测控系统是性能分析的重要手段。通过系统总体方案设计,确认了系统参数及指标;分析了四桥臂DC/DC拓扑,并基于高端电流采样和隔离采样技术实现了系统电压、电流、功率和频率的采集,设计了功率驱动及单片机控制电路;探讨了适合单片机的MPPT爬坡搜索法软件;给出了基于LabVIEW的上位机操控系统的软件设计实现;搭建了测控系统,进行了MPPT和两种拓扑测控结果对比实验。实验结果表明测控系统达到了设计目标。展开更多
风速-功率曲线广泛应用于风电机组的功率预测、状态监测和故障诊断,其主要构建方法是使用风电场SCADA(supervisory control and data acquisition)数据进行拟合。然而由于弃风限电、仪表故障等因素,SCADA数据中存在部分功率异常数据。...风速-功率曲线广泛应用于风电机组的功率预测、状态监测和故障诊断,其主要构建方法是使用风电场SCADA(supervisory control and data acquisition)数据进行拟合。然而由于弃风限电、仪表故障等因素,SCADA数据中存在部分功率异常数据。为保证拟合结果的准确可靠,应首先剔除这些异常数据。文中提出了一种风电机组功率异常数据剔除方法:首先使用分位数方法剔除距离正常数据较远的离散点,而后结合K-means聚类方法和改进时序方法剔除中部堆积点,最后使用分位数方法和DBSCAN(density-based spatial clustering of applications with noise)聚类方法的组合方法剔除距离正常数据较近的离散点。文中分别使用仿真数据集和实测数据集对分位数方法、基本时序方法及文中方法进行对比测试,结果表明,文中方法最优,对中部堆积点和离散点均有良好剔除效果。展开更多
Reducing the input wind and photovoltaic power time series data can improve the efficiency of time sequential simulations.In this paper,a wind and photovoltaic power time series data aggregation method based on an ens...Reducing the input wind and photovoltaic power time series data can improve the efficiency of time sequential simulations.In this paper,a wind and photovoltaic power time series data aggregation method based on an ensemble clustering and Markov chain(ECMC)is proposed.The ECMC method can effectively reduce redundant information in the data.First,the wind and photovoltaic power time series data were divided into scenarios,and ensemble clustering was used to cluster the divided scenarios.At the same time,the Davies-Bouldin Index(DBI)is adopted to select the optimal number of clusters.Then,according to the temporal correlation between wind and photovoltaic scenarios,the wind and photovoltaic clustering results are merged and reduced to form a set of combined typical day scenarios that can reflect the characteristics of historical data within the calculation period.Finally,based on the Markov Chain,the state transition probability matrix of various combined typical day scenarios is constructed,and the aggregation state sequence of random length is generated,and then,the combined typical day scenarios of wind and photovoltaic were sampled in a sequential one-way sequence according to the state sequence and then are built into a representative wind and photovoltaic power time series aggregation sequence.A provincial power grid was chosen as an example to compare the multiple evaluation indexes of different aggregation methods.The results show that the ECMC aggregation method improves the accuracy and efficiency of time sequential simulations.展开更多
基金We gratefully acknowledge the support of National Natural Science Foundation of China(NSFC)(Grant No.51977133&Grant No.U2066209).
文摘Randomness and fluctuations in wind power output may cause changes in important parameters(e.g.,grid frequency and voltage),which in turn affect the stable operation of a power system.However,owing to external factors(such as weather),there are often various anomalies in wind power data,such as missing numerical values and unreasonable data.This significantly affects the accuracy of wind power generation predictions and operational decisions.Therefore,developing and applying reliable wind power interpolation methods is important for promoting the sustainable development of the wind power industry.In this study,the causes of abnormal data in wind power generation were first analyzed from a practical perspective.Second,an improved complete ensemble empirical mode decomposition with adaptive noise(ICEEMDAN)method with a generative adversarial interpolation network(GAIN)network was proposed to preprocess wind power generation and interpolate missing wind power generation sub-components.Finally,a complete wind power generation time series was reconstructed.Compared to traditional methods,the proposed ICEEMDAN-GAIN combination interpolation model has a higher interpolation accuracy and can effectively reduce the error impact caused by wind power generation sequence fluctuations.
文摘According to the Chinese 'Twelfth Five-Year Plan', two large scale wind farms are planned to be built in the shore of Shandong province and Guangdong province to meet the increasing electricity demand with economic development. Before the construction of wind farm, it is necessary to evaluate the wind potential and its temporal variation along the coast of Shandong province, Guangdong province and Zhejiang province that have been studied in this paper. The data used were obtained from Goddard Earth Observing System (GEOS) Data Assimilation System. The results showed that there is rich wind supply in Zhejiang province with small annual variation. Further away from shore, the wind energy will increase fastest in Guangdong area. The yield of wind energy in Shandong province is not as rich as in the other two provinces as predicted in the study. Furthermore, the layout of wind turbines in wind farm was also investigated to absorb wind energy at the highest efficiency by wind farm. Our results provide a reference for the future construction of wind farms.
文摘The author’s research group has been conducting research on applications of various meteorological Grid Point Value (GPV) data offered by the Japan Meteorological Agency (JMA) to the field of wind power generation. In particular, the group’s research has been focusing on the following areas: 1) the use of GPV data from the JMA Meso-Scale Model (MSM-S;horizontal resolution: 5 km) and the JMA Local Forecast Model (LFM-S;horizontal resolution: 2 km), and 2) examinations of the prediction accuracy of local wind assessment with the use of these data. In both the MSM-S and the LFM-S, grid points are fixed at 10 m above the sea (ground) surface. The purpose of the present study is to establish a method in which the values of the MSM-S and LFM-S wind speed data from the 10 m height are used as the reference wind speed and are, using a power law, vertically extrapolated to 80 to 90 m heights, typical hub-heights of offshore wind turbines. For this purpose, the present study examined time-averaged vertical profiles of wind speed over the ocean based on the MSM-S data and in-situ data in the Hibikinada area, Kitakyushu City, Fukuoka Prefecture, Japan. As a result, it was revealed that a strong wind shear existed close to the sea surface, between the 10 and 30 m heights. In order to address the above-mentioned wind shear, a two-step vertical extrapolation method was proposed in the present study. In this method, two values of N, specifically for low and high altitudes (below and above approximately 30 m, respectively), were calculated and used. The data were created for the five years between 2012 and 2016. Similarly to previous analyses, the analysis of the created data set indicated that the potential of offshore wind power generation in the Hibikinada area, Kitakyushu City is quite high.
文摘在中小型永磁同步风力发电机中,桥式整流+DC/DC变换器拓扑具有结构简单的优点,其中DC/DC电路有Boost和Buck两种拓扑,探究两者的最大功率点跟踪(Maximum Power Point Tracking,MPPT)控制性能区别是对其优化设计的关键,测控系统是性能分析的重要手段。通过系统总体方案设计,确认了系统参数及指标;分析了四桥臂DC/DC拓扑,并基于高端电流采样和隔离采样技术实现了系统电压、电流、功率和频率的采集,设计了功率驱动及单片机控制电路;探讨了适合单片机的MPPT爬坡搜索法软件;给出了基于LabVIEW的上位机操控系统的软件设计实现;搭建了测控系统,进行了MPPT和两种拓扑测控结果对比实验。实验结果表明测控系统达到了设计目标。
文摘风速-功率曲线广泛应用于风电机组的功率预测、状态监测和故障诊断,其主要构建方法是使用风电场SCADA(supervisory control and data acquisition)数据进行拟合。然而由于弃风限电、仪表故障等因素,SCADA数据中存在部分功率异常数据。为保证拟合结果的准确可靠,应首先剔除这些异常数据。文中提出了一种风电机组功率异常数据剔除方法:首先使用分位数方法剔除距离正常数据较远的离散点,而后结合K-means聚类方法和改进时序方法剔除中部堆积点,最后使用分位数方法和DBSCAN(density-based spatial clustering of applications with noise)聚类方法的组合方法剔除距离正常数据较近的离散点。文中分别使用仿真数据集和实测数据集对分位数方法、基本时序方法及文中方法进行对比测试,结果表明,文中方法最优,对中部堆积点和离散点均有良好剔除效果。
基金supported by the National Key R&D Program of China(2017YFB0902200)Science and Technology Project of State Grid Corporation of China(4000-202255057A-1-1-ZN,5228001700CW).
文摘Reducing the input wind and photovoltaic power time series data can improve the efficiency of time sequential simulations.In this paper,a wind and photovoltaic power time series data aggregation method based on an ensemble clustering and Markov chain(ECMC)is proposed.The ECMC method can effectively reduce redundant information in the data.First,the wind and photovoltaic power time series data were divided into scenarios,and ensemble clustering was used to cluster the divided scenarios.At the same time,the Davies-Bouldin Index(DBI)is adopted to select the optimal number of clusters.Then,according to the temporal correlation between wind and photovoltaic scenarios,the wind and photovoltaic clustering results are merged and reduced to form a set of combined typical day scenarios that can reflect the characteristics of historical data within the calculation period.Finally,based on the Markov Chain,the state transition probability matrix of various combined typical day scenarios is constructed,and the aggregation state sequence of random length is generated,and then,the combined typical day scenarios of wind and photovoltaic were sampled in a sequential one-way sequence according to the state sequence and then are built into a representative wind and photovoltaic power time series aggregation sequence.A provincial power grid was chosen as an example to compare the multiple evaluation indexes of different aggregation methods.The results show that the ECMC aggregation method improves the accuracy and efficiency of time sequential simulations.
