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
The power infrastructure of the power system is massive in size and dispersed throughout the system.Therefore,how to protect the information security in the operation and maintenance of power equipment is a difficult ...The power infrastructure of the power system is massive in size and dispersed throughout the system.Therefore,how to protect the information security in the operation and maintenance of power equipment is a difficult problem.This paper proposes an improved time-stamped blockchain technology biometric fuzzy feature for electrical equipment maintenance.Compared with previous blockchain transactions,the time-stamped fuzzy biometric signature proposed in this paper overcomes the difficulty that the key is easy to be stolen by hackers and can protect the security of information during operation and maintenance.Finally,the effectiveness of the proposed method is verified by experiments.展开更多
基金funded by the State Grid Science and Technology Project“Research on Key Technologies for Prediction and Early Warning of Large-Scale Offshore Wind Power Ramp Events Based on Meteorological Data Enhancement”(4000-202318098A-1-1-ZN).
文摘The development of wind power clusters has scaled in terms of both scale and coverage,and the impact of weather fluctuations on cluster output changes has become increasingly complex.Accurately identifying the forward-looking information of key wind farms in a cluster under different weather conditions is an effective method to improve the accuracy of ultrashort-term cluster power forecasting.To this end,this paper proposes a refined modeling method for ultrashort-term wind power cluster forecasting based on a convergent cross-mapping algorithm.From the perspective of causality,key meteorological forecasting factors under different cluster power fluctuation processes were screened,and refined training modeling was performed for different fluctuation processes.First,a wind process description index system and classification model at the wind power cluster level are established to realize the classification of typical fluctuation processes.A meteorological-cluster power causal relationship evaluation model based on the convergent cross-mapping algorithm is pro-posed to screen meteorological forecasting factors under multiple types of typical fluctuation processes.Finally,a refined modeling meth-od for a variety of different typical fluctuation processes is proposed,and the strong causal meteorological forecasting factors of each scenario are used as inputs to realize high-precision modeling and forecasting of ultra-short-term wind cluster power.An example anal-ysis shows that the short-term wind power cluster power forecasting accuracy of the proposed method can reach 88.55%,which is 1.57-7.32%higher than that of traditional methods.
基金This research was funded by science and technology project of State Grid JiangSu Electric Power Co.,Ltd.(Research on Key Technologies of power network security digital identity authentication and management and control based on blockchain,Grant No.is J2021021).
文摘The power infrastructure of the power system is massive in size and dispersed throughout the system.Therefore,how to protect the information security in the operation and maintenance of power equipment is a difficult problem.This paper proposes an improved time-stamped blockchain technology biometric fuzzy feature for electrical equipment maintenance.Compared with previous blockchain transactions,the time-stamped fuzzy biometric signature proposed in this paper overcomes the difficulty that the key is easy to be stolen by hackers and can protect the security of information during operation and maintenance.Finally,the effectiveness of the proposed method is verified by experiments.
