Patents by Inventor Cun Dong

Cun Dong has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Patent number: 10290066
    Abstract: A method and device for modeling a long-time-scale photovoltaic output time sequence are provided. The method includes that: historical data of a photovoltaic power station is acquired, and a photovoltaic output with a time length of one year and a time resolution of 15 mins is selected (101); weather types of days corresponding to the photovoltaic output are acquired from a weather station (102), and probabilities of transfer between each type of weather are calculated respectively (103); and a simulated time sequence of the photovoltaic output within a preset time scale is generated (104), and its validity is verified (105). By the method, annual and monthly photovoltaic output simulated time sequences consistent with a random fluctuation rule of a photovoltaic time sequence may be acquired according to different requirements to provide a favorable condition and a data support for analog simulation of time sequence production including massive new energy.
    Type: Grant
    Filed: April 25, 2018
    Date of Patent: May 14, 2019
    Assignees: China Electric Power Research Institute Company Li, State Grid Corporation of China, CLP Puri Zhangbei Wind Power Research & Testing Co
    Inventors: Weisheng Wang, Chun Liu, Chi Li, Yuehui Huang, Yuefeng Wang, Cun Dong, Nan Zhang, Xiaofei Li, Yunfeng Gao, Xiaoyan Xu, Yanping Xu, Xiaofeng Pan
  • Publication number: 20180240200
    Abstract: A method and device for modeling a long-time-scale photovoltaic output time sequence are provided. The method includes that: historical data of a photovoltaic power station is acquired, and a photovoltaic output with a time length of one year and a time resolution of 15 mins is selected (101); weather types of days corresponding to the photovoltaic output are acquired from a weather station (102), and probabilities of transfer between each type of weather are calculated respectively (103); and a simulated time sequence of the photovoltaic output within a preset time scale is generated (104), and its validity is verified (105). By the method, annual and monthly photovoltaic output simulated time sequences consistent with a random fluctuation rule of a photovoltaic time sequence may be acquired according to different requirements to provide a favorable condition and a data support for analogue simulation of time sequence production including massive new energy.
    Type: Application
    Filed: June 30, 2016
    Publication date: August 23, 2018
    Inventors: Weisheng Wang, Chun Liu, Chi Li, Yuehui Huang, Yuefeng Wang, Cun Dong, Nan Zhang, Xiaofei Li, Yunfeng Gao, Xiaoyan Xu, Yanping Xu, Xiaofeng Pan
  • Publication number: 20180240048
    Abstract: A method and device for modeling a long-time-scale photovoltaic output time sequence are provided. The method includes that: historical data of a photovoltaic power station is acquired, and a photovoltaic output with a time length of one year and a time resolution of 15 mins is selected (101); weather types of days corresponding to the photovoltaic output are acquired from a weather station (102), and probabilities of transfer between each type of weather are calculated respectively (103); and a simulated time sequence of the photovoltaic output within a preset time scale is generated (104), and its validity is verified (105). By the method, annual and monthly photovoltaic output simulated time sequences consistent with a random fluctuation rule of a photovoltaic time sequence may be acquired according to different requirements to provide a favorable condition and a data support for analogue simulation of time sequence production including massive new energy.
    Type: Application
    Filed: April 25, 2018
    Publication date: August 23, 2018
    Inventors: Weisheng Wang, Chun Liu, Chi Li, Yuehui Huang, Yuefeng Wang, Cun Dong, Nan Zhang, Xiaofei Li, Yunfeng Gao, Xiaoyan Xu, Yanping Xu, Xiaofeng Pan