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New Wind Farms Study Results Reported from Northeast Electric Power University (Analysis of Wind Farm Output Characteristics Based On Descriptive Statistical Analysis and Envelope Domain)

Energy Daily News

2019 APR 12 (NewsRx) -- By a News Reporter-Staff News Editor at Energy Daily News -- A new study on Energy - Wind Farms is now available. According to news reporting out of Jilin, People’s Republic of China, by NewsRx editors, research stated, “In order to make full use of the built wind farm, it is one of the most basic work to analyze the output characteristics. In this paper, a thought of mining wind power output characteristics with the perspective of descriptive statistical analysis and correlation is put forward.”

Our news journalists obtained a quote from the research from Northeast Electric Power University, “Based on the measured data, the output characteristics of the wind farm are analyzed from two aspects of descriptive statistics: the digital feature and the distribution characteristics. Then, in view of the wind speed-power relationship, an envelope domain is constructed to characterize it, and its rationality is verified by the defined data density index of wind power output.”

According to the news editors, the research concluded: “The method proposed in this paper is helpful to effectively characterize the output characteristics of wind farm connected to the network, and lays a foundation for better operation and management of wind farms.”

For more information on this research see: Analysis of Wind Farm Output Characteristics Based On Descriptive Statistical Analysis and Envelope Domain. Energy, 2019;170():580-591. Energy can be contacted at: Pergamon-Elsevier Science Ltd, The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, England. (Elsevier -; Energy -

Our news journalists report that additional information may be obtained by contacting C. Liu, Northeast Electric Power University, School of Electronics Engineering, Jilin 132012, Jilin, People’s Republic of China. Additional authors for this research include Y.B. Wang, X.Y. Shao, G.W. Cai, L. Kou and Z.Q. Wu.

The direct object identifier (DOI) for that additional information is: This DOI is a link to an online electronic document that is either free or for purchase, and can be your direct source for a journal article and its citation.


(Our reports deliver fact-based news of research and discoveries from around the world.)



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