State Grid Jiangsu Electric Power Company

Xi'an Jiaotong University

State Grid Corporation of China

Hohai University

State Grid Jiangsu Electric Power Co., Ltd. Marketing Center

State Grid Jiangsu Electric Power Co., Ltd. Marketing Service Center

Xiaodong Cao

Shihai Yang

Xiao Chen

Bo Li

Qifeng Huang

Shuangshuang Zhao

Zhixin Li

Qing Xu

Chao Zhou

Guofang Xia

Mingming Chen

Zhengqi Tian

Kun Yu

Meimei Duan

Xingying Chen

Yuxiong Huang

Gengfeng Li

Xiaoquan Lu

Jiangfeng Jiang

Liyin Zhang

Huiling Su

Qirui Qiu

Yuqin Chen

Zengkai Ouyang

Hanmiao Cheng

Tianchang Liu

Dan Gong

Yixuan Huang

Xiaoxing Mu

Kaijie Fang

A quasi-real-time data collection system for a competitive power market includes a distributed data collection unit, a centralized data collection unit, and a main collection station, where the distributed data collection unit is connected to an intelligent watt-hour meter by using an internal downlink communication module of the distributed data collection unit, and configures data item collection parameters and task execution parameters by using an internal collection task and scheme configuration module of the distributed data collection unit; the centralized data collection unit is connected to the main collection station by using an internal file conversion and uplink communication module of the centralized data collection unit; and the centralized data collection unit is connected to multiple distributed data collection units by using a local high speed power line carrier communication (HPLC) network.

Quasi-real-time data collection system and method for competitive power market

A method of a power user classification based on distributed K-means, a storage medium and a classification device are provided. The method includes: obtaining, by N load aggregators, power consumption data of power users managed by respective load aggregators; performing, by each load aggregator, a normalization operation on time series load data of the power users managed by the load aggregator; forming a N&#xd7;N dimensional adjacency matrix A; performing K-means clustering on normalized time series load data, to obtain the respective centroids and user groups characterized by the respective centroids; sharing, by the respective load aggregators, the centroids and the number of users under the respective centroids based on the adjacency matrix A, and obtaining consistent centroids by multiple load aggregators; after an overall iteration ends, obtaining, by the respective load aggregators, the consistent centroids consistent with the K-means centroid based on global data, to realize user classification.

Method of power user classification based on distributed K-means, storage medium and classification device

Provided is a live measurement method for three-winding transformer loss based on windowed frequency shift. The method includes: stepproviding an improved live calculation equation of a three-winding transformer loss; stepprocessing a collected x(t) signal by windowed frequency shift calculation; stepsolving an amplitude and a phase of the collected x(t) signal via discrete Fourier transform of a frequency shift signal; and stepcalculating a no-load loss and a load loss of a three-winding transformer.

Live measurement method for three-winding transformer loss based on windowed frequency shift

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Jiangsu, China

Xiaodong Cao