State Grid Zhejiang Electric Power Company Limited Electric Power Research Institute

Hangzhou Kelin Electric Co., Ltd

Yiming Zheng

Xiang Sun

Chen Li

Ping Qian

Wenhao Wang

Bingxiao Mei

Xiaoxin Chen

Qiaoqun Xia

Huibin Tao

Jiangyang Zhan

Yiyong Zhu

Daolin Lan

Haojun Liu

Xianjun Shao

Zemin Wei

Haibao Mu

Wei Du

Wenlin He

Tieying Tang

Jialong Xu

Zhongsheng Hua

Yifan He

Xixing Hu

A transformer bushing partial discharge monitoring chip device and method are provided. An acquisition unit comprises three sensor modules installed at end screens of three-phase bushings of a transformer respectively for acquiring partial discharge signals and power frequency current signals from the transformer bushing end screens; each of the sensor modules is connected to an SIP digitization unit through a wire; the SIP digitization unit is fixedly installed beneath a base of the acquisition unit for digitizing the partial discharge signals and the power frequency current signals; an output end of the SIP digitization unit is connected to an input end of a background processing terminal.

Transformer bushing partial discharge monitoring chip device and method

Disclosed is an intelligent on-line diagnosis method for winding deformation of power transformer. When a transformer is subjected to short-circuit shock or transportation collision, transformer windings may undergo local twisting, swelling or the like under the action of an electric power or mechanical force, which is called winding deformation and will cause a huge hidden danger to the safe operation of the power network. Commonly used diagnosis methods for winding deformation are all off-line diagnosis methods, which have the disadvantages that transformers need to be shut down and highly skilled operators are required. The present invention provide an intelligent on-line diagnosis method for winding deformation on the basis of combination of information entropy and support vector machine. By carrying out feature extraction of current and voltage signals based on permutation entropy and wavelet entropy, integrating the variation of the monitoring indicators of the power transformers in complexity, time-frequency domain and the like and automatically learning the diagnostic logic from fault features through the machine learning algorithm, intelligent diagnosis of winding deformation is realized, thereby reducing labor costs and improving diagnosis efficiency.

Intelligent on-line diagnosis and positioning method for winding deformation of power transformers

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Zhejiang, China

Yiming Zheng