Prediction model of insulator contamination degree based on adaptive mutation particle swarm optimisation and general regression neural network-Reference-Cited by-同舟云学术

Prediction model of insulator contamination degree based on adaptive mutation particle swarm optimisation and general regression neural network

Published:2018-12-10 Issue:16 Volume:2019 Page:1423-1428
ISSN:2051-3305
Container-title:The Journal of Engineering
language:en
Short-container-title:J. eng.

Author:

Wang Rumeng¹,Zhang Ming¹,Jiang Yunpeng¹,Yang Yong¹

Affiliation:

1. International Joint Research Laboratory of Magnetic Confinement Fusion and Plasma PhysicsState Key Laboratory of Advanced Electromagnetic Engineering and TechnologySchool of Electrical & Electronic Engineering, Huazhong University of Science & TechnologyWuhanHubei ProvincePeople's Republic of China

Publisher

Institution of Engineering and Technology (IET)

Subject

General Engineering,Energy Engineering and Power Technology,Software

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1049/joe.2018.8669

Reference18 articles.

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2. Study on the DC flashover performance of various types of insulators with fan‐shaped nonuniform pollution;Zhang Z.;IEEE Trans. Power Del.,2015

3. Study of the influence of test methods on DC pollution flashover voltage of insulator strings and its flashover process;Zhang Z.;IEEE Trans. Dielectr. Electr. Insul.,2010

4. Pollution flashover calculation model based on characteristics of AC partial arc on top and bottom wet‐polluted dielectric surfaces;Li Y.;IEEE Trans. Dielectr. Electr. Insul.,2014

5. Flashover voltage prediction of composite insulators based on the characteristics of leakage current;Zhao S.;Power Syst. Tech.,2014

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