Predicting lightning outages of transmission lines using generalized regression neural network

Xie, Y, Li, c, Lv, Y and Yu, C 2019, 'Predicting lightning outages of transmission lines using generalized regression neural network', Applied Soft Computing Journal, vol. 78, pp. 438-446.


Document type: Journal Article
Collection: Journal Articles

Title Predicting lightning outages of transmission lines using generalized regression neural network
Author(s) Xie, Y
Li, c
Lv, Y
Yu, C
Year 2019
Journal name Applied Soft Computing Journal
Volume number 78
Start page 438
End page 446
Total pages 9
Publisher Elsevier BV
Abstract Lightning is the major cause of transmission line outages, which can result in large area blackouts of power systems. One effective method to prevent catastrophic consequences is to predict lightning outages before they occur. The abundance of recorded lightning and lightning outage data in power system makes it possible to predict lightning outages of transmission lines. This paper proposes an artificially intelligent algorithm using general regression neural networks (GRNN) to predict lightning outages of transmission lines. First, the data that can be obtained from the operation and management system of a power company are analyzed, and the features that can be used as input parameters of GRNN are extracted. The prediction model based on GRNN is then built to perform lightning outage prediction. Finally, the effectiveness of the proposed method is validated by comparing it with (Back Propagation) BP and (Radial Basis Function) RBF neural networks using actual lightning data and lightning outage data. The simulation results show that the proposed method provides much better prediction performance.
Subject Applied Mathematics not elsewhere classified
Keyword(s) Generalized regression neural network
Lightning outage
Outage prediction
Transmission line
Copyright notice © 2019 Elsevier B.V.
ISSN 1568-4946
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