Voltage stability margin prediction by ensemble based extreme learning machine

Zhang, Rui; Xu, Yan; Dong, Zhao Yang; Zhang, Pei; Wong, Kit Po

Title: Voltage stability margin prediction by ensemble based extreme learning machine
Creator: Zhang, Rui; Xu, Yan; Dong, Zhao Yang; Zhang, Pei; Wong, Kit Po
Relation: 2013 IEEE Power and Energy Society General Meeting (PES). Proceedings of the 2013 IEEE Power and Energy Society General Meeting (Vancouver, BC 21-25 July, 2013) p. 1-5
Relation: ARC
Publisher Link: http://dx.doi.org/10.1109/PESMG.2013.6672489
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Resource Type: conference paper
Date: 2013
Description: Voltage stability margin (VSM) evaluation is one of the essential tasks of power system voltage stability analysis. Conventional methods for VSM calculation is based on continuation-power flow technique. Recently, there is growing interest to apply artificial neural network (ANN) techniques to rapidly predict the VSM. However, traditional ANN learning algorithms usually suffer from excessive training and/or tuning burden and unsatisfactory generalization performance. In this paper, a relatively new and promising learning algorithm called extreme learning machine (ELM) is employed and an ensemble model of ELMs is designed for more accurate and efficient VSM prediction. The inputs of the prediction model are system operating parameters and loading direction, and the output is the VSM. The proposed model is successfully verified on the IEEE 118-bus system.
Subject: continuation-power flow; extreme learning machine; ensemble learning; voltage stability
Identifier: http://hdl.handle.net/1959.13/1332620
Identifier: uon:26911
Identifier: ISBN:9781479913039
Language: eng
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