Assessment of voltage stability risks under stochastic net loads using scalable SVM classification

Antonin Demazy; Tansu Alpcan; Iven Mareels; Sajeeb Saha

doi:10.1109/AUPEC.2017.8282424

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Assessment of voltage stability risks under stochastic net loads using scalable SVM classification

Conference paper

Peer reviewed

Assessment of voltage stability risks under stochastic net loads using scalable SVM classification

Antonin Demazy, Tansu Alpcan, Iven Mareels and Sajeeb Saha

2017 Australasian Universities Power Engineering Conference (AUPEC), pp.1-6

Australasian Universities Power Engineering Conference (AUPEC), 2017 (Melbourne, Australia, 19-Nov-2017–22-Nov-2017)

Institute of Electrical and Electronics Engineers

2017

DOI: https://doi.org/10.1109/AUPEC.2017.8282424

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Abstract

voltage stability risk assessment

support vector machine

intermittent power generation

power system stability

voltage control

SVM

A novel approach is presented to identify and assess voltage stability risk in power networks that include nodes with variable loads and intermittent renewable generation. Given a power network configuration, the approach firstly assesses voltage instability boundaries at selected nodes in response to stochastic operational conditions (variable load and intermittent decentralised generation). By iteratively calculating Saddle Node Bifurcation (SNB) points one node at a time given a set of loads and intermittent generation conditions at the other nodes, those data points are used as training sets for support vector machine (SVM) classifiers. Secondly, a marginal voltage stability risk probability distribution for the intermittent buses is derived using Monte Carlo simulation methods with stochastic net load profiles within the system where instability status at each simulation is derived from the trained set of SVM classifiers. The key advantage of the proposed method is its scalability to higher dimension networks for which the SVM training date set must be calculated only once. The voltage risk probability distribution acquires a significant importance in the design of quantitative risk valuation framework for planning and expansion purposes in a context of network with decentralised and intermittent generation. This paper focuses on describing the approach to derive a voltage risk probability using SVM technique, while the construction and use of the probability distribution in a comprehensive risk valuation framework for planning is left for future work to the authors.

Details

Title: Assessment of voltage stability risks under stochastic net loads using scalable SVM classification
Authors: Antonin Demazy (Author) - The University of Melbourne
Tansu Alpcan (Author) - The University of Melbourne
Iven Mareels (Author) - The University of Melbourne
Sajeeb Saha (Author) - Deakin University
Publication details: 2017 Australasian Universities Power Engineering Conference (AUPEC), pp.1-6
Conference details: Australasian Universities Power Engineering Conference (AUPEC), 2017 (Melbourne, Australia, 19-Nov-2017–22-Nov-2017)
Publisher: Institute of Electrical and Electronics Engineers
Date published: 2017
DOI: 10.1109/AUPEC.2017.8282424
ISSN: 2474-1507
ISBN: 9781538626474
Organisation Unit: University of the Sunshine Coast, Queensland; School of Science, Technology and Engineering
Language: English
Record Identifier: 99532306302621
Output Type: Conference paper

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