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Self-healing network instigated by distributed energy resources

Lookup NU author(s): Dr Thillainathan Logenthiran, Dr Wai Lok Woo

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Abstract

© 2017 IEEE. Power outages have been a troubling issue yet inevitable till to date. In conjunction to the recent paradigm shift in restructuring passive power grid into an active network, grid operators now have little control over the grid's power flow transactions between generations and consumers. Such avocation concedes undeterministic fault origins and capitulate power line oscillatory which degrade the grid's integrity; succumbing to power outage catastrophe. Despite innovations in integrating distributed generations, leveraging demand curves irregularity and deployment of monitoring devices, transmission system operators could not guarantee the resiliency of the grid's operations in real-time due to high traffic of power flow diversifications. In consequence, embed distribution intelligence proceedings are infused to perform self-healing operations to assist grid operators to isolate and diagnose fault-affected regions while dampening overloading phenomenon. This paper proposes an automated transmission line fault restoration operation which employs knowledge-based algorithm to alleviate real-time line fault intrusions. A simulated test bed six-bus mesh network is modelled to identify and define fault events while performing autonomous isolation strategies through re-routing power flow displacements. The presented simulation results and findings are contrived using Power World Simulator (modelling of six-bus system), MATLAB and SimAuto (devising control and fault detection scheme).


Publication metadata

Author(s): Ramadan BMSM, Raj S, Logenthiran T, Naayagi RT, Woo WL

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: Asia-Pacific Power and Energy Engineering Conference, APPEEC

Year of Conference: 2018

Pages: 1-6

Online publication date: 08/03/2018

Acceptance date: 08/11/2017

Publisher: IEEE Computer Society

URL: http://doi.org/10.1109/APPEEC.2017.8308959

DOI: 10.1109/APPEEC.2017.8308959

Library holdings: Search Newcastle University Library for this item

ISBN: 9781538613795


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