Seismic risk assessment of high-voltage transformers using Bayesian belief networks

Abstract

Large-magnitude earthquakes can damage high-voltage transformers, trigger power flow disruption and impact the economy and society. However, methods that enable large transformer vulnerability assessment in a practical and rigorous way are scarce. This paper proposes a probabilistic framework using Bayesian belief networks (BBNs) to predict the damage of high-voltage transformers subjected to seismic events. This framework incorporates major causes of transformer vulnerability at once, such as liquefaction, rocking response of the transformer, and interactions with interconnected equipment, which are otherwise commonly studied in isolation. To demonstrate the application of the framework, the paper elaborates on each step of the BBN framework, which is then validated with historical empirical data. Furthermore, the value of the proposed method is illustrated with high-voltage transformers in substations of the electric value BC Hydro in British Columbia, Canada. The paper also offers a sensitivity analysis that evaluates the effects of input variables on transformer damage. The proposed framework is simple to perform in practice, and the results are expected to support decisions on mitigation measures, seismic risk management, and to provide a step towards modelling the vulnerability of entire electrical substations.

Keywords::

• peak ground acceleration
• seismic risk
• Bayesian belief networks
• transformer vulnerability
• electrical systems

Acknowledgement

The authors acknowledge Prof. Alexis Kwasinski at the University of Texas, Austin, for providing the transformer damage photographs from the 2010 Chile and 2010–2011 Canterbury–Christchurch earthquakes.

Notes

^1. Email: [email protected]

^2. Email: [email protected]

Additional information

Funding

The second author acknowledges the financial support through the NSERC Discovery Grant program and the third author acknowledges the U.S. National Science Foundation for the financial support through Grant CMMI-0748231.