The Influence of Load Characteristics on Early Warning Signs in Power Systems
Critical infrastructure systems like the power system are in danger of abrupt transitions into unstable and oftentimes catastrophic regimes. These critical transitions occur due to the modification of certain control parameters or an external forcing acting on the dynamical system. Bifurcation analysis can help to characterize the critical threshold beyond which systems become unstable. Moreover, some systems emit early warning signs prior to the critical transition, detectable from small-scale signal fluctuations triggered by the stochasticity of the external forcing. We present here our analysis of a time-domain dynamical power system model subjected to an increasing load level and small-scale stochastic load perturbations. We confirm previous findings from other literature that the autocorrelation of system signals increases, as the load level approaches a critical threshold characterized by a Hopf bifurcation point. Furthermore, we analyze the effects of load homogeneity and load connectivity on early warning signs. We assert that load connectivity does not influence autocorrelation coefficients. In addition, we show that changes in load homogeneity shift the location of the critical threshold.
We would like to thank the IT Services at ETH Zürich for the provision of computing resources and access of the EULER cluster. This research was conducted under the Future Resilient Systems program at the Singapore-ETH Centre and funded by the National Research Foundation of Singapore.
- 1.Glavitsch, H.: Large-scale electricity transmission grids: lessons learned from the European electricity blackouts. In: Voeller, J.G. (ed.) Wiley Handbook of Science and Technology for Homeland Security, vol. 3, no. 4, pp. 1–20 (2009)Google Scholar
- 3.Scheffer, M.: Anticipating critical transitions. Am. Assoc. Adv. Sci. 338(6105), 344–348 (2012)Google Scholar
- 10.Ghanavati, G., Hines, P.D.H., Lakoba, T.I.: Investigating early warning signs of oscillatory instability in simulated phasor measurements. arXiv, pp. 1–5 (2013)Google Scholar
- 20.Hines, P., Cotilla-Sanchez, E., Blumsack, S.: Topological models and critical slowing down: two approaches to power system blackout risk analysis. In: 44th Hawaii International Conference on Systems Sciences, pp. 1–10 (2011)Google Scholar
- 24.Chevalier, S., Hines, P.: System-wide early warning signs of instability in stochastically forced power systems. arXiv, January 2016Google Scholar