Risk-Constrained Minimization of Combined Event Detection and Decision Time for Online Transient Stability Assessment

Abstract

This paper addresses the problem of 'quickest possible' online transient stability assessment, by minimizing the decision time of combined event detection that might lead to a system split and unstable generator group prediction, from realtime wide area power system measurements. More importantly it does so by respecting predefined probabilistic error constraints for the prediction. The statistical theory of optimal detection is applied, firstly to choose the detection threshold and secondly to select a flexible assessment time, after using probabilistic neural networks to provide a temporal representation of the data. On simulated wide area measurements from the interconnected New England test system and New York power system this approach is between two and three times faster on average than strategies based on fixed assessment times, despite having comparable error rates.Index Terms-dynamic security assessment, optimal detection, phasor measurement units, probabilistic neural network, transient stability assessment, risk.Jhonny Gonzalez holds a MSc in Mathematical Finance (2011) and a PhD in Mathematics (2015) both from the University of Manchester, UK, where he also completed his postdoctoral research. He is currently a commodities derivatives quantitative analyst with Citigroup in London.

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