Identifying Useful Statistical Indicators of Proximity to Instability in Stochastic Power Systems

Abstract

Prior research has shown that autocorrelation and variance in voltage measurements tend to increase as power systems approach instability. This paper seeks to identify the conditions under which these statistical indicators provide reliable early warning of instability in power systems. First, the paper derives and validates a semi-analytical method for quickly calculating the expected variance and autocorrelation of all voltages and currents in an arbitrary power system model. Building on this approach, the paper describes the conditions under which filtering can be used to detect these signs in the presence of measurement noise. Finally, several experiments show which types of measurements are good indicators of proximity to instability for particular types of state changes. For example, increased variance in voltages can reliably indicate the location of increased stress, while growth of autocorrelation in certain line currents is a reliable indicator of system-wide instability.

8 pages, 12 figures

References in corpus (4)

• Understanding early indicators of critical transitions in power systems from autocorrelation functions
• Voltage Collapse and ODE Approach to Power Flows: Analysis of a Feeder Line with Static Disorder in Consumption/Production
• Investigating early warning signs of oscillatory instability in simulated phasor measurements
• Critical slowing-down as indicator of approach to the loss of stability

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• Stochastic Uncertainty Propagation in Power System Dynamics using Measure-valued Proximal Recursions
• Investigating the Impacts of Stochastic Load Fluctuation on Dynamic Voltage Stability Margin Using Bifurcation Theory