Subsynchronous Resonance Monitoring Using Ambient High Speed Sensor Data

This paper proposes a framework for online monitoring of subsynchronous oscillations using ambient sensor data from high speed sampling devices such as digital fault recorders (DFRs). Ambient data is continuously available in the form of routine system responses to random load fluctuations. This paper shows the usefulness of ambient data for tracking the frequency, damping ratio and mode shape of torsional modes related to subsynchronous resonance (SSR). Frequency domain decomposition (FDD) and recursive least square (RLS) algorithms are tested for ambient monitoring of SSR modes. IEEE second SSR benchmark model is used as the study system. Nonlinear and linearized equations of this system are analyzed for testing the performance of measurement based methods and to compare their results with respective linearized system modes. It is shown that both electrical and mechanical signals can be used for the SSR monitoring while the torsional modes are more observable in mechanical signals such as generator speeds. In addition to estimating the frequency and damping ratio, torsional mode shapes can be identified by implementing FDD as a multi-dimensional method on the speeds of the masses of the turbine shaft. The algorithms are tested on an archived real system data set from a DFR.