Performing Prony method and down sampling factor optimization for power system oscillation analysis

A blackout in the electrical power interconnection system occurs when the stability limits of power system are exceeded. The majority of global power blackouts, including those in Indonesia, are primarily triggered by disturbances within specific segments of the transmission system. These disturbances propagate through interconnected networks, leading to widespread disruptions in the electrical power grid. Oscillations in the electric power system i.e., low-frequency power system oscillations which refer to inter-area oscillations, are distinct phenomena which appear in the interconnection prior to blackout events. These oscillations data are typically obtained from disturbance records after the incidents and used as basis data for post-mortem analysis to describe and identify the oscillating generators in the power system.

The implementation of Phasor Measurement Units (PMUs) for acquiring synchronized phasor data and assessing the potential evolution of system conditions based on these oscillations, prior to significant disruptions in the electrical power system, has been incorporated into IEC/IEEE standards. Additionally, new equipment for synchronized phasor measurement (synchrophasors) utilizing PMUs has been developed in accordance with these standards. A method that is widely used to determine the presence of oscillations in an electric power system is Prony analysis. Prony analysis is a powerful signal processing technique to estimate the parameters of a signal. In power systems, this is applied to estimate the parameters of power system signals, such as voltage, current, and frequency, which are essential for power system control and protection.

This research demonstrates Prony method to monitor oscillations in power system using synchrophasor data, establishing the correlation between oscillation at various busses within the grid and the implication of practical DSF value selection to the model accuracy for selected grid system. A modified Kundur’s four-machine two-area test system model is employed as base model, scaled to the actual measurement data of 45 Extra High Voltage Substation 500 kV in Indonesia power system interconnection. This paper discusses the principles of Prony analysis, its advantages and limitations, including the applications to detect power system oscillation. The performance of Prony analysis is evaluated by comparing the simulation data and real time measurement data from actual Java interconnection system. In practical applications within the selected grid, the DSF to mismatch the actual data and signal construction were then selected and matched as the reference for a particular grid application. The results demonstrate that Prony analysis, when applied to PMU data with a DSF value of 6, provides a reliable and effective method for detecting power system oscillations where the squared errors are reduced from 600–8000% with DSF=2–4 down to 0.03–10% for dominant oscillation frequencies between 0.25–0.65 Hz signifying interarea mode, machine mode and control mode.