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Ambient oscillatory mode assessment in power system using an advanced signal processing method.

Rahul Satheesh1, Sunitha Rajan2

  • 1Amrita School of Artificial Intelligence, Coimbatore, Amrita Vishwa Vidyapeetham, India.

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This study introduces a new method using Time-Varying Filter based Empirical Mode Decomposition (TVF-EMD) and Teager Kaiser Energy Operator (TKEO) to analyze power system oscillations. The approach efficiently estimates electromechanical mode properties from real-world data with reduced computational load.

Keywords:
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Area of Science:

  • Electrical Engineering
  • Power Systems Analysis
  • Signal Processing

Background:

  • Accurate estimation of electromechanical mode properties is vital for power system stability and operator awareness.
  • Existing measurement-based methods often require significant computational resources for mode identification and parameter estimation.
  • Real-world synchrophasor (PMU) data offers a rich source for analyzing system dynamics.

Purpose of the Study:

  • To develop a novel, computationally efficient framework for assessing low-frequency oscillation modes in power systems.
  • To accurately estimate instantaneous mode parameters (frequency, amplitude, damping ratio) using ambient PMU data.
  • To validate the proposed method's robustness and effectiveness on synthetic, simulated, and real-world power grid data.

Main Methods:

  • Utilizing a nonstationary approach, the Time-Varying Filter based Empirical Mode Decomposition (TVF-EMD) technique to identify dominant low-frequency modes.
  • Combining TVF-EMD with the Teager Kaiser Energy Operator (TKEO) for precise estimation of instantaneous mode parameters.
  • Applying and validating the proposed method on synthetic signals, simulated IEEE test systems, and real-world Indian power grid data.

Main Results:

  • The proposed TVF-EMD and TKEO combination effectively identifies low-frequency oscillation modes in ambient PMU data.
  • Instantaneous mode parameters including frequency, amplitude, and damping ratio are precisely estimated.
  • Demonstrated robust performance in analyzing both simulated and real-world power system data.

Conclusions:

  • The novel framework offers a computationally efficient and robust solution for estimating electromechanical mode properties.
  • The method provides accurate assessment of instantaneous mode features, crucial for power system monitoring and control.
  • This approach enhances the ability of operators to gauge system stress using real-world synchrophasor data.