Functional Basis Analysis for the Characterization of Power System Signal Dynamics: Formulation, Implementation and Validation

With the integration of distributed energy resources and the trend towards low-inertia power grids, the frequency and severity of grid dynamics is expected to increase. Conventional phasor-based signal processing methods are proving to be insufficient in the analysis of non-stationary AC voltage and current waveforms, while the computational complexity of many dynamic signal analysis techniques hinders their deployment in operational embedded systems. This paper presents the Functional Basis Analysis (FBA), a signal processing tool capable of capturing the full broadband nature of signal dynamics in power grids while maintaining a streamlined design for real-time monitoring applications. Relying on the Hilbert transform and optimization techniques, the FBA can be user-engineered to identify and characterize combinations of several of the most common signal dynamics in power grids, including amplitude/phase modulations, frequency ramps and steps. This paper describes the theoretical basis and design of the FBA as well as the deployment of the algorithm in embedded hardware systems, with adaptations made to consider latency requirements, finite memory capacity, and fixed-point precision arithmetic. For validation, a PMU calibrator is used to evaluate and compare the algorithm's performance to state-of-the-art static and dynamic phasor methods. The test outcomes demonstrate the FBA method's suitability for implementation in embedded systems to improve grid situational awareness during severe grid events.