  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.
