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 toward low-inertia power grids, the frequency and severity of grid dynamics are expected to increase. Conventional phasor-based signal-processing methods are proving to be insufficient in the analysis of nonstationary ac voltage and current waveforms, while the computational complexity of many dynamic signal analysis techniques hinders their deployment in operational embedded systems. This article presents the functional basis analysis (FBA), a signal-processing tool capable of capturing the broadband nature of common single-component signal dynamics in power grids while maintaining a streamlined design for real-time monitoring applications. Relying on the Hilbert transform (HT) 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 (AMs/PMs), frequency ramps (FRs), and steps. This article 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 phasor measurement unit (PMU) calibrator is used to evaluate and compare the algorithm’s performance to state-of-the-art static and dynamic phasor methods. The test results highlight the potential of the FBA method for implementation in embedded systems to enhance grid situational awareness during critical grid events. Future work will investigate the extraction of multicomponent broadband signals with empirical mode decomposition (EMD) for harmonic analysis.