Grid Monitoring and Protection with Continuous Point-on-Wave
  Measurements and Generative AI
Lang Tong Xinyi Wang Qing Zhao
Purpose This article presents a case for a next-generation grid monitoring
and control system, leveraging recent advances in generative artificial
intelligence (AI), machine learning, and statistical inference. Advancing
beyond earlier generations of wide-area monitoring systems built upon
supervisory control and data acquisition (SCADA) and synchrophasor
technologies, we argue for a monitoring and control framework based on the
streaming of continuous point-on-wave (CPOW) measurements with AI-powered data
compression and fault detection.
  Methods and Results: The architecture of the proposed design originates from
the Wiener-Kallianpur innovation representation of a random process that
transforms causally a stationary random process into an innovation sequence
with independent and identically distributed random variables. This work
presents a generative AI approach that (i) learns an innovation autoencoder
that extracts innovation sequence from CPOW time series, (ii) compresses the
CPOW streaming data with innovation autoencoder and subband coding, and (iii)
detects unknown faults and novel trends via nonparametric sequential hypothesis
testing.
  Conclusion: This work argues that conventional monitoring using SCADA and
phasor measurement unit (PMU) technologies is ill-suited for a future grid with
deep penetration of inverter-based renewable generations and distributed energy
resources. A monitoring system based on CPOW data streaming and AI data
analytics should be the basic building blocks for situational awareness of a
highly dynamic future grid.