Smart DSP for a Smarter Power Grid: Teaching Power System Analysis through Signal Processing
Ahmad Moniri, Anthony G. Constantinides, Danilo P. Mandic
Abstract
The future Smart Grid represents an extraordinary opportunity to transform the ways we currently approach energy into a new era of low-carbon, renewable, and efficient solutions which will ultimately have a significant impact on both the environment and economy. This effort requires close collaboration of experts from the Power, Digital Signal Processing (DSP) and Machine Learning (ML) communities, with the common language between these diverse disciplines an important first step in this endeavour. To promote seamless transition of ideas, we here establish a duality between the Clarke transform, a workhorse in Power Grid analysis, and principal component analysis (PCA), a staple subspace method in DSP/ML. Upon highlighting the limitations of the Clarke transform in off-nominal unbalanced power system conditions, we illuminate a DSP-enabled class of self-balancing solutions, referred to as the Smart Transforms, based on adaptive complex widely linear modelling. Examples on system frequency estimation support the approach.
BibTeX
@inproceedings{icassp2019_smartdspforasmar,
title = {Smart DSP for a Smarter Power Grid: Teaching Power System Analysis through Signal Processing},
author = {Ahmad Moniri and Anthony G. Constantinides and Danilo P. Mandic},
booktitle = {ICASSP 2019},
year = {2019}
}