Tech Briefs Magazine - November 2023 - 16

Edge Intelligence
Solar PV
Power
Conditioning
Sustainable Agriculture
Loads
Grid
DC
Battery Pack
Energy
Management
Interface
Converter
Cloud-Based Monitoring Platform
AC
DC
Meter
Wind
Turbines
and operation of converters using traditional,
statistical, and machine learning-based
approaches. However, these
approaches are usually restricted to
the technology design or prototyping
phases, and they are hardly implemented
online in the final products. The
added number and footprint of circuit
components needed for realizing these
methods, potential interference with
the device characteristics or the regular
converter operation, and the extent of
computing resources (such as laptops)
required for process execution make
the existing techniques impractical for
onboard implementation, especially in
sustainable energy applications that use
many power converters.
Pulsed Power
Converter
Water Purification or
Desalination System
Each component of a power converter
presents a unique challenge in the
onboard or in-situ measurement of the
" health
indicators "
for
practical imFigure
1. Sustainable agriculture - power electronics-based integration of clean energy technologies,
agriculture loads, and water purification/desalination systems with the grid. (Image: University
of Houston)
Strategic Framework for Research
AC/DC grid/
microgrid
Performance improvement
Cyber-Physical System - Reliability and Resiliency
Converter-Level Insights
Converter health models
Converter
Modeling
Analytical
or Statistical
Converter performance,
digital twin
Converter-level RUL Characterization and Health Prediction
Component-Level Insights
Sensing and conditioning systems
for power devices, such as FETs
Capacitive health indicator
Magnetics, transformers, etc.
- more reliable normally
Power FET characterization with
health indicators, like Rds-on
Health prediction of gate
drivers, PCBs, etc.
Characterization knowledge at
adverse operating conditions
Component-Level Characterization and Health Prediction
Figure 2: Three-tier approach to " Edge Intelligence " in power conversion - (i) component level, (ii)
sub-system or module level, and (iii) cyber-physical system level. (Image: University of Houston)
16
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Machine
Learning
System resiliency
Impedance
analysis/pattern
recognition
Cybersecurity
plementation. For a power field-effect
transistor or FET, sensing its " on-state
resistance, " a widely accepted health indicator,
involves precise voltage and current
measurements during the device's
current conducting period. However, the
sensing circuit needs to handle high voltages
and currents along with associated
overshoots, that too near the device to
avoid further intrusions of its own.
Whereas for a capacitor, which is a typically
concerning component in terms of
converter reliability, the health indicators
such as " capacitance " and " effective series
resistance " (or ESR) are more difficult
to evaluate in-situ due to the complex
nature of the measurements involved.
Sensing and monitoring the health indicators
of some of the other important
components, such as the PCB, can be
even more challenging since the material
degradation needs to be monitored.
Enabling Edge Intelligence
across Power Converter Layers
Integrated circuits (ICs) are becoming
popular for onboard PHM of power FETs.
Tell-I's SensAI12
is an integrated hardware
aimed at power and health management,
which is designed to be placed near the
primary FET or transistor and measure
the on-state voltage, current, and resistance.
Some of the other solutions13
target the monolithic or heterogenous
integration of the power FETs along with
the sensing and measurement circuits to
further reduce the footprint, component
count, and performance intrusion.
Considering the case of capacitors,
the net impedances at multiple frequenTech
Briefs, November 2023
Three-Tier Approach for Converter Reliability and Resiliency
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Tech Briefs Magazine - November 2023

Table of Contents for the Digital Edition of Tech Briefs Magazine - November 2023
