Tech Briefs Magazine - November 2023 - 17

cies can be good health indicators and
are relatively easier to sense and measure
onboard. However, for capacitors
and some of the other components, further
technological advancements need
to be made along with thorough testing
for performance and reliability in order
to realize the true benefits of in-situ
PHM and the associated modeling of the
entire power converter, sometimes also
termed as a " Digital Twin. " 14
The sensing and measurement techniques
only form half the story. It is
equally critical to communicate the data,
deduce the information, and execute
the actions - all onboard the converter
hardware to term it " edge intelligence. "
Field Programmable Gate Arrays (FPGAs)
and microcontrollers are popularly
used in power converters to implement
control and modulation schemes.
If the performance characteristics and
RUL patterns of health indicators associated
with each critical component in a
converter are recorded offline via thorough
reliability test procedures under a
wide range of practical mission or operation
profiles, machine learning can be
used to model the overall behavior in a
simple, space-efficient manner.
FPGAs can be excellent solutions for
implementing compact machine-learning
models15
and can easily support this
feature while also executing the control
actions for a converter. An example test
setup and a procedure for the reliability
characterization of gallium nitride
Fuel cell
Experimental Testing Setup
H-Bridge-Based GaN FETs
Controller
/ dSPACE
Control and Data
Acquisition
PCB1Motherboard
In-Situ/
Offline
PCB3-
Vdson
Measurement
Board
FPGA
ANN
PCB2- FET
Board
Figure 3: Harnessing the power of machine learning, FPGAs, and more to deduce/improve the performance
and health of power converters onboard - enabling PHM and " digital twins " at the edge
at minimal or no added cost. (Image: University of Houston)
(GaN) FETs are depicted in Figure 3.
With an increasing number of converters
interfaced with the grid or any
comprehensive power distribution system,
several new challenges emerge at
the CPS level including stability and resiliency.
The impedance-based stability
analysis is widely used in grid-interconnected
applications since this method
doesn't need modeling of the whole
system.
There are several technological constraints
here as well: (1) most of the
studies assume a fixed grid voltage;
there aren't considerable literature
studies available for impedance characterization
of converter-dominated grids
or microgrids; (2) in scenarios with a
vlarge number of converters/inverters
interconnected, there hasn't been sufficient
research on how the impedance or
admittance patterns at different nodes
of the CPS can construe the relative
resiliency of the overall system and the
potential actions that can be taken to
remedy any adverse scenarios; and (3)
there hasn't been any research on practically
incorporating these analyses together
with converter-level health and
performance information using FPGAs
or microcontrollers to enable edge intelligence
at the CPS level.
Once these technical gaps are addressed,
converter impedance at every
node or terminal can be evaluated periodically
(as in Figure 4) and the pattern
Fuel cell
Bidirectional Power flow
Grid/Microgrid
Network
#1
#1
DC residential Load
Energy storage system
Bidirectional Power flow
Can involve AC, DC, or
hybrid grids/microgrids
involving commercial and/or
residential loads
Can involve AC, DC, or
Hybrid Grids/Microgrids
involving commercial and/or
residential loads
Solar power Wind power
* Control Unit
and signal
injection
* Calculating
the grid's
impedance
* Grids' PHM
(Prognostic
Health
Monitoring)
Fuel cell
Grid/Microgrid
Network
#2
#2
DC residential Load
Energy storage system
Solar power Wind power
* Control Unit
and signal
injection
* Control Unit
and signal
injection
* Calculating
the grid's
impedance
* PHM at the
grid side
Grid/Microgrid
Network
#n
Residential or
commercial load
#n
Energy storage system
* Control Unit
and signal
injection
* Calculating
the grid's
impedance
Solar power Wind power
* PHM at the
grid side
* Calculating
the grid's
impedance
* PHM at the
grid side
Part of the power
converter's control unit
Figure 4: Edge intelligence for PHM and performance adaptation at the cyber-physical system (CPS) level using impedance characterization as well as
machine learning-assisted status checks at the converter terminals. (Image: University of Houston)
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Tech Briefs Magazine - November 2023

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