Multiple methods are available
to monitor the health
of a power supply, ultimately
leading to improved
reliability of the power subsystem and,
subsequently, the total system. These
improvements can come from adjusting
system operating parameters based on
these real-time diagnostics or by alerting
the host system that the power subsystem
performance is degraded, allowing the
system to adjust or schedule maintenance.
Because discrete values of the powersystem
states already exist, digital control
makes it convenient for this monitoring
and evaluation to occur within the power
supply itself. It also simplifies monitoring
parameters that otherwise might require
additional circuitry to sense.
One important advantage of a digitally
controlled power solution is that it’s possible
to monitor complex parameters.
In addition to simple parameters (like
switching frequency, duty cycle, input
and output voltage, input and output
current, and the temperature of various
components), complex parameters (such
as power dissipation, efficiency, stability
margin, output ripple voltage, input ripple
voltage, phase-current mismatch, pulsewidth
jitter, and fault history) can be captured
and reported to the host system.
Traditionally, things like current, voltage,
and temperature have been easy to
measure. However, we need the embedded
intelligence of the digital controller
to determine parameters such as stability
margin or pulse-width jitter. Access to
such information and the controller’s
embedded intelligence can allow for complex
operations; for example, adjusting its
own compensation if it senses the stability
margin is unacceptable.
The Bode characteristics of the loop
gain can provide considerable insight into
component values, efficiency, and stability
margins. The ability of a digital controller
to make this measurement while the power
supply is deployed in an actual product
offers a unique opportunity to improve the
reliability of the overall system.
Once the Bode characteristics are
determined, classical stability metrics
like phase margin, gain margin, and loop
bandwidth can be extracted from the
resulting data. In addition, the output filter’s
resonant frequency and quality factor
(Q) also can be extracted. This data then
can be compared to expected values. If the
observed changes are statistically significant,
conclusions about the component
values or efficiency can be made and, if
deemed necessary, a maintenance request
can be sent to the system.
Figure 1 shows a typical power-supply
application. The transfer function from the
switching node to the output has the form
of Equation 1 with passive loss elements
shown in Equation 2.1
The Q of the output filter is related to
the loss elements connected to the energy
storage components L and C; ?Z is related
to the output capacitance and its associated
equivalent series resistance (ESR);
and ?0 is primarily determined by the
resonance of the inductor and capacitor.
While in this example the resonant
frequency is a function of R, ESR, and
DCR (Fig. 1, again), efficiency requirements
demand that R be much larger than
either ESR or DCR. The result is that ?0 is
approximately a function of L and C only.
Because Q is linked to the losses, a large
change in its value means either a passive
component value has changed, or a large
change occurred in the MOSFET’s losses.
Either way, it’s possible to alert the system
that maintenance is needed. A history of
the Bode metrics can be stored in memory
for later statistical analysis.
In addition to making measurements,
the controller must be able to interpret an
appropriate time to take the measurement.
Bode characteristics are only relevant during
steady-state conditions with known
input voltage, load characteristics, and
temperatures. A digital controller can monitor
these items before, after, and during the
measurement. If any of these parameters
are unacceptable, delay the measurement
until such time as they are acceptable.
As an application, the controller can
measure and record critical loop gain
characteristics right before the product is
deployed into the field. If the system can
record bandwidth, Q, and ?0 at a known
load and temperature when the product
is new, the power supply can periodically
monitor these parameters to see if a statistically
significant change has occurred and
alert the host system as appropriate.
System Identification
Measuring the power system’s transfer
function and creating the Bode plot of the
loop gain is called system identification.
The classical way a network analyzer measures
a system is to inject an excitation signal
at a summing junction at one location
around the loop and measure the response
at another point. If we chose locations
within the controller where the control signals
are discrete samples, we can use digital
techniques to apply the excitation and measurement.
The power system can be excited
by injecting a signal at x1 or x2 (Fig. 2). The
response to the excitation can be measured
at e, c, d, or u. Reference 2 describes the
associated math for each case.
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