[Design View / Design Solution]
Digital PWM Controllers Augment System Reliability
Intelligent digital controllers can simplify power-supply design as well as enhance overall reliability through monitoring and statistical interpretation of key performance metrics.
As an example, assume that during development the mean and sigma of the open-loop bandwidth are µ = 55.0 kHz and s = 0.750 kHz. Then, during normal operation, we periodically identify the 0-dB bandwidth by exciting the system at frequencies around the last bandwidth estimate and adjust the measurement frequency until a 0-dB gain is found.
This process of detecting the 0-dB crossover is repeated four times, resulting in the values [56, 58, 53, 55] kHz. The average value is 55.5 kHz. To determine with 95% confidence whether the mean has changed, assign za/2 to be 1.96. Then the interval k is 1.96 × 0.750/v(4) = 0.735 kHz and the confidence interval is [54.2650 kHz, 55.7350 kHz]. Since 55.5 kHz is within this interval, we can say with 95% confidence that the mean has not changed.
Health Metrics Using the aforementioned system ID techniques and applying the confidence intervals borrowed from statistical process control, we can define a set of metrics upon which to make decisions about the power supply’s health.
Phase margin: This is one of the most important parameters relating to the closed-loop system’s behavior. If the voltage-regulation circuits don’t have sufficient phase margin, the response to changes in commanded voltage or load current will be a large ringing disturbance in the regulated output voltage. If severe enough, the result could damage the circuits powered by the regulator. This makes phase margin a strong candidate for a health metric.
To calculate phase margin, the loop gain is measured and the magnitude of the measured values is inspected to find the frequency at which the magnitude of the gain is equal to 1.0. The distance of the measured loop phase response at this frequency from 180º is the phase margin.
Power stage ?0 and Q: By exciting the system over a range of frequencies that include the expected resonant frequency of the power stage, we can construct a health metric for power-supply components that otherwise would be difficult to measure. A health metric based on ?0 can be an indicator of a change in output capacitance or inductor value. This could be due to damage to the capacitor dielectric or a cracked inductor.
A health metric based on the quality factor of the output filter can be used to identify changes in the series resistance of the filter components. At low load currents, the load resistance is larger than the ESR of the capacitors and DCR of the inductor and MOSFETs. In this case, the Q of the power stage response is:
Therefore, Q will decrease with increasing series resistance.
Average duty cycle: Separate from the dynamic measurements used to estimate ?0 and Q of the plant, we can estimate the series losses by comparing the average duty cycle to the measured voltage and supply current. This is an indicator of efficiency, a performance metric that’s become increasingly important in today’s world.
In addition to steady-state duty cycle, the digital controller can collect statistics on the duty-cycle jitter. This jitter can be used as an extra input when determining the optimal loop compensation. For example, if the controller determines a given Bode response from the TFA algorithm and then implements what it believes to be an appropriate compensation, the results of that compensation on the system duty cycle can be checked. Variations in duty cycle can be interpreted as having a direct impact on the system noise and output voltage ripple. If the jitter is deemed excessive, an alternate compensation can be chosen with a larger gain margin to quiet the duty-cycle jitter.
Experimental Results Figure 5 shows the measured plant response for a single-phase power stage driven by a UCD9240 digital PWM controller.4 The controller accepts commands over a serial interface to excite the loop at a given frequency and returns a complex (real and imaginary) response for that frequency.
In this case, a host computer was used to issue the commands and collect the complex data. From the closed-loop response to the excitation, the open-loop gain was calculated. Then the gains of the error voltage analog-to-digital converter (ADC), compensation filter, and PWM modulator were divided out of the openloop response, yielding the transfer function for the power stage.
To simulate a fault, the DCR of the inductor was increased from 2 mO to 42 mO. As you can see, the Q of the power-stage response dropped substantially.
References: 1. R.W. Erickson and D. Maksimovic, Fundamentals of Power Electronics, Second Edition, Springer Science + Business Media Inc., 2001 2. Mark Hagen, “In Situ Transfer Function Analysis,” 2006 Digital Power Forum 3. Daniel Zwillinger (editor), Standard Mathematical Tables and Formula, 30th Edition, CRC Press, 1996 4. UCD9240 Digital Point-of-Load System Controller, Rev. C, Texas Instruments, 2008: http://focus.ti.com/docs/prod/folders/print/ucd9240.html
Please refresh the page if you have trouble reading this text.
Search Electronic Design
Email Newsletter
Sponsored By:
The Find Power Products monthly newsletter brings you the most important new developments within the world of power design. The newsletter includes exerpts from industry leader Sam Davis's exclusive blog, as well as overviews of the latest new products.
Enter Email to Subscribe
Web Seminar
Sponsored By:
Title: Exploring How Good GUIs Drive Adoption in the Digital Power Management Space
Reprints
