Systems and methods for improved root mean square (RMS) measurement
Systems and methods are provided for improving the operation of a computer or other electronic device that utilizes root-mean-square (RMS) measurements, e.g., RMS current measurements, by reducing error in the RMS measurement. A series of measurement samples are received at a processor, which executes a noise-decorrelated RMS algorithm including: calculating a current-squared value for each measurement sample by multiplying the measurement sample by a prior measurement sample in the series (rather by simply squaring each measurement sample as in conventional techniques), summing the current-squared values, and calculating an RMS value based on the summed values. The processor may also execute a frequency-dependent magnitude correction filter to correct for frequency-dependent attenuation associated with the noise-decorrelated RMS algorithm. The calculated RMS value has a reduced error, particularly for lower-end current measurements, which may improve the operation of the computer or electronic device that utilizes the RMS value.
1. A method for improving the operation of a computer or other electronic device that utilizes root-mean-square (RMS) measurements, by reducing error in the RMS measurement, the method comprising:
receiving, at a processor of the computer or other electronic device, a series of measurement samples;
executing, by the processor, a noise-decorrelated RMS algorithm including:
for each received measurement sample, calculating an adjusted sample value by a mathematical combination step comprising multiplying each respective measurement sample by a prior measurement sample in the sequence; and
summing the adjusted sample values for the sequence of measurement samples; and
calculating an RMS value based on the summed adjusted sample values;
executing, by the processor, a frequency-dependent magnitude correction filter to the series of measurement samples to correct for a frequency-dependent attenuation associated with the noise-decorrelated RMS algorithm; and
controlling at least one component of the computer or other electronic device based on the calculated RMS value.
2. The method of claim 1 , wherein the series of measurement samples comprise a series of current measurement samples, and the RMS value calculated by execution of the noise-decorrelated RMS algorithm is an RMS current value.
3. The method of claim 1 , wherein multiplying each respective measurement sample by a prior measurement sample in the sequence comprises multiplying each received measurement sample by the immediately previous measurement sample in the sequence to calculate a current-squared value.
4. The method of claim 1 , wherein the frequency-dependent magnitude correction filter comprises an infinite impulse response (IIR) filter.
5. The method of claim 1 , comprising executing the frequency-dependent magnitude correction filter for each measurement sample prior to executing the noise-decorrelated RMS algorithm for each respective received measurement sample.
6. The method of claim 1 , wherein the frequency-dependent magnitude correction filter is configured to achieve a stable pole and zero placement using a single multiplication and two additions.
7. The method of claim 1 , wherein the computer or other electronic device that utilizes root-mean-square (RMS) measurements comprises a microcontroller.
8. The method of claim 1 , wherein the computer or other electronic device that utilizes root-mean-square (RMS) measurements comprises a microprocessor.
9. The method of claim 1 , wherein the computer or other electronic device that utilizes root-mean-square (RMS) measurements comprises a computer including at least one microcontroller or microprocessor.
10. A system for improving the operation of a computer or other electronic device that utilizes root-mean-square (RMS) measurements, by reducing error in the RMS current, the system comprising:
a memory device storing a noise-decorrelated RMS algorithm; and
a processor configured to:
receive a series of measurement samples; and
execute the noise-decorrelated RMS algorithm including:
for each received measurement sample, calculating an adjusted sample value by a mathematical combination step comprising multiplying each respective measurement sample by a prior measurement sample in the sequence; and
summing the adjusted sample values for the sequence of measurement samples; and
calculating the RMS value based on the summed adjusted sample values; and
execute a frequency-dependent magnitude correction filter to the series of measurement samples to correct for a frequency-dependent attenuation associated with the noise-decorrelated RMS algorithm.
11. The system of claim 10 , wherein the series of measurement samples comprise a series of current measurement samples, and the RMS value calculated by execution of the noise-decorrelated RMS algorithm is an RMS current value.
12. The system of claim 10 , wherein the processor is integrated in a microcontroller.
13. The system of claim 10 , wherein the processor comprises a microprocessor.
14. The system of claim 10 , wherein multiplying each respective measurement sample by a prior measurement sample in the sequence multiplying each received measurement sample by the immediately previous measurement sample in the sequence to calculate a current-squared value.
15. The system of claim 10 , wherein the frequency-dependent magnitude correction filter comprises an infinite impulse response (IIR) filter.
16. The system of claim 10 , wherein the processor is configured to execute the frequency-dependent magnitude correction filter for each measurement sample prior to executing the noise-decorrelated RMS algorithm for each respective received measurement sample.
17. The system of claim 10 , wherein the frequency-dependent magnitude correction filter is configured to achieve a stable pole and zero placement using a single multiplication and two additions.
18. A method for improving the operation of a computer or other electronic device that utilizes root-mean-square (RMS) measurements, by reducing error in the RMS measurement, the method comprising:
receiving, at a processor of the computer or other electronic device, a series of measurement samples;
executing, by the processor, a noise-decorrelated RMS algorithm to calculate RMS values from the series of measurement samples, the noise-decorrelated RMS algorithm including:
a noise-decorrelated RMS routine that decorrelates noise in the measurement samples; and
a frequency-dependent gain correction filter that corrects for frequency-dependent attenuation introduced by the noise-decorrelated RMS routine;
wherein the frequency-dependent gain correction filter precedes the noise-decorrelated RMS routine in the noise-decorrelated RMS algorithm;
controlling at least one component of the computer or other electronic device based on the calculated RMS values.
19. The method of claim 18 , wherein the noise-decorrelated RMS routine includes multiplying each measurement sample in the series of measurement samples by a prior measurement sample in the series of measurement samples.