Choice and properties of adaptive and tunable digital boxcar (moving average) filters for power systems and other signal processing applications

Andrew J. Roscoe, Steven M. Blair

Research output: Contribution to conferencePaper

4 Citations (Scopus)

Abstract

The humble boxcar (or moving average) filter has many uses, perhaps the most well-known being the Dirichlet kernel inside a short-time discrete Fourier transform. A particularly useful feature of the boxcar filter is the ease of placement of (and tuning of) regular filter zeros, simply by defining (and varying) the time length of the boxcar window. This is of particular use within power system measurements to eliminate harmonics, inter-harmonics and image components from Fourier, Park and Clarke transforms, and other measurements related to power flow, power quality, protection, and converter control. However, implementation of the filter in real-time requires care, to minimise the execution time, provide the best frequency-domain response, know (exactly) the group delay, and avoid cumulative numerical precision errors over long periods. This paper reviews the basic properties of the boxcar filter, and explores different digital implementations, which have subtle differences in performance and computational intensity. It is shown that generally, an algorithm using trapezoidal integration and interpolation has the most desirable characteristics.

Conference

Conference IEEE Applied Measurements for Power Systems (AMPS 2016)
CountryGermany
CityAachen
Period28/09/1630/09/16
Internet address

Fingerprint

Electric power system measurement
Group delay
Power quality
Discrete Fourier transforms
Interpolation
Signal processing
Tuning

Keywords

  • boxcar filters
  • moving average
  • power system measurements
  • transforms
  • execution time
  • power quality
  • converter control
  • frequency-domain response
  • digital implementations
  • computational intensity

Cite this

Roscoe, Andrew J. ; Blair, Steven M. / Choice and properties of adaptive and tunable digital boxcar (moving average) filters for power systems and other signal processing applications. Paper presented at IEEE Applied Measurements for Power Systems (AMPS 2016), Aachen, Germany.6 p.
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Roscoe, AJ & Blair, SM 2016, 'Choice and properties of adaptive and tunable digital boxcar (moving average) filters for power systems and other signal processing applications' Paper presented at IEEE Applied Measurements for Power Systems (AMPS 2016), Aachen, Germany, 28/09/16 - 30/09/16, . https://doi.org/10.1109/AMPS.2016.7602853

Choice and properties of adaptive and tunable digital boxcar (moving average) filters for power systems and other signal processing applications. / Roscoe, Andrew J.; Blair, Steven M.

2016. Paper presented at IEEE Applied Measurements for Power Systems (AMPS 2016), Aachen, Germany.

Research output: Contribution to conferencePaper

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AU - Roscoe, Andrew J.

AU - Blair, Steven M.

N1 - © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

PY - 2016/10/24

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N2 - The humble boxcar (or moving average) filter has many uses, perhaps the most well-known being the Dirichlet kernel inside a short-time discrete Fourier transform. A particularly useful feature of the boxcar filter is the ease of placement of (and tuning of) regular filter zeros, simply by defining (and varying) the time length of the boxcar window. This is of particular use within power system measurements to eliminate harmonics, inter-harmonics and image components from Fourier, Park and Clarke transforms, and other measurements related to power flow, power quality, protection, and converter control. However, implementation of the filter in real-time requires care, to minimise the execution time, provide the best frequency-domain response, know (exactly) the group delay, and avoid cumulative numerical precision errors over long periods. This paper reviews the basic properties of the boxcar filter, and explores different digital implementations, which have subtle differences in performance and computational intensity. It is shown that generally, an algorithm using trapezoidal integration and interpolation has the most desirable characteristics.

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