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The Moving Average Filter of Stator Current as Alarm for Overload of Induction Motor

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  • Goran Kekovic
  • Negovan Stamenkovic

Abstract

In this paper, the method of detecting the overload of the induction motors (IMs) by applying moving average filter of the stator current in the time domain, was suggested. This method can be analyzed as the part of the analysis of the motor current signature analysis (MCSA). The stator current is analysed as the signal and it is divided into regular intervals – windows, within moving average filter applied. The minimum length of the moving window is determined by the sampling frequency detector, as well as the frequency of the stator current. The better resolution of peaks in moving average filter, can be achieved by the greater length of moving window. This is particularly important in areas of variable torque induction motor. This opens the possibility of detecting finer changes in the value of the stator current. It is shown that within the time window of changes in load of IMs, the signal of stator current becomes non-stationary. Otherwise, it is stationary. The changes of stationarity of the signal could be the alarm of overload of induction motor. The advantage of this method is improved response time of detector due to changes in the stator current. Software solution of proposed method is relatively simple: time series of the stator current is mapped according to the action of the moving average filter in different time series, suitable for use in real-time.

Suggested Citation

  • Goran Kekovic & Negovan Stamenkovic, 2020. "The Moving Average Filter of Stator Current as Alarm for Overload of Induction Motor," European Journal of Electrical Engineering and Computer Science, European Open Science, vol. 4(2), March.
    • Handle: RePEc:epw:ejece0:v:4:y:2020:i:2:id:19199
    DOI: 10.24018/ejece.2020.4.2.199
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    1. Hammoudeh, Shawkat & Li, Huimin, 2008. "Sudden changes in volatility in emerging markets: The case of Gulf Arab stock markets," International Review of Financial Analysis, Elsevier, vol. 17(1), pages 47-63.
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