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This study introduces a deep learning method to measure electrical current intensity and frequency using magnetic field spectrograms. This contactless approach offers a novel way to analyze electrical properties via magnetic field imaging.

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Area of Science:

  • Electrical Engineering
  • Signal Processing
  • Machine Learning

Background:

  • Accurate measurement of current intensity and frequency is crucial in various electrical systems.
  • Traditional methods may require direct contact or complex sensor setups.
  • Magnetic field analysis offers a potential non-invasive alternative.

Purpose of the Study:

  • To develop and present a deep learning-based approach for identifying current intensity and frequency.
  • To leverage magnetic field data, specifically spectrograms, for electrical parameter estimation.
  • To demonstrate a contactless method for current measurement.

Main Methods:

  • Utilized magnetic field measurements from a conductor carrying current.
  • Employed a magnetic probe to generate time-frequency spectrograms of the magnetic field.
  • Applied a convolutional neural network (CNN) model with spectrogram images as input.

Main Results:

  • The CNN model successfully estimated current intensity and frequency from spectrogram images.
  • The approach demonstrated contactless current estimation capabilities.
  • Spectrograms visually represented magnetic field induction values across frequencies over time.

Conclusions:

  • Deep learning, specifically CNNs, can effectively reconstruct current intensity and frequency from magnetic field spectrograms.
  • Contactless current measurement using magnetic field probes and deep learning is feasible and advantageous.
  • This method provides a novel, non-invasive technique for electrical parameter monitoring.