Noise Cancellation in Partial Discharge Measurement Signal using Adaptive Neuro-Fuzzy Inference Systems (ANFIS)

Marungsri B., Boonpoke S., Oonsivilai A.

9th WSEAS International Conference on Power Systems, Budapest, Hungary, 3 - 05 September 2009, pp.146-147 identifier

  • Publication Type: Conference Paper / Full Text
  • City: Budapest
  • Country: Hungary
  • Page Numbers: pp.146-147
  • Istanbul Technical University Affiliated: No


This paper presents application of Adaptive Neuro-Fuzzy Inference Systems (ANFIS) for noise cancellation in partial discharge measurement signal which was detected by current transducer. The measurable output noisy partial discharge with 1dB, -1dB, 5dB, and 10 dB SNR level is defined as the contaminated signal of the interference to compare with the output data of the filter. The white noise source is acquired as the input. The ANFIS uses a hybrid learning algorithm to identify parameters of Sugeno-type fuzzy inference systems. It applies a combination of the least-squares method and the back propagation gradient descent method for training FIS membership function parameters to emulate a given training data set. Finally, after training, the ANFIS output is demonstrated. Then the estimated information signal is calculated as the difference between the measured signal and the estimated interference. The ANFIS could do a practically superior situation in adaptive de-noising of PD signal.