Pendeteksian Harmonisa Arus Berbasis Feed Forward Neural Network Secara Real Time

Endro Wahjono, Dimas Okky Anggriawan, Achmad Luki Satriawan, Aji Akbar Firdaus, Eka Prasetyono, Indhana Sudiharto, Anang Tjahjono, Anang Budikarso


The development of power electronics converters has been widespread in the industrial, commercial, and home applications. The device is considered to produce harmonics in non-linear loads. Harmonics cause a decrease in power quality in the electric power system. To prevent a decrease in power quality caused by harmonics in the power system, the detection of harmonics has an important role. Therefore, this paper proposed feed forward neural network (FFNN) for harmonic detection. The design of harmonic detection device is designed with a feed forward neural network method that it has two stages of information processing, namely the training stage and the testing stage. FFNN has input harmonics and THDi as output. To detect harmonics, frst training is conducted to recognize waveform patterns and calculate the fast fourier transform (FFT) process offline. Prototype using the AMC1100DUB current sensor, microcontroller and display. To validate the proposed algorithm, compared by standard measurement tool and FFT. The results show the proposed algorithm has good performance with the average percentage error compared by standard measurement tool and FFT of 5.33 %.


Feed Forward neural network; Monitoring; FFT; Harmonisa; Realtime

Full Text:



C. F. Nascimento, A. A. Oliveira, A. Goedtel, dan A. B. Dietrich,"Harmonic Distortion Monitoring for Non Linear Loads Using Neural Network Method,” Applied Soft Comp., pp. 475-482,

Sept. 2012.

H. C. Lin,”Fast tracking of time-varying power system frequency and harmonics using iterative-loop approaching algorithm” IEEE Trans. Ind. Electron., Vol. 54, No. 2, pp.974-983, Apr. 2007.

Pujiantara, M., Anggriawan, D. O., Tjahjono, A., Permadi,D., Priyadi, A., & Purnomo, M. H. A Real-Time Current Harmonic Monitoring System Based in Stockwell Transform Method. International Review of Electrical Engineering, Vol. 11, No.2.

R. M. Hidalgo, J. G. Fernandez, R. R. Rivera, and H. A. Larrondo, ” A simple adjustable window algorithm to improve FFT measurements, "IEEE Trans. Instrum. Meas., vol 51, pp. 31-6, Feb. 2002.

D. Agrez, ”Weighted multipoint interpolated DFT to improve amplitude estimation of multifrequency signal.” IEEE Trans. Instrum. Meas., Vol. 51, No. 2, pp. 287-292, Apr. 2002.

G. W. Chang, C. I. Chen, dan Y. F. Teng, ”Radial Basis Function Based Neural Network for Harmonic Detection,” IEEE Trans. Act. Ind. Elec., Vol. 57, No. 6, pp. 2171-2173, Jun. 2010.

W. W. L. Keerthipala, L. T. Chong, dan T. C Leong, ” Artificial Neural Network Model for Analysis of Power System Harmonics” Proceedings of ICNN’95, 2002.

I.S. Faradisa, D.O. Anggriawan, T.A. Sardjono, M.H. Purnomo, "Identification of Phonocardiogram Signal Based on STFT and Marquardt Lavenberg Backpropagation” IEEE International Seminar on Intelligent and Its Applications, 2016.

Sudiharto, I., Anggriawan, D. O., & Tjahjono, A., "Harmonic Load Identification Based on Fast Fourier Transform and Levenberg Marquardt Backpropagation. Journal of Theoretical and Applied Information Technology, Vol. 95, No. 5, pp. 1080.

Anggriawan, D. O., Satriawan, A. L., Sudiharto, I., Wahjono, E. Prasetyono, E., & Tjahjono, A. Levenberg Marquardt Backpropagation Neural Network for Harmonic Detection", In 2018 International Electronics Symposium on Engineering Technology and Applications IES-ETA, pp. 129-132, Oct. 2018.

Syafi’i, M. H. R. A., Prasetyono, E., Khafdli, M. K., Anggriawan, D. O., and Tjahjono, A.. Real-Time Series DC Arc Fault Detection Based on Fast Fourier Transform", International Electronics Symposium on Engineering Technology and Applications (IES-ETA), pp. 25-30, Oct. 2018.

Hassoun, M. H. (1995). Fundamentals of artificial neural networks. MIT press.



  • There are currently no refbacks.

View My Stats


Creative Commons License

Jurnal Rekayasa Elektrika (JRE) is published under license of a Creative Commons Attribution-ShareAlike 4.0 International License.