Load Identification Using Harmonic Based on Probabilistic Neural Network
AbstractDue to increase power quality which are caused by harmonic distortion it could be affected malfunction electrical equipment. Therefore, identification of harmonic loads become important attentionÂ in the power system. According to those problems, this paper proposes a Load Identification using harmonic based on probabilistic neural network (PNN). Harmonic is obtained by experiment using prototype, which it consists of microcontroller and current sensor. Fast Fourier Transform (FFT) method to analyze of current waveform on loads become harmonic load data. PNN is used to identify the type of load. To load identification, PNN is trained to get the new weight. Testing is conducted To evaluate of the accuracy of the PNN from combination of four loads. The results demonstrate that this method has high accuracy to determine type of loads based on harmonic load
IEEE std. 519-1992 IEEE Recommended Practices and Requirement for Harmonic Control in Electrical Power Systems.
G.T. Heydt, Identification of Hharmonic sources by a state estimation technieque, IEEE Trans. Power Del. Vol. 4 no. 1, pp. 569-576, jan. 1989.
H. Ma and A. A. Girgis, Identification and tracking of harmonic sources in a power system using a Kalman FIlter, IEEE Trans. Power Del., vol. 11, no. 3, pp. 1659-1665, jul. 1996.
F. Sultanem, Using appliance signatures for monitoring residential loads at meter panel level, IEEE Trans. Power Del., vol. 6, no. 4, pp. 1380â€“1385, Oct. 1991.
G.W. Hart, Nonintrusive appliance load monitoring, Proc. IEEE, vol. 80, no. 12, pp. 1870â€“1891, Dec. 1992.
D. C. Robertson, O. I. Camps, J. S. Mayer, and W. B. Gish, Sr., Wavelets and electromagnetic power system transients, IEEE Trans. Power Del., vol. 11, no. 2, pp. 1050â€“1056, Apr. 1996.
A. I. Cole and A. Albicki, Data extraction for effective non-intrusive identification of residential power loads, in Proc. IEEE Instrum. Meas.Technol. Conf., 1998, pp. 812â€“815.
I. Cole and A. Albicki, Algorithm for non-intrusive identification of residential appliances, in Proc. IEEE Int. Symp. Circuits Syst., 1998, pp. 338â€“341.
Anggriawan, D.O., Satriawan, A.L., Sudiharto, I., Wahjono, E., Prasetyono, E., Tjahjono, A., â€œLevenberg Marquardt Backpropagation Neural Network for Harmonic Detectionâ€, International Electronics Symposium on Engineering Technology and Application (IES-ETA), 2018
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, Iss. 5, pp. 1080, 2017
Mubarok, A.F., Octavira, T., Sudiharto, I., Wahjono, E., Anggriawan, D.O., â€œIdentification of Harmonic Loads Using Fast Fourier Transform and Radial Basis Function Neural Networkâ€, International Electronics Symposium on Engineering Technology and Application (IES-ETA), 2017
M. T. Musav1, W. Ahmed, K. H. Chan, K. B. Faris, T And D. M. Hummels, On the Training of Radial Basis Function Classifiers, Pergamon Press Ltd. Neural Networks vol. 5, pp. 595-603. 1992.
Young-Sup Hwang and Sung-Yang Bang, An Efficient Method to Construct a Radial Basis Function Neural Network Classifier, Elsevier Science Ltd, Neural Networks. vol. 10, no. 8, 1997.
J.A Leonard, M.A Kramer, Radial basis function networks for classifying process faults, IEEE Control Systems, Vol. 11, No. 3, April. 1991.
Wu, Sthephen Gang. 2007. A leaf recognition algorithm for plant clasification using probabilistic neural network. IEEE ISSPIT 2007 on computer science and electrical engineering involve artificial intelligence and Neurology.
F. Zhang, Z. Geng, W. Yuan, The Algorithm of interpolating Windowed FFT for harmonic Analysis of electric Power System, IEEE Trans. Power Del., Vol. 16, No. 2, Apr. 2001.
H.Qian, R. Zhao, T. Chen, Interhamonics Analysis Based on Interpolating Windowed FFT Algorithm IEEE Trans. Power Del, Vol. 22, no. 2, Apr. 2007.
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