PREDICTION of POWER SYSTEMS HARMONIC USING FUZZY LOGIC

Yıl 2022, Sayı: 050, 98 - 105, 30.09.2022

Ersen Kuru Leyla Kuru

Öz

This paper presents a new approach for predicting the Voltage Total Harmonic Distortion (THDV ) in power systems. We benefit from a power system with nonlinear dynamic load belonging to an Iron and Steel Industry. In this power system the nonlinear load consist of DC motor drives, high frequency welding machine, thyristor controlled AC chopper, rectifier and invertor. Especially high frequency machines used in heating and welding process in an iron and steel industry are playing rol in voltage distortions. Basic relationships about harmonics, effects of the harmonics and ways for the THDV measurement are described in the firstly and prediction of THDV using Fuzzy Inference Systems (FIS) are examined in the secondly part of the paper. Power Factor (PF), and 3rd phase current (IL3) values are measured for an example system. After FIS is designed for prediction of THDV and method is tested using both FIS simulation and field measurements, the proposed fuzzy prediction approach is successfully applied to predict THDV

Anahtar Kelimeler

Harmonic, Fuzzy Inference System, Power Factor, Active Filter, Distortion Factor

Teşekkür

I would like to thank Güven Özdemir, the owner of the Steel Industry , for giving the opportunity for this research.

Kaynakça

• [1] Yiğit, E., Özkaya, U., Öztürk, Ş., Singh, D., Gritli, H. (2021), Automatic detection of power quality disturbance using convolutional neural network structure with gated recurrent unit. Mobile Information Systems, 2021.
• [2] Fang Z. P. (2001), Harmonic Sources and Filtering Approaches. IEEE Industry Applications Magazine, 18-25.
• [3] Probabilistic Aspects Task Force of the Harmonics Working Group Subcommittee of the Transmission and Distribution Committee, Time-Varying Harmonics:Part I – Characterizing Measured Data, IEEE Transactions on Power Delivery, 13(3), 1998, 938-944.
• [4] Janik, P., Lobos, T. (2006), Automated classification of power-quality disturbances using SVM and RBF networks. IEEE Transactions on Power Delivery, 21(3), 1663-1669.
• [5] Yılmaz, A., Küçüker, A., Bayrak, G. (2022), Automated classification of power quality disturbances in a SOFC&PV-based distributed generator using a hybrid machine learning method with high noise immunity. International Journal of Hydrogen Energy, 47(45), 19797-19809.
• [6] Wang, J., Zhang, D., Zhou, Y. (2022), Ensemble deep learning for automated classification of power quality disturbances signals. Electric Power Systems Research, 213, 108695.
• [7] Zadeh L.A. (1965), Fuzzy sets. Information Control, 8, 338-353.
• [8] Hung T. N., Elbert A.W. (2000), A first Course in Fuzzy Logic, Chapman and Hall / CRC .
• [9] Fuzzy Logic Toolbox User’s Guide (2013), The MathWorks, Inc., Massachusetts, USA.
• [10] Mamdani E.H., Assilian S. (1975), An experiment in linguistic synthesis with a fuzzy logic controller. International Journal of Man-Machine Studies, 7(1), 1-13.