Variable determined for optimization of alternating energy on the load by the adaptive Taguchi method


CAN E.

Journal of Engineering Research (Kuwait), cilt.10, sa.4, ss.316-335, 2022 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 10 Sayı: 4
  • Basım Tarihi: 2022
  • Doi Numarası: 10.36909/jer.12657
  • Dergi Adı: Journal of Engineering Research (Kuwait)
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Arab World Research Source, Directory of Open Access Journals
  • Sayfa Sayıları: ss.316-335
  • Anahtar Kelimeler: Optimum point, ATM, Prediction results, Fuzzy Logic
  • Erzincan Binali Yıldırım Üniversitesi Adresli: Evet

Özet

© 2022 University of Kuwait. All rights reserved.It is important to find the optimum point in terms of energy quality in the studies for electrical energy conversions with the converter and inverter circuits. In such studies, the authors tried to find the optimum operating point by using control systems such as PID and Fuzzy Logic in closed-loop controls. When a large number of variables are involved in finding the optimum point, closed-loop control methods such as these may be insufficient to find the optimum point. Therefore, in this study, the Adaptive Taguchi Method (ATM) with the maximum-minimum value is used for the estimation of variables providing the optimum point for energy quality by using it in a multilevel inverter with a double DC-DC converter. While in traditional Taguchi Methods, dependent variables predict the results by revealing their effects on independent variables, in ATM, besides the effects of the independent variables, the effects of different dependent variables on each other are also estimated. First, the system to which the ATM will be applied is introduced. Then, the principles of applying the ATM are explained. In the known Taguchi method, variable values to be found with 34 = 81 trials, variables are estimated with 27 trials, while the values of two different dependent variables in the system can be found with 54 trials instead of 2×34= 2×81 trials. The resulting values to be estimated with the proposed ATM method are estimated with 27 trials instead of 2×81 or 54 trials. Finally, the observation results to be used for estimation are analyzed and evaluated. By trying the prediction results, it is seen that the proposed system is quite effective because the estimated result value that is %3.71 in the experiments gives a lesser distortion value than the values used for prediction.