An adaptive neuro-fuzzy based methodology for harmonic analysis of a power transformer

• Autorzy: Urooj Shabana , Amir Mohammad , Khan Aiman , Tariq Mohd
• Języki publikacji: EN

Abstrakty

EN

The interfering nature of harmonics always causes various power quality issues that impacts on both efficiency, and expected transformer life. Optimal analysis of the three-phase core power transformers using harmonic spectrum can limit these power quality issues. This paper designs the Adaptive Neuro-Fuzzy Inference System (ANFIS) based model for the estimation of losses. Further optimal parameters selection of three-phase power transformer using iron and ferrite core materials. This paper demonstrates factors that deteriorate the power quality, responsible for harmonics distortions and inefficiency in power transformers. The proposed ANFIS based analysis provides an optimal solution to harmonic reduction and improves overall efficiency. Also, providing a comparative study of various core parameters that will be suitable for a three-phase core transformer. The proposed parameters are demonstrated for improving the overall transformer efficiency using iron and ferrite core material. ANSYS Maxwell simulation estimates the Total Harmonic Distortion (THD) and enhances THD in contributing to the optimal core material. The design of a three-phase power transformer and the performance evaluation of the proposed methodology performed in MATLAB simulation environment.

Słowa kluczowe

EN

Adaptive Neuro Fuzzy Inference System; ANFIS; artificial intelligence; AI; transformer; Total Harmonic Distortion; THD; electromagnetic; ANSYS Maxwell

PL

adaptacyjny system neuronowo-rozmyty; ANFIS; sztuczna inteligencja; transformator; współczynnik zawartości harmonicznych; THD; system ANSYS

• Wydawca: Institute of Heat Engineering, Warsaw University of Technology
• Czasopismo: Journal of Power Technologies
• Rocznik: 2021
• Tom: Vol. 101, nr 1
• Strony: 1--10
• Opis fizyczny: Bibliogr. 30 poz., rys., tab., wykr.

Twórcy

autor

Urooj Shabana

• Department of Electrical Engineering, College of Engineering, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia

autor

Amir Mohammad

• Department of Electrical Engineering, Madan Mohan Malaviya University of Technology (MMMUT), Gorakhpur, India

autor

Khan Aiman

• Department of Electrical Engineering, Indian Institute of Technology (IIT), Ropar, Punjab, India

autor

Tariq Mohd

• Department of Electrical Engineering, ZHCET, Aligarh Muslim University, Aligarh, India

Bibliografia

• 1. Suppitaksakul, C., and Saelee, V. (2009) Application of Artificial Neural Networks for electrical losses estimation in three-phase transformer. 2009 6th International Conference on Electrical Engineering/Electronics Computer, Telecommunications and Information Technology.
• 2. Rahman, M.A., and Jeyasurya, B. (1988) A state-of-the-art review of transformer protection algorithms. IEEE Transactions on Power Delivery, 3 (2), 534-544.
• 3. Prasojo, R.A., Diwyacitta, K., Suwarno, and Gumilang, H. (2017) Transformer Paper Expected Life Estimation Using ANFIS Based on Oil Characteristics and Dissolved Gases (Case Study: Indonesian Transformers). Energies, 10 (8), 1135.
• 4. Singh, R., and Singh, A. (2010) Aging of distribution transformers due to harmonics. Proceedings of 14th International Conference on Harmonics and Quality of Power - ICHQP 2010.
• 5. Phaengkieo, D., and Ruangsinchaiwanich, S. (2015) Design optimization of electrical transformer using artificial intelligence techniques. 2015 18th International Conference on Electrical Machines and Systems (ICEMS).
• 6. Urooj, S., Singh, T., Amir, M., and Tariq, M. (2020) Optimal Design of Power Transformer with Advance Core Material using ANSYS Technique. European Journal of Electrical Engineering and Computer Science, 4 (5).
• 7. Mohammadpour, H., and Dashti, R. (2011) Localization of short circuit faults in transformers using harmonic analysis and pattern recognition. 2011 International Conference on Advanced Power System Automation and Protection.
• 8. Turker, T., Yorukeren, N., Sengul, M., and Alboyaci, B. (2008) An artificial neural-net based method for predicting distribution transformer’s total harmonic distortions. 2008 IEEE 2nd International Power and Energy Conference.
• 9. Wakileh, G.J. (2001) Mitigation of Power System Harmonics, in Power Systems, Springer Berlin Heidelberg, pp. 105-136.
• 10. Johnson, J.R. (2008) Managing harmonics and resonance with active harmonic filters in an offshore ring main oil field. 2008 13th International Conference on Harmonics and Quality of Power.
• 11. Digalovski, M., Najdenkoski, K., and Rafajlovski, G. (2013) Impact of current high order harmonic to core losses of three-phase distribution transformer. Eurocon 2013.
• 12. Ferreira, V.A., and Teixeira, M.D. (2018) The influence of harmonic voltage distortion on power transformer equivalent circuit and losses. 2018 Simposio Brasileiro de Sistemas Eletricos (SBSE).
• 13. Sadati, S.B., Tahani, A., Darvishi, B., Dargahi, M., and yousefi, H. (2008) Comparison of distribution transformer losses and capacity under linear and harmonic loads. 2008 IEEE 2nd International Power and Energy Conference.
• 14. Kazem, H.A. (2013) Harmonic Mitigation Techniques Applied to Power Distribution Networks. Advances in Power Electronics, 2013, 1-10.
• 15. Awadallah, M.A., Xu, T., Venkatesh, B., and Singh, B.N. (2016) On the effects of Solar Panels - newline on Distribution Transformers. IEEE Transactions on Power Delivery, 31 (3), 1176-1185.
• 16. Platero, C.A., Granizo, R., Blazquez, F., and Marchesi, E. (2018) Testing of non-toroidal shape primary pass-through current transformer for electrical machine monitoring and protection. 2018 IEEE International Conference on Industrial Technology (ICIT).
• 17. Al-Hmouz, A., Shen, J., Al-Hmouz, R., and Yan, J. (2012) Modeling and Simulation of an Adaptive Neuro-Fuzzy Inference System (ANFIS) for Mobile Learning. IEEE Transactions on Learning Technologies, 5 (3), 226-237.
• 18. Amir, M., and Zaheeruddin (2019) ANN Based Approach for the Estimation and Enhancement of Power Transfer Capability. 2019 International Conference on Power Electronics Control and Automation (ICPECA).
• 19. Subbanna, S.R., and Suryakalavarthi, M. (2016) Performance analysis of artificial intelligence techniques to control the saturation level in the magnetic core of a welding transformer. 2016 International Conference on Electrical Electronics, and Optimization Techniques (ICEEOT).
• 20. Şahin, M., and Erol, R. (2017) A Comparative Study of Neural Networks and ANFIS for Forecasting Attendance Rate of Soccer Games. Mathematical and Computational Applications, 22 (4), 43.
• 21. Fan, J., Wang, F., Sun, Q., Bin, F., Liang, F., and Xiao, X. (2017) Hybrid RVM-ANFIS algorithm for transformer fault diagnosis. IET Generation Transmission & Distribution, 11 (14), 3637-3643.
• 22. Forouhari, S., and Abu-Siada, A. (2018) Application of adaptive neuro fuzzy inference system to support power transformer life estimation and asset management decision. IEEE Transactions on Dielectrics and Electrical Insulation, 25 (3), 845-852.
• 23. Kim, J., Lai, J.-S., and Liu, X. (2018) Analysis of Harmonic Cancellation Performance of a Shunt Phase-Shift Transformer Rectifier. 2018 IEEE 4th Southern Power Electronics Conference (SPEC).
• 24. Raharja, L.P.S., Q., O.A., Arief, Z., and Windarko, N.A. (2017) Reduction of Total Harmonic Distortion (THD) on Multilevel Inverter with Modified PWM using Genetic Algorithm. EMITTER International Journal of Engineering Technology, 5 (1), 91-118.
• 25. Amir, M., and Srivastava, S.K. (2018) Analysis of MPPT Based Grid Connected Hybrid Renewable Energy System with Battery Backup. 2018 International Conference on Computing Power and Communication Technologies (GUCON).
• 26. Amir, M., and Srivastava, S.K. (2019) Analysis of Harmonic Distortion in PV-Wind-Battery Based Hybrid Renewable Energy System for Microgrid Development, in Lecture Notes in Electrical Engineering, Springer Singapore, pp. 1223-1231.
• 27. Sambariya, D.K., and Prasad, R. (2016) Selection of Membership Functions Based on Fuzzy Rules to Design an Efficient Power System Stabilizer. International Journal of Fuzzy Systems, 19 (3), 813-828.
• 28. Er, M.J., and Sun, Y.L. (2001) Hybrid fuzzy proportional-integral plus conventional derivative control of linear and nonlinear systems. IEEE Transactions on Industrial Electronics, 48 (6), 1109-1117.
• 29. Faiz, J., Ghazizadeh, M., and Oraee, H. (2015) Derating of transformers under non-linear load current and non-sinusoidal voltage - an overview. IET Electric Power Applications, 9 (7), 486-495.
• 30. Chowdhury, A.H., Grady, W.M., and Fuchs, E.F. (1999) An investigation of the harmonic characteristics of transformer excitation current under nonsinusoidal supply voltage. IEEE Transactions on Power Delivery, 14 (2), 450-458.

Uwagi

PL

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).