Adaptive Neuro-Fuzzy Voltage Control for LCL-Filter Grid-Connected Converter

Olwie, Safa S.; Humod, Abdulrahim T.; Hasan, Fadhil A.

doi:10.30684/etj.2022.132342.1115

	Adaptive Neuro-Fuzzy Voltage Control for LCL-Filter Grid-Connected Converter
Engineering and Technology Journal
Article 5, Volume 41, Issue 2, 2023, Pages 316-332 PDF (2.59 M)
Document Type: Research Paper
DOI: 10.30684/etj.2022.132342.1115
Authors
Safa S. Olwie^* ¹; Abdulrahim T. Humod²; Fadhil A. Hasan³
¹Department of Electrical Engineering, University of Technology -Baghdad- Iraq
²b Department of Electrical Engineering, University of Technology, Baghdad - Iraq
³Department of Electrical Engineering, University of Technology ,Baghdad - Iraq
Abstract
Inductance – Capacitance – Inductance (LCL) filter is a very attractive candidate for renewable energy system applications due to its high efficiency. High attenuation of the switching frequency harmonics, small size, low fee, and improving the overall harmonic distortion (THD). This paper presents how voltage is affected by increased loads or voltage sag. Therefore it is necessary to control it with certain controllers. The Adaptive Neuro-Fuzzy Inference System (ANFIS) is used as an intelligent controller, the voltage constraint as training data for ANFIS obtained from PI. The filter works in a good connection between the inverter and the grid and rewords unwanted harmonics from using the inverter. The mathematical models for the LCL filter are investigated. The proposed approach shows more effective results than previous performance for voltage controlling and harmonic reduction. It gives overshoot (0.5%), steady state error (0.005), settling time (0.03 sec), rise time (0.005 sec), and improving THD 8.67% to 2.33% by comparing these results of ANFIS respectively with the results of PI which gave(3%),(0.01),(0.2sec)and( 0.02sec).
Highlights
This paper presents how voltage is affected by increased loads or voltage sag. The Adaptive neuro-Fuzzy Inference System (ANFIS) is used as an Intelligent controller. The mathematical models for the LCL filter are investigated. The proposed approach shows more effective results than previous performance for voltage controlling and harmonic reduction.
Keywords
Distributed power generation; LCL filter; mathematical models of LCL filter; PI control; ANFIS

References
[1] Y. He, H. S.-H. Chung, C.-T. Lai, X. Zhang, and W. Wu, Active cancelation of equivalent grid impedance for improving stability and injected power quality of grid-connected inverter under variable grid condition, IEEE Trans. Power Electron. 33 (2018)9387-9398,doi: 10.1109/TPEL.2018.2793459 [2] J. Xu, T. Tang, and S. Xie, Research on low‐order current harmonics rejections for grid‐connected LCL‐filtered inverters, IET Power Electron. 7 (2014) 1227-1234,doi: 10.1049/iet-pel.2013.0477. [3] W. Choi, W. Lee, D. Han, and B. Sarlioglu, New configuration of multifunctional grid-connected inverter to improve both current-based and voltage-based power quality, IEEE Trans. Ind. Appl. 54 (2018) 6374-6382, doi: 10.1109/TIA.2018.2861737. [4] Y. Gui, Q. Xu, F. Blaabjerg, and H. Gong, Sliding mode control with grid voltage modulated DPC for voltage source inverters under distorted grid voltage, CPSS Trans. Power Electron. Appl. 4 (2019) 244-254, doi: 10.24295/CPSSTPEA.2019.00023 . [5] M. El-Habrouk, M. Darwish, and P. Mehta, Active power filters: A review, IEE Proceedings-Electric Power Applications, 147(2000) 403-413. [6] H. Akagi, Active harmonic filters, Proceedings of the IEEE, 93 (2005) 2128-2141, doi: 10.1109/JPROC.2005.859603 . [7] H. Goh, M. Armstrong, and B. Zahawi, Adaptive control technique for suppression of resonance in grid-connected PV inverters, IET Power Electron. 12 (2019) 1479-1486, https://doi.org/10.1049/iet-pel.2018.5170. [8] M. N. Arafat, A. Elrayyah, and Y. Sozer, An effective smooth transition control strategy using droop-based synchronization for parallel inverters, IEEE Trans. Ind. Appl. 51(2014) 2443-2454, doi: 10.1109/TIA.2014.2369826. [9] A. B. Rad, W. L. Lo, and K. Tsang, Self-tuning PID controller using Newton-Raphson search method, IEEE Trans. Ind. Electron.44 (1997)717-725,doi: 10.1109/41.633479. [10] M. Tursini, F. Parasiliti, and D. Zhang, Real-time gain tuning of PI controllers for high-performance PMSM drives, IEEE Trans. Ind. Appl.38 (2002)1018-1026, doi 10.1109/TIA.2002.800564. [11] V. A. Shankar, S. Umashankar, S. Padmanaban, and S. Paramasivam, Adaptive neuro-fuzzy inference system (anfis) based direct torque control of pmsm driven centrifugal pump, International Journal of Renewable Energy Research (IJRER), 7(2017)1436-1447. [12] W. A. A. Salem, G. F. Osman, and S. H. Arfa, Adaptive neuro-fuzzy inference system based field oriented control of PMSM & speed estimation, in Twentieth International Middle East Power Systems Conference (MEPCON), 2018, 626-631, doi: 10.1109/MEPCON.2018.8635179. [13] A. Parviainen, J. Pyrhönen, and M. Niemelä, Axial flux interior permanent magnet synchronous motor with sinusoidally shaped magnets,Electromagnetic Fields in Electrical Engineering, (2002) 271. [14] S. Hussain and M. A. Bazaz, Comparative analysis of speed control strategies for vector controlled PMSM drive,in International Conference on Computing, Communication and Automation (ICCCA), 2016,1314-1319, doi: 10.1109/CCAA.2016.7813950. [15] X. d. T. Garcia, B. Zigmund, A. A. Terlizzi, R. Pavlanin, and L. Salvatore, Comparison between FOC and DTC strategies for permanent magnet synchronous motors, Recent Adv. Electr. Electron. Eng.Recent.5 (2011) 76-81. [16] R. Krishnan, Electric motor drives: modeling, analysis, and control: Pearson, 2001. [17] A. Darba, M. Esmalifalak, and E. S. Barazandeh, Implementing SVPWM technique to axial flux permanent magnet synchronous motor drive with internal model current controller, in 4th International Power Engineering and Optimization Conference (PEOCO), 2010,126-131, doi: 10.1109/PEOCO.2010.5559197. [18] K. Sitapati and R. Krishnan, Performance comparisons of radial and axial field, permanent-magnet, brushless machines, IEEE Trans. Ind. Appl. 37 (2001) 1219-1226, doi: 10.1109/28.952495. [19] F. Daldaban and E. Çetin, Prototyping of axial flux permanent magnet motors,in 3rd Int. Symp. on Innovative Technologies in Engineering and Science (ISITES2015), Valencia, Spain, 2015. [20] S. Nakashima, Y. Inagaki, and I. Miki, Sensorless initial rotor position estimation of surface permanent-magnet synchronous motor, IEEE Trans. Ind. Appl. 36 ( 2000) 1598-1603, doi: 10.1109/28.887211. [21] F. Semiconductor, Sensorless PMSM vector control with a sliding mode observer for compressors using MC56F8013,Document Number, 2008. [22] A. Verma, A. Singh, and A. Agrawal, Speed control of pmsm drive using anfis based speed controller,Development, 4(2017). [23] K. Jash, P. K. Saha, and G. K. Panda, Vector control of permanent magnet synchronous motor based on sinusoidal pulse width modulated inverter with proportional integral controller, International Journal of Engineering Research and Applications, 3 (2013) 913-917. [24] D. V. Lukichev, G. L. Demidova, A. Y. Kuzin, and A. V. Saushev, Application of adaptive Neuro Fuzzy Inference System (ANFIS) controller in servodrive with multi-mass object, in 25th International Workshop on Electric Drives: Optimization in Control of Electric Drives (IWED), 2018,1-6, doi: 10.1109/IWED.2018.8321388. [25] G. Joshi and A. P. Pius, ANFIS controller for vector control of three phase induction motor, Indonesian Journal of Electrical Engineering and Computer Science (IJEECS), 19 (2020) 1177-1185. [26] Zue, Aslain Ovono, and Ambrish Chandra. Simulation and stability analysis of a 100 kW grid connected LCL photovoltaic inverter for industry IEEE Power Engineering Society General Meeting. IEEE, (2006)6 pp.-, doi: 10.1109/PES.2006.1709455.. [27] IEEE Std 519-2014. "Recommended Practice and Requirements for Harmonic Control in Electric Power Systems" in Revision of IEEE Std 519-1992, (2014), pp.1-29, June 11.
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