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Comparative analysis of Solar Generation System with 21- CHB-MLI integrated SAPF based ANN and AGPSO tuned PI controller to enhance power quality

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
This paper represents comparative analysis of artificial neural network (ANN) and AGPSO tuned PI controller based power quality improvement solar generation system. Now a day's Power quality is a major problem due to non-liner load based on power electronics. SAPF is solution to overcome such power quality issues in dynamic manner. With the use of both soft computing controllers based Shunt active power filter, it is tried to reduce harmonics (distortions), compensate reactive power, enhance power quality and power factor correction of supply voltage. System comprises 21-Level cascaded H-bridge inverter supplied from photovoltaic panel, series coupling inductor and self supported DC (capacitor) bus. Voltage harmonics of supplied voltage from PV is reduced by 21-level cascades H-bridge inverter in which switching signal is generated by carrier based in phase level shifted pulse width modulation technique. Incremental conductance (IC) MPPT technique is incorporated to maximize PV panel output. Phase locked loop based unit template generation and Levenberg Marquardt algorithm trained ANN and AGPSO tuned PI controller based DC bus voltage regulation is utilized for current quality improvement in SAPF. Comparative results show the effectiveness of ANN controller than A GPSO tuned PI controller. Suggested model is simulated in Matlab/Simulink 2016(b) for effectiveness.
Rocznik
Strony
121--131
Opis fizyczny
Bibliogr. 20 poz., rys., tab., wykr.
Twórcy
  • Department of Electrical Engineering, Rajasthan Technical University, Kota-324010
  • Faculty of Engineering and Technology, Datta Meghe Institute of Medical Sciences (Deemed to be University), Sawangi (Meghe), Wardha, Maharashtra, India
  • Department of Electrical Engineering, Rajasthan Technical University, Kota-324010
Bibliografia
  • [1] Rahmani S., Hamadi A., Al-Haddad K., & Dessaint L. A, "A Combination of Shunt Hybrid Power Filter and Thyristor-Controlled Reactor for Power Quality", IEEE Transactions on Industrial Electronics, 61(5), 2152-2164. doi:10.1109/tie.2013.2272271.
  • [2] X. Liang, "Emerging power quality challenges due to integration of renewable energy sources", IEEE Transactions on Industry Applications, vol. 53, no. 2, pp. 855-866, 2017.
  • [3] Jain, Sachin, and VenuSonti. "A Highly Efficient and Reliable Inverter Configuration Based Cascaded Multilevel Inverter for PV Systems." IEEE Transactions on Industrial Electronics 64.4 (2017): 2865-2875.
  • [4] Kumar, N., Saha, T. K., & Dey, J. (2019). Multilevel Inverter (MLI)-Based Stand-Alone Photovoltaic System: Modeling, Analysis, and Control. IEEE Systems Journal, 1-7. doi:10.1109/jsyst.2019.2900485.
  • [5] A. K. Podder, N. K. Roy and H. R. Pota, "MPPT methods for solar PV systems: a critical review based on tracking nature, " in lET Renewable Power Generation, vol. 13, no. 10, pp. 1615-1632, 29 7 2019. doi: 10.1049/iet-rpg.2018.5946
  • [6] M.G. Villalva, J.R. Gazoli and E. Ruppert F. "Comprehensive Approach to Modeling and Simulation of Photovoltaic Arrays". IEEE Transactions on power electronics. Vol. 24, No. 5, 2009. pp 1198-1208.
  • [7] R. Dogga and M. K. Pathak, "Recent trends in solar PV inverter topologies." Sol. Energy, vol. 183, no. February, pp. 57-73, 2019.
  • [8] Rodriguez, Jose, Jih-Sheng Lai, and Fang Zheng Peng. "Multilevel inverters: a survey of topologies, controls and applications." IEEE Transactions on industrial electronics 49.4 (2002): 724-738.
  • [9] McGrath, Brendan Peter, and Donald Grahame Holmes. "Multicarrier PWM strategies for multilevel inverters." IEEE Transactions on industrial electronics 49.4 (2002): 858-867.
  • [10] V. F. Pires, A. Cordeiro, D. Foito, and J. F. Silva, "Three-phase multilevel inverter for grid connected distributed photovoltaic systems based in three three-phase two-level inverters," Sol. Energy, vol. 174, no. September, pp. 1026-1034, 2018.
  • [11] Bhattacharya, Avik, and Chandan Chakraborty. "A shunt active power filter with enhanced performance using ANN-based predictive and adaptive controllers. " IEEE transactions on industrial electronics 58.2 (2011): 421-428.
  • [12] P. Acuna, L.Moran, M. Rivera, J. Dixon, and J. Rodriguez, "Improved active power filter performance for renewable power generation systems" , IEEE Transactions on Power Electronics, vol. 29, no. 2, pp. 687-694, 2014
  • [13] S. Agrawal, D. K. Palwalia, and M. Kumar, "Performance Analysis of ANN Based three-phase four-wire Shunt Active Power Filter for Harmonic Mitigation under Distorted Supply Voltage Conditions Performance Analysis of ANN Based three-phase four-wire Shunt Active Power, " IETE J. Res., vol. 0, no. 0, pp. 1-9, 2019
  • [14] M. N. Akhter, S. Mekhilef, H. Mokhlis and N. Mohamed Shah, "Review on forecasting of photovoltaic power generation based on machine learning and metaheuristic techniques," in lET Renewable Power Generation, vol. 13, no. 7, pp. 1009-1023, 20 5 2019.doi: 10.1049/iet-rpg.2018.5649.
  • [15] Agrawal S., Kumar P., & Palwalia D. K., "Artificial neural network based three phase shunt active power filter", 2016 IEEE 7th Power India International Conference (PIICON).doi: 10.1109/poweri.2016.8077153.
  • [16] Panda,A. K., & Patnaik, S. S. Optimal Harmonic Compensation In Three-Phase Power System Employing Voltage Source Inverter Based Active Power Filter And Particle Swarm Optimization. Journal of Electrical Engineering, 12(1), 6-6, 2012.
  • [17] O. B. Belghith, L. Sbita and F. Bettaher , "MPPT Design Using PSO Technique for Photovoltaic System Control Comparing to Fuzzy Logic and P&O Controllers ", Energy and Power Engineering, 2016.
  • [18] Mohamed, A.; Eltamaly, A.; Alolah, I. Swarm intelligence-based optimization of grid dependent hybrid renewable energy systems. Renew. Sustain. Energy Rev. 2017, 77, 515-524.
  • [19] M. Kumar, S. Agrawal and T. H. Mohamed, "Application of AGPSO Algorithm in Frequency Controller Design for Isolated Microgrid, " 2021 IEEE Texas Power and Energy Conference (TPEC), 2021, pp. 1-6,doi: 10.1109/TPEC51183.2021.9384910.
  • [20] Mirjalili, S., Lewis, A. and Sadiq, A.S. Autonomous Particles Groups for Particle Swarm Optimization. Arab J Sci Eng 39, 4683-4697 (2014). https//:doi.org/10.1007/s13369-014-1156-x
Uwagi
PL
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-b7fb281b-dec0-44db-a02a-6460adbe16da
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