Implementation of an ANN based Controllers for Three Phase NPC Grid Connected PV Inverter

Yıl 2025, Cilt: 25 Sayı: 3, 544 - 557, 10.06.2025

Yunus Emre Yağan , Kadir Vardar

https://doi.org/10.35414/akufemubid.1559119

Öz

In this study, an Artificial Neural Network-based Current Controller (ANN-CC) and an ANN-based Capacitor Voltage Balancer (ANN-CVB) for a three-phase three-level Neutral Point Clamped (NPC) single-stage and transformerless grid-connected Photovoltaic (PV) inverter system is designed. ANNs are trained using the data obtained from the inverter simulation prepared using Proportional-Resonant (PR) and Proportional-Integral (PI) controllers in MATLAB/Simulink environment. The designed ANN-CVB is a PI-based static network. ANN-CC, on the other hand, is PR based and has a dynamic structure. When the obtained ANN controllers are compared with the other ANN controllers found in the literature on the basis of the size and tasks of the network structures, it is seen that they do not require much computational burden and are easy to implement. The performances of ANN controllers and classical controllers have been comparatively tested both in the simulation environment and in the laboratory environment. Simulation tests are carried out in a cooperation simulation approach. In the cooperation simulation, the entire controller of the grid connected PV inverter is programmed on the prototype inverter control card whereas its power circuits are kept in the Simulink. Using such a cooperation approach in power electronics applications is a generalized method for testing the prepared software safely before the experimental work. In the prototype inverter control card, STM32F407VGT6 DISCOVERY Board is used and the control software is prepared in MikroC language. Laboratory tests are carried out with a 9kVA 3-phase NPC inverter circuit.

Anahtar Kelimeler

Artificial Neural Networks , Capacitor Voltage Balancer , Current Controller , NPC inverter , PR Controller.

Etik Beyan

Bu çalışma, Doç.Dr. Kadir VARDAR danışmanlığında Yunus Emre YAĞAN tarafından hazırlanan 26/4/2023 tarihinde tamamlanan “Üç fazlı nötr noktası kenetli eviriciler için ysa tabanlı kontrolcüler tasarlanması ve uygulanması” başlıklı doktora tezi (Tez No: 803115) kapsamında gerçekleştirilmiş ve bu tezden türetilmiştir. Bu çalışmanın hazırlanma sürecinde bilimsel ve etik ilkelere uyulduğu ve yararlanılan tüm çalışmaların kaynakçada belirtildiği beyan olunur.

Üç Fazlı NNK Şebeke Bağlantılı Fotovoltaik Eviriciler için YSA Tabanlı Kontrolörlerin Uygulanması

Öz

Bu çalışmada, üç fazlı üç seviyeli Nötr Noktası Kenetli (NNK) tek aşamalı ve transformatörsüz şebeke bağlantılı bir Fotovoltaik (FV) evirici sistemi için bir Yapay Sinir Ağı tabanlı Akım Kontrolcüsü (YSA-AK) ve bir YSA tabanlı Kapasitör Gerilim Dengeleyici (YSA-KGD) tasarlanmıştır. MATLAB/Simulink ortamında, Orantısal-Rezonans (PR) ve Orantısal-İntegral (PI) kontrolcüler kullanılarak hazırlanan evirici benzetimden elde edilen veriler kullanılarak YSA’lar eğitilmiştir. Tasarlanan YSA-KGD, PI tabanlı statik yapıda bir ağdır. YSA-AK ise PR tabanlıdır ve dinamik bir yapıya sahiptir. Elde edilen YSA kontrolcüler, literatürde bulanan diğer YSA kontrolcüler ile ağ yapılarının büyüklüğü ve görevleri esas alınarak karşılaştırıldığında, fazla işlem yükü gerektirmeyen, uygulanması kolay yapıda oldukları görülmüştür. YSA ve klasik kontrolcülerin performansları hem benzetim ortamında hem de laboratuvar ortamında karşılaştırmalı olarak test edilmiştir. Benzetim ortamındaki testler, FV evirici sisteminin güç katı MATLAB ortamında, tüm kontrol yazılımları prototip evirici kontrol kartı içerisinde olacak şekilde bir ortak çalışma yaklaşımı ile gerçekleştirilmiştir. Bu yaklaşım güç elektroniği uygulamalarında deneysel çalışmaya geçilmeden önce, hazırlanan yazılımın güvenli şekilde test edilebilmesi için genelleştirilmiş bir yöntemdir. Prototip evirici kontrol kartı tasarımında STM32F407VGT6 DISCOVERY Board kiti kullanılmıştır ve kontrol yazılımları MikroC dilinde hazırlanmıştır. Laboratuvar testleri 9kVA’lik 3 fazlı bir NNK evirici devresi ile gerçekleştirilmiştir.

Anahtar Kelimeler

Yapay Sinir Ağları , Kapasitör Gerilim Dengeleyici , Akım Kontrolcü , NNK Evirici , PR Kontrolcü.

Kaynakça

• Babaie, M., Sebaaly, F., Sharifzadeh, M., Kanaan, H.Y., and Al-Haddad, K., 2019. Design of an artificial neural network control based on levenberg-marquart algorithm for grid-connected packed U-cell inverter, International Conference on Industrial Technology (ICIT), 1202-1207. https://doi.org/10.1109/ICIT.2019.8755098
• Babaie, M., Sharifzadeh, M., and Al-Haddad, K., 2020a. Three-phase grid-connected NPC inverter based on a robust artificial neural network controller, IEEE Power & Energy Society General Meeting (PESGM), 1-5. https://doi.org/10.1109/PESGM41954.2020.9281531
• Babaie, M., Saeidi, M., Sharifzadeh, M., Hamadi, A., Al-Haddad, K., and Chandra, A., 2020b. Hybrid ANN-linear controller for maximum PV energy harvesting in grid-tied packed E-cell inverter, International Symposium on Power Electronics, Electrical Drives, Automation and Motion (SPEEDAM), 871-875. https://doi.org/10.1109/SPEEDAM48782.2020.9161935
• Babaie, M., Mehrasa, M., Sharifzadeh, M., and Al-Haddad, K., 2022. Floating weighting factors ANN-MPC based on lyapunov stability for seven-level nodified PUC active rectifier. IEEE Trans. Ind. Electron., 69(1),387-398. https://doi.org/10.1109/TIE.2021.3050375
• Bana, P. R., and Amin, M., 2023. State-space modelling and stability analysis of ANN controller for grid-connected VSC system, IEEE IAS Global Conference on Renewable Energy and Hydrogen Technologies (GlobConHT), 1-6. https://doi.org/10.1109/GlobConHT56829.2023.10087412
• Beşer, E., ve Beşer, E. K., 2021. Endüstriyel Amaçlı Yarım H-Köprü Modüllü Üç Fazlı Çok Seviyeli EviriciYapısı, Afyon Kocatepe Üniversitesi Fen ve Mühendislik Bilimleri Dergisi, 21(3), 579 - 585. https://doi.org/10.35414/akufemubid.702382
• Bouaouaou, H., Lalili, D., and Boudjerda, N., 2022. Model predictive control and ANN-based MPPT for a multi-level grid-connected photovoltaic inverter, Electrical Engineering, 104(3), 1229-1246. https://doi.org/10.1007/s00202-021-01355-w
• Cha, H., Vu, T., and Kim, J., 2009. Design and control of proportional-resonant controller based photovoltaic power conditioning system, IEEE Energy Conversion Congress and Exposition, 2198-2205. https://doi.org/10.1109/ECCE.2009.5316374
• Chatterjee, A., and Mohanty, K.B., 2018. Current control strategies for single phase grid integrated inverters for photovoltaic applications-a review, Renewable Sustainable Energy Rev., 92, 554–569. https://doi.org/10.1016/j.rser.2018.04.115
• Chenai, S., and Benchouia, M., 2014. Three-phase Three-level (NPC) shunt active power filter performances based on PWM and ANN’s controllers for harmonic current compensation, International Journal on Electrical Engineering and Informatics, 6(3),532-552. https://doi.org/10.15676/ijeei.2014.6.3.7
• Crescimbini, F., Lidozzi, A., Rovelli, E., Salvadore, A., and Solero, L., 2012. A 30kw transformerless PV NPC inverter, 15th International Power Electronics and Motion Control Conference (EPE/PEMC), DS2c–17. https://doi.org/10.1109/EPEPEMC.2012.6397301
• Dong, W., Li, S., Fu, X., Li, Z., Fairbank, M., and Gao, Y., 2021. Control of a buck DC/DC converter using approximate dynamic programming and artificial neural networks, IEEE Trans. Circuits Syst. I Regul. Pap., 68(4), 1760-1768. https://doi.org/10.1109/TCSI.2021.3053468
• Faraji, F., Hajirayat, A., Birjandi, A.A.M., and Al-Haddad, K., 2017. Single stage single-phase three-level neutral-point-clamped transformerless grid-connected photovoltaic inverters: Topology review, Renewable Sustainable Energy Rev., 80, 197–214, https://doi.org/10.1016/j.rser.2017.05.181
• Goh, H. H., Dong, H., Liang, X., Zhang, D., Dai, W., Song, S., Kurniawan, T.A., and Goh, K. C., 2024. Enhancing performance of shipboard photovoltaic grid-connected inverter through CRNN-LM-BP control optimized by particle swarm optimization of LCL parameters, Engineering Science and Technology, an International Journal, 57, 101816. https://doi.org/10.1016/j.jestch.2024.101816
• Hagan, M.T., Demuth, H.B., Beale, M.H., and Jesus, O.D., 2014. Neural network desing. 2nd Edition, eBook. Hlaili, M., Mechergui, H., Gonc¸alves, H., Exposto, B., and Afonso, J.L., 2016. Single phase NPC inverter controller with integrated MPPT for PV grid connection, 17th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA), 598–605. https://doi.org/10.1109/STA.2016.7952025
• İbrahim, S., ve Altın, N., 2009. Güneş pili ile beslenen şebeke etkileşimli eviriciler—genel bir bakış, Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 24(3).
• Kinga, S., Megahed, T. F., Kanaya, H., and Mansour, D. E. A., 2024. Enhancing robustness and control performance of voltage source inverters using Kalman filter adaptive observer and ANN-based model predictive controller, Neural Computing and Applications, 36(33), 21073-21090. https://doi.org/10.1007/s00521-024-10243-w
• Latran, M.B., and Teke, A., 2015. Investigation of multilevel multifunctional grid connected inverter topologies and control strategies used in photovoltaic systems, Renewable Sustainable Energy Rev., 42, 361–376. https://doi.org/10.1016/j.rser.2014.10.030
• Li, S., Fairbank, M., Johnson, C., Wunsch, D.C., Alonso, E., and Proao, J.L., 2014. Artificial neural networks for control of a grid-connected rectifier/inverter under disturbance, dynamic and power converter switching conditions, IEEE Trans. Neural Networks Learn. Syst., 25(4), 738-750. https://doi.org/10.1109/TNNLS.2013.2280906
• Li, S., Sun, Y., Ramezani, M., and Xiao, Y., 2019. Artificial neural networks for volt/VAR control of DER inverters at the grid edge. IEEE Trans. Smart Grid, 10(5), 5564-5573. https://doi.org/10.1109/TSG.2018.2887080
• Lim, S.K., Kim, J.H., and Nam, K, 1999. A DC-link voltage balancing algorithm for 3-level converter using the zero sequence current, 30th Annual IEEE Power Electronics Specialists Conference, 2, 1083-1088. https://doi.org/10.1109/PESC.1999.785646
• Massaro, A., 2024. ANNs Predicting Noisy Signals in Electronic Circuits: A Model Predicting the Signal Trend in Amplification Systems, AI, 5(2), 533-549. https://doi.org/10.3390/ai5020027
• Mehrasa, M., Babaie, M., Sharifzadeh, M., and Al-Haddad, K., 2021. An input–output feedback linearization control method synthesized by artificial neural network for grid-tied packed E-Cell inverter, IEEE Trans. Ind. Appl., 57(3), 3131-3142. https://doi.org/10.1109/TIA.2021.3049456
• Mohamed, I.S., Rovetta, S., Do, T.D., Dragicević, T., and Diab, A.A.Z., 2019. A neural-network-based model predictive control of three-phase inverter with an output LC filter. IEEE Access, 7, 124737-124749. https://doi.org/10.1109/ACCESS.2019.2938220
• Nazeri, A.A., Zacharias, P., Ibanez, F.M., and Somkun, S., 2019. Design of proportional-resonant controller with zero steady-state error for a single-phase grid-connected voltage source inverter with an LCL output filter, IEEE Milan PowerTech, 1-6. https://doi.org/10.1109/PTC.2019.8810554
• Özdemir, Ş., 2013. Yenilenebilir enerji kaynakları için tek aşamalı MPPT denetimli çok seviyeli eviricinin gerçekleştirilmesi, Doktora Tezi, Gazi Üniversitesi Fen Bilimler Enstitüsü, Ankara.
• Panigrahi, R., Mishra, S.K., and Srivastava, S., 2018. Grid integration of small-scale photovoltaic systems-a review, IEEE Industry Applications Society Annual Meeting (IAS), 1–8. https://doi.org/10.1109/IAS.2018.8544503
• Parvez, M., Elias, M.F.M., Rahim, N.A., Blaabjerg, F., Abbott, D., and Al-Sarawi, S.F., 2020. Comparative study of discrete PI and PR controls for single-phase UPS inverter, IEEE Access, 8, 45584-45595. https://doi.org/10.1109/ACCESS.2020.2964603
• Patel, H., and Agarwal, V., 2008. MATLAB-based modeling to study the effects of partial shading on PV array characteristics, IEEE Trans. Energy Convers., 23(1), 302–310. https://doi.org/10.1109/TEC.2007.914308
• Sun, Y., Li, S., Lin, B., Fu, X., Ramezani, M., and Jaithwa, I., 2017. Artificial neural network for control and grid integration of residential solar photovoltaic systems, IEEE Trans. Sustainable Energy ,8(4), 1484-1495. https://doi.org/10.1109/TSTE.2017.2691669
• Sun, Y., Li, S., Ramezani, M., Balasubramanian, B., Jin, B., and Gao, Y., 2019. DSP implementation of a neural network vector controller for IPM motor drives, Energies, 12(13), 2558. https://doi.org/10.3390/en12132558
• Sujatha, B.G., and Anitha, G.S., 2018. Enhancement of PQ in grid connected PV system using hybrid technique, Ain Shams Eng. J., 9(4), 869-881. https://doi.org/10.1016/j.asej.2016.04.007
• Tran, H.N, Le, K.M, and Jeon, J.W., 2019. Adaptive current controller based on neural network and double phase compensator for a stepper motor, IEEE Trans. Power Electron., 34(8), 8092-8103. https://doi.org/10.1109/TPEL.2018.2878928
• Teodorescu, R., Blaabjerg, F., Liserre, M., and Loh, P.C., 2006. Proportional resonant controllers and filters for grid-connected voltage-source converters, IEE Proceedings - Electric Power Applications, 153(5), 750-762. https://doi.org/10.1049/ip-epa:20060008
• Venkatesan, A.K., Subramaniam, U., Bhaskar, M.S., Gnana Swathika, O.V., Padmanaban, S., Almakhles, D.J., and Mitolo, M., 2021. Small-signal stability analysis for microgrids under uncertainty using MALANN control technique, IEEE Systems Journal, 15(3), 3797-3807. https://doi.org/10.1109/JSYST.2020.3020509
• Vinnakoti, S., and Kota, V.R., 2018. Implementation of artificial neural network based controller for a five-level converter based UPQC, Alexandria Eng. J., 57(3), 1475-1488. https://doi.org/10.1016/j.aej.2017.03.027
• Villalva, M.G., and Ruppert, E.F., 2004. Current controller with artificial neural network for 3-phase 4-wire active filter, 35th Annual Power Electronics Specialists Conference (IEEE Cat. No.04CH37551) 2, 993-998. https://doi.org/10.1109/PESC.2004.1355556
• Yacine, A.A., Noureddine, A.A., Aissa, K., and Brahim, M., 2017. Design and implementation of three-level inverter for grid-tied PV systems, 2017 5th International Conference on Electrical Engineering-Boumerdes (ICEE-B), 1–6. https://doi.org/10.1109/ICEE-B.2017.8191993
• Yağan, Y.E., Vardar, K., and Ebeoğlu, M.A., 2018a. Modeling and simulation of PV systems, IOSR Journal of Electrical and Electronics Engineering (IOSR-JEEE), 13(2), 1–11.
• Yağan, Y.E., Vardar, K., and Ebeoğlu, M.A., 2018b. Investigation of MPPT methods used in PV systems, Journal of Electrical and Electronics Engineering, 13(2), 84-95.
• Yağan, Y.E., and Vardar K., 2021. Prototype development and verification for grid connected three-phase three-level NPC PV inverter, 2021 13th International Conference on Electrical and Electronics Engineering (ELECO2021), 500-504. https://doi.org/10.23919/ELECO54474.2021.9677753
• Yarıkkaya, S., and Vardar, K., 2020. Rapid prototype development of single-phase grid connected PV inverter using STM32F4 and MATLAB, Avrupa Bilim ve Teknoloji Dergisi, 18, 213–223. https://doi.org/10.31590/ejosat.680586
• Zeb, K., Khan, I., Uddin, W., Khan, M.A., Sathishkumar, P., Busarello, T.D.C., Ahmad, I., and Kim, H., 2018a. A review on recent advances and future trends of transformerless inverter structures for single-phase grid connected photovoltaic systems, Energies, 11(8), 1968. https://doi.org/10.3390/en11081968
• Zeb, K., Uddi,n W., Khan, M.A., Ali, Z., Ali, M.U., Christofides, N., and Kim, H., 2018b. A comprehensive review on inverter topologies and control strategies for grid connected photovoltaic system, Renewable Sustainable Energy Rev., 94, 1120–1141. https://doi.org/10.1016/j.rser.2018.06.053