A sliding-neural network control of induction-motor-pump supplied by photovoltaic generator

Hichem Hamdi1, Chiheb Ben Regaya1, Abderrahmen Zaafouri1
1University of Tunis, Higher National Engineering School Of Tunis (ENSIT), Engineering Laboratory of Industrial Systems and Renewable Energies (LISIER), 5 avenue Taha Hussein, PO Box 56, 1008, Tunis, Tunisia

Tóm tắt

AbstractEnergy production from renewable sources offers an efficient alternative non-polluting and sustainable solution. Among renewable energies, solar energy represents the most important source, the most efficient and the least expensive compared to other renewable sources. Electric power generation systems from the sun’s energy typically characterized by their low efficiency. However, it is known that photovoltaic pumping systems are the most economical solution especially in rural areas. This work deals with the modeling and the vector control of a solar photovoltaic (PV) pumping system. The main objective of this study is to improve optimization techniques that maximize the overall efficiency of the pumping system. In order to optimize their energy efficiency whatever, the weather conditions, we inserted between the inverter and the photovoltaic generator (GPV) a maximum power point adapter known as Maximum Power Point Tracking (MPPT). Among the various MPPT techniques presented in the literature, we adopted the adaptive neuro-fuzzy controller (ANFIS). In addition, the performance of the sliding vector control associated with the neural network was developed and evaluated. Finally, simulation work under Matlab / Simulink was achieved to examine the performance of a photovoltaic conversion chain intended for pumping and to verify the effectiveness of the speed control under various instructions applied to the system. According to the study, we have done on the improvement of sliding mode control with neural network. Note that the sliding-neuron control provides better results compared to other techniques in terms of improved chattering phenomenon and less deviation from its reference.

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Tài liệu tham khảo

Richard, N. (2009). Traversing the mountaintop: World fossil fuel production to 2050. Philos Trans R Soc Lond Ser B Biol Sci, 3067–3079.

Roberto, F., & Sonia, L. (2008). Energy comparison of MPPT techniques for PV systems. WSEAS Transcations Power Syst, 3, 446–455.

Fangrui, L., Yong, K., Yu, Z., & Shanxu, D. (2008). Comparison of P&O and hill climbing MPPT methods for grid-connected PV converter (pp. 804–807). ICIEA: Industrial Electronics and Applications.

Galotto, M. A. G., Sampaio, L. P., Azevedo, M., Canesin, G., & Canesin, C. A. (2013). Evaluation of the main MPPT techniques for photovoltaic applications. IEEE Trans Ind Electron, 1156–1167.

AdelMellit Soteris, A. (2008). Artificial intelligence techniques for photovoltaic applications: A review. Elsevier -Prog Energy Combustion Sci, 574–632.

Li, X., Li, Y., & Seem, J. E. (2013). Maximum power point tracking for photovoltaic system using adaptive extremum seeking control. IEEE Trans Control Syst Technol, 2315–2322.

Ramaprabha, R., Gothandaraman, V., Kanimozhi, K., Divya, R., & Mathur, B. L. (2011). Maximum power point tracking using GA-optimized artificial neural network for solar PV system. Electrical Energy Systems (ICEES), 264–268.

Hamdi, H., Ben Regaya, C., & Zaafouri, A. (2019). Real-time study of a photovoltaic system with boost converter using the PSO-RBF neural network algorithms in a MyRio controller. Solar Energy, 183, 1–16.

Chokri, B. S., & Mohamed, O. (2011). Comparison of fuzzy logic and neural network in maximum power point tracker for PV systems. Electr Power Syst Res, 43–50.

Bendib, B., Krim, F., Belmili, H., Almi, M.F., Bolouma, S. (2014). An intelligent MPPT approach based on neural-network voltage estimator and fuzzy controller, applied to a stand-alone PV system. IEEE 23rd Int. Symp on Industrial Electronics (ISIE), 404–409.

Rajan, K. B. S. (2014). Buck-boost converter fed BLDC motor drive for solar PV array-based water pumpin. IEEE international conference on power electronics drives and energy systems (PEDES), 1–6.

Farhani, F., Ben Regaya, C., Zaafouri, A., & Chaari, A. (2013). Adaptive full order observer for Sensorless induction motor control. Trans Mach Des, 10–18.

Zaafouri, A., Ben Regaya, C., & Chaari, A. (2013). Backstepping approach applied for control and on-line adaptation of the rotor resistance. World Appl Sci J, 1120–1126.

Srinu, B. N. (2014). Comparison of Direct and Indirect Vector Control of Induction Motor. Int J New Technol Sci Eng, 110–131.

Domenico, C., Francesco, P., Giovanni, S., & Angelo, T. (2002). FOC and DTC: Two viable schemes for induction motors torque control. IEEE Trans Power Electron, 779–787.

Ben Regaya, C., Zaafouri, A. Z., & Chaari, A. (2014). A New Sliding Mode Speed Observer Of Electric Motor Drive Based on Fuzzy-Logic. Acta Polytechnica Hungarica, 219–232.

Mohd, Z., Mohd, R., Soh, A. C., Abdul Rahim, N. (2012). Adaptive P&O- fuzzy control MPPT for PV boost dc-dc converter. IEEE international conference on power and energy, 524–529.

Ben Regaya, C., Farhani, F., Zaafouri, A., & Chaari, A. (2018). A novel adaptive control method for induction motor based on Backstepping approach using dSpace DS 1104 control board. Mech Syst Signal Process, 466–481.

Farhani, F., Ben Regaya, C., Zaafouri, A., & Chaari, A. (2017). Real time PI-backstepping induction machine drive with efficiency optimization. ISA Trans, 348–356.

Arrouf, M., & Ghabrour, S. (2007). Modelling and simulation of a pumping system fed by photovoltaic generator within the Matlab/Simulink programming environment. Desalination, 23–30.

Serir, C., & Rekioua, D. (2015). Control of photovoltaic water pumping system. J Electr Eng, 339–344.

Esram, T., & Chapman, P. L. (2007). Comparison of photovoltaic array maximum power point tracking techniques. IEEE TRANS ENERGY CONVERSION EC, 439–449.

Otieno, C. A., Nyakoe, G. N., & Wekesa, C. W. (2009). A neural fuzzy based maximum power point tracker for a photovoltaic system. IEEE AFRICON, 1–6.

Denaï, M. A., Palis, F., & Zeghbib, A. (2007). Modeling and control of non- linear systems using soft computing techniques. Appl Soft Comput, 728–738.

Mahmoudi, M. O., Madani, N., Benkhoris, M. F., & Boudjema, F. (1999). Cascade sliding mode control of a field-oriented induction machine drive. Eur Phys J AP, 217–225.

Hardik, A.S., Ami, T.P. (2014). Controller design via sliding mode control approach of induction motor. IEEE international conference on Advanced Computing & Communication Technologies, 541–546.

Saghafinia, A., Wooi, P. H., & Khalaf, G. (2015). Adaptive fuzzy sliding-mode control into chattering-free IM drive. IEEE Trans Ind Appl, 692–701.

Ertugrul, M., & Kaynak, O. (2000). Neuro sliding mode control of robotic manipulators. Mechatronics, 243–267.