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 Volume 5, Issue 2 (February 2018), Pages: 90-96


 Original Research Paper

 Title: ANFIS-based PI controller for maximum power point tracking in PV systems

 Author(s): Hisham Hussein 1, *, Ali Aloui 1, Badr AlShammari 2


 1Department of Electronics Engineering, Community College, Ha’il University, Hai’l, Saudi Arabia
 2College of Engineering, Ha’il University, Hai’l, Saudi Arabia

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This paper presents a maximum power point tracking (MPPT) control system which is designed to increase the energy generation efficiency of Photovoltaic (PV) arrays. Usually Maximum power point tracking control system uses dc-to-dc converters to compensate for the output voltage of the PV array in order to keep the voltage at the value, which maximizes the output power. The purpose of the work is to develop an adaptive neuro-fuzzy inference system (ANFIS)-based proportional integral controller. The operating temperature and level of irradiance constitute inputs for the ANFIS controller, allowing it to determine the maximum available power that the PV array possesses. The error between the reference power from the ANFIS controller and the measured voltage and current of the PV array enables the proportional integral controller to generate the duty cycle. It is shown that ANFIS-based PI controller gives better performance criteria, unlike conventional techniques which usually give associations at steady state operating conditions. Eventually, the proposed MPPT control system based on ANFIS could provide better results than conventional techniques in terms of performance, accuracy and stability. 

 © 2017 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license (

 Keywords: Adaptive neuro-fuzzy inference system, Maximum power point, Fuzzy logic, Neural networks

 Article History: Received 15 September 2017, Received in revised form 12 December 2017, Accepted 18 December 2017

 Digital Object Identifier:


 Hussein H, Aloui A, and AlShammari B (2018). ANFIS-based PI controller for maximum power point tracking in PV systems. International Journal of Advanced and Applied Sciences, 5(2): 90-96

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 References (23)

  1. Alabedin AZ, El-Saadany EF, and Salama MMA (2011). Maximum power point tracking for Photovoltaic systems using fuzzy logic and artificial neural networks. In the Power and Energy Society General Meeting, IEEE, Detroit, MI, USA: 1-9. 
  2. Ansari MF, Kharb RK, Luthra S, Shimmi SL, and Chatterji S (2013). Analysis of barriers to implement solar power installations in India using interpretive structural modeling technique. Renewable and Sustainable Energy Reviews, 27:163-174. 
  3. Atallah AM, Abdelaziz AY, and Jumaah RS (2014). Implementation of perturb and observe MPPT of PV system with direct control method using buck and buck-boost converters. Emerging Trends in Electrical, Electronics and Instrumentation Engineering: An International Journal, 1(1): 31-44.     
  4. Azab MA (2009). New maximum power point tracking for photovoltaic systems. International Journal of Electrical and Electronics Engineering, 3(11):702-705.     
  5. Chao KH and Li CJ (2010). An intelligent maximum power point tracking method based on extension theory for PV systems. Expert Systems with Applications, 37(2): 1050-1055. 
  6. Chaouachi A, Kamel RM, and Nagasaka K (2010). A novel multi-model neuro-fuzzy-based MPPT for three-phase grid-connected photovoltaic system. Solar Energy, 84(12): 2219-2229. 
  7. Govinda RT, Kurdgelashvili L, and Patrick AN (2011). Review of solar energy: Markets. Economics and Policies. No. WPS 5845; Paper is Funded by the Knowledge for Change Program (KCP). World Bank, Washington, DC, USA. PMCid:PMC3267313     
  8. Gow JA and Manning CD (1999). Development of a photovoltaic array model for use in power-electronics simulation studies. IEEE Proceedings-Electric Power Applications, 146(2): 193-200. 
  9. Jang JSR (1991). Fuzzy modeling using generalized neural networks and kalman filter algorithm. In the 9th National Conference on Artificial Intelligence, Anaheim, CA, USA, 91: 762-767.     
  10. Jang JSR (1993). ANFIS: adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, Man, and Cybernetics, 23(3): 665-685. 
  11. KACARE (2016). Hail college of technology station. Solar Resource Reports for King Abdullah City for Atomic and Renewable Energy: As Part of the Renewable Resource Monitoring and Mapping (RRMM) Program, King Abdullah City for Atomic and Renewable Energy, Government agency, Saudi Arabia.     
  12. Kumari JS and Babu S (2011). Comparison of maximum power point tracking algorithms for photovoltaic system. International Journal of Advances in Engineering and Technology, 1(5):133–148.     
  13. Li J and Wang H (2009). Maximum power point tracking of photovoltaic generation based on the fuzzy control method. In the International Conference on Sustainable Power Generation and Supply, IEEE, Nanjing, China: 1-6. 
  14. Liu C, Wu B, and Cheung R (2004). Advanced algorithm for MPPT control of photovoltaic systems. In the Canadian solar buildings conference, Montreal, Québec, Canada: 20–24.     
  15. Lokanadham M and Bhaskar KV (2012). Incremental conductance based maximum power point tracking (MPPT) for photovoltaic system. International Journal of Engineering Research and Applications, 2(2): 1420-1424.     
  16. Mirbagheri SZ, Mekhilef S, and Mirhassani SM (2013). MPPT with Inc. Cond method using conventional interleaved boost converter. Energy Procedia, 42: 24-32. 
  17. Pierre G, Gilbert S, and Carlos O (2012). Grid-connected PV array. The Mathworks, Hydro-Quebec Research Institute (IREQ) and Shripad Chandrachood, Varennes, Canada.     
  18. Singh R and Pandit M (2013). Controlling output voltage of photovoltaic cells using ANFIS and interfacing it with closed loop boost converter. International Journal of Current Engineering and Technology, 3(2): 417-423.     
  19. Takun P, Kaitwanidvilai S, and Jettanasen C (2011). Maximum power point tracking using fuzzy logic control for photovoltaic systems. In the International MultiConference of Engineers and Computer Scientists, Hong Kong, 2: 978-988.     
  20. Villalva MG, Gazoli JR, and Ruppert Filho E (2009). Modeling and circuit-based simulation of photovoltaic arrays. In the Brazilian Power Electronics Conference, IEEE, Bonito-Mato Grosso do Sul, Brazil: 1244-1254. 
  21. Wu YE, Shen CL, and Wu CY (2009). Research and improvement of maximum power point tracking for photovoltaic systems. In the International Conference on Power Electronics and Drive Systems, IEEE, Taipei, Taiwan: 1308-1312. 
  22. Yousef HA (1999). Design and implementation of a fuzzy logic computer-controlled sun tracking system. In the IEEE International Symposium on Industrial Electronics, IEEE, Bled, Slovenia, 3: 1030-1034. 
  23. Zhou W, Yang H, and Fang Z (2007). A novel model for photovoltaic array performance prediction. Applied Energy, 84(12): 1187-1198.