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

Article history: Received 15 September 2017 Received in revised form 12 December 2017 Accepted 18 December 2017 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.

[1]  Moumi Pandit,et al.  Controlling Output Voltage of Photovoltaic Cells using ANFIS and Interfacing it with Closed Loop Boost Converter , 2013 .

[2]  Hassan Yousef,et al.  Design and implementation of a fuzzy logic computer-controlled sun tracking system , 1999, ISIE '99. Proceedings of the IEEE International Symposium on Industrial Electronics (Cat. No.99TH8465).

[3]  Marcelo Gradella Villalva,et al.  Modeling and circuit-based simulation of photovoltaic arrays , 2009, 2009 Brazilian Power Electronics Conference.

[4]  E. El-Saadany,et al.  Maximum power point tracking for Photovoltaic systems using fuzzy logic and artificial neural networks , 2011, 2011 IEEE Power and Energy Society General Meeting.

[5]  Rashad M. Kamel,et al.  A novel multi-model neuro-fuzzy-based MPPT for three-phase grid-connected photovoltaic system , 2010 .

[6]  Wei Zhou,et al.  A novel model for photovoltaic array performance prediction , 2007 .

[7]  Ch. Sai Babu,et al.  COMPARISON OF MAXIMUM POWER POINT TRACKING ALGORITHMS FOR PHOTOVOLTAIC SYSTEM , 2011 .

[8]  J. A. Gow,et al.  Development of a photovoltaic array model for use in power-electronics simulation studies , 1999 .

[9]  Harold A. Innis Comments on Russia , 1946 .

[10]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[11]  Almoataz Y. Abdelaziz,et al.  IMPLEMENTATION OFPERTURB AND OBSERVE MPPT OFPV SYSTEM W ITH DIRECT CONTROL M ETHOD USING BUCK AND BUCK - BOOST CONVERTERS , 2014 .

[12]  Mohamed Azab,et al.  A New Maximum Power Point Tracking for Photovoltaic Systems , 2008 .

[13]  Saad Mekhilef,et al.  MPPT with Inc.Cond method using conventional interleaved boost converter , 2013 .

[14]  Yu-En Wu,et al.  Research and improvement of maximum power point tracking for photovoltaic systems , 2009, 2009 International Conference on Power Electronics and Drive Systems (PEDS).

[15]  Kuei-Hsiang Chao,et al.  An intelligent maximum power point tracking method based on extension theory for PV systems , 2010, Expert Syst. Appl..

[16]  M. Lokanadham,et al.  Incremental Conductance Based Maximum Power Point Tracking ( MPPT ) for Photovoltaic System , 2012 .

[17]  Honghua Wang,et al.  Maximum power point tracking of photovoltaic generation based on the fuzzy control method , 2009, 2009 International Conference on Sustainable Power Generation and Supply.

[18]  Somyot Kaitwanidvilai,et al.  Maximum Power Point Tracking using Fuzzy Logic Control for Photovoltaic Systems , 2011 .

[19]  Jyh-Shing Roger Jang,et al.  Fuzzy Modeling Using Generalized Neural Networks and Kalman Filter Algorithm , 1991, AAAI.

[20]  Sunil Luthra,et al.  Analysis of barriers to implement solar power installations in India using interpretive structural modeling technique , 2013 .