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Abstract

Maximum Power Point Tracking (MPPT) is one of the major functions of PV system because of the performance of PV system directly affected by atmospheric conditions. This paper first presents a novel configuration for MPPT of PV system. This configuration is combined with modified positive luo converter that is high gain dc-dc converter and Type-2 fuzzy neural network controller (T2FNNC). Modified positive luo converter that has high voltage gain and conversion efficiency is suitable for the two-stage grid integrated solar photovoltaic system. The main advantage of proposed configuration is high performance and stability against to disturbance inputs such as solar radiation and panel temperature variations. To validate the performance of this configuration, a simulation model is built by using Matlab/Simulink. The simulation results show that proposed configuration based on modified positive output luo converter with T2FNN has a superior performance than conventional PI controllers in terms of MPP tracking efficiency under changing atmospheric conditions.

Keywords

Robust control Type-2 FNN MPPT modified positive output Luo converter

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References

Sivakumar ,

M.J.

Sathik ,

P.S.

Manoj and

Sundararajan , An assessment on performance of DC-DC converters for renewable energy applications, Renew Sust Energ Rev 58 (2016), 1475–1485.

Mohapatra ,

Nayak ,

Das and

K.B.

Mohanty , A review on MPPT techniques of PV system under partial shading condition, Renew Sust Energ Rev 80 (2017), 854–867.

Yilmaz ,

Kircay and

Borekci , PV system fuzzy logic MPPT method and PI control as a charge controller, Renew Sust Energ Rev 81 (2018), 994–1001.

Pansare ,

S.K.

Sharma ,

Jain and

Saxena , Analysis of a modified positive output Luo converter and its application to solar PV system, IEEE Industry Applications Society Annual Meeting (2017), 1–6.

Abid ,

Masmoudi and

Derbel , Comparative Study of the Performances of the DC/DC Luo-Converter in Photovoltaic Applications, International Conference on Smart, Monitored and Controlled Cities (2017), 117–122.

Amirtharaj and

Premalatha , Design of an Efficient Positive Output Self-Lift and Negative Output Self-Lift Luo Converters using Drift Free Technique for Photovoltaic Applications, Enrgy Proced 117 (2017), 651–657.

Soltani and

M.J.

Kouhanjani , Fuzzy Logic Type-2 Controller Design for MPPT in Photovoltaic system, Conference on Electrical Power Distribution Networks Conference (2017), 149–155.

J.K.

Shiau ,

Y.C.

Wei and

B.C.

Chen , A Study on the Fuzzy-Logic-Based Solar Power MPPT Algorithms Using Different Fuzzy Input Variables, Algorithms 8(2) (2015), 100–127.

Abid ,

Zaidi ,

Toumi and

Chaabane , T–S Fuzzy Algorithm for Photovoltaic Panel, Int J Fuzzy Syst 17(2) (2015), 215–223.

10.

He and

F.L.

Luo , Analysis of Luo converters with voltagelift circuit, IEE P-Elect Pow Appl 152(5) (2005), 1239–1252.

11.

Xiaofan ,

Fang Lin and

Hong , Modified positive output Luo converters, International Conference on Power Electronics and Drive Systems (1999), 450–455.

12.

F.L.

Luo , Positive output Luo converters: voltage lift technique, IEE P-Elect Pow Appl 146(4) (1999), 415–432.

13.

Acikgoz

H, O.F.

Kececioglu ,

Karadol ,

Gani and

Sekkeli , Adaptive Control of Solid State Transformer Using Type-2 Fuzzy Neural System, Stud Inform Control 26(2) (2017), 171–181.

14.

Kilic ,

H.R.

Ozcalik and

Sit , Adaptive controller with RBF neural network for induction motor drive, Int J Numer Model El 31(3) (2018), e2280.

15.

Logeswaran ,

Senthilkumar and

Karuppusamy , Adaptive Neuro-Fuzzy Model for Grid-Connected Photovoltaic System, Int J Fuzzy Syst 17(4) (2015), 585–594.

16.

D.S.

Karanjkar ,

Chatterji ,

Kumar and

S.L.

Shimi , Fuzzy adaptive proportional-integral-derivative controller with dynamic set-point adjustment for maximum power point tracking in solar photovoltaic system, Systems Science & Control Engineering 2(1) (2014), 562–582.

17.

Bendib ,

Krim ,

Belmili ,

M.F.

Almi and

Boulouma , Advanced Fuzzy MPPT Controller for a stand-alone PV system, Enrgy Proced 50 (2014), 383–392.

18.

Coteli ,

Acikgoz ,

Ucar and

Dandil , Design and implementation of Type-2 fuzzy neural system controller for PWM rectifiers, Int J Hydrogen Energ 42(32) (2017), 20759–20771.

19.

C.Y.

Chuang ,

P.S.

Chen ,

C.C

Hsu ,

J.Y.

Li ,

J.F.

Chen and

C.L.

Lin , Novel maximum power point tracker for PV systems using interval type-2 fuzzy logic controller. In: IEEE 3rd International Future Energy Electronics Conference; (2017). pp. 1505–1507.

20.

Acikgoz ,

Coteli ,

Ustundag and

Dandil , Robust Control of Current Controlled PWM Rectifiers Using Type-2 Fuzzy Neural Networks for Unity Power Factor Operation, J Electr Eng Technol 13(2) (2018), 822–828.

21.

D.S.

Karanjkar ,

Chatterji and

Kumar , Development of Linear Quadratic Regulator Based PI Controller for Maximum Power Point Tracking in Solar Photo-Voltaic System, Recent Advances in Engineering and Computational Sciences (2014), 1–6.

22.

Venkatesan and

Saravanan , Simulation and experimental validation of new MPPT algorithm with direct control method for PV application, J Renew Sustain Ener 8(4) (2016), 043503.

23.

A.S.

Tekade ,

Juneja ,

Kurwale and

Debre , Design of Positive Output Super-Lift Luo Boost Converter for Solar Inverter, International Conference on Energy Efficient Technologies for Sustainability (2016), 153–156.

Robust control of high gain DC-DC converter using Type-2 fuzzy neural network controller for MPPT

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