Fuzzy MPPT Control of PV Connected Grid Distribution System


This work is to simulate and test the fuzzy logic for the MPPT control in photo voltaic array and grid distribution system to enhance the maximum power transfer to grid from photo voltaic array. The fuzzy logic gives the output as duty cycle of boost converter at PV side. This package contains:

  1. Simulink Model
  2. How to run.txt

Note:  We don’t claim the documentation file to be plagiarism free and neither support to copy this code for your academic submission. This is to ease your pain to start writing code from scratch. We suggest to modify the code for your work.


Here at free-thesis.com,  we have developed fuzzy Logic MPPT control approach in our work work using MATLAB simulink with 2016a version. the simpower toolbox is the main on which our model is based on. The proposed work is compared with the previous work too which is using Incremental & conductance method and also developed in the same model. 10-6 seconds are considered as sampling simulation time. model view is shown in figure  which is the main modelling layer. In layers beneath it we have proposed MPPT control technique and I&C method. Each submodel is connected with the PV array with grid and boost converter which is fed by MPPT controlled pulse on gate terminal. Except MPPT method, rest model is same for both. The machine learning based algorithm provides efficient outcomes of control system.

Main MATLAB simulink model for PV connected grid for MPPT control: free-thesis.com

The PV array plant generates the DC current and voltage and before putting it on distribution lines, it has to boost up. Boost converter does this task and to gain maximum power from the generating plant, fuzzy logic controller controls the boost converter to get as high power as possible. The boosted power is then transferred to grid and before that it is converted to 3 phase AC at point of common coupling (PCC). This coupling point is responsible for any disturbance in synchronisation of both plants. For stability these must be in sync. So a 3 phase converter is used at PCC which takes the grid voltage, current and DC voltage at PCC as input and a controllers gives the gating pulse to IGBT of this 3 phase converter. This controller is called voltage source controller (VSC). The stability of the whole system is analysed by the constant DC at PCC. If both grid and PV plant are in sync then this DC voltage will be constant else disturbed. As much constant and smooth will be this PCC DC voltage, as will be the controlled power generation. A table of plant parameters considered for designing and simulation is shown  in table 1.

Table 1: Parameters for grid connected PV array modelling

PV array ~22 KW
Grid Power ~25 KW
Vdc(coupling voltage) 500 V
Grid load 35KW
Irradiance 900-1000 w/m2
PV array MPPT control Yes
PV array MPPT control Method Incremental Conductance method, Fuzzy Logic Method
PWM carrier frequency in MPPT controller 5000 Hz
PV array inductance and capacitance 0.01 mH, 100microF
Wind plant inductance and capacitance L1=L2=2mH, c4=1000microF

Free-thesis.com used 66 arrays of PV cell in string and 5 PV cells array in series. Thsi combination is still non linear power generating and to analyse the voltage and current generation by complete array collectively, a V-V and P-V curve is plotted for different radiation intensity. Varying  temperature and solar irradiations are the input to the model.

Machine learning Fuzzy Logic based MPPT control requires some basic set of rules and input. WE have fed the fuzzy logic with error and change in error. Mathematically it is represented as:

Error = 

Change in error=

here P and V are power and voltage from PV array. The input to the fuzzy system are fuzzified in fuzzy set representation by membership functions whose range is defined by programmer based on previous behavior of system and knowing the desired output. Decision of membership function range is hit and trial process near to best optimal set. Rules are created based on input and output membership functions which are used to create output signal. In our controller ‘E’ and ‘CE’ are defined by linguistic variables (fuzzy set of variables) like NB, NS, ZE, PS, PB. These are membership functions for every input and output. Basically an input/ output is defined by five membership functions. These membership functions are definite curve which represents a particular area  and define how can each input point be mapped in between particular range which is -0.032 to 0.032 and -100 to 100 for ‘E’ and ‘CE’ respectively.  The 3D surface view of fuzzy rules is shown in figure.

3D surface view fo fuzzy rules for MPPt control of PV grid: free-thesis.com

The table for fuzzy rules is


Under machine learning fuzzy logic MPPt control the improvement in PV array output power is achieved and an improvement upto 1.3% is reached than incremental & control method.

I&C Fuzzy method Improvement
PV array output power 77.7198 kW 78.6939 kW 1.3 %




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