Description
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.
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.
The table for fuzzy rules is
| E/CE | NB | NS | ZE | PS | PB |
| NB | ZE | ZE | PB | PB | PB |
| NS | ZE | ZE | PS | PS | PS |
| ZE | PS | ZE | ZE | ZE | NS |
| PS | NS | NS | NS | ZE | ZE |
| PB | NB | NB | NB | ZE | ZE |
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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