MPPT Design Using PSO Technique for Photovoltaic System Control Comparing to Fuzzy Logic and P&O Controllers

The Maximum Power Point Tracker (MPPT) is the optimum operating point of a photovoltaic module. It plays a very important role to obtain the maximum power of a solar panel as it allows an optimal use of a photovoltaic system, regardless of irradiation and temperature variations. In this research, we present a novel technique to improve the control’s performances optimization of the system consisting of a photovoltaic panel, a buck converter and a load. Simulations of different parts of the system are developed under Matlab/Simulink, thus allowing a comparison between the performances of the three studied controllers: “Fuzzy TS”, “P&O” and “PSO”. The three algorithms of MPPT associated with these techniques are tested in different meteorological conditions. The obtained results, in different operating conditions, reveal a clear improvement of controlling performances of MPPT of a photovoltaic system when the PSO tracking technique is used.

• Photovoltaic System
• MPPT Controller
• Buck Converter
• Perturb and Observe “P&O”
• Fuzzy Logic “Fuzzy TS”
• Particle Swarm Optimization “PSO”

1. Introduction

The photovoltaic solar energy deriving from the direct transformation of a part of solar irradiation into electric energy faces, inter alia, a maximization problem of power transfer of the photovoltaic generator (PVG) to the load. This is due to the non-linear feature of the electric characteristics I-V (Current-Voltage) of photovoltaic cells [1] . These characteristics depend on the illumination level, the temperature of the cell and the load. In order to increase the output power of a photovoltaic energy system, it is indispensable to make the photovoltaic panel operate at Maximum Power Point (MPP), to extract, at every moment, the maximum of power available at the PVG boundaries. The technique commonly used is to insert an adapting interface between the PVG and the load. This adapting interface consists of a static converter driven by Pulse Width Modulation (PWM) [2] [3] . The majority of articles dealing with control algorithms (MPPT) are based on the incremental conductance method (IncCon) or on Perturb and Observe (P&O) [4] [5] [6] . The fuzzy logic controller type Mamdani, as well, has been studied [7] [8] . In these last years, the analysis and synthesis of non-linear systems, described by fuzzy models type Takagi-Sugeno (TS), have also been widely studied in the literature [9] [10] .

In this work, we present a robust technique which permits to track the MMP of the PV panel system, thanks to the controller using PSO. This control technique reduces the calculating time and keeps a good precision. In addition, it can be implemented in a low-cost microcontroller [11] [12] . This controller “PSO” developed as such, will be subsequently used and compared to other classic tracking algorithms.

This paper is organized as follows. We present, in Section 2, the working environment and the electric modelling of the studied system, as well as the description of the developed algorithms for tracking techniques of MPP: “P&O”, “Fuzzy TS” and “PSO”. Section 3 is dedicated to the presentation of models under Matlab/Simulink version 2014 associated with different components of the tested conversion chain. In Section 4, we show and interpret simulation’s results concerning the PV system behaviors under the effect of one of the three controllers “P&O”, “Fuzzy TS” or “PSO” under different irradiation changes S and temperature T. We present also the evaluation of the performances of each of the studied MPP controllers. Finally, we finish our contribution with a summary of our research works.

2. PV Conversion Chain

As the PV conversion chain illustrates in Figure 1, MPP is reached through controlling the DC-DC converter with a system using a MPPT controller. The strategy of the MPPT controller allows to optimize the transfer of power from the PV panel to the load.

Figure 1

Elementary chain of photovoltaic conversion

The studied PV panel (Figure 1) is BPSX 150S type [13] . Table 1 sums up the electric characteristics of the PV panel in the Standard Test Conditions (STC): 1000 W/m², 25˚C and AM 1.5. We will present the Matlab-Simulink model which is based on these characteristic values.

2.1. Electric Modeling of the Studied System

2.1.1. PV Panel Model

In order to model our PV panel, we start with a simple model which is one of a PV elementary cell. The configuration that Figure 2 presents is the most common equivalent schema of a solar cell. It is composed of a source of variable current I_pv, connected in parallel with a diode D, characterizing the junction of semi-conductors which make the solar cell, and a parallel resistance R_p. To this assembly, another resistance R_s is added in series.

The model of a PVG issues from this schema, defined by the following equations [1] :

With:

- a: Ideality factor of the solar cell.

- ΔT = T − T_n (Kelvin), T: Real temperature of the cells and T_n: nominal temperature of the cells in the Standard Test Conditions (STC): 1000 W/m², 25˚C and AM 1.5.

Table 1

PV panel parameters (type: BPSX 150S)

Parameters	Values
Pmax: maximum power	150 W
Imp: maximum power current	4.35 A
Vmp: maximum power voltage	34.5 V
Ns: number of series cells	36
Isc: short circuit current	4.75 A
Voc: open circuit voltage	43.5 V

Figure 2

Equivalent electric circuit of solar cell

- S: Real Irradiation (W/m²).

- S_n: Nominal Irradiation in the Standard Test Conditions (W/m²).

- I₀: Diode reverse saturation current (A).

- I_pv,n: Current measured under Standard Test Conditions (A).

- I, V: PVG current (A) and voltage (V).

- I_sc,n and V_oc,n: Short circuit current (A) and Open circuit voltage (V) measured under Standard Test Conditions.

- V_t = N_sKT/q: Thermal voltage.

- N_s: Number of series-connected cells.

- K: Boltzmann constant (1.38 10 - 23 J/K).

- K_v: temperature coefficient of the open circuit voltage(=80 ± 10 mV/˚C).

- K_i: temperature coefficient of the short circuit current (=0.065 ± 0.015) %/˚C.

- q: Electron charge (1.6 10 - 19 C).

- R_s, R_p: Series resistance (=0.2365 Ω) and parallel resistance (=415.405 Ω) respectively.

2.1.2. Static Converter Type Buck

In our research, the suggested system in Figure 1 contains a power converter DC-DC type Buck, driven by using the Pulse Width Modulation principle. This converter is modeled by the equivalent electric schema in Figure 3.

As far as the simulations of the studied converter are concerned, the parameters we have used are: the resistance of the load being R_c = 3 Ω, the capacitance of the capacitor being C = 4.7 μF, the inductance of the inductor being L = 2 mH, D being freewheeling diode and T being a transistor type MOSFET. During the operation in continuous mode of this buck converter, the average values of the output voltage V_s and input voltage V_e are proportional as follows:

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