COMPARISON OF MPPT ALGORITHMS FOR PHOTOVOLTAIC SYSTEMS UNDER UNIFORM IRRIADIANCE BETWEEN PSO AND P&O

1 Electrical and Control Engineering Department, College of Engineering Arab Academy for Science and Technology, Maritime Transport, Cairo, Egypt 2 Electrical Power and Machines Department, Faculty of Engineering-ain Shams University, Cairo, Egypt 3 Electrical and Control Engineering Department, College of Engineering Arab Academy for Science and Technology, Maritime Transport, Cairo, Egypt Abstract: This paper shows a Comparison between Conventional Method [P&O] and particle swarm optimization [PSO] Based on MPPT Algorithms for Photovoltaic Systems under uniform irradiance and temperature. The main idea is to show that PSO method has a very high tracking speed and has the ability to track MPP under different environmental conditions in addition to an easy hardware implementation using a low-cost microcontroller. MATLAB simulations are carried out under very challenging conditions, namely irradiance and temperature, which reflect a change in the load [KW]. The proposed PSO tracking method Results will be compared with conventional method called [P&O] through MATLAB/SIMULINK.


Introduction
Renewable energy resources play an important role in electric power generation. There are various renewable resources which are used for electric power generation, such as solar energy, wind energy, geothermal, Biomass and hydro-electric etc. Solar Energy is a good choice for electric power generation, since the solar energy is directly converted into electrical energy by solar photovoltaic arrays. These arrays which consist of N-modules are made up of silicon cells or semi-conductor material. When many such cells are connected in series or parallel, we get a solar PV module. From the P-V characteristics curve of the PV module we can track the maximum point for extraction the maximum power, these criteria called maximum power point tracking [MPP]. From this point of view, we had a various MPPT algorithms. Two MPP tracking techniques are studied and compared in this paper. The first technique is based on particle swarm optimization and the second one is based on the perturb and observe [P&O method]. The objective of MPPT is to ensure that the system can always crop the Maximum power generated by the PV arrays. However, due to the varying environmental conditions, namely temperature and solar irradiance, as we mentioned before that P-V characteristic curve exhibits a maximum Power point (MPP) that varies nonlinearly with these conditions. To capitalize that particular advantage, this paper proposes a MPPT method based on a PSO algorithm and its simulation results compared with the conventional P&O. The main feature of the proposed method [PSO] is the ability to track the MPP under the effect of changing the irradiance and temperature in the presence of uniform condition. And if we compared it to other conventional MMPT techniques, it has a faster tracking speed. Furthermore, the algorithm is simple and can be computed very rapidly; thus, its implementation using allow-cost controller is possible. This paper is organized by the following. In Section II discusses the modeling of the PV module and array. In Section III, The PV module performance. In Section IV, the techniques of MPPT algorithms. In Section V, system modeling and simulation results. Finally, the Conclusion is made in last section.  The diode current can be written as:

Modeling of the PV Cells
Where a is the ideality factor of the diode and is the thermal voltage, which is given by the equation: = . . / By using KVL, the output voltage terminal can be written as:

V= -I
Where, I, V -the PV cell output current and voltage terminal respectively, Iph[ ]is the photon generated current, and Io is the diode reverse saturation currents. is the number of cells in series. a is the ideality factor, T is the cell temperature in Kelvin, k is the Boltzmann's constant (K=1.380 x 10-23 J/K) and q is the Electronic charge =1.6×10^−19C. The selected PV module for this study is SUNPOWER T5-SPR-308E PV module. Its electrical specifications are shown in (see Table 1)   From the above figures, it is clear that the PV module has a non-linear characteristic which differs according to solar radiation, temperature and the load condition. Each curve has a different operating point due to the variation in irradiance and temperature as well,at which the module can produce its maximum power. Hence, in order to overcome this problem, a MPPT controller is required, in order to hunt only one global peak point. The main role for MPPT system is to choose a high efficiency DC-DC converter that can operate the PV module at its MPP and be the interface between PV module and the Load.

Perturbation and Observation Algorithm (P&O)
This technique is based on the relationship between the PV module output power and its output voltage, and we can get the maximum power point by adjusting duty cycle ratio until reaches that dp/dv is equal to zero. From the disadvantages of P&O algorithm is that it produces oscillations around the MPP in the steady state condition. In addition, we cannot operate the module at its maximum output power under various environmental conditions, we can see that from the following flowchart in fig.4.

Incremental Conductance (IncCond) Algorithm
The Incremental Conductance (IncCond) algorithm appeared to overcome the disadvantages of the P&O method under the environmental effects. The relationship between the voltage and power can be expressed as follows: >0 on the left side of MPP <0 on the right side of MPP Hence, the PV module operating point at its maximum output power can be calculated based on the relationship of and its tracking search around MPP whatever on it, left or on the right side.
The above relationships shows that the PV module operates at its MPP when the IncCond dI/dV is equal to its direct conductance -I/V. while if the PV module IncCond dI/dV is greater than the its conductance -I

Particle Swarm Optimization [PSO]
Particle swarm optimization (PSO) is an intelligence optimization theory was developed by Eberhart and Kennedy in 1995. The basic concept of this algorithm was inspired from the foraging behavior of birds and fish schooling, and this concept appeared to reduce search and optimization problems that related to the conventional ones. In this algorithm, a group of fishes or birds are used as a particle; each particle flying in the search space has its location and velocity that is updated through every iteration. In order to decide the direction and distance of their movement, each particle marked as ,and the best particle among all of them marked  As we can see from the above equations, the location and the velocity of the particle is updated in every iteration till reached the required value from the fitness function which is the maximum power of the PV module in this paper : Personal best position for the particle itself : Global best position among all particles : The location of particle

System Modeling and Simulation Results
A 100-KW grid connected PV array system model simulated in MATLAB/SIMULINK and consist of PV array connected with a signal builder which corresponding to irradiance and temperature as well. And MPPT controller to track MPP by using two codes for P&O and PSO. that output duty cycle that is adjusted through DC-DC boost converter and VSC (average model), moreover through step down transformer till reached utility grid, and its implemented from powersim/simulink library. We used a variable irradiance with different step size of w/ 2 and temperature as well, that will show a simulation resultsfor current, voltage, duty cycle and power tracked respectively. Simulation results are carried out under uniform irradiance with different cases 1000,600,800 w/ 2 respectively, between PSO and P&O.And as shown in the following table.2 that PSO has a fast-tracking speed and has the ability to track MPP under various irradiance than P&O algorithm which is slow in tracking As shown from the above table. When, a step change of irradiance happened its corresponding to sampling time. At 1000 w/ 2 occurs at t=0.06 sec, 600 w/ 2 at t= 2.0 sec and 800 w/ 2 at t=4.01 sec. respectively. On the other hand, P&O simulation results are taken also as a comparison with PSO algorithms in order to show the tracking performance at the same step change of irradiance at 25 Cas shown in the following table.3 As shown in from the previous tables, PSO algorithm has a perfect tracking speed from power point of view than P&O algorithm. Simulation curves are showed fig.9 consists of current, voltage, duty cycle and power tracked under step change of irradiance at25C.   fig.11 we have three redideal points of irradiance at 1000,800, 600 w/m 2 which has the maximum power of 100.7, 80.7, 59.8 KW respectively. Its shown the tracking performance on P-V curve characteristics between both techniques PSO and P&O, and it's clear that the swarm particles are nearby the MPP with very low tolerance, but P&O are far away from MPP , which reflect that PSO is more convenient for tracking maximum power point than P&O. As a comparison point of view between PSO and P&O its seems that:  1) High tracking efficiency 2) Fast convergence to reach GMPP with oscillation of the particles 3) Tracking MPP with complex Simulink model and with minimum number of iterations.
If we see a uniform example with four pv modules with 1000 w/m 2 , the therotical max. power: 84.31 KW with my PSO control method its tracking is 84.10 KW with optimization time @ 0.057 which is better a little bit from ref [11]

Conclusion
In this paper, a PSO algorithm has the ability to crop MPP by the source of particle oscillation around MPP to get accurate and efficient results as shown in the previous tables. Simulation/MATLAB results are carried under step change of irradiance to show the dynamic behavior and response of the controller. And from the above pros of PSO it could locate the MPP under partial shading conditions due its accuracy and fast-tracking speed also it could be implemented in a simple way with low-cost microcontrollers.