MPPT Control Using GA And Load Voltage Of PV Systems

if the battery is fully charged it shows and display that the battery is charged and MPPT get off but if the battery is not charged it checks that the sun light is available or not as it is day time or night ?if night it displays that the battery is not charged and MPPT gets off if there were day it senses the solar position to sense the best voltage to charge the battery if there is problem in sensing it changes the position as if there is need to change the position of panel it generates PWM to move panel and displays solar panel tracking message. If there is no need to change the position of panel MPPT starts sensing the PV Volt and the current then it displays the charging current and so the algorithm reaches to its

The PV module has been designed by considering the irradiance, temperature and number of PV cells connected in series and parallel. Figure 5.1 shows the simulink model of solar system. Here only function file used to show the solar system, the program code is given in the appendix. Here we are using a battery. The system is generated 240volt and current is 2.9 A.

Fig.3. shows algorithm for the proposed method. This algorithm uses voltage and current of the PV module. A permitted error of 0.06 as shown in (8) is used in modified algorithm to eliminate the steady state oscillation in the system. The permitted error is written as  I + V dI/dV   0.06 Using the permitted error of 0.06, the steady state error is approximately 0.7% based on the duty cycle step size of 0.005.

Similar to the previous work [17], the second ANN architecture design adopts the PV module short circuit(ISC) in Ampere,and the open circuit voltage (VOC) in Volts, as the inputs of the network and the PV generator output power (P) in Watt, as the output of the network. The data set stored in the data acquisition is trained until new patterns may be presented to them forprediction. Thus the trained data are compared to the experimental measurements, recorded during the characterization tests.

Figure 10 shows the results between the hybridg algorithm compared to other algorithms with various mathematical functions as presented by privous sessions. It is cleard to see that the hybride algorithm performed better than other techniques. Surrprisingly, the Ackley function performed closely to the hybride algorithm. Figure 11 - 14 shows that PSO reached the optimal result very quickly because this algorithm works as a local search which makes a narrow space for the search of a solution, rather than other algorithms which work as a global search

Fig. 9(a) shows the DC-link voltage across the DC-link capacitor of the PV array. The DC-link reference voltage corresponds to maximum power point of the PV array. Fig. 9(b) shows the power demand by the load, and power flows from the grid and PV system generated power. As the PV system generated power could not meet complete power demand of load, remainining power is supplied by the grid. At 1secocond the PV system switched from partially shaded condition to uniform solar insolation. Thus, at this point power supplied by the inverter and grid also varies. Fig. 10(a) shows the grid current which is fully sinusoidal. Fig. 10(b) shows the inverter output current. Fig. 11 shows the performance of system under reactive load condition. In this study, a 500VA reactive load is considered. The figure shows the complete reactive power is supplied by the inverter. Thus the power factor of the system is maintained nearly at

Z. Salameh and D.Taylor, “Step-up maximum power point tracker for photovoltaicarrays,” Solar Energy, vol. 44, pp. 57–61,

Department of Mechanical and Aerospace Engineering, Jacobs School of Engineering, Center for Renewable Resource Integration, University of

However, taking into account the economic and feasibility factors, PV power forecasting is still one of the most effective and economical ways to solve the uncertainty and fluctuation problems in PV system. Day-ahead PV power forecasting can provide the expected future PV power output of the next day 24 hours, which is an important reference information for power generation planning and system scheduling [7]. Accurate solar forecasting is not only able to help system operators and planners to better manage the variability and uncertainty of PV power in advance, but also can benefit PV plant managers as they avoid possible penalties that are incurred due to deviations between forecasted and produced energy [8], [9].

The power conversion efficiency is evaluated with respect to standard reporting conditions (SRC) defined by temperature, spectral irradiance, and total irradiance. 23 The SRC for rating the performance of terrestrial PV cells are the following: 1000 W∙m-2 irradiance, AM 1.5 (AM: air mass) global reference spectrum, and 25 °C cell temperature. 24-28 The PCE (η) of a PV cell is given as30

This method does also suffer from the exploitative deficiencies like GA method, which could not precisely find the global optimum. Moreover, the original PSO is weakness in exploration that may results in local optimum. Therefore, some modified PSO may add a mutation process to ensure the global optimum.

“ The Energy Pay Back Time is defined by EPBT = Einput/Esaved, where Einput is the energy input during the module life cycle (which includes the energy required for manufacturing, installation, energy use during operation, and energy needed for decommissioning) and Esaved the annual energy savings due to electricity generated by the PV module. EPBT depends on a number of factors: cell technology, type of encapsulation, frame and array support, module size & efficiency, PV system application type (autonomous or grid-connected) and, finally, PV system performance as determined by irradiation and the performance ratio. EPBT is also affected by factors that do not directly relate to the characteristics of the PV power system itself: conversion efficiency of the electricity supply system and energy requirements of materials like glass, aluminum etc. ”

A flat plate solar collector has a dynamic behaviour in response to variations in the intensity of solar radiation at different times of the day and also variations in weather conditions. The characteristics governing the input-output behaviour of a flat plate collector can be described by a mathematical model which serves as a prerequisite for simulation and control. The steady state and transient characteristics of flat plate solar collectors have been studied in \cite{Hilmer1999Solar,Dhariwal2005Solar,deRon1980,RodriguezHidalgo2011,Refaie1980}. Depending on the complexity of the flat plate solar collector under observation, deriving a mathematical model may lead to a high order model which requires high computational effort and longer

Balancing load of distribution feeders is significant for falling of power loss and mitigating power flow. As the loop power controller is implanting for the active power and reactive power flows by change in voltage ratio and phase shift. So that the balancing of the load distribution feeders can be achieved. However it can include photovoltaic power producing in feeder balancing load, as a Taipower distribution feeder consisting of two feeders with large amount of photovoltaic equipment considered. The balancing load can be determine in distribution feeders with photovoltaic set up by using the loop power controllers as the changing of solar energy and power loading of feeders. With implanting the control algorithm in MATLAB for loop power controller by changing the voltage ratio and phase shift connecting to the feeder the proper amount of active power and

Performance Improvement and MPPT of an Autonomous Hybrid PV/Storage System Powering a Dynamic Load Based on Artificial Bee Colony Algorithm