This study presents a novel search algorithm of maximum power point tracking for photovoltaic power generation systems. The

It is now widely accepted that the nonrenewable sources in the world are finite and it is only a matter of time before reserves will essentially be consumed [

The power produced by a PV module depends on the operating temperature, the amount of falling solar irradiance over the PV Cells array, and the load connected [

The main methods used to achieve MPPT for PV cells are Estimation Methods, Heuristic Method, and Search Algorithms. System modeling method [

Heuristic methods are recently developed to overcome the problem associated with the inaccuracy of the PV cell mathematical model. The succesful development of these methods is attributed to the recent advances in nonlinear control method. Fuzzy Logic Control methods [

Unlike estimation methods, Search Algorithms track the actual MPP rather than an estimated MPP. However, they continuously search for the MPP by increasing or decreasing the PV cell output voltage. Many methods are developed implementing the search algorithms concept. Some of those methods are the differentiation method [

All the developed methods found in the literature still suffer from weak performance under extreme weather conditions. It is the objective of this study to develop an efficient novel search algorithm that deals with rapidly changing, partial shading and sudden partial shading conditions to track the MPP of PV cells. Minimal user interface for wider adaptation to different solar panels and different environmental conditions is considered in developing the proposed search algorithm.

The modern solar cells are fabricated from a p-n junction and prepared in a small thickness semiconductor layer. These cells create electric current when subjected to sunlight. The PV cells act as a form of diode in which the parameters of this diode define the circuit model. Walker [

Simple PV equivalent circuit model.

Villalva et al. suggested adding several parameters in order to capture the behavior of actual panels which consist of a number of connected photovoltaic cells [

Single-diode model of the theoretical PV with additional parameters for the improved PV model.

Figure ^{2}),

MATLAB/Simulink model of improved PV panel.

MATLAB/Simulink model for calculating the

PV cell MATLAB/Simulink model for calculating

MATLAB/Simulink model for calculating

From the above equations, it is obvious that output power of a PV module depends on the solar irradiance values and ambient temperature.

The temperature, the irradiation levels, and the shading of the system affect the performance of photovoltaic cells. Partial shading problems arise due to the existence of clouds or building shadows. This problem makes the photovoltaic power characteristics more complicated with multiple peaks in power. This reduces the efficiency of most MPPT techniques. The effect of partial shading problem appears significantly for large arrangement of panels. Under partially shaded condition, it has been found that the

In this paper, we utilized a boost convertor to change the PV panel operating point to its MPP. The operation of the DC-DC converters is controlled by the MPPT algorithm making the power output of the panel operate at its the maximum level. The MPPT algorithms are usually implemented using either digital signal processors (DSP) or a microcontroller.

DC-DC boost converter shown in Figure

Circuit diagram of boost converter.

Boost converters have two modes of operation. The closed-switch mode starts the diode reverse bias mode. It causes the input power source to store energy in the inductor as well as the capacitor to discharge into the storage battery, while the open-switch mode starts the diode forward bias mode causing higher output energy supply from both the power source and the inductor to the capacitor and the load. The equation defining the ratio between the input voltage and the output voltage is given as

In order to design an appropriate boost converter, the following steps are carried out.

The boost converter specifications listed in Table

A 200 kHz switching frequency is chosen to reduce the size of the boost converter components and decrease the power loss.

The boost inductor value plays a key role in determining the operational mode of the system. In order to have continuous operation mode, the inductance should satisfy the following equation:

where

The maximum duty cycle is calculated according to (

The capacitance value is given by

where

Specification | Min | Max | Units |
---|---|---|---|

Input voltage | 0 | 30 | V |

Output voltage | 50 | 51 | V |

Output power | 0 | 150 | W |

Operation frequency | 1 | MHz | |

Voltage allowed output ripple | 50 | mV |

To sooth the signal, almost twice of the calculated capacitor value is used, that is, 330

Full simulator model.

There are several factors to consider when developing and choosing the techniques for performing MPPT, such as the ability of an algorithm to detect multiple maxima, costs, and convergence speed. MPPT is naturally a maxima-finding process. The proposed Maximum Power Point tracking algorithm implements the search algorithms concept. The reasons behind this choice as mentioned previously are no previous knowledge about the PV cell characteristics is required, simple implementation, and guaranteed convergence. The main disadvantage of search algorithms are that; they waste energy as they continuously oscillate around the MPP and they show inadequate response under partial shading conditions and fail under sudden partial shading conditions.

Most search algorithms model the data as a 1-D function and go about a Brute-Force method of finding the maxima of the function. These kinds of algorithms require a large amount of processing time. Other algorithms like the Shubert algorithm [

Eliminating the need to specify the constant and making the algorithm consider both local and global search are the criteria for developing the new algorithm. In this study, we followed Jones et al. methodology [

The goal is to find the maximum functional value of

Lipschitz continuity states that a function

Let us take a hypothetical function

The inequalities (

An initial lower bound for

The point of intersection for the two lines

The initial lower bound of

The Shubert algorithm uses this straightforward idea in finding the minimum or the maximum of

Iterations of the Shubert algorithm in dividing the intervals of minimum

Dividing strategy of DIRECT algorithm.

Evaluating the function at the center of any interval rather than the bounds of the interval is the main idea of DIRECT algorithm developed by Jones et al. [

Figure

The inequality (

Find the point in

Calculate the angles in radians that each of the points makes with

If

Repeat step number (3) until you encounter

Set of potentially optimal intervals.

Change in power under partially shaded condition identification in (a)

A simplified approach is to calculate the direction cross product of the two vectors formed from three points

The golden section algorithm is used to detect the environmental change by continuously oscillating around the maximum power point. The Golden Section Search method is used to find the maximum or the minimum of a unimodal function by calculating the function at three different points. In this study, Golden Section Search (GSS) MPPT algorithm uses the voltage as the search variable. The main advantage of GSS algorithm is its fast convergence compared to many other MPPT algorithms. The MPPT algorithm is developed with the limiting parameters for fast convergence. The main steps in GSS algorithm are as follows.

Determine

Determine two intermediate points

If

If

If

The intermediate points

Theoretically,

As mentioned previously, the

Flowchart of the proposed search algorithm.

Masked Simulink model of PV cells.

The simulation results are carried out using MATLAB/Simulink to validate the performance of the proposed MPPT algorithm.

The developed photovoltaic cell Simulink model built and shown in Figure

To investigate partial shading conditions, first, a masked model of a single PV cell is built as shown in Figure

Masked Simulink model to calculate

Simulink model for partial shading.

In order to clarify the complexity associated with partial shading, sample simulations are carried out and their results are shown in Figures

The variation of the^{2}).

The variation of the^{2}).

The Performance and operation of the proposed search algorithm have been evaluated using MATLAB/Simulink. The sampling time is chosen to be 0.05 s. For the implemented proposed MPPT algorithms, the simulation results have been obtained during starting up of the system. The results have been obtained for a solar irradiance value of the proposed system that is tested under two uniform radiation levels: 1000 W/m^{2} and 2000 W/m^{2}. As shown in Figures

The simulated power curves for the 1000 W/m^{2}, 25°C fully shaded designed algorithm.

The simulated power curves for the 2000 W/m^{2}, 25°C.

The simulated power curves for the 2000 W/m^{2}, 25°C Perturbation and Observe [

The proposed scheme for MPPT algorithm is tested under partially shaded conditions. The simulations were conducted with two consecutive scenarios. In the first scenario, the PV panels are subjected to uniform insolation condition. This condition is maintained for 0.4 s before it is changed to partially shaded condition. The cells temperatures are kept constant at 25°C. Figure

MMP tracking under partial shading (600, 200, and 100 W/m^{2}).

A novel algorithm of maximum power point tracking for photovoltaic power generation system is presented. A mathematical model of the PV panel is presented based on the theory of photovoltaic. The

The authors declare that there is no conflict of interests regarding the publication of this paper.