Comparability Research of Highest Power Point Tracker Approaches For Photovoltaic Programs

Recently, the maximum power point tracking technology becomes very important for photovoltaic systems. Consequently, many developments have been done in this area until it reached the saturation phase. As a result, the interest of the authors when implementing this work has been focused on achieving a certain added value in the proposed system, which can be found in the accurateness, speed and low cost. Thus, the developed algorithm presents the advantage of its simplicity and its high speed which also helps to improve photovoltaic system efficiency. Source in many cases. Most commonly applied PV systems can be found in remote and rural areas where no public grid is available. A typical small photovoltaic power system (off-grid) can contain the following components: solar PV array, with a number of series/parallel interconnected solar modules and

Typically, in photovoltaic (PV) systems a high voltage is obtained by connecting multiple modules in series to form a string of modules. Since modules in a string are connected in series, a mismatch of currents among the individual modules reduces the output current of the whole string, inserting losses which are disproportional to the shading. In attempting to address this problem, module level maximum power point tracking (MPPT) methods, otherwise known as distributed maximum power point tracking (DMPPT), are increasingly reported. A MPPT is used for extracting the maximum power from the solar PV module and transferring that power to the load. A dc/dc converter (step up/ step down) serves the purpose of transferring maximum power from the solar PV module to the load. A dc/dc converter acts as an interface between the load and the module. By changing the duty cycle the load impedance as seen by the source is varied and matched at the point of the peak power with the source so as to transfer the maximum power. Therefore MPPT techniques are needed to maintain the PV array’s operating at its MPP. Many MPPT techniques have been proposed in the literature; example are the Perturb and Observe (P&O) methods, Incremental Conductance (IC) methods, Fuzzy Logic Method etc.

Figure shows a simple model of a PV cell. RS is the series resistance associated with connecting to the active portion of a cell or module consisting of a series of equivalent cells.

A solar cell basically is a p-n semiconductor junction. When exposed to light, a dc current is generated. The generated current varies linearly with the solar irradiance. The standard equivalent circuit of the PV cell. It is assumed that a maximum power point of a particular solar PV module lies at about 0.75 times the open circuit voltage of the module. So by measuring the open circuit voltage a reference voltage can be generated and feed forward voltage control scheme can be implemented to bring the solar PV module voltage to the point of maximum power. One problem of this technique is the open circuit voltage of the module varies with the temperature.

With the increasing deployment of PV generation and the constant need to reduce the cost of PV generated electricity, mismatch losses can no longer be tolerated. This gave rise to a new group of PV architectures, called Distributed Maximum Power Point Tracking (DMPPT). DMPPT facilitates the extraction of maximum power from each individual module, thus eliminating the mismatch losses. The second class DMPPT employs Front-end DC optimizers, also known as Module Integrated Converters (MICs). The MICs are connected in front of the PV modules and their outputs are connected in series to form a string. In this architecture, the MPPT is performed on a per-module basis, thereby allowing PV modules to operate at different currents – each module at its MPP current. Thus, underperforming modules do not limit the whole string, nor are they bypassed. Each module contributes its entire potential power.

A real PV array has been modeled. It consists of 30 PV modules with 36x2 monocrystalline silicon solar cells each one, connected in series and parallel. Each module can produce 106 W of DC electrical power with an area of 126.5 square centimeters. The array is configured as follows: fifteen modules are connected in series, resulting a nominal operating

rated power of the PV array is 2.6 kW (DC). In order to allow the interaction between a DC/DC converter and the PV array, a simulation model for a PV array has been developed, including the dependence of the PV array output with the irradiance and temperature. The model was implemented in Simulink, helped by the Sim Power System block set. Most MPPT techniques attempt to find (search) the PV voltage that results in the maximum power point VMPP , or to find the PV current IMPP corresponding to the maximum power point. The proposed algorithm tracks neither the VMPP nor the IMPP. However, it tracks directly the maximum possible power PMAX that can be extracted from the PV. In the current-control simulation, each algorithm tracks the MPPT of the low-pass-filtered irradiance data. The current control dynamics are not easily controllable and fail for the rapid irradiance changes found in the unfiltered data. However, on the low-pass-filtered data, current control with PO and ES admits almost perfect MPPT. ES has efficiency = 0.9963 with rise time of 0.02 s, and PO has efficiency 0.9898 with rise time of 6.9 s. The step size for PO is = 0.5 A.

All simulation and result for every converter have been recorded to make sure the comparison of the circuit can be determined accurately. The input, output, voltage, current and power is the main comparison to take into consideration. The complexity and simplicity of the circuit have been determined based on the literature. Convergence speed, hardware required and range of effectiveness. To ensure the effectiveness of the proposed algorithm, a MATALB code for the proposed system in figure 4 has been developed. The modeled system consists of one PV module (200 Wp, 26.3 V, 7.61 A), (60 Ah/24 V) storage battery and load. Figure 6 shows the simulated load profile. The total consumption is 930 Wh where the peak of the load is 120 Watt. The peak solar radiation is 800 W/m2 while the solar day long is about 12 hours.

A novel ES algorithm that utilizes the natural inverter ripple was tested on a simulated array inverter system. Thismethod was benchmarked against the popular PO method using 25 min of rapidly varying irradiance data taken on June 2007 at Princeton University. The irradiance data represent a worst case scenario for PO in total power efficiency, and drastically outperforms in transient rise time to the maximum power point, with two orders of magnitude speed-up. Moreover, ES has guaranteed convergence and stability properties, which are ideal for variable weather conditions and unmodeled dynamics. In this paper, a sliding mode integral control of the boost converter has been analyzed. The reported controller uses the output power of the PV array, output voltage of the converter and an input signal, which in this case is the switching signal. The control law provides voltage regulation at the converter output, and guarantees the maximum power point of the PV array. A practical case developed in a Matlab/Simulink simulation platform has been presented, and the results confirm the adequate performance of designed control. So, the dynamical response of the control is around one period of the voltage supply. This work presents high-level architectural aspects of photovoltaic DMPPT systems and their performance in terms of efficiency, control and stability. PV string operation under inhomogeneous insolation is clarified. In addition, a distinction between feedback based regulation and MPPT is emphasized. MPPT units’ location is discussed and possible instabilities are demonstrated. The architectures are classified in two categories: the full power processing category, of which mainly the front end DC optimizers architecture is analyzed, and the minimal power processing category, consisting of the shuffling, the RECC, and the Energy Feed Forward architectures. This paper has presented a comparison of two most popular MPPT controller, Perturb and Observe Controller with Incremental Conductance Controller. This paper focus on comparison of three different converter which will connected with the controller. One simple solar panel that has standard value of insolation and temperature has been included in the simulation circuit. From all the cases, the best controller for MPPT is incremental conductance controller. This controller gives a better output value for buck, boost and cuk converter. Hence this controller will give different kind of curves for the entire converter. In simulation Buck converter show the best performance the controller work at the best condition using buck controller. The proposed method offers different advantages which are: good tracking efficiency, relatively high convergence speed and well control for the extracted power thanks to the direct power control unit based on the ON/OFF hysteresis controller. bounce around the maximum power point due to noise present in the power conversion system. Of course, an accurate and fast method would be preferred but the cost of implementation needs to be considered.

• ADVANCED ALGORITHM FOR MPPT CONTROL OF PHOTOVOLTAIC SYSTEMS, C. Liu, B. Wu and R. Cheung

• B. K. Bose, Modern Power Electronics: Evolution, Technology, and Applications. New York: IEEE Press, 1992.

• Energy comparison of MPPT techniques for PV Systems, ROBERTO FARANDA, SONIA LEVA

• R. A. Mastromauro, M. Liserre, T.Kerekes, and A. Dell’Aquila, “A singlephase voltage-controlled grid-connected photovoltaic system with power quality conditioner functionality,” IEEE Trans. Ind. Electron., vol. 56, no. 11, pp. 4436–4444, Nov. 2009.

• R. Faranda and S. Leva, "Energy Comparison of MPPT techniques for PV Systems," WSES Transaction on Power Systems, vol. 3, pp. 446-455, 2008.

• R.D. Middlebrook and Slobodan Cuk, “A General Unified Approach to Modelling Switching-Converter Power Stages”, IEEE PESC’76 Rec., pp. 18-34, Cleveland, OH, June 8-10.

• S. Jain and V. Agarwal, “A New Algorithm for Rapid Tracking of Approximate Maximum Power Point in Photovoltaic Systems,” IEEE Power Electronic Letter., Vol. 2, pp. 16-19, Mar. 2004.

• V. Utkin, J. Guldner, and J. Shi, “Sliding Modes in Electromechanical Systems”, London, U.K.: Taylor & Francis, 1999.

• V.Salas, E. O., A.Barrado, A.lazaro. Review of the maximum power point tracking algorithms for stand-alone photovoltice systems . Solar Energy Materials & Solar Cells 90(2006): 1555–1578.

• Vikrant.A.Chaudhari, "Automatic Peak Power Traker for Solar PV Modules Using dSpacer Software.," in Maulana Azad National Institute Of Technologyvol. Degree of Master of Technology In Energy. Bhopal: Deemed University, 2005, pp. 98.

• W. Xiao and W. G. Dunford,“A modified adaptive hill climbing MPPT method for photovoltaic power systems,” 35th. Annual IEEE Power Electron. Specialists Conf. , pp. 1957-1963, 2004.

IEEE Trans. Ind. Electron., Vol. 48, pp. 594-601, 2001.