• Solar Energy
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• v.18 no.1
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• pp.27-34
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• 1998

A maximum power point tracking(MPPT) converter, to enhance the converter efficiency is evaluated within the laboratory. The converter is controlled to track the maximum power point of the input photovoltaic(PV) source by varying the input and output parameter-conditions of irradiation, temperature, etc. The purpose of this paper is to develop a new maximum power point tracking(MPPT) using neural controller. Neural controller are applied to control of MPPT by boosting converter duty ratios compensation effect with 8 bit single chip 8051 microcontroller.

• Journal of Power Electronics
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• v.18 no.4
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• pp.1201-1210
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• 2018

The applications of thermoelectric generators (TEGs) have received a lot of attention both in terms of harvesting waste thermal energy and the need for multi-levels of power. It is critical to track the optimum electrical operating point using DC to DC converters controlled by a pulse that is generated through a maximum power point tracking algorithm (MPPT). In this paper, the hardware implementation of a short-current pulse algorithm has been demonstrated under steady stated and transient conditions. In addition, the MPPT algorithm has been proposed, which is one of the most effective and applicable algorithms for obtaining the maximum power point of TEGs. During this study, the proposed prototype has been validated both analytically and experimentally. It has also demonstrated successful performance, which highlights the claimed advantages of the proposed MPPT solution.

• The Transactions of the Korean Institute of Power Electronics
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• v.26 no.3
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• pp.159-166
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• 2021

This study compares the maximum power point tracking (MPPT) control methods of a thermoelectric generator on vehicles. The researchers conduct comparisons on five different MPPT methods, including a fractional open circuit voltage method, a perturbation and observation (P&O) method, an incremental conductance method, a linear extrapolation-based MPPT (LEMPPT) method, and a LEMPPT/P&O hybrid method. These five MPPT methods are theoretically analyzed in detail, and the comparisons are conducted through MATLAB/Simulink simulation results. The comparison outcomes reveal that linear MPPT methods, including LEMPPT and LEMPPT/P&O hybrid methods, are more suitable for a thermoelectric generator on vehicles than the other MPPT methods examined in this work.

• The Transactions of the Korean Institute of Power Electronics
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• v.24 no.2
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• pp.99-104
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• 2019

Solar power generation systems require maximum power point tracking (MPPT) control to acquire maximum power using inefficient and high-cost PV modules. Most conventional MPPT algorithms are based on the slope-tracking concept. The perturb and observe (P&O) algorithm is a typical slope-tracking method. The two factors that determine the MPPT performance of P&O algorithm are the MPPT control period and the magnitude of the perturbation voltage. The MPPT controller quickly moves to the new maximum power point at insolation change when the perturbation voltage is set to large, and the error of output power will be huge in the steady state even when insolation is not changing. The dynamics of the MPPT controller can be accelerated even though the perturbation voltage is set to small when the MPPT control period is set to short. However, too short MPPT control period does not improve MPPT performance but consumes the MPPT controller resources. Therefore, analyzing the performance of the MPPT controller is necessary for actual insolation conditions in real weather environment to determine the optimum MPPT control period and the magnitude of the perturbation voltage. This study proposes an optimum MPPT control period that maximizes MPPT efficiency by measuring and analyzing actual insolation profiles in typical clear and cloudy weather in central Korea.

• IEIE Transactions on Smart Processing and Computing
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• v.5 no.6
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• pp.454-461
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• 2016

A maximum power point tracking (MPPT) system with fast-tracked time and high power efficiency is presented in this paper. The proposed MPPT system uses an unbounded binary search (UBS) algorithm that continuously tracks the maximum power point (MPP) with a binary system to follow the MPP under rapid-weather-change conditions. The proposed algorithm can decide the correct direction of the MPPT system while comparing the previous power point with the present power point. And then, by fixing the MPP until finding the next MPP, there is no oscillation of voltage MPP, which maximizes the overall power efficiency of the photovoltaic module. With these advantages, this proposed UBS is able to detect the MPP more effectively. This MPPT system is based on a boost converter with a micro-control unit to control analog-to-digital converters and pulse width modulation. Analysis of this work and experimental results show that the proposed UBS MPPT provides fast, accurate tracking with no oscillation in situations where weather rapidly changes and shadow is caused by all sorts of things. The tracking time is reduced by 87.3% and 66.1% under dynamic-state and steady-state operation, respectively, as compared with the conventional 7-bit perturb and observe technique.

• The Transactions of the Korean Institute of Power Electronics
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• v.24 no.2
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• pp.111-119
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• 2019

In this study, constant power generation (CPG) algorithms are introduced for whole range power point tracking in photovoltaic systems. Currently, maximum power point tracking (MPPT) algorithm is widely used for high-power photovoltaic systems. However, MPPT algorithm cannot flexibly control such systems according to changing grid conditions. Maintaining grid stability has become important as the capacity of grid-connected photovoltaic systems is increased. CPG algorithms are required to generate the desired power depending on grid conditions. A grid-connected photovoltaic system is configured, and CPG algorithms are implemented. The performances of the implemented algorithms are compared and analyzed by experimental results.

• Journal of Power Electronics
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• v.16 no.1
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• pp.277-286
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• 2016

This paper offers two Maximum Power Point Tracking (MPPT) systems for Photovoltaic (PV) applications. The first MPPT method is based on a fixed frequency Model Predictive Control (MPC). The second MPPT technique is based on the Predictive Hysteresis Control (PHC). An experimental demonstration shows that the proposed techniques are fast, accurate and robust in tracking the maximum power under different environmental conditions. A DC/DC converter with a high voltage gain is obligatory to track PV applications at the maximum power and to boost a low voltage to a higher voltage level. For this purpose, a high gain Switched Inductor Quadratic Boost Converter (SIQBC) for PV applications is presented in this paper. The proposed converter has a higher gain than the other transformerless topologies in the literature. It is shown that at a high gain the proposed SIQBC has moderate efficiency.

• The Transactions of the Korean Institute of Power Electronics
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• v.17 no.5
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• pp.423-430
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• 2012

An adaptive maximum power point tracking (MPPT) scheme employing a variable scaling factor is presented. A MPPT control loop was constructed analytically and the magnitude variation in the MPPT loop gain according to the operating point of the PV array was identified due to the nonlinear characteristics of the PV array output. To make the crossover frequency of the MPPT loop gain consistent, the variable scaling factor was determined using an approximate curve-fitted polynomial equation about linear expression of the error. Therefore, a desirable dynamic response and the stability of the MPPT scheme were maintained across the entire MPPT voltage range. The simulation and experimental results obtained from a 3 KW rated prototype demonstrated the effectiveness of the proposed MPPT scheme.

• Proceedings of the KIPE Conference
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• 2003.11a
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• pp.234-237
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• 2003

As the maximum power operating point (MPOP) of photovoltaic (PV) power systems changes with changing atmospheric conditions, the efficiency of maximum power point tracking (MPPT) is important in PV power systems. Many MPPT techniques have been considered in the past, but techniques using microprocessors with appropriate MPPT algorithms are favored because of their flexibility and compatibility with different PV arrays. Although the efficiency of these MPPT algorithms is usually high, it drops noticeably in case of rapidly changing atmospheric conditions. In this paper, we proposed a new MPPT control method called improved perturb and observe method (ImP&O), anda simple voltage and current characteristic equation of a PV array for PV array simulator. Experimental results verify the accuracy and excellent performance of the proposed MPPT method. ImP&O algorithm is very simple, and has successful tracked the MPOP, even in case of rapidly changing atmospheric conditions.

• The Transactions of the Korean Institute of Power Electronics
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• v.27 no.4
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• pp.297-304
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• 2022

This study compares the performance of artificial intelligence (AI)-based maximum power point tracking (MPPT) methods under partial shading conditions in a photovoltaic generation system. Although many studies on AI-based MPPT have been conducted, few studies comparing the tracking performance of various AI-based global MPPT methods seem to exist in the literature. Therefore, this study compares four representative AI-based global MPPT methods including fuzzy logic control (FLC), particle swarm optimization (PSO), grey wolf optimization (GWO), and genetic algorithm (GA). Each method is theoretically analyzed in detail and compared through simulation studies with MATLAB/Simulink under the same conditions. Based on the results of performance comparison, PSO, GWO, and GA successfully tracked the global maximum power point. In particular, the tracking speed of GA was the fastest among the investigated methods under the given conditions.