The many different techniques for maximum power point tracking of photovoltaic (PV) arrays are discussed. The techniques are taken from the literature dating back to the earliest methods. It is shown that at least 19 distinct methods have been introduced in the literature, with many variations on implementation. This paper should serve as a convenient reference for future work in PV power generation.

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Comparison of Photovoltaic Array Maximum Power Point Tracking Techniques

Review of the maximum power point tracking algorithms for stand-alone photovoltaic systems

Detailed theoretical and experimental analyses are presented for the comparison of two simple fast and reliable maximum power point tracking (MPPT) techniques for photovoltaic systems (PV): the voltage-based (VMPPT) and the current-based (CMPPT) approaches. A microprocessor-controlled tracker capable of online voltage and current measurements and programmed with both VMPPT and CMPPT algorithms is constructed. The load of the solar system is either a water pump or a resistance. Simulink facilities are used for simulation and modeling of the novel trackers. The main advantage of this new MPPT, as compared with present trackers, is the elimination of reference (dummy) cells, which results in a more efficient, less expensive, and more reliable PV system.

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Closure on "Theoretical and experimental analyses of photovoltaic systems with voltage and current-based maximum power point tracking"

A novel maximum-power-point-tracking (MPPT) controller for a photovoltaic (PV) energy conversion system is presented. Using the slope of power versus voltage of a PV array, the proposed MPPT controller allows the conversion system to track the maximum power point very rapidly. As opposed to conventional two-stage designs, a single-stage configuration is implemented, resulting in size and weight reduction and increased efficiency. The proposed system acts as a solar generator on sunny days, in addition to working as an active power line conditioner on rainy days. Finally, computer simulations and experimental results demonstrate the superior performance of the proposed technique.

Novel maximum-power-point-tracking controller for photovoltaic energy conversion system

Power processing systems will be a key factor of future photovoltaic (PV) applications. They will play a central role in transferring, to the load and/or to the grid, the electric power produced by the high-efficiency PV cells of the next generation. In order to come up the expectations related to the use of solar energy for producing electrical energy, such systems must ensure high efficiency, modularity, and, particularly, high reliability. The goal of this paper is to provide an overview of the open problems related to PV power processing systems and to focus the attention of researchers and industries on present and future challenges in this field.

Reliability Issues in Photovoltaic Power Processing Systems

This paper presents an application of a neural network for the identification of the optimal operating point of PV modules for the real time maximum power tracking control. The output power from the modules depends on the environmental factors such as insolation, cell temperature, and so on. Therefore, accurate identification of optimal operating point and real time continuous control are required to achieve the maximum output efficiency. The proposed neural network has a quite simple structure and provides a highly accurate identification of the optimal operating point and also a highly accurate estimation of the maximum power from the PV modules. >

Identification of optimal operating point of PV modules using neural network for real time maximum power tracking control

This paper presents a neural network based maximum power tracking controller for interconnected PV power systems The neural network is utilized to identify the optimal operating voltage of the PV power system The controller generates the control signal in real-time, and the control signal is fed back to the voltage control loop of the inverter to shift the terminal voltage of the PV power system to its identified optimum, which yields maximum power generation The controller is of the PI type The proportional and the integral gains are set to their optimal values to achieve fast response and also to prevent overshoot and also undershoot Continuous measurement is required for the open circuit voltage on the monitoring cell, and also for the terminal voltage of the PV power system Because of the accurate identification of the optimal operating voltage of the PV power system, more than 99% power is drawn from the actual maximum power >

Evaluation of neural network based real time maximum power tracking controller for PV system

This paper presents a neural network based real time maximum power tracking controller for interconnected photovoltaic (PV) systems to commercial power sources through inverters. The neural network is utilized to identify the optimal operating voltage of the PV array to achieve maximum output efficiency. The proposed controller generates the control signal in real time, and the control signal is fed back to the voltage control loop of the inverter to shift the terminal voltage of the PV array to the identified optimal one, which yields the maximum power generation. The controller is simply configured by using proportional and integral (PI) control loops. The proportional and the integral gains are tuned to their optimal values to achieve the quick response and also to prevent the overshoot and the undershoot in the transient state. The continuous measurement is required for the open circuit voltage on the monitoring cell, and also the terminal voltage of the PV array. The measurements are quite easy and straightforward. Because of the accurate identification of the optimal operating voltage through the neural network, more than 99%

Tomofumi Imakubo

Papers

Identification of optimal operating point of PV modules using neural network for real time maximum power tracking control

Evaluation of neural network based real time maximum power tracking controller for PV system

Experimental Evaluation of Neural Network Based Real Time Maximum Power Tracking Controller for PV System