scholarly journals Die ontwikkeling en implementering van ’n saamgestelde omsetter vir gebruik in sonenergiestelsels

1992 ◽  
Vol 11 (4) ◽  
pp. 142-146
Author(s):  
S. J. B. Hartman

The converter proposed here is a compound photovoltaic converter system that has been implemented with a power rating of 1,5 kVA at an array voltage of 96 V, using a 24 V battery bank. The converter system combines the functions of inversion, battery regulation and maximum power point tracking of the solar array into a single cost-effective converter. Maximum power point tracking is performed by controlling the voltage and frequency of the AC output. A description of this converter, and an explanation of the control strategy employed, are provided together with practical results measured on the prototype converter. This compound topology has a high conversion efficiency from solar array to load.

2011 ◽  
Vol 21 (2) ◽  
pp. 151-165 ◽  
Author(s):  
Abdelaziz Talha ◽  
Houria Boumaaraf ◽  
Omar Bouhali

Evaluation of maximum power point tracking methods for photovoltaic systems The output characteristics of photovoltaic (PV) arrays are nonlinear and change with the solar irradiance and the cell's temperature. Therefore, a maximum power point tracking (MPPT) technique is needed to draw peak power from the solar array to maximize the produced energy. Among the hill climbing methods, the perturb and observe (P&O) method tracks the maximum power point (MPP) by repeatedly increasing or decreasing the output voltage at the MPP of the PV module. The implementation of the method is relatively simple, but it cannot track the MPP when the irradiance varies quickly with time. In addition, itmay cause system oscillation around the peak power points due to the effect of measurement noise. The incremental conductance (IncCond) method is also often used in PV systems. This method tracks the MPPs by comparing the incremental and instantaneous conductances of the solar array. This method requires longer conversion time, and a large amount of power loss results. In addition, extra hardware circuitry is required to implement the system. In this paper, it is shown that the negative effects associated with such a drawback can be greatly reduced if the intelligent method is used to improve P&O and IncCond algorithms. The perturbation step is continuously approximated by using fuzzy logic controller (FLC). By the digital simulation, the validity of the proposed control algorithm is proved.


2016 ◽  
Vol 4 (8) ◽  
pp. 152-156
Author(s):  
Smriti Dwivedi ◽  
Sunil Kumar Bhatt

Maximum power point tracking (MPPT) is a technique that charge controllers use for wind turbines and PV solar systems to maximize power output. PV solar systems exist in several different configurations. The most basic version sends power from collector panels directly to the DC-AC inverter and from there directly to the electrical grid. A second version, called a hybrid inverter, might split the power at the inverter, where a percentage of the power goes to the grid and the remainder goes to a battery bank. The third version is not connected at all to the grid but employs a dedicated PV inverter that features the MPPT. In this configuration, power flows directly to a battery bank.


Author(s):  
Afshin Balal & Shahab Balali

By considering the limitation of the size of houses, the permanent inaccessibility of the solar radiation energy, and also the low efficiency of solar cells, a PV system requires the maximum power point tracking(MPPT). The main issue with using solar cells is to reach its maximum power, which is intensified by change in the temperature and radiation. In this paper, among MPPT methods, the Perturb and Observe (P&O) method, has been designed which has high reliability and traceability. However, due to the output power oscillation around the operating point in P&O method, an optimization fuzzy/bee algorithm is used for maximum power point tracking so that without the need for temperature and light sensors, reduction of output power oscillations can be achieved. Simulation results indicate that by using the fuzzy/ bee method, in addition to reducing the fluctuation around the operating point, the speed of reaching to the optimal point is maximized.


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