# Mathematical Technique to find best PV technology per location

Mar 2, 2021 11:43 AM ET
• To decide the size of the PV device, divide the necessary power output by the derate factor.
• Literature is available to approximate the field efficiency of the standalone PV array device. Any models of power efficiency can also estimate the average output of the PV system under varying climatic conditions.

Considering solar radiation and the temperature of the battery, a power output model of the PV module was suggested. The neural network model is more dynamic, using different inputs such as solar radiation, atmospheric temperature, and temperature of the module. The architecture of a neural network model is based on trial and error methods and requires past knowledge to be successfully applied.

### Issues in finding the best PV technology

The successful fit of the power load with the solar resource profile contributes to an economic structure. Most of the above models require comprehensive details and are difficult or limited in the estimation of economic results. These shortcomings of the literature are an obstacle to simple machine output manipulation.

Another concern is that in the same climatic scenario different forms of PV technology behave differently. The efficiency of amorphous silicon-based modules in Mexico City is greater than that of other technologies. Amorphous silicone output is well in the hot and wet tropical climate of Malaysia. For use in the Gobi Desert region, the pv module is advantageous with a high temperature coefficient such as crystalline sycamide.

### PV Module efficiency

For photovoltaics, the voltage and current may be determined and multiplied to achieve output. Divide it by the module's area. The power of the sun per square metre separates us to achieve efficiency.

### Energy of different PV modules

The energy generation of various types of PV modules is estimated by numbers of power prediction models. PV power generation prediction using three maths and both poly-cristalline and monocristallin pv units. PV power generation prediction The PV power performance forecast using the hybrid and neural networks. Any of the esteemed models used to forecast, build and evaluate the PV power plant economically. The most common PV manner for hybrid optimisation is the Electric Renewables Optimization Model (HOMER), RetScreen, Model Device Advisor (SAM), PVSyst, PVSol, PVWATT.

### Mathematical calculation of PV efficiency

The efficiency of the mathematical formula r=P/C, where P is the amount of the useful result ("product") generated in the amount of C ("cost"), of the resources consumed. Efficiency may also be calculated by a ratio of useful results to a total input.

In normal test conditions monocrystalline solar panels are known for their optimum performance, relative to the other 2 solar cell types. The latest supplied performance of the monocrystalline panel is 22-27%.

For photovoltaics, the voltage and current may be determined and multiplied to achieve output. Divide it by the module's area. The force of the sun separates us per metre in order to generate efficiency.

### Mathematical parameters for PV

Solar cell and modular behaviour under different operating conditions can be effectively calculated by correctly measuring and simulating current-voltage (IV) properties with its own intrinsic parameters. This work suggested a new computer method based on the Approximation and Correction Technique (ACT) to remove solar cells and modules from the single-diode model in a simple and effective way. The initial value of parallel resistance (R) was obtained from this technique and used to calculate the initial value of (Rp). Subsequently, the final right values of Rs and Rp were reached by replacing their estimated values and using the manipulated equations in a five-loop iteration. A software framework was also developed using the MATLAB programme to easily test and verify the proposed methodology. On five types of solar cells and modules worked at varying temperatures and irradiances, the correctness and robustness of the technology was validated. In a simple but competitive approach to implementation the validity of ACT extracted parameters was identified by comparing developed techniques with several advanced methods recently published in the literature.

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