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Inference method for inverter sizing ratio for PV plant using satellite-derived solar irradiance data

Tan, Hui Li (2022) Inference method for inverter sizing ratio for PV plant using satellite-derived solar irradiance data. Final Year Project, UTAR.

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    Inverter Sizing Ratio (ISR) is the ratio of the installed DC capacity of a photovoltaic (PV) power plant to the AC output power rating of an inverter. It is important to choose an optimal ISR to design an economical design of a utility-scale PV power plant. The ground-based measurement solar irradiance datasets can have higher temporal resolution, which can be made up to 1-min intervals, whereas the satellite-derived datasets are only available for a lower temporal resolution such as 15 minutes to 1-hour intervals. Therefore, an optimal ISR computed by satellite-derived irradiance datasets might be less reliable as compared to the optimal ISR computed by ground-based datasets. In executing the first objective of this project, ISR of a 10MW PV plant was computer-simulated, and the optimal ISR value was chosen based on the lowest Levelized Cost of Energy (LCOE) for a project lifespan. Both groundbased measurement and satellite-derived irradiance datasets were used to simulate the optimal ISR. The optimal ISR values were compared and to the relationship was investigate the relationship between them. It is found that different types of solar irradiance sensors can cause different solar irradiance distribution profile and thus impact on the optimal ISR value. The result shows that the irradiance sensor with low accuracy will contribute to the overestimation of optimal ISR. The second objective is to infer the optimal ISR to the other sites using satellite-derived datasets. The inference was done by using an Artificial Neural Network (ANN) model since the relationship between optimal ISR simulated by ground-based and satellite-derived datasets is complicated to be derived. The percentage of difference and errors between optimal ISR simulated by groundbased and satellite-derived datasets is reduced after the inferencing of optimal ISR using the ANN model developed. The percentage of difference is reduced to 5.8%, Mean Squared Error is 0.0128 and Root Mean Squared Error is 0.1132.

    Item Type: Final Year Project / Dissertation / Thesis (Final Year Project)
    Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
    Divisions: Lee Kong Chian Faculty of Engineering and Science > Bachelor of Engineering (Honours) Electrical and Electronic Engineering
    Depositing User: Sg Long Library
    Date Deposited: 23 Dec 2022 17:05
    Last Modified: 23 Dec 2022 17:05
    URI: http://eprints.utar.edu.my/id/eprint/4956

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