Matching Items (2)
Filtering by
- Status: Published
Description
Photovoltaic modules degrade in the field. This thesis aims to answer two questions: 1. Do photovoltaic modules degrade linearly or not? 2. Do soiled modules operate at lower temperatures than clean modules? Answers to these questions are provided in part 1 and part 2 of this thesis respectively.
Part 1: Linearity determination in degradation: The electricity output from PV power plants degrades every year. Generally, a system’s life is considered to last for 20-25 years and rate of degradation is commonly assumed as 1% per year. PV degradation can be found out using Performance Ratio (PR), Performance Index (PI) and raw kWh output. The rate of degradation is considered linear for simplicity of calculations. In this thesis, statistical methods are used to check whether systems in Arizona are degrading linearly or not. Time series modeling such as Winters’ method and ARIMA are used to model the data. Winters’ method and Seasonal ARIMA consider the seasonality component and perform well for small data sets of about 10 years. Rate of degradation is found out as linear for all the evaluated systems.
Part 2: Temperature analysis of clean and soiled modules: Soiling and temperature are important parameters in performance of PV modules. In this paper, an analysis is carried out on a soiling station located in Mesa, Arizona. The soiling station consists of 10 different c-Si coupons with tilt angles varying from 0° to 45° with the difference of 5°. These coupons are cut in half, one is cleaned periodically and the other is remained soiled naturally. The analysis involves data worth for 19 months. 6 dry spells in all four seasons within 19 months were analyzed. The temperature difference between a clean module and a soiled module (ΔT) is compared with the soiling loss factor (SLF). The analysis concludes stating in which season a soiled module is hotter or cooler than a clean module.
Part 1: Linearity determination in degradation: The electricity output from PV power plants degrades every year. Generally, a system’s life is considered to last for 20-25 years and rate of degradation is commonly assumed as 1% per year. PV degradation can be found out using Performance Ratio (PR), Performance Index (PI) and raw kWh output. The rate of degradation is considered linear for simplicity of calculations. In this thesis, statistical methods are used to check whether systems in Arizona are degrading linearly or not. Time series modeling such as Winters’ method and ARIMA are used to model the data. Winters’ method and Seasonal ARIMA consider the seasonality component and perform well for small data sets of about 10 years. Rate of degradation is found out as linear for all the evaluated systems.
Part 2: Temperature analysis of clean and soiled modules: Soiling and temperature are important parameters in performance of PV modules. In this paper, an analysis is carried out on a soiling station located in Mesa, Arizona. The soiling station consists of 10 different c-Si coupons with tilt angles varying from 0° to 45° with the difference of 5°. These coupons are cut in half, one is cleaned periodically and the other is remained soiled naturally. The analysis involves data worth for 19 months. 6 dry spells in all four seasons within 19 months were analyzed. The temperature difference between a clean module and a soiled module (ΔT) is compared with the soiling loss factor (SLF). The analysis concludes stating in which season a soiled module is hotter or cooler than a clean module.
ContributorsPatankar, Adit (Author) / Tamizhmani, Govindasamy (Thesis advisor) / Wang, Liping (Thesis advisor) / Phelan, Patrick (Committee member) / Arizona State University (Publisher)
Created2017
Description
Complex animal societies consist of a plethora of interactions between members. To successfully thrive they must be able to recognize members and their kin, and to understand how they do this we need sufficient and reliable methods of testing. Eusocial insects are especially good at recognizing their nestmates, but the exact mechanism or how well they can discriminate is unknown. Ants achieve nestmate recognition by identifying varying proportions of cuticular hydrocarbons. Previous studies have shown ants can be trained to discriminate between pairs of hydrocarbons. This study aims to compare two methodologies previously shown to demonstrate odor learning to identify which one is the most promising to use for future odor learning experiments. The two methods tested were adapted from Sharma et al. (2015) and Guerrieri and d’Ettorre (2010). The results showed that the Guerrieri method demonstrated learning better and was more reliable and faster than the Sharma method. The Guerrieri method should be used in future experiments regarding odor learning discrimination
ContributorsDavis, Cole (Author) / Liebig, Juergen (Thesis director) / Stephen, Pratt (Committee member) / School of Life Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05