Zokkia

Numerical Validation of an Algorithm for Combined Soiling and Degradation Analysis of Photovoltaic Systems Publisher: IEEE Michael G. Deceglie ; Matthew Muller ; Dirk C. Jordan ; Chris Deline Abstract: We describe and demonstrate an open-source algorithm for simultaneously quantifying degradation and soiling of photovoltaic (PV) systems from energy-production time series data. The new analysis is based on year-on-year degradation rate analysis combined with stochastic rate and recovery soiling analysis. The algorithm is designed to fit into the workflow provided by RdTools, a Python module maintained by NREL and collaboratively developed with the community, which provides a framework and functions for degradation and loss-factor analysis of PV field data. We demonstrate the method on numerically simulated PV data sets and show that it reduces the root-mean-square error of the P50 degradation rate estimate when soiling is present https://ieeexplore.ieee.org/document/8981183/authors#auth...

Assessment of Various Dry Photovoltaic Cleaning Techniques and Frequencies on the Power Output of CdTe-Type Modules in Dusty Environments by Mohammed Al-Housani,Yusuf Bicer *OrcID andMuammer Koç Division of Sustainable Development (DSD), Hamad Bin Khalifa University (HBKU), Qatar Foundation (QF), Education City, Doha 5825, Qatar Published: 19 May 2019 Abstract This study presents the conditions and results of experimental investigations on various photovoltaic (PV) module cleaning methods and the effects on the performance of cadmium-telluride CdTe-type photovoltaic (PV) modules located in Doha, Qatar. The study aims to find the optimum cleaning technique and frequency based on cleaning performance and cost. PV modules are in a dusty and rocky area in the western part of Doha, Qatar within the north campus of Education City. Maximum power point tracking (MPPT) technology is employed for five different PV modules. The results show that microfiber-based wiper along with microfiber & ...

Effects of soiling on photovoltaic (PV) modules in the Atacama Desert R. R. Cordero, A. Damiani, D. Laroze, S. MacDonell, J. Jorquera, E. Sepúlveda, S. Feron, P. Llanillo, F. Labbe, J. Carrasco, J. Ferrer & G. Torres  Published: 17 September 2018 Abstract Soiling by dry deposition affects the power output of photovoltaic (PV) modules, especially under dry and arid conditions that favor natural atmospheric aerosols (wind-blown dust). In this paper, we report on measurements of the soiling effect on the energy yield of grid-connected crystalline silicon PV modules deployed in five cities across a north-south transect of approximately 1300 km in the Atacama Desert ranging from latitude 18°S to latitude 30°S. Energy losses were assessed by comparing side-by-side outputs of four co-planar PV modules. Two of the PV modules of the array were kept clean as a control, while we allowed the other two to naturally accumulate soiling for 12 months (from January 2017 to January 2018). We fou...

Photovoltaics (PV) System Energy Forecast on the Basis of the Local Weather Forecast: Problems, Uncertainties and Solutions  by Kristijan Brecl *OrcID andMarko TopičOrcID Laboratory of Photovoltaics and Optoelectronics—LPVO, Faculty of Electrical Engineering, University of Ljubljana, Tržaška 25, SI-1000 Ljubljana, Slovenia Published: 4 May 2018 Abstract When integrating a photovoltaic system into a smart zero-energy or energy-plus building, or just to lower the electricity bill by rising the share of the self-consumption in a private house, it is very important to have a photovoltaic power energy forecast for the next day(s). While the commercially available forecasting services might not meet the household prosumers interests due to the price or complexity we have developed a forecasting methodology that is based on the common weather forecast. Since the forecasted meteorological data does not include the solar irradiance information, but only the weather condition, the uncertaint...

Robust PV Degradation Methodology and Application Published in: IEEE Journal of Photovoltaics ( Volume: 8 , Issue: 2 , March 2018 ) Dirk C. Jordan  National Renewable Energy Laboratory, Golden, CO, USA; Chris Deline  ; Sarah R. Kurtz ; Gregory M. Kimball  ; Mike Anderson  Abstract: The degradation rate plays an important role in predicting and assessing the long-term energy generation of photovoltaics (PV) systems. Many methods have been proposed for extracting the degradation rate from operational data of PV systems, but most of the published approaches are susceptible to bias due to inverter clipping, module soiling, temporary outages, seasonality, and sensor degradation. In this paper, we propose a methodology for determining PV degradation leveraging available modeled clear-sky irradiance data rather than site sensor data, and a robust year-over-year rate calculation. We show the method to provide reliable degradation rate estimates even in the case of sensor dri...

Short-term Memory Solar Energy Forecasting at University of Illinois  Adele Kuzmiakova ”akuzmiakova”, Gael Colas ”colasg”, Alex McKeehan ”mckeehan” December 2017  Abstract:  Climate change and energy crisis have motivated the use and development of solar power generation. Since solar power generation is highly intermittent and dependent on local weather characteristics, we apply a variety of linear and non-linear ensemble machine learning models to predict short-term solar energy output at the University of Illinois campus. Implemented models include weighted linear regression with and without dimension reduction, boosting regression trees, and artificial neural networks with and without vanishing temporal gradient. Additionally, we apply a variety of variable selection techniques, which suggest that air temperature, relative humidity, and dew point are the most informative weather parameters for predicting the solar energy generation at the Illinois power plant. Findings...

An investigation of the key parameters for predicting PV soiling losses Leonardo Micheli, Matthew S. Muller Published 2017 Chemistry Progress in Photovoltaics One hundred and two environmental and meteorological parameters have been investigated and compared with the performance of 20 soiling stations installed in the USA, in order to determine their ability to predict the soiling losses occurring on PV systems. The results of this investigation showed that the annual average of the daily mean particulate matter values recorded by monitoring stations deployed near the PV systems are the best soiling predictors, with coefficients of determination (R2) as high as 0.82. The precipitation pattern was also found to be relevant: among the different meteorological parameters, the average length of dry periods had the best correlation with the soiling ratio. A preliminary investigation of two-variable regressions was attempted and resulted in an adjusted R2 of 0.90 when a combination of PM2.5 ...