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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 ...

PV monitoring and fault detection: Evaluation of machine learning for prediction of PV soiling in Northern Cape, South-Africa Gard Inge Rosvold Published 2017 Engineering Renewable energy sources, and thus PV are experiencing exponential growth due to most current energy production still relies on fossil fuels, and energy demands are steadily increasing. If the performance of PV could be increased, the result will be more production per installation. One significant performance loss for PV is soiling on the modules. Research has been done to statistically indicate optimal cleaning intervals. Some attempts using conventional methods to predict soiling have been conducted as well, suggesting environmental features like wind and humidity are relevant factors for predicting soiling. With the increase in popularity and availability of machine learning – is it possible to use machine learning to predict soiling? If it is possible, this could lead to quick and precise implementation of algori...

A novel model to estimate the cleaning frequency for dirty solar photovoltaic (PV) modules in desert environmen YuJiang LinLu HaoLu Highlights •A novel model is developed to estimate the cleaning frequency for dirty PV modules in desert areas. •The cleaning criterion was 5% reduction in power with the accumulated dust density of 2 g/m2. •The cleaning time for PV modules in desert regions is about 20 days. •The effect of environmental parameters on the cleaning frequency are discussed respectively. Abstract Accumulated dust on solar photovoltaic (PV) modules can significantly decrease their energy output in desert environment. Therefore, cleaning the deposited dust on the PV module surface is crucial in engineering applications to maintain the high power output of solar power plants, especially in desert areas. Nevertheless, it is difficult to predict the reasonable cleaning frequency for PV modules by traditional methods. In this paper, a novel model to simply estimate the cleaning fre...

Photovoltaic faults recognition method based on data mining techniques Lucía Serrano-Luján, José Manuel Cadenas, +1 author Antonio Urbina Published 2016 Engineering Journal of Renewable and Sustainable Energy Data Mining techniques have been applied to data collected from a 222 kWp CdTe (Cadmium Telluride) photovoltaic (PV) generator to predict faults or special conditions that occurs due to shadows, bad weather, soiling, and technical faults. Five types of errors have been distinguished and its impact on the PV system performance has been evaluated. Up to date, this computing approach has needed the simultaneous measurement of environmental attributes that an array of sensors collected. This study presents a model to assess the state of the PV (photovoltaic) generator and an algorithm that classifies its state without measuring ambient conditions. The result of a 222 kWp CdTe PV case study shows how the application of computing learning algorithms can be used to improve the management...

Optimized Cleaning Cost and Schedule Based on Observed Soiling Conditions for Photovoltaic Plants in Central Saudi Arabia Russell K. Jones, Abdulaziz Baras, +5 authors Shafi A. Al-Hsaien Published 2016 Physics IEEE Journal of Photovoltaics For photovoltaic (PV) systems suffering energy loss due to an accumulation of soil, there is an optimum cleaning interval that balances the economic cost of lost revenues due to soiling loss with the cost of cleaning operations and, thereby, minimizes the total cost per unit energy delivered by the system and, thus, the levelized cost of electricity. This optimum interval may vary, depending on cleaning costs, as well as seasonal variations in soiling loss and energy production. In this work, a general expression has been derived for the optimal cleaning interval and then applied to losses actually observed over a one-year interval at a test site in central Saudi Arabia and fitted to a simple exponential loss model. The results show that costs attrib...

Towards Real-Time Estimation of Solar Generation From Micro-Synchrophasor Measurements Emre C. Kara emrecan@slac.stanford.edu Grid Integration Systems and Mobility Group, SLAC National Accelerator Laboratory, Menlo Park, CA, USA Ciaran M. Roberts Energy Storage and Distributed Resources Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA Michaelangelo Tabone Energy and Resources Group, University of California, Berkeley, CA, USA Lilliana Alvarez Riverside Public Utilities, Riverside, CA, USA Duncan S. Callaway Energy and Resources Group, University of California, Berkeley, CA, USA Emma M. Stewart Energy Storage and Distributed Resources Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA Submitted on Jul 11, 2016   Abstract This paper presents a set of methods for estimating the renewable energy generation downstream of a measurement device using real-world measurements. First, we present a generation disaggregation scheme where the only informati...

Measuring Soiling Losses at Utility-scale PV Power Plants  Michael Gostein1, J. Riley Caron2, Bodo Littmann2 1Atonometrics, 8900 Shoal Creek Blvd., Suite 116, Austin, TX 78757, USA 2First Solar, 135 Main St., Suite 600, San Francisco, CA 94105, USA   Abstract   The effect  of dust,  dirt, and  other contaminant accumulation on PV modules, commonly referred to as soiling, is an important environmental factor that causes reduced PV power plant energy generation. Accurate monitoring of soiling losses has become  increasingly  important,  especially  for  utility-scale  PV power plants,  and soiling  measurement systems  are now  widely deployed at First Solar power plants. In this work we show  how soiling  monitoring  data  are  indicative  of  actual  power  plant performance  and we outline  how such  data should be  collected and...