The document summarizes a study that measured photovoltaic (PV) performance, ambient dust levels, and weather conditions in Doha, Qatar from June to December 2014. Three PV arrays were monitored - one cleaned every week, one every two months, and one every six months. A "cleanness index" showed the arrays cleaned every six months lost on average 0.0042 of performance per day, while the two-month cleaned arrays lost 0.0045 per day. Daily changes in cleanness index were negatively correlated with dust levels and humidity but positively correlated with wind speed. A regression model related cleanness index changes to dust, wind, and humidity and showed dust deposition significantly reduced PV power generation
This document analyzes soiling losses for different solar photovoltaic technologies installed on the rooftop of an Infosys building in Bangalore, India over a one month period. Soiling, or the accumulation of dust on solar panels, reduces efficiency. The study found soiling losses ranged from 1-9% initially to 17-22% after one month, with cadmium telluride panels experiencing the highest losses. Losses were quantified by comparing generation of cleaned versus uncleaned panels of each technology. The study provides field data on how dust impacts performance of different solar modules in a metropolitan environment.
The document describes UKC Holdings Corporation's Dust Detection System (DDS) for monitoring soiling levels at photovoltaic power plants. Field tests of the DDS in Egypt and the UAE found that soiling levels varied significantly depending on environmental conditions like humidity and wind speed, and did not accumulate at a constant linear rate. The DDS provides real-time soiling loss data and cleaning timing recommendations to maximize plant output and revenue.
The document summarizes research on the impact of soiling on PV module performance in various climates. Mini-module tests were conducted over 1-2 years in Tempe, AZ; Chennai, India; Ancona, Italy; Cologne, Germany; and Thuwal, Saudi Arabia. Results showed significant annual soiling losses of over 3% in Tempe and over 2% in Chennai due to dust accumulation between rainfall events. Frequent rainfall in Ancona and Cologne led to negligible losses. Periodic cleaning was found necessary to maintain performance in arid Thuwal. Soiling patterns varied substantially between climates and years.
This document summarizes a presentation on estimating the influence of solar spectrum variations on PV performance using satellite-based spectral irradiance data. It describes methods for calculating spectral correction factors and module performance ratio given spectral response curves and spectrally resolved irradiance data. Maps of Europe show estimated annual spectral effects on c-Si, CdTe, a-Si tandem, and III-V multi-junction modules, with effects ranging from -3% to 3%. The analysis demonstrates how satellite data can be used to model large-scale spectral impacts, though results in some climates need validation.
The document discusses the development of an energy-based parameter for photovoltaic classification by the PhotoClass consortium. The consortium aims to define a new metric based on the annual energy yield of photovoltaic modules under real-world conditions, rather than just peak power. This will provide a more accurate assessment of module performance and energy generation. The PhotoClass work plan includes modelling to calculate energy yields, characterizing reference devices, detector characterization, source characterization, and developing standards. The goal is to establish a standardized energy rating that supports the renewable energy industry and implementation of EU directives.
The document discusses the impact of spectral irradiance on the energy yield of different PV module technologies measured at test sites in various climates. Spectral response measurements showed variations between module types and locations. One year of spectral irradiance data from four test sites showed seasonal shifts from the AM1.5 standard spectrum. For c-Si modules, spectral effects had a minor impact on energy yield of up to 1.6% higher in Chennai. CdTe modules saw gains of up to 5.3% in Chennai due to their spectral response. While daily and seasonal shifts in spectrum compensated over the year for most modules, spectral irradiance was found to influence energy yield predictions, particularly for thin-film technologies
The document discusses local and regional PV power forecasting based on PV measurements, satellite data, and numerical weather predictions. It summarizes a PV power prediction system that uses different data sources and models for various forecast horizons, from 15 minutes to 5 hours ahead. The system combines persistence forecasts, satellite-derived cloud motion forecasts, PV simulations using numerical weather predictions, and statistical models to improve forecast accuracy compared to individual models. Regional forecasts for Germany show lower errors than single site forecasts.
This document analyzes soiling losses for different solar photovoltaic technologies installed on the rooftop of an Infosys building in Bangalore, India over a one month period. Soiling, or the accumulation of dust on solar panels, reduces efficiency. The study found soiling losses ranged from 1-9% initially to 17-22% after one month, with cadmium telluride panels experiencing the highest losses. Losses were quantified by comparing generation of cleaned versus uncleaned panels of each technology. The study provides field data on how dust impacts performance of different solar modules in a metropolitan environment.
The document describes UKC Holdings Corporation's Dust Detection System (DDS) for monitoring soiling levels at photovoltaic power plants. Field tests of the DDS in Egypt and the UAE found that soiling levels varied significantly depending on environmental conditions like humidity and wind speed, and did not accumulate at a constant linear rate. The DDS provides real-time soiling loss data and cleaning timing recommendations to maximize plant output and revenue.
The document summarizes research on the impact of soiling on PV module performance in various climates. Mini-module tests were conducted over 1-2 years in Tempe, AZ; Chennai, India; Ancona, Italy; Cologne, Germany; and Thuwal, Saudi Arabia. Results showed significant annual soiling losses of over 3% in Tempe and over 2% in Chennai due to dust accumulation between rainfall events. Frequent rainfall in Ancona and Cologne led to negligible losses. Periodic cleaning was found necessary to maintain performance in arid Thuwal. Soiling patterns varied substantially between climates and years.
This document summarizes a presentation on estimating the influence of solar spectrum variations on PV performance using satellite-based spectral irradiance data. It describes methods for calculating spectral correction factors and module performance ratio given spectral response curves and spectrally resolved irradiance data. Maps of Europe show estimated annual spectral effects on c-Si, CdTe, a-Si tandem, and III-V multi-junction modules, with effects ranging from -3% to 3%. The analysis demonstrates how satellite data can be used to model large-scale spectral impacts, though results in some climates need validation.
The document discusses the development of an energy-based parameter for photovoltaic classification by the PhotoClass consortium. The consortium aims to define a new metric based on the annual energy yield of photovoltaic modules under real-world conditions, rather than just peak power. This will provide a more accurate assessment of module performance and energy generation. The PhotoClass work plan includes modelling to calculate energy yields, characterizing reference devices, detector characterization, source characterization, and developing standards. The goal is to establish a standardized energy rating that supports the renewable energy industry and implementation of EU directives.
The document discusses the impact of spectral irradiance on the energy yield of different PV module technologies measured at test sites in various climates. Spectral response measurements showed variations between module types and locations. One year of spectral irradiance data from four test sites showed seasonal shifts from the AM1.5 standard spectrum. For c-Si modules, spectral effects had a minor impact on energy yield of up to 1.6% higher in Chennai. CdTe modules saw gains of up to 5.3% in Chennai due to their spectral response. While daily and seasonal shifts in spectrum compensated over the year for most modules, spectral irradiance was found to influence energy yield predictions, particularly for thin-film technologies
The document discusses local and regional PV power forecasting based on PV measurements, satellite data, and numerical weather predictions. It summarizes a PV power prediction system that uses different data sources and models for various forecast horizons, from 15 minutes to 5 hours ahead. The system combines persistence forecasts, satellite-derived cloud motion forecasts, PV simulations using numerical weather predictions, and statistical models to improve forecast accuracy compared to individual models. Regional forecasts for Germany show lower errors than single site forecasts.
This document discusses improved methods for predicting the spectral impact on PV systems based on commonly available data. It presents a modified CREST model that uses the true spectral response rather than a useful fraction to better account for variations due to cloudy conditions. Evaluation shows this modified CREST-WUF model significantly outperforms existing models in predicting daily spectral impact factors for different module types. While this approach captures more variation than previous models, further work is still needed to fully characterize the relationship between site properties and spectral offset to enable general predictions of spectral impact.
This document discusses using spectral corrections to better model PV performance. It presents an overview of how the spectral distribution of sunlight changes over the day and affects PV module performance. Models that account for the spectral response of modules and changing spectral irradiance can provide more accurate estimates of performance metrics like current and power output. The document shares results of analyzing spectral data that demonstrate variations in average module response under direct normal, diffuse horizontal, and global irradiance over the course of a day due to these spectral effects. Future work aims to refine estimates of air mass effects and integrate spectral modeling into broader PV modeling.
This document summarizes the results of multiple outdoor measurements and modeling of bifacial PV module performance. Outdoor measurements were taken of 12 PV modules in Lugano, Switzerland between 2014-2015. Models were developed and validated to predict cell temperature, irradiation, and power output. The best-fit models estimated bifacial module performance using two parameters for ground-reflected irradiance and could predict power within 4-5 Watts. Further work is needed to improve cell temperature modeling and optimize models for different tilt/azimuth conditions.
The document summarizes the results of system performance and degradation analysis of different PV technologies conducted by Yuzuru Ueda of Tokyo University of Science. It describes monitoring and analysis of PV systems at two test sites: the Hokuto testing site in Japan, which has evaluated various PV technologies since 2008; and the Los Alamos testing site in New Mexico for the Japan-U.S. Smart Grid Collaborative Demonstration Project. The analysis determined performance ratios and effective array peak power over time to analyze degradation of different PV technologies. Results showed some technologies like crystalline silicon degrading more slowly than others like amorphous silicon.
The document describes pvSpot, a PV simulation tool that uses the SolarGIS solar and meteorological database. PvSpot allows users to model PV system production based on system configuration and location. It provides both interactive and non-interactive services, including long-term averages, recent daily data, forecasts, and analysis of measured vs simulated production for monitoring and performance assessment of PV systems.
1. The document discusses using sky imagers for short-term solar forecasting, as traditional methods lack sufficient spatial and temporal resolution for small-scale applications.
2. The proposed sky imager forecast model involves 7 steps: image analysis, cloud detection, cloud projection, shadow projection, irradiance modeling, predicting cloud motion to generate forecasts, and PV power modeling.
3. Accurate cloud detection, projection, and shadow projection are challenging due to issues like cloud inhomogeneity, perspective errors with distance from camera, and sensitivity to errors in estimated cloud base height.
The document discusses modeling energy losses from snow on photovoltaic (PV) systems using the System Advisor Model (SAM). It describes a snow loss model implemented in SAM that estimates snow coverage over PV modules based on factors like system tilt, irradiance, temperature and snow depth. The model was validated against measurements from two PV systems, showing much better accuracy when including snow losses. National modeling for the U.S. estimated average snow losses by region and their correlation with total snow depth. Future work may consider snow losses for tracking systems or improving accuracy at shorter timescales.
VR4PV is a virtual reality software that enables fast visualization and simulation of photovoltaic (PV) systems. It allows users to render shades on PV systems from surrounding objects or self-shading during the design process. VR4PV has been used for cases involving PV integration in the built environment, designing movable PV boats, shadow analysis of a PV-powered street lamp, and allocating PV and renewable energy technologies on small islands. Future work may involve extending VR4PV's capabilities and transitioning to more performant gaming engines like Unity to improve processing speed while separating animation from energy simulations.
This document discusses uncertainty in satellite-based solar resource data and its importance for photovoltaic applications. It notes that while satellite data provides continuous global coverage, the accuracy of estimates can be limited. Older satellite models and ground measurements often had low spatial/temporal resolution and accuracy that could lead to uncertainty of over 10-15% in long-term solar resource assessments. Modern satellite models have improved but rely on inputs like meteorological data that can change over time. Ground measurements provide higher accuracy but are limited in scope. The document emphasizes the need for validated high quality data to support solar energy applications.
This document describes a data analysis method to automatically detect energy losses from shadows on a partially shaded residential PV system using only production data. The method defines an error barrier between a benchmark PV system and the studied system. Times when the error exceeds the barrier are marked in red, otherwise green. Periods with high red concentrations indicate shadowing. Shadowed times are then analyzed daily to distinguish between shaded and unshaded days, and further analyze shadowing within expected shadow hours only on shaded days. The goal is to distinguish energy losses due to shadows from other faults using just production data from the inverter.
1) The document describes a high-speed monitoring system for multiple grid-tied photovoltaic array configurations located at the National Institute of Standards and Technology (NIST).
2) The monitoring system collects data every second from four PV arrays totaling over 500 kW, including two ground-mounted arrays, a canopy array, and a roof array.
3) In addition to PV performance data, the system also monitors weather conditions, sky images, and module temperatures to better understand PV system behavior and validation of models.
Prism Solar Technologies conducted a multi-year study of bifacial solar module energy gains under various field conditions. The study found that bifacial modules produced significantly higher energy yields than monofacial modules under different tilt angles, heights, albedos, and orientations. For example, at a site in Tucson, AZ with a ground albedo of 77% and module height of 0.76m, the bifacial modules produced 36.8% more energy than the monofacial reference modules. However, standard STC ratings do not accurately represent the true energy generation potential of bifacial modules under real-world conditions.
The document summarizes the development of satellite modeling for the National Solar Radiation Database (NSRDB) to provide accurate surface solar radiation data. It describes the evolution from empirical to physical models using satellite measurements and ancillary data as inputs to radiative transfer models. Validation shows the new 2005-2012 dataset has a mean bias error of less than 5% for GHI and DNI compared to surface measurements, though uncertainty remains for cloudy cases. Future work aims to improve the model with higher resolution data and better representation of aerosols and surfaces.
This document summarizes a workshop on PV performance modeling. It discusses developing models to estimate yearly PV yield in Germany for regulatory reporting. Models are selected based on existing algorithms, computational efficiency, accuracy, and using satellite and weather data. Models are validated using irradiation, temperature, and generation data. The best irradiation model was Perez Model. A ZIPcode-based model called ZIPSoP estimates yield by classifying systems, optimizing parameters like temperature coefficients and comparing to measured generation. Current work focuses on improving ZIPSoP by determining the best aggregation level and parameter fitting.
This document discusses using ray tracing to model bifacial photovoltaic systems. Ray tracing allows for more accurate modelling of rear-side irradiances compared to traditional view factor methods by accounting for complex shading effects. Experimental measurements of rear-side irradiances on a 15 kW bifacial array showed good agreement with ray tracing simulations. The ray tracing model was also integrated with an electrical model in Dymola, and validation against voltage, current, power and yield measurements also showed good accuracy within 1%. Ray tracing is presented as a powerful tool for modelling large bifacial PV installations and quantifying losses from rear-side shading effects.
The document describes models developed to predict soiling-caused photovoltaic (PV) power output degradation in Doha, Qatar based on environmental variables. Field data on PV performance and dust concentration, wind speed, and relative humidity were collected. A linear model and a semi-physical model were developed to correlate daily changes in PV performance (∆CI) with the daily average environmental conditions. The semi-physical model, which considers dust deposition and resuspension processes, predicted daily ∆CI slightly more accurately than the linear model based on R-squared values. However, both models performed roughly the same in predicting ∆CI over longer periods. The results suggest it is possible to use the models to estimate PV
effect of environmental variables on photovoltaic performance based on experi...IJCMESJOURNAL
This paper investigated the effect of environment variables on Photovoltaic PV performance. It is surely understood that local climate can dramatically affect the power generation from a PV system. The most obvious components are the solar radiation hitting the panels, air temperature, humidity and wind speed. The local climatic conditions and precipitation influence the extent to which the panels get to be dusty or polluted, which affects the electrical power generation. The high air temperature caused a reduction in the PV panel output power rated from 1.85 to 20.22%, as well as, increased relative humidity where the largest decline recorded was 32.24%. The wind has a cooling effect on the PV panel that limits the power reduction due to increased solar radiation or panel back temperature. Besides, the wind blows away the accumulated dust that enhances the resulted PV panel power.
This document discusses improved methods for predicting the spectral impact on PV systems based on commonly available data. It presents a modified CREST model that uses the true spectral response rather than a useful fraction to better account for variations due to cloudy conditions. Evaluation shows this modified CREST-WUF model significantly outperforms existing models in predicting daily spectral impact factors for different module types. While this approach captures more variation than previous models, further work is still needed to fully characterize the relationship between site properties and spectral offset to enable general predictions of spectral impact.
This document discusses using spectral corrections to better model PV performance. It presents an overview of how the spectral distribution of sunlight changes over the day and affects PV module performance. Models that account for the spectral response of modules and changing spectral irradiance can provide more accurate estimates of performance metrics like current and power output. The document shares results of analyzing spectral data that demonstrate variations in average module response under direct normal, diffuse horizontal, and global irradiance over the course of a day due to these spectral effects. Future work aims to refine estimates of air mass effects and integrate spectral modeling into broader PV modeling.
This document summarizes the results of multiple outdoor measurements and modeling of bifacial PV module performance. Outdoor measurements were taken of 12 PV modules in Lugano, Switzerland between 2014-2015. Models were developed and validated to predict cell temperature, irradiation, and power output. The best-fit models estimated bifacial module performance using two parameters for ground-reflected irradiance and could predict power within 4-5 Watts. Further work is needed to improve cell temperature modeling and optimize models for different tilt/azimuth conditions.
The document summarizes the results of system performance and degradation analysis of different PV technologies conducted by Yuzuru Ueda of Tokyo University of Science. It describes monitoring and analysis of PV systems at two test sites: the Hokuto testing site in Japan, which has evaluated various PV technologies since 2008; and the Los Alamos testing site in New Mexico for the Japan-U.S. Smart Grid Collaborative Demonstration Project. The analysis determined performance ratios and effective array peak power over time to analyze degradation of different PV technologies. Results showed some technologies like crystalline silicon degrading more slowly than others like amorphous silicon.
The document describes pvSpot, a PV simulation tool that uses the SolarGIS solar and meteorological database. PvSpot allows users to model PV system production based on system configuration and location. It provides both interactive and non-interactive services, including long-term averages, recent daily data, forecasts, and analysis of measured vs simulated production for monitoring and performance assessment of PV systems.
1. The document discusses using sky imagers for short-term solar forecasting, as traditional methods lack sufficient spatial and temporal resolution for small-scale applications.
2. The proposed sky imager forecast model involves 7 steps: image analysis, cloud detection, cloud projection, shadow projection, irradiance modeling, predicting cloud motion to generate forecasts, and PV power modeling.
3. Accurate cloud detection, projection, and shadow projection are challenging due to issues like cloud inhomogeneity, perspective errors with distance from camera, and sensitivity to errors in estimated cloud base height.
The document discusses modeling energy losses from snow on photovoltaic (PV) systems using the System Advisor Model (SAM). It describes a snow loss model implemented in SAM that estimates snow coverage over PV modules based on factors like system tilt, irradiance, temperature and snow depth. The model was validated against measurements from two PV systems, showing much better accuracy when including snow losses. National modeling for the U.S. estimated average snow losses by region and their correlation with total snow depth. Future work may consider snow losses for tracking systems or improving accuracy at shorter timescales.
VR4PV is a virtual reality software that enables fast visualization and simulation of photovoltaic (PV) systems. It allows users to render shades on PV systems from surrounding objects or self-shading during the design process. VR4PV has been used for cases involving PV integration in the built environment, designing movable PV boats, shadow analysis of a PV-powered street lamp, and allocating PV and renewable energy technologies on small islands. Future work may involve extending VR4PV's capabilities and transitioning to more performant gaming engines like Unity to improve processing speed while separating animation from energy simulations.
This document discusses uncertainty in satellite-based solar resource data and its importance for photovoltaic applications. It notes that while satellite data provides continuous global coverage, the accuracy of estimates can be limited. Older satellite models and ground measurements often had low spatial/temporal resolution and accuracy that could lead to uncertainty of over 10-15% in long-term solar resource assessments. Modern satellite models have improved but rely on inputs like meteorological data that can change over time. Ground measurements provide higher accuracy but are limited in scope. The document emphasizes the need for validated high quality data to support solar energy applications.
This document describes a data analysis method to automatically detect energy losses from shadows on a partially shaded residential PV system using only production data. The method defines an error barrier between a benchmark PV system and the studied system. Times when the error exceeds the barrier are marked in red, otherwise green. Periods with high red concentrations indicate shadowing. Shadowed times are then analyzed daily to distinguish between shaded and unshaded days, and further analyze shadowing within expected shadow hours only on shaded days. The goal is to distinguish energy losses due to shadows from other faults using just production data from the inverter.
1) The document describes a high-speed monitoring system for multiple grid-tied photovoltaic array configurations located at the National Institute of Standards and Technology (NIST).
2) The monitoring system collects data every second from four PV arrays totaling over 500 kW, including two ground-mounted arrays, a canopy array, and a roof array.
3) In addition to PV performance data, the system also monitors weather conditions, sky images, and module temperatures to better understand PV system behavior and validation of models.
Prism Solar Technologies conducted a multi-year study of bifacial solar module energy gains under various field conditions. The study found that bifacial modules produced significantly higher energy yields than monofacial modules under different tilt angles, heights, albedos, and orientations. For example, at a site in Tucson, AZ with a ground albedo of 77% and module height of 0.76m, the bifacial modules produced 36.8% more energy than the monofacial reference modules. However, standard STC ratings do not accurately represent the true energy generation potential of bifacial modules under real-world conditions.
The document summarizes the development of satellite modeling for the National Solar Radiation Database (NSRDB) to provide accurate surface solar radiation data. It describes the evolution from empirical to physical models using satellite measurements and ancillary data as inputs to radiative transfer models. Validation shows the new 2005-2012 dataset has a mean bias error of less than 5% for GHI and DNI compared to surface measurements, though uncertainty remains for cloudy cases. Future work aims to improve the model with higher resolution data and better representation of aerosols and surfaces.
This document summarizes a workshop on PV performance modeling. It discusses developing models to estimate yearly PV yield in Germany for regulatory reporting. Models are selected based on existing algorithms, computational efficiency, accuracy, and using satellite and weather data. Models are validated using irradiation, temperature, and generation data. The best irradiation model was Perez Model. A ZIPcode-based model called ZIPSoP estimates yield by classifying systems, optimizing parameters like temperature coefficients and comparing to measured generation. Current work focuses on improving ZIPSoP by determining the best aggregation level and parameter fitting.
This document discusses using ray tracing to model bifacial photovoltaic systems. Ray tracing allows for more accurate modelling of rear-side irradiances compared to traditional view factor methods by accounting for complex shading effects. Experimental measurements of rear-side irradiances on a 15 kW bifacial array showed good agreement with ray tracing simulations. The ray tracing model was also integrated with an electrical model in Dymola, and validation against voltage, current, power and yield measurements also showed good accuracy within 1%. Ray tracing is presented as a powerful tool for modelling large bifacial PV installations and quantifying losses from rear-side shading effects.
The document describes models developed to predict soiling-caused photovoltaic (PV) power output degradation in Doha, Qatar based on environmental variables. Field data on PV performance and dust concentration, wind speed, and relative humidity were collected. A linear model and a semi-physical model were developed to correlate daily changes in PV performance (∆CI) with the daily average environmental conditions. The semi-physical model, which considers dust deposition and resuspension processes, predicted daily ∆CI slightly more accurately than the linear model based on R-squared values. However, both models performed roughly the same in predicting ∆CI over longer periods. The results suggest it is possible to use the models to estimate PV
effect of environmental variables on photovoltaic performance based on experi...IJCMESJOURNAL
This paper investigated the effect of environment variables on Photovoltaic PV performance. It is surely understood that local climate can dramatically affect the power generation from a PV system. The most obvious components are the solar radiation hitting the panels, air temperature, humidity and wind speed. The local climatic conditions and precipitation influence the extent to which the panels get to be dusty or polluted, which affects the electrical power generation. The high air temperature caused a reduction in the PV panel output power rated from 1.85 to 20.22%, as well as, increased relative humidity where the largest decline recorded was 32.24%. The wind has a cooling effect on the PV panel that limits the power reduction due to increased solar radiation or panel back temperature. Besides, the wind blows away the accumulated dust that enhances the resulted PV panel power.
This study examines the impact of four environmental factors - dust accumulation, water droplets, bird droppings, and partial shading conditions - on photovoltaic (PV) system performance. The results show that dust accumulation, shading, and bird fouling significantly reduce PV current, voltage, and harvested energy. Shading had the strongest negative effect, with power reduction of 33.7-92.6% depending on the shaded area. However, water droplets decreased PV panel temperature and slightly increased power output. Dust reduced power by 8.8% and efficiency by 11.86%. Bird fouling reduced performance by around 7.4%. The study provides quantitative analysis of how each factor individually affects key PV parameters and overall system
This study examines the impact of four environmental factors - dust accumulation, water droplets, bird droppings, and partial shading conditions - on photovoltaic (PV) system performance. The results show that dust accumulation, shading, and bird fouling significantly reduce PV current, voltage, and harvested energy. Shading had the strongest negative effect, with power reduction of 33.7-92.6% depending on the shaded area. However, water droplets decreased PV panel temperature and slightly increased power output. Dust reduced power by 8.8% and efficiency by 11.86%. Bird fouling reduced performance by around 7.4%. The study provides quantitative analysis of how each factor individually affects key PV parameters and overall system
Reviewing the factors of the Renewable Energy systems for Improving the Energ...IJERA Editor
Electricity demand around the globe has increased alarmingly and is increasing at high rates. Therefore,
electricity supply by the conventional resources is not sufficient right now and the generation of electricity by
these resources is causing pollution worldwide. As the recent world is moving towards the alternative and
renewable resources of energy that include sun, wind, water, and air. This paper focuses on reviewing the
renewable energy sources used to improve the energy efficiency. This paper presents how the maximum power
generation capacity can be achieved using these sources. Main focus of this paper is on solar and wind power
that is freely available all around the globe. This paper concludes that there are certain factors that should be
considered while generating power from these sources. The factors include the calculation of radiation data,
storage size and capacity calculation, and geographic dispersion of the plants.
Effect of pollution and dust on PV performanceIJCMESJOURNAL
This document summarizes an experimental study that evaluated the effect of three Iraqi construction materials (cement, plaster, and borax) on the performance of photovoltaic (PV) cells. The materials were applied in weights from 2 to 10 grams to PV cell samples. Plaster was found to have the greatest negative impact on current, power, and efficiency of the cells, reducing efficiency by up to 25.8% compared to clean cells. Borax had the smallest effect on current and caused the lowest degradation to PV cell performance. The results indicate the importance of keeping PV surfaces clean to maximize their economic benefits.
The document summarizes an experimental study on the impact of environmental factors on the efficiency of household PV panels. Key findings include:
- The study measured factors like temperature, wind speed, and solar irradiance and their effect on PV panel performance over one week in Minqin County, China.
- PV panel efficiency varied by approximately 3% due to environmental impacts with surface temperature having the biggest effect.
- Equations were presented relating ambient temperature, wind speed, and solar irradiance to PV surface temperature and efficiency.
- The study aimed to quantify PV panel performance and operating characteristics under real-world environmental conditions.
RENEWABLE ENERGY ALTERNATIVE FOR WASTEWATER TREATMENT PLANTS IN TRINIDAD - A ...ijmech
The feasibility of using solar photovoltaic (PV) as an alternative to power a waste water treatment plant
(WWTP) in Trinidad was investigated. The site data and power consumption of the Orangefield WWTP
was used to size a PV system with and without grid tied and tracking and non-tracking options. Present day
costing was determined and a simple payback period for Trinidad and Tobago was calculated. The
analysis indicated a minimum and maximum payback period of 27 years and 97.4 years, respectively.
Therefore, in Trinidad and Tobago, even the most cost effective PV system was not financially feasible. A
comparative payback period for neighbouring countries of Barbados and St. Vincent, with three times
higher power cost than Trinidad and Tobago, indicated that solar power is more attractive and feasible
with a minimum and maximum payback period of 9.1 and 8.6 years and 32.8 and 31.2 years, respectively
Using Design of Experiments Approach to analysis Factors Effecting on the PV ...ijtsrd
Many factors affect the performance of a PV module. In this experiment, we will use the factorial experimental design method to investigate these factors. Several factors are studied in this experiment such as phase change martial type, PCM thickness, fin length thickness, fin count and the wind speed. A factorial design is often used by scientists wishing to understand the effect of two or more independent variables upon a single dependent variable so applying factorial design in PV parameters will give us the most significant parameter on the temperature of the cells. The statistical results showed that the most significant factors affected on the temperature of the cells are PCM thickness and wind speed. Malik Al-Abed Allah | Mahdy Migdady "Using Design of Experiments Approach to analysis Factors Effecting on the PV Cells" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-5 , August 2020, URL: https://www.ijtsrd.com/papers/ijtsrd33065.pdf Paper Url :https://www.ijtsrd.com/engineering/mechanical-engineering/33065/using-design-of-experiments-approach-to-analysis-factors-effecting-on-the-pv-cells/malik-alabed-allah
This document analyzes the performance of a 954,809 kWp photovoltaic array located at the Sheikh Zayed solar power plant in Nouakchott, Mauritania over one year from September 2014 to August 2015. The array consists of micro-amorphous silicon modules. The results show that the array's performance depends on solar insolation and environmental conditions like temperature. The array's capture loss ranged from 1.63 to 2.46 hours per day on average. The monthly performance ratio varied from 0.61% to 0.71% and the average capacity factor was highest in October at 20.54% and lowest in January at 11.66%. Two linear models are proposed to evaluate the
IRJET Wind Data Estimation of Kolhapur District using Improved Hybrid Opt...IRJET Journal
This document compares wind data for Kolhapur district in India obtained from the iHOGA and NASA POWER software programs. The iHOGA program was developed in C++ for optimizing hybrid renewable energy systems, while NASA POWER provides satellite-derived data on renewable resources. Both programs estimate the average wind speed at various locations in Kolhapur. The results show that locations like Hatkangale, Karveer and Shirol have the highest average wind speeds of around 5.5-6 m/s based on both models. Locations like Chandhgad, Shahuwadi and Radhanagari showed the lowest speeds of around 4.5-5 m/s. In general, the results from both
A Review on Integration of Wind Turbines in Distributed Generation Power Systemijtsrd
This document summarizes a review on integrating wind turbines into distributed generation power systems. It discusses how wind power is an important renewable energy source that provides challenges for grid integration due to its variability. Energy storage and control systems that can mimic conventional power plants are needed to maintain grid stability when integrating intermittent wind power. The document reviews various European grid codes and their requirements for frequency control by wind power, including definitions of different reserve types (immediate, primary, secondary, tertiary) and their timescales of activation to balance supply and demand.
Technical and Economic Performance of 1MW Grid-connected PV system in Saudi A...IJERA Editor
In this paper, a feasibility study has been done utilizing real time solar irradiance data for a 1MW grid-connected PV system in Qassim region in the middle of Saudi Arabia. The analysis has been done using both technical and economic indicators. Technical performance indicators are; Yield Factor, Capacity Factor and Performance Ratio. Economic indicators are; Levelized cost of energy and simple payback time. The simulation results show high energy productivity, and both technical and economic indicators are high compared with similar systems in different countries. Also, the greenhouse gas emission reduction has been estimated. The prices of PV modules and balance of system components are up to date. The analysis results proved the viability of the proposed system supposing there is no any governmental incentives or grants which could make big difference.
INFLUENCE OF SITE AND SYSTEM PARAMETERS ON THE PERFORMANCE OF ROOF-TOP GRID-C...IRJET Journal
This document analyzes the influence of site and system parameters on the performance of a 49.92 kW rooftop solar PV system in Belfast, Northern Ireland over five years. The researchers examined how ambient temperature, relative humidity, solar irradiation, wind speed, and air pressure (site parameters) as well as inverter efficiency, system performance ratio, and other metrics (system parameters) affected the system's output. Their results showed that higher temperatures, humidity, and solar irradiation decreased performance while higher wind speed increased it. Higher air pressure also increased solar irradiation and power output. The goal was to better understand how environmental conditions and system design impact the real-world efficiency of solar energy generation.
This document reviews the effect of environmental factors like dust, temperature, and wind on photovoltaic module performance. Dust deposition reduces light transmission and module output, and high temperatures also decrease efficiency. Wind can help remove dust but also spread it; higher wind speeds generally result in lower module temperatures through increased cooling. The document examines past research on these impacts and promising strategies for mitigating dust accumulation, like electrostatic cleaning and surface treatments.
This study investigated the impact of dust deposition on photovoltaic panel performance by comparing the I-V curves of identical panels with and without dust under indoor and outdoor conditions. Indoor testing found dust reduced short circuit current by 30-40% and power output by 45-55%, while outdoor testing found reductions of 4-5% in current and 5-6% in power. Both settings found dust did not significantly impact open circuit voltage. The study concluded that dust deposition significantly reduces panel performance and I-V curve analysis can be used to quantify power losses and economic impacts for PV plants from dust accumulation.
Indoor and outdoor investigation comparison of photovoltaic thermal air colle...journalBEEI
Photovoltaic technology is one of renewable energy technology very hopeful, especially photovoltaic thermal system or PVT system. A PVT system solar air collector produces hot air and electricity simultaneously. In this study, indoor and outdoor investigation comparison of PVT system solar air collector has tested at the National University of Malaysia. The indoor and outdoor investigation conducted with variation mass flow rates from 0.01 kg/s to 0.05 kg/s at the solar intensity of 820 W/m2. Indoor and outdoor evaluation is conducted to precisely evaluate the performance improvement theorized by the researcher. The comparison between the indoor and outdoor outcome purposed to confirm each testing and attraction decision. The outdoor investigation outcomes were agreement with indoor results. Indoor investigation outcomes reliably with outdoor investigation outcomes indicated by accuracy results.
The document discusses an automated cleaning system for removing dust from solar photovoltaic (PV) modules. Dust accumulation reduces light transmission and PV performance. The system uses a combination of mechanical cleaning methods like water jets, air jets, and module vibration on an 8 kW pilot-scale PV testbed in Saudi Arabia. Preliminary results found that water jets were most effective at increasing array power output by over 27% by removing sand, while air jets and vibration were less effective. The testbed allows testing different cleaning combinations and solutions to minimize water and energy use for cleaning large-scale PV arrays.
Design And Analysis of Buoyant Wind TurbineIRJET Journal
This document describes the design and analysis of a buoyant air turbine (BAT), a type of airborne wind energy system. The BAT differs from a traditional wind turbine in that it floats in the air, anchored to the ground by cables. CFD analysis is performed on a BAT model designed in CREO software to analyze pressure, velocity, lift and drag forces at wind velocities of 3-6 m/s. Results show that maximum pressure, outlet velocity, drag force, and lift force all increase with higher wind velocity. The analysis demonstrates the BAT concept and evaluates its performance under different wind conditions.
Sizing of Hybrid PV/Battery Power System in Sohag cityiosrjce
This paper gives the feasibility analysis of PV- Battery system for an off-grid power station in Sohag
city. Hybrid PV-battery system was used for supplying a combined pumping and residential load. A simple cost
effective method for sizing stand-alone PV hybrid systems was introduced. The aim of sizing hybrid system is to
determine the cost effective PV configuration and to meet the estimated load at minimum cost. This requires
assessing the climate conditions which determine the temporal variation of the insolation in Sohag city. Sizing
of the hybrid system components was investigated using RETscreen and HOMER programs. The sizing software
tools require a set of data on energy resource demand and system specifications. The energy cost values of the
hybrid system agrees reasonably with those published before.
Similar to Guo dust weather conditions PV performance SGRE2015 (20)
2. 2
available in Qatar until now. There is also a need to study the
relationship between impact of dust and environmental
conditions such as airborne particulate matter concentration,
wind, temperature, and humidity [17].
The objective of the study was to obtain data of PV system
performance degradation due to dust deposition, to obtain data
of ambient dust and weather conditions, and to determine the
correlation between the former and the latter. In the following
sections the methods and results of this study are described,
following which the conclusions are presented.
II. METHODS
Data collection of this study was carried out in the Solar
Test Facility located at the Qatar Science & Technology Park
(QSTP), Doha, Qatar. Data collection for this study occurred
in June 01 through December 31, 2014.
Fig. 1. A DustTrak® airborne dust concentration monitor
installed at the Solar Test Facility
A. Measurement and Calculation of PV Module Performance
Three PV arrays were used in this study, each comprising
eight 220 Wp polysilicon PV modules, tilted at 22° and facing
due South, in a single string connected to identical grid-tied
inverters. The arrays’ DC electrical parameters and module
back surface temperatures were measured at maximum power
point condition once per minute. DC power, voltage and
current were measured via transducers with +/- 0.5%
accuracy. Module temperatures were measured via
permanently attached thermocouple sensors, with unspecified
accuracy.
One array was cleaned every week (“high wash”), one
every second month (“medium wash”), and one every sixth
month (“low wash”). During the test period, the “low wash”
event occurred on 25th
June 2014, and the “medium wash”
events on 25th
June, 2nd
September, and 4th
November 2014.
There were two significant rain events, which occurred on 24th
November and 1st
December 2014.
A “cleanness index” was used in this study as a metric for
the effect of soiling on PV performance ratio. It is defined as
the ratio of a PV module’s temperature-corrected performance
ratio to that of a clean PV module. Its physical meaning is
similar to the “soiling ratio” that has used by other researchers
[3]. The temperature-corrected performance ratio of a PV
module is determined as:
PRT _corr =
PDC _i
1+δ Tcell _i −TSTC( )i
∑
PSTC
GPOA_i
GSTC
#
$
%
&
'
(
i
∑
(1)
Where:
The summation is over ever 24-hour day, from the first
minute after midnight to the last minute before midnight.
PDC_i is the array’s power at maximum power point in the
ith
minute of a day [kW].
PSTC is the array’s power rating at maximum power point,
at standard test conditions (STC), from flash-test data
[kW].
GPOA_i is the measured plane of array (POA) irradiance in
the ith
minute of the day [kW/m2
].
GSTC is the irradiance at the standard test conditions (1 kW
m-2
).
Tcell_i is the average array temperature in the ith
minute of
the day [˚C].
TSTC is the temperature at the standard test conditions (25
˚C).
δ is the temperature coefficient for power of the arrays
(-0.485 % ˚C-1
)
The temperature-corrected performance ratio is similar in
concept to the “weather-corrected performance ratio” defined
in a NREL report [18]. The temperature-corrected
performance ratio in this study uses the PV module’s DC
power output, and is corrected to the temperature at STC (25
˚C). In contrast, the “weather-corrected performance ratio”
uses the PV module’s AC power output, and is corrected to a
locality-dependent temperature based on the project weather
file [18].
The cleanness index of a PV module, in a 24-hour day, is
then calculated as follows:
CI =
PRT _corr
PRT _corr _clean
(2)
Where:
PRT_corr is the temperature-corrected performance ratio of
the PV module whose cleanness index is being evaluated.
PRT_corr_clean is the temperature-corrected performance ratio
of a “clean” PV module. Based on the average PRT_corr of a
weekly-cleaned PV module during the test period, a
constant value of 0.88 was assigned for PRT_corr_clean.
The metric CI is a measure of a PV module’s cleanness. Its
value decreases as the PV module’s soiling level increases. It
takes into account the effect of soiling on module temperature.
It was found through experiment that more heavily soiled
modules tended to be several degrees cooler than clean
modules, presumably because the deposited dust served as a
thermal barrier from the sun’s irradiation. A clean PV module
should have a CI value of unity. Because a constant value of
0.88 was used for the clean module’s temperature-corrected
performance ratio, some daily CI values were slightly greater
3. than unity due to measurement uncertainty. However, this
should have no effect on the objectives and conclusions of this
study.
The daily change of CI for each day was calculated using
the following equation:
∆ CIn = CIn −CIn−1 (3)
Where:
∆CIn is the change in cleanness index of a PV module
attributed to the nth
day.
CIn is the cleanness index of the PV module on the nth
day.
CIn-1 is the cleanness index of the PV module on the (n-1)th
day.
B. Measurement of Ambient Dust and Weather Conditions
Ambient dust concentration (mg m-3
) in term of PM10 was
continuously measured by using a TSI 8533EP DustTrak®
DRX Aerosol Monitor (TSI Inc., Shoreview, MN, USA) with
a temporal resolution of 2 min, stationed at the Solar Test
Facility over the entire study period. This instrument is a
continuous 90° light-scattering laser photometer that produces
size-segregated mass fraction concentrations corresponding to
PM1, PM2.5, PM4, PM10 size fractions. It has a minimum
detectable particle size of 0.1 µm and a sensitivity of 0.001 mg
m-3
. It uses a constant sample flow rate of 3 l m-1
,
automatically controlled by an external pump. The instrument
was set to auto-zero once every 15 minutes using an external
zeroing module, in order to minimize the effect of zero drift.
The instrument was placed inside an environmental enclosure,
which was mounted on a tripod at a height of 1.5 m above
ground.
Ambient air temperature, relative humidity, wind speed and
wind direction were recorded at one minute intervals
continuously every day during the test period. Daily average
of dust concentration, temperature, relative humidity and wind
speed was computed using the usual arithmetic mean for all
data points within a 24-hour day. Daily wind direction was
computed by treating all angular measurements as point on the
unit circle and computing the resultant vector of the unit
vectors determined by data points [19].
C. Data Processing by Multi-Variable Regression
In this study, a multi-variable linear regression model was
used to examine the correlation between daily change of the
cleanness index cleanness index and the daily ambient
environmental conditions. Daily ∆CI was used as the
dependent variable. Three daily average ambient
environmental parameters, namely dust concentration, wind
speed, and relative humidity were used as the independent
variables. The regression model predicts the dependent
variable as a linear function of the independent variables:
∆ CIPre = β0 + β1PM10 + β2WS + β3RH (4)
Where:
∆CIPre is the predicted value of ∆CI on a 24-hour day, which
is described in Eqn. (3).
PM10 is the 24-hour average concentration of particles
smaller than 10 µm in aerodynamic diameter in ambient air,
measured experimentally as described in previous sections.
WS is the 24-hour average of wind speed based on
experimental measurement, as described in previous
sections.
RH is the 24-hour average of relative humidity measured
experimentally as described in previous sections.
β0, β1, β2, β3 are coefficients to be determined using the
experimental data, by minimizing the sum of squares of the
error.
III. RESULTS
The cleanness index of the “low wash” and “medium wash”
PV arrays decreased substantially over the course of this
study. Ambient dust concentration had a significant effect on
the daily change of cleanness index. Weather conditions also
affected the daily change of the cleanness index.
A. Cleanness Index and Daily Change
Fig. 2 shows the cleanness index of the three test PV arrays
in the months of June through December 2014. On average,
the cleanness index decreased 0.0042 (standard deviation
0.0080) per day and 0.0045 (standard deviation 0.0091) per
day over the study period (seven months) for the “low wash”
and “medium wash” PV arrays, respectively.
Two individual dust episodes on the days of 25th
August
and 12th
December caused the cleanness index to decrease
0.027 and 0.024, respectively (for both “low wash” and
“medium wash” arrays).
The PV arrays were also naturally cleaned by rain, on 24th
November and 1st
December 2014, which restored the test
arrays’ cleanness index to around unity.
Fig. 2. The cleanness index of the PV arrays with different
cleaning frequencies
B. Correlation with Dust and Meteorological Data
Examination of the data revealed that, dust concentration
(PM10), wind speed (WS), and relative humidity (RH) had the
most significant correlation with the daily ∆CI. The
correlation coefficient between any single environmental
variable with ∆CI was lower than 0.5, suggesting the
complexity of the soiling process. The daily ∆CI after a
cleaning or rain event was not included in the correlation
analysis. The mean and standard deviation values for PM10,
WS, and RH are given in Table I, which will be referred to in
the following sections.
As shown in Fig. 3, daily ∆CI (excluding cleaning and rainy
days) and daily PM10 both varied significantly from day to day
4. in the months studied. As can be seen from Fig. 3, ∆CI is in
fact positive on many days, suggesting that the level of soiling
actually reduced on those days. By examining the weekly
moving averages of daily ∆CI and daily PM10, one may see
that the trends of these two variables are generally opposite to
each other. In other words, in periods when ∆CI was
increasing, PM10 would generally decrease. This correlation
between ∆CI and PM10 may also be observed in Fig. 4.
However, it may also be observed that the relation between
daily ∆CI and daily PM10 was complex, suggesting there was
interaction with other variables as well.
Fig. 3. Daily ∆CI of the “medium wash” PV array and daily
dust concentration PM10 (lines are weekly moving average of
the daily values)
The correlation between wind speed and daily ∆CI may be
seen in Fig. 5. In general, the daily loss of PV performance
loss due to soiling is greater at lower wind speeds (i.e., daily
∆CI is more negative). On days with high wind speed, it is
more likely to see positive daily ∆CI (partial performance
recovery of soiled PV modules), except during a dust storm.
One explanation for this observation is that higher wind
speeds cause higher re-suspension of deposited dust on PV
panels [20]. Therefore it is possible for the dust deposition of a
PV module to decrease on a high-wind day, and hence the PV
performance would actually recover. The effect of wind speed
on dust deposition on solar surfaces has been noted by other
researchers, albeit under laboratory conditions that are very
different from this study, using Belgian Brabantian loess dust
at high concentrations (0.56 – 2.25 g m-3
) and a narrower
range of wind speeds (0.63 – 2.59 m s-1
) [21, 22].
It should be pointed out that higher wind speeds may cause
PV performance ratio to increase due to the stronger cooling
of PV under high wind conditions [18, 23]. However, since the
daily ∆CI metric uses temperature-corrected performance
ratio, the effect of wind speed on module temperature is taken
into account. Therefore, the wind speed effect on daily ∆ CI
observed in this study should be attributed to the role of wind
speed in dust deposition and re-suspension of deposited dust.
Fig. 4. Daily ∆CI and daily average dust concentration
Fig. 5. Daily ∆CI and daily average wind speed
Fig. 6. Daily ∆CI and daily average relative humidity
The relation between daily ∆CI and daily average relative
humidity is shown in Fig. 6, which suggests that relative
humidity has some impact on the PV soiling. Overall, daily
∆CI was more negative on days with higher relative humidity
levels. This is intuitively consistent with the perception that
higher relative humidity causes dust particles to more likely
“stick” to the PV module, and less likely to be re-suspended
by wind. In other words, with increasing higher relative
TABLE I
STATISTICS OF AMBIENT CONDITION VARIABLES
Variable Mean Standard Deviation
PM10 (mg m-3
) 0.094 0.032
WS (m s-3
) 2.0 0.78
RH 49% 14%
5. humidity, PV soiling is likely to be more severe, provided
other parameters are kept the same.
It should be noted that the opposing diurnal patterns wind
speed and relative humidity might have enhanced the effect of
wind speed on PV surface soiling. The daily peak of wind
speed was found to occur at the same time as the daily
minimum of the relative humidity (data no shown). This
suggests that the dust resuspension effect of high winds is
enhanced when the deposited dust particles contain the lowest
moisture, which make them less sticky and more likely to be
carried away by the wind.
Fig. 7. Daily ∆CI and daily average wind direction (Note: the
inside solid circular line represent a ∆CI value of zero.)
Fig. 8. Wind rose showing distribution of wind speed and
direction
The relation between wind direction and daily ∆CI is
complex, as can be seen in Fig. 7. The daily ∆CI is mostly
negative when the wind comes from the south; when the wind
comes from the north, both negative and positive values are
possible for ∆CI. The prevailing wind is from the northwest,
as shown in Fig. 8. The prevailing wind covers the entire
spectrum of wind speed, but winds from other directions
appear to be only available at relatively low wind speeds. In
other words, wind direction and wind speed are not
independent of each other. On the other hand, the PM10 and
wind direction plot (Fig. 9) shows that, the prevailing wind is
associated with the entire range of dust concentration, but
wind from other directions is generally associated with
medium-to-high dust concentrations. In other words, wind
direction was not included in the multi-variable regression in
this study. Due to the fact that wind direction is not
independent of wind speed and dust concentration, it is not
included in the multivariable regression.
Fig. 9. Plot of daily average PM10 and daily average wind
direction
C. Multivariable Regression Results
The coefficients for Eqn. (4) derived from the multivariable
regression analysis are given in Table II.
Using Eqn. (4) and the set of coefficients in Table II, one
can calculate the predicted ∆CI under various ambient
conditions. Using the mean values from Table I, we can
calculate the predicted ∆CI under “Mean Ambient
Conditions”. We can see that the predicted ∆CI under “Mean
Ambient Conditions” is significantly larger in magnitude
(more negative) than the mean measured ∆CI. This
discrepancy may be partly attributed to the fact that ∆CI is
apparently not a linear function of the ambient environmental
variables, and hence the predicted ∆CI under “Mean Ambient
Conditions” should not be expected to equal the mean
experimental ∆CI. Nevertheless, as a semi-quantitative
approximation, the linear regression model may be used to
assess each environmental variable’s contribution to the
variation of the daily ∆CI. Using the standard deviation values
from Table I, one can calculate the predicted ∆CI with each
variable increased or decreased by one standard deviation,
while keeping the other variables at their mean values. Such
results are shown in the “Varied by One Standard Deviation”
rows in Table III. We can see that a one standard deviation
change in WS or RH causes significantly greater variation in
the predicted ∆CI, than a one standard deviation change in
PM10 would. This suggests that the variation of ∆CI observed
in this study may have been caused by wind speed or relative
humidity variation more than by dust concentration variation.
The multivariable regression model is only a preliminary
approximation of the relationship between ∆CI and the
environmental variables. Dust deposition velocity, which
relates dust deposition flux and ambient dust concentration,
6. has a non-linear dependence on wind speed and particle size
[24], and likely has a non-linear relationship with relative
humidity. Therefore, additional work is needed to have a
better understanding of such relationships, so that one may be
able to more accurately predict the effect of dust and weather
conditions of PV performance loss over long periods of time.
IV. CONCLUSIONS
The results of this study show that surface soiling due to
dust deposition causes significant loss in PV power output in
Doha, Qatar. A “cleanness index”, defined as the ratio of
temperature-corrected performance ratio of a soiled PV
module to that of a clean module placed in identical position
and environment, was introduced to quantify the soiling effect
on PV power output. On average, the cleanness index of a PV
module cleaned every second month may decrease by 0.45
percentage points per day, or 10-20 percentage points per
month, due to dust deposition alone. Dust concentration, wind
speed, and relative humidity are the most important factors
affecting surface soiling. The mathematical relationship
between daily change of the cleanness index and the ambient
environmental variables is yet to be determined through
further research.
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TABLE II
MULTIVARIABLE REGRESSION RESULTS
Coefficient Value and Unit
2.3×10-3
-5.7×10-2
m3
mg-1
3.5×10-3
s m-1
-2.0×10-1
TABLE III
PREDICTED ∆CI UNDER VARIOUS AMBIENT CONDITIONS
Independent Variables Predicted ∆CI
Mean Ambient Conditions -0.0058
Single Variable Varied by
One Standard Deviation
PM10 -0.0076 to -0.0040
WS -0.0086 to -0.0031
RH -0.0086 to -0.0030