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- All Subjects: Photovoltaic power systems
- Creators: Tamizhmani, Govindasamy
- Creators: Sugar, Thomas
Description
Photovoltaic (PV) module nameplates typically provide the module's electrical characteristics at standard test conditions (STC). The STC conditions are: irradiance of 1000 W/m2, cell temperature of 25oC and sunlight spectrum at air mass 1.5. However, modules in the field experience a wide range of environmental conditions which affect their electrical characteristics and render the nameplate data insufficient in determining a module's overall, actual field performance. To make sound technical and financial decisions, designers and investors need additional performance data to determine the energy produced by modules operating under various field conditions. The angle of incidence (AOI) of sunlight on PV modules is one of the major parameters which dictate the amount of light reaching the solar cells. The experiment was carried out at the Arizona State University- Photovoltaic Reliability Laboratory (ASU-PRL). The data obtained was processed in accordance with the IEC 61853-2 model to obtain relative optical response of the modules (response which does not include the cosine effect). The results were then compared with theoretical models for air-glass interface and also with the empirical model developed by Sandia National Laboratories. The results showed that all modules with glass as the superstrate had identical optical response and were in agreement with both the IEC 61853-2 model and other theoretical and empirical models. The performance degradation of module over years of exposure in the field is dependent upon factors such as environmental conditions, system configuration, etc. Analyzing the degradation of power and other related performance parameters over time will provide vital information regarding possible degradation rates and mechanisms of the modules. An extensive study was conducted by previous ASU-PRL students on approximately 1700 modules which have over 13 years of hot- dry climatic field condition. An analysis of the results obtained in previous ASU-PRL studies show that the major degradation in crystalline silicon modules having glass/polymer construction is encapsulant discoloration (causing short circuit current drop) and solder bond degradation (causing fill factor drop due to series resistance increase). The power degradation for crystalline silicon modules having glass/glass construction was primarily attributed to encapsulant delamination (causing open-circuit voltage drop).
ContributorsVasantha Janakeeraman, Suryanarayana (Author) / Tamizhmani, Govindasamy (Thesis advisor) / Rogers, Bradley (Committee member) / Macia, Narciso (Committee member) / Arizona State University (Publisher)
Created2013
Description
The object of this study was a 26 year old residential Photovoltaic (PV) monocrystalline silicon (c-Si) power plant, called Solar One, built by developer John F. Long in Phoenix, Arizona (a hot-dry field condition). The task for Arizona State University Photovoltaic Reliability Laboratory (ASU-PRL) graduate students was to evaluate the power plant through visual inspection, electrical performance, and infrared thermography. The purpose of this evaluation was to measure and understand the extent of degradation to the system along with the identification of the failure modes in this hot-dry climatic condition. This 4000 module bipolar system was originally installed with a 200 kW DC output of PV array (17 degree fixed tilt) and an AC output of 175 kVA. The system was shown to degrade approximately at a rate of 2.3% per year with no apparent potential induced degradation (PID) effect. The power plant is made of two arrays, the north array and the south array. Due to a limited time frame to execute this large project, this work was performed by two masters students (Jonathan Belmont and Kolapo Olakonu) and the test results are presented in two masters theses. This thesis presents the results obtained on the north array and the other thesis presents the results obtained on the south array. The resulting study showed that PV module design, array configuration, vandalism, installation methods and Arizona environmental conditions have had an effect on this system's longevity and reliability. Ultimately, encapsulation browning, higher series resistance (potentially due to solder bond fatigue) and non-cell interconnect ribbon breakages outside the modules were determined to be the primary causes for the power loss.
ContributorsBelmont, Jonathan (Author) / Tamizhmani, Govindasamy (Thesis advisor) / Henderson, Mark (Committee member) / Rogers, Bradley (Committee member) / Arizona State University (Publisher)
Created2013
Description
Potential induced degradation (PID) due to high system voltages is one of the major degradation mechanisms in photovoltaic (PV) modules, adversely affecting their performance due to the combined effects of the following factors: system voltage, superstrate/glass surface conductivity, encapsulant conductivity, silicon nitride anti-reflection coating property and interface property (glass/encapsulant; encapsulant/cell; encapsulant/backsheet). Previous studies carried out at ASU's Photovoltaic Reliability Laboratory (ASU-PRL) showed that only negative voltage bias (positive grounded systems) adversely affects the performance of commonly available crystalline silicon modules. In previous studies, the surface conductivity of the glass surface was obtained using either conductive carbon layer extending from the glass surface to the frame or humidity inside an environmental chamber. This thesis investigates the influence of glass surface conductivity disruption on PV modules. In this study, conductive carbon was applied only on the module's glass surface without extending to the frame and the surface conductivity was disrupted (no carbon layer) at 2cm distance from the periphery of frame inner edges. This study was carried out under dry heat at two different temperatures (60 °C and 85 °C) and three different negative bias voltages (-300V, -400V, and -600V). To replicate closeness to the field conditions, half of the selected modules were pre-stressed under damp heat for 1000 hours (DH 1000) and the remaining half under 200 hours of thermal cycling (TC 200). When the surface continuity was disrupted by maintaining a 2 cm gap from the frame to the edge of the conductive layer, as demonstrated in this study, the degradation was found to be absent or negligibly small even after 35 hours of negative bias at elevated temperatures. This preliminary study appears to indicate that the modules could become immune to PID losses if the continuity of the glass surface conductivity is disrupted at the inside boundary of the frame. The surface conductivity of the glass, due to water layer formation in a humid condition, close to the frame could be disrupted just by applying a water repelling (hydrophobic) but high transmittance surface coating (such as Teflon) or modifying the frame/glass edges with water repellent properties.
ContributorsTatapudi, Sai Ravi Vasista (Author) / Tamizhmani, Govindasamy (Thesis advisor) / Srinivasan, Devarajan (Committee member) / Rogers, Bradley (Committee member) / Arizona State University (Publisher)
Created2012
Description
ABSTRACT As the use of photovoltaic (PV) modules in large power plants continues to increase globally, more studies on degradation, reliability, failure modes, and mechanisms of field aged modules are needed to predict module life expectancy based on accelerated lifetime testing of PV modules. In this work, a 26+ year old PV power plant in Phoenix, Arizona has been evaluated for performance, reliability, and durability. The PV power plant, called Solar One, is owned and operated by John F. Long's homeowners association. It is a 200 kWdc, standard test conditions (STC) rated power plant comprised of 4000 PV modules or frameless laminates, in 100 panel groups (rated at 175 kWac). The power plant is made of two center-tapped bipolar arrays, the north array and the south array. Due to a limited time frame to execute this large project, this work was performed by two masters students (Jonathan Belmont and Kolapo Olakonu) and the test results are presented in two masters theses. This thesis presents the results obtained on the south array and the other thesis presents the results obtained on the north array. Each of these two arrays is made of four sub arrays, the east sub arrays (positive and negative polarities) and the west sub arrays (positive and negative polarities), making up eight sub arrays. The evaluation and analyses of the power plant included in this thesis consists of: visual inspection, electrical performance measurements, and infrared thermography. A possible presence of potential induced degradation (PID) due to potential difference between ground and strings was also investigated. Some installation practices were also studied and found to contribute to the power loss observed in this investigation. The power output measured in 2011 for all eight sub arrays at STC is approximately 76 kWdc and represents a power loss of 62% (from 200 kW to 76 kW) over 26+ years. The 2011 measured power output for the four south sub arrays at STC is 39 kWdc and represents a power loss of 61% (from 100 kW to 39 kW) over 26+ years. Encapsulation browning and non-cell interconnect ribbon breakages were determined to be the primary causes for the power loss.
ContributorsOlakonu, Kolapo (Author) / Tamizhmani, Govindasamy (Thesis advisor) / Srinivasan, Devarajan (Committee member) / Rogers, Bradley (Committee member) / Arizona State University (Publisher)
Created2012
Description
To increase the deployment of photovoltaic (PV) systems, a higher level of performance for PV modules should be sought. Soiling, or dust accumulation on the PV modules, is one of the conditions that negatively affect the performance of the PV modules by reducing the light incident onto the surface of the PV module. This thesis presents two studies that focus on investigating the soiling effect on the performance of the PV modules installed in Metro Phoenix area.
The first study was conducted to investigate the optimum cleaning frequency for cleaning PV modules installed in Mesa, AZ. By monitoring the soiling loss of PV modules mounted on a mock rooftop at ASU-PRL, a detailed soiling modeling was obtained. Same setup was also used for other soiling-related investigations like studying the effect of soiling density on angle of incidence (AOI) dependence, the climatological relevance (CR) to soiling, and spatial variation of the soiling loss. During the first dry season (May to June), the daily soiling rate was found as -0.061% for 20o tilted modules. Based on the obtained soiling rate, cleaning PV modules, when the soiling is just due to dust on 20o tilted residential arrays, was found economically not justifiable.
The second study focuses on evaluating the soiling loss in different locations of Metro Phoenix area of Arizona. The main goal behind the second study was to validate the daily soiling rate obtained from the mock rooftop setup in the first part of this thesis. By collaborating with local solar panel cleaning companies, soiling data for six residential systems in 5 different cities in and around Phoenix was collected, processed, and analyzed. The range of daily soiling rate in the Phoenix area was found as -0.057% to -0.085% for 13-28o tilted arrays. The soiling rate found in the first part of the thesis (-0.061%) for 20o tilted array, was validated since it falls within the range obtained from the second part of the thesis.
The first study was conducted to investigate the optimum cleaning frequency for cleaning PV modules installed in Mesa, AZ. By monitoring the soiling loss of PV modules mounted on a mock rooftop at ASU-PRL, a detailed soiling modeling was obtained. Same setup was also used for other soiling-related investigations like studying the effect of soiling density on angle of incidence (AOI) dependence, the climatological relevance (CR) to soiling, and spatial variation of the soiling loss. During the first dry season (May to June), the daily soiling rate was found as -0.061% for 20o tilted modules. Based on the obtained soiling rate, cleaning PV modules, when the soiling is just due to dust on 20o tilted residential arrays, was found economically not justifiable.
The second study focuses on evaluating the soiling loss in different locations of Metro Phoenix area of Arizona. The main goal behind the second study was to validate the daily soiling rate obtained from the mock rooftop setup in the first part of this thesis. By collaborating with local solar panel cleaning companies, soiling data for six residential systems in 5 different cities in and around Phoenix was collected, processed, and analyzed. The range of daily soiling rate in the Phoenix area was found as -0.057% to -0.085% for 13-28o tilted arrays. The soiling rate found in the first part of the thesis (-0.061%) for 20o tilted array, was validated since it falls within the range obtained from the second part of the thesis.
ContributorsNaeem, Mohammad Hussain (Author) / Tamizhmani, Govindasamy (Thesis advisor) / Rogers, Bradley (Committee member) / Srinivasan, Devarajan (Committee member) / Arizona State University (Publisher)
Created2014
Description
Photovoltaic (PV) systems are one of the next generation's renewable energy sources for our world energy demand. PV modules are highly reliable. However, in polluted environments, over time, they will collect grime and dust. There are also limited field data studies about soiling losses on PV modules. The study showed how important it is to investigate the effect of tilt angle on soiling. The study includes two sets of mini-modules. Each set has 9 PV modules tilted at 0, 5, 10, 15, 20, 23, 30, 33 and 40°. The first set called "Cleaned" was cleaned every other day. The second set called "Soiled" was never cleaned after the first day. The short circuit current, a measure of irradiance, and module temperature was monitored and recorded every two minutes over three months (January-March 2011). The data were analyzed to investigate the effect of tilt angle on daily and monthly soiling, and hence transmitted solar insolation and energy production by PV modules. The study shows that during the period of January through March 2011 there was an average loss due to soiling of approximately 2.02% for 0° tilt angle. Modules at tilt anlges 23° and 33° also have some insolation losses but do not come close to the module at 0° tilt angle. Tilt anlge 23° has approximately 1.05% monthly insolation loss, and 33° tilt angle has an insolation loss of approximately 0.96%. The soiling effect is present at any tilt angle, but the magnitude is evident: the flatter the solar module is placed the more energy it will lose.
ContributorsCano Valero, José (Author) / Tamizhmani, Govindasamy (Thesis advisor) / Madakannan, Arunachalanadar (Committee member) / Macia, Narciso (Committee member) / Arizona State University (Publisher)
Created2011
Description
Photovoltaic (PV) modules undergo performance degradation depending on climatic conditions, applications, and system configurations. The performance degradation prediction of PV modules is primarily based on Accelerated Life Testing (ALT) procedures. In order to further strengthen the ALT process, additional investigation of the power degradation of field aged PV modules in various configurations is required. A detailed investigation of 1,900 field aged (12-18 years) PV modules deployed in a power plant application was conducted for this study. Analysis was based on the current-voltage (I-V) measurement of all the 1,900 modules individually. I-V curve data of individual modules formed the basis for calculating the performance degradation of the modules. The percentage performance degradation and rates of degradation were compared to an earlier study done at the same plant. The current research was primarily focused on identifying the extent of potential induced degradation (PID) of individual modules with reference to the negative ground potential. To investigate this, the arrangement and connection of the individual modules/strings was examined in detail. The study also examined the extent of underperformance of every series string due to performance mismatch of individual modules in that string. The power loss due to individual module degradation and module mismatch at string level was then compared to the rated value.
ContributorsJaspreet Singh (Author) / Tamizhmani, Govindasamy (Thesis advisor) / Srinivasan, Devarajan (Committee member) / Rogers, Bradley (Committee member) / Arizona State University (Publisher)
Created2011
Description
Photovoltaic (PV) modules are typically rated at three test conditions: STC (standard test conditions), NOCT (nominal operating cell temperature) and Low E (low irradiance). The current thesis deals with the power rating of PV modules at twenty-three test conditions as per the recent International Electrotechnical Commission (IEC) standard of IEC 61853 – 1. In the current research, an automation software tool developed by a previous researcher of ASU – PRL (ASU Photovoltaic Reliability Laboratory) is validated at various stages. Also in the current research, the power rating of PV modules for four different manufacturers is carried out according to IEC 61853 – 1 standard using a new outdoor test method. The new outdoor method described in this thesis is very different from the one reported by a previous researcher of ASU – PRL. The new method was designed to reduce the labor hours in collecting the current-voltage ( I – V) curves at various temperatures and irradiance levels. The power matrices for all the four manufacturers were generated using the I – V data generated at different temperatures and irradiance levels and the translation procedures described in IEC 60891 standard. All the measurements were carried out on both clear and cloudy days using an automated 2 – axis tracker located at ASU – PRL, Mesa, Arizona. The modules were left on the 2 – axis tracker for 12 continuous days and the data was continuously and automatically collected for every two minutes from 6 am to 6 pm. In order to obtain the I – V data at wide range of temperatures and irradiance levels, four identical (or nearly identical) modules were simultaneously installed on the 2 – axis tracker with and without thermal insulators on the back of the modules and with and without mesh screens on the front of the modules. Several issues related to the automation software were uncovered and the required improvement in the software has been suggested. The power matrices for four manufacturers have been successfully generated using the new outdoor test method developed in this work. The data generated in this work has been extensively analyzed for accuracy and for performance efficiency comparison at various temperatures and irradiance levels.
ContributorsVemula, Meena Gupta (Author) / Tamizhmani, Govindasamy (Thesis advisor) / Macia, Narcio F. (Committee member) / Rogers, Bradley (Committee member) / Arizona State University (Publisher)
Created2012
Description
Brain Computer Interfaces are becoming the next generation controllers not only in the medical devices for disabled individuals but also in the gaming and entertainment industries. In order to build an effective Brain Computer Interface, which accurately translates the user thoughts into machine commands, it is important to have robust and fail proof signal processing and machine learning modules which operate on the raw EEG signals and estimate the current thought of the user.
In this thesis, several techniques used to perform EEG signal pre-processing, feature extraction and signal classification have been discussed, implemented, validated and verified; efficient supervised machine learning models, for the EEG motor imagery signal classification are identified. To further improve the performance of system unsupervised feature learning techniques have been investigated by pre-training the Deep Learning models. Use of pre-training stacked autoencoders have been proposed to solve the problems caused by random initialization of weights in neural networks.
Motor Imagery (imaginary hand and leg movements) signals are acquire using the Emotiv EEG headset. Different kinds of features like mean signal, band powers, RMS of the signal have been extracted and supplied to the machine learning (ML) stage, wherein, several ML techniques like LDA, KNN, SVM, Logistic regression and Neural Networks are applied and validated. During the validation phase the performances of various techniques are compared and some important observations are reported. Further, deep Learning techniques like autoencoding have been used to perform unsupervised feature learning. The reliability of the features is analyzed by performing classification by using the ML techniques mentioned earlier. The performance of the neural networks has been further improved by pre-training the network in an unsupervised fashion using stacked autoencoders and supplying the stacked autoencoders’ network parameters as initial parameters to the neural network. All the findings in this research, during each phase (pre-processing, feature extraction, classification) are directly relevant and can be used by the BCI research community for building motor imagery based BCI applications.
Additionally, this thesis attempts to develop, test, and compare the performance of an alternative method for classifying human driving behavior. This thesis proposes the use of driver affective states to know the driving behavior. The purpose of this part of the thesis was to classify the EEG data collected from several subjects while driving simulated vehicle and compare the classification results with those obtained by classifying the driving behavior using vehicle parameters collected simultaneously from all the subjects. The objective here is to see if the drivers’ mental state is reflected in his driving behavior.
In this thesis, several techniques used to perform EEG signal pre-processing, feature extraction and signal classification have been discussed, implemented, validated and verified; efficient supervised machine learning models, for the EEG motor imagery signal classification are identified. To further improve the performance of system unsupervised feature learning techniques have been investigated by pre-training the Deep Learning models. Use of pre-training stacked autoencoders have been proposed to solve the problems caused by random initialization of weights in neural networks.
Motor Imagery (imaginary hand and leg movements) signals are acquire using the Emotiv EEG headset. Different kinds of features like mean signal, band powers, RMS of the signal have been extracted and supplied to the machine learning (ML) stage, wherein, several ML techniques like LDA, KNN, SVM, Logistic regression and Neural Networks are applied and validated. During the validation phase the performances of various techniques are compared and some important observations are reported. Further, deep Learning techniques like autoencoding have been used to perform unsupervised feature learning. The reliability of the features is analyzed by performing classification by using the ML techniques mentioned earlier. The performance of the neural networks has been further improved by pre-training the network in an unsupervised fashion using stacked autoencoders and supplying the stacked autoencoders’ network parameters as initial parameters to the neural network. All the findings in this research, during each phase (pre-processing, feature extraction, classification) are directly relevant and can be used by the BCI research community for building motor imagery based BCI applications.
Additionally, this thesis attempts to develop, test, and compare the performance of an alternative method for classifying human driving behavior. This thesis proposes the use of driver affective states to know the driving behavior. The purpose of this part of the thesis was to classify the EEG data collected from several subjects while driving simulated vehicle and compare the classification results with those obtained by classifying the driving behavior using vehicle parameters collected simultaneously from all the subjects. The objective here is to see if the drivers’ mental state is reflected in his driving behavior.
ContributorsManchala, Vamsi Krishna (Author) / Redkar, Sangram (Thesis advisor) / Rogers, Bradley (Committee member) / Sugar, Thomas (Committee member) / Arizona State University (Publisher)
Created2015
Characterization and analysis of long term field aged photovoltaic modules and encapsulant materials
Description
Photovoltaic (PV) module degradation is a well-known issue, however understanding the mechanistic pathways in which modules degrade is still a major task for the PV industry. In order to study the mechanisms responsible for PV module degradation, the effects of these degradation mechanisms must be quantitatively measured to determine the severity of each degradation mode. In this thesis multiple modules from three climate zones (Arizona, California and Colorado) were investigated for a single module glass/polymer construction (Siemens M55) to determine the degree to which they had degraded, and the main factors that contributed to that degradation. To explain the loss in power, various nondestructive and destructive techniques were used to indicate possible causes of loss in performance. This is a two-part thesis. Part 1 presents non-destructive test results and analysis and Part 2 presents destructive test results and analysis.
ContributorsChicca, Matthew (Author) / Tamizhmani, Govindasamy (Thesis advisor) / Rogers, Bradley (Committee member) / Srinivasan, Devarajan (Committee member) / Arizona State University (Publisher)
Created2015