@phdthesis{phinikaridesDegradationRateEstimation2017,
  title = {Degradation {{Rate Estimation}} in {{Photovoltaics}}},
  author = {Phinikarides, Alexander},
  year = {2017},
  month = feb,
  address = {{Nicosia, Cyprus}},
  abstract = {Recent advances in photovoltaic (PV) module manufacturing have resulted in the production of highly efficient cells and modules and the significant reduction of the levelized cost of electricity (LCOE) due to the increased demand for the technology. Two key factors that will increase the demand and reduce the LCOE even further are: 1) improving operations and maintenance (O\&M) to ensure the optimal operation of deployed PV plants, and 2) accurately estimating the well-known effect of gradual performance degradation and guaranteeing their lifetime energy yield. Both of these key factors require active monitoring and supervision of the deployed PV plants, analysis of field measurement data for estimation of the long-term degradation rate, RD with statistical confidence and comparison with the warranty. This analysis will in turn enable the planning of actions to mitigate the causes of low performance and minimize the amount of energy lost. The accurate estimation of the RD for a deployed PV plant will also enable more accurate and precise lifetime energy yield forecasting and stricter performance guarantees, further reducing investment risk and increasing confidence in the technology. This work deals with developing a generalized data analysis methodology based on statistical principles, for estimating the energy degradation rate, using field measurement data from eleven different grid-connected PV plants operating side-by-side since June 2006 at the PV Technology test site of the University of Cyprus. The methodology was designed to provide accurate and robust un-supervised estimation with a measure of uncertainty. Also, it was designed for application on commercial PV plants, where sensor deployment is sparse and data logging capabilities are low due to cost. Therefore, the minimum requirements for the realization of the developed methodology are accurate measurements of power and an accurate measurement of irradiance. The methodology was developed to address four main issues in the field of PV degradation: 1) measurement qualification and creation of a clean data set from uncertain sources, 2) detection of suboptimal performance from the measurement data and assessment of the effect on the actual degradation rate, 3) analysis of time series of constructed performance metrics to extract and analyse the trend in either a linear or non-linear fashion, and 4) substitution of adhoc analyses and empirical parametrisation with formal statistical tests, to enable the applicability of the methodology in an unsupervised way. Therefore, a data pipeline consisting of measurement qualification, creation of performance metrics, detection and treatment of outliers and trend modelling procedures was developed. In addition, the computational expense of implementing such a methodology was explored and alternative ways were proposed to further reduce it. Extensive experimental work has also been performed in order to estimate the RD of the studied PV arrays, under standard test conditions (STC). These experiments were performed ex situ, using high quality laboratory equipment (i.e. flasher, electroluminescence (EL), infrared (IR) thermography), with traceable calibration throughout the evaluation period. Even though the results were more traceable and certain, indoor testing required a significant amount of manual labour and system downtime and introduced risk due to mounting/dismounting and transporting the PV modules to the laboratory. The PV modules under study were characterized in the laboratory and the results were used to benchmark the accuracy of the developed unsupervised methodology. In this way, PV performance measured under a broad spectrum of prevailing meteorological conditions was compared to the results of indoor testing under international standards. This was one of the most important outcomes of this work as this kind of long-term comparison on multiple, co-located PV technologies which were monitored and characterized in a research-grade environment was extremely rare in the bibliography.},
  copyright = {All rights reserved},
  langid = {english},
  school = {University of Cyprus},
  file = {/home/alexis/Zotero/storage/DBN43GLC/Phinikarides - 2017 - Degradation Rate Estimation in Photovoltaics.pdf;/home/alexis/Zotero/storage/SE8FT4U6/Phinikarides - 2017 - Degradation Rate Estimation in Photovoltaics.pdf}
}

{"_id":"3vqtFb7pW8kG4zGpS","bibbaseid":"phinikarides-degradationrateestimationinphotovoltaics-2017","author_short":["Phinikarides, A."],"bibdata":{"bibtype":"phdthesis","type":"phdthesis","title":"Degradation Rate Estimation in Photovoltaics","author":[{"propositions":[],"lastnames":["Phinikarides"],"firstnames":["Alexander"],"suffixes":[]}],"year":"2017","month":"February","address":"Nicosia, Cyprus","abstract":"Recent advances in photovoltaic (PV) module manufacturing have resulted in the production of highly efficient cells and modules and the significant reduction of the levelized cost of electricity (LCOE) due to the increased demand for the technology. Two key factors that will increase the demand and reduce the LCOE even further are: 1) improving operations and maintenance (O&M) to ensure the optimal operation of deployed PV plants, and 2) accurately estimating the well-known effect of gradual performance degradation and guaranteeing their lifetime energy yield. Both of these key factors require active monitoring and supervision of the deployed PV plants, analysis of field measurement data for estimation of the long-term degradation rate, RD with statistical confidence and comparison with the warranty. This analysis will in turn enable the planning of actions to mitigate the causes of low performance and minimize the amount of energy lost. The accurate estimation of the RD for a deployed PV plant will also enable more accurate and precise lifetime energy yield forecasting and stricter performance guarantees, further reducing investment risk and increasing confidence in the technology. This work deals with developing a generalized data analysis methodology based on statistical principles, for estimating the energy degradation rate, using field measurement data from eleven different grid-connected PV plants operating side-by-side since June 2006 at the PV Technology test site of the University of Cyprus. The methodology was designed to provide accurate and robust un-supervised estimation with a measure of uncertainty. Also, it was designed for application on commercial PV plants, where sensor deployment is sparse and data logging capabilities are low due to cost. Therefore, the minimum requirements for the realization of the developed methodology are accurate measurements of power and an accurate measurement of irradiance. The methodology was developed to address four main issues in the field of PV degradation: 1) measurement qualification and creation of a clean data set from uncertain sources, 2) detection of suboptimal performance from the measurement data and assessment of the effect on the actual degradation rate, 3) analysis of time series of constructed performance metrics to extract and analyse the trend in either a linear or non-linear fashion, and 4) substitution of adhoc analyses and empirical parametrisation with formal statistical tests, to enable the applicability of the methodology in an unsupervised way. Therefore, a data pipeline consisting of measurement qualification, creation of performance metrics, detection and treatment of outliers and trend modelling procedures was developed. In addition, the computational expense of implementing such a methodology was explored and alternative ways were proposed to further reduce it. Extensive experimental work has also been performed in order to estimate the RD of the studied PV arrays, under standard test conditions (STC). These experiments were performed ex situ, using high quality laboratory equipment (i.e. flasher, electroluminescence (EL), infrared (IR) thermography), with traceable calibration throughout the evaluation period. Even though the results were more traceable and certain, indoor testing required a significant amount of manual labour and system downtime and introduced risk due to mounting/dismounting and transporting the PV modules to the laboratory. The PV modules under study were characterized in the laboratory and the results were used to benchmark the accuracy of the developed unsupervised methodology. In this way, PV performance measured under a broad spectrum of prevailing meteorological conditions was compared to the results of indoor testing under international standards. This was one of the most important outcomes of this work as this kind of long-term comparison on multiple, co-located PV technologies which were monitored and characterized in a research-grade environment was extremely rare in the bibliography.","copyright":"All rights reserved","langid":"english","school":"University of Cyprus","file":"/home/alexis/Zotero/storage/DBN43GLC/Phinikarides - 2017 - Degradation Rate Estimation in Photovoltaics.pdf;/home/alexis/Zotero/storage/SE8FT4U6/Phinikarides - 2017 - Degradation Rate Estimation in Photovoltaics.pdf","bibtex":"@phdthesis{phinikaridesDegradationRateEstimation2017,\n title = {Degradation {{Rate Estimation}} in {{Photovoltaics}}},\n author = {Phinikarides, Alexander},\n year = {2017},\n month = feb,\n address = {{Nicosia, Cyprus}},\n abstract = {Recent advances in photovoltaic (PV) module manufacturing have resulted in the production of highly efficient cells and modules and the significant reduction of the levelized cost of electricity (LCOE) due to the increased demand for the technology. Two key factors that will increase the demand and reduce the LCOE even further are: 1) improving operations and maintenance (O\\&M) to ensure the optimal operation of deployed PV plants, and 2) accurately estimating the well-known effect of gradual performance degradation and guaranteeing their lifetime energy yield. Both of these key factors require active monitoring and supervision of the deployed PV plants, analysis of field measurement data for estimation of the long-term degradation rate, RD with statistical confidence and comparison with the warranty. This analysis will in turn enable the planning of actions to mitigate the causes of low performance and minimize the amount of energy lost. The accurate estimation of the RD for a deployed PV plant will also enable more accurate and precise lifetime energy yield forecasting and stricter performance guarantees, further reducing investment risk and increasing confidence in the technology. This work deals with developing a generalized data analysis methodology based on statistical principles, for estimating the energy degradation rate, using field measurement data from eleven different grid-connected PV plants operating side-by-side since June 2006 at the PV Technology test site of the University of Cyprus. The methodology was designed to provide accurate and robust un-supervised estimation with a measure of uncertainty. Also, it was designed for application on commercial PV plants, where sensor deployment is sparse and data logging capabilities are low due to cost. Therefore, the minimum requirements for the realization of the developed methodology are accurate measurements of power and an accurate measurement of irradiance. The methodology was developed to address four main issues in the field of PV degradation: 1) measurement qualification and creation of a clean data set from uncertain sources, 2) detection of suboptimal performance from the measurement data and assessment of the effect on the actual degradation rate, 3) analysis of time series of constructed performance metrics to extract and analyse the trend in either a linear or non-linear fashion, and 4) substitution of adhoc analyses and empirical parametrisation with formal statistical tests, to enable the applicability of the methodology in an unsupervised way. Therefore, a data pipeline consisting of measurement qualification, creation of performance metrics, detection and treatment of outliers and trend modelling procedures was developed. In addition, the computational expense of implementing such a methodology was explored and alternative ways were proposed to further reduce it. Extensive experimental work has also been performed in order to estimate the RD of the studied PV arrays, under standard test conditions (STC). These experiments were performed ex situ, using high quality laboratory equipment (i.e. flasher, electroluminescence (EL), infrared (IR) thermography), with traceable calibration throughout the evaluation period. Even though the results were more traceable and certain, indoor testing required a significant amount of manual labour and system downtime and introduced risk due to mounting/dismounting and transporting the PV modules to the laboratory. The PV modules under study were characterized in the laboratory and the results were used to benchmark the accuracy of the developed unsupervised methodology. In this way, PV performance measured under a broad spectrum of prevailing meteorological conditions was compared to the results of indoor testing under international standards. This was one of the most important outcomes of this work as this kind of long-term comparison on multiple, co-located PV technologies which were monitored and characterized in a research-grade environment was extremely rare in the bibliography.},\n copyright = {All rights reserved},\n langid = {english},\n school = {University of Cyprus},\n file = {/home/alexis/Zotero/storage/DBN43GLC/Phinikarides - 2017 - Degradation Rate Estimation in Photovoltaics.pdf;/home/alexis/Zotero/storage/SE8FT4U6/Phinikarides - 2017 - Degradation Rate Estimation in Photovoltaics.pdf}\n}\n\n","author_short":["Phinikarides, A."],"key":"phinikaridesDegradationRateEstimation2017","id":"phinikaridesDegradationRateEstimation2017","bibbaseid":"phinikarides-degradationrateestimationinphotovoltaics-2017","role":"author","urls":{},"metadata":{"authorlinks":{}},"html":""},"bibtype":"phdthesis","biburl":"https://alexisph.github.io/alexander.bib","dataSources":["EdXGkEcjMZQF5CuiR","wkdr2EqAZ8v976o9g"],"keywords":[],"search_terms":["degradation","rate","estimation","photovoltaics","phinikarides"],"title":"Degradation Rate Estimation in Photovoltaics","year":2017}