The Time-Variant Degradation of a Photovoltaic System

Charki, Abdérafi; Laronde, Rémi; Bigaud, David

doi:10.1115/1.4007771

Cited by 36 publications

(11 citation statements)

References 4 publications

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“…According to bibliographic studies, particle filter is a hybrid method that meets our requirements and has shown its capability in multiple applications [56,57]. The degradation model that we aim to use in future work is that of output power, as mentioned in [58].…”

Section: Future Work: Improving the Resultsmentioning

confidence: 99%

Intelligent Prognostic Framework for Degradation Assessment and Remaining Useful Life Estimation of Photovoltaic Module

Laayouj

Jamouli

Hail

2016

J. Eng. Technol. Sci.

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Abstract. All industrial systems and machines are subjected to degradation processes, which can be related to the operating conditions. This degradation can cause unwanted stops at any time and major maintenance work sometimes. The accurate prediction of the remaining useful life (RUL) is an important challenge in condition-based maintenance. Prognostic activity allows estimating the RUL before failure occurs and triggering actions to mitigate faults in time when needed. In this study, a new smart prognostic method for photovoltaic module health degradation was developed based on two approaches to achieve more accurate predictions: online diagnosis and data-driven prognosis. This framework of forecasting integrates the strengths of real-time monitoring in the first approach and relevant vector machine in the second. The results show that the proposed method is plausible due to its good prediction of RUL and can be effectively applied to many systems for monitoring and prognostics.

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Section: Future Work: Improving the Resultsmentioning

confidence: 99%

Intelligent Prognostic Framework for Degradation Assessment and Remaining Useful Life Estimation of Photovoltaic Module

Laayouj

Jamouli

Hail

2016

J. Eng. Technol. Sci.

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“…This degradation results from exposure to environmental conditions and has significant consequences with regard to PV performance lifetimes [1][2][3][4] . Specifically, weather conditions in the area of PV installations play a large role in the degradation of PV module performance [5,6] .…”

Section: Introductionmentioning

confidence: 99%

“…Common chamber-based accelerated degradation tests can be achieved by (1) elevating the ambient temperature/humidity above normal field temperature/humidity, (2) shifting the exposure spectrum to wavelengths shorter than that of natural light, or (3) increasing the average intensity of solar irradiance received by the module [16, ,21-23] . Under these conditions it is critical to evaluate the correlation between the accelerated test data and in-situ, test yard degradation data in order to optimize the prediction accuracy of product lifetimes.…”

Section: Introductionmentioning

confidence: 99%

Environmental aging in polycrystalline-Si photovoltaic modules: comparison of chamber-based accelerated degradation studies with field-test data

et al. 2015

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Lifecycle degradation testing of photovoltaic (PV) modules in accelerated-degradation chambers can enable the prediction both of PV performance lifetimes and of return-on-investment for installations of PV systems. With degradation results strongly dependent on chamber test parameters, the validity of such studies relative to fielded, installed PV systems must be determined. In the present work, accelerated aging of a 250 W polycrystalline silicon module is compared to real-time performance degradation in a similar polycrystalline-silicon, fielded, PV technology that has been operating since October 2013. Investigation of environmental aging effects are performed in a full-scale, industrial-standard environmental chamber equipped with single-sun irradiance capability providing illumination uniformity of 98% over a 2 x 1.6 m area. Time-dependent, photovoltaic performance (J-V) is evaluated over a recurring, compressed night-day cycle providing representative local daily solar insolation for the southwestern United States, followed by dark (night) cycling. This cycle is synchronized with thermal and humidity environmental variations that are designed to mimic, as closely as possible, test-yard conditions specific to a 12 month weather profile for a fielded system in Tucson, AZ. Results confirm the impact of environmental conditions on the module long-term performance. While the effects of temperature de-rating can be clearly seen in the data, removal of these effects enables the clear interpretation of module efficiency degradation with time and environmental exposure. With the temperature-dependent effect removed, the normalized efficiency is computed and compared to performance results from another panel of similar technology that has previously experienced identical climate changes in the test yard. Analysis of relative PV module efficiency degradation for the chamber-tested system shows good comparison to the field-tested system with ~2.5% degradation following an equivalent year of testing.

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“…PV system degradation is important because is directly related to the loss of power output and therefore the lifetime and reliability of the installation [3]. Charki et al [4,5] proposed a methodology using an accelerated degradation model to estimate the production of a photovoltaic system, taking into account electrical losses and time to failure. However, it was still difficult to study in real conditions.…”

Section: Introductionmentioning

confidence: 99%

Measurement of Thermal and Electrical Parameters in Photovoltaic Systems for Predictive and Cross-Correlated Monitorization

et al. 2019

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Photovoltaic electricity generation is growing at an almost exponential rate worldwide, reaching 400 GWp of installed capacity in 2018. Different types of installations, ranging from small building integrated systems to large plants, require different maintenance strategies, including strategies for monitorization and data processing. In this article, we present three case studies at different scales (from hundreds of Wp to a 2.1 MWp plant), where automated parameter monitorization and data analysis has been carried out, aiming to detect failures and provide recommendations for optimum maintenance procedures. For larger systems, the data collected by the inverters provides the best source of information, and the cross-correlated analysis which uses these data is the best strategy to detect failures in module strings and failures in the inverters themselves (an average of 32.2% of inverters with failures was found after ten years of operation). In regards to determining which module is failing, the analysis of thermographic images is reliable and allows the detection of the failed module within the string (up to 1.5% for grave failures and 9.1% of medium failures for the solar plant after eleven years of activity). Photovoltaic (PV) systems at different scales require different methods for monitorization: Medium and large systems depend on inverter automated data acquisition, which can be complemented with thermographic images. Nevertheless, if the purpose of the monitorization is to obtain detailed information about the degradation processes of the solar cells, it becomes necessary to measure the environmental (irradiance and ambient temperature), thermal and electrical parameters (I-V characterization) of the modules and compare the experimental data with the modelling results. This is only achievable in small systems.

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The Time-Variant Degradation of a Photovoltaic System

Cited by 36 publications

References 4 publications

Intelligent Prognostic Framework for Degradation Assessment and Remaining Useful Life Estimation of Photovoltaic Module

Intelligent Prognostic Framework for Degradation Assessment and Remaining Useful Life Estimation of Photovoltaic Module

Environmental aging in polycrystalline-Si photovoltaic modules: comparison of chamber-based accelerated degradation studies with field-test data

Measurement of Thermal and Electrical Parameters in Photovoltaic Systems for Predictive and Cross-Correlated Monitorization

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