Reliability of photovoltaic modules based on climatic measurement data

Laronde, Rémi; Charki, Abdérafi; Bigaud, David

doi:10.1051/ijmqe/2010012

Cited by 22 publications

(5 citation statements)

References 7 publications

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“…Table 1 presents the different distribution laws which are implemented in the Petri networks. Exponential μ = 2.98 × 10 -3 h -1 T 10 Exponential λ = 7.61 × 10 -6 h -1 T 11 Exponential μ = 0.926 × 10 -3 h -1 T 12 Exponential λ = 13 × 10 -9 h -1 T 13 Exponential μ = 2.98 × 10 -3 h -1 Moreover, as mentioned by Maish [15], the ultimate metric that is common to all aspects of reliability is cost. Thus, the maintenance cost of the system for each case are estimated using maintenance costs of the different components as shown in Table 2.…”

Section: Simulationsmentioning

confidence: 99%

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Photovoltaic system lifetime prediction using Petri networks method

Laronde¹,

Charki²,

Bigaud³

et al. 2010

SPIE Proceedings

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Photovoltaic modules and systems lifetime and availability are difficult to determine and not really well-known. This information is an important data to insure the installation performance of such a system and to prepare its recycling. The aim of this article is to present a methodology for the availability and lifetime evaluation of a photovoltaic system using the Petri networks method. Each component -module, wires and inverter -is detailed in Petri networks and several laws are used in order to estimate the reliability. Several guides (FIDES, MIL-HDBK-217 ...) allow determining the reliability of electronic components using collections of data. For photovoltaic modules, accelerated life testing are carried out for the evaluation of the lifetime which is described by a Weibull distribution. Results obtained show that Petri networks are very useful to simulate lifetime thanks to its intrinsic modularity.

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Section: Simulationsmentioning

confidence: 99%

“…It can be estimated either carrying out a feedback analysis or with realizing accelerated life testing thanks to the method of Laronde [12][13] using influent parameters like module temperature and relative humidity.…”

Section: Photovoltaic Arraymentioning

confidence: 99%

Photovoltaic system lifetime prediction using Petri networks method

Laronde¹,

Charki²,

Bigaud³

et al. 2010

SPIE Proceedings

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“…In [ 12 ], an evaluation was conducted to estimate the power loss resulting from various PV failures, leading to the conclusion that power loss due to cell cracks can range from 1% to 15%. This decrease in power output has a substantial impact on the financial well-being of system owners [ 13 , 14 ].…”

Section: Introductionmentioning

confidence: 99%

Methodology for Calculating the Damaged Surface and Its Relationship with Power Loss in Photovoltaic Modules by Electroluminescence Inspection for Corrective Maintenance

Saborido-Barba,

García-López,

Clavijo-Blanco

et al. 2024

Sensors

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Photovoltaic panels are exposed to various external factors that can cause damage, with the formation of cracks in the photovoltaic cells being one of the most recurrent issues affecting their production capacity. Electroluminescence (EL) tests are employed to detect these cracks. In this study, a methodology developed according to the IEC TS 60904-13 standard is presented, allowing for the calculation of the percentage of type C cracks in a PV panel and subsequently estimating the associated power loss. To validate the methodology, it was applied to a polycrystalline silicon module subjected to incremental damage through multiple impacts on its rear surface. After each impact, electroluminescence images and I-V curves were obtained and used to verify power loss estimates. More accurate estimates were achieved by assessing cracks at the PV cell level rather than by substring or considering the entire module. In this context, cell-level analysis becomes indispensable, as the most damaged cell significantly influences the performance of the photovoltaic model. Subsequently, the developed methodology was applied to evaluate the conditions of four photovoltaic panels that had been in operation, exemplifying its application in maintenance tasks. The results assisted in decision making regarding whether to replace or continue using the panels.

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“…In fact, in this study, we use a large corrosion growth database of polycrystalline PV module that is reported in [29]. This data set consists of damage trajectory containing 10 measurement points (see Figure 2) based on a constant stress damp heat of T = 85°C and HR = 85%.…”

Section: Numerical Examplementioning

confidence: 99%

The PV Corrosion Fault Prognosis Based on Ensemble Kalman Filter

Ouladsine

Outbib

Bakhouya

2017

International Journal of Photoenergy

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The degradation of photovoltaic (PV) modules remains a major concern on the control and the development of the photovoltaic field, particularly, in regions with difficult climatic conditions. The main degradation modes of the PV modules are corrosion, discoloration, glass breaks, and cracks of cells. However, corrosion and discoloration remain the predominant degradation modes that still require further investigations. In this paper, a model-based PV corrosion prognostic approach, based on an ensemble Kalman filter (EnKF), is introduced to identify the PV corrosion parameters and then estimate the remaining useful life (RUL). Simulations have been conducted using measured data set, and results are reported to show the efficiency of the proposed approach.

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Reliability of photovoltaic modules based on climatic measurement data

Cited by 22 publications

References 7 publications

Photovoltaic system lifetime prediction using Petri networks method

Photovoltaic system lifetime prediction using Petri networks method

Methodology for Calculating the Damaged Surface and Its Relationship with Power Loss in Photovoltaic Modules by Electroluminescence Inspection for Corrective Maintenance

The PV Corrosion Fault Prognosis Based on Ensemble Kalman Filter

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