Diagnostic architecture: A procedure based on the analysis of the failure causes applied to photovoltaic plants

Cristaldi, L.; Faifer, Marco; Lazzaroni, M.; Khalil, Mohamed; Catelani, Marcantonio; Ciani, Lorenzo

doi:10.1016/j.measurement.2015.02.023

Cited by 46 publications

(17 citation statements)

References 41 publications

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“…EVA is consistently drafted with additives like cross linking agent, antioxidants, UV absorber, hindered amine light stabilizers and adhesive elements. Cristaldi et al 47,48 stated that electrochemical corrosion of the string interconnects of a cell due to encapsulant results in the degradation of PV module. The discoloration results were noticed in different patterns and were examined for complexity due to oxygen diffusion and acetic acid from additive reactions.…”

Section: Review Of Failures Found In Silicon Wafer-based Pv Modulesmentioning

confidence: 99%

“…These factors are generally dependent on the temperature, humidity, system voltage, type of material used, and the refractive index of the cell. Cristaldi et al 47,48 stated that electrochemical corrosion of the string interconnects of a cell due to encapsulant results in the degradation of PV module. This phenomenon leads to high series resistance and low parallel resistance of the PV modules.…”

Section: Review Of Failures Found In Silicon Wafer-based Pv Modulesmentioning

confidence: 99%

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Fault diagnosis of Photovoltaic Modules

Haque

Bharath

Khan

et al. 2019

Energy Science & Engineering

129

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Fault diagnosis and condition monitoring are important to increase the efficiency and reliability of photovoltaic modules. This paper reviews the challenges and limitations associated with fault diagnosis of solar modules. A thorough analysis of various faults responsible for failure of solar modules has been discussed. After reviewing relevant work, a monitoring tool is designed using thermography and artificial intelligent systems that allows the detection of various types of faults in PV modules and at the same time the designed tool aims to filter the nonsignificant anomalies. A neural network (NN) classifier is applied to the transfer characteristics (I‐V data) of the faulty PV module for the diagnosis which adapts multilayer perceptron (MLP) networks to identify the type and location of occurring faults. The Discrete wavelet transform (DWT) based signal processing technique is utilized in the feature extraction process to reduce the NN input size. The developed detection algorithm is adapted for 24/7 automated surveillance. For a given fault condition, the average fault detection time is observed to be <9 seconds, which is lower than the previous work done. The developed algorithm achieved 100% accuracy when tested on a predetermined fault data set.

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Section: Review Of Failures Found In Silicon Wafer-based Pv Modulesmentioning

confidence: 99%

Section: Review Of Failures Found In Silicon Wafer-based Pv Modulesmentioning

confidence: 99%

Fault diagnosis of Photovoltaic Modules

Haque

Bharath

Khan

et al. 2019

Energy Science & Engineering

129

View full text Add to dashboard Cite

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“…Production forecasting has received significant attention in the last decades [5][6] [7]. There are many approaches for trend identification and forecasting in time domain to ensure with the required level of production quantity and quality.…”

Section: Production Trend Forecast Methodsmentioning

confidence: 99%

Production trend identification and forecast for shop-floor business intelligence

Viharos

Csanaki²,

Nacsa

et al. 2016

ACTA IMEKO

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The paper introduces a methodology to define production trend classes and also the results to serve with trend prognosis in a given manufacturing situation. The prognosis is valid for one, selected production measure (e.g. a quality dimension of one product, like diameters, angles, surface roughness, pressure, basis position, etc.) but the applied model takes into account the past values of many other, related production data collected typically on the shop-floor, too. Consequently, it is useful in batch or (customized) mass production environments. The proposed solution is applicable to realize production control inside the tolerance limits to proactively avoid the production process going outside from the given upper and lower tolerance limits.The solution was developed and validated on real data collected on the shop-floor; the paper also summarizes the validated application results of the proposed methodology.

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“…Consequently, comprehensive studies have been carried out to explain the failures of PV modules. For instance, Quintana et al [2] reported the different degradation mechanisms of aged PV modules in the field, Chattopadhyay et al [3] presented an analysis of degradation data of the Indian survey on PV modules, Cristaldi et al [4] proposed a diagnostic monitoring architecture based upon the analysis of the PV module failure causes, and a detailed failure analysis of PV module was reported by an international energy agency in [5], etc. Based on the available studies, seven main failure causes for a PV module can be identified in the field: dust, host spot, corrosion, cell cracks, semiconductor ageing, and broken interconnects and soldier busses.…”

Section: Introductionmentioning

confidence: 99%

Markov process reliability model for photovoltaic module failures

2017

Self Cite

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This paper presents a Markov process reliability model for a photovoltaic module. This model includes all the possible failures associated to the operation of a Photovoltaic (PV) module. Failure modes are identified in detail, afterwards all the possible failure states of the PV module during the service are considered. Finally, a complete Markov process is attained to assess the probability of each failure mode occurrence and estimate the mean time to failure, probability density function of the time to module failure, hazard and survival functions of a PV module.

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Diagnostic architecture: A procedure based on the analysis of the failure causes applied to photovoltaic plants

Cited by 46 publications

References 41 publications

Fault diagnosis of Photovoltaic Modules

Fault diagnosis of Photovoltaic Modules

Production trend identification and forecast for shop-floor business intelligence

Markov process reliability model for photovoltaic module failures

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