Contribution to fault detection of PV modules using I-V curves Article Swipe
Continuous monitoring of the health status of PV modules is mandatory to maintain high efficiency and minimize power losses due to faults or failures. In this work, a low-cost embedded tracer is developed and optimized to measure the I-V curve in less than 0.2 s to minimize the duration of power generation interruption. The proposed tracer is validated with a commercial analyzer. The experimental data is used to validate the analytical model of the PV module. This model is based on the single diode electrical circuit's five parameters (Iph,Rs,Rsh,I0 and n) . It is combined with the Matlab-Simulink numerical model to set up the hybrid model that will be used as a reference for the diagnosis. This model is validated with a relative error of less than 3% for several environmental data (irradiance and temperature). The measured data are used to extract the five parameters of the equivalent electrical model and the main characteristics of the I-V curve (current, voltage, Voc, Isc, and Pmpp). The measured I-V curves are also used to evaluate two fault diagnosis methods (denoted M1 and M2). The method M1 uses the analytical models of the five parameters(Iph,Rs,Rsh,I0 and n) while M2 uses the five characteristics (Ipv,Vpv,Pmpp,Voc and Isc) of the I-V curves as fault features, and the hybrid model to generate the I-V reference curves. The residuals are calculated between the fault indicators extracted from the experimental measurements and those from the reference curves. Three fault cases were studied: degradation of the series resistance, degradation of the shunt resistance, and partial shading. The results based on experimental data, obtained under different temperatures and illuminations, showed that the I-V curves' characteristics are more sensitive to series and shunt resistance degradation and partial shading than the parameters.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://www.theses.fr/2022UPAST149/document
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4392726354
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4392726354Canonical identifier for this work in OpenAlex
- Title
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Contribution to fault detection of PV modules using I-V curvesWork title
- Type
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preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-11-25Full publication date if available
- Authors
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Vorachack KongphetList of authors in order
- Landing page
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https://www.theses.fr/2022UPAST149/documentPublisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://www.theses.fr/2022UPAST149/documentDirect OA link when available
- Concepts
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Fault detection and isolation, Computer science, Reliability engineering, Engineering, Artificial intelligence, ActuatorTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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