Simulation of fault detection in photovoltaic arrays
In solar systems, faults in the module and inverter occur in proportion to increased operating time. The identification of fault types and their effects is important information not only for manufacturers but also for investors, solar operators and researchers. Monitoring and diagnosing the conditio...
Elmentve itt :
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| Dokumentumtípus: | Cikk |
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2021
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| Sorozat: | Analecta technica Szegedinensia
15 No. 2 |
| Kulcsszavak: | Napenergia felhasználása, Megújuló energiaforrás - hasznosítás |
| Tárgyszavak: | |
| Online Access: | http://acta.bibl.u-szeged.hu/74112 |
| Tartalmi kivonat: | In solar systems, faults in the module and inverter occur in proportion to increased operating time. The identification of fault types and their effects is important information not only for manufacturers but also for investors, solar operators and researchers. Monitoring and diagnosing the condition of photovoltaic (PV) systems is becoming essential to maximize electric power generation, increase the reliability and lifetime of PV power plants. Any faults in the PV modules cause negative economic and safety impacts, reducing the performance of the system and making unwanted electric connections that can be dangerous for the user. In this paper have been classified all possible faults that happen in the PV system, and is presented to detect common PV array faults, such as open-circuit fault, line-to-line fault, ground fault, shading condition, degradation fault and bypass diode fault. In this studies examines the equivalent circuits of PV arrays with different topological configurations and fault conditions to evaluate the effects of these faults on the performance of a solar system, taking into account the influence of temperature and solar radiation. This work presents the validation of a simulated solar network by measuring the output curves of a low-power photovoltaic array system under real outdoor conditions. This method can be useful in future solar systems. |
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| Terjedelem/Fizikai jellemzők: | 31-40 |
| ISSN: | 2064-7964 |