Fault Detection and Diagnosis of PV Systems Using Kalman-Filter Algorithm Based on Multi-zone Polynomial Regression
| dc.rights.license | open | en_US |
| hal.structure.identifier | ESTIA - Institute of technology [ESTIA] | |
| dc.contributor.author | AL RIFAI, Yehya
ORCID: 0000-0002-7195-2346 | |
| hal.structure.identifier | ESTIA - Institute of technology [ESTIA] | |
| dc.contributor.author | AGUILERA GONZALEZ, Adriana
ORCID: 0000-0003-1166-0648 IDREF: 253127653 | |
| hal.structure.identifier | ESTIA - Institute of technology [ESTIA] | |
| dc.contributor.author | VECHIU, Ionel
ORCID: 0000-0003-4108-3546 IDREF: 102417741 | |
| dc.date.accessioned | 2024-09-23T13:30:18Z | |
| dc.date.available | 2024-09-23T13:30:18Z | |
| dc.date.issued | 2023-06-14 | |
| dc.identifier.uri | https://oskar-bordeaux.fr/handle/20.500.12278/201746 | |
| dc.description.abstractEn | The correct functioning of the PV system with the desired operational efficiency, under all circumstances, has become a strategic research challenge. Toward this end, faults must be timely diagnosed, detected, and identified to enhance reliability, maintainability, and safety of the PV system. In this context, this paper presents a novel FDD methodology based on Kalman filter (KF) through a model-based approach for online health monitoring of the PV system’s DC side. This approach is formulated on the comparison of two different regression analysis techniques of PV characteristics under global max power point (GMPP) at array level. The first is based on a non-linear polynomial regression equation, whereas the second is based on a multivariate regression method. In particular, the proposed methods show their effectiveness to detected anomalies at low-current with sensor-less irradiation. The performance of the proposed diagnosis methods is evaluated under Matlab/Simulink® with varying environmental conditions. | |
| dc.language.iso | EN | en_US |
| dc.source.title | Recent Developments in Model-Based and Data-Driven Methods for Advanced Control and Diagnosis. ACD 2022. Studies in Systems, Decision and Control | en_US |
| dc.subject.en | PV system | |
| dc.subject.en | Monitoring | |
| dc.subject.en | Kalman filter | |
| dc.subject.en | Non-linear polynomial regression | |
| dc.subject.en | Multivariate polynomial regression | |
| dc.subject.en | Fault diagnosis and detection | |
| dc.title.en | Fault Detection and Diagnosis of PV Systems Using Kalman-Filter Algorithm Based on Multi-zone Polynomial Regression | |
| dc.type | Chapitre d'ouvrage | en_US |
| dc.subject.hal | Sciences de l'ingénieur [physics] | en_US |
| bordeaux.hal.laboratories | ESTIA - Recherche | en_US |
| bordeaux.institution | Université de Bordeaux | en_US |
| bordeaux.inpress | non | en_US |
| hal.popular | non | en_US |
| hal.audience | Internationale | en_US |
| hal.export | false | |
| workflow.import.source | dissemin | |
| dc.rights.cc | Pas de Licence CC | en_US |
| bordeaux.COinS | ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.btitle=Recent%20Developments%20in%20Model-Based%20and%20Data-Driven%20Methods%20for%20Advanced%20Control%20and%20Diagnosis.%20ACD%202022.%20Studies%20in%20Systems,%20Decision%2&rft.date=2023-06-14&rft.au=AL%20RIFAI,%20Yehya&AGUILERA%20GONZALEZ,%20Adriana&VECHIU,%20Ionel&rft.genre=unknown |
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