Data-Driven Two-Stage Fault Detection and Diagnosis Method for Photovoltaic Power Generation

Detection of abnormal photovoltaic (PV) system operation is essential to ensure safe and uninterrupted performance. In this study, the authors present a data-driven two-stage method for PV fault detection and diagnosis (FDD). We exploit an inherent characteristic of PV systems, i.e., voltage and cur...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:IEEE transactions on instrumentation and measurement 2024, Vol.73, p.1-11
Hauptverfasser: Ha, Jihun, Ram, J. Prasanth, Kim, Young-Jin, Hong, Junho
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Detection of abnormal photovoltaic (PV) system operation is essential to ensure safe and uninterrupted performance. In this study, the authors present a data-driven two-stage method for PV fault detection and diagnosis (FDD). We exploit an inherent characteristic of PV systems, i.e., voltage and current changes at maximum power point (MPP) caused by faults. In the first stage, fault occurrences are detected using predefined criteria based on the MPP values. The second stage employs [Formula Omitted]–[Formula Omitted] characteristic curve data and a densely connected convolutional network (DenseNet) model to diagnose the fault type. The DenseNet model is rigorously trained using a very large dataset of [Formula Omitted]–[Formula Omitted] curves; this ensures precise and efficient fault diagnosis. We validate our approach via simulations and hardware analyses employing a [Formula Omitted] PV array that initially operates normally, but then develops line-to-line faults (LLFs), open-circuit faults (OCFs), degradation faults (DFs), and partial shading faults (PSFs). We compare our DenseNet-based PV FDD model to the latest PV FDD models. The results confirmed that the new method accurately detect and diagnose PV faults.
ISSN:0018-9456
1557-9662
DOI:10.1109/TIM.2024.3351249