Detection of abnormal photovoltaic systems’ operation with minimum data requirements based on Recursive Least Squares algorithms

In the last years, the massive deployment of new photovoltaic (PV) power plants has launched the connection of PV inverters to the electrical network. A single medium-sized ground-mounted PV plant may have thousands of these inverters linked to the grid and even more PV panels on the DC side. Upon r...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Solar energy Jg. 274; S. 112556
Hauptverfasser: Laguna, Gerard, Moreno, Pablo, Cipriano, Jordi, Mor, Gerard, Gabaldón, Eloi, Luna, Alvaro
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 15.05.2024
Schlagworte:
Energy prediction
Fault detection in PV plants
Low-data methods
Machine learning
Renewable energy
Smart grids
Energy prediction
Low-data methods
Fault detection in PV plants
Smart grids
Renewable energy
Machine learning
ISSN:0038-092X
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:In the last years, the massive deployment of new photovoltaic (PV) power plants has launched the connection of PV inverters to the electrical network. A single medium-sized ground-mounted PV plant may have thousands of these inverters linked to the grid and even more PV panels on the DC side. Upon reaching such a substantial magnitude of devices involved in grid-connected installations, the effective operation, management, predictive maintenance, and fault detection becomes increasingly challenging without integrating advanced prediction and automated anomaly detection systems. Artificial intelligence algorithms, grounded in data measurements, can be pivotal in addressing this challenge. This paper proposes several regression-based methods to predict PV plants’ energy generation, which is useful for detecting transient and long-term anomalies. These models are trained using a Recursive Least Squares (RLS) method and require a minimum number of variables to yield satisfactory outcomes, which is one of the paper’s contributions. They mainly rely on energy generation measurements and geolocation. Within the scope of this research, two distinct algorithms have been implemented and validated. The first algorithm, a simplified model, is engineered to analyse the daily efficiency variation, prioritizing the identification of faults and abnormal operational profiles in PV plants. On the other hand, the second algorithm adopts a more intricate model tailored to facilitate long-term diagnosis, enabling the assessment of PV efficiency degradation. In this work, both algorithms are described and their performance is validated using the historical data from more than 20 PV plants placed in different climatic regions. •Photovoltaic systems’ modelling using linear regression models.•Photovoltaic fault detection using linear regression coefficients evaluation.•Automatic fault detection for photovoltaic plants with low sensors’ availability.•Short-term and long-term efficiency trend evaluation of photovoltaic plants.
ISSN:0038-092X
DOI:10.1016/j.solener.2024.112556