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21 October 2024
Reseach Article

Methodology for Anomaly Detection and Alert Generation in Photovoltaic Systems

by Roberto Alexandre Dias, Mário de Noronha Neto, Gregory Chagas da Costa Gomes, Fernando Benites, Stefan Gürtler, Siqueira de Moraes Neto, Níkola Zaia de Figueiredo
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 186 - Number 36
Year of Publication: 2024
Authors: Roberto Alexandre Dias, Mário de Noronha Neto, Gregory Chagas da Costa Gomes, Fernando Benites, Stefan Gürtler, Siqueira de Moraes Neto, Níkola Zaia de Figueiredo
10.5120/ijca2024923966

Roberto Alexandre Dias, Mário de Noronha Neto, Gregory Chagas da Costa Gomes, Fernando Benites, Stefan Gürtler, Siqueira de Moraes Neto, Níkola Zaia de Figueiredo . Methodology for Anomaly Detection and Alert Generation in Photovoltaic Systems. International Journal of Computer Applications. 186, 36 ( Aug 2024), 9-15. DOI=10.5120/ijca2024923966

@article{ 10.5120/ijca2024923966,
author = { Roberto Alexandre Dias, Mário de Noronha Neto, Gregory Chagas da Costa Gomes, Fernando Benites, Stefan Gürtler, Siqueira de Moraes Neto, Níkola Zaia de Figueiredo },
title = { Methodology for Anomaly Detection and Alert Generation in Photovoltaic Systems },
journal = { International Journal of Computer Applications },
issue_date = { Aug 2024 },
volume = { 186 },
number = { 36 },
month = { Aug },
year = { 2024 },
issn = { 0975-8887 },
pages = { 9-15 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume186/number36/methodology-for-anomaly-detection-and-alert-generation-in-photovoltaic-systems/ },
doi = { 10.5120/ijca2024923966 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-08-26T20:51:51+05:30
%A Roberto Alexandre Dias
%A Mário de Noronha Neto
%A Gregory Chagas da Costa Gomes
%A Fernando Benites
%A Stefan Gürtler
%A Siqueira de Moraes Neto
%A Níkola Zaia de Figueiredo
%T Methodology for Anomaly Detection and Alert Generation in Photovoltaic Systems
%J International Journal of Computer Applications
%@ 0975-8887
%V 186
%N 36
%P 9-15
%D 2024
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This article presents a methodology for identifying anomalies and generating alerts during the management of photovoltaic plants. The approach is mainly based on the analysis of AC power from inverters, eliminating the need for additional instrumentation. The methodology can be enhanced with solar irradiance data, enabling more precise anomaly detection and alert generation based on the Performance Ratio (PR) concept. The autoencoder technique was employed to detect anomalies in inverters using custom models based on equipment size, region, as well as specific times of day and year. Alert generation considers the quantity of detected anomalies and PR variation over a 30-day period. To validate the results, plants with previously recorded shading and 5k inverters in the regions of Santa Catarina and São Paulo (Brazil) were used. The obtained results demonstrated excellent performance in plant management, allowing for the analysis of anomaly recurrence and alert level variations over time.

References
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Index Terms

Computer Science
Information Sciences
Anomaly Detection; Pattern Recognition; Photovoltaic Fault Detection; Machine Learning

Keywords

Anomaly Detection; Machine Learning; Photovoltaic Systems; Autoencoder; Fault Detection