Online voltage prediction using gaussian process regression for fault-tolerant photovoltaic standalone applications

Sanz Alcaine, José Miguel (Universidad de Zaragoza) ; Sebastián, Eduardo (Universidad de Zaragoza) ; Sanz-Gorrachategui, Iván (Universidad de Zaragoza) ; Bernal-Ruiz, Carlos (Universidad de Zaragoza) ; Bono-Nuez, Antonio (Universidad de Zaragoza) ; Pajovic, Milutin ; Orlik, Philip V.

Resumen: This paper presents a fault detection system for photovoltaic standalone applications based on Gaussian Process Regression (GPR). The installation is a communication repeater from the Confederacion Hidrografica del Ebro (CHE), public institution which manages the hydrographic system of Aragon, Spain. Therefore, fault-tolerance is a mandatory requirement, complex to fulfill since it depends on the meteorology, the state of the batteries and the power demand. To solve it, we propose an online voltage prediction solution where GPR is applied in a real and large dataset of two years to predict the behavior of the installation up to 48 hour. The dataset captures electrical and thermal measures of the lead-acid batteries which sustain the installation. In particular, the crucial aspect to avoid failures is to determine the voltage at the end of the night, so different GPR methods are studied. Firstly, the photovoltaic standalone installation is described, along with the dataset. Then, there is an overview of GPR, emphasizing in the key aspects to deal with real and large datasets. Besides, three online recursive multistep GPR model alternatives are tailored, justifying the selection of the hyperparameters: Regular GPR, Sparse GPR and Multiple Experts (ME) GPR. An exhaustive assessment is performed, validating the results with those obtained by Long Short-Term Memory (LSTM) and Nonlinear Autoregressive Exogenous Model (NARX) networks. A maximum error of 127 mV and 308 mV at the end of the night with Sparse and ME, respectively, corroborates GPR as a promising tool.

Idioma: Inglés

DOI: 10.1007/s00521-021-06254-6

Año: 2021

Publicado en: Neural Computing and Applications 33 (2021), 16577–16590
ISSN: 0941-0643

Factor impacto JCR: 5.102 (2021)

Categ. JCR: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE rank: 45 / 146 = 0.308 (2021) - Q2 - T1

Factor impacto CITESCORE: 8.7 - Computer Science (Q1)

Factor impacto SCIMAGO: 1.072 - Software (Q1) - Artificial Intelligence (Q1)

Financiación: info:eu-repo/grantAgreement/ES/DGA-FEDER/RIS3-LMP16-18
Financiación: info:eu-repo/grantAgreement/ES/MINECO/RTC2015-3358-5

Tipo y forma: Artículo (Versión definitiva)

Área (Departamento): Área Ingen.Sistemas y Automát. (Dpto. Informát.Ingenie.Sistms.)
Área (Departamento): Área Tecnología Electrónica (Dpto. Ingeniería Electrón.Com.)