Details
Name
Felipe YamadaRole
ResearcherSince
21st March 2022
Nationality
BrasilCentre
Industrial Engineering and ManagementContacts
+351222094000
felipe.yamada@inesctec.pt
2023
Authors
Bacalhau, ET; Barbosa, F; Casacio, L; Yamada, F; Guimarães, L;
Publication
Proceeding of the 33rd European Safety and Reliability Conference
Abstract
2023
Authors
Yamada, L; Rampazzo, P; Yamada, F; Guimaraes, L; Leitao, A; Barbosa, F;
Publication
OPERATIONAL RESEARCH, IO 2022-OR
Abstract
Data clustering combined with multiobjective optimization has become attractive when the structure and the number of clusters in a dataset are unknown. Data clustering is the main task of exploratory data mining and a standard statistical data analysis technique used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. This project analyzes data to extract possible failure patterns in Solar Photovoltaic (PV) Panels. When managing PV Panels, preventive maintenance procedures focus on identifying and monitoring potential equipment problems. Failure patterns such as soiling, shadowing, and equipment damage can disturb the PV system from operating efficiently. We propose a multiobjective evolutionary algorithm that uses different distance functions to explore the conflicts between different perspectives of the problem. By the end, we obtain a non-dominated set, where each solution carries out information about a possible clustering structure. After that, we pursue a-posteriori analysis to exploit the knowledge of non-dominated solutions and enhance the fault detection process of PV panels.
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