Open AccessEvaluation of artificial intelligence techniques used in the diagnosis of failures in power plants
Author(s) -
Jesus Filander Caratar Chaux,
Andrés Mauricio-Valencia,
Gladys Caicedo-Delgado,
Cristian Chamorro
Publication year - 2020
Publication title -
respuestas
Language(s) - English
Resource type - Journals
eISSN - 2422-5053
pISSN - 0122-820X
DOI - 10.22463/0122820x.2966
Subject(s) - computer science , hydroelectricity , novelty , artificial intelligence , artificial neural network , fuzzy logic , fault (geology) , focus (optics) , electric power system , novelty detection , machine learning , reliability engineering , power (physics) , engineering , philosophy , physics , theology , optics , quantum mechanics , seismology , geology , electrical engineering
This article presents an evaluation about the research related to the development of computational tools based on artificial intelligence techniques, which focus on the detection and diagnosis of faults in the different processes associated with a power generation plant such as: hydroelectric, thermoelectric and nuclear power plants. Initially, the main techniques of artificial intelligence that allow the construction of intelligent systems in the area of fault diagnosis is described in a general way, techniques such as: fuzzy logic, neural networks, knowledge-based systems and hybrid techniques Subsequently A summary of the research based on each of these techniques is presented. Subsequently, the different articles found for each of the techniques are presented in tables, illustrating the year of publication and the description of the research carried out. The result of this work is the comparison and evaluation of each technique focused on the diagnosis of failures in power plants. The novelty of this work is that it presents an extensive bibliography of the applications of the different intelligent techniques in solving the problem of detection and diagnosis of failure in power plants