Evaluación de técnicas de inteligencia artificial utilizadas en el diagnóstico de fallas en plantas de potencia
Evaluation of artificial intelligence techniques used in the diagnosis of failures in power plants
Barra lateral del artículo
PDF (English)
FLIP
(English)
HTML
Contenido principal del artículo
Perfil Google Scholar
Este artículo presenta una evaluación de herramientas computacionales basadas en técnicas de inteligencia artificial, las cuales se enfocan en la detección y diagnóstico de fallas en los diferentes procesos asociados a una central de generación de energía tal como: hidroeléctricas, termoeléctricas y centrales nucleares. Inicialmente, se describen de manera general las principales técnicas de inteligencia artificial que permiten la construcción de sistemas inteligentes para el diagnóstico de fallas en centrales eléctricas, se presentan técnicas como: lógica difusa, redes neuronales, sistemas basados en el conocimiento y técnicas hibridas. Posteriormente se presentan en tablas los diferentes artículos encontrados para cada una de las técnicas, ilustrando el año de publicación y una descripción de cada publicación. El resultado de este trabajo es la comparación y evaluación de cada técnica enfocada al diagnóstico de fallas en centrales eléctricas. Lo novedoso de este trabajo, es que presenta una extensa bibliografía de las aplicaciones de las diferentes técnicas inteligentes en la solución del problema de detección y diagnóstico de falla en centrales de generación eléctrica
Descargas
Detalles del artículo
T. Wildi, R. Navarro Salas, and L. M. Ortega González, Máquinas eléctricas y sistemas de potencia, Sexta edic. Mexico: Pearson Educación, 2007.
F. cembranos N. Jesus, Automatismos Electricos Neumaticos E Hidraulicos, Quinta. Thomson, 2008.
A. R. Penin, Sistemas SCADA, 2nd ed. 2007.
J. Roldán Viloria, Fuentes de energía, 1st ed. Madrid España: Paraninfo, 2008.
M. A. A. Larrahondo and A. J. B. Arias, “Desastres en Plantas Nucleares,” Bucaramanga, 2000.
B. Shan, D. Zhao, X. Zhang, F. Guan, and Z. Liu, “Research on relay protection setting expert system for main equipment in power plant,” 1st Int. Conf. Sustain. Power Gener. Supply, SUPERGEN ’09, pp. 1–4, 2009.
H. Arroyo, E. L. Tigre, L. A. Máquina, D. E. E. Como, and D. E. Diseño, “Sistema de automatización, supervisión y control del ‘aprovechamiento hidroeléctrico arroyo el tigre’. la máquina de estado como herramienta de diseño.,” Av. en Energías Renov. y Medio Ambient., vol. 13, pp. 195–201, 2009.
S. Ramirez, Protección de Sistemas Eléctricos, 1st ed. Universidad Nacional de Colombia Manizales, 2005.
P. P. Cruz, Inteligencia artificial con aplicaciones a la ingeniería, 1st ed. Alfaomega, 2011.
G. R. Joseph Giarratano, Sistemas expertos: principios y programación. Thomson, 2001.
R. P. Marcos, “Fundamentos De La Lógica Difusa,” Ing. e Investig., vol. 3, pp. 101–101, 2000.
A. Evsukoff and S. Gentil, “Recurrent neuro-fuzzy system for fault detection and isolation in nuclear reactors,” Adv. Eng. Informatics, vol. 19, no. 1, pp. 55–66, 2005.
J. Falqueto and M. S. Telles, “Automation of diagnosis of electric power transformers in Itaipu Hydroelectric Plant with a fuzzy expert system,” IEEE Int. Conf. Emerg. Technol. Fact. Autom. ETFA, pp. 577–584, 2007.
J. a. Calderón, G. Zapata, and D. Ovalle, “Algoritmo Neuro Difuso para la Detección y Clasificación de Fallas en Líneas de Transmisión Eléctrica Usando ANFIS,” Rev. Av. en Sist. e Informática, vol. 4, no. 1, 2007.
E. J. Amaya and A. J. Alvares, “SIMPREBAL: An expert system for real-time fault diagnosis of hydrogenerators machinery,” Proc. 15th IEEE Int. Conf. Emerg. Technol. Fact. Autom. ETFA 2010, 2010.
Y. Ting, W. B. Lu, C. H. Chen, and G. K. Wang, “A fuzzy reasoning design for fault detection and diagnosis of a computer-controlled system,” Eng. Appl. Artif. Intell., vol. 21, no. 2, pp. 157–170, 2008.
K. El-kobbah, I. Conference, and A. M. Aboshosha, “Neurofuzzy Computing aided Fault Diagnosis of Nuclear Power Reactors,” 7th Int. Conf. Electr. Eng. ICEENG 2010, pp. 25–27, 2010.
P.-C. Lin, M.-T. Yang, and J.-C. Gu, “Intelligent maintenance model for condition assessment of circuit breakers using fuzzy set theory and evidential reasoning,” IET Gener. Transm. Distrib., vol. 8, no. 7, pp. 1244–1253, 2014.
P. N. Montes Dorantes, J. P. Nieto Gonzalez, and G. M. Mendez, “Fault Detection Systems via a Novel hybrid Methodology for Fuzzy Logic Systems based on Individual base inference and Statistical Process Control,” IEEE Lat. Am. Trans., vol. 12, no. 4, pp. 706–712, 2014.
C. Octavio, H. Morales, J. Pablo, N. González, E. Gabriel, and C. Siller, “Detección y diagnóstico de fallas en sistemas eléctricos de potencia (SEP) combinando lógica difusa, métricas y una red neuronal probabilística,” Research in Computing Science, vol. 72. pp. 47–59, 2014.
L. Tarba and P. MacH, “Analysis on quality of diagnostic processes in power electrical engineering using combined methods of lead six sigma and fuzzy approaches,” Proc. Int. Conf. - 2016 Conf. Diagnostics Electr. Eng. Diagnostika, 2016.
D. J. Matich, Redes Neuronales: Conceptos Básicos y Aplicaciones. 2001.
E. J. Amaya Simeón, “Aplicação de Técnicas de Inteligência Artificial no Desenvolvimento de um Sistema de Manutenção Baseada em Condição,” Universidade de Brasília, 2008.
A. A. Bittencourt, M. R. De Carvalho, and J. G. R. M. Ieee, “Adaptive Strategies in Power Systems Protection using Artificial Intelligence Techniques,” 2009 15th Int. Conf. Intell. Syst. Appl. to Power Syst., pp. 1–6, 2009.
W. Chine, A. Mellit, V. Lughi, A. Malek, G. Sulligoi, and A. Massi Pavan, “A novel fault diagnosis technique for photovoltaic systems based on artificial neural networks,” Renew. Energy, vol. 90, pp. 501–512, 2016.
A. D. S. Nicolau, J. P. D. S. C. Augusto, and R. Schirru, “Accident diagnosis system based on real-time decision tree expert system,” AIP Conf. Proc., vol. 1836, 2017.
M. Talaat, M. H. Gobran, and M. Wasfi, “A hybrid model of an artificial neural network with thermodynamic model for system diagnosis of electrical power plant gas turbine,” Eng. Appl. Artif. Intell., vol. 68, no. November 2017, pp. 222–235, 2018.
S. Zhao, C. Jiru, and S. Qian, “Research on artificial intelligent nuclear power plant emergency operating guide,” in International Conference on Nuclear Engineering, Proceedings, ICONE, 2019, vol. 2019-May.
H. A. Saeed, H. Wang, M. Peng, A. Hussain, and A. Nawaz, “Online fault monitoring based on deep neural network & sliding window technique,” Prog. Nucl. Energy, vol. 121, no. January, p. 103236, 2020.
R. P. Díez, A. G. Gómez, and N. de A. Martínez, Introducción a la inteligencia artificial: sistemas expertos, redes neuronales artificiales y computación evolutiva. 2001.
S. D. J. McArthur, J. R. McDonald, S. C. Bell, and G. M. Burt, “Expert systems and model-based reasoning for protection performance analysis,” Artif. Intell. Appl. Power Syst. IEE Colloq., p. 1/1-1/4, 1995.
K. El-Arroudi, D. McGillis, and G. Joos, “A methodology for power system protection design based on an intelligent system approach,” Electr. Comput. …, pp. 1164–1169, 1999.
F. Filippetti, M. Martelli, G. Franceschini, and C. Tassoni, “Development of expert system knowledge base to on-line diagnosis ofnrotor electrical faults of induction motors,” Conf. Rec. 1992 IEEE Ind. Appl. Soc. Annu. Meet., 1992.
T. K. Saha and P. Purkait, “Investigation of an expert system for the condition assessment of transformer insulation based on dielectric response measurements,” IEEE Trans. Power Deliv., vol. 19, no. 3, pp. 1127–1134, 2004.
X. Luo and M. Kezunovic, “An expert system for diagnosis of digital relay operation,” Proc. 13th Int. Conf. Intell. Syst. Appl. to Power Syst. ISAP’05, vol. 2005, pp. 175–180, 2005.
L. Amendola, “Sistemas expertos monitoreo de condiciones en máquinas rotativas,” Valencia, pp. 1–4, 2008.
B. Rodrigo, N. Felipe, C. Aldo, and P. Rodrigo, “Expert fault detection and diagnosis for the refrigeration process of a hydraulic power plant,” Proc. 27th Chinese Control Conf. CCC, pp. 122–126, 2008.
E. J. Amaya and A. J. Alvares, “Expert system for power generation fault diagnosis using hierarchical meta-rules,” Proc. 2012 IEEE 17th Int. Conf. Emerg. Technol. Fact. Autom. (ETFA 2012), pp. 1–8, 2012.
S. Saludes, L. j. de Miguel, and J. R. Perán, “Sistema experto para el mantenimiento predictivo de una central hidroeléctrica,” ResearchGate, no. December 2013, pp. 148–159, 2013.
I. Buaphan and S. Premrudeepreechacharn, “Development of expert system for fault diagnosis of an 8-MW bulb turbine downstream irrigation hydro power plant,” 2017 6th Int. Youth Conf. Energy, IYCE 2017, p. 8003740, 2017.
A. J. Alvares and R. Gudwin, “Integrated system of predictive maintenance and operation of eletronorte based on expert system,” IEEE Lat. Am. Trans., vol. 17, no. 1, pp. 155–166, 2019.
C. Y. Wu X., Guo C., “A new fault diagnosis approach of powe system based on bayesian network and temporal order information,” Proc. CSEE 25, vol. 13, pp. 14–18, 2005.
Z. Yongli, H. Limin, and L. Jinling, “Bayesian networks-Based approach for power systems fault diagnosis,” IEEE Trans. Power Deliv., vol. 21, no. 2, pp. 634–639, 2006.
M. C. Method, “Uncertain Fault,” pp. 1–6, 2006.
Q. Z. Qin Li, Zhi Bin Li, “Research of Power Transformer Fault Diagnosis System Based on Rough Sets and Bayesian Networks,” Adv. Mater. Res., vol. 320, pp. 524–529, 2011.
Y. Zhao, F. Xiao, and S. Wang, “An intelligent chiller fault detection and diagnosis methodology using Bayesian belief network,” vol. 57. pp. 278–288, 2013.
B. Cai et al., “Multi-source information fusion-based fault diagnosis of ground-source heat pump using Bayesian network,” Applied Energy, vol. 114. 2014.
B. Cai, H. Liu, and M. Xie, “A real-time fault diagnosis methodology of complex systems using object-oriented Bayesian networks,” Mech. Syst. Signal Process., vol. 80, pp. 31–44, 2016.
H.-B. Jun and D. Kim, “A Bayesian network-based approach for fault analysis,” Expert Syst. Appl., vol. 81, pp. 332–348, 2017.
G. Wu, J. Tong, L. Zhang, Y. Zhao, and Z. Duan, “Framework for fault diagnosis with multi-source sensor nodes in nuclear power plants based on a Bayesian network,” Ann. Nucl. Energy, vol. 122, pp. 297–308, 2018.
J. Rohmer and P. Gehl, “Sensitivity analysis of Bayesian networks to parameters of the conditional probability model using a Beta regression approach,” Expert Syst. Appl., vol. 145, 2020.
S. Sarkar, T. Sharma, A. Baral, B. Chatterjee, D. Dey, and S. Chakravorti, “An expert system approach for transformer insulation diagnosis combining conventional diagnostic tests and PDC, RVM data,” IEEE Trans. Dielectr. Electr. Insul., vol. 21, no. 2, pp. 882–891, 2014.
Y. Liu, C. Xie, M. Peng, and S. Ling, “Improvement of fault diagnosis efficiency in nuclear power plants using hybrid intelligence approach,” Prog. Nucl. Energy, vol. 76, pp. 122–136, Sep. 2014.
G. Muller and D. Falcão, “A Fuzzy Knowledge-Based System to Assess the Impact of Demand Response on the Long-Term Demand of Electricity: Application to the Brazilian Interconnected Power System,” 2019 IEEE PES Conf. Innov. Smart Grid Technol. ISGT Lat. Am. 2019, p. 8894988, 2019.
F. B. Ismail Alnaimi, R. I. Bin Ismail, P. J. Ker, and S. K. B. Wahidin, “Development of intelligent early warning system for steam turbine,” J. Eng. Sci. Technol., vol. 14, no. 2, pp. 844–858, 2019.
H. Wang, M. jun Peng, J. Wesley Hines, G. yang Zheng, Y. kuo Liu, and B. R. Upadhyaya, “A hybrid fault diagnosis methodology with support vector machine and improved particle swarm optimization for nuclear power plants,” ISA Trans., vol. 95, pp. 358–371, 2019.
Y. Zhao, J. Tong, L. Zhang, and G. Wu, “Diagnosis of operational failures and on-demand failures in nuclear power plants: An approach based on dynamic Bayesian networks,” Ann. Nucl. Energy, vol. 138, p. 107181, 2020.
