dor_id: 4110109
506.#.#.a: Público
590.#.#.d: Los artículos enviados a la revista "Journal of Applied Research and Technology", se juzgan por medio de un proceso de revisión por pares
510.0.#.a: Scopus, Directory of Open Access Journals (DOAJ); Sistema Regional de Información en Línea para Revistas Científicas de América Latina, el Caribe, España y Portugal (Latindex); Indice de Revistas Latinoamericanas en Ciencias (Periódica); La Red de Revistas Científicas de América Latina y el Caribe, España y Portugal (Redalyc); Consejo Nacional de Ciencia y Tecnología (CONACyT); Google Scholar Citation
561.#.#.u: https://www.icat.unam.mx/
650.#.4.x: Ingenierías
336.#.#.b: article
336.#.#.3: Artículo de Investigación
336.#.#.a: Artículo
351.#.#.6: https://jart.icat.unam.mx/index.php/jart
351.#.#.b: Journal of Applied Research and Technology
351.#.#.a: Artículos
harvesting_group: RevistasUNAM
270.1.#.p: Revistas UNAM. Dirección General de Publicaciones y Fomento Editorial, UNAM en revistas@unam.mx
590.#.#.c: Open Journal Systems (OJS)
270.#.#.d: MX
270.1.#.d: México
590.#.#.b: Concentrador
883.#.#.u: https://revistas.unam.mx/catalogo/
883.#.#.a: Revistas UNAM
590.#.#.a: Coordinación de Difusión Cultural
883.#.#.1: https://www.publicaciones.unam.mx/
883.#.#.q: Dirección General de Publicaciones y Fomento Editorial
850.#.#.a: Universidad Nacional Autónoma de México
856.4.0.u: https://jart.icat.unam.mx/index.php/jart/article/view/646/630
100.1.#.a: Selvaraju, Ramesh Kumar; Somaskandan, Ganapathy
524.#.#.a: Selvaraju, Ramesh Kumar, et al. (2017). ACS algorithm tuned ANFIS-based controller for LFC in deregulated environment. Journal of Applied Research and Technology; Vol. 15 Núm. 2. Recuperado de https://repositorio.unam.mx/contenidos/4110109
245.1.0.a: ACS algorithm tuned ANFIS-based controller for LFC in deregulated environment
502.#.#.c: Universidad Nacional Autónoma de México
561.1.#.a: Instituto de Ciencias Aplicadas y Tecnología, UNAM
264.#.0.c: 2017
264.#.1.c: 2019-06-06
653.#.#.a: Adaptive network-based fuzzy inference system; Artificial cooperative search algorithm; Deregulated power system; Load frequency control
506.1.#.a: La titularidad de los derechos patrimoniales de esta obra pertenece a las instituciones editoras. Su uso se rige por una licencia Creative Commons BY-NC-SA 4.0 Internacional, https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode.es, para un uso diferente consultar al responsable jurídico del repositorio por medio del correo electrónico gabriel.ascanio@icat.unam.mx
884.#.#.k: https://jart.icat.unam.mx/index.php/jart/article/view/646
001.#.#.#: 074.oai:ojs2.localhost:article/646
041.#.7.h: eng
520.3.#.a: In this paper, an artificial cooperative search (ACS) algorithm tuned adaptive network-based fuzzy inference system (ANFIS) controller for optimal gain tuning of load frequency control (LFC) operation in deregulated scenario has been offered. The conventional controllers for load frequency control operation are having fixed gain values intended for nominal operating conditions of the power system and they do not afford effective and efficient performance over a large range of operating scenarios in the deregulated environment. To progress the system performance to its near optimum for all probable operating circumstances of the power system, the controller gains have to be computed for the equivalent operating conditions by using the restructured parameters. For this intention, a controller based on an adaptive network-based fuzzy inference system seems to be the most excellent and valuable preference. The ANFIS is trained by off-line data obtained using a new optimization technique, artificial cooperative search optimization algorithm and the corresponding gains are updated in real-time as per the changing operating conditions. ACS is a swarm intelligence algorithm developed for solving numerical optimization problems. The swarm intelligence philosophy behind ACS algorithmis based on the migration of two artificial superorganisms as they biologically interact to achieve the global minimum value pertaining to the problem. To exhibit the competence and robustness of the projected ACS algorithm tuned ANFIS controller, the controller has been implementedon a two-area two-unit interconnected deregulated power system having one reheat unit and one non-reheat unit in each area. The simulation results exhibit the ability of the designed ACS algorithm tuned ANFIS controller for online LFC operation in deregulated environment.
773.1.#.t: Journal of Applied Research and Technology; Vol. 15 Núm. 2
773.1.#.o: https://jart.icat.unam.mx/index.php/jart
022.#.#.a: ISSN electrónico: 2448-6736; ISSN: 1665-6423
310.#.#.a: Bimestral
264.#.1.b: Instituto de Ciencias Aplicadas y Tecnología, UNAM
doi: https://doi.org/10.1016/j.jart.2017.01.010
harvesting_date: 2023-11-08 13:10:00.0
856.#.0.q: application/pdf
file_creation_date: 2017-04-11 11:43:50.0
file_modification_date: 2017-04-11 07:25:18.0
file_creator: Ramesh Kumar Selvaraju
file_name: e35c95cb351724169b76bb810080b45a30d6bedb0611c1df02cc54f2a290cc78.pdf
file_pages_number: 15
file_format_version: application/pdf; version=1.7
file_size: 1730047
last_modified: 2024-03-19 14:00:00
license_url: https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode.es
license_type: by-nc-sa
No entro en nada
No entro en nada 2