Volume 10, Issue 3 (September 2014)                   IJEEE 2014, 10(3): 212-222 | Back to browse issues page

XML Print

Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Khoshsaadat A, Mosavi M R, Moghani J S. A Controller Design with ANFIS Architecture Attendant Learning Ability for SSSC-Based Damping Controller Applied in Single Machine Infinite Bus System. IJEEE 2014; 10 (3) :212-222
URL: http://ijeee.iust.ac.ir/article-1-599-en.html
A Controller Design with ANFIS Architecture Attendant Learning Ability for SSSC-Based Damping Controller Applied in Single Machine Infinite Bus System
A. Khoshsaadat , M. R. Mosavi , J. S. Moghani
Abstract:   (5601 Views)
Static Synchronous Series Compensator (SSSC) is a series compensating Flexible AC Transmission System (FACTS) controller for maintaining to the power flow control on a transmission line by injecting a voltage in quadrature with the line current and in series mode with the line. In this work, an Adaptive Network-based Fuzzy Inference System controller (ANFISC) has been proposed for controlling of the SSSC-based damping system and applied to a Single Machine Infinite Bus (SMIB) power system. For implementation of the learning process in this controller, we use of the one approach of the learning ability that named as Forward Signal and Backward Error Back-Propagation (FSBEBP) method for improving of the system efficiency. This artificial intelligence-based control model leads to a controller with adaptive structure, improved correctness, high damping ability and dynamic performance. System implementation is easy and it requires 49 fuzzy rules for inference engine of the system. As compared with the other complex neuro-fuzzy systems, this controller has medium number of the fuzzy rules and low number of layers, but it has high accuracy. In order to demonstrate of the proposed controller ability, it is simulated and its output compared with that of classic Lead-Lag-based Controller (LLC) and PI controller.
Keywords: ANFISC , Inference , Learning , LLC , SSSC , SMIB
Full-Text [PDF 573 kb]   (2883 Downloads)    
Type of Study: Research Paper | Subject: System Dynamics and Control
Received: 2013/07/07 | Revised: 2014/09/28 | Accepted: 2014/09/24
Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Creative Commons License
© 2022 by the authors. Licensee IUST, Tehran, Iran. This is an open access journal distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license.