Design of an Adaptive Neurofuzzy Inference Control System for the Unified Power-Flow Controller

Farrag, Mohamed and Putrus, Ghanim (2012) Design of an Adaptive Neurofuzzy Inference Control System for the Unified Power-Flow Controller. IEEE Transactions on Power Delivery, 27 (1). pp. 53-61. ISSN 0885-8977

[img]
Preview
PDF
Farag_IEEE_Transactions_on_Power_Delivery_27_1.pdf - Accepted Version

Download (402kB) | Preview
Official URL: http://dx.doi.org/10.1109/TPWRD.2011.2171061

Abstract

This paper presents a new approach to control the operation of the unified power-flow controller (UPFC) based on the adaptive neurofuzzy inference controller (ANFIC) concept. The training data for the controller are extracted from an analytical model of the transmission system incorporating a UPFC. The operating points' space is dynamically partitioned into two regions: 1) an inner region where the desired operating point can be achieved without violating any of the UPFC constraints and 2) an outer region where it is necessary to operate the UPFC beyond its limits. The controller is designed to achieve the most appropriate operating point based on the real power priority. In this study, the authors investigated and analyzed the effect of the system short-circuit level on the UPFC operating feasible region which defines the limitation of its parameters. In order to illustrate the effectiveness of the control algorithm, simulation and experimental studies have been conducted using the MATLAB/SIMULINK and dSPACE DS1103 data-acquisition board. The obtained results show a clear agreement between simulation and experimental results which verify the effective performance of the ANFIC controller.

Item Type: Article
Additional Information: Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
Uncontrolled Keywords: artificial intelligence , flexible ac transmission systems , fuzzy , neural networks , unified power-flow controller (UPFC)
Subjects: H900 Others in Engineering
J500 Materials Technology not otherwise specified
Department: Faculties > Engineering and Environment > Mathematics, Physics and Electrical Engineering
Depositing User: Ay Okpokam
Date Deposited: 24 Jan 2012 10:44
Last Modified: 24 Oct 2017 16:22
URI: http://nrl.northumbria.ac.uk/id/eprint/5113

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics


Policies: NRL Policies | NRL University Deposit Policy | NRL Deposit Licence