@INPROCEEDINGS { Rsds2544,
author = {Ramanna, Sheela and Feng, H. and Peters, James F.}, 
title = {Adaptive Granular Control of an HVDC System: A Rough Set Approach}, 
booktitle = {Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing}, 
series =  = {Lecture Notes in Artificial Intelligence}, 
volume = 2639, 
pages = {213--220}, 
publisher = {Springer-Verlag}, 
address = {Heidelberg - Berlin, Germany}, 
month = {May}, 
year = 2003, 
editor = {Wang, Guoying and Liu, Quing and Yao, Yiyu and Skowron, Andrzej}, 
issn = {0302-9743}, 
isbn = {3-540-14040-9}, 
abstract = {This article reports the results of a three-year study of adaptive granular control of High-Voltage Direct Current (HVDC) systems using a combination of rough sets and granular computing techniques. A proportional integral (PI) control strategy is commonly used for constant current and extinction angle control in an HVDC system. A PI control strategy is based on a static design where the gains of a PI controller are fixed. Since the response of a HVDC plant dynamically changes with variations in the operating point, a PI controller's performance is far from optimal. By contrast, an adaptive controller makes changes in the gains relative to the observed changes in HVDC system behavior. However, adaptive controllers require for their design, a frequency domain model of the controlled plant. Due to the non-linear operation of the HVDC system, such a model is difficult to establish. Because rough set theory makes it possible to set up a decision-making utility that approximates a control engineer's nowledge about how to tune the controller of a system to improve its behavior, rough sets can be used to design an adaptive controller for the HVDC system. The contribution of this paper is the presentation of the design of a rough set based, granular control scheme. Experimental results that compare the performance of the adaptive control and PI control schemes are also given.}, 
keywords = {adaptive control, granular computing, HVDC, rough sets, }, 
}