Title :
A web based software system for database generation for online dynamic security assessment studies (ML4DSA)
Author :
Geeganage, Janath ; Annakkage, U.D. ; Archer, Brian A. ; Weekes, Tony
Author_Institution :
Dept. of Electr. & Comput. Eng., Univ. of Manitoba, Winnipeg, MB, Canada
Abstract :
This paper presents a software system that generates a database for power system dynamic security assessment. The generated database is intended to be used in machine learning techniques. The development of algorithms to generate data is a very time consuming task. This software tool is aimed at facilitating faster generation of the appropriate database. Further, the system allows the user to plug-in the case specific limit checks and algorithms for system specific corrective actions depending on the type of study. The proposed system automates the Power System Simulator for Engineering (PSSE) which is an industry standard software used in many electrical power utilities. The proposed software system, ML4DSA, is based on Python which is available in the public domain with plenty of supporting communities and powerful libraries. These features enable the user to develop algorithms for system specific corrective actions. The web interface facilitates access to the authenticated users of PSSE over the web, therefore, requires no additional software installed on the client computer. ML4DSA is successfully tested on the 39 Bus New England test system and the Midwest Reliability Organization (MRO) system which has over 50,000 buses.
Keywords :
Internet; learning (artificial intelligence); power engineering computing; power system security; software tools; ML4DSA; PSSE; Python; Web based software system; Web interface; database generation; electrical power utilities; machine learning techniques; midwest reliability organization system; online dynamic security assessment studies; power system dynamic security assessment; power system simulator for engineering; software tool; Databases; Heuristic algorithms; Power system dynamics; Power system stability; Software; Software algorithms; Stability analysis; Dynamic security assessment; Machine learning; Software tools;
Conference_Titel :
Electrical and Computer Engineering (CCECE), 2013 26th Annual IEEE Canadian Conference on
Conference_Location :
Regina, SK
Print_ISBN :
978-1-4799-0031-2
Electronic_ISBN :
0840-7789
DOI :
10.1109/CCECE.2013.6567682