Paper Information

Title: 

PROBABILISTIC STATIC SECURITY ASSESSMENT OF POWER SYSTEMS USING SELF-ORGANIZING MAP

Type: PAPER
Author(s): KAMYAB G.R.,SADATI N.,FOTOOHI M.,ALISHAHI S.
 
 
 
Name of Seminar: INTERNATIONAL POWER SYSTEM CONFRENCE
Type of Seminar:  CONFERENCE
Sponsor:  SHERKATE TAVANIR
Date:  2004Volume 19
 
 
Abstract: 

DUE TO SOME UNCERTAINTIES IN POWER SYSTEMS SUCH AS LOAD CHANGES OR TRANSMISSION LINE OUTAGES, PROBABILISTIC ASSESSMENT OF STATIC SECURITY OF POWER SYSTEMS ARE NECESSARY. PROBABILISTIC ASSESSMENT HAS MORE IMPORTANT IN DEREGULATED ELECTRIC POWER INDUSTRY. IN THIS PAPER THE OCCURRENCE OF TRANSMISSION LINE OUTAGES AND LOADS ARE ASSUMED AS PROBABILISTIC. WE DIVIDE THE SYSTEM OPERATING STATES INTO NORMAL AND ABNORMAL STATES. WE DEFINE THE PROBABILITY OF THE NORMAL STATE AS SECURITY LEVEL INDEX. TO COMPUTE THIS INDEX, WE TRAIN A KOHONEN’S SELF-ORGANIZING MAP NEURAL NETWORK IN THE SPACE OF INPUT SIGNALS IN OFFLINE, TO CLASSIFY THE NORMAL AND ABNORMAL POWER SYSTEM STATES. THEN WE UTILIZE THE TRAINED NEURAL NETWORK TO COMPUTE THE SECURITY INDEX BY MONTE CARLO SIMULATION METHOD. IN THIS METHOD, WE ARE NOT RESTRICTED BY TYPE OF LOAD PROBABILITY DISTRIBUTION. ALSO WE DON’T HAVE ANY PROBLEM FOR POWER SYSTEM COMPLEXITY. IN THIS METHOD, WE NEED A LOW REPETITION FOR ANALYZING OF THE POWER SYSTEMS. THE COMPUTATION RESULTS ON A TYPICAL POWER SYSTEM, INDICATES THAT THE PROPOSED METHOD HAVE SUFFICIENT ACCURACY AND IS SUITABLE TO UTILIZE FOR REAL TIME.

 
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