Paper Information

Title: 

A ROBUST AND ADAPTIVE TEMPORAL DIFFERENCE LEARNING BASED MLP NEURAL NETWORK FOR FLEXIBLE AC TRANSMISSION SYSTEMS

Type: PAPER
Author(s): RASHIDI FARZAN,RASHIDI MEHRAN,MONAVAR HAMID,ARJOMAND ABDOLSAHEB
 
 
 
Name of Seminar: INTERNATIONAL POWER SYSTEM CONFRENCE
Type of Seminar:  CONFERENCE
Sponsor:  SHERKATE TAVANIR
Date:  2004Volume 19
 
 
Abstract: 

A NEURO-CONTROL APPROACH FOR FLEXIBLE AC TRANSMISSION SYSTEMS (FACTS) BASED ON TEMPORAL DIFFERENCE LEARNING BASED MULTILAYER PERCEPTRON NEURAL NETWORK (TDMLP) IS PRESENTED IN THIS PAPER. THE PROPOSED SCHEME CONSISTS OF A SINGLE NEURON NETWORK WHOSE INPUT IS DERIVED FROM THE ACTIVE OR REACTIVE POWER OR VOLTAGE DERIVATION AT THE POWER SYSTEM BUS, WHERE THE FACTS DEVICE (IN THIS CASE AN UNIFIED POWER FLOW CONTROLLER) IS LOCATED. THE PERFORMANCE AND USEFULNESS OF THIS APPROACH IS TESTED AND EVALUATED USING BOTH SINGLE MACHINE INFINITE-BUS AND TWO-MACHINE POWER SYSTEM SUBJECTED TO VARIOUS TRANSIENT DISTURBANCES. IT WAS FOUND THAT THE NEW INTELLIGENT CONTROLLER FOR FACTS EXHIBITS A SUPERIOR DYNAMIC PERFORMANCE IN COMPENSATION TO THE EXISTING CLASSICAL CONTROL SCHEMES. ITS SIMPLE ARCHITECTURE REDUCES THE COMPUTATIONAL OVERHEAD, THEREBY REAL-TIME IMPLEMENTATION.

 
Keyword(s): TEMPORAL DIFFERENCE LEARNING, MLP NEURAL NETWORK, FACTS, REAL AND REACTIVE POWER, TRANSIENT STABILITY
 
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