✦ LIBER ✦

Degree of insecurity estimation in a power system using functional link neural network

✍ Scribed by S. N. Singh; K. N. Srivastava

Publisher: John Wiley and Sons
Year: 2002
Tongue: English
Weight: 720 KB
Volume: 12
Category: Article
ISSN: 1430-144X
DOI: 10.1002/etep.4450120506

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✦ Synopsis

Abstract

Secure operation of power system has always been a challenge to system operators. With increasing interconnection and growing load demand, a power system, sometimes, may go into the insecure operation especially after severe contingencies. It is important to develop a technique to quantify the degree of insecurity in both planning and operational stages. This paper presents such an algorithm.

The distance (Euclidean norm) in parameter space between any insecure point and closest point on feasible (secure) hypersurface is used as a measure of insecurity. Artificial Neural Network (ANN) based Functional Link Network (FLN) is proposed because ANN is very popular for complex problems. The various FLN models are evolved to estimate the degree of insecurity. The sample study is performed on an IEEE 30‐bus test system for different loading patterns and single line outage contingencies. The investigations reveal that the proposed FLN accurately estimates the degree of insecurity with least computational time compared to the existing methods.

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