A new approach using Artificial Neural Network s has been prosed in this paper for real-time network security assessment. Security assessment has two functions the first is violation detection in the actual system operating state. The second, much more demanding, function of security assessment is contingency analysis. In this paper, for the determination of voltage contingency ranking, a method has been suggested, which eliminates misranking and masking effects and security assessment has been determined using Radial Basis Function (RBF) neural network for the real time control of power system. The proposed paradigms are tested on IEEE 14 – bus and 30 – bus systems.
Introduction
Security refers to the ability of the system to withstand the impact of disturbance(contingency). The system is said to be secure if no security limit is seriously violated in the event of contingency. The process of investigating whether the system secure or insecure in a set of proposed contingencies is called Security Analysis.
The three basic elements if real-time security analysis are, Security monitoring, Security assessment. The problem of predicting the static security status of a large power system is a computationally demanding task [2] and it requires large amount of memory. These considerations seriously undermine the application of static security assessment in real time without the support of large computing capability.
In online contingency analysis, it has become quite common to screen contingencies by ranking them according to some severity index. Which is calculatd solely as a measure of limit violations. The methods developed are known as “ranking methods”. In this paper, for the determination of voltage contingency ranking, a method has been suggested, which eliminates misranking and masking effects and security assessment has been determined using Radial Basis Function (RBF) neural network for the real time control of power system. The proposed paradigms are tested on IEEE 14-bus and 30-bus systems.
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References
Technical Paper on Real Time Power System Security Assessment Using Artificial Neural Networks
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