NEURAL NETWORK
REAL TIME POWER SYSTEM SECURITY ASSESSMENT USING ARTIFICIAL NEURAL NETWORKS
REAL TIME POWER SYSTEM SECURITY ASSESSMENT USING ARTIFICIAL NEURAL NETWORKS
Abstract
Contingency analysis of a power system is a major activity in power system planning and operation. In general an outage of one transmission line or transformer may lead to over loads in other branches and/or sudden system voltage rise or drop. The traditional approach of security analysis known as exhaustive security analysis involving the simulation of all conceivable contingencies by full AC load flows, becomes prohibitively costly in terms of time and computing resources A new approach using Artificial Neural Network s has been proposed 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 is 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
REAL TIME POWER SYSTEM SECURITY ASSESSMENT USING ARTIFICIAL NEURAL NETWORKS
Abstract
Contingency analysis of a power system is a major activity in power system planning and operation. In general an outage of one transmission line or transformer may lead to over loads in other branches and/or sudden system voltage rise or drop. The traditional approach of security analysis known as exhaustive security analysis involving the simulation of all conceivable contingencies by full AC load flows, becomes prohibitively costly in terms of time and computing resources A new approach using Artificial Neural Network s has been proposed 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 is 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
References: 1) A.J.Wood and B.F. Woolenberg, power generation, operation and control. 2) K.F.Schafer and J.F . verstege, adaptive procedure for masking effect compensation in contingency selection algorithms, IEEE trans. On power systems, vol – pwrs-5,no.2,pp,539-546, may 1990