NEURAL NETWORK

Topics: Artificial neural network, Neural network, Artificial neuron Pages: 11 (2214 words) Published: September 24, 2014
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 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 calculated 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.

Artificial Neural Networks

Artificial neural networks (ANN) are massively parallel inter connected networks of simple elements known as artificial neurons and their connectivity is intended to interact with the objects of real world, in a similar manner as the biological nerves systems do.

The simple neuron model is shown in fig (a). ∑ Unit multiplies each input ‘x’ by a weight ‘w’ and sums the weighted inputs. The output of the figure is NET = x1w1 + x2w2 + …. +xnwn: OUT = f (NET)

2.1) Basic features of ANNs are:
1. High computational rates due to the massive parallelism.
2. Fault tolerance.
3. Training the network adopts itself, based on the information received from the environment. 4. Programmed rules are not necessary.
5. Primitive computational elements.
2.2) Radial basis function networks:
The Radial Basis Function is similar to the Gaussian function, which is defined by a center and a width parameter. The Gaussian function gives the highest output when the incoming variables are closest to the center apposition and decreases monotonically as the rate of decrease.

The weights of each hidden layer neuron are assigned the values of input training vector.

The output neuron produces the linear weighted summation of these,...

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
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