A comparative study on the performance of modern function estimation tools for estimation of non-linear functions in high voltage system

Abstract EN

To safeguard the electric power system, protecting the overhead transmission line from lightning stroke is an important task.

For evaluating the lightning performance of transmission line several algorithms were used.

Choosing a proper learning algorithm to train the network is very important for better estimation.

Gradient based function estimation tools like Radial Basis Function (RBF) and back propagation Artificial Neural Network (ANN) are mainly used for this purpose.

In this paper an attempt is made to compare the performance of modern function estimation tools, viz.

Particle Swarm optimization (PSO) aided Wavelet neural Network (WNN), gradient based WNN, and back propagation ANN for estimation of such non-linear functions.

The simulated input, output data of lightning impulse voltage, non-sinusoidal current and chopped impulse voltage were used for training and testing data of developed network for estimation.

Results are resented for modern function estimation tools for learning these non-linear functions and showed that the PSO aided network estimates function more accurately than gradient based algorithms.

The PSO aided WNN method can be used by electric power utilities as a useful tool for the design of electric power systems, alternative to the conventional analytical method.