Loss prediction for an IEEE 30 bus power system using ANN

Transmission loss Loss prediction for an IEEE 30 bus power system using an artificial neural network with training and testing data.

A supervised learning algorithm is used to train a feed-forward neural network to predict the total transmission loss of the IEEE 30 bus power system when provided with the schedules of the power system generators (available in file lossdb.xlsx). A feedforward neural network with a back-propagation algorithm is trained and implemented using MATLAB. The first six columns in lossdb.xlsx are the schedules of the generators in the IEEE 30 bus system and the last column is the transmission loss obtained by load flow analysis.

This technique has been employed in my following publications

1. Arunachalam Sundaram, Multiobjective multi verse optimization algorithm to solve dynamic economic emission dispatch problem with transmission loss prediction by an artificial neural network, Applied Soft Computing, Volume 124, 2022, 109021, ISSN 1568-4946, https://doi.org/10.1016/j.asoc.2022.109021.
2. Sundaram, Arunachalam, and Nasser S. Alkhaldi. 2024. "Multi-Objective Stochastic Paint Optimizer for Solving Dynamic Economic Emission Dispatch with Transmission Loss Prediction Using Random Forest Machine Learning Model" Energies 17, no. 4: 860. https://doi.org/10.3390/en17040860