An Estimation of Total Real Power Losses in Electrical Systems via Artificial Neural Network

This work presents the application of an artificial neural network to estimate the total real power losses of an electrical power system. The network used is the Multilayer Perceptron composed of 3 neurons in the input layer (loading factor, real and reactive power generated in the slack bus), 10 neurons in the intermediate layer and 1 neuron in the output layer, representing the total real power losses. The training used is the backpropagation, which uses the desired output (target) to adjust the weights. From the results (IEEE systems of 14 and 30 buses), the network performed well, with mean squared error around 10 ^-4 , and R-value at 0.99. For validation and testing, with 20% of samples, the network proved to be efficient, with MSE also around 10 ^-4 . In this context, the network was able to estimate the total real power losses in function of loading, showing that the obtained output was very close to the one desired.