On-line evaluation of capacity and energy losses in power transmission systems by using artificial neural networks

An adaptive loss evaluation algorithm for power transmission systems is proposed in this paper. The algorithm is based on training of artificial neural networks (ANNs) using backpropagation. Due to the capability of parallel information processing of the ANNs, the proposed method is fast and yet accurate. Active and reactive powers of generators and loads, as well as the magnitudes of voltages at voltage-controlled buses are chosen as inputs to the ANN. System losses are chosen as the outputs. Training data are obtained by load flow studies, assuming that the state variables of the power system to be studied take the values uniformly distributed in the ranges of their lower and upper limits. Load flow studies for different system topologies are carried out and the results are compiled to form the training set. Numerical results are presented in the paper to demonstrate the effectiveness of the proposed algorithm in terms of accuracy and speed. It is concluded that the trained ANN can be utilized for both off-line simulation studies and on-line calculation of demand and energy losses. High performance has been achieved through complex mappings, modeled by the ANN, between system losses and system topologies, operating conditions and load variations