Extended Delta-Bar-Delta Algorithm Application to Evaluate Transmission Lines Overvoltages

In this paper an intelligent approach is introduced to study switching overvoltages during transmission lines energization. In most countries, the main step in the process of power system restoration, following a complete/partial is energization of primary restorative lines. An artificial neural network (ANN) has been used to evaluate the overvoltages due to transmission lines energization. Three learning algorithms, delta-bar-delta (DBD), extended delta-bar-delta (EDBD) and directed random search (DRS), were used to train the ANNs. Proposed ANN is trained with equivalent circuit parameters of the network as input parameters; therefore developed ANNs have proper generalization capability. The simulated results for 39-bus New England test system, that the proposed technique can estimate the peak values and of switching overvoltages with acceptable accuracy and EDBD algorithm presents best performance.