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In this paper an intelligent-based approach is introduced to evaluate harmonic overvoltages during three-phase transformer energization. In a power system that appears in an early stage of a black ‎start of a power system, an overvoltage could be caused by core ‎saturation on the energization of a three-phase transformer with residual flux. ‎Such an overvoltage might damage some equipment and delay ‎power system restoration. A new approach based on worst case determination is proposed to reduce time-domain simulations. Also, an artificial neural network (ANN) has been used to estimate the temporary overvoltages (TOVs) due to three-phase transformer ‎energization. ‎ Three learning algorithms, delta-bar-delta (DBD), extended delta-bar-delta (EDBD), and directed random search (DRS), were used to train the ANNs. ANN Training is performed based on equivalent circuit parameters of the network; thus trained ANN is applicable to every studied system. The ‎developed ANN is trained with the worst case of the switching condition and remanent flux, and ‎tested for typical cases. The simulated results for a partial of 39-bus New England test system, ‎show that the proposed technique can estimate the peak values and ‎durations of switching overvoltages with good accuracy and EDBD algorithm presents best performance.

Study of Transformer Switching Overvoltages during Power System Restoration Using Delta-Bar-Delta and Directed Random Search Algorithms