International Journal on Electrical Engineering and Informatics - Volume 5, Number 1, March 2013 Delta-Bar-Delta and Directed Random Search Algorithms Application to Reduce Transformer Switching Overvoltages Iman Sadeghkhani 1 , Abbas Ketabi 2 , and Rene Feuillet 3 1 Department of Electrical Engineering, Najafabad Branch, Islamic Azad University, Najafabad 85141-43131, Iran 2 Department of Electrical Engineering, University of Kashan, Kashan 87317-51167, Iran 3 Grenoble Electrical Engineering Lab (G2ELab), Grenoble INP, France Abstract: This paper proposed an artificial neural network (ANN)-based approach to mitigate harmonic overvoltages during transformer energization. Uncontrolled energization of large power transformers may result in magnetizing inrush current of high amplitude and switching overvoltages. The most effective method for the limitation of the switching overvoltages is controlled switching since the magnitudes of the produced transients are strongly dependent on the closing instants of the switch. We introduce a harmonic index that it’s minimum value is corresponding to the best case switching time. Also, in this paper three learning algorithms, delta-bar-delta (DBD), extended delta-bar-delta (EDBD) and directed random search (DRS) were used to train ANNs to estimate the optimum switching instants for real time applications. ANNs training is performed based on equivalent circuit parameters of the network. Thus, trained ANN is applicable to every studied system. To verify the effectiveness of the proposed index and accuracy of the ANN-based approach, two case studies are presented and demonstrated. Keywords: Artificial neural networks, delta-bar-delta, directed random search algorithm, harmonic index, switching overvoltages, transformer energization. 1. Introduction A major process of power system restoration following a blackout would be energization of primary restorative transmission lines in most countries [1-4]. The energizing process begins by starting black-start generators such as hydro generators or gas turbines, and then charging some pre-defined transmission lines to supply cranking power for large generation plants [5,6]. Then the energization of unloaded transformers would be followed by switching action, and that is an inevitable process of bottom-up restoration strategy. During transformer energization, unexpected over-voltage may happen due to nonlinear interaction between the unloaded transformer and the transmission system [1,2]. When a lightly loaded transformer is energized, the initial magnetizing current is generally much larger than the steady-state magnetizing current and often much larger than the rated current of the transformer [7-8]. Controlled switching has been recommended as a reliable method to reduce switching overvoltage during energization of capacitor banks, transformers, and transmission lines [9]. This technique is the most effective method for the limitation of the switching transients since the magnitudes of the created transients are strongly dependent on the closing instants of the switch [10]. The fundamental requirement for all controlled switching applications is the precise definition of the optimum switching instants [10]. This paper presents a novel method for controlled energization of transformers in order to minimize temporary overvoltages. We introduce a harmonic index to determine the best case switching time. Using numerical algorithm we can find the time that the harmonic index is minimum, i.e., harmonic overvoltages is minimum. Also, for real time applications, this paper presents an Artificial Received: May 3 rd , 2012. Accepted: March 8 th , 2013 55