Estimation of switching transient peak overvoltages during transmission line energization using artificial neural network

• Published in: Electric power systems research 2006, Vol.76 (4), p.259-269
• Main Authors: Thukaram, D., Khincha, H.P., Khandelwal, Sulabh
• Format: Article
• Language: English
• Subjects: Applied sciences; Artificial neural networks; Electric, optical and optoelectronic circuits; Electrical engineering. Electrical power engineering; Electrical power engineering; Electromagnetic transients; Electronics; Exact sciences and technology; Line energization; Neural networks; Operation. Load control. Reliability; Power networks and lines; Restoration; Switching surges
• ISSN: 0378-7796; 1873-2046
• DOI: 10.1016/j.epsr.2005.07.001

Overvoltages are one of the most frequently encountered problems during line energization. At the time of restoration transmission line switching is also one of the major causes, which creates overvoltage. The magnitude and shape of the switching overvoltages vary with the system parameters and network configuration and the point-on-wave where the switching operation takes place. Though detailed electromagnetic transient studies carried out for the design of transmission systems, such studies are not common in a day-to-day operation of power system. However it is important for the operator to ensure that peak overvoltages resulting from the switching operations are well within safe limits. This paper presents an Artificial Neural Network (ANN)-based approach to estimate the peak overvoltage generated by switching transients during line energization. In proposed methodology Levenberg–Marquardt method is used to train the multilayer perceptron. The developed ANN is trained with the extensive simulated results, and tested for typical cases. The simulated results presented clearly show that the proposed technique can estimate the peak values of switching overvoltages with good accuracy.