Affiliations: [a] Intelligent Systems Laboratory & Automation Facility (ISLAF), University of the Punjab, Lahore, Pakistan
| [b] Department of Electrical & Computer Engineering, Dalhousie University, Halifax, N.S., Canada | [c] Department of Automatics & Applied Informatics, Aurel Vlaicu University, Arad, Romania
| [d] Department of Electrical Engineering, King Fahd University of Petroleum and Minerals (KFUPM), Dhahran, Saudi Arabia
| [e] Department of Electrical Engineering, Texas A&M University, College Station, TX, USA
| [f] Department of Electrical Engineering, The University of Lahore, Lahore, Pakistan
Correspondence:
[*]
Corresponding author. Jason Gu, Department of Electrical & Computer Engineering, Dalhousie University, Halifax, N.S., B3H4R2 Canada. E-mail: Jason.gu@dal.ca.
Abstract: This paper is devoted to develop interest of power system engineers in learning basic concepts of image processing and consequently using deep networks to solve problems of complex power system networks. To this end, we study fault classification in a power system through automation of equal area (EAC) criterion. By considering EAC graphs as images and using classical image processing techniques, we successfully distinguish between different transient conditions including sudden change of input power as well as short circuit at the sending end and middle points of a single and double circuit transmission lines. In addition to classification, some parameters are also determined from EAC images such as initial rotor angle, clearing angle, and maximum rotor angle. Further, the use of deep networks is introduced to perform the same task of fault classification and a comparison is drawn with multilayer perceptron neural networks. Developed algorithms are tested in MATLAB as well as Pytorch environments.
Keywords: Engineering education, power system, equal area criterion, image processing, deep neural networks, MATLAB, pytorch
