Open AccessAnalysis of unsymmetrical faults based on artificial neural network using 11 kV distribution network of University of Lagos as case study
Author(s) -
Akintunde Samson Alayande,
Ignatius K. Okakwu,
O. E. Olabode,
Okwuchukwu K. Nwankwoh
Publication year - 2021
Publication title -
journal of advances in sciences and engineering
Language(s) - English
Resource type - Journals
ISSN - 2636-607X
DOI - 10.37121/jase.v4i1.91
Subject(s) - artificial neural network , identification (biology) , computer science , python (programming language) , data mining , fault (geology) , reliability engineering , artificial intelligence , reliability (semiconductor) , electric power system , machine learning , engineering , power (physics) , botany , physics , quantum mechanics , seismology , biology , geology , operating system
The occurrence of faults in any operational power system network is inevitable, and many of the causative factors such as lightning, thunderstorm among others is usually beyond human control. Consequently, there is the need to set up models capable of prompt identification and classification of these faults for immediate action. This paper, explored the use of artificial neural network (ANN) technique to identify and classify various faults on the 11 kV distribution network of University of Lagos. The ANN is applied because it offers high speed, higher efficiency and requires less human intervention. Datasets of the case study obtained were sectioned proportionately for training, testing, and validation. The mathematical formulations for the method are presented with python used as the programming tools for the analysis. The results obtained from this study, for both the voltage and current under different scenarios of faults, are displayed in graphical forms and discussed. The results showed the effectiveness of the ANN in fault identification and classification in a distribution network as the model yielded satisfactory results for the available limited datasets used. The information obtained from this study could be helpful to the system operators in faults identification and classification for making informed decisions regarding power system design and reliability.