Journal of Advances in Science and Engineering (JASE)
DOI Prefix: 10.37121
The occurrence of faults in any operational power system network is inevitable, and many of the causative factors such as lightning, thunderstorm among others are 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, therefore, explored the use of artificial neural network (ANN) technique to identify and classify various faults on the 11-kV University of Lagos Distribution network. The ANN is applied in this paper 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 the study, for both the voltage and current under different scenarios of faults, are displayed in graphical forms, and discussion of the results presented. 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.
The wire rod mill of the Ajaokuta Steel Company Limited produces coils, wire rods and re-bars of different sizes. Without the furnace hangers, it will be difficult for the mill to continue to operate. This paper describes the production of furnace roof hangers that are required for re-heating furnace using the spheroidal graphite iron (SGI), highlighting the sand casting process, charge calculation, and the chemical compositions. The facilities within the foundry shop of the steel company are used to produce furnace roof hangers. The available materials used for the casting of the hangers are the pig iron, scrap ends, foundry returns and magnesium. The process of production was performed through the reheating furnace for the heating of 120m x 120m x 120m billets. One ton induction furnace of low frequency was used as the melting vessel. Also, 6kg of magnesium was introduced in the ladle before the liquid metal was teemed into it. A Spectro analytical instrument was used to determine the chemical compositions of the materials before and after the casting processes. The analysis of the chemical compositions of produced sample of SGI are presented and discussed.
This work studied the critical load analysis of rectangular plates, carrying uniformly distributed loads utilizing direct variational energy calculus. The aim of this study is to establish the techniques for calculating the critical lateral imposed loads of the plate before deflection attains the specified maximum threshold, qiw as well as its corresponding critical lateral imposed load before the plate reaches an elastic yield point. The formulated potential energy by the static elastic theory of the plate was minimized to get the shear deformation and coefficient of deflection. The plates under consideration are clamped at the first and second edges, free of support at the third edge and simply supported at the fourth edge (CCFS). From the numerical analysis obtained, it is found that the critical lateral imposed loads (qiw and qip) increase as the thickness (t) of plate increases, and decrease as the length to width ratio increases. This suggests that as the thickness increases, the allowable deflection improves the safety of the plate, whereas an increase in the span (length) of the plate increases the failure tendency of the plate structure.