An intelligent transformer load estimation model using artificial neural networks

Abstract

This paper discusses the development of an intelligent system to estimate the transformer peak load from the asset management point of view in a large electric utility. This utility has over 50,000 distribution transformers, some of these transformers are approaching their design life and therefore it is important that their loading levels are continuously monitored. From the resource point of view, however, it is not cost effective to monitor individual transformer peak loading. It is therefore very much desirable to develop a system to estimate the peak loads on individual transformers from available data. The utility recently conducted a series of power quality tests on a number of transformers and from these data, a database was developed containing hourly average kVA demand and other related information. The measurements spanned over a variety of load centres including residential, commercial, industrial, and combinations thereof. An intelligent model is developed based on Artificial Neural Networks (ANNs) to estimate hourly average loads on distribution transformers from these data. In this paper, results from the development of a predominantly residential load model is reported. The ANN model was developed based on 5818 cases of residential transformer loads taken from the available database. The model was constructed to predict the load using 12 input variables that deemed important in determining the transformer hourly loading. The peak load was extracted from the hourly average loads. Once the ANN model’s training was completed, the resulting ANN was evaluated on 318 transformers selected randomly that was not used in the training set. Results indicate that the ANN model showed good agreement with actual loads. The R2 value for the training set was 0.855 and for the testing set was 0.854 respectively. Using contribution analysis, it was found that the highest contributions were from kVA ratings (13.8%), LV length (13.2%), HV rating (10.1%) and hour of the day (10.1%). While the lowest contributions were number of transformers connected (6.7%) on a feeder, temperature (5.8%), humidity (5.5%) and wind speed (2.2%).

Key Words:

• Transformer loading
• asset management
• artificial neural networks
• intelligent systems

Additional information

Notes on contributors

Saleh M A Al-Alawi

Saleh Al-Alawi

Saleh Al-Alawi is an associate professor at Sultan Qaboos University, Oman. He has served as the Assistant Vice Chancellor for Research and Graduate Studies, Head of Department of Electrical & Electronic Engineering, and Dean of College of Commerce and Economics at Sultan Qaboos University. His research interests are in, Load Forecasting, Artificial Intelligence and Neural Network applications in Engineering systems. He has published many papers in his area of expertise. He spent his sabbatical leave at Curtin University of Technology, Perth in 2003.

Syed M. Islam

Syed Islam

Syed Islam is the Chair Professor of Electrical Power Engineering and Head of Department of Electrical & Computer Engineering at Curtin University of Technology, Perth, Australia. He is the current Chair of the Australasian Committee for Power Engineering (ACPE). He received the Dean’s Medallion for Research at Curtin in 1999 and the T Burke Haye’s faculty Recognition Award from the IEEE in 2000. His research interests are in transformer condition monitoring, artificial intelligence application to power systems, wind energy conversion systems and power system harmonics. He has published over 130 papers in his area of expertise. He is Fellow of the Engineers Australia, Senior member of the IEEE, Member of the IEE, member of CIGRE Australia panel on transformers and materials and a chartered engineer in the UK.

Josh Hurley

Josh Hurley

Josh Hurley is an electrical engineering and mathematics double degree student Curtin University of Technology. He has won many awards and prizes for excellence in academic achievements including a prestigious Clough Engineering scholarship. His research interests are in transformer load estimation and lightning generated overvoltage calculations.

Shane Dureya

Shane Dureya

Shane Dureya is a senior electrical engineer with Western Power Corporation. He is currently the Acting Head of the Distribution section. He graduated with a Bachelor of Engineering in Electrical Engineering from Curtin University of Technology, Perth, Australia. His research interests are in distributed automation and solar charge controller. He is a member of the Engineers Australia.