This content is not included in
your SAE MOBILUS subscription, or you are not logged in.
High Impedance Fault Detection Using Artificial Neural Network Techniques
Annotation ability available
Sector:
Language:
English
Abstract
Artificial Neural Network(ANN) techniques are used to develop a system to detect High Impedance Faults(HIFs) in electric power distribution lines. Encouraging results were observed with a simple Multi-layer Perceptron(MLP) trained with the backpropagation learning algorithm. Although the results are not significantly better than those reported with other algorithmic approaches, ANN techniques have potential advantages over the other approaches; namely, ability to train the system easily to accommodate different feeder characteristics, ability to adapt and so become a better detector with experience and better fault tolerance. When these features are incorporated, the system is expected to perform better than existing systems.
The system we developed for the current phase, the training strategies used, the tests conducted and the results obtained are discussed in this paper. Also background discussions on existing HIF detection techniques, and ANN techniques can be found in this paper. A feature extraction method based on fuzzy logic and a bin structure is discussed.
Recommended Content
| Journal Article | Requirement Analysis and Development using MATLAB Models |
| Technical Paper | The Development of Auto Temp II |
Topic
Citation
Fernando, S. and Watson, K., "High Impedance Fault Detection Using Artificial Neural Network Techniques," SAE Technical Paper 929103, 1992, https://doi.org/10.4271/929103.Also In
References
- Power Technologies Inc. “Detection of High Impedance Faults,” Electric Power System Research Institute 1982
- Aucoin B. M. Russell B. D. “Distribution High Impedance Fault Detection Utilizing High Frequency Current Component,” IEEE Transactions on Power Apparatus and Systems 101 1596 1606 June 1982
- Russell B. D. Mehta K. Chinchali R. P. “An Arcing Fault Detection Technique using Low Frequency Current Components - Performance Evaluation using Recorded Field Data,” IEEE Transactions on Power Delivery 3 1493 1500 October 1988
- Benner C. L. Carswell P. W. Russell B. D. “Improved Algorithm for Detecting Arcing Faults using Random Fault Behavior,” Southern Electric Industry Applications Symposium New Orleans, LA Nov. 15-16 1988
- Kim C. J. “An Intelligent Decision Making System for Detecting High Impedance Faults,” Texas A&M University, College Station 1989
- Sultan A. F. Swift G. W. Fedirchuk D. J. “Detection of High Impedance Arcing Faults using a Multi-Layer Perceptron,” IEEE-PES Winter Meeting New York Jan. 26-30 1992 1 7 IEEE 1992
- Rumelhart D. E. McClelland J. L. Parallel Distributed Processing Cambridge, Massachusetts MIT Press 1986
- Lippmann R. “An Introduction to Computing with Neural Nets,” IEEE ASSP Magazine 4 22 April 1987
- Fernando S. Watson K. “An Artificial Neural Network Architecture to Detect HI Faults in Power Distribution Lines,” Intelligent Engineering Systems through Artificial Neural Networks Dagli Kumara Shin St. Louis Nov. 1991 677 682 ASME Press 1991
- Fernando S. Islam F. Utama P. Watson K. “High Impedance Fault Detection using Recurrent Networks,” International Conference of Artificial Neural Networks (ICANN 92) Aleksander I. Brighton, UK Sept. 1992