Introduction
As a vital lifeline of the modern society for maintaining adequate and reliable flows of energy, power distribution systems deliver the electricity from high voltage transmission circuits to customers. Any interruption in their service may cause economical loss, damage equipments, and even endanger people lives. When a power outage (i.e., the loss of the electricity supply to an area) occurs, it is of great importance to diagnose the fault and restore the system in a timely manner in order to maintain the system availability and reliability. However, the restoration process may take from tens of minutes to hours. Most utilities for safety concerns do not restore the system until the outage cause has been found: linemen may need to inspect the distribution lines section by section in an attempt to find evidences (e.g., burn marks on the pole for possible lightning caused faults, dead animal bodies for possible animal activity related faults) and to ensure safety prior to re-energizing the system (e.g., no down distribution lines). Sometimes specific crew need to be further dispatched for advanced tasks such as the removal of fallen trees. Effective identification of either the cause or the location of the outage can provide valuable information to expedite the restoration procedure.
This work is supported by National Science Foundation Grant ECS-0245383.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Brown, R.E.: Electric Power Distribution Reliability. Marcel Dekker, New York (2002)
Castro, L.N., Timmis, J.: Artificial Immune Systems: A New Computational Approach. Springer, London (2002)
Chow, M.Y., Taylor, L.S.: Analysis and prevention of animal-caused faults in power distribution systems. IEEE Transaction Power Delivery 10(2), 95–1001 (1995)
Han, J., Kamber, M.: Data Mining: Concepts and Techniques. San Francisco. Academic Press, Morgan Kaufmann Publishers, London, San Francisco (2001)
Ishibuchi, H., Nakashima, T., Murata, T.: Voting in fuzzy rule-based systems for pattern classification problems. Fuzzy Sets and Systems 103(2), 223–238 (1999)
Ishibuchi, H., Yamamoto, T.: Comparison of heuristic rule weight specification methods. In: Proc. IEEE International Conference on Fuzzy Systems, pp. 908–913 (2002)
Kubat, M., Holte, R., Matwin, S.: Machine Learning for the Detection of Oil Spills in Radar Images. Machine Learning 30, 195–215 (1998)
Walpole, R.E., et al.: Probability and Statistics for Engineers and Scientists, 7th edn. Prentice Hall, Englewood Cliffs (2002)
Watkins, A.: AIRS: A Resource Limited Artificial Immune Classifier. M.S. Thesis, Mississippi State University (2001)
Watkins, A.: Artificial Immune Recognition System (AIRS) C++ source code (2002)
Watkins, A., Timmis, J.: Artificial Immune Recognition System (AIRS): Revisions and Refinements. In: Proc. 1st International Conference on Artificial Immune Systems (ICARIS), pp. 173–181 (2002)
Watkins, A., Timmis, J., Boggess, L.: Artificial Immune Recognition System (AIRS): An Immune Inspired Supervised Machine Learning Algorithm. Genetic Programming and Evolvable Machines 5(1) (2004)
Xu, L., Chow, M.Y., Taylor, L.S.: Analysis of Tree-Caused Faults in Power Distribution Systems. In: Proc North American Power Symposium, pp. 122–127 (2003)
Xu, L., Chow, M.Y., Taylor, L.S.: Data Mining Based Fuzzy Classification Algorithm for Imbalanced Data. In: Proc. IEEE World Congress on Computational Intelligence, pp. 4216–4221 (2006)
Xu, L., et al.: On the Investigation of Artificial Immune Systems on Imbalanced Data Classification. In: Proc. IEEE World Congress on Computational Intelligence, pp. 1629–1634 (2006)
Xu, L., Chow, M.Y.: A Classification Approach for Power Distribution Systems Fault Cause Identification. IEEE Transaction on Power Systems 21(1), 53–60 (2006)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Xu, L., Chow, MY. (2008). Power Distribution System Fault Diagnosis Using Hybrid Algorithm of Fuzzy Classification and Artificial Immune Systems. In: Prasad, B. (eds) Soft Computing Applications in Industry. Studies in Fuzziness and Soft Computing, vol 226. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77465-5_18
Download citation
DOI: https://doi.org/10.1007/978-3-540-77465-5_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-77464-8
Online ISBN: 978-3-540-77465-5
eBook Packages: EngineeringEngineering (R0)