Title :
Unsupervised learning strategies for the detection and classification of transient phenomena on electric power distribution systems
Author :
Lubkeman, David L. ; Fallon, Chris D. ; Girgis, Adly A.
Author_Institution :
Dept. of Electr. & Comput. Eng., Clemson Univ., SC, USA
Abstract :
A number of utilities are currently installing high-speed data acquisition equipment in their distribution substations. This equipment will make it possible to record the transient waveforms due to events such as low and high-impedance faults, capacitor switching, and load switching. The authors describe the potential of applying unsupervised learning strategies to the classification of the various events observed by a substation recorder. Several strategies are tested using simulation studies and the effectiveness of unsupervised learning is compared to current classification strategies as well as supervised learning
Keywords :
distribution networks; learning (artificial intelligence); neural nets; power engineering computing; transients; unsupervised learning; capacitor switching; electric power distribution systems; high-impedance faults; high-speed data acquisition; load switching; low-impedance faults; neural networks; substations; transient phenomena; unsupervised learning strategies; Backpropagation; Discrete event simulation; EMTP; Event detection; Neural networks; Power engineering and energy; Sampling methods; Substations; Supervised learning; Unsupervised learning;
Conference_Titel :
Neural Networks to Power Systems, 1991., Proceedings of the First International Forum on Applications of
Conference_Location :
Seattle, WA
Print_ISBN :
0-7803-0065-3
DOI :
10.1109/ANN.1991.213506
