Title :
On the application of a machine learning technique to fault diagnosis of power distribution lines
Author :
Togami, Masato ; Abe, Norihiro ; Kitahashi, Tadahiro ; Ogawa, Harunao
Author_Institution :
Nagoya Mfg., Japan
fDate :
10/1/1995 12:00:00 AM
Abstract :
This paper presents one method for fault diagnosis of power distribution lines by using a decision tree. The conventional method, using a decision tree, applies only to discrete attribute values. To apply it to fault diagnosis of power distribution lines in practice, it must be revised in order to treat attributes whose values range over certain widths. This is because the sensor value or attribute value varies owing to the resistance of the fault point or is influenced by noise. The proposed method is useful when the attribute value has such a property, and it takes into consideration the cost of acquiring the information and the probability of the occurrence of a fault
Keywords :
decision theory; diagnostic expert systems; distribution networks; fault diagnosis; fault location; learning (artificial intelligence); neural nets; power system analysis computing; decision tree; discrete attribute values; expert systems; fault diagnosis; fault occurrence probability; fault point resistance; machine learning technique; neural nets; power distribution lines; Circuit faults; Decision trees; Diagnostic expert systems; Electrical fault detection; Fault diagnosis; Immune system; Machine learning; Power distribution; Power distribution lines; Sensor phenomena and characterization;
Journal_Title :
Power Delivery, IEEE Transactions on
