Abstract
This paper proposes a new connectionist (or neural network) expert diagnostic system for on-line fault diagnosis of a power substation. The connectionist expert diagnostic system has similar profile of an expert system, but can be constructed much more easily from elemental samples. These sample indicate the association of fault with their protective relays and breakers, as well as the bus voltage and feeder currents. Through an elaborately designed structure, these two types of alarm signals are processed by different connectionist models. The outputs of the connectionist models are then integrated to provide the final conclusion with confidence level. The proposed approach has been practically verified testing on a typical Taiwan Power (Taipower) secondary substation. The test results suggest our system can be implemented by various electric utilities with relatively low customization effort.
| Original language | English |
|---|---|
| Pages (from-to) | 871-877 |
| Number of pages | 7 |
| Journal | IEE Conference Publication |
| Volume | 2 |
| Issue number | 388 |
| Publication status | Published - 1994 Jan 1 |
| Event | Proceedings of the 2nd International Conference on Advances in Power System Control, Operation & Management - Hong Kong, Hong Kong Duration: 1993 Dec 7 → 1993 Dec 10 |
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All Science Journal Classification (ASJC) codes
- Electrical and Electronic Engineering
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On-line power system fault estimation using connectionist models. / Yang, Hong Tzer; Chang, Wen Yeau; Chen, Chi Fung; Huang, Ching Lien.
In: IEE Conference Publication, Vol. 2, No. 388, 01.01.1994, p. 871-877.Research output: Contribution to journal › Conference article
TY - JOUR
T1 - On-line power system fault estimation using connectionist models
AU - Yang, Hong Tzer
AU - Chang, Wen Yeau
AU - Chen, Chi Fung
AU - Huang, Ching Lien
PY - 1994/1/1
Y1 - 1994/1/1
N2 - This paper proposes a new connectionist (or neural network) expert diagnostic system for on-line fault diagnosis of a power substation. The connectionist expert diagnostic system has similar profile of an expert system, but can be constructed much more easily from elemental samples. These sample indicate the association of fault with their protective relays and breakers, as well as the bus voltage and feeder currents. Through an elaborately designed structure, these two types of alarm signals are processed by different connectionist models. The outputs of the connectionist models are then integrated to provide the final conclusion with confidence level. The proposed approach has been practically verified testing on a typical Taiwan Power (Taipower) secondary substation. The test results suggest our system can be implemented by various electric utilities with relatively low customization effort.
AB - This paper proposes a new connectionist (or neural network) expert diagnostic system for on-line fault diagnosis of a power substation. The connectionist expert diagnostic system has similar profile of an expert system, but can be constructed much more easily from elemental samples. These sample indicate the association of fault with their protective relays and breakers, as well as the bus voltage and feeder currents. Through an elaborately designed structure, these two types of alarm signals are processed by different connectionist models. The outputs of the connectionist models are then integrated to provide the final conclusion with confidence level. The proposed approach has been practically verified testing on a typical Taiwan Power (Taipower) secondary substation. The test results suggest our system can be implemented by various electric utilities with relatively low customization effort.
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UR - http://www.scopus.com/inward/citedby.url?scp=0028043075&partnerID=8YFLogxK
M3 - Conference article
AN - SCOPUS:0028043075
VL - 2
SP - 871
EP - 877
JO - IEE Conference Publication
JF - IEE Conference Publication
SN - 0537-9989
IS - 388
ER -