Abstract
This paper describes the applications of discrete wavelet transforms (DWT) coupled with conventional artificial neural networks (ANN) to the development of a fault location technique under an improved TCSC transmission system model. The fault location scheme is modular based whereby fault type is verified before identifying the fault location using ANN. This method relies on utilising DWT to decompose the line currents obtained from a single terminal into a series of time-scale representations. A feature model using self-organising maps (SOM) is applied herein to verify the fault location capability of the extracted features. Simulation results indicate that this approach can be used as an effective tool for accurate fault location in TCSC systems.
Original language | English |
---|---|
Pages | 224-227 Vol.1 |
Publication status | Published - 2004 |
Event | Developments in Power System Protection, 2004. Eighth IEE International Conference on - Duration: 1 Jan 2004 → … |
Conference
Conference | Developments in Power System Protection, 2004. Eighth IEE International Conference on |
---|---|
Period | 1/01/04 → … |
Keywords
- line current signals
- power transmission faults
- DWT
- thyristor applications
- TCSC transmission system model
- self-organising feature maps
- ANN
- fault classification
- self organising maps
- thyristor controlled series compensated transmission lines
- SOM
- fault location technique
- series of time-scale representations
- neural nets
- artificial neural networks
- fault location
- discrete wavelet transforms