Fault location in transmission lines based on stationary wavelet transform, determinant function feature and support vector regression

• We simulated a power system with real life data from a power utility company in South Africa.
• We used stationary wavelet transform (SWT) in filtering decaying DC offset in post fault measurements in power transmission line.
• We extracted fault type and location information based on a determinant fault features (DFF) algorithm from a 1/4 cycle post fault record.
• We used support vector regression (SVR) in estimating fault location on power transmission line.
• The result shows that fault location can be estimated correctly irrespective of fault impedance.

This paper proposes a novel transmission line fault location scheme, combining stationary wavelet transform (SWT), determinant function feature (DFF), support vector machine (SVM) and support vector regression (SVR). Various types of faults at different locations, fault impedance and fault inception angles on a 400 kV, 361.297 km transmission line are investigated. The system only utilizes single-end measurements. DFF is used to extract distinctive fault features from 1/4 cycle of post fault signals after noise and the decaying DC offset have been eliminated by a filtering scheme based on SWT. A classifier (SVM) and regression (SVR) schemes are subsequently trained with features obtained from DFF. The scheme is then used in precise location of fault on the transmission line. The result shows that fault location on transmission lines can be determined rapidly and correctly irrespective of fault impedance.