A novel nonintrusive fault identification for power transmission networks using power-spectrum-based hyperbolic S-transform — Part I: Fault classification
- 1 May 2018
- conference paper
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE) in 2018 IEEE/IAS 54th Industrial and Commercial Power Systems Technical Conference (I&CPS)
Abstract
This paper presents a novel nonintrusive protection scheme for fault classification of power transmission networks in a wide-area measurement system (WAMS) using fault information for decision making. The protection scheme is a non-communication without GPS as it depends completely on locally measured currents for the non-intrusive fault monitoring (NIFM) using the power-spectrum-based hyperbolic S-transform (PS-HST). In this work, the HST is used to extract the high frequency components of the current signals generated by an electric fault. To effectively select the HST coefficients (HSTCs) representing fault transient signals with increasing performance, a power spectrum of the HSTCs in different scales calculated by Parseval's Theorem is proposed in this paper. Finally, back-propagation artificial neural networks (BP-ANNs) and PS-HST are used to identify fault types in power transmission networks. The proposed method is tested for different breaker ON/ OFF conditions by simulations using electromagnetic transients program (EMTP) software. The results obtained have proved that the proposed method is promising and demonstrate a high success rates and reliability for considering different fault resistances and inception angles in NIFM applications.Keywords
This publication has 21 references indexed in Scilit:
- Non-intrusive fault identification of power distribution systems in intelligent buildings based on power-spectrum-based wavelet transformEnergy and Buildings, 2016
- Divisional fault diagnosis of large-scale power systems based on radial basis function neural network and fuzzy integralElectric Power Systems Research, 2013
- Power-Spectrum-Based Wavelet Transform for Nonintrusive Demand Monitoring and Load IdentificationIEEE Transactions on Industry Applications, 2013
- Particle-Swarm-Optimization-Based Nonintrusive Demand Monitoring and Load Identification in Smart MetersIEEE Transactions on Industry Applications, 2013
- Transmission lines distance protection using artificial neural networksInternational Journal of Electrical Power & Energy Systems, 2011
- High impedance fault detection methodology using wavelet transform and artificial neural networksElectric Power Systems Research, 2011
- Fault diagnosis system for tapped power transmission linesElectric Power Systems Research, 2010
- Fault classification and location using HS-transform and radial basis function neural networkElectric Power Systems Research, 2006
- Fuzzy ART Neural Network Algorithm for Classifying the Power System FaultsIEEE Transactions on Power Delivery, 2005
- Nonintrusive appliance load monitoringProceedings of the IEEE, 1992