Unbalanced Underground Distribution Systems Fault Detection and Section Estimation

This paper presents a novel fault detection and section estimation method for unbalanced underground distribution systems (UDS). The method proposed is based on artificial neural networks (ANNs) and wavelet transforms (WTs). The majority of UDS are characterized by having several single/double phase laterals and non-symmetrical lines. Also, Digital Fourier Transforms (DFT), used in the majority of traditional protection relays, supplies a low level of robustness to the fault diagnosis process due to its inversely proportional time-frequency characteristic. These characteristics compromise the traditional fault diagnosis methods performance. ANNs are capable of learning and generalizing, whereas WTs are robust tools capable of evaluating a signal’s frequency range that can characterize the fault phenomenon. This paper describes the proposed diagnosis method and discusses the results obtained from simulated implementation. The obtained results demonstrate the capability and robustness ...

• Type: Conference
• Year: 2007
• Where: ICIC
• Authors: Karen Rezende Caino de Oliveira, Rodrigo Hartstein Salim, André Darós Filomena, Mariana Resener, Arturo Suman Bretas
• Keywords: Fault Diagnosis, Fault Diagnosis Process, ICIC 2007, Traditional Fault Diagnosis