A wavelet-based algorithm to analyze oscillographic data with single and multiple disturbances

This paper presents a discrete wavelet transform approach to recognize disturbances by the analysis of electromagnetic transients in transmission lines using oscillographic data. It is usual that different types of disturbances simultaneously exist in an oscillographic record supplied by a monitoring device in a transmission line. However, most of the existing methods treat the events as a single type. Thus, the performance of these methods might be limited and impracticable for real applications in power systems. In this framework, the proposed approach overcome this problem by the ability to analyze single and multiple disturbances in actual oscillographic records. Other distinctive feature of the proposed method is that it can recognize faults, some events related to power quality and normal maintenance operation in transmission lines. Sliding data windows of the detail spectral energy with length equivalent to one cycle of the fundamental power frequency are used to achieve wavelet-based information for detection and classification tasks. The performance of the method was evaluated for actual data and excellent results have been obtained.