Detecting discontinuities in electrical signals from recorded oscillograms makes it possible to segment them. This is the first step in implementing automated methods which will ensure disturbances in electrical power systems are detected, classified and stored. In this context, this paper presents a way of determining an adaptive threshold based on the decomposition of electrical signals through the Discrete Wavelet Transform (DWT) using Daubechies family filter banks, allowing for the segmentation of signals and, as a consequence, the analysis of disturbances related to Power Quality (PQ). Considering this, the proposed approach was initially evaluated for signals originating from mathematical models representing short-term voltage fluctuations, transients (impulsive and oscillatory) and harmonic distortions. In the synthetic signal database, either single or combined occurrences of more than one disturbance were considered. By applying the DWT, the amount of energy and entropy of energy were then calculated for the leaves of the second level of decomposition. Based on these calculations, a unique adaptive threshold could be determined for each analyzed signal. Afterwards, the amount of existing intersections between the threshold and the curve of details obtained for the second level of decomposition was then defined. Thus, the intersections determine the beginning and end of the segments. In order to validate the approach, the performance of the proposed methodology was analyzed considering the signals obtained from oscillograms provided by IEEE 1159.3 Task Force, as well as real oscillograms obtained from a regional distribution utility. After these analyses, it was observed that the proposed approach is efficient and applicable to automatic segmentation of events related to PQ. 
Adaptive threshold based on wavelet transform applied to the segmentation of single and combined power quality disturbances