A general view of voltage dip analysis is presented in this paper. The objectives to perform voltage dip analysis, activities inside of this analysis and methodological aspects are described from a data mining perspective. Basic data mining principles were taken as the basis to identify similar steps in power quality works involving classification and knowledge discovery tasks. This paper is centred in voltage dip event diagnosis in order to reduce the study and focus the analysis.

<p>This paper presents a new method for the characterization and diagnosis of electrical disturbances caused by fuses operation in the electrical distribution systems. A set of descriptors is proposed in order to quantify the typical features of the distortions caused by operation of expulsion and current limiting fuses. A multivariate statistical analysis is performed to select the descriptors with the best profiles qualifiers and the optimal decision thresholds are selected through of machine learning algorithms. Voltage and current signals of the fuses operation are obtained from the ATP-EMTP simulation, as well as some real signals, to be all used in the validation of the new proposed algorithm, obtaining optimal performance and efficiency results. The algorithm was implemented in Matlab and the computational requirements are minimal.</p>