%0 Journal Article 
%A González de la Rosa, Juan José
%A Florencias Oliveros, Olivia
%A Remigio Carmona, Paula
%T Strategy for Visual Measurement of Power Quality Based on Higher-Order Statistics and Exploratory Big Data Analysis
%D 2025
%@ 2076-3417
%U http://hdl.handle.net/10498/38262
%X This article proposes a strategy for the visual characterization of power quality in
big data analysis contexts, culminating in the development of a visualization tool based on
higher-order statistics, which exhibits an efficiency between 83.33% and 100% in detecting
50 Hz synthetic and real-life simple and hybrid events, showing its significant potential
for real-world applications marked by non-linear loads and non-Gaussian behaviors and
surpassing the detection of traditional tools such as boxplot by up to 50%. Efficient energy
management is closely accompanied by an optimum energy data management (EDM). It
implies the acquisition, analysis, and interpretation of data to make decisions regarding the
best energy usage with subsequent cost reductions. Through a study of indicators, including
higher-order statistics, crest factor, SNR and THD, the article establishes nominal values and
behavioral patterns, expanding the previous knowledge of these parameters. The indicators
are presented as vertices in a radar-type charting tool, providing a multidimensional spatial
visualization from individual indices that allows the behavioral pattern associated with
each type of disturbance to be characterized combined with a decision tree. In addition,
boxplots reflecting data processing are included, which facilitates the comparison and
discussion of both visualization instruments: radar chart and boxplot.
%K higher-order statistics
%K observational data analysis
%K power quality
%K signal processing
%K visualization tool
%~ Universidad de Cádiz