RT journal article
T1 Strategy for Visual Measurement of Power Quality Based on Higher-Order Statistics and Exploratory Big Data Analysis
A1 González de la Rosa, Juan José
A1 Florencias Oliveros, Olivia
A1 Remigio Carmona, Paula
A2 Ingeniería en AutomáticaElectrónica, Arquitectura y Redes de Computadores
K1 higher-order statistics
K1 observational data analysis
K1 power quality
K1 signal processing
K1 visualization tool
AB This article proposes a strategy for the visual characterization of power quality inbig data analysis contexts, culminating in the development of a visualization tool based onhigher-order statistics, which exhibits an efficiency between 83.33% and 100% in detecting50 Hz synthetic and real-life simple and hybrid events, showing its significant potentialfor real-world applications marked by non-linear loads and non-Gaussian behaviors andsurpassing the detection of traditional tools such as boxplot by up to 50%. Efficient energymanagement is closely accompanied by an optimum energy data management (EDM). Itimplies the acquisition, analysis, and interpretation of data to make decisions regarding thebest energy usage with subsequent cost reductions. Through a study of indicators, includinghigher-order statistics, crest factor, SNR and THD, the article establishes nominal values andbehavioral patterns, expanding the previous knowledge of these parameters. The indicatorsare presented as vertices in a radar-type charting tool, providing a multidimensional spatialvisualization from individual indices that allows the behavioral pattern associated witheach type of disturbance to be characterized combined with a decision tree. In addition,boxplots reflecting data processing are included, which facilitates the comparison anddiscussion of both visualization instruments: radar chart and boxplot.
PB MDPI
SN 2076-3417
YR 2025
FD 2025-06-07
LK http://hdl.handle.net/10498/38262
UL http://hdl.handle.net/10498/38262
LA eng
NO Spanish Ministry of Science and Education and the State Investigation Agency for funding the research project PID2019-108953RBC21, entitled ‘Strategies for Aggregated Generation of Photo-Voltaic Plants-Energy and MeteorologicalData’ (SAGPV-EMOD), and the Andalusian Government for supporting the Research Group PAIDITIC-168, in Computational Instrumentation and Industrial Electronics (ICEI).
DS Repositorio Institucional de la Universidad de Cádiz
RD 09-may-2026