Strategy for Visual Measurement of Power Quality Based on Higher-Order Statistics and Exploratory Big Data Analysis
Source abstract
Abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Evaluation Methods in Various Fields · Advanced Decision-Making Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Research Group PAIDI-TIC-168, Department of Automation Engineering, Electronics, Architecture and Computers Networks, University of Cadiz, 11202 Algeciras, Cadiz, Spain
References (32)
Show source-provided references
1. Martinez, R., Castro, P., Arroyo, A., Manana, M., Galan, N., Moreno, F.S., Bustamante, S., and Laso, A. (2022). Techniques to Locate the Origin of Power Quality Disturbances in a Power System: A Review. Sustainability, 14.10.3390/su14127428 ↗
2. (2023). Voltage Characteristics of Electricity Supplied by Public Electricity Networks (Standard No. EN 50160).
3. An Advanced Scheme for Range Ambiguity Suppression of Spaceborne SAR Based on Blind Source Separation10.1109/TGRS.2022.3184709 ↗
4. Comprehensive Review on Detection and Classification of Power Quality Disturbances in Utility Grid With Renewable Energy Penetration10.1109/ACCESS.2020.3014732 ↗
5. Artificial Intelligent Techniques for Identifying the Cause of Disturbances in the Power Grid
6. Deep learning for power quality10.1016/j.epsr.2022.108887 ↗
7. A Review on Intelligent Detection and Classification of Power Quality Disturbances: Trends, Methodologies, and Prospects
8. A systematic review of real-time detection and classification of power quality disturbances10.1186/s41601-023-00277-y ↗
9. Intelligent Methods for Characterization of Electrical Power Quality Signals using Higher Order Statistical Features
10. Detection and classification of power quality disturbances in wind-grid integrated system using fast time-time transform and small residual-extreme learning machine10.1002/etep.2519 ↗
11. Methodology based on higher-order statistics and genetic algorithms for the classification of power quality disturbances10.1049/iet-gtd.2020.0366 ↗
12. Power quality event dynamics characterization via 2D trajectories using deviations of higher-order statistics10.1016/j.measurement.2018.04.098 ↗
13. Zanoni, M., Chiappa, C., Chiumeo, R., Tenti, L., and Shadmehr, H. (2020, January 23–25). Higher-Order Statistics for Voltage Dips Characterization on Italian MV Networks. Proceedings of the 2020 AEIT International Annual Conference (AEIT), Catania, Italy.10.23919/AEIT50178.2020.9241206 ↗
14. A Critical Analysis of Methodologies for Detection and Classification of Power Quality Events in Smart Grid10.1109/ACCESS.2021.3087016 ↗
15. Power Quality Indices for Transient Disturbances10.1109/TPWRD.2005.855444 ↗
16. Support vector machine for classification of voltage disturbances10.1109/TPWRD.2007.900065 ↗
17. Ribeiro, E.G., Dias, G.L., Barbosa, B.H.G., and Ferreira, D.D. (2016, January 16–19). Real-time system for automatic classification of power quality disturbances. Proceedings of the International Conference on Harmonics and Quality of Power, ICHQP, Belo Horizonte, Brazil.10.1109/ICHQP.2016.7783405 ↗
18. Identification of optimal features for fast and accurate classification of power quality disturbances10.1016/j.measurement.2017.10.034 ↗
19. Shen, Y., Abubakar, M., Liu, H., and Hussain, F. (2019). Power Quality Disturbance Monitoring and Classification Based on Improved PCA and Convolution Neural Network for Wind-Grid Distribution Systems. Energies, 12.10.3390/en12071280 ↗
20. A classification method for multiple power quality disturbances using EWT based adaptive filtering and multiclass SVM10.1016/j.neucom.2019.01.038 ↗
21. Site Characterization Index for Continuous Power Quality Monitoring Based on Higher-order Statistics10.35833/MPCE.2020.000041 ↗
22. Characterisation of Electrical Power Systems Based on Electrical Curves and their properties
23. Ignatova, V., Villard, D., and Hypolite, J.M. (2015, January 10–13). Simple indicators for an effective Power Quality monitoring and analysis. Proceedings of the 2015 IEEE 15th International Conference on Environment and Electrical Engineering, EEEIC 2015-Conference Proceedings, Rome, Italy.10.1109/EEEIC.2015.7165321 ↗
24. Kattmann, C., and Tenbohlen, S. (2017, January 18–22). Visualization of power quality data. Proceedings of the 2017 IEEE Manchester PowerTech, Manchester, UK.10.1109/PTC.2017.7981158 ↗
25. Open source dataset generator for power quality disturbances with deep-learning reference classifiers10.1016/j.epsr.2021.107152 ↗
26. Florencias-Oliveros, O., Espinosa-Gavira, M.J., González de la Rosa, J.-J., Agüera-Pérez, A., Palomares-Salas, J.C., and Sierra-Fernández, J.M. (2017). Real-Life Power Quality Sags, Institute of Electrical and Electronics Engineers (IEEE). IEEE Dataport.
27. Florencias-Oliveros, O., Espinosa-Gavira, M.J., González-de-la-Rosa, J.J., Agüera-Pérez, A., Palomares-Salas, J.C., and Sierra-Fernández, J.M. (2017). Real-Life Power Quality Transients, Institute of Electrical and Electronics Engineers (IEEE).
28. Recognition of Complex Power Quality Disturbances Using S-Transform Based Ruled Decision Tree10.1109/ACCESS.2020.3025190 ↗
29. Detection and classification of power quality disturbances using discrete wavelet transform and wavelet networks10.1049/iet-smt.2009.0006 ↗
30. A New Power System Digital Harmonic Analyzer10.1109/TPWRD.2007.893372 ↗
31. SED-Bench: Benchmark for Simulated Electrical Disturbance10.1109/TIM.2019.2943989 ↗
32. (2002). Electromagnetic Compatibility (EMC). Testing and Measurement Techniques-General Guide on Harmonics and Interharmonics Measurements and Instrumentation, for Power Supply Systems and Equipment Connected Thereto (Standard No. IEC 61000-4-7).