Simulation-Based Study of Structural Changes in Electrical Time-Series Signals

Authors

DOI:

https://doi.org/10.18690/jet.18.2.75-84.2025

Keywords:

break points, energy system , noise , segmentation , signals , simulation , time series

Abstract

This paper addresses the detection of changes in electrical signals typical for industrial and power systems, using statistical indicators. A dedicated MATLAB algorithm was developed to identify change points by tracking shifts in signal behavior and statistical properties. To evaluate the method, synthetic signals were generated through simulation to reproduce common patterns observed in these systems, allowing testing under different operating conditions and varying noise levels. The results demonstrate that the algorithm reliably detects change points across multiple scenarios, showing both flexibility and robustness. This study highlights the value of simulation-based signal generation as a controlled environment for testing detection methods and provides a foundation for future application to more complex real-world electrical signal analysis tasks.

Downloads

Download data is not yet available.

Author Biographies

  • Luka Živković, Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek

    Assistant, Chair of Fundamentals of Electrical Engineering and Measurements. Osijek. Croatia.

    E-mail: luka.zivkovic@ferit.hr

  • Željko Hederić, Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek

    Head of the Department of Electromechanical Engineering, Full Professor (Faculty of Electrical Engineering, Computer Science and Information Technology Osijek), Head of Research Laboratory for Hybrid Electric Drives. 

    Osijek, Croatia. E-mail: zeljko.hederic@ferit.hr

  • Tin Benšić, Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek

    Assistant Professor, Chair of Fundamentals of Electrical Engineering and Measurements.

    Osijek, Croatia. E-mail: tin.bensic@ferit.hr

  • Goran Kurtović, Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek

    Senior Laboratory Technician, Chair of Electric Machines and Power Electronics.

    Osijek, Croatia. E-mail: goran.kurtovic@ferit.hr

  • Marinko Stojkov, University of Slavonski Brod, Mechanical Engineering Faculty

    Full Professor (Department for Energetics).

    Osijek, Croatia. E-mail: mstojskov@unisb.hr

References

[1] A. Benigni, T. Strasser, G. De Carne, M. Liserre, M. Cupelli and A. Monti, "Real-Time Simulation-Based Testing of Modern Energy Systems: A Review and Discussion," in IEEE Industrial Electronics Magazine, vol. 14, no. 2, pp. 28-39, June 2020.

[2] S. Aminikhanghahi and D. J. Cook, “A Survey of Methods for Time Series Change Point Detection,” Knowledge and Information Systems, vol. 51, no. 2, pp. 339–367, 2016.

[3] A. Hazra, N. Gogtay, "Biostatistics Series Module 10: Brief Overview of Multivariate Methods," Indian J. Dermatol., Jul-Aug 2017.

[4] C. Truong, L. Oudre, and N. Vayatis, “Selective review of offline change point detection methods,” Signal Processing, vol. 167, p. 107299, 2020.

[5] T. Dawn, A. Roy, A. Manna, and A. K. Ghosh, “Some clustering-based change-point detection methods applicable to high dimension, low sample size data,” Journal of Statistical Planning and Inference, vol. 234, 2025.

[6] A. Pushkar, M. Gupta, R. Wadhvani, and M. Gyanchandani, “A Comparative Study on Change-Point Detection Methods in Time Series Data,” in 2022 2nd International Conference on Intelligent Technologies (CONIT), Hubli, India, pp. 1–7, 2022.

[7] A. Ryzhikov, M. Hushchyn, and D. Derkach, “Latent Stochastic Differential Equations for Change Point Detection,” IEEE Access, vol. 11, pp. 104700–104711, 2023.

[8] S. Liu, M. Yamada, N. Collier, and M. Sugiyama, “Change-point detection in time-series data by relative density-ratio estimation,” Neural Networks, vol. 43, pp. 72–83, 2013.

[9] X. Ding, J. Wang, Y. Liu, and U. Jung, “Multivariate time series anomaly detection using working memory connections in bi-directional long short-term memory autoencoder network,” Appl. Sci., vol. 15, no. 2861, 2025.

Downloads

Published

29.09.2025

How to Cite

Živković, L., Hederić, Željko, Benšić, T., Kurtović, G., & Stojkov, M. (2025). Simulation-Based Study of Structural Changes in Electrical Time-Series Signals. Journal of Energy Technology, 18(2), 75-84. https://doi.org/10.18690/jet.18.2.75-84.2025