Frequency and phase tracking for excitation capacitor fault detection in a self-excited induction generator using the ZCT method
DOI:
https://doi.org/10.14311/AP.2026.66.0375Keywords:
wind energy, self-excitation generator, diagnostic, finite elements method, zero-crossing time rateAbstract
This paper presents a methodology for detecting excitation capacitor faults in self-excited induction generators (SEIGs) used in wind energy systems operating in isolated or rural areas. The proposed approach combines frequency and phase tracking with zero-crossing time rate analysis for real-time anomaly detection. The SEIG is modelled using the finite element method (FEM) to simulate its electromechanical behaviour under various fault conditions. Fault scenarios are introduced by progressively disconnecting excitation capacitors connected in parallel and series, thus simulating failures ranging from one to five units. Monitoring zero-crossing intervals and phase variations enables accurate fault detection without relying on computationally intensive spectral techniques. The results confirm the effectiveness of the proposed methodology in identifying fault-induced distortions and improving the reliability of SEIG-based wind energy systems.
Downloads
References
[1] M. J. B. Kabeyi, O. A. Olanrewaju. Sustainable energy transition for renewable and low carbon grid electricity generation and supply. Frontiers in Energy Research 9:743114, 2022. https://doi.org/10.3389/fenrg.2021.743114
[2] A. Q. Al-Shetwi. Sustainable development of renewable energy integrated power sector: Trends, environmental impacts, and recent challenges. Science of The Total Environment 822:153645, 2022. https://doi.org/10.1016/j.scitotenv.2022.153645
[3] G. K. Singh. Self-excited induction generator research – A survey. Electric Power Systems Research 69(2–3):107–114, 2004. https://doi.org/10.1016/j.epsr.2003.08.004
[4] V. B. M. Krishna, V. Sandeep, S. S. Murthy, K. Yadlapati. Experimental investigation on performance comparison of self excited induction generator and permanent magnet synchronous generator for small scale renewable energy applications. Renewable Energy 195:431–441, 2022. https://doi.org/10.1016/j.renene.2022.06.051
[5] A. Dilmi, A. Bouzida, N. Yassa, et al. Enhancing the reliability of self-excited induction generators used in wind energy microgrids through a hybrid Renewable and Sustainable Energy 18(1):016306, 2026. https://doi.org/10.1063/5.0312967
[6] C. F. Wagner. Self-excitation of induction motors. Electrical Engineering 58(2):47–51, 1939. https://doi.org/10.1109/ee.1939.6431777
[7] F. Belynda, R. Abdelli, A. Bouzida. Stator current signal crossing for fault diagnosis of self-excited induction generators. Acta Polytechnica 63(5):293–304, 2023. https://doi.org/10.14311/ap.2023.63.0293
[8] S. P. Singh, S. K. Jain, J. Sharma. Voltage regulation optimization of compensated self-excited induction generator with dynamic load. IEEE Transactions on Energy Conversion 19(4):724–732, 2004. https://doi.org/10.1109/tec.2004.827711
[9] A. Najafi, I. Iskender. Electromagnetic force investigation on distribution transformer under unbalanced faults based on time stepping finite element methods. International Journal of Electrical Power & Energy Systems 76:147–155, 2016. https://doi.org/10.1016/j.ijepes.2015.09.020
[10] R. de Jesus Romero-Troncoso. Multirate signal processing to improve FFT-based analysis for detecting faults in induction motors. IEEE Transactions on Industrial Informatics 13(3):1291–1300, 2017. https://doi.org/10.1109/tii.2016.2603968
[11] A. Kucuker, M. Bayrak. Detection of mechanical imbalances of induction motors with instantaneous power signature analysis. Journal of Electrical Engineering and Technology 8(5):1116–1121, 2013. https://doi.org/10.5370/jeet.2013.8.5.1116
[12] A. Ukil, S. Chen, A. Andenna. Detection of stator short circuit faults in three-phase induction motors using motor current zero crossing instants. Electric Power Systems Research 81(4):1036–1044, 2011. https://doi.org/10.1016/j.epsr.2010.12.003
[13] Z. Zou, M. Chen, C. Yang, et al. Fault diagnosis method for marine electric propulsion systems based on zero-crossing tacholess order tracking. Journal of Marine Science and Engineering 12(11):1899, 2024. https://doi.org/10.3390/jmse12111899
[14] S. Joo, J. Choi, N. Kim, M. C. Lee. Zero-crossing rate method as an efficient tool for combustion instability diagnosis. Experimental Thermal and Fluid Science 123:110340, 2021. https://doi.org/10.1016/j.expthermflusci.2020.110340
[15] M. Z. Ali, M. N. S. K. Shabbir, X. Liang, et al. Machine learning-based fault diagnosis for single- and multi-faults in induction motors using measured stator currents and vibration signals. IEEE Transactions on Industry Applications 55(3):2378–2391, 2019. https://doi.org/10.1109/tia.2019.2895797
[16] J. Faiz, B. M. Ebrahimi, B. Akin, H. A. Toliyat. Finite-element transient analysis of induction motors under mixed eccentricity fault. IEEE Transactions on Magnetics 44(1):66–74, 2008. https://doi.org/10.1109/tmag.2007.908479
[17] A. Bouzida, R. Abdelli, O. Touhami, et al. Dynamic eccentricity fault diagnosis in induction motors using finite element method and experimental tests. International Journal of Industrial Electronics and Drives 3(4):199–209, 2017. https://doi.org/10.1504/ijied.2017.087610
[18] N. H. Malik, A. A. Mazi. Capacitance requirements for isolated self excited induction generators. IEEE Transactions on Energy Conversion EC-2(1):62–69, 1987. https://doi.org/10.1109/tec.1987.4765805
[19] A. K. Al Jabri, A. I. Alolah. Capacitance requirement for isolated self-exicted induction generator. IEE Proceedings B (Electric Power Applications) 137(3):154–159, 1990. https://doi.org/10.1049/ip-b.1990.0016
[20] T. F. Chan. Capacitance requirements of self-excited induction generators. IEEE Transactions on Energy Conversion 8(2):304–311, 1993. https://doi.org/10.1109/60.222721
[21] W. E. Vanco, F. B. Silva, F. A. S. Goncalves, C. A. Bissochi. Evaluation of the capacitor bank design for self-excitation in induction generators. IEEE Latin America Transactions 16(2):482–488, 2018. https://doi.org/10.1109/tla.2018.8327403
[22] R. R. Shenoy, C. S. Seelamantula. Spectral zero-crossings: Localization properties and applications. IEEE Transactions on Signal Processing 63(12):3177–3190, 2015. https://doi.org/10.1109/tsp.2015.2420538
[23] Q. Chen, T. Liu, X. Wu, H. Li. Modeling and optimization for fault diagnosis of electromechanical systems based on zero crossing algorithm. Mathematical Problems in Engineering 2020:9267838, 2020. https://doi.org/10.1155/2020/9267838
[24] K. Maresch, L. F. Freitas-Gutierres, A. L. Oliveira, et al. Advanced diagnostic approach for high-voltage insulators: Analyzing partial discharges through zero-crossing rate and fundamental frequency estimation of acoustic raw data. Energies 16(16):6033, 2023. https://doi.org/10.3390/en16166033
[25] R. C. Guido. ZCR-aided neurocomputing: A study with applications. Knowledge-Based Systems 105:248–269, 2016. https://doi.org/10.1016/j.knosys.2016.05.011
[26] D. A. Ramli, N. Ghazali, L. Tay. Ischemic stroke detection system with computer aided diagnostic capability. Procedia Computer Science 126:393–402, 2018. https://doi.org/10.1016/j.procs.2018.07.273
[27] B. Trajin, M. Chabert, J. Regnier, J. Faucher. Hilbert versus Concordia transform for three-phase machine stator current time-frequency monitoring. Mechanical Systems and Signal Processing 23(8):2648–2657, 2009. https://doi.org/10.1016/j.ymssp.2009.05.015
[28] D. J. Gross, A. Matytsin. Instanton induced large N phase transitions in two- and four-dimensional QCD. Nuclear Physics B 429(1):50–74, 1994. https://doi.org/10.1016/s0550-3213(94)80041-3
[29] B. Kedem. Search for periodicities by axis-crossings of filtered time series. Signal Processing 10(2):129–144, 1986. https://doi.org/10.1016/0165-1684(86)90015-0
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Ali Dilmi, Ahcene Bouzida, Nacera Yassa, Belynda Fares

This work is licensed under a Creative Commons Attribution 4.0 International License.


