Power quality analysis and energy consumption monitoring across hospitals in Cameroon

Authors

Aristide Tolok Nelem1,2,3,4*  ID,  Yannick Antoine Abanda1,3,4 ID,  Théodore Patrice Nna Nna4,5,6 ID,  Junior Ngaba Mbezele1,2,3,4 ID,  Pierre Ele2,3,4 ID
Show Less
1 Higher Technical Teachers’ Training College, University of Ebolowa, Ebolowa 118, Cameroon
2 University Research Centre on Energy for Health, University of Yaounde I, Yaounde 8390, Cameroon
3 Electrotechnical, Automatic and Energetic Laboratory, University of Ebolowa, Ebolowa 118, Cameroon
4 Signal and System Laboratory, University of Ebolowa, Ebolowa 118, Cameroon
5 Cameroonian Association for Research and Innovation in Energy, Technology and Environment, Ebolowa 118, Cameroon
6 Laboratory of Technology and Applied Sciences, University of Douala, Douala 8698, Cameroon
Article ID: 802
86 Views

DOI:

https://doi.org/10.18686/cest802

Keywords:

power quality; fault diagnosis; anomaly detection; principal component analysis; K-means clustering; vector autoregressive model; biomedical equipment

Abstract

The quality of electrical supply is the main cause of premature failure of biomedical equipment in Cameroon, where nearly 95% of hospital devices are imported and designed to operate under stable mains conditions. This study, based on real hospital data, utilises data collected by CURES (University Centre for Energy Research in Healthcare) in four Cameroonian hospitals. The data collection yielded 3,474 ten-minute intervals. A model-free algorithm enabled, in turn: the profiling of regulatory compliance against the EN 50160 and IEEE 519-2014 standards; the detection of anomalies via SPE and Hotelling’s T2 statistics derived from PCA; the localization of faults through residual structuring; unsupervised classification using the K-means method validated by the elbow and silhouette criteria; and finally, the modelling of temporal dependence via a robust vector autoregressive model with parameter estimation based on the M-estimator. Four disturbance profiles were identified. A Spearman’s correlation of ρ = −1.00 between anomaly rates and compliance scores indicates the internal consistency between the compliance scoring and the anomaly detection outputs. With a frequency compliance rate of 28.9% and a score of 47.8%, Djoum Hospital is the most critical site, while Obala achieved the highest score (72.6%). These results highlight the urgent need to deploy reactive compensation and frequency stabilisation systems in the rural hospital facilities included in this study.

References

1. Zafar MW, Shahbaz M, Sinha A, et al. How renewable energy consumption contribute to environmental quality? The role of education in OECD countries. Journal of Cleaner Production. 2020; 268: 122149. doi: 10.1016/j.jclepro.2020.122149

2. Ortiz-Peña A, Honrubia-Escribano A, Gallego-Giner I, et al. Analysis and impact of electrical energy consumption in the academic sector. A case study of the university of Castilla-La Mancha. Energy Conversion and Management: X. 2025; 26: 100894. doi: 10.1016/j.ecmx.2025.100894

3. Pontificia Universidad Católica de Chile, Aguayo-Ulloa E, Valderrama-Ulloa C, et al. Analysis of energy data of existing buildings in a University Campus. Revista de la construcción. 2018; 172–182. doi: 10.7764/RDLC.17.1.172

4. Bastida-Molina P, Torres-Navarro J, Honrubia-Escribano A, et al. A detailed analysis of electricity consumption at the University of Castilla-La Mancha (Spain). Energy and Buildings. 2023; 289: 113046. doi: 10.1016/j.enbuild.2023.113046

5. Popa G. Electric Power Quality through Analysis and Experiment. Energies. 2022; 15(21): 7947. doi: 10.3390/en15217947

6. Paustian F, Gøl R, Wolfe Julsgart H, et al. Medical equipment in the global south: perspective of sustainability and donations. Frontiers in Health Services. 2025; 5: 1638305. doi: 10.3389/frhs.2025.1638305

7. Miles SB, Mughuma JM, Moner-Girona M, et al. The power of health in the Democratic Republic of the Congo: Electricity quality and reliability in medical facilities of North Kivu Province. Applied Energy. 2025; 401: 126830. doi: 10.1016/j.apenergy.2025.126830

8. Styslo B, Danylchenko D, Fedorchuk S, et al. Analysis of Methods for Improving the Quality of Electrical Energy Through the Use of Energy Storage Systems. In: Systems, Decision and Control in Energy VI, Studies in Systems, Decision and Control. Springer Nature; 2024. pp. 51–75. doi: 10.1007/978-3-031-67091-6_3

9. Balasubramaniam PM, Prabha SU. Power quality issues, solutions and standards: A technology review. Journal of Applied Science and Engineering. 2015; 18(4): 371–380. Available online: https://www.researchgate.net/publication/290452952_Power_quality_issues_solutions_and_standards_A_technology_review

10. Akkaya S. Optimization of Convolutional Neural Networks for Classifying Power Quality Disturbances Using Wavelet Synchrosqueezed Transform. Traitement du Signal. 2024; 41(2): 599–614. doi: 10.18280/ts.410205

11. Sinha P, Snehalika S, Gatla RK, et al. Dynamic Lissajous patterns for real time identification and localization of power quality disturbance. Scientific Reports. 2025; 15(1): 32372. doi: 10.1038/s41598-025-10218-4

12. Wang Y, Yang J, Chen Y, et al. Analysis of Electric Energy Full Coverage Acquisition. In: Proceedings of the 2022 IEEE 5th International Conference on Automation, Electronics and Electrical Engineering (AUTEEE); 18–20 November 2022; Shenyang, China. pp. 865–869. doi: 10.1109/AUTEEE56487.2022.9994295

13. Moulum PA, Mandeng JJ, Kom CH. Holistic analysis of the dynamic stability of the Southern Cameroon Interconnected grid in contingency situations. Scientific African. 2024; 26: e02446. doi: 10.1016/j.sciaf.2024.e02446

14. Onanena R, Tchuidjan R, Motto FB, et al. Improvement of the Voltage Quality in an Electrical Network via the Loopback: Case of the Southern Interconnected Grid (SIG) of Cameroon. Journal of Power and Energy Engineering. 2021; 09(03): 25–41. doi: 10.4236/jpee.2021.93002

15. Muyulema-Masaquiza D, Ayala-Chauvin M. Segmentation of Energy Consumption Using K-Means: Applications in Tariffing, Outlier Detection, and Demand Prediction in Non-Smart Metering Systems. Energies. 2025; 18(12): 3083. doi: 10.3390/en18123083

16. Patrizi G, Alfonso CG, Calandroni L, et al. Anomaly Detection for Power Quality Analysis Using Smart Metering Systems. Sensors. 2024; 24(17): 5807. doi: 10.3390/s24175807

17. Li N, Zhu L, Li Y. Power quality disturbance detection based on IEWT. Energy Reports. 2023; 9: 512–521. doi: 10.1016/j.egyr.2023.05.105

18. Alsabaan M, Elsayed A, Bondok A, et al. Robust Federated-Learning-Based Classifier for Smart Grid Power Quality Disturbances. Sensors. 2025; 25(22): 6880. doi: 10.3390/s25226880

19. Sekar K, Kanagarathinam K, Subramanian S, et al. An Improved Power Quality Disturbance Detection Using Deep Learning Approach. Mathematical Problems in Engineering. 2022; 2022: 1–12. doi: 10.1155/2022/7020979

20. Tong Z, Zhong J, Li J, et al. A power quality disturbances classification method based on multi-modal parallel feature extraction. Scientific Reports. 2023; 13(1): 17655. doi: 10.1038/s41598-023-44399-7

21. Chothani N, Desai I, Chan CK, et al. PCA and CNN-based detection and classification of faults in distribution network with distributed energy resources. Applications in Engineering Science. 2026; 25: 100300. doi: 10.1016/j.apples.2026.100300

22. Shantaram MS. Smart Detection and Mitigation of Power Quality Issues in Smart Grids Using MATLAB-Based Simulation and Optimization. Journal of Information Systems Engineering and Management. 2025; 10(26s): 1003–1022. doi: 10.52783/jisem.v10i26s.4328

23. Wu Y, Wu K, Qian C, et al. Research on Power Quality Disturbance Identification by Multi-Scale Feature Fusion. Big Data and Cognitive Computing. 2026; 10(1): 18. doi: 10.3390/bdcc10010018

24. Choudhury AR, Mallick RK, Agrawal R, et al. Power Quality Disturbance Monitoring in PV Integrated Power System with Mode Decomposition and Ensemble Extreme Learning Machine. Nigerian Journal of Technological Development. 2024; 21(3): 127–135. doi: 10.4314/njtd.v21i3.2140

25. Tong CD, Yan XF, Ma YX. Statistical process monitoring based on improved principal component analysis and its application to chemical processes. Journal of Zhejiang University SCIENCE A. 2013; 14(7): 520–534. Available online: https://www.researchgate.net/profile/Xuefeng-Yan-3/publication/257896662_Statistical_process_monitoring_based_on_improved_principal_component_analysis_and_its_application_to_chemical_processes/links/56e7515a08ae438aab8824a0/Statistical-process-monitoring-based-on-improved-principal-component-analysis-and-its-application-to-chemical-processes.pdf

Downloads

Published

2026-09-07

How to Cite

Tolok Nelem, A., Antoine Abanda, Y., Nna Nna, T. P., Mbezele, J. N., & Ele, P. (2026). Power quality analysis and energy consumption monitoring across hospitals in Cameroon. Clean Energy Science and Technology, 4(5). https://doi.org/10.18686/cest802