International Journal of Advance Research Publication and Reviews

International Journal of Advance Research Publication and Reviews
Peer-Reviewed | Multi-Disciplinary Journal

Predictive Failure Analytics for Power Electronic Converters Supporting Renewable Energy Operations Across Enterprise Banking Architectures

Author

Bridjaa Akeem

Abstract

 Renewable-energy integration increasingly depends on power electronic converters that sustain efficient energy transfer between distributed generation, storage infrastructure, electrical grids, and mission-critical enterprise facilities. Within banking architectures, converter reliability becomes particularly significant because data centres, transaction platforms, branch systems, payment-processing infrastructure, and continuity environments require stable power despite fluctuating renewable generation and demanding operational loads. This study develops a predictive failure analytics framework for identifying converter degradation before functional disruption occurs. The approach integrates electrical, thermal, switching, environmental, and operational measurements, including voltage, current, junction temperature, switching frequency, harmonic distortion, efficiency, load variability, and fault histories. Condition indicators are transformed into temporal degradation features and analysed using statistical learning, machine-learning regression, anomaly detection, and remaining-useful-life estimation. The framework associates evolving component signatures with capacitor deterioration, semiconductor stress, thermal cycling, switching abnormalities, and converter-level failure probability. Predictive outputs are subsequently incorporated into enterprise banking resilience processes to support maintenance prioritization, renewable-power continuity, and risk-informed intervention. The proposed approach establishes a quantitative connection between converter health, renewable-energy variability, failure prediction, and operational resilience across geographically distributed banking technology environments.

Keywords

Power Electronic Converters; Predictive Failure Analytics; Renewable Energy; Enterprise Banking Architecture; Remaining Useful Life; Operational Resilience

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References

  1. Masood A, Ahmed U, Hassan SZ, Khan AR, Mahmood A. Economic value creation of artificial intelligence in supporting variable renewable energy resource integration to power systems: a systematic review. Sustainability. 2025 Mar 15;17(6):2599.
  2. Ojo KE, Saha AK, Srivastava VM. Review of advances in renewable energy-based microgrid systems: Control strategies, emerging trends, and future possibilities. Energies. 2025 Jul 14;18(14):3704.
  3. Adegboyega AW, Sepasi S, Howlader HO, Griswold B, Matsuura M, Roose LR. DC microgrid deployments and challenges: A comprehensive review of academic and corporate implementations. Energies. 2025 Feb 22;18(5):1064.
  4. Prajapati A, Paraye P, Ahirwar BK, Tam CF. Artificial intelligence integration in solar-powered EV charging systems: challenges, opportunities, and future perspectives: A. Prajapati et al. Journal of Thermal Analysis and Calorimetry. 2025 Aug;150(16):12103-34.
  5. Vimal VR. Next Generation Enterprise Architecture for SAP Cloud Systems Leveraging AI Driven Analytics and Hybrid Infrastructure. International Journal of Engineering & Extended Technologies Research (IJEETR). 2025 Nov 30;7(6):11174-82.
  6. Elboughdiri N, Kriaa K, Bakare MS, Abdulkarim A, Alaneme GU, Maatki C. Intelligent demand-side energy management via optimized ANFIS–gene expression programming in hybrid renewable–grid systems. Scientific Reports. 2025 Dec 3;15(1):43065.
  7. Chukwunweike J. Coordinating PLC-based load shedding and variable-speed drives to prevent transformer overloading during industrial production peaks. Int J Electr Data Commun. 2025;6(2 Pt B):118-135. doi:10.22271/27083969.2025.v6.i2b.109.
  8. Ahmed MM, Mirsaeidi S, Koondhar MA, Karami N, Tag-Eldin EM, Ghamry NA, El-Sehiemy RA, Alaas ZM, Mahariq I, Sharaf AM. Mitigating uncertainty problems of renewable energy resources through efficient integration of hybrid solar PV/wind systems into power networks. IEEe Access. 2024 Feb 26;12:30311-28.
  9. Kabeyi MJ, Olanrewaju OA. Smart grid technologies and application in the sustainable energy transition: a review. International Journal of Sustainable Energy. 2023 Dec 14;42(1):685-758.
  10. Kumar M. AI Driven Self Healing Cloud Architectures for Intelligent Enterprise Reliability Engineering. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM). 2025 Oct 16;8(5):12944-51.
  11. Ramadhan HR, Osman M, Ben-Dalla L, Rashid T, Albaraesi M, EL-sseid MA, Alnnale T. A New Approach of the Machine Learning Framework Integrating Policy Design to Predict Renewable Electricity Penetration in Resource-Constrained Settings. Comprehensive Journal of Science. 2025;10(ملحق 38):2929-50.‎
  12. Fazle AB, Prodhan RK, Islam MM. AI-powered predictive failure analysis in pressure vessels using real-time sensor fusion: Enhancing industrial safety and infrastructure reliability. American Journal of Scholarly Research and Innovation. 2023 Dec 1;2(02):102-34.
  13. Denkyi, Felix, Olusegun Olayinka Sofowoke, Confidence Adimchi Chinonyerem, and Idowu Wasiu Adebola. 2026. “Design and Optimization of Intelligent Power Electronics Converters for Renewable Energy Systems: A Systematic Review”. Asian Journal of Advanced Research and Reports20 (2):41-59. https://doi.org/10.9734/ajarr/2026/v20i21277.
  14. Ukoba K, Olatunji KO, Adeoye E, Jen TC, Madyira DM. Optimizing renewable energy systems through artificial intelligence: Review and future prospects. Energy & environment. 2024 Nov;35(7):3833-79.
  15. Hassan A, Khan SA, Li R, Su W, Zhou X, Wang M, Wang B. Second-life batteries: A review on power grid applications, degradation mechanisms, and power electronics interface architectures. Batteries. 2023 Nov 27;9(12):571.
  16. Shil SK. AI driven predictive maintenance in petroleum and power systems using random forest regression model for reliability engineering framework. American Journal of Scholarly Research and Innovation. 2025 Aug 30;4(01):363-91.
  17. Hackney M. Enterprise Architecture As A Catalyst For Real-Time Risk Monitoring And Predictive Analytics In Financial Services. Available at SSRN 5390423. 2025 Aug 13.
  18. Xie L, Huang T, Kumar PR, Thatte AA, Mitter SK. On an information and control architecture for future electric energy systems. Proceedings of the IEEE. 2022 Nov 15;110(12):1940-62.
  19. Racheal Kikachukwu Ogan. Machine-learning-based API failure prediction and root-cause analytics for improving reliability of enterprise banking integration architectures at scale. Int J Comput Artif Intell 2021;2(2):142-153. DOI: 33545/27076571.2021.v2.i2a.378
  20. Han C, Yang L. Financing and management strategies for expanding green development projects: A case study of energy corporation in China’s renewable energy sector using machine learning (ML) modeling. Sustainability. 2024 May 21;16(11):4338.
  21. Tuyen ND, Quan NS, Linh VB, Van Tuyen V, Fujita G. A comprehensive review of cybersecurity in inverter-based smart power system amid the boom of renewable energy. Ieee Access. 2022 Mar 30;10:35846-75.
  22. Saboori H, Pishbahar H, Dehghan S, Strbac G, Amjady N, Novosel D, Terzija V. Reactive power implications of penetrating inverter-based renewable and storage resources in future grids toward energy transition—A review. Proceedings of the IEEE. 2025 Apr 18;113(1):66-104.
  23. Peng FZ, Liu CC, Li Y, Jain AK, Vinnikov D. Envisioning the future renewable and resilient energy grids—A power grid revolution enabled by renewables, energy storage, and energy electronics. IEEE Journal of Emerging and Selected Topics in Industrial Electronics. 2023 Dec 14;5(1):8-26.
  24. Parsa Z, Oleksandr H, Jacek R. Data Centers as a Driving Force for the Renewable Energy Sector. Energies. 2025;19(1):236.
  25. Oluwatobi Alebiosu. Comparative analysis of public-private partnership legislation driving electricity infrastructure expansion, renewable energy investment, and economic resilience nationally. Int J Foreign Trade Int Bus 2025;7(2):239-249. DOI: 33545/26633140.2025.v7.i2c.262

 

  1. Kumar P, Kumar K, Adhikary N, Tesfaye EL. Analysis of control and computational strategies for green energy integration for sociotechnical ecological power infrastructure in Indian and African markets. Scientific Reports. 2025 Apr 22;15(1):13953.
  2. Cavus M. Advancing power systems with renewable energy and intelligent technologies: A comprehensive review on grid transformation and integration. Electronics. 2025 Mar 15;14(6):1159.
  3. Islam MS. Integrated Modeling of Condition Monitoring Data for Predictive Maintenance of Electrical Power Plant Systems. American Journal of Scholarly Research and Innovation. 2025 Dec 28;4(01):695-731.
  4. Arun Sampaul Thomas G, Muthukaruppasamy S, Saravanan K, Muleta N. Revolutionizing smart grids with big data analytics: A case study on integrating renewable energy and predicting faults. InData Analytics for Smart Grids Applications—A Key to Smart City Development 2023 Nov 30 (pp. 179-198). Cham: Springer Nature Switzerland.
  5. Rana S. AI-driven fault detection and predictive maintenance in electrical power systems: A systematic review of data-driven approaches, digital twins, and self-healing grids. American Journal of Advanced Technology and Engineering Solutions. 2025 Feb 3;1(01):258-89.
  6. Ogan RK. Risk-based API governance for predicting integration failures, security anomalies, and service degradation across hybrid enterprise application environments. Int J Finance Manage Econ. 2024;7(2):871-882. doi:10.33545/26179210.2024.v7.i2.946.
  7. Erhueh OV, Elete T, Akano OA, Nwakile C, Hanson E. Application of Internet of Things (IoT) in energy infrastructure: Lessons for the future of operations and maintenance. Comprehensive Research and Reviews in Science and Technology. 2024 Oct;2(2):28-54.
  8. Almihat M, Munda J. Comprehensive review on challenges of integration of renewable energy systems into microgrid. Solar Energy and Sustainable Development Journal. 2025 Mar 4;14(1):199-236.
  9. Ejiyi CJ, Cai D, Thomas D, Obiora S, Osei-Mensah E, Acen C, Eze FO, Sam F, Zhang Q, Bamisile OO. Comprehensive review of artificial intelligence applications in renewable energy systems: current implementations and emerging trends. Journal of Big Data. 2025 Jul 11;12(1):169.
  10. Rajendran G, Raute R, Caruana C. The brain behind the grid: A comprehensive review on advanced control strategies for smart energy management systems. Energies. 2025 Jul 24;18(15):3963.
  11. Yaghouti S, Hayati MM, Majidi H, Sorouri H, Jabari F, Abapour M, Oshnoei A, Blaabjerg F. Grid modernization and transitioning toward sustainability: An in-depth survey of the latest transformative directions in modern power systems. Reference Module in Materials Science and Materials Engineering; Elsevier: Amsterdam, The Netherlands. 2025 Jan 1.
  12. Oyeleke AV, Denkyi F, Ojajuni BA, Eze FC. Intelligent thermal management framework combining liquid cooling technologies and predictive analytics for sustainable AI infrastructure operations. Int J Sci Eng Appl. 2026;15(7):29-43. doi:10.7753/IJSEA1507.
  13. Iannotta S. AI and Machine Learning Driven Multi-Cloud Enterprise Platforms for Digital Banking Renewable Energy and Secure Mobile Systems. International Journal of Engineering & Extended Technologies Research (IJEETR). 2025 Oct 21;7(5):16022-32.
  14. Kaushal RK, Raveendra K, Nagabhooshanam N, Azam M, Brindha G, Anand D, Natrayan L, Rambabu K. Fault prediction and awareness for power distribution in grid connected res using hybrid machine learning. Electric Power Components and Systems. 2025 Aug 27;53(14):1744-65.
  15. Kull K, Asad B, Khan MA, Naseer MU, Kallaste A, Vaimann T. Faults, failures, reliability, and predictive maintenance of grid-connected solar systems: A comprehensive review. Applied Sciences. 2025 Oct 27;15(21):11461.

 

 

 

 

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