Digital Twin-Based Predictive Maintenance and Voltage Stability Enhancement of Distribution Networks with High Solar PV Penetration Using Artificial Intelligence

• Author(s): Chukwuemeka, S. N.; Anigbogu, C. C.; Benjamin, A. O. E.; Ezenwa, K. I.
• Paper ID: 1719424
• Page: 423-431
• Published Date: 06-07-2026
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 10 Issue 1 July-2026

Abstract

The integration of solar photovoltaic (PV) systems into electrical distribution networks introduces challenges such as voltage fluctuations, increased equipment stress, and reduced system reliability. This study presents a Digital Twin-based framework for predictive maintenance and voltage stability enhancement in PV-rich distribution networks using Artificial Intelligence (AI) .The proposed framework combines a real-time digital twin with machine learning models to continuously monitor system conditions, predict equipment failures, and detect abnormal operating states from voltage profiles, load variations, and asset health indicators. An adaptive voltage regulation scheme is also implemented to maintain acceptable voltage limits under high PV penetration and dynamic loading. Simulation results show that the proposed method reduces voltage deviation by 18–25%, maintains bus voltages within 0.95–1.05 p u., and decreases system power losses by 12–20% compared to conventional methods. In addition, predictive maintenance improves fault detection accuracy to 92–97%, reduces unexpected outages by 20–35%, and lowers maintenance costs by 15–28%. Overall, the results demonstrate that integrating digital twin and AI techniques significantly enhances voltage stability, system reliability, and operational efficiency in modern PV-integrated distribution networks.

Keywords

Digital Twin, Artificial Intelligence, Predictive Maintenance, Voltage Stability, Solar Photovoltaic Systems, Distribution Networks, Smart Grid, Renewable Energy Integration.

Citations

IRE Journals:
Chukwuemeka, S. N., Anigbogu, C. C., Benjamin, A. O. E., Ezenwa, K. I. "Digital Twin-Based Predictive Maintenance and Voltage Stability Enhancement of Distribution Networks with High Solar PV Penetration Using Artificial Intelligence" Iconic Research And Engineering Journals Volume 10 Issue 1 2026 Page 423-431

IEEE:
Chukwuemeka, S. N., Anigbogu, C. C., Benjamin, A. O. E., Ezenwa, K. I. "Digital Twin-Based Predictive Maintenance and Voltage Stability Enhancement of Distribution Networks with High Solar PV Penetration Using Artificial Intelligence" Iconic Research And Engineering Journals, vol. 10, no. 1, Jul. 2026

APA:
Chukwuemeka, S. N., Anigbogu, C. C., Benjamin, A. O. E., Ezenwa, K. I. (2026). Digital Twin-Based Predictive Maintenance and Voltage Stability Enhancement of Distribution Networks with High Solar PV Penetration Using Artificial Intelligence. Iconic Research And Engineering Journals, 10(1).

MLA:
Chukwuemeka, S. N., Anigbogu, C. C., Benjamin, A. O. E., Ezenwa, K. I. "Digital Twin-Based Predictive Maintenance and Voltage Stability Enhancement of Distribution Networks with High Solar PV Penetration Using Artificial Intelligence" Iconic Research And Engineering Journals, vol. 10, no. 1, Jul. 2026.