Research Article
Predictive Maintenance and Digital Twins for Greener Power Generation: Case Studies from China, Germany, Norway, and the Netherlands
Agil Mammadov*
,
Yaroslav Danilov
Issue:
Volume 14, Issue 5, October 2025
Pages:
115-121
Received:
18 September 2025
Accepted:
4 October 2025
Published:
3 December 2025
DOI:
10.11648/j.ijepe.20251405.11
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Abstract: Predictive maintenance (PdM), supported by artificial intelligence (AI) and digital twin methods, is gaining attention as a practical and cost efficient way to manage power generation assets. In the renewable energy sector, where performance, stability, and cost control are central concerns, PdM enables operators to anticipate equipment faults, schedule interventions more effectively, and reduce unplanned downtime. This paper reviews how such approaches are being applied in four different national contexts: China, Germany, Norway, and the Netherlands, and considers their contribution to cleaner and more reliable energy systems. The discussion highlights several patterns that emerge across these countries. In China, the rapid expansion of wind and solar capacity has driven the use of PdM to improve fault detection and optimize turbine and panel performance. Germany demonstrates how PdM can be integrated into broader energy transition policies, using digital twins and AI to balance fluctuating renewable output with grid demands. Norway shows the value of predictive tools in extending the life and efficiency of hydropower equipment, while the Netherlands illustrates the particular benefits of PdM in offshore wind projects, where remote monitoring and early fault recognition are critical. Evidence from these cases points to three consistent outcomes: improved uptime of renewable assets, measurable reductions in maintenance costs, and smoother integration of intermittent power sources through more advanced grid management. Taken together, these findings suggest that PdM is not only a set of technical tools but also a strategic component in building sustainable, resilient, and economically viable energy systems. Its wider adoption may help accelerate the transition toward low carbon power on a global scale.
Abstract: Predictive maintenance (PdM), supported by artificial intelligence (AI) and digital twin methods, is gaining attention as a practical and cost efficient way to manage power generation assets. In the renewable energy sector, where performance, stability, and cost control are central concerns, PdM enables operators to anticipate equipment faults, sch...
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Research Article
A Multi-agent Based Distributed Voltage Control Scheme Using Machine Learning and Smart Inverters
Md Rayhan Tanvir*
Issue:
Volume 14, Issue 5, October 2025
Pages:
122-141
Received:
27 October 2025
Accepted:
5 November 2025
Published:
9 December 2025
DOI:
10.11648/j.ijepe.20251405.12
Downloads:
Views:
Abstract: A new distributed voltage control strategy for PV power systems that does not need support from centralized SVCs is proposed. The methodology uses smart inverters, agent-based coordination, and machine learning-based forecasting to offer a scalable and economical solution for decoupling voltage variations in the context of high penetration of PV. Each inverter acts as an autonomous agent that regulates its reactive power output using local voltage measurements and short-term irradiance predictions derived from a Long Short-Term Memory (LSTM) model. The agents cooperate with their neighbors, utilizing a consensus algorithm for coordinated voltage control throughout the network. This decentralized strategy enables fast, adaptive, and cost-effective voltage stabilization without relying on hardware-intensive centralized devices. The effectiveness and reliability of the proposed control strategy are verified through a simulation study using a five-bus radial distributed generation (DG) system with high PV penetration. Simulation results on a five-bus radial distribution feeder show better voltage stability, fault recovery, and reactive power utilization as compared with conventional and existing distributed control strategies. The findings confirm the feasibility of software-defined, inverter-based voltage regulation as a practical alternative for future smart grids. In addition, the proposed framework offers extensibility to hybrid renewable energy systems, such as wind and storage, supporting the transition toward resilient, low-carbon, and data-driven energy infrastructures.
Abstract: A new distributed voltage control strategy for PV power systems that does not need support from centralized SVCs is proposed. The methodology uses smart inverters, agent-based coordination, and machine learning-based forecasting to offer a scalable and economical solution for decoupling voltage variations in the context of high penetration of PV. E...
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