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This is a Quantitative Study study.

October 15, 2025Open Access

Construction and Practice of Digital Twin Operation and Maintenance Platform for Water Supply and Drainage Systems in New Energy Power Stations

Key Points

The platform reduced fault response time by 91.7%, improving operational reliability in water supply systems.
Using a Convolutional Neural Network, the platform achieved 98.2% accuracy in equipment fault diagnosis, enhancing maintenance efficiency.
Overall operational costs decreased by 32.4%, providing a scalable solution for efficient energy resource management.
Field tests demonstrate a 41.2% increase in annual power generation, supporting further integration of intelligent technologies in the energy sector.

Abstract

To address the critical challenges of low operational reliability, high energy consumption, and inefficient fault response in water supply and drainage (WSD) systems of new energy power stations (NEPS), this study proposes a digital twin (DT)-enabled operation and maintenance (O root mean square error, RMSE = 0.05 NTU for turbidity) and a Convolutional Neural Network (CNN)-driven equipment fault diagnosis model (accuracy = 98.2%, F1-score = 0.978 for pump bearing faults). Second, a cross-protocol data adaptation framework was designed to achieve seamless integration with Vestas wind turbine equipment, supporting Modbus, IEC 61850, and ThingWorx protocols, with data transmission latency reduced to 87 ± 5 ms and packet loss rate < 0.1%. Finally, the platform was validated in the Inner Mongolia 6000 MW Wind Power Demonstration Station, a world-class onshore wind project. Field test results showed that the platform shortened fault response time from 4.0 ± 0.5 hours to 28 ± 3 minutes (a 91.7% reduction), reduced O&M costs by 32.4% (from (12.6 million/year to )8.5 million/year), and improved annual power generation by 41.2% (from 12.7 GWh to 18.0 GWh) by optimizing anti-freezing energy consumption and reducing equipment downtime. This study provides a scalable technical paradigm for intelligent O&M of NEPS WSD systems, with significant implications for advancing the decarbonization and digitalization of the global energy sector.

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