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Intelligent Operation and Maintenance and Prediction Model Optimization for Improving Wind Power Generation Efficiency

Year: 2025

Type: conference-paper

Abstract: This study explores the effectiveness of predictive maintenance models and the optimization of intelligent Operation and Maintenance (O&M) systems in improving wind power generation efficiency. Through qualitative research, structured interviews were conducted with five wind farm engineers and maintenance managers, each with extensive experience in turbine operations. Using thematic analysis, the study revealed that while predictive maintenance models effectively reduce downtime by identifying major faults, they struggle with detecting smaller, gradual failures. Key challenges, such as false positives, sensor malfunctions, and the integration of new models with older turbines, were also identified. Advanced technologies like digital twins, SCADA systems, and condition monitoring have enhanced turbine maintenance but require further improvements, particularly in AI refinement and real-time data integration. The findings highlight the need for continued development to optimize wind turbine performance and support the broader adoption of renewable energy.

Source: International Congress on Human-Computer Interaction, Optimization and Robotic Applications (ICHORA)

DOI: 10.1109/ichora65333.2025.11017307

Authors: Xun Liu, Xiaobin Wu, Jiaqi He, Rajan Das Gupta

Institutions: Shinawatra University, Chengdu Industry & Trade College, Tilburg University, Jahangirnagar University

Language: English

Topic: Reliability and Maintenance Optimization

Subfield: Safety, Risk, Reliability and Quality

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure

Open Access status: green

OpenAlex ID: W4411206261

FWCI: 9.488

Cites:

Cited by: 3

Related to: 10