Application of Power System in Sustainable Energy Perspective

Dear Colleagues,

The extensive and long-term use of non-renewable energy resources, such as coal, oil, and natural gas, not only leads to the gradual depletion of global energy, but also continuously pollutes the environment; therefore, renewable energy sources are receiving increasing attention. In recent years, as the main force of renewable energy, wind power has become increasingly important in the field of power generation. With the steady growth of the amount of energy generated by wind power, the failure of wind turbines has gradually become a  challenge for their development. Nowadays, not only university researchers in related fields but also large wind power system enterprises, including Goldwind, Mingyang, Envision Energy, Yunda, Siemens, and General Electric, are also committed to the research and development of condition monitoring and fault diagnosis technology of wind turbines. Researching effective methods for monitoring and diagnosing wind turbine conditions, in order to comprehensively improve the operational safety and stability of wind turbines and improve the quality and efficiency of wind power generation, is an important trend in line with the current development of renewable energy systems.

This Special Issue aims to gather and propagate the most recent research results and breakthroughs in condition monitoring and fault diagnosis of wind power systems, including the application of numerous theories and technologies, such as dynamics modeling, sensor layout, data acquisition, parameter measurement, signal analysis, feature extraction, fault diagnosis, anomaly detection, structural damage identification, early warning, health condition assessment, residual life prediction, and other related software, and even hardware technology.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

1. Dynamic modeling and simulation techniques;
2. Sensor optimization layout strategy;
3. Sensor network-based data acquisition and processing technology;
4. Advanced signal processing and filtering technology;
5. Multi-sensor information fusion technology;
6. 4G/5G/ Internet of Things communication technology;
7. Big data and artificial intelligence-driven fault diagnosis;
8. Intelligent fault diagnosis of digital-analog linkage;
9. Physics/statistics/machine learning model-based life prediction techniques;
10. Prediction and health management (PHM) technology;
11. Structural health monitoring (SHM) and damage recognition theory;
12. Data-driven intelligent security management and control;
13. Intelligent operation, maintenance, and monitoring system;
14. System reliability analysis and evaluation.

We look forward to receiving your contributions.

Prof. Dr. Ling Xiang
Dr. Xiaoan Yan
Guest Editors

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• system dynamics
• sensor technology
• signal processing
• fault diagnosis
• artificial intelligence
• machine learning
• prediction and health management (PHM)
• structural health monitoring (SHM)
• intelligent operation and management
• system reliability