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Application of artificial intelligence for EV charging and discharging scheduling and dynamic pricing: A review
The high penetration of electric vehicles (EVs) will burden the existing power delivery
infrastructure if their charging and discharging are not adequately coordinated. Dynamic …
infrastructure if their charging and discharging are not adequately coordinated. Dynamic …
Machine learning applications in health monitoring of renewable energy systems
Rapidly evolving renewable energy generation technologies and the ever-increasing scale
of renewable energy installations are driving the need for more accurate, faster, and smarter …
of renewable energy installations are driving the need for more accurate, faster, and smarter …
Adaptive transfer learning for multimode process monitoring and unsupervised anomaly detection in steam turbines
Through condition-based maintenance strategy, engineers can monitor the health states of
equipment and take actions based on the sensor data. Limited by the low failure frequency …
equipment and take actions based on the sensor data. Limited by the low failure frequency …
Unsupervised anomaly detection using graph neural networks integrated with physical-statistical feature fusion and local-global learning
Efficient and feasible anomaly detection scheme that could utilize data collected by
supervisory-control-and-data-acquisition (SCADA) system is essential for wind turbines …
supervisory-control-and-data-acquisition (SCADA) system is essential for wind turbines …
Conditional feature disentanglement learning for anomaly detection in machines operating under time-varying conditions
Anomaly detection (AD) is an important task of machines' condition monitoring (CM). Data-
driven policies can be used in a more intelligent way to achieve anomaly detection and …
driven policies can be used in a more intelligent way to achieve anomaly detection and …
[HTML][HTML] Artificial intelligence based abnormal detection system and method for wind power equipment
X Ding, Y Gong, C Wang, Z Zheng - International Journal of Thermofluids, 2024 - Elsevier
The existing traditional monitoring methods are not ideal for identifying abnormal states in
remote real-time monitoring of power system equipment due to adverse weather conditions …
remote real-time monitoring of power system equipment due to adverse weather conditions …
Wind turbine condition monitoring based on a novel multivariate state estimation technique
Z Wang, C Liu - Measurement, 2021 - Elsevier
With the development of the wind energy industry, condition monitoring (CM) has become
one of the important ways to improve the reliability of wind turbines (WTs). A data driven …
one of the important ways to improve the reliability of wind turbines (WTs). A data driven …
Probabilistic prognosis of wind turbine faults with feature selection and confidence calibration
With the ever-increasing expansion of the installed capacity of wind power generation,
reliable condition monitoring for wind turbines (WTs) has become increasingly important. To …
reliable condition monitoring for wind turbines (WTs) has become increasingly important. To …
Condition monitoring of wind turbine based on incremental learning and multivariate state estimation technique
Z Wang, C Liu, F Yan - Renewable energy, 2022 - Elsevier
With the development of wind turbine (WT) operation and maintenance technologies, the
condition monitoring (CM) method based on the data of supervisory control and data …
condition monitoring (CM) method based on the data of supervisory control and data …
Hierarchical spatial–temporal autocorrelation graph neural network for online wind turbine pitch system fault detection
The advancement of low-cost, non-manually labeled big data technologies for fault detection
in wind turbines is crucial to guarantee their safe and efficient operation. In this context …
in wind turbines is crucial to guarantee their safe and efficient operation. In this context …