SCADA‐based wind turbine anomaly detection using Gaussian process models for wind turbine condition monitoring purposes-Reference-Cited by-同舟云学术

SCADA‐based wind turbine anomaly detection using Gaussian process models for wind turbine condition monitoring purposes

Published:2018-07-09 Issue:11 Volume:12 Page:1249-1255
ISSN:1752-1416
Container-title:IET Renewable Power Generation
language:en
Short-container-title:IET Renewable Power Generation

Author:

Pandit Ravi Kumar¹^ORCID,Infield David¹

Affiliation:

1. Electronics and Electrical Engineering DepartmentUniversity of Strathclyde16 Richmond StGlasgowG1 1XQScotlandUK

Publisher

Institution of Engineering and Technology (IET)

Subject

Renewable Energy, Sustainability and the Environment

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1049/iet-rpg.2018.0156

Reference49 articles.

1. IEC 61400‐12‐1: ‘International electro‐technical commission wind turbines — part 12‐1: power performance measurements of electricity producing wind turbines’ 2016

2. Wind turbine power production and annual energy production depend on atmospheric stability and turbulence;Martin C. M.;Wind Energy Sci.,2014

3. Wind turbine power curves incorporating turbulence intensity

4. Wind power curve modeling in simple and complex terrain using statistical models;Bulaevskaya V.;J. Renew. Sustain. Energy,2015

5. Data-Driven Wind Turbine Power Generation Performance Monitoring

Cited by 96 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Condition-based maintenance of wind turbine structures: A state-of-the-art review;Renewable and Sustainable Energy Reviews;2024-10

2. Collaborative monitoring of wind turbine performance based on probabilistic power curve comparison;Renewable Energy;2024-09

3. Unlocking the potential: A review of artificial intelligence applications in wind energy;Expert Systems;2024-08-27

4. Hierarchical Service Quality Regulation Method in Wind Farms Based on Optimal Generating Strategy;IEEE Transactions on Sustainable Energy;2024-07

5. Robust Machine Learning Predictive Models for Real-Time Determination of Confined Compressive Strength of Rock Using Mudlogging Data;Rock Mechanics and Rock Engineering;2024-04-11