Design of an ADSP-Based Distributed Wind Turbines Condition Monitoring System for Wind Farms

2017 ◽  
Author(s):  
Xiao Wang ◽  
Xin Sui ◽  
Wei Ma ◽  
Yujun Xue
Keyword(s):  
Monitoring System ◽  
Condition Monitoring ◽  
Wind Turbines ◽  
Wind Farms ◽  
Condition Monitoring System

How to Improve the Reliability of Wind Turbines

2011 ◽  
Vol 58-60 ◽  
pp. 771-775
Author(s):  
Hai Bo Zhang ◽  
Liang Liu
Keyword(s):  
Wind Turbine ◽  
Monitoring System ◽  
Condition Monitoring ◽  
Wind Turbines ◽  
Lubrication System ◽  
Component Failure ◽  
Time Condition ◽  
Automatic Lubrication ◽  
Potential Component ◽  
Condition Monitoring System

According to the failure of wind turbines in operation, the failure cause and phenomenon of wind turbines is analyzed, combined with the reliability of wind turbine subsystems, measures aiming at cooperation parts and purchased parts are proposed, the reliability of the whole wind turbines is improved in a certain extent. At the same time, condition monitoring system can carry through the early detecting and diagnosing to potential component failure maintain. Besides, automatic lubrication system can realize accurate and timeliness lubrication, also can reduce maintenance workload, preserve correct lubrication and smooth running of all parts.

scholarly journals Design and Development of Condition Monitoring System for Wind Turbines Based on Generator Output Voltages

2020 ◽  
Vol 32 (3) ◽  
pp. 895
Author(s):  
Rong-Mao Lee ◽  
Shih-Hsuan Hu ◽  
Cheng-Chi Wang ◽  
Tsung-Chia Chen ◽  
Jui-Hung Liu
Keyword(s):  
Monitoring System ◽  
Condition Monitoring ◽  
Wind Turbines ◽  
Design And Development ◽  
Condition Monitoring System ◽  
Generator Output

scholarly journals Design of a Condition Monitoring System for Wind Turbines

Energies ◽  
2022 ◽  
Vol 15 (2) ◽  
pp. 464
Author(s):  
Jinje Park ◽  
Changhyun Kim ◽  
Minh-Chau Dinh ◽  
Minwon Park
Keyword(s):  
Neural Network ◽  
Wind Turbine ◽  
Monitoring System ◽  
Condition Monitoring ◽  
Wind Turbines ◽  
Offshore Wind ◽  
Offshore Wind Turbines ◽  
Diagnosis Model ◽  
Condition Monitoring System ◽  
Condition Diagnosis

Renewable energy is being adopted worldwide, and the proportion of offshore wind turbines is increasing. Offshore wind turbines operate in harsh weather conditions, resulting in various failures and high maintenance costs. In this paper, a condition diagnosis model for condition monitoring of an offshore wind turbine has been developed. The generator, main bearing, pitch system, and yaw system were selected as components subject to the condition monitoring by considering the failure rate and downtime of the wind turbine. The condition diagnosis model works by comparing real-time and predictive operating data of the wind turbine, and about four years of Supervisory Control and Data Acquisition (SCADA) data from a 2 MW wind turbine was used to develop the model. A deep neural network and an artificial neural network were used as machine learning to predict the operational data in the condition diagnosis model, and a confusion matrix was used to measure the accuracy of the failure determination. As a result of the condition monitoring derived by inputting SCADA data to the designed system, it was possible to maintain the failure determination accuracy of more than 90%. The proposed condition monitoring system will be effectively utilized for the maintenance of wind turbines.

scholarly journals Perspectives on condition monitoring techniques of wind turbines

2018 ◽  
Vol 43 (5) ◽  
pp. 539-555 ◽  
Author(s):  
R Moeini ◽  
M Entezami ◽  
M Ratkovac ◽  
P Tricoli ◽  
H Hemida ◽  
...  
Keyword(s):  
Wind Turbine ◽  
Monitoring System ◽  
Condition Monitoring ◽  
Wind Turbines ◽  
Power Plants ◽  
High Reliability ◽  
Monitoring Methods ◽  
Technical Literature ◽  
Monitoring Techniques ◽  
Condition Monitoring System

The ever-increasing development of wind power plants has raised awareness that an appropriate condition monitoring system is required to achieve high reliability of wind turbines. In order to develop an efficient, accurate and reliable condition monitoring system, the operations of wind turbines need to be fully understood. This article focuses on the online condition monitoring of electrical, mechanical and structural components of a wind turbine to diminish downtime due to maintenance. Failure mechanisms of the most vulnerable parts of wind turbines and their root causes are discussed. State-of-the-art condition monitoring methods of the different parts of wind turbine such as generators, power converters, DC-links, bearings, gearboxes, brake systems and tower structure are reviewed. This article addresses the existing problems in some areas of condition monitoring systems and provides a novel method to overcome these problems. In this article, a comparison between existing condition monitoring techniques is carried out and recommendations on appropriate methods are provided. In the analysis of the technical literature, it is noted that the effect of wind speed variation is not considered for traditional condition monitoring schemes.

scholarly journals Performance analysis of different predictive models for condition monitoring of direct drive wind turbine generator

2021 ◽  
pp. 002029402110039
Author(s):  
Jui-Hung Liu ◽  
Nelson T Corbita
Keyword(s):  
Performance Analysis ◽  
Monitoring System ◽  
Condition Monitoring ◽  
Wind Turbines ◽  
Predictive Models ◽  
Wind Farm ◽  
Direct Drive ◽  
The Neural Network ◽  
Neural Network Algorithm ◽  
Condition Monitoring System

This paper presents a performance analysis of predictive models for the generator module which can be used as a reference for improvement in the condition monitoring system using wind turbines in a wind farm in Taiwan. With the generator being a critical component prone to failures, it is important to perform data analysis on its parameters that could be used for condition monitoring. The main innovative feature in this framework is the conduct of performance analysis before the development of the condition monitoring system. Also, the consistency of the performance between the different wind turbines in the wind farm is evaluated. The predictive models are generated using the neural network algorithm with a different combination of parameters from the SCADA system. The correlation of the parameters as well as the mean square error of the predictive models were then computed for analysis. Results showed that pairing of input parameters with a higher correlation to the output parameter would give better performance for the predictive model. Furthermore, the performance of the different models was consistent throughout the different wind turbines in the wind farm which indicates that the same model can be developed and used for wind turbines belonging to the same wind farm. Employing a preliminary performance analysis of different combinations of component parameters could help in optimizing predictive models for condition monitoring.

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