Analysis Technique of Remote Condition Monitoring System for Wind Turbines Based on Internet of Things

2018 ◽  
Author(s):  
Hongwu Qin ◽  
Xian Zhang ◽  
Qinyin Fan ◽  
En Un Chye ◽  
A.V. Levenets
Keyword(s):  
Internet Of Things ◽  
Monitoring System ◽  
Condition Monitoring ◽  
Wind Turbines ◽  
Analysis Technique ◽  
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 SISTEM PEMANTAUAN KONSENTRASI CO KEBAKARAN HUTAN RIAU MENGGUNAKAN TEKNOLOGI WIRELESS SENSOR NETWORK (WSN) DAN INTERNET OF THINGS (IOT)

2019 ◽  
Vol 4 (2) ◽  
pp. 135-140
Author(s):  
Eko Prayitno ◽  
Desi Amirullah
Keyword(s):  
Carbon Monoxide ◽  
Wireless Sensor Network ◽  
Internet Of Things ◽  
Sensor Network ◽  
Monitoring System ◽  
Condition Monitoring ◽  
Wireless Sensor ◽  
Test Results ◽  
Temperature And Humidity ◽  
Condition Monitoring System

The purpose of this research is how to make an air condition monitoring system by considering the concentration value of carbon monoxide in Riau Province. The technology used to support monitoring system of carbon monoxide concentration, using Wireless Sensor Network Technology (WSN) and Internet of Things (IoT). One of the WSN concepts to be used is a combination of several sensors, the only sensors used to detect the level of carbonmonoxide concentration include: carbon monoxide, temperature and humidity sensors. Air condition data derived from the sensor in the form of concentration value of carbon monoxide, temperature and humidity of air sent to server connected to network using IoT technology. Based on the test results it can be concluded that the air condition monitoring system using WSN and IoT technology can be applied in realtime, this can be proven with the data shown in the monitoring tool. the detection of a fire source using a sensor can be done by using a distance between a smoke source (hotspot) and a device 90cm. From the observation result there is difference between sensing data without smoke and using smoke, such as temperature has 60C difference, humidity 20 rh and carbon monoxide about 17ppm

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.

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