Study of Cognitive Wireless Sensor Networks for Wind Turbine Monitoring
Primary Faculty Mentor’s Name
Dr. Chandra Bajracharya and Dr. Danda B. Rawat
Proposal Track
Student
Session Format
Poster
Abstract
Wind turbines have become symbolic ambassadors for cleaner energy resources. Due to increasing demand of more efficient and reliable renewable energy sources, wind energy has become more popular than it has ever been. The maintenance and monitoring of these wind turbines is critically to the improvement of wind energy technology. However, acquisition of data from large and remote wind farms can be a costly and time-consuming process. Without having a proper data acquisition and monitoring process, a simple undetected damage can lead to a sudden and catastrophic failure resulting in expensive repair and significant downtime. Using wireless sensors, data collection and monitoring process could be automated and minor damages or disturbances can be corrected before they turn into a more serious interruption. Moreover, wireless sensors can make wind turbine testing much more efficient since wireless sensors can be easily incorporated for both temporary or emergency and permanent measurements. In this case, a common predictive maintenance application involves monitoring bearings, shafts, generators for excessive vibration, etc. Once data is collected using wireless sensors on a wind farm, this information can be wirelessly transmitted to a central control point for analysis and archiving. When sensed data has to be transmitted for a long distance, data may have to be sent via satellite or cell modem, or multi-hop wireless communication needs to be implemented. Regardless of physical locations of the plants, users with the help of wireless networks will be able to access the data though handheld mobile devices to interact and monitor the plants. Wireless sensors can also determine wind speed and kilowatt-hour production automatically so that the supervisors take less time to collect data and make insightful analysis and decisions. Moreover, wireless video systems can also be installed to guard the facility in addition to keeping an eye on plant and processes. In this presentation, we will present the feasibility study and a prototype of wireless cognitive sensor networks for wind turbine monitoring where wireless devices will adapt their operating parameters on the fly based on the their operating wireless environment.
Keywords
Wireless sensor networks, Wireless sensors for wind turbine monitoring
Location
Concourse/Atrium
Presentation Year
2014
Start Date
11-15-2014 2:55 PM
End Date
11-15-2014 4:10 PM
Publication Type and Release Option
Presentation (Open Access)
Recommended Citation
source:http://digitalcommons.georgiasouthern.edu/gurc/2014/2014/93/
Study of Cognitive Wireless Sensor Networks for Wind Turbine Monitoring
Concourse/Atrium
Wind turbines have become symbolic ambassadors for cleaner energy resources. Due to increasing demand of more efficient and reliable renewable energy sources, wind energy has become more popular than it has ever been. The maintenance and monitoring of these wind turbines is critically to the improvement of wind energy technology. However, acquisition of data from large and remote wind farms can be a costly and time-consuming process. Without having a proper data acquisition and monitoring process, a simple undetected damage can lead to a sudden and catastrophic failure resulting in expensive repair and significant downtime. Using wireless sensors, data collection and monitoring process could be automated and minor damages or disturbances can be corrected before they turn into a more serious interruption. Moreover, wireless sensors can make wind turbine testing much more efficient since wireless sensors can be easily incorporated for both temporary or emergency and permanent measurements. In this case, a common predictive maintenance application involves monitoring bearings, shafts, generators for excessive vibration, etc. Once data is collected using wireless sensors on a wind farm, this information can be wirelessly transmitted to a central control point for analysis and archiving. When sensed data has to be transmitted for a long distance, data may have to be sent via satellite or cell modem, or multi-hop wireless communication needs to be implemented. Regardless of physical locations of the plants, users with the help of wireless networks will be able to access the data though handheld mobile devices to interact and monitor the plants. Wireless sensors can also determine wind speed and kilowatt-hour production automatically so that the supervisors take less time to collect data and make insightful analysis and decisions. Moreover, wireless video systems can also be installed to guard the facility in addition to keeping an eye on plant and processes. In this presentation, we will present the feasibility study and a prototype of wireless cognitive sensor networks for wind turbine monitoring where wireless devices will adapt their operating parameters on the fly based on the their operating wireless environment.
