Internship Program : Development of a Spatial Predictive Model for Estimation of Flow Rate in Penstock Networks
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kuttu80
Oct 8, 2023, 11:09:12 PM10/8/23
to Indian Hydrology
About this project:
Penstocks transport water from the reservoir to the prime mover, which then rotates the mover to produce electricity from the coupled generator. The head difference between the powerhouse and the reservoir of any hydropower plant is ensured by these pipe-like structures. The pressure at the reservoir and power plant must be continuously monitored in order to detect and prevent any faults such as sudden negative pressure, hammering, or other water distribution issues. If the pressure at the beginning, end, and between nodes is specifically monitored, such monitoring systems can detect faults in real time and predict the outcome of that fault at the end. This will also assist system engineers in preventing or correcting faults before they become a large-scale risk to the system. In this case, GIS can be used to digitize the entire network, and polynomial neural network models based on the Group Method of Data Handling (GMDH) can be used to predict pressure at each node and at the end. With the help of GMDH, cascading models can be prepared to develop such models. A connected Distributed Control System and SCADA, as well as a network of pressure sensors, can induce a real-time system in the penstock network, capable of detecting and preventing faults in real-time. This Real-time Artificial Intelligence-based Pressure Detection and Monitoring (RAIPDM) system can improve efficiency and reduce penstock costs.
At the end of this project if we can successfully accomplish our objectives then we can try for possible publication in a Journal or a Monograph. Off-course certificates will be provided to each participant who will complete their assignments.
Penstocks transport water from the reservoir to the prime mover, which then rotates the mover to produce electricity from the coupled generator. The head difference between the powerhouse and the reservoir of any hydropower plant is ensured by these pipe-like structures. The pressure at the reservoir and power plant must be continuously monitored in order to detect and prevent any faults such as sudden negative pressure, hammering, or other water distribution issues. If the pressure at the beginning, end, and between nodes is specifically monitored, such monitoring systems can detect faults in real time and predict the outcome of that fault at the end. This will also assist system engineers in preventing or correcting faults before they become a large-scale risk to the system. In this case, GIS can be used to digitize the entire network, and polynomial neural network models based on the Group Method of Data Handling (GMDH) can be used to predict pressure at each node and at the end. With the help of GMDH, cascading models can be prepared to develop such models. A connected Distributed Control System and SCADA, as well as a network of pressure sensors, can induce a real-time system in the penstock network, capable of detecting and preventing faults in real-time. This Real-time Artificial Intelligence-based Pressure Detection and Monitoring (RAIPDM) system can improve efficiency and reduce penstock costs.
At the end of this project if we can successfully accomplish our objectives then we can try for possible publication in a Journal or a Monograph. Off-course certificates will be provided to each participant who will complete their assignments.
Kindly apply through energyinstyle.website. The project id is EIS04.
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