Abstract
When a leak event happens in energy transportation system, emergency procedures would be taken by staff according to the abnormal signal evaluation result. The wrong evaluation result leads to inadequate treatment and preparation, and then the leak event would evolve to fire and explosion hazards. To solve it, a data-driven signal evaluation method based on adaptive dynamic programming is proposed in this paper. First, a signal estimation model based on three-layer neural networks is proposed to describe the pressure signal change along pipeline. Then, based on the estimation result of pressure change, a flow signal evaluation method based on value iteration scheme is proposed to obtain the leak flow rate. Moreover, the abnormal signal analysis principles are added into the iterative solving process to close to the actual leak event situation. For different leak scenarios, the proposed method could give the reasonable and reliable result through the designed neural network structures. Finally, different types of case results demonstrate that the proposed method could be applied to the signal evaluation problem.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: IEEE Transactions on Instrumentation and Measurement
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
