Year-end Sale % OFF 
Abstract
Aiming at the uneven distribution of the electric field and the difficulty in achieving low false alarms for operators approaching charged bodies in the complex operating environment in the power system, a mask region convolutional neural network (MR-CNN) based complex operation scene recognizer is first constructed in this paper to perform image feature extraction, target box regression, and classification training. Secondly, a simulated annealing-based mirror-image charge method is proposed to calculate the electric field distribution characteristics. The simulated annealing algorithm is used to optimize the number of charges of the mirror-image charges method to minimize the weighted sum of the calculation error and the electric field strength calculation time. Finally, the feasibility and effectiveness of the proposed algorithm are verified through simulation experiments.
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 callDisclaimer: 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.
