Abstract
Power cables are increasingly popular in daily life and industrial production. The long-term use will make various cable faults. To reduce the losses caused by the faults, the cable faults should be recognized correctly and timely. In this paper, we developed an improved particle swarm optimization and support vector machine (IPSO-SVM) algorithm to recognize the power cable faults. The algorithm used the improved PSO to optimize the SVM kernel function parameter and the penalty parameter simultaneously. Two advantages were illustrated by the simulation experiments. The first one is the recognition accuracy which was increased from 81.8% to 90.9%; the second advantage is the SVM training time which decreased from 0.0247 second to 0.0202 second.
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.
