HGCS—A Multiplex Transmission Method for Wide-Area Unmanned Aerial Vehicle Cluster Inspection Data in Hybrid Networked Transmission Lines
The use of intelligent inspection technologies for fault detection in transmission lines plays a key role in optimizing inspection accuracy by leveraging Unmanned Aerial Vehicles (UAVs) and carrier wave differential positioning. The recent technologies effectively manage the challenges of inspection...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10771723/ |
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| Summary: | The use of intelligent inspection technologies for fault detection in transmission lines plays a key role in optimizing inspection accuracy by leveraging Unmanned Aerial Vehicles (UAVs) and carrier wave differential positioning. The recent technologies effectively manage the challenges of inspection accuracy and algorithmic accuracy, ensuring reliable and secure power transmission services. There are multiple challenges in using intelligent inspection technologies for fault detection in power services for transmission lines. The key challenge is to ensure the Quality of Service (QoS) while maintaining the network’s utility function value. Moreover, achieving the maximum inspection accuracy for using the carrier wave differential positioning through UAVs for transmission line inspection is another important challenge. Furthermore, the execution efficiency and fairness index to use the available resources for inspection are also the key parameters that need to be investigated. To address these challenges, an effective optimization algorithm is required to balance the algorithmic performance while considering the fairness and QoS parameters. We propose the Hybrid Grouping Cluster Selection (HGCS) algorithm, which outperforms the recently devised algorithms like Hybrid Cluster Access Selection (HCAS) and Hybrid Grouped Network Selection (HGNS) by maximizing the execution efficiency and QoS parameters. The proposed HGCS algorithm reduces the execution time from 2.2 seconds (HCAS) and 2.8 seconds (HGNS) to 1.2 seconds, demonstrating the optimized design of the algorithm. Moreover, Jain’s fairness index (JFI) comparison outperforms the HGCS’s in ensuring a fair distribution of resources. The algorithm achieves the JFI score of 0.9, in comparison to HCAS and HGNS where the JFI scores of 0.72 and 0.62 are achieved, respectively. The HGCS algorithm in comparison to the existing algorithms uses the innovative hybrid approach, exploiting the strengths of different components to get an optimal balance between computational efficiency and resource utilization. These results show that HGCS is a robust choice to deal with fault detection in less time with a fair distribution of resources. |
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| ISSN: | 2169-3536 |