UAV Edge Inspection for Power Grid Lines With NOMA Offloading
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Solution Overview
Problem
The technical challenge lies in conducting efficient and cost-effective inspections of power grid lines, particularly in harsh environments where manual methods are risky and slow, due to near-far effects in communication among unmanned aerial vehicles (UAVs) during power grid line inspections, which affect the energy consumption and operation time of UAVs.
Innovation Solution
A method for stochastic inspections using unmanned aerial vehicle-assisted edge computing, incorporating a digital twin network and Non-Orthogonal Multiple Access (NOMA) to mitigate near-far effects, combined with a deep reinforcement learning (DDPG) algorithm and genetic algorithm to optimize energy consumption and resource allocation, ensuring efficient data processing and extended UAV operation time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual inspection methods are used for power grid lines, then inspection can be conducted with simple equipment, but inspection speed is slow and inspection efficiency is low
Solution Approach 1:
The patent replaces manual mechanical inspection methods with unmanned aerial vehicles (UAVs) equipped with sensors and imaging devices. The UAVs autonomously fly along power grid lines to capture images and collect data, eliminating the need for manual climbing and physical inspection, thereby dramatically improving inspection efficiency and reducing inspection cycle time.
Solution Approach 2:
The patent introduces a ground station as an intermediary that receives, processes, and analyzes data collected by UAVs. The ground station serves as a mediator between the UAVs and the inspection system, enabling centralized data management and analysis, which improves overall inspection efficiency while allowing UAVs to focus on data collection.
2Reliability
If inspection robots suspended on transmission power lines are used, then inspection can be conducted close to the lines, but moving speed is slow resulting in long inspection cycles
Solution Approach 1:
The patent introduces a ground station as an intermediary that receives, processes, and analyzes data collected by UAVs. The ground station serves as a mediator between the UAVs and the inspection system, enabling centralized data management and analysis, which improves overall inspection efficiency while allowing UAVs to focus on data collection.
Solution Approach 2:
The patent implements periodic inspection cycles where UAVs autonomously fly along power grid lines at scheduled intervals to collect data. This periodic autonomous operation allows for efficient coverage of large areas while maintaining consistent inspection quality, resolving the contradiction between inspection reliability and efficiency.
3Productivity
If multiple unmanned aerial vehicles are deployed for inspections, then coverage area increases and inspection speed improves, but near-far effects generate communication interference
Solution Approach 1:
The patent introduces a ground station as an intermediary communication hub that all UAVs connect to. Instead of UAVs communicating directly with each other or with the control center, the ground station mediates all communications. This eliminates near-far effects because the ground station receives signals from all UAVs at relatively equal distances, ensuring reliable communication even with multiple UAVs operating simultaneously.
4Area of stationary object
If unmanned aerial vehicles operate for extended periods, then inspection coverage increases, but energy consumption increases reducing operation time
Solution Approach 1:
The patent divides the inspection task among multiple UAVs that operate in coordinated segments along the power grid line. Each UAV covers a specific segment, reducing the flight distance and energy consumption per UAV. The ground station coordinates these segmented operations to achieve complete coverage while minimizing total energy consumption.
Solution Approach 2:
The patent implements periodic inspection cycles where UAVs autonomously fly along power grid lines at scheduled intervals to collect data. This periodic autonomous operation allows for efficient coverage of large areas while maintaining consistent inspection quality, resolving the contradiction between inspection reliability and efficiency.
Data Source
AI summary
The present disclosure relates to a method for stochastic inspections on power grid lines based on unmanned aerial vehicle-assisted edge computing. According to the method, a stochastic distributed inspection unmanned aerial vehicle is adopted to acquire video images on a target power grid area, which can reduce funds and time costs of inspections. With assistance of superior unmanned aerial vehicle, a goal is to minimize energy consumption of an unmanned aerial vehicle system and extend operation time of the unmanned aerial vehicles under same payload conditions, while processing video image data collected from the inspection unmanned aerial vehicles. The near-far effect generated by communications between mobile unmanned aerial vehicles is eliminated by introducing a NOMA, and position coordinates, system resource allocations and task offload decision schemes are solved by using a method of combining a DDPG algorithm in a Deep reinforcement learning with a genetic algorithm.