AI Pylon Identification for Faster Overhead Line Inspection
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Solution Overview
Problem
The manual inspection of overhead power lines and pylons is time-consuming, prone to errors, and cannot be fully automated, posing challenges for efficient and reliable monitoring of high-voltage grid infrastructure.
Innovation Solution
An automated method using geographic data and captured image data assigned to a digital twin model, leveraging trainable artificial intelligence algorithms for identification, anomaly detection, and inspection of power line pylons, simplifying the inspection process and reducing human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual inspection is used, then inspection can be performed with simple equipment, but inspection time is excessive and error-prone
Solution Approach 1:
The patent replaces manual mechanical inspection with an automated optical inspection system using drones equipped with cameras and AI-based image analysis. The system captures images of power line components and uses trained AI algorithms to automatically detect defects, eliminating the need for manual visual inspection while significantly reducing inspection time and improving reliability through consistent automated evaluation.
2Productivity
If digitalization with drones is used, then inspection speed is improved, but automation is incomplete requiring manual evaluation
Solution Approach 1:
The patent implements self-service automation where the inspection system performs complete autonomous operation including image capture, AI-based defect detection, and automatic generation of inspection reports. The trained AI algorithms independently evaluate captured images to identify anomalies such as insulator defects, conductor damage, and pylon issues without requiring manual intervention, achieving full automation from data collection to result interpretation.
3Measurement precision
If manual evaluation of recorded images is performed, then detailed assessment is possible, but the process is time-consuming and prone to human errors
Solution Approach 1:
The patent replaces manual image evaluation with AI-based automated image analysis. Trained neural networks process captured images to detect and classify defects with high precision, identifying patterns and anomalies that may be missed by human inspectors. The system automatically generates detailed inspection reports with defect locations and severity assessments, maintaining measurement precision while reducing evaluation time by a factor of 10 or more compared to manual methods.
Data Source
Figure 1~2
Figure 3~4
Figure 5a~5b
AI summary
The disclosed invention consists of a method for identification and subsequent inspection of an overhead power line, with pylons (1), with conductor ropes (120) and insulators (14-14VI) at the pylon (1), which is to be used in a simplified, reproducible and reliable way to identify and later control the pylons (1). This is achieved by process steps I) to V), wherein at least steps III) and IV) are performed by a pre-trained AI-based model, wherein a grouping step, several processing steps and a matching step are performed. V) defect detection of the power line pylon (1) and mapping of the detected defects to the digital twin is performed either by a person and/or software-supported in the database of the service provider and subsequently damaged spots can be transmitted to the service provider and/or network operator for repair.