Power equipment is exposed to outdoor environments for a long time, causing problems such as insulator damage, foreign objects in wires, tower tilting, and corrosion. Relying on manual inspection is not only inefficient, but also poses many hidden dangers at heights and angles that are difficult for the naked eye to reach. AI power grid inspection is transforming "human inspection" into "machine inspection+AI interpretation", allowing hidden danger detection to shift from "luck" to "full coverage".
1、 AI based inspection chain
A typical AI inspection link is: unmanned aerial vehicles or robots collect images, image feedback, AI models automatically identify defects, generate defect reports and work orders, and manually review and dispose of them.
Traditional manual inspections have a limited number of towers that can be viewed in a day, and highly rely on the experience of experienced technicians; A single takeoff and landing of a drone can cover dozens of towers, and AI can automatically interpret the massive images captured, turning "human eyes looking for defects" into "algorithm circle defects, human review", improving efficiency by an order of magnitude.
2、 What can AI recognize
The most mature application currently is visual recognition tasks:
Equipment appearance defects: insulator damage or self explosion, rust, pollution flash marks, missing pins, displacement of shock absorbers, etc.
Channel hazards: Tree and bamboo growth invading the route channel, construction machinery approaching, foreign objects hanging (kites, plastic film), illegal construction, etc.
Meter and status reading: Meter reading recognition, oil level and pressure gauge status, equipment appearance abnormalities (deformation, leakage).
These recognition models are trained on a large number of annotated images, and their accuracy on clear images has reached a level that can be put into production. The difficulty lies in the robustness under complex weather conditions (fog, backlighting, nighttime), occlusion, and multiple angles, which is also the direction continuously polished by various manufacturers.
3、 From 'taking pictures' to' understanding '
The true watershed of AI inspection is not in photography, but in interpretation. Early drone inspections captured a large number of photos, which still required manual frame by frame viewing, resulting in limited efficiency improvement. After introducing visual big models and specialized defect recognition models, "automatic interpretation" was truly implemented: the system classifies defects by type (critical, serious, general), prioritizes critical defects, and only needs to manually review suspicious targets circled by AI.
The new generation of solutions is still incorporating the capabilities of large models into inspections: using visual language models to understand complex scenes (such as determining whether the foreign object affects the safe distance), using multimodal models to automatically generate inspection reports, and further compressing manual processes.
4、 Not just transmission lines
The application scope of AI inspection is expanding from transmission lines to more scenarios: robot inspection inside substations (meter readings, abnormal equipment temperature), unmanned aerial vehicle inspection of distribution lines, tunnel and cable channel inspection. In the substation scenario, wheeled or track robots combined with infrared thermal imaging and visual recognition can complete most routine inspections, freeing operation and maintenance personnel from repetitive labor.
5、 What to pay attention to when landing
Data loop: Defect samples are the fuel for model iteration, and the results of each manual review should be re annotated to continuously improve recognition rate.
Collaboration between edge and cloud: Due to limited bandwidth for image transmission at remote sites, lightweight models on the edge first perform initial screening, and suspicious images are then refined in the cloud. This is a common architecture.
Human machine division of labor: AI is a tool for "expanding vision", and the final disposal and judgment still need to be completed by humans. The inspection report serves as an auxiliary decision-making tool and cannot replace regulations.
6、 Conclusion
For the power industry, the value of AI inspection is not only to save manpower, but also to upgrade the safety defense line from "human experience" to "systematic full coverage". With the maturity of visual models and the decrease in drone costs, this technological path is still rapidly evolving. For frontline operation and maintenance personnel, mastering "using AI tools for inspection and interpretation" is becoming a fundamental skill rather than a bonus.
[Reference source] This article comprehensively summarizes the application reports and industry practice discussions of intelligent inspection technology publicly released in the power industry.