A new energy station health management system and method based on unmanned aerial vehicle inspection

CN122415073APending Publication Date: 2026-07-17JIANGSU YUNSHAN GREEN ENERGY INVESTMENT HLDG CO LTD
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Patent Information

Authority / Receiving Office
CN Β· China
Patent Type
Applications(China)
Current Assignee / Owner
JIANGSU YUNSHAN GREEN ENERGY INVESTMENT HLDG CO LTD
Filing Date
2026-05-25
Publication Date
2026-07-17

AI Technical Summary

Technical Problem

Traditional operation and maintenance of new energy power stations relies on manual inspections, which are labor-intensive and inefficient. It is difficult to accurately identify equipment faults and achieve high-frequency, all-weather coverage inspections, resulting in delayed detection of equipment faults, potential safety hazards, and difficulty in meeting the needs of intelligent and refined management.

Method used

A health management system based on drone inspection is adopted. The system uses drones equipped with oblique photography and LiDAR to build a 3D model, and combines it with a deep learning model to automatically identify defects, establish a wear prediction model, optimize the cruise path, and generate accurate inspection reports.

Benefits of technology

It enables intelligent and refined operation and maintenance of new energy power plants, reduces operation and maintenance costs, improves power generation efficiency, and ensures the safety and accuracy of full-coverage inspections.

✦ Generated by Eureka AI based on patent content.

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Abstract

The application discloses a new energy station health management system and method based on unmanned aerial vehicle inspection, and relates to the technical field of new energy station operation and maintenance.The method first plans an investigation route through a map software, then constructs a three-dimensional model by using an unmanned aerial vehicle tilt scanning, arranges a multi-machine cooperative operation task by using a dynamic programming algorithm to simultaneously complete photovoltaic panel strip scanning, synchronously returns image and latitude and longitude, position label cloud such as equipment number, identifies defects by using a CNN deep learning model, estimates power loss, establishes a secondary loss prediction model combined with inspection data, predicts equipment degradation trend and calculates maintenance priority, dynamically optimizes model parameters every day, and generates a PDF inspection report containing defect labeling, loss estimation and maintenance suggestions.The application realizes intelligent and refined operation and maintenance of the whole process of the new energy station, solves the problems of low efficiency, high risk and inaccurate defect identification of manual inspection, effectively reduces operation and maintenance cost, and improves power generation benefit and operation reliability of the station.
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