Waveform-Based Inspection Zone Detection in Power Distribution
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current resource distribution systems face challenges in efficiently identifying the location and cause of anomalies and problems, often relying on manual inspections or deploying the same equipment for all issues, which can lead to inefficiencies and missed temporary fault insights.
Innovation Solution
A method that utilizes power parameter data from collection points in a resource distribution system, processed with topological information to determine a geographic region of interest, where an unmanned aerial vehicle (UAV) collects image data to identify the cause of anomalies and deploy the necessary resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual inspection is used to identify the cause of anomalies, then the accuracy of problem diagnosis is improved, but the response time and productivity deteriorate
Solution Approach 1:
The patent replaces manual inspection with an automated system that uses waveform data analysis and image processing to identify anomalies and their causes. The system automatically compares waveform data from multiple collection points, processes images from unmanned aerial vehicles, and identifies equipment issues without human intervention, thereby maintaining diagnostic accuracy while significantly improving response time
Solution Approach 2:
The system enables self-diagnosis of the resource distribution network by automatically analyzing waveform data patterns, comparing data across multiple collection points, and identifying the specific equipment causing anomalies. This self-service capability eliminates the need for manual inspection while maintaining accurate problem identification
2Ease of operation
If the same type of equipment is deployed to every problem, then the ease of operation is improved, but the resource efficiency and problem-solving effectiveness deteriorate
Solution Approach 1:
The patent implements local quality by matching specific equipment and resources to the specific type of anomaly identified. The system analyzes waveform data patterns and image data to determine the precise nature of each problem, then deploys the appropriate specialized equipment for that specific issue, ensuring optimal resource efficiency while maintaining operational simplicity through automated matching
3Ease of operation
If scheduled vegetation management is performed without considering temporary fault information, then the ease of operation is improved, but the reliability and preventive capability deteriorate
Solution Approach 1:
The patent implements feedback by using temporary fault information detected from waveform data to dynamically adjust vegetation management scheduling. The system analyzes patterns of temporary faults, identifies their correlation with vegetation proximity, and uses this feedback to prioritize and reschedule vegetation management activities in affected areas, thereby improving preventive maintenance effectiveness while maintaining operational simplicity through automated scheduling adjustments
Data Source
AI summary
Temporary outages or degradation of a resource, such as electric power, may be detected by identifying anomalies in waveform data collected by collection points. The collection points may be distributed throughout a resource distribution system and configured to communicate data to a headend system. The headend system processes the data to identify anomalies and to correlate waveform data collected by different collection points. The geographic locations of the collection points with correlated data are used to identify a geographic region. An unmanned aerial vehicle may be used to conduct an inspection of the geographic region and to collect inspection data. The inspection data may be communicated to the headend system. The waveform data and the inspection data may be used to determine the correct resources to deploy to address the cause of the anomaly.