Power Line Condition Detection Using Distributed Sensor Data
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Solution Overview
Problem
Power line management systems face challenges in efficiently detecting and predicting maintenance needs for power lines due to environmental conditions and wear, leading to potential power outages and safety risks.
Innovation Solution
A system that utilizes multiple sensor units deployed along power lines to collect data, which is then processed to determine conditions requiring maintenance. This includes using shift-invariant transformations to derive features from the data, independent of sensor location and time, to identify conditions such as faults, sag, and overload.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple sensor units are deployed along power lines to collect data, then measurement precision and reliability of power line condition detection is improved, but device complexity and cost increase
Solution Approach 1:
The power line monitoring system is segmented into multiple independent sensor units distributed along the power line. Each sensor unit independently monitors local conditions (voltage, current, temperature, mechanical stress) and transmits data to a central processing system. This segmentation enables comprehensive coverage of the power line while maintaining modular, manageable system architecture.
Solution Approach 2:
The sensor units are designed with multi-functionality, capable of measuring multiple parameters (electrical and mechanical conditions) simultaneously. This universal design reduces the need for separate specialized sensors for each parameter, thereby improving measurement precision across multiple dimensions while controlling overall system complexity through standardized multi-functional components.
2Reliability
If data from multiple sensor units is collected and processed to determine power line conditions, then reliability of maintenance scheduling is improved, but loss of time for data processing increases
Solution Approach 1:
The system performs preliminary actions by continuously collecting and pre-processing data from multiple sensor units in real-time. Data is aggregated, validated, and preliminary analysis is conducted before formal maintenance scheduling decisions are made. This preliminary processing ensures that when maintenance scheduling is required, the decision-making process can proceed quickly with pre-analyzed data, improving both reliability and reducing actual processing time.
Solution Approach 2:
The system implements feedback mechanisms where processed data from sensor units is continuously monitored and used to adjust maintenance scheduling in real-time. The feedback loop enables the system to learn from historical data patterns, improving the reliability of maintenance predictions while optimizing processing time by focusing computational resources on anomalies and critical conditions rather than processing all data uniformly.
3Adaptability or versatility
If shift-invariant transformations are applied to derive features from sensor data, then adaptability to different sensor locations and times is improved, but device complexity increases
Solution Approach 1:
The system applies shift-invariant transformations that change the parameter representation of sensor data from time-domain to transform-domain (e.g., wavelet transform coefficients). This parameter transformation makes the extracted features invariant to shifts in time and location, enabling the same feature extraction methodology to work universally across different sensor positions and time periods without requiring recalibration or location-specific adjustments.
Solution Approach 2:
The system replaces complex mechanical or manual adjustment mechanisms with mathematical transformations. Instead of physically adjusting sensors or processing parameters based on location and time, the patent uses shift-invariant mathematical transformations (such as wavelet transforms) that automatically provide location and time independence. This substitution of mathematical operations for mechanical adjustments reduces physical system complexity while achieving the desired adaptability.
4Reliability
If power lines are monitored for environmental damage and overload conditions, then safety and reliability are improved, but loss of energy for monitoring increases
Solution Approach 1:
The monitoring system utilizes the power line's existing electrical infrastructure to power the sensor units and communication devices. Sensor units draw minimal power from the line they are monitoring, and the system leverages existing electrical signals for both power transmission and condition detection. This self-service approach enables comprehensive safety monitoring while minimizing additional energy loss, as the system uses the power line's own resources rather than requiring separate external power sources.
Data Source
AI summary
Techniques for determining conditions of power lines in a power distribution system based on data collected by a plurality of sensor units deployed in the power distribution system. The techniques include obtaining data associated with measurements collected by at least two sensor units in the plurality of sensor units, and determining, by using at least one processor, at least one condition of at least one power line in the power distribution system by using the data obtained by the at least two sensor units.