AI Balloting for Electrical Disturbance Location Detection
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Solution Overview
Problem
Existing electrical systems require manual analysis by experienced professionals to diagnose voltage disturbances, which is inefficient and costly, and existing disturbance direction detection methods lack accuracy and consistency in determining the source of electrical events.
Innovation Solution
A method and system using multiple algorithms to independently ascertain the location of disturbances in an electrical system, combining outputs to provide a weighted indication of the disturbance's location, leveraging IEDs to capture and analyze energy-related data, and employing machine learning to enhance accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis by experienced professionals is used to diagnose voltage disturbances, then diagnostic accuracy is improved, but labor cost and time consumption increase
Solution Approach 1:
The system enables self-service by implementing automated disturbance direction detection that operates independently without requiring human intervention. Multiple detection algorithms automatically analyze waveform data and determine disturbance directions, eliminating the need for manual analysis by experienced professionals while maintaining diagnostic accuracy.
Solution Approach 2:
The patent replaces the mechanical system of manual human analysis with automated electronic detection algorithms. The system uses computer-based algorithms to process waveform capture data and determine disturbance directions, substituting human cognitive processing with automated computational methods that are both accurate and time-efficient.
2Measurement precision
If multiple disturbance direction detection algorithms are applied, then detection accuracy is improved, but system complexity increases
Solution Approach 1:
The system segments the detection process by implementing multiple independent disturbance direction detection algorithms that each analyze the waveform data separately. Each algorithm operates as an independent module, and their results are combined through a balloting mechanism. This segmentation allows the system to achieve high detection accuracy through multiple perspectives while managing complexity through modular design.
Solution Approach 2:
The patent merges the outputs of multiple independent detection algorithms through a balloting mechanism. Each algorithm independently determines disturbance directions, and the balloting process combines these results to produce a final determination. This merging approach consolidates the strengths of multiple algorithms while presenting a unified, simplified output to the user.
3Productivity
If automated analysis algorithms are implemented, then productivity is improved, but algorithm selection and configuration complexity increases
Solution Approach 1:
The system implements universality by designing a multi-functional balloting mechanism that can handle multiple types of detection algorithms through a single unified interface. The balloting mechanism serves as a universal coordinator that manages various algorithms without requiring separate configuration systems for each, thereby improving productivity while minimizing configuration complexity.
Data Source
AI summary
Fault or disturbance location detection in an electrical system. Energy-related data in the electrical system is captured using at least one intelligent electronic device (IED) and analyzed to identify fault or disturbance events in the electrical system. The outputs of a plurality of fault or disturbance location detection algorithms include independently ascertained location(s) of the faults or disturbances and a weighted ballot is applied to each of the outputs. The outputs are compiled for determining and providing an indication of the location(s). Compiling the plurality of outputs strengthens the conclusiveness and indicates a higher confidence in the determined location.