Arc Fault Detection Using Noise Probability Density Classification
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Solution Overview
Problem
Existing arc fault circuit interrupters often trip due to oversensitive detection or erroneous identification of arcing, particularly distinguishing between normal periodic arcing and potentially unwanted arcing caused by electrical wire damage.
Innovation Solution
A method and system that involve obtaining data indicative of voltage and current, determining the waveform of a primary load current, identifying noise signals, calculating their probability density, and comparing it to model probability densities to accurately detect and identify arcing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional arc fault circuit interrupters use sensitive detection methods, then arcing detection capability is improved, but false trips increase due to normal periodic arcing being misidentified
Solution Approach 1:
The patent segments the arcing detection problem into two distinct parts: detecting the presence of arcing signals and identifying the type of arcing (normal periodic vs. potentially unwanted). This is achieved by separating the detection function into initial arc detection followed by classification analysis using probability density comparison, allowing the system to handle different arc types differently and avoid false trips from normal periodic arcing while maintaining sensitivity to dangerous arcs
Solution Approach 2:
The patent changes the detection parameter from simple arc presence detection to probability density analysis. By calculating and comparing probability density functions of detected signals against model probability densities for different arc types, the system transforms the detection problem into a statistical classification problem, improving both detection precision and reliability by distinguishing between normal and abnormal arcing patterns
2Quantity of substance
If arc detection sensitivity is increased to detect all arcing events, then detection coverage is improved, but identification accuracy deteriorates due to inability to distinguish arc types
Solution Approach 1:
The patent introduces probability density analysis as an intermediary step between raw signal detection and arc type identification. The probability density function acts as a mediator that transforms detected noise signals into a form that can be compared against model probability densities for different arc types, enabling accurate classification while maintaining comprehensive detection coverage
Solution Approach 2:
The patent replaces simple threshold-based detection mechanisms with a statistical probability density comparison system. Instead of using fixed thresholds to identify arcs, the system uses probabilistic modeling and comparison, substituting deterministic mechanical detection with statistical analysis to improve identification accuracy while maintaining detection coverage
Data Source
AI summary
Systems and methods for detecting and identifying arcing are disclosed. A method of detecting arcing includes obtaining data indicative of voltage and data indicative of current, determining a waveform of a cycle of a primary load current according to the data indicative of current, determining at least one noise signal according to the determined waveform of a cycle of the primary load current and the data indicative of current, determining a probability density of the noise signal according to a time window, and comparing the probability density of the noise signal with at least one model probability density.