Partial Discharge Monitoring via Differential Waveform Analysis
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Solution Overview
Problem
Existing methods for detecting partial discharge in alternating-current electrical equipment struggle to accurately distinguish between partial discharge signals and disturbance noise, especially when the noise is synchronous with the powerline period, leading to missed detections and inaccurate evaluations.
Innovation Solution
The proposed solution involves analyzing the differential waveform of detected signals by comparing them to shifted versions of themselves by integer periods of the power supply frequency. This approach allows for the separation of disturbance noise synchronous with the powerline period from partial discharge signals, improving the accuracy of signal extraction and evaluation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If frequency analysis is performed on the waveform to extract periodicity components, then partial discharge detection capability is improved, but disturbance noise with powerline period coincides with partial discharge signals causing false detection
Solution Approach 1:
The invention segments the waveform analysis by dividing the detection period into multiple sub-periods and analyzing each segment separately. This allows the system to distinguish between periodic disturbance noise and irregular partial discharge signals by examining the temporal distribution patterns within segmented intervals, thereby reducing false detections while maintaining detection accuracy.
Solution Approach 2:
The invention exploits the asymmetry in timing patterns by comparing the actual occurrence times of detected signals against the expected periodic timing of powerline frequency noise. Partial discharge signals exhibit asymmetric, irregular timing patterns whereas disturbance noise follows symmetric periodic timing, enabling the system to filter out false detections while preserving true partial discharge events.
2Measurement precision
If correlation coefficient calculation is performed to distinguish partial discharge from disturbance noise, then signal identification accuracy is improved, but computational complexity increases
Solution Approach 1:
The invention performs preliminary actions by pre-calculating and storing reference waveform patterns and their corresponding correlation coefficients before actual detection occurs. During operation, the system only needs to compare detected signals against these pre-computed references, significantly reducing real-time computational complexity while maintaining high signal identification accuracy.
Solution Approach 2:
The invention creates a library of reference waveform copies representing typical partial discharge patterns and disturbance noise characteristics. By comparing detected signals against these stored copies rather than performing complex real-time analysis, the system achieves accurate signal identification with reduced computational burden during operational phases.
Data Source
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AI summary
The accuracy of monitoring of electrical equipment for the occurrence of partial discharge is improved more. A physical quantity to be caused when partial discharge occurs in electrical equipment (1) under inspection is detected as an electrical signal. Based on a detected waveform indicative of a change over time in the amplitude of the electrical signal thus detected, a differential waveform constituted by the difference in amplitude between the detected waveform and a waveform obtained by shifting the detected waveform by m periods (m: an integer equal to or more than 1) of the power supply frequency of the electrical equipment (1) is calculated. In an amplitude distribution of data of the differential waveform, a distribution near zero is regarded as a normal distribution. An amplitude threshold (TA) is calculated from a standard deviation (σ) and a scale factor (X) set in advance in the normal distribution. The time when the amplitude exceeds the amplitude threshold (TA) is taken as partial discharge occurrence time, a partial discharge signal is extracted from the differential waveform thus calculated, and the occurrence state of the partial discharge in the electrical equipment (1) under inspection is evaluated based on the partial discharge signal thus extracted.