Partial Discharge Detection with Single-Sensor Statistical Filtering
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Solution Overview
Problem
Existing partial discharge detection methods require multiple sensors, which complicates construction and measurement, and are prone to noise interference, especially at higher noise levels.
Innovation Solution
A method utilizing a single measuring device for partial discharge detection, involving signal preprocessing with a band-pass filter, calculation of variances, and recursive estimation of mean and variance to identify and locate partial discharges, without the need for sensor synchronization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple sensors are used for partial discharge detection, then measurement accuracy is improved, but device complexity increases
Solution Approach 1:
The patent segments the detection function into two parts: a single sensor captures the signal, while signal processing algorithms (Wavelet transform, statistical processing) separately perform noise filtering and discharge detection. This segmentation allows using a simple single-sensor hardware configuration while achieving accurate detection through sophisticated software-based signal analysis.
2Reliability
If multiple sensors are used for partial discharge detection, then detection reliability is improved, but ease of operation deteriorates
Solution Approach 1:
The patent merges the functions of multiple sensors into a single sensor system. Instead of using multiple sensors that would require synchronization and complex coordination, the invention combines multiple signal processing techniques (Wavelet transform, statistical methods, thresholding) to achieve the same detection reliability with a single sensor, greatly simplifying operation.
3Measurement precision
If Wavelet transform method is used for noise filtering, then signal detection capability is improved, but robustness to high noise levels deteriorates
Solution Approach 1:
The patent introduces statistical processing as an intermediary step between the Wavelet transform and discharge detection. The statistical methods (calculating mean, standard deviation, and using thresholding based on these statistics) act as a mediator that enhances the Wavelet transform's ability to handle high noise levels, making the overall system more robust while maintaining signal detection capability.
Data Source
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AI summary
A method of detecting a partial discharge signal, in particular a method of detecting a partial discharge signal from a measured signal by a measuring device at at least one point of an electrical network, according to which the variables in the detection device are first initialised and the parameters Nvar, lag_max, dead_max are loaded, then the measured wide-spectrum is loaded at then least once an analog signal that is converted into a digital signal in the digitising means, whích is further band-pass filtered so that components unrelated to the manifestation of a partial discharge are removed, with their frequency being identified as a frequency region where typical broadband transient excitation does not occur at an amplitude due to partial discharges, with the variance of the filtered signal, which contains at least one time constant of the filter, being subsequently calculated from the filtered signal, such that: Formula (I).