A transformer fault prediction method based on multi-source data fusion

Abstract

The present application relates to the field of power equipment state monitoring, and particularly relates to a transformer fault prediction method based on multi-source data fusion. The method comprises the following steps: synchronously collecting ultra-high frequency partial discharge and contact acoustic signature signals, extracting atlas and frequency band features, calculating low-frequency baseband energy derivatives to preliminarily screen transient impact states, comparing the acoustic signature library by using a dynamic time warping algorithm to confirm transient decoupling modes or output unknown early warnings, removing shallow interference and extracting phase atlas and high-frequency residuals as target features for diagnosis in the decoupling mode, and finally outputting prediction warnings or suppressing working condition artifacts according to power frequency phase aggregation degree and resonance energy peak value. The present application effectively improves the system false alarm problem caused by extreme transient working conditions, and realizes feature decoupling and early prediction of deep composite faults of the transformer under strong interference.

Claims

1. A transformer fault prediction method based on multi-source data fusion, characterized in that, include: S1. Synchronously acquire the UHF partial discharge signal and contact acoustic pattern signal of the transformer, divide the contact acoustic pattern signal into low-frequency baseband signal and high-frequency residual band signal, and extract the UHF partial discharge signal into time-domain amplitude features and phase spectrum features. S2. Calculate the first derivative of the energy of the low-frequency baseband signal in real time. If the first derivative of the energy exceeds the preset derivative threshold, it is determined that the transformer has entered the transient impact screening state and the waveform comparison process is triggered. S3. In the transient impact screening state, the envelope features of the contact acoustic signal are extracted, and the envelope features are compared with the preset operation acoustic library using the dynamic time warping algorithm. If the matching similarity reaches the preset similarity threshold, it is confirmed that the transient decoupling mode has been entered; otherwise, it is determined that the transformer has experienced an unknown abnormal impact and an abnormal impact warning is directly output. S4. When in the transient decoupling mode, the low-frequency baseband signal and the time-domain amplitude feature are removed, and the phase spectrum feature and the high-frequency residual band signal are extracted as the target decoupling feature. S5. Diagnose the target decoupling features. If the phase spectrum features show power frequency phase clustering and the energy peak of the high frequency residual band signal exceeds the preset resonance threshold, output a fault prediction alarm. Otherwise, determine it as a working condition impact artifact and suppress the alarm. In step S2, the step of determining that the transformer has entered the transient impact initial screening state and triggering the waveform comparison process includes: Extract the basis noise derivative sequence of the transformer during its historical steady-state operation, and calculate the preset derivative threshold based on the statistical distribution model; The first derivative of the energy is compared with the preset derivative threshold. When the first derivative of the energy is greater than the preset derivative threshold within a continuous preset sampling period, a preliminary screening trigger command is generated. In response to the initial screening trigger command, the working mode of the monitoring system is switched to the transient impact initial screening state, and the waveform comparison process is triggered. Step S5, specifically the step of outputting a fault prediction alarm, includes: The pulse distribution density of the phase spectrum features within a preset power frequency cycle is statistically analyzed, and the phase aggregation probability of the pulse distribution density within a preset phase interval is calculated. In the target decoupling features, determine the maximum energy peak value corresponding to the high-frequency residual band signal; When the phase convergence probability is greater than a preset density threshold and the maximum energy peak is greater than the preset resonance threshold, the fault prediction alarm is generated.

2. The transformer fault prediction method based on multi-source data fusion according to claim 1, characterized in that, Step S1, the step of dividing the signal into a low-frequency baseband signal and a high-frequency residual band signal, includes: The contact acoustic pattern signal is processed by low-pass filtering to extract the vibration component of a preset low-frequency band on the transformer surface to generate the low-frequency baseband signal. The contact acoustic signal is processed by bandpass filtering to extract the vibration component of a preset high-frequency band; A time delay compensation operation is performed on the extracted vibration components of the preset high-frequency band to generate the high-frequency residual band signal that is synchronously aligned with the low-frequency baseband signal on the time axis.

3. The transformer fault prediction method based on multi-source data fusion according to claim 1, characterized in that, Step S2, the step of calculating the first derivative of the energy of the low-frequency baseband signal in real time, includes: Construct a sliding time window along the time axis and calculate the cumulative energy value of the low-frequency baseband signal within the current sliding time window; Obtain the difference sequence of the accumulated energy values ​​between adjacent sliding time windows, and calculate the time change rate of the difference sequence; The time rate of change is subjected to noise reduction by applying a smoothing filter operator, and the smoothed numerical sequence is output as the first derivative of the energy.

4. The transformer fault prediction method based on multi-source data fusion according to claim 1, characterized in that, Step S3, the step of extracting the envelope features of the contact acoustic signature signal, includes: The contact acoustic signal is analyzed using an envelope extraction operator to obtain an initial envelope curve; The initial envelope curve is resampled using an interpolation algorithm to obtain a resampled curve; An amplitude normalization operation is performed on the resampled curve to generate the envelope feature with a unified data dimension.

5. The transformer fault prediction method based on multi-source data fusion according to claim 1, characterized in that, Step S3, the step of comparing the envelope feature with a preset operation voiceprint database, includes: Retrieve the standard operation template from the preset operation voiceprint library and construct the cost matrix between the envelope feature and the standard operation template; Find the alignment path with the minimum cumulative distance in the cost matrix, and obtain the best matching distance between the envelope feature and the standard operation template on the time axis; Based on the optimal matching distance, the matching similarity between the envelope feature and the standard operation template is calculated using a mapping function.

6. The transformer fault prediction method based on multi-source data fusion according to claim 1, characterized in that, Step S4, the step of extracting the phase spectrum features and the high-frequency residual band signal as target decoupling features, includes: The synchronous reference signal of the transformer is obtained, and the ultra-high frequency partial discharge signal is mapped to the phase interval corresponding to the synchronous reference signal to construct the two-dimensional distributed phase spectrum features. Perform time-frequency transformation processing on the high-frequency residual band signal to extract the spectral amplitude distribution data of the high-frequency residual band signal in a preset high-frequency band; The phase map features are fused and spliced ​​with the spectral amplitude distribution data to generate the target decoupling features.

7. The transformer fault prediction method based on multi-source data fusion according to claim 1, characterized in that, In step S5, the step of otherwise determining it as an impact artifact and suppressing the alarm includes: When the phase spectrum features do not show power frequency phase clustering, or the energy peak value of the high frequency residual band signal is not greater than the preset resonance threshold, the ultra-high frequency partial discharge signal and the contact acoustic pattern signal are determined to be working condition impact artifacts. Intercept the corresponding over-limit alarm signals and generate artifact masking instructions for the monitoring alarm link; Assign working condition impact artifact labels to the current UHF partial discharge signal and the contact acoustic pattern signal, and update them to the local background noise dataset.

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