Method and device for diagnosing transformer winding inter-turn discharge fault

Abstract

The embodiment of the present specification provides a transformer winding interturn discharge fault diagnosis method and device, wherein the transformer winding interturn discharge fault diagnosis method comprises: obtaining two original signals collected by two sensors arranged in a transformer and preprocessing to obtain two to-be-tested signals, wherein each sensor is arranged on the two sides of the winding interturn of the transformer; performing pulse screening on the to-be-tested signals to obtain a plurality of target pulse pairs; performing double clustering on the target pulse pairs to screen out a first fault point and a second fault point, wherein the second fault point is obtained by screening and splitting from the first fault point; and judging the fault type of the first / second fault point and determining the position information. The embodiment of the present application can improve the efficiency and accuracy of the transformer discharge fault detection, and ensure the stable operation of the power system and the safety of the equipment.

Description

Technical Field

[0001] The embodiments in this specification relate to the field of power equipment testing technology, and in particular to a method and device for diagnosing inter-turn discharge faults in transformer windings. Background Technology

[0002] Transformers are critical electrical devices in power systems, and their safe and stable operation is essential for the power system. However, during long-term service, transformers are affected by various factors such as electric fields, magnetic fields, heat, and mechanical forces, which can cause aging and damage to their internal insulation structure, leading to partial discharge. Partial discharge generates high temperatures and heat in the insulation material, causing a decline in insulation performance, accelerating insulation aging, and potentially even causing serious faults such as inter-turn short circuits and breakdowns in the transformer windings, posing a serious threat to the safe and stable operation of the power system.

[0003] Currently, commonly used partial discharge detection methods include ultrasonic methods, high-frequency pulse current methods, and ultra-high frequency methods. However, each of these methods has certain limitations: while ultrasonic methods have good anti-interference performance, their detection sensitivity is low and their detection range is small, making them only suitable for detecting discharge sources close to the transformer tank wall; high-frequency pulse current methods, although highly sensitive and with excellent waveform recognition capabilities, are easily affected by interference in complex electromagnetic environments; ultra-high frequency methods, while having a high detection bandwidth and high sensitivity, have high requirements for sensor installation and are difficult to accurately identify discharge signals in environments with strong interference. Furthermore, in actual substation sites, there are numerous sources of electromagnetic interference, which can severely affect partial discharge detection, leading to misjudgments or missed detections, reducing the accuracy and reliability of the detection. In addition, most existing partial discharge detection methods cannot achieve precise location of the discharge source, which makes it difficult to promptly detect and handle potential faults inside the transformer, hindering the maintenance and management of power equipment.

[0004] With the continuous development of power systems and the increasing demands for power supply reliability, the safe operation of transformers has become a major concern. Therefore, there is an urgent need for a technology that can overcome the shortcomings of existing technologies, improve the efficiency and accuracy of transformer discharge fault detection, and ensure the stable operation of power systems and the safety of equipment. Summary of the Invention

[0005] In view of this, embodiments of this specification provide a method for diagnosing inter-turn discharge faults in transformer windings. One or more embodiments of this specification also relate to a device for diagnosing inter-turn discharge faults in transformer windings, a computing device, a computer-readable storage medium, and a computer program, to address the technical deficiencies existing in the prior art.

[0006] According to a first aspect of the embodiments of this specification, a method for diagnosing inter-turn discharge faults in transformer windings is provided, comprising:

[0007] Two raw signals collected by two sensors installed in the transformer are acquired and preprocessed to obtain two signals to be measured. Each sensor is installed on both sides of the winding turns of the transformer.

[0008] Multiple target pulse pairs are obtained by pulse filtering from the signal under test;

[0009] The target pulse pairs are subjected to double clustering to filter out the first fault point and the second fault point, wherein the second fault point is obtained by filtering and splitting the first fault point.

[0010] Determine the fault type of the first / second fault point and determine its location information.

[0011] In some embodiments, the preprocessing step includes:

[0012] Each original signal is bandpass filtered to obtain the filtered signal;

[0013] The filtered signal is subjected to wavelet denoising to obtain the signal to be tested.

[0014] In some embodiments, pulse detection is performed from the signal under test to obtain multiple target pulse pairs, including:

[0015] Obtain the dynamic threshold for each sensor;

[0016] The effective pulse group of each signal to be tested is extracted based on the dynamic threshold.

[0017] Based on a preset jitter threshold, pulses in two valid pulse groups are time-matched to select target pulse pairs.

[0018] In some embodiments, the step of identifying the first fault point includes:

[0019] Calculate the amplitude ratio and time delay difference for each target pulse pair;

[0020] A scatter plot is generated based on the amplitude ratio and time delay difference;

[0021] Perform the first clustering on the scatter plot, select the first cluster, and determine the first fault point based on the first cluster.

[0022] In some embodiments, the step of filtering and splitting from the first fault point to obtain the second fault point includes:

[0023] From the first cluster, select outlier clusters whose delay difference is greater than a preset delay threshold;

[0024] A second cluster is selected from the clusters of outliers;

[0025] A second fault point is determined for each second cluster.

[0026] In some embodiments, the step of determining the fault type of the first / second fault point includes:

[0027] Obtain preset comparison indicators, including amplitude ratio;

[0028] The fault type of the first / second fault point is determined based on the comparison indicators. The fault types include partial discharge fault, spark discharge fault and arc discharge fault.

[0029] In some embodiments, the step of determining the location information of the first / second fault point includes:

[0030] Obtain the propagation speed;

[0031] Based on the measured propagation speed and the time delay difference of the pulse pair corresponding to each first fault point or second fault point, calculate the distance from each first fault point or second fault point to the two sensors respectively.

[0032] The discharge coordinates of each first or second fault point in the preset coordinate system are calculated using a preset attenuation model.

[0033] The location information of the first / second fault point is determined based on the discharge coordinates.

[0034] According to a second aspect of the embodiments of this specification, a transformer winding inter-turn discharge fault diagnosis device is provided, comprising:

[0035] The acquisition module is configured to acquire two raw signals collected by two sensors installed in the transformer and preprocess them to obtain two signals to be measured. Each sensor is installed on both sides of the winding turns of the transformer.

[0036] The first filtering module is configured to perform pulse filtering on the signal to be tested to obtain multiple target pulse pairs;

[0037] The second screening module is configured to perform double clustering on the target pulse pairs to screen out the first fault point and the second fault point, wherein the second fault point is obtained by screening and splitting from the first fault point.

[0038] The determination module is configured to determine the fault type of the first / second fault point and determine the location information.

[0039] In some embodiments, the preprocessing step includes:

[0040] Each original signal is bandpass filtered to obtain the filtered signal;

[0041] The filtered signal is subjected to wavelet denoising to obtain the signal to be tested.

[0042] In some embodiments, pulse detection is performed from the signal under test to obtain multiple target pulse pairs, including:

[0043] Obtain the dynamic threshold for each sensor;

[0044] The effective pulse group of each signal to be tested is extracted based on the dynamic threshold.

[0045] Based on a preset jitter threshold, pulses in two valid pulse groups are time-matched to select target pulse pairs.

[0046] In some embodiments, the step of identifying the first fault point includes:

[0047] Calculate the amplitude ratio and time delay difference for each target pulse pair;

[0048] A scatter plot is generated based on the amplitude ratio and time delay difference;

[0049] Perform the first clustering on the scatter plot, select the first cluster, and determine the first fault point based on the first cluster.

[0050] In some embodiments, the step of filtering and splitting from the first fault point to obtain the second fault point includes:

[0051] From the first cluster, select outlier clusters whose delay difference is greater than a preset delay threshold;

[0052] A second cluster is selected from the clusters of outliers;

[0053] A second fault point is determined for each second cluster.

[0054] In some embodiments, the step of determining the fault type of the first / second fault point includes:

[0055] Obtain preset comparison indicators, including amplitude ratio;

[0056] The fault type of the first / second fault point is determined based on the comparison indicators. The fault types include partial discharge fault, spark discharge fault and arc discharge fault.

[0057] In some embodiments, the step of determining the location information of the first / second fault point includes:

[0058] Obtain the propagation speed;

[0059] Based on the measured propagation speed and the time delay difference of the pulse pair corresponding to each first fault point or second fault point, calculate the distance from each first fault point or second fault point to the two sensors respectively.

[0060] The discharge coordinates of each first or second fault point in the preset coordinate system are calculated using a preset attenuation model.

[0061] The location information of the first / second fault point is determined based on the discharge coordinates.

[0062] According to a third aspect of the embodiments of this specification, a computing device is provided, comprising:

[0063] Memory and processor;

[0064] The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, they implement the steps of the above-described method for diagnosing inter-turn discharge faults in transformer windings.

[0065] According to a fourth aspect of the embodiments of this specification, a computer-readable storage medium is provided that stores computer-executable instructions, which, when executed by a processor, implement the steps of the above-described method for diagnosing inter-turn discharge faults in transformer windings.

[0066] According to a fifth aspect of the embodiments of this specification, a computer program is provided, wherein when the computer program is executed in a computer, it causes the computer to perform the steps of the above-described method for diagnosing inter-turn discharge faults in transformer windings.

[0067] At least one embodiment of this specification obtains two raw signals collected by two sensors installed in the transformer and preprocesses them to obtain two signals to be measured, wherein each sensor is respectively installed on both sides of the winding turns of the transformer; pulse filtering is performed on the signals to be measured to obtain multiple target pulse pairs; binary clustering is performed on the target pulse pairs to filter out a first fault point and a second fault point, wherein the second fault point is obtained by filtering and splitting from the first fault point; the fault type of the first / second fault point is determined and the location information is determined, which can improve the efficiency and accuracy of transformer discharge fault detection, and ensure the stable operation of the power system and the safety of equipment. Attached Figure Description

[0068] Figure 1 This is a flowchart of some embodiments of a transformer winding inter-turn discharge fault diagnosis method provided in some embodiments of this specification;

[0069] Figure 2 This is a flowchart of some embodiments of a transformer winding inter-turn discharge fault diagnosis method provided in some embodiments of this specification;

[0070] Figure 3 This is a simplified structural diagram of a transformer winding inter-turn discharge fault diagnosis device provided in some embodiments of this specification;

[0071] Figure 4 This is a structural block diagram of a computing device provided in some embodiments of this specification. Detailed Implementation

[0072] Many specific details are set forth in the following description to provide a full understanding of this specification. However, this specification can be implemented in many other ways than those described herein, and those skilled in the art can make similar extensions without departing from the spirit of this specification. Therefore, this specification is not limited to the specific implementations disclosed below.

[0073] The terminology used in one or more embodiments of this specification is for the purpose of describing particular embodiments only and is not intended to limit the scope of the one or more embodiments of this specification. The singular forms “a” and “the” as used in one or more embodiments of this specification and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in one or more embodiments of this specification refers to and includes any or all possible combinations of one or more associated listed items. The modifications “a” and “a plurality” as used in this disclosure are illustrative and not restrictive, and those skilled in the art will understand that they should be understood as “one or more” unless the context clearly indicates otherwise.

[0074] It should be understood that although the terms first, second, etc., may be used to describe various information in one or more embodiments of this specification, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, first may also be referred to as second without departing from the scope of one or more embodiments of this specification, and similarly, second may also be referred to as first. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to a determination."

[0075] First, the terms and concepts used in one or more embodiments of this specification will be explained.

[0076] UHF signal: Ultra high frequency signal.

[0077] HF signal: high frequency signal.

[0078] AE signal: Acoustic Emission signal, also known as ultrasonic signal.

[0079] See Figure 1 , Figure 1 A flowchart of a method for diagnosing inter-turn discharge faults in transformer windings according to some embodiments of this specification is shown, specifically including the following steps.

[0080] Step 101: Obtain the two raw signals collected by the two sensors installed in the transformer and perform preprocessing to obtain two signals to be measured.

[0081] In some embodiments, the execution subject of the transformer winding inter-turn discharge fault diagnosis method (such as a preset computing device) can be connected to the target device via a wired or wireless connection. Then, it acquires two raw signals collected by two sensors installed in the transformer and performs preprocessing to obtain two signals to be tested. The two sensors are installed on both sides of the transformer winding inter-turns; that is, one transformer is installed on one side of the winding inter-turns, and the other transformer is installed on the other side of the winding inter-turns, to test whether a discharge fault has occurred in the winding inter-turns.

[0082] In some optional implementations, the preprocessing steps include: bandpass filtering each original signal to obtain a filtered signal; and wavelet denoising processing of the filtered signal to obtain the signal to be tested. Bandpass filtering can filter a specific frequency range, effectively extracting useful components while suppressing out-of-band interference and noise. The multi-resolution characteristics of wavelet transform can separate signals and noise at different scales, effectively removing noise while preserving signal edges, peaks, and other detailed features, avoiding the blurring or noise amplification problems of traditional low-pass / high-pass filtering. Combining these two methods, while removing noise, can preserve more details of the target signal.

[0083] It should be noted that the aforementioned wireless connection methods may include, but are not limited to, 3G / 4G / 5G / 6G connections, WiFi connections, Bluetooth connections, WiMAX connections, Zigbee connections, UWB (ultra wideband) connections, and other currently known or future wireless connection methods.

[0084] Step 102: Perform pulse filtering on the signal to be tested to obtain multiple target pulse pairs.

[0085] In some embodiments, the aforementioned execution entity can obtain multiple target pulse pairs by performing pulse detection from the signal under test through the following steps:

[0086] The first step is to obtain the dynamic threshold for each sensor.

[0087] The dynamic threshold can refer to a preset multiple of the sensor's noise standard deviation. As an example, if a sensor has a noise standard deviation of approximately 3 (mV) and a preset multiple of 5, then the dynamic threshold of that sensor can be 15.

[0088] The second step is to extract the effective pulse group of each corresponding signal to be tested based on the dynamic threshold.

[0089] When screening valid pulse pairs, pulses that are greater than the corresponding dynamic threshold in each signal to be tested can be deleted, thereby selecting valid pulses and obtaining two valid pulse groups.

[0090] The third step involves time-pairing the pulses in the two valid pulse groups based on a preset jitter threshold to select the target pulse pair.

[0091] After selecting valid pulse groups, time pairing of the two valid pulse groups is performed based on a preset jitter threshold, and single pulses without corresponding pulses are deleted, thereby selecting target pulse pairs. Since the two signals may experience slight time jitter due to differences in materials, structures, etc. during actual transmission, a certain jitter range can be set to select more pulse pairs without affecting signal accuracy.

[0092] As a concrete example, suppose sensor A selects 60 pulses A2 based on the corresponding dynamic threshold A1, and sensor B selects 59 pulses B2 based on the corresponding dynamic threshold B1. Setting the jitter threshold to 2 microseconds, the 60 pulses A2 and 59 pulses B2 are matched (e.g., based on a preset timestamp or by calculating the pulse leading-edge time), and pulses that cannot be matched are discarded, ultimately resulting in 45 pulse pairs.

[0093] Through the above processing, more representative characteristic pulses can be selected, providing a highly accurate data foundation for subsequent discharge prediction.

[0094] Step 103: Perform binary clustering on the target pulse pairs to filter out the first fault point and the second fault point, wherein the second fault point is obtained by filtering and splitting from the first fault point.

[0095] Double clustering refers to clustering target impulse pairs twice. As an example, this clustering method can be Gaussian mixture model clustering.

[0096] In some embodiments, the first fault point can be identified by the following steps:

[0097] The first step is to calculate the amplitude ratio and time delay difference for each target pulse pair.

[0098] Amplitude ratio refers to the ratio of the amplitudes of each pulse pair. Delay difference refers to the difference in delay between each pulse pair.

[0099] The second step is to generate a scatter plot based on the amplitude ratio and time delay difference.

[0100] In some alternative implementations, a scatter plot can be a data graph with the amplitude ratio on the horizontal axis and the delay difference on the vertical axis. Conversely, the horizontal axis of a scatter plot can be the delay difference, and the vertical axis can be the amplitude ratio, depending on the need.

[0101] The third step is to perform the first clustering on the scatter plot, select the first cluster, and determine the first fault point based on the first cluster. As mentioned above, the first clustering can be Gaussian mixture model clustering. The first cluster can refer to the data aggregation point obtained after clustering, and this aggregation point can be considered as the corresponding fault point.

[0102] It should be noted that the first cluster is the initial separation, and the first cluster may also contain clusters of multiple fault points. Therefore, a second cluster analysis is required for the first cluster.

[0103] After identifying the first fault point, further analysis is required. The second fault point is obtained by filtering and breaking down the first fault point through the following steps:

[0104] The first step is to select outlier clusters from the first clusters whose delay difference is greater than a preset delay threshold.

[0105] Since there may be two or more fault points that are relatively close to each other, a single cluster may contain two or more fault points. In this case, further analysis of each pulse pair in the first cluster is required. Anomaly clusters represent the aggregation points of pulse pairs whose delay difference is greater than a preset delay threshold. For example, assuming that the delay difference of most pulse pairs is around 50 microseconds (plus or minus 10 microseconds), the delay threshold can be set to 50 + 10 = 60 microseconds. Pulse pairs with delays greater than 60 microseconds are considered anomaly clusters.

[0106] The second step is to select a second cluster from the outlier clusters.

[0107] Anomaly clusters could be caused by other noise or anomalies during signal acquisition, or they could be due to multiple fault points. Further analysis can be performed by examining the peak values. For example, if most peaks are around 50 millivolts, but one or more peaks are 80 or 100 millivolts, significantly higher than normal peaks, then this pulse pair likely involves discharges from two or more fault points, causing peak overlap and resulting in an abnormally high peak value.

[0108] In some embodiments, the step of filtering out a second cluster from the outlier clusters includes:

[0109] The first step is to obtain the peak of each pulse in the pulse pair corresponding to the anomaly cluster and judge it according to the preset peak threshold.

[0110] The second step is to identify pulse pairs with peak values ​​greater than the peak threshold as overlapping pulse pairs.

[0111] The third step is to cluster the overlapping pulse pairs to obtain a second cluster.

[0112] The fourth step is to identify a second fault point for each second cluster.

[0113] Secondary clustering can further subdivide previously undetectable fault points, resulting in more accurate classification results.

[0114] Step 104: Determine the fault type of the first / second fault point and determine its location information.

[0115] In some embodiments, the step of determining the fault type of the first / second fault point includes: first, obtaining a preset comparison index, wherein the comparison index includes an amplitude ratio; second, determining the fault type of the first / second fault point based on the comparison index, wherein the fault type includes partial discharge fault, spark discharge fault, and arc discharge fault.

[0116] The comparison index can refer to preset limiting data for judging the fault type based on the amplitude ratio.

[0117] As an example, the comparison metrics can include partial discharge metrics, spark discharge metrics, and arc discharge metrics, among which,

[0118] The partial discharge index is: amplitude ratio range: 1.2-2.5;

[0119] The spark discharge parameters are: amplitude ratio range: 0.8-1.5;

[0120] The arc discharge index is: amplitude ratio range: 3.0-10.0.

[0121] The comparison index can be obtained by artificially injecting known discharge types (such as partial discharge simulated by needle plate electrodes) under fault-free conditions of the transformer and recording the amplitude distribution corresponding to each type.

[0122] Based on this indicator, the fault types of the first / second fault points can be determined as partial discharge fault, spark discharge fault, or arc discharge fault, respectively.

[0123] In some embodiments, the step of determining the location information of the first / second fault point includes:

[0124] The first step is to obtain the propagation velocity. The propagation velocity refers to the speed at which the sensor signal travels within the transformer components; this velocity is a measured value. The second step is to calculate the distance from each first or second fault point to the two sensors based on the measured propagation velocity and the time delay difference of the pulse pairs corresponding to each first or second fault point. The third step is to calculate the discharge coordinates of each first or second fault point in a preset coordinate system using a pre-defined attenuation model. The fourth step is to determine the location information of the first / second fault points based on the discharge coordinates.

[0125] Due to the complex structure of transformers, which incorporate various materials and configurations, a certain degree of signal attenuation occurs during transmission. The attenuation model refers to a model trained based on transformer characteristics. This model can be used to determine the transmission distance based on the degree of signal attenuation, and further, to more accurately pinpoint the location of the fault. Discharge coordinates refer to the coordinates of the discharge fault point within a preset coordinate system. This preset coordinate system can be any two-dimensional or three-dimensional coordinate system that matches actual operating conditions, and can be selected as needed.

[0126] The beneficial effects of one embodiment in the various embodiments of this specification include at least the following: by acquiring two raw signals collected by two sensors installed in the transformer and preprocessing them, two signals to be measured are obtained, wherein each sensor is respectively installed on both sides of the winding turns of the transformer; pulse filtering is performed on the signals to be measured to obtain multiple target pulse pairs; binary clustering is performed on the target pulse pairs to filter out the first fault point and the second fault point, wherein the second fault point is obtained by filtering and splitting from the first fault point; the fault type of the first / second fault point and the location information are determined, which can improve the efficiency and accuracy of transformer discharge fault detection, and ensure the stable operation of the power system and the safety of equipment.

[0127] In some optional implementations, the above-mentioned method for diagnosing inter-turn discharge faults in transformer windings further includes: generating a target spectrum based on a first cluster and a second cluster; displaying the target spectrum on a target display interface; and making a secondary judgment on the fault type of the first / second fault point based on the shape of the target spectrum.

[0128] The target spectrum can be any of the following: PRPD (Phase-Resolved Partial Discharge), PRPS (Phase-Resolved Pulse Sequence), N-Φ [Number-Φ (Phase), discharge count-phase], V-Φ [Voltage-Φ (Phase), discharge amplitude-phase], NV (Number-Voltage), or other spectra. Since partial discharge, spark discharge, and arc discharge exhibit different display shapes in the aforementioned spectra, secondary verification can be performed based on this spectrum, increasing the accuracy of fault type identification.

[0129] The following is in conjunction with the appendix Figure 2 (Taking the application of the transformer winding inter-turn discharge fault diagnosis method provided in this specification in a 00000 environment as an example, the transformer winding inter-turn discharge fault diagnosis method will be further explained. Among them,) Figure 2The following is a flowchart illustrating the processing steps of a transformer winding inter-turn discharge fault diagnosis method provided in some other embodiments of this specification, specifically including the following steps.

[0130] Step 201: Obtain the two raw signals collected by the two sensors installed in the transformer and perform preprocessing to obtain two signals to be measured.

[0131] Step 202: Perform pulse filtering on the signal to be tested to obtain multiple target pulse pairs.

[0132] Step 203: Calculate the amplitude ratio and time delay difference for each target pulse pair.

[0133] Step 204: Generate a scatter plot based on the amplitude ratio and time delay difference.

[0134] Step 205: Perform a first clustering on the scatter plot, filter out the first cluster, and determine the first fault point based on the first cluster.

[0135] Step 206: Select outlier clusters from the first clusters whose delay difference is greater than a preset delay threshold.

[0136] Step 207: Select the second cluster from the anomaly cluster.

[0137] Step 208: Determine a second fault point for each of the second clusters.

[0138] Step 209: Determine the fault type of the first / second fault point and determine its location information.

[0139] Step 210: Generate the target spectrum based on the first cluster and the second cluster.

[0140] Step 211: Display the target spectrum on the target display interface, and make a secondary judgment on the fault type of the first / second fault point based on the shape of the target spectrum.

[0141] In some embodiments, the specific implementation and technical effects of steps 201-209 corresponding to those in the foregoing embodiments can refer to those steps, and will not be repeated here.

[0142] Corresponding to the above method embodiments, this specification also provides embodiments of a transformer winding inter-turn discharge fault diagnosis device. Figure 3 This specification illustrates a schematic diagram of the structure of a transformer winding inter-turn discharge fault diagnosis device according to some embodiments. Figure 3 As shown, the device includes:

[0143] The acquisition module 301 is configured to acquire two raw signals collected by two sensors installed in the transformer and preprocess them to obtain two signals to be measured. Each sensor is installed on both sides of the winding turns of the transformer.

[0144] The first screening module 302 is configured to perform pulse screening from the signal to be tested to obtain multiple target pulse pairs;

[0145] The second screening module 303 is configured to perform double clustering on the target pulse pairs to screen out the first fault point and the second fault point, wherein the second fault point is obtained by screening and splitting from the first fault point.

[0146] The determination module 304 is configured to determine the fault type of the first / second fault point and determine the location information.

[0147] In some embodiments, the preprocessing step includes:

[0148] Each original signal is bandpass filtered to obtain the filtered signal;

[0149] The filtered signal is subjected to wavelet denoising to obtain the signal to be tested.

[0150] In some embodiments, pulse detection is performed from the signal under test to obtain multiple target pulse pairs, including:

[0151] Obtain the dynamic threshold for each sensor;

[0152] The effective pulse group of each signal to be tested is extracted based on the dynamic threshold.

[0153] Based on a preset jitter threshold, pulses in two valid pulse groups are time-matched to select target pulse pairs.

[0154] In some embodiments, the step of identifying the first fault point includes:

[0155] Calculate the amplitude ratio and time delay difference for each target pulse pair;

[0156] A scatter plot is generated based on the amplitude ratio and time delay difference;

[0157] Perform the first clustering on the scatter plot, select the first cluster, and determine the first fault point based on the first cluster.

[0158] In some embodiments, the step of filtering and splitting from the first fault point to obtain the second fault point includes:

[0159] From the first cluster, select outlier clusters whose delay difference is greater than a preset delay threshold;

[0160] A second cluster is selected from the clusters of outliers;

[0161] A second fault point is determined for each second cluster.

[0162] In some embodiments, the step of determining the fault type of the first / second fault point includes:

[0163] Obtain preset comparison indicators, including amplitude ratio;

[0164] The fault type of the first / second fault point is determined based on the comparison indicators. The fault types include partial discharge fault, spark discharge fault and arc discharge fault.

[0165] In some embodiments, the step of determining the location information of the first / second fault point includes:

[0166] Obtain the propagation speed;

[0167] Based on the measured propagation speed and the time delay difference of the pulse pair corresponding to each first fault point or second fault point, calculate the distance from each first fault point or second fault point to the two sensors respectively.

[0168] The discharge coordinates of each first or second fault point in the preset coordinate system are calculated using a preset attenuation model.

[0169] The location information of the first / second fault point is determined based on the discharge coordinates.

[0170] The above is a schematic scheme of a transformer winding inter-turn discharge fault diagnosis device according to this embodiment. It should be noted that the technical solution of this transformer winding inter-turn discharge fault diagnosis device and the technical solution of the above-described transformer winding inter-turn discharge fault diagnosis method belong to the same concept. For details not described in detail in the technical solution of the transformer winding inter-turn discharge fault diagnosis device, please refer to the description of the technical solution of the above-described transformer winding inter-turn discharge fault diagnosis method.

[0171] Figure 4 A structural block diagram of a computing device 400 according to some embodiments of this specification is shown. The components of the computing device 400 include, but are not limited to, a memory 401 and a processor 402. The processor 402 is connected to the memory 401 via a bus 403, and a database 405 is used to store data.

[0172] The computing device 400 also includes an access device 404 that enables the computing device 400 to communicate via one or more networks 406. Examples of such networks include Public Switched Telephone Network (PSTN), Local Area Network (LAN), Wide Area Network (WAN), Personal Area Network (PAN), or combinations of communication networks such as the Internet. The access device 404 may include one or more of any type of wired or wireless network interface (e.g., a network interface card (NIC)), such as an IEEE 802.11 Wireless Local Area Network (WLAN) wireless interface, a Wi-MAX (Worldwide Interoperability for Microwave Access) interface, an Ethernet interface, a Universal Serial Bus (USB) interface, a cellular network interface, a Bluetooth interface, or a Near Field Communication (NFC) interface.

[0173] In one embodiment of this specification, the aforementioned components of the computing device 400 and Figure 4 Other components, not shown, can also be connected to each other, for example, via a bus. It should be understood that... Figure 4 The block diagram of the computing device shown is for illustrative purposes only and is not intended to limit the scope of this specification. Those skilled in the art can add or replace other components as needed.

[0174] The computing device 400 can be any type of stationary or mobile computing device, including mobile computers or mobile computing devices (e.g., tablet computers, personal digital assistants, laptop computers, notebook computers, netbooks, etc.), mobile phones (e.g., smartphones), wearable computing devices (e.g., smartwatches, smart glasses, etc.) or other types of mobile devices, or stationary computing devices such as desktop computers or personal computers (PCs). The computing device 400 can also be a mobile or stationary server.

[0175] The processor 402 executes computer-executable instructions, which, when executed by the processor, implement the steps of the above-described method for diagnosing inter-turn discharge faults in transformer windings. The above is a schematic representation of a computing device according to this embodiment. It should be noted that the technical solution of this computing device and the technical solution of the above-described method for diagnosing inter-turn discharge faults in transformer windings belong to the same concept. Details not described in detail in the technical solution of the computing device can be found in the description of the technical solution of the above-described method for diagnosing inter-turn discharge faults in transformer windings.

[0176] An embodiment of this specification also provides a computer-readable storage medium storing computer-executable instructions that, when executed by a processor, implement the steps of the above-described method for diagnosing inter-turn discharge faults in transformer windings.

[0177] The above is an illustrative scheme of a computer-readable storage medium according to this embodiment. It should be noted that the technical solution of this storage medium belongs to the same concept as the technical solution of the above-described method for diagnosing inter-turn discharge faults in transformer windings. For details not described in detail in the technical solution of the storage medium, please refer to the description of the technical solution of the above-described method for diagnosing inter-turn discharge faults in transformer windings.

[0178] An embodiment of this specification also provides a computer program, wherein when the computer program is executed in a computer, it causes the computer to perform the steps of the above-described method for diagnosing inter-turn discharge faults in transformer windings.

[0179] The above is an illustrative scheme of a computer program according to this embodiment. It should be noted that the technical solution of this computer program and the technical solution of the above-described method for diagnosing inter-turn discharge faults in transformer windings belong to the same concept. For details not described in detail in the technical solution of the computer program, please refer to the description of the technical solution of the above-described method for diagnosing inter-turn discharge faults in transformer windings.

[0180] The foregoing has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0181] Computer instructions include computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. Computer-readable media can include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in computer-readable media can be appropriately added to or removed according to the requirements of legislation and patent practice in a jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electrical carrier signals and telecommunication signals.

[0182] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that the embodiments in this specification are not limited to the described order of actions, because according to the embodiments in this specification, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in this specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to the embodiments in this specification.

[0183] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0184] The preferred embodiments disclosed above are merely illustrative of this specification. The optional embodiments do not exhaustively describe all details, nor do they limit the invention to specific implementations. Clearly, many modifications and variations can be made based on the embodiments described in this specification. These embodiments are selected and specifically described in this specification to better explain the principles and practical applications of the embodiments, thereby enabling those skilled in the art to better understand and utilize this specification. This specification is limited only by the claims and their full scope and equivalents.

Claims

1. A transformer winding interturn discharge fault diagnostic method characterized by, The method comprises the following steps: obtaining two original signals collected by two sensors arranged in a transformer and pre-processing the two original signals to obtain two to-be-tested signals, wherein each of the two sensors is arranged on the two sides of the inter-turn winding of the transformer; performing pulse screening on the to-be-tested signals to obtain a plurality of target pulse pairs, specifically comprising: obtaining a dynamic threshold of each sensor; extracting an effective pulse group of each to-be-tested signal based on the dynamic threshold; and performing time pairing on the pulses in the two effective pulse groups based on a preset jitter threshold to screen out the target pulse pairs; performing double clustering on the target pulse pairs to screen out a first fault point and a second fault point, wherein the second fault point is obtained by splitting from the first fault point, specifically comprising: calculating an amplitude ratio and a time delay difference of each target pulse pair; generating a scatter plot based on the amplitude ratio and the time delay difference; performing first clustering on the scatter plot to screen out a first clustering cluster; screening out an abnormal point clustering cluster with a time delay difference greater than a preset time delay threshold from the first clustering cluster; screening out a second clustering cluster from the abnormal point clustering cluster; and determining one second fault point according to each second clustering cluster; judging the fault type of the first / second fault point and determining the position information, wherein the step of judging the fault type of the first / second fault point comprises: obtaining a preset comparison index, wherein the comparison index comprises an amplitude ratio; and determining the fault type of the first / second fault point according to the comparison index, wherein the fault type comprises a partial discharge fault, a spark discharge fault and an arc discharge fault.

2. The method of claim 1, wherein, The pre-processing step comprises: performing band-pass filtering on each of the original signals to obtain a filtered signal; performing wavelet denoising processing on the filtered signal to obtain the to-be-tested signal.

3. The method of claim 1, wherein, The step of determining the position information of the first / second fault point comprises: obtaining a measured propagation speed; calculating the distance from each of the first fault points or second fault points to the two sensors respectively according to the measured propagation speed and the time delay difference of the pulse pair corresponding to each of the first fault points or second fault points; calculating the discharge coordinates of each of the first fault points or second fault points in a preset coordinate system through a preset attenuation model; determining the position information of the first / second fault point according to the discharge coordinates.

4. A transformer winding interturn discharge fault diagnostic apparatus characterized by comprising: The method comprises the following steps: an obtaining module configured to obtain two original signals collected by two sensors arranged in a transformer and pre-process the two original signals to obtain two to-be-tested signals, wherein each of the two sensors is arranged on the two sides of the inter-turn winding of the transformer; a first screening module configured to perform pulse screening on the to-be-tested signals to obtain a plurality of target pulse pairs, specifically comprising: obtaining a dynamic threshold of each sensor; extracting an effective pulse group of each to-be-tested signal based on the dynamic threshold; and performing time pairing on the pulses in the two effective pulse groups based on a preset jitter threshold to screen out the target pulse pairs; The second screening module is configured to perform double clustering on the target pulse pairs to screen out first fault points and second fault points, wherein the second fault points are obtained by screening out from the first fault points, and specifically includes: calculating an amplitude ratio and a time delay difference of each target pulse pair; generating a scatter plot based on the amplitude ratio and the time delay difference; performing first clustering on the scatter plot to screen out first clustering clusters; screening out an abnormal point clustering cluster with a time delay difference greater than a preset time delay threshold from the first clustering clusters; screening out second clustering clusters from the abnormal point clustering cluster; and determining one second fault point according to each second clustering cluster; The determining module is configured to determine a fault type and position information of the first / second fault point, wherein the step of determining the fault type of the first / second fault point includes: obtaining a preset comparison index, wherein the comparison index includes an amplitude ratio; and determining the fault type of the first / second fault point according to the comparison index, wherein the fault type includes a partial discharge fault, a spark discharge fault and an arc discharge fault.

5. A computing device, comprising: Comprise: a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions, and the computer executable instructions, when executed by the processor, realize the steps of the transformer winding interturn discharge fault diagnosis method in any one of claims 1 to 3.

6. A computer-readable storage medium storing computer-executable instructions, wherein execution of the computer-executable instructions by one or more processors causes performance of operations comprising: The computer executable instructions, when executed by the processor, realize the steps of the transformer winding interturn discharge fault diagnosis method in any one of claims 1 to 3.

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