Active protection live detection method and device for main substation equipment

Abstract

This application applies to the field of fault detection technology and provides a method and device for active defense detection of live power in substation main equipment. The method comprises: acquiring a partial discharge signal from the substation main equipment; obtaining a correlation between the partial discharge signal and historical partial discharge signals; wherein the correlation indicates the degree of correlation between the partial discharge signal and the historical partial discharge signal and a predetermined fault, and at least two synchronously collected signals from the partial discharge signal participate in determining the correlation; and obtaining a detection result based on the correlation. This method solves the problem of low fault identification rate in the prior art method of using single data for analysis, improves the fault identification rate, provides strong support for active defense of substation main equipment, and further extends the service life of the equipment.

Description

Technical Field

[0001] The present application belongs to the field of detection technology, and in particular relates to a method and device for active protection detection of live power of a main substation equipment. Background Art

[0002] Safe, high-quality, and economical transmission of electric energy is a fundamental requirement for modern power system operation. Transformers and / or reactors (hereinafter referred to as main substation equipment, with transformers used as an example) are key components of power systems and grid architectures.

[0003] Because transformers in power systems require long-term operation under load, they are typically more prone to failure than other equipment. In today's power grid, with increasing grid upgrades, cross-regional interconnections, and dispatching, transformer failures can easily trigger chain reactions within a specific area of ​​the grid. Therefore, routine transformer fault detection and diagnosis—detecting potential faults before they occur and repairing them before they occur—is crucial for maintaining stable grid operation.

[0004] Transformer fault diagnosis involves performing data analysis on acquired transformer fault information and using the results of this data analysis to determine the transformer's intended fault. In actual engineering, six methods for diagnosing transformer conditions exist: oil chromatography, partial discharge, infrared temperature measurement, electrical testing, oiling, and winding deformation testing. The most basic method is oil chromatography, which is an effective method for diagnosing transformer faults. However, both oil chromatography and other analysis methods are based on a single parameter. Therefore, the inventors have discovered that the prior art method of using a single data point for analysis results in a low fault identification rate. Summary of the Invention

[0005] The embodiments of the present application provide a method and device for active protection detection of live power of main substation equipment, which can improve the accuracy and reliability of fault detection.

[0006] According to one aspect of an embodiment of the present application, a method for active defense live detection of substation main equipment is provided. The substation main equipment includes at least one of the following: a transformer, a reactor, and a gas-insulated switchgear (GIS). The method is applied to a live detection device including multiple channels, wherein each channel is used to receive a signal from a sensor. The partial discharge signal includes at least two signals, and the partial discharge signal is obtained by synchronously acquiring at least two of the multiple channels. A correlation between the partial discharge signal and a historical partial discharge signal is obtained. The correlation indicates the degree of correlation between the partial discharge signal and the historical partial discharge signal and a predetermined fault. The at least two synchronously acquired signals in the partial discharge signal participate in determining the correlation. The historical partial discharge signal is a previously acquired partial discharge signal. Each predetermined fault corresponds to its own historical partial discharge signal, and the historical partial discharge signal includes at least one of the following: a partial discharge signal acquired before the fault occurs, a partial discharge signal acquired during the fault occurs, and a partial discharge signal acquired within a predetermined time period after the fault occurs. A detection result is obtained based on the correlation.

[0007] Furthermore, the at least two signals are at least two of the following signals: ultrasonic signal, high frequency signal, ultra-high frequency signal, radio frequency partial discharge signal, soundprint signal, vibration signal, bushing end screen signal, pulse current method partial discharge signal, transient ground wave signal, core addition ground current signal; and / or, the sensor includes at least two of the following: ultrasonic sensor, high frequency sensor, ultra-high frequency sensor, radio frequency sensor, soundprint sensor, vibration sensor, bushing end screen sensor, partial discharge input unit, ground wave sensor, ground current sensor.

[0008] Furthermore, obtaining the correlation between the partial discharge signal and historical partial discharge signals includes: obtaining a signal type of the partial discharge signal; and obtaining the correlation of each signal in the partial discharge signal according to the signal type.

[0009] Furthermore, obtaining the correlation between the partial discharge signal and the historical partial discharge signal includes: fusing multiple signals in the historical partial discharge signal according to the correlation to obtain historical fused data; fusing multiple signals in the partial discharge signal according to the correlation to obtain fused data, wherein the partial discharge signal and the multiple signals in the historical partial discharge signal are fused in the same manner; and obtaining the detection result according to the correlation includes: obtaining the detection result according to the fused data and the historical fused data.

[0010] Furthermore, obtaining the detection result based on the fused data and the historical fused data includes: using the historical fused data and the corresponding predetermined fault as training data to train a fault detection model; and inputting the fused data into the fault detection model to obtain the detection result.

[0011] Furthermore, fusing multiple signals in the partial discharge signal according to correlation to obtain fused data includes: obtaining a feature sequence corresponding to each signal in the partial discharge signal under each predetermined fault; dividing the feature sequence corresponding to each signal in the partial discharge signal into a main feature sequence and a secondary feature sequence according to the correlation between the partial discharge signal of each signal type and the predetermined fault and a preset correlation threshold; wherein, if the correlation of a signal with the predetermined fault is less than the correlation threshold, the feature sequence corresponding to the signal is a secondary feature sequence; if the correlation of a signal with the predetermined fault is greater than or equal to the correlation threshold, the feature sequence corresponding to the signal is a main feature sequence; and fusing the main feature sequence and the secondary feature sequence to obtain fused data under each predetermined fault.

[0012] Furthermore, obtaining a characteristic sequence corresponding to each signal in the partial discharge signal includes: collecting each signal in the partial discharge signal at a predetermined frequency to obtain a data sequence corresponding to each signal; when the correlation of the data sequence corresponding to a collected signal is lower than a preset condition, using data enhancement to expand the data sequence to obtain an expanded data sequence; and obtaining a corresponding characteristic sequence based on the data sequence and the expanded data sequence.

[0013] Furthermore, fusing the main feature sequence and the secondary feature sequence to obtain the fused data includes: fusing the main feature sequence and the secondary feature sequence according to channel numbers to form a fused data matrix.

[0014] Furthermore, the historical fusion data and the corresponding predetermined faults are used as training data to train a fault detection model, including: using the main feature sequence and the secondary feature sequence to train the first fault detection model to obtain a trained first fault detection model; using the secondary feature sequence to train the second fault detection model to obtain a trained second fault detection model; wherein, when the main feature sequence is missing in the fusion data corresponding to the partial discharge signal, the second fault detection model is used to output the detection result; when the secondary feature sequence is missing or exists in the fusion data corresponding to the partial discharge signal, the first fault detection model is used.

[0015] Furthermore, after obtaining the fault detection result, the active defense detection method for live transformer main equipment further includes: determining the spatial coordinates of the local discharge source using the ultrasonic signal and the placement position of the sensor; and determining the specific location of the fault based on the spatial coordinates of the local discharge source and the spatial coordinates of the physical structure of the transformer.

[0016] According to another aspect of the embodiment of the present application, a device for actively defending against live detection of substation main equipment is also provided, comprising: a plurality of channels, wherein each channel is used to access the signal of a sensor, wherein the sensor comprises at least two of the following: an ultrasonic sensor, a high-frequency sensor, and an ultra-high-frequency sensor; a processor for executing software, wherein the software is used to execute the above-mentioned method for actively defending against live detection of substation main equipment.

[0017] Compared with the related art, the embodiments of the present application have the following beneficial effects:

[0018] The active defense live detection method and device for substation main equipment in the embodiments of the present application obtain a partial discharge signal from the substation main equipment; wherein the partial discharge signal includes at least two signals; the partial discharge signal is obtained by synchronously collecting at least two of the multiple channels; and the correlation between the partial discharge signal and a historical partial discharge signal is obtained; wherein the correlation is used to indicate the degree of correlation between the partial discharge signal and the historical partial discharge signal and a predetermined fault. The at least two synchronously collected signals in the partial discharge signal participate in the correlation determination, and the historical partial discharge signal is a previously collected partial discharge signal. Each predetermined fault corresponds to its own historical partial discharge signal, and the historical partial discharge signal includes at least one of the following: a partial discharge signal collected before the fault occurs, a partial discharge signal collected during the fault occurs, and a partial discharge signal collected within a predetermined time period after the fault occurs; and a detection result is obtained based on the correlation. This method solves the problem of low fault recognition rate in the prior art method of using single data analysis, improves the fault recognition rate, provides strong support for active defense of substation main equipment, and further extends the service life of the equipment.

[0019] The beneficial effects of the above-mentioned second aspect embodiment refer to the beneficial effects of the first aspect embodiment, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the embodiments or related technical descriptions. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0021] Figure 1 This is a diagram of an application scenario of a transformer active protection live detection device using multi-channel data fusion provided in an embodiment of the present application;

[0022] Figure 2 This is a flow chart of a method for active protection of live detection of substation main equipment provided by an embodiment of the present application;

[0023] Figure 3 This is a schematic diagram of a process for obtaining historical fusion data provided by an embodiment of the present application;

[0024] Figure 4 This is a flowchart of actually obtaining a fault detection result provided by an embodiment of the present application;

[0025] Figure 5 This is a structural diagram of a transformer active defense live detection device with multi-channel data fusion provided in one embodiment of the present application. DETAILED DESCRIPTION

[0026] In the following description, specific details such as specific system structures and technologies are provided for illustration rather than limitation to facilitate a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid obstructing the description of the present application with unnecessary details.

[0027] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements and / or components, but does not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or collections thereof.

[0028] It will also be understood that the term "and / or" used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0029] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0030] References to "an embodiment," "one embodiment," or "some embodiments" in this specification mean that one or more embodiments of the present application include a particular feature, structure, or characteristic described in conjunction with that embodiment. Thus, phrases such as "in one embodiment," "in some embodiments," "in other embodiments," and "in other embodiments" appearing in various places in this specification do not necessarily refer to the same embodiment, but rather mean "one or more but not all embodiments," unless otherwise specifically emphasized. The terms "including," "comprising," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0031] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings and specific implementation methods. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0032] It should be noted that the transformer is used as an example in the following embodiments, but the following embodiments can also be applied to other main substation equipment such as reactors and GIS. The application method is similar to that of transformers and will not be repeated here. The live detection device in the following embodiments uses multiple channels, each channel is used to access the signal of a sensor, and the sensor includes at least two of the following: ultrasonic sensor, high frequency sensor, ultra-high frequency sensor, radio frequency sensor, voice print sensor, vibration sensor, bushing end screen sensor, partial discharge input unit, ground wave sensor, ground current sensor; in the following embodiments, ultrasonic sensor, high frequency sensor, and ultra-high frequency sensor are used as examples for illustration, and in the following embodiments, at least two signals are used for analysis, which is a data fusion approach. Therefore, in the following embodiments, the live detection device is also referred to as a transformer active defense live detection device with multi-channel data fusion. The so-called active defense here is because the power workers can actively use the detection device for detection, thereby achieving a defensive effect.

[0033] Figure 1 The figure shows an application scenario diagram of the transformer active defense live detection device using multi-channel data fusion according to an embodiment of the present application. Figure 1As shown in this application scenario, the transformer's partial discharge signals are first collected using multiple channels on a live detection instrument deployed with a multi-channel data fusion active transformer protection live detection device. The partial discharge signals are then processed using the multi-channel data fusion active transformer protection live detection device. The processed results can be used to determine the transformer's fault. It is important to note that the fault can be a future fault or a current fault. Faults typically present anomalies before they occur. If these anomalies are present in the collected partial discharge information, even if a fault has not yet occurred, it can be used to determine the future occurrence of a fault or the probability of a future fault. Furthermore, information such as the time of a future fault can be predicted. After the fault is detected, a predetermined fault indicating the transformer fault can be generated based on the collected partial discharge signals.

[0034] Among them, the live detection instrument deployed with a transformer active defense live detection device with multi-channel data fusion can be designed as a multi-functional detection instrument or a portable detection instrument.

[0035] Figure 2 This is a flow chart of a method for active protection of live detection of main substation equipment provided by an embodiment of the present application, with reference to Figure 2 The active defense live detection method for the main substation equipment includes:

[0036] Step 101: Acquire a partial discharge signal of a main power transformation device (eg, a transformer).

[0037] It should be noted that the sensors include at least two of the following: an ultrasonic sensor, a high-frequency sensor, an ultra-high-frequency sensor, a radio frequency sensor, a soundprint sensor, a vibration sensor, a bushing end screen sensor, a partial discharge input unit, a ground wave sensor, and a ground current sensor; the partial discharge signal includes at least two signals, and the partial discharge signal is acquired through synchronous acquisition of at least two of the multiple channels. For example, the at least two signals include at least two of the following: an ultrasonic signal, a high-frequency signal, an ultra-high-frequency signal, a radio frequency partial discharge signal, a soundprint signal, a vibration signal, a bushing end screen signal, a pulse current method partial discharge signal, a transient ground wave signal, and a core addition ground current signal. In the following embodiments, at least two of the ultrasonic signal, the high-frequency signal, and the ultra-high-frequency signal are used as examples.

[0038] Exemplarily, the multiple channels may include a first preset number of ultrasound channels and a second preset number of high frequency channels. For example, the first preset number is 5, the second preset number is 3, and there are 8 channels in total. Alternatively, it may include at least one ultrasound channel and at least one ultra-high frequency channel; or it may include at least one high frequency channel and at least one ultra-high frequency channel. The number of these channels can be set according to actual needs. Figure 1 The illustrated device has eight channels, including at least one ultrasonic channel, at least one high-frequency channel, and at least one ultra-high-frequency channel. The collected partial discharge signals may also include ultrasonic, high-frequency, and ultra-high-frequency signals. In the following embodiments, partial discharge signals including ultrasonic and high-frequency signals are used as an example. The fusion processing methods for partial discharge signals including other signals are similar and are not further described here.

[0039] The live detection device may also have a network function, which allows the live detection device to be connected to a server. The server may store historically collected partial discharge signals, or the historical partial discharge signals may be stored on the live detection device. It should be noted that in the following embodiments, training of the artificial intelligence model can be performed on the server. The artificial intelligence model can be stored on the server or on the live detection device. When stored on the server, the live detection device needs to connect to the server via the network to exchange data with the service. If the model is stored in the live detection device, there is no need to connect to the server via the network. In specific implementation, flexible selection can be made according to different needs, and will not be detailed here.

[0040] These partial discharge signals can be divided into multiple signal types based on the signals they include. For example, a first signal type includes ultrasonic signals and high-frequency signals, a second signal type includes ultrasonic signals and ultra-high-frequency signals, a third signal type includes high-frequency signals and ultra-high-frequency signals, and a fourth signal type includes ultrasonic signals, high-frequency signals, and ultra-high-frequency signals. Different signal types correspond to different types of historical data.

[0041] This historical data is pre-collected. These partial discharge signals can be collected at the time of a fault, before the fault occurs, or even after the fault occurs. The collected partial discharge signals can be a time series, for example, collected at 5-millisecond intervals. Each acquisition simultaneously collects signals from each of the multiple channels. This is referred to as synchronization in the following embodiments.

[0042] After acquisition, partial discharge signals corresponding to different predetermined faults (the predetermined faults may include information such as fault type, fault condition, fault characteristics, fault development level, and fault severity, with fault type being used as an example for explanation below) can be obtained. It should be noted that these partial discharge signals need to be processed after acquisition. In the following embodiments, signals that have not been processed after acquisition are referred to as initial partial discharge signals. Because these faults have already occurred and are equivalent to history at the current time, they are also referred to as historical partial discharge signals. The processing of initial partial discharge signals is described below.

[0043] An initial partial discharge signal of each preset fault of the transformer is obtained; data cleaning, data standardization and feature extraction operations are performed on the initial historical partial discharge signal to obtain a processed partial discharge signal.

[0044] Data cleaning includes at least one of the following: removing noise, outliers, and duplicate data from the collected data to ensure data accuracy and reliability. Data standardization involves standardizing or normalizing different types of data to eliminate differences in dimensions and numerical ranges, facilitating subsequent data fusion. Feature extraction involves extracting characteristic indicators related to transformer faults, such as waveform characteristics and spectral features, from the initial historical partial discharge signals.

[0045] Typically, each channel has a designated monitoring point. High-frequency channel monitoring is suitable for capturing discharge activity within the transformer, such as faults at the bushing end shield, transformer core, and clamps. Partial discharge events also generate ultrasonic signals, which can propagate through air or transformer oil. Ultrasonic sensors can be mounted on the transformer casing to capture these ultrasonic signals. Ultrasonic monitoring is useful for detecting discharge locations because the rapid attenuation of ultrasonic signals means the signal source is typically close to the sensor.

[0046] However, if fault detection relies solely on data from a single sensor channel in the fault detection device for each fault or each designated monitoring point, detection accuracy is low and the system is susceptible to interference. For example, while high-frequency channel monitoring is suitable for capturing discharge activity within a transformer, if the high-frequency current sensor is interfered with or damaged, it will not be able to generate an effective high-frequency signal or will not be able to generate a high-frequency signal at all, and the fault at the monitoring point corresponding to the high-frequency channel may be missed.

[0047] Although other ultrasonic signals are not very sensitive to faults at the monitoring points corresponding to the high-frequency channels, they can also change due to the influence of faults. For example, the performance of partial discharge caused by cracks can change. This subtle change can be used to assist in or replace the identification of the interfered or damaged channels, avoiding the failure to monitor the occurrence of faults in a timely manner during the long period of sensor replacement and preventing missed fault detection.

[0048] It should be noted that the processing of the above-mentioned historical partial discharge signals can be performed by a server or by an energized detection device. After processing, the historical partial discharge signals corresponding to each fault can be obtained. For example, fault A corresponds to a set of historical partial discharge signals, and fault B corresponds to a set of historical partial discharge signals. To distinguish between fault A and fault B, fault A and fault B are referred to as faults of different fault types (i.e., different predetermined faults) in the following embodiments. Faults have different fault types, and partial discharge signals also have different signal types. For example, the stored historical partial discharge signals may include partial discharge signals of different fault types corresponding to a first signal type, partial discharge signals of different fault types corresponding to a second signal type, partial discharge signals of different fault types corresponding to a third signal type, and partial discharge signals of different fault types corresponding to a fourth signal type.

[0049] Step 102: Obtain the correlation between the fused data and historical fused data.

[0050] In this step, correlations with historical partial discharge signals can be determined based on partial discharge signals of different signal types. As an optional embodiment, the signal type of the partial discharge signal can be obtained, for example, one of the following: a first signal type including ultrasonic signals and high-frequency signals, a second signal type including ultrasonic signals and ultra-high-frequency signals, a third signal type including high-frequency signals and ultra-high-frequency signals, or a fourth signal type including ultrasonic signals, high-frequency signals, and ultra-high-frequency signals. Correlations of each signal in the partial discharge signal can be obtained based on the signal type. This optional embodiment allows correlations with corresponding historical partial discharge signals to be found based on different signal types.

[0051] The correlation between the partial discharge signal of each signal type and the historical partial discharge signals corresponding to different predetermined faults under that signal type is determined. As an optional embodiment, since both the partial discharge signal and the historical partial discharge signal include at least two signals (i.e., at least two of the ultrasonic signal, the high-frequency signal, and the ultra-high-frequency signal), at least two of the partial discharge signals can be fused (which can be understood as being processed into a single integrated data set through an algorithm). In the following embodiments, the fused data is referred to as fused data. To determine the correlation, at least two of the collected partial discharge signals can be fused to obtain collected fused data (to distinguish it from historical fused data, in the following embodiments, the fused data may also be referred to as real-time fused data. It should be noted that "real-time" is merely a relative concept to "historical" and has no other special meaning).

[0052] It should be noted that due to the different types of local discharge signals, in an optional embodiment, the signal type of the local discharge signal can be obtained, for example, the signal type is one of the following: a first signal type including an ultrasonic signal and a high-frequency signal, a second signal type including an ultrasonic signal and an ultra-high-frequency signal, a third signal type including a high-frequency signal and an ultra-high-frequency signal, and a fourth signal type including an ultrasonic signal, a high-frequency signal and an ultra-high-frequency signal; and then the correlation of each signal in the local discharge signal is obtained according to the signal type.

[0053] Partial discharge signals consist of at least two signals, such as ultrasonic signals and high-frequency signals. These signals are collected at a specific frequency. Therefore, the collected signals can also be referred to as sequences or data sequences. The collected sequences can be processed to determine correlation. Sequences with correlation determination can be referred to as feature sequences. Similarly, historical partial discharge signals are also sequences. Sequences can be correlated, and this correlation can be referred to as a correlation coefficient. For example, the Pearson correlation coefficient can be used to represent correlation.

[0054] The Pearson correlation coefficient is a statistic used to measure the degree of linear correlation between two continuous variables. Its value ranges from -1 to 1, where 1 indicates a perfect positive correlation, -1 indicates a perfect negative correlation, and 0 indicates no correlation.

[0055] The following uses the example of a partial discharge signal including an ultrasonic signal and a high-frequency signal as an example. In step 102, the correlations between the ultrasonic signal and the high-frequency signal in the partial discharge signal and each predetermined fault can be calculated by respectively calculating the Pearson correlation coefficient between the sample sequences of the ultrasonic signal and the high-frequency signal and each fault feature. It should be noted that since historical partial discharge signals are fault-related signals, the sequences corresponding to the historical partial discharge signals can also be referred to as fault features, fault sequences, or fault data. Using the Pearson correlation coefficient to calculate the correlations is merely one implementation. For example, the correlations can also be calculated using the method described in step 202 below for determining the correlations between each type of historical partial discharge signal and the fault features corresponding to the predetermined faults.

[0056] In an optional embodiment, considering that each partial discharge signal and each historical partial discharge signal includes multiple signals, the sequences corresponding to each signal can also be fused based on correlation. For example, obtaining the correlation between the partial discharge signal and the historical partial discharge signal includes: fusing multiple signals in the historical partial discharge signal based on the correlation to obtain historical fused data; fusing multiple signals in the partial discharge signal based on the correlation to obtain fused data, wherein the partial discharge signal and the multiple signals in the historical partial discharge signal are fused in the same manner; and obtaining a detection result based on the correlation includes: obtaining the detection result based on the fused data and the historical fused data.

[0057] The fused data may correspond to a predetermined fault. For example, based on the correlation, the real-time fused data corresponding to each preset predetermined fault is determined, including:

[0058] The characteristic sequence of each type of partial discharge signal is determined in descending order of correlation.

[0059] Exemplarily, determining the characteristic sequence of each type of partial discharge signal in order of correlation from high to low includes: determining the data sequence of each type of partial discharge signal in order of correlation from high to low; under each preset predetermined fault, using data enhancement technology to expand the data sequence with low correlation to obtain an expanded data sequence; and determining the characteristic sequence of each type of partial discharge signal under each preset predetermined fault based on the expanded data sequence.

[0060] In an optional embodiment, the real-time fused data can also be divided into a main feature sequence and a secondary feature sequence. It should be noted that the real-time fused data (i.e., fused data) is processed in the same way as the historical fused data. Therefore, the historical fused data can also be divided into a main feature sequence and a secondary feature sequence. For example, for the historical fused data, based on each preset predetermined fault, the feature sequences of various types of local discharge signals are fused to obtain the fused data under each preset predetermined fault.

[0061] Exemplarily, based on each preset predetermined fault, the characteristic sequences of various types of partial discharge signals are fused to obtain real-time fused data under each preset predetermined fault, including:

[0062] Under each preset predetermined fault: according to the correlation between each type of partial discharge signal and the preset predetermined fault and the preset correlation threshold, the feature sequence is divided into a main feature sequence and a secondary feature sequence; the main feature sequence is set with a main label, and the secondary feature sequence is set with a secondary label.

[0063] If the correlation between a certain type of partial discharge signal and the predetermined fault is less than a preset correlation threshold, the characteristic sequence corresponding to the certain type of partial discharge signal is a secondary characteristic sequence. If the correlation between a certain type of partial discharge signal and the predetermined fault is greater than or equal to the preset correlation threshold, the characteristic sequence corresponding to the certain type of partial discharge signal is a primary characteristic sequence. For example, for a discharge fault in a liquid medium, the correlation between the high-frequency signal and the predetermined fault is 0.9, and the correlation threshold for the characteristic sequence corresponding to the high-frequency signal and the predetermined fault is 0.3; the correlation between the ultrasonic signal and the predetermined fault is 0.1, and the correlation threshold for the characteristic sequence corresponding to the ultrasonic signal and the predetermined fault is 0.4. Therefore, the characteristic sequence corresponding to the high-frequency signal is the primary characteristic sequence, while the characteristic sequence corresponding to the ultrasonic signal is the secondary characteristic sequence.

[0064] The main feature sequence and the secondary feature sequence are fused to obtain real-time fused data under each preset predetermined fault; the real-time fused data carries the main label and the secondary label.

[0065] Exemplarily, the main feature sequence and the secondary feature sequence may be fused according to the channel number to form a fused data matrix.

[0066] In this embodiment, both the ultrasonic signal and the high-frequency signal are fault-related data, differing only in their correlation. Data augmentation techniques are then used to augment low-correlation data sequences to improve the representation of these faults in real-time fusion data. For example, in the example above, the correlation between the ultrasonic signal and a discharge fault in a liquid medium was originally 0.02. After data augmentation of the characteristic components that cause subtle changes in the ultrasonic signal, the correlation between the ultrasonic signal and the discharge fault in the liquid medium was increased to 0.1. If a highly correlated data set cannot be obtained due to sensor damage, other low-correlation data sequences can still be used for identification.

[0067] This embodiment collects historical partial discharge signals before each preset fault occurs in the transformer, analyzes its subtle signs, and uses parameters such as the amplitude, phase, frequency, growth rate, duration, acoustic-electrical linkage, and positioning of the historical partial discharge signals to form historical fusion data, establish an accurate prediction model, and gradually understand the characteristic patterns of transformer faults.

[0068] See also Figure 3 , obtaining historical fusion data may include the following steps:

[0069] Step 201: Acquire a historical partial discharge signal of each preset fault of the transformer.

[0070] For example, the historical partial discharge signal may be obtained from a database of partial discharge signals stored during historical transformer detection.

[0071] Step 202 : Determine the correlation between each type of signal in the historical partial discharge signal of each preset predetermined fault and the corresponding preset predetermined fault, and determine the historical fusion data corresponding to each preset predetermined fault based on the correlation.

[0072] The historical fusion data includes the main feature sequence and the secondary feature sequence.

[0073] Exemplarily, in step 202, determining the correlation between various types of historical partial discharge signals and preset predetermined faults includes:

[0074] For a type of preset predetermined fault, the accuracy of the ultrasonic signal and the high-frequency signal in judging the preset predetermined fault can be used to calculate the correlation between the ultrasonic signal and the high-frequency signal in the corresponding historical partial discharge signal and the preset predetermined fault. The higher the accuracy, the stronger the correlation.

[0075] For a class of preset predetermined faults, the correlation between the ultrasonic signal and the high-frequency signal in the corresponding historical partial discharge signal and the preset predetermined fault can also be calculated by respectively calculating the Pearson correlation coefficient between the sample sequences of the ultrasonic signal and the high-frequency signal and the corresponding fault characteristics.

[0076] For a type of preset predetermined fault, the correlation between the ultrasonic signal and the high-frequency signal and the preset predetermined fault can be pre-set directly according to the existing known sensitivity.

[0077] Next, in step 202, based on the correlation, the historical fusion data corresponding to each preset predetermined fault is determined, including:

[0078] The characteristic sequences of various types of partial discharge signals corresponding to each preset predetermined fault are determined in descending order of relevance.

[0079] Exemplarily, determining the characteristic sequences of various types of historical partial discharge signals corresponding to each preset predetermined fault (e.g., preset fault type) in order of correlation from high to low includes: determining the data sequences of various types of historical partial discharge signals corresponding to each preset predetermined fault in order of correlation from high to low; under each preset predetermined fault, using data enhancement technology to perform data expansion on the data sequence with low correlation to obtain an expanded data sequence; and determining the characteristic sequences of various types of partial discharge signals corresponding to each preset predetermined fault based on the expanded data sequence.

[0080] The characteristic sequences of various historical partial discharge signals corresponding to each preset predetermined fault are fused to obtain historical fusion data under each preset predetermined fault.

[0081] Exemplarily, the characteristic sequences of various historical partial discharge signals corresponding to each preset predetermined fault are fused to obtain historical fusion data under each preset predetermined fault, including:

[0082] Under each preset predetermined fault: according to the correlation between various types of historical partial discharge signals and the preset predetermined fault and the preset correlation threshold, the feature sequence is divided into a main feature sequence and a secondary feature sequence; the main feature sequence is set with a main label, and the secondary feature sequence is set with a secondary label.

[0083] The main feature sequence and the secondary feature sequence are fused to obtain historical fusion data under each preset predetermined fault; the historical fusion data carries the main label and the secondary label.

[0084] Exemplarily, the main feature sequence and the secondary feature sequence may be fused according to the channel number to form a historical fusion data matrix.

[0085] Step 103: Obtain a detection result according to the correlation.

[0086] There are many ways to obtain detection results. For example, the historical fusion data and the corresponding predetermined faults can be used as training data to train a fault detection model; the fusion data can be input into the fault detection model to obtain the detection results. The above-mentioned fault model can be pre-trained and therefore can also be called a preset fault detection model. The preset fault detection model can be implemented using methods such as support vector machines, random forests, and deep learning models. The model parameters can be optimized through methods such as cross-validation to improve the accuracy of fault detection.

[0087] In the above optional embodiment, a primary feature sequence and a secondary feature sequence are used. Therefore, as an optional manner, the above fault detection model includes a first fault detection model and a second fault detection model, which will be explained below.

[0088] The first fault detection model is trained using the main feature sequence and the secondary feature sequence of the feature sequence of the historical partial discharge signal to obtain a trained first fault detection model.

[0089] The second fault detection model is trained using the secondary feature sequence of the feature sequence of the historical partial discharge signal to obtain a trained second fault detection model. The secondary feature sequence can be stored in a separate file before data fusion, and when training the second fault detection model, the secondary feature sequence and the preset predetermined fault are input into the second fault detection model. Alternatively, the secondary feature sequence can be extracted from the historical fused data using the secondary label, and then the secondary feature sequence and the preset predetermined fault are input into the second fault detection model.

[0090] The real-time fusion data obtained each time will be stored in the database as historical fusion data, and the preset fault detection model will be automatically updated based on the updated historical fusion data.

[0091] This embodiment trains fault detection models in two situations and performs targeted training on data with different characteristics to improve the accuracy of fault identification.

[0092] In this step, the fault detection results are obtained based on the real-time fusion data and the preset fault detection model.

[0093] Exemplarily, the process for obtaining fused data based on partial discharge signals is consistent with the aforementioned process for obtaining fused data based on historical partial discharge signals. This step differs from step 202, after the fused data is obtained, in that it is necessary to identify whether the real-time fused data includes a primary signature sequence or a secondary signature sequence. For example, if a sensor that obtains data from the primary signature sequence is interfered with or damaged, the primary signature sequence will not be present in the real-time fused data. Therefore, after obtaining the real-time fused data, identification of the real-time fused data is necessary.

[0094] Therefore, see Figure 4 :

[0095] Step 1031 : Based on the real-time fusion data, a target fault detection model is determined from the trained first fault detection model and the trained second fault detection model.

[0096] Exemplarily, the main label and sub-label of the real-time fusion data are obtained. When the main label and sub-label of the real-time fusion data exist at the same time, it means that the channels with high correlation and low correlation corresponding to the current predetermined fault are working normally. In this case, the trained first fault detection model is selected as the target fault detection model.

[0097] When the main label of the real-time fusion data does not exist and only the secondary label exists, it means that the channel with high correlation corresponding to the current predetermined fault is not working properly, while the channel with low correlation is working normally. In this case, the trained second fault detection model is selected as the target fault detection model.

[0098] In addition, if the secondary label of the real-time fusion data does not exist and only the primary label exists, since the primary feature sequence is highly correlated with the current fault, the absence of the secondary feature sequence will not have a significant impact on the recognition result, and the first fault detection model is still selected as the target fault detection model.

[0099] Step 1032: Perform detection based on the target fault detection model to obtain a fault detection result.

[0100] The target fault detection model outputs a fault detection result, which includes a predetermined fault.

[0101] For example, after determining the predetermined fault, the approximate location range of the fault can be obtained. There may be multiple locations of the bushing end shield, core, clamp, and winding grounding lead. If the precise fault location is required, further analysis is required. Then, after obtaining the fault detection results, the active defense live detection method for the main substation equipment also includes:

[0102] Based on the fault detection results, the spatial coordinates of the partial discharge source are determined using ultrasonic signals and the placement of the sensor. The specific location of the fault is determined based on the spatial coordinates of the partial discharge source and the spatial coordinates of the transformer's physical structure.

[0103] Using ultrasonic signals and sensor placement, the spatial coordinates of the partial discharge source are determined, including:

[0104] The moment when the local discharge source emits an ultrasonic signal is selected as the timing starting point. Ultrasonic signals of at least three channels with different reception durations are received. The three ultrasonic sensors are placed in fixed and non-collinear positions. The time it takes for the ultrasonic signal of each channel to be received from its respective receiving point is measured. Combined with the ultrasonic propagation velocity, the spatial position of the point where the measured object is located is solved.

[0105] For example, when receiving three-channel ultrasonic signals, because the receiving point of the ultrasonic signals is fixed, their coordinates in the three-dimensional space coordinate system are known: (x1, y1, z1), (x2, y2, z2), and (x3, y3, z3). The propagation velocity k of the ultrasonic wave is also known. If the coordinates of the partial discharge source are set to (x, y, z), the coordinates of the partial discharge source can be calculated using the following equation.

[0106] (x1-x) 2 +(y1-y) 2 +(z1-z) 2 =k 2 t1 2

[0107] (x2-x) 2 +(y2-y) 2 +(z2-z) 2 =k 2 t2 2

[0108] (x3-x) 2 +(y3-y) 2 +(z3-z) 2 =k 2 t3 2

[0109] Among them, t1, t2 and t3 are the time taken for the ultrasonic signals of the three channels to propagate from the local discharge source to the respective receiving points.

[0110] Once the coordinates of the PD source are determined, they are matched with the spatial coordinates of the transformer's physical structure to pinpoint the fault location. The spatial coordinates of the transformer's physical structure, such as the location of the bushing end shield, core, clamps, and winding ground leads, can be pre-stored in the system.

[0111] Exemplarily, after determining the predetermined fault or the specific location of the fault, the active defense live detection method for the main substation equipment further includes:

[0112] An early warning is issued based on the fault detection results and the specific location of the fault, and the fault detection results and the specific location of the fault are visualized.

[0113] In summary, the active defense live detection method for transformer main equipment provided by the embodiment of the present application can more comprehensively capture the various fault characteristics that may occur inside the transformer by comprehensively analyzing different types of partial discharge signals. In combination with a plurality of preset predetermined faults (for example, fault types), the correlation between each type of partial discharge signal and these predetermined faults is analyzed separately, which can achieve comprehensive coverage of multiple potential faults of the transformer. It is not limited to detecting a single type of fault, and can simultaneously identify multiple different types of faults, thereby enhancing the comprehensiveness of fault detection. Through data fusion technology, the partial discharge signals of different channels are integrated into fused data containing the main feature sequence and the secondary feature sequence, which effectively reduces the redundancy of the data, improves the efficiency of subsequent model processing, avoids missed detection, improves the accuracy and reliability of fault detection, provides strong support for the active defense of the transformer, and further extends the service life of the equipment.

[0114] Data enhancement technology is used to expand data sequences with low correlation to improve the representation ability of such faults in fused data, and to perform auxiliary or alternative identification of interfered or damaged channels, so as to avoid the failure to monitor the occurrence of faults in a timely manner during the long period of sensor replacement and prevent missed fault detection.

[0115] See also Figure 5 The embodiment of the present application provides a transformer active defense live detection device with multi-channel data fusion, which executes the above-mentioned active defense live detection method for substation main equipment. The software in the device may include: an acquisition module 301, a data analysis and fusion module 302 and a detection result output module 303.

[0116] An acquisition module 301 is configured to acquire a partial discharge signal from a main substation device; wherein the partial discharge signal includes at least two signals; and the partial discharge signal is acquired by synchronously acquiring at least two of the multiple channels;

[0117] A data analysis and fusion module 302 is configured to obtain a correlation between the partial discharge signal and historical partial discharge signals; wherein the correlation indicates the degree of correlation between the partial discharge signal and the historical partial discharge signal and a predetermined fault; at least two synchronously collected signals in the partial discharge signal participate in determining the correlation; the historical partial discharge signal is a previously collected partial discharge signal; wherein each predetermined fault corresponds to its own historical partial discharge signal, and the historical partial discharge signal includes at least one of the following: a partial discharge signal collected before the fault occurs, a partial discharge signal collected during the fault occurs, and a partial discharge signal collected within a predetermined time period after the fault occurs;

[0118] The detection result output module 303 is used to obtain the detection result according to the correlation.

[0119] The software is used to execute various implementations of the above method embodiments, which are described below.

[0120] Exemplarily, the data analysis and fusion module 302 includes a correlation analysis module and a data fusion module. The data fusion module is used to:

[0121] Determine the characteristic sequence of each type of partial discharge signal in descending order of correlation;

[0122] Based on each preset scheduled fault, the characteristic sequences of various types of partial discharge signals are fused to obtain real-time fused data under each preset scheduled fault.

[0123] Exemplarily, the characteristic sequences of various types of partial discharge signals are determined in descending order of correlation, including:

[0124] Determine the data sequence of each type of partial discharge signal in descending order of correlation;

[0125] Under each preset fault, data enhancement technology is used to expand the data sequence with low correlation to obtain the expanded data sequence;

[0126] Based on the expanded data sequence, a characteristic sequence of each type of partial discharge signal under each preset predetermined fault is determined.

[0127] Exemplarily, based on each preset predetermined fault, the characteristic sequences of various types of partial discharge signals are fused to obtain real-time fused data under each preset predetermined fault, including:

[0128] For each preset scheduled fault:

[0129] According to the correlation between each type of partial discharge signal and the preset predetermined fault and the preset correlation threshold, the feature sequence is divided into a main feature sequence and a secondary feature sequence; the main feature sequence is provided with a main label, and the secondary feature sequence is provided with a secondary label;

[0130] The main feature sequence and the secondary feature sequence are fused to obtain real-time fused data under each preset predetermined fault; the real-time fused data carries the main label and the secondary label.

[0131] Exemplarily, the transformer active defense live detection device with multi-channel data fusion also includes a model training module.

[0132] Before obtaining fault detection results based on real-time fusion data and a preset fault detection model, the model training module is specifically used to:

[0133] Acquire a historical partial discharge signal of each preset fault of the transformer;

[0134] Determining the correlation between each type of signal in the historical partial discharge signal of each preset predetermined fault and the corresponding preset predetermined fault, and determining historical fusion data corresponding to each preset predetermined fault based on the correlation; the historical fusion data includes a main feature sequence and a secondary feature sequence;

[0135] The fault detection model is trained using historical fusion data to obtain a trained preset fault detection model.

[0136] Exemplarily, the preset fault detection model includes a first fault detection model and a second fault detection model;

[0137] Using historical fusion data, a preset fault detection model is trained to obtain a trained preset fault detection model, including:

[0138] Using the primary feature sequence and the secondary feature sequence, a first fault detection model is trained to obtain a trained first fault detection model;

[0139] The secondary feature sequence is used to train a second fault detection model to obtain a trained second fault detection model.

[0140] Exemplarily, after obtaining the fault detection result, the transformer active defense live detection device with multi-channel data fusion further includes a fault location module.

[0141] The fault location module is used to:

[0142] Based on the fault detection results, the ultrasonic signal and the sensor placement are used to determine the spatial coordinates of the PD source. The specific location of the fault is determined based on the spatial coordinates of the PD source and the spatial coordinates of the transformer's physical structure.

[0143] Exemplarily, the transformer active defense live detection device with multi-channel data fusion also includes a visualization module.

[0144] The visualization module is specifically used for:

[0145] An early warning is issued based on the fault detection results and the specific location of the fault, and the fault detection results and the specific location of the fault are visualized.

[0146] Exemplarily, in the signal acquisition module 301 , the multiple channels include a first preset number of ultrasound channels and a second preset number of high-frequency channels.

[0147] For example, the first preset number is 5, the second preset number is 3, and there are 8 channels in total.

[0148] Of course, there is no limit to the number and type of channels, and other related channels and the number of channels for ultra-high frequency, temperature, and gas content can also be expanded on the basis of the present invention.

[0149] The beneficial effects of the embodiment of the transformer active protection live detection device with multi-channel data fusion mentioned above can be found in the beneficial effects of the embodiment of the active protection live detection method for main substation equipment, which will not be repeated here.

[0150] It should be noted that while the detailed description above mentions several units / modules or sub-units / modules of the multi-channel data fusion active transformer protection live detection device, this division is merely exemplary and not mandatory. In practice, depending on the implementation of this application, the features and functions of two or more units / modules described above may be embodied in a single unit / module. Conversely, the features and functions of a single unit / module described above may be further divided and embodied by multiple units / modules.

[0151] Furthermore, although the operations of the method of the present application are described in a particular order in the accompanying drawings, this does not require or imply that the operations must be performed in this particular order, or that all illustrated operations must be performed to achieve the desired results. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step, and / or one step may be decomposed into multiple steps.

[0152] The present application also provides a computer-readable storage medium, which stores computer execution instructions. When the processor executes the computer execution instructions, the active defense live detection method for the substation main equipment provided in the above embodiment of the present application is implemented.

[0153] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0154] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present application, and should all be included in the scope of protection of the present application.

Claims

1. A method for active defense detection of live power of main substation equipment, characterized in that: The main substation equipment includes at least one of the following: a transformer, a reactor, and a gas-insulated fully enclosed switchgear. The method is applied to a live detection device including multiple channels, wherein each channel is used to receive a signal from a sensor. The method includes: Acquire a partial discharge signal of a main substation equipment; wherein the partial discharge signal includes at least two signals; the partial discharge signal is acquired by synchronously acquiring at least two of the multiple channels; Obtaining a correlation between the partial discharge signal and a historical partial discharge signal; wherein the correlation is used to indicate a degree of correlation between the partial discharge signal and the historical partial discharge signal and a predetermined fault; at least two signals synchronously collected from the partial discharge signal participate in determining the correlation; the historical partial discharge signal is a previously collected partial discharge signal; wherein each predetermined fault corresponds to its own historical partial discharge signal; and the historical partial discharge signal includes at least one of the following: a partial discharge signal collected before the fault occurs, a partial discharge signal collected during the fault occurs, and a partial discharge signal collected within a predetermined time period after the fault occurs; Wherein, obtaining the correlation between the partial discharge signal and the historical partial discharge signal includes: fusing multiple signals in the historical partial discharge signal according to the correlation to obtain historical fused data; fusing multiple signals in the partial discharge signal according to the correlation to obtain fused data, wherein the partial discharge signal and the multiple signals in the historical partial discharge signal are fused in the same manner; obtaining the detection result according to the correlation includes: obtaining the detection result according to the fused data and the historical fused data; Wherein, obtaining the detection result according to the fused data and the historical fused data includes: using the historical fused data and the corresponding predetermined fault as training data to train a fault detection model; inputting the fused data into the fault detection model to obtain the detection result; The step of fusing multiple signals in the partial discharge signal according to correlation to obtain fused data includes: obtaining a feature sequence corresponding to each signal in the partial discharge signal under each predetermined fault; dividing the feature sequence corresponding to each signal in the partial discharge signal into a primary feature sequence and a secondary feature sequence according to the correlation between the partial discharge signal of each signal type and the predetermined fault and a preset correlation threshold; if the correlation between a signal and the predetermined fault is less than the correlation threshold, the feature sequence corresponding to the signal is a secondary feature sequence; if the correlation between a signal and the predetermined fault is greater than or equal to the correlation threshold, the feature sequence corresponding to the signal is a primary feature sequence; and fusing the primary feature sequence and the secondary feature sequence to obtain fused data under each predetermined fault. The method of using the historical fusion data and the corresponding predetermined fault as training data to obtain a fault detection model includes: using the primary feature sequence and the secondary feature sequence to train a first fault detection model to obtain a trained first fault detection model; and using the secondary feature sequence to train a second fault detection model to obtain a trained second fault detection model; wherein, when the primary feature sequence is missing in the fusion data corresponding to the partial discharge signal, the second fault detection model is used to output a detection result; and when the secondary feature sequence is missing or exists in the fusion data corresponding to the partial discharge signal, the first fault detection model is used; A detection result is obtained according to the correlation.

2. The method according to claim 1, wherein The at least two signals are at least two of the following signals: ultrasonic signal, high frequency signal, ultra-high frequency signal, radio frequency partial discharge signal, sound print signal, vibration signal, bushing end screen signal, pulse current method partial discharge signal, transient ground wave signal, core clamp grounding current signal.

3. The method according to claim 2, wherein Obtaining the correlation between the partial discharge signal and historical partial discharge signals includes: Acquiring a signal type of the partial discharge signal; The correlation of each signal in the partial discharge signal is obtained according to the signal type; wherein the sensor includes at least two of the following: an ultrasonic sensor, a high-frequency sensor, an ultra-high-frequency sensor, a radio frequency sensor, a soundprint sensor, a vibration sensor, a casing end screen sensor, a partial discharge input unit, a ground wave sensor, and a ground current sensor.

4. The method according to claim 1, wherein Acquiring a characteristic sequence corresponding to each signal in the partial discharge signal includes: collecting each signal in the partial discharge signal at a predetermined frequency to obtain a data sequence corresponding to each signal; When the correlation of a data sequence corresponding to a collected signal is lower than a preset condition, data enhancement is used to expand the data sequence to obtain an expanded data sequence; A corresponding feature sequence is obtained according to the data sequence and the expanded data sequence.

5. The method according to claim 1, wherein The main feature sequence and the secondary feature sequence are fused to obtain the fused data, including: The main feature sequence and the secondary feature sequence are fused according to the channel number to form a fused data matrix.

6. An active protection live detection device for main substation equipment, characterized in that: include: Multiple channels, each channel is used to access the signal of a sensor; A processor for executing software, wherein the software is used to execute the active defense live detection method for substation main equipment according to any one of claims 1 to 5.

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