A method, apparatus, equipment and storage medium for monitoring DC bias in transformers.

Abstract

This invention provides a method, apparatus, device, and storage medium for monitoring DC bias current in transformers, relating to the field of DC bias current technology. The method includes: acquiring neutral point bias current signals from multiple substations based on sensors; identifying a target substation that meets preset conditions; acquiring the target neutral point bias current signal of the target substation; acquiring initial noise signals collected at multiple preset points in the target substation; obtaining a target noise signal; and obtaining the DC bias current detection result of the target substation based on the target neutral point bias current signal and the target noise signal. This invention, by installing sensors on the grounding plate of the substation, can monitor the DC bias current of the transformer in real time and assess the current state of the transformer by considering both transformer temperature and the magnitude of the bias current. This ensures that when a fault occurs in any aspect of the transformer, real-time early warning information is output, guaranteeing the safe operation of the substation.

Description

Technical Field

[0001] This invention relates to the field of transformer DC bias technology, and more specifically, to transformer DC bias monitoring methods, devices, equipment, and readable storage media. Background Technology

[0002] With the continuous development of urban construction, the scale of urban rail transit in my country is growing larger and larger. However, during the operation of subway trains, current leaks from the rails into the ground, forming what is known as stray current in the DC power supply system of rail transit. These stray DC currents may flow into the transformer windings through the grounding grid of urban power grid substations, causing DC bias current in the transformer, leading to increased vibration and noise in the main transformer, and even serious transformer accidents. Current technology typically uses Hall effect sensors to monitor the DC bias current of transformers in real time. However, Hall effect sensors occupy a large amount of space, and some substations cannot install them, making real-time monitoring of the bias current impossible. Summary of the Invention

[0003] The purpose of this invention is to provide a method, apparatus, device, and storage medium for monitoring DC bias in transformers, in order to improve the aforementioned problems. To achieve the above objective, the technical solution adopted by this invention is as follows:

[0004] Firstly, this application provides a method for monitoring DC bias magnetism in a transformer, including:

[0005] Based on the acquisition of neutral point bias current signals from multiple substations using sensors;

[0006] The neutral point bias current signal of each substation is compared with the preset conditions to determine the target substation that meets the preset conditions.

[0007] Acquire the target neutral point bias current signal of the target substation;

[0008] Acquire the initial noise signals collected at multiple preset locations at the target substation;

[0009] Multiple initial noise signals are processed to obtain the target noise signal;

[0010] Based on the target neutral point bias current signal and the target noise signal, the DC bias detection result of the target substation is obtained.

[0011] Secondly, this application also provides a transformer DC bias monitoring device, comprising:

[0012] The acquisition unit is used to acquire neutral point bias current signals from multiple substations based on sensors.

[0013] The first comparison unit is used to compare the neutral point bias current signal of each substation with preset conditions to determine the target substation that meets the preset conditions.

[0014] The first acquisition unit is used to acquire the target neutral point bias current signal of the target substation.

[0015] The second acquisition unit is used to acquire the initial noise signals collected by the target substation at multiple preset locations;

[0016] The first processing unit is used to process multiple initial noise signals to obtain the target noise signal;

[0017] The unit is used to obtain the DC bias detection result of the target substation based on the target neutral point bias current signal and the target noise signal.

[0018] Thirdly, this application also provides a transformer DC bias monitoring device, comprising:

[0019] Memory, used to store computer programs;

[0020] A processor is used to implement the steps of the transformer DC bias monitoring method when executing the computer program.

[0021] Fourthly, this application also provides a readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the above-described transformer-based DC bias monitoring method.

[0022] The beneficial effects of this invention are as follows:

[0023] This invention, by installing sensors on the grounding plate of a substation, can monitor the DC bias current of the transformer in real time and assess the current state of the transformer by measuring both the transformer temperature and the magnitude of the bias current. This ensures that when a fault occurs in any aspect of the transformer, early warning information is output in real time, thus guaranteeing the safe operation of the substation.

[0024] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing embodiments of the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description, claims, and drawings. Attached Figure Description

[0025] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without paying any creative work.

[0026] Figure 1 This is a schematic diagram of the transformer DC bias monitoring method described in this embodiment of the invention;

[0027] Figure 2 This is a schematic diagram of the transformer DC bias monitoring device described in an embodiment of the present invention;

[0028] Figure 3 This is a schematic diagram of the sensor arrangement described in an embodiment of the present invention;

[0029] Figure 4 This is a schematic diagram of the transformer DC bias monitoring device described in an embodiment of the present invention.

[0030] Marked in the image:

[0031] 100. Acquisition Unit; 200. First Comparison Unit; 300. First Acquisition Unit; 400. Second Acquisition Unit; 500. First Processing Unit; 600. Obtaining Unit; 700. Sixth Acquisition Unit; 800. Seventh Acquisition Unit; 900. Analysis Unit; 210. Second Processing Unit; 220. Second Comparison Unit; 230. Third Processing Unit; 240. First Calculation Unit; 250. Drawing Unit; 260. First Determination Unit; 270. Third Comparison Unit; 280. Second Determination Unit; 510. Fourth Processing Unit; 520. Input Unit; 530. Evaluation Unit; 540. Fifth Processing Unit; 610. Third Acquisition Unit; 620. Input Unit; 630. Fourth Acquisition Unit; 640. Fifth Acquisition Unit; 650. Third Determination Unit; 651. Second Calculation Unit; 652. Third Calculation Unit; 653. First Output Unit; 654. Second Output Unit; 655. Third Output Unit;

[0032] 80. Transformer DC bias monitoring equipment; 81. Processor; 82. Memory; 83. Multimedia components; 84. I / O interface; 85. Communication components. Detailed Implementation

[0033] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0034] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this invention, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0035] Example 1:

[0036] This embodiment provides a method for monitoring DC bias magnetic field of a transformer.

[0037] See Figure 1 The figure shows that the method includes steps S100, S200, S300, S400, S500 and S600.

[0038] Step S100. Acquire neutral point bias current signals from multiple substations based on sensors;

[0039] Specifically, in substations where there is no space to install Hall effect sensors, sensors are typically installed on the transformer grounding flat steel to measure the neutral point bias current, such as... Figure 2 The diagram shows the sensor arrangement. A constant current source and a voltage sampling device are installed on the grounding flat steel. After installation, calibration is required so that the calibrated sensor can accurately acquire the neutral point bias current signal of the transformer.

[0040] Step S200. Compare the neutral point bias current signal of each substation with the preset conditions to determine the target substation that meets the preset conditions;

[0041] Specifically, under normal circumstances, slight fluctuations in the neutral point bias current will not have any impact. However, when the neutral point bias current is too large, it will cause the main transformer to vibrate and increase noise, leading to serious transformer accidents. Therefore, it is necessary to identify substations with large fluctuations in bias current from multiple substations for real-time monitoring.

[0042] Specifically, step S200 includes:

[0043] Step S210. Filter all neutral point bias current signals to obtain multiple first current signals;

[0044] Step S220. Compare all first current signals with a first set threshold to obtain a first comparison result. The first comparison result is the first substation corresponding to the first current signal that is greater than the first set threshold.

[0045] Step S230. Normalize the first current signals corresponding to all first substations to obtain multiple second current signals;

[0046] Step S240. Calculate the autocorrelation function of each second current signal to obtain multiple correlation function calculation results;

[0047] Step S250. Based on the calculation results of the correlation function, plot the graph of the correlation function;

[0048] Step S260. Determine the corresponding target peak value based on the correlation function graph;

[0049] Step S270. Compare the target peak corresponding to each correlation function image with the second set threshold to obtain a second comparison result. The second comparison result is the third current signal corresponding to the target peak that is greater than the second set threshold.

[0050] Step S280. Identify the substation corresponding to the third current signal as the target substation;

[0051] Specifically, firstly, the magnitudes of the neutral point bias currents of multiple substations are compared, and substations with larger neutral point bias currents are selected. Secondly, the periodicity of the neutral point bias currents corresponding to the substations identified above is determined, and target substations that meet the periodic change requirements are identified as monitoring objects.

[0052] Step S300. Obtain the target neutral point bias current signal of the target substation;

[0053] Specifically, the target neutral point bias current signal of the transformer in the target substation is acquired through sensors.

[0054] Step S400. Acquire the initial noise signals collected at multiple preset points in the target substation;

[0055] Specifically, at a distance of one meter from the main transformer of the target substation, and on a contour line that is half the height of the main transformer, 12 equally spaced points are selected for noise monitoring for 10-30 seconds to obtain multiple initial noise signals.

[0056] Step S500. Process multiple initial noise signals to obtain the target noise signal;

[0057] Specifically, it is necessary to integrate multiple noises and remove extraneous sounds to obtain the optimal target noise signal.

[0058] Specifically, step S500 includes:

[0059] Step S510. Preprocess all initial noise signals to obtain multiple first noise signals;

[0060] Specifically, all initial noise is input into a bandpass filter for filtering. The desired frequency range can be selected to ensure that the transformer noise is preserved relatively completely.

[0061] Step S520. Input each first noise signal into a preset blind source separation model to obtain multiple second noise signals;

[0062] Step S530. Perform a comprehensive evaluation of the second noise signal to obtain the evaluation result;

[0063] Step S540. Integrate and process the multiple second noise signals corresponding to the evaluation results that meet the preset evaluation conditions to obtain the target noise signal;

[0064] Specifically, multiple filtered noise signals are input into a preset blind source separation model for separation, resulting in multiple second noise signals. It is necessary to evaluate whether the currently separated second noise signals are actual transformer noise signals. Multiple second noise signals that meet the evaluation conditions are integrated to obtain the target noise signal.

[0065] Step S600. Based on the target neutral point bias current signal and the target noise signal, obtain the DC bias detection result of the target substation;

[0066] Specifically, taking into account the characteristics of stray currents and transformer noise during rail transit operation, transformer noise and neutral point bias current are used as early warning criteria for the influence of rail AC DC bias on transformers, thereby accurately determining whether the target substation is currently in a safe operating state.

[0067] Specifically, step S600 includes:

[0068] Step S610. Obtain the target noise signal of the target substation sampled multiple times within the target time period, and calculate the average value as the average noise;

[0069] Step S620. Use the target noise signal sampled initially as the initial noise;

[0070] Step S630. Obtain the target neutral point bias current signal of the target substation sampled multiple times within the target time period, and calculate the average value as the average current;

[0071] Step S640. Obtain the rated current of the target substation;

[0072] Step S650. Based on the average noise, initial noise, average current and rated current, determine the DC bias detection results of the target substation.

[0073] Specifically, step S650 includes:

[0074] Step S651. Calculate the difference between the average noise and the initial noise, and use it as the first result;

[0075] Step S652. Calculate the ratio of average current to rated current as the second result;

[0076] Step S653. When the first result is greater than the third set threshold and the second result is greater than the fourth set threshold, output a level one alarm message;

[0077] Step S654. When the first result is greater than the third set threshold and the second result is less than the fourth set threshold, output a level 2 alarm message;

[0078] Step S655. When the first result is less than the third set threshold, the average noise is greater than the fifth set threshold, and the second result is greater than the sixth set threshold, output the second-level alarm information;

[0079] Specifically, using the initial noise level of the transformer during installation and operation as the baseline, a level two alarm is output when the average noise level of the transformer exceeds the baseline by 10 dB within one minute and the cumulative average value of the neutral point bias current exceeds 0.45% of the rated current within one minute; a level two alarm is output when the cumulative average value of the neutral point bias current exceeds 0.7% of the rated current within one minute, but the average noise level of the transformer within one minute does not exceed the baseline by 15 dB; and a level one alarm is output when the average noise level of the transformer exceeds the baseline by 15 dB within one minute and the cumulative average value of the neutral point bias current exceeds 0.7% of the rated current within one minute.

[0080] Specifically, the magnitude of the transformer's bias current is affected by the operation of the subway within a certain distance, and the correlation between subway operation and the change in bias current can be further analyzed.

[0081] Step S700. Obtain the target subway station whose straight-line distance from the target substation is less than the seventh set threshold;

[0082] Step S800. Obtain the subway arrival and departure times at the target subway station;

[0083] Step S900. Based on the arrival time, departure time, and target neutral point bias current signal, analyze the correlation between the magnitude of the target substation bias current and the subway running time.

[0084] Specifically, based on the above data analysis, it was found that the neutral point bias current of the substation is correlated with the subway line within a certain distance. Specifically, the periodicity of the bias current is basically consistent with the periodicity of the train entering and leaving the nearby subway platform.

[0085] Example 2:

[0086] like Figure 3 As shown, this embodiment provides a transformer DC bias monitoring device, the device including:

[0087] The acquisition unit 100 is used to acquire neutral point bias current signals from multiple substations based on sensors.

[0088] The first comparison unit 200 is used to compare the neutral point bias current signal of each substation with preset conditions to determine the target substation that meets the preset conditions.

[0089] The first acquisition unit 300 is used to acquire the target neutral point bias current signal of the target substation.

[0090] The second acquisition unit 400 is used to acquire the initial noise signals collected at multiple preset points of the target substation.

[0091] The first processing unit 500 is used to process multiple initial noise signals to obtain a target noise signal;

[0092] Unit 600 is obtained to obtain the DC bias detection result of the target substation based on the target neutral point bias current signal and the target noise signal.

[0093] In one specific embodiment disclosed in this application, the first comparison unit 200 includes:

[0094] The second processing unit 210 is used to filter all neutral point bias current signals to obtain multiple first current signals.

[0095] The second comparison unit 220 is used to compare all first current signals with a first set threshold to obtain a first comparison result. The first comparison result is the first substation corresponding to the first current signal that is greater than the first set threshold.

[0096] The third processing unit 230 is used to normalize the first current signals corresponding to all the first substations to obtain multiple second current signals.

[0097] The first calculation unit 240 is used to calculate the autocorrelation function of each second current signal respectively, and obtain multiple correlation function calculation results;

[0098] The plotting unit 250 is used to plot the graph of the correlation function based on the calculation results of the correlation function;

[0099] The first determining unit 260 is used to determine the corresponding target peak value based on the correlation function graph;

[0100] The third comparison unit 270 is used to compare the target peak corresponding to each correlation function image with the second set threshold to obtain a second comparison result. The second comparison result is the third current signal corresponding to the target peak that is greater than the second set threshold.

[0101] The second determining unit 280 is used to determine the substation corresponding to the third current signal as the target substation.

[0102] In one specific embodiment disclosed in this application, the first processing unit 500 includes:

[0103] The fourth processing unit 510 is used to preprocess all the initial noise signals to obtain multiple first noise signals;

[0104] The input unit 520 is used to input each first noise signal into a preset blind source separation model to obtain multiple second noise signals;

[0105] Evaluation unit 530 is used to comprehensively evaluate the second noise signal and obtain the evaluation result;

[0106] The fifth processing unit 540 is used to integrate and process multiple second noise signals corresponding to the evaluation results that meet the preset evaluation conditions to obtain the target noise signal.

[0107] In one specific embodiment disclosed in this application, the obtaining unit 600 includes:

[0108] The third acquisition unit 610 is used to acquire the target noise signal of the target substation sampled multiple times within the target time period, and calculate the average value as the average noise.

[0109] Unit 620 is used to take the target noise signal sampled initially as the initial noise;

[0110] The fourth acquisition unit 630 is used to acquire the target neutral point bias current signal of the target substation sampled multiple times within the target time period, and calculate the average value as the average current.

[0111] The fifth acquisition unit 640 is used to acquire the rated current of the target substation;

[0112] The third determining unit 650 is used to determine the DC bias detection results of the target substation based on the average noise, initial noise, average current and rated current.

[0113] In one specific embodiment disclosed in this application, the third determining unit 650 includes:

[0114] The second calculation unit 651 is used to calculate the difference between the average noise and the initial noise, as the first result;

[0115] The third calculation unit 652 is used to calculate the ratio of the average current to the rated current as the second result;

[0116] The first output unit 653 is used to output a first-level alarm message when the first result is greater than the third set threshold and the second result is greater than the fourth set threshold.

[0117] The second output unit 654 is used to output a secondary alarm message when the first result is greater than the third set threshold and the second result is less than the fourth set threshold.

[0118] The third output unit 655 is used to output a secondary alarm message when the first result is less than the third set threshold, the average noise is greater than the fifth set threshold, and the second result is greater than the sixth set threshold.

[0119] In one specific embodiment disclosed in this application, the apparatus further includes:

[0120] The sixth acquisition unit 700 is used to acquire target subway stations whose straight-line distance from the target substation is less than the seventh set threshold.

[0121] The seventh acquisition unit 800 is used to acquire the subway entry time and exit time of the target subway station;

[0122] Analysis unit 900 is used to analyze the correlation between the magnitude of the target substation's bias current and the subway's running time based on the arrival time, departure time, and the target neutral point bias current signal.

[0123] It should be noted that the specific manner in which each module performs its operation in the apparatus described in the above embodiments has been described in detail in the embodiments of the method, and will not be elaborated here.

[0124] Example 3:

[0125] Corresponding to the above method embodiments, this embodiment also provides a transformer DC bias monitoring device. The transformer DC bias monitoring device described below and the transformer DC bias monitoring method described above can be referred to each other.

[0126] Figure 4 This is a block diagram illustrating a transformer DC bias monitoring device 80 according to an exemplary embodiment. Figure 4 As shown, the transformer DC bias monitoring device 80 may include: a processor 81 and a memory 82. The transformer DC bias monitoring device 80 may also include one or more of the following: a multimedia component 83, an I / O interface 84, and a communication component 85.

[0127] The processor 81 controls the overall operation of the transformer DC bias monitoring device 80 to complete all or part of the steps in the aforementioned transformer DC bias monitoring method. The memory 82 stores various types of data to support the operation of the transformer DC bias monitoring device 80. This data may include, for example, instructions for any application or method used to operate on the transformer DC bias monitoring device 80, as well as application-related data such as contact data, sent and received messages, images, audio, video, etc. The memory 82 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The multimedia component 83 may include a screen and an audio component. The screen may be, for example, a touchscreen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signals may be further stored in the memory 82 or transmitted via the communication component 85. The audio component also includes at least one speaker for outputting audio signals. I / O interface 84 provides an interface between processor 81 and other interface modules, such as keyboards, mice, and buttons. These buttons can be virtual or physical. Communication component 85 is used for wired or wireless communication between the transformer DC bias monitoring device 80 and other devices. Wireless communication includes Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, or 4G, or a combination thereof. Therefore, the corresponding communication component 85 may include a Wi-Fi module, a Bluetooth module, or an NFC module.

[0128] In an exemplary embodiment, the transformer DC bias monitoring device 80 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the aforementioned transformer DC bias monitoring method.

[0129] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided, which, when executed by a processor, implement the steps of the transformer DC bias monitoring method described above. For example, the computer-readable storage medium may be the memory 82 including the program instructions described above, which may be executed by the processor 81 of the transformer DC bias monitoring device 80 to complete the transformer DC bias monitoring method described above.

[0130] Example 4:

[0131] Corresponding to the above method embodiments, this embodiment also provides a readable storage medium. The readable storage medium described below can be referred to in conjunction with the transformer DC bias monitoring method described above.

[0132] A readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the transformer DC bias monitoring method described in the above method embodiments.

[0133] Specifically, the readable storage medium can be a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, or any other readable storage medium capable of storing program code.

[0134] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

[0135] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for monitoring DC bias magnetism in a transformer, characterized in that, include: Based on the acquisition of neutral point bias current signals from multiple substations using sensors; The neutral point bias current signal of each substation is compared with the preset conditions to determine the target substation that meets the preset conditions. Acquire the target neutral point bias current signal of the target substation; Acquire the initial noise signals collected at multiple preset locations at the target substation; Multiple initial noise signals are processed to obtain the target noise signal; Based on the target neutral point bias current signal and the target noise signal, the DC bias detection result of the target substation is obtained; The DC bias detection result of the target substation is obtained based on the target neutral point bias current signal and the target noise signal, including: Acquire the target noise signal of the target substation from multiple consecutive samples within the target time period, and calculate the average value as the average noise. The target noise signal sampled initially is used as the initial noise; Acquire the target neutral point bias current signal of the target substation from multiple consecutive samples within the target time period, and calculate the average value as the average current. Obtain the rated current of the target substation; The DC bias detection results of the target substation are determined based on the average noise, initial noise, average current, and rated current. Identify target subway stations whose straight-line distance from the target substation is less than the seventh preset threshold; Obtain the subway arrival and departure times at the target subway station; Based on the arrival time, departure time, and target neutral point bias current signal, the correlation between the magnitude of the target substation bias current and the subway running time is analyzed.

2. The transformer DC bias monitoring method according to claim 1, characterized in that... The neutral point bias current signal of each substation is compared with preset conditions to determine the target substations that meet the preset conditions, including: All neutral point bias current signals are filtered to obtain multiple first current signals; All first current signals are compared with a first set threshold to obtain a first comparison result. The first comparison result is the first substation corresponding to the first current signal that is greater than the first set threshold. The first current signals corresponding to all the first substations are normalized to obtain multiple second current signals. The autocorrelation function of each second current signal is calculated separately, and multiple correlation function calculation results are obtained; Based on the calculation results of the correlation function, plot the correlation function graph; The corresponding target peak value is determined based on the graph of the correlation function; The target peak corresponding to each correlation function image is compared with a second set threshold to obtain a second comparison result. The second comparison result is the third current signal corresponding to the target peak that is greater than the second set threshold. The substation corresponding to the third current signal is identified as the target substation.

3. The transformer DC bias monitoring method according to claim 1, characterized in that... Multiple initial noise signals are processed to obtain the target noise signal, including: All initial noise signals are preprocessed to obtain multiple first noise signals; Each first noise signal is input into a preset blind source separation model to obtain multiple second noise signals; The second noise signal is comprehensively evaluated to obtain the evaluation result; The multiple second noise signals corresponding to the evaluation results that meet the preset evaluation conditions are integrated and processed to obtain the target noise signal.

4. The transformer DC bias monitoring method according to claim 1, characterized in that... The step of obtaining the DC bias detection result of the target substation based on the target neutral point bias current signal and the target noise signal includes: The target noise signal is obtained by multiple consecutive samplings of the target substation within the target time period, and the average value is calculated as the average noise. The target noise signal sampled initially is used as the initial noise; Acquire the target neutral point bias current signal of the target substation from multiple consecutive samples within the target time period, and calculate the average value as the average current; Obtain the rated current of the target substation; Based on the average noise, the initial noise, the average current, and the rated current, the DC bias detection result of the target substation is determined.

5. A transformer DC bias monitoring device, characterized in that, include: The acquisition unit is used to acquire neutral point bias current signals from multiple substations based on sensors. The first comparison unit is used to compare the neutral point bias current signal of each substation with preset conditions to determine the target substation that meets the preset conditions. The first acquisition unit is used to acquire the target neutral point bias current signal of the target substation. The second acquisition unit is used to acquire the initial noise signals collected by the target substation at multiple preset locations; The first processing unit is used to process multiple initial noise signals to obtain the target noise signal; The unit is used to obtain the DC bias detection result of the target substation based on the target neutral point bias current signal and the target noise signal; The obtained units include: The third acquisition unit is used to acquire the target noise signal of the target substation sampled multiple times within the target time period, and calculate the average value as the average noise. As a unit, it is used to take the target noise signal sampled in the first sampling as the initial noise; The fourth acquisition unit is used to acquire the target neutral point bias current signal of the target substation sampled multiple times within the target time period, and calculate the average value as the average current. The fifth acquisition unit is used to acquire the rated current of the target substation; The third determining unit is used to determine the DC bias detection results of the target substation based on the average noise, initial noise, average current and rated current. The sixth acquisition unit is used to acquire target subway stations whose straight-line distance from the target substation is less than the seventh set threshold. The seventh acquisition unit is used to acquire the subway arrival and departure times of the target subway station; The analysis unit is used to analyze the correlation between the magnitude of the target substation's bias current and the subway's running time based on the arrival time, departure time, and the target neutral point bias current signal.

6. The transformer DC bias monitoring device according to claim 5, characterized in that, The first comparison unit includes: The second processing unit is used to filter all neutral point bias current signals to obtain multiple first current signals. The second comparison unit is used to compare all first current signals with a first set threshold to obtain a first comparison result, wherein the first comparison result is the first substation corresponding to the first current signal that is greater than the first set threshold. The third processing unit is used to normalize the first current signals corresponding to all the first substations to obtain multiple second current signals. The first calculation unit is used to calculate the autocorrelation function of each second current signal, and obtain multiple autocorrelation function calculation results; A drawing unit is used to draw the graph of the relevant function based on the calculation results of the relevant function; The first determining unit is used to determine the corresponding target peak value based on the correlation function image; The third comparison unit is used to compare the target peak corresponding to each correlation function image with the second set threshold to obtain a second comparison result. The second comparison result is the third current signal corresponding to the target peak that is greater than the second set threshold. The second determining unit is used to determine the substation corresponding to the third current signal as the target substation.

7. The transformer DC bias monitoring device according to claim 5, characterized in that, The first processing unit includes: The fourth processing unit is used to preprocess all the initial noise signals to obtain multiple first noise signals; The input unit is used to input each first noise signal into a preset blind source separation model to obtain multiple second noise signals; An evaluation unit is used to comprehensively evaluate the second noise signal and obtain an evaluation result; The fifth processing unit is used to integrate and process multiple second noise signals corresponding to the evaluation results that meet the preset evaluation conditions to obtain the target noise signal.

8. The transformer DC bias monitoring device according to claim 5, characterized in that, The obtaining unit includes: The third acquisition unit is used to acquire the target noise signal sampled multiple times for the target substation within the target time period, and calculate the average value as the average noise. As a unit, it is used to take the target noise signal sampled initially as initial noise; The fourth acquisition unit is used to acquire the target neutral point bias current signal of the target substation sampled multiple times within the target time period, and calculate the average value as the average current. The fifth acquisition unit is used to acquire the rated current of the target substation; The third determining unit is used to determine the DC bias detection result of the target substation based on the average noise, the initial noise, the average current, and the rated current.

9. A transformer DC bias monitoring device, characterized in that, include: Memory, used to store computer programs; A processor, configured to implement the steps of the transformer DC bias monitoring method as described in any one of claims 1 to 4 when executing the computer program.

10. A readable storage medium, characterized in that, The readable storage medium stores a computer program that, when executed by a processor, implements the steps of the transformer DC bias monitoring method as described in any one of claims 1 to 4.

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