Partial discharge detection method, system, device and equipment and storage medium

Abstract

The invention relates to a partial discharge detection method, system, device and equipment and a storage medium. The method comprises the following steps: acquiring a first change curve of the fluorescence intensity of a quantum sensor in target electrical equipment, determining a first microwave frequency corresponding to a trough in the first change curve, and acquiring a second change curve of the fluorescence intensity of the quantum sensor in a plurality of acquisition periods, determining a first microwave frequency corresponding to a wave trough in each first change curve, determining a second microwave frequency corresponding to a wave trough in each second change curve, determining electric field intensity of the target electrical equipment in each acquisition period according to the first microwave frequency and each second microwave frequency, and determining whether the target electrical equipment has a partial discharge condition according to each electric field intensity and a preset partial discharge identification model. And when the target electrical equipment has partial discharge, outputting the partial discharge type. By adopting the method, the accuracy of partial discharge detection and partial discharge type determination can be improved.

Description

Technical Field

[0001] The present application relates to the technical field of power equipment detection, and in particular to a partial discharge detection method, system, device, equipment and storage medium. Background Art

[0002] The power distribution room is an important power supply node of the distribution network, and the environmental factors in the power distribution room may cause the electrical equipment to be in an abnormal working state. The insulation aging and damage caused by the high temperature, humidity and other environmental problems in the power distribution room can easily lead to insulation breakdown, partial discharge and short circuit, which pose a serious threat to the safe operation of electrical equipment and personal safety. A reliable partial discharge detection system in the power distribution room is an effective means to reduce the risk of distribution network operation.

[0003] In the prior art, non-electrical detection methods such as ultrasonic detection, optical detection and gas detection are usually used. These methods have the problem of inaccuracy in detecting whether there is partial discharge and determining the type of partial discharge. Summary of the invention

[0004] Based on this, it is necessary to provide a partial discharge detection method, system, device, equipment and storage medium that can improve detection accuracy and accurately determine the type of partial discharge in response to the above technical problems.

[0005] In a first aspect, the present application provides a partial discharge detection method, which is used in a partial discharge detection device in a partial discharge detection system, comprising:

[0006] A first change curve of the fluorescence intensity of the quantum sensor in the target electrical device is obtained, and a first microwave frequency corresponding to a trough in the first change curve is determined, wherein the first change curve is a fluorescence intensity change curve of the quantum sensor in a first preset microwave scanning frequency interval when the target electrical device is not in operation; a second change curve of the fluorescence intensity of the quantum sensor in a plurality of acquisition cycles is obtained, and a second microwave frequency corresponding to a trough in each second change curve is determined, wherein the second change curve is a fluorescence intensity change curve of the quantum sensor in a second preset microwave scanning frequency interval when the target electrical device is in operation; an electric field strength of the target electrical device in each acquisition cycle is determined according to the first microwave frequency and each second microwave frequency; and whether the target electrical device has a partial discharge according to each electric field strength and a preset partial discharge identification model, and when the target electrical device has a partial discharge, a partial discharge type is output.

[0007] In one embodiment, determining whether a target electrical device has a partial discharge condition based on each electric field strength and a preset partial discharge identification model, and outputting a partial discharge type when the target electrical device has a partial discharge condition includes:

[0008] A curve of electric field strength changing with time is determined according to each electric field strength and the corresponding acquisition period; the curve is input into a preset partial discharge identification model to determine the partial discharge amount, the number of partial discharges and the type of partial discharge; if the partial discharge amount is greater than a preset partial discharge amount threshold, and / or the number of partial discharges is greater than a preset number threshold, it is determined that partial discharge exists in the target electrical equipment, and the type of partial discharge is output.

[0009] In one embodiment, the curve is input into a preset partial discharge identification model to determine the partial discharge amount, the number of partial discharges and the type of partial discharge, including:

[0010] The curve is subjected to feature extraction processing to obtain a plurality of first feature data; the plurality of first feature data is subjected to dimensionality reduction processing to obtain a plurality of second feature data; the plurality of second feature data is input into a preset partial discharge recognition model to determine the partial discharge amount, the number of partial discharges and the type of partial discharge.

[0011] In one embodiment, the local discharge types include internal discharge, tip discharge, surface discharge and suspended discharge.

[0012] In one embodiment, determining the electric field strength of the target electrical device in each acquisition cycle according to the first microwave frequency and each second microwave frequency includes:

[0013] A preset constant coefficient is obtained, where the constant coefficient is related to the performance of the quantum sensor; a plurality of microwave frequency offsets are determined based on the frequency difference between the first microwave frequency and each second microwave frequency; and the electric field strength of the target electrical equipment in each acquisition period is determined based on the ratio of each microwave frequency offset to the preset constant coefficient.

[0014] In a second aspect, the present application also provides a partial discharge detection system, including a quantum sensor, a data transmission unit and a partial discharge detection device;

[0015] The quantum sensor includes an NV color center and a microwave antenna. The microwave antenna is located below the NV color center and is insulated from the NV color center.

[0016] A data transmission unit, used to connect with the quantum sensor, to obtain multiple fluorescence intensities of the quantum sensor in a preset microwave scanning frequency interval, and to send each fluorescence intensity to the partial discharge detection device;

[0017] A partial discharge detection device is used to obtain a first change curve of the fluorescence intensity of a quantum sensor in a target electrical device, and determine a first microwave frequency corresponding to a trough in the first change curve, wherein the first change curve is a fluorescence intensity change curve of the quantum sensor in a first preset microwave scanning frequency interval when the target electrical device is not in operation; obtain a second change curve of the fluorescence intensity of the quantum sensor in multiple acquisition cycles, and determine a second microwave frequency corresponding to a trough in each second change curve, wherein the second change curve is a fluorescence intensity change curve of the quantum sensor in a second preset microwave scanning frequency interval when the target electrical device is in operation; determine an electric field strength of the target electrical device in each acquisition cycle according to the first microwave frequency and each second microwave frequency; determine whether the target electrical device has a partial discharge according to each electric field strength and a preset partial discharge identification model, and output a partial discharge type when the target electrical device has a partial discharge.

[0018] In a third aspect, the present application further provides a partial discharge detection device, which is used for a partial discharge detection device in a partial discharge detection system, comprising:

[0019] A first acquisition module is used to acquire a first change curve of the fluorescence intensity of the quantum sensor in the target electrical device, and determine a first microwave frequency corresponding to a trough in the first change curve, wherein the first change curve is a fluorescence intensity change curve of the quantum sensor in a first preset microwave scanning frequency interval when the target electrical device is not in operation;

[0020] A second acquisition module is used to acquire a second variation curve of the fluorescence intensity of the quantum sensor within a plurality of acquisition cycles, and determine a second microwave frequency corresponding to a trough in each second variation curve, wherein the second variation curve is a fluorescence intensity variation curve of the quantum sensor in a second preset microwave scanning frequency interval when the target electrical device is in operation;

[0021] A calculation module, used to determine the electric field strength of the target electrical equipment in each acquisition cycle according to the first microwave frequency and each second microwave frequency;

[0022] The determination module is used to determine whether the target electrical equipment has partial discharge according to each electric field strength and a preset partial discharge identification model, and output the partial discharge type when the target electrical equipment has partial discharge.

[0023] In one of the embodiments, the determination module is specifically used to determine a curve of electric field strength changing with time based on each electric field strength and the corresponding acquisition period; input the curve into a preset partial discharge identification model to determine the partial discharge amount, the number of partial discharges and the type of partial discharge; if the partial discharge amount is greater than a preset partial discharge amount threshold, and / or the number of partial discharges is greater than a preset number threshold, it is determined that partial discharge exists in the target electrical equipment, and the type of partial discharge is output.

[0024] In one of the embodiments, the determination module is specifically used to perform feature extraction processing on the curve to obtain multiple first feature data; perform dimensionality reduction processing on the multiple first feature data to obtain multiple second feature data; input the multiple second feature data into a preset partial discharge recognition model to determine the partial discharge amount, the number of partial discharges and the partial discharge type.

[0025] In one embodiment, the local discharge types include internal discharge, tip discharge, surface discharge and suspended discharge.

[0026] In one of the embodiments, the calculation module is specifically used to obtain a preset constant coefficient, which is related to the performance of the quantum sensor; determine multiple microwave frequency offsets based on the frequency difference between the first microwave frequency and each second microwave frequency; and determine the electric field strength of the target electrical equipment in each acquisition period based on the ratio of each microwave frequency offset to the preset constant coefficient.

[0027] In a fourth aspect, the present application further provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements any of the methods described in the first aspect when executing the computer program.

[0028] In a fifth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements any of the methods described in the first aspect above.

[0029] In a sixth aspect, the present application further provides a computer program product, including a computer program, which, when executed by a processor, implements any of the methods described in the first aspect above.

[0030] The above-mentioned partial discharge detection method, system, device, equipment and storage medium obtain a first change curve of the fluorescence intensity of the quantum sensor in the target electrical equipment, and determine the first microwave frequency corresponding to the trough in the first change curve, the first change curve is the fluorescence intensity change curve of the quantum sensor in the first preset microwave scanning frequency interval when the target electrical equipment is not in operation; obtain a second change curve of the fluorescence intensity of the quantum sensor in multiple acquisition cycles, and determine the second microwave frequency corresponding to the trough in each second change curve, the second change curve is the fluorescence intensity change curve of the quantum sensor in the second preset microwave scanning frequency interval when the target electrical equipment is in operation; determine the electric field strength of the target electrical equipment in each acquisition cycle according to the first microwave frequency and each second microwave frequency; determine whether the target electrical equipment has a partial discharge according to each electric field strength and a preset partial discharge identification model, and output the partial discharge type when the target electrical equipment has a partial discharge. In this way, local discharge detection for target electrical equipment is achieved based on quantum sensors and local discharge recognition models. The electric field strength change of the target electrical equipment is determined by obtaining the fluorescence intensity change of the quantum sensor. The quantum sensor can prevent weak electric signals from being affected by medium interference, electromagnetic interference, etc., and can accurately determine the electric field strength change. Then, the local discharge type is determined through the preset local discharge recognition model, which can further improve the accuracy of local discharge detection and local discharge type determination. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments of the present application or related technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0032] FIG1 is a diagram showing an application environment of a partial discharge detection method according to an embodiment;

[0033] FIG2 is a schematic flow chart of a partial discharge detection method in one embodiment;

[0034] FIG3 is a schematic diagram of the energy level structure of the NV color center of a quantum sensor in one embodiment;

[0035] FIG4 is a diagram showing a first change curve and a second change curve of a target electrical device measured by a quantum sensor in one embodiment;

[0036] FIG5 is a flow chart of the steps of determining whether a target electrical device has a partial discharge condition in one embodiment;

[0037] FIG6 is a schematic flow chart of the steps of determining the electric field strength of the target electrical device in each acquisition cycle in one embodiment;

[0038] FIG7 is a schematic flow chart of a partial discharge detection method in another embodiment;

[0039] FIG8 is a schematic diagram of the structure of a quantum sensor in one embodiment;

[0040] FIG9 is a block diagram of a partial discharge detection device in one embodiment;

[0041] FIG. 10 is a diagram showing the internal structure of a partial discharge detection device in one embodiment. DETAILED DESCRIPTION

[0042] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0043] The partial discharge detection method provided in the embodiment of the present application can be applied to the partial discharge detection device in the partial discharge detection system as shown in FIG1 . The partial discharge detection system includes a quantum sensor, a data transmission unit and a partial discharge detection device. The execution subject of the partial discharge detection method provided in the embodiment of the present application can be a partial discharge detection device, which can be implemented by software, hardware or a combination of software and hardware, and can be embedded in or independent of the processor in the partial discharge detection device in the form of hardware, or can be stored in the memory in the partial discharge detection device in the form of software. In the following method embodiments, the execution subject is taken as an example of a partial discharge detection device.

[0044] In an exemplary embodiment, as shown in FIG2 , a partial discharge detection method is provided, which is described by taking the method applied to the partial discharge detection device in FIG1 as an example, and includes the following steps 201 to 204. Among them:

[0045] S201, obtaining a first variation curve of the fluorescence intensity of a quantum sensor in a target electrical device, and determining a first microwave frequency corresponding to a trough in the first variation curve.

[0046] The first variation curve is a fluorescence intensity variation curve of the quantum sensor in a first preset microwave scanning frequency interval when the target electrical device is not in operation.

[0047] Optionally, the target electrical equipment may be electrical equipment in a distribution room such as a converter transformer, a reactor, or a switch cabinet. Taking the switch cabinet as an example, local discharge in the distribution room may be caused by insulation defects in the switch cabinet. During the operation of the switch cabinet, if the breakdown field strength of the weak insulation part is lower than the electric field strength, local discharge will occur.

[0048] Optionally, the quantum sensor is disposed at a specified location in the target electrical device.

[0049] Alternatively, a quantum sensor can be a device that uses quantum mechanical effects to perform high-precision measurements, has high sensitivity and accuracy, and can respond to tiny changes in physical quantities.

[0050] Optionally, the quantum sensor can be a quantum sensor based on diamond NV color centers, a quantum sensor based on silicon color center quantum dots, or a quantum sensor based on other types of solid-state doping systems "artificial atoms", which is not limited in the embodiments of the present application.

[0051] For example, taking a quantum sensor based on a diamond NV color center as an example, two adjacent carbon atoms in the diamond are replaced by a nitrogen atom and a vacancy to form a special structure, called an NV color center, and the connecting axis between the nitrogen atom and the vacancy is called the NV axis. As shown in Figure 3, 3 A 2 and 3 E are the ground state and excited state of the NV color center, respectively, and have triplet structures, namely, m s =0, m s =+1, m s =-1. Continuously irradiating the NV center with a laser (usually at a wavelength of 532nm) can cause it to transition to an excited state, after which the NV center emits red light (usually at a wavelength of 637nm-800nm) and returns to the ground state. There are two pathways for the NV center to decay from the excited state back to the ground state. One of them is through radiation transition (path 301 in Figure 3), directly from the excited state to the ground state. 3 E transitions to the ground state 3 A 2 , this process radiates photons. Another way is to go through the spin state transition process (path shown as 302 in Figure 3), through the temporary steady state ( 1 A1 and 1 E), and finally returns to the ground state. This method is mainly completed by ms=+1 and ms=-1. When there is no external magnetic field or external electric field, m s =+1 and m s =-1 completely overlap, forming a degenerate state m s = ±1. When there is an external magnetic field or electric field, m s =+1 and m s=-1 energy levels separate, and the distance between them is proportional to the magnetic field projection of the NV axis, specifically 2gμ B B (g and μ B are all constants, and B is the magnetic field component on the NV axis), and the fluorescence signal of the NV color center also changes accordingly. Based on this characteristic, the fluorescence intensity change of the NV color center after laser irradiation can be observed by applying microwaves of a specific frequency, which can also be called continuous wave optical detection magnetic resonance (CW-ODMR). The obtained fluorescence intensity change curve can be called ODMR spectrum.

[0052] Optionally, when the target electrical equipment is not in operation, it can be considered that there is no external magnetic field or electric field, and the ODMR spectrum has only one resonance peak. The frequency corresponding to the trough is determined as the first microwave frequency, also called the center frequency. Alternatively, there may be a weak magnetic field or weak electric field, and the energy level is degenerate, and two peaks appear in the ODMR spectrum, corresponding to m and m respectively. s =0 to m s =+1 or m s =-1, and is symmetrical about the center frequency, and the distance between the two peaks is proportional to the magnetic field component on the NV axis.

[0053] Optionally, the fitting curve of the ODRM spectrum can be in the form of a Gaussian function ,in, is the microwave frequency, y is the fluorescence intensity, and the parameters of the Gaussian function can be determined based on the fitting curve of the obtained ODMR spectrum. ,w, ,y0, is the first microwave frequency.

[0054] Optionally, when there is a trough in the first change curve, the first microwave frequency may be a microwave frequency value corresponding to the trough; when there are multiple troughs in the first change curve, the first microwave frequency may be an average value of the microwave frequency values ​​corresponding to the troughs.

[0055] S202, obtaining a second variation curve of the fluorescence intensity of the quantum sensor in a plurality of acquisition cycles, and determining a second microwave frequency corresponding to a trough in each second variation curve.

[0056] The second variation curve is a fluorescence intensity variation curve of the quantum sensor in a second preset microwave scanning frequency interval when the target electrical device is in operation.

[0057] Optionally, when the target device is in operation, that is, when there is an external magnetic field or an external electric field, the energy level structure of the NV color center will shift, causing the shape and position of the ODRM spectrum to change, as shown in Figure 4. The local enlarged view in the lower right corner of Figure 4 includes the first change curve and the second change curve measured by the NV color center quantum sensor before and after the electric field changes. It can be seen from the figure that the position of the ODRM spectrum shifts after the external electric field exists.

[0058] Optionally, when there is a trough in the second change curve, the second microwave frequency may be the microwave frequency value corresponding to the trough; when there are multiple troughs in the second change curve, the second microwave frequency may be the average value of the microwave frequency values ​​corresponding to the troughs.

[0059] Optionally, the second preset microwave scanning frequency interval may be the same as the first preset microwave scanning frequency interval, or may have partial frequency overlap. This is not limited in the embodiment of the present application, and it is sufficient to ensure that the first microwave frequency and the second microwave frequency are determined from the ODRM spectrum obtained in each microwave scanning frequency interval.

[0060] S203: Determine the electric field strength of the target electrical device in each acquisition cycle according to the first microwave frequency and each second microwave frequency.

[0061] Optionally, the electric field strength of the target electrical device in each acquisition cycle can be determined based on the Gaussian function and Stark effect determined in S201, wherein the Stark effect can be a phenomenon in which the energy level and spectrum of atoms or molecules are split under the action of an external electric field.

[0062] Optionally, the electric field intensity in each acquisition period may be determined according to an offset between a first microwave frequency and a second microwave frequency corresponding to the acquisition period.

[0063] S204, determining whether the target electrical equipment has a partial discharge condition according to each electric field strength and a preset partial discharge identification model, and outputting a partial discharge type when the target electrical equipment has a partial discharge condition.

[0064] Optionally, the preset partial discharge identification model may be obtained by training based on a large amount of historical electric field strength data when partial discharge occurs.

[0065] Optionally, multiple groups of electric field strength data and the local discharge type corresponding to each group of data can be used as training samples to train the initial local discharge recognition model to obtain a local discharge recognition model, the input of which is the electric field strength in each acquisition cycle, and the output is whether local discharge exists and the type of local discharge.

[0066] Optionally, the partial discharge types include internal discharge, tip discharge, surface discharge and suspension discharge.

[0067] Optionally, the partial discharge identification model may be implemented based on a neural network, or based on a support vector machine algorithm, or based on a decision tree algorithm. The embodiment of the present application does not limit the implementation method of the partial discharge identification model.

[0068] Optionally, environmental data of the target electrical equipment, such as temperature information, humidity information, voltage and current, etc., can also be obtained. When determining whether partial discharge exists, the environmental data is also used as input to the partial discharge identification model. The partial discharge identification model can use the environmental data as a background to more accurately analyze the strength of each electric field, so as to further improve the accuracy of partial discharge detection and determination of partial discharge type.

[0069] Optionally, when it is determined that the target electrical equipment has a partial discharge, abnormal alarm information may be generated and the type of partial discharge may be output.

[0070] The above-mentioned partial discharge detection method obtains a first change curve of the fluorescence intensity of the quantum sensor in the target electrical equipment, and determines a first microwave frequency corresponding to a trough in the first change curve, the first change curve being a fluorescence intensity change curve of the quantum sensor in a first preset microwave scanning frequency interval when the target electrical equipment is not in operation; obtains a second change curve of the fluorescence intensity of the quantum sensor in multiple acquisition cycles, and determines a second microwave frequency corresponding to a trough in each second change curve, the second change curve being a fluorescence intensity change curve of the quantum sensor in a second preset microwave scanning frequency interval when the target electrical equipment is in operation; determines the electric field strength of the target electrical equipment in each acquisition cycle according to the first microwave frequency and each second microwave frequency; determines whether the target electrical equipment has a partial discharge according to each electric field strength and a preset partial discharge identification model, and outputs the partial discharge type when the target electrical equipment has a partial discharge. In this way, local discharge detection for target electrical equipment is achieved based on quantum sensors and local discharge recognition models. The electric field strength change of the target electrical equipment is determined by obtaining the fluorescence intensity change of the quantum sensor. The quantum sensor can prevent weak electric signals from being affected by medium interference, electromagnetic interference, etc., and can accurately determine the electric field strength change. Then, the local discharge type is determined through the preset local discharge recognition model, which can further improve the accuracy of local discharge detection and local discharge type determination.

[0071] In an exemplary embodiment, as shown in FIG5 , optionally, determining whether the target electrical equipment has a partial discharge condition according to each electric field strength and a preset partial discharge identification model, and outputting the partial discharge type when the target electrical equipment has a partial discharge condition, includes the following steps 501 to 503. Among them:

[0072] S501, determining a curve of electric field intensity variation with time according to each electric field intensity and a corresponding acquisition period.

[0073] Optionally, a curve showing the change of electric field intensity over time is determined by taking the time corresponding to the acquisition cycle as the horizontal coordinate and taking the electric field intensity corresponding to each acquisition cycle as the vertical coordinate.

[0074] S502, inputting the curve into a preset partial discharge identification model to determine the partial discharge amount, the number of partial discharges and the type of partial discharge.

[0075] In one possible implementation, a preset partial discharge identification model can determine the severity of the periodic change of the electric field strength based on the curve, and then determine the partial discharge amount and the number of partial discharges. After determining the existence of partial discharge based on the partial discharge amount and the number of partial discharges, feature extraction is performed on the electric field strength and time in the curve, and the type of partial discharge is determined based on the extracted feature data.

[0076] In another possible implementation, the partial discharge amount, the number of partial discharges and the type of partial discharge may be determined by the following steps:

[0077] Step 1: Perform feature extraction processing on the curve to obtain a plurality of first feature data.

[0078] In one possible implementation, feature extraction can be performed on the curve through time domain analysis. For example, statistical features such as mean, variance, standard deviation, maximum value, minimum value and peak value can be determined from the curve, and the statistical features can be used as the first feature data. Alternatively, feature extraction can be performed through autocorrelation function or cross-correlation function to obtain multiple first feature data.

[0079] In another possible implementation, feature extraction may be performed on the curve through frequency domain analysis. For example, the time domain curve may be converted to the frequency domain through Fourier transform to obtain frequency components, and feature extraction may be performed to obtain a plurality of first feature data.

[0080] In another possible implementation, feature extraction can be performed on the curve through time-frequency analysis, for example, through wavelet transform or short-time Fourier transform, wherein wavelet transform extracts the first feature data in the curve through multi-scale analysis and can provide time and frequency information at the same time.

[0081] Step 2: Perform dimensionality reduction processing on the multiple first feature data to obtain multiple second feature data.

[0082] Optionally, dimensionality reduction of multiple first feature data can reduce the dimension of the input data while retaining important information as much as possible. Feature dimensionality reduction can be performed through principal component analysis, linear discriminant analysis, nonlinear dimensionality reduction, autoencoder, and local linear embedding. The embodiments of the present application do not limit this and it is determined according to actual needs in the application.

[0083] Step 3: Input the plurality of second characteristic data into a preset partial discharge recognition model to determine the partial discharge amount, the number of partial discharges and the type of partial discharge.

[0084] Optionally, a plurality of second characteristic data are input into a preset partial discharge recognition model, and the preset partial discharge recognition model can determine the partial discharge amount, the number of partial discharges and the partial discharge type.

[0085] S503: If the partial discharge amount is greater than a preset partial discharge amount threshold, and / or the number of partial discharges is greater than a preset number threshold, it is determined that the target electrical equipment has partial discharge, and the partial discharge type is output.

[0086] Optionally, a local discharge amount threshold and a preset number threshold in the local discharge identification model are compared with the local discharge amount and the number of local discharges determined by the local discharge identification model according to the characteristic data to determine whether local discharge exists in the target electrical equipment, and if it is determined that local discharge exists, the type of local discharge is output.

[0087] In a possible implementation, when the partial discharge amount is greater than a preset partial discharge amount threshold, a partial discharge condition exists in the target electrical equipment.

[0088] In another possible implementation, when the number of partial discharges is greater than a preset number threshold, a partial discharge condition exists in the target electrical equipment.

[0089] In another possible implementation, when the amount of partial discharge is greater than a preset partial discharge amount threshold, and the number of partial discharges is greater than a preset number threshold, a partial discharge situation exists in the target electrical equipment.

[0090] In an exemplary embodiment, as shown in FIG6 , optionally, determining the electric field strength of the target electrical device in each acquisition cycle according to the first microwave frequency and each second microwave frequency includes the following steps 601 to 603. Wherein:

[0091] S601, obtaining a preset constant coefficient.

[0092] Among them, the constant coefficient is related to the performance of the quantum sensor.

[0093] Optionally, the preset constant coefficient is saved in a database or configuration file after the quantum sensor is determined, and when the electric field strength is determined, the preset constant coefficient is obtained from the database or configuration file according to the identifier of the quantum sensor.

[0094] S602, determining a plurality of microwave frequency offsets by using a frequency difference between the first microwave frequency and each second microwave frequency.

[0095] S603, determining the electric field strength of the target electrical equipment in each acquisition cycle according to the ratio of each microwave frequency offset to a preset constant coefficient.

[0096] Optionally, the electric field strength in each acquisition period can be determined according to the following formula:

[0097]

[0098] in, is the second microwave frequency, is the first microwave frequency, and m is a preset constant coefficient.

[0099] As an optional implementation, as shown in FIG. 7 , the partial discharge detection method provided in the embodiment of the present application may include the following specific steps:

[0100] S701, obtaining a first variation curve of the fluorescence intensity of a quantum sensor in a target electrical device, and determining a first microwave frequency corresponding to a trough in the first variation curve.

[0101] The first variation curve is a fluorescence intensity variation curve of the quantum sensor in a first preset microwave scanning frequency interval when the target electrical device is not in operation.

[0102] S702, obtaining a second variation curve of the fluorescence intensity of the quantum sensor in a plurality of acquisition cycles, and determining a second microwave frequency corresponding to a trough in each second variation curve.

[0103] The second variation curve is a fluorescence intensity variation curve of the quantum sensor in the second preset microwave scanning frequency interval when the target electrical equipment is in operation;

[0104] S703, obtaining a preset constant coefficient.

[0105] Among them, the constant coefficient is related to the performance of the quantum sensor.

[0106] S704, determining a plurality of microwave frequency offsets by using the frequency differences between the first microwave frequency and each of the second microwave frequencies.

[0107] S705, determining the electric field strength of the target electrical equipment in each acquisition cycle according to the ratio of each microwave frequency offset to a preset constant coefficient.

[0108] S707, determining a curve of electric field intensity variation with time according to each electric field intensity and a corresponding acquisition period.

[0109] S707: Perform feature extraction processing on the curve to obtain a plurality of first feature data.

[0110] S708, performing dimensionality reduction processing on the plurality of first feature data to obtain a plurality of second feature data.

[0111] S709, inputting the plurality of second characteristic data into a preset partial discharge recognition model to determine the partial discharge amount, the number of partial discharges and the partial discharge type.

[0112] S710: If the partial discharge amount is greater than a preset partial discharge amount threshold, and / or the number of partial discharges is greater than a preset number threshold, it is determined that the target electrical device has partial discharge, and the partial discharge type of the target electrical device is output.

[0113] Optionally, the partial discharge types include internal discharge, tip discharge, surface discharge and suspension discharge.

[0114] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0115] In an exemplary embodiment, the embodiment of the present application provides a partial discharge detection system for implementing the above-mentioned partial discharge detection method. The implementation scheme for solving the problem provided by the system is similar to the implementation scheme recorded in the above-mentioned method. Therefore, the specific limitations in one or more partial discharge detection system embodiments provided below can refer to the limitations of the partial discharge detection method above, and will not be repeated here. The structure of the partial discharge detection system can refer to Figure 1, including: a quantum sensor, a data transmission unit and a partial discharge detection device;

[0116] The quantum sensor includes an NV color center and a microwave antenna. The microwave antenna is located below the NV color center and is insulated from the NV color center.

[0117] A data transmission unit, used to connect with the quantum sensor, to obtain multiple fluorescence intensities of the quantum sensor in a preset microwave scanning frequency interval, and to send each fluorescence intensity to the partial discharge detection device;

[0118] A partial discharge detection device is used to obtain a first change curve of the fluorescence intensity of a quantum sensor in a target electrical device, and determine a first microwave frequency corresponding to a trough in the first change curve, wherein the first change curve is a fluorescence intensity change curve of the quantum sensor in a first preset microwave scanning frequency interval when the target electrical device is not in operation; obtain a second change curve of the fluorescence intensity of the quantum sensor in multiple acquisition cycles, and determine a second microwave frequency corresponding to a trough in each second change curve, wherein the second change curve is a fluorescence intensity change curve of the quantum sensor in a second preset microwave scanning frequency interval when the target electrical device is in operation; determine an electric field strength of the target electrical device in each acquisition cycle according to the first microwave frequency and each second microwave frequency; determine whether the target electrical device has a partial discharge according to each electric field strength and a preset partial discharge identification model, and output a partial discharge type when the target electrical device has a partial discharge.

[0119] Optionally, the partial discharge detection system may also include a laser emitter and a microwave generator. The laser emitter is used to generate a laser signal, which is connected to the quantum sensor via an optical fiber, and the laser signal is input into the NV color center of the quantum sensor via the optical fiber to put the NV color center in an excited state to generate a fluorescence signal; the microwave generator is used to generate microwaves in a preset microwave scanning frequency range, and is connected to the microwave antenna in the quantum sensor via a cable.

[0120] Optionally, as shown in Figure 8, it is a schematic diagram of the structure of a possible quantum sensor, the quantum sensor includes an NV color center 801, a microwave antenna 802, a housing 803, a PCB board 804, an optical fiber 805, a cable 806, an optical fiber interface 807 and a cable interface 808, wherein the NV color center is used to detect weak local discharge signals; the NV color center 801 is adhered to the surface of one end of the optical fiber 805, and the other end is connected to the optical fiber interface 807, so as to be connected to the laser transmitter through the optical fiber interface 807 to transmit laser signals and fluorescence signals; the microwave antenna 802 is placed on the PCB board 804, below the NV color center 801 and the optical fiber 805 and insulated from the NV color center 801 and the optical fiber 805, one end of the cable 806 is connected to the microwave antenna 802, and the other end is connected to the cable interface 808, so as to be connected to the microwave generator through the cable interface 808 for transmitting microwave signals.

[0121] Based on the same inventive concept, the embodiment of the present application also provides a partial discharge detection device for implementing the partial discharge detection method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more partial discharge detection device embodiments provided below can refer to the limitations on the partial discharge detection method above, and will not be repeated here.

[0122] In an exemplary embodiment, as shown in FIG. 9 , a partial discharge detection device 900 is provided, comprising: a first acquisition module 901 , a second acquisition module 902 , a calculation module 903 and a determination module 904 , wherein:

[0123] A first acquisition module 901 is used to acquire a first change curve of the fluorescence intensity of the quantum sensor in the target electrical device, and determine a first microwave frequency corresponding to a trough in the first change curve, wherein the first change curve is a fluorescence intensity change curve of the quantum sensor in a first preset microwave scanning frequency interval when the target electrical device is not in operation;

[0124] The second acquisition module 902 is used to acquire a second variation curve of the fluorescence intensity of the quantum sensor in a plurality of acquisition cycles, and determine a second microwave frequency corresponding to a trough in each second variation curve, wherein the second variation curve is a fluorescence intensity variation curve of the quantum sensor in a second preset microwave scanning frequency interval when the target electrical device is in operation;

[0125] A calculation module 903, used to determine the electric field strength of the target electrical device in each acquisition cycle according to the first microwave frequency and each second microwave frequency;

[0126] The determination module 904 is used to determine whether the target electrical equipment has partial discharge according to each electric field strength and a preset partial discharge identification model, and output the partial discharge type when the target electrical equipment has partial discharge.

[0127] In an exemplary embodiment, the determination module 904 is specifically used to determine a curve of electric field strength changing with time based on each electric field strength and the corresponding acquisition period; input the curve into a preset partial discharge identification model to determine the partial discharge amount, the number of partial discharges and the type of partial discharge; if the partial discharge amount is greater than a preset partial discharge amount threshold, and / or the number of partial discharges is greater than a preset number threshold, it is determined that partial discharge exists in the target electrical equipment, and the type of partial discharge is output.

[0128] In an exemplary embodiment, the determination module 904 is specifically used to perform feature extraction processing on the curve to obtain multiple first feature data; perform dimensionality reduction processing on the multiple first feature data to obtain multiple second feature data; input the multiple second feature data into a preset partial discharge identification model to determine the partial discharge amount, the number of partial discharges and the partial discharge type.

[0129] In an exemplary embodiment, the partial discharge types include internal discharge, tip discharge, creeping discharge, and floating discharge.

[0130] In an exemplary embodiment, the calculation module 903 is specifically used to obtain a preset constant coefficient, which is related to the performance of the quantum sensor; determine multiple microwave frequency offsets based on the frequency difference between the first microwave frequency and each second microwave frequency; and determine the electric field strength of the target electrical equipment in each acquisition period based on the ratio of each microwave frequency offset to the preset constant coefficient.

[0131] Each module in the above partial discharge detection device can be implemented in whole or in part by software, hardware or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to each module.

[0132] In an exemplary embodiment, a partial discharge detection device is provided, which may be a server, and its internal structure diagram may be shown in FIG10. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a partial discharge detection method is implemented.

[0133] Those skilled in the art will understand that the structure shown in FIG. 10 is merely a block diagram of a partial structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different arrangement of components.

[0134] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps described in any of the above method embodiments when executing the computer program.

[0135] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps described in any of the above method embodiments are implemented.

[0136] In one embodiment, a computer program product is provided, including a computer program, which implements the steps described in any of the above method embodiments when executed by a processor.

[0137] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., but are not limited to this.

[0138] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0139] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A partial discharge detection method, characterized in that: A partial discharge detection device used in a partial discharge detection system, the method comprising: Acquire a first change curve of the fluorescence intensity of the quantum sensor in the target electrical device, and determine a first microwave frequency corresponding to a trough in the first change curve, wherein the first change curve is a fluorescence intensity change curve of the quantum sensor in a first preset microwave scanning frequency interval when the target electrical device is not in operation; Acquire a second change curve of the fluorescence intensity of the quantum sensor in a plurality of acquisition cycles, and determine a second microwave frequency corresponding to a trough in each of the second change curves, wherein the second change curve is a fluorescence intensity change curve of the quantum sensor in a second preset microwave scanning frequency interval when the target electrical device is in operation; determining the electric field strength of the target electrical equipment in each of the acquisition cycles according to the first microwave frequency and each of the second microwave frequencies; According to each of the electric field strengths and a preset partial discharge identification model, it is determined whether the target electrical equipment has a partial discharge condition, and when the target electrical equipment has a partial discharge condition, the partial discharge type is output.

2. The method according to claim 1, characterized in that: The determining whether the target electrical equipment has a partial discharge condition according to each of the electric field strengths and a preset partial discharge identification model, and outputting a partial discharge type when the target electrical equipment has a partial discharge condition, comprises: Determine a curve of electric field strength changing with time according to each of the electric field strengths and the corresponding acquisition period; Inputting the curve into the preset partial discharge identification model to determine the partial discharge amount, the number of partial discharges and the type of partial discharge; If the partial discharge amount is greater than a preset partial discharge amount threshold, and / or the number of partial discharges is greater than a preset number threshold, it is determined that partial discharge exists in the target electrical equipment, and the partial discharge type is output.

3. The method according to claim 2, characterized in that The step of inputting the curve into the preset partial discharge identification model to determine the partial discharge amount, the number of partial discharges and the type of partial discharge comprises: Performing feature extraction processing on the curve to obtain a plurality of first feature data; Performing dimensionality reduction processing on the plurality of first feature data to obtain a plurality of second feature data; The plurality of second characteristic data are input into a preset partial discharge recognition model to determine the partial discharge amount, the number of partial discharges and the type of partial discharge.

4. The method according to claim 3, characterized in that The local discharge types include internal discharge, tip discharge, surface discharge and suspension discharge.

5. The method according to claim 1, characterized in that The step of determining the electric field strength of the target electrical device in each of the acquisition cycles according to the first microwave frequency and each of the second microwave frequencies includes: Obtaining a preset constant coefficient, wherein the constant coefficient is related to the performance of the quantum sensor; Determine a plurality of microwave frequency offsets using a frequency difference between the first microwave frequency and each of the second microwave frequencies; The electric field strength of the target electrical equipment in each acquisition cycle is determined according to the ratio of each microwave frequency offset to the preset constant coefficient.

6. A partial discharge detection system, characterized in that: The system includes a quantum sensor, a data transmission unit and a partial discharge detection device; The quantum sensor comprises an NV color center and a microwave antenna, wherein the microwave antenna is located below the NV color center and is insulated from the NV color center; The data transmission unit is used to connect with the quantum sensor, to obtain multiple fluorescence intensities of the quantum sensor in a preset microwave scanning frequency interval, and to send each of the fluorescence intensities to the partial discharge detection device; The partial discharge detection device is used to obtain a first change curve of the fluorescence intensity of a quantum sensor in a target electrical device, and determine a first microwave frequency corresponding to a trough in the first change curve, wherein the first change curve is a fluorescence intensity change curve of the quantum sensor in a first preset microwave scanning frequency interval when the target electrical device is not in operation; obtain a second change curve of the fluorescence intensity of the quantum sensor in a plurality of acquisition cycles, and determine a second microwave frequency corresponding to a trough in each of the second change curves, wherein the second change curve is a fluorescence intensity change curve of the quantum sensor in a second preset microwave scanning frequency interval when the target electrical device is in operation; determine an electric field strength of the target electrical device in each of the acquisition cycles according to the first microwave frequency and each of the second microwave frequencies; determine whether the target electrical device has a partial discharge according to each of the electric field strengths and a preset partial discharge identification model, and output a partial discharge type when the target electrical device has a partial discharge.

7. A partial discharge detection device, characterized in that: A partial discharge detection device used in a partial discharge detection system, the device comprising: A first acquisition module is used to acquire a first change curve of the fluorescence intensity of the quantum sensor in the target electrical device, and determine a first microwave frequency corresponding to a trough in the first change curve, wherein the first change curve is a fluorescence intensity change curve of the quantum sensor in a first preset microwave scanning frequency interval when the target electrical device is not in operation; A second acquisition module is used to acquire a second change curve of the fluorescence intensity of the quantum sensor within a plurality of acquisition cycles, and determine a second microwave frequency corresponding to a trough in each of the second change curves, wherein the second change curve is a fluorescence intensity change curve of the quantum sensor in a second preset microwave scanning frequency interval when the target electrical device is in operation; a calculation module, used for determining the electric field strength of the target electrical equipment in each of the acquisition cycles according to the first microwave frequency and each of the second microwave frequencies; The determination module is used to determine whether the target electrical equipment has partial discharge according to each of the electric field strengths and a preset partial discharge identification model, and output the partial discharge type when the target electrical equipment has partial discharge.

8. A partial discharge detection device, comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 5 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

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