CN113866569B - Multi-partial discharge source fingerprint positioning method and device based on broadband radio frequency detection - Google Patents

Abstract

The present invention provides a method and device for locating fingerprints of multiple PD sources based on broadband radio frequency detection, wherein the method includes the following steps: when receiving signal data collected by multiple PD sensors, according to the multiple PD sensors The collected signal data forms a signal fingerprint; the signal fingerprint is matched with a pre-established fingerprint database to obtain the location information of the partial discharge source, and the pre-established fingerprint database is used for a plurality of partial discharge sensors to monitor the data in the monitoring area. The test points are respectively obtained by the simulated partial discharge test. By implementing the present invention, the signal data collected by multiple PD sensors is received, cluster analysis is performed to form various signal fingerprints, and the signal fingerprints are matched with the pre-established fingerprint database to obtain the positions of various PD sources. information. Compared with positioning methods such as time difference of arrival, it does not require high-performance acquisition hardware to ensure high time synchronization accuracy, and can also achieve accurate positioning of partial discharge sources.

Description

A method and device for multi-PD source fingerprint location based on broadband radio frequency detection

technical field

The invention relates to the technical field of fault detection of high-voltage equipment, in particular to a method and device for locating fingerprints of multiple partial discharge sources based on broadband radio frequency detection.

Background technique

With the construction and development of UHV power transmission projects, the coverage area of interconnected power grids has gradually expanded, and the safety of power transmission and transformation equipment has a more prominent impact on the safe and reliable operation of power grids. The state monitoring, fault diagnosis and life cycle management of power transmission and transformation equipment have important scientific significance and application value for improving the operation reliability and utilization rate of power transmission and transformation equipment and realizing the optimal management of equipment. Partial discharge detection is the focus and difficulty of power equipment condition monitoring. Before the insulation failure of power equipment, there is generally a gradual development of partial discharge process. Partial discharge monitoring and positioning of operating equipment, and treatment of defects in advance can effectively avoid the occurrence of insulation breakdown faults, help to formulate targeted maintenance plans, reduce power outage time, and ensure the safe and reliable operation and status of power equipment. Maintenance provides an important reference basis.

In related technologies, ranging and positioning algorithms are usually used for positioning. The ranging and positioning algorithms include Time of Arrival (TOA), Time Difference of Arrival (TDOA), Angle of Arrival (AOA), etc. . The above methods usually have high requirements on the device hardware. For example, the arrival time and the arrival time difference need to maintain high time synchronization accuracy, and the performance requirements of the acquisition hardware are high. When the performance of the acquisition hardware is poor, the positioning accuracy is difficult to guarantee.

SUMMARY OF THE INVENTION

In view of this, embodiments of the present invention provide a multi-PD source fingerprint positioning method and device based on broadband radio frequency detection, so as to solve the problem that the prior art has high requirements on the performance of acquisition hardware, and when the performance of the acquisition hardware is poor, its positioning Accuracy is difficult to guarantee defects.

According to a first aspect, an embodiment of the present invention provides a multi-PD source fingerprint location method based on broadband radio frequency detection, including the following steps: when receiving signal data collected by multiple PD sensors, according to the multiple PD sources The signal data collected by the sensor forms a signal fingerprint; the signal fingerprint is matched with a pre-established fingerprint database to obtain the location information of the partial discharge source, and the pre-established fingerprint database is used by a plurality of partial discharge sensors to monitor the area within the monitoring area. The test points are respectively obtained by simulated partial discharge test.

Optionally, forming a signal fingerprint according to the signal data collected by the multiple PD sensors includes: according to a target clustering algorithm, respectively clustering the signal data collected by each PD sensor to obtain a classified signal; From the classified signals, at least one set of signal fingerprints is formed.

Optionally, the matching of the signal fingerprint with a pre-established fingerprint database to obtain the position information of the PD source includes: constructing a relationship matrix based on a sparse vector according to the signal fingerprint and the pre-established fingerprint database; using The reconstruction algorithm in compressed sensing solves the relationship matrix to obtain the position information of the partial discharge source.

Optionally, forming a signal fingerprint according to the signal data collected by the multiple PD sensors includes: performing analog signal processing and digital signal processing on the signal data to obtain processed signal data; signal data to form a signal fingerprint.

Optionally, the pre-established fingerprint database is obtained by performing simulated partial discharge tests on test points in the monitoring area by multiple partial discharge sensors, including: multiple partial discharge sensors perform multiple simulated partial discharge tests on the same test point. The obtained signal strengths of multiple tests; according to the signal strengths of the multiple tests, the average signal strengths of the simulated partial discharge sensors at the test points are obtained; the simulated partial discharge sensors are respectively performed on the grid test points in the monitoring area. The average signal intensity of each test point is obtained by the put test, and normalized to construct a fingerprint library.

Optionally, the target clustering algorithm is any one of a partition-based clustering algorithm, a hierarchical clustering algorithm, and a density clustering algorithm.

According to a second aspect, an embodiment of the present invention provides a fingerprint positioning device for multiple PD sources based on broadband radio frequency detection, including: a signal fingerprint determination module, configured to receive signal data collected by multiple PD sensors, according to the multiple PD sources. The signal data collected by each PD sensor forms a signal fingerprint; a location information determination module is used to match the signal fingerprint with a pre-established fingerprint database to obtain the location information of the PD source, and the pre-established fingerprint database It is obtained by performing simulated partial discharge tests on the test points in the monitoring area by multiple partial discharge sensors.

Optionally, the signal fingerprint determination module includes: a clustering module, configured to perform cluster analysis on the signal data collected by the partial discharge sensor according to a target clustering algorithm, to obtain a classified signal; a grouping module, configured to perform cluster analysis according to the classification signal to form at least one set of signal fingerprints.

According to a third aspect, an embodiment of the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the first aspect or the computer program when the processor executes the program. In the first aspect, the steps of the fingerprint location method for multiple PD sources based on broadband radio frequency detection according to any one of the embodiments.

According to a fourth aspect, an embodiment of the present invention provides a storage medium on which computer instructions are stored, and when the instructions are executed by a processor, implement the broadband radio frequency detection-based multiplexing described in the first aspect or any implementation manner of the first aspect. The steps of the partial discharge source fingerprint location method.

The technical scheme of the present invention has the following advantages:

1. The multi-PD source fingerprint positioning method/device based on broadband radio frequency detection provided by the present invention forms a signal fingerprint by receiving signal data collected by a plurality of PD sensors, and matches with a pre-established fingerprint database according to the signal fingerprint , so that the position information of the PD source can be obtained without the need for high-precision hardware acquisition equipment to ensure high time synchronization accuracy. Even if the acquisition hardware performance is poor, the accurate positioning of the PD source can be achieved.

2. The multi-PD source fingerprint positioning method/device based on broadband radio frequency detection provided by this embodiment performs cluster analysis on PD source data to obtain classified signals, and then forms signal fingerprints to achieve accurate detection of multiple PD sources. Positioning, improving the detection ability of multiple PD sources, and providing an important reference for the safe and reliable operation and condition maintenance of power equipment.

Description of drawings

In order to illustrate the specific embodiments of the present invention or the technical solutions in the prior art more clearly, the following briefly introduces the accompanying drawings that need to be used in the description of the specific embodiments or the prior art. Obviously, the accompanying drawings in the following description The drawings are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings without creative efforts.

1 is a flowchart of a specific example of a multi-PD source fingerprint positioning method based on broadband radio frequency detection in an embodiment of the present invention;

2 is a specific example diagram of a multi-PD source fingerprint positioning method based on broadband radio frequency detection in an embodiment of the present invention;

3 is a specific example diagram of a multi-PD source fingerprint positioning method based on broadband radio frequency detection in an embodiment of the present invention;

4 is a specific example diagram of a multi-PD source fingerprint positioning method based on broadband radio frequency detection in an embodiment of the present invention;

5 is a schematic block diagram of a specific example of a multi-PD source fingerprint positioning device based on broadband radio frequency detection in an embodiment of the present invention;

FIG. 6 is a schematic block diagram of a specific example of an electronic device in an embodiment of the present invention.

Detailed ways

The technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Apparently, the described embodiments are some of the embodiments of the present invention, but not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. The indicated orientation or positional relationship is based on the orientation or positional relationship shown in the accompanying drawings, which is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the indicated device or element must have a specific orientation or a specific orientation. construction and operation, and therefore should not be construed as limiting the invention. Furthermore, the terms "first", "second", and "third" are used for descriptive purposes only and should not be construed to indicate or imply relative importance.

In the description of the present invention, it should be noted that the terms "installed", "connected" and "connected" should be understood in a broad sense, unless otherwise expressly specified and limited, for example, it may be a fixed connection or a detachable connection connection, or integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, or it can be the internal connection of two components, which can be a wireless connection or a wired connection connect. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood in specific situations.

In addition, the technical features involved in the different embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

This embodiment provides a multi-PD source fingerprint location method based on broadband radio frequency detection, as shown in FIG. 1 , including the following steps:

S101, when receiving signal data collected by multiple PD sensors, a signal fingerprint is formed according to the signal data collected by multiple PD sensors.

Exemplarily, the PD sensor may be a signal acquisition device pre-deployed in the monitoring area for collecting signal data, such as a broadband antenna, capable of collecting electromagnetic wave signals in the monitoring area. The signal data may include signal characteristic data such as signal strength, time-domain waveform of the signal, amplitude mean and variance, and frequency-domain characteristics. The signal fingerprint characterizes the signal characteristics formed by the signal from any PD source at each PD sensor.

The way of receiving the signal data collected by the multiple partial discharge sensors may be to connect the processor implementing the method with the signal collecting equipment deployed in the monitoring area, so as to receive the data collected by the multiple partial discharge sensors in a wired or wireless manner. signal data. The way to form a signal fingerprint can be to combine the signal data of the same classification collected by multiple PD sensors, and its representation form can be: ψ′ _j =[ψ′ _1,j ,ψ′ _2,j ,... ,ψ′ _L,j ] ^T , where ψ′ _L,j represents the signal data collected by the Lth PD sensor and sent out at the grid test point j.

S102: Match various signal fingerprints with a pre-established fingerprint database to obtain location information of the PD source. The pre-established fingerprint database is obtained by performing simulated PD tests on test points in the monitoring area by multiple PD sensors.

Exemplarily, the specific construction process of the pre-established fingerprint library can be described by taking the following construction process as an example:

Divide the monitoring area into N evenly distributed grids, each grid is denoted as QY _j in turn, j=1, 2, ..., N, and the center of each grid is used as the test point for simulating partial discharge; L PD sensors, denoted as CG _i , i=1, 2, ..., L. Simulate partial discharge at each test point in turn. When partial discharge is simulated at point QY _j , the electromagnetic wave signal intensity measured by the partial discharge sensor CG _i is ψ _i,j . Based on the signal strength, the following fingerprint library containing the signal propagation characteristics of the monitored area can be constructed, namely:

其中，ψ_i,j(η)，η＝1,…k，k>1，k为测量次数；ψ_i,j(η)表征第η次局部放电发生在QY_j点时，局放传感器CG_i测得的电磁波信号强度。Further, when constructing the fingerprint database, in order to ensure the accuracy of the fingerprints in the fingerprint database, multiple signal intensities obtained by simulating partial discharge detection on the same test point by the partial discharge sensor for many times can be obtained. The average signal strength detected by the PD sensor at the time of the PD occurrence at this test point, i.e.,

Among them, ψ _i,j (η), η=1,...k, k>1, k is the number of measurements; ψ _i,j (η) indicates that when the nth partial discharge occurs at point QY _j , the partial discharge sensor CG _i Measured electromagnetic wave signal strength.

Thirdly, normalize the average signal intensities detected by the PD sensors to obtain a normalized fingerprint database. The normalization process can reduce the error caused by the fluctuation of the discharge power supply, and highlight the relative magnitude relationship between the signal strengths received by each PD sensor.

The specific normalization processing method can be realized according to the following formula:

This embodiment does not limit the normalization method, which can be determined by those skilled in the art as required.

The method of matching the signal fingerprint with the pre-established fingerprint database to obtain the position information of the PD source can be illustrated by the following matching process as an example:

Assuming that partial discharge occurs at point QY _j , the signal strength value measured by each partial discharge sensor is ψ′ _j =[ψ′ _1,j ,ψ′ _2,j ,...,ψ′ _L,j ] ^T , that is, the generation of a fingerprint. Input the fingerprint into the fingerprint database Ψ for pattern matching, and match it to the jth column in the fingerprint database: ψ _j =[ψ _1,j ,ψ _2,j ,...,ψ _L,j ] ^T , then the bureau The source position positioning result is the jth measurement point QY _j .

The multi-PD source fingerprint positioning method based on broadband radio frequency detection provided in this embodiment forms a signal fingerprint by receiving signal data collected by multiple PD sensors, and matches the signal fingerprint with a pre-established fingerprint database to obtain The location information of the partial discharge source does not need to be collected by high-precision hardware to ensure high time synchronization accuracy. Even if the hardware performance of the collection is poor, the precise positioning of the partial discharge source can be achieved.

As an optional implementation of this embodiment, a signal fingerprint is formed according to signal data collected by multiple PD sensors, including:

Firstly, according to the target clustering algorithm, the signal data collected by each PD sensor is clustered to obtain the classified signal;

Exemplarily, since the signal data (pulse signals) excited by each PD source are different, the signal characteristics such as time domain waveform, amplitude mean and variance, and frequency domain characteristics in the collected signal data can be used to analyze the pulse signal. The similarity of the signals, set the correlation threshold, and classify the pulse signals through the target clustering algorithm, so as to effectively mine the similarity and difference of the PD pulse signals, and realize the classification and identification of multiple PD sources. The target clustering algorithm may be a partition-based clustering algorithm, a hierarchical clustering algorithm, a density clustering algorithm, etc., which is not limited in this embodiment, and can be determined by those skilled in the art as required.

According to the target clustering algorithm, the signal data collected by the PD sensor is clustered respectively, and the specific process of obtaining the classified signal can be described by taking the following process as an example:

Assuming that four PD sensors are deployed in the monitoring area, and the PD sensors are synchronized to trigger acquisition, any PD sensor will collect 100 pieces of signal data within 1 minute, but these 100 pieces of data may not come from the same office. Therefore, it is necessary to perform cluster analysis on 100 pieces of signal data, so as to distinguish the signal data from different PD sources. It is explained that the signal waveform characteristics emitted by different PD sources are different. The waveform characteristics of 100 pieces of signal data are extracted respectively, and the hierarchical clustering method is applied. The distance (similarity) between different data samples is calculated by Euclidean distance, and the number of clusters Corresponding settings can be made according to different detection requirements, which are not limited here. Thus, the 100 pieces of signal data can be classified.

Then, based on the classified signals, at least one set of signal fingerprints is formed.

Exemplarily, since the PD sensors maintain synchronization to trigger the acquisition, it can be determined which signals of the four PD sensors are generated by the same PD source according to the time of each acquisition data record.

Further, the average value of the signal strength collected by each PD sensor is obtained for the same type of signal, and the average value of the signal strength of each PD sensor is combined to form a signal fingerprint, and the specific expression form of the combination can be: ψ′ _j = [ ψ′ _1,j , ψ′ _2,j , ψ′ _3,j , ψ′ _4,j ], where ψ′ _1,j represents the jth PD source collected by the first PD sensor. Average signal strength, ψ′ _2,j indicates the average signal strength of the jth PD source collected by the second hurricane sensor, ψ′ _3,j indicates the jth PD collected by the third PD sensor The average strength of the signal emitted by the source, ψ′ _4,j represents the average strength of the signal emitted by the jth PD source collected by the fourth PD sensor.

The fingerprint positioning method for multiple PD sources based on broadband radio frequency detection provided in this embodiment, clustering signal data to obtain classified signals, forming a signal fingerprint of the classified signals, realizing accurate positioning of multiple PD sources, and improving the accuracy of multiple PD sources. The detection ability of the discharge source provides an important reference for the safe and reliable operation and condition maintenance of power equipment.

In order to verify the above method, this embodiment selects an area of 4m × 3m, as shown in Figure 2, and arranges 12 test points (1-12 in the circle in the figure), and the measurement points are evenly distributed in a grid shape, with a distance of 1m from each other. 4 PD sensors are distributed in the four corners, and the analog PD sources are placed at test points 1 and 4. According to the cluster analysis of the signal data collected by the PD sensor, the collected signals are divided into two groups, and the signal strength of each group is as follows:

The average strength of the first group of signals is: PD sensor 1: -26.78dBm; PD sensor 2: -20.35dBm; PD sensor 3: -21.7dBm; PD sensor 4: -23.14dBm; Expressed as: [-26.78,-20.35,-21.7,-23.14];

The average strength of the second group of signals is: PD sensor 1: -20.98dBm; PD sensor 2: -25.32dBm; PD sensor 3: -24.44dBm; PD sensor 4: -22.98dBm. The formed signal fingerprint can be expressed as: [-20.98,-25.32,-24.44,-22.98];

The fingerprint positioning method is applied to each classified signal after cluster analysis to locate and match the signal. As shown in Figure 3, the first group of signal positioning results is located at position 4; as shown in Figure 4, the second group of signal positioning results is at position 1.

As an optional implementation of this embodiment, matching the signal fingerprint with a pre-established fingerprint database to obtain the position information of the PD source includes: constructing a sparse vector-based relationship matrix according to the signal fingerprint and the pre-established fingerprint database ; Solve the relationship matrix according to the reconstruction algorithm in compressed sensing, and obtain the position information of the partial discharge source.

Exemplarily, the position of the PD source in the monitoring area has natural _sparsity , and the position of the PD source is converted into a vector with a sparsity of 1 through discrete physical space. In addition to its coordinates, it can also be expressed as W=[0,...,0,1,0,...,0] ^T , only the position of the QY _j point is marked as 1, and the rest of the positions are is 0. Therefore, the PD source localization problem is transformed into a sparse vector reconstruction problem to solve the monitoring grid where the PD source is located.

A sparse vector based relation matrix can be:

P=ΨW; (1)

Among them, P=ψ _j =[ψ _1,j ,ψ _2,j ,...,ψ _L,j ] ^T , that is, the signal data measured by each PD sensor when point QY _j is the PD source, that is, the signal Fingerprint, Ψ is a pre-established fingerprint database.

Multiply both sides of equation (1) by the measurement matrix Φ to get

ΦP=ΦΨW (2)

Let ΦP=Y, then

Y=ΦΨW (3)

Since W is an N×1-dimensional vector with a sparsity of 1, it has sparsity, so the most sparse vector in Y=ΦΨW is the solution. Among them, Φ is the measurement matrix, which is m×L dimension, m<<N, and a Gaussian random measurement matrix can be used, which conforms to a normal distribution with a mean of 0 and a variance of 1. Θ is the sensing matrix, let Θ=ΦΨ, and Θ is the m×N dimension.

According to the theory of compressed sensing, using Y in formula (3) and the sensing matrix Θ, through the reconstruction algorithm, W can be obtained, that is, the positioning result can be obtained.

Due to the sparsity of W, the problem of reconstructing W can be transformed into the problem of solving the minimum norm l ₀ , and solving this optimization problem can reconstruct W with a high probability.

argmin‖W‖ ₀ stY=ΘW (4)

Among them, ‖W‖ ₀ represents the number of non-zero elements in the vector W.

表示向量x中非零元素的个数。范数l₁定义为

表示向量x中非零元素的绝对值之和。最小范数l₁优化模型为：Since the l ₀ norm is the number of non-zero items in the solution vector, the solution of equation (4) is relatively difficult. In order to simplify the difficulty of solving, under the condition of RIP (restricted isometric property), an equivalent solution can be obtained by converting the minimum norm l ₀ model to the minimum norm l ₁ model, where the norm l ₀ is defined as

Represents the number of non-zero elements in the vector x. The norm _l1 is defined as

represents the sum of the absolute values of the non-zero elements in the vector x. The minimum norm l ₁ optimization model is:

arg min‖W‖ ₁ stY=ΘW (5)

To solve this model, a greedy algorithm with smaller computational complexity and faster computational speed is usually used, such as the Orthogonal Matching Pursuit (OMP) algorithm, the SOMP algorithm, and the like.

As an optional implementation of this embodiment, forming a signal fingerprint according to signal data collected by multiple PD sensors includes: performing analog signal processing and digital signal processing on the signal data to obtain processed signal data; The processed signal data forms a signal fingerprint.

Exemplarily, the main purpose of analog signal processing is to improve the signal-to-noise ratio, and the detection frequency band is set in a desired range, such as a 100MHz-1.5GHz frequency band, through a band-pass filtering module. The filtered signal is subjected to low-noise amplification with a fixed gain by a low-noise amplifier. Gain control is used to dynamically control the amplification or attenuation of the acquired signal, so that the acquired signal fits within the range of the input signal amplitude of the digital-to-analog converter and fully utilizes its digital resolution.

After the signal processed by the analog signal, the collected signal is digitized, and the digitized signal is further subjected to noise reduction and feature extraction analysis, so as to realize the identification of the partial discharge pulse signal. Noise reduction can usually be done by methods such as wavelet transform, and feature extraction can usually be the duration of the steep pulse of the partial discharge pulse signal, the maximum value of the signal amplitude, and the like.

This embodiment provides a multi-PD source fingerprint positioning device based on broadband radio frequency detection, as shown in FIG. 5 , including:

The signal fingerprint determination module 201 is used to form a signal fingerprint according to the signal data collected by the multiple PD sensors when receiving the signal data collected by the multiple PD sensors; for details, please refer to the corresponding part of the above embodiment, in This will not be repeated here.

The location information determination module 202 is used for matching the signal fingerprint with a pre-established fingerprint database to obtain the location information of the PD source, and the pre-established fingerprint database is used for the test points in the monitoring area by a plurality of PD sensors. The simulated partial discharge test was carried out respectively. For details, refer to the corresponding part of the above-mentioned embodiment, and details are not repeated here.

As an optional implementation manner of this embodiment, the location information determination module 202 includes:

The clustering module is used for clustering the signal data collected by the partial discharge sensor according to the target clustering algorithm to obtain the classified signal; for details, refer to the corresponding part of the above-mentioned embodiment, which will not be repeated here.

a grouping module, configured to form at least one set of signal fingerprints according to the classified signals. For details, refer to the corresponding part of the above-mentioned embodiment, and details are not repeated here.

As an optional implementation manner of this embodiment, the location information determination module 202 includes:

The relationship matrix determination module is configured to construct a sparse vector-based relationship matrix according to the signal fingerprint and the pre-established fingerprint library; for details, refer to the corresponding part of the above-mentioned embodiment, which will not be repeated here.

The position information determination sub-module is used for solving the relationship matrix according to the reconstruction algorithm in compressed sensing to obtain the position information of the partial discharge source. For details, refer to the corresponding part of the above-mentioned embodiment, and details are not repeated here.

As an optional implementation manner of this embodiment, the signal fingerprint determination module 201 includes:

The processing module is configured to perform analog signal processing and digital signal processing on the signal data to obtain processed signal data; for details, refer to the corresponding part of the above-mentioned embodiment, which will not be repeated here.

The signal fingerprint determination sub-module is configured to form a signal fingerprint according to the processed signal data. For details, refer to the corresponding part of the above-mentioned embodiment, and details are not repeated here.

As an optional implementation manner of this embodiment, the location information determination module 202 includes:

The multiple groups of signal strength information receiving modules are used to receive multiple signal strengths obtained by multiple detections and collections by the partial discharge sensor; for details, refer to the corresponding parts of the above embodiments, which will not be repeated here.

The average signal strength determination module is configured to calculate the average signal strength of each classified signal after the clustering according to the plurality of signal strengths; for details, refer to the corresponding part of the above embodiment, which will not be repeated here.

A normalization module, configured to perform normalization processing on the average signal strength detected by each PD sensor. For details, refer to the corresponding part of the above-mentioned embodiment, and details are not repeated here.

The fingerprint library building module is used to construct a fingerprint library according to the normalized average signal strength; for details, refer to the corresponding part of the above embodiment, which will not be repeated here.

As an optional implementation manner of this embodiment, the clustering module includes: any one of a division-based clustering module, a hierarchical clustering module, and a density clustering module. For details, refer to the corresponding part of the above-mentioned embodiment, and details are not repeated here.

This embodiment of the present application further provides an electronic device, as shown in FIG. 6 , a processor 310 and a memory 320, where the processor 310 and the memory 320 may be connected through a bus or in other ways.

The processor 310 may be a central processing unit (Central Processing Unit, CPU). The processor 310 may also be other general-purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuit (Application Specific Integrated Circuit, ASIC), Field-Programmable Gate Array (Field-Programmable Gate Array, FPGA) or Other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components and other chips, or a combination of the above types of chips.

The memory 320, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs, non-transitory computer-executable programs and modules, such as the multi-PD source fingerprint based on broadband radio frequency detection in the embodiment of the present invention The program instruction/module corresponding to the positioning method. The processor executes various functional applications and data processing of the processor by executing non-transitory software programs, instructions and modules stored in the memory.

The memory 320 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required by at least one function; the storage data area may store data created by the processor, and the like. Additionally, the memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, memory 320 may optionally include memory located remotely from the processor, which may be connected to the processor through a network. Examples of such networks include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

The one or more modules are stored in the memory 320, and when executed by the processor 310, execute the multi-PD source fingerprint positioning method based on broadband radio frequency detection in the embodiment shown in FIG. 1 .

The specific details of the above electronic device can be understood by referring to the corresponding description and effects in the embodiment shown in FIG. 1 , and details are not repeated here.

This embodiment also provides a computer storage medium, where computer-executable instructions are stored in the computer storage medium, and the computer-executable instructions can execute the broadband radio frequency detection-based fingerprint positioning method in any of the foregoing method embodiments. The storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM), a random access memory (RAM), a flash memory (Flash Memory), a hard disk (Hard) Disk Drive, abbreviation: HDD) or solid-state drive (Solid-State Drive, SSD), etc.; the storage medium may also include a combination of the above-mentioned types of memories.

Obviously, the above -mentioned examples are just a clear explanation of the examples, not the limitation of the embodiment. For those of ordinary skill in the art, changes or modifications in other different forms can also be made on the basis of the above description. There is no need and cannot be exhaustive of all implementations here. And the obvious changes or changes derived from this are still within the protection scope of the present invention.

Claims (7)

1. a multi-partial discharge source fingerprint positioning method based on broadband radio frequency detection, is characterized in that, comprises the steps: When receiving signal data collected by multiple PD sensors; According to the target clustering algorithm, cluster analysis is performed on the signal data collected by the PD sensor, and the classified signal is obtained; forming at least one set of signal fingerprints based on the classified signals; A sparse vector-based relationship matrix is constructed according to the signal fingerprint and a pre-established fingerprint library, and the pre-established fingerprint library is obtained by performing simulated partial discharge tests on test points in the monitoring area by a plurality of partial discharge sensors; The relationship matrix is solved according to the reconstruction algorithm in compressed sensing, and the position information of the partial discharge source is obtained. 2. The method according to claim 1, wherein forming a signal fingerprint according to the signal data collected by the plurality of partial discharge sensors, comprising: Perform analog signal processing and digital signal processing on the signal data to obtain processed signal data; According to the processed signal data, a signal fingerprint is formed. 3. The method according to claim 1, wherein the pre-established fingerprint database is obtained by performing simulated partial discharge tests on test points in the monitoring area by a plurality of partial discharge sensors, comprising: The signal strength of multiple tests obtained by multiple partial discharge sensors performing multiple simulated partial discharge tests on the same test point; According to the signal strengths of the multiple tests, obtain the average signal strengths of the simulated partial discharge sensors at the test point of the multiple partial discharge sensors; The grid test points in the monitoring area are respectively subjected to simulated partial discharge test to obtain the average signal strength of each test point, which is then normalized to construct a fingerprint database. 4 . The method according to claim 1 , wherein the target clustering algorithm is any one of a partition-based clustering algorithm, a hierarchical clustering algorithm, and a density clustering algorithm. 5 . 5. A multi-partial discharge source fingerprint positioning device based on broadband radio frequency detection is characterized in that, comprising: The signal fingerprint determination module is used to perform cluster analysis on the signal data collected by the partial discharge sensor according to the target clustering algorithm when receiving the signal data collected by multiple partial discharge sensors to obtain a classification signal; according to the classification signal , forming at least one set of signal fingerprints; The location information determination module is used to construct a sparse vector-based relationship matrix according to the signal fingerprint and a pre-established fingerprint library, and the pre-established fingerprint library is used to simulate the test points in the monitoring area by a plurality of partial discharge sensors. The relationship matrix is solved according to the reconstruction algorithm in compressed sensing, and the position information of the partial discharge source is obtained. 6. An electronic device, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor implements any one of claims 1-4 when executing the program. The steps of the multi-PD source fingerprint location method based on broadband radio frequency detection described above. 7. A storage medium on which computer instructions are stored, characterized in that, when the instructions are executed by a processor, the steps of any one of claims 1-4 based on the broadband radio frequency detection-based fingerprint positioning method for multiple PD sources are implemented .

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