WO2022205834A1 - 一种基于宽带射频检测的多局放源指纹定位方法及装置 - Google Patents

一种基于宽带射频检测的多局放源指纹定位方法及装置 Download PDF

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WO2022205834A1
WO2022205834A1 PCT/CN2021/124034 CN2021124034W WO2022205834A1 WO 2022205834 A1 WO2022205834 A1 WO 2022205834A1 CN 2021124034 W CN2021124034 W CN 2021124034W WO 2022205834 A1 WO2022205834 A1 WO 2022205834A1
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signal
partial discharge
fingerprint
signal data
source
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PCT/CN2021/124034
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English (en)
French (fr)
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杨智豪
黄辉
邓辉
黄凤
张磊
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全球能源互联网研究院有限公司
国网山东省电力公司
国网山东省电力公司电力科学研究院
国家电网有限公司
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Publication of WO2022205834A1 publication Critical patent/WO2022205834A1/zh

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/12—Testing dielectric strength or breakdown voltage ; Testing or monitoring effectiveness or level of insulation, e.g. of a cable or of an apparatus, for example using partial discharge measurements; Electrostatic testing
    • G01R31/1227—Testing dielectric strength or breakdown voltage ; Testing or monitoring effectiveness or level of insulation, e.g. of a cable or of an apparatus, for example using partial discharge measurements; Electrostatic testing of components, parts or materials
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/23—Clustering techniques
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
    • G06F2218/08—Feature extraction
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
    • G06F2218/12—Classification; Matching

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  • the present application relates to the technical field of fault detection of high-voltage equipment, and in particular to a method and device for locating fingerprints of multiple partial discharge sources based on broadband radio frequency detection.
  • 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.
  • 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. .
  • TOA Time of Arrival
  • TDOA Time Difference of Arrival
  • AOA Angle of Arrival
  • 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, its positioning accuracy is difficult to guarantee.
  • the embodiments of the present application 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.
  • an embodiment of the present application provides a multi-PD source fingerprint positioning 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.
  • 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, A classification signal is obtained; based on the classification signal, at least one set of signal fingerprints is formed.
  • the matching of the signal fingerprint with a pre-established fingerprint database to obtain the position information of the partial discharge source includes: constructing a sparse vector-based algorithm according to the signal fingerprint and the pre-established fingerprint database. Relationship matrix; the relationship matrix is solved by using the reconstruction algorithm in compressed sensing, and the position information of the PD source is obtained.
  • 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; The processed signal data forms a signal fingerprint.
  • 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 tests on the same test point.
  • the signal strength of the multiple tests obtained by simulating the partial discharge test; according to the signal strength of the multiple tests, the average signal strength of the simulated partial discharge sensors at the test point is obtained; for the grid test points in the monitoring area
  • the simulated partial discharge test was carried out to obtain the average signal strength of each test point, and normalized to construct a fingerprint database.
  • the target clustering algorithm is any one of a partition-based clustering algorithm, a hierarchical clustering algorithm, and a density clustering algorithm.
  • an embodiment of the present application 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.
  • 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 From the classified signals, at least one set of signal fingerprints is formed.
  • an embodiment of the present application 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.
  • the steps of the fingerprint location method for multiple PD sources based on broadband radio frequency detection according to any one of the embodiments.
  • an embodiment of the present application 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 method described in the first aspect or any implementation manner of the first aspect.
  • the steps of the partial discharge source fingerprint location method are performed by a processor.
  • the multi-PD source fingerprint positioning method/device based on broadband radio frequency detection 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.
  • 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.
  • FIG. 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 application;
  • FIG. 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 application
  • FIG. 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 application;
  • FIG. 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 application
  • FIG. 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 application
  • FIG. 6 is a schematic block diagram of a specific example of an electronic device in an embodiment of the present application.
  • the terms “installed”, “connected” and “connected” should be understood in a broad sense, 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.
  • installed should be understood in a broad sense, 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.
  • 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:
  • 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.
  • 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.
  • the electromagnetic wave signal intensity measured by the partial discharge sensor CG i is ⁇ i,j .
  • the following fingerprint library containing the signal propagation characteristics of the monitored area can be constructed, namely:
  • 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.
  • 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:
  • ⁇ ′ j [ ⁇ 1,j , ⁇ 2,j ,..., ⁇ L,j ] T
  • 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 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.
  • a signal fingerprint is formed according to signal data collected by multiple PD sensors, including:
  • the signal data collected by each PD sensor is clustered to obtain the classified signal
  • 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.
  • 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:
  • 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.
  • 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.
  • Average signal strength, ⁇ ′ 2,j represents the average signal strength of the jth PD source collected by the second hurricane sensor, ⁇ ′ 3,j represents the jth PD collected by the third PD sensor The average strength of the signal emitted by the source, ⁇ ′ 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.
  • 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.
  • 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; Represented 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 resulting signal fingerprint can be represented 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.
  • 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.
  • the position of the PD source in the monitoring area has natural sparsity
  • the position of the PD source is converted into a vector with a sparsity of 1 through discrete physical space.
  • 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:
  • ⁇ is the measurement matrix, which is m ⁇ L dimension, m ⁇ N, and Gaussian random measurement matrix can be used, which conforms to a normal distribution with a mean of 0 and a variance of 1.
  • 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.
  • the solution of equation (4) is relatively difficult.
  • 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:
  • OMP Orthogonal Matching Pursuit
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • the clustering module includes: any one of a division-based clustering module, a hierarchical clustering module, and a density clustering module.
  • a division-based clustering module a clustering module
  • a hierarchical clustering module a clustering module
  • a density clustering module a clustering module
  • 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.
  • DSP Digital Signal Processor
  • ASIC Application Specific Integrated Circuit
  • FPGA Field-Programmable Gate Array
  • 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 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 application.
  • the program instruction/module corresponding to the positioning method.
  • the processor performs various functional applications and data processing of the processor by executing non-transitory software programs, instructions, and modules stored in 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 storage medium may be a magnetic disk, an optical disk, a read-only memory (Read-Only Memory, ROM), a random access memory (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.

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Abstract

本申请提供一种基于宽带射频检测的多局放源指纹定位方法及装置，其中，方法，包括如下步骤：当接收到多个局放传感器采集到的信号数据，根据所述多个局放传感器采集到的信号数据，形成信号指纹；将所述信号指纹与预先建立的指纹库进行匹配，得到局放源的位置信息，所述预先建立的指纹库由多个局放传感器对监测区域内的测试点分别进行模拟局放测试得到。通过实施本申请，接收多个局放传感器采集到的信号数据，进行聚类分析进而形成各类信号指纹，并根据信号指纹与预先建立的指纹库进行匹配，从而得到各类局放源的位置信息。相比于到达时间差等定位方法，其不需要高性能的采集硬件来保证高时间同步精度，也能实现局放源的精确定位。

Description

一种基于宽带射频检测的多局放源指纹定位方法及装置
相关申请的交叉引用
本申请基于申请号为2021103586700、申请日为2021年04月01日的中国专利申请提出，并要求该中国专利申请的优先权，该中国专利申请的全部内容在此引入本申请作为参考。
技术领域
本申请涉及高压设备故障检测技术领域，具体涉及一种基于宽带射频检测的多局放源指纹定位方法及装置。
背景技术
随着特高压输电工程的建设和发展，互联电网的覆盖区域逐步扩大，输变电设备运行安全对电网安全可靠运行的影响更为突出。对输变电设备进行状态监测、故障诊断及全寿命周期管理，对于提高输变电设备的运行可靠性与利用率，实现设备的优化管理具有重要的科学意义与应用价值。局部放电检测是电力设备状态监测的重点和难点。电力设备发生绝缘故障前，一般都会有一个逐渐发展的局部放电过程。对运行设备进行局部放电监测和定位，提前对缺陷进行处理，能有效避免绝缘击穿故障的发生，有助于制定有针对性的检修方案，减少停电时间，为电力设备的安全可靠运行和状态检修提供重要参考依据。
相关技术中，通常使用测距定位算法进行定位，测距定位算法包括有到达时间(Time of Arrival,TOA)、到达时间差(Time Difference of Arrival,TDOA)、到达角度(Angle of Arrival,AOA)等。上述方法通常对设备硬件要求高，如到达时间和到达时间差需要保持高时间同步精度，对采集硬件 的性能要求高，当采集硬件性能较差时，其定位精度就难以保证。
发明内容
有鉴于此，本申请实施例提供了一种基于宽带射频检测的多局放源指纹定位方法及装置，以解决现有技术中对采集硬件性能要求高，当采集硬件性能较差时，其定位精度就难以保证的缺陷。
根据第一方面，本申请实施例提供一种基于宽带射频检测的多局放源指纹定位方法，包括如下步骤：当接收到多个局放传感器采集到的信号数据，根据所述多个局放传感器采集到的信号数据，形成信号指纹；将所述信号指纹与预先建立的指纹库进行匹配，得到局放源的位置信息，所述预先建立的指纹库由多个局放传感器对监测区域内的测试点分别进行模拟局放测试得到。
在一些可选实施方式中，根据所述多个局放传感器采集到的信号数据，形成信号指纹，包括：根据目标聚类算法，分别对每个局放传感器采集到的信号数据进行聚类，得到分类信号；根据所述分类信号，形成至少一组信号指纹。
在一些可选实施方式中，所述将所述信号指纹与预先建立的指纹库进行匹配，得到局放源的位置信息，包括：根据所述信号指纹与预先建立的指纹库构建基于稀疏向量的关系矩阵；利用压缩感知中的重构算法对所述关系矩阵进行求解，得到局放源的位置信息。
在一些可选实施方式中，根据所述多个局放传感器采集到的信号数据，形成信号指纹，包括：对所述信号数据进行模拟信号处理和数字信号处理，得到处理后的信号数据；根据所述处理后的信号数据，形成信号指纹。
在一些可选实施方式中，所述预先建立的指纹库由多个局放传感器对监测区域内的测试点分别进行模拟局放测试得到，包括：多个局放传感器对同一测试点进行多次模拟局部放电测试得到的多次测试的信号强度；根 据所述多次测试的信号强度，得到所述测试点的模拟局放在各局放传感器的平均信号强度；对监测区域内的网格测试点分别进行模拟局放测试得到每个测试点的平均信号强度，并进行归一化处理，构建指纹库。
在一些可选实施方式中，所述目标聚类算法为基于划分的聚类算法、层次聚类算法、密度聚类算法中的任意一种。
根据第二方面，本申请实施例提供基于宽带射频检测的多局放源指纹定位装置，包括：信号指纹确定模块，用于当接收到多个局放传感器采集到的信号数据，根据所述多个局放传感器采集到的信号数据，形成信号指纹；位置信息确定模块，用于将所述信号指纹与预先建立的指纹库进行匹配，得到局放源的位置信息，所述预先建立的指纹库由多个局放传感器对监测区域内的测试点分别进行模拟局放测试得到。
在一些可选实施方式中，信号指纹确定模块，包括：聚类模块，用于根据目标聚类算法，对局放传感器采集到的信号数据进行聚类分析，得到分类信号；分组模块，用于根据所述分类信号，形成至少一组信号指纹。
根据第三方面，本申请实施例提供一种电子设备，包括存储器、处理器及存储在存储器上并可在处理器上运行的计算机程序，所述处理器执行所述程序时实现第一方面或第一方面任一实施方式所述的基于宽带射频检测的多局放源指纹定位方法的步骤。
根据第四方面，本申请实施例提供一种存储介质，其上存储有计算机指令，该指令被处理器执行时实现第一方面或第一方面任一实施方式所述的基于宽带射频检测的多局放源指纹定位方法的步骤。
本申请技术方案，具有如下优点：
1.本申请提供的基于宽带射频检测的多局放源指纹定位方法/装置，通过接收多个局放传感器采集到的信号数据，形成信号指纹，并根据信号指纹与预先建立的指纹库进行匹配，从而得到局放源的位置信息，而不需要 通过高精度的硬件采集设备以保证高时间同步精度，即使采集硬件性能较差，也能实现局放源的精确定位。
2.本实施例提供的基于宽带射频检测的多局放源指纹定位方法/装置，对局放源数据进行聚类分析，得到分类信号，进而形成信号指纹，实现对多个局放源的准确定位，提升对多局放源的检测能力，为电力设备的安全可靠运行和状态检修提供重要参考依据。
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为了更清楚地说明本申请具体实施方式或现有技术中的技术方案，下面将对具体实施方式或现有技术描述中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图是本申请的一些实施方式，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他的附图。
图1为本申请实施例中基于宽带射频检测的多局放源指纹定位方法的一个具体示例的流程图；
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图4为本申请实施例中基于宽带射频检测的多局放源指纹定位方法的一个具体示例图；
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图6为本申请实施例中电子设备的一个具体示例的原理框图。
具体实施方式
下面将结合附图对本申请的技术方案进行清楚、完整地描述，显然，所描述的实施例是本申请一部分实施例，而不是全部的实施例。基于本申请中的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例，都属于本申请保护的范围。
在本申请的描述中，需要说明的是，术语“中心”、“上”、“下”、“左”、“右”、“竖直”、“水平”、“内”、“外”等指示的方位或位置关系为基于附图所示的方位或位置关系，仅是为了便于描述本申请和简化描述，而不是指示或暗示所指的装置或元件必须具有特定的方位、以特定的方位构造和操作，因此不能理解为对本申请的限制。此外，术语“第一”、“第二”、“第三”仅用于描述目的，而不能理解为指示或暗示相对重要性。
在本申请的描述中，需要说明的是，除非另有明确的规定和限定，术语“安装”、“相连”、“连接”应做广义理解，例如，可以是固定连接，也可以是可拆卸连接，或一体地连接；可以是机械连接，也可以是电连接；可以是直接相连，也可以通过中间媒介间接相连，还可以是两个元件内部的连通，可以是无线连接，也可以是有线连接。对于本领域的普通技术人员而言，可以具体情况理解上述术语在本申请中的具体含义。
此外，下面所描述的本申请不同实施方式中所涉及的技术特征只要彼此之间未构成冲突就可以相互结合。
本实施例提供一种基于宽带射频检测的多局放源指纹定位方法，如图1所示，包括如下步骤：
S101，当接收到多个局放传感器采集到的信号数据，根据多个局放传感器采集到的信号数据，形成信号指纹。
示例性地，局放传感器可以是预先部署在监测区域内用于采集信号数据的信号采集设备，比如宽带天线，能够采集监测区域内的电磁波信号。 信号数据可以包括信号强度、信号的时域波形、幅值均值和方差、频域特性等信号特征数据。信号指纹表征任意一个局放源发出的信号在每个局放传感器处形成的信号特征。
接收多个局放传感器采集到的信号数据的方式可以是将实施本方法的处理器与部署于监测区域内的信号采集设备通信连接，从而通过有线或无线方式接收多个局放传感器采集到的信号数据。形成信号指纹的方式可以是将多个局放传感器采集到的同一分类的信号数据进行组合，其表征形式可以是：ψ′ j＝[ψ′ 1,j,ψ′ 2,j,...,ψ′ L,j] T，其中，ψ′ L，j表征第L个局放传感器采集到在网格测试点j发出的信号数据。
S102，将各类信号指纹与预先建立的指纹库进行匹配，得到局放源的位置信息，预先建立的指纹库由多个局放传感器对监测区域内的测试点分别进行模拟局放测试得到。
示例性地，预先建立的指纹库具体构建过程可以通过以下构建过程为例进行说明：
将监测区域划分成均匀分布的N个网格，每个网格依次记为QY j，j＝1,2,…,N，以每个网格的中心作为模拟局放的测试点；另外布置L个局放传感器，记为CG i，i＝1,2,…,L。依次在每个测试点进行模拟局部放电，当在QY j点模拟局部放电时，局放传感器CG i测得的电磁波信号强度为ψ i，j。基于信号强度可以构建以下包含被监测区域的信号传播特性的指纹库，即：
Figure PCTCN2021124034-appb-000001
进一步地，在构建指纹库时，为了保证指纹库中指纹的准确性，还可以通过局放传感器多次对同一测试点进行模拟局部放电检测得到的多个信号强度，根据多个信号强度，得到在该测试点发生局放时局放传感器检测 到的平均信号强度，也即，
Figure PCTCN2021124034-appb-000002
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。
再次，对所述各局放传感器检测到的平均信号强度进行归一化处理，得到归一化后的指纹库。归一化处理可以减弱放电电源波动引起的误差，并突出各个局放传感器接收的信号强度之间的相对大小关系。
具体归一化处理的方法可根据以下公式实现：
Figure PCTCN2021124034-appb-000003
本实施例对归一化方法不做限定，本领域技术人员可以根据需要确定。
将信号指纹与预先建立的指纹库进行匹配，得到局放源的位置信息的方式可以通过以下匹配过程为例进行说明：
假设QY j点发生了局部放电，各局放传感器测得的信号强度值为ψ′ j＝[ψ′ 1,j,ψ′ 2,j,...,ψ′ L,j] T，即生成了一个指纹。将该指纹输入到指纹库Ψ中进行模式匹配，匹配到指纹库中的第j列：ψ j＝[ψ 1,j,ψ 2,j,...,ψ L,j] T，则局放源位置定位结果就是第j个测量点QY j。
本实施例提供的基于宽带射频检测的多局放源指纹定位方法，通过接收多个局放传感器采集到的信号数据，形成信号指纹，并根据信号指纹与预先建立的指纹库进行匹配，从而得到局放源的位置信息，而不需要通过高精度的硬件采集设备以保证高时间同步精度，即使采集硬件性能较差，也能实现局放源的精确定位。
作为本实施例一种可选的实施方式，根据多个局放传感器采集到的信号数据，形成信号指纹，包括：
首先，根据目标聚类算法，分别对各局放传感器采集到的信号数据进 行聚类，得到分类信号；
示例性地，由于各局放源激发的信号数据(脉冲信号)是存在差异的，因此可利用采集到的信号数据中的时域波形、幅值均值和方差、频域特性等信号特征，分析脉冲信号的相似性，设定相关性门限，通过目标聚类算法对脉冲信号进行分类，从而有效挖掘各局放脉冲信号的相似性和差异性，实现对多局放源的分类辨别。其中，目标聚类算法可以是基于划分的聚类算法、层次聚类算法、密度聚类算法等等，本实施例对此不做限定，本领域技术人员可以根据需要确定。
根据目标聚类算法，分别对局放传感器采集到的信号数据进行聚类，得到分类信号的具体过程可以通过以下过程为例进行说明：
假设在监测区域部署有四个局放传感器，各局放传感器之间保持同步触发采集，在1分钟内任意一个局放传感器采集到100条信号数据，但这100条数据可能不是来自于同一个局放源，因此，需要对着100条信号数据进行聚类分析，从而区分出来自不同局放源的信号数据，以信号数据中陡脉冲时长、信号幅值最大值两个特征量进行聚类进行说明，不同的局放源发出的信号波形特征不同，将100条信号数据分别提取出其波形特征应用层次聚类方法，通过欧式距离来计算不同数据样本间的距离(相似度)，聚类数量可以按照不同检测需求进行相应设置，在此不作限定。由此，可以将这100条信号数据进行分类。
然后，根据分类信号，形成至少一组信号指纹。
示例性地，由于各局放传感器之间保持同步触发采集，因此可以通过各采集数据记录的时刻判断四个局放传感器中哪些信号是同一局放源产生的。
进一步，对同一类信号分别求取各局放传感器采集到的信号强度平均值，并将各局放传感器的信号强度平均值进行组合，形成信号指纹，组合 的具体表现形式可以是：ψ′ j＝[ψ′ 1，j，ψ′ 2，j，ψ′ 3，j，ψ′ 4，j]，其中，ψ′ 1，j表征第一个局放传感器采集到的第j个局放源发出的信号平均强度，ψ′ 2，j表征第二个飓风传感器采集到的第j个局放源发出的信号平均强度，ψ′ 3，j表征第三个局放传感器采集到的第j个局放源发出的信号平均强度，ψ′ 4，j表征第四个局放传感器采集到的第j个局放源发出的信号平均强度。
本实施例提供的基于宽带射频检测的多局放源指纹定位方法，对信号数据进行聚类，得到分类信号，形成分类信号的信号指纹，实现对多局放源的准确定位，提升对多局放源的检测能力，为电力设备的安全可靠运行和状态检修提供重要参考依据。
为了验证上述方法，本实施例选取4m×3m的区域，如图2所示，布置12个测试点(图中圆圈内的1-12)，测量点成网格状均匀分布，互相间隔1m。4个局放传感器分布于四个角，将模拟局放源置于1号测试点和4号测试点。根据对局放传感器采集的信号数据进行聚类分析，将采集信号分成了2组，各组信号的信号强度如下所示：
第一组信号的平均强度为：局放传感器1：-26.78dBm；局放传感器2：-20.35dBm；局放传感器3：-21.7dBm；局放传感器4：-23.14dBm；形成的信号指纹可以表示为：[-26.78，-20.35，-21.7，-23.14]
第二组信号的平均强度为：局放传感器1：-20.98dBm；局放传感器2：-25.32dBm；局放传感器3：-24.44dBm；局放传感器4：-22.98dBm。形成的信号指纹可以表示为：[-20.98，-25.32，-24.44，-22.98]
对聚类分析后的各分类信号分别应用指纹定位方法对信号进行定位匹配，如图3所示，第一组信号定位结果是位于4号位置；如图4所示，第二组信号定位结果是位于1号位置。
作为本实施例一种可选的实施方式，将信号指纹与预先建立的指纹库进行匹配，得到局放源的位置信息，包括：根据信号指纹与预先建立的指 纹库构建基于稀疏向量的关系矩阵；根据压缩感知中的重构算法对关系矩阵进行求解，得到局放源的位置信息。
示例性地，在监测区域中局放源的位置具有天然稀疏性，通过离散物理空间将局放源的位置转化成稀疏度为1的向量，比如当局放源在QY j点，那么定位结果除了可以用其坐标表示外，还可表示为W＝[0,...,0,1,0,...,0] T，只有QY j点所在的位置被标为1，其余的位置均为0。从而将局放源定位问题转化成稀疏向量重构问题，以求解出局放源所在的监测网格。
基于稀疏向量的关系矩阵可以是：
P＝ΨW；         (1)
其中，P＝ψ j＝[ψ 1,j,ψ 2,j,...,ψ L,j] T，即QY j点为局放源时各局放传感器测得的信号数据，也即信号指纹，Ψ为预先建立的指纹库。
式(1)两边同乘以测量矩阵Φ得
ΦP＝ΦΨW        (2)
令ΦP＝Y，则
Y＝ΦΨW           (3)
由于W是一个稀疏度为1的N×1维向量，具有稀疏性，因此满足Y＝ΦΨW中最稀疏的向量就是所求解。其中，Φ为测量矩阵，是m×L维，m<<N，可采用高斯随机测量矩阵，其符合均值为0，方差为1的正态分布。Θ为传感矩阵，令Θ＝ΦΨ，Θ是m×N维。
根据压缩感知理论，利用式(3)中的Y以及传感矩阵Θ，通过重构算法，就可以求出W，即得到定位结果。
由于W具有稀疏性，因此重构W的问题就可以转换为求解最小范数l 0的问题，求解这一优化问题就可以较高概率地重构出W。
arg min||W|| 0 s.t. Y＝ΘW           (4)
其中，||W|| 0表示向量W中非零元素个数。
由于l 0范数是求解向量中非零项的数量，因此式(4)的求解相对困难。为了简化求解难度，在满足RIP条件(受限等距性质)下，将最小范数l 0模型转换为最小范数l 1模型也可以得到等价的解，其中，范数l 0定义为
Figure PCTCN2021124034-appb-000004
表示向量x中非零元素的个数。范数l 1定义为
Figure PCTCN2021124034-appb-000005
表示向量x中非零元素的绝对值之和。最小范数l 1优化模型为：
arg min||W|| 1 s.t. Y＝ΘW           (5)
求解这一模型通常可用运算复杂度较小、运算速度较快的贪婪算法，如正交匹配追踪(Orthogonal Matching Pursuit,OMP)算法、SOMP算法等。
作为本实施例一种可选的实施方式，根据多个局放传感器集到的信号数据，形成信号指纹，包括：对信号数据进行模拟信号处理和数字信号处理，得到处理后的信号数据；根据处理后的信号数据，形成信号指纹。
示例性地，模拟信号处理主要是为了提高信噪比，通过带通滤波模块将检测频段设置在期望的范围，例如100MHz-1.5GHz频段。通过低噪声放大器对滤波后的信号进行固定增益的低噪声放大。通过增益控制实现对采集信号的放大或衰减进行动态控制，使得采集信号适配于数模转换器的输入信号幅值范围内，充分利用其数字化分辨率。
经模拟信号处理后的信号后对采集信号进行数字化，再进一步对数字化信号进行降噪和特征提取分析，从而实现对局放脉冲信号的辨识。降噪通常可用小波变换等方法，特征提取通常可以是局放脉冲信号的陡脉冲时长、信号幅值最大值等。
本实施例提供一种基于宽带射频检测的多局放源指纹定位装置，如图5所示，包括：
信号指纹确定模块201，用于当接收到多个局放传感器采集到的信号数 据，根据所述多个局放传感器采集到的信号数据，形成信号指纹；具体内容参见上述实施例对应部分，在此不再赘述。
位置信息确定模块202，用于将所述信号指纹与预先建立的指纹库进行匹配，得到局放源的位置信息，所述预先建立的指纹库由多个局放传感器对监测区域内的测试点分别进行模拟局放测试得到。具体内容参见上述实施例对应部分，在此不再赘述。
作为本实施例一种可选的实施方式，位置信息确定模块202，包括：
聚类模块，用于根据目标聚类算法，对局放传感器采集到的信号数据进行聚类，得到分类信号；具体内容参见上述实施例对应部分，在此不再赘述。
分组模块，用于根据所述分类信号，形成至少一组信号指纹。具体内容参见上述实施例对应部分，在此不再赘述。
作为本实施例一种可选的实施方式，位置信息确定模块202，包括：
关系矩阵确定模块，用于根据所述信号指纹与预先建立的指纹库构建基于稀疏向量的关系矩阵；具体内容参见上述实施例对应部分，在此不再赘述。
位置信息确定子模块，用于根据压缩感知中的重构算法对所述关系矩阵进行求解，得到局放源的位置信息。具体内容参见上述实施例对应部分，在此不再赘述。
作为本实施例一种可选的实施方式，信号指纹确定模块201，包括：
处理模块，用于对所述信号数据进行模拟信号处理和数字信号处理，得到处理后的信号数据；具体内容参见上述实施例对应部分，在此不再赘述。
信号指纹确定子模块，用于根据所述处理后的信号数据，形成信号指纹。具体内容参见上述实施例对应部分，在此不再赘述。
作为本实施例一种可选的实施方式，位置信息确定模块202，包括：
多组信号强度信息接收模块，用于接收局放传感器多次检测采集得到的多个信号强度；具体内容参见上述实施例对应部分，在此不再赘述。
平均信号强度确定模块，用于根据所述多个信号强度，计算所述聚类后各分类信号的平均信号强度；具体内容参见上述实施例对应部分，在此不再赘述。
归一化模块，用于对所述各局放传感器检测到的平均信号强度进行归一化处理。具体内容参见上述实施例对应部分，在此不再赘述。
指纹库构建模块，用于根据归一化处理后的平均信号强度，构建指纹库；具体内容参见上述实施例对应部分，在此不再赘述。
作为本实施例一种可选的实施方式，聚类模块包括：基于划分的聚类模块、层次聚类模块、密度聚类模块中的任意一种。具体内容参见上述实施例对应部分，在此不再赘述。
本申请实施例还提供一种电子设备，如图6所示，处理器310和存储器320，其中处理器310和存储器320可以通过总线或者其他方式连接。
处理器310可以为中央处理器(Central Processing Unit，CPU)。处理器310还可以为其他通用处理器、数字信号处理器(Digital Signal Processor，DSP)、专用集成电路(Application Specific Integrated Circuit，ASIC)、现场可编程门阵列(Field-Programmable Gate Array，FPGA)或者其他可编程逻辑器件、分立门或者晶体管逻辑器件、分立硬件组件等芯片，或者上述各类芯片的组合。
存储器320作为一种非暂态计算机可读存储介质，可用于存储非暂态软件程序、非暂态计算机可执行程序以及模块，如本申请实施例中的基于宽带射频检测的多局放源指纹定位方法对应的程序指令/模块。处理器通过运行存储在存储器中的非暂态软件程序、指令以及模块，从而执行处理器 的各种功能应用以及数据处理。
存储器320可以包括存储程序区和存储数据区，其中，存储程序区可存储操作系统、至少一个功能所需要的应用程序；存储数据区可存储处理器所创建的数据等。此外，存储器可以包括高速随机存取存储器，还可以包括非暂态存储器，例如至少一个磁盘存储器件、闪存器件、或其他非暂态固态存储器件。在一些实施例中，存储器320可选包括相对于处理器远程设置的存储器，这些远程存储器可以通过网络连接至处理器。上述网络的实例包括但不限于互联网、企业内部网、局域网、移动通信网及其组合。
所述一个或者多个模块存储在所述存储器320中，当被所述处理器310执行时，执行如图1所示实施例中的基于宽带射频检测的多局放源指纹定位方法。
上述电子设备的具体细节可以对应参阅图1所示的实施例中对应的相关描述和效果进行理解，此处不再赘述。
本实施例还提供了一种计算机存储介质，所述计算机存储介质存储有计算机可执行指令，该计算机可执行指令可执行上述任意方法实施例中基于宽带射频检测的多局放源指纹定位方法。其中，所述存储介质可为磁碟、光盘、只读存储记忆体(Read-Only Memory，ROM)、随机存储记忆体(Random Access Memory，RAM)、快闪存储器(Flash Memory)、硬盘(Hard Disk Drive，缩写：HDD)或固态硬盘(Solid-State Drive，SSD)等；所述存储介质还可以包括上述种类的存储器的组合。
显然，上述实施例仅仅是为清楚地说明所作的举例，而并非对实施方式的限定。对于所属领域的普通技术人员来说，在上述说明的基础上还可以做出其它不同形式的变化或变动。这里无需也无法对所有的实施方式予以穷举。而由此所引伸出的显而易见的变化或变动仍处于本申请创造的保护范围之中。

Claims (10)

  1. 一种基于宽带射频检测的多局放源指纹定位方法，包括：
    当接收到多个局放传感器采集到的信号数据，根据所述多个局放传感器采集到的信号数据，形成信号指纹；
    将所述信号指纹与预先建立的指纹库进行匹配，得到局放源的位置信息，所述预先建立的指纹库由多个局放传感器对监测区域内的测试点分别进行模拟局放测试得到。
  2. 根据权利要求1所述的方法，其中，根据所述多个局放传感器采集到的信号数据，形成信号指纹，包括：
    根据目标聚类算法，对局放传感器采集到的信号数据进行聚类分析，得到分类信号；
    根据所述分类信号，形成至少一组信号指纹。
  3. 根据权利要求1或2所述的方法，其中，将所述信号指纹与预先建立的指纹库进行匹配，得到局放源的位置信息，包括：
    根据所述信号指纹与预先建立的指纹库构建基于稀疏向量的关系矩阵；
    根据压缩感知中的重构算法对所述关系矩阵进行求解，得到局放源的位置信息。
  4. 根据权利要求1或2所述的方法，其中，根据所述多个局放传感器采集到的信号数据，形成信号指纹，包括：
    对所述信号数据进行模拟信号处理和数字信号处理，得到处理后的信号数据；
    根据所述处理后的信号数据，形成信号指纹。
  5. 根据权利要求1或2所述的方法，其中，所述预先建立的指纹库由多个局放传感器对监测区域内的测试点分别进行模拟局放测试得到，包 括：
    多个局放传感器对同一测试点进行多次模拟局部放电测试得到的多次测试的信号强度；
    根据所述多次测试的信号强度，得到所述测试点的模拟局放在所述多个局放传感器的平均信号强度；
    对监测区域内的网格测试点分别进行模拟局放测试得到每个测试点的平均信号强度，并进行归一化处理，构建指纹库。
  6. 根据权利要求2所述的方法，其中，所述目标聚类算法为基于划分的聚类算法、层次聚类算法、密度聚类算法中的任意一种。
  7. 一种基于宽带射频检测的多局放源指纹定位装置，包括：
    信号指纹确定模块，用于当接收到多个局放传感器采集到的信号数据，根据所述多个局放传感器采集到的信号数据，形成信号指纹；
    位置信息确定模块，用于将所述信号指纹与预先建立的指纹库进行匹配，得到局放源的位置信息，所述预先建立的指纹库由多个局放传感器对监测区域内的测试点分别进行模拟局放测试得到。
  8. 根据权利要求7所述的装置，其中，信号指纹确定模块，包括：
    聚类模块，用于根据目标聚类算法，对局放传感器采集到的信号数据进行聚类分析，得到分类信号；
    分组模块，用于根据所述分类信号，形成至少一组信号指纹。
  9. 一种电子设备，包括存储器、处理器及存储在存储器上并可在处理器上运行的计算机程序，所述处理器执行所述程序时实现权利要求1-6任一所述的基于宽带射频检测的多局放源指纹定位方法的步骤。
  10. 一种存储介质，其上存储有计算机指令，该指令被处理器执行时实现权利要求1-6任一所述的基于宽带射频检测的多局放源指纹定位方法的步骤。
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