Cable partial discharge type identification method and device, and electronic equipment

Abstract

The invention discloses a cable partial discharge type identification method and device and electronic equipment, and relates to the technical field of power grid safety, and the method comprises the steps: collecting a partial discharge data set corresponding to a power cable partial discharge signal; preprocessing data in the partial discharge data set to obtain preprocessed partial discharge data; drawing a PRPD atlas, a discharge pulse sequence atlas and a discharge characteristic statistical atlas based on the preprocessed partial discharge data; partial discharge features are extracted from the PRPD atlas, the discharge pulse sequence atlas and the discharge feature statistical atlas, and partial discharge feature vectors are constructed based on the partial discharge features; and calculating a similarity value between the partial discharge feature vector and a defect feature vector in the defect fingerprint database, and identifying the partial discharge type of the power cable based on the similarity value. The technical problem that the partial discharge type is difficult to accurately identify by adopting a single partial discharge detection method in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid safety, and in particular to a method for identifying the type of partial discharge of a cable, a device thereof, and electronic equipment. Background Art

[0002] Reliability, quality, economy and environmental protection are the basic requirements for the operation of modern power systems. Among them, it is particularly important to ensure reliable and continuous power supply of the power system. Once a large-scale power outage occurs in the power system, economic development and even social public safety will be seriously affected. As an important power transmission equipment, power cables are widely used in power grid assembly and construction. Power cables may be damaged by excessive stretching or bending during installation, or physical damage caused during transportation, such as damage to the insulation sheath of power cables; or many problems may occur during the design and manufacturing process, causing more or less damage to the insulation of power cables, which will leave hidden dangers of failure. In addition, during the long-term operation of power cables, due to the influence of relevant factors such as the surrounding environment, the aging and deterioration of the insulation of power cables will be accelerated, resulting in further deterioration of cable insulation defects. Due to the influence of the above-mentioned possible insulation defects, the electric field strength of some areas on the surface or inside of the insulating material will be higher than the average field strength, and the breakdown strength of some areas will be higher than the average breakdown field strength. At this time, these areas will be the first to discharge, and the remaining areas will still maintain the original insulation characteristics, thereby producing partial discharge. Partial discharge is an important cause of insulation breakdown in high-voltage electrical equipment and an important indicator of insulation degradation.

[0003] In the related technology, the recognition effect of partial discharge signals of power cables is not ideal. In actual detection, there is noise interference caused by equipment and environment. More importantly, the feature extraction and analysis of partial discharge signals are not accurate enough, which greatly weakens the guidance of partial discharge identification for actual projects. The generation of partial discharge is the result of the joint action of multiple factors, such as environment, voltage, shape, size, insulation defects, etc. The influence of multiple factors and the different degrees of influence of different factors make the partial discharge signal complex and changeable, and it is difficult to identify the type and characteristics of discharge from its intrinsic mechanism. At present, statistical analysis and time-frequency domain analysis are more commonly used, and both methods extract the external manifestation characteristics of discharge, such as phase, amplitude, frequency domain, and time-frequency characteristics. Different types of discharges often have similarities in some characteristics, which makes it difficult to accurately detect the type of discharge defects using a single identification method.

[0004] To address the above-mentioned problems, no effective solution has been proposed yet. Summary of the invention

[0005] The embodiment of the present invention provides a method for identifying the type of partial discharge of a cable and a device and electronic equipment thereof, so as to at least solve the technical problem in the related art that it is difficult to accurately identify the type of partial discharge by using a single partial discharge detection method.

[0006] According to one aspect of an embodiment of the present invention, a method for identifying the type of partial discharge of a cable is provided, comprising: collecting a partial discharge data set corresponding to a partial discharge signal of a power cable, wherein the partial discharge data set includes at least: a discharge voltage, a discharge amount, a discharge phase, and a discharge number; preprocessing the partial discharge data in the partial discharge data set to obtain preprocessed partial discharge data; and drawing a PRPD spectrum, a discharge pulse sequence spectrum, and a discharge characteristic statistical spectrum based on the preprocessed partial discharge data, wherein the PRPD spectrum is a distribution of the discharge number of the partial discharge signal with the discharge amount and the discharge phase, the discharge pulse sequence spectrum represents the relationship distribution between the discharge voltages at adjacent time points, and the discharge characteristic statistical spectrum represents the relationship distribution between the discharge voltages at adjacent time points. The method comprises the following steps: a characteristic statistical spectrum represents the distribution of the number of discharges with the voltage difference of the discharge voltage, wherein the voltage difference is the difference between the discharge voltages at two adjacent time points; extracting local discharge features from the PRPD spectrum, the discharge pulse sequence spectrum and the discharge characteristic statistical spectrum, and constructing a local discharge feature vector based on the local discharge features; calculating a similarity value between the local discharge feature vector and a defect feature vector in a defect fingerprint library, and identifying the local discharge type of the power cable based on the similarity value, wherein the defect fingerprint library stores defect feature vectors corresponding to N types of local discharge defects, where N is a positive integer, and the defect feature vectors in the defect fingerprint library at least include: a defect feature vector obtained based on simulating spike discharge using a needle-plate electrode.

[0007] Optionally, the step of obtaining a defect feature vector based on simulating spike discharge by a needle-plate electrode includes: constructing the needle-plate electrode, and using the needle-plate electrode to simulate local discharge defects under different voltages; obtaining simulated local discharge data of the local discharge signal of the needle-plate electrode under different voltages; drawing the PRPD spectrum, the discharge pulse sequence spectrum and the discharge feature statistical spectrum based on the simulated local discharge data; performing feature extraction on the PRPD spectrum, the discharge pulse sequence spectrum and the discharge feature statistical spectrum to obtain defect features corresponding to the simulated local discharge defects of the needle-plate electrode under different voltages; constructing the defect feature vector based on the defect features to obtain the defect feature vectors corresponding to the simulated local discharge defects under different voltages, and storing the defect feature vectors corresponding to the simulated local discharge defects under different voltages in the defect fingerprint library.

[0008] Optionally, the step of drawing the PRPD spectrum, the discharge pulse sequence spectrum and the discharge characteristic statistical spectrum based on the simulated local discharge data includes: extracting leader discharge data from the simulated local discharge data, wherein the leader discharge data is data corresponding to the leader discharge part in the local discharge process, and the leader discharge part represents the part of the local discharge process in which the discharge stability is lower than the stability threshold; drawing the leader PRPD spectrum, the leader discharge pulse sequence spectrum and the leader discharge characteristic statistical spectrum of the leader discharge part based on the leader discharge data; and drawing the complete PRPD spectrum, the complete discharge pulse sequence spectrum and the complete discharge characteristic statistical spectrum of the complete discharge part based on the simulated local discharge data.

[0009] Optionally, the step of extracting features from the PRPD spectrum, the discharge pulse sequence spectrum and the discharge feature statistical spectrum to obtain defect features corresponding to the simulated local discharge defects of the needle-plate electrode at different voltages includes: extracting features from the leading PRPD spectrum to obtain discharge relationship features of the leading discharge part, wherein the discharge relationship features at least include: the relationship between the discharge amount and the discharge phase of the leading discharge part, the relationship between the discharge amount and the number of discharges of the leading discharge part, and the relationship between the discharge phase and the number of discharges of the leading discharge part; extracting features from the leading discharge pulse sequence spectrum to obtain local discharge event distribution features of the leading discharge part; extracting features from the leading discharge feature statistical spectrum to obtain the number, peak value and width of the voltage difference characteristic peaks of the leading discharge part to obtain the voltage difference features of the leading discharge part; and obtaining the defect features corresponding to the simulated local discharge defects of the needle-plate electrode at different voltages based on the discharge relationship features, the local discharge event distribution features and the voltage difference features.

[0010] Optionally, the step of extracting features from the PRPD spectrum, the discharge pulse sequence spectrum and the discharge characteristic statistical spectrum to obtain defect features corresponding to the simulated partial discharge defect of the needle-plate electrode at different voltages includes: obtaining partial discharge distribution data of the complete discharge part in a discharge cycle based on the complete PRPD spectrum of the complete discharge part; calculating mean data of the first voltage difference absolute value and the second voltage difference absolute value of the complete discharge part based on the partial discharge distribution data, the complete discharge pulse sequence spectrum and the complete discharge characteristic statistical spectrum, wherein the first voltage difference absolute value represents the absolute value of the complete discharge part in a discharge cycle. The absolute value of the voltage difference between the first local discharge in the negative half-cycle and the local discharge in the positive half-cycle in a discharge cycle, the absolute value of the second voltage difference represents the absolute value of the voltage difference between two consecutive local discharges in the falling part of the negative half-cycle of the complete discharge part in one signal cycle; the starting voltage value and the ending voltage value of the negative half-cycle in the target discharge cycle are calculated based on the average data of the complete discharge part and the starting voltage value of the positive half-cycle in the target discharge cycle; the defect characteristics corresponding to the simulated local discharge defects of the needle plate electrode at different voltages are obtained based on the starting voltage value and the ending voltage value of the negative half-cycle of the complete discharge part in the target discharge cycle.

[0011] Optionally, the step of identifying the partial discharge type of the power cable based on the similarity value includes: selecting a defect feature vector having the largest similarity value with the partial discharge feature vector as a target defect feature vector; and determining the partial discharge type corresponding to the target defect feature vector, thereby obtaining the partial discharge type of the power cable.

[0012] Optionally, the N types of partial discharge defects are caused by insulation defects of the power cable, and the insulation defects include at least one of the following: metal particles in the insulation, breath in the semiconductor, scratches in the insulation, unqualified cable eccentricity, and sharp tips in the semiconductor.

[0013] According to another aspect of an embodiment of the present invention, a device for identifying the type of partial discharge of a cable is provided, comprising: a collection unit, for collecting a partial discharge data set corresponding to a partial discharge signal of a power cable, wherein the partial discharge data set includes at least: a discharge voltage, a discharge amount, a discharge phase, and a discharge number; a processing unit, for preprocessing the partial discharge data in the partial discharge data set to obtain preprocessed partial discharge data; a drawing unit, for drawing a PRPD spectrum, a discharge pulse sequence spectrum, and a discharge characteristic statistical spectrum based on the preprocessed partial discharge data, wherein the PRPD spectrum is the distribution of the discharge number of the partial discharge signal with the discharge amount and the discharge phase, the discharge pulse sequence spectrum represents the relationship distribution between the discharge voltages at adjacent time points, and the discharge characteristic statistical spectrum is the distribution of the local discharge signal with the discharge amount and the discharge phase. The discharge characteristic statistical spectrum represents the distribution of the number of discharges with the voltage difference of the discharge voltage, and the voltage difference is the difference between the discharge voltages at two adjacent time points; an extraction unit is used to extract local discharge features from the PRPD spectrum, the discharge pulse sequence spectrum and the discharge characteristic statistical spectrum, and construct a local discharge feature vector based on the local discharge features; an identification unit is used to calculate the similarity value between the local discharge feature vector and the defect feature vector in the defect fingerprint library, and identify the local discharge type of the power cable based on the similarity value, wherein the defect fingerprint library stores defect feature vectors corresponding to N types of local discharge defects, N is a positive integer, and the defect feature vectors in the defect fingerprint library at least include: defect feature vectors obtained based on needle-plate electrode simulation spike discharge.

[0014] Optionally, the device for identifying the type of cable partial discharge also includes: a first construction module, used to construct the needle plate electrode, and use the needle plate electrode to simulate partial discharge defects under different voltages; a first acquisition module, used to obtain simulated partial discharge data of the partial discharge signal of the needle plate electrode under different voltages; a first drawing module, used to draw the PRPD spectrum, the discharge pulse sequence spectrum and the discharge characteristic statistical spectrum based on the simulated partial discharge data; a first extraction module, used to extract features from the PRPD spectrum, the discharge pulse sequence spectrum and the discharge characteristic statistical spectrum to obtain defect features corresponding to the simulated partial discharge defects of the needle plate electrode under different voltages; a first construction module, used to construct the defect feature vector based on the defect feature, obtain the defect feature vector corresponding to the simulated partial discharge defect under different voltages, and store the defect feature vector corresponding to the simulated partial discharge defect under different voltages in the defect fingerprint library.

[0015] Optionally, the first drawing module includes: a first extraction submodule, used to extract leader discharge data from the simulated local discharge data, wherein the leader discharge data is data corresponding to the leader discharge part in the local discharge process, and the leader discharge part represents the part of the local discharge process in which the discharge stability is lower than the stability threshold; a first drawing submodule, used to draw the leader PRPD spectrum, leader discharge pulse sequence spectrum and leader discharge characteristic statistical spectrum of the leader discharge part based on the leader discharge data; and a second drawing submodule, used to draw the complete PRPD spectrum, complete discharge pulse sequence spectrum and complete discharge characteristic statistical spectrum of the complete discharge part based on the simulated local discharge data.

[0016] Optionally, the first extraction module includes: a first extraction submodule, used to perform feature extraction on the leader PRPD spectrum to obtain the discharge relationship characteristics of the leader discharge part, wherein the discharge relationship characteristics at least include: the relationship between the discharge amount and the discharge phase of the leader discharge part, the relationship between the discharge amount and the number of discharges of the leader discharge part, and the relationship between the discharge phase and the number of discharges of the leader discharge part; a second extraction submodule, used to perform feature extraction on the leader discharge pulse sequence spectrum to obtain the local discharge event distribution characteristics of the leader discharge part; a third extraction submodule, used to perform feature extraction on the leader discharge feature statistical spectrum to obtain the number, peak value and width of the voltage difference characteristic peaks of the leader discharge part, and obtain the voltage difference characteristics of the leader discharge part; the first acquisition submodule is used to obtain the defect characteristics corresponding to the simulated local discharge defects of the needle plate electrode at different voltages based on the discharge relationship characteristics, the local discharge event distribution characteristics and the voltage difference characteristics.

[0017] Optionally, the first extraction module also includes: a second acquisition submodule, which is used to acquire the local discharge distribution data of the complete discharge part in a discharge cycle based on the complete PRPD spectrum of the complete discharge part; a first calculation submodule, which is used to calculate the mean data of the first voltage difference absolute value and the second voltage difference absolute value of the complete discharge part based on the local discharge distribution data, the complete discharge pulse sequence spectrum and the complete discharge feature statistical spectrum, wherein the first voltage difference absolute value represents the absolute value of the voltage difference between the first local discharge in the negative half cycle and the local discharge in the positive half cycle of the complete discharge part in a discharge cycle, and the second voltage difference absolute value represents the absolute value of the voltage difference between two consecutive local discharges in the falling part of the negative half cycle of the complete discharge part in a signal cycle; the second calculation submodule is used to calculate the starting voltage value and the ending voltage value of the negative half cycle in the target discharge cycle based on the mean data of the complete discharge part and the starting voltage value of the positive half cycle in the target discharge cycle; the third acquisition submodule is used to obtain the defect characteristics corresponding to the simulated local discharge defects of the needle plate electrode at different voltages based on the starting voltage value and the ending voltage value of the negative half cycle of the complete discharge part in the target discharge cycle.

[0018] Optionally, the identification unit includes: a first selection module, used to select a defect feature vector with the largest similarity value with the local discharge feature vector as a target defect feature vector; and a first determination module, used to determine the local discharge type corresponding to the target defect feature vector, thereby obtaining the local discharge type of the power cable.

[0019] Optionally, the N types of partial discharge defects are caused by insulation defects of the power cable, and the insulation defects include at least one of the following: metal particles in the insulation, breath in the semiconductor, scratches in the insulation, unqualified cable eccentricity, and sharp tips in the semiconductor.

[0020] According to another aspect of an embodiment of the present invention, a computer-readable storage medium is further provided, wherein the computer-readable storage medium includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute any one of the above-mentioned methods for identifying the type of partial discharge of a cable.

[0021] According to another aspect of an embodiment of the present invention, there is also provided an electronic device, comprising one or more processors and a memory, wherein the memory is used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement any one of the above-mentioned methods for identifying the type of partial discharge of a cable.

[0022] In the present application, the following steps are performed: first, a local discharge data set corresponding to a local discharge signal of a power cable is collected, wherein the local discharge data set includes at least: a discharge voltage, a discharge amount, a discharge phase, and a discharge number, and the local discharge data in the local discharge data set is preprocessed to obtain the preprocessed local discharge data, and then a PRPD spectrum, a discharge pulse sequence spectrum, and a discharge characteristic statistical spectrum are drawn based on the preprocessed local discharge data, wherein the PRPD spectrum is the distribution of the discharge number of the local discharge signal with the discharge amount and the discharge phase, the discharge pulse sequence spectrum represents the relationship distribution between the discharge voltages at adjacent time points, and the discharge characteristic statistical spectrum represents the discharge characteristic distribution between the discharge voltages at adjacent time points. The distribution of the number of discharges with the voltage difference of the discharge voltage is analyzed, where the voltage difference is the difference between the discharge voltages at two adjacent time points, and local discharge features are extracted from the PRPD spectrum, the discharge pulse sequence spectrum and the discharge feature statistical spectrum, and a local discharge feature vector is constructed based on the local discharge features. Finally, the similarity value between the local discharge feature vector and the defect feature vector in the defect fingerprint library is calculated, and the local discharge type of the power cable is identified based on the similarity value, wherein the defect feature vectors corresponding to N types of local discharge defects are stored in the defect fingerprint library, where N is a positive integer, and the defect feature vectors in the defect fingerprint library at least include: a defect feature vector obtained based on the needle-plate electrode simulating spike discharge.

[0023] In the present application, the local discharge signal of the power cable is analyzed in multiple dimensions such as PRPD spectrum, discharge pulse sequence spectrum and discharge characteristic statistical spectrum, and the external manifestation characteristics and internal intrinsic characteristics of the local discharge signal are deeply excavated, so that the local discharge type of the power cable can be accurately identified based on the multi-dimensional local discharge characteristics, and the recognition accuracy can be improved, thereby ensuring the stable operation of the power system, and further solving the technical problem in the related technology that a single local discharge detection method is used and it is difficult to accurately identify the local discharge type. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0025] Figure 1 is a flow chart of an optional method for identifying cable partial discharge type according to an embodiment of the present invention;

[0026] Figure 2 is a schematic diagram of an optional needle plate electrode according to an embodiment of the present invention;

[0027] Figure 3 is a schematic diagram of an optional device for identifying cable partial discharge type according to an embodiment of the present invention;

[0028] Figure 4 The present invention is a hardware structure block diagram of an electronic device (or mobile device) for identifying a cable partial discharge type according to an embodiment of the present invention.

[0029] Reference numerals:

[0030] Metal columnar electrode 1, electrode plate 2, needle tip 3, insulating shell 4. DETAILED DESCRIPTION

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

[0032] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0033] To facilitate those skilled in the art to understand the present invention, some terms or nouns involved in the embodiments of the present invention are explained below:

[0034] Phase Reso lved Part ia l Discharge, referred to as PRPD diagram, is a tool used to analyze partial discharge activity in high-voltage insulation systems. It intuitively displays partial discharge activity relative to 360 degrees of the AC cycle.

[0035] It should be noted that the cable partial discharge type identification method and device in the present application can be used in the field of power grid safety technology when identifying the partial discharge fault type of the power cable, and can also be used in any field other than the field of power grid safety technology when identifying the partial discharge fault type of the power cable. The application does not limit the application field of the cable partial discharge type identification method and device in the present application.

[0036] The following embodiments of the present invention can be applied to various cable partial discharge type identification systems / applications / equipment. The present invention provides a method for analyzing the type of discharge signal based on PRPD spectrum and discharge pulse sequence spectrum, extracting discharge features by simulating the spike discharge process through needle plate electrodes to establish a defect fingerprint library, thereby providing a reference basis for typical defect identification problems of power cables. PRPD spectrum and discharge pulse sequence spectrum can reflect the external manifestation characteristics and internal intrinsic characteristics of partial discharge from multiple dimensions, thereby accurately identifying the type of partial discharge of power cables, and then quickly maintaining the safety of power cables and ensuring the stable operation of power systems.

[0037] The present invention is described in detail below in conjunction with various embodiments.

[0038] Embodiment 1

[0039] According to an embodiment of the present invention, an embodiment of a method for identifying the type of partial discharge of a cable is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0040] Figure 1 is a flow chart of an optional method for identifying cable partial discharge types according to an embodiment of the present invention, such as Figure 1 As shown, the method comprises the following steps:

[0041] Step S101, collecting a partial discharge data set corresponding to a partial discharge signal of a power cable, wherein the partial discharge data set at least includes: a discharge voltage, a discharge amount, a discharge phase, and a discharge number;

[0042] Step S102, preprocessing the partial discharge data in the partial discharge data set to obtain preprocessed partial discharge data;

[0043] Step S103, based on the pre-processed partial discharge data, a PRPD spectrum, a discharge pulse sequence spectrum and a discharge characteristic statistical spectrum are drawn, wherein the PRPD spectrum is the distribution of the number of discharges of the partial discharge signal with the discharge amount and the discharge phase, the discharge pulse sequence spectrum represents the relationship distribution between the discharge voltages at adjacent time points, and the discharge characteristic statistical spectrum represents the distribution of the number of discharges with the voltage difference of the discharge voltage, where the voltage difference is the difference between the discharge voltages at two adjacent time points;

[0044] Step S104, extracting local discharge features from the PRPD spectrum, the discharge pulse sequence spectrum and the discharge feature statistical spectrum, and constructing a local discharge feature vector based on the local discharge features;

[0045] Step S105, calculating the similarity value between the partial discharge feature vector and the defect feature vector in the defect fingerprint library, and identifying the partial discharge type of the power cable based on the similarity value, wherein the defect fingerprint library stores defect feature vectors corresponding to N types of partial discharge defects, N is a positive integer, and the defect feature vectors in the defect fingerprint library at least include: defect feature vectors obtained based on needle-plate electrode simulation of spike discharge.

[0046] Through the above steps, firstly, a local discharge data set corresponding to the local discharge signal of the power cable is collected, wherein the local discharge data set at least includes: discharge voltage, discharge amount, discharge phase and discharge number, and the local discharge data in the local discharge data set are preprocessed to obtain the preprocessed local discharge data, and then a PRPD spectrum, a discharge pulse sequence spectrum and a discharge characteristic statistical spectrum are drawn based on the preprocessed local discharge data, wherein the PRPD spectrum is the distribution of the discharge number of the local discharge signal with the discharge amount and the discharge phase, the discharge pulse sequence spectrum represents the relationship distribution between the discharge voltages at adjacent time points, and the discharge characteristic statistical spectrum represents the discharge number The distribution of voltage difference with discharge voltage, where the voltage difference is the difference between the discharge voltages at two adjacent time points, extracts local discharge features from PRPD spectrum, discharge pulse sequence spectrum and discharge feature statistical spectrum, and constructs local discharge feature vectors based on local discharge features. Finally, the similarity value between the local discharge feature vector and the defect feature vector in the defect fingerprint library is calculated, and the local discharge type of the power cable is identified based on the similarity value, wherein the defect fingerprint library stores defect feature vectors corresponding to N types of local discharge defects, N is a positive integer, and the defect feature vectors in the defect fingerprint library at least include: defect feature vectors obtained based on needle-plate electrode simulation of spike discharge.

[0047] In this embodiment, the local discharge signal of the power cable is analyzed in multiple dimensions such as the PRPD spectrum, the discharge pulse sequence spectrum, and the discharge characteristic statistical spectrum, and the external manifestation characteristics and internal intrinsic characteristics of the local discharge signal are deeply excavated, so that the local discharge type of the power cable can be accurately identified based on the multi-dimensional local discharge characteristics, and the identification accuracy is improved, thereby ensuring the stable operation of the power system, and further solving the technical problem in the related technology that a single local discharge detection method is used and it is difficult to accurately identify the local discharge type.

[0048] The embodiment of the present invention is described in detail below in combination with the above steps.

[0049] Optionally, the N types of partial discharge defects are caused by insulation defects of the power cable, and the insulation defects include at least one of the following: metal particles in the insulation, breath in the semiconductor, scratches in the insulation, unqualified cable eccentricity, and sharp tips in the semiconductor.

[0050] It should be noted that power cables, as important power transmission equipment, need to be important monitoring objects to ensure their safety and stability. On power cables, most local discharge defects are caused by insulation defects of the cables. Insulation defects can be metal particles in the insulation, odor in the semiconductor, scratches in the insulation, unqualified cable eccentricity, sharp points in the semiconductor, etc. There are also various reasons for insulation defects, such as physical damage to the power cables during transportation, damage to the power cables due to excessive stretching or bending during use, etc.

[0051] It should be noted that the embodiment of the present invention simulates the spike discharge process through the needle plate electrode to enrich the power cable defect fingerprint library, thereby providing a certain recognition basis for the identification of the type of local discharge of the power cable. Spike discharge is a typical type of local discharge on the power cable. It is a local discharge phenomenon caused by factors such as the presence of fine burrs or pointed protrusions inside the power cable. When the external electric field strength increases, the electric field near the tip will accumulate to form an extremely uneven electric field, thereby triggering spike local discharge. Over time, the insulation of the power cable may be broken down. Spike local discharge usually occurs at the tip of the spike, and it is easy to accumulate charge at the top of the spike with a small radius of curvature or a spike with a large curvature. During the long-term operation of the power cable, the charge accumulation increases and the electric field strength increases, resulting in more spike local discharge at the spike site.

[0052] It should be noted that in addition to spike discharge, power cables can also include various types such as suspended partial discharge, internal air gap partial discharge and surface partial discharge. Suspended partial discharge is caused by the presence of freely suspended metal particles inside the conductor or accessories of the power cable. When the external electric field is enhanced, these metal particles may generate a high potential to the ground, causing partial discharge. This discharge usually occurs in places where the voltage regulating winding, the metal bolts used for fixing and the high-voltage bushing are not in close contact; internal air gap partial discharge is called bubble discharge or air gap discharge, which often occurs in the dielectric of the power cable insulation layer or in the bubbles at the interface between the dielectric layer and the cable core. The electric field strength to which these tiny bubbles are subjected is higher than the electric field strength of the cable insulation layer dielectric. When the external electric field strength is high, partial discharge will occur; surface partial discharge often occurs between the interface of rubber and epoxy resin. When there is a gap between the two, the electric field strength to which the interface is subjected is higher than its minimum breakdown electric field, and partial discharge occurs along the surface of the insulating medium. This type of discharge often occurs on the outer surfaces of electrical equipment such as disconnectors, bushings, and cables. The embodiments of the present invention can also design other physical models to simulate various local discharge processes, so as to obtain local discharge characteristics of different local discharge types and improve the defect fingerprint library. When a defect occurs in a power cable, the defect type can be quickly located and solutions can be provided for specific defect types, thereby speeding up the defect handling efficiency and ensuring the safe and stable operation of the power cable.

[0053] Optionally, the step of obtaining a defect feature vector based on simulating spike discharge with a needle-plate electrode includes: constructing a needle-plate electrode, and using the needle-plate electrode to simulate local discharge defects under different voltages; obtaining simulated local discharge data of local discharge signals of the needle-plate electrode under different voltages; drawing a PRPD spectrum, a discharge pulse sequence spectrum and a discharge feature statistical spectrum based on the simulated local discharge data; performing feature extraction on the PRPD spectrum, the discharge pulse sequence spectrum and the discharge feature statistical spectrum to obtain defect features corresponding to the simulated local discharge defects of the needle-plate electrode under different voltages; constructing a defect feature vector based on the defect features to obtain defect feature vectors corresponding to the simulated local discharge defects under different voltages, and storing the defect feature vectors corresponding to the simulated local discharge defects under different voltages in a defect fingerprint library.

[0054] In the embodiment of the present invention, a needle plate electrode is first constructed to simulate partial discharge defects under multiple voltages. Figure 2 is a schematic diagram of an optional needle plate electrode according to an embodiment of the present invention, such as Figure 1As shown, the needle plate electrode includes a metal columnar electrode 1 and an electrode plate 2 which are arranged at intervals. The metal columnar electrode 1 is connected to high voltage, and the electrode plate 2 is grounded. A needle tip 3 is arranged at one end of the metal columnar electrode 1 relative to the electrode plate 2. The distance between the needle tip 3 and the electrode plate 2 is 6 mm. The space between the metal columnar electrode 1 and the electrode plate 2 is filled with air. The needle plate electrode also includes an insulating shell 4 for protecting the internal structure of the needle plate electrode.

[0055] It should be noted that, in the process of simulating local discharge of the needle-plate electrode, a pulse current test device is used to measure the voltage, discharge amount, phase information and other simulated local discharge data of the local discharge signal of the needle-plate electrode under different voltages, and the PRPD spectrum, discharge pulse sequence spectrum and discharge characteristic statistical spectrum are drawn according to the measured simulated local discharge data. Specifically, the PRPD spectrum uses the number of discharges of a local discharge event as the horizontal coordinate and the discharge amount of the local discharge event as the vertical coordinate, indicating the distribution of the number of discharges of the local discharge signal with the discharge amount and the discharge phase. The discharge pulse sequence spectrum uses the voltage value at time point t as the vertical coordinate and the voltage value at time t-1 as the horizontal coordinate, indicating the distribution of the relationship between the discharge voltages at adjacent time points. The discharge characteristic statistical spectrum uses the voltage difference as the horizontal coordinate and the number of discharges as the vertical coordinate, indicating the distribution of the voltage difference between the discharge number and the discharge voltage, and the voltage difference is the difference between the discharge voltages at two adjacent time points.

[0056] Optionally, the step of drawing a PRPD spectrum, a discharge pulse sequence spectrum and a discharge characteristic statistical spectrum based on simulated local discharge data includes: extracting leader discharge data from the simulated local discharge data, wherein the leader discharge data is data corresponding to a leader discharge part in the local discharge process, and the leader discharge part represents a part in the local discharge process where the discharge stability is lower than a stability threshold; drawing a leader PRPD spectrum, a leader discharge pulse sequence spectrum and a leader discharge characteristic statistical spectrum of the leader discharge part based on the leader discharge data; and drawing a complete PRPD spectrum, a complete discharge pulse sequence spectrum and a complete discharge characteristic statistical spectrum of the complete discharge part based on the simulated local discharge data.

[0057] It should be noted that after obtaining the simulated partial discharge data, it is necessary to extract the leader discharge part from the data, so as to divide the data analysis into data analysis of the leader discharge part and data analysis of the complete discharge part. Leader discharge refers to the unstable partial discharge at the beginning of the simulated partial discharge process, that is, the leader discharge part represents the part of the partial discharge process where the discharge stability is lower than the stability threshold. The leader discharge data is used to draw the leader PRPD spectrum, leader discharge pulse sequence spectrum and leader discharge characteristic statistical spectrum corresponding to the leader discharge part, and the leader PRPD spectrum, leader discharge pulse sequence spectrum and leader discharge characteristic statistical spectrum under different voltages are compared and analyzed respectively, so as to extract the partial discharge characteristics of the leader discharge part.

[0058] Similarly, for the complete discharge data of the complete discharge part, the complete PRPD spectrum, complete discharge pulse sequence spectrum and complete discharge feature statistical spectrum corresponding to the complete discharge part are drawn, and the complete PRPD spectrum, complete discharge pulse sequence spectrum and complete discharge feature statistical spectrum under different voltages are compared and analyzed respectively to extract the local discharge characteristics of the complete discharge part, so as to improve the defect fingerprint library based on the local discharge characteristics of the leader discharge part and the local discharge characteristics of the complete discharge part.

[0059] Optionally, the step of extracting features from the PRPD spectrum, the discharge pulse sequence spectrum and the discharge feature statistical spectrum to obtain defect features corresponding to the simulated local discharge defects of the needle-plate electrode at different voltages includes: extracting features from the leading PRPD spectrum to obtain the discharge relationship features of the leading discharge part, wherein the discharge relationship features at least include: the relationship between the discharge amount and the discharge phase of the leading discharge part, the relationship between the discharge amount and the number of discharges of the leading discharge part, and the relationship between the discharge phase and the number of discharges of the leading discharge part; extracting features from the leading discharge pulse sequence spectrum to obtain the local discharge event distribution features of the leading discharge part; extracting features from the leading discharge feature statistical spectrum to obtain the number, peak value and width of the voltage difference characteristic peaks of the leading discharge part to obtain the voltage difference features of the leading discharge part; and obtaining the defect features corresponding to the simulated local discharge defects of the needle-plate electrode at different voltages based on the discharge relationship features, the local discharge event distribution features and the voltage difference features.

[0060] It should be noted that when extracting features from the PRPD spectrum, discharge pulse sequence spectrum and discharge feature statistical spectrum of the leading discharge part, the relationship between the discharge amount and the discharge phase of the leading discharge part, the relationship between the discharge amount and the number of discharges, and the relationship between the discharge phase and the number of discharges of the leading discharge part are first obtained based on the PRPD spectrum. Then, the local discharge event distribution characteristics of the leading discharge part are extracted from the leading discharge pulse sequence spectrum. At the same time, based on the leading discharge feature statistical spectrum, the number, peak value and width of the voltage difference characteristic peaks are extracted to obtain the voltage difference characteristics of the leading discharge part. Finally, the extracted discharge relationship characteristics, local discharge event distribution characteristics and voltage difference characteristics and their corresponding input voltages are stored in the defect fingerprint library.

[0061] Optionally, the steps of extracting features from the PRPD spectrum, the discharge pulse sequence spectrum and the discharge feature statistical spectrum to obtain defect features corresponding to the simulated local discharge defect of the needle-plate electrode at different voltages include: obtaining local discharge distribution data of the complete discharge part in a discharge cycle based on the complete PRPD spectrum of the complete discharge part; calculating the mean data of the first voltage difference absolute value and the second voltage difference absolute value of the complete discharge part based on the local discharge distribution data, the complete discharge pulse sequence spectrum and the complete discharge feature statistical spectrum, wherein the first voltage difference absolute value represents the absolute value of the voltage difference between the first local discharge in the negative half cycle and the local discharge in the positive half cycle of the complete discharge part in a discharge cycle, and the second voltage difference absolute value represents the absolute value of the voltage difference between two consecutive local discharges in the falling part of the negative half cycle of the complete discharge part in a signal cycle; calculating the starting voltage value and the ending voltage value of the negative half cycle in the target discharge cycle based on the mean data of the complete discharge part and the starting voltage value of the positive half cycle in the target discharge cycle; and obtaining the defect features corresponding to the simulated local discharge defect of the needle-plate electrode at different voltages based on the starting voltage value and the ending voltage value of the negative half cycle of the complete discharge part in the target discharge cycle.

[0062] It should be noted that when extracting features from the PRPD spectrum, discharge pulse sequence spectrum and discharge characteristic statistical spectrum of the complete discharge part, the complete PRPD spectrum of the complete partial discharge data is analyzed to obtain the partial discharge distribution data within a discharge cycle. A discharge cycle refers to a complete discharge phase cycle. According to the PRPD spectrum of each cycle, dV1+, dV1- (corresponding to the absolute value of the second voltage difference), dV2+ and dV2- (corresponding to the absolute value of the first voltage difference) are defined, where dV2- is the partial discharge voltage between the first partial discharge in the negative half cycle and the partial discharge voltage in the positive half cycle. The absolute value of the voltage difference; dV1- is the absolute value of the voltage difference between two consecutive partial discharges in the falling part of the negative half cycle; dV1+ is the absolute value of the voltage difference between two consecutive partial discharges in the rising part of the negative half cycle; dV2+ is the absolute value of the voltage difference between the partial discharge in the positive half cycle and the last partial discharge in the negative half cycle. Based on the absolute value of the voltage difference, i.e., the average of dV1- and dV2+ and the starting voltage VI+ of the positive half cycle of the partial discharge, the estimated values ​​of the starting voltage VI- and the ending voltage VX- of the negative half cycle of the partial discharge are obtained. The calculation expressions for the estimated values ​​of the starting voltage VI- and the ending voltage VX- of the negative half cycle of the partial discharge are expressed as follows:

[0063]

[0064] Finally, based on the starting voltage value and ending voltage value of the negative half-cycle of the complete discharge part in the target discharge cycle, the defect characteristics corresponding to the simulated partial discharge defect of the needle-plate electrode at different voltages are obtained, and compared with the numerical values ​​of other defects to obtain the defect characteristics and feature vectors of spike discharge, thereby improving the defect fingerprint library.

[0065] Specifically, when the applied voltage rises to the point where the field strength reaches the breakdown field strength of the gas, corona discharge occurs near the needle tip. Spike discharge is a self-sustaining discharge form unique to extremely non-uniform electric fields. In this embodiment, by analyzing data from different discharge scenarios at different voltages, it is possible to learn the degree of spike discharge, the distribution characteristics of spike discharge events, and the number of spike discharges as the voltage changes. This allows the spike discharge to be distinguished from other defect types, providing a data basis for identifying local discharge types.

[0066] Step S101, collecting a partial discharge data set corresponding to a partial discharge signal of a power cable.

[0067] It should be noted that during the operation of the power cable, the actual state of the power cable is monitored in real time, the partial discharge signal of the power cable is collected, and the partial discharge data of the partial discharge signal is obtained. Partial discharge is accompanied by the generation of high-frequency current, and high-frequency current sensors can be deployed around the power cable to collect partial discharge signals. The collected partial discharge data set includes at least: discharge voltage, discharge amount, discharge phase and discharge times.

[0068] Step S102, preprocessing the partial discharge data in the partial discharge data set to obtain preprocessed partial discharge data.

[0069] It should be noted that when collecting data, due to the influence of equipment and environment, the collected partial discharge data needs to be preprocessed. The preprocessing can include removing noise, filling missing values, etc., so as to obtain processed partial discharge data.

[0070] Step S103, drawing a PRPD spectrum, a discharge pulse sequence spectrum and a discharge characteristic statistical spectrum based on the pre-processed partial discharge data.

[0071] It should be noted that after the local discharge data is processed, the data of the leader discharge part can be extracted from the local discharge data, thereby splitting the local discharge data into the data of the leader discharge part and the data of the complete discharge part, and drawing the PRPD spectrum, discharge pulse sequence spectrum and discharge characteristic statistical spectrum for the data of the leader discharge part and the complete discharge part respectively, to obtain multiple spectra under different scenarios.

[0072] Step S104, extracting local discharge features from the PRPD spectrum, the discharge pulse sequence spectrum and the discharge feature statistical spectrum, and constructing a local discharge feature vector based on the local discharge features.

[0073] It should be noted that after constructing the data of the leading discharge part and the complete discharge part to draw the PRPD spectrum, the discharge pulse sequence spectrum and the discharge characteristic statistical spectrum, the multi-dimensional local discharge characteristics are extracted from the spectrum, and the local discharge characteristics are converted into local discharge feature vectors. The external manifestation characteristics of the local discharge phenomenon can be extracted from the PRPD spectrum, such as phase characteristics, amplitude characteristics, frequency domain characteristics, etc. The internal intrinsic characteristics of the local discharge phenomenon can be obtained based on the feature analysis of the discharge pulse sequence spectrum and the discharge characteristic statistical spectrum, such as: voltage difference characteristics, local discharge event distribution characteristics, etc. For each type of local discharge, the internal intrinsic characteristics are different.

[0074] Step S105, calculating a similarity value between the partial discharge feature vector and a defect feature vector in a defect fingerprint library, and identifying the partial discharge type of the power cable based on the similarity value.

[0075] It should be noted that when identifying the type of local discharge through defect feature vectors, the defect fingerprint library is called. The defect feature vectors corresponding to various types of defects are stored in the defect fingerprint library. The Euclidean distance between the local feature vector and each defect feature vector in the fingerprint defect library is calculated, and the inverse of the Euclidean distance is calculated to obtain the similarity value between the local feature vector and the defect feature vector. The defect type corresponding to the defect feature vector with the largest similarity value is selected as the local discharge type of the power cable.

[0076] It should be noted that the defect fingerprint library stores defect feature vectors corresponding to N types of partial discharge defects, where N is a positive integer. The defect feature vectors in the defect fingerprint library at least include: defect feature vectors obtained based on needle-plate electrode simulation of spike discharge.

[0077] Optionally, the step of identifying the partial discharge type of the power cable based on the similarity value includes: selecting a defect feature vector having the largest similarity value with the partial discharge feature vector as a target defect feature vector; and determining the partial discharge type corresponding to the target defect feature vector, thereby obtaining the partial discharge type of the power cable.

[0078] It should be noted that, among all defect feature vectors, the defect feature vector with a large similarity value to the local feature vector is selected as the target defect feature vector. The larger the similarity value, the more similar the characteristics of this local discharge are to the defect feature vectors in the defect fingerprint library. Therefore, the local discharge type corresponding to the target defect feature vector can be used as the local discharge type of the power cable to obtain the local discharge type identification result of the power cable.

[0079] The following is a detailed description in conjunction with another embodiment.

[0080] Embodiment 2

[0081] A cable partial discharge type identification device provided in this embodiment includes multiple implementation units, each implementation unit corresponds to each implementation step in the above-mentioned embodiment 1. Its specific implementation method and beneficial effects can refer to the above-mentioned method embodiment and will not be repeated here.

[0082] Figure 3 is a schematic diagram of an optional cable partial discharge type identification device according to an embodiment of the present invention, such as Figure 3 As shown, the cable partial discharge type identification device may include: a collection unit 31, a processing unit 32, a drawing unit 33, an extraction unit 34, and an identification unit 35, wherein:

[0083] The collecting unit 31 is used to collect a partial discharge data set corresponding to the partial discharge signal of the power cable, wherein the partial discharge data set at least includes: discharge voltage, discharge amount, discharge phase and discharge times;

[0084] A processing unit 32, configured to pre-process the partial discharge data in the partial discharge data set to obtain pre-processed partial discharge data;

[0085] A drawing unit 33 is used to draw a PRPD spectrum, a discharge pulse sequence spectrum and a discharge characteristic statistical spectrum based on the pre-processed partial discharge data, wherein the PRPD spectrum is the distribution of the discharge times of the partial discharge signal with the discharge amount and the discharge phase, the discharge pulse sequence spectrum represents the relationship distribution between the discharge voltages at adjacent time points, and the discharge characteristic statistical spectrum represents the distribution of the discharge times with the voltage difference of the discharge voltage, where the voltage difference is the difference between the discharge voltages at two adjacent time points;

[0086] An extraction unit 34 is used to extract local discharge features from the PRPD spectrum, the discharge pulse sequence spectrum and the discharge feature statistical spectrum, and construct a local discharge feature vector based on the local discharge features;

[0087] The identification unit 35 is used to calculate the similarity value between the local discharge feature vector and the defect feature vector in the defect fingerprint library, and identify the local discharge type of the power cable based on the similarity value, wherein the defect fingerprint library stores defect feature vectors corresponding to N types of local discharge defects, N is a positive integer, and the defect feature vectors in the defect fingerprint library at least include: defect feature vectors obtained based on needle-plate electrode simulation of spike discharge.

[0088] The above-mentioned cable partial discharge type identification device collects a partial discharge data set corresponding to the partial discharge signal of the power cable through the collection unit 31, wherein the partial discharge data set at least includes: discharge voltage, discharge amount, discharge phase and discharge number; the partial discharge data in the partial discharge data set is preprocessed by the processing unit 32 to obtain the preprocessed partial discharge data; the PRPD spectrum, the discharge pulse sequence spectrum and the discharge characteristic statistical spectrum are drawn based on the preprocessed partial discharge data by the drawing unit 33, wherein the PRPD spectrum is the distribution of the discharge number of the partial discharge signal with the discharge amount and the discharge phase, the discharge pulse sequence spectrum represents the relationship distribution between the discharge voltages at adjacent time points, and the discharge characteristic statistical spectrum represents the relationship distribution between the discharge voltages at adjacent time points. The spectrum represents the distribution of the number of discharges with the voltage difference of the discharge voltage, where the voltage difference is the difference between the discharge voltages at two adjacent time points; the extraction unit 34 extracts local discharge features from the PRPD spectrum, the discharge pulse sequence spectrum and the discharge feature statistical spectrum, and constructs a local discharge feature vector based on the local discharge features; the identification unit 35 calculates the similarity value between the local discharge feature vector and the defect feature vector in the defect fingerprint library, and identifies the local discharge type of the power cable based on the similarity value, wherein the defect fingerprint library stores defect feature vectors corresponding to N types of local discharge defects, N is a positive integer, and the defect feature vectors in the defect fingerprint library at least include: defect feature vectors obtained based on needle-plate electrode simulation of spike discharge.

[0089] In this embodiment, the local discharge signal of the power cable is analyzed in multiple dimensions such as the PRPD spectrum, the discharge pulse sequence spectrum, and the discharge characteristic statistical spectrum, and the external manifestation characteristics and internal intrinsic characteristics of the local discharge signal are deeply excavated, so that the local discharge type of the power cable can be accurately identified based on the multi-dimensional local discharge characteristics, and the identification accuracy is improved, thereby ensuring the stable operation of the power system, and further solving the technical problem in the related technology that a single local discharge detection method is used and it is difficult to accurately identify the local discharge type.

[0090] Optionally, the device for identifying the type of cable partial discharge also includes: a first construction module, used to construct a needle-plate electrode, and use the needle-plate electrode to simulate partial discharge defects under different voltages; a first acquisition module, used to obtain simulated partial discharge data of the partial discharge signal of the needle-plate electrode under different voltages; a first drawing module, used to draw PRPD spectra, discharge pulse sequence spectra and discharge feature statistical spectra based on the simulated partial discharge data; a first extraction module, used to extract features from the PRPD spectra, discharge pulse sequence spectra and discharge feature statistical spectra, and obtain defect features corresponding to the simulated partial discharge defects of the needle-plate electrode under different voltages; a first construction module, used to construct a defect feature vector based on the defect features, obtain the defect feature vectors corresponding to the simulated partial discharge defects under different voltages, and store the defect feature vectors corresponding to the simulated partial discharge defects under different voltages in a defect fingerprint library.

[0091] Optionally, the first drawing module includes: a first extraction submodule, used to extract leader discharge data from simulated local discharge data, wherein the leader discharge data is data corresponding to the leader discharge part in the local discharge process, and the leader discharge part represents the part of the local discharge process in which the discharge stability is lower than the stability threshold; a first drawing submodule, used to draw the leader PRPD spectrum, leader discharge pulse sequence spectrum and leader discharge characteristic statistical spectrum of the leader discharge part based on the leader discharge data; and a second drawing submodule, used to draw the complete PRPD spectrum, complete discharge pulse sequence spectrum and complete discharge characteristic statistical spectrum of the complete discharge part based on the simulated local discharge data.

[0092] Optionally, the first extraction module includes: a first extraction submodule, which is used to perform feature extraction on the leader PRPD spectrum to obtain the discharge relationship characteristics of the leader discharge part, wherein the discharge relationship characteristics at least include: the relationship between the discharge amount and the discharge phase of the leader discharge part, the relationship between the discharge amount and the number of discharges of the leader discharge part, and the relationship between the discharge phase and the number of discharges of the leader discharge part; a second extraction submodule, which is used to perform feature extraction on the leader discharge pulse sequence spectrum to obtain the local discharge event distribution characteristics of the leader discharge part; a third extraction submodule, which is used to perform feature extraction on the leader discharge feature statistical spectrum to obtain the number, peak value and width of the voltage difference characteristic peaks of the leader discharge part, and obtain the voltage difference characteristics of the leader discharge part; the first acquisition submodule is used to obtain the defect characteristics corresponding to the simulated local discharge defects of the needle-plate electrode at different voltages based on the discharge relationship characteristics, the local discharge event distribution characteristics and the voltage difference characteristics.

[0093] Optionally, the first extraction module also includes: a second acquisition submodule, which is used to acquire local discharge distribution data of the complete discharge part within a discharge cycle based on the complete PRPD spectrum of the complete discharge part; a first calculation submodule, which is used to calculate the mean data of the first voltage difference absolute value and the second voltage difference absolute value of the complete discharge part based on the local discharge distribution data, the complete discharge pulse sequence spectrum and the complete discharge feature statistical spectrum, wherein the first voltage difference absolute value represents the absolute value of the voltage difference between the first local discharge in the negative half cycle and the local discharge in the positive half cycle of the complete discharge part within a discharge cycle, and the second voltage difference absolute value represents the absolute value of the voltage difference between two consecutive local discharges in the falling part of the negative half cycle of the complete discharge part within a signal cycle; the second calculation submodule is used to calculate the starting voltage value and the ending voltage value of the negative half cycle in the target discharge cycle based on the mean data of the complete discharge part and the starting voltage value of the positive half cycle in the target discharge cycle; the third acquisition submodule is used to obtain the defect characteristics corresponding to the simulated local discharge defects of the needle plate electrode at different voltages based on the starting voltage value and the ending voltage value of the negative half cycle of the complete discharge part in the target discharge cycle.

[0094] Optionally, the identification unit 35 includes: a first selection module, used to select the defect feature vector with the largest similarity value with the local discharge feature vector as the target defect feature vector; a first determination module, used to determine the local discharge type corresponding to the target defect feature vector, thereby obtaining the local discharge type of the power cable.

[0095] Optionally, the N types of partial discharge defects are caused by insulation defects of the power cable, and the insulation defects include at least one of the following: metal particles in the insulation, breath in the semiconductor, scratches in the insulation, unqualified cable eccentricity, and sharp tips in the semiconductor.

[0096] The above-mentioned cable partial discharge type identification device may also include a processor and a memory. The above-mentioned acquisition unit 31, processing unit 32, drawing unit 33, extraction unit 34, identification unit 35, etc. are all stored in the memory as program units, and the processor executes the above-mentioned program units stored in the memory to realize corresponding functions.

[0097] The processor includes a kernel, which retrieves the corresponding program unit from the memory. One or more kernels can be set, and the cable partial discharge type can be identified by adjusting kernel parameters.

[0098] The above-mentioned memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash memory (fl ash RAM), and the memory includes at least one storage chip.

[0099] According to another aspect of an embodiment of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium including a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute any one of the above-mentioned methods for identifying the type of partial discharge of a cable.

[0100] According to another aspect of an embodiment of the present invention, there is also provided an electronic device, comprising one or more processors and a memory, wherein the memory is used to store one or more programs, wherein when the one or more programs are executed by one or more processors, the one or more processors implement any one of the above-mentioned methods for identifying the type of partial discharge of a cable.

[0101] According to another aspect of an embodiment of the present invention, a computer program product is provided. The computer program product includes a computer program, wherein when the computer program is executed by a processor, any one of the above-mentioned methods for identifying a cable partial discharge type is implemented.

[0102] The present application also provides a computer program product, which, when executed on a data processing device, is suitable for executing a program having the following method steps: collecting a local discharge data set corresponding to a local discharge signal of a power cable, wherein the local discharge data set includes at least: a discharge voltage, a discharge amount, a discharge phase, and a discharge number; preprocessing the local discharge data in the local discharge data set to obtain preprocessed local discharge data; and drawing a PRPD spectrum, a discharge pulse sequence spectrum, and a discharge characteristic statistical spectrum based on the preprocessed local discharge data, wherein the PRPD spectrum is the distribution of the discharge number of the local discharge signal with the discharge amount and the discharge phase, and the discharge pulse sequence spectrum represents the difference between the discharge voltages at adjacent time points. The relationship distribution of the discharge characteristics statistical map represents the distribution of the discharge times with the voltage difference of the discharge voltage, and the voltage difference is the difference between the discharge voltages at two adjacent time points; the local discharge features are extracted from the PRPD map, the discharge pulse sequence map and the discharge characteristics statistical map, and the local discharge feature vector is constructed based on the local discharge features; the similarity value between the local discharge feature vector and the defect feature vector in the defect fingerprint library is calculated, and the local discharge type of the power cable is identified based on the similarity value, wherein the defect fingerprint library stores defect feature vectors corresponding to N types of local discharge defects, N is a positive integer, and the defect feature vectors in the defect fingerprint library at least include: defect feature vectors obtained based on needle-plate electrode simulation spike discharge.

[0103] Figure 4 FIG. 1 is a hardware structure block diagram of an electronic device (or mobile device) for a method for identifying a cable partial discharge type according to an embodiment of the present invention. Figure 4 As shown, the electronic device may include one or more ( Figure 4402a, 402b, ..., 402n are used to illustrate) processor 402 (processor 402 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA), memory 404 for storing data. In addition, it may also include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of the I / O interface), a network interface, a keyboard, a power supply and / or a camera. It can be understood by those skilled in the art that Figure 4 The structure shown is only for illustration and does not limit the structure of the above electronic device. Figure 4 More or fewer components as shown, or with Figure 4 Different configurations shown.

[0104] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0105] In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0106] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0107] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0108] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0109] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk and other media that can store program codes.

[0110] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for identifying the type of partial discharge of a cable, characterized in that: include: Collecting a partial discharge data set corresponding to a partial discharge signal of a power cable, wherein the partial discharge data set at least includes: a discharge voltage, a discharge amount, a discharge phase, and a discharge number; Preprocessing the partial discharge data in the partial discharge data set to obtain preprocessed partial discharge data; Based on the pre-processed partial discharge data, a PRPD spectrum, a discharge pulse sequence spectrum and a discharge characteristic statistical spectrum are drawn, wherein the PRPD spectrum is the distribution of the discharge times of the partial discharge signal with the discharge amount and the discharge phase, the discharge pulse sequence spectrum represents the relationship distribution between the discharge voltages at adjacent time points, and the discharge characteristic statistical spectrum represents the distribution of the discharge times with the voltage difference of the discharge voltage, wherein the voltage difference is the difference between the discharge voltages at two adjacent time points; Extracting local discharge features from the PRPD spectrum, the discharge pulse sequence spectrum and the discharge feature statistical spectrum, and constructing a local discharge feature vector based on the local discharge features; Calculate the similarity value between the partial discharge feature vector and the defect feature vector in the defect fingerprint library, and identify the partial discharge type of the power cable based on the similarity value, wherein the defect fingerprint library stores defect feature vectors corresponding to N types of partial discharge defects, N is a positive integer, and the defect feature vectors in the defect fingerprint library at least include: a defect feature vector obtained based on needle-plate electrode simulation of spike discharge.

2. The identification method according to claim 1, characterized in that: The steps of obtaining the defect feature vector based on the needle-plate electrode simulation spike discharge include: constructing the needle-plate electrode, and using the needle-plate electrode to simulate partial discharge defects under different voltages; Acquire simulated partial discharge data of partial discharge signals of needle-plate electrodes at different voltages; Drawing the PRPD spectrum, the discharge pulse sequence spectrum and the discharge characteristic statistical spectrum based on the simulated partial discharge data; Extracting features from the PRPD spectrum, the discharge pulse sequence spectrum and the discharge characteristic statistical spectrum to obtain defect features corresponding to the simulated partial discharge defects of the needle-plate electrode at different voltages; The defect feature vector is constructed based on the defect feature, the defect feature vector corresponding to the simulated partial discharge defect under different voltages is obtained, and the defect feature vector corresponding to the simulated partial discharge defect under different voltages is stored in the defect fingerprint library.

3. The identification method according to claim 2, characterized in that: The step of drawing the PRPD spectrum, the discharge pulse sequence spectrum and the discharge characteristic statistical spectrum based on the simulated partial discharge data comprises: Extracting leader discharge data from the simulated partial discharge data, wherein the leader discharge data is data corresponding to a leader discharge portion in a partial discharge process, and the leader discharge portion indicates a portion in the partial discharge process where a discharge stability level is lower than a stability level threshold; Based on the leader discharge data, a leader PRPD spectrum, a leader discharge pulse sequence spectrum and a leader discharge characteristic statistical spectrum of the leader discharge part are drawn; Based on the simulated partial discharge data, a complete PRPD spectrum of the complete discharge part, a complete discharge pulse sequence spectrum and a complete discharge characteristic statistical spectrum are drawn.

4. The identification method according to claim 2, characterized in that: The step of extracting features from the PRPD spectrum, the discharge pulse sequence spectrum and the discharge characteristic statistical spectrum to obtain defect features corresponding to the simulated partial discharge defects of the needle-plate electrode at different voltages comprises: Extracting features from the leading PRPD spectrum to obtain discharge relationship features of the leading discharge part, wherein the discharge relationship features at least include: a relationship between a discharge amount and a discharge phase of the leading discharge part, a relationship between a discharge amount and a discharge number of the leading discharge part, and a relationship between a discharge phase and a discharge number of the leading discharge part; Extracting features from the leader discharge pulse sequence spectrum to obtain distribution features of local discharge events in the leader discharge portion; Extracting features from the leader discharge feature statistical spectrum, obtaining the number, peak value and width of the voltage difference feature peaks of the leader discharge portion, and obtaining the voltage difference feature of the leader discharge portion; Based on the discharge relationship characteristics, the local discharge event distribution characteristics and the voltage difference characteristics, defect characteristics corresponding to the simulated local discharge defects of the needle-plate electrode at different voltages are obtained.

5. The identification method according to claim 2, characterized in that: The step of extracting features from the PRPD spectrum, the discharge pulse sequence spectrum and the discharge characteristic statistical spectrum to obtain defect features corresponding to the simulated partial discharge defects of the needle-plate electrode at different voltages comprises: Acquiring partial discharge distribution data of the complete discharge part within a discharge cycle based on a complete PRPD spectrum of the complete discharge part; Based on the partial discharge distribution data, the complete discharge pulse sequence spectrum and the complete discharge characteristic statistical spectrum, the mean data of the first voltage difference absolute value and the second voltage difference absolute value of the complete discharge part are calculated, wherein the first voltage difference absolute value represents the absolute value of the voltage difference between the first partial discharge in the negative half cycle and the partial discharge in the positive half cycle of the complete discharge part within a discharge cycle, and the second voltage difference absolute value represents the absolute value of the voltage difference between two consecutive partial discharges in the falling part of the negative half cycle of the complete discharge part within a signal cycle; Calculate the starting voltage value and the ending voltage value of the negative half cycle in the target discharge cycle based on the average data of the complete discharge part and the starting voltage value of the positive half cycle in the target discharge cycle; Based on the starting voltage value and the ending voltage value of the complete discharge part in the negative half cycle within the target discharge cycle, the defect characteristics corresponding to the simulated partial discharge defects of the needle plate electrode at different voltages are obtained.

6. The identification method according to claim 1, characterized in that: The step of identifying the partial discharge type of the power cable based on the similarity value comprises: Selecting a defect feature vector having the largest similarity value with the partial discharge feature vector as a target defect feature vector; The partial discharge type corresponding to the target defect feature vector is determined, thereby obtaining the partial discharge type of the power cable.

7. The identification method according to claim 1, characterized in that: The N types of partial discharge defects are caused by insulation defects of the power cable, and the insulation defects include at least one of the following: metal particles in insulation, breath in semiconductor, scratches in insulation, unqualified cable eccentricity, and sharp points in semiconductor.

8. A device for identifying the type of partial discharge of a cable, characterized in that: include: A collection unit, used for collecting a partial discharge data set corresponding to a partial discharge signal of a power cable, wherein the partial discharge data set at least includes: a discharge voltage, a discharge amount, a discharge phase, and a discharge number; A processing unit, configured to preprocess the partial discharge data in the partial discharge data set to obtain preprocessed partial discharge data; A drawing unit, for drawing a PRPD spectrum, a discharge pulse sequence spectrum and a discharge characteristic statistical spectrum based on the pre-processed partial discharge data, wherein the PRPD spectrum is the distribution of the number of discharges of the partial discharge signal with the discharge amount and the discharge phase, the discharge pulse sequence spectrum represents the relationship distribution between the discharge voltages at adjacent time points, and the discharge characteristic statistical spectrum represents the distribution of the number of discharges with the voltage difference of the discharge voltage, wherein the voltage difference is the difference between the discharge voltages at two adjacent time points; An extraction unit is used to extract local discharge features from the PRPD spectrum, the discharge pulse sequence spectrum and the discharge feature statistical spectrum, and to construct a local discharge feature vector based on the local discharge features; an identification unit is used to calculate a similarity value between the local discharge feature vector and a defect feature vector in a defect fingerprint library, and to identify the local discharge type of the power cable based on the similarity value, wherein the defect fingerprint library stores defect feature vectors corresponding to N types of local discharge defects, N is a positive integer, and the defect feature vectors in the defect fingerprint library at least include: a defect feature vector obtained based on needle-plate electrode simulation of spike discharge.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored computer program, wherein when the computer program is executed, the device where the computer-readable storage medium is located is controlled to execute the method for identifying the type of partial discharge of a cable according to any one of claims 1 to 7.

10. An electronic device, characterized in that: It comprises one or more processors and a memory, wherein the memory is used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the method for identifying the cable partial discharge type as described in any one of claims 1 to 7.

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