Partial discharge type identification method and system for 10kV distribution cable

Abstract

The invention discloses a partial discharge type identification method and system for a 10kV distribution cable, and the method comprises the steps: obtaining the primary sampling data of the discharge data of the distribution cable, carrying out the first preprocessing of the primary sampling data, and carrying out the second preprocessing of the primary sampling data based on a first preprocessing result; and carrying out secondary sampling on the primary sampling data after the second preprocessing, and carrying out similarity analysis on the secondary sampling data and typical discharge data to determine the discharge type. The partial discharge characteristic method is adopted to perform similarity analysis on the discharge data of the power distribution cable and typical spectrogram data, and state monitoring of the power distribution cable is realized, so that intelligent operation and maintenance of the power distribution cable are realized, the operation reliability and stability of power distribution equipment are improved, and the method is of great significance to improvement of social production efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution automation status monitoring, and in particular to a method and system for identifying the partial discharge type of a 10kV distribution cable. Background Art

[0002] The distribution network is a key node in the production and consumption of electric energy, and the distribution cable carries the key core mission of transmitting electric energy. At present, there are problems such as lack of maintenance, insufficient maintenance, and untimely maintenance in the operation and maintenance of distribution cables, which leads to the emergence of insulation aging problems in distribution cables, which affects the reliability of power supply at the least and personal safety at the worst. Therefore, a 10kV distribution cable partial discharge type identification method and system based on high-frequency current channel sampling is urgently needed to solve the problem of distribution cable operation and maintenance. Summary of the invention

[0003] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a method and system for identifying the partial discharge type of a 10kV distribution cable to solve the problem.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, an embodiment of the present invention provides a method for identifying the partial discharge type of a 10kV distribution cable, comprising: obtaining primary sampling data of the distribution cable discharge data, performing a first preprocessing on the primary sampling data, and performing a second preprocessing on the primary sampling data based on the first preprocessing result;

[0008] The first sampling data after the second preprocessing is resampled, and similarity analysis is performed between the resampled data and typical discharge data to determine the discharge type.

[0009] As a preferred solution of the method for identifying partial discharge types of 10kV distribution cables described in the present invention, the acquisition of typical discharge data includes: acquiring typical characteristics of partial discharge based on typical partial discharge fault types of the cable, and collecting samples for each partial discharge fault type.

[0010] As a preferred solution of the method for identifying the partial discharge type of a 10kV distribution cable of the present invention, wherein: obtaining a primary sampling data of the distribution cable discharge data, and performing a first preprocessing on the primary sampling data comprises: using N sampling points per cycle, a total of M cycles of discharge data;

[0011] Each phase counts the total number of discharges in M ​​cycles at its phase point to form a sampling point of the number of discharges in each phase to generate various types of discharge features;

[0012] Before forming the discharge number sampling points of each phase to generate various types of discharge features, the discharge data is normalized.

[0013] As a preferred solution of the method for identifying the partial discharge type of a 10kV distribution cable of the present invention, wherein: performing a second preprocessing on the primary sampling data based on the first preprocessing result comprises: taking the discharge frequency as a data source, obtaining all sampling points;

[0014] By calculating the amplitude difference and periodicity between sampling points, it is determined whether there is a periodic signal;

[0015] If a periodic signal is found, the periodic noise is filtered out.

[0016] As a preferred solution of the method for identifying partial discharge type of a 10kV distribution cable according to the present invention, the secondary sampling of the first sampling data after the second preprocessing comprises:

[0017] The sampling points of one sampling are combined into one sampling point every three consecutive phase points, and the sampling is repeated again by summing up to obtain a new discharge feature.

[0018] As a preferred solution of the method for identifying the partial discharge type of a 10kV distribution cable of the present invention, wherein: performing similarity analysis on the secondary sampling data and the typical discharge data to determine the discharge type includes: calculating the distance measurement of the discharge characteristics of the secondary sampling data and the typical discharge data, expressed as:

[0019]

[0020] Where, j is the sampling point, Y(j) is the discharge number difference sequence, X1(j) is the amplitude of sampling point 1, and X2(j) is the amplitude of sampling point 2;

[0021] The distance L(N) column is calculated, where N is the number of typical samples, and the new discharge feature is compared with the partial discharge fault type samples to determine the discharge type.

[0022] As a preferred solution of the method for identifying the partial discharge type of a 10kV distribution cable described in the present invention, it also includes: continuously monitoring the discharge, statistically analyzing the results in a daily, weekly, monthly and annual manner, and storing the results in respective database systems; in a daily statistical module, if there are more than 3 discharge records on the day, a daily alarm is activated; in a weekly statistical module, if there are more than 1 record out of a total of 7 discharge records this week, a weekly alarm is activated; in a monthly statistical module, if there are 1 or more weekly discharge records within 4 weeks, a monthly alarm is activated; in an annual statistical module, if there are 3 or more discharge records within 12 months of this year, an annual alarm is activated.

[0023] In a second aspect, the present invention provides a partial discharge type identification system for a 10kV distribution cable, comprising:

[0024] A data acquisition and processing module, used for acquiring primary sampling data of the distribution cable discharge data, performing a first preprocessing on the primary sampling data, and performing a second preprocessing on the primary sampling data based on the first preprocessing result;

[0025] The partial discharge type identification module is used to perform secondary sampling on the primary sampling data after the second preprocessing, perform similarity analysis on the secondary sampling data and typical discharge data, and determine the discharge type.

[0026] In a third aspect, the present invention provides a computing device, comprising:

[0027] Memory and processor;

[0028] The memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions. When the computer executable instructions are executed by the processor, the steps of the method for identifying the partial discharge type of the 10kV distribution cable are implemented.

[0029] In a fourth aspect, the present invention provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the method for identifying the partial discharge type of the 10kV distribution cable.

[0030] Compared with the prior art, the present invention has the following beneficial effects: the present invention adopts the local discharge characteristic method to perform similarity analysis on the discharge data of the distribution cable and the typical spectrum data, realizes the status monitoring of the distribution cable, thereby realizes the intelligent operation and maintenance of the distribution cable, improves the operating reliability and stability of the distribution equipment, and is of great significance to the improvement of social production efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them:

[0032] Figure 1 A method flow chart of a method and system for identifying partial discharge types of a 10kV distribution cable according to an embodiment of the present invention;

[0033] Figure 2 A method for identifying the partial discharge type of a 10kV distribution cable and a base value calculation flow chart of the system according to an embodiment of the present invention;

[0034] Figure 3 A schematic diagram of raw discharge data processing of a method and system for identifying partial discharge types of a 10kV distribution cable according to an embodiment of the present invention;

[0035] Figure 4 A schematic diagram of data secondary sampling processing of a method and system for identifying partial discharge types of a 10kV distribution cable according to an embodiment of the present invention;

[0036] Figure 5 This is an original discharge signal diagram of a partial discharge type identification method and system for a 10kV distribution cable according to an embodiment of the present invention;

[0037] Figure 6 A schematic diagram of periodic signal recognition of a method and system for identifying partial discharge types of a 10kV distribution cable according to an embodiment of the present invention;

[0038] Figure 7 A discharge identification flow chart of a method and system for identifying partial discharge types of a 10kV distribution cable according to an embodiment of the present invention;

[0039] Figure 8 A schematic diagram of the distance between a discharge signal to be identified and a typical discharge signal in a method and system for identifying the partial discharge type of a 10kV distribution cable according to an embodiment of the present invention;

[0040] Fig. 9 A schematic diagram of a method for identifying partial discharge types of a 10kV distribution cable and a discharge continuity monitoring system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0041] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0042] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0043] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0044] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0045] At the same time, in the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the directions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0046] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0047] Example 1

[0048] Reference Figures 1 to 9, is an embodiment of the present invention, which provides a method for identifying the partial discharge type of a 10kV distribution cable, such as Figure 1 As shown, including:

[0049] S100: acquiring primary sampling data of the distribution cable discharge data, performing a first preprocessing on the primary sampling data, and performing a second preprocessing on the primary sampling data based on the first preprocessing result;

[0050] S200: performing secondary sampling on the primary sampling data after the second preprocessing, performing similarity analysis between the secondary sampling data and the typical discharge data, and determining the discharge type.

[0051] It should be noted that partial discharge of distribution cables is a branch of online monitoring of power systems and an important research direction for distribution reliability. At present, online monitoring of partial discharge has achieved significant results in the fields of power transmission and substation, but there are still problems such as lack of maintenance, insufficient maintenance, and untimely maintenance in the operation and maintenance of distribution cables. This application is based on a partial discharge type identification method for high-frequency current channel sampling, which makes the operation and maintenance of distribution cables intelligent, networked, and digital. According to the actual operating characteristics of 10kV distribution cables, a typical partial discharge model feature library is established. After the actual discharge signal is filtered out by noise, software is used to realize the similarity analysis of the actual discharge signal and the typical discharge signal, so as to realize the analysis of the insulation fault type of the distribution cable and the trend of the insulation fault, and finally realize the intelligent operation and maintenance of the distribution cable.

[0052] In the embodiment of the present application, the acquisition of typical discharge data includes: based on typical partial discharge fault types of the cable, acquiring typical characteristics of partial discharge, and collecting samples for each partial discharge fault type.

[0053] Exemplarily, partial discharge online monitoring technology is used for distribution cables in the distribution room. First, typical characteristic spectra of partial discharge are collected. The collected fault types may include spikes, air gaps, suspensions, and particles. 500 samples are collected for each partial discharge fault type, for a total of 2000 samples.

[0054] In the embodiment of the present application, obtaining a sampling data of the distribution cable discharge data and performing a first preprocessing on the sampling data includes: using N sampling points per cycle, a total of M cycles of discharge data;

[0055] Each phase counts the total number of discharges in M ​​cycles at its phase point to form a sampling point of the number of discharges in each phase to generate various types of discharge features;

[0056] Before forming the discharge number sampling points of each phase to generate various types of discharge features, the discharge data is normalized.

[0057] It should be noted that since the dimensions of the original discharge data are mostly mv / dbm, they are all natural numbers after being converted into the number of discharges. In order to better reflect the proportional relationship when calculating the statistical algorithm, the discharge data needs to be normalized in advance.

[0058] Specifically, the normalization process is expressed as:

[0059]

[0060] Among them, x min Indicates the minimum value 0, x max represents the maximum value of 50, and x represents a data point, which is the maximum and minimum values ​​of the discharge times of 50 cycles counted in all 360 phase points.

[0061] For example, Figures 2 to 3 As shown, calculate the discharge base value, first remove the typical discharge characteristic phase in the spectrum, and then find the average value of the corresponding amplitude of the remaining phase as the base value, so the interval of the calculated base value is: 270 degrees to 360 degrees. Analyze the spectrum sample data, 360 sampling points per cycle, a total of 50 cycles of discharge data. Each phase counts the total number of discharges in 50 cycles at its phase point to form the original discharge number sampling points of each phase. Count the number of discharge amplitudes in all cycles at this phase point that are greater than the discharge base value. For example: there are 50 cycles of data at phase 1°. If the number of discharge amplitudes in 50 cycles that are greater than the discharge base value is 30, then the number of discharges at this phase point is 30.

[0062] In the embodiment of the present application, performing a second preprocessing on the primary sampling data based on the first preprocessing result includes: using the discharge frequency as a data source to obtain all sampling points;

[0063] By calculating the amplitude difference and periodicity between sampling points, it is determined whether there is a periodic signal;

[0064] If a periodic signal is found, the periodic noise is filtered out.

[0065] In an optional embodiment, the process of calculating the background noise includes the following steps:

[0066] The data of the high-frequency spectrum is read into a buffer in the computer memory, and the number of the accumulated effective discharge amplitudes of the counter is initialized to 0;

[0067] The discharge amplitude between 270 degrees and 360 degrees in 50 cycles;

[0068] Check whether the absolute value of each discharge amplitude is greater than 0. If the discharge amplitude is very small or close to zero, it is meaningless to calculate the background noise.

[0069] If the absolute value of the discharge amplitude is greater than 0, the discharge amplitude is accumulated into the variable, and the counter accumulates the effective discharge amplitude by 1, which means that a new effective discharge amplitude is found; if the absolute value of the discharge amplitude is not greater than 0, the program skips the accumulation operation and continues to check the next discharge amplitude;

[0070] When all effective discharge amplitudes are accumulated, the background noise is calculated. The background noise is equal to the sum variable of all the accumulated effective discharge amplitudes divided by the number of effective discharge amplitudes.

[0071] It should be noted that most of the environmental noise and interference signals at the distribution cable site have periodic characteristics, such as Figure 5 As shown, the original sampling signal can be preprocessed with discharge characteristics, and the generated phase-discharge frequency relationship also has periodic characteristics. The sliding window tracking method is then used to determine whether there is a periodic law in the phase-discharge frequency relationship. Based on this characteristic law, most of the influence of environmental noise on partial discharge analysis can be filtered out, thereby improving the accuracy of partial discharge identification.

[0072] For example, the following Figure 6 As shown, four points x1, x2, x3, and x4 in the partial discharge signal are selected as signal sampling points. If the signal interval W satisfies T1≈T2≈T3 and its amplitude y1≈y2≈y3≈y4, it indicates that the signal is a periodic signal.

[0073] Using the sliding window method, the window and threshold are selected. Each window roughly contains one period. Then the window is slid to find out the distance difference between the first maximum values ​​of adjacent windows, which is the period of the ambient noise signal.

[0074] Step 1: Use the discharge frequency as the data source to obtain all endpoints. Method: If a falling edge of the signal appears from a certain endpoint, then this point is the endpoint (maximum value).

[0075] Step 2: First take the first endpoint as the sampling point, and then find the second sampling point. The method is: calculate whether the amplitude difference between endpoint n (n = 2 ~ N) and sampling point 1 is within δ. If endpoint n is not found, slide to endpoint 2 as sampling point 1. If no point that meets the conditions is still not found, exit directly; the distance between sampling point 1 and endpoint n is used as the estimated period T. Within the delay range of |T±δt| with endpoint n as the starting point, if endpoint m whose amplitude difference with endpoint n is also within δ can be found, it is judged as a periodic signal; if endpoint 2 that meets the conditions is not found, exit directly, and the output does not have periodic characteristics.

[0076] Step 3: If it is not satisfied, continue to slide the time axis, considering endpoints 3...N, until it is satisfied; if not found, exit, and the output does not have periodic characteristics.

[0077] In an embodiment of the present application, performing secondary sampling on the primary sampling data after the second preprocessing includes:

[0078] The sampling points of one sampling are combined into one sampling point every three consecutive phase points, and the sampling is repeated again by summing up to obtain a new discharge feature.

[0079] For example, Figure 4 As shown, every three consecutive phase points of the original discharge number sampling points are merged into one sampling point by summing, such as X(j)=x(i)+x(i+1)+x(i+2). After secondary sampling, the new sampling data buffer has a total of 120 phase sampling point data.

[0080] It should be noted that the discharge times of two discharge data sets to be compared (such as the sample and actual discharge data sets) are subtracted at each corresponding phase sampling point to obtain a discharge times difference sequence Y(j)=X1(j)-X2(j).

[0081] In the embodiments of the present application, Figure 7 As shown, similarity analysis is performed on the resampled data and the typical discharge data to determine the discharge type, including: calculating the distance measurement between the discharge characteristics of the resampled data and the typical discharge data, expressed as:

[0082]

[0083] Where, j is the sampling point, Y(j) is the discharge number difference sequence, X1(j) is the amplitude of sampling point 1, and X2(j) is the amplitude of sampling point 2;

[0084] Calculate the distance L(N) sequence, where N is the number of typical samples, and compare the new discharge characteristics with the partial discharge fault type samples to determine the discharge type.

[0085] For example, Figure 8 As shown, by calculating the distance L(N) between the actual sampled data and each spectrum in the typical sample spectrum library, the typical sample spectrum with the smallest distance is found to determine the type of the spectrum to be identified. For example, when the calculated distance is L(0), the spectrum to be identified of the actual sampled data is considered to be of the type of "spike 1"; when the calculated distance is L(1), the spectrum to be identified of the actual sampled data is considered to be of the type of "along the surface 1", and so on.

[0086] In the embodiment of the present application, it also includes: continuously monitoring the discharge, counting the results in a daily, weekly, monthly and annual manner, and storing the results in respective database systems; in the daily statistics module, if there are more than 3 discharge records on the day, a daily alarm is activated; in the weekly statistics module, if more than 1 record appears in a total of 7 discharge records this week, a weekly alarm is activated; in the monthly statistics module, if 1 or more weekly discharge records appear within 4 weeks, a monthly alarm is activated; in the annual statistics module, if 3 or more discharge records appear within 12 months of this year, an annual alarm is activated.

[0087] It should be noted that discharge continuity monitoring is used for continuous observation of discharge, which can avoid the identification error caused by instantaneous partial discharge, and can effectively reduce the error and improve the identification accuracy.

[0088] Specifically, Fig. 9 As shown, the discharge continuous monitoring process includes the following steps:

[0089] Step 1: Perform discharge data collection;

[0090] Step 2: Check whether the collected data is complete; if the data is incomplete, re-execute the discharge data collection. If the data is complete, the current discharge information is recorded in the memory, including the discharge type compared with the typical spectrum, the discharge similarity distance L and other information;

[0091] Step 3: Determine whether the 1-hour time has been reached; if it has not been reached, repeat the above steps; if it has been reached, record the average value of the discharge distance L under various discharge types within one hour and the information of the discharge type;

[0092] Step 4: Determine whether a day has been reached; if not, repeat the above steps; if it has been reached, enter the daily discharge statistics logic;

[0093] Step 5: Determine whether a week has been reached; if it has not been reached, repeat the above steps; if it has been reached, enter the weekly discharge statistics logic;

[0094] Step 6: Determine whether one month has passed; if not, repeat the above steps; if one month has passed, enter the monthly discharge statistics logic;

[0095] Step 7: Determine whether the one-year period has been reached; if it has not been reached, repeat the above steps; if it has been reached, enter the annual discharge statistical logic to summarize and analyze the annual discharge data.

[0096] It should be noted that this application is based on high-frequency current channel sampling. After data preprocessing and sliding window denoising, the characteristic signal of partial discharge, i.e., the relationship spectrum of phase-discharge frequency, is obtained. By calculating the Minkowski distance between the actual discharge signal and the characteristic quantity of the typical discharge signal, the correlation between the actual partial discharge signal and the typical discharge signal is analyzed, thereby analyzing the potential insulation faults, partial discharge types and discharge trend development of the distribution cable, which can improve the operating reliability and stability of the distribution cable.

[0097] Example 2

[0098] In an exemplary embodiment, a partial discharge type identification system for a 10kV distribution cable is also provided in this embodiment, including:

[0099] A data acquisition and processing module, used for acquiring primary sampling data of the distribution cable discharge data, performing a first preprocessing on the primary sampling data, and performing a second preprocessing on the primary sampling data based on the first preprocessing result;

[0100] The partial discharge type identification module is used to perform secondary sampling on the primary sampling data after the second preprocessing, perform similarity analysis between the secondary sampling data and the typical discharge data, and determine the discharge type.

[0101] Furthermore, the partial discharge type identification system for 10kV distribution cables also includes: based on typical partial discharge fault types of the cables, obtaining typical characteristics of partial discharge, and collecting samples for each partial discharge fault type.

[0102] In an optional embodiment, the partial discharge type identification system of a 10kV distribution cable may perform the following steps:

[0103] Use N sampling points per cycle, a total of M cycles of discharge data;

[0104] Each phase counts the total number of discharges in M ​​cycles at its phase point to form a sampling point of the number of discharges in each phase to generate various types of discharge features;

[0105] Before forming the discharge number sampling points of each phase to generate various types of discharge features, the discharge data is normalized.

[0106] Using the discharge frequency as the data source, all sampling points are obtained;

[0107] By calculating the amplitude difference and periodicity between sampling points, it is determined whether there is a periodic signal;

[0108] If a periodic signal is found, the periodic noise is filtered out.

[0109] The sampling points of one sampling are combined into one sampling point every three consecutive phase points, and the sampling is repeated again by summing up to obtain a new discharge feature.

[0110] Calculate the distance measure between the discharge characteristics of the subsampled data and the typical discharge data, expressed as:

[0111]

[0112] Where, j is the sampling point, Y(j) is the discharge number difference sequence, X1(j) is the amplitude of sampling point 1, and X2(j) is the amplitude of sampling point 2;

[0113] Calculate the distance L[N] sequence, where N is the number of typical samples, and compare the new discharge characteristics with the partial discharge fault type samples to determine the discharge type.

[0114] The discharge is continuously monitored, and the results are counted on a daily, weekly, monthly and annual basis, and the results are stored in their respective database systems. In the daily statistics module, if there are more than 3 discharge records on the day, the daily alarm will be activated. In the weekly statistics module, if there are more than 1 record out of a total of 7 discharge records this week, the weekly alarm will be activated. In the monthly statistics module, if there are 1 or more weekly discharge records within 4 weeks, the monthly alarm will be activated. In the annual statistics module, if there are 3 or more discharge records within 12 months of this year, the annual alarm will be activated.

[0115] This embodiment further provides a computing device, which is applicable to the method for identifying the partial discharge type of a 10 kV distribution cable, and includes:

[0116] Memory and processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the method for identifying the partial discharge type of a 10kV distribution cable as proposed in the above embodiment.

[0117] This embodiment further provides a storage medium on which a computer program is stored. When the program is executed by a processor, the method for identifying the partial discharge type of a 10 kV distribution cable as proposed in the above embodiment is implemented.

[0118] The storage medium proposed in this embodiment and the method for identifying the partial discharge type of a 10kV distribution cable proposed in the above embodiment belong to the same inventive concept. The technical details not described in detail in this embodiment can be referred to the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0119] Through the above description of the implementation methods, the technicians in the relevant field can clearly understand that the present invention can be implemented by means of software and necessary general hardware, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ReadOnly, Memory, ROM), random access memory (RandomAccess Memory, RAM), flash memory (FLASH), hard disk or optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform the methods of various embodiments of the present invention.

[0120] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for identifying the partial discharge type of a 10kV distribution cable, characterized in that: include: Acquire primary sampling data of the distribution cable discharge data, perform a first preprocessing on the primary sampling data, and perform a second preprocessing on the primary sampling data based on the first preprocessing result; The first sampling data after the second preprocessing is resampled, and similarity analysis is performed between the resampled data and typical discharge data to determine the discharge type.

2. The method for identifying the partial discharge type of a 10kV distribution cable according to claim 1, characterized in that: The acquisition of typical discharge data includes: obtaining typical characteristics of partial discharge based on typical partial discharge fault types of cables, and collecting samples for each partial discharge fault type.

3. The method for identifying the partial discharge type of a 10kV distribution cable according to claim 2, characterized in that: Acquiring primary sampling data of the distribution cable discharge data, and performing a first preprocessing on the primary sampling data includes: using N sampling points per cycle, a total of M cycles of discharge data; Each phase counts the total number of discharges in M ​​cycles at its phase point to form a sampling point of the number of discharges in each phase to generate various types of discharge features; Before forming the discharge number sampling points of each phase to generate various types of discharge features, the discharge data is normalized.

4. The method for identifying the partial discharge type of a 10kV distribution cable according to claim 3, characterized in that: Performing a second preprocessing on the primary sampling data based on the first preprocessing result includes: taking the discharge frequency as a data source to obtain all sampling points; By calculating the amplitude difference and periodicity between sampling points, it is determined whether there is a periodic signal; If a periodic signal is found, the periodic noise is filtered out.

5. The method for identifying the partial discharge type of a 10kV distribution cable according to claim 4, characterized in that: The second pre-processing of the first sampling data includes: The sampling points of one sampling are combined into one sampling point every three consecutive phase points, and the sampling is repeated again by summing up to obtain a new discharge feature.

6. The method for identifying the partial discharge type of a 10kV distribution cable according to claim 5, characterized in that: Performing similarity analysis on the resampled data and the typical discharge data to determine the discharge type includes: calculating a distance measurement between the discharge characteristics of the resampled data and the typical discharge data, expressed as: Where, j is the sampling point, Y(j) is the discharge number difference sequence, X1(j) is the amplitude of sampling point 1, and X2(j) is the amplitude of sampling point 2; The distance L(N) column is calculated, where N is the number of typical samples, and the new discharge feature is compared with the partial discharge fault type samples to determine the discharge type.

7. The method for identifying the partial discharge type of a 10 kV distribution cable according to claim 6, characterized in that: Also includes: Continuously monitor the discharge, collect statistics on daily, weekly, monthly and annual basis, and store the results in their respective database systems; In the daily statistics module, if there are more than 3 discharge records on the day, the daily alarm will be activated; In the weekly statistics module, if more than one record appears in the seven discharge records this week, a weekly alarm will be triggered; in the monthly statistics module, if one or more weekly discharge records appear within four weeks, a monthly alarm will be triggered; In the annual statistics module, if 3 or more discharge records appear within 12 months of the year, the annual alarm will be activated.

8. A partial discharge type identification system for 10kV distribution cables, characterized in that: include: A data acquisition and processing module, used for acquiring primary sampling data of the distribution cable discharge data, performing a first preprocessing on the primary sampling data, and performing a second preprocessing on the primary sampling data based on the first preprocessing result; The partial discharge type identification module is used to perform secondary sampling on the primary sampling data after the second preprocessing, perform similarity analysis on the secondary sampling data and typical discharge data, and determine the discharge type.

9. An electronic device, comprising: Memory and processor; The memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions. When the computer executable instructions are executed by the processor, the steps of the method for identifying the partial discharge type of a 10kV distribution cable as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the method for identifying the partial discharge type of a 10 kV distribution cable according to any one of claims 1 to 7.

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