Insulator life assessment method, device, equipment and medium for distribution network lines

Abstract

The present invention discloses an insulator life assessment method, device, equipment and medium for a distribution network line. The method comprises: obtaining insulator lightning fault data; then determining the insulator non-failure time period according to the insulator lightning fault data; then determining the insulator non-failure data sample according to the insulator non-failure time period and the corresponding equipment ledger information; and constructing a Weibull probability density function and a Weibull distribution function according to the insulator non-failure data sample; finally determining the insulator life curve according to the Weibull probability density function and the Weibull distribution function. The present invention combines the non-failure data and the Weibull model to obtain the insulator life curve, and the present invention can be widely used in the field of power grid operation and maintenance technology.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid operation and maintenance, and in particular to a method, device, equipment and medium for evaluating the life of insulators of distribution lines. Background Art

[0002] Glossary:

[0003] Ledger data: records information related to the equipment in operation, such as commissioning time, equipment model, etc.

[0004] Insulator lightning failure data: records the time when the lightning insulator failure occurred and the commissioning time of the faulty equipment.

[0005] With the proposal and continuous development of smart distribution networks, mastering the operating status of various distribution network equipment can help grid workers make timely judgments on equipment reliability and risk probability, and then make adjustments in advance to prevent various serious accidents. As insulators that reflect the lightning resistance level of the line, the state of the insulator directly affects the operational reliability of the line when the line is subjected to lightning overvoltage, so it is required to have indicators that can reflect the state of the line insulator.

[0006] Since insulators have no electrical signal feedback, the relationship between their operation time and failure rate can be used to reflect the status of insulators. In general, the insulators installed on 10kV overhead lines in cities have problems such as single model, small number of lightning tripping damage, and no ledger data. The laboratory breakdown test of insulators can only obtain their breakdown voltage, which cannot reflect the failure risk when they are struck by lightning at different operation times. Generally, the Weibull distribution model can be used to evaluate the life of equipment, but it requires that the tested samples have sufficient failure data for parameter fitting. However, 10kV overhead line insulators not only have little tripping data, but the power grid company also has no ledger information for 10kV insulators. Therefore, the general Weibull model is difficult to evaluate the life status of 10kV insulators under the above circumstances. Summary of the invention

[0007] In view of this, embodiments of the present invention provide a method, device, equipment and medium for evaluating the life of insulators of a distribution network line.

[0008] A first aspect of the present invention provides a method for evaluating the life of an insulator of a distribution network line, comprising:

[0009] Obtain insulator lightning fault data;

[0010] Determine an insulator failure-free time period according to the insulator lightning fault data;

[0011] Determine an insulator non-failure data sample according to the insulator non-failure time period and the corresponding equipment ledger information;

[0012] Constructing a Weibull probability density function and a Weibull distribution function according to the insulator zero-failure data sample;

[0013] The life curve of the insulator is determined according to the Weibull probability density function and the Weibull distribution function.

[0014] Optionally, determining the insulator non-failure time period according to the insulator lightning fault data includes:

[0015] Sort the commissioning dates of faulty insulators;

[0016] The latest commissioning date of the faulty equipment is taken as the starting section, the end time of the fault data recording is taken as the end section, and the insulator failure-free time period is determined according to the starting section and the end section;

[0017] Optionally, determining the insulator non-failure data sample according to the insulator non-failure time period and corresponding equipment ledger information includes:

[0018] According to the insulator failure-free time period, matching corresponding equipment ledger information;

[0019] Taking days as units, intercept the tower ledger data within the zero-failure time period to record the number of towers put into operation every day, and then construct the zero-failure data sample of the insulator;

[0020] The data format of the insulator zero-failure data sample is <operation duration, operation quantity>.

[0021] Alternatively, the expression for the Weibull probability density function is:

[0022]

[0023] Where f(t) represents the Weibull probability density function; t is the running time before failure; β is the shape parameter; η is the scale parameter; when β>1, it indicates that the failure rate is increasing; when β<1, it indicates that the failure rate is decreasing; when β=1, it indicates that the failure rate remains unchanged.

[0024] Optionally, the expression of the Weibull distribution function is:

[0025]

[0026] Where F(t) represents the Weibull distribution function; t is the running time before failure; β is the shape parameter; and η is the scale parameter.

[0027] Optionally, determining the life curve of the insulator according to the Weibull probability density function and the Weibull distribution function includes:

[0028] Estimate the parameter values ​​of the Weibull probability density function and the Weibull distribution function by using the maximum likelihood function method;

[0029] The life curve of the insulator is determined according to the Weibull probability density function and the Weibull distribution function after the parameters are determined.

[0030] Optionally, the expression of the maximum likelihood function is:

[0031]

[0032] Among them, L M represents the maximum likelihood function; C is a constant; k is the number of samples; f() represents the Weibull probability density function; S i is the correction coefficient of the i-th sample; t i represents the failure time of the i-th sample; F() represents the Weibull distribution function; n i is the number of samples put into operation of the ith sample; t is the running time before failure; β is the shape parameter; η is the scale parameter.

[0033] Another aspect of an embodiment of the present invention provides a device for evaluating the life of an insulator of a distribution network line, comprising:

[0034] The first module is used to obtain insulator lightning fault data;

[0035] The second module is used to determine the insulator failure-free time period according to the insulator lightning fault data;

[0036] The third module is used to determine an insulator non-failure data sample according to the insulator non-failure time period and the corresponding equipment ledger information;

[0037] A fourth module is used to construct a Weibull probability density function and a Weibull distribution function according to the insulator zero-failure data sample;

[0038] The fifth module is used to determine the life curve of the insulator according to the Weibull probability density function and the Weibull distribution function.

[0039] Another aspect of an embodiment of the present invention provides an electronic device, including a processor and a memory;

[0040] The memory is used to store programs;

[0041] The processor executes the program to implement the method described above.

[0042] Another aspect of an embodiment of the present invention provides a computer-readable storage medium, wherein the storage medium stores a program, and the program is executed by a processor to implement the method described above.

[0043] The embodiment of the present invention also discloses a computer program product or a computer program, which includes a computer instruction stored in a computer-readable storage medium. A processor of a computer device can read the computer instruction from the computer-readable storage medium, and the processor executes the computer instruction, so that the computer device executes the above method.

[0044] The embodiment of the present invention first obtains insulator lightning fault data; then determines the insulator non-failure time period according to the insulator lightning fault data; then determines the insulator non-failure data sample according to the insulator non-failure time period and the corresponding equipment ledger information; and constructs the Weibull probability density function and the Weibull distribution function according to the insulator non-failure data sample; finally, determines the life curve of the insulator according to the Weibull probability density function and the Weibull distribution function. The present invention combines the non-failure data and the Weibull model to obtain the life curve of the insulator. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0046] Figure 1 The figure is a flowchart of the overall steps of an embodiment of the present invention. DETAILED DESCRIPTION

[0047] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0048] In view of the problems existing in the prior art, an embodiment of the present invention provides a method for evaluating the life of an insulator of a distribution network line. Figure 1 As shown, the method specifically comprises the following steps:

[0049] Obtain insulator lightning fault data;

[0050] Determine an insulator failure-free time period according to the insulator lightning fault data;

[0051] Determine an insulator non-failure data sample according to the insulator non-failure time period and the corresponding equipment ledger information;

[0052] Constructing a Weibull probability density function and a Weibull distribution function according to the insulator zero-failure data sample;

[0053] The life curve of the insulator is determined according to the Weibull probability density function and the Weibull distribution function.

[0054] Optionally, determining the insulator non-failure time period according to the insulator lightning fault data includes:

[0055] Sort the commissioning dates of faulty insulators;

[0056] The latest commissioning date of the faulty equipment is taken as the starting section, the end time of the fault data recording is taken as the end section, and the insulator failure-free time period is determined according to the starting section and the end section;

[0057] Optionally, determining the insulator non-failure data sample according to the insulator non-failure time period and corresponding equipment ledger information includes:

[0058] According to the insulator failure-free time period, matching corresponding equipment ledger information;

[0059] Taking days as units, intercept the tower ledger data within the zero-failure time period to record the number of towers put into operation every day, and then construct the zero-failure data sample of the insulator;

[0060] The data format of the insulator zero-failure data sample is <operation duration, operation quantity>.

[0061] Alternatively, the expression for the Weibull probability density function is:

[0062]

[0063] Where f(t) represents the Weibull probability density function; t is the running time before failure; β is the shape parameter; η is the scale parameter; when β>1, it indicates that the failure rate is increasing; when β<1, it indicates that the failure rate is decreasing; when β=1, it indicates that the failure rate remains unchanged.

[0064] Optionally, the expression of the Weibull distribution function is:

[0065]

[0066] Where F(t) represents the Weibull distribution function; t is the running time before failure; β is the shape parameter; and η is the scale parameter.

[0067] Optionally, determining the life curve of the insulator according to the Weibull probability density function and the Weibull distribution function includes:

[0068] Estimate the parameter values ​​of the Weibull probability density function and the Weibull distribution function by using the maximum likelihood function method;

[0069] The life curve of the insulator is determined according to the Weibull probability density function and the Weibull distribution function after the parameters are determined.

[0070] Optionally, the expression of the maximum likelihood function is:

[0071]

[0072] Among them, L M represents the maximum likelihood function; C is a constant; k is the number of samples; f() represents the Weibull probability density function; S i is the correction coefficient of the i-th sample; t i represents the failure time of the i-th sample; F() represents the Weibull distribution function; n i is the number of samples put into operation of the ith sample; t is the running time before failure; β is the shape parameter; η is the scale parameter; exp is the exponential function e^().

[0073] Another aspect of an embodiment of the present invention provides a device for evaluating the life of an insulator of a distribution network line, comprising:

[0074] The first module is used to obtain insulator lightning fault data;

[0075] The second module is used to determine the insulator failure-free time period according to the insulator lightning fault data;

[0076] The third module is used to determine an insulator non-failure data sample according to the insulator non-failure time period and the corresponding equipment ledger information;

[0077] A fourth module is used to construct a Weibull probability density function and a Weibull distribution function according to the insulator zero-failure data sample;

[0078] The fifth module is used to determine the life curve of the insulator according to the Weibull probability density function and the Weibull distribution function.

[0079] Another aspect of an embodiment of the present invention provides an electronic device, including a processor and a memory;

[0080] The memory is used to store programs;

[0081] The processor executes the program to implement the method described above.

[0082] Another aspect of an embodiment of the present invention provides a computer-readable storage medium, wherein the storage medium stores a program, and the program is executed by a processor to implement the method described above.

[0083] The embodiment of the present invention also discloses a computer program product or a computer program, which includes a computer instruction stored in a computer-readable storage medium. A processor of a computer device can read the computer instruction from the computer-readable storage medium, and the processor executes the computer instruction, so that the computer device executes the above method.

[0084] The implementation principle of the method for evaluating the life of insulators in distribution lines of the present invention is described in detail below.

[0085] Specifically, the present invention is based on the ledger information of 10kV overhead line towers. Since the erection of towers necessarily requires the installation of insulators, and the lightning tripping failure rate of 10kV insulators is relatively low, it can be considered that on towers where no insulator failure occurs, the operation time of the tower is equal to the operation time of the insulator. Combined with the lightning tripping failure data of the insulator, a time period in which no lightning tripping insulator failure occurs is selected, and an insulator sample with no failure data is constructed using the insulators (towers) put into operation during this time period. The Weibull model with no failure data is used to perform parameter fitting on the sample, and finally the failure probability curve of the insulator equipment group is obtained. The insulator failure rate can be judged according to the operation time of the insulator, and then the state of the insulator can be mastered, which is conducive to the staff to replace high-risk insulators in advance to ensure the lightning resistance level of the line.

[0086] The present invention can adopt the method of equivalent replacement (pole tower operation time equivalent to insulator operation time) in the absence of key data, and combine the Weibull model for failure-free data to make a 10kV insulator life curve, so that the staff can understand the lightning failure risk of 10kV insulators.

[0087] The present invention aims to solve the problem of evaluating the life of 10kV distribution network overhead line insulators in the absence of data.

[0088] First, according to the insulator lightning fault data, the insulator non-failure time period is screened. Taking the fault data up to July 31, 2020 as an example, the commissioning date of the faulty insulator is sorted. Assuming that the latest commissioning date of the faulty insulator is December 31, 2019, it can be considered that from January 1, 2020 to July 31, 2020 is the insulator non-failure time period, that is, the insulators commissioned during this period have no failure data. To generalize the above text, according to the insulator lightning fault data, the latest commissioning date of the faulty equipment is screened as the starting segment, and the end time of the fault data record is the end segment. This period is the insulator non-failure time period.

[0089] According to the zero-failure time period, corresponding to the equipment inventory information, since there is no insulator inventory information, there is no insulator failure in the zero-failure time period, it can be considered that the operation time of the tower is the operation time of the insulator. Therefore, the tower inventory data of the zero-failure time period is intercepted, and the number of towers put into operation every day is recorded in days. The operation time to the deadline is calculated to form a data sample with the data format of "operation time, operation quantity". For example, the deadline is July 31, 2020, and 21 towers are put into operation on July 30, 2020, then the corresponding sample is (1,21). If there are k days of towers put into operation in the zero-failure time period, d groups of sample data are formed. An example of sample data is shown in Table 1:

[0090] Table 1

[0091] Operation time (d) Number of units put into operation 1 N1 2 N2 3 …… k N

[0092] Weibull distribution is widely used in the study of mechanical, chemical, electrical, electronic, material failure, and even human diseases. There are many forms of Weibull distribution, among which the most commonly used are 2-parameter and 3-parameter Weibull distributions. The probability density function of the 2-parameter Weibull distribution is:

[0093]

[0094] Where t is the running time before failure; β is the shape parameter; η is the scale parameter. When β>1, it indicates that the failure rate is increasing; when β<1, it indicates that the failure rate is decreasing; when β=1, it indicates that the failure rate remains unchanged.

[0095] The distribution function of the 2-parameter Weibull distribution is:

[0096]

[0097] It is denoted as T~Wei(β,η). The decisive factor in determining a two-parameter Weibull distribution is the value of β and η. When calculating the parameters of the Weibull distribution model with zero or few failure data, the modified maximum likelihood function method is usually used for estimation.

[0098] Maximum Likelihood Function Method (MLE): Assume that the product life distribution function is F(t), the density function is f(t), and there are n products undergoing timed truncated life tests, with the truncation time being t0. Assume that r products fail within the observation time, and the observed failure time is 0≤t1≤t2≤…≤tr≤t0 (in this case, the operation time of different groups), then its modified maximum likelihood function is:

[0099]

[0100] In the formula, C is a constant, k is the number of samples, S i is the correction coefficient of the i-th sample, a constant, n i is the number of samples put into operation.

[0101] Simultaneous equations:

[0102]

[0103] Solved:

[0104]

[0105]

[0106] In the formula, and represents the numerical solutions of β and η, which can be considered as approximate values, and 1-α is the confidence level of the average life.

[0107] By setting different α values ​​according to the needs, the β and η values ​​are solved, and the insulator life curve is obtained by substituting the probability density function and distribution function. According to the commissioning time, the failure probability of insulators on each tower can be obtained, so that the staff can replace high-risk insulators in time.

[0108] In summary, compared with the prior art, the present invention uses the tower commissioning time to replace the insulator commissioning time, and combines the zero-failure data Weibull model to obtain the 10kV insulator life curve, which specifically has the following distinguishing technical features:

[0109] 1. Extract failure-free data samples based on insulator lightning failure data and tower ledger data;

[0110] 2. Fit the Weibull model parameters based on the improved maximum likelihood estimation method;

[0111] 3. The life curve of the insulator is derived based on the Weibull model with no failure data. The failure probability of the insulators on each tower can be obtained according to the commissioning time, so that the staff can replace the high-risk insulators in time.

[0112] Compared with traditional insulator life assessment, the present invention is data-driven and combined with the information of insulators actually put into operation. Compared with traditional experiments, it is more in line with the actual situation. At the same time, it adopts the Weibull model without failure data for fitting, which solves the bottleneck of the traditional Weibull function requiring a large amount of failure data.

[0113] In some selectable embodiments, the function / operation mentioned in the block diagram may not occur in the order mentioned in the operation diagram. For example, depending on the function / operation involved, the two boxes shown in succession can actually be executed substantially simultaneously or the boxes can sometimes be executed in reverse order. In addition, the embodiment presented and described in the flow chart of the present invention is provided by way of example, for the purpose of providing a more comprehensive understanding of technology. The disclosed method is not limited to the operation and logic flow presented herein. Selectable embodiments are expected, wherein the order of various operations is changed and the sub-operation of a part for which is described as a larger operation is performed independently.

[0114] In addition, although the present invention is described in the context of functional modules, it should be understood that, unless otherwise specified, one or more of the functions and / or features described may be integrated into a single physical device and / or software module, or one or more functions and / or features may be implemented in separate physical devices or software modules. It is also understood that a detailed discussion of the actual implementation of each module is unnecessary for understanding the present invention. More specifically, in view of the properties, functions, and internal relationships of the various functional modules in the device disclosed herein, the actual implementation of the module will be understood within the conventional skills of the engineer. Therefore, those skilled in the art can implement the present invention set forth in the claims without excessive experimentation using ordinary techniques. It is also understood that the specific concepts disclosed are merely illustrative and are not intended to limit the scope of the present invention, which is determined by the full scope of the appended claims and their equivalents.

[0115] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several 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 methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0116] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0117] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0118] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0119] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0120] Although the embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the claims and their equivalents.

[0121] The above is a specific description of the preferred implementation of the present invention, but the present invention is not limited to the described embodiments. Those skilled in the art may make various equivalent modifications or substitutions without violating the spirit of the present invention. These equivalent modifications or substitutions are all included in the scope defined by the claims of this application.

Claims

1. A method for evaluating the life of insulators of distribution lines, characterized in that: include: Obtain insulator lightning fault data; Determine an insulator failure-free time period according to the insulator lightning fault data; According to the insulator-free failure time period and the corresponding equipment ledger information, determine the insulator-free failure data sample, intercept the pole tower ledger data within the non-failure time period in units of days to record the number of pole towers put into operation every day, and then construct the insulator-free failure data sample, wherein the data format of the insulator-free failure data sample is <operation time, operation number>, and the pole tower operation time is equivalent to the insulator operation time; Constructing a Weibull probability density function and a Weibull distribution function according to the insulator zero-failure data sample; Determining a life curve of an insulator according to the Weibull probability density function and the Weibull distribution function; Characterized in that, determining the life curve of the insulator according to the Weibull probability density function and the Weibull distribution function comprises: Estimate the parameter values ​​of the Weibull probability density function and the Weibull distribution function by using the maximum likelihood function method; Determining a life curve of an insulator according to the Weibull probability density function and the Weibull distribution function after determining parameters; The expression of the maximum likelihood function is: Among them, L M represents the maximum likelihood function; C is a constant; k is the number of samples; f() represents the Weibull probability density function; Si is the correction coefficient of the i-th sample; t i represents the failure time of the i-th sample; F() represents the Weibull distribution function; ni is the number of commissioning of the i-th sample; t is the operating time before failure; β is the shape parameter; η is the scale parameter.

2. The method for evaluating the life of insulators of distribution lines according to claim 1, characterized in that: The determining of the insulator non-failure time period according to the insulator lightning fault data comprises: Sort the commissioning dates of faulty insulators; The latest commissioning date of the faulty equipment is taken as the starting section, the end time of the fault data recording is taken as the end section, and the insulator failure-free time period is determined according to the starting section and the end section.

3. The method for evaluating the life of insulators of distribution lines according to claim 1, characterized in that: The expression of the Weibull probability density function is: Where f(t) represents the Weibull probability density function; t is the running time before failure; β is the shape parameter; η is the scale parameter; when β>1, it indicates that the failure rate is increasing; when β<1, it indicates that the failure rate is decreasing; when β=1, it indicates that the failure rate remains unchanged.

4. The method for evaluating the life of insulators of distribution lines according to claim 1, characterized in that: The expression of the Weibull distribution function is: Where F(t) represents the Weibull distribution function; t is the running time before failure; β is the shape parameter; and η is the scale parameter.

5. The insulator life evaluation device for distribution network lines is characterized by The method for evaluating the life of an insulator of a distribution network line according to claim 1 comprises: The first module is used to obtain insulator lightning fault data; The second module is used to determine the insulator failure-free time period according to the insulator lightning fault data; The third module is used to determine the insulator non-failure data sample according to the insulator non-failure time period and the corresponding equipment ledger information, wherein the tower commissioning time is equivalent to the insulator commissioning time; The fourth module is used to construct a Weibull probability density function and a Weibull distribution function according to the insulator zero-failure data sample; The fifth module is used to determine the life curve of the insulator according to the Weibull probability density function and the Weibull distribution function.

6. An electronic device, characterized in that: including a processor and a memory; The memory is used to store programs; The processor executes the program to implement the method according to any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that: The storage medium stores a program, and the program is executed by a processor to implement the method according to any one of claims 1 to 4.

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