A method for analyzing double-threshold alarm rules of lightning arresters considering grey correlation

Abstract

The present invention relates to the technical field of metal oxide arrester state diagnosis, and discloses a method for analyzing the double threshold alarm rules of arresters by considering grey correlation, and the specific steps are as follows: S1, model confirmation; S2, normal state data acquisition. The invention selects the grey correlation analysis method as the basic method, utilizes the advantages of the grey system theory for sample systems with inaccurate and incompletely known information, clarifies the incompletely known relationship between evaluation factors, sets the correlation threshold, and integrates it with the alarm threshold of the monitoring equipment, and constructs a double threshold alarm rule method, which can effectively eliminate false alarms caused by abnormal current values ​​caused by environmental influences. The method not only improves the efficiency of real-time monitoring, but also enhances the effectiveness of preventive measures and maintenance work, and the safety of the power grid, the service life of the equipment and the overall economic benefits are significantly improved.

Description

Technical Field

[0001] The invention relates to the technical field of metal oxide arrester state diagnosis, in particular to a method for analyzing arrester double threshold alarm rules by considering grey correlation. Background Art

[0002] Metal oxide arresters are vital electrical equipment in power grids, protecting power equipment from overvoltage hazards. Their reliability directly affects the safety of the power system. Different environmental factors such as lightning, rain, snow, dirt and corrosion may cause damage to the arrester, affecting its normal operation, and even causing power accidents, resulting in huge economic losses. Resistive current is a key parameter for judging the state of the arrester and is widely used in state monitoring and alarm. Although the State Grid has formulated relevant standards for metal oxide arrester maintenance based on resistive current, resistive current is easily affected by environmental influences such as humid weather, resulting in abnormal monitoring data, and it is difficult for operation and maintenance personnel to make accurate insulation status Online diagnosis. In recent years, operation and maintenance personnel have also used a series of live detection technologies such as resistive current testing, infrared temperature measurement, and high-frequency current partial discharge to discover defects in lightning arresters. Combined with the power outage test and dismantling process, they comprehensively analyze the causes and propose the next step of processing and prevention measures. However, the operation and maintenance personnel use a variety of live detection technologies to diagnose the status. The steps are cumbersome and the interference caused by the environment cannot be eliminated, which seriously affects the reliability and safe operation of the power grid. Inaccurate monitoring data or data interfered by environmental factors may lead to diagnostic errors, which not only increases the risk of equipment failure, but may also cause more extensive power system interruptions or accidents due to the inability to identify or prevent problems in a timely manner. 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] To achieve the above object, the present invention provides the following technical solutions:

[0005] A method for analyzing the double-threshold alarm rules of lightning arresters by considering grey correlation, the method steps are as follows:

[0006] S1. Model confirmation: determine the model of the metal oxide arrester to be monitored;

[0007] S2. Normal state data acquisition: collect the resistive current data of the selected arrester under normal operating conditions within 24 hours, and set the data as an auxiliary feature vector, i.e., a reference sequence, for use in the correlation analysis in the subsequent steps;

[0008] S3. Abnormal state data acquisition: collect the resistive current data of the same arrester under known abnormal state within 24 hours as a comparison sequence ;

[0009] S4, data dimensionless: standardize or normalize the two sets of data obtained in S2 and S3 to eliminate the dimension effect;

[0010] S5, finding the difference sequence: using the dimensionless sequence obtained in S4 to find the difference sequence;

[0011] S6, solving the extreme difference value: solving the difference sequence obtained in S5 for the maximum value M in the maximum difference sequence and the minimum value N in the minimum difference sequence;

[0012] S7, calculating the correlation coefficient and correlation degree: calculating the correlation degree using the correlation coefficient calculated in S6;

[0013] S8. Draw a scatter plot for visualization and determine the threshold: Draw a scatter plot of the correlation calculated in S7.

[0014] As a further solution of the present invention: the specific operation of step S2 is as follows:

[0015] The data in the typical normal state is taken as the auxiliary feature vector , the auxiliary feature vector is used as a reference sequence to analyze the correlation with the vector sequence at other moments;

[0016] ;

[0017] In the formula, is the resistive current sequence of the selected typical arrester; The reference sequence The resistive current at the moment, is the total number of time points.

[0018] As a further solution of the present invention: the specific operation of step S3 is as follows:

[0019] The data in typical abnormal state is taken as auxiliary feature vector , the auxiliary feature vector is used as a reference sequence to analyze the correlation with the vector sequence at other moments;

[0020] ;

[0021] In the formula, is the resistive current sequence of the selected typical arrester under abnormal conditions; For the The resistive current at the moment, is the total number of time points.

[0022] As a further solution of the present invention: in step S4, the two sets of data obtained in step S2 and step S3 are standardized or normalized to eliminate the dimension effect, and the formula to ensure the validity of the data is as follows:

[0023] ;

[0024] In the formula, .

[0025] As a further solution of the present invention: in step S5, the dimensionless sequence obtained in step S4 is used to obtain the difference sequence , calculate the absolute difference between the reference sequence and the comparison sequence at each moment, the formula is as follows:

[0026] .

[0027] As a further solution of the present invention: in step S6, the difference sequence obtained in step S5 needs to be Solve the maximum value M in the maximum difference sequence and the minimum value N in the minimum difference sequence. The formula is as follows:

[0028] ;

[0029] ;

[0030] And calculate the correlation coefficient with the maximum value M and minimum value N :

[0031] ;

[0032] In the formula is the resolution coefficient, Different resolution coefficients can be selected according to the correlation analysis between factors. The online monitoring of the arrester insulation status is generally A satisfactory resolution can be obtained.

[0033] As a further solution of the present invention: in step S7, the correlation coefficient calculated in step S6 is used Calculate the correlation :

[0034] ;

[0035] The correlation of resistive currents in different states of the arrester can be calculated through the correlation degree.

[0036] As a further solution of the present invention: in step S8, the association degree calculated in step S7 is Draw a scatter plot, observe the main clustering areas of invalid abnormal data and valid abnormal data on the scatter plot, determine the correlation threshold, and merge it with the existing alarm rule threshold to form a dual-threshold alarm rule to exclude invalid abnormal data.

[0037] Compared with the prior art, the present invention has the following beneficial effects:

[0038] By selecting the grey correlation analysis method as the basic method, the advantages of grey system theory for sample systems with inaccurate and incomplete information are used to clarify the incompletely known relationship between the evaluation factors. The method of the alarm rule is based on the relevant standards for the state maintenance of metal oxide lightning arresters resistive current. Combined with historical data, the correlation relationship between the resistive current in normal and abnormal states is analyzed, and the correlation threshold is set. It is integrated with the alarm threshold of the monitoring equipment, and the double threshold alarm rule method is constructed. It can effectively eliminate false alarms caused by abnormal current values ​​caused by environmental influences. The method not only improves the efficiency of real-time monitoring, but also enhances the effectiveness of preventive measures and maintenance work. The safety of the power grid, the service life of equipment and the overall economic benefits have been significantly improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 A double-threshold alarm rule diagnosis flow chart of a method for analyzing double-threshold alarm rules of lightning arresters by considering grey correlation;

[0040] Figure 2 A scatter plot showing the determination of correlation threshold in a method for analyzing the double-threshold alarm rule of lightning arresters by considering grey correlation;

[0041] Figure 3 This is a comparison chart before and after the optimization of the alarm rules in a method for analyzing the dual-threshold alarm rules of lightning arresters using grey correlation. DETAILED DESCRIPTION

[0042] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and understandable, the specific implementation modes of the present invention are described in detail below in conjunction with the accompanying drawings.

[0043] 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.

[0044] 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.

[0045] Example

[0046] See also Figure 1-3 , which is the first embodiment of the present invention, provides a method for analyzing the double-threshold alarm rules of lightning arresters by considering grey correlation, and the method steps are as follows:

[0047] Step 1: Collect data from December 15 to January 15 of the 220 kV A phase lightning arrester of the #1 main transformer of a 220 kV substation;

[0048] Step 2: Screen the current data obtained in step 1. The resistive current data on January 12 is stable and within the normal range. Therefore, the auxiliary feature vector of 24 sets of resistive current data on January 12 is selected as the reference vector.

[0049] ;

[0050] Step 3: Select the resistive current data from December 15 to January 11 and January 13 to January 15 as the comparison sequence, calculate the correlation sequence matrix, and take the resistive current on December 15 as an example to calculate the correlation. The reference sequences are:

[0051] ;

[0052] Step 4: Combine the sequences obtained in steps 2 and 3 and Perform dimensionless operations;

[0053] ;

[0054] In the formula, ;

[0055] The dimensionless normalized sequence is:

[0056] ;

[0057] ;

[0058] Step 5: Use the dimensionless sequence obtained in step 4 to find the difference sequence , find the absolute difference between the reference sequence and the comparison sequence:

[0059] ;

[0060] The calculated absolute difference matrix is:

[0061] ;

[0062] Step 6: Substitute the difference sequence obtained in step 5 Solve the maximum value M in the maximum difference sequence and the minimum value N in the minimum difference sequence, and calculate the correlation coefficient :

[0063] ;

[0064] ;

[0065] ;

[0066] The correlation coefficient matrix calculated according to the above formula is:

[0067] ;

[0068] Step 7: Use the correlation coefficient calculated in step 6 Calculate the correlation :

[0069] ;

[0070] The resistive current correlation on December 15 is:

[0071] ;

[0072] Step 8: Similar to the above steps, the resistive current data from December 16 to January 11 and January 13 to January 15 are used as comparison sequences to calculate the correlation degree and form a correlation degree sequence matrix. The calculated correlation degree sequence matrix is ​​as follows:

[0073] (1) Draw a scatter plot of the correlation calculated in step 8. For details, see Figure 3 , when invalid abnormal data appeared, the correlation was lower than 0.85, while the correlations of the other days without invalid abnormal data were all higher than 0.85, so the correlation threshold of this type of arrester was set to 0.85;

[0074] (2) The abnormal alarm threshold of the resistive current data of this type of arrester is 0.08. The correlation threshold obtained in (1) is integrated with the existing standard threshold to form an improved arrester alarm rule, as shown in the following table:

[0075] In summary, the example proves that the optimized dual-threshold alarm rule will effectively clean invalid abnormal data, reduce the number of lightning arrester alarms by at least 90%, and basically solve the problem of frequent false alarms.

[0076] Importantly, it should be noted that the construction and arrangement of the present application shown in a number of different exemplary embodiments are only exemplary. Although only a few embodiments are described in detail in this disclosure, it should be readily understood by those who refer to this disclosure that many modifications are possible (e.g., the size, scale, structure, shape and proportion of various elements, and parameter values ​​(e.g., temperature, pressure, etc.), mounting arrangements, use of materials, color, changes in orientation, etc.) without substantially departing from the novel teachings and advantages of the subject matter described in the application. For example, an element shown as integrally formed may be composed of multiple parts or elements, the position of the element may be inverted or otherwise changed, and the nature or number or position of the discrete element may be changed or changed. Therefore, all such modifications are intended to be included within the scope of the present invention. The order or sequence of any process or method steps may be changed or reordered according to an alternative embodiment. In the claims, any "device plus function" clause is intended to cover the structure described herein that performs the function, and is not only structurally equivalent but also equivalent structure. Other substitutions, modifications, changes and omissions may be made in the design, operating conditions and arrangement of the exemplary embodiments without departing from the scope of the present invention. Therefore, the invention is not limited to a specific embodiment, but extends to numerous modifications still falling within the scope of the appended claims.

[0077] Additionally, in order to provide a concise description of exemplary embodiments, all features of an actual embodiment may not be described (ie, those features that are not relevant to the best mode presently contemplated for carrying out the invention or those features that are not relevant to implementing the invention).

[0078] It will be appreciated that in the development of any actual implementation, as in any engineering or design project, numerous implementation-specific decisions may be made. Such a development effort may be complex and time-consuming, but will be a routine task of design, fabrication, and production for those of ordinary skill having the benefit of this disclosure without undue experimentation.

[0079] 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 analyzing the double-threshold alarm rules of lightning arresters by considering grey correlation, characterized in that: The method steps are as follows: S1. Model confirmation: determine the model of the metal oxide arrester to be monitored; S2. Normal state data acquisition: collect the resistive current data of the selected arrester under normal operating conditions within 24 hours, and set the data as an auxiliary feature vector, i.e., a reference sequence, for use in the correlation analysis in the subsequent steps; S3. Abnormal state data acquisition: collect the resistive current data of the same arrester under known abnormal state within 24 hours as a comparison sequence ; S4, data dimensionless: standardize or normalize the two sets of data obtained in S2 and S3 to eliminate the dimension effect; S5, finding the difference sequence: using the dimensionless sequence obtained in S4 to find the difference sequence; S6, solving the extreme difference value: solving the difference sequence obtained in S5 for the maximum value M in the maximum difference sequence and the minimum value N in the minimum difference sequence; S7, calculating the correlation coefficient and correlation degree: calculating the correlation degree using the correlation coefficient calculated in S6; S8. Draw a scatter plot for visualization and determine the threshold: Draw a scatter plot of the correlation calculated in S7.

2. According to claim 1, a method for analyzing the double-threshold alarm rules of lightning arresters by considering grey correlation, characterized in that: The specific operations of step S2 are as follows: The data in the typical normal state is taken as the auxiliary feature vector , the auxiliary feature vector is used as a reference sequence to analyze the correlation with the vector sequence at other moments; ； In the formula, is the resistive current sequence of the selected typical arrester; The reference sequence The resistive current at the moment, is the total number of time points.

3. The method for analyzing the double-threshold alarm rules of lightning arresters by considering grey correlation according to claim 2 is characterized in that: The specific operation of step S3 is as follows: The data in typical abnormal state is taken as auxiliary feature vector , the auxiliary feature vector is used as a reference sequence to analyze the correlation with the vector sequence at other moments; ； In the formula, is the resistive current sequence of the selected typical arrester under abnormal conditions; For the The resistive current at the moment, is the total number of time points.

4. The method for analyzing the double-threshold alarm rules of lightning arresters by considering grey correlation according to claim 3 is characterized in that: In step S4, the two sets of data obtained in step S2 and step S3 are standardized or normalized to eliminate the dimension effect, and the formula to ensure the validity of the data is as follows: ； In the formula, .

5. The method for analyzing the double-threshold alarm rules of lightning arresters by considering grey correlation according to claim 4 is characterized in that: In step S5, the dimensionless sequence obtained in step S4 is used to obtain the difference sequence , calculate the absolute difference between the reference sequence and the comparison sequence at each moment, the formula is as follows: 。 6. The method for analyzing the double-threshold alarm rules of lightning arresters by considering grey correlation according to claim 5, characterized in that: In step S6, the difference sequence obtained in step S5 needs to be Solve the maximum value M in the maximum difference sequence and the minimum value N in the minimum difference sequence. The formula is as follows: ； ； And calculate the correlation coefficient with the maximum value M and minimum value N : ； In the formula is the resolution coefficient, According to the correlation analysis between factors, different resolution coefficients can be selected to monitor the insulation status of the arrester online. Get a satisfactory resolution.

7. The method for analyzing the double-threshold alarm rules of lightning arresters by considering grey correlation according to claim 6, characterized in that: In step S7, the correlation coefficient calculated in step S6 is used Calculate the correlation : ； The correlation of resistive currents in different states of the arrester can be calculated through the correlation degree.

8. The method for analyzing the double-threshold alarm rules of lightning arresters by considering grey correlation according to claim 7, characterized in that: In step S8, the correlation calculated in step S7 is Draw a scatter plot, observe the main clustering areas of invalid abnormal data and valid abnormal data on the scatter plot, determine the correlation threshold, and merge it with the existing alarm rule threshold to form a dual-threshold alarm rule to exclude invalid abnormal data.

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