A method for evaluating the operating status of a substation terminal box

Abstract

The present application discloses a method for evaluating the operating status of a transformer substation terminal box, comprising the following steps: obtaining real-time operating status parameters of the terminal box; S2, performing data cleaning, filtering and calibration on the real-time operating status parameters to obtain preprocessed data; S3, comparing and judging the preprocessed data with normal data, and generating an operating status result, wherein the operating status result includes a normal result and an abnormal result; S4, analyzing the changes in the preprocessed data within different time periods for the terminal box corresponding to the normal result, judging the potential risk situation, and generating a judgment result; S5, performing an abnormal analysis on the terminal box corresponding to the abnormal result and determining the cause of the abnormality, and performing a risk analysis on the operating status of the terminal box according to the cause of the abnormality, and generating a risk analysis result; the method is used to solve the technical problem that the existing terminal box operating status evaluation method cannot reasonably utilize and analyze the data, resulting in one-sided evaluation.

Description

Technical Field

[0001] The present application relates to the technical field of terminal box status assessment, and in particular to a method for assessing the operating status of a substation terminal box. Background Art

[0002] With the continuous development and intelligent upgrading of power systems, substations play a vital role in power transmission and distribution. As an important part of the substation, the substation terminal box contains numerous electrical connections and equipment. Its operating status is directly related to the safe and stable operation of the substation.

[0003] Chinese patent publication number CN115545505A discloses a terminal box operating status evaluation method, which effectively solves the problem that the existing technology lacks monitoring of the insulation performance of the terminal box, thereby affecting the safety of the terminal box. The specific method is: detect the local discharge phenomenon of the terminal box, and normalize the obtained status evaluation index to obtain the weight set A and the fuzzy evaluation matrix P corresponding to the evaluation index, and finally obtain the quantization result B. According to the value of the quantization result B, the operating status of the terminal box at this time is judged, thereby realizing effective monitoring of the operating status of the terminal box and ensuring the safe and reliable operation of the power equipment.

[0004] However, the above patented method performs status assessment after directly judging the collected data. Such an assessment method may result in inaccurate assessment. The data is not reasonably utilized and analyzed, resulting in one-sided assessment, which leads to low accuracy in terminal box operating status assessment.

[0005] Therefore, in order to solve the problems existing in the above-mentioned prior art, a method for evaluating the operating status of a substation terminal box is proposed. Summary of the invention

[0006] An embodiment of the present application provides a method for evaluating the operating status of a substation terminal box, which is used to solve the technical problem that the existing terminal box operating status evaluation method cannot reasonably utilize and analyze data, resulting in one-sided evaluation and low accuracy of terminal box operating status evaluation.

[0007] In view of this, the present application provides a method for evaluating the operating status of a substation terminal box, comprising the following steps:

[0008] S1. Obtain the real-time operating status parameters of the terminal box;

[0009] S2. Perform data cleaning, filtering and calibration on the real-time operating status parameters to obtain pre-processed data;

[0010] S3, comparing and judging the preprocessed data with the normal data, and generating an operation status result, wherein the operation status result includes a normal result and an abnormal result;

[0011] S4, analyzing the changes of pre-processed data in different time periods for the terminal box corresponding to the normal result, judging the potential risk situation, and generating a judgment result;

[0012] S5. Perform an abnormal analysis on the terminal box corresponding to the abnormal result and determine the cause of the abnormality. At the same time, perform a risk analysis on the operating status of the terminal box according to the cause of the abnormality and generate a risk analysis result.

[0013] Optionally, the specific method steps for obtaining the preprocessed data in step S2 are:

[0014] S21, acquiring the operating status parameter, and acquiring error data in the operating status parameter to obtain error data;

[0015] S22, calibrating the erroneous data and eliminating the erroneous data to obtain correct data;

[0016] S23, obtain the correct data of the same type, and mark it as i, where i=1, 2, ..., j, where j represents the number of the correct data, and obtain the value corresponding to the correct data as g i , substitute the correct data i into the formula The calculated y is used as a filtering value, and the preprocessing data is obtained according to the filtering value.

[0017] Optionally, the specific method steps for generating the operating status result in step S3 are:

[0018] S31, obtaining the normal data;

[0019] S32. Compare the preprocessed data with the normal data. If the preprocessed data does not match the normal data, determine that the preprocessed data is abnormal, mark it as abnormal data, and generate an abnormal result. If the preprocessed data matches the normal data, determine that the preprocessed data is normal, and generate a normal result.

[0020] Optionally, in step S4, the change of pre-processed data in different time periods is analyzed for the terminal box corresponding to the normal result, and the potential risk situation is judged. The specific method steps for generating the judgment result are:

[0021] S41, obtaining preprocessed data corresponding to the normal result, determining a time period t, and obtaining a value change of the preprocessed data within the time period t;

[0022] S42. Acquire historical data, and at the same time acquire the corresponding potential risk records in the historical data, and organize the obtained potential risk records to obtain risk judgment standards, and then compare the numerical change of the pre-processed data with the risk judgment standard. If the numerical change of the pre-processed data is greater than the risk judgment standard, it indicates that there is a potential risk and a risk warning signal is generated. If the numerical change of the pre-processed data is less than the risk judgment standard, it indicates that there is no potential risk and a normal detection signal is generated.

[0023] Optionally, the method steps for performing abnormal analysis on the terminal box corresponding to the abnormal result in step S5 are:

[0024] S511, obtaining preprocessed data corresponding to the abnormal result, and determining the abnormal data according to a comparison result between the preprocessed data and the normal data;

[0025] S512, acquiring historical data, and filtering the historical data according to the abnormal data to obtain pre-selected records, and labeling the pre-selected records as n, where n=1, 2, ..., m, and then analyzing and extracting data features corresponding to the pre-selected record n.

[0026] Optionally, the specific method steps for analyzing and extracting the data features corresponding to the pre-selected record n ​​in step S512 are:

[0027] S5121, obtaining a change value of the change data corresponding to the pre-selected record n ​​within a time T, and using the obtained change value as the first feature data;

[0028] S5122. Acquire the number of associated data corresponding to the changed data in the pre-selected record n, and use the number of associated data as second feature data, and combine the first feature data with the second feature data to obtain data features.

[0029] Optionally, the specific method steps for performing risk analysis on the operating state of the terminal box according to the abnormal cause in step S5 are:

[0030] S521, obtaining the abnormal cause, and simultaneously obtaining the historical data corresponding to the abnormal cause;

[0031] S522. Label the historical data as data to be analyzed o, where o=1, 2, ..., p, where p represents the number of data to be analyzed. Then, screen the data to be analyzed o with operational risks and record them as comparison data. Meanwhile, perform corresponding analysis according to the risk levels corresponding to different comparison data.

[0032] Optionally, the specific method steps for performing corresponding analysis according to the risk levels corresponding to different comparison data in step S522 are:

[0033] S5221. Obtain the risk level corresponding to the comparison data, and at the same time obtain the abnormal parameter corresponding to the comparison data, and obtain the change value of the abnormal parameter within time t1, recorded as Ga, and a=1, 2, ..., b, where b represents different specific data of the abnormal parameter, and θ and α are both constants;

[0034] S5222. Obtain the maximum change peak value of the abnormal parameter a, record it as GaMax, and obtain the duration of the maximum change peak value, record it as T1, and substitute the obtained parameters into the formula The judgment value F corresponding to the comparison data is calculated, and the judgment values ​​corresponding to all the comparison data of the same risk level are calculated in this way, and the judgment interval is generated based on the judgment value.

[0035] It can be seen from the above technical solutions that the embodiments of the present application have the following advantages:

[0036] The present application provides a method for evaluating the operating status of a substation terminal box. The method effectively removes erroneous data and noise interference by preprocessing the data, including data cleaning, filtering and calibration, and improves the quality and reliability of the data. It provides an accurate data basis for subsequent operating status judgment and risk analysis. By screening and analyzing historical data, it accurately finds records similar to the current abnormal situation, thereby providing strong support for fault diagnosis and risk analysis. This helps to take corresponding measures before a fault occurs, avoid the expansion of accidents, and improve the operating safety of the substation. By extracting and analyzing various data features, such as the change value of abnormal data, the number of related data, etc., and matching and comparing these features with historical data, it is possible to deeply explore the information behind the data, provide a comprehensive and scientific decision-making basis for the operation and maintenance of the substation terminal box, and help improve the overall stability and reliability of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly express the technical solutions of the embodiments of the present application, the drawings required for describing the embodiments are briefly introduced below. 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 work.

[0038] Figure 1 A step diagram of a method for evaluating the operating status of a substation terminal box provided in an embodiment of the present application. DETAILED DESCRIPTION

[0039] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0040] For easier understanding, see Figure 1 The present invention provides a method for evaluating the operating status of a transformer substation terminal box, comprising the following steps:

[0041] S1. Obtain the real-time operating status parameters of the terminal box; specifically, obtain the operating status parameters of the terminal box based on multiple sensors, and the sensors here include temperature sensors, humidity sensors, insulation current sensors, etc., to further obtain corresponding temperature data, humidity data and insulation current data.

[0042] S2. Perform data cleaning, filtering and calibration on the real-time operation status parameters to obtain preprocessed data; specifically, obtain the operation status parameters, then obtain the erroneous data in the operation status parameters, calibrate the erroneous data, and eliminate the erroneous data to obtain the correct data, then filter all the correct data, the filtering processing includes arithmetic mean filtering method, sliding average filtering method, median filtering method and limiting filtering method. The arithmetic mean filtering method is used in this application. The specific processing method is: obtain the correct data of the same type, and label it as i, and i=1, 2, ..., j, where j represents the number of correct data, and obtain the numerical value corresponding to the correct data as gi, and substitute the obtained correct data i into the formula The calculated y is used as the filtered value, and the preprocessed data is obtained through the above processing. For example, for the temperature data in the terminal box collected by the temperature sensor, it is assumed that 10 temperature values ​​are collected within 1 minute, which are 25.1℃, 25.2℃, 24.9℃, 25.0℃, 25.3℃, 24.8℃, 25.1℃, 25.0℃, 24.9℃, and 25.2℃ respectively. Substituting the obtained data into the above formula, the temperature value obtained is 25.05℃, which is the filtered value after arithmetic mean filtering.

[0043] S3, comparing and judging the preprocessed data with the normal data, and generating an operation status result, wherein the operation status result includes a normal result and an abnormal result;

[0044] Specifically, normal data is obtained, and the normal data here refers to the data corresponding to the normal working condition of the terminal box. The normal data includes the data obtained by various sensors in step S1, and the normal working condition is set by the operator. The specific normal data is the range value. Then the preprocessed data is compared with the normal data, and the comparison here is to compare each sensor data corresponding to the preprocessed data with the data corresponding to the normal data. For example, the preprocessed data includes temperature, humidity and insulation current. The three kinds of data are further compared with the corresponding normal data. If the preprocessed data does not match the normal data, it means that the preprocessed data is abnormal, and it is marked as abnormal data, and an abnormal result is generated; if the preprocessed data matches the normal data, it means that the preprocessed data is normal, and a normal result is generated. Specifically, only when all the preprocessed data matches the normal data does it mean that the terminal box is operating normally, and a normal result will be generated at this time. If any group of preprocessed data does not match the normal data, an abnormal result will be generated.

[0045] S4. Analyze the changes of pre-processed data in different time periods for the terminal box corresponding to the normal result, judge the potential risk situation, and generate a judgment result;

[0046] Specifically, the preprocessed data corresponding to the normal result is obtained, and the time period t is determined, and the numerical changes of the preprocessed data within the time period t are obtained, and the time value of the time period t here is 10 minutes. Then the numerical changes are analyzed, and the specific analysis method is: historical data is obtained, and the corresponding potential risk records in the historical data are obtained, and the potential risk records here are expressed as data changes obtained according to the corresponding data analysis. There will be potential risks, and the obtained potential risk records are sorted out to obtain risk judgment standards, and then the changes in the preprocessed data are compared with the risk judgment standards; if the numerical change of the preprocessed data is greater than the risk judgment standard, it indicates that there is a potential risk, and a risk warning signal is generated; if the numerical change of the preprocessed data is less than the risk judgment standard, it indicates that there is no potential risk, and a normal detection signal is generated.

[0047] Specifically, assuming that when the substation terminal box operates normally, the temperature generally fluctuates between 20℃-30℃, the humidity varies between 40%-60%RH, and the cycle is set to 10 minutes. It is found in the historical operation data that when the temperature rises by more than 6℃ within 10 minutes, there are several subsequent minor failures caused by poor equipment heat dissipation; when the humidity changes by more than 15%RH within 10 minutes, there have been cases of electrical component short circuit failures caused by excessive humidity. At the same time, all potential risk records are analyzed and integrated, and the risk judgment standard for setting the temperature is a rise of more than 4℃ within 10 minutes, and the risk judgment standard for the humidity is a change of more than 10%RH within 10 minutes.

[0048] S5. Perform an abnormal analysis on the terminal box corresponding to the abnormal result and determine the abnormal cause. At the same time, perform a risk analysis on the operation status of the terminal box according to the abnormal cause and generate a risk analysis result.

[0049] Specifically, the preprocessed data corresponding to the abnormal result is obtained, and the abnormal data is determined according to the comparison result between the preprocessed data and the normal data, and then the historical data is obtained, and the historical data is screened according to the abnormal data to obtain the preselected records, and the preselected records here are represented as the same data records as the abnormal data, and are all records under abnormal operation, and the preselected records are numbered as n, and n=1, 2, ..., m, and then the data features corresponding to the preselected record n ​​are analyzed and extracted, and the specific analysis and extraction method is:

[0050] Obtain the change data corresponding to the preselected record n, and obtain the change value within the time T, and the change data here is represented as the change of the corresponding abnormal data, and the obtained change value is used as the first characteristic data, and at the same time, the associated data corresponding to the change data in the preselected record n ​​is obtained, and the associated data here is represented as data that is positively or negatively correlated with the change data. For example, when the change data is temperature, when the terminal box is actually working, the change of voltage and current will affect the temperature, so the voltage data and the current data are marked as associated data, and the number of associated data is obtained, and then the number of associated data is used as the second characteristic data, and the first characteristic data and the second characteristic data are combined to obtain the data feature;

[0051] Similarly, the above analysis obtains the first characteristic data and the second characteristic data of the preprocessed data, and matches the first characteristic data and the second characteristic data of the preprocessed data with the data characteristics of the preselected record n, and records the matched preselected record as the abnormal reason record, and generates the abnormal reason;

[0052] Then, the cause of the abnormality is obtained, and at the same time, the historical data corresponding to the cause of the abnormality is obtained, and the historical data here is the historical data corresponding to the specific fault obtained by the current analysis, and the historical data is labeled as data to be analyzed o, and o=1, 2, ..., p, where p represents the number of data to be analyzed, and then the data to be analyzed o with operation risks are screened and recorded as comparison data, and corresponding analysis is performed according to the risk levels corresponding to different comparison data; and the risk level here is set by the operator according to the actual situation, and the risk level is specifically classified into level one, level two and level three, and the risk increases step by step from level one to level three.

[0053] Obtain the risk level corresponding to the comparison data, and at the same time obtain the abnormal parameters corresponding to the comparison data, and obtain the change value of the abnormal parameters within time t1, recorded as Ga, and a = 1, 2, ..., b, where b represents the specific data of different abnormal parameters, θ and α are constants, then obtain the maximum change peak value of the abnormal parameter a, recorded as GaMax, and obtain the duration of the maximum change peak value, recorded as T1, and the duration here represents the time from the appearance of the maximum change peak to the end, which can be obtained by adding multiple time periods, and substitute the obtained parameters into the formula The judgment value F corresponding to the comparison data is calculated, and the judgment values ​​corresponding to all the comparison data of the same risk level are calculated in this way. At the same time, the judgment interval is generated based on the judgment value. Specifically, the judgment value of the same risk level is calculated here, and the corresponding judgment interval is obtained by integration. Similarly, the judgment intervals of other risk levels are integrated.

[0054] Similarly, the above calculation method is used to calculate the judgment value of the preprocessed data, which is recorded as F1. The calculated judgment interval is compared with the judgment value F1. If the judgment value F1 exists in the corresponding judgment interval, a risk level corresponding to the judgment interval is generated, and a risk analysis result is generated.

[0055] Specifically, the value of the time period t in step S4 is 20 minutes. The specific value of the time period t is determined according to actual conditions, and the value selection rule is t+10. Specifically, the unit here is minutes.

[0056] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein, for example. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0057] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for evaluating the operating status of a substation terminal box, characterized in that: The following steps are involved: S1. Obtain the real-time operating status parameters of the terminal box; S2. Perform data cleaning, filtering and calibration on the real-time operating status parameters to obtain pre-processed data; S3, comparing and judging the preprocessed data with the normal data, and generating an operation status result, wherein the operation status result includes a normal result and an abnormal result; S4, analyzing the changes of pre-processed data in different time periods for the terminal box corresponding to the normal result, judging the potential risk situation, and generating a judgment result; S5. Perform an abnormal analysis on the terminal box corresponding to the abnormal result and determine the cause of the abnormality. At the same time, perform a risk analysis on the operating status of the terminal box according to the cause of the abnormality and generate a risk analysis result.

2. A method for evaluating the operating status of a substation terminal box according to claim 1, characterized in that: The specific method steps for obtaining the pre-processed data in step S2 are: S21, acquiring the operating status parameter, and acquiring error data in the operating status parameter to obtain error data; S22, calibrating the erroneous data and eliminating the erroneous data to obtain correct data; S23, obtain the correct data of the same type, and mark it as i, where i=1, 2, ..., j, where j represents the number of the correct data, and obtain the value corresponding to the correct data as g i , substitute the correct data i into the formula The calculated y is used as a filtering value, and the preprocessing data is obtained according to the filtering value.

3. A method for evaluating the operating status of a substation terminal box according to claim 1, characterized in that: The specific method steps for generating the operating status result in step S3 are: S31, obtaining the normal data; S32. Compare the preprocessed data with the normal data. If the preprocessed data does not match the normal data, determine that the preprocessed data is abnormal, mark it as abnormal data, and generate an abnormal result. If the preprocessed data matches the normal data, determine that the preprocessed data is normal, and generate a normal result.

4. A method for evaluating the operating status of a transformer substation terminal box according to claim 1, characterized in that: In step S4, the change of pre-processed data in different time periods of the terminal box corresponding to the normal result is analyzed, and the potential risk situation is judged. The specific method steps for generating the judgment result are: S41, obtaining preprocessed data corresponding to the normal result, determining a time period t, and obtaining a value change of the preprocessed data within the time period t; S42. Acquire historical data, and at the same time acquire the corresponding potential risk records in the historical data, and organize the obtained potential risk records to obtain risk judgment standards, and then compare the numerical change of the pre-processed data with the risk judgment standard. If the numerical change of the pre-processed data is greater than the risk judgment standard, it indicates that there is a potential risk and a risk warning signal is generated. If the numerical change of the pre-processed data is less than the risk judgment standard, it indicates that there is no potential risk and a normal detection signal is generated.

5. A method for evaluating the operating status of a substation terminal box according to claim 1, characterized in that: The method steps for performing abnormal analysis on the terminal box corresponding to the abnormal result in step S5 are: S511, obtaining preprocessed data corresponding to the abnormal result, and determining the abnormal data according to a comparison result between the preprocessed data and the normal data; S512, acquiring historical data, and filtering the historical data according to the abnormal data to obtain pre-selected records, and labeling the pre-selected records as n, where n=1, 2, ..., m, and then analyzing and extracting data features corresponding to the pre-selected record n.

6. A method for evaluating the operating status of a substation terminal box according to claim 5, characterized in that: The specific method steps for analyzing and extracting the data features corresponding to the pre-selected record n ​​in step S512 are: S5121, obtaining a change value of the change data corresponding to the pre-selected record n ​​within a time T, and using the obtained change value as the first feature data; S5122. Acquire the number of associated data corresponding to the changed data in the pre-selected record n, and use the number of associated data as second feature data, and combine the first feature data with the second feature data to obtain data features.

7. A method for evaluating the operating status of a substation terminal box according to claim 1, characterized in that: The specific method steps for performing risk analysis on the operating status of the terminal box according to the abnormal cause in step S5 are: S521, obtaining the abnormal cause, and simultaneously obtaining the historical data corresponding to the abnormal cause; S522. Label the historical data as data to be analyzed o, where o=1, 2, ..., p, where p represents the number of data to be analyzed. Then, screen the data to be analyzed o with operational risks and record them as comparison data. Meanwhile, perform corresponding analysis according to the risk levels corresponding to different comparison data.

8. A method for evaluating the operating status of a transformer substation terminal box according to claim 7, characterized in that: The specific method steps for performing corresponding analysis according to the risk levels corresponding to different comparison data in step S522 are: S5221. Obtain the risk level corresponding to the comparison data, and at the same time obtain the abnormal parameter corresponding to the comparison data, and obtain the change value of the abnormal parameter within time t1, recorded as Ga, and a=1, 2, ..., b, where b represents different specific data of the abnormal parameter, and θ and α are both constants; S5222. Obtain the maximum change peak value of the abnormal parameter a, record it as GaMax, and obtain the duration of the maximum change peak value, record it as T1, and substitute the obtained parameters into the formula The judgment value F corresponding to the comparison data is calculated, wherein the judgment values ​​corresponding to all the comparison data of the same risk level are calculated in this way, and the judgment interval is generated based on the judgment value.

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