Railway power supply line data inspection method and system

Abstract

The invention relates to the technical field of power system operation and maintenance, and provides an inspection method and system for railway power supply line data, and the method comprises the steps: collecting the power supply data of a main station side; classifying the power supply data according to data types; judging abnormal states according to classification; and setting an inspection period, and sequentially inspecting the same data belonging to multiple classes according to the set priority. According to the power supply line data inspection method provided by the invention, in the dispatching master station, according to the characteristics of the power supply data, the judgment data is divided into four classes, data inspection, abnormity judgment and classification and analysis processing of data calculation are performed on each class, the method is simple to implement, the monitoring workload of dispatchers can be reduced, and the working efficiency of the dispatchers is improved. And early warning of an abnormal state can be realized.

Description

Technical Field

[0001] The present invention relates to the technical field of power system operation and maintenance, and in particular to a patrol inspection method and system for railway power supply line data. Background Art

[0002] Regular inspections of power lines can promptly detect power outages caused by equipment aging or external damage. Inspections and analysis of collected data at the master station can identify hidden equipment failures and provide early warnings.

[0003] By leveraging currently collected data and implementing dispatch inspections at the master station, technical prevention measures can provide early warnings of power line operating status at minimal cost. However, the current means of implementing technical inspections at the master station to prevent accidents are relatively limited, and a comprehensive, efficient, and intelligent technical inspection system has yet to be established, failing to fully meet the growing demand for power supply reliability. Therefore, further exploration and development of technical inspection methods based on master station data collection and analysis are urgently needed to enhance the power supply line's operational status early warning capabilities and ensure a safe and stable power supply. Summary of the Invention

[0004] In view of the defects existing in the above-mentioned prior art, the technical problem to be solved by the present invention is to provide a power supply line data inspection method, which adopts a data classification method to realize the data inspection on the master station side.

[0005] In order to solve the above technical problems, the present invention provides a method for inspecting railway power supply line data, the method comprising:

[0006] (1) Collect power supply data on the master station side;

[0007] (2) Classify the power supply data according to the data type:

[0008] (3) Determine abnormal status based on classification;

[0009] (4) Set the inspection cycle and inspect the same data belonging to multiple categories in sequence according to the set priority.

[0010] Furthermore, the power supply data is divided into the following types according to the data type:

[0011] Category A, data with normal value range;

[0012] Category B, statistical data for a period after processing the collected data;

[0013] Category C: calculation result data obtained through mutual verification of related data;

[0014] Category D: When Category A data is judged to be in an abnormal state, it searches for manual intervention information and corrects the abnormal state according to the type of manual intervention.

[0015] Furthermore, the following abnormal status judgment is performed on Class A data:

[0016]

[0017] Wherein, V is the collected power supply data, Vmin and Vmax are the minimum and maximum values in the normal range, respectively, and f is the abnormal state of the data.

[0018] Furthermore, the following abnormal status judgment is performed on Class B data:

[0019]

[0020] Among them, Rm and Ry are the monthly and annual statistical data of a certain data in the power supply line, Rms and Rys are the judgment values set for the monthly and annual statistical data respectively, and f1 and f2 are the abnormal states of the data. The abnormal state is determined by combining the logical combination of f1 and f2, that is, the judgment is performed in two ways: f1 independent judgment and f2 independent judgment.

[0021] Furthermore, the following abnormal status judgment is performed on Class C data:

[0022]

[0023] Among them, W is a data value of the power supply line, Ws is the set judgment value, Ly and Lys are the associated data value and the set judgment value respectively, and P1, P2 and P3 are the abnormal states of the data; the abnormal state is determined by combining the logical combination of P1, P2 and P3, that is, there are five judgment methods: P1 independent judgment, P2 independent judgment, P3 independent judgment, P1 and P3 are established at the same time, and P2 and P3 are established at the same time.

[0024] Furthermore, the following abnormal status judgment is performed on Class D data:

[0025]

[0026] Among them, f is the Class A calculated value of a data value of the power supply line, Sy is the manually intervened data value and the set judgment value, Q1 and Q2 are the abnormal states of the data respectively; judgment is performed through two methods: Q1 independent judgment and Q2 independent judgment.

[0027] Furthermore, in the Class D calculation, if the Class A abnormal data corresponds to the manual intervention setting of "abnormal" and there is a manual operation record, the data is determined to be abnormal; if the Class A abnormal data corresponds to the manual intervention setting of "normal", the data is determined to be normal.

[0028] Furthermore, the priority of the inspection of the same data belonging to multiple categories is: give priority to calculating Class A data and Class D data. If Class D data issues an alarm, the alarm will take effect; if the Class A calculation data is abnormal and the Class D calculation data is normal, the alarm will be cleared; if the Class A calculation data is abnormal and there is no calculation corresponding to the information in the Class D calculation, the alarm will take effect; then calculate Class B data, if the statistical value is greater than the set value, the alarm will take effect; finally calculate Class C data, if the verification result is abnormal, the calculation of the next category after the alarm takes effect will only be executed when the previous category calculation has not triggered an alarm or requires additional judgment.

[0029] In a second aspect, the present invention provides an inspection system for railway power supply line data, the inspection system comprising: at least one processor; and a memory communicatively connected to at least one of the processors; wherein the memory stores instructions executable by the processor, the instructions being executed by the processor to implement an inspection method.

[0030] The present invention has the following beneficial effects:

[0031] The power supply line data inspection method provided by the present invention divides the judgment data into four categories according to the characteristics of the power supply data in the dispatching master station, performs data inspection on each category, judges anomalies, and realizes classification and analytical processing of data calculation. The method is simple to implement, can reduce the workload of dispatching personnel in monitoring the panel, and can realize early warning of abnormal conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 It is an inspection flow chart. DETAILED DESCRIPTION

[0033] The technical solution of the present invention is further described in detail below in conjunction with specific embodiments, but this embodiment is not intended to limit the present invention. All similar structures and similar variations of the present invention should be included in the scope of protection of the present invention. The semicolons in the present invention represent the relationship of and, and the English letters in the present invention are case-sensitive.

[0034] Example 2

[0035] like Figure 1 As shown, this embodiment provides a method for inspecting railway power supply line data, the inspection method comprising:

[0036] S1, collects power supply data on the master station side;

[0037] S2, classify the power supply data according to the data type: the power supply data is divided into:

[0038] Category A, data with normal value range;

[0039] Category B, statistical data for a period after processing the collected data;

[0040] Category C: calculation result data obtained through mutual verification of related data;

[0041] Category D: When Category A data is judged to be in an abnormal state, it searches for manual intervention information and corrects the abnormal state according to the type of manual intervention.

[0042] S3, judge the abnormal status according to the classification;

[0043] The following abnormal status judgment is performed for Class A data:

[0044]

[0045] Wherein, V is the collected power supply data, Vmin and Vmax are the minimum and maximum values in the normal range, respectively, and f is the abnormal state of the data.

[0046] Class A data, such as current and voltage data and circuit breaker operating status data, is judged as abnormal as follows: if the measured current is greater than the maximum value of the normal range or less than the minimum value of the normal range, it is judged as abnormal data.

[0047] The following abnormal status judgment is performed for Class B data:

[0048]

[0049] Among them, Rm and Ry are the monthly and annual statistical data of a certain data in the power supply line, Rms and Rys are the judgment values set for the monthly and annual statistical data respectively, and f1 and f2 are the abnormal states of the data. The abnormal state is determined by combining the logical combination of f1 and f2, that is, the judgment is performed in two ways: f1 independent judgment and f2 independent judgment.

[0050] Category B data includes the number of circuit breaker operations in the last month and the last year; the number of data channel anomalies in the last month and the last year; and the duration of frequency anomalies in the last month and the last year. The anomaly judgment is as follows: if the statistical data of the circuit breaker in the last month or the operations in the last year are greater than the set judgment value, it is judged as abnormal data; if the statistical data of the circuit breaker in the last month and the operations in the last year are greater than the set judgment value, it is judged as abnormal data;

[0051] The following abnormal status judgment is performed for Class C data:

[0052]

[0053] Among them, W is a data value of the power supply line, Ws is the set judgment value, Ly and Lys are the associated data value and the set judgment value respectively, and P1, P2 and P3 are the abnormal states of the data; the abnormal state is determined by combining the logical combination of P1, P2 and P3, that is, there are five judgment methods: P1 independent judgment, P2 independent judgment, P3 independent judgment, P1 and P3 are established at the same time, and P2 and P3 are established at the same time.

[0054] For example, when the collected current and voltage are within the normal range, the power is calculated. If the calculated power does not match the collected power value, the data is judged as abnormal data.

[0055] The following abnormal status judgment is performed for Class D data:

[0056]

[0057] Among them, f is the Class A calculated value of a data value of the power supply line, Sy is the manually intervened data value and the set judgment value, Q1 and Q2 are the abnormal states of the data respectively; judgment is performed through two methods: Q1 independent judgment and Q2 independent judgment.

[0058] In the Class D calculation, if the manual intervention corresponding to Class A abnormal data is set to "abnormal" and there is a manual operation record, the data is determined to be abnormal; if the manual intervention corresponding to Class A abnormal data is set to "normal", the data is determined to be normal.

[0059] For example, if the manual intervention condition is set to abnormal for Class A circuit breaker status data, then if abnormal information about the Class A circuit breaker status is detected and manual operation is found, it will be judged as abnormal. Another example is if the manual intervention condition is set to normal for Class A grounding switch status data, then if abnormal information about the Class A grounding switch status is detected and manual operation is found, it will be judged as normal and the Class A status abnormality will be changed to normal to avoid false alarms.

[0060] S4, setting an inspection cycle, and inspecting the same data belonging to multiple categories in sequence according to the set priority.

[0061] The inspection priority is: first calculate Class A data and Class D data. If Class D data issues an alarm, the alarm takes effect; if Class A calculated data is abnormal and Class D calculated data is normal, the alarm is cleared; if Class A calculated data is abnormal and there is no calculation corresponding to this information in Class D calculation, the alarm takes effect; then calculate Class B data. If the statistical value is greater than the set value, the alarm takes effect; finally calculate Class C data. If the verification result is abnormal, the alarm takes effect. The latter type of calculation is only performed when the former type of calculation does not trigger an alarm or requires additional judgment.

[0062] The power supply line data inspection method provided by the present invention divides the judgment data into four categories according to the characteristics of the power supply data in the dispatching master station, performs data inspection on each category, judges anomalies, and realizes classification and analytical processing of data calculation. The method is simple to implement, can reduce the workload of dispatching personnel in monitoring the panel, and can realize early warning of abnormal conditions.

[0063] Example 2

[0064] This embodiment provides an inspection system for railway power supply line data, the inspection system comprising: at least one processor; and a memory communicatively connected to at least one of the processors; wherein the memory stores instructions executable by the processor, the instructions being executed by the processor to implement an inspection method.

[0065] Although the preferred embodiments of the present application have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present application.

Claims

1. A method for inspecting railway power supply line data, characterized in that: The inspection method includes: (1) Collect power supply data on the master station side; (2) Classify the power supply data according to the data type: (3) Determine abnormal status based on classification; (4) Set the inspection cycle and inspect the same data belonging to multiple categories in sequence according to the set priority.

2. A method for inspecting railway power supply line data according to claim 1, characterized in that: Power supply data is divided into the following types according to data type: Category A, data with normal value range; Category B, statistical data for a period after processing the collected data; Category C: calculation result data obtained through mutual verification of related data; Category D: When Category A data is judged to be in an abnormal state, it searches for manual intervention information and corrects the abnormal state according to the type of manual intervention.

3. The inspection method for railway power supply line data according to claim 2, characterized in that: The following abnormal status judgment is performed for Class A data: Wherein, V is the collected power supply data, Vmin and Vmax are the minimum and maximum values in the normal range, respectively, and f is the abnormal state of the data.

4. The inspection method for railway power supply line data according to claim 2, characterized in that: The following abnormal status judgment is performed for Class B data: Among them, Rm and Ry are the monthly and annual statistical data of a certain data in the power supply line, Rms and Rys are the judgment values set for the monthly and annual statistical data respectively, and f1 and f2 are the abnormal states of the data. The abnormal state is determined by combining the logical combination of f1 and f2, that is, the judgment is performed in two ways: f1 independent judgment and f2 independent judgment.

5. The inspection method for railway power supply line data according to claim 2, characterized in that: The following abnormal status judgment is performed for Class C data: Among them, W is a data value of the power supply line, Ws is the set judgment value, Ly and Lys are the associated data value and the set judgment value respectively, and P1, P2 and P3 are the abnormal states of the data; the abnormal state is determined by combining the logical combination of P1, P2 and P3, that is, there are five judgment methods: P1 independent judgment, P2 independent judgment, P3 independent judgment, P1 and P3 are established at the same time, and P2 and P3 are established at the same time.

6. The inspection method for railway power supply line data according to claim 2, characterized in that: The following abnormal status judgment is performed for Class D data: Among them, f is the Class A calculated value of a data value of the power supply line, Sy is the manually intervened data value and the set judgment value, Q1 and Q2 are the abnormal states of the data respectively; judgment is performed through two methods: Q1 independent judgment and Q2 independent judgment.

7. A method for inspecting railway power supply line data according to claim 6, characterized in that: In the Class D calculation, if the manual intervention corresponding to the Class A abnormal data is set to "abnormal" and there is a manual operation record, the data is determined to be abnormal; If the manual intervention setting for Class A abnormal data is set to "normal", the data is judged to be normal.

8. The inspection method for railway power supply line data according to claim 2, characterized in that: The priority of the inspection of the same data belonging to multiple categories is as follows: first calculate the data of category A and category D. If the alarm is issued for category D data, the alarm will take effect; if the calculated data of category A is abnormal and the calculated data of category D is normal, the alarm will be cleared; if the calculated data of category A is abnormal and there is no calculation corresponding to the information in the calculation of category D, the alarm will take effect; Then calculate the Class B data. If the statistical value is greater than the set value, the alarm will take effect; Finally, calculate the Class C data. If the verification result is abnormal, the alarm will take effect.

9. A patrol inspection system for railway power supply line data, characterized in that: The inspection system includes: at least one processor; and a memory communicatively connected to at least one of the processors; wherein the memory stores instructions executable by the processor, and the instructions are used to be executed by the processor to implement the inspection method according to any one of claims 1 to 8.

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