CN107506832B - Hidden danger mining method for assisting monitoring tour - Google Patents
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Abstract
The embodiment provides a hidden danger excavation method for assisting monitoring tour, which belongs to the field of power equipment monitoring and comprises the following steps: acquiring real-time monitoring data of each power grid device, and performing incremental coverage on historical monitoring data of each power grid device according to the real-time monitoring data to obtain a sample database; setting a reference threshold value for key data in a sample database, and selecting sample data exceeding the threshold value; and according to the inspection requirement, carrying out merging operation on the sample data to obtain an operation result, and taking the power grid equipment corresponding to the operation result as an object to be processed. By means of threshold judgment, comparison logic, correlation analysis and the like, the judgment, calculation and analysis results are stored in a computer cache to support upper-layer application, the problems that the traditional method needs frequent inspection by manpower, the information is not complete in cross-specialty acquisition and the relational data is slow to inquire are solved, different specialties and related data inspection operations and logic algorithms are packaged, the data value is fully mined, and potential problems and hidden dangers are effectively found.
Description
Technical Field
The invention belongs to the field of power equipment monitoring, and particularly relates to a hidden danger mining method for assisting monitoring inspection.
Background
The operation monitoring of the current power equipment mainly depends on three modes of real-time monitoring, professional inspection and operation and maintenance inspection. The real-time monitoring patrol cycle is too long, the means is single, and the quality is not high; the 'professional patrol' is long in period, due to different emphasis of each specialty, data is not comprehensive enough, and the 'operation and maintenance patrol' is a passive waiting type and poor in on-site feeling.
In conclusion, the existing monitoring mode cannot effectively mine the data value, and potential problems and hidden dangers are effectively discovered.
Disclosure of Invention
In order to solve the defects and shortcomings in the prior art, the invention provides a hidden danger mining method for screening out power grid equipment with potential dangers through a threshold value.
In order to achieve the technical purpose, the invention provides a hidden danger mining method for assisting monitoring tour, which comprises the following steps:
acquiring real-time monitoring data of each power grid device, and performing incremental coverage on historical monitoring data of each power grid device according to the real-time monitoring data to obtain a sample database representing the latest state of each power grid device;
setting a reference threshold value for key data in a sample database, and selecting sample data exceeding the threshold value;
and according to the inspection requirement, carrying out merging operation on the sample data to obtain an operation result, and taking the power grid equipment corresponding to the operation result as an object to be processed.
Optionally, the real-time detecting data includes:
the method comprises the following steps of (1) transforming plant, interval, power grid equipment name, telemetering description, telemetering type, out-of-limit state, telemetering state, measured value, occurrence time and limit value;
wherein the telemetry types comprise current, voltage and active power;
the out-of-limit state comprises normal, upper limit and upper limit;
telemetry states include blockade, suppression;
limits include messages from the EMS and the limit of the operation.
Optionally, the hidden danger excavation method includes:
acquiring real-time monitoring data of each power grid device at each moment;
performing cluster evaluation on the real-time monitoring data at each moment to obtain a monitoring tour evaluation result;
wherein the real-time monitoring data comprises: the method comprises the following steps of power grid equipment encoding, power grid equipment running state, power grid equipment monitoring parameters and power grid equipment fault parameters.
Optionally, the hidden danger excavation method includes:
displaying the monitoring patrol evaluation result in a monitoring patrol popup window corresponding to each power grid device;
optionally, the hidden danger excavation method includes:
filtering monitoring and inspecting evaluation results of the plurality of power grid devices;
and displaying the monitoring and inspecting evaluation results of the power grid equipment meeting the filtering condition in a popup window corresponding to each power grid equipment.
Optionally, the hidden danger excavation method includes:
according to the monitoring sample data of the power grid equipment at each historical moment, simulating replay by adopting a preset accident inversion simulation algorithm;
and analyzing the simulation replay process to obtain the fault information of each power grid device.
Optionally, the hidden danger excavation method includes:
establishing a power grid equipment fault tree according to the power grid equipment structure;
calculating the fault probability of each level of events in the fault tree of the power grid equipment by using a fault model probability calculation model and a fault probability calculation model and utilizing the historical defect records of the power grid equipment;
the power grid equipment fault tree comprises multilevel events forming a tree structure.
Optionally, the hidden danger excavation method includes:
making a depth inspection theme according to the power grid maintenance working condition, the new equipment commissioning starting, the climate environment characteristics, the power grid load condition and the like;
and according to the theme of deep inspection, deeply inspecting various monitoring charts and pictures, and performing trend comparison and correlation analysis on the monitoring data.
Optionally, the hidden danger excavation method includes:
and selecting a customized auxiliary tool kit, and carrying out deep inspection by using tools provided in the tool kit.
Optionally, the hidden danger excavation method includes:
and manually confirming and analyzing the object to be processed, and judging whether the power grid equipment corresponding to the object to be processed has hidden danger or not.
The technical scheme provided by the invention has the beneficial effects that:
by means of threshold judgment, comparison logic, correlation analysis and the like, the judgment, calculation and analysis results are stored in a computer cache to support upper-layer application, the problems that the traditional method needs frequent inspection by manpower, the information is not complete in cross-specialty acquisition and the relational data is slow to inquire are solved, different specialties and related data inspection operations and logic algorithms are packaged, the data value is fully mined, and potential problems and hidden dangers are effectively found.
Drawings
In order to more clearly illustrate the technical solution of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on the drawings without creative efforts.
FIG. 1 is a schematic flow chart of a hidden danger mining method for assisting in monitoring and patrolling provided by the invention;
fig. 2 is another schematic flow chart of the hidden danger mining method for assisting in monitoring tour provided by the present invention.
Detailed Description
To make the structure and advantages of the present invention clearer, the structure of the present invention will be further described with reference to the accompanying drawings.
Example one
The invention provides a hidden danger mining method for assisting monitoring and inspection, as shown in fig. 1, the hidden danger mining method comprises the following steps:
11. acquiring real-time monitoring data of each power grid device, and performing incremental coverage on historical monitoring data of each power grid device according to the real-time monitoring data to obtain a sample database representing the latest state of each power grid device;
12. setting a reference threshold value for key data in a sample database, and selecting sample data exceeding the threshold value;
13. and according to the inspection requirement, carrying out merging operation on the sample data to obtain an operation result, and taking the power grid equipment corresponding to the operation result as an object to be processed.
In implementation, in order to find potential hazards that may exist in the power grid equipment, the embodiment provides a potential hazard mining method for determining whether the power grid equipment has the potential hazards based on monitoring data of the power grid equipment. The specific method comprises the following three steps:
firstly, acquiring real-time monitoring data of each power grid device, and covering historical monitoring data increment based on the real-time monitoring data. The incremental coverage is performed here because the latest state of the power grid equipment is known in a data tracing manner from the historical monitoring data as a traditional patrol result, and the method is to perform full-table scanning on the historical monitoring data table, so that the efficiency is poor, a great burden is also brought to the database, and the overall function of the system is affected. In view of the defects, the historical monitoring data is subjected to real-time incremental coverage based on the acquired real-time monitoring data, namely, the extra part of the real-time monitoring data relative to the historical monitoring data is supplemented and updated on the basis of the historical monitoring data, so that the latest state value of the power grid equipment can be obtained at the first time during patrol.
Secondly, after the real-time monitoring data is obtained, if the amount of the accumulated data is small, problem data can be found quickly, but if the large amount of the data is generated continuously, automatic screening is needed, a threshold value is set in a key dimension in the obtained monitoring data, and only the data exceeds the threshold value, the data can be selected. The actually established threshold is an upper limit or a lower limit in an acceptable range, the upper limit can be subdivided according to the severity of the problem caused by the hidden trouble, for example, the upper limit and the lower limit are required to be set, and the range can be reduced.
Finally, after sample data is obtained, merging operation may be required with other module data, and the result is recorded. The merging operation refers to adding real-time monitoring data generated by other power grid equipment connected with the power grid equipment corresponding to the sample data on the basis of screening the sample data. The merging operation of the sample data and the real-time monitoring data of other power grid equipment considers that the power grid equipment is not completely independent and certain faults are caused by a plurality of modules formed by a plurality of power grid equipment together, so that the merging operation is carried out on the sample data obtained by a single power grid equipment in combination with the real-time monitoring data of other power grid equipment, and the defects of each module are conveniently predicted.
Optionally, the real-time detecting data includes:
the method comprises the following steps of (1) transforming plant, interval, power grid equipment name, telemetering description, telemetering type, out-of-limit state, telemetering state, measured value, occurrence time and limit value;
wherein the telemetry types comprise current, voltage and active power;
the out-of-limit state comprises normal, upper limit and upper limit;
telemetry states include blockade, suppression;
limits include messages from the EMS and the limit of the operation.
In implementation, in order to sufficiently detect the state of the power grid equipment, the obtained real-time monitoring data includes a plurality of contents, which typically include a substation, an interval, a power grid equipment name, a telemetry description, a telemetry type, an out-of-limit state, a telemetry state, an actual measurement value, an occurrence time, and a limit value.
Optionally, as shown in fig. 2, the hidden danger excavation method includes:
21. acquiring real-time monitoring data of each power grid device at each moment;
22. performing cluster evaluation on the real-time monitoring data at each moment to obtain a monitoring tour evaluation result;
wherein the real-time monitoring data comprises: the method comprises the following steps of power grid equipment encoding, power grid equipment running state, power grid equipment monitoring parameters and power grid equipment fault parameters.
In practice, the method for potential hazard mining proposed in this embodiment includes steps 21 to 22 in addition to the aforementioned steps 11 to 13.
Wherein the content of step 21 is similar to the content of the real-time monitoring data obtained in step 11, and the obtained real-time monitoring data needs to be subjected to cluster evaluation in step 22.
As the name implies, clustering evaluation is a process of evaluating results obtained by clustering. In the present case, the method of assessment mainly comprises:
(1) and the external method is used for evaluating the clustering analysis result according to the known real grouping, constructing a confusion matrix, summarizing whether any pair of records containing the two records belong to the same grouping or not, and calculating the accuracy of the clustering analysis according to the confusion matrix.
(2) The internal method can calculate the inner square sum and the outer square sum as evaluation indexes by using the recorded feature vectors when the real grouping situation is unknown.
(3) When only two variables exist, a visual method is adopted to evaluate the clustering result. For example, a ggplot () function may be called to draw a scatter diagram, and each cluster and each central point are identified, generally considering the following factors:
1) whether the clusters are well separated from each other or not;
2) whether there are clusters of only a few points;
3) whether there is a close center point.
After the obtained real-time monitoring data are subjected to cluster evaluation, typical data represented by the classified real-time monitoring data are obtained, and then a monitoring patrol evaluation result formed by power grid equipment fault parameters corresponding to the typical data is determined.
Optionally, the hidden danger excavation method includes:
23. the patrol evaluation result is displayed in a monitoring patrol popup window corresponding to each power grid device;
in implementation, after the monitoring patrol evaluation results are obtained in steps 21 to 22, the monitoring patrol evaluation results need to be sent to the monitoring patrol terminal, so that the monitoring patrol evaluation results are displayed in a monitoring patrol popup window in a display interface of the monitoring patrol terminal, and a result reminding process is completed.
Optionally, the hidden danger excavation method includes:
231. filtering the monitoring and inspecting evaluation result of the power grid equipment;
232. and displaying the monitoring and inspecting evaluation results of the power grid equipment meeting the filtering condition in a popup window corresponding to each power grid equipment.
In practice, the foregoing step 23 further includes the contents shown in steps 231 and 232.
After the monitoring patrol evaluation result is obtained, the monitoring patrol evaluation result needs to be displayed in a pop-up window mode. However, considering that the display mode of the pop-up window may limit the content to be displayed, before displaying, the content shown in step 231 needs to be filtered, so as to filter out the content that is not suitable for displaying in the pop-up window mode, and send the filtered content to the display interface of the monitoring tour terminal corresponding to each power grid device to display in the pop-up window mode.
Optionally, the hidden danger excavation method includes:
according to the monitoring sample data of the power grid equipment at each historical moment, simulating replay by adopting a preset accident inversion simulation algorithm;
and analyzing the simulation replay process to obtain the fault information of each power grid device.
In implementation, various accident simulation deduction logics are established according to a preset accident model, and fault information of each power grid device is deduced by adopting monitoring sample data of each historical time of the power grid device.
Optionally, the hidden danger excavation method includes:
establishing a power grid equipment fault tree according to the power grid equipment structure;
calculating the fault probability of each level of events in the fault tree of the power grid equipment by using a fault model probability calculation model and a fault probability calculation model and utilizing the historical defect records of the power grid equipment;
the power grid equipment fault tree comprises multilevel events forming a tree structure.
In implementation, according to an equipment structure, an equipment fault tree is established, wherein the equipment fault tree comprises a plurality of levels of events forming a tree structure; and calculating the fault probability of each level of event in the equipment fault tree by using the equipment historical defect record by adopting a fault model probability calculation model and a fault probability calculation model.
Optionally, the hidden danger excavation method includes:
making a depth inspection theme according to the power grid maintenance working condition, the new equipment commissioning starting, the climate environment characteristics, the power grid load condition and the like;
and according to the theme of deep inspection, deeply inspecting various monitoring charts and pictures, and performing trend comparison and correlation analysis on the monitoring data.
In the implementation, in the specific implementation process, in order to analyze the potential hazards of the power grid equipment based on the real-time monitoring data, a deep inspection theme is formulated in advance according to the power grid maintenance working condition, the new equipment commissioning starting, the climate environment characteristics, the power grid load condition and the like, then the power grid equipment is monitored according to the deep inspection theme, a monitoring chart and a monitoring picture are obtained, so that trend comparison and correlation analysis are performed on the monitoring data, and the defects possibly existing in the power grid equipment are determined.
Optionally, the hidden danger excavation method includes:
and selecting a customized auxiliary tool kit, and carrying out deep inspection by using tools provided in the tool kit.
In implementation, a monitoring information analyst formulates a deep inspection theme according to the power grid maintenance working condition, the new equipment commissioning starting, the climate environment characteristics, the power grid load condition and the like, deeply inspects various monitoring charts and pictures, performs trend comparison and correlation analysis on monitoring data, can also select a customized auxiliary tool kit, and performs deep inspection by using tools provided in the tool kit.
Optionally, the hidden danger excavation method includes:
and manually confirming and analyzing the object to be processed, and judging whether the power grid equipment corresponding to the object to be processed has hidden danger or not.
In implementation, in order to improve the reliability of the hidden danger mining method provided by the present application, on the basis of processing by the foregoing contents, a step of manual analysis needs to be introduced, and the to-be-processed correspondence obtained in step 13 is further analyzed by working experience to determine whether a hidden danger exists in the to-be-processed object.
The embodiment provides a hidden danger excavation method for assisting monitoring tour, which comprises the following steps: acquiring real-time monitoring data of each power grid device, and performing incremental coverage on historical monitoring data of each power grid device according to the real-time monitoring data to obtain a sample database representing the latest state of each power grid device; setting a reference threshold value for key data in a sample database, and selecting sample data exceeding the threshold value; and according to the inspection requirement, carrying out merging operation on the sample data to obtain an operation result, and taking the power grid equipment corresponding to the operation result as an object to be processed. By means of threshold judgment, comparison logic, correlation analysis and the like, the judgment, calculation and analysis results are stored in a computer cache to support upper-layer application, the problems that the traditional method needs frequent inspection by manpower, the information is not complete in cross-specialty acquisition and the relational data is slow to inquire are solved, different specialties and related data inspection operations and logic algorithms are packaged, the data value is fully mined, and potential problems and hidden dangers are effectively found.
The sequence numbers in the above embodiments are merely for description, and do not represent the sequence of the assembly or the use of the components.
The above description is only exemplary of the present invention and should not be taken as limiting the invention, as any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. The hidden danger excavation method for assisting monitoring tour is characterized by comprising the following steps:
acquiring real-time monitoring data of each power grid device, and performing incremental coverage on historical monitoring data of each power grid device according to the real-time monitoring data to obtain a sample database representing the latest state of each power grid device;
setting a reference threshold value for key data in a sample database, and selecting sample data exceeding the threshold value;
according to the inspection requirement, carrying out merging operation on the sample data to obtain an operation result, and taking the power grid equipment corresponding to the operation result as an object to be processed;
the method specifically comprises the following steps:
firstly, acquiring real-time monitoring data of each power grid device, and covering historical monitoring data increment based on the real-time monitoring data;
secondly, after the real-time monitoring data are obtained, if the amount of the accumulated data is small, problem data are found, and if the amount of the accumulated data is large, automatic screening is carried out;
and finally, after the sample data is obtained, carrying out merging operation with data of other modules, recording the result, merging the sample data obtained by the single power grid equipment with the real-time monitoring data of other power grid equipment, and predicting the defects of each module.
2. The method according to claim 1, wherein the real-time monitoring data comprises:
the method comprises the following steps of (1) transforming plant, interval, power grid equipment name, telemetering description, telemetering type, out-of-limit state, telemetering state, measured value, occurrence time and limit value;
wherein the telemetry types comprise current, voltage and active power;
the out-of-limit state comprises normal, upper limit and upper limit;
telemetry states include blockade, suppression;
limits include messages from the EMS and the limit of the operation.
3. The hidden danger mining method for assisting in monitoring rounds as claimed in claim 1, characterized in that the hidden danger mining method comprises:
acquiring real-time monitoring data of each power grid device at each moment;
performing cluster evaluation on the real-time monitoring data at each moment to obtain a monitoring tour evaluation result;
wherein the real-time monitoring data comprises: the method comprises the following steps of power grid equipment encoding, power grid equipment running state, power grid equipment monitoring parameters and power grid equipment fault parameters.
4. The hidden danger mining method for assisting in monitoring patrol according to claim 3, wherein the hidden danger mining method comprises:
and displaying the monitoring patrol evaluation result in a monitoring patrol popup window corresponding to each power grid device.
5. The hidden danger mining method for assisting in monitoring patrol according to claim 3 or 4, characterized in that the hidden danger mining method comprises:
filtering monitoring and inspecting evaluation results of the plurality of power grid devices;
and displaying the monitoring and inspecting evaluation results of the power grid equipment meeting the filtering condition in a popup window corresponding to each power grid equipment.
6. The hidden danger mining method for assisting in monitoring rounds as claimed in claim 1, characterized in that the hidden danger mining method comprises:
according to the monitoring sample data of the power grid equipment at each historical moment, simulating replay by adopting a preset accident inversion simulation algorithm;
and analyzing the simulation replay process to obtain the fault information of each power grid device.
7. The hidden danger mining method for assisting in monitoring rounds as claimed in claim 1, characterized in that the hidden danger mining method comprises:
establishing a power grid equipment fault tree according to the power grid equipment structure;
calculating the fault probability of each level of events in the fault tree of the power grid equipment by using a fault model probability calculation model and a fault probability calculation model and utilizing the historical defect records of the power grid equipment;
the power grid equipment fault tree comprises multilevel events forming a tree structure.
8. The hidden danger mining method for assisting in monitoring rounds as claimed in claim 1, characterized in that the hidden danger mining method comprises:
making a depth inspection theme according to the power grid maintenance working condition, the new equipment commissioning starting, the climate environment characteristics, the power grid load condition and the like;
and according to the theme of deep inspection, deeply inspecting various monitoring charts and pictures, and performing trend comparison and correlation analysis on the monitoring data.
9. The hidden danger mining method for assisting in monitoring rounds as set forth in claim 8, characterized in that the hidden danger mining method comprises:
and selecting a customized auxiliary tool kit, and carrying out deep inspection by using tools provided in the tool kit.
10. The hidden danger mining method for assisting in monitoring rounds as claimed in claim 1, characterized in that the hidden danger mining method comprises:
and manually confirming and analyzing the object to be processed, and judging whether the power grid equipment corresponding to the object to be processed has hidden danger or not.
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| CN113537415A (en) * | 2021-09-17 | 2021-10-22 | 中国南方电网有限责任公司超高压输电公司广州局 | Convertor station inspection method and device based on multi-information fusion and computer equipment |
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