CN114742247B - Feature extraction method and device based on distribution network distribution variation normal alarm information - Google Patents
Feature extraction method and device based on distribution network distribution variation normal alarm information Download PDFInfo
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Abstract
The invention discloses a feature extraction method and device based on distribution network distribution variation normal alarm information, wherein the method comprises the following steps: after receiving current alarm information of the power distribution network, respectively searching historical alarm information and feedback information corresponding to the current alarm information; calculating an alarm association value by using the current alarm information, the historical alarm information and the feedback information; when the alarm association value is larger than a preset association value, determining that the current alarm information is triggered by a first-leave-behind problem, and extracting a plurality of abnormal feature types from the historical alarm information; and extracting feature information from the current alarm information according to the abnormal feature types. The invention can determine whether the current triggering abnormal alarm is in alarm connection with the prior maintenance operation, and when the current triggering abnormal alarm is associated with the prior maintenance operation, the characteristic is extracted based on the prior alarm information so as to increase the accuracy of extracting the abnormal information.
Description
Technical Field
The invention relates to the technical field of distribution networks, in particular to a feature extraction method, device electronic equipment and computer readable storage medium based on distribution network distribution variation constant alarm information.
Background
As the power demand of people increases, the scale of the power system increases, and the requirements on the safety and reliability of the power system become more and more strict, so that the maintenance and management of each power distribution network device becomes more important.
Because of the various kinds of equipment of the power distribution network and the huge quantity, each piece of equipment of the power distribution network needs to be monitored, updated and maintained in real time. The current common monitoring mode is to monitor the real-time operation data of each device of the power distribution network in real time, record the abnormal alarm information in the data when faults occur, and inform maintenance personnel to secondarily detect the devices and carry out field maintenance according to the abnormal alarm information by management personnel so as to ensure the stable operation of each device of the power distribution network.
However, the current extraction method of the common abnormal alarm information has the following technical problems: because the detection capability of each maintainer is different in the secondary detection process, the detection result is greatly different from the actual detection result, the detection accuracy is reduced, and a plurality of later alarm information are alarms which are generated again due to imperfect maintenance operation which is executed before, so that the fault probability is increased.
Disclosure of Invention
The invention provides a feature extraction method and device based on distribution network distribution variation normal alarm information, wherein when abnormal alarm information occurs, the method can determine whether alarm connection exists between the current alarm information, historical alarm information and feedback information of maintenance operation performed by maintenance personnel and the prior maintenance operation so as to improve the accuracy of extracting the abnormal alarm information.
The first aspect of the invention provides a feature extraction method based on distribution network distribution variation constant alarm information, which comprises the following steps: after receiving current alarm information of a power distribution network, respectively searching historical alarm information and feedback information corresponding to the current alarm information, wherein the feedback information is maintenance information recorded in advance;
calculating an alarm association value by using the current alarm information, the historical alarm information and the feedback information;
when the alarm association value is larger than a preset association value, determining that the current alarm information is triggered by a prior legacy problem, and extracting a plurality of abnormal feature types from the historical alarm information;
And extracting feature information from the current alarm information according to the abnormal feature types.
In a possible implementation manner of the first aspect, the calculating an alarm association value using the current alarm information, the historical alarm information, and the feedback information includes:
Calculating the information similarity of the current alarm information and the historical alarm information, and inputting the feedback information into a preset neural network for fault recurrence analysis to obtain an alarm recurrence probability value;
Determining weight percentages based on the information similarity;
and multiplying the weight percentage by the alarm recurrence probability value to obtain an alarm association value.
In a possible implementation manner of the first aspect, the searching for the historical alarm information and the feedback information corresponding to the current alarm information includes:
determining equipment numbers of equipment of the power distribution network based on the current alarm information;
and searching historical alarm information and feedback information by using the equipment number.
In a possible implementation manner of the first aspect, after the step of extracting feature information from the current alert information according to the number of abnormal feature types, the method further includes:
searching a plurality of maintenance schemes based on the plurality of characteristic information, and sending the plurality of maintenance schemes to maintenance personnel so that the maintenance personnel can select corresponding maintenance schemes for maintenance treatment.
The second aspect of the invention provides a feature extraction device based on distribution network distribution variation constant alarm information, which comprises: the searching module is used for respectively searching historical alarm information and feedback information corresponding to the current alarm information after receiving the current alarm information of the power distribution network, wherein the feedback information is maintenance information recorded in the prior time;
the calculation module is used for calculating an alarm association value by utilizing the current alarm information, the historical alarm information and the feedback information;
The determining module is used for determining that the current alarm information is triggered by a prior legacy problem when the alarm association value is larger than a preset association value, and extracting a plurality of abnormal feature types from the historical alarm information;
and the extraction module is used for extracting the characteristic information from the current alarm information according to the abnormal characteristic types.
In a possible implementation manner of the second aspect, the computing module is further configured to:
Calculating the information similarity of the current alarm information and the historical alarm information, and inputting the feedback information into a preset neural network for fault recurrence analysis to obtain an alarm recurrence probability value;
Determining weight percentages based on the information similarity;
and multiplying the weight percentage by the alarm recurrence probability value to obtain an alarm association value.
In a possible implementation manner of the second aspect, the search module is further configured to:
determining equipment numbers of equipment of the power distribution network based on the current alarm information;
and searching historical alarm information and feedback information by using the equipment number.
In a possible implementation manner of the second aspect, the apparatus further includes:
the scheme sending module is used for searching a plurality of maintenance schemes based on the plurality of characteristic information and sending the plurality of maintenance schemes to maintenance staff so that the maintenance staff can select corresponding maintenance schemes to carry out maintenance treatment.
Compared with the prior art, the feature extraction method and device based on the distribution network distribution variation constant alarm information provided by the invention have the beneficial effects that: the invention can respectively acquire the current alarm information, the historical alarm information and the feedback information of maintenance operation carried out by maintenance personnel when the equipment triggers abnormal alarm information due to faults, determine whether the current alarm information, the historical alarm information and the feedback information are in alarm connection with the prior maintenance operation or not, and extract information based on the alarm information of the prior faults after determining that the current alarm information, the historical alarm information and the feedback information are associated with the prior faults so as to increase the accuracy of extracting the abnormal alarm information.
Drawings
Fig. 1 is a flow chart of a feature extraction method based on distribution network distribution variation constant alarm information according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a feature extraction device based on distribution network distribution variation normal alarm information according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The current common extraction mode of the abnormal alarm information has the following technical problems: because the detection capability of each maintainer is different in the secondary detection process, the detection result is greatly different from the actual detection result, the detection accuracy is reduced, and a plurality of later alarm information are alarms which are generated again due to imperfect maintenance operation performed before, so that the fault probability is increased.
In order to solve the above problems, the following detailed description and the description will be given in detail of a feature extraction method based on distribution network configuration variation normal alarm information provided by the embodiment of the present application.
Referring to fig. 1, a flow chart of a feature extraction method based on distribution network distribution variation constant alarm information according to an embodiment of the present invention is shown.
In an embodiment, the method may be applied to a management system of the power distribution network device, where the management system may monitor the power distribution network device in real time.
For example, the feature extraction method based on distribution network distribution constant alarm information may include:
s11, after receiving current alarm information of the power distribution network, respectively searching historical alarm information and feedback information corresponding to the current alarm information, wherein the feedback information is maintenance information recorded in the prior time.
In an embodiment, the management system may monitor the power parameters of each power distribution network device in real time, and when the power parameters are abnormal, use the power parameters as current alarm information, and determine to trigger receiving the current alarm information of the power distribution network device when the power distribution network device is configured to be abnormal.
And then searching corresponding historical alarm information and feedback information respectively by utilizing the current alarm information. The historical alarm information is abnormal alarm information detected by the monitoring equipment in advance; the feedback information is information recorded by a maintenance person after the maintenance operation is performed on the power distribution network equipment at a previous time, for example, the fault is a short circuit of the ground terminal, the ground terminal is reconnected by using the xx line, the fault is eliminated, the maintenance time is 2020, 2 months and 2 days, and the maintenance person is numbered 9527.
Since there are a plurality of monitored devices, in order to accurately find the corresponding historical alert information and feedback information, step S11 may include the following sub-steps, as an example:
And a substep S111, determining the equipment number of the equipment of the distribution network based on the current alarm information.
Specifically, each device may be assigned a corresponding preamble number, and then a corresponding number is added to the preamble number to form a device number.
For example, there are three transformers, and the equipment numbers thereof are ABC001, ABC002, and ABC003, respectively.
It should be noted that the device number of each device is unique.
And a substep S112, searching historical alarm information and feedback information by using the equipment number.
In an embodiment, the historical alarm information and the feedback information corresponding to the type of equipment are searched from a preset database based on the equipment number of the equipment.
S12, calculating an alarm association value by using the current alarm information, the historical alarm information and the feedback information.
The alarm association value can be used for indicating the association degree of the current alarm information and the prior fault alarm, if the alarm association value is higher, the association degree is higher, otherwise, the association degree is smaller. The alarm correlation value is calculated to determine that the alarm is caused by imperfect maintenance operation of the prior alarm, if yes, the corresponding feature extraction can be performed according to the rule of the prior alarm, so as to improve the accuracy and the processing efficiency of the feature extraction.
In order to increase the accuracy of the calculation, in an alternative embodiment, step S12 may comprise the sub-steps of:
And S121, calculating the information similarity of the current alarm information and the historical alarm information, and inputting the feedback information into a preset neural network for fault recurrence analysis to obtain an alarm recurrence probability value.
Specifically, the current alarm information and the historical alarm information can be subjected to information similarity calculation, so that the information similarity of the two information is calculated. And then, the feedback information can be input into a preset neural network to perform fault recurrence analysis to obtain an alarm recurrence probability value.
The preset neural network is a neural network trained through an evolution algorithm. The fault recurrence analysis may be a repeated evolution of use with repair schemes and fault types in the feedback information to analyze whether the device will have the same fault here.
Substep S122, determining weight percentages based on the information similarity.
In an embodiment, a user may preset a plurality of different weight percentages, where each weight percentage corresponds to a segment of information similarity. After calculating the information similarity, the corresponding different weight percentages may be determined based on the different information similarities.
For example, the interval of the information similarity is [1, 10 ], and the weight percentage is 10%; the interval of the information similarity is 10,20, and the weight percentage is 20%; the interval of the information similarity is [ 20,30 ], and the weight percentage is 30%; the interval of the information similarity is [ 90,100 ], the weight percentage is 100%, and so on.
It should be noted that the weight percentage may be set by the user according to different manners. For example, different weight percentages can be set according to the reciprocal of the similarity of the information, and the weight percentages can be specifically adjusted according to actual needs.
And step 123, multiplying the weight percentage by the alarm recurrence probability value to obtain an alarm association value.
Finally, the weight percentage can be multiplied by the alarm recurrence probability value to obtain an alarm association value.
For example, the information similarity is 35, and falls within the interval of [ 30,40 ], the weight percentage is 40%, the calculated alarm recurrence probability value is 80%, and the alarm association value is 40% ×80% =32%.
And S13, when the alarm association value is larger than a preset association value, determining that the current alarm information is triggered by a prior legacy problem, and extracting a plurality of abnormal feature types from the historical alarm information.
After the alarm association value is calculated, whether the alarm association value is larger than a preset association value or not can be judged, when the alarm association value is larger than the preset association value, the alarm is possibly caused by the problem of missing of the prior maintenance operation, and a plurality of abnormal feature types can be obtained from the historical alarm information, wherein the abnormal feature types are corresponding abnormal information in the abnormal information.
For example, the history warning information includes the output current, the input resistance, and the output phase, and the output current, the input resistance, and the output phase may be the abnormal feature types.
S14, extracting feature information from the current alarm information according to the abnormal feature types.
Specifically, feature information of the current alarm information can be extracted according to a plurality of abnormal feature types so as to acquire corresponding abnormal information.
In one embodiment, after determining a specific abnormal characteristic, maintenance treatment is required for the device to ensure stable operation of the device.
Wherein, as an example, the method may further comprise:
s15, searching a plurality of maintenance schemes based on the plurality of characteristic information, and sending the plurality of maintenance schemes to maintenance staff so that the maintenance staff can select corresponding maintenance schemes to carry out maintenance treatment.
Specifically, a maintenance scheme containing a plurality of characteristic information can be searched in a preset database to obtain a plurality of maintenance schemes, and finally the plurality of maintenance schemes are sent to maintenance staff, so that the maintenance staff can select a proper maintenance scheme from the plurality of maintenance schemes to carry out maintenance treatment.
In this embodiment, the embodiment of the invention provides a feature extraction method based on distribution network distribution variation constant alarm information, which has the beneficial effects that: the invention can respectively acquire the current alarm information, the historical alarm information and the feedback information of maintenance operation carried out by maintenance personnel when the equipment triggers abnormal alarm information due to faults, determine whether the current alarm information, the historical alarm information and the feedback information are in alarm connection with the prior maintenance operation or not, and extract information based on the characteristics of the alarm information of the prior faults after determining that the current alarm information, the historical alarm information and the feedback information are associated with the prior faults so as to improve the accuracy of extracting the abnormal alarm information.
The embodiment of the invention also provides a feature extraction device based on the distribution network distribution variation normal alarm information, and referring to fig. 2, a schematic structural diagram of the feature extraction device based on the distribution network distribution variation normal alarm information is shown.
As an example, the feature extraction device based on distribution network distribution constant alarm information may include:
the searching module 201 is configured to, after receiving current alarm information of the power distribution network, search historical alarm information and feedback information corresponding to the current alarm information, where the feedback information is maintenance information recorded in a previous time;
a calculation module 202, configured to calculate an alarm association value using the current alarm information, the historical alarm information, and the feedback information;
A determining module 203, configured to determine that the current alarm information is triggered by a previous legacy problem when the alarm association value is greater than a preset association value, and extract a plurality of abnormal feature types from the historical alarm information;
And the extracting module 204 is configured to extract feature information from the current alarm information according to the plurality of abnormal feature types.
Optionally, the computing module is further configured to:
Calculating the information similarity of the current alarm information and the historical alarm information, and inputting the feedback information into a preset neural network for fault recurrence analysis to obtain an alarm recurrence probability value;
Determining weight percentages based on the information similarity;
and multiplying the weight percentage by the alarm recurrence probability value to obtain an alarm association value.
Optionally, the search module is further configured to:
determining equipment numbers of equipment of the power distribution network based on the current alarm information;
and searching historical alarm information and feedback information by using the equipment number.
Optionally, the apparatus further comprises:
the scheme sending module is used for searching a plurality of maintenance schemes based on the plurality of characteristic information and sending the plurality of maintenance schemes to maintenance staff so that the maintenance staff can select corresponding maintenance schemes to carry out maintenance treatment.
Further, an embodiment of the present application further provides an electronic device, including: the system comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the processor is used for realizing the characteristic extraction method based on the distribution network distribution variation normal alarm information according to the embodiment.
Further, an embodiment of the present application further provides a computer readable storage medium, where computer executable instructions are stored, where the computer executable instructions are configured to cause a computer to perform the feature extraction method based on distribution network configuration variable normal alarm information according to the foregoing embodiment.
While the foregoing is directed to the preferred embodiments of the present invention, it will be appreciated by those skilled in the art that changes and modifications may be made without departing from the principles of the invention, such changes and modifications are also intended to be within the scope of the invention.
Claims (8)
1. The characteristic extraction method based on distribution network distribution variation constant alarm information is characterized by comprising the following steps:
after receiving current alarm information of a power distribution network, respectively searching historical alarm information and feedback information corresponding to the current alarm information, wherein the feedback information is maintenance information recorded in the prior time;
calculating an alarm association value by using the current alarm information, the historical alarm information and the feedback information;
when the alarm association value is larger than a preset association value, determining that the current alarm information is triggered by a prior legacy problem, and extracting a plurality of abnormal feature types from the historical alarm information;
Extracting feature information from the current alarm information according to the abnormal feature types;
The calculating the alarm association value by using the current alarm information, the historical alarm information and the feedback information comprises the following steps:
Calculating the information similarity of the current alarm information and the historical alarm information, and inputting the feedback information into a preset neural network for fault recurrence analysis to obtain an alarm recurrence probability value;
Determining weight percentages based on the information similarity;
and multiplying the weight percentage by the alarm recurrence probability value to obtain an alarm association value.
2. The method for extracting the characteristic based on the distribution network distribution constant alarm information according to claim 1, wherein the searching for the historical alarm information and the feedback information corresponding to the current alarm information respectively comprises:
determining equipment numbers of equipment of the power distribution network based on the current alarm information;
and searching historical alarm information and feedback information by using the equipment number.
3. The method for extracting features based on distribution network distribution constant alarm information according to claim 1, wherein after the step of extracting features from the current alarm information according to the plurality of abnormal feature types, the method further comprises:
Searching a plurality of maintenance schemes based on the plurality of characteristic information, and sending the plurality of maintenance schemes to maintenance staff for the maintenance staff to select corresponding maintenance schemes for maintenance processing.
4. The utility model provides a characteristic extraction device based on distribution network distribution variation normal alarm information which characterized in that, the device includes:
the searching module is used for respectively searching historical alarm information and feedback information corresponding to the current alarm information after receiving the current alarm information of the power distribution network, wherein the feedback information is maintenance information recorded in the prior time;
the calculation module is used for calculating an alarm association value by utilizing the current alarm information, the historical alarm information and the feedback information;
The determining module is used for determining that the current alarm information is triggered by a prior legacy problem when the alarm association value is larger than a preset association value, and extracting a plurality of abnormal feature types from the historical alarm information;
the extraction module is used for extracting feature information from the current alarm information according to the plurality of abnormal feature types;
The computing module is further for:
Calculating the information similarity of the current alarm information and the historical alarm information, and inputting the feedback information into a preset neural network for fault recurrence analysis to obtain an alarm recurrence probability value;
Determining weight percentages based on the information similarity;
and multiplying the weight percentage by the alarm recurrence probability value to obtain an alarm association value.
5. The feature extraction device based on distribution network distribution constant alarm information according to claim 4, wherein the search module is further configured to:
determining equipment numbers of equipment of the power distribution network based on the current alarm information;
and searching historical alarm information and feedback information by using the equipment number.
6. The power distribution network distribution constant alarm information-based feature extraction device according to claim 4, further comprising:
The scheme sending module is used for searching a plurality of maintenance schemes based on the plurality of characteristic information and sending the plurality of maintenance schemes to maintenance personnel so that the maintenance personnel can select corresponding maintenance schemes to carry out maintenance treatment.
7. An electronic device, comprising: a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the feature extraction method based on distribution network distribution constant alarm information according to any one of claims 1-3 when executing the program.
8. A computer-readable storage medium storing computer-executable instructions for causing a computer to perform the method for feature extraction based on distribution network distribution constant alert information according to any one of claims 1 to 3.
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Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105182122A (en) * | 2015-09-02 | 2015-12-23 | 许继集团有限公司 | Fault early warning method of random power supply access equipment |
| CN108521113A (en) * | 2018-04-25 | 2018-09-11 | 云南电网有限责任公司大理供电局 | The warning information rendering method of secondary operation management system |
Family Cites Families (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103888282A (en) * | 2013-08-19 | 2014-06-25 | 中广核工程有限公司 | Network intrusion alarm method and system based on nuclear power plant |
| US10614702B2 (en) * | 2018-06-21 | 2020-04-07 | Honeywell International Inc. | Alarm tuning using alarm and process data for condition monitoring |
| CN110309009B (en) * | 2019-05-21 | 2022-05-13 | 北京云集智造科技有限公司 | Situation-based operation and maintenance fault root cause positioning method, device, equipment and medium |
| US11496353B2 (en) * | 2019-05-30 | 2022-11-08 | Samsung Electronics Co., Ltd. | Root cause analysis and automation using machine learning |
| CN110245056A (en) * | 2019-06-10 | 2019-09-17 | 中国工商银行股份有限公司 | O&M alarm information processing method and device |
| CN110752942B (en) * | 2019-09-06 | 2021-09-17 | 平安科技（深圳）有限公司 | Alarm information decision method and device, computer equipment and storage medium |
| CN113497716B (en) * | 2020-03-18 | 2023-03-10 | 华为技术有限公司 | Recommended methods and related equipment for similar faults |
| CN113282461B (en) * | 2021-05-28 | 2023-06-23 | 中国联合网络通信集团有限公司 | Alarm recognition method and device for transmission network |
| CN114091704B (en) * | 2021-11-26 | 2022-07-12 | 奇点浩翰数据技术(北京)有限公司 | Alarm suppression method and device |
-
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Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105182122A (en) * | 2015-09-02 | 2015-12-23 | 许继集团有限公司 | Fault early warning method of random power supply access equipment |
| CN108521113A (en) * | 2018-04-25 | 2018-09-11 | 云南电网有限责任公司大理供电局 | The warning information rendering method of secondary operation management system |
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