CN115880871A - High-voltage circuit breaker state early warning method, system and equipment based on deep learning - Google Patents
High-voltage circuit breaker state early warning method, system and equipment based on deep learning Download PDFInfo
- Publication number
- CN115880871A CN115880871A CN202211517489.0A CN202211517489A CN115880871A CN 115880871 A CN115880871 A CN 115880871A CN 202211517489 A CN202211517489 A CN 202211517489A CN 115880871 A CN115880871 A CN 115880871A
- Authority
- CN
- China
- Prior art keywords
- state
- circuit breaker
- voltage circuit
- early warning
- parameter group
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000013135 deep learning Methods 0.000 title claims abstract description 30
- 238000000034 method Methods 0.000 title claims abstract description 26
- 238000012544 monitoring process Methods 0.000 claims abstract description 56
- 238000009413 insulation Methods 0.000 claims abstract description 20
- 230000009471 action Effects 0.000 claims description 57
- 239000013598 vector Substances 0.000 claims description 16
- 238000012549 training Methods 0.000 claims description 14
- 238000003745 diagnosis Methods 0.000 claims description 12
- 238000004590 computer program Methods 0.000 claims description 9
- 238000012545 processing Methods 0.000 claims description 9
- 238000013136 deep learning model Methods 0.000 claims description 8
- 238000012360 testing method Methods 0.000 claims description 7
- 238000002790 cross-validation Methods 0.000 claims description 3
- 230000007774 longterm Effects 0.000 claims description 3
- 238000010606 normalization Methods 0.000 claims description 3
- 238000001514 detection method Methods 0.000 claims 1
- 230000009286 beneficial effect Effects 0.000 abstract description 5
- 238000012986 modification Methods 0.000 description 7
- 230000004048 modification Effects 0.000 description 7
- 238000005299 abrasion Methods 0.000 description 1
- 239000003086 colorant Substances 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 238000012552 review Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Images
Landscapes
- Testing Electric Properties And Detecting Electric Faults (AREA)
Abstract
The application discloses high-voltage circuit breaker state early warning method based on deep learning includes: collecting a state parameter group reflecting operation state information in the operation of the high-voltage circuit breaker; the set of state parameters includes: a first parameter set reflecting mechanical characteristics; a second parameter set reflecting the electrical characteristic; a third parameter group reflecting the insulation characteristic; when the working state of the high-voltage circuit breaker acquired in the acquisition time period and the working state error obtained by monitoring of the state monitoring model exceed the error tolerance interval, iterating the state parameter set to form a new state parameter set; refreshing the monitoring model according to the original state parameter group and the state parameter group after iteration; and monitoring and early warning the state of the high-voltage circuit breaker by adopting the refreshed monitoring model. Compared with the prior art, the early warning method and the early warning device can improve the early warning accuracy of the operation state of the high-voltage circuit breaker. The application also relates to a high-voltage circuit breaker state early warning system and electronic equipment based on deep learning, and the high-voltage circuit breaker state early warning system and the electronic equipment also have the beneficial effects.
Description
Technical Field
The invention relates to the technical field of smart power grids, in particular to a high-voltage circuit breaker state early warning method based on deep learning, and further relates to a high-voltage circuit breaker state early warning system based on deep learning and electronic equipment.
Background
The reliability of the high-voltage circuit breaker is directly related to the overall operation safety of the power system, the power system fault is caused by the high-voltage circuit breaker with high probability, the high-voltage circuit breaker switch causes certain abrasion to the contact, and once the service life limit is reached, the fault or accident of the circuit breaker can be caused, so that the monitoring and early warning of the operation state of the high-voltage circuit breaker are indispensable. However, the accuracy of the early warning of the operation state of the high-voltage circuit breaker in the prior art is obviously insufficient.
Therefore, how to provide a high-voltage circuit breaker state early warning method based on deep learning, which can overcome the above technical problems and improve the accuracy of the high-voltage circuit breaker operation state early warning, has become a technical problem to be solved by those skilled in the art.
Disclosure of Invention
In order to solve the technical problem, the application provides a high-voltage circuit breaker state early warning method based on deep learning, which can overcome the technical problem and improve the accuracy of the high-voltage circuit breaker operation state early warning. The application also relates to a high-voltage circuit breaker state early warning system and electronic equipment based on deep learning, and the high-voltage circuit breaker state early warning system and the electronic equipment also have the beneficial effects.
The technical scheme provided by the application is as follows:
the application provides a high-voltage circuit breaker state early warning method based on deep learning, which comprises the following steps: acquiring a state parameter group reflecting operation state information in the operation of the high-voltage circuit breaker;
the set of state parameters includes: a first set of parameters reflecting mechanical properties; a second parameter set reflecting the electrical characteristic; a third parameter group reflecting the insulation characteristic;
establishing a characteristic vector according to the state parameter group of the high-voltage circuit breaker and the corresponding acquisition time;
training the deep learning model according to the characteristic vector, and establishing a state monitoring model of the high-voltage circuit breaker;
setting a collection time period and an error tolerance interval; collecting the working state of the high-voltage circuit breaker on the spot in a collection time period; when the working state of the high-voltage circuit breaker collected in the collection time period and the working state error obtained by monitoring of the state monitoring model exceed the error tolerance interval, iterating the state parameter set to form a new state parameter set; refreshing the monitoring model according to the original state parameter group and the state parameter group after iteration;
and monitoring and early warning the state of the high-voltage circuit breaker by adopting the refreshed monitoring model.
Further, in a preferred mode of the present invention, the first parameter group includes: time parameter information, speed parameter information, opening and closing coil information and direct current resistance information.
Further, in a preferred aspect of the present invention, the second parameter set includes: the on-off wear information, the lowest action voltage information and the main loop resistance information.
Further, in a preferred aspect of the present invention, the third parameter set includes insulation medium information, long-term power frequency withstand voltage test information, and primary circuit ground insulation information.
Further, in a preferred mode of the present invention, the method further includes the steps of: predicting future action data through historical action of the circuit breaker; and carrying out latent fault diagnosis on the high-voltage circuit breaker through mechanical action data of the predicted future action data.
Further, in a preferred mode of the present invention, the step "predicts future action data by a historical action of the circuit breaker; the specific steps of performing latent fault diagnosis on the high-voltage circuit breaker through the predicted mechanical action data of the future action data are as follows: predicting next or later action data through the contact strokes of the first action and the current curves of the operating coil; and carrying out latent fault diagnosis on the high-voltage circuit breaker through mechanical action data of the predicted future action data.
Further, in a preferred mode of the invention, the 10-time action data is predicted through the contact stroke and the operating coil current curve of the previous 10-time action, and the algorithm precision is improved through normalization, cross validation and grid search processing.
In addition, the invention also relates to a high-voltage circuit breaker state early warning system based on deep learning, which comprises: the first acquisition module is used for acquiring a first parameter group reflecting mechanical characteristics; the second acquisition module is used for acquiring a second parameter group reflecting the electrical characteristics; the third acquisition module is used for acquiring a third parameter group reflecting the insulation characteristic; the comprehensive processing module is used for establishing a characteristic vector according to the state parameter group of the high-voltage circuit breaker and the corresponding acquisition time; the model training module is used for training the monitoring model, training the deep learning model according to the characteristic vector and establishing a state monitoring model of the high-voltage circuit breaker; the comprehensive processing module is also used for collecting the working state of the high-voltage circuit breaker on site in a collecting time period; when the working state of the high-voltage circuit breaker acquired in the acquisition time period and the working state error obtained by monitoring of the state monitoring model are large, updating the state parameter group to form a new state parameter group; updating the monitoring model according to the original state parameter group and the new state parameter group; and monitoring and early warning the state of the high-voltage circuit breaker by adopting the updated monitoring model.
Further, in a preferred mode of the present invention, the first acquisition module includes: the device comprises a time parameter information acquisition unit, a speed parameter information acquisition unit, a switching-on and switching-off coil information acquisition unit and a direct current resistance information acquisition unit; the second acquisition module comprises: the system comprises a breaking wear information acquisition unit, a lowest action voltage information acquisition unit and a main loop resistance information acquisition unit; the third parameter group comprises an insulating medium information acquisition unit, a long-time power frequency withstand voltage test information acquisition unit and a primary circuit ground insulation information acquisition unit.
Furthermore, the invention relates to a device comprising: a computer program for executing the deep learning-based high voltage circuit breaker state warning method as described above; a memory for storing a computer program; a processor for executing a computer program.
Compared with the prior art, the high-voltage circuit breaker state early warning method based on deep learning provided by the invention comprises the following steps: a state parameter group reflecting operation state information in the operation of the high-voltage breaker is collected; the set of state parameters includes: a first parameter set reflecting mechanical characteristics; a second parameter set reflecting the electrical characteristic; a third parameter group reflecting the insulation characteristic; establishing a characteristic vector according to the state parameter group of the high-voltage circuit breaker and the corresponding acquisition time; training the deep learning model according to the characteristic vector, and establishing a state monitoring model of the high-voltage circuit breaker; setting a collection time period and an error tolerance interval; collecting the working state of the high-voltage circuit breaker on the spot in a collecting time period; when the working state of the high-voltage circuit breaker collected in the collection time period and the working state error obtained by monitoring of the state monitoring model exceed the error tolerance interval, iterating the state parameter set to form a new state parameter set; refreshing the monitoring model according to the original state parameter group and the state parameter group after iteration; and monitoring and early warning the state of the high-voltage circuit breaker by adopting the refreshed monitoring model. According to the technical scheme, the technical operation steps are mutually and organically matched to coordinate operation, compared with the prior art, the technical problem can be solved, and the early warning accuracy of the running state of the high-voltage circuit breaker is improved. The application also relates to a high-voltage circuit breaker state early warning system and electronic equipment based on deep learning, and the high-voltage circuit breaker state early warning system and the electronic equipment also have the beneficial effects.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic flow chart of a high-voltage circuit breaker state early warning method based on deep learning according to an embodiment of the present invention.
Fig. 2 is a schematic diagram of a high-voltage circuit breaker state early warning system based on deep learning according to an embodiment of the present invention.
Detailed Description
In order to make those skilled in the art better understand the technical solutions in the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
It will be understood that when an element is referred to as being "fixed" or "disposed" to another element, it can be directly on the other element or be indirectly disposed on the other element; when an element is referred to as being "connected to" another element, it can be directly connected to the other element or be indirectly connected to the other element.
It will be understood that the terms "length," "width," "upper," "lower," "front," "rear," "first," "second," "vertical," "horizontal," "top," "bottom," "inner," "outer," and the like, as used herein, refer to an orientation or positional relationship indicated in the drawings that is solely for the purpose of facilitating the description and simplifying the description, and do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus should not be construed as limiting the application.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or to implicitly indicate the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the present application, "plurality" or "a plurality" means two or more unless specifically limited otherwise.
It should be understood that the structures, ratios, sizes, and the like shown in the drawings are only used for matching the disclosure of the specification, so as to be understood and read by those skilled in the art, and are not used to limit the practical limit conditions of the present application, so that the modifications of the structures, the changes of the ratio relationships, or the adjustment of the sizes, do not have the technical essence, and the modifications, the changes of the ratio relationships, or the adjustment of the sizes, are all within the scope of the technical contents disclosed in the present application without affecting the efficacy and the achievable purpose of the present application.
As shown in fig. 1 to fig. 2, an embodiment of the present application provides a method for early warning a state of a high-voltage circuit breaker based on deep learning, including: collecting a state parameter group reflecting operation state information in the operation of the high-voltage circuit breaker; the set of state parameters includes: a first parameter set reflecting mechanical characteristics; a second parameter set reflecting the electrical characteristic; a third parameter group reflecting the insulation characteristic; establishing a characteristic vector according to the state parameter group of the high-voltage circuit breaker and the corresponding acquisition time; training the deep learning model according to the characteristic vector, and establishing a state monitoring model of the high-voltage circuit breaker; setting a collection time period and an error tolerance interval; collecting the working state of the high-voltage circuit breaker on the spot in a collection time period; when the working state of the high-voltage circuit breaker acquired in the acquisition time period and the working state error obtained by monitoring of the state monitoring model exceed the error tolerance interval, iterating the state parameter set to form a new state parameter set; refreshing the monitoring model according to the original state parameter group and the state parameter group after iteration; and monitoring and early warning the state of the high-voltage circuit breaker by adopting the refreshed monitoring model. According to the technical scheme, the technical operation steps are mutually and organically matched to coordinate operation, compared with the prior art, the technical problem can be solved, and the early warning accuracy of the running state of the high-voltage circuit breaker is improved. The application also relates to a high-voltage circuit breaker state early warning system and electronic equipment based on deep learning, and the high-voltage circuit breaker state early warning system and the electronic equipment also have the beneficial effects.
Specifically, in an embodiment of the present invention, the first parameter group includes: time parameter information, speed parameter information, opening and closing coil information and direct current resistance information.
Specifically, in an embodiment of the present invention, the second parameter group includes: the on-off wear information, the lowest operating voltage information and the main loop resistance information.
Specifically, in the embodiment of the present invention, the third parameter set includes insulation medium information, long-term power frequency withstand voltage test information, and primary loop ground insulation information.
Specifically, in the embodiment of the present invention, the method further includes the steps of: predicting future action data through historical action of the circuit breaker; and carrying out latent fault diagnosis on the high-voltage circuit breaker through mechanical action data of the predicted future action data.
Specifically, in an embodiment of the present invention, the step "predict future action data from circuit breaker historical actions; the specific steps of performing latent fault diagnosis on the high-voltage circuit breaker through the predicted mechanical action data of the future action data are as follows: predicting next or later action data through the contact strokes of the first action and the current curves of the operating coil; and carrying out latent fault diagnosis on the high-voltage circuit breaker through mechanical action data of the predicted future action data.
Specifically, in the embodiment of the invention, the 10-time action data is predicted through the contact stroke and the operating coil current curve of the previous 10-time action, and the algorithm precision is improved through normalization, cross validation and grid search processing.
Example 1
The embodiment of the application provides a high-voltage circuit breaker state early warning method based on deep learning, which comprises the following steps: collecting a state parameter group reflecting operation state information in the operation of the high-voltage circuit breaker; the set of state parameters includes: a first parameter set reflecting mechanical characteristics; a second parameter set reflecting the electrical characteristic; a third parameter group reflecting the insulation characteristic; establishing a characteristic vector according to the state parameter group of the high-voltage circuit breaker and the corresponding acquisition time; training the deep learning model according to the characteristic vector, and establishing a state monitoring model of the high-voltage circuit breaker; setting an acquisition time period and an error tolerance interval; collecting the working state of the high-voltage circuit breaker on the spot in a collection time period; when the working state of the high-voltage circuit breaker collected in the collection time period and the working state error obtained by monitoring of the state monitoring model exceed the error tolerance interval, iterating the state parameter set to form a new state parameter set; refreshing the monitoring model according to the original state parameter group and the state parameter group after iteration; and monitoring and early warning the state of the high-voltage circuit breaker by adopting the refreshed monitoring model. According to the technical scheme, the technical operation steps are mutually and organically matched to coordinate operation, compared with the prior art, the technical problem can be solved, and the early warning accuracy of the running state of the high-voltage circuit breaker is improved. The application also relates to a high-voltage circuit breaker state early warning system and electronic equipment based on deep learning, and the high-voltage circuit breaker state early warning system and the electronic equipment also have the beneficial effects.
Example 2
Compared with the previous embodiment, the present embodiment has the following differences: in an embodiment of the present invention, the first parameter set includes: time parameter information, speed parameter information, opening and closing coil information and direct current resistance information; the second parameter set includes: the on-off wear information, the lowest action voltage information and the main loop resistance information; the third parameter group comprises insulation medium information, long-time power frequency withstand voltage test information and primary loop ground insulation information.
Example 3
Compared with the previous embodiment, the present embodiment has the following differences: in an embodiment of the present invention, further comprising the steps of: predicting future action data through historical action of the circuit breaker; the method comprises the following steps of carrying out latent fault diagnosis on the high-voltage circuit breaker through predicted mechanical action data of future action data, specifically: the specific steps of performing latent fault diagnosis on the high-voltage circuit breaker through the predicted mechanical action data of the future action data are as follows: predicting next or later action data through the contact strokes of the first action and the current curves of the operating coil; and carrying out latent fault diagnosis on the high-voltage circuit breaker through mechanical action data of the predicted future action data.
In addition, the invention also relates to a high-voltage circuit breaker state early warning system based on deep learning, which comprises: the first acquisition module is used for acquiring a first parameter group reflecting mechanical characteristics; the second acquisition module is used for acquiring a second parameter group reflecting the electrical characteristics; the third acquisition module is used for acquiring a third parameter group reflecting the insulation characteristic; the comprehensive processing module is used for establishing a characteristic vector according to the state parameter group of the high-voltage circuit breaker and the corresponding acquisition time; the model training module is used for training the monitoring model, training the deep learning model according to the characteristic vector and establishing a state monitoring model of the high-voltage circuit breaker; the comprehensive processing module is also used for collecting the working state of the high-voltage circuit breaker on site in a collecting time period; when the working state of the high-voltage circuit breaker acquired in the acquisition time period and the working state error obtained by monitoring of the state monitoring model are large, updating the state parameter group to form a new state parameter group; updating the monitoring model according to the original state parameter group and the new state parameter group; the state of the high-voltage circuit breaker is monitored and early-warned by adopting the updated monitoring model, and the high-voltage circuit breaker further comprises an early-warning prompting module which is used for sending early-warning information to an operator on duty.
Specifically, in an embodiment of the present invention, the first acquisition module includes: the device comprises a time parameter information acquisition unit, a speed parameter information acquisition unit, a switching-on and switching-off coil information acquisition unit and a direct current resistance information acquisition unit; the second acquisition module comprises: the system comprises a breaking wear information acquisition unit, a lowest action voltage information acquisition unit and a main loop resistance information acquisition unit; the third parameter group comprises an insulating medium information acquisition unit, a long-time power frequency withstand voltage test information acquisition unit and a primary circuit ground insulation information acquisition unit.
Furthermore, the invention relates to a device comprising: a computer program for executing the deep learning-based high voltage circuit breaker state early warning method as described above; a memory for storing a computer program; a processor for executing a computer program.
It should be noted that the construction and arrangement of the present application as shown in the various exemplary embodiments is illustrative only. Although only a few embodiments have been described in detail in this disclosure, those skilled in the art who review this disclosure will readily appreciate that many modifications are possible (e.g., variations in sizes, dimensions, structures, shapes and proportions of the various elements, values of parameters (e.g., temperatures, pressures, etc.), mounting arrangements, use of materials, colors, orientations, etc.) without materially departing from the novel teachings and advantages of the subject matter recited in this application. For example, elements shown as integrally formed may be constructed of multiple parts or elements, the position of elements may be reversed or otherwise varied, and the nature or number of discrete elements or positions may be altered or varied. Accordingly, all such modifications are intended to be included within the scope of this invention. The order or sequence of any process or method steps may be varied or re-sequenced according to alternative embodiments. In the claims, any means-plus-function clause is intended to cover the structures described herein as performing the recited function and not only structural equivalents but also equivalent structures. Other substitutions, modifications, changes and omissions may be made in the design, operating conditions and arrangement of the exemplary embodiments without departing from the scope of the present inventions. Therefore, the present invention is not limited to a particular embodiment, but extends to various modifications that nevertheless fall within the scope of the appended claims.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. A high-voltage circuit breaker state early warning method based on deep learning is characterized by comprising the following steps:
acquiring a state parameter group reflecting operation state information in the operation of the high-voltage circuit breaker;
the set of state parameters includes: a first parameter set reflecting mechanical characteristics; a second parameter set reflecting the electrical characteristic; a third parameter group reflecting the insulation characteristic;
establishing a characteristic vector according to the state parameter group of the high-voltage circuit breaker and the corresponding acquisition time;
training the deep learning model according to the feature vectors, and establishing a state monitoring model of the high-voltage circuit breaker;
setting a collection time period and an error tolerance interval; collecting the working state of the high-voltage circuit breaker on the spot in a collecting time period; when the working state of the high-voltage circuit breaker collected in the collection time period and the working state error obtained by monitoring of the state monitoring model exceed the error tolerance interval, iterating the state parameter set to form a new state parameter set; refreshing the monitoring model according to the original state parameter group and the state parameter group after iteration;
and monitoring and early warning the state of the high-voltage circuit breaker by adopting the refreshed monitoring model.
2. The deep learning-based high voltage circuit breaker state warning method as claimed in claim 1, wherein the first parameter group comprises: time parameter information, speed parameter information, opening and closing coil information and direct current resistance information.
3. The deep learning based high voltage circuit breaker state warning method of claim 2, wherein the second parameter set comprises: the on-off wear information, the lowest operating voltage information and the main loop resistance information.
4. The deep learning-based high-voltage circuit breaker state early warning method as claimed in claim 3, wherein the third parameter group comprises insulation medium information, long-term power frequency withstand voltage test information and primary circuit ground insulation information.
5. The deep learning-based high-voltage circuit breaker state early warning method as claimed in claim 4, further comprising the steps of: predicting future action data through historical action of the circuit breaker; and carrying out latent fault diagnosis on the high-voltage circuit breaker through mechanical action data of the predicted future action data.
6. The deep learning-based high-voltage circuit breaker state early warning method as claimed in claim 5, wherein the step of predicting future action data by historical action of the circuit breaker; the specific steps of performing latent fault diagnosis on the high-voltage circuit breaker through the predicted mechanical action data of the future action data are as follows: predicting next or later action data through the contact strokes of the first action and the current curves of the operating coil; and carrying out latent fault diagnosis on the high-voltage circuit breaker through mechanical action data of the predicted future action data.
7. The distribution network capacitance current safety detection system according to claim 6, wherein the data of the 10 last actions are predicted according to the contact stroke of the first 10 actions and the current curve of the operating coil, and the algorithm precision is improved through normalization, cross validation and grid search processing.
8. The utility model provides a high voltage circuit breaker state early warning system based on degree of depth study which characterized in that includes: the first acquisition module is used for acquiring a first parameter group reflecting mechanical characteristics; the second acquisition module is used for acquiring a second parameter group reflecting the electrical characteristics; the third acquisition module is used for acquiring a third parameter group reflecting the insulation characteristic; the comprehensive processing module is used for establishing a characteristic vector according to the state parameter group of the high-voltage circuit breaker and the corresponding acquisition time; the model training module is used for training the monitoring model, training the deep learning model according to the characteristic vector and establishing a state monitoring model of the high-voltage circuit breaker; the comprehensive processing module is also used for collecting the working state of the high-voltage circuit breaker on site in a collecting time period; when the working state of the high-voltage circuit breaker acquired in the acquisition time period and the working state error obtained by monitoring of the state monitoring model are large, updating the state parameter group to form a new state parameter group; updating the monitoring model according to the original state parameter group and the new state parameter group; and monitoring and early warning the state of the high-voltage circuit breaker by adopting the updated monitoring model.
9. The deep learning based high voltage circuit breaker state warning system of claim 8, wherein the first acquisition module comprises: the device comprises a time parameter information acquisition unit, a speed parameter information acquisition unit, a switching-on/off coil information acquisition unit and a direct current resistance information acquisition unit; the second acquisition module comprises: the system comprises a breaking wear information acquisition unit, a lowest action voltage information acquisition unit and a main loop resistance information acquisition unit; the third parameter group comprises an insulating medium information acquisition unit, a long-time power frequency withstand voltage test information acquisition unit and a primary circuit ground insulation information acquisition unit.
10. An apparatus, comprising:
a computer program for executing the high-voltage circuit breaker state early warning method based on deep learning according to any one of claims 1 to 7;
a memory for storing a computer program;
a processor for executing a computer program.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211517489.0A CN115880871A (en) | 2022-11-29 | 2022-11-29 | High-voltage circuit breaker state early warning method, system and equipment based on deep learning |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211517489.0A CN115880871A (en) | 2022-11-29 | 2022-11-29 | High-voltage circuit breaker state early warning method, system and equipment based on deep learning |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115880871A true CN115880871A (en) | 2023-03-31 |
Family
ID=85764819
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211517489.0A Pending CN115880871A (en) | 2022-11-29 | 2022-11-29 | High-voltage circuit breaker state early warning method, system and equipment based on deep learning |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115880871A (en) |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101825894A (en) * | 2010-04-30 | 2010-09-08 | 北京航空航天大学 | SF6 high-voltage circuit breaker state intelligent monitoring and health management system |
| CN107015145A (en) * | 2017-06-01 | 2017-08-04 | 湖州知维技术服务有限公司 | Primary cut-out is monitored on-line and state evaluation system |
| US20180082568A1 (en) * | 2016-09-21 | 2018-03-22 | General Electric Technology Gmbh | Machine Protection and Diagnostic Systems and Methods |
| CN111145482A (en) * | 2019-12-31 | 2020-05-12 | 南京南瑞继保工程技术有限公司 | Fire control command control system |
| CN114355182A (en) * | 2021-12-03 | 2022-04-15 | 国网黑龙江省电力有限公司检修公司 | Online monitoring and fault diagnosis method for high-voltage circuit breaker |
| CN114355184A (en) * | 2022-01-05 | 2022-04-15 | 国网江苏省电力有限公司宿迁供电分公司 | High-voltage circuit breaker state monitoring and early warning system and method based on online learning |
-
2022
- 2022-11-29 CN CN202211517489.0A patent/CN115880871A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101825894A (en) * | 2010-04-30 | 2010-09-08 | 北京航空航天大学 | SF6 high-voltage circuit breaker state intelligent monitoring and health management system |
| US20180082568A1 (en) * | 2016-09-21 | 2018-03-22 | General Electric Technology Gmbh | Machine Protection and Diagnostic Systems and Methods |
| CN107015145A (en) * | 2017-06-01 | 2017-08-04 | 湖州知维技术服务有限公司 | Primary cut-out is monitored on-line and state evaluation system |
| CN111145482A (en) * | 2019-12-31 | 2020-05-12 | 南京南瑞继保工程技术有限公司 | Fire control command control system |
| CN114355182A (en) * | 2021-12-03 | 2022-04-15 | 国网黑龙江省电力有限公司检修公司 | Online monitoring and fault diagnosis method for high-voltage circuit breaker |
| CN114355184A (en) * | 2022-01-05 | 2022-04-15 | 国网江苏省电力有限公司宿迁供电分公司 | High-voltage circuit breaker state monitoring and early warning system and method based on online learning |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN113448763B (en) | Dynamic expansion grouping alarm service method for full life cycle management | |
| CN106908674A (en) | A kind of Transformer condition evaluation based on the prediction of multimode amount | |
| KR102079813B1 (en) | 25.8kV class ENVIRONMENT-FRIENDLY SWITCHGEAR HAVING PARTIAL DISCHARGE DIAGNOSIS FUNCTION AND IOT TECHNOLOGY | |
| CN108020781B (en) | Breaker fault diagnosis method | |
| CN116956639B (en) | High-heat-dissipation storage battery assessment method and system based on data analysis | |
| CN102289582A (en) | Method for recognizing risk state of power distribution network | |
| CN114154728A (en) | Fault prediction method and device, electronic equipment and storage medium | |
| CN115880871A (en) | High-voltage circuit breaker state early warning method, system and equipment based on deep learning | |
| CN112600309B (en) | Low-voltage power distribution intelligent diagnosis system with wave recording function | |
| CN105548873B (en) | The method for realizing switchgear Gernral Check-up for on-Line Monitor Device | |
| CN117411183B (en) | Abnormal electricity consumption monitoring system of circuit breaker | |
| CN116937758B (en) | Household energy storage power supply system and operation method thereof | |
| CN106291158A (en) | Real time load monitoring and insulating oil deterioration diagnosis method and intelligent transformer | |
| CN116298844B (en) | Semi-dynamic arrangement-based substation high-voltage circuit breaker state monitoring system and method | |
| CN112485630A (en) | IGBT health state monitoring method based on parameter transformation | |
| CN116704731A (en) | Safe electricity utilization pre-alarm method based on current fingerprint technology | |
| CN107274088B (en) | Risk field calculation method and system for underground power distribution room | |
| CN114201983A (en) | Health state diagnosis method and system for switch cabinet | |
| CN115800534A (en) | Real-time comprehensive management system for state of high-voltage circuit breaker | |
| CN115905646A (en) | High-voltage circuit breaker fault judgment method, system and equipment based on Internet of things technology | |
| CN115754638A (en) | High-voltage circuit breaker based partial discharge comprehensive monitoring method and system and electronic equipment | |
| CN116402492B (en) | Household appliance maintenance early warning method and related equipment thereof | |
| CN117852093B (en) | Electric power engineering data key information protection method | |
| Sheng et al. | An overview of fault identification techniques in power distribution networks: methods and models | |
| CN114035039B (en) | Multidimensional analysis method for action state of circuit breaker |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |