CN118061839A - Charging pile charging control method based on information monitoring - Google Patents
Charging pile charging control method based on information monitoring Download PDFInfo
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- CN118061839A CN118061839A CN202410471734.1A CN202410471734A CN118061839A CN 118061839 A CN118061839 A CN 118061839A CN 202410471734 A CN202410471734 A CN 202410471734A CN 118061839 A CN118061839 A CN 118061839A
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- 238000012544 monitoring process Methods 0.000 title claims abstract description 16
- 239000011159 matrix material Substances 0.000 claims abstract description 41
- 238000012549 training Methods 0.000 claims abstract description 22
- 238000001514 detection method Methods 0.000 claims abstract description 11
- 238000007635 classification algorithm Methods 0.000 claims abstract description 5
- 238000010801 machine learning Methods 0.000 claims abstract description 5
- 230000003111 delayed effect Effects 0.000 claims description 6
- 238000003745 diagnosis Methods 0.000 abstract description 2
- 238000012986 modification Methods 0.000 description 3
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- 238000013528 artificial neural network Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000003066 decision tree Methods 0.000 description 1
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- 238000007637 random forest analysis Methods 0.000 description 1
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Abstract
The invention discloses a charging pile charging control method based on information monitoring, which comprises the following steps: s1, presetting a control scheme for a plurality of charging piles with known fault types; s2, acquiring a magnetic field intensity matrix set of the fault-free charging pile; s3, acquiring a magnetic field intensity matrix set of the charging pile with a known fault type; s4, constructing a training sample set; s5, constructing a classifier model based on a classification algorithm in a machine learning method, and training the classifier model by using a training sample set to obtain a mature classifier model; s6, acquiring a magnetic field intensity matrix of the charging pile to be detected; s7, sending the magnetic field intensity matrix of the charging pile to be detected into a mature classifier model, and controlling the charging pile according to the output detection result. According to the invention, fault diagnosis of the charging pile is carried out by carrying out magnetic field information around the charging pile, and on the basis, the precise control of different fault types of the charging pile can be realized.
Description
Technical Field
The invention relates to charging pile control, in particular to a charging pile charging control method based on information monitoring.
Background
The charging pile can help the electric automobile to charge more conveniently; however, in the use process of the charging pile, the problem of faults is often involved, and in the present situation, when the charging pile breaks down, even emergency control processing such as immediate power-off, delayed power-off, immediate restarting, delayed restarting and the like is often required to be performed while the fault is reported; however, the specific control mode needs to be refined according to different fault types, which requires fault judgment, the existing judgment is generally performed by staff, which requires strong professional ability, and some of the control modes are also performed by collecting and judging current and voltage parameters in the charging pile, but the additional sensor is required to be installed in the charging pile.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, and provides a charging pile charging control method based on information monitoring, which is used for diagnosing faults of a charging pile by carrying out magnetic field information around the charging pile and realizing the fine control on different fault types of the charging pile on the basis.
The aim of the invention is realized by the following technical scheme: a charging pile charging control method based on information monitoring comprises the following steps:
s1, presetting a control scheme for a plurality of charging piles with known fault types;
S2, for a plurality of fault-free charging piles in a working state, arranging a plurality of magnetic field intensity sensors around the charging piles at a position away from the charging piles L, and collecting magnetic field intensities around the charging piles to obtain a magnetic field intensity matrix set of the fault-free charging piles;
S3, for a plurality of charging piles of known fault types in a working state, arranging a plurality of magnetic field intensity sensors around the charging piles at a distance L from the charging piles, and collecting magnetic field intensities around the charging piles at a plurality of moments to obtain a magnetic field intensity matrix set of the charging piles of known fault types;
S4, marking data in a magnetic field intensity matrix set of the fault-free charging pile and the magnetic field intensity matrix set of the charging pile with a known fault type, and constructing a training sample set;
s5, constructing a classifier model based on a classification algorithm in a machine learning method, and training the classifier model by using a training sample set to obtain a mature classifier model;
S6, for the charging pile to be detected, enabling the charging pile to be in a working state, and arranging a plurality of magnetic field intensity sensors around the charging pile at a position distant from the charging pile L, and collecting magnetic field intensity around the charging pile to obtain a magnetic field intensity matrix of the charging pile to be detected;
S7, sending the magnetic field intensity matrix of the charging pile to be detected into a mature classifier model, outputting a detection result by the classifier model, and controlling the charging pile according to the detection result.
The beneficial effects of the invention are as follows: according to the invention, firstly, when the charging pile without faults and various known fault types is in a working state, surrounding magnetic field information is collected, a training sample set is constructed, then a classifier model constructed by a machine learning algorithm is trained by utilizing the training sample set, the classifier model has the capability of identifying whether the charging pile has faults and the fault types, then the fault diagnosis of the charging pile is carried out by collecting and constructing a matrix by the magnetic field information around the charging pile to be tested, and on the basis, the refined control of different fault types of the charging pile is realized according to a preset control scheme.
Drawings
FIG. 1 is a flow chart of the method of the present invention.
Detailed Description
The technical solution of the present invention will be described in further detail with reference to the accompanying drawings, but the scope of the present invention is not limited to the following description.
As shown in fig. 1, a charging pile charging control method based on information monitoring is characterized in that: the method comprises the following steps:
s1, presetting a control scheme for a plurality of charging piles with known fault types;
S2, for a plurality of fault-free charging piles in a working state, arranging a plurality of magnetic field intensity sensors around the charging piles at a position away from the charging piles L, and collecting magnetic field intensities around the charging piles to obtain a magnetic field intensity matrix set of the fault-free charging piles;
S3, for a plurality of charging piles of known fault types in a working state, arranging a plurality of magnetic field intensity sensors around the charging piles at a distance L from the charging piles, and collecting magnetic field intensities around the charging piles at a plurality of moments to obtain a magnetic field intensity matrix set of the charging piles of known fault types;
S4, marking data in a magnetic field intensity matrix set of the fault-free charging pile and the magnetic field intensity matrix set of the charging pile with a known fault type, and constructing a training sample set;
S5, constructing a classifier model based on a classification algorithm in a machine learning method, and training the classifier model by using a training sample set to obtain a mature classifier model; the classification algorithm comprises one of a support vector machine, a decision tree, a random forest and a deep neural network algorithm.
S6, for the charging pile to be detected, enabling the charging pile to be in a working state, and arranging a plurality of magnetic field intensity sensors around the charging pile at a position distant from the charging pile L, and collecting magnetic field intensity around the charging pile to obtain a magnetic field intensity matrix of the charging pile to be detected;
S7, sending the magnetic field intensity matrix of the charging pile to be detected into a mature classifier model, outputting a detection result by the classifier model, and controlling the charging pile according to the detection result.
In the step S1, a total of K charging piles of known fault types are set, and the fault type number is 1~K;
a control scheme is preset for each fault type number; the control scheme includes, but is not limited to, one of an immediate power-off, a delayed power-off, an immediate restart, and a delayed restart.
The fault-free charging pile, the charging piles of various known fault types and the charging pile to be detected belong to charging piles delivered from the same manufacturer in the same batch; the L is 1 meter.
The step S2 includes:
S201, setting the number of magnetic field intensity sensors to be M for any fault-free charging pile in a working state, and collecting magnetic field intensity data at N moments, so as to obtain a magnetic field intensity matrix The method comprises the following steps:
;
wherein, Representing the magnetic field strength acquired by the mth sensor at the nth moment,；
S202, repeatedly executing the step S201 for each non-fault charging pile to obtain magnetic field intensity matrixes of a plurality of non-fault charging piles, and adding the magnetic field intensity matrixes into the same set to form a magnetic field intensity matrix set of the non-fault charging piles.
The step S3 includes:
S301, setting the number of magnetic field intensity sensors to be M for any charging pile with known fault type in a working state, and collecting magnetic field intensity data at N moments, so as to obtain a magnetic field intensity matrix The method comprises the following steps:
;
wherein, Representing the magnetic field strength acquired by the mth sensor at the nth time,；
S302, for each charging pile with a known fault type, repeating the step S301 to obtain magnetic field intensity matrixes of a plurality of charging piles with known fault types, and adding the magnetic field intensity matrixes into the same set to form a magnetic field intensity matrix set of the charging piles with known fault types.
The step S4 includes:
For each magnetic field intensity matrix obtained in step S202, adding a tag of 0 thereto;
For each magnetic field intensity matrix obtained in step S302, the corresponding fault type number of the charging pile is the label: namely, marking each sample characteristic by using a fault type number 1~K;
and adding all the magnetic field intensity matrixes into one set after marking is finished to form a training sample set.
In step S5, the magnetic field intensity matrix in the training sample set is used as the input of the classifier model, the label corresponding to the magnetic field intensity matrix is used as the expected output for training, and when all the magnetic field intensity matrices in the training sample set are trained, the mature classifier model is obtained.
In the step S6, the number of the arranged magnetic field intensity sensors is M, and the magnetic field intensity data of N moments are collected, so that a magnetic field intensity matrix of the charging pile to be detected is obtainedThe method comprises the following steps:
;
wherein, Representing the magnetic field strength acquired by the mth sensor at the nth time,。
In step S7, if the detection result output by the mature classifier model is 0, the charging pile is considered to continue to work if no fault occurs, if the detection result output by the mature classifier model is 1~K, any one of the marks is considered to occur, the output mark is the fault type number, a preset control scheme is searched according to the fault type number, and the charging pile is controlled according to the corresponding control scheme.
The foregoing is a preferred embodiment of the invention, and it is to be understood that the invention is not limited to the form disclosed herein, but is not to be construed as limited to other embodiments, but is capable of other combinations, modifications and environments and is capable of changes or modifications within the scope of the inventive concept, either as a result of the foregoing teachings or as a result of the knowledge or knowledge of the relevant art. And that modifications and variations which do not depart from the spirit and scope of the invention are intended to be within the scope of the appended claims.
Claims (9)
1. The charging pile charging control method based on information monitoring is characterized by comprising the following steps of: the method comprises the following steps:
s1, presetting a control scheme for a plurality of charging piles with known fault types;
S2, for a plurality of fault-free charging piles in a working state, arranging a plurality of magnetic field intensity sensors around the charging piles at a position away from the charging piles L, and collecting magnetic field intensities around the charging piles to obtain a magnetic field intensity matrix set of the fault-free charging piles;
S3, for a plurality of charging piles of known fault types in a working state, arranging a plurality of magnetic field intensity sensors around the charging piles at a distance L from the charging piles, and collecting magnetic field intensities around the charging piles at a plurality of moments to obtain a magnetic field intensity matrix set of the charging piles of known fault types;
S4, marking data in a magnetic field intensity matrix set of the fault-free charging pile and the magnetic field intensity matrix set of the charging pile with a known fault type, and constructing a training sample set;
s5, constructing a classifier model based on a classification algorithm in a machine learning method, and training the classifier model by using a training sample set to obtain a mature classifier model;
S6, for the charging pile to be detected, enabling the charging pile to be in a working state, and arranging a plurality of magnetic field intensity sensors around the charging pile at a position distant from the charging pile L, and collecting magnetic field intensity around the charging pile to obtain a magnetic field intensity matrix of the charging pile to be detected;
S7, sending the magnetic field intensity matrix of the charging pile to be detected into a mature classifier model, outputting a detection result by the classifier model, and controlling the charging pile according to the detection result.
2. The charging pile charging control method based on information monitoring according to claim 1, wherein: in the step S1, a total of K charging piles of known fault types are set, and the fault type number is 1~K;
a control scheme is preset for each fault type number; the control scheme includes, but is not limited to, one of an immediate power-off, a delayed power-off, an immediate restart, and a delayed restart.
3. The charging pile charging control method based on information monitoring according to claim 1, wherein: the fault-free charging pile, the charging piles of various known fault types and the charging pile to be detected belong to charging piles delivered from the same manufacturer in the same batch; the L is 1 meter.
4. The charging pile charging control method based on information monitoring according to claim 2, characterized in that: the step S2 includes:
S201, setting the number of magnetic field intensity sensors to be M for any fault-free charging pile in a working state, and collecting magnetic field intensity data at N moments, so as to obtain a magnetic field intensity matrix The method comprises the following steps:
;
wherein, Representing the magnetic field intensity acquired by the mth sensor at the nth moment,/>；
S202, repeatedly executing the step S201 for each non-fault charging pile to obtain magnetic field intensity matrixes of a plurality of non-fault charging piles, and adding the magnetic field intensity matrixes into the same set to form a magnetic field intensity matrix set of the non-fault charging piles.
5. The charging pile charging control method based on information monitoring according to claim 4, wherein: the step S3 includes:
S301, setting the number of magnetic field intensity sensors to be M for any charging pile with known fault type in a working state, and collecting magnetic field intensity data at N moments, so as to obtain a magnetic field intensity matrix The method comprises the following steps:
;
wherein, Representing the intensity of the magnetic field acquired by the Mth sensor at the nth moment,/>；
S302, for each charging pile with a known fault type, repeating the step S301 to obtain magnetic field intensity matrixes of a plurality of charging piles with known fault types, and adding the magnetic field intensity matrixes into the same set to form a magnetic field intensity matrix set of the charging piles with known fault types.
6. The charging pile charging control method based on information monitoring according to claim 5, wherein: the step S4 includes:
For each magnetic field intensity matrix obtained in step S202, adding a tag of 0 thereto;
For each magnetic field intensity matrix obtained in step S302, the corresponding fault type number of the charging pile is the label: namely, marking each sample characteristic by using a fault type number 1~K;
and adding all the magnetic field intensity matrixes into one set after marking is finished to form a training sample set.
7. The charging pile charging control method based on information monitoring according to claim 1, wherein: in step S5, the magnetic field intensity matrix in the training sample set is used as the input of the classifier model, the label corresponding to the magnetic field intensity matrix is used as the expected output for training, and when all the magnetic field intensity matrices in the training sample set are trained, the mature classifier model is obtained.
8. The charging pile charging control method based on information monitoring according to claim 1, wherein: in the step S6, the number of the arranged magnetic field intensity sensors is M, and the magnetic field intensity data of N moments are collected, so that a magnetic field intensity matrix of the charging pile to be detected is obtainedThe method comprises the following steps:
;
wherein, Representing the intensity of the magnetic field acquired by the Mth sensor at the nth moment,/>。
9. The charging pile charging control method based on information monitoring according to claim 1, wherein: in step S7, if the detection result output by the mature classifier model is 0, the charging pile is considered to continue to work if no fault occurs, if the detection result output by the mature classifier model is 1~K, any one of the marks is considered to occur, the output mark is the fault type number, a preset control scheme is searched according to the fault type number, and the charging pile is controlled according to the corresponding control scheme.
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| CN202410471734.1A CN118061839B (en) | 2024-04-19 | 2024-04-19 | Charging pile charging control method based on information monitoring |
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9985465B1 (en) * | 2017-05-16 | 2018-05-29 | Ahmad L. D. Glover | Systems, devices, and/or methods for managing electrical energy |
| CN112234707A (en) * | 2020-09-07 | 2021-01-15 | 北京师范大学 | High-energy synchrotron radiation light source magnet power failure recognition system |
| CN115310843A (en) * | 2022-08-17 | 2022-11-08 | 国网浙江省电力有限公司电力科学研究院 | Environment monitoring and early warning method, device and equipment for electric power operation safety |
| CN116552306A (en) * | 2023-07-12 | 2023-08-08 | 江西驴充充充电技术有限公司 | Monitoring system and method for direct current pile |
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Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9985465B1 (en) * | 2017-05-16 | 2018-05-29 | Ahmad L. D. Glover | Systems, devices, and/or methods for managing electrical energy |
| CN112234707A (en) * | 2020-09-07 | 2021-01-15 | 北京师范大学 | High-energy synchrotron radiation light source magnet power failure recognition system |
| CN115310843A (en) * | 2022-08-17 | 2022-11-08 | 国网浙江省电力有限公司电力科学研究院 | Environment monitoring and early warning method, device and equipment for electric power operation safety |
| CN116552306A (en) * | 2023-07-12 | 2023-08-08 | 江西驴充充充电技术有限公司 | Monitoring system and method for direct current pile |
Non-Patent Citations (1)
| Title |
|---|
| 叶友;: "汽车充电系统的故障诊断", 汽车维护与修理, no. 02, 15 January 2020 (2020-01-15) * |
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