CN111047015A - Power transmission and transformation data mutation analysis method based on neural network - Google Patents
Power transmission and transformation data mutation analysis method based on neural network Download PDFInfo
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- CN111047015A CN111047015A CN201911267272.7A CN201911267272A CN111047015A CN 111047015 A CN111047015 A CN 111047015A CN 201911267272 A CN201911267272 A CN 201911267272A CN 111047015 A CN111047015 A CN 111047015A
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- G06N3/00—Computing arrangements based on biological models
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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Abstract
The invention belongs to the technical field of power transmission and transformation, and provides a power transmission and transformation data mutation analysis method based on a neural network, which comprises the following steps: collecting different types of parameter data of the power transmission and transformation equipment in real time; setting standard values according to different types of parameters of the power transmission and transformation equipment; calculating the difference value between the currently output parameter data and the last output single parameter data; comparing the difference with a set standard value, if the difference is larger than the standard value, judging that the parameters of the electric transmission and transformation equipment are mutated once and recording the mutation times, otherwise, returning to continue calculating the difference; judging whether the mutation times of the parameters of the electric transmission and transformation equipment are more than or equal to three times, if so, sending warning information, and if not, returning to continuously record the mutation times; and the operation and inspection personnel carry out equipment defect elimination according to the received early warning information. The method for analyzing the power transmission and transformation data mutation based on the neural network simplifies the data mutation analysis process and has the characteristic of quick response.
Description
Technical Field
The invention relates to the technical field of power transmission and transformation, in particular to a power transmission and transformation data mutation analysis method based on a neural network.
Background
With the development of society and the wide application of electronic equipment in daily life, the demand for power consumption is higher and higher, and therefore, the power supply pressure borne by the power grid is higher and higher. In the daily monitoring and maintenance process of the power grid, faults of power grid lines and equipment are comprehensively and efficiently solved, so that time is saved, and inconvenience brought to people due to power failure is reduced. The power transmission and transformation is a part of a power grid, and the abnormal parameters of the power transmission and transformation equipment not only affect the normal work of the power transmission and transformation equipment, but also reduce the power supply stability of the power grid.
In a power transmission and transformation network, power transmission and transformation equipment usually comprises a wire, a transformer, an isolating switch, a grounding switch, a high-voltage insulator and the like, and power transmission and transformation protection equipment usually comprises a mutual inductor, a relay protector, a lightning arrester current mutual inductor, a voltage mutual inductor, a reactor and the like, so that when power is supplied to a target electric appliance, parameters of the power transmission and transformation equipment and the power transmission and transformation protection equipment need to be monitored in real time, whether the power transmission and transformation equipment and the power transmission and transformation protection equipment normally operate is judged, and potential fault factors are eliminated.
Disclosure of Invention
Aiming at the defects in the prior art, the method for analyzing the power transmission and transformation data mutation based on the neural network simplifies the data mutation analysis process, improves the speed of data mutation analysis, and has the characteristic of quick response.
In order to solve the technical problems, the invention provides the following technical scheme:
the method for analyzing the mutation of the power transmission and transformation data based on the neural network comprises the following steps:
s01: collecting different types of parameter data of the power transmission and transformation equipment in real time;
s02: setting standard values according to different types of parameters of the power transmission and transformation equipment;
s03: the parameters of different types of the electric transmission and transformation equipment collected in the step S01 are used as the input of the neural network model, the output parameters of different types of the neural network model are used as the data mutation analysis object, and the difference value between the single-parameter data output by the current neural network model and the single-parameter data output last time is calculated;
s04: comparing the difference value in the step S03 with the standard value set in the step S02, if the difference value is larger than the standard value, judging that the parameters of the power transmission and transformation equipment have mutation once and recording the mutation times, otherwise, returning to the step S03;
s05: judging whether the number of times of sudden change of the parameters of the electric transmission and transformation equipment is more than or equal to three times, if so, sending out early warning information, and if not, returning to the step S04;
s06: and the operation and inspection personnel carry out equipment defect elimination according to the received early warning information.
Further, the parameters of the electric transmission and transformation equipment in S01 include electric currents of the electric transmission and transformation equipment and voltages of the electric transmission and transformation equipment, the electric currents of the electric transmission and transformation equipment are obtained by measuring current values of different electric transmission and transformation equipment through a current sensor, and the voltages of the electric transmission and transformation equipment are measured by a voltage transformer.
Further, the power transmission and transformation equipment comprises one or more of a wire, a transformer, an isolating switch, a mutual inductor, a relay protector and a lightning arrester.
Further, the neural network model in the step S03 is a BP neural network model.
Further, the step S06 includes the following steps:
s0601: the transport inspection personnel receive the warning information;
s0602: the early warning information is checked and compared with the operation condition of the field equipment, and the reason of the equipment with problems is determined;
s0603: performing equipment maintenance or replacement;
s0604: and (5) recovering the equipment data to be normal, and completing the equipment defect elimination.
According to the technical scheme, the invention has the beneficial effects that: the parameter data of the power transmission and transformation equipment are collected in real time, the difference value calculation is carried out after the parameter data are processed by the neural network model, and the difference value is directly compared with a set standard value to judge whether the parameter of the power transmission and transformation equipment has mutation or not, so that the data mutation analysis process is simplified, the data mutation analysis speed is increased, and the characteristic of quick response is realized.
Drawings
In order to more clearly illustrate the detailed description of the invention or the technical solutions in the prior art, the drawings that are needed in the detailed description of the invention or the prior art will be briefly described below. Throughout the drawings, like elements or portions are generally identified by like reference numerals. In the drawings, elements or portions are not necessarily drawn to scale.
FIG. 1 is a flow chart of the present invention;
fig. 2 is a flowchart of the equipment defect elimination process performed by the inspection staff in step S06 according to the present invention.
Detailed Description
Embodiments of the present invention will be described in detail below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and therefore are only examples, and the protection scope of the present invention is not limited thereby.
Referring to fig. 1, the method for analyzing mutation of electric transmission and transformation data based on a neural network according to the present embodiment includes the following steps:
s01: collecting different types of parameter data of the power transmission and transformation equipment in real time;
s02: setting standard values according to different types of parameters of the power transmission and transformation equipment;
s03: the parameters of different types of the electric transmission and transformation equipment collected in the step S01 are used as the input of the neural network model, the output parameters of different types of the neural network model are used as the data mutation analysis object, and the difference value between the single-parameter data output by the current neural network model and the single-parameter data output last time is calculated;
s04: comparing the difference value in the step S03 with the standard value set in the step S02, if the difference value is larger than the standard value, judging that the parameters of the power transmission and transformation equipment have mutation once and recording the mutation times, otherwise, returning to the step S03;
s05: judging whether the number of times of sudden change of the parameters of the electric transmission and transformation equipment is more than or equal to three times, if so, sending out early warning information, and if not, returning to the step S04;
s06: and the operation and inspection personnel carry out equipment defect elimination according to the received early warning information.
In actual use, parameter data of the power transmission and transformation equipment are collected in real time, the difference value between the current power transmission and transformation equipment parameter and the last power transmission and transformation equipment parameter is calculated after being processed by the neural network model, and the difference value is directly compared with a set standard value to judge whether the power transmission and transformation equipment parameter has mutation or not, so that the data mutation analysis process is simplified, the data mutation analysis speed is increased, and the characteristic of quick response is realized.
In this embodiment, the parameters of the power transmission and transformation equipment in S01 include currents of the power transmission and transformation equipment and voltages of the power transmission and transformation equipment, the currents of the power transmission and transformation equipment are obtained by measuring current values of different power transmission and transformation equipment through a current sensor, the voltages of the power transmission and transformation equipment are measured through a voltage transformer, and the current sensor and the voltage transformer can collect the currents and voltages of all the power transmission and transformation equipment in the power grid, so as to facilitate monitoring of the operation state of the whole power grid equipment.
In this embodiment, the power transmission and transformation equipment includes one or more of a wire, a transformer, an isolating switch, a mutual inductor, a relay protector and a lightning arrester.
In this embodiment, the neural network model in step S03 is a BP neural network model, and the voltage and current parameters of a large number of power transmission and transformation devices in the power grid are processed in a distributed manner, so that the parameters of the power transmission and transformation devices in the whole power grid can be processed conveniently.
Referring to fig. 2, the step S06 includes the following steps:
s0601: the transport inspection personnel receive the warning information;
s0602: the early warning information is checked and compared with the operation condition of the field equipment, and the reason of the equipment with problems is determined;
s0603: performing equipment maintenance or replacement;
s0604: and (5) recovering the equipment data to be normal, and completing the equipment defect elimination.
In actual use, the operation and inspection personnel judge the root cause of the problem of the power transmission and transformation equipment according to the early warning information and the operation condition of the field equipment, and the power transmission and transformation equipment is convenient to maintain.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit and scope of the present invention, and they should be construed as being included in the following claims and description.
Claims (5)
1. The method for analyzing the mutation of the power transmission and transformation data based on the neural network is characterized by comprising the following steps of:
s01: collecting different types of parameter data of the power transmission and transformation equipment in real time;
s02: setting standard values according to different types of parameters of the power transmission and transformation equipment;
s03: the parameters of different types of the electric transmission and transformation equipment collected in the step S01 are used as the input of the neural network model, the output parameters of different types of the neural network model are used as the data mutation analysis object, and the difference value between the single-parameter data output by the current neural network model and the single-parameter data output last time is calculated;
s04: comparing the difference value in the step S03 with the standard value set in the step S02, if the difference value is larger than the standard value, judging that the parameters of the power transmission and transformation equipment have mutation once and recording the mutation times, otherwise, returning to the step S03;
s05: judging whether the number of times of sudden change of the parameters of the electric transmission and transformation equipment is more than or equal to three times, if so, sending out early warning information, and if not, returning to the step S04;
s06: and the operation and inspection personnel carry out equipment defect elimination according to the received early warning information.
2. The neural network-based method for analyzing sudden changes in electric transmission and transformation data according to claim 1, wherein the parameters of the electric transmission and transformation equipment in S01 include electric currents of the electric transmission and transformation equipment and voltages of the electric transmission and transformation equipment, the electric currents of the electric transmission and transformation equipment are obtained by measuring current values of different electric transmission and transformation equipment through a current sensor, and the voltages of the electric transmission and transformation equipment are obtained by measuring voltage values of the electric transmission and transformation equipment through a voltage transformer.
3. The neural network-based power transmission and transformation data mutation analysis method according to claim 2, wherein the power transmission and transformation equipment comprises one or more of a wire, a transformer, a disconnecting switch, a mutual inductor, a relay protector and a lightning arrester.
4. The method for analyzing mutation in electric transmission and transformation data based on neural network as claimed in claim 1, wherein the neural network model in step S03 is a BP neural network model.
5. The neural network-based power transmission and transformation data mutation analysis method according to claim 1, wherein the step S06 includes the steps of:
s0601: the transport inspection personnel receive the warning information;
s0602: the early warning information is checked and compared with the operation condition of the field equipment, and the reason of the equipment with problems is determined;
s0603: performing equipment maintenance or replacement;
s0604: and (5) recovering the equipment data to be normal, and completing the equipment defect elimination.
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| CN201911267272.7A CN111047015A (en) | 2019-12-11 | 2019-12-11 | Power transmission and transformation data mutation analysis method based on neural network |
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| CN201911267272.7A CN111047015A (en) | 2019-12-11 | 2019-12-11 | Power transmission and transformation data mutation analysis method based on neural network |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112557946A (en) * | 2020-11-20 | 2021-03-26 | 台州学院 | Low-voltage SPD intelligent online detection device based on digital filtering and artificial neural network |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112557946A (en) * | 2020-11-20 | 2021-03-26 | 台州学院 | Low-voltage SPD intelligent online detection device based on digital filtering and artificial neural network |
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