CN112269104A - Power line fault judgment system and method based on big data analysis - Google Patents
Power line fault judgment system and method based on big data analysis Download PDFInfo
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- CN112269104A CN112269104A CN202011175154.6A CN202011175154A CN112269104A CN 112269104 A CN112269104 A CN 112269104A CN 202011175154 A CN202011175154 A CN 202011175154A CN 112269104 A CN112269104 A CN 112269104A
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- 238000007405 data analysis Methods 0.000 title claims abstract description 14
- 238000004458 analytical method Methods 0.000 claims description 8
- 238000005259 measurement Methods 0.000 claims description 6
- 238000013459 approach Methods 0.000 claims description 2
- 238000004220 aggregation Methods 0.000 claims 5
- 230000002776 aggregation Effects 0.000 claims 5
- 238000003745 diagnosis Methods 0.000 claims 4
- 230000007423 decrease Effects 0.000 claims 1
- 238000012544 monitoring process Methods 0.000 abstract description 7
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- 238000004891 communication Methods 0.000 description 2
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- 230000002159 abnormal effect Effects 0.000 description 1
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/08—Locating faults in cables, transmission lines, or networks
- G01R31/081—Locating faults in cables, transmission lines, or networks according to type of conductors
- G01R31/083—Locating faults in cables, transmission lines, or networks according to type of conductors in cables, e.g. underground
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
- Y04S10/52—Outage or fault management, e.g. fault detection or location
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Abstract
The invention discloses a power line fault judgment system and method based on big data analysis, wherein the fault judgment system comprises a circuit monitor, a collecting transponder, a cloud program and a user side program; the fault judgment method comprises the following steps: s1, the circuit monitor monitors the data of each phase line of the branch, the tail end and the node of the power line in real time; s2: the circuit monitor transmits the monitoring data to the collecting transponder, and the collecting transponder analyzes the data; s3: and the collection forwarder analyzes the data and uploads the data to the cloud. The fault judging system can more accurately judge whether the circuit at one end has faults and fault types by comparing the data of the circuit monitors arranged before and after one section of the circuit with the data of the adjacent other phase circuit monitors through the cloud program, and can reduce misjudgment caused by instantaneous sudden changes of loads, no-load closing inrush current of the transformer and missed judgment after a small-current grounding protection device or a demarcation switch is arranged.
Description
Technical Field
The invention relates to power network fault monitoring, in particular to a power line fault judgment system and method based on big data analysis.
Background
Faults such as short circuit, grounding and the like often occur in an overhead power line, and power failure accidents are caused to cause economic loss; and the fault type and the fault position can be accurately and quickly found, which is the premise for maintaining the line. The existing method comprises monitoring devices such as a filter type fault indicator, a transient characteristic type fault indicator and an external signal type fault indicator, and the like, and the monitoring devices are judged according to monitoring data of a single fault indicator, so that the fault with atypical characteristics of a line provided with a low-current grounding protection device or a high-sensitivity demarcation switch and in a non-fault state such as normal manual large-load switching, instantaneous sudden change of load, no-load switching-on inrush current and non-fault reclosing inrush current of a transformer and the like and a low-current grounding fault can not be effectively distinguished; more fault false reports and false reports are caused, the work of maintenance personnel is increased, and the maintenance efficiency is reduced.
Disclosure of Invention
The invention aims to provide a power line fault judgment system and method based on big data analysis, which compare data of circuit monitors arranged before and after a section of line with data of circuit monitors of other adjacent phases by using a cloud program, accurately judge whether the line at the end has faults and fault types, and reduce load instantaneous mutation, misjudgment caused by transformer no-load closing inrush current and missed judgment after a small-current grounding protection device or a demarcation switch is arranged; the circuit monitor and the collecting transponder are used for preliminary fault pre-judgment, so that data reporting in a non-fault state is reduced, communication flow and occupation of a server memory are reduced, and the use cost of the system is reduced.
The purpose of the invention can be realized by the following technical scheme:
the utility model provides a power line fault judgment system based on big data analysis, fault judgment system includes circuit monitor, collects repeater, high in the clouds procedure and user end program, the circuit monitor is installed on each looks line of branch department, circuit end and node of power line, monitors and the analysis circuit data, and the circuit monitor is uploaded unusual circuit data to collecting the repeater, collects the repeater and uploads the high in the clouds after to data analysis, and the high in the clouds procedure is judged to the trouble to fault information propelling movement to user end program.
Furthermore, the fault judging system comprises a plurality of collecting repeaters, and data transmission is carried out between the collecting repeaters and a plurality of adjacent circuit monitors.
A power line fault judgment method based on big data analysis comprises the following steps:
s1, the circuit monitor monitors the data of each phase line of the branch, the tail end and the node of the power line in real time;
s2: when the monitoring data changes abnormally, the circuit monitor transmits the monitoring data to the collecting transponder, and the collecting transponder analyzes the data;
s3: the collecting transponder analyzes the data and uploads the data to the cloud, the cloud program judges the fault by combining front and back positions on the power line, simultaneous segment data and historical data of different phase circuit monitors, and fault information is pushed to the user program.
Further, the cloud program adjusts current measurement deviation of the front and rear circuit monitors according to data in a normal circuit state and fault data fed back by a user.
Furthermore, the collecting repeater corrects the measurement precision of each phase of circuit monitor according to the data of the adjacent three-phase circuit monitor in the normal state of the line.
Further, the cloud program receives the data of the collecting transponder, calculates that when the current data of the circuit monitor is larger than the current data of the rear circuit monitor or the sum of the current data of a plurality of circuit monitors of a rear branch circuit exceeds an early warning value, judges that a fault occurs in a circuit between the circuit monitor and the rear circuit monitor, and judges that the fault type is a short-circuit fault if the current data of adjacent other phase circuit monitors are similar, and pushes information to the user terminal program.
Further, the cloud program receives the data of the collecting transponder, calculates that the current data of the circuit monitor is larger than the current data of the rear circuit monitor or the sum of the current data of a plurality of circuit monitors of a rear branch circuit exceeds an early warning value, judges that a fault occurs in a circuit between the circuit monitor and the rear circuit monitor, judges that the fault type is a ground fault if the current data of adjacent other phase circuit monitors does not have similar conditions, and pushes information to the user terminal program.
Further, the collecting transponder calculates that the current data of the two-phase circuit monitor is suddenly increased and the amplitude is close to that of the current data of the other two-phase circuit monitor, and if the current data of the other two-phase circuit monitor is not increased, it is judged that a highly suspicious short circuit fault occurs in the rear-end circuit of the collecting transponder, and the highly suspicious short circuit fault is reported to the cloud program for further analysis.
Further, the collecting repeater calculates that the current data of the monitor close to a certain phase circuit is suddenly increased, and when the voltage is suddenly reduced and the current data of the other two phase monitors is not increased, the current data of the circuit at the rear end of the collecting repeater is judged to have a highly suspicious ground fault, and the current data is reported to a cloud program for further analysis.
The invention has the beneficial effects that:
1. the fault judging system compares the data of the circuit monitors arranged in front of and behind one section of line with the data of the circuit monitors of other adjacent phases through the cloud program, can more accurately judge whether the line at the end has faults and fault types, and can reduce the misjudgment caused by instantaneous sudden changes of load and no-load closing inrush current of a transformer and the misjudgment after a small-current grounding protection device or a demarcation switch is arranged;
2. the fault judgment system of the invention carries out preliminary fault pre-judgment by the circuit monitor and the collecting transponder, reduces the data report of the non-fault state, reduces the communication flow and the occupation of the memory of the server, and reduces the use cost of the system.
Drawings
The invention will be further described with reference to the accompanying drawings.
FIG. 1 is a schematic diagram of a fault determination system according to the present invention;
FIG. 2 is a schematic illustration of a branch line installation of the present invention;
FIG. 3 is a schematic of the ground fault of the present invention;
FIG. 4 is a schematic diagram of a short circuit fault of the present invention;
FIG. 5 is a logic diagram illustrating a cloud-based program fault determination according to the present invention;
FIG. 6 is a diagram of the aggregate forwarder failure prejudgment logic of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the 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 invention.
A power line fault judgment system based on big data analysis comprises circuit monitors 1, collecting repeaters 2, a cloud program 3 and a user side program 4, the fault judgment system comprises a plurality of collecting repeaters 2, data transmission is carried out between the collecting repeaters 2 and a plurality of adjacent circuit monitors 1, as shown in figures 1, 2, 5 and 6, the circuit monitors 1 are installed on each phase line of branches, tail ends and nodes of a power line, data such as voltage and current waveforms are monitored and analyzed in real time, when data change is found to be abnormal, the circuit monitors 1 are uploaded to the collecting repeaters 2, further data analysis is carried out on the data, then the data are uploaded to the cloud, the cloud program 3 judges faults, and fault information is pushed to the user side program 4.
A power line fault judgment method based on big data analysis comprises the following steps:
s1, the circuit monitor 1 monitors the data of each phase line of the branch, the tail end and the node of the power line in real time;
s2: when the monitoring data changes abnormally, the circuit monitor 1 transmits the monitoring data to the collecting transponder 2, and the collecting transponder 2 analyzes the data;
s3: the collecting transponder 2 analyzes the data and uploads the data to a cloud, the cloud program 3 judges a fault by combining front and rear positions on a power line, and simultaneous segment data and historical data of different phase circuit monitors 1, and transmits fault information to the user side program 4; the cloud program 3 adjusts current measurement deviation of the front and rear circuit monitors 1 according to data in a normal circuit state and fault data fed back by a user; the collecting repeater 2 corrects the measurement accuracy of each phase of the circuit monitor 1 according to the data of the adjacent three-phase circuit monitor 1 in the normal state of the line.
Example 1
As shown in fig. 1, fig. 2, fig. 4, and fig. 5, the cloud program 3 receives the data of the sink repeater 2, calculates that the current data of the circuit monitor 1 is greater than the current data of its back-end circuit monitor or the sum of the current data of multiple circuit monitors of the back-end branch line exceeds an early warning value, determines that a fault occurs in the line from the circuit monitor 1 to the back-end circuit monitor, and determines that the fault type is a short-circuit fault if the current data of adjacent other phase circuit monitors are similar, and pushes information to the client program 4.
Example 2
As shown in fig. 1, fig. 2, fig. 3, and fig. 5, the cloud program 3 receives the data of the sink repeater 2, calculates that the current data of the circuit monitor 1 is greater than the current data of its back-end circuit monitor or the sum of the current data of multiple circuit monitors of the back-end branch line exceeds an early warning value, determines that a fault occurs in the line from the circuit monitor 1 to the back-end circuit monitor, and determines that the fault type is a ground fault if the current data of adjacent other phase circuit monitors does not have similar conditions, and pushes information to the client program 4.
Example 3
As shown in fig. 4 and 6, the collecting repeater 2 calculates that the current data near a certain two-phase circuit monitor 1 suddenly increases and approaches the amplitude, and the current data of another two-phase circuit monitor 1 does not increase, and then determines that a highly suspicious short-circuit fault occurs in the rear-end line thereof, and reports the short-circuit fault to the cloud program 3 for further analysis.
Example 4
As shown in fig. 3 and fig. 6, the collecting repeater 2 calculates that the current data of the monitor 1 near a certain phase circuit is suddenly increased, and when the voltage is suddenly decreased, the current data of the other two phase monitors 1 is not increased, and then determines that a highly suspicious ground fault occurs in the rear-end line thereof, and reports the ground fault to the cloud program 3 for further analysis.
In the description herein, references to the description of "one embodiment," "an example," "a specific example" or the like are intended to mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The foregoing shows and describes the general principles, essential features, and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed.
Claims (9)
1. The utility model provides a power line fault judgment system based on big data analysis, fault judgment system includes circuit monitor (1), collects repeater (2), high in the clouds procedure (3) and user end procedure (4), its characterized in that, circuit monitor (1) is installed on each looks line of branch department, circuit end and node of power line, monitors and analysis circuit data, and circuit monitor (1) uploads unusual circuit data to collecting repeater (2), collects repeater (2) and uploads the high in the clouds after data analysis, and high in the clouds procedure (3) are judged the trouble to give user end procedure (4) fault information propelling movement.
2. A big data analysis based power line fault diagnosis system according to claim 1, characterized in that the fault diagnosis system comprises a plurality of aggregation repeaters (2), and the aggregation repeaters (2) transmit data to and from the adjacent circuit monitors (1).
3. The fault determination method of the power line fault determination system according to any one of claims 1 to 2, characterized by comprising:
s1, the circuit monitor (1) monitors the data of each phase line of the branch, the tail end and the node of the power line in real time;
s2: when the monitored data changes abnormally, the circuit monitor (1) transmits the monitored data to the collecting transponder (2), and the collecting transponder (2) analyzes the data;
s3: the collecting forwarder (2) analyzes the data and uploads the data to the cloud, the cloud program (3) judges the fault by combining the front and back positions on the power line, the simultaneous segment data and the historical data of the circuit monitors (1) with different phases, and the fault information is pushed to the user side program (4).
4. The fault diagnosis method according to claim 3, wherein the cloud program (3) adjusts the current measurement deviation of the circuit monitor (1) before and after the fault diagnosis according to the data in the normal state of the line and the fault data fed back by the user.
5. The failure determination method according to claim 3, wherein the collective repeater (2) corrects the measurement accuracy of each phase circuit monitor (1) based on data of adjacent three phase circuit monitors (1) in a line normal state.
6. The fault determination method according to claim 3, wherein the cloud program (3) receives data of the aggregation repeater (2), calculates that when the current data of the circuit monitor (1) is larger than the current data of the rear-end circuit monitor thereof or the sum of the current data of a plurality of circuit monitors of a rear-end branch circuit exceeds an early warning value, determines that the circuit between the circuit monitor (1) and the rear-end circuit monitor has a fault, determines that the fault type is a short-circuit fault if the current data of adjacent other phase circuit monitors have similar conditions, and pushes information to the client program (4).
7. The fault determination method according to claim 3, wherein the cloud program (3) receives data of the aggregation repeater (2), calculates that when the current data of the circuit monitor (1) is larger than the current data of the rear-end circuit monitor thereof or the sum of the current data of a plurality of circuit monitors of a rear-end branch line exceeds an early warning value, determines that the line from the circuit monitor (1) to the rear-end circuit monitor has a fault, determines that the fault type is a ground fault if the current data of adjacent other phase circuit monitors does not have similar conditions, and pushes information to the user side program (4).
8. The fault determination method according to claim 3, wherein the collecting repeater (2) calculates that the current data near a certain two-phase circuit monitor (1) suddenly increases and approaches the amplitude, and the current data of another one-phase circuit monitor (1) does not increase, and determines that a highly suspicious short-circuit fault occurs in the rear-end line thereof, and reports the short-circuit fault to the cloud program (3) for further analysis.
9. The fault determination method according to claim 3, wherein the aggregation repeater (2) calculates that the current data of the circuit monitor (1) adjacent to one phase suddenly increases, and when the voltage suddenly decreases, the current data of the other two phase monitors (1) does not increase, and determines that the rear-end line thereof has a highly suspicious ground fault, and reports the ground fault to the cloud program (3) for further analysis.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117571117A (en) * | 2023-11-21 | 2024-02-20 | 固力发电气有限公司 | Method for judging overhead line state through vibration frequency monitoring |
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