CN112269104B - Power line fault judging system and method based on big data analysis - Google Patents
Power line fault judging system and method based on big data analysis Download PDFInfo
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- CN112269104B CN112269104B CN202011175154.6A CN202011175154A CN112269104B CN 112269104 B CN112269104 B CN 112269104B CN 202011175154 A CN202011175154 A CN 202011175154A CN 112269104 B CN112269104 B CN 112269104B
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- 238000000034 method Methods 0.000 title claims abstract description 20
- 238000007405 data analysis Methods 0.000 title claims abstract description 14
- 230000002776 aggregation Effects 0.000 claims abstract description 27
- 238000004220 aggregation Methods 0.000 claims abstract description 27
- 238000012544 monitoring process Methods 0.000 claims abstract description 9
- 238000004458 analytical method Methods 0.000 claims description 6
- 238000005259 measurement Methods 0.000 claims description 6
- 230000002159 abnormal effect Effects 0.000 claims description 5
- 230000005540 biological transmission Effects 0.000 claims description 3
- 230000007423 decrease Effects 0.000 claims description 3
- 238000010586 diagram Methods 0.000 description 5
- 238000004891 communication Methods 0.000 description 2
- 238000012423 maintenance Methods 0.000 description 2
- 238000012806 monitoring device Methods 0.000 description 2
- 230000001052 transient effect Effects 0.000 description 2
- 238000013459 approach Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
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Classifications
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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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- General Physics & Mathematics (AREA)
- Locating Faults (AREA)
- Remote Monitoring And Control Of Power-Distribution Networks (AREA)
Abstract
The invention discloses a power line fault judging system and a method based on big data analysis, wherein the fault judging system comprises a circuit monitor, a collection transponder, a cloud program and a user terminal program; the fault judging method comprises the following steps: the method comprises the following steps that S1, a circuit monitor monitors data of each phase line of a branch position, a line end and a node of a power line in real time; s2: the circuit monitor transmits the monitoring data to the collecting and forwarding device, and the collecting and forwarding device analyzes the data; s3: and the aggregation transponder analyzes the data and then uploads the data to the cloud. The fault judging system provided by the invention can more accurately judge whether the end circuit has faults and fault types by comparing the data of the circuit monitors installed before and after one section of circuit with the data of the adjacent other phase circuit monitors through the cloud program, and can reduce false judgment caused by instantaneous abrupt change of load and no-load switching-on surge current of the transformer and missed judgment after installing a small-current grounding protection device or a demarcation switch.
Description
Technical Field
The invention relates to power network fault monitoring, in particular to a power line fault judging system and method based on big data analysis.
Background
The overhead power line is frequently subjected to faults such as short circuit, grounding and the like, so that power failure accidents are caused to cause economic losses; the fault type and the fault position are accurately and rapidly found, and the method is a precondition for maintaining the line. The traditional method comprises monitoring devices such as a filtering type fault indicator, a transient characteristic type fault indicator, an externally applied signal type fault indicator and the like, and the monitoring devices are judged according to the monitoring data of a single fault indicator, so that the normal non-fault states such as manual heavy load switching, load transient abrupt change, no-load switching-on inrush current of a transformer, non-fault phase reclosing inrush current and the like and the faults with atypical characteristics of small current ground faults and lines of a high-sensitivity demarcation switch provided with a small current ground protection device or the like cannot be effectively distinguished; more faults are misreported and missed, 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 judging system and method based on big data analysis, which utilize a cloud program to compare the data of a circuit monitor installed before and after a section of line with the data of adjacent other phase circuit monitors to accurately judge whether the end line has faults and fault types, so as to reduce the misjudgment caused by instantaneous abrupt change of load and no-load closing inrush current of a transformer and the missed judgment after installing a small-current grounding protection device or a demarcation switch; the circuit monitor and the aggregation transponder are used for preliminary fault pre-judgment, so that the data reporting of a non-fault state is reduced, the communication flow and the occupation of the memory of the server are reduced, and the use cost of the system is reduced.
The aim of the invention can be achieved 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 the transponder, high in the clouds procedure and user terminal procedure, circuit monitor installs on the branch department of power line, each phase line of circuit end and node, monitors and analyzes circuit data, and circuit monitor is with unusual circuit data uploading to collect the transponder, collects the transponder and uploads to the high in the clouds after data analysis, and the high in the clouds procedure judges the trouble to the user terminal procedure is given to fault information push.
Further, the fault judging system comprises a plurality of aggregation repeaters, and data transmission is carried out between the aggregation repeaters and a plurality of adjacent circuit monitors.
A power line fault determination method based on big data analysis, the fault determination method comprising the steps of:
the method comprises the following steps that S1, a circuit monitor monitors data of each phase line of a branch position, a line end and a node of a power line in real time;
s2: when the monitoring data change is abnormal, the circuit monitor transmits the monitoring data to the collecting and forwarding device, and the collecting and forwarding device analyzes the data;
s3: the data of the aggregation transponder are analyzed and then uploaded to the cloud, and the cloud program judges faults by combining the front and rear positions of the power lines, the simultaneous period data and the historical data of the circuit monitors with different phases, and the fault information is pushed to the user side program.
Further, the cloud program adjusts current measurement deviation of the front and rear circuit monitors according to data in a normal state of the circuit and fault data fed back by a user.
Furthermore, the collecting and forwarding device corrects the measurement precision of each phase of circuit monitor according to the data of adjacent three-phase circuit monitors in the normal state of the circuit.
Further, the cloud program receives the data of the aggregation transponder, calculates that when the current data of the circuit monitor is larger than the current data of the rear-end circuit monitor or the sum of the current data of the plurality of circuit monitors of the rear-end branch line exceeds an early warning value, judges that a fault occurs in a line between the circuit monitor and the rear-end circuit monitor, if similar conditions occur in the current data of adjacent other phase circuit monitors, judges that the fault type is a short circuit fault, and pushes information to the user program.
Further, the cloud program receives the data of the aggregation transponder, calculates that when the current data of the circuit monitor is larger than the current data of the rear-end circuit monitor or the sum of the current data of the plurality of circuit monitors of the rear-end branch line exceeds an early warning value, judges that a line between the circuit monitor and the rear-end circuit monitor has a fault, if the current data of adjacent other phase circuit monitors have no similar condition, judges that the fault type is a ground fault, and pushes information to the user program.
Further, the collecting and forwarding device calculates that current data of a certain two-phase circuit monitor is suddenly increased and is close in amplitude, and current data of another two-phase circuit monitor is not increased, so that a highly suspicious short circuit fault occurs in a rear-end circuit of the collecting and forwarding device, and the highly suspicious short circuit fault is reported to a cloud program for further analysis.
Further, the collecting and forwarding device calculates that current data of a circuit monitor adjacent to a certain phase suddenly increases, and when voltage suddenly decreases, current data of another two-phase monitor does not increase, and then the highly suspicious ground fault of a circuit at the rear end of the collecting and forwarding device is judged, and the highly suspicious ground fault is reported to a cloud program for further analysis.
The invention has the beneficial effects that:
1. the fault judging system provided by the invention compares the data of the circuit monitors installed before and after a section of line with the data of the adjacent other phase circuit monitors through the cloud program, can more accurately judge whether the end line has faults and fault types, and can reduce misjudgment caused by instantaneous abrupt change of load and no-load switching-on surge current of the transformer and missed judgment after installing a small-current grounding protection device or a demarcation switch;
2. the fault judging system of the invention uses the circuit monitor and the aggregation transponder to perform primary fault pre-judgment, reduces the data reporting 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 is further described below 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 diagram of a branch circuit installation of the present invention;
FIG. 3 is a schematic view 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 of cloud program failure determination according to the present invention;
fig. 6 is a logic diagram of the present invention, aggregation repeater failure prediction.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The fault judging system comprises a circuit monitor 1, an aggregation transponder 2, a cloud program 3 and a user side program 4, wherein the fault judging system comprises a plurality of aggregation transponders 2, data transmission is carried out between the aggregation transponder 2 and the adjacent circuit monitors 1, as shown in fig. 1, 2, 5 and 6, the circuit monitor 1 is arranged at a branch position of a power line, on each phase line of the tail end of the line and a node, data such as voltage and current waveforms are monitored and analyzed in real time, when data fluctuation is abnormal, the circuit monitor 1 is uploaded to the aggregation transponder 2 for further data analysis and then is uploaded to the cloud side, and the cloud program 3 judges faults and pushes fault information to the user side program 4.
A power line fault judging method based on big data analysis comprises the following steps:
the method comprises the following steps that S1, a circuit monitor 1 monitors data of each phase line of a branch position, a line end and a node of a power line in real time;
s2: when the monitoring data change is abnormal, the circuit monitor 1 transmits the monitoring data to the aggregation transponder 2, and the aggregation transponder 2 analyzes the data;
s3: the data are analyzed by the aggregation repeater 2 and then uploaded to the cloud, and the cloud program 3 judges faults by combining the front and rear positions of the power lines, the simultaneous period data and the historical data of the different-phase circuit monitors 1 and pushes fault information to the user terminal program 4; the cloud program 3 adjusts current measurement deviation of the front and rear circuit monitors 1 according to the data of the circuit in the normal state and fault data fed back by a user; the aggregation repeater 2 corrects the measurement accuracy of each phase of the circuit monitor 1 based on 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 aggregation transponder 2, calculates that when the current data of the circuit monitor 1 is greater than the current data of the back-end circuit monitor or the sum of the current data of a plurality of circuit monitors of the back-end branch line exceeds the early warning value, it will determine that a fault occurs in the line between the circuit monitor 1 and the back-end circuit monitor, and if the current data of adjacent other phase circuit monitors have similar conditions, it will determine that the fault type is a short circuit fault, and push the information to the user side 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 aggregation transponder 2, calculates that when the current data of the circuit monitor 1 is greater than the current data of the back-end circuit monitor or the sum of the current data of a plurality of circuit monitors of the back-end branch line exceeds the early warning value, it will determine that the line between the circuit monitor 1 and the back-end circuit monitor has a fault, and if the current data of adjacent other phase circuit monitors have no similar condition, it determines that the fault type is a ground fault, and pushes information to the user program 4.
Example 3
As shown in fig. 4 and fig. 6, the sink repeater 2 calculates that the current data of the adjacent two-phase circuit monitor 1 suddenly increases and approaches to each other in amplitude, and if the current data of the other two-phase circuit monitor 1 does not increase, it determines that a highly suspicious short-circuit fault occurs in the line at the rear end of the sink repeater, and reports the highly suspicious short-circuit fault to the cloud program 3 for further analysis.
Example 4
As shown in fig. 3 and fig. 6, the sink repeater 2 calculates that the current data of the circuit monitor 1 adjacent to a certain phase suddenly increases, and when the voltage suddenly decreases, the current data of the other two phase monitor 1 does not increase, and then determines that a highly suspicious ground fault occurs on the line at the rear end of the sink repeater, and reports the highly suspicious ground fault to the cloud program 3 for further analysis.
In the description of the present specification, the descriptions of the terms "one embodiment," "example," "specific example," and the like, 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 present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. 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 has shown and described the basic principles, principal 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, and that the above embodiments and descriptions are merely illustrative of the principles of the present invention, and various changes and modifications may be made without departing from the spirit and scope of the invention, which is defined in the appended claims.
Claims (4)
1. The power line fault judging system based on big data analysis comprises a circuit monitor (1), a collecting transponder (2), a cloud program (3) and a user side program (4), and is characterized in that the circuit monitor (1) is arranged at a branch position of a power line, at the tail end of the line and on each phase line of a node, monitors and analyzes circuit data, the circuit monitor (1) uploads abnormal circuit data to the collecting transponder (2), the collecting transponder (2) uploads the data after analyzing the data to the cloud, and the cloud program (3) judges faults and pushes fault information to the user side program (4);
the fault judging method of the power line fault judging system comprises the following steps:
the method comprises the following steps that S1, a circuit monitor (1) monitors data of each phase line of a branch position, a line end and a node of a power line in real time;
s2: when the monitoring data change is abnormal, the circuit monitor (1) transmits the monitoring data to the aggregation transponder (2), and the aggregation transponder (2) analyzes the data;
s3: the data are analyzed by the aggregation transponder (2) and then uploaded to the cloud, and the cloud program (3) judges faults by combining the front and back positions of the power lines and the simultaneous segment data and the historical data of the circuit monitors (1) with different phases and pushes fault information to the user program (4);
the cloud program (3) receives the data of the aggregation transponder (2), calculates that when the current data of the circuit monitor (1) is larger than the current data of the rear-end circuit monitor or the sum of the current data of a plurality of circuit monitors of the rear-end branch line exceeds an early warning value, judges that a line between the circuit monitor (1) and the rear-end circuit monitor has faults, if the current data of adjacent other-phase circuit monitors have similar conditions, judges that the fault type is short-circuit fault, and pushes information to the user program (4);
the cloud program (3) receives the data of the aggregation transponder (2), calculates that when the current data of the circuit monitor (1) is larger than the current data of the rear-end circuit monitor or the sum of the current data of a plurality of circuit monitors of the rear-end branch line exceeds an early warning value, judges that a line between the circuit monitor (1) and the rear-end circuit monitor has faults, if the current data of adjacent other-phase circuit monitors have no similar condition, judges that the fault type is a ground fault, and pushes information to the user program (4);
the collecting and forwarding device (2) calculates that current data of a certain two-phase circuit monitor (1) is suddenly increased and is close in amplitude, and if the current data of another two-phase circuit monitor (1) is not increased, the situation that a highly suspicious short circuit fault occurs in a rear-end circuit is judged, and the highly suspicious short circuit fault is reported to the cloud program (3) for further analysis;
the collecting and forwarding device (2) calculates that current data of a circuit monitor (1) adjacent to a certain phase suddenly increases, and when the voltage suddenly decreases, current data of another two-phase monitor (1) does not increase, then the highly suspicious ground fault of a rear-end circuit is judged, and the highly suspicious ground fault is reported to the cloud program (3) for further analysis.
2. A power line fault determination system based on big data analysis according to claim 1, characterized in that the fault determination system comprises a plurality of aggregation transponders (2), the aggregation transponders (2) being in data transmission with a plurality of adjacent circuit monitors (1).
3. The system for judging the power line fault based on big data analysis according to claim 1, wherein the cloud program (3) adjusts the current measurement deviation of the front and rear circuit monitors (1) according to the data of the normal state of the line and the fault data fed back by the user.
4. The system for judging power line faults based on big data analysis according to claim 1 is characterized in that the aggregation transponder (2) corrects the measurement precision of each phase of the circuit monitor (1) according to the data of adjacent three phases of the circuit monitor (1) in the normal state of the line.
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| CN202011175154.6A CN112269104B (en) | 2020-10-28 | 2020-10-28 | Power line fault judging system and method based on big data analysis |
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| CN202011175154.6A CN112269104B (en) | 2020-10-28 | 2020-10-28 | Power line fault judging system and method based on big data analysis |
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| CN112269104B true CN112269104B (en) | 2024-02-13 |
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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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| CN106556754A (en) * | 2016-10-12 | 2017-04-05 | 国网上海市电力公司 | A kind of online acquisition method of distribution line failure waveform |
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| CN109765459A (en) * | 2019-01-22 | 2019-05-17 | 清大智能（北京）科技有限公司 | It is a kind of based on the method for locating single-phase ground fault studied and judged on the spot and system |
| CN111157838A (en) * | 2018-11-08 | 2020-05-15 | 中国铁路沈阳局集团有限公司科学技术研究所 | Intelligent management system for big data of running state of railway power distribution network |
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- 2020-10-28 CN CN202011175154.6A patent/CN112269104B/en active Active
Patent Citations (6)
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| CN106501671A (en) * | 2016-10-12 | 2017-03-15 | 国网上海市电力公司 | A kind of monitoring method of electric distribution network overhead wire failure |
| CN106501656A (en) * | 2016-10-12 | 2017-03-15 | 国网上海市电力公司 | A kind of on-line acquisition system of distribution line failure waveform |
| CN106556754A (en) * | 2016-10-12 | 2017-04-05 | 国网上海市电力公司 | A kind of online acquisition method of distribution line failure waveform |
| CN108132425A (en) * | 2017-12-18 | 2018-06-08 | 云南电网有限责任公司电力科学研究院 | Power grid distribution line failure on-line monitoring method and system |
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