CN110505288A - A kind of monitoring method and system of power transmission network - Google Patents
A kind of monitoring method and system of power transmission network Download PDFInfo
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- CN110505288A CN110505288A CN201910713560.4A CN201910713560A CN110505288A CN 110505288 A CN110505288 A CN 110505288A CN 201910713560 A CN201910713560 A CN 201910713560A CN 110505288 A CN110505288 A CN 110505288A
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/06—Management of faults, events, alarms or notifications
- H04L41/0631—Management of faults, events, alarms or notifications using root cause analysis; using analysis of correlation between notifications, alarms or events based on decision criteria, e.g. hierarchy, tree or time analysis
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/02—Protocols based on web technology, e.g. hypertext transfer protocol [HTTP]
- H04L67/025—Protocols based on web technology, e.g. hypertext transfer protocol [HTTP] for remote control or remote monitoring of applications
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
-
- 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
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
- Y02B70/00—Technologies for an efficient end-user side electric power management and consumption
- Y02B70/30—Systems integrating technologies related to power network operation and communication or information technologies for improving the carbon footprint of the management of residential or tertiary loads, i.e. smart grids as climate change mitigation technology in the buildings sector, including also the last stages of power distribution and the control, monitoring or operating management systems at local level
-
- 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
- Y04S20/00—Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof
- Y04S20/20—End-user application control systems
Abstract
A kind of monitoring method and system of power transmission network, edge calculations node and access gateway are set between the center calculation station of power transmission network and end sensor, power transmission network is monitored by end sensor based on edge calculations, the edge calculations node includes aggregation node and access node, after multiple terminal data sensors are converged by aggregation node, center calculation station is connected to by access gateway through access node.The present invention provides a kind of monitoring method of power transmission network and its systems, by being divided into aggregation node and access node to edge calculate node, the calculating work of edge calculations is further segmented, monitoring response quickly can be made to the power transmission network state monitored by end sensor；By the state judgement to edge calculate node and end sensor, guarantee the safety and stability of monitoring system.
Description
Technical field
The invention belongs to technical field of electric power, are related to the condition monitoring of power transmission network, are a kind of monitoring side of power transmission network
Method and its system.
Background technique
Aorta of the power transmission network as electric system undertakes over long distances, the critical function of large capacity electrical energy transportation.Therefore it needs
Effective monitoring is carried out to power transmission network, collect related data.Such as the monitoring for shaft tower, the monitoring etc. for substation.
Specifically, sensor is terminal data source.Sensor includes wireless sensor and wired sensor.Wireless sensing
Device includes micro energy lose and low-power consumption sensor again.
How edge calculations are used for power domain as research hotspot as emerging technology scheme in recent years.Side
It is the operation program that completion is brought using the border land close to data source that edge, which calculates, to alleviate mass data transfers to central master station
Bring calculates pressure and processing delay, the power transmission network huge in face of complexity, how in end sensor and central master station
Between configure edge calculations scheme, will affect the real-time entirely monitored, accuracy effect.
In addition, generally using rule-based method in central master station in conventional method in safety in operation, stability
Whether normal judge to collect data back, for other than many rules the case where can not just judge.Further, since edge meter
The node of calculation is chronically at exposed state, once since environment influences to damage, the data of report are with regard to unreliable.Therefore it is also required to
A kind of method is next to be monitored in time.
Summary of the invention
The technical problem to be solved by the present invention is how edge calculations are efficiently applied to the monitoring of electric power transmission network
In, and guarantee real-time, stability.
The technical solution of the present invention is as follows: a kind of monitoring method of power transmission network, at the center calculation station of power transmission network and end
Edge calculations node and access gateway are set between end sensor, based on edge calculations by end sensor to power transmission network into
Row monitoring, the edge calculations node includes aggregation node and access node, and multiple terminal data sensors are converged by aggregation node
After poly-, center calculation station is connected to by access gateway through access node, when carrying out edge calculations, aggregation node carries out simple
Edge calculations, access node carry out complex edge calculating, and the simple edges calculate the calculating for referring to threshold decision one kind, complicated side
Edge calculates the calculating for referring to machine learning one kind.
Further, system mode judgement is carried out during monitoring, including following judges process: using original state as base
Standard judge whether edge calculations node works normally, such as it is abnormal, issue corresponding alarm prompt, normally then continue judge end
Whether end sensor normal, then issues corresponding alarm prompt if any abnormal end sensor, and continue with it is other just
Normal terminal sensor data continues with whole terminal sensor datas if whole is normal.
As a kind of implementation, the judgment module for judging process and module is judged as different subjects:
If the judgement process is carried out in access gateway, the edge calculations node being judged refers to the accession to node, judges mould
Block is access gateway or center calculation station；If the judgement process is carried out in access node, it is judged edge calculations node and refers to
Aggregation node, judgment module are access node or access gateway or center calculation station；Judgment module collects edge calculations node
Original state, including the data power size for responding the time span of judgment module poll and receiving, using original state as base
Standard judges whether fringe node is normal.
It is described judged on the basis of original state fringe node whether normally include:
If the weighted array value and original state of pair the correspondence parameter or parameter that are successfully received are same calculate after difference
Absolute value is more than predetermined threshold, then judges that fringe node is abnormal；
If the ratio after the calculating same as original state of the weighted array value for the correspondence parameter or parameter being successfully received is super
Predetermined threshold is crossed, then judges that fringe node is abnormal.
As another implementation, the judgment module for judging process and module is judged as same body: if
The judgement process is carried out in access node, and judgment module is access node, if the process is carried out in aggregation node, judgement
Module is aggregation node；
Judgment mode includes: the abnormal threshold values that data are arranged using artificial experience, once the data being collected into be more than/are lower than
Abnormal threshold values, then alarm prompt；Or judged using the same class sensing data collected, if there is some in same class sensor
Or the data of certain several sensor are in the same side edge in the space that most of sensing datas are constituted, then alarm prompt.
The machine learning model of access node of the present invention is downloaded from Internet of Things server or in local training, machine
The algorithm of device study includes the algorithm of supervised learning and the algorithm of unsupervised learning, and the machine learning model uses incremental learning
Method is trained.
It is preferred that the data of the incremental learning use aggregation node and the common acknowledged data of access node
It is trained, i.e. the positive sample of incremental learning is the sample that aggregation node and access node are all judged as positive sample, incremental learning
Negative sample be sample that aggregation node and access node are all judged as negative sample.
The present invention also provides a kind of monitoring systems of power transmission network, including center calculation station, access gateway, edge calculations section
Point and end sensor, edge calculations node include aggregation node and access node, and multiple terminal data sensors are saved by convergence
After point convergence, center calculation station, center calculation station, access gateway and edge calculations are connected to by access gateway through access node
Computer program is configured in node, the computer program, which is performed, realizes above-mentioned monitoring method.
The present invention provides a kind of monitoring method of power transmission network and its systems, by being divided into remittance to edge calculate node
The calculating work of edge calculations is further segmented, can quickly be supervised to by end sensor by poly- node and access node
The power transmission network state of control makes monitoring response；By the state judgement to edge calculate node and end sensor, guarantee prison
The safety and stability of control system.
Detailed description of the invention
Fig. 1 is the structural schematic diagram of monitoring system of the present invention.
The judgement flow diagram that state judges in the monitoring method of the present invention of the position Fig. 2.
Specific embodiment
Specific implementation of the invention is described below.
As shown in Figure 1, the present invention includes center calculation station, access gateway, edge calculations node and end sensor, edge
Calculate node includes aggregation node and access node, after multiple terminal data sensors are converged by aggregation node, through access node
It is connected to center calculation station by access gateway, power transmission network is monitored by end sensor based on edge calculations, In
When carrying out edge calculations, aggregation node carries out simple edges calculating, and access node carries out complex edge calculating, the simple edges
The calculating for referring to threshold decision one kind is calculated, complex edge calculates the calculating for referring to machine learning one kind.
Access node is sent to after the terminal data of aggregation node collecting sensor.One access node can be by wired
And/or it is wirelessly connected one or more aggregation node (star-like connection).Aggregation node supports simple edge calculations.Convergence section
Point can send feedback information to sensor.Aggregation node can send edge calculations result to access node.
Access node is connected to center calculation station (i.e. server) by access gateway.One access gateway can pass through
Wiredly and/or wirelessly connect one or more access node (star-like connection).Access node supports complicated edge calculations.It connects
Ingress can issue feedback information to aggregation node.Access node can send edge calculations result to access gateway.
It can be transmitted using technologies such as 3G/4G/NB-IoT between access node and access gateway, sensor and convergence
Using the connection of the technologies such as LoRa/WiFi/Bluetooth between node, LoRa/WiFi/ is used between aggregation node and sensor
The connection of the technologies such as Bluetooth/Zigbee.
For the stability for guaranteeing monitoring, the present invention carries out system mode judgement, including following judgement stream during monitoring
Journey, as shown in Figure 2: judging whether edge calculations node works normally on the basis of original state, as abnormal, issue correspondence
Alarm prompt, normally then continue to judge whether end sensor normal, then issue correspondence if any abnormal end sensor
Alarm prompt, and continue with other normal terminal sensing datas, continue with whole terminals sensings if all normal
Device data.
Above-mentioned judgement process can be realized in multiple spot.
As a kind of implementation, the judgment module for judging process and module is judged as different subjects:
If the judgement process is carried out in access gateway, the edge calculations node being judged refers to the accession to node, judges mould
Block is access gateway or center calculation station；If the judgement process is carried out in access node, it is judged edge calculations node and refers to
Aggregation node, judgment module are access node or access gateway or center calculation station；Judgment module collects edge calculations node
Original state, including the data power size for responding the time span of judgment module poll and receiving, using original state as base
Standard judges whether fringe node is normal.If it is described judged on the basis of original state fringe node whether normally include: pair after
Absolute value of the difference after the calculating same as original state of the weighted array value of the continuous correspondence parameter or parameter received is more than predetermined door
Limit, then judge that fringe node is abnormal；If the weighted array value and original state of the correspondence parameter or parameter that are successfully received are same
Ratio after sample calculates is more than predetermined threshold, then judges that fringe node is abnormal.
As another implementation, the judgment module for judging process and module is judged as same body: if
The judgement process is carried out in access node, and judgment module is access node, if the process is carried out in aggregation node, judgement
Module is aggregation node；Judgment mode includes: the abnormal threshold values that data are arranged using artificial experience, once the data being collected into are super
Abnormal threshold values is crossed/is lower than, then alarm prompt；Or judged using the same class sensing data collected, if there is some or certain
The data of several sensors are in the same side edge in the space that most of sensing datas are constituted, then alarm prompt.Normal data point
Cloth should be the normal distribution for comparing concentration.If one or several data edge distant in the same side in this space
(such as being greater than three times variance apart from mean value) then illustrates that obvious deviation occur in these data, carries out alarm prompt.
Above two mode can be configured individually or simultaneously.
The machine learning model of access node of the present invention is downloaded from Internet of Things server or in local training, machine
The algorithm of device study includes the algorithm of supervised learning and the algorithm of unsupervised learning, and the machine learning model uses incremental learning
Method is trained.
It is preferred that the data of the incremental learning use aggregation node and the common acknowledged data of access node
It is trained, i.e. the positive sample of incremental learning is the sample that aggregation node and access node are all judged as positive sample, incremental learning
Negative sample be sample that aggregation node and access node are all judged as negative sample.
Monitoring embodiment below by transmission of electricity Internet of Things illustrates implementation of the invention.
Transmission of electricity Internet of Things itself is deployed with inclination sensor, meteorological element sensor, leakage current sensor etc..Each
Aggregation node is installed to be collected sensing data on tower.Access section about is installed in each shaft tower every one kilometer or so range
Point carries out data back.Certain scenes that data back can not be carried out for conventional network communications, can provide the small station of passback
Magnification scheme is relayed with signal to realize the communication with remote internet of things management platform.
In the present embodiment, aggregation node state is on the one hand judged by access node, access node is in system initialization
When collect the original state of aggregation node, the including but not limited to time span of response access node poll and the number that receives
According to watt level.If judged on the basis of original state aggregation node whether normally include: the correspondence parameter being successfully received or
Absolute value of the difference after the calculating same as original state of the weighted array value of parameter is more than predetermined threshold, then judges fringe node not
Normally.Such as the time span of responsive node poll is more than initial state value predetermined threshold one, or the data power received
Less than the data power predetermined threshold two being initially received.If the weighted array value of the correspondence parameter or parameter that are successfully received with
Ratio after original state equally calculates is more than predetermined threshold, then judges that fringe node is abnormal.Such as responsive node poll
Time span is greater than predetermined threshold three divided by initial state value, or the data power received is divided by the data being initially received
Power is less than predetermined threshold four or the time span of responsive node poll subtracts the data function received divided by initial state value
Rate is greater than predetermined threshold five divided by the data power being initially received.
On the other hand sensor states are judged by aggregation node, it, can be with when the aggregation node simple edge calculations of support
Using rule, data exception detection can be handled rapidly.Particularly, it can use artificial experience, that is, the abnormal threshold values of data be set.
Such as by inclination sensor, meteorological element, such as atmospheric temperature, atmospheric humidity, wind speed, wind direction, air pressure, rainfall sensor,
The data that leakage current sensor is collected into are more than preset abnormal threshold values, then alert.Or by meteorological element sensor
The data being collected into then are alerted lower than preset abnormal threshold values.Also it can use the same class sensing data being collected into
Directly judge.If there is the data of some or certain several sensors in same class sensor are constituted in most of sensing datas
The same side edge in space, then alert.Such as multiple same class sensors (inclination sensor, meteorological element, leakage current) are received
There is several especially big or especially small, i.e., the average value difference of average value and other data within a specified time in the data collected
Value is more than preset thresholding, then alerts.The feedback that aggregation node provides includes accelerating sensing data to send frequency, is closed
Control valve etc..
Present invention may also apply to the monitoring of substation, are correspondingly arranged the monitoring center of substation, and substation equipment itself is set
There are electric current, voltage, temperature-humidity sensor etc. to monitor sensor, aggregation node is configured to sensor, aggregation node passes through again to be connect
Ingress, access gateway are connected to monitoring center, are monitored according to equipment state of the above-mentioned process to substation.
Power transmission network monitoring method and system provided by the invention, acquisition fringe node working condition that can be efficient and convenient
Information.Aggregation node can carry out simple edge calculations according to rule and/or data, improve the abnormal efficiency of detection.It connects
Ingress supports complicated edge calculations, such as the model of operation machine learning, can more effectively handle data exception detection, and
Execute the function of prediction data exception.
Claims (8)
1. a kind of monitoring method of power transmission network, it is characterized in that being set between the center calculation station of power transmission network and end sensor
Edge calculations node and access gateway are set, power transmission network is monitored by end sensor based on edge calculations, the side
Edge calculate node includes aggregation node and access node, after multiple terminal data sensors are converged by aggregation node, is saved through access
Point is connected to center calculation station by access gateway, and when carrying out edge calculations, aggregation node carries out simple edges calculating, access
Node carries out complex edge calculating, and the simple edges calculate the calculating for referring to threshold decision one kind, and complex edge calculating refers to machine
Learn a kind of calculating.
2. the monitoring method of a kind of power transmission network according to claim 1, it is characterized in that carrying out system during monitoring
State judgement, including following judge process: judging whether edge calculations node works normally on the basis of original state, if not just
It is normal then issue corresponding alarm prompt, normally then continue to judge whether end sensor is normal, be sensed if any abnormal terminal
Device then issues corresponding alarm prompt, and continues with other normal terminal sensing datas, continues with if whole is normal
Whole terminal sensor datas.
3. the monitoring method of a kind of power transmission network according to claim 2, it is characterized in that the judgement mould of the judgement process
Block is different subjects with module is judged:
If the judgement process is carried out in access gateway, the edge calculations node being judged refers to the accession to node, and judgment module is
Access gateway or center calculation station；If the judgement process is carried out in access node, it is judged edge calculations node and refers to convergence
Node, judgment module are access node or access gateway or center calculation station；Judgment module collects the initial of edge calculations node
State is sentenced on the basis of original state including the data power size for responding the time span of judgment module poll and receiving
Whether disconnected fringe node is normal.
4. the monitoring method of a kind of power transmission network according to claim 3, it is characterized in that being judged on the basis of original state
Fringe node whether normally include:
If the weighted array value and original state of pair the correspondence parameter or parameter that are successfully received it is same calculate after difference it is absolute
Value is more than predetermined threshold, then judges that fringe node is abnormal；
If the ratio after the calculating same as original state of the weighted array value for the correspondence parameter or parameter being successfully received is more than pre-
Determine thresholding, then judges that fringe node is abnormal.
5. the monitoring method of a kind of power transmission network according to claim 2, it is characterized in that the judgement mould of the judgement process
Block is same body with module is judged: if the judgement process is carried out in access node, judgment module is access node, such as
Process described in fruit is carried out in aggregation node, and judgment module is aggregation node；
Judgment mode include: using the abnormal threshold values of artificial experience setting data, once the data being collected into be more than/lower than abnormal
Threshold values, then alarm prompt；Or judged using the same class sensing data collected, sensing data is done into data distribution, if
The same side edge of data space for having the data of some or certain several sensors to constitute in most of sensing datas, then alert
Prompt.
6. the monitoring method of a kind of power transmission network according to claim 1, it is characterized in that the machine learning mould of access node
Type is downloaded from Internet of Things server or in local training, and the algorithm of machine learning includes the algorithm of supervised learning and unsupervised
The algorithm of study, the machine learning model are trained using Increment Learning Algorithm.
7. the monitoring method of a kind of power transmission network according to claim 6, it is characterized in that the data of the incremental learning make
It is trained with aggregation node and the common acknowledged data of access node, i.e. the positive sample of incremental learning is aggregation node and connects
Ingress is all judged as that the sample of positive sample, the negative sample of incremental learning are that aggregation node and access node are all judged as negative sample
Sample.
8. a kind of monitoring system of power transmission network, it is characterized in that including center calculation station, access gateway, edge calculations node and end
End sensor, edge calculations node include aggregation node and access node, and multiple terminal data sensors are converged by aggregation node
Afterwards, center calculation station is connected to by access gateway through access node, in center calculation station, access gateway and edge calculations node
Configured with computer program, the computer program, which is performed, realizes the described in any item monitoring methods of claim 1-7.
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| CN110994798A (en) * | 2019-12-16 | 2020-04-10 | 深圳供电局有限公司 | Substation equipment monitoring system |
| CN111857015A (en) * | 2020-08-06 | 2020-10-30 | 山东科宏电子科技有限公司 | Power transmission and transformation cloud intelligent controller |
| 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 |
| CN112710915A (en) * | 2020-12-18 | 2021-04-27 | 北京百度网讯科技有限公司 | Method and device for monitoring power equipment, electronic equipment and computer storage medium |
| CN112800110A (en) * | 2021-01-22 | 2021-05-14 | 国家电网有限公司技术学院分公司 | Weak sensitive data abnormity detection system of power internet of things sensor |
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Application publication date: 20191126 |