[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

CN115002579A - Electric power information acquisition system based on thing networking - Google Patents

Electric power information acquisition system based on thing networking Download PDF

Info

Publication number
CN115002579A
CN115002579A CN202210844247.6A CN202210844247A CN115002579A CN 115002579 A CN115002579 A CN 115002579A CN 202210844247 A CN202210844247 A CN 202210844247A CN 115002579 A CN115002579 A CN 115002579A
Authority
CN
China
Prior art keywords
sub
range
power information
partition
wireless sensor
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN202210844247.6A
Other languages
Chinese (zh)
Other versions
CN115002579B (en
Inventor
余明阳
金波
查志勇
郑蕾
董晨曦
吴耿
龙菲
姚渭菁
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Information and Telecommunication Branch of State Grid Hubei Electric Power Co Ltd
Original Assignee
Information and Telecommunication Branch of State Grid Hubei Electric Power Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Information and Telecommunication Branch of State Grid Hubei Electric Power Co Ltd filed Critical Information and Telecommunication Branch of State Grid Hubei Electric Power Co Ltd
Priority to CN202210844247.6A priority Critical patent/CN115002579B/en
Publication of CN115002579A publication Critical patent/CN115002579A/en
Application granted granted Critical
Publication of CN115002579B publication Critical patent/CN115002579B/en
Expired - Fee Related legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04QSELECTING
    • H04Q9/00Arrangements in telecontrol or telemetry systems for selectively calling a substation from a main station, in which substation desired apparatus is selected for applying a control signal thereto or for obtaining measured values therefrom
    • GPHYSICS
    • G16INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16YINFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE INTERNET OF THINGS [IoT]
    • G16Y10/00Economic sectors
    • G16Y10/35Utilities, e.g. electricity, gas or water
    • GPHYSICS
    • G16INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16YINFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE INTERNET OF THINGS [IoT]
    • G16Y10/00Economic sectors
    • G16Y10/75Information technology; Communication
    • GPHYSICS
    • G16INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16YINFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE INTERNET OF THINGS [IoT]
    • G16Y20/00Information sensed or collected by the things
    • G16Y20/30Information sensed or collected by the things relating to resources, e.g. consumed power
    • GPHYSICS
    • G16INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16YINFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE INTERNET OF THINGS [IoT]
    • G16Y40/00IoT characterised by the purpose of the information processing
    • G16Y40/10Detection; Monitoring
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04WWIRELESS COMMUNICATION NETWORKS
    • H04W52/00Power management, e.g. TPC [Transmission Power Control], power saving or power classes
    • H04W52/02Power saving arrangements
    • H04W52/0209Power saving arrangements in terminal devices
    • H04W52/0212Power saving arrangements in terminal devices managed by the network, e.g. network or access point is master and terminal is slave
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04WWIRELESS COMMUNICATION NETWORKS
    • H04W84/00Network topologies
    • H04W84/18Self-organising networks, e.g. ad-hoc networks or sensor networks
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04QSELECTING
    • H04Q2209/00Arrangements in telecontrol or telemetry systems
    • H04Q2209/20Arrangements in telecontrol or telemetry systems using a distributed architecture
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04QSELECTING
    • H04Q2209/00Arrangements in telecontrol or telemetry systems
    • H04Q2209/80Arrangements in the sub-station, i.e. sensing device
    • YGENERAL 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
    • Y04INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04SSYSTEMS 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
    • Y04S40/00Systems for electrical power generation, transmission, distribution or end-user application management characterised by the use of communication or information technologies, or communication or information technology specific aspects supporting them
    • Y04S40/12Systems for electrical power generation, transmission, distribution or end-user application management characterised by the use of communication or information technologies, or communication or information technology specific aspects supporting them characterised by data transport means between the monitoring, controlling or managing units and monitored, controlled or operated electrical equipment
    • Y04S40/128Systems for electrical power generation, transmission, distribution or end-user application management characterised by the use of communication or information technologies, or communication or information technology specific aspects supporting them characterised by data transport means between the monitoring, controlling or managing units and monitored, controlled or operated electrical equipment involving the use of Internet protocol

Landscapes

  • Engineering & Computer Science (AREA)
  • Computing Systems (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Business, Economics & Management (AREA)
  • Accounting & Taxation (AREA)
  • Development Economics (AREA)
  • Economics (AREA)
  • General Business, Economics & Management (AREA)
  • Mobile Radio Communication Systems (AREA)
  • Remote Monitoring And Control Of Power-Distribution Networks (AREA)

Abstract

The invention discloses an electric power information acquisition system based on the Internet of things, which comprises an Internet of things module; the Internet of things module comprises a wireless sensor node and a control base station; the control base station is used for clustering the wireless sensor nodes in the following modes: dividing the distribution range of the power equipment into a plurality of sub-ranges by adopting a self-adaptive partition mode; clustering the wireless sensor nodes in each sub-range respectively, and dividing the wireless sensor nodes into cluster head nodes and member nodes; the member nodes are used for acquiring power information of the power equipment and transmitting the power information to the cluster head nodes; the cluster head node is used for forwarding the power information to the control base station. Because the partition is firstly carried out, the distribution of the cluster head nodes is not random any more, the distribution of the cluster head nodes is more reasonable, and the workload of the transformer substation workers is effectively reduced.

Description

Electric power information acquisition system based on thing networking
Technical Field
The invention relates to the field of information acquisition, in particular to an electric power information acquisition system based on the Internet of things.
Background
The transformer substation is a place for converting voltage and current, receiving electric energy and distributing electric energy in an electric power system. The substations in the power plant are step-up substations, which are used to boost up the electrical energy generated by the generator and feed it into the high-voltage network. The transformer substation comprises various types of power equipment, and in the operation process, the power information of the power equipment needs to be acquired so as to monitor the states of the power equipment.
In the prior art, the power information of the power equipment is generally acquired through the technology of internet of things, that is, the power information is acquired by forming a wireless sensor network by using wireless sensor nodes. The existing wireless sensor network generally adopts a mode of randomly generating cluster heads to perform clustering processing on wireless sensor nodes in the operation process, and the cluster heads are unreasonably distributed in the clustering mode to influence the service life of the wireless sensor nodes, so that the wireless sensor nodes are required to be subjected to battery replacement operation more frequently, and the workload of transformer substation workers is increased.
Disclosure of Invention
The invention aims to disclose an electric power information acquisition system based on the Internet of things, and solve the problems that in the prior art, when electric power information of electric power equipment is acquired by a wireless sensor network, cluster heads are clustered by randomly generating cluster heads, so that the cluster heads are unreasonably distributed, and the workload of transformer substation workers is increased.
In order to achieve the purpose, the invention adopts the following technical scheme:
an electric power information acquisition system based on the Internet of things comprises an Internet of things module;
the Internet of things module comprises a wireless sensor node and a control base station;
the control base station is used for clustering the wireless sensor nodes in the following modes:
dividing the distribution range of the power equipment into a plurality of sub-ranges in a self-adaptive partitioning mode;
clustering the wireless sensor nodes in each sub-range respectively, and dividing the wireless sensor nodes into cluster head nodes and member nodes;
the member nodes are used for acquiring power information of the power equipment and transmitting the power information to the cluster head nodes;
the cluster head node is used for forwarding the power information to the control base station.
Preferably, the power information acquisition system based on the internet of things further comprises a data transmission module;
and the control base station is used for sending the power information sent by the cluster head node to the data transmission module.
Preferably, the power information acquisition system based on the internet of things further comprises a data storage module;
the data transmission module is used for transmitting the power information transmitted by the control base station to the data storage module.
Preferably, the data transmission module includes a wired transmission unit or a wireless transmission unit.
Preferably, the data storage module is configured to store the power information.
Preferably, the power information includes temperature, voltage, and current.
Preferably, the power equipment comprises a transformer, a high voltage circuit breaker, a disconnector and a bus bar.
Preferably, the dividing the distribution range of the power equipment into a plurality of sub-ranges by using an adaptive partition method includes:
acquiring a minimum circumscribed rectangle of a distribution range of the power equipment;
partitioning for the first time, dividing the minimum circumscribed rectangle into H sub-ranges with the same area, and storing the sub-ranges obtained by the partitioning into a set
Figure 527081DEST_PATH_IMAGE001
Respectively judge
Figure 506538DEST_PATH_IMAGE001
Whether each sub-range in (2) enters the next partition, storing the sub-range entering the next partition into the set
Figure 863439DEST_PATH_IMAGE002
Storing sub-ranges not entering next partition into the set
Figure 55386DEST_PATH_IMAGE003
The nth partition, n is greater than or equal to 2, respectively collecting
Figure 682807DEST_PATH_IMAGE004
Each element in the set is divided into H sub-ranges with the same area, and the sub-ranges obtained by the current partition are stored into a set
Figure 567587DEST_PATH_IMAGE005
Respectively judge
Figure 146205DEST_PATH_IMAGE005
Whether each sub-range in (a) enters the next partition, storing the sub-ranges entering the next partition into the set
Figure 407422DEST_PATH_IMAGE006
Storing the sub-ranges not entering the next partition into the set
Figure 889350DEST_PATH_IMAGE003
If it is
Figure 945030DEST_PATH_IMAGE006
If the number of elements in (1) is less than the set number threshold, the elements are aggregated
Figure 807682DEST_PATH_IMAGE003
As the sub-ranges finally obtained.
Preferably, whether the sub-range enters the next partition is judged by the following method:
calculating partition coefficients for the subranges:
Figure 607011DEST_PATH_IMAGE008
in the formula (I), the compound is shown in the specification,
Figure 943445DEST_PATH_IMAGE009
the partition coefficients representing the sub-ranges,
Figure 435606DEST_PATH_IMAGE010
which is indicative of a set scale parameter,
Figure 723237DEST_PATH_IMAGE011
indicating the number of wireless sensor nodes contained within the sub-range,
Figure 60678DEST_PATH_IMAGE012
represents the total number of wireless sensor nodes contained in the internet of things module,
Figure 782777DEST_PATH_IMAGE013
the footprint of a sub-range is indicated,
Figure 180260DEST_PATH_IMAGE014
represents the area of the distribution range of the power equipment,
Figure 220766DEST_PATH_IMAGE015
representing the set of all wireless sensor nodes within a sub-range,
Figure 361898DEST_PATH_IMAGE016
representing the distance between the wireless sensor node within the sub-range and the control base station,
Figure 672924DEST_PATH_IMAGE017
representing the maximum value of the distance between a wireless sensor node in the Internet of things module and a control base station;
and comparing the partition coefficient with a set partition coefficient threshold value, if the partition coefficient is greater than the partition coefficient threshold value, indicating that the sub-range enters the next partition, and if the partition coefficient is less than or equal to the partition coefficient threshold value, indicating that the sub-range does not enter the next partition.
Preferably, the shape of the sub-range is rectangular.
When the wireless sensor nodes are used for acquiring the power information, the method is used for partitioning the distribution range of the power equipment to obtain a plurality of sub-ranges, and then clustering processing is performed on each sub-range to realize clustering processing. Because the partition is firstly carried out, the distribution of the cluster head nodes is not random any more, the distribution of the cluster head nodes is more reasonable, and the workload of the transformer substation workers is effectively reduced.
Drawings
The invention is further illustrated by means of the attached drawings, but the embodiments in the drawings do not constitute any limitation to the invention, and for a person skilled in the art, other drawings can be obtained on the basis of the following drawings without inventive effort.
Fig. 1 is a diagram of an exemplary embodiment of an internet-of-things-based power information acquisition system according to the present invention.
Fig. 2 is a diagram illustrating an exemplary embodiment of a clustering process according to the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention and are not to be construed as limiting the present invention.
As shown in fig. 1, an embodiment of the present invention provides an electric power information obtaining system based on internet of things, which includes
An electric power information acquisition system based on the Internet of things comprises an Internet of things module;
the Internet of things module comprises a wireless sensor node and a control base station;
the control base station is used for clustering the wireless sensor nodes in the following modes:
dividing the distribution range of the power equipment into a plurality of sub-ranges in a self-adaptive partitioning mode;
clustering the wireless sensor nodes in each sub-range respectively, and dividing the wireless sensor nodes into cluster head nodes and member nodes;
the member nodes are used for acquiring power information of the power equipment and transmitting the power information to the cluster head nodes;
the cluster head node is used for forwarding the power information to the control base station.
When the wireless sensor nodes are used for acquiring the power information, the method is used for partitioning the distribution range of the power equipment to obtain a plurality of sub-ranges, and then clustering processing is performed on each sub-range to realize clustering processing. Because the partition is firstly carried out, the distribution of the cluster head nodes is not random any more, the distribution of the cluster head nodes is more reasonable, and the workload of the transformer substation workers is effectively reduced.
Preferably, the power information acquisition system based on the Internet of things further comprises a data transmission module;
the control base station is used for sending the power information sent by the cluster head node to the data transmission module.
Preferably, the power information acquisition system based on the internet of things further comprises a data storage module;
and the data transmission module is used for transmitting the power information transmitted by the control base station to the data storage module.
Preferably, the data transmission module includes a wired transmission unit or a wireless transmission unit.
The wired transmission unit may include a fiber optic communication network, while the wireless sensor unit may include a 4G communication network, a 5G communication network, and the like.
Preferably, the data storage module is configured to store the power information.
The storage module may be a cloud server.
Preferably, the power information includes temperature, voltage, and current.
The power information is only an example, and the power information required to be acquired by different types of power devices is not the same, and therefore, the present invention is not limited to acquiring only the type of power information of the distance.
Preferably, the power equipment comprises a transformer, a high voltage circuit breaker, a disconnector and a bus bar.
There are many types of electrical equipment other than those listed above, and the equipment in the substation may be part of the electrical equipment.
Preferably, the dividing the distribution range of the power equipment into a plurality of sub-ranges by using an adaptive partition method includes:
acquiring a minimum circumscribed rectangle of a distribution range of the power equipment;
partitioning for the first time, dividing the minimum circumscribed rectangle into H sub-ranges with the same area, and storing the sub-ranges obtained by the partitioning into a set
Figure 506888DEST_PATH_IMAGE001
Respectively judge
Figure 769111DEST_PATH_IMAGE001
Whether each sub-range in (a) enters the next partition, storing the sub-ranges entering the next partition into the set
Figure 713933DEST_PATH_IMAGE002
Storing sub-ranges not entering next partition into the set
Figure 879467DEST_PATH_IMAGE003
The nth partition, n is greater than or equal to 2, respectively collecting the sets
Figure 884332DEST_PATH_IMAGE018
Each element in the set is divided into H sub-ranges with the same area, and the sub-ranges obtained by the current partition are stored into a set
Figure 379990DEST_PATH_IMAGE005
Respectively judge
Figure 862924DEST_PATH_IMAGE005
Whether each sub-range in (a) enters the next partition, storing the sub-ranges entering the next partition into the set
Figure 69915DEST_PATH_IMAGE006
Storing sub-ranges not entering next partition into the set
Figure 996414DEST_PATH_IMAGE003
If it is
Figure 983961DEST_PATH_IMAGE006
If the number of elements in (1) is less than the set number threshold, the elements are aggregated
Figure 254274DEST_PATH_IMAGE003
As the sub-ranges finally obtained.
The final sub-range is obtained through a multi-time partition mode, and the distribution range of the power equipment is not directly divided into multiple sub-ranges with the same size. By the arrangement mode, the situation that the obtained sub-range does not contain the wireless sensor node, invalid partition is caused, and the calculation force is wasted can be avoided. In addition, the size of the sub-range is adaptive to the actual distribution condition of the wireless sensor nodes, and the practicability of the wireless sensor node distribution method is improved.
Preferably, whether the sub-range enters the next partition is judged by the following method:
calculating partition coefficients of the sub-ranges:
Figure 378088DEST_PATH_IMAGE019
in the formula (I), the compound is shown in the specification,
Figure 475488DEST_PATH_IMAGE009
the partition coefficients representing the sub-ranges,
Figure 950332DEST_PATH_IMAGE010
which is indicative of a set scale parameter,
Figure 289915DEST_PATH_IMAGE011
indicating the number of wireless sensor nodes contained within the sub-range,
Figure 330553DEST_PATH_IMAGE012
represents the total number of wireless sensor nodes contained in the internet of things module,
Figure 333275DEST_PATH_IMAGE013
the footprint of a sub-range is indicated,
Figure 295415DEST_PATH_IMAGE014
represents the area of the distribution range of the power equipment,
Figure 907530DEST_PATH_IMAGE015
representing the set of all wireless sensor nodes within a sub-range,
Figure 271516DEST_PATH_IMAGE016
representing the distance between the wireless sensor node within the sub-range and the control base station,
Figure 445139DEST_PATH_IMAGE017
the maximum value of the distance between the wireless sensor node in the Internet of things module and the control base station is represented;
and comparing the partition coefficient with a set partition coefficient threshold value, if the partition coefficient is greater than the partition coefficient threshold value, indicating that the sub-range enters the next partition, and if the partition coefficient is less than or equal to the partition coefficient threshold value, indicating that the sub-range does not enter the next partition.
The partition coefficient is mainly considered comprehensively from the three aspects of the number of the wireless sensor nodes, the occupied area of the sub-range and the distance between the wireless sensor nodes in the sub-range and the control base station, and the probability of entering the next partition is higher when the number is larger, the occupied area is larger, and the distance between the wireless sensor nodes in the sub-range and the control base station is smaller. The setting mode can make the area of the sub-range which is farther from the control base station larger as much as possible, and the area of the sub-range which is closer to the control base station smaller, so that the number of cluster head nodes in the area which is closer to the control base station is more, the situation that the area which is close to the control base station consumes the electric quantity quickly due to the too small number and quits the work is avoided, the power change operation needs to be carried out on the wireless sensor nodes more frequently, and the workload of substation workers is increased.
Preferably, the shape of the sub-range is rectangular.
Preferably, as shown in fig. 2, the clustering the wireless sensor nodes in each sub-range, and dividing the wireless sensor nodes into cluster head nodes and member nodes, includes:
calculating the clustering probability value of each wireless sensor node in the sub-range;
calculating the number of cluster head nodes within a sub-range
Figure 160154DEST_PATH_IMAGE020
Rank the clustering probability values ahead
Figure 841540DEST_PATH_IMAGE021
The wireless sensor nodes of the bits are used as cluster head nodes, and the rest wireless sensor nodes are used as member nodes.
The invention does not only have one cluster head node in a sub-range, but adaptively determines the number of cluster head nodes according to the distribution of the wireless sensor nodes, and the number and the distribution change. The method is favorable for avoiding the problem that a single cluster head node needs to be responsible for too many data forwarding tasks, so that the energy consumption is too fast.
Preferably, the clustering probability value of the wireless sensor node is calculated by adopting the following formula:
Figure 997715DEST_PATH_IMAGE022
in the formula (I), the compound is shown in the specification,
Figure 404557DEST_PATH_IMAGE023
and
Figure 75709DEST_PATH_IMAGE024
respectively representing the remaining energy and the initial energy of the wireless sensor node,
Figure 560786DEST_PATH_IMAGE025
representing the minimum distance between the wireless sensor node and the edge of the sub-range,
Figure 633784DEST_PATH_IMAGE026
represents a preset first distance criterion value,
Figure 414790DEST_PATH_IMAGE027
representing the distance between the wireless sensor node and the control base station,
Figure 573239DEST_PATH_IMAGE028
representing a preset second distance criterion value.
The clustering probability value is related to the residual energy, the minimum distance between the residual energy and the edge and the distance between the control base stations, and the clustering probability value is larger when the residual energy is more, the minimum distance between the residual energy and the edge is smaller, and the distance between the residual energy and the control base stations is smaller. Therefore, the wireless sensor nodes which are close to the control base station, close to the edge of the sub-range and have more residual energy can be selected as cluster head nodes. The energy consumed by the data forwarding of the cluster head nodes is less, and the success rate of one-hop communication with the cluster head nodes in other sub-ranges is also obviously improved.
Preferably, the number of cluster head nodes
Figure 596427DEST_PATH_IMAGE029
The following formula is adopted for calculation:
Figure 523932DEST_PATH_IMAGE030
in the formula (I), the compound is shown in the specification,
Figure 475839DEST_PATH_IMAGE031
indicating the number of wireless sensor nodes contained within the sub-range,
Figure 121584DEST_PATH_IMAGE032
the footprint of a sub-range is indicated,
Figure 682884DEST_PATH_IMAGE033
represents a preset density standard value and a preset density standard value,
Figure 730474DEST_PATH_IMAGE034
representing a preset quantity standard value.
The number of cluster head nodes is mainly related to the distribution density of the wireless sensor nodes, and the larger the density is, the larger the number of cluster head nodes is. The number of cluster head nodes can be adaptively changed along with the actual distribution of the wireless sensor nodes.
It should be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
In the foregoing description of the specification, reference to the description of "one embodiment/example," "another embodiment/example," or "certain embodiments/examples," etc., means 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, schematic representations of the above terms 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.
While embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that: various changes, modifications, substitutions and alterations can be made to the embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.
While the preferred embodiments of the present invention have been illustrated and described, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.

Claims (10)

1. An electric power information acquisition system based on the Internet of things is characterized by comprising an Internet of things module;
the Internet of things module comprises a wireless sensor node and a control base station;
the control base station is used for clustering the wireless sensor nodes in the following way:
dividing the distribution range of the power equipment into a plurality of sub-ranges in a self-adaptive partitioning mode;
clustering the wireless sensor nodes in each sub-range respectively, and dividing the wireless sensor nodes into cluster head nodes and member nodes;
the member nodes are used for acquiring power information of the power equipment and transmitting the power information to the cluster head nodes;
the cluster head node is used for forwarding the power information to the control base station.
2. The system for acquiring the power information based on the internet of things as claimed in claim 1, further comprising a data transmission module;
the control base station is used for sending the power information sent by the cluster head node to the data transmission module.
3. The power information acquisition system based on the internet of things as claimed in claim 2, further comprising a data storage module;
the data transmission module is used for transmitting the power information transmitted by the control base station to the data storage module.
4. The internet-of-things-based power information acquisition system according to claim 2, wherein the data transmission module comprises a wired transmission unit or a wireless transmission unit.
5. The internet-of-things-based power information acquisition system according to claim 3, wherein the data storage module is configured to store the power information.
6. The internet-of-things-based power information acquisition system according to claim 1, wherein the power information comprises temperature, voltage and current.
7. The internet of things-based power information acquisition system according to claim 1, wherein the power equipment comprises a transformer, a high-voltage circuit breaker, a disconnecting switch and a bus.
8. The system according to claim 1, wherein the dividing of the distribution range of the power equipment into a plurality of sub-ranges in an adaptive partition manner includes:
acquiring a minimum circumscribed rectangle of a distribution range of the power equipment;
primary partition, dividing the minimum external rectangle into H sub-ranges with the same area, storing the sub-ranges obtained by the current partition into a set
Figure 287347DEST_PATH_IMAGE001
Respectively judge
Figure 852189DEST_PATH_IMAGE001
Whether each sub-range in (2) enters the next partition, storing the sub-range entering the next partition into the set
Figure 875771DEST_PATH_IMAGE002
Storing sub-ranges not entering next partition into the set
Figure 89846DEST_PATH_IMAGE003
The nth partition, n is greater than or equal to 2, respectively collecting
Figure 762136DEST_PATH_IMAGE004
Each element in the set is divided into H sub-ranges with the same area, and the sub-ranges obtained by the current partition are stored into a set
Figure 682949DEST_PATH_IMAGE005
Respectively judge
Figure 928031DEST_PATH_IMAGE005
Whether each sub-range in (2) enters the next partition, storing the sub-range entering the next partition into the set
Figure 301506DEST_PATH_IMAGE006
Storing sub-ranges not entering next partition into the set
Figure 43066DEST_PATH_IMAGE003
If it is
Figure 83965DEST_PATH_IMAGE006
If the number of elements in (1) is less than the set number threshold, the elements are aggregated
Figure 714929DEST_PATH_IMAGE003
As the sub-ranges finally obtained.
9. The system according to claim 8, wherein whether the sub-range enters the next partition is determined by:
calculating partition coefficients for the subranges:
Figure 605393DEST_PATH_IMAGE008
in the formula (I), the compound is shown in the specification,
Figure 386530DEST_PATH_IMAGE009
the partition coefficients representing the sub-ranges are,
Figure 281936DEST_PATH_IMAGE010
which is indicative of a set scale parameter,
Figure 785598DEST_PATH_IMAGE011
indicating the number of wireless sensor nodes contained within the sub-range,
Figure 664824DEST_PATH_IMAGE012
represents the total number of wireless sensor nodes contained in the internet of things module,
Figure 482607DEST_PATH_IMAGE013
the footprint of a sub-range is indicated,
Figure 29257DEST_PATH_IMAGE014
represents the area of the distribution range of the power equipment,
Figure 154688DEST_PATH_IMAGE015
representing the set of all wireless sensor nodes within a sub-range,
Figure 957427DEST_PATH_IMAGE016
representing the distance between the wireless sensor node within the sub-range and the control base station,
Figure 408262DEST_PATH_IMAGE017
representing the maximum value of the distance between a wireless sensor node in the Internet of things module and a control base station;
and comparing the partition coefficient with a set partition coefficient threshold value, if the partition coefficient is greater than the partition coefficient threshold value, indicating that the sub-range enters the next partition, and if the partition coefficient is less than or equal to the partition coefficient threshold value, indicating that the sub-range does not enter the next partition.
10. The internet of things-based power information acquisition system according to claim 8, wherein the shape of the sub-range is rectangular.
CN202210844247.6A 2022-07-19 2022-07-19 Electric power information acquisition system based on thing networking Expired - Fee Related CN115002579B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202210844247.6A CN115002579B (en) 2022-07-19 2022-07-19 Electric power information acquisition system based on thing networking

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202210844247.6A CN115002579B (en) 2022-07-19 2022-07-19 Electric power information acquisition system based on thing networking

Publications (2)

Publication Number Publication Date
CN115002579A true CN115002579A (en) 2022-09-02
CN115002579B CN115002579B (en) 2022-12-20

Family

ID=83021930

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202210844247.6A Expired - Fee Related CN115002579B (en) 2022-07-19 2022-07-19 Electric power information acquisition system based on thing networking

Country Status (1)

Country Link
CN (1) CN115002579B (en)

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115866554A (en) * 2023-03-02 2023-03-28 吉林省信息技术研究所 Information security transmission system of Internet of things
CN115955491A (en) * 2022-11-30 2023-04-11 山东和同信息科技股份有限公司 Heating power station operation monitoring system based on internet of things
CN116488336A (en) * 2023-04-14 2023-07-25 深圳市威能讯电子有限公司 Energy storage battery safety monitoring system based on Internet of things
CN116539096A (en) * 2023-05-12 2023-08-04 广东康德威电气股份有限公司 Transformer state monitoring system based on Internet of things
CN117042008A (en) * 2023-08-18 2023-11-10 国网四川省电力公司电力应急中心 Power equipment control system based on wireless sensor network
CN117320112A (en) * 2023-10-26 2023-12-29 陕西思极科技有限公司 Dual-mode communication network energy consumption balancing method and system

Citations (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2009112937A1 (en) * 2008-03-14 2009-09-17 The University Of Cape Town Wireless sensor network model
GB201008643D0 (en) * 2005-03-22 2010-07-07 Itt Mfg Enterprises Inc Energy-efficient network protocol and node device for sensor networks
KR20110070704A (en) * 2009-12-18 2011-06-24 한국전자통신연구원 Method for controlling electric power of electric power controller , lrwpan-ethernet bridge and senser nod
CN104410997A (en) * 2014-12-29 2015-03-11 重庆邮电大学 Method for establishing hierarchical topology structure applied to wireless sensor network
KR101531791B1 (en) * 2014-03-31 2015-06-26 한국기술교육대학교 산학협력단 Method of head node selection and clustering using direction of node at mobile ad hoc network
CN105704775A (en) * 2016-01-13 2016-06-22 湖南工业大学 Improved low energy adaptive clustering hierarchy (LEACH) method
CN106413031A (en) * 2016-09-13 2017-02-15 中国人民解放军后勤工程学院 Self-adaptive clustering algorithm for heterogeneous network based on node level
CN110618616A (en) * 2019-09-23 2019-12-27 上海大学 Environmental safety monitoring system of transformer substation
CN111031506A (en) * 2019-12-16 2020-04-17 南京邮电大学 Wireless sensor network clustering algorithm based on Voronoi graph domain processing
KR102254230B1 (en) * 2020-10-08 2021-05-21 (주)스타라이트 System and method for saving power utilizing public big data

Patent Citations (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
GB201008643D0 (en) * 2005-03-22 2010-07-07 Itt Mfg Enterprises Inc Energy-efficient network protocol and node device for sensor networks
WO2009112937A1 (en) * 2008-03-14 2009-09-17 The University Of Cape Town Wireless sensor network model
KR20110070704A (en) * 2009-12-18 2011-06-24 한국전자통신연구원 Method for controlling electric power of electric power controller , lrwpan-ethernet bridge and senser nod
KR101531791B1 (en) * 2014-03-31 2015-06-26 한국기술교육대학교 산학협력단 Method of head node selection and clustering using direction of node at mobile ad hoc network
CN104410997A (en) * 2014-12-29 2015-03-11 重庆邮电大学 Method for establishing hierarchical topology structure applied to wireless sensor network
CN105704775A (en) * 2016-01-13 2016-06-22 湖南工业大学 Improved low energy adaptive clustering hierarchy (LEACH) method
CN106413031A (en) * 2016-09-13 2017-02-15 中国人民解放军后勤工程学院 Self-adaptive clustering algorithm for heterogeneous network based on node level
CN110618616A (en) * 2019-09-23 2019-12-27 上海大学 Environmental safety monitoring system of transformer substation
CN111031506A (en) * 2019-12-16 2020-04-17 南京邮电大学 Wireless sensor network clustering algorithm based on Voronoi graph domain processing
KR102254230B1 (en) * 2020-10-08 2021-05-21 (주)스타라이트 System and method for saving power utilizing public big data

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
尹翔等: "一种优化的基于博弈论的无线传感器网络区域分簇算法", 《计算机科学》 *
胥楚贵等: "无线传感器网络通信半径动态调整的能耗均衡策略", 《传感技术学报》 *

Cited By (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115955491A (en) * 2022-11-30 2023-04-11 山东和同信息科技股份有限公司 Heating power station operation monitoring system based on internet of things
CN115955491B (en) * 2022-11-30 2024-03-08 山东和同信息科技股份有限公司 Heating power station operation monitoring system based on internet of things technology
CN115866554A (en) * 2023-03-02 2023-03-28 吉林省信息技术研究所 Information security transmission system of Internet of things
CN116488336A (en) * 2023-04-14 2023-07-25 深圳市威能讯电子有限公司 Energy storage battery safety monitoring system based on Internet of things
CN116488336B (en) * 2023-04-14 2024-05-24 荣森茂(深圳)科技有限公司 Energy storage battery safety monitoring system based on Internet of things
CN116539096A (en) * 2023-05-12 2023-08-04 广东康德威电气股份有限公司 Transformer state monitoring system based on Internet of things
CN116539096B (en) * 2023-05-12 2024-07-05 广东康德威电气股份有限公司 Transformer state monitoring system based on Internet of things
CN117042008A (en) * 2023-08-18 2023-11-10 国网四川省电力公司电力应急中心 Power equipment control system based on wireless sensor network
CN117042008B (en) * 2023-08-18 2024-04-12 国网四川省电力公司电力应急中心 Power equipment control system based on wireless sensor network
CN117320112A (en) * 2023-10-26 2023-12-29 陕西思极科技有限公司 Dual-mode communication network energy consumption balancing method and system
CN117320112B (en) * 2023-10-26 2024-05-03 陕西思极科技有限公司 Dual-mode communication network energy consumption balancing method and system

Also Published As

Publication number Publication date
CN115002579B (en) 2022-12-20

Similar Documents

Publication Publication Date Title
CN115002579B (en) Electric power information acquisition system based on thing networking
Kong Cost efficient data aggregation point placement with interdependent communication and power networks in smart grid
US9209629B2 (en) Charge-discharge control device, charge-discharge control system, and computer program product
HUE032333T2 (en) Method and apparatus for spatiotemporal control of the electric power draw of a telecommunication network on the basis of states of the power supply system
CN104065168A (en) Dynamic variable-frequency data collection method for monitoring wind-PV-ES hybrid power generation status
CN114050621A (en) Distributed energy storage power distribution system and method
CN114598034A (en) Transformer substation monitoring system based on Internet of things technology
JP2014512157A (en) Centralized management of power supply to multiple local power networks
CN114784394A (en) Method for optimizing battery management of energy storage system and battery safety management system
CN115173450A (en) Self-regulation microgrid energy storage capacity configuration method
EP3314278B1 (en) Adaptive polling of data in energy distribution systems
CN111474480B (en) Battery array state parameter detection method, energy management system and energy storage system
Admaja et al. Leach distributed clustering improvement for wireless sensor networks
CN116826810A (en) Digital twin energy storage power station management system and method and electronic equipment
CN112332428B (en) Adopt RTU to optimize AGC signal correction system
CN110620389B (en) Micro-grid new energy hybrid energy storage system
Gupta et al. A novel K-means L-layer algorithm for uneven clustering in WSN
KR102568487B1 (en) Apparatus and method for controlling energy storagy system
Hoang et al. Cooperative bidding of data transmission and wireless energy transfer
CN115917903A (en) Power converter, control method for power converter, power system, control method for power system, and program
Masoodi et al. Efficient Modified-LEACH Protocol for Enhancing WSNs’ Lifetime
CN117277593B (en) Hierarchical grouping miniature power buffering method and system
US20230155380A1 (en) Power converter, method of controlling power converter, and computer readable recording medium
KR20180113089A (en) Method for managing Energy Storage System
Scheel et al. Maximization of the feed-in of renewable energy into high-voltage grids by optimal switching

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant
CF01 Termination of patent right due to non-payment of annual fee

Granted publication date: 20221220

CF01 Termination of patent right due to non-payment of annual fee