CN202872464U - Secondary network measurement and multimode decision apparatus of intelligent substation - Google Patents
Secondary network measurement and multimode decision apparatus of intelligent substation Download PDFInfo
- Publication number
- CN202872464U CN202872464U CN2012204808729U CN201220480872U CN202872464U CN 202872464 U CN202872464 U CN 202872464U CN 2012204808729 U CN2012204808729 U CN 2012204808729U CN 201220480872 U CN201220480872 U CN 201220480872U CN 202872464 U CN202872464 U CN 202872464U
- Authority
- CN
- China
- Prior art keywords
- network
- module
- intelligent substation
- network quality
- data
- 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.)
- Expired - Lifetime
Links
- 238000005259 measurement Methods 0.000 title claims abstract description 12
- 238000012549 training Methods 0.000 claims description 18
- 238000013528 artificial neural network Methods 0.000 claims description 12
- 238000000034 method Methods 0.000 claims description 12
- 241000272814 Anser sp. Species 0.000 claims description 10
- 230000003287 optical effect Effects 0.000 claims description 9
- 238000004458 analytical method Methods 0.000 claims description 7
- 238000012423 maintenance Methods 0.000 claims description 7
- 230000004888 barrier function Effects 0.000 claims description 6
- 238000012545 processing Methods 0.000 claims description 3
- 238000012360 testing method Methods 0.000 abstract description 4
- 238000004519 manufacturing process Methods 0.000 abstract description 3
- 238000013439 planning Methods 0.000 abstract description 3
- 230000008569 process Effects 0.000 description 6
- 230000006870 function Effects 0.000 description 5
- 239000002245 particle Substances 0.000 description 5
- 230000005540 biological transmission Effects 0.000 description 4
- 230000006854 communication Effects 0.000 description 4
- 238000004891 communication Methods 0.000 description 3
- 238000005457 optimization Methods 0.000 description 3
- 238000001514 detection method Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000007689 inspection Methods 0.000 description 2
- 238000007726 management method Methods 0.000 description 2
- 239000011159 matrix material Substances 0.000 description 2
- 230000006855 networking Effects 0.000 description 2
- 230000001537 neural effect Effects 0.000 description 2
- 210000002569 neuron Anatomy 0.000 description 2
- 238000003860 storage Methods 0.000 description 2
- 230000007704 transition Effects 0.000 description 2
- 230000015572 biosynthetic process Effects 0.000 description 1
- 230000000903 blocking effect Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 239000000835 fiber Substances 0.000 description 1
- 230000008676 import Effects 0.000 description 1
- 230000002452 interceptive effect Effects 0.000 description 1
- 238000011068 loading method Methods 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 238000003062 neural network model Methods 0.000 description 1
- 238000010606 normalization Methods 0.000 description 1
- 239000013307 optical fiber Substances 0.000 description 1
- 238000010223 real-time analysis Methods 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
- 238000013519 translation Methods 0.000 description 1
Images
Classifications
-
- 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
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
-
- 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
- Y04S10/16—Electric power substations
-
- 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
-
- 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
Landscapes
- Data Exchanges In Wide-Area Networks (AREA)
Abstract
The utility model provides a secondary network measurement and multimode decision apparatus of an intelligent substation. The secondary network measurement and multimode decision apparatus comprises a network configuration file reading module, a network configuration file parse module, a network measurement module, a network measurement parse module, a network quality prediction module, a network quality decision module, a network quality database, a network quality model database and a network quality decision database. The secondary network measurement and multimode decision apparatus can comprehensively detect secondary network quality of the intelligent substation network, thereby guaranteeing security and stability of the intelligent substation network, providing technical bases for planning design, update reconstruction, network tests and the like of the intelligent substation network at 110kV, 220kV or above, and facilitating safety in production and stable operation of a power grid.
Description
Technical field
The utility model relates to the detection field of intelligent substation, and specifically a kind of intelligent substation secondary network is measured and the multimode decision making device.
Background technology
Day by day perfect along with the intelligent substation correlation technique, intelligent substation progressively enters the extensive practical stage.In intelligent substation, network connection primary equipment and bay device, finish the real time information exchange between bay device and the primary equipment, between the bay device, it has substituted the secondary cable of continuing to use in the conventional substation for many years with digital transmission technology (fiber optic Ethernet), for the interoperability that realizes data sharing and equipment provides necessary technical foundation, this major transformation will all will produce deep effect to the various aspects such as form, equipment debugging flow process, station and operations specification of secondary device in the future substation.Through domestic and international intelligent substation Construction Practice and operating experience for many years; towards the success of the transformer substation case (GOOSE) of general object be applied to transmit real-time trip signal, interval logic blocking, inspection same period etc., the digital protection testing equipment of transmission Network Based is gradual perfection also.Communication process is own through becoming the key of intelligent substation success accurately between the reliable and stable operation of network and the IED equipment, and is own through becoming an urgent demand of intelligent substation safe operation to real-time analysis, monitoring, management and the prediction of network operation situation and IED communication between devices process.Yet traditional fault oscillograph be because the interactive information in the network can not be monitored, be resolved to its theory structure and set up pattern effectively, and when electric power system was broken down, it can not satisfy the needs of accident record and analysis under the intelligent substation network condition.For above problem, need a cover record to monitor the network service message, and carry out online analyzing for the message of communication protocol and transmission, carry out timely alarm for the hidden danger that exists in the network and exception message, the lay equal stress on overall process of existing network service, and then accurately locate and analyzing failure cause, for investigating rapidly fault, the operation maintenance personnel provide effective supplementary means.
Summary of the invention
The utility model provides a kind of intelligent substation secondary network to measure and the multimode decision making device, can detect all sidedly the secondary network quality of intelligent substation network, thereby ensure the safety and stability of intelligent substation network, for the planning and designing of 110kV, 220kV and above intelligent substation network, upgrading, network test etc. provide technical basis, be conducive to power grid security production and stable operation.
A kind of intelligent substation secondary network is measured and the multimode decision making device, comprises network profile read module, network profile parsing module, network measure module, network measure parsing module, network quality prediction module, network quality decision-making module, network quality data storehouse, network quality model library and network quality solution bank;
The input of network profile read module is connected with the output on intelligent substation backstage, is used for reading the substation configuration description file that derive on the intelligent substation backstage;
The input of network profile parsing module is connected with the output of network profile read module, be used for to resolve the substation configuration description file that the network profile read module reads, generate the intelligent substation allocation list of entirely standing after the configuration data in the network profile is resolved;
An input of network measure module is connected with the network profile parsing module, be used for utilizing full station allocation list to obtain whole topology of networks, in addition two inputs are connected with two optical splitters in the intelligent substation, obtain respectively the GOOSE message and the SV message that transmit in the network, the GOOSE message that utilization is received, SV message and whole topology of networks, and by snmp protocol the mass parameter of intelligent substation secondary network is carried out comprehensive measurement, form the initial data of network measure;
The input of network measure parsing module is connected with network measure module output, be used for the network raw data that obtains of network measure module is resolved, utilize the characteristics that flow to of data message content analysis network data in the network raw data, utilize the mass parameter in the network raw data to analyze whole network of network quality, for the processing of network data prediction provides the data basis, and the network quality data that obtains after the network raw data of network measure module resolved deposits the network quality data storehouse in;
The network quality prediction module is used for utilizing the multiple model of network quality data dynamic training in network quality data storehouse to set up the network quality model library, the future network running status predicted, and in intelligent substation backstage Dynamic Display;
The network quality decision-making module is used for utilizing the network quality prediction module to the prediction of future network running status, carry out reasoning by the decision-making technique that the network quality solution bank provides, the result obtains safe class and the early warning scheme of future network running status by inference, provides effective aid decision means for the operation maintenance personnel keep away barrier in advance.
The intelligent substation secondary network that the utility model provides is measured and the multimode decision making device, by the feeler inspection message intelligent substation secondary network is measured, and the network quality data that obtains after measurement result is resolved deposits the network quality data storehouse in.Utilize the multiple model of network quality data dynamic training to set up model library, and then the network future running status is predicted.Solution bank carries out early warning by predicting the outcome unusually to what the intelligent substation secondary network may occur, provides effective aid decision means for the operation maintenance personnel keep away barrier in advance.The utility model can be used for complete detection intelligent substation secondary network quality, ensure the safety and stability of intelligent substation network, for the planning and designing of 110kV, 220kV and above intelligent substation network, upgrading, network test etc. provide technical basis, be conducive to power grid security production and stable operation.
Description of drawings
Fig. 1 is the structural representation of a kind of intelligent substation secondary network measurement of the utility model and multimode decision making device;
Fig. 2 is the interface arrangement figure of a kind of intelligent substation secondary network measurement of the utility model and multimode decision making device;
Fig. 3 is that the utility model is the structural representation of network quality prediction module and network quality decision-making module and network quality prediction module.
Among the figure: 10-intelligent acquisition card, 11-intelligent terminal, 12-intelligent substation backstage, the 13-Intelligent substation merging unit, 14-SV optical splitter, 15-GOOSE optical splitter, 20-network profile read module, 21-network profile parsing module, 22-network measure module, 23-network measure parsing module, 24-network quality prediction module, 25-network quality decision-making module, 30-network quality data storehouse, 31-network quality model library, 32-network quality solution bank.
Embodiment
Below in conjunction with the accompanying drawing in the utility model, the technical scheme in the utility model is clearly and completely described.
Figure 1 shows that a kind of intelligent substation secondary network of the utility model is measured and the structural representation of multimode decision making device, comprise network profile read module 20, network profile parsing module 21, network measure module 22, network measure parsing module 23, network quality prediction module 24, network quality decision-making module 25, network quality data storehouse 30, network quality model library 31, network quality solution bank 32.
The input of network profile read module 20 is connected with the output on intelligent substation backstage 12, be used for reading substation configuration description (the Substation Configuration Description that derive on intelligent substation backstage 12, SCD) file is hereinafter to be referred as the SCD file.Described SCD file is described model and the communication connection of particular substation with substation configuration description language (Substation Configuration description Language, SCL).
The input of network profile parsing module 21 is connected with the output of network profile read module 20, be used for to resolve the substation configuration description file that network profile read module 20 reads, generate the intelligent substation allocation list of entirely standing after the configuration data in the network profile is resolved.
An input of network measure module 22 is connected with network profile parsing module 21, be used for reading the described transformer station allocation list of entirely standing and obtain whole topology of networks, two other input is connected with the GOOSE optical splitter with SV optical splitter 14 respectively and is connected, and is used for obtaining SV message and the GOOSE message that the intelligent substation network transmits.The telemetered signal of 10 pairs of loadings of smart machine capture card is delivered to Intelligent substation merging unit 13 by the rear FT3 form message that forms of A/D conversion by optical fiber transmission, and 13 pairs of FT3 forms of Intelligent substation merging unit message is processed output SV message through data.Intelligent substation merging unit 13 is connected with the input of SV optical splitter 14, utilizes SV optical splitter 14 to gather the SV message that Intelligent substation merging unit 13 sends, and is sent at last network measure module 22; Another one GOOSE optical splitter 15 gathers the GOOSE message that intelligent terminal 11 sends over, and is sent to network measure module 22.
The input of network measure parsing module 23 is connected with network measure module 22 outputs, is used for that the network raw data that network measure module 22 is obtained is carried out the real-time online parsing and deposits the later network quality data that obtains of network raw data parsing in network quality data storehouse 30.Concrete, utilize the characteristics that flow to of the data message content analysis network data in the network raw data, utilize the mass parameter in the network raw data, such as throughput, time delay, packet loss etc., analyze whole network of network quality, predicting for the processing of network data provides the data basis.
Network quality prediction module 24 utilizes the multiple model of network quality data dynamic training in the network quality data storehouse 30 to set up network quality model library 31, and becomes Markov during by grey or these two kinds of algorithms of population wavelet neural network are predicted future network quality (mainly referring to the parameters such as throughput, time delay, packet loss) and in intelligent substation backstage 12 Dynamic Display.Two kinds of concrete Forecasting Methodology steps are as follows:
Become Markov during grey:
Steps A 1: network quality data is obtained.Network quality prediction module 24 constantly obtains original data sequence from network quality parsing module 23;
Step B1: network quality data preliminary treatment.The length l ength of setting-up time sliding window W, step-length is step, utilizes BX data method of formation that the data in the sliding window are carried out preliminary treatment, the fluctuation of rule changes to make it present, avoids the training process of forecast model is produced interference;
Step C1: set up Grey System Model.Pretreated data are carried out match, set up Grey System Model, and calculate the residual error of this Grey System Model;
Step D1: set up Markov model.The residual error of Grey System Model is divided into five time domain states, and then structure Markov state transition probability matrix;
Step e 1: determine predicted value.According to the constant interval of five attitude Markov state transition probability predict future states, determine following constantly predicted value;
Step F 1: continue to obtain network quality data, after m*step minute, with sliding window W mobile step length backward, skip to step B and continue next modeling and prediction.
The population wavelet neural network:
Steps A 2: sample normalized.The intelligent substation network quality has sudden and randomness, for avoiding that the training process of forecast model is produced interference, must carry out normalized to from network quality parsing module 23, obtaining network quality data first before the training, all data normalizations are arrived [0.1,0.9];
Step B2: set up wavelet neural network.The network quality data of getting continuous front 24 time periods is input layer, predicts the network quality of a rear time period, so the output layer nodes is made as 1.x
iBe i input sample of input layer, W
jFor connecting the weights of hidden layer node j and output layer node, a
jAnd b
jBe respectively the flexible and translation yardstick of j hidden layer Wavelet Element;
Step C2: parameter coding.At first by real number coding method weight matrix in the wavelet neural network and the number of hidden nodes are encoded into real number code string list and are shown as individuality; Initialization wavelet neural network parameter. the neuron number of the input layer of setting network, hidden layer and output layer, and with wavelet neural network parameter (a
1, b
1, w
1) ..., (a
k, b
k, w
k) as the position vector of each particle, that is: present (i)=[w
1, w
2..., w
k, a
1, a
2..., a
k, b
1, b
2..., b
k], wherein k is the hidden layer neuron number.The scale number i of initialization population, produce at random the individual composition of i population according to above-mentioned individual configurations simultaneously, wherein different particles represent two weight matrixs and the number of hidden nodes of wavelet neural network, and the individual optimal value pBest of particle and global optimum gBest are carried out initialization;
Step D2: wavelet neural network training.Each individuality in the population is decoded into wavelet neural network.Each individual corresponding neural net is learnt the input sample.By study and the optimization to sample, thereby guarantee that the neural net of training has stronger generalization ability;
Step e 2: fitness calculates.Particle position and speed are upgraded and are calculated each individual fitness function value in each population, and compare with current individual optimal value pBest and global optimum gBest, upgrade particle position and speed, fitness function adopts " closely related function " to substitute " energy function ".
Step F 2: algorithm finishes.When target function value during less than the pre-value of measuring, the parameter optimization algorithm just stops, and perhaps reaches predefined error, finally dopes following period network quality.
The concrete Fig. 3 that please refer to, when comprising network measure data preliminary treatment submodule, network quality model training submodule and grey, described network quality prediction module 24 becomes the Prediction of Markov submodule, in another embodiment, described network quality prediction module 24 comprises network measure data preliminary treatment submodule, network quality model training submodule and population wavelet neural network predictor module.
Network quality prediction module 24 obtains network quality data from network quality data storehouse 30, by 24 network quality data preliminary treatment submodule in the network quality prediction module network quality data that obtains is carried out preliminary treatment, avoid training process to forecast model to produce and disturb; Network quality model training submodule in the network quality prediction module 24 (becoming Markov model training submodule or population wavelet-neural network model training submodule during grey) is trained respectively the input sample by the various forecast models that call in the network quality model library, deposits network quality model library 31 after having trained in.By study and the optimization to sample, thereby guarantee that the various models of training have stronger generalization ability; Become Prediction of Markov submodule or population wavelet neural network predictor module during grey in the network quality prediction module 24 and utilize training pattern that the input sample is predicted, and obtain the intelligent substation network quality in future.
Network quality decision-making module 25 utilizes predicting the outcome of 24 pairs of future network running statuses of network quality prediction module, carry out reasoning by the decision rule that network quality solution bank 32 provides, the result obtains safe class and the early warning scheme of future network running status by inference, provides effective aid decision means for the operation maintenance personnel keep away barrier in advance.The output of network quality decision-making module 25 is connected with the input on intelligent substation backstage 12, and intelligent substation backstage 12 can demonstrate the early warning scheme, provides effective aid decision means for the operation maintenance personnel keep away barrier in advance.
Concrete, please refer to Fig. 3, network quality decision-making module 25 comprises predicting the outcome to be analyzed submodule, issue handling submodule and early warning scheme and generates submodule, and predicting the outcome of obtaining in the network quality prediction module 24 imported the analysis submodule that predicts the outcome in the network quality decision-making module 25 into.The analysis submodule that predicts the outcome is analysed and compared to precision and the convergence rate of multiple model prediction, finds out optimum forecast model, the accuracy of the prediction of improving network quality; Import more excellent the predicting the outcome that the analysis submodule that predicts the outcome obtains into the issue handling module.Carry out reasoning to predicting the outcome by the integrated machine of decision model, issue handling and the inference machine that calls in the network quality solution bank.The reasoning results that obtains by the issue handling submodule, early warning scheme generation module in the network quality decision-making module 25 generates safe class and the early warning scheme of future network running status, and the early warning scheme is sent to the intelligent substation backstage, for keeping away barrier in advance, the operation maintenance personnel provide effective aid decision means.
Connection is connected in network quality data storehouse 30 with the network measure parsing module, be mainly used in storage networking and measure the network quality data of resolving, and predicting for network quality prediction module 24 provides the data basis.
Network quality model library 31 is connected with network quality prediction module 24, is mainly used in the multiple model of storage networking prediction of quality module 24 training, is network quality prediction module 24 basis that supplies a model.
Network quality solution bank 32 is connected with network decision module 25, is mainly used in storing various decision rules, for 25 decision supports of network decision module provide the reasoning foundation
The above; it only is embodiment of the present utility model; but protection range of the present utility model is not limited to this; anyly belong to those skilled in the art in the technical scope that the utility model discloses; the variation that can expect easily or replacement all should be encompassed within the protection range of the present utility model.Therefore, protection range of the present utility model should be as the criterion with the protection range of claim.
Claims (3)
1. an intelligent substation secondary network is measured and the multimode decision making device, it is characterized in that: comprise network profile read module (20), network profile parsing module (21), network measure module (22), network measure parsing module (23), network quality prediction module (24), network quality decision-making module (25), network quality data storehouse (30), network quality model library (31) and network quality solution bank (32);
The input of network profile read module (20) is connected with the output of intelligent substation backstage (12), is used for reading the substation configuration description file that derive on intelligent substation backstage (12);
The input of network profile parsing module (21) is connected with the output of network profile read module (20), be used for to resolve the substation configuration description file that network profile read module (20) reads, generate the intelligent substation allocation list of entirely standing after the configuration data in the network profile is resolved;
An input of network measure module (22) is connected with network profile parsing module (21), be used for utilizing full station allocation list to obtain whole topology of networks, in addition two inputs are connected with two optical splitters in the intelligent substation, obtain respectively the GOOSE message and the SV message that transmit in the network, the GOOSE message that utilization is received, SV message and whole topology of networks, and by snmp protocol the mass parameter of intelligent substation secondary network is carried out comprehensive measurement, form the initial data of network measure;
The input of network measure parsing module (23) is connected with network measure module (22) output, be used for the network raw data that obtains of network measure module (22) is resolved, utilize the characteristics that flow to of data message content analysis network data in the network raw data, utilize the mass parameter in the network raw data to analyze whole network of network quality, for the processing of network data prediction provides the data basis, and the network quality data that obtains after the network raw data of network measure module (22) resolved deposits network quality data storehouse (30) in;
Network quality prediction module (24) is used for utilizing the multiple model of network quality data dynamic training in network quality data storehouse (30) to set up network quality model library (31), the future network running status is predicted, and in intelligent substation backstage (12) Dynamic Display;
Network quality decision-making module (25) is used for utilizing network quality prediction module (24) to the prediction of future network running status, carry out reasoning by the decision-making technique that network quality solution bank (32) provides, the result obtains safe class and the early warning scheme of future network running status by inference, provides effective aid decision means for the operation maintenance personnel keep away barrier in advance.
2. intelligent substation secondary network as claimed in claim 1 is measured and the multimode decision making device, it is characterized in that: network quality prediction module (24) becomes Markov during by grey or population wavelet neural network algorithm is predicted the future network running status.
3. intelligent substation secondary network as claimed in claim 1 is measured and the multimode decision making device, and it is characterized in that: the mass parameter of described intelligent substation secondary network comprises throughput, time delay, packet loss, bandwidth availability ratio and network retractility.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN2012204808729U CN202872464U (en) | 2012-09-20 | 2012-09-20 | Secondary network measurement and multimode decision apparatus of intelligent substation |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN2012204808729U CN202872464U (en) | 2012-09-20 | 2012-09-20 | Secondary network measurement and multimode decision apparatus of intelligent substation |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN202872464U true CN202872464U (en) | 2013-04-10 |
Family
ID=48039153
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN2012204808729U Expired - Lifetime CN202872464U (en) | 2012-09-20 | 2012-09-20 | Secondary network measurement and multimode decision apparatus of intelligent substation |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN202872464U (en) |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102868224A (en) * | 2012-09-20 | 2013-01-09 | 湖北省电力公司电力科学研究院 | Secondary network measurement and multimode decision-making method and device for intelligent substation |
| CN104535853A (en) * | 2014-12-12 | 2015-04-22 | 国家电网公司 | Distributed test terminal of LET wireless communication intelligent substation test system |
| CN104699903A (en) * | 2015-03-16 | 2015-06-10 | 国家电网公司 | Intelligent substation secondary signal transmission system evaluation apparatus and method |
| CN106936654A (en) * | 2015-12-29 | 2017-07-07 | 国网智能电网研究院 | A kind of test system and method for testing of Packet Transport Network business isolation |
| CN111327487A (en) * | 2018-12-14 | 2020-06-23 | 国网山西省电力公司信息通信分公司 | Power communication network running state monitoring method and device based on deep learning |
-
2012
- 2012-09-20 CN CN2012204808729U patent/CN202872464U/en not_active Expired - Lifetime
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102868224A (en) * | 2012-09-20 | 2013-01-09 | 湖北省电力公司电力科学研究院 | Secondary network measurement and multimode decision-making method and device for intelligent substation |
| CN104535853A (en) * | 2014-12-12 | 2015-04-22 | 国家电网公司 | Distributed test terminal of LET wireless communication intelligent substation test system |
| CN104535853B (en) * | 2014-12-12 | 2017-08-04 | 国家电网公司 | The distributed testing terminal of LTE radio communication intelligent substation test systems |
| CN104699903A (en) * | 2015-03-16 | 2015-06-10 | 国家电网公司 | Intelligent substation secondary signal transmission system evaluation apparatus and method |
| CN104699903B (en) * | 2015-03-16 | 2018-05-22 | 国家电网公司 | A kind of intelligent substation secondary signal transmission system apparatus for evaluating and method |
| CN106936654A (en) * | 2015-12-29 | 2017-07-07 | 国网智能电网研究院 | A kind of test system and method for testing of Packet Transport Network business isolation |
| CN111327487A (en) * | 2018-12-14 | 2020-06-23 | 国网山西省电力公司信息通信分公司 | Power communication network running state monitoring method and device based on deep learning |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN102868224B (en) | Secondary network measurement and multimode decision-making method and device for intelligent substation | |
| US10164431B2 (en) | Outage management and prediction for a power grid system | |
| CN101413981B (en) | Electric power system operation standby reliability testing system | |
| CN110401262A (en) | GIS device state intelligent monitoring system and method based on edge calculations technology | |
| CN202872464U (en) | Secondary network measurement and multimode decision apparatus of intelligent substation | |
| CN109146093A (en) | A kind of electric power equipment on-site exploration method based on study | |
| CN105335816A (en) | Electric power communication operation trend and business risk analyzing method based on deep learning | |
| CN107180314B (en) | Operation and maintenance business model modeling method based on primary and secondary system incidence relation | |
| Wang et al. | On machine learning-based techniques for future sustainable and resilient energy systems | |
| CN103701637A (en) | Method for analyzing running trend of electric power communication transmission network | |
| CN104578408A (en) | State monitoring and tendency estimation device for secondary equipment of intelligent substation | |
| CN105574604A (en) | Power network operation event-oriented monitoring, pre-judging and analyzing system | |
| Guo et al. | Evidence-based approach to power transmission risk assessment with component failure risk analysis | |
| CN110826228A (en) | Regional power grid operation quality limit evaluation method | |
| CN110412417B (en) | Micro-grid data fault diagnosis method based on intelligent power monitoring instrument | |
| Amini et al. | Electrical energy systems resilience: A comprehensive review on definitions, challenges, enhancements and future proceedings | |
| Guo et al. | Power transmission risk assessment considering component condition | |
| CN114142614A (en) | Highway power distribution room intelligent operation and maintenance management system based on SD-WAN network | |
| Gao et al. | Concepts, structure and developments of high-reliability cyber-physical fusion based coordinated planning for distribution system | |
| CN115936663A (en) | Maintenance method and device for power system | |
| Li et al. | Risk prediction of the SCADA communication network based on entropy-gray model | |
| Liu et al. | Evaluation method for voltage fluctuation performance considering cyber soft failure | |
| Gong et al. | State detection method of secondary equipment in smart substation based on deep belief network and trend prediction | |
| CN205809183U (en) | Power automation network analyzing apparatus | |
| Li et al. | Node Vulnerability-Aware co-deployment of D-PMUs and FTUs for active distribution networks |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant | ||
| AV01 | Patent right actively abandoned |
Granted publication date: 20130410 Effective date of abandoning: 20140709 |
|
| RGAV | Abandon patent right to avoid regrant |