CN102593830B - Parallel identification method for model parameters of electric power system - Google Patents
Parallel identification method for model parameters of electric power system Download PDFInfo
- Publication number
- CN102593830B CN102593830B CN2012100644921A CN201210064492A CN102593830B CN 102593830 B CN102593830 B CN 102593830B CN 2012100644921 A CN2012100644921 A CN 2012100644921A CN 201210064492 A CN201210064492 A CN 201210064492A CN 102593830 B CN102593830 B CN 102593830B
- Authority
- CN
- China
- Prior art keywords
- parallel
- optimized algorithm
- power system
- parameter
- electric power
- 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 - Fee Related
Links
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
- 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
Landscapes
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The invention discloses a parallel identification method for model parameters of an electric power system, and belongs to the technical field of electric power system modeling. According to the parallel identification method, parameter identification of a model of the wide area electric power system is realized through establishing interactive iteration between an optimization algorithm and simulation software of the electric power system, and more importantly, parallelization of parameter identification is realized through parallelizing simulation and computation tasks of the electric power system in a computer cluster environment, so that time needed for parallel identification is effectively shortened and the practicality of model parameter identification of the wide area electric power system is improved. Besides, the parallel identification method can be applied to various simulation software of the electric power system used in the electric power industry in China, can be also applied to numerous modern optimization algorithms such as a particle swarm algorithm, an ant colony algorithm, a simulated evolutionary algorithm, and the like, and has good popularization and application prospect.
Description
Technical field
The present invention relates to a kind of parallel identification method for model parameters of electric power system, belong to electric power system modeling technique field.
Background technology
The result that electric system simulation calculates is the basic foundation of electrical production department for instructing actual electric network to move, and whether simulation result correctly depends on that whether model parameter is accurate to a great extent.Aspect model parameters of electric power system obtains, the method for main flow is parameter identification at present.So-called parameter identification, utilize model to be measured measured input, output data in a certain dynamic process, by the continuous adjustment model parameter of optimized algorithm so that the model emulation result approaches measured result as far as possible.
When the model parameter of identification wide area power system, when need to carry out the multiclass parameter on the one hand, optimize, need on the other hand to calculate the target function value of reflection electric power system overall dynamics behavior, optimizing process and target function value computational process are mutual.The calculating of target function value be take dynamic process of electrical power system emulation as basis, this just need to set up exchanges data between optimized algorithm and electric system simulation software for calculation, optimize the intermediate parameters result once obtained and automatically substitute the parameter in analogue system, then obtain the system dynamic response and export to optimizer by simulation calculation software, optimizer calculates and obtains target function value, and the one-step optimization of going forward side by side obtains new parameter value.
But, when in wide area power system parameter identification process, relating to multi-parameter, optimize, this has increased the amount of calculation of optimized algorithm greatly, and the emulation of wide area power system also needs the time of growing, so one group of parameter of identification needs tens hours a few days even usually.Therefore, raising parameter identification speed is practical significant for the wide area power system parameter identification, and adopt parallel processing technique, is a comparatively desirable solution.
Computer cluster technology is many computer organizations to be got up to carry out a kind of parallel processing technique of collaborative work.This technology utilizes high-speed communicating network that one group of work station (computing node) is coupled together by certain structure, forms a loosely-coupled parallel computation environment; Then the support by parallel Programming and visual man-machine interactive environment realizes United Dispatching and Coordination Treatment, thereby form the system that efficient parallel processes, carrys out the same problem of Cooperative Solving.
Summary of the invention
The object of the invention is to overcome defect of the prior art, a kind of parallel identification method for model parameters of electric power system has been proposed, the method is by setting up the mutual parameter identification of realizing the wide area power system model between optimized algorithm and power system simulation software, main is, realized the parallelization of parameter identification by the parallel processing to the electric system simulation calculation task under the computer cluster environment, effectively shorten the time of parameter identification, improved the practicality of wide area power system identification of Model Parameters.The following technical scheme of the concrete employing of the present invention:
By setting up the mutual parameter identification of realizing the wide area power system model between optimized algorithm and power system simulation software, being controlled by optimized algorithm generally alternately of optimized algorithm and power system simulation software in the parameter identification process, and realize by an interactive interface program, realized the parallelization of parameter identification by the parallel processing to the electric system simulation calculation task under the computer cluster environment, specifically using the carrier of computer cluster as the Parameter Parallel identification, adopt a kind of parallel computation mode of master slave mode, be that a computer in computer cluster is as parallel computation task management machine, mainly bear the execution of optimized algorithm and the scheduling of parallel computation task, all the other computers are as computing node, mainly bear the electric system simulation calculation task, the actual representative of each electric system simulation calculation task be one group of parameter to be identified may value, in each of optimized algorithm is taken turns iteration, numerous calculation tasks are assigned to each computing node of computer cluster to realize the parallelization of simulation calculation task simultaneously.
The invention has the beneficial effects as follows: solved wide area power system parameter identification long problem consuming time in the past, improved the practicality of wide area power system identification of Model Parameters, thereby to improving the electric system simulation analysis precision, correctly formulate power grid construction planning and operational mode and produce positive role.In addition, the present invention goes for the various power system simulation softwares that current China power industry is used, and goes for numerous modern optimization algorithms such as particle cluster algorithm, ant group algorithm, simulated evolutionary algorithm, therefore has good popularizing application prospect.
the accompanying drawing explanation:
Accompanying drawing 1 is optimized algorithm and the mutual schematic diagram of power system simulation software in the parameter identification process
Accompanying drawing 2 is flow charts of parallel identification method for model parameters of electric power system proposed by the invention
Accompanying drawing 3 is through the general flowchart of the optimized algorithm of parallel programming
embodiment:
Parallel identification method for model parameters of electric power system of the present invention is by setting up the mutual parameter identification of realizing the wide area power system model between optimized algorithm and power system simulation software, being controlled by optimized algorithm generally alternately of optimized algorithm and power system simulation software in the parameter identification process, and realize by an interactive interface program (or title difference and functionally similar program), realized the parallelization of parameter identification by the parallel processing to the electric system simulation calculation task under the computer cluster environment, specifically using the carrier of computer cluster as the Parameter Parallel identification, adopt a kind of parallel computation mode of master slave mode, be that a computer in computer cluster is as parallel computation task management machine (hereinafter to be referred as " supervisor "), mainly bear the execution of optimized algorithm and the scheduling of parallel computation task, all the other computers are as computing node, mainly bear the electric system simulation calculation task, the actual representative of each electric system simulation calculation task be one group of parameter to be identified may value, in each of optimized algorithm is taken turns iteration, numerous calculation tasks are assigned to each computing node of computer cluster to realize the parallelization of simulation calculation task simultaneously.
" Based on Power System Analysis Software Package " (Power System Analysis Software Package, PSASP) is that current China electric power enterprise carries out one of main tool of electric system simulation.Below take PSASP as embodiment, by reference to the accompanying drawings embodiments of the present invention are elaborated.
Fig. 1 is optimized algorithm and the mutual schematic diagram of power system simulation software in the parameter identification process, and the parameter identification process is controlled by optimized algorithm generally, and concrete reciprocal process is:
1. at first optimized algorithm determines one group of value of parameter to be identified.
2. by the interactive interface program, this group parameter value (after format conversion) is handed down to power system simulation software.
3. interactive interface routine call power system simulation software carries out simulation calculation.
4., after having calculated, the interactive interface program is according to simulation data calculation optimization target function value and return to optimized algorithm.
5. optimized algorithm is determined next step Optimum Operation according to the target function value returned.
6. repeat above step, until optimized algorithm reaches iterations or the target function value of setting, reach expection, last output parameter optimum results.
Fig. 2 is the flow chart of parallel identification method for model parameters of electric power system proposed by the invention, when take PSASP during as embodiment, specifically comprises following steps:
1. prepare the electric system simulation packet for parameter identification on supervisor, main contents comprise target response curve (the general dynamic response curve that the adopts actual measurement) format of data, the coupling of power system simulation software output content etc.According to different power system simulation softwares, sometimes also need to treat identified parameters and carry out the code setting, in the emulated data bag, copy emulation caller etc.If model is called " PowerSystem " in the name of catalogue, concrete operations comprise:
(1) by parameter identification, required target response curve (being generally the Electrical Power System Dynamic Response curve of actual measurement) data are arranged by the form of PSASP simulation data result, and called after " FN1.DAT " leaves under " PowerSystem " catalogue.PSASP simulation data file is " PowerSystem temp FN1.DAT ", and wherein each curve of output is deposited by row, between every row, with ", ", cuts apart.
(2) according to the actual conditions of target response curve, corresponding network failure and simulation data content are set in the PSASP interface, require simulation data consistent with content, quantity, the time span of target response curve.
(3) treat identified parameters in the PSASP interface and carry out " code " setting.
Because PSASP itself does not support directly calling of external program, so the programming modify feature of parameter is not provided yet.In the PSASP interface, can the various parameters of analogue system be arranged, before carrying out calculating, need computational tasks is carried out to " refreshing " subsequently, its effect is various parameter read-ins that emulation is required in the specific file under the catalogue of analogue system place, and (simulation system parameters writes " PowerSystem Lib DATALIB.DAT ", static load ratio in load model writes last row of " PowerSystem Temp ST.S6 "), so just make model parameter break away from database environment so that the reading of calculation procedure.The parameter identification program can realize the modification to simulation system parameters by the ad-hoc location of revising in " DATALIB.DAT " and " ST.S6 " file.But because the content of these two files can change along with the change of analogue system, therefore consider versatility, need in above-mentioned file, treat identified parameters setting " code ", to play the effect of identification parameter location revision.
The code of parameter to be identified can directly arrange in the PSASP interface, and the parameter code of setting is also numeral, but need to significant difference be arranged with the parameter value that adopts mark the one system.Code after setting completed, need to refresh computational tasks.Can also continue subsequently to make the parameter code in " DATALIB.DAT " and " ST.S6 " file into alphabetical form, but this only just is necessary when the static load proportion of a large amount of identifications of needs.
(4) set the hunting zone of parameter to be identified.Hunting zone can be based on experience value, representative value or node level identification result are determined.
(5) the PSASP calculation procedure is copied in the model catalogue.
Numerous simulation calculation functions of PSASP are that the different executable programs by calling under its installation directory are realized, i.e. the corresponding a kind of simulation calculation function of each executable program.Although PSASP does not directly provide the call instruction of simulation calculation, only need to move executable program corresponding to required computing function and can realize calling.Mainly use trend (" Wmlf.exe "), transient stability (" Wmud.exe "), the transient stability/UPI(" Wmupst.exe " of PSASP in the present invention) three computing modules.These above-mentioned three programs and " lforDLL.DLL " are copied under " PowerSystem Temp " catalogue, can break away from subsequently the PSASP environment and directly be called by external program.The method is not to the cracking of PSASP, and while therefore calling computing function, the software security dog of PSASP still needs, and does not damage PSASP author's commercial interest.
2. supervisor notifies each computing node to start the parallel computation client.Calculation task (and returning to result of calculation to supervisor) when the electric system simulation packet that this client issues for the receiving management machine and parameter identification.
3. supervisor is handed down to each computing node by the electric system simulation packet, and this needs the parallel computation client on supervisor and computing node to cooperatively interact.
4. supervisor starts the optimized algorithm scheduler program through parallel programming.
The present invention is usingd computer cluster as the carrier of Parameter Parallel identification, and has adopted a kind of parallel computation mode of master slave mode.According to these characteristics, the thinking of the modern optimization algorithm being carried out to parallelization is that the parallelization of carrying out calculation task is processed, each calculation task can be one " ant ", one " particle " in particle cluster algorithm or " individuality " in genetic algorithm in ant group algorithm etc., its actual representative be that a group of parameter to be identified may value.All have many calculation tasks in each of optimized algorithm is taken turns iteration simultaneously, by by these distribution of computation tasks on each computing node of computer cluster to realize the parallelization of optimized algorithm.
Be one through the optimized algorithm of parallel programming and operate in parallel optimization algorithm scheduler program on the computer cluster supervisor (or title different and functionally similar program).No matter specifically adopt which kind of optimized algorithm, as shown in Figure 3, concrete steps are its general execution flow process:
(1) the parallel optimization algorithm scheduler program starts, and some parameters of optimized algorithm itself are arranged, such as the inertia weight of the mobile number of times of ant group's maximum, population, variation probability of genetic algorithm etc.
(2) all calculation tasks of calculative determination when the previous round iteration (in such as ant group algorithm in the position of ant, particle cluster algorithm in the position of particle, genetic algorithm individual genomic constitution etc.).
(3) the idle computing node to computer cluster issues calculation task, if there is no idle computing node waited for.
(4), after all calculation tasks of epicycle iteration all issue, wait for that all computing nodes return to result of calculation.If have computing node to fail to return at the appointed time result of calculation, distribution of computation tasks recalculated to other computing nodes.
(5) check whether iterations restriction and minimal error reach desired value, to determine whether to carry out the next round iteration.If also need iteration, get back to step (2) and continue to carry out, otherwise the output optimum results.
5. at first the optimized algorithm scheduler program on supervisor calculates all calculation tasks of epicycle iteration, then to each computing node, issues task.Calculation task is actual is one group of possibility value of parameter to be identified.
6. each computing node calls PSASP calculating and returns to the error numerical value (being the target function value of optimized algorithm) between simulation data curve and target response curve to supervisor after receiving calculation task, and concrete steps are:
(1) " DATALIB.DAT " in the electric system simulation packet and " ST.S6 " file are modified, making the wherein numerical value of parameter to be identified is current calculation task value.
(2) " Wmud.exe " (transient stability) or " Wmupst.exe " (transient stability/UPI) of calling as required under " PowerSystem Temp " catalogue are calculated
(3) end to be calculated such as, then read the data in PSASP output file " PowerSystem Temp FN1.DAT ", and contrasted error of calculation numerical value with the data of target response curve (depositing in " PowerSystem FN1.DAT ").In this step, likely because in calculation task, the value of parameter is unreasonable and cause calculating abnormal end, this just needs to judge by the modification time of inspection " PowerSystem Temp FN1.DAT " file, if abnormal end occurs to calculate, just should return to supervisor the value code of an agreement, unreasonable to mean the parameter current combination.
(4) return to the error numerical value between simulation data curve and target response curve to supervisor, then wait for next calculation task.
7. the result that the optimized algorithm scheduler program on supervisor returns according to each computing node, judge whether to carry out the next round optimizing that changes.If the continuation optimizing, repeating step 5 and step 6, otherwise output parameter identification result.
Claims (2)
1. a parallel identification method for model parameters of electric power system, it is characterized in that: by setting up the mutual parameter identification of realizing the wide area power system model between optimized algorithm and PSASP software, being controlled by optimized algorithm generally alternately of optimized algorithm and PSASP software in the parameter identification process, and realize by an interactive interface program, realized the parallelization of parameter identification by the parallel processing to the electric system simulation calculation task under the computer cluster environment, specifically using the carrier of computer cluster as the Parameter Parallel identification, adopt a kind of parallel computation mode of master slave mode, be that a computer in computer cluster is as parallel computation task management machine, mainly bear the execution of optimized algorithm and the scheduling of parallel computation task, all the other computers are as computing node, mainly bear the electric system simulation calculation task, the actual representative of each electric system simulation calculation task be one group of parameter to be identified may value, in each of optimized algorithm is taken turns iteration, numerous calculation tasks are assigned to each computing node of computer cluster to realize the parallelization of simulation calculation task simultaneously, the concrete reciprocal process of optimized algorithm and PSASP software is: at first (1) optimized algorithm determines one group of value of parameter to be identified, (2) will after this group parameter value format transformation, be handed down to PSASP software by the interactive interface program, (3) interactive interface routine call PSASP software carries out simulation calculation, (4), after having calculated, the interactive interface program is according to simulation data calculation optimization target function value and return to optimized algorithm, (5) optimized algorithm is determined next step Optimum Operation according to the target function value returned, (6) repeat above step, until optimized algorithm reaches iterations or the target function value of setting, reach expection, last output parameter optimum results.
2. a kind of parallel identification method for model parameters of electric power system according to claim 1, is characterized in that the method specifically comprises the steps:
(1) prepare the electric system simulation packet for parameter identification on parallel computation task management machine;
(2) parallel computation task management machine notifies each computing node to start the parallel computation client;
(3) parallel computation task management machine is handed down to each computing node by the electric system simulation packet;
(4) parallel computation task management machine starts the optimized algorithm scheduler program through parallel programming;
(5) the optimized algorithm scheduler program on parallel computation task management machine is assigned calculation task to each computing node;
(6) each computing node calls PSASP software according to calculation task and is calculated, and returns to the error numerical value between simulation data curve and target response curve to parallel computation task management machine;
(7) result that the optimized algorithm scheduler program on parallel computation task management machine returns according to each computing node, judge whether to carry out the next round optimizing, if continue optimizing, and repeating step (5) and step (6), otherwise output parameter identification result.
3. a kind of parallel identification method for model parameters of electric power system according to claim 1 and 2, it is characterized in that: described optimized algorithm is a process parallel programming, operate in the parallel optimization algorithm scheduler program on parallel computation task management machine, no matter specifically adopt which kind of optimized algorithm, its general execution step is:
1 parallel optimization algorithm scheduler program starts, and some parameters of optimized algorithm itself are arranged;
The all calculation tasks of 2 calculative determinations when the previous round iteration;
The 3 idle computing nodes to computer cluster issue calculation task, if there is no idle computing node wait for;
After the 4 all calculation tasks when the epicycle iteration all issue, wait for that all computing nodes return to result of calculation, if there is computing node to fail to return at the appointed time result of calculation, its distribution of computation tasks is recalculated to other computing nodes;
5 check whether iterations restriction and minimal error reach desired value, thereby determine whether to carry out the next round iteration, if also need iteration, get back to step 2 and continue to carry out, otherwise the output optimum results.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN2012100644921A CN102593830B (en) | 2012-03-13 | 2012-03-13 | Parallel identification method for model parameters of electric power system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN2012100644921A CN102593830B (en) | 2012-03-13 | 2012-03-13 | Parallel identification method for model parameters of electric power system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN102593830A CN102593830A (en) | 2012-07-18 |
| CN102593830B true CN102593830B (en) | 2013-12-25 |
Family
ID=46482159
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN2012100644921A Expired - Fee Related CN102593830B (en) | 2012-03-13 | 2012-03-13 | Parallel identification method for model parameters of electric power system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN102593830B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106383974A (en) * | 2016-11-08 | 2017-02-08 | 广西电网有限责任公司电力科学研究院 | Method for establishing simulation model of excitation system |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110187647B (en) * | 2018-02-23 | 2024-08-16 | 北京京东尚科信息技术有限公司 | Model training method and system |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101363886A (en) * | 2008-10-08 | 2009-02-11 | 河海大学 | High and low pressure side power load modelling approach based on single side sampling |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP3683466B2 (en) * | 2000-03-15 | 2005-08-17 | 三菱電機株式会社 | Power system simulation system |
-
2012
- 2012-03-13 CN CN2012100644921A patent/CN102593830B/en not_active Expired - Fee Related
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101363886A (en) * | 2008-10-08 | 2009-02-11 | 河海大学 | High and low pressure side power load modelling approach based on single side sampling |
Non-Patent Citations (5)
| Title |
|---|
| 《基于计算机集群的电力系统暂态风险评估》;郭琦等;《电网技术》;20050831;第29卷(第15期);第13-17,50页 * |
| 《构建高性能集群计算机系统的关键技术》;王文义等;《郑州工业大学学报》;20010331;第22卷(第1期);第6-9页 * |
| JP特开2001-268791A 2001.09.28 |
| 王文义等.《构建高性能集群计算机系统的关键技术》.《郑州工业大学学报》.2001,第22卷(第1期),第6-9页. |
| 郭琦等.《基于计算机集群的电力系统暂态风险评估》.《电网技术》.2005,第29卷(第15期),第13-17,50页. |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106383974A (en) * | 2016-11-08 | 2017-02-08 | 广西电网有限责任公司电力科学研究院 | Method for establishing simulation model of excitation system |
| CN106383974B (en) * | 2016-11-08 | 2019-12-17 | 广西电网有限责任公司电力科学研究院 | Method for establishing simulation model of excitation system |
Also Published As
| Publication number | Publication date |
|---|---|
| CN102593830A (en) | 2012-07-18 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN105069524B (en) | Planned dispatching optimization method based on big data analysis | |
| CN106960103B (en) | Automatic generation method and device of power grid graph | |
| CN104408518B (en) | Based on the neural network learning optimization method of particle swarm optimization algorithm | |
| CN102945516B (en) | The progress analysis method of comprehensive network plans | |
| CN104252652A (en) | Space load predicting method in electricity system | |
| CN111752710B (en) | Data center PUE dynamic optimization method, system and equipment and readable storage medium | |
| CN105719126A (en) | System and method for internet big data task scheduling based on life cycle model | |
| CN113642069B (en) | Building wind load rapid iteration design method based on BIM and heterogeneous system | |
| CN103577926A (en) | Method for achieving real-time calculation and high accuracy of large-scale power grid theoretical line loss | |
| CN102624865A (en) | Cluster load prediction method and distributed cluster management system | |
| Tao et al. | Construction project scheduling under workspace interference | |
| CN102593830B (en) | Parallel identification method for model parameters of electric power system | |
| CN113312748A (en) | Online modeling method and system for load model | |
| CN115965154A (en) | Knowledge graph-based digital twin machining process scheduling method | |
| CN103679289A (en) | Power load prediction method based on multiple regression extrapolation method | |
| CN105718318A (en) | Assembly type scheduling optimization method based on auxiliary engineering design software | |
| Lordan et al. | Energy-aware programming model for distributed infrastructures | |
| US9460240B2 (en) | Method for determining a partial-load condition of a system | |
| CN101923598A (en) | Optimization method of parametric modeling of gliding transfer model of building based on VB | |
| CN115599772A (en) | Localization migration adaptation method of embedded edge computing service application system | |
| CN114825337A (en) | Power grid distributed voltage control distributed power supply optimization system | |
| CN109698498A (en) | Using the power grid security assessment system and method for voltage stability margin index | |
| CN108170532A (en) | A kind of cloud computing resource scheduling method applied towards big data | |
| CN104252552A (en) | Process simulation method for final assembly technology | |
| CN113761633A (en) | Shear wall structure optimization design method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20131225 Termination date: 20210313 |
|
| CF01 | Termination of patent right due to non-payment of annual fee |