CN110795889A - Simulation confirmation method for simulating wind power generation system based on deep learning - Google Patents
Simulation confirmation method for simulating wind power generation system based on deep learning Download PDFInfo
- Publication number
- CN110795889A CN110795889A CN201910875612.8A CN201910875612A CN110795889A CN 110795889 A CN110795889 A CN 110795889A CN 201910875612 A CN201910875612 A CN 201910875612A CN 110795889 A CN110795889 A CN 110795889A
- Authority
- CN
- China
- Prior art keywords
- simulation
- power generation
- data
- wind power
- generation system
- 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.)
- Pending
Links
- 238000004088 simulation Methods 0.000 title claims abstract description 101
- 238000010248 power generation Methods 0.000 title claims abstract description 37
- 238000000034 method Methods 0.000 title claims abstract description 36
- 238000013135 deep learning Methods 0.000 title claims abstract description 11
- 238000012790 confirmation Methods 0.000 title claims abstract description 7
- 230000006870 function Effects 0.000 description 11
- 239000011159 matrix material Substances 0.000 description 10
- 230000008859 change Effects 0.000 description 8
- 239000012634 fragment Substances 0.000 description 8
- 230000014509 gene expression Effects 0.000 description 8
- 230000000694 effects Effects 0.000 description 6
- 238000011156 evaluation Methods 0.000 description 5
- 238000000605 extraction Methods 0.000 description 5
- 238000012795 verification Methods 0.000 description 4
- 238000013527 convolutional neural network Methods 0.000 description 3
- 230000008569 process Effects 0.000 description 3
- 238000012360 testing method Methods 0.000 description 3
- 230000008901 benefit Effects 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 238000010200 validation analysis Methods 0.000 description 2
- 238000004364 calculation method Methods 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000005094 computer simulation Methods 0.000 description 1
- 238000009795 derivation Methods 0.000 description 1
- 239000011521 glass Substances 0.000 description 1
- 238000013178 mathematical model Methods 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 230000008707 rearrangement Effects 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 230000000630 rising effect Effects 0.000 description 1
- 230000011218 segmentation Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
Abstract
The invention provides a simulation confirmation method for simulating a wind power generation system based on deep learning, which comprises the following steps: calculating a simulation credibility value of the wind power generation system according to the simulation similarity level of each field of the wind power generation system; and evaluating the credibility of the simulation method according to the credibility numerical value.
Description
Technical Field
The invention relates to a simulation confirmation method for simulating a wind power generation system based on deep learning.
Background
The wind power generation system has important position in the energy field and the economic field, for establishing a wind power generation facility in one region, the cost of one unit is estimated to be 200 plus 300 million, the patent researches the wind power generation system of Mongolia in China, judges which places are suitable for building wind power generation equipment, and the like ….
At present, a simulation method is adopted to research and generate a weather simulation data set MαTerrain data set M of inner Mongolia regionβElectric energy simulation data set M of each regional unit of wind power generationpEconomic benefit simulation data MqThe data together form a system simulation data set M of the wind power generation system in inner Mongolia regionα,Mβ,...,Mp,...Mη. The specific simulation field related to the method has a terrain evaluation system omega1Wind direction evaluation System omega2Electrical effect evaluation system omega3Loss evaluation system omega4Dynamic environment monitoring system omega5。
The wind power generation system is a power system which is formed by a plurality of different fields working in sequence and is used in the whole inner Mongolia region. Generally, such systems typically employ HLA-based distributed simulation systems for simulation. VV & a mainly comprises three parts: checking, verifying and confirming. Checking means verifying whether the conversion from the mathematical model to the computer model has a certain degree of accuracy. The verification representation indicates whether the model is a real description of the original system and expresses the real world. Experts in the field confirm to evaluate the simulation model to determine if the simulation model meets the requirements.
Disclosure of Invention
The invention aims to provide a simulation confirmation method for simulating a wind power generation system based on deep learning, which can confirm the reliability of the simulation method.
In order to achieve the above object, an aspect of the present invention provides a simulation validation method for simulating a wind turbine system based on deep learning, including:
calculating a simulation credibility value of the wind power generation system according to the simulation similarity level of each field of the wind power generation system;
and evaluating the credibility of the simulation method according to the credibility numerical value.
In a preferred embodiment, said calculating a simulation credibility value for the wind power generation system is calculated by:
where δ represents the number of domain simulations.
The scheme provided by the embodiment of the invention can confirm the reliability of the simulation method of the wind power generation simulation system.
Drawings
Other features, objects and advantages of the invention will become more apparent upon reading of the detailed description of non-limiting embodiments made with reference to the following drawings:
FIG. 1 is a flow chart of a simulation validation method for deep learning based simulation of a wind power generation system in an embodiment of the present invention;
FIG. 2 is a flowchart of a simulation verification method for simulating a wind power generation system based on deep learning in an embodiment of the present invention;
FIG. 3 is a domain simulation model of independent relationships;
FIG. 4 is a domain simulation model of a partnership;
FIG. 5 is a domain simulation model of containment relationships.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some of the structures related to the present invention are shown in the drawings, not all of the structures.
The main idea of the invention is that: confirming the credibility value of the domain simulation, and then confirming the credibility of the reflective memory-based frame heated network relationships method according to the value.
Referring to fig. 1, a simulation confirmation method for simulating a wind power generation system based on deep learning according to an embodiment of the present invention includes the following steps:
step S101: and calculating the simulation credibility value of the wind power generation system according to the simulation similarity level of each field of the wind power generation system.
According to the relation between the fields, the fields are classified and divided into independent modules, a plurality of fields with intersecting and containing relations are calculated to be an independent large field, and then the number of the independent fields under each relation is counted.
The similarity level of the simulation in each field in the wind power generation system may be calculated based on the process shown in fig. 2, and specifically includes:
step S201: and performing simulation verification on a plurality of independent fields in the system by adopting an improved simulation verification method based on SGD and qualitative trend extraction.
Firstly, the lifting format method is adopted to remove the noise which may be contained in the data, including white noise, gross error and the like, and various noises generated by the data during encoding and decoding are removed.
Obtaining:
step S202: and extracting data with a plurality of sliding window lengths through a convolutional neural network, extracting data characteristics for a plurality of times to obtain high-grade data characteristics, and judging the optimal length L of the data.
And acquiring windows with different sizes by using a convolutional neural network to extract data length data l.
Arranging L in a matrix form and internally containing a data type M'pThe data of n sliding window lengths are extracted to obtain a data length matrix:
the matrix eigenvalue calculation method comprises the following steps:
|λE-Lm T|＝0
wherein the identity matrix E is:
after the eigenvalues of the matrix are obtained according to the above formula, the following formula is used:
order:
E-Lm T＝H
then:
(E-Lm T)X＝HX
the matrix H is simplified to obtain H0Where X is a feature matrix, expressed as:
(E-Lm T)X＝H0X
XT＝[x1,x2,...,xn]
by studying the eigenvalues, the feature matrix X is further obtained. Introducing X into convolutional neural network, and introducing L by the methodmThe matrix is set as an input layer, and an output layer is obtained through the following formula:
the 200 th convolution can extract the low-level feature c which is the feature of the low level.
The 300 th convolution can extract the middle-level feature, the middle-level feature b.
The 500 th convolution can extract a high-level feature, i.e., a high-level feature a.
Multiple convolutions may be performed through hout,woutObtain a matrix LmMatching L according to the characteristic value of the high-level characteristic value amThe best sliding window size of the two, one or more optimal solutions L are determinedbestObtaining one M 'through researching an optimal solution'pHow long the window should be chosen.
Step S203: after the simulation data set of the wind power generation system is subjected to checking operation and noise reduction operation, simulation data of multiple fields in the wind power generation system are obtained, and the simulation data are fitted through a unitary linear fitting method; and acquiring the qualitative trend of the simulation data of each field, and judging the derivative of the qualitative trend according to the qualitative trend.
For the fixed-potential extraction method based on the sliding window, if a linear relation exists between simulation data. When the fitting effect cannot meet the requirement, the method can be divided into two cases, one case is that a nonlinear relation exists between data, the window width is required to be reduced, namely, the used data is reduced, and the other case is that the data adopted in the fitting process is re-fitted, namely, the data has steady state, such as a variable is basically unchanged. When extracting and identifying, the two situations need to be distinguished.
And after the size of the sliding window is confirmed according to a deep learning method, extracting the qualitative trend of the simulation data by using a qualitative trend extraction method of the sliding window. Wherein the i-th field sub-moduleQuantification of the trend change as piOne domain simulation consists of k domain sub-modules. The domain simulation trend composed of domain sub-modules is expressed as:
the qualitative trend extraction method of the sliding window comprises the following steps:
the first step is as follows: simulation data M of multiple fields in wind power generation system after noise removal is assumedα′,Mβ′,...,Mp′,...Mη', degree L is y1，y2，...，yLThe time variable is t1，t2，...，tL. Wherein the subscript represents a sequence number, the larger the sequence number, the newer the data. Confirming that the initial width of a sliding window is M according to the data type and the data length, and enabling M original data y1，y2，...，yMPutting the glass into a sliding window; and simultaneously deleting corresponding data from the original data.
The second step is that: and performing unary linear fitting on the simulation data of the sliding window by adopting a least square method as follows:
y(x)＝f(x)＝ax+b
then, if the fitting effect is judged to meet the requirement by adopting an F test, namely the data of the window is linear, the window can be continuously expanded, new data are moved from the original data, and fitting is continuously carried out; and F, checking and judging whether the requirements are met or not until the requirements cannot be met. Determining a linear segment, and going to a fourth step to further divide the segment in the sliding window; if the requirements cannot be met, the process goes to the third step.
The third step is to divide the fitting effect into two cases which can not meet the requirement. In one case, the initial sliding window is set to be large, and data in the window is nonlinear, so that the fitting effect cannot meet the requirement, and in this case, the window width needs to be reduced and fitting needs to be performed again; another situation is that the data within the window is constant, and a distinction needs to be made between these two situations:
(1) and calculating the variance of y according to the data in the sliding window, comparing the variance with a threshold value, if the variance is smaller than the threshold value, indicating that the data is unchanged, continuously moving new data from the original data, calculating the variance, judging whether the data is unchanged until the variance is larger than the threshold value, and extracting the unchanged segment. Go to the fifth step. The threshold value is typically 3 times the steady state variance of the variable.
(2) If the variance of y is larger than the threshold, the data is changed, the sliding window is set to be larger, the window is reduced, the newly moved data is put back into the original data, and fitting and F test are carried out until the requirement is met or the data variance in the window is smaller than the threshold. In this way, a new fragment is extracted. Go to the fifth step.
The fourth step: the data in the sliding window has been F-checked to meet the linearity requirement. However, since the criterion for judging whether the data in the window is linear by using the F test is relaxed, the data larger than or equal to the initial width of the sliding window needs to be further divided. Suppose the window width is W and the data is y1，y2，...，yw. Dividing the window data into two parts and respectively performing linear fitting, namely y1，y2，...，yiAnd yi，yi+1，...，yw，(1<i<w) performing linear fitting, calculating the error sum of the two parts, and taking i with the minimum error sum as the optimal segmentation point. A new segment y1，y2，...，yiThe remaining data is put back into the original data. Go to the fifth step.
The fifth step: and (4) generating a new fragment, putting the last data of the new fragment into the original data in order to ensure the continuity of all fragments, clearing the data of the sliding window, continuously loading M data from the original data, and turning to the second step. If the original data length is zero, it indicates that all fragments have been extracted. Then all of the extracted fragments are identified. If the fragment is not changed during extraction, the fragment is unchanged; otherwise, if a >0, the segment is rising, a <0, the segment is falling.
Step S204: and acquiring the qualitative trend of the simulation data of each field, and judging the derivative of the qualitative trend according to the qualitative trend.
And (3) judging the qualitative trend P of the system simulation data set of the wind power generation system in the inner Mongolia region according to the method:
wherein, the simulation domain data set is as follows:
the first derivative of the qualitative trend P corresponds to a trend:
the second derivative of the qualitative trend P corresponds to a trend:
the third derivative of the qualitative trend P corresponds to the trend:
step S205: classifying the domain simulations according to the relationship to obtain independent domain simulations, comparing each domain simulation with a corresponding real scene, and judging whether the model is consistent with the simulation to obtain a similarity grade; and outputting the simulation similarity grade value K of all the fields.
And (3) establishing a model in the scene, and determining the trend change in the actual scene according to a simulation method adopted by the system in simulation check. For example, for a continuous system, the simulation model adopts a differential form to confirm the expression; for a discrete system, the simulation model adopts a probability theory method to confirm the trend change. Since various errors exist in the real world, only functional expressions can be obtained.
The model expression obtained by the method according to the reflective memory-based frame heated managed networks is approximate to:
the f function represents a function expression of a first-level federal simulation framework, g represents a function expression of a second-level federal simulation framework, and a, b, c and d represent parameters of the second-level federal simulation framework; c (t), d (t) respectively represent the change of the parameters, and N is an integer. X and Y represent data values of human world population phenomena faced by the wind power generation simulation system in inner Mongolia region.
Confirming a function expression corresponding to the simulation method of the actual scene, and then confirming that a derivative function expression is as follows:
f(x)→f′(x)→f″(x)→f′″(x)
and expressing the change of the trend of the function at each stage by a derivative function expression of a simulation method. And simultaneously, matching and comparing the trend with the trend of the simulation data to obtain the similarity value of the simulation data. (P denotes simulation data trend, x denotes function of field sub-module)
f′(x1)→P1
f″(x1)→P1
f′″(x1)→P1
The above equation describes the matching between the trend of a sub-module in a certain field and the trend of a certain section of simulation data, the three formulas represent the matching situation between the derivative of different stages and the simulation data, if the model is consistent with the simulation trend, 1 is output, and if the model is false, 0 is output. Summing the output results to obtain a field submodule and M'pSimilarity level K of simulated data trends1。
And (3) carrying out derivation comparison on the simulation function and a researched target field model, judging which level the field simulation belongs to according to the following judgment standard, and then outputting the level value, wherein the K value represents the similarity level value of the simulation.
The evaluation system of the simulation model and the simulation data, namely the similarity degree of the model and the simulation (M & S), is as follows:
first-order similarity: (the values of the current nodes are the same (more than the upper limit is "1", less than the lower limit is "-1", and the interval between the upper limit and the lower limit is "0") (K ═ 1; 0; -1)
Secondary similarity: the first derivative of the two trends is the same, i.e. the direction of change is identical. (K2)
Third-order similarity: the first and second derivatives of the two trends are the same, and the rate of change in direction is also the same (K ═ 3)
Four-level similarity: the third derivative of the two trends is the same, the change is basically the same, only the initial value is different (K is 4) (K is a credibility value, the trend function is judged according to the model and the simulation (M & S), after the similarity index is judged, the similarity is quantified by a value)
According to the relations of the figures 3, 4 and 5, the similarity grade value of the simulation of each field is obtained.
<1> independent relationship: the multiple domains are independent relations, and each credibility should be evaluated independently when verifying.
Kindependence＝K1+K2+...+Kε(ε<z)
<2> cooperative relationship: the two fields are in an intersecting relationship, the credibility of each field is sequentially verified according to the functions of the intersecting fields, a weight is divided according to the effect, the weight with large contribution is higher, and the total credibility is calculated: (K is the confidence of the domain simulation module; w is the weight, available from FAHP)
<3> intimacy: the two domains are contained relations, and only the credibility of the domain 2 needs to be evaluated when the domain is verified. Domain 1 is also trusted if Domain 2 is satisfied. (Vi denotes the size of the field i)
And dividing the simulation domains into independent domain simulations according to the relationship of the simulation domains, and outputting a similarity grade value K of each domain simulation.
After the similarity level of simulation of each field in the wind power generation system, the simulation credibility value of the wind power generation system in the whole inner Mongolia region is calculated according to the following formula
Where δ represents the number of domain simulations.
Step S102: and evaluating the credibility of the simulation method according to the credibility numerical value.
The wind power generation system generated by simulation, and relevant experts in the field confirm the simulation credibility value of the field by analyzing the credibility value
Then, the simulation method of the wind power generation simulation system in the inner Mongolia region is confirmed according to the numerical values: reflective memory-based frame forward network dependencies, the method has credibility.
Please note that the above description is only for the preferred embodiment of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments illustrated herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.
Claims (2)
1. A simulation confirmation method for simulating a wind power generation system based on deep learning is characterized by comprising the following steps:
calculating a simulation credibility value of the wind power generation system according to the simulation similarity level of each field of the wind power generation system;
and evaluating the credibility of the simulation method according to the credibility numerical value.
2. The method of claim 1, wherein said calculating a simulation credibility value for a wind power generation system is calculated by:
where δ represents the number of domain simulations.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910875612.8A CN110795889A (en) | 2019-09-17 | 2019-09-17 | Simulation confirmation method for simulating wind power generation system based on deep learning |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910875612.8A CN110795889A (en) | 2019-09-17 | 2019-09-17 | Simulation confirmation method for simulating wind power generation system based on deep learning |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN110795889A true CN110795889A (en) | 2020-02-14 |
Family
ID=69427232
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910875612.8A Pending CN110795889A (en) | 2019-09-17 | 2019-09-17 | Simulation confirmation method for simulating wind power generation system based on deep learning |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110795889A (en) |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20120053983A1 (en) * | 2011-08-03 | 2012-03-01 | Sameer Vittal | Risk management system for use with service agreements |
| CN103246762A (en) * | 2013-04-10 | 2013-08-14 | 哈尔滨工程大学 | Method of comprehensive evaluation for simulation credibility of electric propulsion system |
| CN103344437A (en) * | 2013-07-19 | 2013-10-09 | 华北电力大学 | Semi-physical real-time simulation platform for wind generating set |
| EP2869144A1 (en) * | 2013-11-05 | 2015-05-06 | Jinan Railway Vehicles Equipment Co., Ltd. | Simulation testing platform for wind power plant and testing method thereof |
| KR20150122848A (en) * | 2014-04-23 | 2015-11-03 | 전자부품연구원 | Method for Modeling and Simulation in Combined Generation of Wind Power and Wave Power |
| CN105224812A (en) * | 2015-10-21 | 2016-01-06 | 中国电力科学研究院 | Static load frequency factor polymerization in a kind of load model |
| CN105260786A (en) * | 2015-09-08 | 2016-01-20 | 哈尔滨工程大学 | Comprehensive optimization method of simulation credibility evaluation model of electric propulsion system |
| CN107451364A (en) * | 2017-08-03 | 2017-12-08 | 华北电力大学 | A kind of discrimination method of DFIG wind power plants equivalent parameters |
| CN108875158A (en) * | 2018-05-31 | 2018-11-23 | 浙江工业大学 | A kind of battery discharge time prediction technique based on Polynomial curve-fit and BP neural network |
-
2019
- 2019-09-17 CN CN201910875612.8A patent/CN110795889A/en active Pending
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20120053983A1 (en) * | 2011-08-03 | 2012-03-01 | Sameer Vittal | Risk management system for use with service agreements |
| CN103246762A (en) * | 2013-04-10 | 2013-08-14 | 哈尔滨工程大学 | Method of comprehensive evaluation for simulation credibility of electric propulsion system |
| CN103344437A (en) * | 2013-07-19 | 2013-10-09 | 华北电力大学 | Semi-physical real-time simulation platform for wind generating set |
| EP2869144A1 (en) * | 2013-11-05 | 2015-05-06 | Jinan Railway Vehicles Equipment Co., Ltd. | Simulation testing platform for wind power plant and testing method thereof |
| KR20150122848A (en) * | 2014-04-23 | 2015-11-03 | 전자부품연구원 | Method for Modeling and Simulation in Combined Generation of Wind Power and Wave Power |
| CN105260786A (en) * | 2015-09-08 | 2016-01-20 | 哈尔滨工程大学 | Comprehensive optimization method of simulation credibility evaluation model of electric propulsion system |
| CN105224812A (en) * | 2015-10-21 | 2016-01-06 | 中国电力科学研究院 | Static load frequency factor polymerization in a kind of load model |
| CN107451364A (en) * | 2017-08-03 | 2017-12-08 | 华北电力大学 | A kind of discrimination method of DFIG wind power plants equivalent parameters |
| CN108875158A (en) * | 2018-05-31 | 2018-11-23 | 浙江工业大学 | A kind of battery discharge time prediction technique based on Polynomial curve-fit and BP neural network |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111784041B (en) | Wind power prediction method and system based on graph convolution neural network | |
| CN110941716A (en) | Automatic construction method of information security knowledge graph based on deep learning | |
| CN111079430A (en) | Power failure event extraction method combining deep learning and concept map | |
| CN112365171A (en) | Risk prediction method, device and equipment based on knowledge graph and storage medium | |
| CN112685504B (en) | Production process-oriented distributed migration chart learning method | |
| CN112910690A (en) | Network traffic prediction method, device and equipment based on neural network model | |
| CN108664512B (en) | Text object classification method and device | |
| CN114978613B (en) | Network intrusion detection method based on data enhancement and self-supervision feature enhancement | |
| CN105760649A (en) | Big-data-oriented creditability measuring method | |
| CN110335160B (en) | Medical care migration behavior prediction method and system based on grouping and attention improvement Bi-GRU | |
| CN113628059A (en) | Associated user identification method and device based on multilayer graph attention network | |
| CN117078048A (en) | Digital twinning-based intelligent city resource management method and system | |
| Ji et al. | Dictionary-based active learning for sound event classification | |
| CN114021432A (en) | Stress corrosion cracking crack propagation rate prediction method and system | |
| CN110705044A (en) | Simulation verification method for simulating wind power generation system based on deep learning | |
| CN110795889A (en) | Simulation confirmation method for simulating wind power generation system based on deep learning | |
| CN113408816B (en) | Power grid disaster situation assessment method based on deep neural network | |
| CN110705638A (en) | Credit rating prediction classification method using deep network learning fuzzy information feature technology | |
| CN116228014A (en) | DC power distribution network benefit evaluation system and method for infrastructure network access | |
| CN115579069A (en) | Construction method and device of scRNA-Seq cell type annotation database and electronic equipment | |
| CN112488805B (en) | Long-renting market early warning method based on multiple regression time series analysis | |
| CN116050579A (en) | Building energy consumption prediction method and system based on depth feature fusion network | |
| CN112712855B (en) | Joint training-based clustering method for gene microarray containing deletion value | |
| CN115860147A (en) | Customs sheet pre-judging model training method and device based on unbalanced ensemble learning | |
| CN113240219A (en) | Land utilization simulation and prediction method |
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 | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20200214 |