WO2020133735A1 - 基于类人概念学习的配电网早期故障检测方法 - Google Patents
基于类人概念学习的配电网早期故障检测方法 Download PDFInfo
- Publication number
- WO2020133735A1 WO2020133735A1 PCT/CN2019/078575 CN2019078575W WO2020133735A1 WO 2020133735 A1 WO2020133735 A1 WO 2020133735A1 CN 2019078575 W CN2019078575 W CN 2019078575W WO 2020133735 A1 WO2020133735 A1 WO 2020133735A1
- Authority
- WO
- WIPO (PCT)
- Prior art keywords
- distortion
- primitives
- waveform
- distribution network
- humanoid
- Prior art date
Links
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/08—Locating faults in cables, transmission lines, or networks
Definitions
- the invention relates to the technical field of early fault detection of a distribution network, in particular to a method for early fault detection of a distribution network based on humanoid concept learning.
- Power supply reliability is the most important evaluation index of distribution network. Due to the large number of distribution network equipment and wide area, its operation and maintenance work is mainly focused on the post-fault treatment, such as fault location, fault isolation, and fault recovery. However, with the increase in the reliability requirements of power supply in the country and the electricity sales market, the troubleshooting of the distribution network must not only pay attention to the restoration of power supply after the failure, but also pay attention to the early warning of the equipment before the failure. "Inspection” has been changed to "active early warning”, which eliminates permanent faults before they occur, greatly reducing the probability of power outages caused by equipment failures.
- Early fault detection as a way of power state detection, provides new ideas for distribution network operation and maintenance, so that defective equipment can be replaced in advance, improving power supply reliability. At the same time, it reduces the operation and maintenance workload and saves costs.
- Model-driven is often limited to a single model and cannot adapt to complex actual situations. Therefore, data-driven methods are often used. Traditional data-driven methods require large amounts of data and often only target individual scenarios, so improvements are needed.
- the concept of humanoid learns to decompose waveforms by analogy with the process of human observation of waveforms, reconstruct the waveform generation process according to the decomposition results, and realize the waveform recognition by learning this process.
- how to apply humanoid concept learning to early fault diagnosis of distribution networks is still a problem that needs to be solved urgently in this field.
- the purpose of the present invention is to introduce the relevant theories and methods of humanoid concept learning into early fault diagnosis of the distribution network, and to propose a method for early fault detection of the distribution network through theoretical analysis Distribution network early fault detection method based on human-like concept learning, and verify the rationality of the method.
- the concept of humanoid learning simulates the process of humans recognizing waveforms, decomposes the waveform into a superposition of different components, and recognizes the waveform by learning the components and the time relationship between the components.
- the early fault detection method of distribution network based on humanoid concept learning proposed by the present invention has the characteristics of introducing a priori knowledge, requiring fewer samples, and having higher accuracy.
- the present invention is achieved by the following technical solutions.
- An early fault detection method for distribution network based on humanoid concept learning includes the following steps:
- the wavelet transform is used to decompose the waveform into an approximate part and a detail part.
- the approximate part is called a rough shape primitive, and the detail part is called a distortion primitive;
- the wavelet transform function is selected as a 5-layer Meyer function
- the approximate shape is taken as a 5 coefficient
- the distortion is taken as the difference between the original waveform and the a 5 coefficient.
- the distortion is decomposed, the curve is split into multiple segments according to the extreme points in the distortion curve, and each segment is combined with adjacent segments to form pulses, harmonics and Three other primitive distortions.
- the rule for combining each segment with adjacent segments is:
- the amplitude threshold is set to 0.5 times the fundamental wave amplitude; the time length threshold is set to 0.25 times the fundamental wave cycle.
- feature extraction of primitives includes the following extraction principles:
- a o extract the amplitude of each cycle, the length of time T o and a DC component A oft; for harmonic z h, extract the amplitude A h, the frequency f h and the length of the total time t h; for pulse z p , extract amplitude A p , pulse width t p ; for other distortions z other , do not extract features.
- the time relationship between the primitives includes the time relationship between the approximate shape and distortion and the time relationship between distortion and distortion; wherein:
- the time relationship between the approximate shape and the distortion is called the relative fundamental wave position Po , and the relative fundamental wave position Po describes the position on the approximate shape at the initial moment of distortion, and this position is expressed by the phase angle;
- the temporal relationship between distortion and distortion includes:
- the single-phase element pair PP uni describes two distortions that are the same or close at the initial moment in the voltage or current waveform of the same phase
- the three-phase element pair PP tri describes three distortions in the three-phase voltage or current waveform that are the same or close at the initial moment.
- the time relationship related to other distortions is ignored.
- the constructed waveform probability distribution formula is:
- ⁇ w is a known waveform type
- the noise follows a normal distribution S N ⁇ N( ⁇ , ⁇ 2 )
- the number of primitives is ⁇
- the primitive feature parameter is p
- the time relationship between primitives is R;
- the neutral point current is summed to produce 7 different types of waveforms (I A , I B , I C , I N , U A , U B , U C ),
- the probability distribution formula in different types of waveforms to obtain abnormal events is:
- ⁇ E is a known kind of event
- the waveform w ⁇ I A, I B , I C, I N, U A, U B, U C ⁇ .
- the type of abnormal event is determined according to the probability distribution of the abnormal event in different types of waveforms, that is, the comparison In the size of different types of waveforms, the waveform type corresponding to the maximum value is taken to obtain the waveform judgment result.
- the present invention has the following beneficial effects:
- the present invention provides an early fault detection method for distribution network based on humanoid concept learning. It introduces related theories and methods of humanoid concept learning into early fault detection of distribution network. Fault detection algorithm and verify the rationality of the algorithm. As a kind of visual concept, voltage and current waveforms are decomposed into rough shapes and various distortions. By calculating the probability distribution of each component, the probability distribution of the entire waveform can be obtained to determine the type of waveform.
- the early fault detection method for distribution network based on humanoid concept learning provided by the present invention is much better than traditional detection in terms of required data volume and accuracy, and is of great significance for the detection and processing of early faults in distribution network.
- FIG. 1 is a schematic diagram of waveform decomposition provided by an embodiment of the present invention.
- FIG. 2 is a schematic diagram of distortion decomposition provided by an embodiment of the present invention.
- FIG. 3 is a schematic diagram of the definition of the time relationship between primitives provided by an embodiment of the present invention. wherein, (a) is a schematic diagram of the definition of the time relationship between the general shape and distortion; (b) is the time relationship between the distortion and distortion Define the schematic diagram;
- FIG. 4 is a schematic diagram of a waveform generation process provided by an embodiment of the present invention.
- FIG. 5 is a working flowchart of an early fault detection method for a distribution network based on humanoid concept learning provided by an embodiment of the present invention.
- this embodiment provides an early fault detection method for a distribution network based on humanoid concept learning, including the following steps:
- Step S1 The wavelet transform is used to decompose the waveform into an approximate part and a detail part, wherein the two parts, the approximate part and the detail part, are called a rough shape primitive and a distortion primitive, respectively.
- Step S2 According to the extreme point, the distortion (detail part) is divided into three elements: harmonics, pulses, and other distortions.
- Step S3 Extract the features of the primitives and the time relationship between the primitives.
- Step S4 Construct the probability distribution of the waveform according to the characteristics of the primitives and the time relationship between the primitives.
- Step S5 Obtain the judgment result of the waveform according to the probability distribution of different types of waveforms.
- the wavelet function is selected as a 5-layer Meyer function
- the approximate shape is taken as a 5 coefficient
- the distortion is taken as the difference between the original waveform and the a 5 coefficient. Decompose the results.
- split the curve into small segments according to the extreme points in the distortion curve as shown in the first step of Figure 2, combine each segment with the surrounding small segments to form a pulse , Harmonics, and other distortions are shown in the second step shown in Figure 2.
- the combination rule is as follows: 1) If the adjacent small segments have opposite monotonicity, the amplitude and time length are small, and there are more than three segments, they constitute harmonics (as shown in the combination of 1, 2, 3, and 4 segments in Figure 2) ; 2) If the two adjacent sections have opposite monotonicity, the amplitude exceeds the threshold (here set to 0.5 times the fundamental amplitude), and the length of time is less than the threshold (here set to 0.25 times the fundamental frequency), then constitute a pulse (as shown in the figure The combination of 5 and 6 segments shown in 2); 3) The small segments that cannot form harmonics and pulses are called other distortions (7 and 8 segments shown in Figure 2).
- the general shape, harmonics, pulses, and other distortions are collectively referred to as primitives.
- the primitive features are extracted as follows: for the general shape z o , the amplitude A o of each cycle, the time length T o and the DC component A oft are extracted; for the harmonic z h , the amplitude A h , the frequency f h and the total time are extracted Length t h ; for pulse z p , amplitude A p and pulse width t p are extracted; for other distortion z other , since z other is not related to early faults, its characteristics are not extracted. There is also a time relationship between primitives. Primitives can be divided into two types: approximate shape and distortion (harmonics, pulses, other distortions), and the time relationship between primitives can also be divided into approximate shape and distortion.
- Time relationship between distortion and distortion Similarly, the time relationship related to z other is not discussed.
- Time relationship between the shape of a substantially distortion fundamental primitives called relative position P o, P o is the position described distortion primitive initial moment in the general shape, the position indicated by the phase angle.
- the time relationship between the distortion primitives is as follows: interval time t int , single-phase primitive pair PP uni and three-phase primitive pair PP tri .
- t int describes the time interval between two adjacent distortion primitives.
- Single-phase primitives describe PP uni as two distortion primitives with the same initial voltage and close to each other in the voltage and current waveforms of the same phase.
- the three-phase element pair PP tri describes three distortion element elements that are the same at the initial moment in the three-phase voltage/current waveform and are close to them.
- PP uni and PP tri describe a correlation.
- n uni and n tri are used to denote the number of the two.
- the schematic diagram of the above time relationship is shown in Figures 3(a) and (b).
- a waveform has been uniquely decomposed into a combination of the above primitives, and primitive features and temporal relationships are used to describe this combination.
- a waveform generation process is proposed based on the above decomposition, as shown in FIG. 4. This process is described as follows: First, select some primitives in the primitive library (the primitive type is 4, the number of each primitive is arbitrary), each primitive has its own characteristic parameters; combine these primitives in order Formed into a waveform, these sequences constitute the time relationship between the primitives. The above belongs to the category level. The type of waveform is determined by the cause of the fault, the faulty device, and the faulty location.
- the waveforms of the same type are affected by line parameters, grid structure, load conditions, sensor parameters, noise, etc., and will appear as different cases.
- the characteristic parameters and time relationship of the primitive will change to some extent, and noise will be introduced. The above belongs to the case level.
- the probability distribution formula of the waveform can be derived:
- ⁇ w is a known waveform type
- the noise follows a normal distribution S N ⁇ N( ⁇ , ⁇ 2 )
- the number of primitives is ⁇
- the primitive feature parameter is p
- the time relationship between primitives is R.
- the corresponding three-phase voltage and three-phase current are often recorded.
- the neutral point current can be obtained by summing the three-phase current, so that 7 different types of waveforms (I A , I B , I C , I N , U A , U B , U C ). Therefore, the probability distribution of an abnormal event in different types of waveforms can be written as:
- ⁇ E is a known kind of event
- the waveform w ⁇ I A, I B , I C, I N, U A, U B, U C ⁇ .
- the type of abnormal events can be determined, that is, the comparison
- the comparison For the size of different types of waveforms, take the waveform type corresponding to the maximum value.
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Locating Faults (AREA)
- Remote Monitoring And Control Of Power-Distribution Networks (AREA)
- Complex Calculations (AREA)
Abstract
Description
Claims (10)
- 一种基于类人概念学习的配电网早期故障检测方法,其特征在于,包括如下步骤:S1:利用小波变换分解波形为近似部分和细节部分,其中近似部分称为大致形状基元,细节部分称为畸变基元;S2:根据极值点将畸变基元拆分为谐波、脉冲以及其他畸变三个基元;S3:提取基元的特征以及基元间的时间关系;S4:根据基元的特征以及基元间的时间关系,构造波形的概率分布;S5:根据不同种类波形的概率分布,得到波形的判断结果。
- 根据权利要求1所述的基于类人概念学习的配电网早期故障检测方法,其特征在于,所述S1中,小波变换函数选取为5层Meyer函数,大致形状取a 5系数,畸变取原始波形与a 5系数的差。
- 根据权利要求1所述的基于类人概念学习的配电网早期故障检测方法,其特征在于,所述S2中,对畸变进行分解,根据畸变曲线中的极值点将曲线拆分为多个分段,将每一分段与相邻分段进行组合,构成脉冲、谐波以及其他畸变三个基元。
- 根据权利要求3所述的基于类人概念学习的配电网早期故障检测方法,其特征在于,将每一分段与相邻分段进行组合的规则为:-如果相邻分段单调性相反,幅值与时间长度差异在0.8倍到1.2倍之内且存在三段以上,则构成谐波;-如果相邻两段分段单调性相反,幅值超过阈值,时间长度小于阈值,则构成脉冲;-无法构成谐波和脉冲的分段则为其他畸变。
- 根据权利要求4所述的基于类人概念学习的配电网早期故障检测方法,其特征在于,幅值阈值设置为0.5倍基波幅值;时间长度阈值设置为0.25倍基波周波。
- 根据权利要求1所述的基于类人概念学习的配电网早期故障检测方法,其特征在于,所述S3中,基元的特征提取,包括如下提取原则:对于大致形状z o,提取每个周波的幅值A o、时间长度T o以及直流分量A oft;对于谐波z h,提取幅值A h、频率f h以及总时间长度t h;对于脉冲z p,提取幅值A p、脉宽t p;对于其他畸变z other,不提取特征。
- 根据权利要求1所述的基于类人概念学习的配电网早期故障检测方法,其特征在于,所述S3中,基元间的时间关系包括大致形状和畸变之间的时间关系以及畸变与畸变之间的时间关系;其中:所述大致形状与畸变之间的时间关系称为相对基波位置P o,相对基波位置P o描述畸变初始时刻在大致形状上的位置,这一位置采用相角表示;畸变与畸变之间的时间关系包括:-间隔时间t int,描述两个相邻畸变之间的时间间隔;-单相基元对PP uni,描述同一相电压或电流波形中初始时刻相同或接近的两个畸变;-三相基元对PP tri描述三相电压或电流波形中初始时刻相同或接近的三个畸变。
- 根据权利要求7所述的基于类人概念学习的配电网早期故障检测方法,其特征在于,所述大致形状和畸变之间的时间关系以及畸变与畸变之间的时间关系中,忽略与其他畸变相关的时间关系。
- 根据权利要求1所述的基于类人概念学习的配电网早期故障检测方法,其特征在于,所述S4中,构造的波形概率分布公式为:根据配网异常事件中记录的三相电流,求和得到中性点电流,产生7个不同种类的波形(I A,I B,I C,I N,U A,U B,U C),得到异常事件的在不同种类波形中的概率分布公式为:
Priority Applications (3)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
GB1912002.1A GB2582676B (en) | 2018-12-27 | 2019-03-19 | Early Failure Detection Method for Power Distribution Network Equipment Based On Human-Level Concept Learning |
JP2019538613A JP7107580B2 (ja) | 2018-12-27 | 2019-03-19 | 配電網の早期故障の検出方法 |
US16/674,233 US11143686B2 (en) | 2018-12-27 | 2019-11-05 | Method for detecting power distribution network early failure |
Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201811607507.8A CN109782126B (zh) | 2018-12-27 | 2018-12-27 | 基于类人概念学习的配电网早期故障检测方法 |
CN201811607507.8 | 2018-12-27 |
Related Child Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
US16/674,233 Continuation US11143686B2 (en) | 2018-12-27 | 2019-11-05 | Method for detecting power distribution network early failure |
Publications (1)
Publication Number | Publication Date |
---|---|
WO2020133735A1 true WO2020133735A1 (zh) | 2020-07-02 |
Family
ID=66497717
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
PCT/CN2019/078575 WO2020133735A1 (zh) | 2018-12-27 | 2019-03-19 | 基于类人概念学习的配电网早期故障检测方法 |
Country Status (3)
Country | Link |
---|---|
JP (1) | JP7107580B2 (zh) |
CN (1) | CN109782126B (zh) |
WO (1) | WO2020133735A1 (zh) |
Cited By (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN117454096A (zh) * | 2023-12-25 | 2024-01-26 | 西安高商智能科技有限责任公司 | 一种电机生产质量检测方法及*** |
Families Citing this family (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN113884803A (zh) * | 2021-08-27 | 2022-01-04 | 国网山东省电力公司日照供电公司 | 配网故障研判方法 |
CN113985733B (zh) * | 2021-10-26 | 2023-11-17 | 云南电网有限责任公司电力科学研究院 | 一种基于自适应概率学习的配电网故障辨识方法 |
Citations (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN103091096A (zh) * | 2013-01-23 | 2013-05-08 | 北京信息科技大学 | 基于eemd和小波包变换的早期故障敏感特征提取方法 |
CN104122486A (zh) * | 2014-07-30 | 2014-10-29 | 浙江群力电气有限公司 | 一种电缆早期故障检测方法及装置 |
CN107025433A (zh) * | 2017-03-03 | 2017-08-08 | 深圳大学 | 视频事件类人概念学习方法及装置 |
CN107329049A (zh) * | 2017-08-21 | 2017-11-07 | 集美大学 | 一种基于卡尔曼滤波器的输电线路早期故障检测方法 |
CN107340456A (zh) * | 2017-05-25 | 2017-11-10 | 国家电网公司 | 基于多特征分析的配电网工况智能识别方法 |
Family Cites Families (9)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN100546143C (zh) * | 2007-09-26 | 2009-09-30 | 东北大学 | 一种小电流接地故障检测与定位的装置及方法 |
CN101726690B (zh) * | 2009-11-30 | 2012-01-11 | 上海电力学院 | 电力***短路全电流各分量分解方法 |
CN102129013B (zh) * | 2011-01-21 | 2013-11-27 | 昆明理工大学 | 一种利用自然频率和人工神经网络的配网故障测距方法 |
US9508612B2 (en) * | 2012-03-15 | 2016-11-29 | Applied Materials, Inc. | Method to detect wafer arcing in semiconductor manufacturing equipment |
KR101308003B1 (ko) | 2012-07-31 | 2013-09-12 | 서울과학기술대학교 산학협력단 | 웨이블릿 기반 아크 판별방법 |
CN104597344A (zh) * | 2015-01-08 | 2015-05-06 | 上海交通大学 | 基于小波一层高频分量相关性的故障电弧在线检测方法 |
CN106501667B (zh) * | 2016-03-16 | 2019-06-21 | 国网山东省电力公司济宁供电公司 | 一种含分布式电源配电网单相断线故障选线方法 |
CN106501668B (zh) * | 2016-03-16 | 2019-06-28 | 国网山东省电力公司济宁供电公司 | 一种传统配电网单相断线故障选线方法 |
CN108279364B (zh) * | 2018-01-30 | 2020-01-14 | 福州大学 | 基于卷积神经网络的配电网单相接地故障选线方法 |
-
2018
- 2018-12-27 CN CN201811607507.8A patent/CN109782126B/zh active Active
-
2019
- 2019-03-19 JP JP2019538613A patent/JP7107580B2/ja active Active
- 2019-03-19 WO PCT/CN2019/078575 patent/WO2020133735A1/zh active Application Filing
Patent Citations (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN103091096A (zh) * | 2013-01-23 | 2013-05-08 | 北京信息科技大学 | 基于eemd和小波包变换的早期故障敏感特征提取方法 |
CN104122486A (zh) * | 2014-07-30 | 2014-10-29 | 浙江群力电气有限公司 | 一种电缆早期故障检测方法及装置 |
CN107025433A (zh) * | 2017-03-03 | 2017-08-08 | 深圳大学 | 视频事件类人概念学习方法及装置 |
CN107340456A (zh) * | 2017-05-25 | 2017-11-10 | 国家电网公司 | 基于多特征分析的配电网工况智能识别方法 |
CN107329049A (zh) * | 2017-08-21 | 2017-11-07 | 集美大学 | 一种基于卡尔曼滤波器的输电线路早期故障检测方法 |
Cited By (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN117454096A (zh) * | 2023-12-25 | 2024-01-26 | 西安高商智能科技有限责任公司 | 一种电机生产质量检测方法及*** |
CN117454096B (zh) * | 2023-12-25 | 2024-03-01 | 西安高商智能科技有限责任公司 | 一种电机生产质量检测方法及*** |
Also Published As
Publication number | Publication date |
---|---|
JP7107580B2 (ja) | 2022-07-27 |
JP2021512377A (ja) | 2021-05-13 |
CN109782126A (zh) | 2019-05-21 |
CN109782126B (zh) | 2021-01-01 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
WO2020133735A1 (zh) | 基于类人概念学习的配电网早期故障检测方法 | |
US11143686B2 (en) | Method for detecting power distribution network early failure | |
CN109444667B (zh) | 基于卷积神经网络的配电网早期故障分类方法及装置 | |
CN103713237B (zh) | 一种电力***输电线路短路故障诊断方法 | |
CN109406949B (zh) | 基于支持向量机的配电网早期故障检测方法及装置 | |
CN106330095B (zh) | 一种大型光伏电站汇集***故障定位方法 | |
CN104280644B (zh) | 一种直流输电工程典型暂态故障识别方法 | |
CN101499659B (zh) | 基于基尔霍夫电流定律的变电站分布式状态估计方法 | |
CN112345968A (zh) | 一种直流***接地故障综合检测方法 | |
Qu et al. | Model-free underground cable incipient fault location using two-terminal zero-sequence measurements | |
CN111339624B (zh) | 基于psasp和emtp/atp短路电流直流分量计算方法 | |
CN109324268B (zh) | 基于贝叶斯推理的配电网早期故障检测方法及装置 | |
CN113933647A (zh) | 一种基于首半波功率方向的小电流接地***故障选线方法 | |
Lopes et al. | A transient based approach to diagnose high impedance faults on smart distribution networks | |
CN105629144B (zh) | 基于指纹库的高压开关设备局部放电诊断方法及*** | |
CN108646145B (zh) | 一种输电线路闪络塔定位方法及*** | |
CN116660679A (zh) | 一种基于网络拓扑的配电网故障分析方法 | |
CN115980515A (zh) | 一种单相接地故障定位方法 | |
Fahim et al. | An agreement based dynamic routing method for fault diagnosis in power network with enhanced noise immunity | |
CN111257687B (zh) | 一种基于改进的meemd的单相接地故障选线方法 | |
Cui et al. | High impedance fault detection method based on Data divergence in the distribution network | |
Tian et al. | Enhancing Arc Fault Detection in DC Series Circuits Using a Sample Entropy and High-Order Cumulants-Based System | |
Datta et al. | Line to ground and line to line fault analysis in ieee standard 9 BUS system | |
Yuan et al. | Faulty feeder detection based on sparse waveform encoding and simple convolutional neural network with multiscale filters and one layer of convolution | |
Sheng et al. | An overview of fault identification techniques in power distribution networks: methods and models |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
ENP | Entry into the national phase |
Ref document number: 2019538613 Country of ref document: JP Kind code of ref document: A |
|
ENP | Entry into the national phase |
Ref document number: 201912002 Country of ref document: GB Kind code of ref document: A Free format text: PCT FILING DATE = 20190319 |
|
121 | Ep: the epo has been informed by wipo that ep was designated in this application |
Ref document number: 19903499 Country of ref document: EP Kind code of ref document: A1 |
|
NENP | Non-entry into the national phase |
Ref country code: DE |
|
32PN | Ep: public notification in the ep bulletin as address of the adressee cannot be established |
Free format text: NOTING OF LOSS OF RIGHTS PURSUANT TO RULE 112(1) EPC (EPO FORM 1205A DATED 22.10.2021) |
|
122 | Ep: pct application non-entry in european phase |
Ref document number: 19903499 Country of ref document: EP Kind code of ref document: A1 |