CN112444758B - Intelligent power distribution network line fault diagnosis and classification evaluation method - Google Patents

Intelligent power distribution network line fault diagnosis and classification evaluation method Download PDF

Info

Publication number
CN112444758B
CN112444758B CN202011295829.0A CN202011295829A CN112444758B CN 112444758 B CN112444758 B CN 112444758B CN 202011295829 A CN202011295829 A CN 202011295829A CN 112444758 B CN112444758 B CN 112444758B
Authority
CN
China
Prior art keywords
phase
phase voltage
characteristic
power grid
short
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.)
Active
Application number
CN202011295829.0A
Other languages
Chinese (zh)
Other versions
CN112444758A (en
Inventor
张国荣
张宇
汤彬
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Hefei University of Technology
Original Assignee
Hefei University of Technology
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Hefei University of Technology filed Critical Hefei University of Technology
Priority to CN202011295829.0A priority Critical patent/CN112444758B/en
Publication of CN112444758A publication Critical patent/CN112444758A/en
Application granted granted Critical
Publication of CN112444758B publication Critical patent/CN112444758B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

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
    • G01R31/50—Testing of electric apparatus, lines, cables or components for short-circuits, continuity, leakage current or incorrect line connections
    • G01R31/52—Testing for short-circuits, leakage current or ground faults
    • 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
    • G01R31/081—Locating faults in cables, transmission lines, or networks according to type of conductors
    • G01R31/086—Locating faults in cables, transmission lines, or networks according to type of conductors in power transmission or distribution networks, i.e. with interconnected conductors
    • 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
    • G01R31/088—Aspects of digital computing
    • 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

Abstract

The invention discloses a line fault diagnosis and classification evaluation method for an intelligent power distribution network, which comprises the following steps: 1. on-line obtaining three-phase voltage signal U of power grida(t)、Ub(t)、Uc(t); 2. decomposing the three-phase voltage signals by 5 layers through discrete wavelets, and reconstructing three-phase voltage characteristic signals u by taking a5 signalsa(t)、ub(t)、uc(t); 3. fourier transform to calculate phase information of three-phase voltage
Figure DDA0002785381400000011
Figure DDA0002785381400000012
4. Drawing a polar coordinate graph, and obtaining a matrix Z with the size of n x n through gray level binary conversiona、Zb、Zc(ii) a 5. Calculating the characteristic matrix C, D and the characteristic parameter V to obtain the characteristic coefficient E of the three-phase voltagea、Eb、Ec(ii) a 5. According to the characteristic coefficient Ea、Eb、EcAnd judging the line state of the intelligent power distribution network. The invention can accurately judge the fault type, is beneficial to fault phase selection and fault analysis, and can improve the power supply reliability of the power grid and ensure the safe operation and maintenance efficiency of the power grid.

Description

Intelligent power distribution network line fault diagnosis and classification evaluation method
Technical Field
The invention relates to the field of intelligent power distribution network line fault detection, in particular to a detection and evaluation method for a power grid short-circuit fault.
Background
The power grid is a high-efficiency and rapid energy transmission channel and an optimized configuration platform, is a key link of sustainable development of energy and power, plays an important pivotal role in a modern energy supply system and is related to national energy safety. With the comprehensive construction of intelligent, digital and information technology power grids, data of the power industry shows an explosive growth situation, a power system in operation generates a large amount of information, the occurrence rate of faults in the increasingly complex power grid system is increased, the influence of natural or human factors on the power grid is more obvious, faults are caused to occur frequently, and the quality of electric energy is reduced or electric equipment is damaged. In recent years, power failure events with large scale and serious harm occur in all countries, and no event causes huge economic loss and social influence. Meanwhile, the rapid development of the smart power grid also provides higher standards for researchers under the background of the big data era, and how to rapidly and accurately detect and position the fault is an important research topic at present.
In order to meet the requirements of the national people on the power grid, a plurality of automatic equipment devices are used, when a fault occurs, the corresponding devices such as a circuit breaker protector and the like can immediately react and give a series of alarm information, but the alarm information is complex in content or has wrong report and missing report, so that the safety and the reliability of the power grid cannot be guaranteed.
Disclosure of Invention
The invention aims to overcome the defects in the prior art, and provides the intelligent power distribution network line fault diagnosis and classification evaluation method, so that the short-circuit fault of the power distribution network can be accurately and rapidly detected, and the fault type can be accurately judged in time, thereby facilitating fault phase selection and accident analysis, further improving the operation reliability of the power distribution network, and ensuring the safe operation and maintenance efficiency of the power grid.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
the invention discloses a method for diagnosing and classifying and evaluating line faults of an intelligent power distribution network, which is characterized by comprising the following steps:
step 1, collecting three-phase voltage signal U of power grida(t)、Ub(t)、Uc(t)；
Step 2, using db5 mother wave to pair the three-phase voltage data Ua(t)、Ub(t)、Uc(t) performing a 5-level discrete wavelet decomposition,the obtained detail signal d5 is used for reconstructing a three-phase voltage characteristic signal ua(t)、ub(t)、uc(t)；
Step 3, comparing the three-phase voltage characteristic signal ua(t)、ub(t)、uc(t) Fourier transform to obtain phase information of three-phase voltage
Figure BDA0002785381380000011
Step 4, constructing polar coordinate signals of three-phase voltage
Figure BDA0002785381380000012
Thereby drawing a three-phase voltage polar coordinate graph;
step 5, converting the three-phase voltage polar coordinate graph into a gray binary matrix Z with the pixel size of n multiplied by n through gray binary conversiona、Zb、Zc；
Step 6, calculating a short-circuit fault characteristic coefficient:
step 6.1, calculating characteristic matrixes C of voltage waveform changes respectively by using the formulas (1) and (2)a1And Da1：
Ca1＝(Za-E(Za))(za-E(za))T (1)
Da1＝(Za-za)TCa1 -1(Za-za) (2)
In the formulae (1) and (2), zaA conversion matrix Z of a polar coordinate diagram when the A-phase voltage of the power grid is normalaAcquiring a conversion matrix of a polar coordinate graph of the phase voltage A of the power grid on line; e (Z)a) Is a conversion matrix ZaAverage value of each column in (1); e (z)a) Is a transformation matrix zaAverage value of each column in (1);
step 6.2, calculating four characteristic parameters { V) by using the formula (3)ai|i＝1,2,3,4}：
Figure BDA0002785381380000021
In the formula (3), Δ (i, j) is a conversion matrix zaAnd a conversion matrix ZaAbsolute value difference on ith row and jth column element; m is the maximum value of the absolute value difference; m is the minimum of the absolute difference, α is the resolution factor;
step 6.3, obtaining a power grid A-phase short-circuit fault characteristic coefficient E by using the formula (4)a：
Figure BDA0002785381380000022
Step 7, calculating short-circuit fault characteristic coefficients E of the B phase and the C phase of the power grid according to the process of the step 6b、Ec；
Step 8, according to the three characteristic coefficients Ea、Eb、EcJudging the line state of the intelligent power distribution network:
if 0.1<Eb≈Ec<0.6 and 1.6<Ea<2; it represents that phase A is grounded and short-circuited;
if 0.8<Eb≈Ec<1.2 and 1.2<Ea<1.6; indicating BC two-phase short circuit;
if 0.2<Eb≈Ec<0.8 and 0.6<Ea<1.0; indicating the BC two-phase grounding short circuit;
if Ea≈Eb≈Ec>2; then a three-phase short is indicated;
if Ea<0.1 and Eb<0.1 and Ec<0.1; it indicates no failure.
Compared with the prior art, the invention has the beneficial effects that:
the three-phase voltage data acquired online from the voltage transformer end is evaluated, the fault type and the distance can be accurately and quickly judged, when the power grid has a short-circuit fault, maintainers can accurately judge the operation state of the power grid in time through three characteristic coefficients, the fault type can be identified, fault phase selection and accident analysis are facilitated, and the method has important significance for improving the power supply reliability, safe operation, maintenance and the like of the system.
Drawings
FIG. 1 is a flow chart of a method for detecting a short-circuit fault of a power grid according to the present invention;
FIG. 2 is a polar diagram of the phase A short circuit ground fault of the present invention;
FIG. 3 is a gray scale binary transition diagram for single-phase ground fault of the present invention;
FIG. 4 is a diagram of a three-phase voltage acquisition and processing apparatus of the present invention;
reference numbers in the figures: 1 high voltage current limiting fuse; 2 a capacitive voltage divider; 3, sampling a controller; 4 signal processing means.
Detailed Description
In this embodiment, as shown in fig. 1, a method for diagnosing and classifying and evaluating line faults of an intelligent power distribution network includes:
step 1, as shown in fig. 4, a voltage signal is transmitted to a capacitive voltage divider 2 through a high-voltage current-limiting fuse 1 on a power distribution network, and the three-phase voltage U of the power network is connected to a high-precision sampling controller 3a(t)、Ub(t)、Uc(t) and transmitted to the signal processing means 4;
step 2, three-phase voltage data U are compareda(t)、Ub(t)、Uc(t) carrying out discrete wavelet transform, carrying out 5-layer decomposition by using db5 mother wave to obtain a detail signal d5 for reconstructing a three-phase voltage characteristic signal ua(t)、ub(t)、uc(t)；
Step 3, three-phase voltage characteristic signal ua(t)、ub(t)、uc(t) Fourier transform to obtain phase information of three-phase voltage
Figure BDA0002785381380000031
Step 4, constructing polar coordinate signals of three-phase voltage
Figure BDA0002785381380000032
Thereby drawing a three-phase voltage polar coordinate graph; in this embodiment, the polar coordinates of the a-phase short-circuit ground fault are shown in fig. 2;
step 5, Matla is addedb, setting the background of the image to be white, and obtaining a three-primary-color light mode diagram in a polar coordinate form. The RGB map is converted into a gray image by the expression (1), and the three-phase voltage polar coordinate gray map is converted into a gray binary matrix Z with the matrix value of 0 and 1(1 represents the background pixel of the image, and 0 represents the pixel corresponding to the voltage signal) and the pixel size of n multiplied by n by the expression (2)a、Zb、Zc(ii) a The single-phase earth fault gray scale binary conversion is shown in fig. 3;
Figure BDA0002785381380000041
Figure BDA0002785381380000042
in formula (1), g (i, j) is a two-dimensional matrix of polar coordinate gray scale transformation, i, j represents each element position in the matrix, and u (g) is an average value of the sum of all elements in the two-dimensional matrix g;
step 6, calculating a short-circuit fault characteristic coefficient:
step 6.1, calculating a characteristic matrix C of voltage waveform change by using the formula (3) and the formula (4) respectivelya1And Da1：
Ca1＝(Za-E(Za))(za-E(za))T (3)
Da1＝(Za-za)TCa1 -1(Za-za) (4)
In the formulae (3) and (4), zaThe transformation matrix is a polar coordinate graph when the phase A voltage of the power grid is normal; e (Z)a) Is a conversion matrix ZaAverage value of each column in (1); e (Z)a) Is a conversion matrix ZaAverage value of each column in (1); e (z)a) Is a transformation matrix zaAverage value of each column in (1);
step 6.2, calculating four characteristic parameters { V) by using the formula (5)ai|i＝1,2,3,4}：
Figure BDA0002785381380000043
In the formula (5), Δ (i, j) is a conversion matrix zaAnd a conversion matrix ZaAbsolute value difference on ith row and jth column element; m is the maximum value of the absolute value difference; m is the minimum value of the absolute value difference, α is the resolution coefficient, and is 0.5 in this example;
step 6.3, obtaining a power grid A-phase short-circuit fault characteristic coefficient E by using the formula (6)a：
Figure BDA0002785381380000051
Step 7, calculating short-circuit fault characteristic coefficients E of the B phase and the C phase of the power grid according to the process of the step 6b、Ec；
Step 8, according to the three characteristic coefficients Ea、Eb、EcJudging the line state of the intelligent power distribution network, in this embodiment, judging the line state of the intelligent power distribution network according to the following table 1;
TABLE 1
State of the grid Characteristic coefficient of short circuit fault
Single-phase grounding short circuit 0.1<Eb≈Ec<0.6,1.6<Ea<2(A phase short circuit)
Two-phase short circuit 0.8<Eb≈Ec<1.2,1.2<Ea<1.6(BC two-phase short circuit)
Two phasesShort circuit to ground 0.2<Eb≈Ec<0.8,0.6<Ea<1.0(BC two-phase short circuit grounding)
Three-phase short circuit Ea≈Eb≈Ec>2；
Is normal Ea<0.1；Eb<0.1；Ec<0.1；
If 0.1<Eb≈Ec<0.6 and 1.6<Ea<2; it represents that phase A is grounded and short-circuited;
if 0.8<Eb≈Ec<1.2 and 1.2<Ea<1.6; indicating BC two-phase short circuit;
if 0.2<Eb≈Ec<0.8 and 0.6<Ea<1.0; indicating the BC two-phase grounding short circuit;
if Ea≈Eb≈Ec>2; then a three-phase short is indicated;
if Ea<0.1 and Eb<0.1 and Ec<0.1; it indicates no failure.

Claims (1)

1. A fault diagnosis and classification evaluation method for an intelligent power distribution network line is characterized by comprising the following steps:
step 1, collecting three-phase voltage signal U of power grida(t)、Ub(t)、Uc(t)；
Step 2, using db5 mother wave to pair the three-phase voltage data Ua(t)、Ub(t)、Uc(t) carrying out 5-layer discrete wavelet decomposition, and using the obtained detail signal d5 to reconstruct three-phase voltage characteristic signal ua(t)、ub(t)、uc(t)；
Step 3, for theThree-phase voltage characteristic signal ua(t)、ub(t)、uc(t) carrying out Fourier transform to obtain phase information of three-phase voltage
Figure FDA0002785381370000011
Step 4, constructing polar coordinate signals of three-phase voltage
Figure FDA0002785381370000012
Thereby drawing a three-phase voltage polar coordinate graph;
step 5, converting the three-phase voltage polar coordinate graph into a gray binary matrix Z with the pixel size of n multiplied by n through gray binary conversiona、Zb、Zc；
Step 6, calculating a short-circuit fault characteristic coefficient:
step 6.1, calculating characteristic matrixes C of voltage waveform changes respectively by using the formulas (1) and (2)a1And Da1：
Ca1＝(Za-E(Za))(za-E(za))T (1)
Da1＝(Za-za)TCa1 -1(Za-za) (2)
In the formulae (1) and (2), zaA conversion matrix Z of a polar coordinate diagram when the A-phase voltage of the power grid is normalaAcquiring a conversion matrix of a polar coordinate graph of the phase voltage A of the power grid on line; e (Z)a) Is a conversion matrix ZaAverage value of each column in (1); e (z)a) Is a transformation matrix zaAverage value of each column in (1);
step 6.2, calculating four characteristic parameters { V) by using the formula (3)ai|i＝1,2,3,4}：
Figure FDA0002785381370000013
In the formula (3), Δ (i, j) is a conversion matrix zaAnd a conversion matrix ZaOn the ith row and jth column elementThe absolute value difference of; m is the maximum value of the absolute value difference; m is the minimum of the absolute difference, α is the resolution factor;
step 6.3, obtaining a power grid A-phase short-circuit fault characteristic coefficient E by using the formula (4)a：
Figure FDA0002785381370000021
Step 7, calculating short-circuit fault characteristic coefficients E of the B phase and the C phase of the power grid according to the process of the step 6b、Ec；
Step 8, according to the three characteristic coefficients Ea、Eb、EcJudging the line state of the intelligent power distribution network:
if 0.1<Eb≈Ec<0.6 and 1.6<Ea<2; it represents that phase A is grounded and short-circuited;
if 0.8<Eb≈Ec<1.2 and 1.2<Ea<1.6; indicating BC two-phase short circuit;
if 0.2<Eb≈Ec<0.8 and 0.6<Ea<1.0; indicating the BC two-phase grounding short circuit;
if Ea≈Eb≈Ec>2; then a three-phase short is indicated;
if Ea<0.1 and Eb<0.1 and Ec<0.1; it indicates no failure.
CN202011295829.0A 2020-11-18 2020-11-18 Intelligent power distribution network line fault diagnosis and classification evaluation method Active CN112444758B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202011295829.0A CN112444758B (en) 2020-11-18 2020-11-18 Intelligent power distribution network line fault diagnosis and classification evaluation method

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202011295829.0A CN112444758B (en) 2020-11-18 2020-11-18 Intelligent power distribution network line fault diagnosis and classification evaluation method

Publications (2)

Publication Number Publication Date
CN112444758A CN112444758A (en) 2021-03-05
CN112444758B true CN112444758B (en) 2021-09-14

Family

ID=74738208

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202011295829.0A Active CN112444758B (en) 2020-11-18 2020-11-18 Intelligent power distribution network line fault diagnosis and classification evaluation method

Country Status (1)

Country Link
CN (1) CN112444758B (en)

Families Citing this family (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113526282B (en) * 2021-06-28 2022-09-06 江苏威尔曼科技有限公司 Method, device, medium and equipment for diagnosing medium and long-term aging faults of elevator
CN113820620A (en) * 2021-08-17 2021-12-21 捍防（深圳）实业有限公司 Fault analysis method and fault analysis device for power supply system

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2006242815A (en) * 2005-03-04 2006-09-14 Kansai Electric Power Co Inc:The Fault point location method of transmission line, fault point location device of transmission line, and fault point location program
CN103323748A (en) * 2013-06-29 2013-09-25 西安科技大学 System and method for recognizing power cable fault based on fractal and wavelet transform
KR101317561B1 (en) * 2012-05-16 2013-10-11 용인송담대학교 산학협력단 Method for detecting ground fault of power line using wavelet transform
CN107451557A (en) * 2017-07-29 2017-12-08 吉林化工学院 Transmission line short-circuit fault diagnostic method based on experience wavelet transformation and local energy
CN110518557A (en) * 2019-04-08 2019-11-29 西安交通大学 A kind of fault current limiter input control method based on short circuit current integrated information

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2006242815A (en) * 2005-03-04 2006-09-14 Kansai Electric Power Co Inc:The Fault point location method of transmission line, fault point location device of transmission line, and fault point location program
KR101317561B1 (en) * 2012-05-16 2013-10-11 용인송담대학교 산학협력단 Method for detecting ground fault of power line using wavelet transform
CN103323748A (en) * 2013-06-29 2013-09-25 西安科技大学 System and method for recognizing power cable fault based on fractal and wavelet transform
CN107451557A (en) * 2017-07-29 2017-12-08 吉林化工学院 Transmission line short-circuit fault diagnostic method based on experience wavelet transformation and local energy
CN110518557A (en) * 2019-04-08 2019-11-29 西安交通大学 A kind of fault current limiter input control method based on short circuit current integrated information

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
《Method of cable incipient faults detection and identification based on wavelet transform and gray correlation analysis》;chuan zhou etc;《 2015 IEEE Innovative Smart Grid Technologies - Asia (ISGT ASIA)》;20160121;第1-5页 *
《一种基于小波分时灰度矩与概率神经网络的电网故障诊断方法》;罗毅 等;《重庆邮电大学学报》;20120228;第121-126页 *

Also Published As

Publication number Publication date
CN112444758A (en) 2021-03-05

Similar Documents

Publication Publication Date Title
US11402438B2 (en) Intelligent on-line diagnosis and positioning method for winding deformation of power transformers
Bíscaro et al. Integrated fault location and power-quality analysis in electric power distribution systems
CN112444758B (en) Intelligent power distribution network line fault diagnosis and classification evaluation method
CN110361686B (en) Multi-parameter-based fault detection method for capacitive voltage transformer
CN103713237A (en) Power system electric transmission line fault diagnosis method
CN106330095B (en) Collect system fault locating method in a kind of large-sized photovoltaic power station
CN101907437A (en) Wavelet difference algorithm-based cable fault localization method
CN107247204B (en) State monitoring system and monitoring method for voltage limiters in ultra-high and extra-high voltage series compensation device
CN105137158A (en) Power system temporary overvoltage monitoring system
CN207730875U (en) A kind of wind-driven generator winding insulation failure detector
CN112381668B (en) Information extraction method for power grid faults
CN112083270A (en) Wind power plant current collection line single-phase earth fault line selection method based on correlation coefficient
CN111999591B (en) Method for identifying abnormal state of primary equipment of power distribution network
CN109900465B (en) On-load tap-changer fault diagnosis method based on sound signals
CN103823158A (en) Resonant earthed system fault line selection method with invariant moments
CN106646138B (en) Distribution net work earthing fault localization method based on the conversion of more sample frequency wavelet character energy
CN107632237A (en) A kind of fault line selection method for single-phase-to-ground fault based on wavelet character amount correlation after failure
CN109283385B (en) Lightning arrester monitoring data online processing method and system
CN112255493A (en) Multi-criterion comprehensive voting power distribution network fault line selection method
CN112964948B (en) Series reactor running state online monitoring method and system based on wave recording data inversion
CN113759206A (en) Method and system for judging fault type of power distribution network
CN113608066B (en) Distribution network overhead line single-phase grounding fault positioning method based on magnetic field detection
CN113325269A (en) Distribution network high-resistance fault monitoring method, system, equipment and storage medium
CN113776421A (en) Transformer winding deformation diagnosis method and system
CN114062846A (en) Alternating current transmission line protection method and system

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant