CN110687393A - Valve short-circuit protection fault positioning method based on VMD-SVD-FCM - Google Patents

Valve short-circuit protection fault positioning method based on VMD-SVD-FCM Download PDF

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CN110687393A
CN110687393A CN201910827072.6A CN201910827072A CN110687393A CN 110687393 A CN110687393 A CN 110687393A CN 201910827072 A CN201910827072 A CN 201910827072A CN 110687393 A CN110687393 A CN 110687393A
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fault
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CN110687393B (en
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李铭
蒋海峰
王冰冰
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Nanjing Tech University
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    • 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
    • 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
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/23—Clustering techniques
    • 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 valve short-circuit protection fault positioning method based on VMD-SVD-FCM, which comprises the steps of collecting current signals of alternating current and direct current sides of a current converter at three known fault occurrence points, and calculating valve short-circuit protection action current; acquiring current signals of an AC side and a DC side of the converter of a fault point to be detected through a current transformer, and calculating valve short-circuit protection action current to be used as an actual sample to be positioned by the fault; setting VMD decomposition parameters including the number of modal decomposition and a secondary punishment factor, and performing VMD decomposition on the valve short-circuit protection action current within the set parameter range to obtain a plurality of IMF components of the action current; reconstructing a Hankel matrix according to each IMF component, performing SVD to obtain a singular value matrix, and selecting the maximum singular value of each IMF component to form a fault feature vector; and analyzing the fault characteristic vector by adopting an FCM clustering algorithm, determining a clustering center where an actual sample to be subjected to fault positioning is located, and judging a fault occurrence place. The invention can quickly find the fault occurrence place after the short-circuit protection action of the converter valve of the flexible direct current transmission system.

Description

Valve short-circuit protection fault positioning method based on VMD-SVD-FCM
Technical Field
The invention relates to a relay protection technology of a power system, in particular to a valve short-circuit protection fault positioning method based on VMD-SVD-FCM.
Background
The flexible direct current transmission technology can provide an excellent solution for long-distance transmission of a power grid, and the stability of a flexible direct current transmission system can be improved due to the safe operation of the current converter. The valve short-circuit protection action principle of the converter of the flexible direct current transmission system is that when the action current exceeds a setting value, protection action is carried out, so that valve short-circuit protection can reflect faults in a protection area of the converter, but a fault point cannot be positioned, and whether the faults occur in an alternating current circuit part, a direct current circuit part or the inside of the converter cannot be judged. In addition, the flexible direct current transmission system requires to remove the fault within 5ms, and less sampling information is left for fault location, so that a suitable fault location method is needed to be found to remove the converter fault as soon as possible.
Disclosure of Invention
The invention aims to provide a valve short-circuit protection fault positioning method based on VMD-SVD-FCM.
The technical solution for realizing the purpose of the invention is as follows: a fault positioning method for converter valve short-circuit protection based on VMD-SVD-FCM comprises the following steps:
step 1: collecting current signals of alternating current and direct current sides of the current converter at three known fault occurrence points, and calculating valve short-circuit protection action current;
step 2: acquiring current signals of an AC side and a DC side of the converter of a fault point to be detected through a current transformer, and calculating valve short-circuit protection action current to be used as an actual sample to be positioned by the fault;
and step 3: setting VMD decomposition parameters including the number of modal decomposition and a secondary punishment factor, and performing VMD decomposition on the valve short-circuit protection action current within the set parameter range to obtain a plurality of IMF components of the action current;
and 4, step 4: reconstructing a Hankel matrix according to each IMF component, performing SVD to obtain a singular value matrix, and selecting the maximum singular value of each IMF component to form a fault feature vector;
and 5: and analyzing the fault characteristic vector by adopting an FCM clustering algorithm, determining a clustering center where an actual sample to be subjected to fault positioning is located, and judging a fault occurrence place.
Compared with the prior art, the invention has the remarkable advantages that: 1) the problem that the short-circuit protection fault of the modular multilevel converter valve is difficult to locate can be well solved; 2) the VMD algorithm is adopted to decompose the short-circuit protection current signal of the converter valve, and compared with the EMD and the improved algorithm thereof in the traditional non-stable signal processing method, the method has a solid theoretical foundation and has a better decomposition effect; 3) when SVD is adopted, all IMF components are not combined into a matrix for decomposition, but a Hankel matrix construction form is adopted to decompose the IMF components respectively, and the fault information of each IMF component is effectively reserved.
Drawings
FIG. 1 is a flow chart of a valve short-circuit protection fault positioning method based on VMD-SVD-FCM.
Fig. 2 is a waveform diagram of valve short-circuit protection action current when the converter ac side, the converter inside and the converter dc side of the invention are in fault.
Fig. 3 is a waveform diagram of an IMF component in a single-phase ground fault on the ac side of the converter according to the present invention.
FIG. 4 is a graph of a cluster membership matrix when decomposed using a VMD algorithm according to the present invention.
FIG. 5 is a graph of a cluster membership matrix when decomposed using the CEEMD algorithm of the present invention.
Detailed Description
The invention is further illustrated by the following examples in conjunction with the accompanying drawings.
As shown in figure 1, the VMD-SVD-FCM-based valve short-circuit protection fault positioning method comprises the steps of decomposing a valve short-circuit protection action current of a converter of a flexible direct-current transmission system by adopting a VMD algorithm to obtain IMF components, reconstructing each IMF component into a matrix by using a Hankel matrix construction form, obtaining a singular value matrix by adopting SVD, selecting the largest singular value of each IMF component to form a characteristic vector, and classifying through an FCM clustering algorithm to realize accurate positioning of valve short-circuit faults. The method comprises the following specific steps:
step 1: collecting current signals of alternating current and direct current sides of a current converter at three known fault points, calculating to obtain valve short-circuit protection action current for subsequent reference clustering center calculation, wherein the data quantity is not required, but at least one group of data is required at each fault point;
step 2: acquiring current signals of an AC side and a DC side of a current converter of a fault point to be detected through a current transformer, and calculating to obtain valve short-circuit protection action current as an actual sample to be positioned by the fault;
three-phase current i at alternating current side of valve short-circuit protection action current taking converterA、iB、iCHalf of the sum of absolute values and the positive and negative current i of the DC side of the converterP、iNThe expression for the difference between the maximum values in (1) is as follows:
Id＝IacY-max(IP,IN) (1)
wherein, IdIs valve short circuit protection action current; i isacYIs half of the sum of the absolute values of the three-phase currents on the alternating-current side of the converter.
And step 3: setting VMD decomposition parameters including the modal decomposition number and the secondary punishment factor alpha, and within the set parameter range, carrying out VMD decomposition on the valve short-circuit protection action current to obtain a plurality of IMF components of the action current, specifically:
the VMD decomposition process can be regarded as a constraint variational problem, and the model is expressed as:
Figure BDA0002189440640000031
wherein the content of the first and second substances,
Figure BDA0002189440640000032
representing partial derivative over time; δ (t) represents a unit pulse function; { ukExpressing K IMF components obtained by action current decomposition; { omega [ [ omega ] ]kExpressing the center frequency of each IMF component obtained by decomposition;
the constraint variational problem can be solved by the extended Lagrange equation of equation 2, the expression is as follows:
Figure BDA0002189440640000033
wherein, L represents the Lagrange equation expression expanded by formula 2; alpha represents a secondary penalty factor; λ denotes the Lagrange multiplier.
Calculating the saddle point problem of formula 3 by adopting a multiplier operator alternating direction method to obtain K IMF components of action current decomposition, wherein the specific method comprises the following steps:
first initializing IMF components with random numbers
Figure BDA0002189440640000034
And its center frequency
Figure BDA0002189440640000035
The sum of IMF components is equal to the action current, and is continuously updated through formula 4, formula 5 and formula 6
Figure BDA0002189440640000036
The optimal solution of equation 3 is solved.
Figure BDA0002189440640000037
Figure BDA0002189440640000039
In the formula (I), the compound is shown in the specification,
Figure BDA00021894406400000310
to represent
Figure BDA00021894406400000311
Wiener filtering of (1);
Figure BDA00021894406400000312
representing a modal power spectrum center of gravity; to pair
Figure BDA00021894406400000313
Performing inverse Fourier transform to obtain the real part of the result as uk(t); τ denotes noise tolerance.
The given iteration termination condition in the update process is as follows:
Figure BDA0002189440640000041
wherein epsilon is a convergence criterion tolerance value, the iterative process is stopped when formula 7 is satisfied, and in the final result, { ukAnd the K IMF components obtained by the action current decomposition are obtained.
And 4, step 4: and reconstructing a Hankel matrix construction form of each IMF component, performing SVD (singular value decomposition) to obtain a singular value matrix, and selecting the maximum singular value of each IMF component to form a fault characteristic vector.
Let IMF component be uk＝[x1,x2,x3,…,xN]Where N is the IMF component length, x1,x2,x3,…,xNFor time sampling values of the IMF component, the Hankel matrix of the IMF component is as follows:
Figure BDA0002189440640000042
after the IMF component reconstruction matrix is obtained, SVD decomposition is carried out on the IMF reconstruction matrix to obtain a singular value matrix of the IMF component. The basic principle of SVD decomposition is that if the rank is r, the real matrix uk∈Rm×nThen there are an m n orthogonal matrix U and an n orthogonal matrix V, such that
uk＝USVT＝σ1U1V1+σ2U2V2+…+σkUkVk(9)
In the formula, the matrix U is called a left singular matrix; the matrix V is called a right singular matrix; the matrix S is a singular value diagonal matrix arranged in descending order. After a singular value matrix of each IMF component is obtained, the maximum singular value of each IMF component is selected to form a fault characteristic vector Zj＝[z1,z2,z3,…,zK]Wherein j is the jth sample, and K is the number of IMF components.
And 5: the FCM clustering algorithm is adopted to analyze the fault characteristic vector, a clustering analysis result is generated, a clustering center where an actual sample to be subjected to fault location is located is obtained, a fault occurrence place is judged, and the purpose of fault location is achieved, and the method specifically comprises the following steps:
a: initializing a membership matrix by using random numbers between intervals (0,1) to meet the condition that the sum of membership degrees of a sample set is 1;
b: determining the cluster center for each sample subset byiAs the cluster center of the sample set, pijIs sample membership, n is total number of samples, i is ith clustering center;
Figure BDA0002189440640000043
c: the objective function is calculated from the following formula, where pijIs sample membership, m is a fuzzy coefficient, q is the number of clustering centers, dijDistance of sample point j to ith cluster center:
Figure BDA0002189440640000051
if the change quantity of the objective function value relative to the last iteration is smaller than a set threshold, turning to the step e, otherwise, turning to the step d to update the membership matrix;
d: by using
Figure BDA0002189440640000052
Updating the membership degree matrix, and then returning to b;
e: and judging a fault classification result according to the membership matrix, namely classifying the actual samples to be subjected to fault positioning to the class with the maximum membership value.
Examples
Embodiments are further described below in connection with the specific example, flexible direct current transmission system valve short circuit protection fault location. A simulation model is built by using a simulation platform Matlab/Simulink, a flexible direct current transmission system in the simulation model adopts a modular multilevel converter topological structure, an alternating current side is connected with a 10kV alternating current power grid, a direct current side is connected with a +/-10 kV direct current line, direct current transformation is carried out on the direct current line through a high-frequency transformer, then an inverter is connected to output low-voltage alternating current to supply to an alternating current load, the normal load power of the power system is 50kW, the signal sampling frequency is 10kHz, and current information within 5ms after a fault is sampled. When the load condition is 40kW, 45kW, 50kW, 55kW or 60kW, fault points are arranged in an AC circuit part of the converter, the inside of the converter or a DC circuit part of the converter, and 10 times of fault simulation tests are repeatedly carried out to obtain 150 groups of test data in total. Wherein the 1 st group to 50 th group faults occur in the converter alternating current line part, the 51 st group to 100 th group faults occur in the converter, and the 101 st group to 150 th group faults occur in the converter direct current line part.
The valve short-circuit protection operating current when a fault occurs in the converter ac line portion, the converter interior, and the converter dc line portion is shown in fig. 2, where a shows the valve short-circuit protection operating current when a fault occurs in the converter ac line portion, b shows the valve short-circuit protection operating current when a fault occurs in the converter interior, and c shows the valve short-circuit protection operating current when a fault occurs in the converter dc line portion. As can be seen from the figure, the valve short-circuit protection removes a fault by an increase in the operating current, but cannot numerically distinguish the point of occurrence of the fault, thereby hindering the subsequent troubleshooting operation.
The first step is as follows: and acquiring current sampling values of an AC side and a DC side of the converter through a current transformer, and calculating the valve short-circuit protection action current.
The second step is that: and setting VMD decomposition parameters, setting the number of modal decompositions to be 7, and setting a secondary punishment factor alpha to be 2000.
The third step: VMD decomposition is performed on the valve short-circuit protection action current to obtain 7 IMF components, wherein a waveform diagram of the IMF components when a single-phase ground fault occurs on the ac side of the converter is shown in fig. 3.
The fourth step: performing matrix reconstruction on IMF components obtained by decomposition, performing SVD (singular value decomposition), selecting the largest singular value of each IMF to form a fault feature vector for clustering analysis, and performing per-unit processing on the fault feature vector.
The fifth step: and repeating the first step to the fifth step to obtain 150 groups of fault feature vectors of the test data.
And a sixth step: the FCM is used to perform cluster analysis on 150 sets of fault feature vectors, and the obtained cluster membership matrix value of 150 sets of samples is shown in fig. 4. And judging that the sample point belongs to the clustering center when the membership value of the sample point is more than 0.5, wherein the judgment result is shown in the table 1.
TABLE 1 Fault location discrimination results when VMD algorithm is employed
Figure BDA0002189440640000061
As can be seen from table 1, the fault location method provided by the present invention has high accuracy, is not limited by load conditions, and is suitable for valve short circuit protection of a flexible direct current power transmission system.
Meanwhile, in order to further explain the superiority of the method of the invention, an improved algorithm CEEMD of EMD is used for decomposing the valve short-circuit protection action current to obtain IMF components, then the IMF components are subjected to SVD in the same way, the largest singular value of each IMF is selected to form fault characteristic vectors, and FCM cluster analysis is carried out on 150 groups of fault characteristic vectors to obtain cluster membership matrix values as shown in FIG. 5. The discrimination results when the CEEMD algorithm was used are shown in Table 2.
TABLE 2 Fault location discrimination results using CEEMD algorithm
Figure BDA0002189440640000062
Comparing tables 1 and 2, it is obvious that the VMD adopted by the invention has better decomposition effect and more accurate clustering result.

Claims (6)

1. A fault positioning method for converter valve short-circuit protection based on VMD-SVD-FCM is characterized by comprising the following steps:
step 1: collecting current signals of alternating current and direct current sides of the current converter at three known fault occurrence points, and calculating valve short-circuit protection action current;
step 2: acquiring current signals of an AC side and a DC side of the converter of a fault point to be detected through a current transformer, and calculating valve short-circuit protection action current to be used as an actual sample to be positioned by the fault;
and step 3: setting VMD decomposition parameters including the number of modal decomposition and a secondary punishment factor, and performing VMD decomposition on the valve short-circuit protection action current within the set parameter range to obtain a plurality of IMF components of the action current;
and 4, step 4: reconstructing a Hankel matrix according to each IMF component, performing SVD to obtain a singular value matrix, and selecting the maximum singular value of each IMF component to form a fault feature vector;
and 5: and analyzing the fault characteristic vector by adopting an FCM clustering algorithm, determining a clustering center where an actual sample to be subjected to fault positioning is located, and judging a fault occurrence place.
2. The fault location method for VMD-SVD-FCM based converter valve short circuit protection according to claim 1, wherein in step 1 there is no requirement for known fault point current data amount, but at least one set of data is required for each fault point.
3. The fault location method for VMD-SVD-FCM-based converter valve short-circuit protection according to claim 1, characterized in that in steps 1 and 2, the valve short-circuit protection action current takes the converter AC side three-phase current iA、iB、iCHalf of the sum of absolute values and the positive and negative current i of the DC side of the converterP、iNThe expression for the difference between the maximum values in (1) is as follows:
Id＝IacY-max(IP,IN) (1)
wherein, IdIs valve short circuit protection action current; i isacYIs half of the sum of the absolute values of the three-phase currents on the alternating-current side of the converter.
4. The fault location method for short-circuit protection of the converter valve based on the VMD-SVD-FCM as claimed in claim 1, wherein in step 3, the VMD decomposition process can be regarded as a constraint variation problem, and the model is expressed as:
Figure FDA0002189440630000011
wherein the content of the first and second substances,
Figure FDA0002189440630000012
representing partial derivative over time; δ (t) represents a unit pulse function; { ukExpressing K IMF components obtained by action current decomposition; { omega [ [ omega ] ]kExpressing the center frequency of each IMF component obtained by decomposition;
the constraint variational problem can be solved by the extended Lagrange equation of equation 2, the expression is as follows:
Figure FDA0002189440630000021
wherein, L represents the Lagrange equation expression expanded by formula 2; alpha represents a secondary penalty factor; λ represents a Lagrange multiplier;
the saddle point problem of formula 3 is calculated by adopting a multiplier operator alternating direction method, K IMF components of action current decomposition can be obtained, and the specific method is as follows: initializing IMF components using random numbers
Figure FDA0002189440630000022
And its center frequency
Figure FDA0002189440630000023
The sum of IMF components is equal to the action current, and is continuously updated through formula 4, formula 5 and formula 6
Figure FDA0002189440630000024
Solving the optimal solution of formula 3;
Figure FDA0002189440630000025
Figure FDA0002189440630000026
Figure FDA0002189440630000027
in the formula (I), the compound is shown in the specification,
Figure FDA0002189440630000028
to represent
Figure FDA0002189440630000029
Wiener filtering of (1);
Figure FDA00021894406300000210
representing a modal power spectrum center of gravity; to pair
Figure FDA00021894406300000211
Performing inverse Fourier transform to obtain the real part of the result as uk(t); τ represents noise tolerance;
the given iteration termination condition in the update process is as follows:
Figure FDA00021894406300000212
wherein epsilon is a convergence criterion tolerance value, the iterative process is stopped when formula 7 is satisfied, and in the final result, { ukAnd the K IMF components obtained by the action current decomposition are obtained.
5. The fault location method for short-circuit protection of the VMD-SVD-FCM-based converter valve according to claim 1, wherein in step 4, the specific method for constructing the fault feature vector is:
let IMF component be uk＝[x1,x2,x3,…,xN]Wherein N is the length of IMF componentDegree, x1,x2,x3,…,xNFor time sampling values of the IMF component, the Hankel matrix of the IMF component is as follows:
Figure FDA0002189440630000031
after obtaining an IMF component reconstruction matrix, carrying out SVD on the IMF reconstruction matrix to obtain a singular value matrix of the IMF component; the basic principle of SVD decomposition is that if the rank is r, the real matrix uk∈Rm×nThen there are an m n orthogonal matrix U and an n orthogonal matrix V, such that
uk＝USVT＝σ1U1V1+σ2U2V2+…+σkUkVk(9)
In the formula, the matrix U is called a left singular matrix; the matrix V is called a right singular matrix; the matrix S is a singular value diagonal matrix arranged in a descending order; after a singular value matrix of each IMF component is obtained, the maximum singular value of each IMF component is selected to form a fault characteristic vector Zj＝[z1,z2,z3,…,zK]Wherein j is the jth sample, and K is the number of IMF components.
6. The fault location method for converter valve short-circuit protection based on the VMD-SVD-FCM as claimed in claim 1, wherein in step 5, the specific method for fault location is as follows:
a: initializing a membership matrix by using random numbers between intervals (0,1) to meet the condition that the sum of membership degrees of a sample set is 1;
b: determining the cluster center for each sample subset byiAs the cluster center of the sample set, pijIs sample membership, n is total number of samples, i is ith clustering center;
Figure FDA0002189440630000032
c: calculating an objective function from the formula wherein，pijIs sample membership, m is a fuzzy coefficient, q is the number of clustering centers, dijDistance of sample point j to ith cluster center:
Figure FDA0002189440630000033
if the change quantity of the objective function value relative to the last iteration is smaller than a set threshold, turning to the step e, otherwise, turning to the step d to update the membership matrix;
d: by using
Figure FDA0002189440630000034
Updating the membership degree matrix, and then returning to b;
e: and judging a fault classification result according to the membership matrix, namely classifying the actual samples to be subjected to fault positioning to the class with the maximum membership value.
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Cited By (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111308272A (en) * 2020-03-09 2020-06-19 西南交通大学 Positioning method for low-current ground fault section
CN111413588A (en) * 2020-03-31 2020-07-14 陕西省地方电力（集团）有限公司咸阳供电分公司 Power distribution network single-phase earth fault line selection method
CN111612074A (en) * 2020-05-22 2020-09-01 王彬 Identification method and device of non-invasive load monitoring electric equipment and related equipment
CN111751671A (en) * 2020-06-29 2020-10-09 三峡大学 VMD-DTW cluster-based low-current grounding system fault line selection method
CN113159100A (en) * 2021-02-19 2021-07-23 湖南第一师范学院 Circuit fault diagnosis method, circuit fault diagnosis device, electronic equipment and storage medium
CN113552224A (en) * 2021-07-28 2021-10-26 中国石油大学(华东) Sealing state detection method for liquid film sealing end face
CN113820564A (en) * 2021-09-24 2021-12-21 国家电网有限公司 Fault detection method suitable for source network load storage complex power grid

Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102195276A (en) * 2011-05-19 2011-09-21 南方电网科学研究院有限责任公司 Method for acquiring set preparative quantity of DC (direct current) transmission relaying protection
CN107064720A (en) * 2017-06-02 2017-08-18 西南交通大学 A kind of valve short trouble classification of high voltage direct current transmission device and localization method
CN107370174A (en) * 2017-06-29 2017-11-21 国家电网公司 A kind of HVDC transmission system simplifies modeling method
CN107632239A (en) * 2017-08-25 2018-01-26 南京理工大学 A kind of photovoltaic based on IMF Energy-Entropies sends out line fault phase-selecting method
CN108414226A (en) * 2017-12-25 2018-08-17 哈尔滨理工大学 Fault Diagnosis of Roller Bearings under the variable working condition of feature based transfer learning
CN108469557A (en) * 2018-03-07 2018-08-31 西南交通大学 High voltage direct current transmission device Fault Locating Method based on transverter differential protection
CN109708875A (en) * 2019-01-24 2019-05-03 北华大学 A kind of Fault Diagnosis of Rotor based on variation mode decomposition Volterra model singular value entropy

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102195276A (en) * 2011-05-19 2011-09-21 南方电网科学研究院有限责任公司 Method for acquiring set preparative quantity of DC (direct current) transmission relaying protection
CN107064720A (en) * 2017-06-02 2017-08-18 西南交通大学 A kind of valve short trouble classification of high voltage direct current transmission device and localization method
CN107370174A (en) * 2017-06-29 2017-11-21 国家电网公司 A kind of HVDC transmission system simplifies modeling method
CN107632239A (en) * 2017-08-25 2018-01-26 南京理工大学 A kind of photovoltaic based on IMF Energy-Entropies sends out line fault phase-selecting method
CN108414226A (en) * 2017-12-25 2018-08-17 哈尔滨理工大学 Fault Diagnosis of Roller Bearings under the variable working condition of feature based transfer learning
CN108469557A (en) * 2018-03-07 2018-08-31 西南交通大学 High voltage direct current transmission device Fault Locating Method based on transverter differential protection
CN109708875A (en) * 2019-01-24 2019-05-03 北华大学 A kind of Fault Diagnosis of Rotor based on variation mode decomposition Volterra model singular value entropy

Non-Patent Citations (4)

* Cited by examiner, † Cited by third party
Title
SHUTING WAN 等: "Fault Diagnosis of High-Voltage Circuit Breakers Using Mechanism Action Time and Hybrid Classifier", 《IEEE ACCESS》 *
张伟 等: "基于VMD和奇异值能量差分谱的风机滚动轴承故障特征提取方法", 《华北电力技术》 *
洪剑锋: "基于变分模态分解算法的高速列车万向轴动不平衡检测方法研究", 《中国优秀硕士学位论文全文数据库 工程科技II辑》 *
郑涛 等: "基于阀短路保护的HVDC换流器区内故障定位新方法", 《电力系统自动化》 *

Cited By (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111308272A (en) * 2020-03-09 2020-06-19 西南交通大学 Positioning method for low-current ground fault section
CN111413588A (en) * 2020-03-31 2020-07-14 陕西省地方电力（集团）有限公司咸阳供电分公司 Power distribution network single-phase earth fault line selection method
CN111612074A (en) * 2020-05-22 2020-09-01 王彬 Identification method and device of non-invasive load monitoring electric equipment and related equipment
CN111612074B (en) * 2020-05-22 2024-02-02 王彬 Identification method and device of non-invasive load monitoring electric equipment and related equipment
CN111751671A (en) * 2020-06-29 2020-10-09 三峡大学 VMD-DTW cluster-based low-current grounding system fault line selection method
CN113159100A (en) * 2021-02-19 2021-07-23 湖南第一师范学院 Circuit fault diagnosis method, circuit fault diagnosis device, electronic equipment and storage medium
CN113552224A (en) * 2021-07-28 2021-10-26 中国石油大学(华东) Sealing state detection method for liquid film sealing end face
CN113552224B (en) * 2021-07-28 2024-02-13 中国石油大学(华东) Sealing state detection method for liquid film sealing end face
CN113820564A (en) * 2021-09-24 2021-12-21 国家电网有限公司 Fault detection method suitable for source network load storage complex power grid

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