CN110346692A - A kind of wire selection method for power distribution network single phase earthing failure based on time-frequency image information - Google Patents
A kind of wire selection method for power distribution network single phase earthing failure based on time-frequency image information Download PDFInfo
- Publication number
- CN110346692A CN110346692A CN201910769648.8A CN201910769648A CN110346692A CN 110346692 A CN110346692 A CN 110346692A CN 201910769648 A CN201910769648 A CN 201910769648A CN 110346692 A CN110346692 A CN 110346692A
- Authority
- CN
- China
- Prior art keywords
- distribution network
- time
- frequency image
- model
- image information
- 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
- 238000010187 selection method Methods 0.000 title claims abstract description 16
- 238000013527 convolutional neural network Methods 0.000 claims abstract description 23
- 238000000034 method Methods 0.000 claims abstract description 21
- 230000005611 electricity Effects 0.000 claims abstract description 16
- 238000012549 training Methods 0.000 claims abstract description 13
- 239000011159 matrix material Substances 0.000 claims description 18
- 238000004088 simulation Methods 0.000 claims description 4
- 230000007257 malfunction Effects 0.000 claims description 3
- 238000010606 normalization Methods 0.000 claims description 3
- 230000009466 transformation Effects 0.000 claims description 3
- 230000001537 neural effect Effects 0.000 claims 1
- 238000003745 diagnosis Methods 0.000 abstract description 3
- 230000000694 effects Effects 0.000 abstract description 3
- 238000012360 testing method Methods 0.000 abstract description 2
- 238000007796 conventional method Methods 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 230000007935 neutral effect Effects 0.000 description 3
- 230000007704 transition Effects 0.000 description 3
- 238000003062 neural network model Methods 0.000 description 2
- 230000004913 activation Effects 0.000 description 1
- 238000013528 artificial neural network Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
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
- 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
-
- 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
-
- 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
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Mathematical Physics (AREA)
- Theoretical Computer Science (AREA)
- Supply And Distribution Of Alternating Current (AREA)
- Locating Faults (AREA)
Abstract
The invention discloses a kind of wire selection method for power distribution network single phase earthing failure based on time-frequency image information, this method utilizes the electricity distribution network model built, each route head end zero-sequence current waveform after generating singlephase earth fault, corresponding time-frequency image has been obtained after converting accordingly, and corresponding training set is generated with this.And convolutional neural networks are had trained with the training set, obtain failure line selection model, then failure time-frequency image is generated again using same electricity distribution network model, as test set, input convolutional neural networks obtain the corresponding network output valve of each route, the selection maximum route of output valve is faulty line, finally achieves good route selection effect.The method of the present invention can accurately select faulty line when singlephase earth fault occurs for power distribution network, this is changeable to structure is complicated, and the fault diagnosis of the faint power distribution network of fault-signal, reliability service are of great significance.
Description
Technical field
The invention belongs to field of power systems, are related to Fault Diagnosis of Distribution Network field, and in particular to one kind is based on time-frequency figure
As the wire selection method for power distribution network single phase earthing failure of information.
Background technique
Power distribution network is the pivotal player for completing " last one kilometer " electrical energy transportation task, directly affect the reliability of power supply with
Quality.However in a practical situation, the probability that power distribution network breaks down is higher, and wherein singlephase earth fault is most commonly seen.It presses
It is provided according to State Grid Corporation of China's electric power safety working regulation, single-phase connect occurs for the power distribution network of isolated neutral or non_direct ground
After earth fault, in order to not influence the continuous power supply to user, system can still operate with failure 1~2h.Nevertheless, if failure obtains
Less than timely eliminating, then failure may develop into failure between more serious line from singlephase earth fault.
The characteristics of power distribution network has outlet more, the complicated network structure.In view of China major part power distribution network is all to take
Property point non_direct ground earthing mode, if occur singlephase earth fault, zero-sequence current is smaller, though the energy on route head end bus
Failure phase is found by way of finding the minimum phase of voltage, but not can determine that the feeder line where fault point.
Summary of the invention
The purpose of the present invention is to provide a kind of wire selection method for power distribution network single phase earthing failure based on time-frequency image information,
To solve the problems, such as Single-phase Earth-fault Selection in Distribution Systems, time-frequency image information, convolutional neural networks are used for distribution by the present invention
Net singlephase earth fault model, power distribution network fault diagnosis, in terms of all have critically important realistic meaning.
In order to achieve the above objectives, the present invention adopts the following technical scheme:
A kind of wire selection method for power distribution network single phase earthing failure based on time-frequency image information, comprising the following steps:
Step 1 establishes electricity distribution network model, and the model is by synchro generator model, transformer model, circuit model and load
Model composition, and one-phase earthing failure in electric distribution network emulation is carried out using the electricity distribution network model；
Step 2, the data for obtaining fault simulation are converted to corresponding time-frequency image information；
Step 3, the time-frequency image information data training convolutional neural networks obtained using emulation, obtain convolutional neural networks
Route selection model；
Step 4 obtains the fault data after one-phase earthing failure in electric distribution network, and is converted into time-frequency image information, utilizes volume
Product neural network model carries out route selection to singlephase earth fault.
Further, the circuit model in electricity distribution network model established in step 1 is Bergeron model, and load model is perseverance
Constant impedance model.
It further, is the zero sequence electricity of each route head end after failure to the data that the fault simulation described in step 2 obtains
Flow waveform.
Further, the method for zero-sequence current waveform being converted into time-frequency image in step 2 are as follows: first to zero-sequence current waveform
Short time discrete Fourier transform is carried out, carries out following normalization again after the complex matrix elder generation modulus obtained to transformation, ultimately forms 8 ranks ash
Spend image:
A2=10*logl0 A1
A4=255*A3
Wherein, A1For the parameter matrix that zero-sequence current waveform obtains after Short Time Fourier Transform is squared mould, A2For matrix
A1Parameter matrix after logarithm process, A3For matrix A2Parameter matrix through maximum-minimum normalized, A4For output
8 rank gray level image parameters.
Further, zero-sequence current waveform is converted into time-frequency image in step 2, obtained time-frequency image is 128 pixels
8 rank gray level images of × 128 pixels.
Further, the input image size of the convolutional neural networks used in step 3 is 128 pixels × 128 pixel, volume
The output of product neural network is a floating number, characterizes the probability of malfunction of route；The function that convolutional neural networks are realized is to return function
Can, the training label of the time-frequency image of faulty line is 1, and the training label of the time-frequency image of non-fault line is 0；Training process
The middle loss function used is root-mean-square error function.
Compared with prior art, the invention has the following beneficial technical effects:
The invention proposes the wire selection method for power distribution network single phase earthing failure based on time-frequency image information, this method passes through short
When Fourier transformation, fault current waveform is converted into two dimensional gray figure, has preferably excavated potential fault message, and make
The fault picture is diagnosed with convolutional neural networks, to carry out failure line selection.This method compares conventional method with good
Good route selection performance.100% route selection accuracy was all obtained on used electricity distribution network model, in addition the method for the present invention is not required to
Upgrade spot measurement device, have at low cost, easy to spread, the characteristics of meeting application request has good popularization
Application prospect.
Detailed description of the invention
Fig. 1 the method for the present invention flow chart；
Fig. 2 electricity distribution network model；
Zero-sequence current waveform diagram after Fig. 3 failure；
The time-frequency image that Fig. 4 is converted by zero-sequence current；
Fig. 5 convolutional neural networks design a model schematic diagram；
Novel method route selection performance under Fig. 6 difference transition resistance；
Novel method route selection performance under Fig. 7 different faults initial phase angle；
Novel method route selection performance under Fig. 8 different faults place.
Specific embodiment
Implementation process of the invention is described in further detail with reference to the accompanying drawing:
As shown in Figure 1, the present invention is a kind of wire selection method for power distribution network single phase earthing failure based on time-frequency image information, tool
Body the following steps are included:
Step 1 establishes electricity distribution network model, and the model is by synchro generator model, transformer model, circuit model, load
Model composition, and one-phase earthing failure in electric distribution network emulation is carried out using the electricity distribution network model, wherein circuit model uses Bei Ruilong
Model, load use constant impedance model, and neutral point is grounded using overcompensation mode, overcompensation degree 8%, as shown in Figure 2.
Step 2, the zero-sequence current that each route head end of power distribution network after failure is obtained using one-phase earthing failure in electric distribution network model,
As shown in Figure 3.And short time discrete Fourier transform is done to waveform, obtain complex matrix, first modulus, after carry out following normalization again, connect
Cutting image to 128 pixels × 128 pixels size, and include fault message, 8 rank gray level images ultimately formed, such as Fig. 4 institute
Show.
A2=10*logl0 A1
A4=255*A3
Wherein, A1For the parameter matrix that zero-sequence current waveform obtains after Short Time Fourier Transform is squared mould, A2For matrix
A1Parameter matrix after logarithm process, A3For matrix A2Parameter matrix through maximum-minimum normalized, A4For output
8 rank gray scale graph parameters.
Step 3, the time-frequency image information data training convolutional neural networks obtained using emulation, obtain convolutional neural networks
The schematic diagram of route selection model, the convolutional neural networks structure of use is as shown in Figure 5.Wherein the input of convolutional neural networks is the line
The time-frequency image that routing zero-sequence current converts, exports the probability of malfunction for the route, faulty line 1, non-fault line
0。
Step 4 obtains the fault data after one-phase earthing failure in electric distribution network, and is converted into time-frequency image information, utilizes volume
Product neural network model carries out route selection to singlephase earth fault.The deterministic process of route selection are as follows: by obtained after each line fault when
Frequency image information all inputs convolutional neural networks, obtains respective output valve, and respective output valve is compared, and is worth maximum line
Road is judged as faulty line.
Embodiment
For electricity distribution network model as shown in Figure 1, this power distribution network has 7 feeder lines, length is respectively 20km, 10km, 11km, 12km,
17km, 15km, 8km.Line parameter circuit value is as shown in the table.
Each transformer parameter is as shown in the table in power distribution network.
Neutral grounding in distribution power network is overcompensation ground connection, and overcompensation degree is 8%.The load of each feeder terminal is
The impedance of 0.16+0.032 Ω.
Using above-mentioned electricity distribution network model, according to faulty line, the position of fault, transition resistance, failure initial phase angle is different, generates
Many failure zero-sequence current waveforms add the noise of some strength on waveform, generate time-frequency image after conversion, constitute
Training set is trained convolutional neural networks using training set.
The convolutional neural networks structure of the present embodiment uses multilayer convolutional layer as shown in Figure 5, and activation primitive all selects
With ReLu function, final full articulamentum is output layer.
Hereafter the fault data composition test set generated again being reused in above-mentioned electricity distribution network model, to convolutional Neural
Network is tested, and according to the route selection principle that the maximum route of convolutional neural networks output valve is faulty line is chosen, is obtained
Corresponding faulty line.Finally obtained route selection effect such as Fig. 6, shown in 7,8.CNN method in figure is to be based on time-frequency image letter
The wire selection method for power distribution network single phase earthing failure of breath, in addition two methods are conventional method, to compare.Fig. 6 analyzes difference
Route selection performance under transition resistance, Fig. 7 analyze the route selection performance under different faults initial phase angle, and Fig. 8 is with analyzing different faults
Route selection performance under point.The results show that the wire selection method for power distribution network single phase earthing failure based on time-frequency image information is functional,
It is much better than conventional method.
Claims (6)
1. a kind of wire selection method for power distribution network single phase earthing failure based on time-frequency image information, which is characterized in that including following step
It is rapid:
Step 1 establishes electricity distribution network model, and the model is by synchro generator model, transformer model, circuit model and load model
Composition, and one-phase earthing failure in electric distribution network emulation is carried out using the electricity distribution network model；
Step 2, the data for obtaining fault simulation are converted to corresponding time-frequency image information；
Step 3, the time-frequency image information data training convolutional neural networks obtained using emulation, obtain convolutional neural networks route selection
Model；
Step 4 obtains the fault data after one-phase earthing failure in electric distribution network, and is converted into time-frequency image information, utilizes convolution mind
Route selection is carried out to singlephase earth fault through network model.
2. a kind of wire selection method for power distribution network single phase earthing failure based on time-frequency image information according to claim 1,
It is characterized in that, the circuit model in electricity distribution network model established in step 1 is Bergeron model, and load model is constant impedance mould
Type.
3. a kind of wire selection method for power distribution network single phase earthing failure based on time-frequency image information according to claim 1,
It is characterized in that, is the zero-sequence current waveform of each route head end after failure to the data that the fault simulation described in step 2 obtains.
4. a kind of wire selection method for power distribution network single phase earthing failure based on time-frequency image information according to claim 3,
It is characterized in that, the method that zero-sequence current waveform is converted into time-frequency image in step 2 are as follows: first zero-sequence current waveform is carried out in short-term
Fourier transform ultimately forms 8 rank gray level images to following normalization are carried out again after the obtained complex matrix elder generation modulus of transformation:
A2=10*log10A1
A4=255*A3
Wherein, A1For the parameter matrix that zero-sequence current waveform obtains after Short Time Fourier Transform is squared mould, A2For matrix A1Through
Parameter matrix after crossing logarithm process, A3For matrix A2Parameter matrix through maximum-minimum normalized, A4For 8 ranks of output
Gray level image parameter.
5. a kind of wire selection method for power distribution network single phase earthing failure based on time-frequency image information according to claim 4,
It is characterized in that, zero-sequence current waveform is converted into time-frequency image in step 2, obtained time-frequency image is 128 pixels × 128 pixels
8 rank gray level images.
6. a kind of wire selection method for power distribution network single phase earthing failure based on time-frequency image information according to claim 5,
It is characterized in that, the input image size of the convolutional neural networks used in step 3 is 128 pixels × 128 pixels, convolutional Neural net
The output of network is a floating number, characterizes the probability of malfunction of route；The function that convolutional neural networks are realized is to return function, fault wire
The training label of the time-frequency image on road is 1, and the training label of the time-frequency image of non-fault line is 0；It is used in training process
Loss function is root-mean-square error function.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910769648.8A CN110346692A (en) | 2019-08-20 | 2019-08-20 | A kind of wire selection method for power distribution network single phase earthing failure based on time-frequency image information |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910769648.8A CN110346692A (en) | 2019-08-20 | 2019-08-20 | A kind of wire selection method for power distribution network single phase earthing failure based on time-frequency image information |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN110346692A true CN110346692A (en) | 2019-10-18 |
Family
ID=68180842
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910769648.8A Pending CN110346692A (en) | 2019-08-20 | 2019-08-20 | A kind of wire selection method for power distribution network single phase earthing failure based on time-frequency image information |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110346692A (en) |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112198232A (en) * | 2020-09-14 | 2021-01-08 | 昆明理工大学 | Drainage pipeline working condition detection and identification method |
| CN112200881A (en) * | 2020-08-24 | 2021-01-08 | 贵州大学 | Method for converting motor current into gray level image |
| CN114252739A (en) * | 2021-12-24 | 2022-03-29 | 国家电网有限公司 | Power distribution network single-phase earth fault distinguishing method, system, equipment and storage medium |
| CN115047296A (en) * | 2022-08-15 | 2022-09-13 | 四川轻化工大学 | Power distribution network fault section positioning method |
| CN117706277A (en) * | 2024-02-02 | 2024-03-15 | 昆明理工大学 | Power distribution network fault line selection method based on graphic analysis and identification |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108279364A (en) * | 2018-01-30 | 2018-07-13 | 福州大学 | Wire selection method for power distribution network single phase earthing failure based on convolutional neural networks |
| CN108732465A (en) * | 2018-05-30 | 2018-11-02 | 广东电网有限责任公司 | A kind of electrical power distribution network fault location method based on wavelet transformation and CNN |
| WO2019021095A1 (en) * | 2017-07-26 | 2019-01-31 | 株式会社半導体エネルギー研究所 | System for controlling charging of secondary cell and method for detecting abnormality in secondary cell |
| CN109444863A (en) * | 2018-10-23 | 2019-03-08 | 广西民族大学 | A kind of estimation method of the narrowband ultrasonic echo number based on convolutional neural networks |
| CN109596326A (en) * | 2018-11-30 | 2019-04-09 | 电子科技大学 | Rotary machinery fault diagnosis method based on optimization structure convolutional neural networks |
| CN109657739A (en) * | 2019-01-09 | 2019-04-19 | 西北大学 | A kind of hand-written Letter Identification Method based on high frequency sound wave Short Time Fourier Transform |
-
2019
- 2019-08-20 CN CN201910769648.8A patent/CN110346692A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2019021095A1 (en) * | 2017-07-26 | 2019-01-31 | 株式会社半導体エネルギー研究所 | System for controlling charging of secondary cell and method for detecting abnormality in secondary cell |
| CN108279364A (en) * | 2018-01-30 | 2018-07-13 | 福州大学 | Wire selection method for power distribution network single phase earthing failure based on convolutional neural networks |
| CN108732465A (en) * | 2018-05-30 | 2018-11-02 | 广东电网有限责任公司 | A kind of electrical power distribution network fault location method based on wavelet transformation and CNN |
| CN109444863A (en) * | 2018-10-23 | 2019-03-08 | 广西民族大学 | A kind of estimation method of the narrowband ultrasonic echo number based on convolutional neural networks |
| CN109596326A (en) * | 2018-11-30 | 2019-04-09 | 电子科技大学 | Rotary machinery fault diagnosis method based on optimization structure convolutional neural networks |
| CN109657739A (en) * | 2019-01-09 | 2019-04-19 | 西北大学 | A kind of hand-written Letter Identification Method based on high frequency sound wave Short Time Fourier Transform |
Non-Patent Citations (1)
| Title |
|---|
| 何正友: "《配电网故障诊断》", 31 January 2011 * |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112200881A (en) * | 2020-08-24 | 2021-01-08 | 贵州大学 | Method for converting motor current into gray level image |
| CN112198232A (en) * | 2020-09-14 | 2021-01-08 | 昆明理工大学 | Drainage pipeline working condition detection and identification method |
| CN114252739A (en) * | 2021-12-24 | 2022-03-29 | 国家电网有限公司 | Power distribution network single-phase earth fault distinguishing method, system, equipment and storage medium |
| CN114252739B (en) * | 2021-12-24 | 2023-11-03 | 国家电网有限公司 | Power distribution network single-phase earth fault discrimination method, system, equipment and storage medium |
| CN115047296A (en) * | 2022-08-15 | 2022-09-13 | 四川轻化工大学 | Power distribution network fault section positioning method |
| CN115047296B (en) * | 2022-08-15 | 2022-10-25 | 四川轻化工大学 | Power distribution network fault section positioning method |
| CN117706277A (en) * | 2024-02-02 | 2024-03-15 | 昆明理工大学 | Power distribution network fault line selection method based on graphic analysis and identification |
| CN117706277B (en) * | 2024-02-02 | 2024-06-07 | 昆明理工大学 | Power distribution network fault line selection method based on graphic analysis and identification |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110346692A (en) | A kind of wire selection method for power distribution network single phase earthing failure based on time-frequency image information | |
| Goswami et al. | IOT based fault detection of underground cables through node MCU module | |
| Aslan | An alternative approach to fault location on power distribution feeders with embedded remote-end power generation using artificial neural networks | |
| CN103760464B (en) | Solve and the low current neutral grounding system fault route selecting method of SVM based on analysis diagram | |
| CN103926509B (en) | Distribution network failure staged selection method based on spectrum kurtosis relative energy entropy | |
| CN103018627A (en) | Adaptive fault type fault line detection method for non-effectively earthed system | |
| CN107329044A (en) | A kind of wire selection method for power distribution network single phase earthing failure based on electric arc transient state component | |
| Resmi et al. | Detection, classification and zone location of fault in transmission line using artificial neural network | |
| Koley et al. | Detection and classification of faults on six phase transmission line using ANN | |
| CN105445585B (en) | The method for diagnosing faults and system of power grid primary circuit | |
| CN112240965A (en) | Grounding line selection device and method based on deep learning algorithm | |
| CN107765139A (en) | A kind of resonant earthed system fault line selection method for single-phase-to-ground fault of high-accuracy | |
| CN109709441B (en) | Low-current grounding line selection method and device | |
| CN105974254A (en) | Transient and steady state line selection method based on voltage calculation comprehensive weight | |
| CN115587329A (en) | Power distribution network fault classification method and system based on convolutional neural network extraction features | |
| CN114966326A (en) | Single-phase earth fault section positioning method and system based on current negative sequence fault | |
| CN112710923B (en) | Data-driven single-phase earth fault line selection method based on post-fault steady-state information | |
| CN107290624B (en) | Three-phase distribution line model suitable for non-effective ground connection distribution network | |
| Wang et al. | Transmission line fault diagnosis based on wavelet packet analysis and convolutional neural network | |
| CN112255493A (en) | Multi-criterion comprehensive voting power distribution network fault line selection method | |
| CN112710924B (en) | Single-phase earth fault line selection method based on steady-state variable quantity before and after fault | |
| CN113991652B (en) | Data-driven multi-output calculation method for short-circuit current of distribution network containing IIDG | |
| CN113406437B (en) | Power transmission line fault detection method for generating countermeasure network based on auxiliary classification | |
| CN109581142B (en) | Novel multi-dimensional fusion high-voltage generator set stator single-phase earth fault detection method | |
| CN109713662B (en) | Method for equivalence of load model identification parameters of power system to low-voltage nodes |
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 | ||
| TA01 | Transfer of patent application right |
Effective date of registration: 20200927 Address after: 450052 No. 85 South Songshan Road, 27 District, Henan, Zhengzhou Applicant after: STATE GRID HENAN ELECTRIC POWER CORPORATION ELECTRIC POWER SCIENCE Research Institute Applicant after: XI'AN JIAOTONG University Applicant after: STATE GRID CORPORATION OF CHINA Address before: 450000 No. 85 South Songshan Road, 27 District, Henan, Zhengzhou Applicant before: STATE GRID HENAN ELECTRIC POWER CORPORATION ELECTRIC POWER SCIENCE Research Institute Applicant before: XI'AN JIAOTONG University |
|
| TA01 | Transfer of patent application right | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20191018 |
|
| RJ01 | Rejection of invention patent application after publication |