CN113759208A - Abnormal waveform identification method based on fault indicator - Google Patents
Abnormal waveform identification method based on fault indicator Download PDFInfo
- Publication number
- CN113759208A CN113759208A CN202110616402.4A CN202110616402A CN113759208A CN 113759208 A CN113759208 A CN 113759208A CN 202110616402 A CN202110616402 A CN 202110616402A CN 113759208 A CN113759208 A CN 113759208A
- Authority
- CN
- China
- Prior art keywords
- data
- waveform
- fault indicator
- direct current
- original
- 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
- 238000000034 method Methods 0.000 title claims abstract description 34
- 230000002159 abnormal effect Effects 0.000 title claims abstract description 20
- 238000012549 training Methods 0.000 claims abstract description 18
- 238000012360 testing method Methods 0.000 claims abstract description 15
- 238000012545 processing Methods 0.000 claims abstract description 14
- 230000005684 electric field Effects 0.000 claims description 6
- 238000005070 sampling Methods 0.000 claims description 4
- 238000000605 extraction Methods 0.000 abstract 1
- 238000010586 diagram Methods 0.000 description 5
- 238000004891 communication Methods 0.000 description 2
- 238000005259 measurement Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/08—Locating faults in cables, transmission lines, or networks
- 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
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Mathematical Physics (AREA)
- Theoretical Computer Science (AREA)
- Remote Monitoring And Control Of Power-Distribution Networks (AREA)
Abstract
The invention discloses an abnormal waveform identification method based on a fault indicator, which comprises the following steps: collecting original waveforms uploaded to a main station by a fault indicator and carrying out direct current removal processing on the original waveforms; manually adding classification labels to all collected waveform data; combining the original waveform data and the waveform data after the direct current is removed into new data, dividing the new data and the corresponding labels into a training set and a test set, and feeding the training set and the test set into a TextCNN network model; performing parameter adjustment, training and testing on the network model to obtain an optimal model; and performing direct current removal processing on the waveform to be recognized, and inputting the original data of the waveform to be recognized and the processed data into the optimal model to obtain a recognition result. According to the method, only the original waveform needs to be subjected to direct current removal processing, no further feature extraction is needed, and compared with the traditional CNN model, the TextCNN model can more accurately capture the relation between voltage and current, so that the identification accuracy is improved, and the advantages of simple model and high engineering practicability are achieved on the basis of ensuring the running state of a power grid.
Description
Technical Field
The invention relates to the technical field of medium-voltage power distribution networks, in particular to an abnormal waveform identification method based on a fault indicator.
Background
The running state of the power distribution network is crucial to the stable running of the power grid, and when a power system fails, the power system needs to be positioned and cleared as soon as possible. The fault indicator (comprising an acquisition unit and a collection unit) is used as a fault indicating and warning device, can automatically record all electric quantities before and after a fault moment and transmit the electric quantities to a main station in a waveform mode, and the power distribution main station analyzes the fault type according to the waveform uploaded by the fault indicator and the change rule of the electric quantities when a power system is grounded or short-circuit fault occurs and judges the fault occurrence position. The fault indicator also has the advantages of no maintenance, low power consumption, charged loading and unloading and the like, and is widely applied to a 10kV power distribution network system based on the advantages. However, due to the complex working environment, problems may occur in the aspects of sampling by the acquisition unit, communication between the acquisition unit and the collection unit, communication between the collection unit and the master station, and the like, and the waveform quality uploaded to the master station by the fault indicator is difficult to guarantee, and may contain many abnormal waveforms, thereby affecting the judgment of the fault position, and causing the occurrence of the conditions of missed judgment and wrong judgment.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides an abnormal waveform identification method based on a fault indicator, which is used for carrying out direct current removal processing on a waveform uploaded to a main station by the fault indicator, combining the waveform with original data and inputting the combined waveform into a trained TextCNN model for calculation so as to realize the identification of the abnormal waveform.
The purpose of the invention can be realized by the following technical scheme:
an abnormal waveform identification method based on a fault indicator comprises the following steps:
step 1: collecting original waveforms uploaded to a main station by a fault indicator and carrying out direct current removal processing on the original waveforms;
step 2: manually adding classification labels to all collected waveform data;
and step 3: combining the original waveform data and the waveform data after the direct current is removed into new data, dividing the new data and the labels corresponding to the new data into a training set and a test set, and feeding the training set and the test set into a TextCNN network model;
and 4, step 4: performing parameter adjustment, training and testing on the network model to obtain an optimal model;
and 5: and performing direct current removal processing on the waveform to be recognized, and inputting the original data of the waveform to be recognized and the processed data into the optimal model to obtain a recognition result.
Further, each group of waveforms of the main station uploaded by the fault indicator in the step 1 includes 8 channel data including three-phase electric field and zero-sequence electric field data and three-phase current and zero-sequence current data, and after performing dc removal processing, 8 channel data are obtained again, and finally 16 channel data are obtained;
further, the dc removal method used in step 1 is a segmented dc removal method, and the calculation formula is as follows:
wherein, data _ newiFor removing the data of the ith point after DCiFor the data of the ith point before the direct current is removed, the cyclePoint is the total point number of a cycle;
further, the label classification method in step 2 is as follows: marking the normal waveform as 1, and marking the abnormal waveform as-1;
further, the method used in the step 3 for dividing the training set and the test set is hierarchical sampling;
further, the constraint overfitting method used in the training process in step 4 includes Early Stopping, L1, L2 regularization, and Dropout.
The invention has the beneficial effects that: the original waveform is only required to be subjected to direct current removal processing, waveform characteristics are not required to be further extracted, and compared with the traditional CNN method, the TextCNN method can more accurately capture the relation between voltage and current, the identification accuracy is improved, the method has the advantages of being simple in model and easy to realize on the basis of ensuring the running state of the power grid, and has strong engineering practicability.
Drawings
FIG. 1 is a general flow diagram of the present invention.
FIG. 2 is a diagram illustrating the structure of the TextCNN model according to the present invention.
Fig. 3 is a schematic diagram of dimensional changes of waveform data in a training or testing process according to the present invention.
Fig. 4 is a diagram of a field topology provided by an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and do not limit the invention.
With reference to fig. 1, a method for identifying an abnormal waveform based on a fault indicator includes the following steps:
step 1: collecting original waveforms uploaded to a main station by a fault indicator and carrying out direct current removal processing on the original waveforms; each group of waveforms of the main station uploaded by the fault indicator comprise 8 channel data including three-phase electric field and zero-sequence electric field data and three-phase current and zero-sequence current data, the 8 channel data are obtained after the direct current removal processing is carried out, and finally 16 channel data are obtained; the used DC removing method is segmented DC removing, and the calculation formula is as follows:
wherein, data _ newiFor removing the data of the ith point after DCiFor the data of the ith point before the direct current is removed, the cyclePoint is the total point number of a cycle;
step 2: manually add classification labels to all collected waveform data: marking the normal waveform as 1, and marking the abnormal waveform as-1;
and step 3: combining original waveform data and waveform data subjected to direct current removal into new data, dividing the new data and labels corresponding to the new data into a training set and a test set by a hierarchical sampling method, and feeding the training set and the test set into a TextCNN network model, wherein the structure of the model network is shown in FIG. 2;
and 4, step 4: performing parameter adjustment, training and testing on the network model to obtain an optimal model; the limiting overfitting methods used in the training process include Early Stopping, L1, L2 regularization and Dropout;
and 5: and performing direct current removal processing on the waveform to be recognized, and inputting the original data of the waveform to be recognized and the processed data into the optimal model to obtain a recognition result. The dimension change of the waveform data from input to output is shown in fig. 3, the final output dimension is (number of samples, 2), and the sample identification result is obtained when the probability value in each row is larger.
Fig. 4 shows a field topology diagram, which includes 6 index measurement points, and the classification result obtained by inputting the waveform of each measurement point into the model is as follows:
indicating a measuring point | Model output results | Classification result |
1 | (0.95,0.05) | Normal waveform |
2 | (0.1,0.9) | Abnormal waveform |
3 | (0.83,0.17) | Normal waveform |
4 | (0.76,0.24) | Normal waveform |
5 | (0.91,0.09) | Normal waveform |
6 | (0.22,0.78) | Abnormal waveform |
In the embodiment, the identification result of the method is accurate and consistent with the actual result.
The above-mentioned embodiments are illustrative of the specific embodiments of the present invention, and are not restrictive, and those skilled in the relevant art can make various changes and modifications to obtain corresponding equivalent technical solutions without departing from the spirit and scope of the present invention, so that all equivalent technical solutions should be included in the scope of the present invention.
Claims (6)
1. An abnormal waveform identification method based on a fault indicator is characterized by comprising the following steps:
step 1: collecting original waveforms uploaded to a main station by a fault indicator and carrying out direct current removal processing on the original waveforms;
step 2: manually adding classification labels to all collected waveform data;
and step 3: combining the original waveform data and the waveform data after the direct current is removed into new data, dividing the new data and the corresponding labels into a training set and a test set, and feeding the training set and the test set into a TextCNN network model;
and 4, step 4: performing parameter adjustment, training and testing on the network model to obtain an optimal model;
and 5: and performing direct current removal processing on the waveform to be recognized, and inputting the original data of the waveform to be recognized and the processed data into the optimal model to obtain a recognition result.
2. The method for identifying the abnormal waveform based on the fault indicator according to claim 1, wherein each group of waveforms uploaded to the main station by the fault indicator in the step 1 comprises 8 channel data including three-phase electric field and zero-sequence electric field data and three-phase current and zero-sequence current data, and the 8 channel data are obtained after the dc removal processing is performed, so that 16 channel data are obtained finally;
3. the method for identifying the abnormal waveform based on the fault indicator according to claim 1, wherein the dc removing method used in the step 1 is segmented dc removing, and the calculation formula is as follows:
wherein, data _ newiFor removing the data of the ith point after DCiThe total number of points of a cycle of the CyclePoint is the data of the ith point before direct current.
4. The abnormal waveform identification method based on the fault indicator as claimed in claim 1, wherein the label classification method in the step 2 is as follows: marking the normal waveform as 1, and marking the abnormal waveform as-1;
5. the abnormal waveform identification method based on the fault indicator as claimed in claim 1, wherein the method used for dividing the training set and the test set in the step 3 is hierarchical sampling;
6. the fault indicator-based abnormal waveform identification method according to claim 1, wherein the constraint overfitting methods used in the training process in the step 4 comprise Early Stopping, L1, L2 regularization and Dropout.
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202110616402.4A CN113759208A (en) | 2021-06-02 | 2021-06-02 | Abnormal waveform identification method based on fault indicator |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202110616402.4A CN113759208A (en) | 2021-06-02 | 2021-06-02 | Abnormal waveform identification method based on fault indicator |
Publications (1)
Publication Number | Publication Date |
---|---|
CN113759208A true CN113759208A (en) | 2021-12-07 |
Family
ID=78787451
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN202110616402.4A Pending CN113759208A (en) | 2021-06-02 | 2021-06-02 | Abnormal waveform identification method based on fault indicator |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN113759208A (en) |
Cited By (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN116449151A (en) * | 2023-06-15 | 2023-07-18 | 青岛鼎信通讯股份有限公司 | Fault indicator-based power distribution network fault positioning method |
Citations (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN102760444A (en) * | 2012-04-25 | 2012-10-31 | 清华大学 | Support vector machine based classification method of base-band time-domain voice-frequency signal |
CN103701755A (en) * | 2014-01-09 | 2014-04-02 | 上海创远仪器技术股份有限公司 | Method for estimating IQ imbalance in communication system |
CN109116196A (en) * | 2018-07-06 | 2019-01-01 | 山东科汇电力自动化股份有限公司 | A kind of power cable fault discharging sound intelligent identification Method |
CN110263172A (en) * | 2019-06-26 | 2019-09-20 | 国网江苏省电力有限公司南京供电分公司 | A kind of evented autonomous classification method of power system monitor warning information |
CN112285489A (en) * | 2020-10-26 | 2021-01-29 | 青岛鼎信通讯股份有限公司 | Fault indicator fault positioning method based on feature fusion and model fusion |
-
2021
- 2021-06-02 CN CN202110616402.4A patent/CN113759208A/en active Pending
Patent Citations (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN102760444A (en) * | 2012-04-25 | 2012-10-31 | 清华大学 | Support vector machine based classification method of base-band time-domain voice-frequency signal |
CN103701755A (en) * | 2014-01-09 | 2014-04-02 | 上海创远仪器技术股份有限公司 | Method for estimating IQ imbalance in communication system |
CN109116196A (en) * | 2018-07-06 | 2019-01-01 | 山东科汇电力自动化股份有限公司 | A kind of power cable fault discharging sound intelligent identification Method |
CN110263172A (en) * | 2019-06-26 | 2019-09-20 | 国网江苏省电力有限公司南京供电分公司 | A kind of evented autonomous classification method of power system monitor warning information |
CN112285489A (en) * | 2020-10-26 | 2021-01-29 | 青岛鼎信通讯股份有限公司 | Fault indicator fault positioning method based on feature fusion and model fusion |
Cited By (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN116449151A (en) * | 2023-06-15 | 2023-07-18 | 青岛鼎信通讯股份有限公司 | Fault indicator-based power distribution network fault positioning method |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN109359271B (en) | Transformer winding deformation degree online detection method based on logistic regression | |
CN111798412B (en) | Intelligent diagnosis method and system for defects of power transformation equipment based on infrared image | |
CN103291544B (en) | Digitizing Wind turbines power curve method for drafting | |
CN112285489B (en) | Fault indicator fault positioning method based on feature fusion and model fusion | |
CN108304567B (en) | Method and system for identifying working condition mode and classifying data of high-voltage transformer | |
CN111199213B (en) | Method and device for detecting defects of equipment for transformer substation | |
CN112098889B (en) | Single-phase earth fault positioning method based on neural network and feature matrix | |
CN103631681A (en) | Method for online restoring abnormal data of wind power plant | |
CN106570790B (en) | Wind power plant output data restoration method considering wind speed data segmentation characteristics | |
CN112131956B (en) | Voltage sag source classification method based on difference hash algorithm | |
CN116028887B (en) | Analysis method of continuous industrial production data | |
CN111666978B (en) | Intelligent fault early warning system for IT system operation and maintenance big data | |
CN114460439A (en) | Digital integrated circuit test system | |
CN113759208A (en) | Abnormal waveform identification method based on fault indicator | |
CN112180188A (en) | Medium-voltage distribution network switching operation identification method and device based on machine learning | |
CN114186617B (en) | Mechanical fault diagnosis method based on distributed deep learning | |
CN113313403B (en) | Power distribution network comprehensive evaluation method, device and system based on large-scale high-power electric vehicle charging and discharging and storage medium | |
CN112287979B (en) | Mutual information-based energy storage battery state judging method | |
CN115587312B (en) | Fault point positioning method and system based on multidimensional big data analysis | |
CN113586423B (en) | Electric energy stability monitoring system for air compressor | |
CN115409335A (en) | Electric power system disturbance identification method based on deep learning and considering unknown disturbance types | |
CN112051493A (en) | Hidden danger type identification method and device for power distribution network equipment | |
Guo et al. | Based on the improved YOLOv3 under the catenary insulator image recognition | |
Jiang et al. | Optimization algorithm of transmission line defect detection and identification based on embedded AI | |
CN116449151A (en) | Fault indicator-based power distribution network fault positioning method |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
PB01 | Publication | ||
PB01 | Publication | ||
SE01 | Entry into force of request for substantive examination | ||
SE01 | Entry into force of request for substantive examination | ||
RJ01 | Rejection of invention patent application after publication |
Application publication date: 20211207 |