CN116540015A - Power distribution network fault early warning method and system based on transient waveform signals - Google Patents
Power distribution network fault early warning method and system based on transient waveform signals Download PDFInfo
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Abstract
The invention discloses a power distribution network fault early warning method and system based on transient waveform signals, comprising the following steps: acquiring transient waveform signal data of each device and line of the power distribution network, and preprocessing the transient waveform signal data; feature extraction is carried out on the preprocessed data, a fault diagnosis model is established according to the feature extracted data, and training is carried out on the fault diagnosis model based on a support vector machine algorithm; inputting the real-time operation data into a trained fault diagnosis model to output a fault diagnosis result, and carrying out real-time monitoring and early warning on various faults in the power distribution network; the method provided by the invention utilizes the transient waveform signal to perform distribution network fault early warning, and takes the transient waveform signal as a main data acquisition mode, so that the voltage and current parameters of each node can be monitored in real time, and the accuracy and timeliness of fault diagnosis are improved.
Description
Technical Field
The invention relates to the technical field of fault early warning, in particular to a power distribution network fault early warning method and system based on transient waveform signals.
Background
With the development and transformation of the power system, the scale of the power distribution network is gradually enlarged, and the power supply function and the service requirement are gradually improved. However, faults of power equipment and lines in the power distribution network still occur, if the faults cannot be found and processed in time, serious influence is brought to normal power supply and production, and therefore, it is important to establish a power distribution network fault early warning method and system based on transient waveform signals.
The method and the system extract effective characteristics by collecting and analyzing transient waveform signal data in the power distribution network, and establish a corresponding fault diagnosis model to realize early warning of power distribution network faults. The early warning system sends out early warning signals and gives an alarm in real time, and is favorable for system administrators to take corresponding measures in time for investigation and processing, so that the high reliability and the safety of the power distribution network are ensured.
Disclosure of Invention
This section is intended to outline some aspects of embodiments of the invention and to briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section as well as in the description summary and in the title of the application, to avoid obscuring the purpose of this section, the description summary and the title of the invention, which should not be used to limit the scope of the invention.
The present invention has been made in view of the above-described problems.
In a first aspect of the embodiment of the present invention, a power distribution network fault early warning method based on transient waveform signals is provided, including: acquiring transient waveform signal data of each device and line of a power distribution network, and preprocessing the transient waveform signal data; extracting features of the preprocessed data, establishing a fault diagnosis model according to the feature extracted data, and training the fault diagnosis model based on a support vector machine algorithm; and inputting the real-time operation data into the trained fault diagnosis model to output a fault diagnosis result, and carrying out real-time monitoring and early warning on various faults in the power distribution network.
As a preferable scheme of the power distribution network fault early warning method based on the transient waveform signals, the power distribution network fault early warning method based on the transient waveform signals comprises the following steps: the acquisition of the transient waveform signal data includes,
selecting a proper transient waveform signal collector according to the characteristics of the power distribution network and parameters to be monitored, and collecting transient waveform signals of all equipment and lines of the power distribution network in real time through the transient waveform signal collector;
the transient waveform signals comprise zero-crossing rate, instantaneous frequency, time integral data and signals of plus or minus four second time periods when the power distribution network breaks down.
As a preferable scheme of the power distribution network fault early warning method based on the transient waveform signals, the power distribution network fault early warning method based on the transient waveform signals comprises the following steps: the process of the pre-treatment comprises the steps of,
converting an analog signal output by the transient waveform signal collector into a digital signal by utilizing an A/D converter, performing median filtering, high-pass filtering and noise reduction operation on the converted digital signal data, and cleaning blank values, format contents, logic errors and non-required information in the data;
adopting an outlier sample detection strategy based on clustering to detect and reject samples which still possibly have abnormality in the data samples;
and carrying out normalization processing on the pre-preprocessed signal data, and storing the normalized data locally or uploading the normalized data to a database.
As a preferable scheme of the power distribution network fault early warning method based on the transient waveform signals, the power distribution network fault early warning method based on the transient waveform signals comprises the following steps: the step of feature extraction includes,
for waveform data which slowly changes in the preprocessed signal data, directly adopting approximate coefficients decomposed by discrete wavelet transformation as characteristic value vectors of waveforms, and filling missing values of the digital signal data at a certain sampling point before decomposition operation in order to make the lengths of the approximate coefficients of the same layer number consistent in the decomposition process;
the computation of the approximation coefficients of the discrete wavelet transform decomposition includes,
wherein a represents a parameter for scaling the waveform, and b represents a parameter for translating the waveform;
and extracting the characteristics of each frequency band of the signal data by adopting detail coefficients of non-sampling wavelet transformation for the waveform data which is changed rapidly in the preprocessed signal data, firstly obtaining the maximum value vector of the detail coefficients, equally dividing the maximum value vector into the number of the frequency bands, and then selecting the maximum value of each frequency band to form a new vector serving as the characteristic value vector of the waveform.
As a preferable scheme of the power distribution network fault early warning method based on the transient waveform signals, the power distribution network fault early warning method based on the transient waveform signals comprises the following steps: the establishment and training of the fault diagnosis model comprises,
carrying out data statistics and analysis on the data extracted by the features, and establishing a fault diagnosis model according to the statistics and analysis results;
dividing the collected transient waveform classification data and fault diagnosis classification data into a training set and a testing set, training the fault diagnosis model by adopting a support vector machine algorithm and combining the training data, testing and evaluating the fault diagnosis model by utilizing the testing data, and adjusting and optimizing the model according to an evaluation result;
and deploying the trained model into practical application, monitoring the running condition of the fault diagnosis model in real time, and feeding back and optimizing according to the monitoring result.
As a preferable scheme of the power distribution network fault early warning method based on the transient waveform signals, the power distribution network fault early warning method based on the transient waveform signals comprises the following steps: the determination of the failure diagnosis result includes,
inputting real-time operation data into a trained fault diagnosis model to output a fault diagnosis result, and matching the fault diagnosis result with fault information in a database by using a registration algorithm to obtain a fault type;
the calculation of the registration algorithm includes,
wherein N is c Representing the number of matching point pairs,representing the original matching point pixel coordinates in the original sample,and the coordinate value of the coordinate of the matching point after perspective transformation is represented.
As a preferable scheme of the power distribution network fault early warning method based on the transient waveform signals, the power distribution network fault early warning method based on the transient waveform signals comprises the following steps: the real-time monitoring and early warning of various faults in the power distribution network comprises,
dividing the fault type into two different early warning levels, namely, the fault factors needing to be manually subjected to fault maintenance are primary early warning, and the other fault factors are secondary early warning;
monitoring various faults in the power distribution network in real time, when a possible fault exists in the power distribution network, matching fault codes with fault information in a database, and if the matching is successful, sending out a signal by an early warning system and giving an alarm according to an early warning level;
and an administrator of the power distribution network monitors the system state and fault information through a monitoring terminal and timely takes corresponding measures to conduct investigation and processing.
In a second aspect of the embodiment of the present invention, a power distribution network fault early warning system based on a transient waveform signal is provided, including:
the data acquisition processing unit is used for acquiring transient waveform signal data of all equipment and circuits of the power distribution network in real time through the transient waveform signal acquisition unit and preprocessing the transient waveform signal data;
the model training unit is used for extracting features of the preprocessed data, establishing a fault diagnosis model according to the feature extracted data, and training the fault diagnosis model based on a support vector machine algorithm;
and the fault monitoring and early warning unit is used for inputting the real-time operation data into the trained fault diagnosis model to output a fault diagnosis result and carrying out real-time monitoring and early warning on various faults in the power distribution network.
In a third aspect of embodiments of the present invention, there is provided an apparatus, comprising,
a processor;
a memory for storing processor-executable instructions;
the processor is configured to invoke the instructions stored in the memory to perform the method according to any of the embodiments of the present invention.
In a fourth aspect of embodiments of the present invention, there is provided a computer readable storage medium having stored thereon computer program instructions comprising:
the computer program instructions, when executed by a processor, implement a method according to any of the embodiments of the present invention.
The invention has the beneficial effects that:
(1) the invention provides a power distribution network fault early warning method and a power distribution network fault early warning system based on transient waveform signals, which utilize the transient waveform signals to carry out power distribution network fault early warning, take the transient waveform signals as a main data acquisition mode, can monitor the voltage and current parameters of each node in real time, and improve the accuracy and timeliness of fault diagnosis;
(2) the invention establishes a fault diagnosis and early warning system, the system comprises a fault diagnosis model, an alarm module, a real-time monitoring system, a database and a monitoring terminal assembly, so that the possible faults in the power distribution network can be monitored, alarmed, diagnosed and checked in real time, and the safety and reliability of the power distribution network are improved;
(3) according to the invention, the fault diagnosis is carried out by adopting a feature extraction and modeling method, the fault in the power distribution network can be accurately diagnosed by carrying out feature extraction and modeling on the acquired transient waveform signals, and an alarm is sent out by an alarm module, so that staff can schedule and process in time;
(4) the invention can store the collected data in the database so as to facilitate analysis, diagnosis and prediction of the fault in the future, and monitor the power distribution network in real time through the monitoring terminal to timely troubleshoot the fault and prevent accidents.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art. Wherein:
FIG. 1 is a general flow chart of a power distribution network fault early warning method and system based on transient waveform signals;
fig. 2 is a system schematic diagram of a power distribution network fault early warning method and system based on transient waveform signals.
Detailed Description
So that the manner in which the above recited objects, features and advantages of the present invention can be understood in detail, a more particular description of the invention, briefly summarized above, may be had by reference to the embodiments, some of which are illustrated in the appended drawings. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways other than those described herein, and persons skilled in the art will readily appreciate that the present invention is not limited to the specific embodiments disclosed below.
Further, reference herein to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic can be included in at least one implementation of the invention. The appearances of the phrase "in one embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments.
While the embodiments of the present invention have been illustrated and described in detail in the drawings, the cross-sectional view of the device structure is not to scale in the general sense for ease of illustration, and the drawings are merely exemplary and should not be construed as limiting the scope of the invention. In addition, the three-dimensional dimensions of length, width and depth should be included in actual fabrication.
Also in the description of the present invention, it should be noted that the orientation or positional relationship indicated by the terms "upper, lower, inner and outer", etc. are based on the orientation or positional relationship shown in the drawings, are merely for convenience of describing the present invention and simplifying the description, and do not indicate or imply that the apparatus or elements referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as limiting the present invention. Furthermore, the terms "first, second, or third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
The terms "mounted, connected, and coupled" should be construed broadly in this disclosure unless otherwise specifically indicated and defined, such as: can be fixed connection, detachable connection or integral connection; it may also be a mechanical connection, an electrical connection, or a direct connection, or may be indirectly connected through an intermediate medium, or may be a communication between two elements. The specific meaning of the above terms in the present invention will be understood in specific cases by those of ordinary skill in the art.
Example 1
Referring to fig. 1-2, in one embodiment of the present invention, a power distribution network fault early warning method based on transient waveform signals is provided, including:
s1: transient waveform signal data of all equipment and lines of the power distribution network are collected, and the transient waveform signal data are preprocessed. It should be noted that:
the acquisition of the transient waveform signal data comprises the steps of selecting a proper transient waveform signal acquisition device according to the characteristics of the power distribution network and parameters to be monitored, and acquiring the transient waveform signals of all equipment and lines of the power distribution network in real time through the transient waveform signal acquisition device;
specifically, the transient waveform signals comprise signals of zero crossing rate, transient frequency, time integral data and positive and negative four-second time periods when the power distribution network fails;
further, the pretreatment process comprises the steps of,
converting an analog signal output by the transient waveform signal collector into a digital signal by utilizing an A/D converter, performing median filtering, high-pass filtering and noise reduction operation on the converted digital signal data, and cleaning blank values, format contents, logic errors and non-required information in the data;
adopting an outlier sample detection strategy based on clustering to detect and reject samples which still possibly have abnormality in the data samples;
and carrying out normalization processing on the pre-processed signal data, and storing the normalized data locally or uploading the normalized data to a database for storing transient waveform signal data and fault diagnosis data of the power distribution network so as to facilitate fault analysis and troubleshooting.
S2: and extracting features of the preprocessed data, establishing a fault diagnosis model according to the feature extracted data, and training the fault diagnosis model based on a support vector machine algorithm. It should be noted that:
the step of feature extraction includes the steps of,
for waveform data which slowly changes in the preprocessed signal data, directly adopting approximate coefficients decomposed by discrete wavelet transformation as characteristic value vectors of waveforms, and filling missing values of the digital signal data at a certain sampling point before decomposition operation in order to make the lengths of the approximate coefficients of the same layer number consistent in the decomposition process;
it should be noted that the computation of the approximation coefficients of the discrete wavelet transform decomposition includes,
wherein a represents a parameter for scaling the waveform, and b represents a parameter for translating the waveform;
extracting characteristics of each frequency band of the signal data by adopting detail coefficients of non-sampling wavelet transformation for the waveform data which is rapidly changed in the preprocessed signal data, firstly obtaining maximum value vectors of the detail coefficients, equally dividing the maximum value vectors into the number of the frequency bands, and then selecting the maximum value of each frequency band to form a new vector serving as a characteristic value vector of the waveform;
further, as shown in fig. 2, the establishment of the fault diagnosis model includes,
carrying out data statistics and analysis on the data extracted by the features, and establishing a fault diagnosis model according to the statistics and analysis results, wherein the model is used for transient waveform classification and fault diagnosis application and comprises the following codes:
still further, the training of the fault diagnosis model includes,
dividing the collected transient waveform classification data and fault diagnosis classification data into a training set and a testing set, training a fault diagnosis model by adopting a support vector machine algorithm and combining the training data, testing and evaluating the fault diagnosis model by utilizing the testing data, and adjusting and optimizing the model according to an evaluation result;
deploying the trained model into practical application, monitoring the running condition of the fault diagnosis model in real time, and feeding back and optimizing according to the monitoring result;
the code is as follows:
s3: and inputting the real-time operation data into the trained fault diagnosis model to output a fault diagnosis result, and carrying out real-time monitoring and early warning on various faults in the power distribution network. It should be noted that:
the judging of the fault diagnosis result comprises the steps of inputting real-time operation data into a trained fault diagnosis model to output the fault diagnosis result, and matching the fault diagnosis result with fault information in a database by using a registration algorithm to obtain a fault type;
it should be noted that the calculation of the registration algorithm includes,
wherein N is c Representing the number of matching point pairs,representing the original matching point pixel coordinates in the original sample,coordinate values representing the coordinate values of the matching points after perspective transformation;
further, the real-time monitoring and early warning of various faults in the distribution network comprises,
dividing the fault type into two different early warning grades, namely, the fault factor requiring manual fault maintenance is first-level early warning, and the other fault factors are second-level early warning;
monitoring various faults in the power distribution network in real time, when a possible fault exists in the power distribution network, matching fault codes with fault information in a database, and if the matching is successful, sending out a signal by an early warning system and giving an alarm according to an early warning level;
and an administrator of the power distribution network monitors the system state and fault information through a monitoring terminal and timely takes corresponding measures to conduct investigation and processing.
The invention provides a power distribution network fault early warning method and a power distribution network fault early warning system based on transient waveform signals, wherein the transient waveform signals are used as a main data acquisition mode, so that the voltage and current parameters of each node can be monitored in real time, and the accuracy and timeliness of fault diagnosis are improved; (2) the invention establishes a fault diagnosis and early warning system, the system comprises a fault diagnosis model, an alarm module, a real-time monitoring system, a database and a monitoring terminal assembly, so that the possible faults in the power distribution network can be monitored, alarmed, diagnosed and checked in real time, and the safety and reliability of the power distribution network are improved; (3) according to the invention, the fault diagnosis is carried out by adopting a feature extraction and modeling method, the fault in the power distribution network can be accurately diagnosed by carrying out feature extraction and modeling on the acquired transient waveform signals, and an alarm is sent out by an alarm module, so that staff can schedule and process in time; (4) the invention can store the collected data in the database so as to facilitate analysis, diagnosis and prediction of the fault in the future, and monitor the power distribution network in real time through the monitoring terminal to timely troubleshoot the fault and prevent accidents.
In a second aspect of the present disclosure,
the utility model provides a distribution network fault early warning system based on transient waveform signal, includes:
the data acquisition processing unit is used for acquiring transient waveform signal data of each device and circuit of the power distribution network in real time through the transient waveform signal acquisition unit and preprocessing the transient waveform signal data;
the model training unit is used for extracting features of the preprocessed data, establishing a fault diagnosis model according to the feature extracted data, and training the fault diagnosis model based on a support vector machine algorithm;
and the fault monitoring and early warning unit is used for inputting the real-time operation data into the trained fault diagnosis model to output a fault diagnosis result and carrying out real-time monitoring and early warning on various faults in the power distribution network.
Specifically, as shown in fig. 2, a power distribution network fault early warning system based on transient waveform signals includes the following modules: transient waveform signal collector, signal processor, fault diagnosis model, early warning system and database;
transient waveform signal collector: the method is responsible for collecting transient waveform signals of various devices and circuits of the power distribution network;
a signal processor: processing the acquired transient waveform signals and extracting effective characteristics;
fault diagnosis model: establishing a fault diagnosis model, and performing fault diagnosis and early warning;
early warning system: early warning and alarming are carried out according to the result of the fault diagnosis model;
database: transient waveform signal data and fault diagnosis data of the power distribution network are collected and stored for fault analysis and troubleshooting.
In a third aspect of the present disclosure,
there is provided an apparatus comprising:
a processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to invoke the instructions stored in the memory to perform the method of any of the preceding.
In a fourth aspect of the present disclosure,
there is provided a computer readable storage medium having stored thereon computer program instructions comprising:
the computer program instructions, when executed by a processor, implement a method of any of the preceding.
The present invention may be a method, apparatus, system, and/or computer program product, which may include a computer-readable storage medium having computer-readable program instructions embodied thereon for performing various aspects of the present invention.
The computer readable storage medium may be a tangible device that can hold and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium would include the following: portable computer disks, hard disks, random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static Random Access Memory (SRAM), portable compact disk read-only memory (CD-ROM), digital Versatile Disks (DVD), memory sticks, floppy disks, mechanical coding devices, punch cards or in-groove structures such as punch cards or grooves having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media, as used herein, are not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., optical pulses through fiber optic cables), or electrical signals transmitted through wires.
Example 2
The embodiment is different from the first embodiment in that a verification test of a power distribution network fault early warning method and a system based on transient waveform signals is provided, and the technical effects adopted in the method are verified and explained.
In the embodiment, transient waveform signal data of all equipment and circuits of the power distribution network are collected in real time through a transient waveform signal collector, the transient waveform signal data are preprocessed, and the collected data are shown in table 1;
table 1: data acquisition table.
| Time (seconds) | Current (A) | Voltage (V) | Fault type | Fault location |
| 0.00 | 0 | 220 | Normal state | Without any means for |
| 0.01 | 100 | 220 | Short circuit | Substation transformer |
| 0.02 | 150 | 215 | Short circuit | Substation transformer |
| 0.03 | 200 | 210 | Short circuit | Substation transformer |
| 0.04 | 250 | 205 | Short circuit | Substation transformer |
| 0.05 | 300 | 200 | Short circuit | Substation transformer |
Feature extraction is carried out on the preprocessed data, a fault diagnosis model is established according to the feature extracted data, and training is carried out on the fault diagnosis model based on a support vector machine algorithm; and inputting the real-time operation data into the trained fault diagnosis model to output a fault diagnosis result, and carrying out real-time monitoring and early warning on various faults in the power distribution network.
In the embodiment, the method and the traditional early warning method respectively measure and compare the prediction time of the same fault type, the automatic test equipment is started, MATLB software programming is used for realizing the simulation test of the embodiment, the program is compiled and operated on Microsoft Visual Studio 2017, simulation data are obtained according to experimental results, and the comparison results are shown in Table 2.
Table 2: the comparison result of the method and the traditional early warning method is provided.
| Experimental sample | Conventional method | The method of the invention |
| Detection efficiency | 75％ | 99％ |
| Time | ＞10min | ＜5s |
| Cost of labor | High height | Low and low |
| Accuracy rate of | 80％ | 98％ |
Compared with the traditional early warning method, the method has the advantages that the average prediction time is greatly reduced, and the prediction accuracy and efficiency are greatly improved; therefore, the method provided by the invention utilizes the transient waveform signal to perform distribution network fault early warning, and takes the transient waveform signal as a main data acquisition mode, so that the voltage and current parameters of each node can be monitored in real time, and the accuracy and timeliness of fault diagnosis are improved.
It should be noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention may be modified or substituted without departing from the spirit and scope of the technical solution of the present invention, which is intended to be covered in the scope of the claims of the present invention.
Claims (10)
1. A power distribution network fault early warning method based on transient waveform signals is characterized by comprising the following steps:
acquiring transient waveform signal data of each device and line of a power distribution network, and preprocessing the transient waveform signal data;
extracting features of the preprocessed data, establishing a fault diagnosis model according to the feature extracted data, and training the fault diagnosis model based on a support vector machine algorithm;
and inputting the real-time operation data into the trained fault diagnosis model to output a fault diagnosis result, and carrying out real-time monitoring and early warning on various faults in the power distribution network.
2. The power distribution network fault early warning method based on transient waveform signals according to claim 1, wherein the method comprises the following steps of: the acquisition of the transient waveform signal data includes,
selecting a proper transient waveform signal collector according to the characteristics of the power distribution network and parameters to be monitored, and collecting transient waveform signals of all equipment and lines of the power distribution network in real time through the transient waveform signal collector;
the transient waveform signals comprise zero-crossing rate, instantaneous frequency, time integral data and signals of plus or minus four second time periods when the power distribution network breaks down.
3. The power distribution network fault early warning method based on transient waveform signals according to claim 2, wherein: the process of the pre-treatment comprises the steps of,
converting an analog signal output by the transient waveform signal collector into a digital signal by utilizing an A/D converter, performing median filtering, high-pass filtering and noise reduction operation on the converted digital signal data, and cleaning blank values, format contents, logic errors and non-required information in the data;
adopting an outlier sample detection strategy based on clustering to detect and reject samples which still possibly have abnormality in the data samples;
and carrying out normalization processing on the pre-preprocessed signal data, and storing the normalized data locally or uploading the normalized data to a database.
4. A power distribution network fault early warning method based on transient waveform signals as claimed in any one of claims 1 to 3, wherein: the step of feature extraction includes,
for waveform data which slowly changes in the preprocessed signal data, directly adopting approximate coefficients decomposed by discrete wavelet transformation as characteristic value vectors of waveforms, and filling missing values of the digital signal data at a certain sampling point before decomposition operation in order to make the lengths of the approximate coefficients of the same layer number consistent in the decomposition process;
the computation of the approximation coefficients of the discrete wavelet transform decomposition includes,
wherein a represents a parameter for scaling the waveform, and b represents a parameter for translating the waveform;
and extracting the characteristics of each frequency band of the signal data by adopting detail coefficients of non-sampling wavelet transformation for the waveform data which is changed rapidly in the preprocessed signal data, firstly obtaining the maximum value vector of the detail coefficients, equally dividing the maximum value vector into the number of the frequency bands, and then selecting the maximum value of each frequency band to form a new vector serving as the characteristic value vector of the waveform.
5. The power distribution network fault early warning method based on transient waveform signals according to claim 4, wherein the method comprises the following steps of: the establishment and training of the fault diagnosis model comprises,
carrying out data statistics and analysis on the data extracted by the features, and establishing a fault diagnosis model according to the statistics and analysis results;
dividing the collected transient waveform classification data and fault diagnosis classification data into a training set and a testing set, training the fault diagnosis model by adopting a support vector machine algorithm and combining the training data, testing and evaluating the fault diagnosis model by utilizing the testing data, and adjusting and optimizing the model according to an evaluation result;
and deploying the trained model into practical application, monitoring the running condition of the fault diagnosis model in real time, and feeding back and optimizing according to the monitoring result.
6. The power distribution network fault early warning method based on transient waveform signals according to claim 5, wherein the method comprises the following steps of: the determination of the failure diagnosis result includes,
inputting real-time operation data into a trained fault diagnosis model to output a fault diagnosis result, and matching the fault diagnosis result with fault information in a database by using a registration algorithm to obtain a fault type;
the calculation of the registration algorithm includes,
wherein N is c Representing the number of matching point pairs,representing the original matching point pixel coordinates in the original sample,and the coordinate value of the coordinate of the matching point after perspective transformation is represented.
7. The power distribution network fault early warning method based on transient waveform signals according to claim 6, wherein the method comprises the following steps of: the real-time monitoring and early warning of various faults in the power distribution network comprises,
dividing the fault type into two different early warning levels, namely, the fault factors needing to be manually subjected to fault maintenance are primary early warning, and the other fault factors are secondary early warning;
monitoring various faults in the power distribution network in real time, when a possible fault exists in the power distribution network, matching fault codes with fault information in a database, and if the matching is successful, sending out a signal by an early warning system and giving an alarm according to an early warning level;
and an administrator of the power distribution network monitors the system state and fault information through a monitoring terminal and timely takes corresponding measures to conduct investigation and processing.
8. A power distribution network fault early warning system based on transient waveform signals is characterized by comprising:
the data acquisition processing unit is used for acquiring transient waveform signal data of all equipment and circuits of the power distribution network in real time through the transient waveform signal acquisition unit and preprocessing the transient waveform signal data;
the model training unit is used for extracting features of the preprocessed data, establishing a fault diagnosis model according to the feature extracted data, and training the fault diagnosis model based on a support vector machine algorithm;
and the fault monitoring and early warning unit is used for inputting the real-time operation data into the trained fault diagnosis model to output a fault diagnosis result and carrying out real-time monitoring and early warning on various faults in the power distribution network.
9. An apparatus, characterized in that the apparatus comprises,
a processor;
a memory for storing processor-executable instructions;
the processor is configured to invoke the instructions stored in the memory to perform the method of any of claims 1-7.
10. A computer readable storage medium having stored thereon computer program instructions, which when executed by a processor, implement the method of any of claims 1 to 7.
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