CN114330437A - Fault detection method and training method and device of fault classification model - Google Patents
Fault detection method and training method and device of fault classification model Download PDFInfo
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Abstract
The disclosure provides a fault detection method and a fault classification model training method and device, and relates to the technical field of artificial intelligence, in particular to the technical field of industrial big data and the like. The specific implementation scheme is as follows: acquiring electric signals of different parts of a long-distance power transmission line to be detected in multiple time periods; aiming at each part, calculating the performance indexes of the electric signals of the part at different time periods to obtain the performance indexes corresponding to the part; respectively constructing index characteristics of the performance indexes corresponding to the parts; and determining whether each part of the long-distance power transmission line to be detected has a fault or not based on the index characteristics of each part. The embodiment of the disclosure can realize fault detection of a long-distance power transmission line.
Description
Technical Field
The disclosure relates to the technical field of artificial intelligence, in particular to the technical field of industrial big data and the like, and specifically relates to a fault detection method and a training method and device of a fault classification model.
Background
Power transmission lines are one of the important infrastructures for transmitting power over long distances, and it is important to assume power stabilization functions, ensure stability of the power transmission lines, and quickly locate a power transmission line fault.
Disclosure of Invention
The disclosure provides a fault detection method and a training method and device of a fault classification model.
According to an aspect of the present disclosure, there is provided a fault detection method including:
acquiring electric signals of different parts of a long-distance power transmission line to be detected in multiple time periods;
aiming at each part, calculating the performance indexes of the electric signals of the part at different time periods to obtain the performance indexes corresponding to the part;
respectively constructing index characteristics of the performance indexes corresponding to the parts;
and determining whether each part of the long-distance power transmission line to be detected has a fault or not based on the index characteristics of each part.
According to another aspect of the present disclosure, there is provided a method for training a fault classification model, including:
the method comprises the steps of obtaining sample electric signals of different parts of a sample remote power transmission line in multiple time periods, wherein the sample electric signals are provided with classification labels which are used for representing whether the sample electric signals belong to a normal category or a fault category;
calculating the sample performance indexes of the sample electric signals of each part at different time periods to obtain the sample performance indexes corresponding to the parts;
respectively constructing sample index characteristics of sample performance indexes corresponding to the parts;
inputting the sample index characteristics of each part into a fault classification model for fault classification to obtain a prediction classification label of each part of the sample long-distance power transmission line at different time periods;
calculating the current loss according to the predicted classification labels of the parts of the sample long-distance power transmission line in different time periods and the classification labels of the parts of the sample electric signal in different time periods, and adjusting the training parameters of the fault classification model according to the current loss until a preset finishing condition is met to obtain a trained fault classification model.
According to another aspect of the present disclosure, there is provided a fault detection apparatus including:
the signal acquisition module is used for acquiring electric signals of different parts of the long-distance power transmission line to be detected in multiple time periods;
the index calculation module is used for calculating the performance indexes of the electric signals of each part under different time periods to obtain the performance indexes corresponding to the part;
the characteristic construction module is used for respectively constructing the index characteristics of the performance indexes corresponding to the parts;
and the fault detection module is used for determining whether each part of the long-distance power transmission line to be detected has a fault or not based on the index characteristics of each part.
According to another aspect of the present disclosure, there is provided a training apparatus for a fault classification model, including:
the system comprises a sample signal acquisition module, a signal processing module and a signal processing module, wherein the sample signal acquisition module is used for acquiring sample electric signals of different parts of a sample remote power transmission line in multiple time periods, the sample electric signals are provided with classification labels, and the classification labels are used for representing whether the sample electric signals belong to a normal category or a fault category;
the sample index calculation module is used for calculating the sample performance indexes of the sample electric signals of each part at different time periods to obtain the sample performance indexes corresponding to the part;
the sample characteristic construction module is used for respectively constructing sample index characteristics of sample performance indexes corresponding to the parts;
the sample fault classification module is used for inputting the sample index characteristics of each part into a fault classification model for fault classification to obtain a prediction classification label of each part of the sample long-distance transmission line at different time periods;
and the classification model training module is used for calculating the current loss according to the predicted classification labels of the parts of the sample remote transmission line in different time periods and the classification labels of the parts of the sample electric signal in different time periods, and adjusting the training parameters of the fault classification model according to the current loss until a preset finishing condition is met to obtain a trained fault classification model.
According to another aspect of the present disclosure, there is provided an electronic device including:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform a fault detection method or a training method of a fault classification model according to any one of the present disclosure.
According to another aspect of the present disclosure, there is provided a non-transitory computer readable storage medium storing computer instructions for causing a computer to perform the fault detection method or the fault classification model training method of any one of the present disclosure.
According to another aspect of the present disclosure, a computer program product is provided, comprising a computer program which, when executed by a processor, implements the fault detection method or the training method of the fault classification model of any one of the present disclosure.
The embodiment of the disclosure realizes fault detection of a long-distance power transmission line.
It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present disclosure, nor do they limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.
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The drawings are included to provide a better understanding of the present solution and are not to be construed as limiting the present disclosure. Wherein:
FIG. 1 is a schematic diagram of a fault detection method according to the present disclosure;
FIG. 2 is a schematic diagram of a method of training a fault classification model according to the present disclosure;
FIG. 3 is a schematic diagram of a fault detection device according to the present disclosure;
FIG. 4 is a schematic diagram of a training apparatus for a fault classification model according to the present disclosure;
fig. 5 is a block diagram of an electronic device for implementing a fault detection method or a training method of a fault classification model according to an embodiment of the present disclosure.
Detailed Description
Exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings, in which various details of the embodiments of the disclosure are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
Power transmission lines are one of the important infrastructures for transmitting power over long distances, and it is very important to ensure the stability of the power transmission lines and the rapid location of power transmission line faults. In practical application, the overlong length of the transmission line easily causes low fault location efficiency, affects the stability of a power system and further affects production activities and resident life. In the related art, a certain number of sensor devices are installed on a power transmission line, data collected by each sensor are transmitted back to a data control center, and further an experienced power engineer observes the collected data to judge whether a fault occurs or whether a fault possibility exists on the corresponding power transmission line. However, the problem of low transmission line fault identification efficiency and low accuracy is easily caused by observing and judging long-time transmission line signals manually, and huge property loss is caused if the faults cannot be identified in time.
In order to implement fault detection of a long-distance power transmission line, an embodiment of the present disclosure provides a fault detection method, including: acquiring electric signals of different parts of a long-distance power transmission line to be detected in multiple time periods; aiming at each part, calculating the performance indexes of the electric signals of the part at different time periods to obtain the performance indexes corresponding to the part; respectively constructing index characteristics of the performance indexes corresponding to the parts; and determining whether each part of the long-distance power transmission line to be detected has a fault or not based on the index characteristics of each part. In the embodiment of the disclosure, electric signals of different parts of a long-distance power transmission line to be detected in multiple time periods are acquired, performance indexes of the electric signals of each part in different time periods are calculated, the performance indexes of the electric signals of each part in different time periods are used for describing signal conditions of different parts of the long-distance power transmission line to be detected in different time periods, direct observation of the electric signals is avoided, index features of the performance indexes corresponding to each part are further constructed respectively, whether each part of the long-distance power transmission line to be detected breaks down or not is determined according to the index features of each part, the problems that manual observation of the electric signals easily causes low power transmission line fault recognition efficiency and low accuracy are avoided, a faulted power transmission line can be found in time based on the index features, so that warning is brought in advance, and greater property loss is avoided.
The fault detection method provided by the present disclosure is explained in detail by specific embodiments below.
The fault detection method provided by the embodiment of the disclosure can be applied to electronic equipment, such as terminal equipment, server equipment and the like. The fault detection method provided by the embodiment of the disclosure can be applied to application scenes such as remote power transmission line fault detection and the like.
Referring to fig. 1, fig. 1 is a schematic flow chart of a fault detection method provided in the embodiment of the present disclosure, including the following steps:
s101, acquiring electric signals of different parts of the long-distance power transmission line to be detected in multiple time periods.
In one example, the remote power transmission line has a corresponding utility pole disposed at a different location, and the utility pole may be provided with a sensor for collecting corresponding electrical signals, such as current, voltage, etc.
Illustratively, when the remote power transmission line is detected, fluctuation data of current, voltage and the like in a plurality of time periods collected by sensors at different telegraph poles of the remote power transmission line to be detected can be obtained, so that the detection of the remote power transmission line is realized. The plurality of periods may be, for example, a plurality of different periods divided in units of minutes, a plurality of different periods divided in units of hours, or the like.
S102, calculating the performance indexes of the electric signals of the part under different time periods aiming at each part to obtain the performance indexes corresponding to the part.
For each part, the statistical analysis can be performed on the electric signal data of the part at different time intervals, the distribution condition of the electric signal data of the part at different time intervals is observed, the performance indexes of the electric signals of the part at different time intervals are calculated, and the performance indexes corresponding to the part are obtained. Specifically, the performance index corresponding to the portion may be a time-series combination of the performance indexes of the electrical signals of the portion at different time periods.
In one possible implementation, the performance indicators may include: at least one of mean, variance, number of local spurs, and phase jitter amplitude.
For each part, statistical analysis can be performed on the electric signal data of the part in different time periods, performance indexes such as the maximum value, the minimum value, the mean value, the variance and the like of the electric signal of the part in each time period are respectively calculated, and the local differences of the electric signal of the part in different time periods can be compared, so that the local numbers of the spurs and the phase jitter amplitude of the electric signal of the part in different time periods can be obtained. And comparing the local differences of the electric signals at different time intervals, and detecting whether the long-distance power transmission line to be detected has a fault or not from a certain moment.
Illustratively, the electrical signal for a period of time at one location of a remote transmission line to be detected may be represented as [1,2,1,2,1,2,1, … … ], typically because the length of the electrical signal is too long and a time index of failure may occur at any one point in time of the electrical signal, therefore, the significance of directly observing the electric signal is not great, so that the corresponding mean value, variance, local spurs number and phase jitter amplitude which are expressed as [1.5,1,2,20] can be obtained by carrying out statistical analysis on the electric signal, and then the electric signal in a period of time of one part of the remote transmission line to be detected can be replaced by observing the performance index data [1.5,1,2,20], the period may be, for example, a time period corresponding to 1 ten thousand seconds, which is a unit of a data sampling interval, or 30 minutes or one hour.
In this embodiment of the disclosure, for each part, performance indexes of the electrical signal of the part at different time periods may be calculated to obtain a performance index corresponding to the part, where the performance index may include: at least one of mean value, variance, local spurs number and phase jitter amplitude to can use the performance index of the signal of telecommunication under different periods of time of each position, replace the signal of telecommunication under different periods of time of each position, avoid direct observation signal of telecommunication, can more quick location detect the trouble of long-distance power transmission line.
And S103, constructing index characteristics of the performance indexes corresponding to each part.
After obtaining the performance indexes corresponding to each location, in one embodiment, the performance indexes of the locations may be combined in order for each location to obtain an index feature. Exemplary performance metrics include: at least one of the mean, the variance, the number of local spurs, and the phase jitter amplitude, then for each part, the performance indicators at different time intervals may be sequentially ordered to form an indicator feature vector, for example, the performance indicators include: the mean, the variance, the number of local spurs, and the phase jitter amplitude, and the obtained index feature vector in a time period can be represented as [ mean, variance, number of local spurs, phase jitter amplitude ], etc.
And S104, determining whether each part of the long-distance transmission line to be detected has a fault or not based on the index characteristics of each part.
The method includes analyzing each index feature of each part, for example, comparing feature ratios to determine whether each part of the remote transmission line to be detected has a fault, or determining whether each part of the remote transmission line to be detected has a fault by using a fault detection model and the index features of each part, where the fault detection model may be a model trained in advance and capable of detecting whether each part of the remote transmission line to be detected has a fault based on the index features of each part.
In the embodiment of the disclosure, electric signals of different parts of a long-distance power transmission line to be detected in multiple time periods are acquired, performance indexes of the electric signals of each part in different time periods are calculated, the performance indexes of the electric signals of each part in different time periods are used for describing signal conditions of different parts of the long-distance power transmission line to be detected in different time periods, direct observation of the electric signals is avoided, index features of the performance indexes corresponding to each part are further constructed respectively, whether each part of the long-distance power transmission line to be detected breaks down or not is determined according to the index features of each part, the problems that manual observation of the electric signals easily causes low power transmission line fault recognition efficiency and low accuracy are avoided, a faulted power transmission line can be found in time based on the index features, so that warning is brought in advance, and greater property loss is avoided.
In a possible embodiment, the step S104 of determining whether each location of the remote transmission line to be detected has a fault based on the index characteristics of each location may include:
and inputting the index characteristics of each part into a pre-trained deep learning model to obtain a fault detection result of each part of the long-distance power transmission line to be detected in a specified time period.
The pre-trained deep learning model is obtained by training according to sample index features of each part of the sample long-distance power transmission line and classification labels of the sample electric signals, and the classification labels are used for representing whether the sample electric signals belong to normal categories or fault categories.
And inputting the index characteristics of each part of the long-distance power transmission line to be detected into a pre-trained deep learning model for fault detection to obtain a fault detection result of each part of the long-distance power transmission line to be detected in a specified time period.
The specified time interval may be the current time interval or a preset time interval after the current time interval, for example, if the time interval division unit is 1 minute, the preset time interval after the current time interval is 1 minute after the current time interval, and if the time interval division unit is 1 hour, the preset time interval after the current time interval is 1 hour after the current time interval.
When the appointed time interval is the current time interval, detecting whether the current time interval of each part of the long-distance transmission line to be detected has a fault; and under the condition that the specified time interval is a preset time interval after the current time interval, predicting whether the fault occurs in the preset time interval after the current time interval of each part of the remote transmission line to be detected.
In the embodiment of the disclosure, the index characteristics of each part of the remote transmission line to be detected are input into the pre-trained deep learning model for fault detection, so that the fault detection result of each part of the remote transmission line to be detected in a specified time period is obtained, the problems of low transmission line fault identification efficiency and low accuracy rate caused by manual observation of electric signals are solved, and the fault transmission line can be timely found based on the index characteristics, so that warning is facilitated in advance, and greater property loss is avoided.
In one possible embodiment, the deep learning model may be a lightgbm tree classification model, the index features may include a plurality of performance index parameters, and accordingly, the obtaining of the fault detection result in the specified time period of each location of the remote power transmission line to be detected by inputting the index features of each location into the deep learning model trained in advance includes:
inputting the index characteristics of each part into a pre-trained lightgbm tree classification model to obtain a fault detection result of each performance index parameter of each part in a specified time period; and obtaining the fault detection result of each part of the long-distance transmission line to be detected in the appointed time period according to the fault detection result of each performance index parameter of each part in the appointed time period.
The light gram (light Gradient Boosting machine) is a framework for realizing a GBDT (Gradient Boosting Decision Tree) algorithm, supports high-efficiency parallel training, and has the advantages of higher training speed, lower memory consumption, higher accuracy, distributed support, capability of quickly processing mass data and the like. The lightgbm tree classification model is a classification model established based on a lightgbm tree model framework.
The features can be processed in parallel in the lightgbm, each working node finds the optimal segmentation point { features, threshold }, the working nodes find the global optimal segmentation point by using point-to-point communication, and each working node splits the node according to the global optimal segmentation point.
In the embodiment of the present disclosure, the index feature may include a plurality of performance index parameters, and after the index feature of each location is obtained, the index feature of each location may be input into a pre-trained lightgbm tree classification model to obtain a fault detection result of each performance index parameter in the index feature of each location in a specified time period, and further obtain a fault detection result of each location of the remote power transmission line to be detected in the specified time period.
Illustratively, the specified time interval is taken as the current time interval, wherein the index characteristic of one part comprises four performance index parameters, namely, a mean value, a variance, a local spur number and a phase jitter amplitude, which are represented as [30,1000,1,345 ]. The first characteristic (the performance index parameter is the mean value) corresponds to a threshold value of 20, the second characteristic (the performance index parameter is the variance) corresponds to a threshold value of 900, the third characteristic (the performance index parameter is the number of local spurs) corresponds to a threshold value of 3, the fourth characteristic (the performance index parameter is the phase jitter amplitude) corresponds to a threshold value of 100. Inputting the index characteristics [30,1000,1,345] of the part into a pre-trained lightgbm tree classification model to obtain the probability of the failure of the first performance index parameter of the part in the current time period as 100% (the mean value 30 is greater than the threshold value 20), the probability of the failure of the second performance index parameter in the current time period as 100% (the variance 1000 is greater than the threshold value 900), the probability of the failure of the third performance index parameter in the current time period as 0% (the number of local spurs is not greater than the threshold value 3), the probability of the failure of the fourth performance index parameter in the current time period as 100% (the phase jitter amplitude 345 is greater than the threshold value 100), and then according to the failure detection result of each performance index parameter of the part in the current time period, the failure detection result of the part in the current time period as 75% (the mean value of the failure probability of each performance index parameter in the current time period) of the remote transmission line to be detected is obtained, or the obtained fault detection result of the part of the remote transmission line to be detected in the current time period can be the weighted sum or the maximum value of the fault probability of each performance index parameter in the current time period, and the like. Of course, the embodiments of the present disclosure are described only by way of example, and do not specifically limit the embodiments of the present disclosure.
In the embodiment of the disclosure, the index characteristics of each part of the remote transmission line to be detected are input into the pre-trained lightgbm tree classification model for fault detection, so that the fault detection result of each part of the remote transmission line to be detected in a specified time period is obtained, the problems that the transmission line fault identification efficiency is low and the accuracy is not high due to manual observation of electric signals are avoided, and the fault transmission line can be found in time based on the index characteristics, so that the alarm is given in advance, and the property loss is avoided being larger.
Based on the above fault detection method, referring to fig. 2, fig. 2 is a schematic flow chart of a training method of a fault classification model provided in the embodiment of the present disclosure, including the following steps:
s201, obtaining sample electric signals of different parts of the sample long-distance power transmission line in multiple time periods.
The sample electric signal is provided with a classification label, and the classification label is used for representing that the sample electric signal belongs to a normal category or a fault category.
The implementation process of step S201 may refer to the implementation process of step S101, and details of the embodiment of the present disclosure are not repeated herein.
In one possible embodiment, the sample electrical signal may include: the method comprises the steps that positive sample electric signals and negative sample electric signals of different parts of a sample remote power transmission line under multiple time periods are obtained, the positive sample electric signals are normal type electric signals, and the negative sample electric signals are fault type electric signals.
In the embodiment of the disclosure, the normal type electrical signal is used as the positive sample electrical signal, the fault type electrical signal is used as the negative sample electrical signal, the positive sample electrical signal and the negative sample electrical signal at different parts of the sample remote power transmission line in multiple time periods are respectively obtained, and further, the training of the fault classification model is realized by using the positive sample electrical signal and the negative sample electrical signal, so that the trained fault classification model is more accurate.
S202, calculating the sample performance indexes of the sample electric signals of each part at different time periods to obtain the sample performance indexes corresponding to the part.
In one possible implementation, the sample performance indicators may include: at least one of mean, variance, number of local spurs, and phase jitter amplitude.
And S203, respectively constructing sample index characteristics of the sample performance indexes corresponding to each part.
The implementation processes of steps S202 to S203 may refer to the implementation processes of steps S102 to S103, which are not described herein again in the embodiments of the present disclosure.
And S204, inputting the sample index characteristics of each part into a fault classification model for fault classification to obtain prediction classification labels of each part of the sample long-distance transmission line in different time periods.
S205, calculating the current loss according to the predicted classification labels of each part of the sample long-distance transmission line in different time periods and the classification labels of each part of the sample electric signal in different time periods, and adjusting the training parameters of the fault classification model according to the current loss until a preset finishing condition is met to obtain the trained fault classification model.
The classification labels of the parts of the sample electrical signal in different time periods are the classification labels (normal category or fault category) of the sample electrical signal. The predetermined ending condition may be a predetermined number of iterations, a depth of the tree, or a loss reaching a predetermined loss threshold.
In the disclosed embodiment, the positive sample electric signals and the negative sample electric signals of different parts of the sample remote transmission line under multiple time periods are obtained, the sample performance indexes of the sample electric signals of all the parts under different time periods are obtained through calculation, the sample performance indexes of the sample electric signals of all the parts under different time periods are used for describing the signal conditions of different parts of the sample remote transmission line under different time periods, the direct observation of the sample electric signals is avoided, the sample index characteristics of the sample performance indexes corresponding to all the parts are further constructed respectively, the fault classification model is trained by utilizing the sample index characteristics and the classification labels of all the parts of the sample electric signals under different time periods, so that the trained fault classification model can detect whether all the parts of the remote transmission line have faults or not, and the problems of low efficiency and low accuracy rate of transmission line fault identification easily caused by manual observation of the electric signals are avoided, the power transmission line with faults can be found in time, so that warning is brought forward, and greater property loss is avoided.
In a possible implementation, the fault classification model may be a lightgbm tree classification model, and the sample indicator feature may include a plurality of sample performance indicator parameters. Correspondingly, the step S204 of inputting the sample index characteristics of each part into the fault classification model for fault classification to obtain the prediction classification labels of each part of the sample long-distance transmission line at different time intervals, which may include:
inputting the sample index characteristics of each part into a lightgbm tree classification model for fault classification to obtain fault detection results of each sample performance index parameter of each part in different time periods;
and obtaining the prediction classification labels of each part of the sample long-distance transmission line in different time periods according to the fault detection results of each sample performance index parameter of each part in different time periods.
An embodiment of the present disclosure provides a fault detection apparatus, referring to fig. 3, the apparatus includes:
the signal acquisition module 301 is used for acquiring electric signals of different parts of the long-distance power transmission line to be detected in multiple time periods;
an index calculation module 302, configured to calculate, for each part, a performance index of the electrical signal of the part at different time periods to obtain a performance index corresponding to the part;
a feature construction module 303, configured to respectively construct index features of the performance indexes corresponding to each part;
and the fault detection module 304 is configured to determine whether each part of the remote transmission line to be detected has a fault based on the index characteristics of each part.
In the embodiment of the disclosure, electric signals of different parts of a long-distance power transmission line to be detected in multiple time periods are acquired, performance indexes of the electric signals of each part in different time periods are calculated, the performance indexes of the electric signals of each part in different time periods are used for describing signal conditions of different parts of the long-distance power transmission line to be detected in different time periods, direct observation of the electric signals is avoided, index features of the performance indexes corresponding to each part are further constructed respectively, whether each part of the long-distance power transmission line to be detected breaks down or not is determined according to the index features of each part, the problems that manual observation of the electric signals easily causes low power transmission line fault recognition efficiency and low accuracy are avoided, a faulted power transmission line can be found in time based on the index features, so that warning is brought in advance, and greater property loss is avoided.
In a possible implementation manner, the failure detection module 304 is specifically configured to:
inputting the index characteristics of each part into a pre-trained deep learning model to obtain a fault detection result of each part of the long-distance transmission line to be detected in a specified time period; the pre-trained deep learning model is obtained by training according to sample index features of each part of the sample long-distance power transmission line and classification labels of the sample electric signals, and the classification labels are used for representing whether the sample electric signals belong to normal categories or fault categories.
In one possible embodiment, the deep learning model is a lightgbm tree classification model, and the index feature includes a plurality of performance index parameters; the fault detection module 304 is specifically configured to:
inputting the index characteristics of each part into a pre-trained lightgbm tree classification model to obtain a fault detection result of each performance index parameter of each part in a specified time period; and obtaining the fault detection result of each part of the long-distance transmission line to be detected in the appointed time period according to the fault detection result of each performance index parameter of each part in the appointed time period.
In a possible embodiment, the performance index includes: at least one of mean, variance, number of local spurs, and phase jitter amplitude.
The embodiment of the present disclosure further provides a training apparatus for a fault classification model, referring to fig. 4, the apparatus includes:
the sample signal acquisition module 401 is configured to acquire sample electrical signals of different parts of the sample remote power transmission line at multiple time periods, where the sample electrical signals have classification labels, and the classification labels are used to represent that the sample electrical signals belong to a normal category or a fault category;
a sample index calculating module 402, configured to calculate, for each part, a sample performance index of the sample electrical signal of the part at different time periods, so as to obtain a sample performance index corresponding to the part;
a sample characteristic construction module 403, configured to respectively construct sample index characteristics of sample performance indexes corresponding to each part;
the sample fault classification module 404 is configured to input the sample index features of each part into a fault classification model to perform fault classification, so as to obtain prediction classification labels of each part of the sample long-distance transmission line at different time intervals;
and the classification model training module 405 is configured to calculate a current loss according to the predicted classification labels of each part of the sample remote transmission line at different time intervals and the classification labels of each part of the sample electrical signal at different time intervals, and adjust the training parameters of the fault classification model according to the current loss until a preset ending condition is met, so as to obtain a trained fault classification model.
In the disclosed embodiment, the positive sample electric signals and the negative sample electric signals of different parts of the sample remote transmission line under multiple time periods are obtained, the sample performance indexes of the sample electric signals of all the parts under different time periods are obtained through calculation, the sample performance indexes of the sample electric signals of all the parts under different time periods are used for describing the signal conditions of different parts of the sample remote transmission line under different time periods, the direct observation of the sample electric signals is avoided, the sample index characteristics of the sample performance indexes corresponding to all the parts are further constructed respectively, the fault classification model is trained by utilizing the sample index characteristics and the classification labels of all the parts of the sample electric signals under different time periods, so that the trained fault classification model can detect whether all the parts of the remote transmission line have faults or not, and the problems of low efficiency and low accuracy rate of transmission line fault identification easily caused by manual observation of the electric signals are avoided, the power transmission line with faults can be found in time, so that warning is brought forward, and greater property loss is avoided.
In a possible embodiment, the sample electrical signal includes: the method comprises the steps that positive sample electric signals and negative sample electric signals of different parts of a sample remote power transmission line under multiple time periods are obtained, the positive sample electric signals are normal type electric signals, and the negative sample electric signals are fault type electric signals.
In one possible implementation, the fault classification model is a lightgbm tree classification model, and the sample indicator features include a plurality of sample performance indicator parameters; the sample fault classification module is specifically configured to:
inputting the sample index characteristics of each part into a lightgbm tree classification model for fault classification to obtain fault detection results of each sample performance index parameter of each part in different time periods;
and obtaining the prediction classification labels of each part of the sample long-distance transmission line in different time periods according to the fault detection results of each sample performance index parameter of each part in different time periods.
In a possible implementation, the sample performance indicators include: at least one of mean, variance, number of local spurs, and phase jitter amplitude.
In the technical scheme of the disclosure, the collection, storage, use, processing, transmission, provision, disclosure and other processing of the personal information of the related user are all in accordance with the regulations of related laws and regulations and do not violate the good customs of the public order. It should be noted that the head model in this embodiment is not a head model for a specific user, and cannot reflect personal information of a specific user. It should be noted that the two-dimensional face image in the present embodiment is from a public data set.
The present disclosure also provides an electronic device, a readable storage medium, and a computer program product according to embodiments of the present disclosure.
Wherein, electronic equipment includes:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any of the present disclosure.
A non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of any one of the present disclosure.
A computer program product comprising a computer program which, when executed by a processor, implements the method of any of the present disclosure.
FIG. 5 illustrates a schematic block diagram of an example electronic device 500 that can be used to implement embodiments of the present disclosure. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the disclosure described and/or claimed herein.
As shown in fig. 5, the apparatus 500 comprises a computing unit 501 which may perform various appropriate actions and processes in accordance with a computer program stored in a Read Only Memory (ROM)502 or a computer program loaded from a storage unit 508 into a Random Access Memory (RAM) 503. In the RAM 503, various programs and data required for the operation of the device 500 can also be stored. The calculation unit 501, the ROM 502, and the RAM 503 are connected to each other by a bus 504. An input/output (I/O) interface 505 is also connected to bus 504.
A number of components in the device 500 are connected to the I/O interface 505, including: an input unit 506 such as a keyboard, a mouse, or the like; an output unit 507 such as various types of displays, speakers, and the like; a storage unit 508, such as a magnetic disk, optical disk, or the like; and a communication unit 509 such as a network card, modem, wireless communication transceiver, etc. The communication unit 509 allows the device 500 to exchange information/data with other devices through a computer network such as the internet and/or various telecommunication networks.
The computing unit 501 may be a variety of general-purpose and/or special-purpose processing components having processing and computing capabilities. Some examples of the computing unit 501 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various dedicated Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, and so forth. The calculation unit 501 performs the respective methods and processes described above, such as a fault detection method or a training method of a fault classification model. For example, in some embodiments, the fault detection method or the training method of the fault classification model may be implemented as a computer software program tangibly embodied in a machine-readable medium, such as storage unit 508. In some embodiments, part or all of the computer program may be loaded and/or installed onto the device 500 via the ROM 502 and/or the communication unit 509. When the computer program is loaded into the RAM 503 and executed by the computing unit 501, one or more steps of the fault detection method or the training method of the fault classification model described above may be performed. Alternatively, in other embodiments, the computing unit 501 may be configured in any other suitable way (e.g., by means of firmware) to perform a fault detection method or a training method of a fault classification model.
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), system on a chip (SOCs), Complex Programmable Logic Devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
Program code for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program codes, when executed by the processor or controller, cause the functions/operations specified in the flowchart and/or block diagram to be performed. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), Wide Area Networks (WANs), and the Internet.
The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server may be a cloud server, a server of a distributed system, or a server with a combined blockchain.
It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present disclosure may be executed in parallel or sequentially or in different orders, and are not limited herein as long as the desired results of the technical solutions disclosed in the present disclosure can be achieved.
The above detailed description should not be construed as limiting the scope of the disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present disclosure should be included in the scope of protection of the present disclosure.
Claims (19)
1. A fault detection method, comprising:
acquiring electric signals of different parts of a long-distance power transmission line to be detected in multiple time periods;
aiming at each part, calculating the performance indexes of the electric signals of the part at different time periods to obtain the performance indexes corresponding to the part;
respectively constructing index characteristics of the performance indexes corresponding to the parts;
and determining whether each part of the long-distance power transmission line to be detected has a fault or not based on the index characteristics of each part.
2. The method of claim 1, wherein the determining whether each of the locations of the long-distance transmission line to be detected is faulty based on the indicator characteristics of each of the locations comprises:
inputting the index characteristics of each part into a pre-trained deep learning model to obtain a fault detection result of each part of the long-distance power transmission line to be detected in a specified time period; the pre-trained deep learning model is obtained by training according to sample index features of each part of the sample long-distance power transmission line and a classification label of a sample electric signal, wherein the classification label is used for representing that the sample electric signal belongs to a normal category or a fault category.
3. The method of claim 2, wherein the deep learning model is a lightgbm tree classification model, the metric features containing a plurality of performance metric parameters;
the method for obtaining the fault detection result of each part of the long-distance power transmission line to be detected in the specified time period by inputting the index characteristics of each part into the pre-trained deep learning model comprises the following steps:
inputting the index characteristics of each part into a pre-trained lightgbm tree classification model to obtain a fault detection result of each performance index parameter of each part in a specified time period;
and obtaining the fault detection result of each part of the long-distance transmission line to be detected in the appointed time period according to the fault detection result of each performance index parameter of each part in the appointed time period.
4. The method of claim 1, wherein the performance indicators comprise: at least one of mean, variance, number of local spurs, and phase jitter amplitude.
5. A method for training a fault classification model comprises the following steps:
the method comprises the steps of obtaining sample electric signals of different parts of a sample remote power transmission line in multiple time periods, wherein the sample electric signals are provided with classification labels which are used for representing whether the sample electric signals belong to a normal category or a fault category;
calculating the sample performance indexes of the sample electric signals of each part at different time periods to obtain the sample performance indexes corresponding to the parts;
respectively constructing sample index characteristics of sample performance indexes corresponding to the parts;
inputting the sample index characteristics of each part into a fault classification model for fault classification to obtain a prediction classification label of each part of the sample long-distance power transmission line at different time periods;
calculating the current loss according to the predicted classification labels of the parts of the sample long-distance power transmission line in different time periods and the classification labels of the parts of the sample electric signal in different time periods, and adjusting the training parameters of the fault classification model according to the current loss until a preset finishing condition is met to obtain a trained fault classification model.
6. The method of claim 5, wherein the sample electrical signal comprises: the method comprises the steps that positive sample electric signals and negative sample electric signals of different parts of a sample long-distance power transmission line under multiple time periods are obtained, the positive sample electric signals are normal type electric signals, and the negative sample electric signals are fault type electric signals.
7. The method of claim 5, wherein the fault classification model is a lightgbm tree classification model, the sample metric features containing a plurality of sample performance metric parameters;
the step of inputting the sample index characteristics of each part into a fault classification model for fault classification to obtain the prediction classification labels of each part of the sample long-distance power transmission line in different time periods comprises the following steps:
inputting the sample index characteristics of each part into a lightgbm tree classification model for fault classification to obtain fault detection results of each sample performance index parameter of each part in different time periods;
and obtaining the prediction classification labels of the parts of the sample long-distance power transmission line in different time periods according to the fault detection results of the performance index parameters of each sample of each part in different time periods.
8. The method of claim 5, wherein the sample performance indicators comprise: at least one of mean, variance, number of local spurs, and phase jitter amplitude.
9. A fault detection device comprising:
the signal acquisition module is used for acquiring electric signals of different parts of the long-distance power transmission line to be detected in multiple time periods;
the index calculation module is used for calculating the performance indexes of the electric signals of each part under different time periods to obtain the performance indexes corresponding to the part;
the characteristic construction module is used for respectively constructing the index characteristics of the performance indexes corresponding to the parts;
and the fault detection module is used for determining whether each part of the long-distance power transmission line to be detected has a fault or not based on the index characteristics of each part.
10. The apparatus according to claim 9, wherein the failure detection module is specifically configured to:
inputting the index characteristics of each part into a pre-trained deep learning model to obtain a fault detection result of each part of the long-distance power transmission line to be detected in a specified time period; the pre-trained deep learning model is obtained by training according to sample index features of each part of the sample long-distance power transmission line and a classification label of a sample electric signal, wherein the classification label is used for representing that the sample electric signal belongs to a normal category or a fault category.
11. The apparatus of claim 10, wherein the deep learning model is a lightgbm tree classification model, the metric features including a plurality of performance metric parameters;
the fault detection module is specifically configured to:
inputting the index characteristics of each part into a pre-trained lightgbm tree classification model to obtain a fault detection result of each performance index parameter of each part in a specified time period; and obtaining the fault detection result of each part of the long-distance transmission line to be detected in the appointed time period according to the fault detection result of each performance index parameter of each part in the appointed time period.
12. The apparatus of claim 9, wherein the performance indicators comprise: at least one of mean, variance, number of local spurs, and phase jitter amplitude.
13. A training apparatus for a fault classification model, comprising:
the system comprises a sample signal acquisition module, a signal processing module and a signal processing module, wherein the sample signal acquisition module is used for acquiring sample electric signals of different parts of a sample remote power transmission line in multiple time periods, the sample electric signals are provided with classification labels, and the classification labels are used for representing whether the sample electric signals belong to a normal category or a fault category;
the sample index calculation module is used for calculating the sample performance indexes of the sample electric signals of each part at different time periods to obtain the sample performance indexes corresponding to the part;
the sample characteristic construction module is used for respectively constructing sample index characteristics of sample performance indexes corresponding to the parts;
the sample fault classification module is used for inputting the sample index characteristics of each part into a fault classification model for fault classification to obtain a prediction classification label of each part of the sample long-distance transmission line at different time periods;
and the classification model training module is used for calculating the current loss according to the predicted classification labels of the parts of the sample remote transmission line in different time periods and the classification labels of the parts of the sample electric signal in different time periods, and adjusting the training parameters of the fault classification model according to the current loss until a preset finishing condition is met to obtain a trained fault classification model.
14. The apparatus of claim 13, wherein the sample electrical signal comprises: the method comprises the steps that positive sample electric signals and negative sample electric signals of different parts of a sample long-distance power transmission line under multiple time periods are obtained, the positive sample electric signals are normal type electric signals, and the negative sample electric signals are fault type electric signals.
15. The apparatus of claim 13, wherein the fault classification model is a lightgbm tree classification model, the sample metric features including a plurality of sample performance metric parameters; the sample fault classification module is specifically configured to:
inputting the sample index characteristics of each part into a lightgbm tree classification model for fault classification to obtain fault detection results of each sample performance index parameter of each part in different time periods;
and obtaining the prediction classification labels of the parts of the sample long-distance power transmission line in different time periods according to the fault detection results of the performance index parameters of each sample of each part in different time periods.
16. The apparatus of claim 13, wherein the sample performance indicators comprise: at least one of mean, variance, number of local spurs, and phase jitter amplitude.
17. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-8.
18. A non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of any one of claims 1-8.
19. A computer program product comprising a computer program which, when executed by a processor, implements the method according to any one of claims 1-8.
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| CN116256602B (en) * | 2023-05-15 | 2023-07-11 | 广东电网有限责任公司中山供电局 | Method and system for identifying state abnormality of low-voltage power distribution network |
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