CN117129815A - Comprehensive detection method and system for multi-degradation insulator based on Internet of things - Google Patents
Comprehensive detection method and system for multi-degradation insulator based on Internet of things Download PDFInfo
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Abstract
The invention relates to the technical field of insulator degradation detection, in particular to a multi-degradation insulator comprehensive detection method and system based on the Internet of things, wherein the specific method comprises the following steps: collecting real-time working state data and real-time appearance image data of an insulator, and constructing a multi-mode fusion database of temperature-humidity-image-sound-electricity; constructing an inspector experience knowledge base and an insulator historical work data driving base; identifying the working state of the insulator, and judging and diagnosing the failure cause and the degradation degree of the insulator; through three-dimensional geometric modeling of the insulator, the working state of the insulator is displayed through a graphical interface, fault warning is automatically sent out to the fault insulator, and self-service maintenance advice is popped up. The invention solves the problems of lack of prediction of insulator degradation trend, difficulty in real-time monitoring and low detection accuracy in insulator degradation detection in the prior art.
Description
Technical Field
The invention relates to the technical field of insulator degradation detection, in particular to a multi-degradation insulator comprehensive detection method and system based on the Internet of things.
Background
With the continuous improvement and upgrade of power grid technology, higher requirements are put on the running performance of the power system. In this situation, more and more insulators are applied to the power distribution system in a large scale, and particularly, an ultra-high voltage and direct current power transmission system which keeps a strong development situation in recent years also adopts a large number of insulators. The insulator not only participates in wire suspension, but also plays a remarkable insulating role, so that high standard requirements are put forward on the operation performance of the insulator, and the operation performance of the insulator directly determines whether a power system can reliably and safely operate to a great extent. Therefore, the degradation state of the insulator is monitored in real time, effective measures are taken for overhauling and maintaining, and the problem to be solved in the power grid industry is solved.
In the prior art, as disclosed in patent application publication No. CN114034997a, a multi-parameter-based insulator degradation degree prediction method and system are disclosed, including: monitoring degradation state parameters of the composite insulator on line; normalizing the degradation state parameters; based on the test means, acquiring degradation degree detection parameters of the composite insulator under different degradation state parameters; based on a gray theory algorithm, establishing a composite insulator degradation degree prediction model; forming an input sequence by using the normalized degradation state parameter and degradation degree detection parameter, and inputting the input sequence into a composite insulator degradation degree prediction model; and outputting a predicted value of the degradation degree of the composite insulator by the model. According to the patent, the degradation degree of the insulator is judged by considering various parameters such as electricity, environment and structure, and the lack of cleaning and correction of data only leads to high system misjudgment rate due to analysis of relevant parameters of the insulator.
As another example, patent with application publication number CN114280434a discloses a method and a system for quantitatively analyzing the degradation degree of a composite insulator, by monitoring the leakage current, relative dielectric loss, relative capacitance and partial discharge signal of the end screen of the composite insulator in real time, whether the composite insulator has obvious abnormal faults or not is accurately judged, and meanwhile, the state of the composite insulator is quantitatively analyzed by a quantitative gray evaluation method, so that the purposes of timely obtaining the running state of the composite insulator and timely troubleshooting the degradation faults are achieved; the gray evaluation method used in the patent relies on subjective experience of an inspector, lacks mathematical deduction, and the relation between parameters causing insulator degradation is nonlinear, and the premise of the gray association degree evaluation method is that the relation between parameters is assumed to be linear, so that the accuracy of judging the insulator degradation degree is low.
The above patents all have problems of high false judgment rate, lack of prediction of insulator degradation trend, difficulty in real-time monitoring and low detection accuracy in insulator degradation detection.
Disclosure of Invention
This section is intended to outline some aspects of embodiments of the application and to briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section as well as in the description of the application and in the title of the application, which may not be used to limit the scope of the application.
Aiming at the problems of lack of prediction of insulator degradation trend, difficulty in real-time monitoring and low detection accuracy in insulator degradation detection in the prior art, the invention provides a multi-degradation insulator comprehensive detection method and system based on the Internet of things.
In order to achieve the above purpose, the technical scheme of the multi-degradation insulator comprehensive detection method based on the Internet of things comprises the following steps:
s1: the method comprises the steps that real-time working state data and real-time appearance image data of an insulator are collected through a built-in sensor and monitoring equipment in the insulator, and the real-time working state data and the real-time appearance image data are transmitted to a local server through wireless communication;
s2: according to the step S1, constructing a temperature-humidity-graph-sound-electricity multi-mode fusion database;
s3: constructing an experience knowledge base of the inspector through the description of the inspector on the insulator problem;
s4: according to the step S2, an insulator historical working data driving library is constructed, and the degradation trend of the insulator is predicted through the real-time data driving which is continuously updated in the multi-mode fusion database;
s5: S2-S4, identifying the working state of the insulator, and judging and diagnosing the failure cause and the degradation degree of the insulator;
S6: through three-dimensional geometric modeling of the insulator, the working state of the insulator is displayed through a graphical interface, fault warning is automatically sent out to the fault insulator, and self-service maintenance advice is popped up.
Specifically, the built-in sensor in the insulator in the step S1 includes:
the temperature sensor is used for collecting temperature data of the insulator in a real-time working state;
the humidity sensor is used for collecting environmental humidity data of the insulator in a real-time working state;
the vibration sensor is used for collecting sound wave vibration data of the insulator in a real-time working state;
the current sensor is used for collecting current data of the insulator in a real-time working state.
Specifically, the data set formed by the real-time working state data and the real-time appearance image data comprises:
；
wherein,is a federated data set;
when (when)When (I)>Is temperature data; when->When (I)>Is humidity data; when->When (I)>Real-time appearance image data; when->When (I)>Is sound wave vibration data; when->When (I)>Is current data;
；
wherein,is a difference dataset; />Is->Middle->Sequence number of the personal modality data.
Specifically, the construction of the multi-modal fusion database of the temperature-humidity-graph-sound-electricity in the step S2 comprises the following specific steps:
S201: carrying out data cleaning and image denoising processing on the collected real-time working state data and real-time appearance image data of the insulator;
s202: according to step S201, the processed real-time working state data and real-time appearance image data are aligned to the same acquisition time point by performing data alignment processing on the processed real-time working state data and real-time appearance image data;
s203: calculating an edge function and an optimal fusion function of data in a multi-mode fusion database of temperature-humidity-graph-sound-electricity;
s204: and calculating the correlation degree of each modal data and insulator fault degradation in the temperature-humidity-graph-sound-electricity multi-modal fusion database.
Specifically, the step S203 includes:
the calculation strategy of the edge function is as follows:
；
wherein:
is->Edge functions of the individual mode data at the detection time point t;
a time period for completing the integrated detection of the insulator;
l is the detection time point t, the firstBandwidth of the personal modality data;
n is atInside, intercepted->An nth data point of the modal data, n being a positive integer;
n is atInside, intercepted->Total number of data points of the individual modality data;
the method comprises the steps that a kernel function related to a detection time point t is used for realizing the linear separability of five modal data in a high-dimensional feature space;
Is an integral function;
the calculation strategy of the optimal fusion function is as follows:
；
wherein:
is->Edge functions of the individual mode data at the detection time point t;
is at->Inside, intercepted->A sample edge function for an nth data point of the modal data;
is at->Inside, intercepted->A sample edge function for an nth data point of the modal data;
selecting a function for the conditions when simultaneously satisfyingWhen the condition selection function value is 1, otherwise, 0.
Specifically, the correlation between each mode data and the insulator fault degradation in step S204The calculation strategy of (2) is as follows:
；
wherein,for->Andrespectively at->And->Integrating;
is->Correlation of the modal data with insulator failure degradation.
Specifically, the construction of the inspector experience knowledge base comprises the following specific steps:
s301: acquisition of near x 1 X in year 2 X in the province area 3 In case of deterioration of insulator x 4 Describing sentences about the insulator fault reasons and degradation degrees in the insulator degradation cases by the patrol inspectors to form an iteration updated prior experience library;
s302: acquiring description sentences of the fault reasons and the degradation degree of the detection insulator by 5 inspectors, processing the description sentences through an NLTK word segmentation tool, and then inputting the description sentences into an priori experience library for comparison;
S303: when the comparison value is larger than the similar threshold value, the confidence of the judgment result of the fault cause and the degradation degree of the detection insulator by the 5 inspectors is 0.75;
s304: when the comparison value is smaller than or equal to the similarity threshold value, the confidence of the judgment result of the fault cause and the degradation degree of the detection insulator by the 5 inspectors is 0.25.
Specifically, the historical work data driving library comprises: an input layer, a filtering convolution layer, a sampling layer, a full connection layer and an output layer.
Specifically, the calculation strategy of the convolution function in the filtering convolution layer is as follows:
；
wherein:
is->Convolution operation function of each modal data when the detection time point is t;
is->The data values of the individual mode data are input to the input layer when the detection time point is t;
to filter the size of the filter in the convolution layer, < >>Is->Is a natural exponential function of (2);
n is atInside, intercepted->An nth data point of the modality data;
n is atInside, intercepted->Total number of data points of the individual modality data.
Specifically, the specific steps of the step S5 for the diagnosis of the failure cause and the degradation degree of the insulator are as follows:
s501: obtaining a time period for completing one-time insulator comprehensive detection Five modal data in the system, wherein the five modal data are temperature numbers respectivelyData of ambient humidity, real-time appearance image data, acoustic vibration data and current data;
s502: predicting the degradation trend of the insulator according to a temperature-humidity-graph-sound-electricity multi-mode fusion database, an inspector experience knowledge base and a historical working data driving base, and calculating the mean value and standard deviation to obtain a health mode data interval of the insulator;
s503: judging whether five modal data at the detection time point is in a healthy modal data interval or not, and outputting a True by a system when the modal data at the time point belongs to the healthy modal data interval, otherwise outputting a False by the system;
s504: according to step S503, when the output is True, the insulator is judged to be healthy;
when the output is False, the step S503 is executed circularly until the 3 rd output is False, the step S503 is stopped circularly, the insulator fault degradation is judged, and the system automatically gives out fault warning.
Specifically, the self-service maintenance advice in step S6 includes:
when the relatedness of the image mode dataIs in the correlation of five modality data +.>When the number set formed by the values of (a) is maximum, a self-service maintenance suggestion is popped up: cleaning the surface of the insulator;
Correlation of current mode dataIs in the correlation of five modality data +.>When the number set formed by the values of (a) is maximum, a self-service maintenance suggestion is popped up: installing an overvoltage and overcurrent protection device;
correlation of data of acoustic wave modesIs in the correlation of five modality data +.>When the number set formed by the values of (a) is maximum, a self-service maintenance suggestion is popped up: the reinforcing insulator is connected with the fixed bracket;
correlation of temperature or humidity mode dataOr->Is in the correlation of five modality data +.>When the number set formed by the values of (a) is maximum, a self-service maintenance suggestion is popped up: replacing the insulator.
In addition, the multi-degradation insulator comprehensive detection system based on the Internet of things comprises the following modules: the system comprises a data acquisition and transmission module, a multi-mode fusion data module, an inspector experience knowledge module, a historical working data driving module, an insulator fault discrimination diagnosis module and an insulator visualization module;
the data acquisition and transmission module acquires working state data and appearance image data of the insulator through a built-in sensor and monitoring equipment in the insulator, and transmits the working state data and the appearance image data to a local server through wireless communication;
The multi-modal fusion data module is used for constructing a multi-modal fusion database of temperature-humidity-graph-sound-electricity;
the inspector experience knowledge module is used for optimizing the prediction result of the degradation trend of the insulator in the historical work data driving module;
the historical working data driving module predicts the degradation trend of the insulator through the real-time data driving which is updated continuously in the multi-mode fusion database;
the insulator fault judging and diagnosing module is used for identifying the working state of the insulator and judging and diagnosing the fault reason and the degradation degree of the insulator;
the insulator visualization module displays the working state of the insulator through a graphical interface by three-dimensional geometric modeling of the insulator, automatically sends out fault warning to the fault insulator, and pops up self-service maintenance suggestions.
A storage medium having instructions stored therein, which when read by a computer, cause the computer to perform any one of the above-described methods for comprehensive detection of multiple-degradation insulators based on the internet of things.
An electronic device comprising a memory, a processor and a computer program stored on the memory and operable on the processor, wherein the processor implements a multi-degradation insulator comprehensive detection method based on the internet of things of any one of the above when executing the computer program.
Compared with the prior art, the invention has the following technical effects:
1. according to the invention, a multi-mode fusion database of temperature-humidity-graph-sound-electricity is constructed, temperature data, environmental humidity data, real-time appearance image data, sound wave vibration data and current data of an insulator are analyzed, and when some data are missing or unreliable, filling correction can be performed through other mode data, so that the comprehensiveness and reliability of the data are improved.
2. The invention constructs the inspector experience knowledge base, supplements and corrects the multi-mode fusion database through the abundant practical experience of the inspector, makes up the deficiency of working data of the insulator, optimizes the performance of the database and enhances the robustness of the database.
3. According to the invention, the historical working data is adopted to drive and predict the degradation development trend of the insulator, so that the deep period degradation rule of the insulator can be accurately captured, the reasonable evaluation and adjustment of the future prediction result can be realized by comparing the iteratively updated historical working data with the actual situation, the subjective assumption influence of individuals is avoided, and the degradation trend of the insulator can be accurately and objectively predicted.
4. According to the insulator visualization module, the working state of the insulator is displayed by a graphical interface, the fault warning is automatically sent out to the fault insulator, and self-service maintenance advice is popped up, so that the fault of the insulator can be timely found and responded in real time, the phenomenon that the power grid line breaks down due to further degradation of the insulator can be avoided, meanwhile, the artificial misinformation and misinformation of the fault of the insulator are reduced, and the reliability is high; the popped self-service maintenance proposal provides a fault removal scheme and operation guidance for inspectors, so that the downtime of a power grid line, the time of insulator fault treatment and maintenance are greatly shortened, and the maintenance cost is reduced.
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 schematic flow chart of a comprehensive detection method of a multi-degradation insulator based on the internet of things according to a first embodiment of the invention;
fig. 2 is a schematic structural diagram of a multi-degradation insulator comprehensive detection system based on internet of things according to a second embodiment of the present invention;
fig. 3 is a schematic diagram of a glass fiber reinforced plastic insulator string on a power transmission line tower according to a first embodiment of the present invention;
fig. 4 is an external image of a glass fiber reinforced plastic insulator on a transmission line tower according to a first embodiment of the present invention;
fig. 5 is a schematic diagram of a porcelain insulator string on a transmission line tower according to a second embodiment of the present invention;
fig. 6 is an external image of a porcelain insulator on a transmission line tower according to a second embodiment of the present invention;
fig. 7 is a flowchart of an insulator failure degradation determination according to the first and second embodiments of the present invention.
Detailed Description
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to specific embodiments thereof which are illustrated in the appended drawings.
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.
Embodiment one:
as shown in fig. 1, 3, 4 and 7, the method for comprehensively detecting the multi-degradation insulator based on the internet of things in the embodiment of the invention, as shown in fig. 1, comprises the following specific steps:
as shown in fig. 3, taking a glass fiber reinforced plastic insulator string on a power transmission line tower in coastal areas as an example, the degradation degree of the glass fiber reinforced plastic insulator string is comprehensively detected, and the specific steps are as follows:
S1: the method comprises the steps that real-time working state data and real-time appearance image data of an insulator are collected through a built-in sensor and monitoring equipment in the insulator, and the real-time working state data and the real-time appearance image data are transmitted to a local server through wireless communication;
the built-in sensor in the insulator in the step S1 includes:
the temperature sensor is used for collecting temperature data of the insulator in a real-time working state;
the humidity sensor is used for collecting environmental humidity data of the insulator in a real-time working state;
the vibration sensor is used for collecting sound wave vibration data of the insulator in a real-time working state;
the current sensor is used for collecting current data of the insulator in a real-time working state.
The data set formed by the real-time working state data and the real-time appearance image data comprises:
；
wherein,is a federated data set;
when (when)When (I)>Is temperature data; when->When (I)>Is humidity data; when->When (I)>Real-time appearance image data; when->When (I)>Is sound wave vibration data; when->When (I)>Is current data;
；
wherein,is a difference dataset; />Is->Middle->Sequence number of the personal modality data.
S2: according to the step S1, constructing a temperature-humidity-graph-sound-electricity multi-mode fusion database;
The construction of the multi-mode fusion database of temperature-humidity-graph-sound-electricity in the step S2 comprises the following specific steps:
s201: carrying out data cleaning and image denoising processing on the collected real-time working state data and real-time appearance image data of the insulator;
s202: according to step S201, the processed real-time working state data and real-time appearance image data are aligned to the same acquisition time point by performing data alignment processing on the processed real-time working state data and real-time appearance image data;
s203: calculating an edge function and an optimal fusion function of data in a multi-mode fusion database of temperature-humidity-graph-sound-electricity;
s204: and calculating the correlation degree of each modal data and insulator fault degradation in the temperature-humidity-graph-sound-electricity multi-modal fusion database.
The step S203 includes:
the calculation strategy of the edge function is as follows:
；
wherein:
is->Edge functions of the individual mode data at the detection time point t;
a time period for completing the integrated detection of the insulator;
l is the detection time point t, the firstBandwidth of the personal modality data;
n is atInside, intercepted->An nth data point of the modal data, n being a positive integer;
n is atInside, intercepted- >Total number of data points of the individual modality data;
the method comprises the steps that a kernel function related to a detection time point t is used for realizing the linear separability of five modal data in a high-dimensional feature space;
is an integral function;
the calculation strategy of the optimal fusion function is as follows:
；
wherein:
is->Edge functions of the individual mode data at the detection time point t;
is at->Inside, intercepted->A sample edge function for an nth data point of the modal data;
is at->Inside, intercepted->A sample edge function for an nth data point of the modal data;
selecting a function for the conditions when simultaneously satisfyingWhen the condition selection function value is 1, otherwise, 0.
Correlation between each mode data and insulator fault degradation in step S204The calculation strategy of (2) is as follows:
；
wherein,for->Andrespectively at->And->Integrating;
is->Correlation degree of the modal data and insulator fault degradation;
calculating and obtaining the correlation degree between each mode data of the glass fiber reinforced plastic insulator and insulator fault degradation:
。
s3: constructing an experience knowledge base of the inspector through the description of the inspector on the insulator problem;
the inspector experience knowledge base comprises the following specific steps:
S301: acquisition of near x 1 X in year 2 X in the province area 3 In case of deterioration of insulator x 4 Describing sentences about the insulator fault reasons and degradation degrees in the insulator degradation cases by the patrol inspectors to form an iteration updated prior experience library;
s302: acquiring description sentences of the fault reasons and the degradation degree of the detection insulator by 5 inspectors, processing the description sentences through an NLTK word segmentation tool, and then inputting the description sentences into an priori experience library for comparison;
s303: when the comparison value is larger than the similar threshold value, the confidence of the judgment result of the fault cause and the degradation degree of the detection insulator by the 5 inspectors is 0.75;
s304: when the comparison value is smaller than or equal to the similarity threshold value, the confidence of the judgment result of the fault cause and the degradation degree of the detection insulator by the 5 inspectors is 0.25.
Wherein the similarity threshold is determined by a person skilled in the art by fitting a number of experimental data, the similarity threshold being 0.866.
S4: according to the step S2, an insulator historical working data driving library is constructed, and the degradation trend of the insulator is predicted through the real-time data driving which is continuously updated in the multi-mode fusion database;
the historical work data driving library comprises: an input layer, a filtering convolution layer, a sampling layer, a full connection layer and an output layer.
The calculation strategy for the convolution function in the filter convolution layer is as follows:
；
wherein:
is->Convolution operation function of each modal data when the detection time point is t;
is->The data values of the individual mode data are input to the input layer when the detection time point is t;
to filter the size of the filter in the convolution layer, < >>Is->Is a natural exponential function of (2);
n is atInside, intercepted->An nth data point of the modality data;
n is atInside, intercepted->Total number of data points of the individual modality data.
S5: S2-S4, identifying the working state of the insulator, and judging and diagnosing the failure cause and the degradation degree of the insulator;
as shown in fig. 7, the specific steps of the diagnosis of the failure cause and the degradation degree of the insulator in the step S5 are as follows:
s501: obtaining a time period for completing one-time insulator comprehensive detectionFive mode data in the system are temperature data, environment humidity data, real-time appearance image data, sound wave vibration data and current data respectively;
s502: predicting the degradation trend of the insulator according to a temperature-humidity-graph-sound-electricity multi-mode fusion database, an inspector experience knowledge base and a historical working data driving base, and calculating the mean value and standard deviation to obtain a health mode data interval of the insulator;
S503: judging whether five modal data at the detection time point is in a healthy modal data interval or not, and outputting a True by a system when the modal data at the time point belongs to the healthy modal data interval, otherwise outputting a False by the system;
s504: according to step S503, when the output is True, the insulator is judged to be healthy;
when the output is False, the step S503 is executed circularly until the 3 rd output is False, the step S503 is stopped circularly, the insulator fault degradation is judged, and the system automatically gives out fault warning.
S6: through three-dimensional geometric modeling of the insulator, the working state of the insulator is displayed through a graphical interface, fault warning is automatically sent out to the fault insulator, and self-service maintenance advice is popped up.
The self-service maintenance advice in step S6 includes:
when the relatedness of the image mode dataIs in the correlation of five modality data +.>When the number set formed by the values of (a) is maximum, a self-service maintenance suggestion is popped up: cleaning the surface of the insulator;
correlation of current mode dataIs in the correlation of five modality data +.>When the number set formed by the values of (a) is maximum, a self-service maintenance suggestion is popped up: installing an overvoltage and overcurrent protection device;
correlation of data of acoustic wave modes Is in the correlation of five modality data +.>When the number set formed by the values of (a) is maximum, the self-service pop-up device pops upMaintenance advice: the reinforcing insulator is connected with the fixed bracket;
correlation of temperature or humidity mode dataOr->Is in the correlation of five modality data +.>When the number set formed by the values of (a) is maximum, a self-service maintenance suggestion is popped up: replacing the insulator.
As shown in fig. 4, the glass fiber reinforced plastic insulator is judged and diagnosed as an insulator failure degradation, wherein, the correlation of temperature mode dataIs in the correlation of five modality data +.>The maximum number set of values of (2) popup self-service maintenance advice: replacing the insulator.
Embodiment two:
as shown in fig. 2, 5, 6 and 7, the multi-degradation insulator comprehensive detection system based on the internet of things in the embodiment of the invention, as shown in fig. 2, comprises the following modules:
the system comprises a data acquisition and transmission module, a multi-mode fusion data module, an inspector experience knowledge module, a historical working data driving module, an insulator fault discrimination diagnosis module and an insulator visualization module;
as shown in fig. 5, taking a porcelain insulator string on a power transmission line tower in a plateau area as an example, the degradation degree of the porcelain insulator string is comprehensively detected, and the specific steps are as follows:
The data acquisition and transmission module acquires working state data and appearance image data of the insulator through a built-in sensor and monitoring equipment in the insulator, and transmits the working state data and the appearance image data to a local server through wireless communication;
the built-in sensor in the insulator includes:
the temperature sensor is used for collecting temperature data of the insulator in a real-time working state;
the humidity sensor is used for collecting environmental humidity data of the insulator in a real-time working state;
the vibration sensor is used for collecting sound wave vibration data of the insulator in a real-time working state;
the current sensor is used for collecting current data of the insulator in a real-time working state.
The data set formed by the real-time working state data and the real-time appearance image data comprises:
；
wherein,is a federated data set;
when (when)When (I)>Is temperature data; when->When (I)>Is humidity data; when->When (I)>Real-time appearance image data; when->When (I)>Is sound wave vibration data; when->When (I)>Is current data;
；
wherein,is a difference dataset; />Is->Middle->Sequence number of the personal modality data.
The multi-modal fusion data module is used for constructing a multi-modal fusion database of temperature-humidity-graph-sound-electricity;
The construction of the multi-modal fusion database of temperature-humidity-graph-sound-electricity comprises the following specific steps:
s201: carrying out data cleaning and image denoising processing on the collected real-time working state data and real-time appearance image data of the insulator;
s202: according to step S201, the processed real-time working state data and real-time appearance image data are aligned to the same acquisition time point by performing data alignment processing on the processed real-time working state data and real-time appearance image data;
s203: calculating an edge function and an optimal fusion function of data in a multi-mode fusion database of temperature-humidity-graph-sound-electricity;
s204: and calculating the correlation degree of each modal data and insulator fault degradation in the temperature-humidity-graph-sound-electricity multi-modal fusion database.
The step S203 includes:
the calculation strategy of the edge function is as follows:
；
wherein:
is->Edge functions of the individual mode data at the detection time point t;
a time period for completing the integrated detection of the insulator;
l is the detection time point t, the firstBandwidth of the personal modality data;
n is atInside, intercepted->An nth data point of the modal data, n being a positive integer;
n is atInside, intercepted->Total number of data points of the individual modality data;
As a kernel function with respect to the detection time t, theThe kernel function is used for realizing the linear separability of the five modal data in the high-dimensional characteristic space;
is an integral function;
the calculation strategy of the optimal fusion function is as follows:
；
wherein:
is->Edge functions of the individual mode data at the detection time point t;
is at->Inside, intercepted->A sample edge function for an nth data point of the modal data;
is at->Inside, intercepted->A sample edge function for an nth data point of the modal data;
selecting a function for a conditionNumber, when meeting at the same timeWhen the condition selection function value is 1, otherwise, 0.
Correlation between each mode data and insulator fault degradation in step S204The calculation strategy of (2) is as follows: />
；
Wherein,to pair(s)And->Respectively at->And->Integrating;
is->Correlation degree of the modal data and insulator fault degradation;
calculating and obtaining the correlation degree between each mode data of the vitreous porcelain insulator and insulator fault degradation:
。
the inspector experience knowledge module is used for optimizing the prediction result of the degradation trend of the insulator in the historical work data driving module;
the construction of the inspector experience knowledge base comprises the following specific steps:
S301: acquisition of near x 1 X in year 2 X in the province area 3 In case of deterioration of insulator x 4 Describing sentences about the insulator fault reasons and degradation degrees in the insulator degradation cases by the patrol inspectors to form an iteration updated prior experience library;
s302: acquiring description sentences of the fault reasons and the degradation degree of the detection insulator by 5 inspectors, processing the description sentences through an NLTK word segmentation tool, and then inputting the description sentences into an priori experience library for comparison;
s303: when the comparison value is larger than the similar threshold value, the confidence of the judgment result of the fault cause and the degradation degree of the detection insulator by the 5 inspectors is 0.75;
s304: when the comparison value is smaller than or equal to the similarity threshold value, the confidence of the judgment result of the fault cause and the degradation degree of the detection insulator by the 5 inspectors is 0.25.
The historical working data driving module predicts the degradation trend of the insulator through the real-time data driving which is updated continuously in the multi-mode fusion database;
the historical work data driving library comprises: an input layer, a filtering convolution layer, a sampling layer, a full connection layer and an output layer.
The calculation strategy for the convolution function in the filter convolution layer is as follows:
；
wherein:
is->Convolution operation function of each modal data when the detection time point is t;
Is->The data values of the individual mode data are input to the input layer when the detection time point is t;
to filter the size of the filter in the convolution layer, < >>Is->Is a natural exponential function of (2);
n is atInside, intercepted->An nth data point of the modality data;
n is atInside, intercepted->Total number of data points of the individual modality data.
The insulator fault judging and diagnosing module is used for identifying the working state of the insulator and judging and diagnosing the fault reason and the degradation degree of the insulator;
as shown in fig. 7, the specific steps for the discrimination diagnosis of the cause of failure and the degree of deterioration of the insulator are as follows:
s501: obtaining a time period for completing one-time insulator comprehensive detectionFive mode data in the system, wherein the five mode data are temperature data, environment humidity data,Real-time appearance image data, acoustic vibration data, and current data;
s502: predicting the degradation trend of the insulator according to a temperature-humidity-graph-sound-electricity multi-mode fusion database, an inspector experience knowledge base and a historical working data driving base, and calculating the mean value and standard deviation to obtain a health mode data interval of the insulator;
s503: judging whether five modal data at the detection time point is in a healthy modal data interval or not, and outputting a True by a system when the modal data at the time point belongs to the healthy modal data interval, otherwise outputting a False by the system;
S504: according to step S503, when the output is True, the insulator is judged to be healthy;
when the output is False, the step S503 is executed circularly until the 3 rd output is False, the step S503 is stopped circularly, the insulator fault degradation is judged, and the system automatically gives out fault warning.
The insulator visualization module displays the working state of the insulator through a graphical interface by three-dimensional geometric modeling of the insulator, automatically sends out fault warning to the fault insulator, and pops up self-service maintenance suggestions.
Self-service maintenance advice includes:
when the relatedness of the image mode dataIs in the correlation of five modality data +.>When the number set formed by the values of (a) is maximum, a self-service maintenance suggestion is popped up: cleaning the surface of the insulator;
correlation of current mode dataIs in the correlation of five modality data +.>When the number set formed by the values of (a) is maximum, a self-service maintenance suggestion is popped up: installation overvoltage and overcurrent protection device；
Correlation of data of acoustic wave modesIs in the correlation of five modality data +.>When the number set formed by the values of (a) is maximum, a self-service maintenance suggestion is popped up: the reinforcing insulator is connected with the fixed bracket;
correlation of temperature or humidity mode data Or->Is in the correlation of five modality data +.>When the number set formed by the values of (a) is maximum, a self-service maintenance suggestion is popped up: replacing the insulator.
As shown in fig. 6, the porcelain insulator is discriminant diagnosed as insulator failure degradation, in which the correlation of image mode dataIs in the correlation of five modality data +.>The maximum number set of values of (2) popup self-service maintenance advice: cleaning the surface of the insulator.
Embodiment III:
the embodiment provides a storage medium, in which instructions are stored, and when a computer reads the instructions, the computer is caused to execute any one of the above-mentioned multi-degradation insulator comprehensive detection method based on the internet of things.
The embodiment also provides an electronic device, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor realizes the multi-degradation insulator comprehensive detection method based on the Internet of things when executing the computer program.
In summary, compared with the prior art, the technical effects of the invention are as follows:
1. according to the invention, a multi-mode fusion database of temperature-humidity-graph-sound-electricity is constructed, temperature data, environmental humidity data, real-time appearance image data, sound wave vibration data and current data of an insulator are analyzed, and when some data are missing or unreliable, filling correction can be performed through other mode data, so that the comprehensiveness and reliability of the data are improved.
2. The invention constructs the inspector experience knowledge base, supplements and corrects the multi-mode fusion database through the abundant practical experience of the inspector, makes up the deficiency of working data of the insulator, optimizes the performance of the database and enhances the robustness of the database.
3. According to the invention, the historical working data is adopted to drive and predict the degradation development trend of the insulator, so that the deep period degradation rule of the insulator can be accurately captured, the reasonable evaluation and adjustment of the future prediction result can be realized by comparing the iteratively updated historical working data with the actual situation, the subjective assumption influence of individuals is avoided, and the degradation trend of the insulator can be accurately and objectively predicted.
4. According to the insulator visualization module, the working state of the insulator is displayed by a graphical interface, the fault warning is automatically sent out to the fault insulator, and self-service maintenance advice is popped up, so that the fault of the insulator can be timely found and responded in real time, the phenomenon that the power grid line breaks down due to further degradation of the insulator can be avoided, meanwhile, the artificial misinformation and misinformation of the fault of the insulator are reduced, and the reliability is high; the popped self-service maintenance proposal provides a fault removal scheme and operation guidance for inspectors, so that the downtime of a power grid line, the time of insulator fault treatment and maintenance are greatly shortened, and the maintenance cost is reduced.
The foregoing has shown and described the basic principles and main features of the present invention and the advantages of the present invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, and that the above embodiments and descriptions are merely illustrative of the principles of the present invention, and various changes and modifications may be made without departing from the spirit and scope of the invention, which is defined in the appended claims. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (14)
1. A multi-degradation insulator comprehensive detection method based on the Internet of things is characterized by comprising the following steps of: the method comprises the following specific steps:
s1: the method comprises the steps that real-time working state data and real-time appearance image data of an insulator are collected through a built-in sensor and monitoring equipment in the insulator, and the real-time working state data and the real-time appearance image data are transmitted to a local server through wireless communication;
s2: according to the step S1, constructing a temperature-humidity-graph-sound-electricity multi-mode fusion database;
s3: constructing an experience knowledge base of the inspector through the description of the inspector on the insulator problem;
s4: according to the step S2, an insulator historical working data driving library is constructed, and the degradation trend of the insulator is predicted through the real-time data driving which is continuously updated in the multi-mode fusion database;
S5: S2-S4, identifying the working state of the insulator, and judging and diagnosing the failure cause and the degradation degree of the insulator;
s6: through three-dimensional geometric modeling of the insulator, the working state of the insulator is displayed through a graphical interface, fault warning is automatically sent out to the fault insulator, and self-service maintenance advice is popped up.
2. The comprehensive detection method of multiple degradation insulators based on the internet of things according to claim 1, wherein the built-in sensor in the insulator in S1 comprises:
the temperature sensor is used for collecting temperature data of the insulator in a real-time working state;
the humidity sensor is used for collecting environmental humidity data of the insulator in a real-time working state;
the vibration sensor is used for collecting sound wave vibration data of the insulator in a real-time working state;
the current sensor is used for collecting current data of the insulator in a real-time working state.
3. The method for comprehensively detecting the multi-degradation insulators based on the internet of things according to claim 2, wherein the dataset formed by the real-time working state data and the real-time appearance image data comprises:
；
wherein,is a federated data set;
when (when)When (I) >Is temperature data; when->When (I)>Is humidity data; when->When (I)>Real-time appearance image data; when->When (I)>Is sound wave vibration data; when->When (I)>Is current data;
；
wherein,is a difference dataset; />Is->Middle->Sequence number of the personal modality data.
4. The comprehensive detection method of multi-degradation insulators based on the internet of things according to claim 3, wherein the construction of the multi-mode fusion database of the temperature-humidity-graph-sound-electricity in the S2 comprises the following specific steps:
s201: carrying out data cleaning and image denoising processing on the collected real-time working state data and real-time appearance image data of the insulator;
s202: according to step S201, the processed real-time working state data and real-time appearance image data are aligned to the same acquisition time point by performing data alignment processing on the processed real-time working state data and real-time appearance image data;
s203: calculating an edge function and an optimal fusion function of data in a multi-mode fusion database of temperature-humidity-graph-sound-electricity;
s204: and calculating the correlation degree of each modal data and insulator fault degradation in the temperature-humidity-graph-sound-electricity multi-modal fusion database.
5. The method for comprehensive detection of multiple degradation insulators based on internet of things according to claim 4, wherein the step S203 comprises:
The calculation strategy of the edge function is as follows:
；
wherein:
is->Edge functions of the individual mode data at the detection time point t;
a time period for completing the integrated detection of the insulator;
l is the detection time point t, the firstBandwidth of the personal modality data;
n is atInside, intercepted->An nth data point of the modal data, n being a positive integer;
n is atInside, intercepted->Total number of data points of the individual modality data;
the method comprises the steps that a kernel function related to a detection time point t is used for realizing the linear separability of five modal data in a high-dimensional feature space;
is an integral function;
the calculation strategy of the optimal fusion function is as follows:
；
wherein:
is->Edge functions of the individual mode data at the detection time point t;
is at->Inside, intercepted->A sample edge function for an nth data point of the modal data;
is at->Inside, intercepted->A sample edge function for an nth data point of the modal data;
selecting a function for the conditions when simultaneously satisfyingWhen the condition selection function value is 1, otherwise, 0.
6. The method for comprehensive detection of multiple-degradation insulators based on internet of things according to claim 5, wherein the correlation degree between each mode data and insulator fault degradation in step S204 The calculation strategy of (2) is as follows:
；
wherein,for->Andrespectively at->And->Integrating;
is->Correlation of the modal data with insulator failure degradation.
7. The comprehensive detection method of the multi-degradation insulator based on the internet of things of claim 6, wherein the construction of the inspector experience knowledge base comprises the following specific steps:
s301: acquisition of near x 1 X in year 2 X in the province area 3 In case of deterioration of insulator x 4 Describing sentences about the insulator fault reasons and degradation degrees in the insulator degradation cases by the patrol inspectors to form an iteration updated prior experience library;
s302: acquiring description sentences of the fault reasons and the degradation degree of the detection insulator by 5 inspectors, processing the description sentences through an NLTK word segmentation tool, and then inputting the description sentences into an priori experience library for comparison;
s303: when the comparison value is larger than the similar threshold value, the confidence of the judgment result of the fault cause and the degradation degree of the detection insulator by the 5 inspectors is 0.75;
s304: when the comparison value is smaller than or equal to the similarity threshold value, the confidence of the judgment result of the fault cause and the degradation degree of the detection insulator by the 5 inspectors is 0.25.
8. The method for comprehensively detecting the multi-degradation insulators based on the internet of things according to claim 7, wherein the historical working data driving library comprises: an input layer, a filtering convolution layer, a sampling layer, a full connection layer and an output layer.
9. The comprehensive detection method of multiple degradation insulators based on the internet of things of claim 8, wherein a calculation strategy of a convolution function in a filtering convolution layer is as follows:
；
wherein:
is->Convolution operation function of each modal data when the detection time point is t;
is->The data values of the individual mode data are input to the input layer when the detection time point is t;
to filter the size of the filter in the convolution layer, < >>Is->Is a natural exponential function of (2);
n is atInside, intercepted->An nth data point of the modality data;
n is atInside, intercepted->Total number of data points of the individual modality data.
10. The comprehensive detection method of multiple degradation insulators based on the internet of things according to claim 9, wherein the specific steps of the judging and diagnosing the failure cause and the degradation degree of the insulators in S5 are as follows:
s501: obtaining a time period for completing one-time insulator comprehensive detectionFive mode data in the system are temperature data, environment humidity data, real-time appearance image data, sound wave vibration data and current data respectively;
s502: predicting the degradation trend of the insulator according to a temperature-humidity-graph-sound-electricity multi-mode fusion database, an inspector experience knowledge base and a historical working data driving base, and calculating the mean value and standard deviation to obtain a health mode data interval of the insulator;
S503: judging whether five modal data at the detection time point is in a healthy modal data interval or not, and outputting a True by a system when the modal data at the time point belongs to the healthy modal data interval, otherwise outputting a False by the system;
s504: according to step S503, when the output is True, the insulator is judged to be healthy;
when the output is False, the step S503 is executed circularly until the 3 rd output is False, the step S503 is stopped circularly, the insulator fault degradation is judged, and the system automatically gives out fault warning.
11. The comprehensive detection method of multiple degradation insulators based on the internet of things of claim 10, wherein the self-service maintenance advice in S6 includes:
when the relatedness of the image mode dataIs in the correlation of five modality data +.>When the number set formed by the values of (a) is maximum, a self-service maintenance suggestion is popped up: cleaning the surface of the insulator;
correlation of current mode dataIs in the correlation of five modality data +.>When the number set formed by the values of (a) is maximum, a self-service maintenance suggestion is popped up: installing an overvoltage and overcurrent protection device;
correlation of data of acoustic wave modesIs in the correlation of five modality data +.>When the number set formed by the values of (a) is maximum, a self-service maintenance suggestion is popped up: the reinforcing insulator is connected with the fixed bracket;
Correlation of temperature or humidity mode dataOr->Is in the correlation of five modality data +.>When the number set formed by the values of (a) is maximum, a self-service maintenance suggestion is popped up: replacing the insulator.
12. A multi-degradation insulator comprehensive detection system based on the internet of things, which is realized based on the multi-degradation insulator comprehensive detection method based on the internet of things as claimed in any one of claims 1 to 11, and is characterized in that the system comprises the following modules: the system comprises a data acquisition and transmission module, a multi-mode fusion data module, an inspector experience knowledge module, a historical working data driving module, an insulator fault discrimination diagnosis module and an insulator visualization module;
the data acquisition and transmission module acquires working state data and appearance image data of the insulator through a built-in sensor and monitoring equipment in the insulator, and transmits the working state data and the appearance image data to a local server through wireless communication;
the multi-modal fusion data module is used for constructing a multi-modal fusion database of temperature-humidity-graph-sound-electricity;
the historical working data driving module predicts the degradation trend of the insulator through the real-time data driving which is updated continuously in the multi-mode fusion database;
The inspector experience knowledge module is used for optimizing the prediction result of the degradation trend of the insulator in the historical work data driving module;
the insulator fault judging and diagnosing module is used for identifying the working state of the insulator and judging and diagnosing the fault reason and the degradation degree of the insulator;
the insulator visualization module displays the working state of the insulator through a graphical interface by three-dimensional geometric modeling of the insulator, automatically sends out fault warning to the fault insulator, and pops up self-service maintenance suggestions.
13. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements a multi-degradation insulator comprehensive detection method based on the internet of things as claimed in any one of claims 1 to 11.
14. An apparatus, comprising:
a memory for storing instructions;
a processor configured to execute the instructions, and cause the device to perform operations for implementing a multi-degradation insulator comprehensive detection method based on the internet of things according to any one of claims 1 to 11.
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