CN116520068B - Diagnostic method, device, equipment and storage medium for electric power data - Google Patents
Diagnostic method, device, equipment and storage medium for electric power data Download PDFInfo
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Abstract
The invention relates to the field of data processing, and discloses a method, a device, equipment and a storage medium for diagnosing electric power data, which are used for realizing intelligent diagnosis of the electric power data and improving the accuracy of diagnosis and analysis of the electric power data. The method comprises the following steps: acquiring third cable parameter data of the first detection point and fourth cable parameter data of the second detection point; calculating a second current difference distribution curve and a second voltage difference distribution curve of the target cable according to the third cable test data and the fourth cable test data; constructing a first characteristic distribution vector according to the first current difference distribution curve and the first voltage difference distribution curve, and constructing a second characteristic distribution vector according to the second current difference distribution curve and the second voltage difference distribution curve; and inputting the first characteristic distribution vector and the second characteristic distribution vector into a preset cable performance diagnosis model to perform power performance diagnosis, so as to obtain a power performance evaluation index.
Description
Technical Field
The present invention relates to the field of data processing, and in particular, to a method, an apparatus, a device, and a storage medium for diagnosing power data.
Background
The electric power system is one of the vital infrastructures in the modern society, and relates to the fields of energy supply, transportation, communication, medical care, national defense and the like. In electrical power systems, cables are an important transmission medium for connecting various electrical devices and systems. Thus, the accuracy and importance of the cable diagnostic technique is critical to ensure stable operation of the power system.
The cable of alternating current needs to take into account some key properties in design and selection to ensure efficiency and safety of power transmission. First, the cable size and wire cross-sectional area need to be determined based on the current load size and the long distance transmission requirements. Second, the insulating material of the cable needs to be able to withstand voltage and current to prevent short circuit or electric shock accidents. In addition, the cable is required to have low resistance and low loss characteristics to reduce energy loss and heat generation. Finally, maintenance and durability of the cable also need to be considered to ensure that it can maintain high quality power transmission over its lifetime. However, the conventional detection methods such as a perspective method, an insulation resistance measurement method, a thermal inertia method and the like are mainly used in the existing scheme, but the detection accuracy of the methods is low.
Disclosure of Invention
The invention provides a diagnosis method, device, equipment and storage medium for electric power data, which are used for realizing intelligent diagnosis of the electric power data and improving the diagnosis analysis accuracy of the electric power data.
The first aspect of the present invention provides a diagnostic method of electric power data, the diagnostic method of electric power data comprising:
acquiring cable attribute parameters of a target cable to be detected, and setting detection points of the target cable to obtain a first detection point and a second detection point;
performing cable test on the target cable based on a preset first current flow direction, and collecting first cable test data of the first detection point position and second cable test data of the second detection point position;
creating a first current difference profile and a first voltage difference profile of the target cable from the first cable test data and the second cable test data;
performing cable test on the target cable based on a preset second current flow direction, and acquiring third cable parameter data of the first detection point and fourth cable parameter data of the second detection point;
calculating a second current difference distribution curve and a second voltage difference distribution curve of the target cable according to the third cable test data and the fourth cable test data;
Constructing a first characteristic distribution vector according to the first current difference distribution curve and the first voltage difference distribution curve, and constructing a second characteristic distribution vector according to the second current difference distribution curve and the second voltage difference distribution curve;
and inputting the first characteristic distribution vector and the second characteristic distribution vector into a preset cable performance diagnosis model to perform power performance diagnosis, so as to obtain a power performance evaluation index of the target cable.
With reference to the first aspect, in a first implementation manner of the first aspect of the present invention, the performing a cable test on the target cable based on a preset first current flow direction, and collecting first cable test data of the first detection point location and second cable test data of the second detection point location includes:
performing cable testing on the target cable based on a preset first current flow direction;
collecting first initial cable test data of the first detection point location, and performing timestamp association and missing value interpolation on the first initial cable test data to obtain first cable test data;
and acquiring second initial cable test data of the second detection point position, and performing timestamp association and missing value interpolation on the second initial cable test data to obtain second cable test data.
With reference to the first aspect, in a second implementation manner of the first aspect of the present invention, the creating a first current difference distribution curve and a first voltage difference distribution curve of the target cable according to the first cable test data and the second cable test data includes:
performing test data classification extraction on the first cable test data to obtain first current data and first voltage data;
performing test data classification extraction on the second cable test data to obtain second current data and second voltage data;
performing difference calculation on the first current data and the second current data to obtain first current difference data, and constructing a first current difference distribution curve of the first current difference data;
and carrying out difference calculation on the first voltage data and the second voltage data to obtain first voltage difference data, and constructing a first voltage difference distribution curve of the first voltage difference data.
With reference to the first aspect, in a third implementation manner of the first aspect of the present invention, the performing test data classification extraction on the first cable test data to obtain first current data and first voltage data includes:
Acquiring current data characteristics and voltage data characteristics;
carrying out current data classification extraction on the first cable test data according to the current data characteristics to obtain first current data;
and carrying out voltage data classification extraction on the first cable test data according to the voltage data characteristics to obtain first voltage data.
With reference to the first aspect, in a fourth implementation manner of the first aspect of the present invention, the constructing a first feature distribution vector according to the first current difference distribution curve and the first voltage difference distribution curve includes:
extracting curve characteristic points of the first current difference distribution curve and the first voltage difference distribution curve respectively to obtain a plurality of target current characteristic points and target voltage characteristic points corresponding to each target current characteristic point;
generating a plurality of current-voltage characteristic point pairs according to the plurality of target current characteristic points and target voltage characteristic points corresponding to each target current characteristic point;
and vector encoding is carried out on the plurality of current-voltage characteristic point pairs to obtain a first characteristic distribution vector.
With reference to the first aspect, in a fifth implementation manner of the first aspect of the present invention, the inputting the first feature distribution vector and the second feature distribution vector into a preset cable performance diagnosis model to perform power performance diagnosis, to obtain a power performance evaluation index of the target cable includes:
Vector fusion is carried out on the first characteristic distribution vector and the second characteristic distribution vector to obtain a target characteristic distribution vector;
inputting the target feature distribution vector into a preset cable performance diagnosis model, wherein the cable performance diagnosis model comprises two layers of models, a first layer of models comprises a plurality of basic models, and a second layer of models comprises a target model;
and carrying out electric power performance diagnosis and prediction on the target characteristic distribution vector through the cable performance diagnosis model to obtain an electric power performance evaluation index of the target cable.
With reference to the first aspect, in a sixth implementation manner of the first aspect of the present invention, the performing, by using the cable performance diagnostic model, power performance diagnostic prediction on the target feature distribution vector, to obtain a power performance evaluation index of the target cable includes:
predicting the target feature distribution vector through a plurality of basic models in the cable performance diagnosis model to obtain a prediction result;
carrying out weight analysis on the prediction result to obtain a first weight, and generating a second weight according to the cable attribute parameter;
performing weighted analysis on the target feature distribution vector according to the first weight to obtain a first weighted vector, and performing weighted analysis on the target feature distribution vector according to the second weight to obtain a second weighted vector;
And inputting the first weighting vector and the second weighting vector into a target model in the cable performance diagnosis model to perform power performance diagnosis prediction, so as to obtain a power performance evaluation index of the target cable.
A second aspect of the present invention provides a diagnostic apparatus of electric power data, the diagnostic apparatus of electric power data including:
the acquisition module is used for acquiring cable attribute parameters of a target cable to be detected, and setting detection points of the target cable to obtain a first detection point and a second detection point;
the first testing module is used for carrying out cable testing on the target cable based on a preset first current flow direction and collecting first cable testing data of the first detection point position and second cable testing data of the second detection point position;
a first creating module, configured to create a first current difference distribution curve and a first voltage difference distribution curve of the target cable according to the first cable test data and the second cable test data;
the second testing module is used for carrying out cable testing on the target cable based on a preset second current flow direction, and acquiring third cable parameter data of the first detection point position and fourth cable parameter data of the second detection point position;
A second creating module, configured to calculate a second current difference distribution curve and a second voltage difference distribution curve of the target cable according to the third cable test data and the fourth cable test data;
the construction module is used for constructing a first characteristic distribution vector according to the first current difference distribution curve and the first voltage difference distribution curve, and constructing a second characteristic distribution vector according to the second current difference distribution curve and the second voltage difference distribution curve;
and the output module is used for inputting the first characteristic distribution vector and the second characteristic distribution vector into a preset cable performance diagnosis model to perform power performance diagnosis, so as to obtain a power performance evaluation index of the target cable.
A third aspect of the present invention provides a diagnostic apparatus of electric power data, comprising: a memory and at least one processor, the memory having instructions stored therein; the at least one processor invokes the instructions in the memory to cause the power data diagnostic device to perform the power data diagnostic method described above.
A fourth aspect of the present invention provides a computer-readable storage medium having instructions stored therein that, when executed on a computer, cause the computer to perform the above-described method of diagnosing power data.
In the technical scheme provided by the invention, third cable parameter data of the first detection point and fourth cable parameter data of the second detection point are obtained; calculating a second current difference distribution curve and a second voltage difference distribution curve of the target cable according to the third cable test data and the fourth cable test data; constructing a first characteristic distribution vector according to the first current difference distribution curve and the first voltage difference distribution curve, and constructing a second characteristic distribution vector according to the second current difference distribution curve and the second voltage difference distribution curve; the first characteristic distribution vector and the second characteristic distribution vector are input into a preset cable performance diagnosis model to perform power performance diagnosis, and a power performance evaluation index is obtained.
Drawings
FIG. 1 is a schematic diagram of an embodiment of a method for diagnosing power data according to an embodiment of the present invention;
FIG. 2 is a flow chart of creating a first current difference profile and a first voltage difference profile according to an embodiment of the present invention;
FIG. 3 is a flowchart of constructing a first feature distribution vector according to an embodiment of the present invention;
FIG. 4 is a flow chart of power performance diagnostics in an embodiment of the present invention;
FIG. 5 is a schematic diagram of an embodiment of a diagnostic device for power data in an embodiment of the invention;
fig. 6 is a schematic diagram of an embodiment of a diagnostic device for power data in an embodiment of the present invention.
Detailed Description
The embodiment of the invention provides a diagnosis method, device and equipment of electric power data and a storage medium, which are used for realizing intelligent diagnosis of the electric power data and improving the diagnosis analysis accuracy of the electric power data. The terms "first," "second," "third," "fourth" and the like in the description and in the claims and in the above drawings, if any, are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments described herein may be implemented in other sequences than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed or inherent to such process, method, article, or apparatus.
For ease of understanding, a specific flow of an embodiment of the present invention is described below with reference to fig. 1, and one embodiment of a method for diagnosing electric power data in an embodiment of the present invention includes:
s101, acquiring cable attribute parameters of a target cable to be detected, and setting detection points of the target cable to obtain a first detection point and a second detection point;
it is to be understood that the execution subject of the present invention may be a diagnostic device of power data, and may also be a terminal or a server, which is not limited herein. The embodiment of the invention is described by taking a server as an execution main body as an example.
Specifically, the server obtains attribute parameters of the target cable according to the test requirement, including characteristics such as cable length, diameter, material, withstand voltage and the like. These parameters may be obtained from the product manual of the cable manufacturer or from a related standard specification. And selecting and setting detection points according to the attribute parameters of the target cable. The detection points of the target cable should include a first detection point and a second detection point. These points are important locations for cable testing for detecting defects and damage inside the cable.
Meanwhile, it should be noted that the computerized design software assists in the detection point setting through calculation and simulation methods. The software can select the attribute parameters of the cable and automatically calculate the optimal detection point position so as to realize the optimal effect of cable test. And recording the acquired attribute parameters and the detection points of the target cable. By the aid of the method, detection accuracy can be optimized, and cable testing in future can be facilitated. And selecting a first detection point according to the detection point setting. The first detection point is typically one end of the cable for detecting damage and breakage of the cable surface and the cable conductors. And selecting a second detection point according to the detection point setting. The second detection point is usually the middle or the other end of the cable and can be used for detecting defects and damages inside the cable.
S102, performing cable test on a target cable based on a preset first current flow direction, and collecting first cable test data of a first detection point position and second cable test data of a second detection point position;
in particular, the server checks whether there is damage to the test equipment or a component that needs replacement. Then, setting test parameters according to the test method requirements. For example, current intensity and voltage range need to be set when using a multimeter. And setting the inflow direction of the first current according to the test requirement. If direct current is used, the positive pole of the device should be connected to the beginning of the cable and the negative pole to the end of the cable. The two probes of the test equipment are connected with the starting point and the ending point of the cable respectively. Ensure that the connection is stable and tried out. And starting the testing equipment and testing. During testing, the device may record data, such as current and voltage information. And waiting for the test result to finish and remembering to store the data file, finally obtaining the cable attribute parameters of the target cable to be detected, and finally acquiring the first cable test data of the first detection point location and the second cable test data of the second detection point location.
S103, creating a first current difference distribution curve and a first voltage difference distribution curve of the target cable according to the first cable test data and the second cable test data;
It should be noted that, the quality of the initial data in the first cable test data and the second cable test data affects the shape of the final curve, and thus data processing is required. For test data, operations such as data processing, filtering, denoising and the like are required to remove signal noise, improve the precision and accuracy of the data, and analysis tools such as MATLAB or Python are utilized to analyze the test data. Through analysis, statistical data such as the mean value, the maximum value, the minimum value, the standard deviation and the like of the test data can be obtained, statistical analysis and data visualization are performed, and after the data analysis, a first current difference distribution curve and a first voltage difference distribution curve are created according to the characteristics of the target cable and the test result. The curves can be made using related software and set up curve colors, lines, and other details.
S104, carrying out cable test on the target cable based on a preset second current flow direction, and acquiring third cable parameter data of the first detection point and fourth cable parameter data of the second detection point;
specifically, test parameters such as current intensity and voltage range are set according to the test method requirements. It is checked whether there is damage to the test equipment or a component that needs replacement. And setting the inflow direction of the second current according to the test requirement. If direct current is used, the positive pole of the device should be connected to the end of the cable and the negative pole to the beginning of the cable. The two probes of the test equipment are connected with the starting point and the ending point of the cable respectively. Ensure that the connection is stable and tried out. And starting the testing equipment and testing. During testing, the device may record data, such as current and voltage information. Wait for the test results to complete and remember to save the data file. After the data files are arranged and typeset, the cable test results can be analyzed by referring to the actual conditions of the test data amount. The first detection point position data (including diameter, resistance, short-circuit capacitance, insulation resistance and the like) are tested and recorded by using a cable quality detector or an electrical testing instrument. And at the second detection point of the target cable, testing by using the same instrument, acquiring fourth cable parameter data and recording.
S105, calculating a second current difference distribution curve and a second voltage difference distribution curve of the target cable according to the third cable test data and the fourth cable test data;
specifically, the test data is subjected to operations such as preprocessing, filtering, denoising and the like. The data preprocessing can improve the data quality and accuracy and avoid erroneous calculation results. And importing the processed third cable test data and fourth cable test data into a computer, converting the data into numerical types, and ensuring the uniformity of a data format and a statistical method. The cable test data is analyzed using an analysis tool, such as MATLAB or Python. Through analysis, statistical data such as the mean value, the maximum value, the minimum value, the standard deviation and the like of the test data can be obtained, and statistical analysis and data visualization are performed. And calculating the current difference value of the target cable according to the third cable test data and the fourth cable test data. The current difference should be the difference between the second current test data and the first current test data. And storing the calculated current difference value into a data file. And calculating the voltage difference value of the target cable according to the third cable test data and the fourth cable test data. The voltage difference should be the difference between the second voltage test data and the first voltage test data. And storing the calculated voltage difference value into a data file. After data analysis, a current difference profile and a voltage difference profile are created from the characteristics of the target cable and the test results.
S106, constructing a first characteristic distribution vector according to the first current difference distribution curve and the first voltage difference distribution curve, and constructing a second characteristic distribution vector according to the second current difference distribution curve and the second voltage difference distribution curve;
specifically, first, data to be analyzed is acquired. Current and voltage data are collected by a sensor, oscilloscope, or the like, and then stored in a computer. Further, the data is processed. Processing is performed in a programming language such as Python. Data can be processed using Pandas, all data can be classified and normalized, and a set of difference profiles can be obtained from the data by calculating the difference between current and voltage across the data. For each set of curves, the differences are processed into a probability density distribution form, such as Parzen-Rosenblatt windowing or Gaussian Mixture Model (GMM) based on kernel density estimation, from which feature distribution vectors can be constructed. The specific method comprises the following steps: dividing the difference distribution curve into a plurality of intervals, calculating the probability density area in each interval, and finally obtaining a group of vector values, specifically, constructing a first characteristic distribution vector according to the first current difference distribution curve and the first voltage difference distribution curve, and constructing a second characteristic distribution vector according to the second current difference distribution curve and the second voltage difference distribution curve.
S107, inputting the first characteristic distribution vector and the second characteristic distribution vector into a preset cable performance diagnosis model to perform power performance diagnosis, and obtaining a power performance evaluation index of the target cable.
Specifically, the server inputs the first feature distribution vector and the second feature distribution vector into a cable performance diagnostic model. The method comprises the steps of constructing a feature vector into a DataFrame through a Pandas tool and the like, then importing the DataFrame into a model for calculation, inputting the constructed feature vector into the model for calculation through the model, extracting a result returned by the model, and carrying out visualization processing to finally obtain an electric power performance evaluation index of a target cable.
In the embodiment of the invention, third cable parameter data of a first detection point and fourth cable parameter data of a second detection point are obtained; calculating a second current difference distribution curve and a second voltage difference distribution curve of the target cable according to the third cable test data and the fourth cable test data; constructing a first characteristic distribution vector according to the first current difference distribution curve and the first voltage difference distribution curve, and constructing a second characteristic distribution vector according to the second current difference distribution curve and the second voltage difference distribution curve; the first characteristic distribution vector and the second characteristic distribution vector are input into a preset cable performance diagnosis model to perform power performance diagnosis, and a power performance evaluation index is obtained.
In a specific embodiment, the process of executing step S102 may specifically include the following steps:
(1) Performing cable test on the target cable based on a preset first current flow direction;
(2) Collecting first initial cable test data of a first detection point location, and performing timestamp association and missing value interpolation on the first initial cable test data to obtain first cable test data;
(3) And acquiring second initial cable test data of the second detection point position, and performing timestamp association and missing value interpolation on the second initial cable test data to obtain second cable test data.
Specifically, a flow direction of a preset first current is first determined so as to perform a cable test. Before cable testing, the cable is grounded and the electrodes of the test instrument are connected to the cable. In the process of cable testing, the testing instrument can record parameters such as current and voltage of the cable in real time. The cable test should take into account the effect of the test duration, and may be selected to be tested for a specific period of time according to the requirements of the user. The test value is ensured to be accurate and reliable during the test, and the subsequent analysis and processing are prevented from being influenced by the error result generated by non-electric power reasons. After the cable test is completed, the data recorded by the test instrument is imported into a computer for subsequent processing. Before this, the cable test data is transmitted from the test instrument to the computer for subsequent processing by means of a data collector or transmitter. Further, the acquired data is time-stamp correlated. The purpose of this operation is to sort the time stamps in the data versus test time for further analysis and processing. For the data collected by different test instruments, the time stamp information is processed because the output time formats are different. The datetime library in Python can be used to convert the data into a unified time format by means of formatting character strings and the like so as to be convenient for processing by a program. During cable testing, some test values may be missing values due to instrument failure or other causes. In order to improve the integrity and stability of the data, interpolation operation of the missing values is performed. Filling in missing values is an important task for data processing. The missing value interpolation method can adopt methods based on statistics, time sequence analysis, machine learning and the like, and can select a proper interpolation method according to specific data conditions, such as linear interpolation, KNN interpolation, spline interpolation and the like. After the timestamp correlation and missing value interpolation are completed, first cable test data can be obtained.
Further, according to the characteristics and detection requirements of the cable, a second detection point is selected and marked. Wiring and equipment is then required to connect the test instrument to the cable. And acquiring initial cable test data of the second detection point position through a test instrument. During testing, it is necessary to ensure accuracy and reliability of the data for subsequent analysis and processing. After the cable test is completed, the data recorded by the test instrument are transmitted to a computer for subsequent processing. This process reads the data from the test instrument via a data collector or transmitter and stores the data in a computer. And carrying out time stamp association on the acquired data. The purpose of this operation is to sort the time stamps in the data against test time for further analysis and processing. And according to different time stamp formats provided by different test instruments, the time stamp information is formatted and processed, so that unified standard identification and comparison are convenient. During cable testing, some test values may be missing values due to instrument failure or other reasons. In order to improve the integrity and stability of data, interpolation operations of missing values are required. Filling in missing values is an important task for data processing. The missing value interpolation method can adopt methods based on statistics, time sequence analysis, machine learning and the like, and can select a proper interpolation method according to specific data conditions, such as linear interpolation, KNN interpolation, spline interpolation, lagrange interpolation and the like. After the timestamp correlation and missing value interpolation are completed, second cable test data can be obtained.
In a specific embodiment, as shown in fig. 2, the process of performing step S103 may specifically include the following steps:
s201, performing test data classification extraction on first cable test data to obtain first current data and first voltage data;
s202, performing test data classification extraction on second cable test data to obtain second current data and second voltage data;
s203, performing difference calculation on the first current data and the second current data to obtain first current difference data, and constructing a first current difference distribution curve of the first current difference data;
s204, performing difference calculation on the first voltage data and the second voltage data to obtain first voltage difference data, and constructing a first voltage difference distribution curve of the first voltage difference data.
Specifically, before classifying and extracting the test data, preprocessing the test data, including data checking, denoising, filtering, data segmentation and other operations, is performed to improve the accuracy and reliability of the data. And classifying and extracting the first cable test data and the second cable test data to obtain first current data and second current data, and first voltage data and second voltage data. The specific flow comprises the following steps: specific values of the current and voltage signals in the test data are set as standards for classification extraction. And according to the predefined classification standard, carrying out signal segmentation on the test data, and extracting the signal segments which accord with the classification standard from the test data. And extracting data from each signal segment to obtain current and voltage signals, and performing data processing operations such as normalization, denoising, filtering and the like. Further, difference calculation is performed on the first current data and the second current data and the first voltage data and the second voltage data. The method comprises the following specific steps: reading in first current data and second current data and first voltage data and second voltage data, carrying out difference calculation on the first current data and the second current data and the first voltage data and the second voltage data to obtain first current and voltage difference data, outputting the first current and voltage difference data to a designated file, and constructing a first current difference distribution curve and a first voltage difference distribution curve after calculating the first current and voltage difference data. The method comprises the following specific steps of counting distribution conditions of first current and voltage difference data, including indexes such as maximum value, minimum value, average value, median, standard deviation and the like, and drawing a first current difference distribution curve and a first voltage difference distribution curve based on the counted difference data. The curve may be plotted using conventional visualization tools, such as matplotlib, ggplot2, to finally obtain a first voltage difference distribution curve for the first voltage difference data.
In a specific embodiment, the process of executing step S201 may specifically include the following steps:
(1) Acquiring current data characteristics and voltage data characteristics;
(2) Carrying out current data classification extraction on the first cable test data according to the current data characteristics to obtain first current data;
(3) And carrying out voltage data classification extraction on the first cable test data according to the voltage data characteristics to obtain first voltage data.
Specifically, corresponding data characteristics including indexes such as waveform shape, spectrum distribution, peak value, mean value, variance, deviation and the like are extracted. The method comprises the following specific steps: and (3) data acquisition: the method comprises the steps of firstly collecting current and voltage through some tools or equipment, ensuring the accuracy and reliability of data, carrying out some data preprocessing including data verification, denoising, filtering, data segmentation and the like before the collected data are processed, carrying out feature extraction on the processed data, extracting various feature data of the current and voltage data, and carrying out classified extraction on first cable test data based on the features after the feature extraction of the current and voltage data is completed. The specific steps are as follows, specific values of current and voltage data are set as standards for classification and extraction, signal segmentation is carried out on test data according to predefined classification standards, signal segments conforming to the classification standards in the test data are extracted, data extraction is carried out on each signal segment to obtain current and voltage signal data, and classification and extraction of the current and voltage data are realized by combining characteristic data.
In a specific embodiment, as shown in fig. 3, the process of executing step S106 may specifically include the following steps:
s301, respectively extracting curve characteristic points of a first current difference distribution curve and a first voltage difference distribution curve to obtain a plurality of target current characteristic points and target voltage characteristic points corresponding to each target current characteristic point;
s302, generating a plurality of current-voltage characteristic point pairs according to a plurality of target current characteristic points and target voltage characteristic points corresponding to each target current characteristic point;
s303, vector encoding is carried out on the plurality of current-voltage characteristic point pairs, and a first characteristic distribution vector is obtained.
Specifically, the server performs characteristic point extraction operation on the first current difference distribution curve and the first voltage difference distribution curve to obtain a plurality of target current characteristic points and target voltage characteristic points corresponding to each target current characteristic point. The server performs secondary curve fitting on the current and voltage difference distribution curve to obtain a smooth curve, extracts characteristic points of the fitted curve, namely, extracts characteristic points such as extremum and inflection point in the curve, and matches voltage characteristic points corresponding to the current characteristic points, namely, determines target voltage characteristic points corresponding to each target current characteristic point. The matching of the characteristic points can be determined according to the distance on the fitting curve, the slope around the characteristic points and other information, and after the characteristic point extraction and the characteristic point matching are completed, a plurality of target current characteristic points and target voltage characteristic points corresponding to each target current characteristic point are required to generate a plurality of current-voltage characteristic point pairs. Specifically, the matched current characteristic points and the voltage characteristic points are combined to generate a plurality of current and voltage characteristic point pairs, the generated current and voltage characteristic point pairs are verified, the relation and logic correctness between the current and voltage characteristics of each point pair are ensured, and further, vector encoding is carried out on the plurality of current and voltage characteristic point pairs to obtain a first characteristic distribution vector. The method comprises the following specific steps: and carrying out normalization processing on the current characteristics and the voltage characteristics in the current-voltage characteristic point pair so as to eliminate the influence of the magnitude difference on the subsequent coding result. The features are screened, the most important features are selected, and the number of the features is reduced on the premise of ensuring the importance, so that the computational complexity is reduced. The screened current and voltage characteristic point pairs are encoded by a computer program to obtain a first characteristic distribution vector, different encoding methods are adopted according to specific requirements, such as simple one-hot encoding or more complex word bag encoding, word2vec encoding and the like, encoding results are verified, and encoding accuracy and reliability are ensured.
In a specific embodiment, as shown in fig. 4, the process of performing step S107 may specifically include the following steps:
s401, carrying out vector fusion on the first characteristic distribution vector and the second characteristic distribution vector to obtain a target characteristic distribution vector;
s402, inputting a target feature distribution vector into a preset cable performance diagnosis model, wherein the cable performance diagnosis model comprises two layers of models, a first layer of models comprises a plurality of basic models, and a second layer of models comprises a target model;
s403, performing electric power performance diagnosis and prediction on the target characteristic distribution vector through a cable performance diagnosis model to obtain an electric power performance evaluation index of the target cable.
Specifically, the server performs vector fusion on the first feature distribution vector and the second feature distribution vector to obtain a target feature distribution vector. Wherein, vector fusion can be achieved by the following fusion formula:
；
wherein, the method comprises the steps of, wherein,for the first feature distribution vector,, is>Is the second characteristic distribution vector, F isThe distribution vector of the characteristic of the object,for the first fusion weight, +.>And the second fusion weight. The method comprises the steps of carrying out normalization processing on a first characteristic distribution vector and a second characteristic distribution vector to eliminate the influence of magnitude difference on a subsequent fusion result, establishing a corresponding weight model aiming at the importance of different characteristic vectors, determining the weight of different characteristics in fusion, and carrying out weighted fusion on the first characteristic distribution vector and the second characteristic distribution vector according to the determined weight to obtain a target characteristic distribution vector. In this embodiment, the cable performance diagnostic model includes two layers of models, the first layer of models including a plurality of base models and the second layer of models including a target model. The basic model refers to various models commonly used in cable testing, such as a resistance model, a capacitance model, an inductance model, and the like. The target model refers to a model for calculating an electric power performance evaluation index of the target cable. The model is typically trained by machine learning or other analytical techniques, and can be predicted for its electrical performance index based on the cable performance to be evaluated. For the calculation formula of the cable performance diagnosis model, the specific formula is different from model to model. Wherein the base model and the target model are each composed of a plurality of input parameters including physical parameters of the cable, power transmission data, and other relevant information. These models may be built using various statistical and machine learning techniques, such as neural networks, regression models, and the like. After the target feature distribution vector is input, the cable performance diagnostic model typically outputs a power performance evaluation index to evaluate the power performance of the target cable.
In a specific embodiment, the process of performing step S403 may specifically include the following steps:
(1) Predicting the target characteristic distribution vector through a plurality of basic models in the cable performance diagnosis model to obtain a prediction result;
(2) Carrying out weight analysis on the prediction result to obtain a first weight, and generating a second weight according to the cable attribute parameter;
(3) Performing weighted analysis on the target feature distribution vector according to the first weight to obtain a first weighted vector, and performing weighted analysis on the target feature distribution vector according to the second weight to obtain a second weighted vector;
(4) And inputting the first weighting vector and the second weighting vector into a target model in the cable performance diagnosis model to perform power performance diagnosis prediction, so as to obtain a power performance evaluation index of the target cable.
Specifically, the target feature distribution vector is input into each basic model, the output result of each model is obtained, and the results are combined and weighted to finally obtain the prediction result. This process may be implemented using various machine learning algorithms and data processing tools, such as logistic regression, decision trees, random forests, and the like. And carrying out weight analysis on the prediction result to obtain a first weight and a second weight. In the process, correlation among variables is required to be considered, normalization processing is used for normalization, and the first weight and the second weight are used for respectively carrying out weighted analysis on the target characteristic distribution vector to obtain a first weighted vector and a second weighted vector. The process involves carrying out weighting processing on each characteristic, combining the characteristics according to a preset proportion, inputting a first weighting vector and a second weighting vector into a target model of a cable performance diagnosis model, carrying out power performance diagnosis prediction, and obtaining a power performance evaluation index of a target cable, wherein the power performance evaluation index is an index for evaluating the power performance of the target cable, which is calculated through cable test data, the value range of the power performance evaluation index is (0, 1), when the power performance evaluation index is (0, 0.5), the power performance of the target cable is determined to be poor, and when the power performance evaluation index is [0.5, 1), the power performance of the target cable is determined to be good.
The method for diagnosing power data in the embodiment of the present invention is described above, and the apparatus for diagnosing power data in the embodiment of the present invention is described below, referring to fig. 5, and one embodiment of the apparatus for diagnosing power data in the embodiment of the present invention includes:
the obtaining module 501 is configured to obtain a cable attribute parameter of a target cable to be detected, and set a detection point location for the target cable, so as to obtain a first detection point location and a second detection point location;
the first test module 502 is configured to perform a cable test on the target cable based on a preset first current flow direction, and collect first cable test data of the first detection point location and second cable test data of the second detection point location;
a first creating module 503, configured to create a first current difference distribution curve and a first voltage difference distribution curve of the target cable according to the first cable test data and the second cable test data;
the second testing module 504 is configured to perform a cable test on the target cable based on a preset second current flow direction, and obtain third cable parameter data of the first detection point location and fourth cable parameter data of the second detection point location;
A second creating module 505, configured to calculate a second current difference distribution curve and a second voltage difference distribution curve of the target cable according to the third cable test data and the fourth cable test data;
a construction module 506, configured to construct a first feature distribution vector according to the first current difference distribution curve and the first voltage difference distribution curve, and construct a second feature distribution vector according to the second current difference distribution curve and the second voltage difference distribution curve;
and the output module 507 is configured to input the first feature distribution vector and the second feature distribution vector into a preset cable performance diagnosis model to perform power performance diagnosis, so as to obtain a power performance evaluation index of the target cable.
Acquiring third cable parameter data of the first detection point and fourth cable parameter data of the second detection point through the cooperative cooperation of the components; calculating a second current difference distribution curve and a second voltage difference distribution curve of the target cable according to the third cable test data and the fourth cable test data; constructing a first characteristic distribution vector according to the first current difference distribution curve and the first voltage difference distribution curve, and constructing a second characteristic distribution vector according to the second current difference distribution curve and the second voltage difference distribution curve; the first characteristic distribution vector and the second characteristic distribution vector are input into a preset cable performance diagnosis model to perform power performance diagnosis, and a power performance evaluation index is obtained.
The above-described diagnostic device for power data in the embodiment of the present invention is described in detail from the point of view of the modularized functional entity in fig. 5, and the below-described diagnostic device for power data in the embodiment of the present invention is described in detail from the point of view of hardware processing.
Fig. 6 is a schematic structural diagram of a power data diagnosis device 600 according to an embodiment of the present invention, where the power data diagnosis device 600 may have a relatively large difference according to a configuration or a performance, and may include one or more processors (central processing units, CPU) 610 (e.g., one or more processors) and a memory 620, and one or more storage media 630 (e.g., one or more mass storage devices) storing application programs 633 or data 632. Wherein the memory 620 and the storage medium 630 may be transitory or persistent storage. The program stored in the storage medium 630 may include one or more modules (not shown), each of which may include a series of instruction operations in the diagnostic device 600 for power data. Still further, the processor 610 may be configured to communicate with the storage medium 630 to execute a series of instruction operations in the storage medium 630 on the diagnostic device 600 for power data.
The power data diagnostic device 600 may also include one or more power supplies 640, one or more wired or wireless network interfaces 650, one or more input/output interfaces 660, and/or one or more operating systems 631, such as windows service, mac OS X, unix, linux, freeBSD, and the like. It will be appreciated by those skilled in the art that the configuration of the diagnostic device for power data shown in fig. 6 does not constitute a limitation of the diagnostic device for power data, and may include more or fewer components than shown, or may combine certain components, or may be arranged in a different arrangement of components.
The present invention also provides a diagnostic apparatus of electrical data comprising a memory and a processor, the memory storing computer readable instructions which, when executed by the processor, cause the processor to perform the steps of the diagnostic method of electrical data in the above embodiments.
The present invention also provides a computer readable storage medium, which may be a non-volatile computer readable storage medium, and which may also be a volatile computer readable storage medium, the computer readable storage medium having stored therein instructions which, when executed on a computer, cause the computer to perform the steps of the method of diagnosing power data.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, which are not repeated herein.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied essentially or in part or all of the technical solution or in part in the form of a software product stored in a storage medium, including instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a read-only memory (ROM), a random access memory (randomacceS memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (10)
1. A method of diagnosing power data, the method comprising:
acquiring cable attribute parameters of a target cable to be detected, and setting detection points of the target cable to obtain a first detection point and a second detection point;
performing cable test on the target cable based on a preset first current flow direction, and collecting first cable test data of the first detection point position and second cable test data of the second detection point position;
creating a first current difference profile and a first voltage difference profile of the target cable from the first cable test data and the second cable test data;
Performing cable test on the target cable based on a preset second current flow direction, and acquiring third cable test data of the first detection point and fourth cable test data of the second detection point;
calculating a second current difference distribution curve and a second voltage difference distribution curve of the target cable according to the third cable test data and the fourth cable test data;
constructing a first characteristic distribution vector according to the first current difference distribution curve and the first voltage difference distribution curve, and constructing a second characteristic distribution vector according to the second current difference distribution curve and the second voltage difference distribution curve;
and inputting the first characteristic distribution vector and the second characteristic distribution vector into a preset cable performance diagnosis model to perform power performance diagnosis, so as to obtain a power performance evaluation index of the target cable.
2. The method for diagnosing power data according to claim 1, wherein the cable testing the target cable based on the preset first current flow direction and collecting the first cable test data of the first detection point and the second cable test data of the second detection point comprises:
Performing cable testing on the target cable based on a preset first current flow direction;
collecting first initial cable test data of the first detection point location, and performing timestamp association and missing value interpolation on the first initial cable test data to obtain first cable test data;
and acquiring second initial cable test data of the second detection point position, and performing timestamp association and missing value interpolation on the second initial cable test data to obtain second cable test data.
3. The method of diagnosing power data according to claim 1, wherein the creating a first current difference profile and a first voltage difference profile of the target cable from the first cable test data and the second cable test data comprises:
performing test data classification extraction on the first cable test data to obtain first current data and first voltage data;
performing test data classification extraction on the second cable test data to obtain second current data and second voltage data;
performing difference calculation on the first current data and the second current data to obtain first current difference data, and constructing a first current difference distribution curve of the first current difference data;
And carrying out difference calculation on the first voltage data and the second voltage data to obtain first voltage difference data, and constructing a first voltage difference distribution curve of the first voltage difference data.
4. The method for diagnosing power data according to claim 3, wherein the step of classifying and extracting the first cable test data to obtain first current data and first voltage data comprises:
acquiring current data characteristics and voltage data characteristics;
carrying out current data classification extraction on the first cable test data according to the current data characteristics to obtain first current data;
and carrying out voltage data classification extraction on the first cable test data according to the voltage data characteristics to obtain first voltage data.
5. The method of diagnosing power data according to claim 1, wherein the constructing a first feature distribution vector from the first current difference distribution curve and the first voltage difference distribution curve includes:
extracting curve characteristic points of the first current difference distribution curve and the first voltage difference distribution curve respectively to obtain a plurality of target current characteristic points and target voltage characteristic points corresponding to each target current characteristic point;
Generating a plurality of current-voltage characteristic point pairs according to the plurality of target current characteristic points and target voltage characteristic points corresponding to each target current characteristic point;
and vector encoding is carried out on the plurality of current-voltage characteristic point pairs to obtain a first characteristic distribution vector.
6. The method for diagnosing power data according to claim 1, wherein inputting the first feature distribution vector and the second feature distribution vector into a preset cable performance diagnosis model for power performance diagnosis, to obtain the power performance evaluation index of the target cable, comprises:
vector fusion is carried out on the first characteristic distribution vector and the second characteristic distribution vector to obtain a target characteristic distribution vector;
inputting the target feature distribution vector into a preset cable performance diagnosis model, wherein the cable performance diagnosis model comprises two layers of models, a first layer of models comprises a plurality of basic models, and a second layer of models comprises a target model;
and carrying out electric power performance diagnosis and prediction on the target characteristic distribution vector through the cable performance diagnosis model to obtain an electric power performance evaluation index of the target cable.
7. The method according to claim 6, wherein the performing, by the cable performance diagnostic model, the power performance diagnostic prediction on the target feature distribution vector to obtain the power performance evaluation index of the target cable includes:
Predicting the target feature distribution vector through a plurality of basic models in the cable performance diagnosis model to obtain a prediction result;
carrying out weight analysis on the prediction result to obtain a first weight, and generating a second weight according to the cable attribute parameter;
performing weighted analysis on the target feature distribution vector according to the first weight to obtain a first weighted vector, and performing weighted analysis on the target feature distribution vector according to the second weight to obtain a second weighted vector;
and inputting the first weighting vector and the second weighting vector into a target model in the cable performance diagnosis model to perform power performance diagnosis prediction, so as to obtain a power performance evaluation index of the target cable.
8. A diagnostic device for electrical data, the diagnostic device comprising:
the acquisition module is used for acquiring cable attribute parameters of a target cable to be detected, and setting detection points of the target cable to obtain a first detection point and a second detection point;
the first testing module is used for carrying out cable testing on the target cable based on a preset first current flow direction and collecting first cable testing data of the first detection point position and second cable testing data of the second detection point position;
A first creating module, configured to create a first current difference distribution curve and a first voltage difference distribution curve of the target cable according to the first cable test data and the second cable test data;
the second testing module is used for carrying out cable testing on the target cable based on a preset second current flow direction, and acquiring third cable testing data of the first detection point position and fourth cable testing data of the second detection point position;
a second creating module, configured to calculate a second current difference distribution curve and a second voltage difference distribution curve of the target cable according to the third cable test data and the fourth cable test data;
the construction module is used for constructing a first characteristic distribution vector according to the first current difference distribution curve and the first voltage difference distribution curve, and constructing a second characteristic distribution vector according to the second current difference distribution curve and the second voltage difference distribution curve;
and the output module is used for inputting the first characteristic distribution vector and the second characteristic distribution vector into a preset cable performance diagnosis model to perform power performance diagnosis, so as to obtain a power performance evaluation index of the target cable.
9. A diagnostic apparatus of electric power data, characterized in that the diagnostic apparatus of electric power data comprises: a memory and at least one processor, the memory having instructions stored therein;
the at least one processor invokes the instructions in the memory to cause the diagnostic device of power data to perform the diagnostic method of power data as claimed in any one of claims 1-7.
10. A computer readable storage medium having instructions stored thereon, which when executed by a processor, implement the method of diagnosing power data according to any one of claims 1-7.
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Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR101367891B1 (en) * | 2012-12-06 | 2014-02-27 | 주식회사 이디엠텍 | Apparatus for deterioration diagnosis of power cable and a method thereof |
| CN104239739A (en) * | 2014-09-26 | 2014-12-24 | 华南理工大学 | Piecewise fitting method and system of lightning current amplitude probability distribution function |
| CN106501662A (en) * | 2016-12-12 | 2017-03-15 | 云南电网有限责任公司西双版纳供电局 | Heart yearn checking device and method |
| CN107632240A (en) * | 2017-09-08 | 2018-01-26 | 河北金能电力科技股份有限公司 | Aerial cable current data analysis method, health status monitoring method and system |
| CN109782127A (en) * | 2019-01-21 | 2019-05-21 | 杭州雷砺自动化科技有限公司 | A kind of parallel intelligent terminal module and cable continuity test method |
| WO2021098611A1 (en) * | 2019-11-19 | 2021-05-27 | 云南电网有限责任公司临沧供电局 | Platform for testing leakage current difference factor of aged xlpe cable, and method |
| JP2021096896A (en) * | 2019-12-13 | 2021-06-24 | 矢崎総業株式会社 | Bus bar and bus bar module |
| CN113092944A (en) * | 2021-04-12 | 2021-07-09 | 广东电网有限责任公司江门供电局 | Insulation fault point detection method and related device for low-voltage line |
| CN113533900A (en) * | 2021-07-02 | 2021-10-22 | 国网浙江省电力有限公司金华供电公司 | System and method for judging fault section of power distribution network cable hybrid line |
| CN115015683A (en) * | 2022-08-09 | 2022-09-06 | 深圳永贵技术有限公司 | Cable production performance test method, device, equipment and storage medium |
| WO2022198899A1 (en) * | 2021-03-24 | 2022-09-29 | 国网河北省电力有限公司沧州供电分公司 | Submarine cable fault diagnosis method and apparatus, and device |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US11293995B2 (en) * | 2020-03-23 | 2022-04-05 | Rosemount Aerospace Inc. | Differential leakage current measurement for heater health monitoring |
-
2023
- 2023-07-04 CN CN202310806188.8A patent/CN116520068B/en active Active
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR101367891B1 (en) * | 2012-12-06 | 2014-02-27 | 주식회사 이디엠텍 | Apparatus for deterioration diagnosis of power cable and a method thereof |
| CN104239739A (en) * | 2014-09-26 | 2014-12-24 | 华南理工大学 | Piecewise fitting method and system of lightning current amplitude probability distribution function |
| CN106501662A (en) * | 2016-12-12 | 2017-03-15 | 云南电网有限责任公司西双版纳供电局 | Heart yearn checking device and method |
| CN107632240A (en) * | 2017-09-08 | 2018-01-26 | 河北金能电力科技股份有限公司 | Aerial cable current data analysis method, health status monitoring method and system |
| CN109782127A (en) * | 2019-01-21 | 2019-05-21 | 杭州雷砺自动化科技有限公司 | A kind of parallel intelligent terminal module and cable continuity test method |
| WO2021098611A1 (en) * | 2019-11-19 | 2021-05-27 | 云南电网有限责任公司临沧供电局 | Platform for testing leakage current difference factor of aged xlpe cable, and method |
| JP2021096896A (en) * | 2019-12-13 | 2021-06-24 | 矢崎総業株式会社 | Bus bar and bus bar module |
| WO2022198899A1 (en) * | 2021-03-24 | 2022-09-29 | 国网河北省电力有限公司沧州供电分公司 | Submarine cable fault diagnosis method and apparatus, and device |
| CN113092944A (en) * | 2021-04-12 | 2021-07-09 | 广东电网有限责任公司江门供电局 | Insulation fault point detection method and related device for low-voltage line |
| CN113533900A (en) * | 2021-07-02 | 2021-10-22 | 国网浙江省电力有限公司金华供电公司 | System and method for judging fault section of power distribution network cable hybrid line |
| CN115015683A (en) * | 2022-08-09 | 2022-09-06 | 深圳永贵技术有限公司 | Cable production performance test method, device, equipment and storage medium |
Non-Patent Citations (3)
| Title |
|---|
| Novel Identification Method of Stator Single Phase-to-Ground Fault for Cable-Wound Generators;Yan Gao et al.;IEEE TRANSACTIONS ON ENERGY CONVERSION;第23卷(第2期);第349-357页 * |
| 基于多状态量特征及变化规律的高压电缆状态综合评估;周路遥;曹俊平;王少华;刘黎;杨勇;江航;;高电压技术;45(第12期);第3954-3963页 * |
| 基于差值电流分析的高压电缆在线监测研究;夏君山 等;高压电器;第55卷(第7期);第165-172页 * |
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