CN116626457A - Transformer ultrahigh frequency partial discharge positioning method and system based on SSA optimization - Google Patents
Transformer ultrahigh frequency partial discharge positioning method and system based on SSA optimization Download PDFInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/12—Testing dielectric strength or breakdown voltage ; Testing or monitoring effectiveness or level of insulation, e.g. of a cable or of an apparatus, for example using partial discharge measurements; Electrostatic testing
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Abstract
The invention discloses a transformer ultrahigh frequency partial discharge positioning method and system based on SSA optimization, which belong to the technical field of power equipment fault positioning, and the method comprises the following steps: collecting transformer partial discharge UHF signals, and carrying out noise reduction treatment on the transformer partial discharge UHF signals; performing cross-correlation calculation on the transformer partial discharge UHF signals after noise reduction treatment, and determining UHF signal time difference; establishing a partial discharge source objective function constraint equation, substituting the partial discharge source objective function constraint equation into UHF signal time difference, and solving the UHF signal time difference by using an SSA algorithm to obtain a plurality of positioning solutions; performing cluster analysis on the obtained positioning solution by using a K-means algorithm to obtain a cluster center; and calculating an optimal solution with the minimum sum of the distances from the cluster centers by using an SSA algorithm to obtain a transformer ultrahigh frequency partial discharge source positioning point. The invention improves the positioning precision of partial discharge, and is convenient for a worker to quickly find the fault position of the equipment, thereby ensuring the safety of the power distribution network.
Description
Technical Field
The invention relates to a transformer ultrahigh frequency partial discharge positioning method and system based on SSA (Sparrow Search Algorithm ) optimization, and belongs to the technical field of power equipment fault positioning.
Background
With the increasing development of the scale of electric power systems, requirements for safe operation and power supply reliability of various electric power equipment are increasing. The power equipment plays an important role in the power grid, and the safe and stable operation of the power equipment plays a decisive role in ensuring the integrity of the power grid structure. In recent years, with the gradual penetration of the national industrialization progress, a large number of various devices are forced to generate strong electricity consumption, so that the safety of a power distribution network is ensured, and meanwhile, it is important to find out the fault position in time.
Transformers are one of the most important devices in power systems, and are key to ensuring safe, reliable, economical and high-quality operation of the power systems. The factors such as natural aging of insulation, severe environmental conditions, excessive operating load and the like can induce faults of the power transformer, so that serious social and economic losses are caused, and particularly, the operation safety of the transformer is seriously affected by partial discharge faults. Partial discharge PD (Partial Discharge) is a major cause of insulation degradation of a transformer, and if not timely discovered and overhauled, the insulation degradation of the transformer is accelerated, and insulation breakdown may be caused to cause a serious power accident. Research shows that the degree of deterioration of transformer insulation is related to not only partial discharge type, discharge amount and discharge repetition rate, but also discharge position, and partial discharge positioning is beneficial to reflecting transformer insulation condition more accurately and adopting a more efficient maintenance strategy.
Partial discharge positioning belongs to a wireless positioning technology, and the algorithm principle of the partial discharge positioning belongs to 3 kinds of based on signal receiving intensity (RSSI), based on signal arrival angle (AOA) and based on signal transmission Time (TOF). Among them, the positioning algorithm based on the arrival time difference TDOA (Time Difference Of Arrival) in the class based on the TOF principle is simple and most widely applied. The ultra-high frequency method (Ultra High Frequency, UHF) is widely applied to the partial discharge detection of equipment such as transformers, gas Insulated Substations (GIS) and the like as an effective means of the partial discharge detection. The common methods based on UHF positioning mainly include: based on the time difference of arrival TDOA and based on the signal received amplitude strength (received signal strength indicator, RSSI). The TDOA method is to arrange an ultrahigh frequency sensor array and solve the coordinates of the partial discharge source by a certain method according to the time difference of signals reaching the sensor array. However, the positioning accuracy of the partial discharge positioning method is low, and the fault position cannot be found timely and accurately, so that the safety of the power distribution network cannot be effectively ensured.
Disclosure of Invention
In order to solve the problems, the invention provides a transformer ultrahigh frequency partial discharge positioning method and system based on SSA optimization, which can improve the positioning accuracy of partial discharge, and timely and accurately find out the fault position, thereby ensuring the safety of a power distribution network.
The technical scheme adopted for solving the technical problems is as follows:
in a first aspect, the embodiment of the invention provides a method for positioning ultrahigh frequency partial discharge of a transformer based on SSA optimization, which comprises the following steps:
collecting transformer partial discharge UHF signals, and carrying out noise reduction treatment on the transformer partial discharge UHF signals;
performing cross-correlation calculation on the transformer partial discharge UHF signals after noise reduction treatment, and determining UHF signal time difference;
establishing a partial discharge source objective function constraint equation, substituting the partial discharge source objective function constraint equation into UHF signal time difference, and solving the UHF signal time difference by using an SSA algorithm to obtain a plurality of positioning solutions;
performing cluster analysis on the obtained positioning solution by using a K-means algorithm to obtain a cluster center;
and calculating an optimal solution with the minimum sum of the distances from the cluster centers by using an SSA algorithm to obtain a transformer ultrahigh frequency partial discharge source positioning point.
As a possible implementation manner of this embodiment, the collecting transformer partially discharges a UHF signal, including:
4 ultra-high frequency intelligent sensing sensors are arranged in a diamond direction, and partial discharge UHF signals of the transformer are collected by the ultra-high frequency intelligent sensing sensors.
As a possible implementation manner of this embodiment, the noise reduction processing for the transformer partial discharge UHF signal includes:
denoising the partial discharge UHF signal by using a wavelet threshold denoising method;
Carrying out normalization processing on the denoised partial discharge UHF signal:
wherein ,for sample data, ++>The normalized result is obtained.
As a possible implementation manner of this embodiment, the denoising processing for the partial discharge UHF signal by using the wavelet threshold denoising method includes:
selecting a wavelet to carry out n layers of wavelet decomposition on the signal;
thresholding the decomposed coefficient of each layer using:
wherein ,wavelet coefficient after thresholding, +.>For the original wavelet coefficients, +.>For unifying threshold value->Is the noise standard deviation, N is the number of discrete signals, ">For adjusting parameters, when->As a soft threshold function, when->Time is a hard threshold function;
and carrying out wavelet reconstruction on the wavelet coefficient subjected to the threshold processing to obtain a denoised UHF signal.
As a possible implementation manner of this embodiment, the cross-correlation calculation is performed on the transformer partial discharge UHF signal after the noise reduction processing, and determining the UHF signal time difference includes:
approximate determination of signal start time and time difference t by threshold method a12 Extracting signals of 3ns before and after the starting time of each signal, and taking the signals as part of signals which are propagated linearly;
calculating a cross-correlation function for the straight-line propagated partial signal:
wherein , and />Respectively extracted partial signals,/->Sampling points for extracting signals;
calculating the time when the cross-correlation function takes the maximum value as the time difference of the extracted signal；
And determining a final time difference by a comprehensive threshold method and a cross-correlation function method:
wherein ,UHF signal time differences are locally discharged for the final transformer.
As a possible implementation manner of this embodiment, the establishing a partial discharge source objective function constraint equation includes:
a space rectangular coordinate system is established by taking the left end point of the bottom of the transformer as an origin (0, 0, 0), a partial discharge source is represented by P (x, y, z), and the positions for collecting partial discharge UHF signals of the transformer are respectively represented by S 1 (x 1 , y 1 , z 1 )、S 2 (x 2 , y 2 , z 2 )、S 3 (x 3 , y 3 , z 3) and S4 (x 4 , y 4 , z 4 ) Representing, establishing a partial discharge source objective function constraint equation:
wherein ,for the purpose of +.>、/>、/>Equivalent transformer length, width, height, i=2,..q, q is the number of positions where UHF signals are acquired, +.>The unit is m/s.
As a possible implementation manner of this embodiment, the performing cluster analysis on the obtained positioning solution by using the K-means algorithm to obtain a cluster center includes:
randomly selecting k initial clustering centers C i， i is less than or equal to 1 and less than or equal to k, k is a positive integer greater than or equal to 3, and the data object and the clustering center C are calculated i Finding the nearest cluster center C from the target data object i And assign data objects to cluster centers C i In the corresponding cluster;
the average value of the data objects in each cluster is calculated as a new cluster center, the error square sum SSE (Sum of Squared Error) of the class clusters is continuously reduced, and the next iteration is performed until the cluster center is not changed any more.
As a possible implementation manner of this embodiment, the calculation formula of the euclidean distance between the data object and the clustering center is:
wherein ,for data comparison, add>For the ith cluster center, m is the dimension of the data object, +.>Is-> and />Is the j-th attribute value of (c).
As a possible implementation manner of this embodiment, the calculation formula of the error square sum SSE is:
wherein, the size of SSE represents the quality of the clustering result, and k is the number of clusters.
As a possible implementation manner of this embodiment, the calculating, by using SSA algorithm, an optimal solution with the smallest sum of distances to the cluster center to obtain a positioning point of a partial discharge source of the transformer, includes:
dividing the multiple positioning results by adopting a K-means clustering algorithm into K classes, and setting the coordinates of K clustering centers asWherein i=1, 2,..k, K is a positive integer greater than or equal to 3;
the problem of finding the point where the sum of the distances of the K cluster centers is minimum is converted into the following constraint optimization problem:
And solving the constraint optimization problem by using an SSA algorithm to obtain a final transformer ultrahigh frequency partial discharge source positioning point.
In a second aspect, an embodiment of the present invention provides a transformer ultrahigh frequency partial discharge positioning system based on SSA optimization, including:
the data acquisition module is used for acquiring the transformer partial discharge UHF signals and carrying out noise reduction treatment on the transformer partial discharge UHF signals;
the cross-correlation calculation module is used for carrying out cross-correlation calculation on the transformer partial discharge UHF signals after the noise reduction treatment and determining the UHF signal time difference;
the positioning solution solving module is used for establishing a partial discharge source objective function constraint equation, substituting the UHF signal time difference and solving the UHF signal time difference by using an SSA algorithm to obtain a plurality of positioning solutions;
the cluster analysis module is used for carrying out cluster analysis on the obtained positioning solution by utilizing a K-means algorithm to obtain a cluster center;
and the partial discharge source positioning module is used for calculating an optimal solution with the minimum sum of the distances from the clustering center by using an SSA algorithm to obtain a transformer ultrahigh frequency partial discharge source positioning point.
As a possible implementation manner of this embodiment, the data acquisition module includes:
the sensor module is used for arranging 4 ultrahigh frequency intelligent sensing sensors in a diamond direction and collecting partial discharge UHF signals of the transformer by using the ultrahigh frequency intelligent sensing sensors;
The noise reduction module is used for carrying out noise reduction processing on the partial discharge UHF signal by adopting a wavelet threshold noise reduction method;
the normalization module is used for carrying out normalization processing on the denoised partial discharge UHF signals.
As a possible implementation manner of this embodiment, the noise reduction module is specifically configured to:
selecting a wavelet to carry out n layers of wavelet decomposition on the signal;
thresholding the decomposed coefficient of each layer using:
wherein ,wavelet coefficient after thresholding, +.>For the original wavelet coefficients, +.>For unifying threshold value->Is the noise standard deviation, N is the number of discrete signals, ">For adjusting parameters, when->As a soft threshold function, when->Time is a hard threshold function;
and carrying out wavelet reconstruction on the wavelet coefficient subjected to the threshold processing to obtain a denoised UHF signal.
As a possible implementation manner of this embodiment, the cross-correlation calculation module includes:
the signal extraction module is used for approximately determining the signal starting time and the time difference t by adopting a threshold method a12 Extracting signals of 3ns before and after the starting time of each signal, and taking the signals as part of signals which are propagated linearly;
a cross-correlation calculation module for calculating a cross-correlation function for the part of the signal propagating in a straight line:
wherein , and />Respectively extracted partial signals,/->Sampling points for extracting signals;
an extracted signal time difference acquisition module for calculating the time when the cross-correlation function takes the maximum value as the time difference of the extracted signal；
The final time difference determining module is used for determining the final time difference by combining a threshold value method and a cross-correlation function method:
wherein ,UHF signal time differences are locally discharged for the final transformer.
As a possible implementation manner of this embodiment, the process of establishing the partial discharge source objective function constraint equation by the positioning solution module is:
a space rectangular coordinate system is established by taking the left end point of the bottom of the transformer as an origin (0, 0, 0), a partial discharge source is represented by P (x, y, z), and the positions for collecting partial discharge UHF signals of the transformer are respectively represented by S 1 (x 1 , y 1 , z 1 )、S 2 (x 2 , y 2 , z 2 )、S 3 (x 3 , y 3 , z 3) and S4 (x 4 , y 4 , z 4 ) Representing, establishing a partial discharge source objective function constraint equation:
wherein ,for the purpose of +.>、/>、/>Equivalent transformer length, width, height, i=2,..q, q is the number of positions where UHF signals are acquired, +.>The unit is m/s.
As a possible implementation manner of this embodiment, the cluster analysis module includes:
the Euclidean distance calculation module is used for randomly selecting k initial clustering centers C i， i is less than or equal to 1 and less than or equal to k, k is a positive integer greater than or equal to 3, and the data object and the clustering center C are calculated i Finding the nearest cluster center C from the target data object i And assign data objects to cluster centers C i In the corresponding cluster;
and the iteration calculation module is used for calculating the average value of the data objects in each cluster as a new cluster center, continuously reducing the error square sum SSE (Sum of Squared Error) of the class clusters, and carrying out the next iteration until the cluster center is not changed.
As a possible implementation manner of this embodiment, the partial discharge source positioning module includes:
the result classification module is used for clustering by adopting K-meansThe multi-time positioning result of the algorithm is divided into K classes, and K clustering center coordinates are set asWherein i=1, 2,..k, K is a positive integer greater than or equal to 3;
the problem conversion module is used for converting the problem of finding the point with the minimum sum of the K cluster center distances into the following constraint optimization problem:
and the problem solving module is used for solving the constraint optimization problem by using an SSA algorithm to obtain a final transformer ultrahigh frequency partial discharge source positioning point.
In a third aspect, an embodiment of the present invention provides a computer device, including a processor, a memory, and a bus, where the memory stores machine-readable instructions executable by the processor, and when the computer device is running, the processor communicates with the memory through the bus, and the processor executes the machine-readable instructions to perform steps of a method for positioning a very high frequency partial discharge of a transformer based on SSA optimization as described above.
In a fourth aspect, an embodiment of the present invention provides a storage medium, where a computer program is stored, where the computer program when executed by a processor performs the steps of any of the above-mentioned SSA-based optimized method for positioning ultrahigh frequency partial discharge of a transformer.
The technical scheme of the embodiment of the invention has the following beneficial effects:
aiming at the fault of partial discharge of the transformer, the ultra-high frequency intelligent sensing sensor is arranged in a proper mode to collect the UHF signal of partial discharge of the transformer and perform noise reduction treatment, and then the interpolation cross-correlation algorithm is utilized to determine the time difference of the UHF signal; then substituting each group of time differences into the established partial discharge source objective function constraint equation in sequence, and solving by using an SSA algorithm to obtain a plurality of positioning solutions; then, carrying out cluster analysis on the obtained positioning solution by using a K-means algorithm; and finally, calculating an optimal solution with the minimum sum of the distances of the K clustering centers by using an SSA algorithm, and obtaining the final determined partial discharge source positioning point. The invention can accurately position the position coordinates of the partial discharge source of the transformer, provides accurate judgment basis for operators to check and timely process equipment faults, and ensures stable and safe operation of the transformer.
Compared with the traditional partial discharge positioning method, the method has the advantages that the K-means cluster analysis is carried out on the solving result of the partial discharge source objective function constraint equation, then the SSA algorithm is utilized for secondary optimization, a plurality of results obtained through the first solving can be corrected, so that the fault position can be timely and accurately found, the positioning precision of partial discharge is improved, the equipment fault position can be conveniently and quickly found by staff, and the safety of a power distribution network is guaranteed.
Drawings
FIG. 1 is a flow chart illustrating a method for locating partial discharge of a transformer at very high frequencies based on SSA optimization, in accordance with an exemplary embodiment;
FIG. 2 is a schematic diagram of a system for locating partial discharge of a transformer at very high frequencies based on SSA optimization, according to an exemplary embodiment;
fig. 3 is a specific flow chart illustrating a positioning of a partial discharge of a transformer with a positioning system according to the present invention, according to an exemplary embodiment.
Detailed Description
The invention is further illustrated by the following examples in conjunction with the accompanying drawings:
in order to clearly illustrate the technical features of the present solution, the present invention will be described in detail below with reference to the following detailed description and the accompanying drawings. The following disclosure provides many different embodiments, or examples, for implementing different structures of the invention. In order to simplify the present disclosure, components and arrangements of specific examples are described below. Furthermore, the present invention may repeat reference numerals and/or letters in the various examples. This repetition is for the purpose of simplicity and clarity and does not in itself dictate a relationship between the various embodiments and/or configurations discussed. It should be noted that the components illustrated in the figures are not necessarily drawn to scale. Descriptions of well-known components and processing techniques and processes are omitted so as to not unnecessarily obscure the present invention.
As shown in fig. 1, the method for positioning ultrahigh frequency partial discharge of a transformer based on SSA optimization provided by the embodiment of the invention comprises the following steps:
collecting transformer partial discharge UHF signals, and carrying out noise reduction treatment on the transformer partial discharge UHF signals;
performing cross-correlation calculation on the transformer partial discharge UHF signals after noise reduction treatment, and determining UHF signal time difference;
establishing a partial discharge source objective function constraint equation, substituting the partial discharge source objective function constraint equation into UHF signal time difference, and solving the UHF signal time difference by using an SSA algorithm to obtain a plurality of positioning solutions;
performing cluster analysis on the obtained positioning solution by using a K-means algorithm to obtain a cluster center;
and calculating an optimal solution with the minimum sum of the distances from the cluster centers by using an SSA algorithm to obtain a transformer ultrahigh frequency partial discharge source positioning point.
Because the object aimed at by the invention is a transformer, the field environment is mostly open-air, the environment is complex and changeable, various environmental noises are often enriched, and a small challenge is provided for the accuracy of positioning the partial discharge source, the invention firstly carries out noise reduction treatment on the collected UHF signals, then utilizes an interpolation cross-correlation algorithm to determine the time difference of the UHF signals, then substitutes each group of time difference into the established constraint optimization problem in sequence, and solves by using an SSA algorithm to obtain a plurality of positioning solutions; then, carrying out cluster analysis on the obtained positioning solution by using a K-means algorithm; and finally, calculating an optimal solution with the minimum sum of the distances from the K clustering centers by using an SSA algorithm to obtain a final partial discharge source positioning point result.
As a possible implementation manner of this embodiment, the collecting transformer partially discharges a UHF signal, including:
4 ultra-high frequency intelligent sensing sensors are arranged in a diamond direction, and partial discharge UHF signals of the transformer are collected by the ultra-high frequency intelligent sensing sensors.
As a possible implementation manner of this embodiment, the noise reduction processing for the transformer partial discharge UHF signal includes:
denoising the partial discharge UHF signal by using a wavelet threshold denoising method;
carrying out normalization processing on the denoised partial discharge UHF signal:
wherein ,for sample data, ++>The normalized result is obtained.
As a possible implementation manner of this embodiment, the denoising processing for the partial discharge UHF signal by using the wavelet threshold denoising method includes:
selecting a wavelet to carry out n layers of wavelet decomposition on the signal;
thresholding the decomposed coefficient of each layer using:
wherein ,wavelet coefficient after thresholding, +.>For the original wavelet coefficients, +.>For unifying threshold value->Is the noise standard deviation, N is the number of discrete signals, ">For adjusting parameters, when->As a soft threshold function, when->Time is a hard threshold function;
and carrying out wavelet reconstruction on the wavelet coefficient subjected to the threshold processing to obtain a denoised UHF signal.
As a possible implementation manner of this embodiment, the cross-correlation calculation is performed on the transformer partial discharge UHF signal after the noise reduction processing, and determining the UHF signal time difference includes:
approximate determination of signal start time and time difference t by threshold method a12 Extracting signals of 3ns before and after the starting time of each signal, and taking the signals as part of signals which are propagated linearly;
calculating a cross-correlation function for the straight-line propagated partial signal:
wherein , and />Respectively extracted partial signals,/->Sampling points for extracting signals;
calculating the time when the cross-correlation function takes the maximum value as the time difference of the extracted signal；
And determining a final time difference by a comprehensive threshold method and a cross-correlation function method:
wherein ,UHF signal time differences are locally discharged for the final transformer.
As a possible implementation manner of this embodiment, the establishing a partial discharge source objective function constraint equation includes:
a space rectangular coordinate system is established by taking the left end point of the bottom of the transformer as an origin (0, 0, 0), a partial discharge source is represented by P (x, y, z), and the positions for collecting partial discharge UHF signals of the transformer are respectively represented by S 1 (x 1 , y 1 , z 1 )、S 2 (x 2 , y 2 , z 2 )、S 3 (x 3 , y 3 , z 3) and S4 (x 4 , y 4 , z 4 ) Representing, establishing a partial discharge source objective function constraint equation:
wherein ,for the purpose of +.>、/>、/>Equivalent transformer length, width, height, i=2,..q, q is the number of positions where UHF signals are acquired, +. >The unit is m/s.
As a possible implementation manner of this embodiment, the performing cluster analysis on the obtained positioning solution by using the K-means algorithm to obtain a cluster center includes:
randomly selecting k initial clustering centers C i， i is less than or equal to 1 and less than or equal to k, k is a positive integer greater than or equal to 3, and the data object and the clustering center C are calculated i Finding the nearest cluster center C from the target data object i And assign data objects to cluster centers C i In the corresponding cluster;
the average value of the data objects in each cluster is calculated as a new cluster center, the error square sum SSE (Sum of Squared Error) of the class clusters is continuously reduced, and the next iteration is performed until the cluster center is not changed any more.
As a possible implementation manner of this embodiment, the calculation formula of the euclidean distance between the data object and the clustering center is:
wherein ,for data comparison, add>For the ith cluster center, m is the dimension of the data object, +.>Is-> and />Is the j-th attribute value of (c).
As a possible implementation manner of this embodiment, the calculation formula of the error square sum SSE is:
wherein, the size of SSE represents the quality of the clustering result, and k is the number of clusters.
As a possible implementation manner of this embodiment, the calculating, by using SSA algorithm, an optimal solution with the smallest sum of distances to the cluster center to obtain a positioning point of a partial discharge source of the transformer, includes:
Dividing the multiple positioning results by adopting a K-means clustering algorithm into K classes, and setting the coordinates of K clustering centers asWherein i=1, 2,..k, K is a positive integer greater than or equal to 3;
the problem of finding the point where the sum of the distances of the K cluster centers is minimum is converted into the following constraint optimization problem:
and solving the constraint optimization problem by using an SSA algorithm to obtain a final transformer ultrahigh frequency partial discharge source positioning point.
As shown in fig. 2, the ultra-high frequency partial discharge positioning system for a transformer based on SSA optimization provided by the embodiment of the invention includes:
the data acquisition module is used for acquiring the transformer partial discharge UHF signals and carrying out noise reduction treatment on the transformer partial discharge UHF signals;
the cross-correlation calculation module is used for carrying out cross-correlation calculation on the transformer partial discharge UHF signals after the noise reduction treatment and determining the UHF signal time difference;
the positioning solution solving module is used for establishing a partial discharge source objective function constraint equation, substituting the UHF signal time difference and solving the UHF signal time difference by using an SSA algorithm to obtain a plurality of positioning solutions;
the cluster analysis module is used for carrying out cluster analysis on the obtained positioning solution by utilizing a K-means algorithm to obtain a cluster center;
and the partial discharge source positioning module is used for calculating an optimal solution with the minimum sum of the distances from the clustering center by using an SSA algorithm to obtain a transformer ultrahigh frequency partial discharge source positioning point.
As a possible implementation manner of this embodiment, the data acquisition module includes:
the sensor module is used for arranging 4 ultrahigh frequency intelligent sensing sensors in a diamond direction and collecting partial discharge UHF signals of the transformer by using the ultrahigh frequency intelligent sensing sensors;
the noise reduction module is used for carrying out noise reduction processing on the partial discharge UHF signal by adopting a wavelet threshold noise reduction method;
the normalization module is used for carrying out normalization processing on the denoised partial discharge UHF signals.
As a possible implementation manner of this embodiment, the noise reduction module is specifically configured to:
selecting a wavelet to carry out n layers of wavelet decomposition on the signal;
thresholding the decomposed coefficient of each layer using:
wherein ,wavelet coefficient after thresholding, +.>For the original wavelet coefficients, +.>For unifying threshold value->Is the noise standard deviation, N is the number of discrete signals, ">For adjusting parameters, when->As a soft threshold function, when->Time is a hard threshold function;
and carrying out wavelet reconstruction on the wavelet coefficient subjected to the threshold processing to obtain a denoised UHF signal.
As a possible implementation manner of this embodiment, the cross-correlation calculation module includes:
the signal extraction module is used for approximately determining the signal starting time and the time difference t by adopting a threshold method a12 Extracting signals of 3ns before and after the starting time of each signal, and taking the signals as part of signals which are propagated linearly;
a cross-correlation calculation module for calculating a cross-correlation function for the part of the signal propagating in a straight line:
wherein , and />Respectively extracted partial signals,/->Sampling points for extracting signals;
an extracted signal time difference acquisition module for calculating the time when the cross-correlation function takes the maximum value as the time difference of the extracted signal；
The final time difference determining module is used for determining the final time difference by combining a threshold value method and a cross-correlation function method:
/>
wherein ,UHF signal time differences are locally discharged for the final transformer.
As a possible implementation manner of this embodiment, the process of establishing the partial discharge source objective function constraint equation by the positioning solution module is:
a space rectangular coordinate system is established by taking the left end point of the bottom of the transformer as an origin (0, 0, 0), a partial discharge source is represented by P (x, y, z), and the positions for collecting partial discharge UHF signals of the transformer are respectively represented by S 1 (x 1 , y 1 , z 1 )、S 2 (x 2 , y 2 , z 2 )、S 3 (x 3 , y 3 , z 3) and S4 (x 4 , y 4 , z 4 ) Representing, establishing a partial discharge source objective function constraint equation:
wherein ,for the purpose of +.>、/>、/>Equivalent transformer length, width, height, i=2,..q, q is the number of positions where UHF signals are acquired, +. >The unit is m/s.
As a possible implementation manner of this embodiment, the cluster analysis module includes:
the Euclidean distance calculation module is used for randomly selecting k initial valuesClustering center C i， i is less than or equal to 1 and less than or equal to k, k is a positive integer greater than or equal to 3, and the data object and the clustering center C are calculated i Finding the nearest cluster center C from the target data object i And assign data objects to cluster centers C i In the corresponding cluster;
and the iteration calculation module is used for calculating the average value of the data objects in each cluster as a new cluster center, continuously reducing the error square sum SSE (Sum of Squared Error) of the class clusters, and carrying out the next iteration until the cluster center is not changed.
As a possible implementation manner of this embodiment, the partial discharge source positioning module includes:
the result classification module is used for classifying the positioning results obtained by adopting the K-means clustering algorithm into K classes and setting K clustering center coordinates asWherein i=1, 2,..k, K is a positive integer greater than or equal to 3;
the problem conversion module is used for converting the problem of finding the point with the minimum sum of the K cluster center distances into the following constraint optimization problem:
and the problem solving module is used for solving the constraint optimization problem by using an SSA algorithm to obtain a final transformer ultrahigh frequency partial discharge source positioning point.
As shown in fig. 3, the specific process of carrying out the ultrahigh frequency partial discharge positioning of the transformer by adopting the positioning system disclosed by the invention is as follows.
Step 1: and (5) collecting and denoising the transformer partial discharge UHF signals.
4 ultra-high frequency intelligent sensing sensors are arranged in any diamond direction to collect the transformer partial discharge UHF signals. And then, noise reduction processing is carried out on the transformer partial discharge UHF signal by using a wavelet threshold denoising method. The wavelet threshold denoising is essentially a process of suppressing the unnecessary part and enhancing the useful part of the signal. The wavelet threshold denoising process is as follows: (1) Wavelet decomposition, namely selecting one wavelet to carry out n layers of wavelet decomposition on signals; (2) Threshold processing, namely, performing threshold processing on each decomposed coefficient of coefficient to obtain an estimated wavelet coefficient; (3) And carrying out wavelet reconstruction according to the denoised wavelet coefficient to obtain a denoised UHF signal. The threshold function formula in this process is as follows:
wherein ,wavelet coefficient after thresholding, +.>For the original wavelet coefficients, +.>For unifying threshold value->Is the noise standard deviation, N is the number of discrete signals, ">For adjusting parameters, when->As a soft threshold function, when->And is a hard threshold function.
And carrying out normalization processing on the denoised partial discharge UHF signal so as to speed up operation. The normalization formula is:
。
wherein For sample data, ++>The normalized result is obtained.
Step 2: the UHF signal time difference is determined using an interpolation cross-correlation algorithm.
Firstly, adopting a threshold method to approximately determine the signal starting moment and the time difference t a12 And extracts signals of 3ns before and after the starting time of each signal, and approximates the signals as part of straight line propagation. A cross-correlation function is then calculated for the partial signal:
wherein , and />Respectively extracted partial signals,/->To extract the number of samples of the signal. Calculating the time when the cross-correlation function takes the maximum as the time difference of the extracted signal +.>. The final time difference is determined by a comprehensive threshold method and a cross-correlation function method as follows:
。
step 3: and substituting each group of time difference into the established partial discharge source objective function constraint equation problem in sequence, and solving by using an SSA optimization algorithm to obtain a plurality of positioning solutions.
The process for establishing the partial discharge source objective function constraint equation is as follows: the left end point of the bottom of the transformer is taken as an origin (0,0, 0) establishing a space rectangular coordinate system, wherein a partial discharge source is represented by P (x, y, z), and each ultrahigh frequency intelligent perception sensor is respectively represented by S 1 (x 1 , y 1 , z 1 )、S 2 (x 2 , y 2 , z 2 )、S 3 (x 3 , y 3 , z 3) and S4 (x 4 , y 4 , z 4 ) Representation, thereby establishing a partial discharge source objective function constraint equation:
wherein ,for the purpose of +.>、/>、/>The equivalent transformer length, width and height are respectively, i=2, & gt, q, q are the number of positions for acquiring UHF signals, the number of ultrahigh frequency intelligent sensing sensors, and the number of the ultrahigh frequency intelligent sensing sensors is + & gt >The unit is m/s.
The SSA algorithm is an intelligent optimization algorithm and is mainly inspired by the foraging behavior and the anti-predation behavior of sparrows, and has the advantages of being novel, strong in optimizing capability and high in convergence speed. In the sparrow search algorithm, individuals are distinguished into discoverers, followers and vignettes, one solution for each individual location. According to the algorithm setting, the alerter accounts for 10% -20% of the population proportion, and the discoverer and the follower are dynamically changed, namely that one individual becomes the discoverer necessarily means that the other individual will become the follower. According to the divisionAnd dividing workers, wherein discoverers mainly provide foraging directions and areas for the whole population, and the discoverers are followed by the followers to forage, so that the alertors are responsible for monitoring the foraging areas. In the foraging process, the positions of the three parts are updated continuously, so that the resource acquisition is completed. If the population has n sparrows, the population consisting of all sparrows can be expressed asThe fitness function corresponding to each individual is +.>。
Discoverer location update mode:
where t represents the current number of iterations,indicating the position of the ith sparrow at the jth in the t th generation, +.>Represents the maximum number of iterations, +. >Is an alarm value->Is a safety threshold value->Representing random numbers subject to normal distribution, L is an all 1 matrix of 1xdim, dim representing the dimension. When->When the food searching device means that predators do not exist around the foraging area, the discoverers can search for food widely; when->When this means that predators are present, all discoverers need to fly to a safe area.
Follower location update mode:
wherein ,indicating the individual position of the t-th generation with worst fitness,/->Indicating the optimal individual position for fitness in the t+1st generation,/->Representing a matrix of 1xdim and each element in the matrix being randomly preset to 1 or-1,/for each element in the matrix>. When (when)The i-th joiner has low adaptability, does not compete with the discoverer for food, and needs to fly to other areas to find food; when->The enrollee will be in the optimal individual +>Nearby forages.
The method for updating the position of the alerter comprises the following steps:
wherein ,represents the global optimum position in the t th generation,/>For controlling step size, obey normal distribution with mean 0 and variance 1, ++>Is constant for avoiding denominator of 0, < ->Indicating the fitness of the current individual->、/>Indicating the fitness of the current globally optimal and worst individuals. When->Meaning that the individual is at the periphery of the population, and needs to adopt anti-predation behavior, and the position is continuously changed to obtain higher fitness; when- >Meaning that the individual is in the center of the population, it will be continually approaching nearby peers, and thus away from the dangerous area.
Step 4: and performing cluster analysis on the obtained positioning solution by using a K-means algorithm.
Firstly, randomly selecting k initial clustering centers C from a data set i (i is less than or equal to 1 is less than or equal to k), and calculating the rest data objects and the clustering center C i Finding the nearest cluster center C from the target data object i And assign data objects to cluster centers C i In the corresponding cluster. The average value of the data objects in each cluster is then calculated as the new cluster center, the sum of squares error (Sum of Squared Error, SSE) of the class clusters is continuously reduced, and the next iteration is performed until the cluster center is no longer changed. The Euclidean distance between the data object and the clustering center in the space is calculated as follows:
wherein ,for data comparison, add>For the ith cluster center, m is the dimension of the data object, +.>Is-> and />Is the j-th attribute value of (c).
The square error sum SSE calculation formula for the whole dataset is:
the size of SSD indicates the quality of the clustering result, k is the number of clusters, and in this embodiment, k is set to 3.
Step 5: and finally, calculating an optimal solution with the minimum sum of the distances between the K clustering centers by using an SSA algorithm, and obtaining the positioning point of the partial discharge source.
The K-means clustering algorithm is divided into K class clusters according to the Euclidean distance between objects, so that the object similarity in the class clusters is ensured to be as high as possible, namely the Euclidean distance is small; and the similarity between the clusters is as low as possible. And (3) dividing the multiple positioning results into K classes by adopting a K-means clustering algorithm, and then finding out the point with the minimum sum of the K clustering center distances, wherein the point is a more accurate positioning point. Let K cluster center coordinates beWhere i=1, 2,..k. This problem is translated into the following constrained optimization problem:
/>
the optimization problem is solved again by using an SSA algorithm, and a more accurate partial discharge source positioning point can be obtained.
According to the scheme, the SSA optimization algorithm is utilized twice to perform optimizing calculation on the partial discharge source positioning points, meanwhile, K=3 is set in the K-means clustering algorithm, and the accuracy of the partial discharge source positioning can be obviously improved by the method in the scheme through verification.
Aiming at the partial discharge fault location identification of a transformer, the invention collects the partial discharge UHF signals of the transformer by arranging an ultrahigh frequency intelligent sensing sensor in a proper mode, carries out noise reduction treatment, then utilizes an interpolation cross-correlation algorithm to determine UHF signal time differences on the UHF signals, then sequentially substitutes each group of time differences into an established constraint optimization problem, uses an SSA optimization algorithm to solve a plurality of positioning solutions, utilizes a K-means algorithm to carry out cluster analysis on the obtained positioning solutions, and finally utilizes the SSA algorithm to calculate the optimal solution with the minimum sum of K cluster center distances, namely the partial discharge source positioning point. Compared with the traditional partial discharge positioning method, the method has the advantages that K-means cluster analysis is carried out on the solving result, then secondary optimization is carried out by using the SSA algorithm, and a plurality of results obtained by the first solving can be corrected, so that more accurate positions are obtained, a worker can conveniently find the equipment fault positions quickly, the method can be applied to the field of large power grid field fault positioning identification, and the method has wide engineering application values.
The embodiment of the invention provides a computer device, which comprises a processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor, when the device runs, the processor and the memory are communicated through the bus, and the processor executes the machine-readable instructions to execute the steps of the transformer ultrahigh frequency partial discharge positioning method based on SSA optimization.
Specifically, the above memory and the processor can be general-purpose memories and processors, which are not limited herein, and when the processor runs the computer program stored in the memory, the above method for positioning ultrahigh frequency partial discharge of a transformer based on SSA optimization can be executed.
It will be appreciated by those skilled in the art that the structure of the computer device is not limiting of the computer device and may include more or fewer components than shown, or may be combined with or separated from certain components, or may be arranged in a different arrangement of components.
In some embodiments, the computer device may further include a touch screen operable to display a graphical user interface (e.g., a launch interface of an application) and to receive user operations with respect to the graphical user interface (e.g., launch operations with respect to the application). A particular touch screen may include a display panel and a touch panel. The display panel may be configured in the form of an LCD (Liquid Crystal Display), an OLED (Organic Light-Emitting Diode), or the like. The touch panel may collect touch or non-touch operations on or near the user and generate preset operation instructions, for example, operations of the user on or near the touch panel using any suitable object or accessory such as a finger, a stylus, or the like. In addition, the touch panel may include two parts, a touch detection device and a touch controller. The touch detection device detects the touch azimuth and the touch gesture of a user, detects signals brought by touch operation and transmits the signals to the touch controller; the touch controller receives touch information from the touch detection device, converts the touch information into information which can be processed by the processor, sends the information to the processor, and can receive and execute commands sent by the processor. In addition, the touch panel may be implemented by various types such as resistive, capacitive, infrared, and surface acoustic wave, or may be implemented by any technology developed in the future. Further, the touch panel may overlay the display panel, and a user may operate on or near the touch panel overlaid on the display panel according to a graphical user interface displayed by the display panel, and upon detection of an operation thereon or thereabout, the touch panel is transferred to the processor to determine a user input, and the processor then provides a corresponding visual output on the display panel in response to the user input. In addition, the touch panel and the display panel may be implemented as two independent components or may be integrated.
Corresponding to the starting method of the application program, the embodiment of the application also provides a storage medium, and the storage medium is stored with a computer program which is executed by a processor to execute the steps of the transformer ultrahigh frequency partial discharge positioning method based on SSA optimization.
The starting device of the application program provided by the embodiment of the application can be specific hardware on the equipment or software or firmware installed on the equipment. The device provided by the embodiment of the present application has the same implementation principle and technical effects as those of the foregoing method embodiment, and for the sake of brevity, reference may be made to the corresponding content in the foregoing method embodiment where the device embodiment is not mentioned. It will be clear to those skilled in the art that, for convenience and brevity, the specific operation of the system, apparatus and unit described above may refer to the corresponding process in the above method embodiment, which is not described in detail herein.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other manners. The above-described apparatus embodiments are merely illustrative, for example, the division of modules is merely a logical function division, and there may be additional divisions in actual implementation, and for example, multiple modules or components may be combined or integrated into another system, or some features may be omitted, or not performed. In addition, the coupling or direct coupling or communication connection shown or discussed with respect to each other may be through some communication interface, indirect coupling or communication connection of devices or modules, electrical, mechanical, or other form.
The modules illustrated as separate components may or may not be physically separate, and components shown as modules may or may not be physical modules, i.e., may be located in one place, or may be distributed over a plurality of network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional module in the embodiment provided by the application may be integrated in one processing module, or each module may exist alone physically, or two or more modules may be integrated in one module.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical aspects of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the above embodiments, it should be understood by those of ordinary skill in the art that: modifications and equivalents may be made to the specific embodiments of the invention without departing from the spirit and scope of the invention, which is intended to be covered by the claims.
Claims (16)
1. The ultra-high frequency partial discharge positioning method for the transformer based on SSA optimization is characterized by comprising the following steps of:
collecting transformer partial discharge UHF signals, and carrying out noise reduction treatment on the transformer partial discharge UHF signals;
performing cross-correlation calculation on the transformer partial discharge UHF signals after noise reduction treatment, and determining UHF signal time difference;
Establishing a partial discharge source objective function constraint equation, substituting the partial discharge source objective function constraint equation into UHF signal time difference, and solving the UHF signal time difference by using an SSA algorithm to obtain a plurality of positioning solutions;
performing cluster analysis on the obtained positioning solution by using a K-means algorithm to obtain a cluster center;
and calculating an optimal solution with the minimum sum of the distances from the cluster centers by using an SSA algorithm to obtain a transformer ultrahigh frequency partial discharge source positioning point.
2. The SSA optimization-based transformer ultrahigh frequency partial discharge positioning method according to claim 1, wherein the collecting the transformer partial discharge UHF signals comprises:
4 ultra-high frequency intelligent sensing sensors are arranged in a diamond direction, and partial discharge UHF signals of the transformer are collected by the ultra-high frequency intelligent sensing sensors.
3. The SSA optimization-based ultrahigh frequency partial discharge positioning method for a transformer according to claim 1, wherein the performing noise reduction processing on the transformer partial discharge UHF signal comprises:
denoising the partial discharge UHF signal by using a wavelet threshold denoising method;
carrying out normalization processing on the denoised partial discharge UHF signal:
wherein ,for sample data, ++>The normalized result is obtained.
4. The method for locating ultrahigh frequency partial discharge of a transformer based on SSA optimization according to claim 3, wherein said denoising the partial discharge UHF signal by using a wavelet threshold denoising method comprises:
Selecting a wavelet to carry out n layers of wavelet decomposition on the signal;
thresholding the decomposed coefficient of each layer using:
wherein ,wavelet coefficient after thresholding, +.>For the original wavelet coefficients, +.>In order to unify the threshold values,is the noise standard deviation, N is the number of discrete signals, ">To adjust parameters;
and carrying out wavelet reconstruction on the wavelet coefficient subjected to the threshold processing to obtain a denoised UHF signal.
5. The SSA optimization-based transformer ultrahigh frequency partial discharge positioning method according to claim 1, wherein the performing cross-correlation calculation on the noise-reduced transformer partial discharge UHF signal to determine a UHF signal time difference comprises:
approximate determination of signal start time and time difference t by threshold method a12 Extracting signals of 3ns before and after the starting time of each signal, and taking the signals as part of signals which are propagated linearly;
calculating a cross-correlation function for the straight-line propagated partial signal:
wherein , and />Respectively extracted partial signals,/->Sampling points for extracting signals;
calculating the time when the cross-correlation function takes the maximum value as the time difference of the extracted signal；
And determining a final time difference by a comprehensive threshold method and a cross-correlation function method:
wherein ,UHF signal time differences are locally discharged for the final transformer.
6. The SSA optimization-based ultrahigh frequency partial discharge positioning method for a transformer according to any one of claims 1 to 5, wherein said establishing a partial discharge source objective function constraint equation comprises:
a space rectangular coordinate system is established by taking the left end point of the bottom of the transformer as an origin (0, 0, 0), a partial discharge source is represented by P (x, y, z), and the positions for collecting partial discharge UHF signals of the transformer are respectively represented by S 1 (x 1 , y 1 , z 1 )、S 2 (x 2 , y 2 , z 2 )、S 3 (x 3 , y 3 , z 3) and S4 (x 4 , y 4 , z 4 ) Representing, establishing a partial discharge source objective function constraint equation:
wherein ,for the purpose of +.>、/>、/>Equivalent transformer length, width, height, i=2,..q, q is the number of positions where UHF signals are acquired, respectively.
7. The SSA optimization-based ultrahigh frequency partial discharge positioning method for a transformer according to any one of claims 1 to 5, wherein the performing cluster analysis on the obtained positioning solution by using a K-means algorithm to obtain a cluster center comprises:
randomly selecting k initial clustering centers C i， i is less than or equal to 1 and less than or equal to k, k is a positive integer greater than or equal to 3, and the data object and the clustering center C are calculated i Finding the nearest cluster center C from the target data object i And assign data objects to cluster centers C i In the corresponding cluster;
and calculating the average value of the data objects in each cluster as a new cluster center, continuously reducing the error square sum SSE of the class clusters, and carrying out the next iteration until the cluster center is not changed.
8. The method for locating the ultrahigh frequency partial discharge of the transformer based on the SSA optimization of claim 7, wherein the calculating the optimal solution with the minimum sum of the distances to the cluster centers by using the SSA algorithm to obtain the locating point of the ultrahigh frequency partial discharge source of the transformer comprises the following steps:
dividing the multiple positioning results by adopting a K-means clustering algorithm into K classes, and setting the coordinates of K clustering centers asWherein i=1, 2,..k, K is a positive integer greater than or equal to 3;
the problem of finding the point where the sum of the distances of the K cluster centers is minimum is converted into the following constraint optimization problem:
and solving the constraint optimization problem by using an SSA algorithm to obtain a final transformer ultrahigh frequency partial discharge source positioning point.
9. The utility model provides a positioning system is put to transformer superfrequency office based on SSA optimizing which characterized in that includes:
the data acquisition module is used for acquiring the transformer partial discharge UHF signals and carrying out noise reduction treatment on the transformer partial discharge UHF signals;
the cross-correlation calculation module is used for carrying out cross-correlation calculation on the transformer partial discharge UHF signals after the noise reduction treatment and determining the UHF signal time difference;
the positioning solution solving module is used for establishing a partial discharge source objective function constraint equation, substituting the UHF signal time difference and solving the UHF signal time difference by using an SSA algorithm to obtain a plurality of positioning solutions;
The cluster analysis module is used for carrying out cluster analysis on the obtained positioning solution by utilizing a K-means algorithm to obtain a cluster center;
and the partial discharge source positioning module is used for calculating an optimal solution with the minimum sum of the distances from the clustering center by using an SSA algorithm to obtain a transformer ultrahigh frequency partial discharge source positioning point.
10. The SSA optimization-based ultrahigh frequency partial discharge positioning system of a transformer of claim 9, wherein said data acquisition module comprises:
the sensor module is used for arranging 4 ultrahigh frequency intelligent sensing sensors in a diamond direction and collecting partial discharge UHF signals of the transformer by using the ultrahigh frequency intelligent sensing sensors;
the noise reduction module is used for carrying out noise reduction processing on the partial discharge UHF signal by adopting a wavelet threshold noise reduction method;
the normalization module is used for carrying out normalization processing on the denoised partial discharge UHF signals.
11. The SSA optimization-based ultrahigh frequency partial discharge positioning system of a transformer according to claim 9, wherein said cross-correlation calculation module comprises:
the signal extraction module is used for approximately determining the signal starting time and the time difference t by adopting a threshold method a12 Extracting signals of 3ns before and after the starting time of each signal, and taking the signals as part of signals which are propagated linearly;
A cross-correlation calculation module for calculating a cross-correlation function for the part of the signal propagating in a straight line:
wherein , and />Respectively extracted partial signals,/->Sampling points for extracting signals;
an extracted signal time difference acquisition module for calculating the time when the cross-correlation function takes the maximum value as the time difference of the extracted signal；
The final time difference determining module is used for determining the final time difference by combining a threshold value method and a cross-correlation function method:
wherein ,UHF signal time differences are locally discharged for the final transformer.
12. The SSA optimization-based ultrahigh frequency partial discharge positioning system for a transformer of claim 9, wherein the positioning solution module establishes a partial discharge source objective function constraint equation by:
a space rectangular coordinate system is established by taking the left end point of the bottom of the transformer as an origin (0, 0, 0), a partial discharge source is represented by P (x, y, z), and the positions for collecting partial discharge UHF signals of the transformer are respectively represented by S 1 (x 1 , y 1 , z 1 )、S 2 (x 2 , y 2 , z 2 )、S 3 (x 3 , y 3 , z 3) and S4 (x 4 , y 4 , z 4 ) Representing, establishing a partial discharge source objective function constraint equation:
wherein ,for the purpose of +.>、/>、/>Equivalent transformer length, width, height, i=2,..q, q is the number of positions where UHF signals are acquired, respectively.
13. The SSA optimization-based transformer ultrahigh frequency partial discharge positioning system according to claim 9, wherein said cluster analysis module comprises:
The Euclidean distance calculation module is used for randomly selecting k initial clustering centers C i， i is less than or equal to 1 and less than or equal to k, k is a positive integer greater than or equal to 3, and the data object and the clustering center C are calculated i Finding the nearest cluster center C from the target data object i And assign data objects to cluster centers C i In the corresponding cluster;
and the iteration calculation module is used for calculating the average value of the data objects in each cluster as a new cluster center, continuously reducing the error square sum SSE of the class clusters, and carrying out the next iteration until the cluster center is not changed.
14. The SSA optimization-based ultrahigh frequency partial discharge positioning system of a transformer according to claim 9, wherein said partial discharge source positioning module comprises:
the result classification module is used for classifying the positioning results obtained by adopting the K-means clustering algorithm into K classes and setting K clustering center coordinates asWherein i=1, 2,..k, K is a positive integer greater than or equal to 3;
the problem conversion module is used for converting the problem of finding the point with the minimum sum of the K cluster center distances into the following constraint optimization problem:
and the problem solving module is used for solving the constraint optimization problem by using an SSA algorithm to obtain a final transformer ultrahigh frequency partial discharge source positioning point.
15. A computer device comprising a processor, a memory and a bus, the memory storing machine-readable instructions executable by the processor, the processor and the memory in communication via the bus when the computer device is in operation, the processor executing the machine-readable instructions to perform the steps of the SSA-based optimized transformer uhf partial discharge positioning method of any one of claims 1-8.
16. A storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the SSA-based optimized transformer very high frequency partial discharge positioning method according to any of claims 1-8.
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