CN115688017A - FRCMDE-based transformer core fault voiceprint diagnosis method and device - Google Patents
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Abstract
The invention belongs to the technical field of power equipment fault diagnosis, and discloses a transformer core fault voiceprint diagnosis method and device based on FRCMDE (frequency-dependent modulation and noise suppression), wherein the method comprises the steps of carrying out successive variation modal decomposition on transformer voiceprint data to obtain a plurality of intrinsic modal components, calculating the kurtosis and energy ratio of each intrinsic modal component, selecting the intrinsic modal components containing useful information, and reconstructing the intrinsic modal components; solving the FRCMDE value of the reconstructed signal in an analysis scale, and removing redundant and invalid parts in the FRCMDE value under different scales by using a Fisher ratio to construct an optimal feature subset; and constructing an improved PODSDOA-LSSVM fault diagnosis model to identify FRCMDE characteristics and output a diagnosis result. The invention can help electric power workers to master the running state of the transformer in time, know latent faults in advance and avoid loss caused by equipment faults.
Description
Technical Field
The invention relates to the technical field of power equipment faults, in particular to a transformer core fault voiceprint diagnosis method and device based on FRCMDE.
Background
The transformer is used as core equipment in a power system, the problems of state perception, fault early warning and the like of the transformer under the condition of complex change are increasingly revealed, and once a fault occurs, serious power supply and distribution problems and great economic loss can be caused. Therefore, an effective fault state diagnosis method is urgently needed to find and diagnose the operation state of the transformer in time and ensure safe and reliable operation of the transformer in a complex environment of a novel power system.
The common trouble of transformer mainly includes mechanical fault, insulation fault and overheated trouble etc. because the electrical fault that mechanical fault leads to takes place the frequency highest among this, and wherein the shared proportion of iron core trouble is great, shows according to statistical studies, and the iron core trouble is mostly aroused because the mechanical structure problem in the initial stage, and can threaten and destroy to iron core clamp force, insulating the fact along with the depth of fault degree, can lead to serious electrical fault appearance such as iron core multiple spot ground even. The sound signal of the transformer during operation contains a large amount of operation state information, and the power transformer fault diagnosis method based on voiceprint analysis obtains enough attention. The method has the characteristics of comprehensive state sensing, efficient information processing, convenient and flexible application and the like, can powerfully improve the fault identification level of the power transformer, reduce the fault probability of the power transformer, and effectively prevent and reduce major accidents caused by transformer faults.
Disclosure of Invention
In order to solve the technical problems mentioned in the background art, the invention provides a transformer core fault voiceprint diagnosis method based on FRCMDE, which comprises the steps of carrying out Successive Variation Modal Decomposition (SVMD) on voiceprint data of a transformer to obtain a plurality of intrinsic modal components (IMF), calculating the kurtosis and the energy ratio of each intrinsic modal component, selecting the intrinsic modal component (IMF) containing useful information, and carrying out reconstruction; solving fractional order fine composite multi-scale spreading entropy (FRCMDE) of the reconstructed signal in an analysis scale, and removing redundant and invalid parts in the fractional order fine composite multi-scale spreading entropy under different scales by using a Fisher ratio to construct an optimal feature subset; and optimizing the parameters of the fractional order fine composite multi-scale dispersion entropy and the super-parameters of the LSSVM by using PODSOA, constructing an improved PODSOA-LSSVM fault diagnosis model, identifying the optimal feature subset, and outputting a diagnosis result. The method can realize non-contact measurement, is simple in equipment installation, fast in measurement speed, easy in signal measurement and free from interference on normal operation of equipment.
In order to achieve the above purpose, the invention adopts a technical scheme that: a transformer core fault voiceprint diagnosis method based on FRCMDE comprises the following steps:
s1, carrying out successive variational modal decomposition on transformer voiceprint data to obtain a plurality of intrinsic modal components, calculating the kurtosis and the energy ratio of each intrinsic modal component, selecting the intrinsic modal components containing useful information, and reconstructing the intrinsic modal components;
s2, solving a fractional order fine composite multi-scale dispersion entropy of the reconstructed signal in an analysis scale;
s3, removing redundant and invalid parts in fractional order fine composite multi-scale scattered entropies under different scales by using a Fisher ratio, and constructing a feature subset of an optimal scale;
and S4, optimizing the super-parameters of the LSSVM by using a PODSDOA algorithm, constructing a PODSDOA-LSSVM fault diagnosis model on the basis of the obtained optimal feature subset, diagnosing unknown transformer voiceprint data and outputting a diagnosis result.
Further preferably, the specific process of step S2 is as follows:
s201: setting successive variation modal decomposition reconstructed signal sequenceX=[x 1 ,x 2 ,⋯x n ]N is the length X of the signal sequence X n For the nth data, in the composite multi-scale entropy-spread algorithm, the initial points are set to be the values in [1,τ]continuously dividing the signal into lengths of
And averaging each region to obtain a coarse grained sequence;
s202: calculating the average of the corresponding scatter pattern probabilities for the coarse-grained sequences:
mapping the coarse grained sequence to [0,1 ] using a standard normal distribution function]Mapping sequences within a rangeY＝[y 1 , y 2 ,⋯,y n ]，y n Mapping the signal for the nth; namely, it is
In the formula (I), the compound is shown in the specification,μ，σfor the expected and standard deviation of the signal sequence X,tas a matter of time, the time is,x i the reconstructed ith signal is decomposed for SVMD,y i mapping the signal for the ith;
mapping the sequence by linear transformation algorithmYThe mapping is to the value of 1,c]among integers within the range, a linear transformation signal is obtained, i.e.
In the formula (I), the compound is shown in the specification,roundis a rounding function;cthe number of the categories;
is as followsiA linear transform signal;
to pair
And performing phase space reconstruction, wherein the embedded vector is as follows:
in the formula (I), the compound is shown in the specification,min order to embed the dimension number, the number of the embedded dimension,dfor time delay, each
Mapping to a scatter patternr v0 , v vm1⋯-1 Wherein, in the step (A),
due to the scattering patternr v0 , v vm1⋯-1 Contains thereinmAn element, each element being taken as [1, c ]]Of any integer of all possible scattering patterns isc m ；
Computingc m Each scatter patternr v0 , v vm1⋯-1 Probability of (2)PNamely:
in the formula (I), the compound is shown in the specification,Num(r v0 , v vm1⋯-1 ) Is composed of
The number of corresponding scatter patterns;
calculating the average value of the probability of the corresponding scattering mode of the coarse graining sequences with different scales, namely:
in the formula (I), the compound is shown in the specification,
is a scale ofτFirst ofkThe probability of the corresponding scatter pattern of the coarse grained sequence,
(r v v vm01⋯-1 ) The average probability of the corresponding walking mode of the coarse graining sequence under different scales;
s203: calculating fractional order fine composite multi-scale dispersion entropy under different scales, namely:
in the formula (I), the compound is shown in the specification,
namely, it is
(r v v vm01⋯-1 )，
Is composed ofgammaThe function of the function(s) is,Ψis composed ofdigammaThe function of the function(s) is,αis a fractional order factor.
Further preferably, the specific process of step S3 is as follows:
s301: calculating a Fisher ratio for a feature vector set formed by fractional order fine composite multi-scale dispersion entropy of transformer voiceprint data, namely:
in the formula (I), the compound is shown in the specification,F k() is shown askThe Fisher ratio of the feature vector of the dimension,
denotes the firstkThe inter-class dispersion of the dimensional feature vectors,
denotes the firstkIntra-class dispersion of dimensional feature vectors;
s302: and sorting the Fisher ratios, and selecting the feature subset with the optimal scale.
Further preferably, the specific process of step S4 is as follows:
s401: dividing the feature subset with the optimal scale into a training set and a test set;
s402: improved butterfly algorithm utilizes punishment factors of training set pairs and least square support vector machinecAnd radial basis inner product function parametersgOptimizing to obtain optimal parameters;
s403: training a least square support vector machine with optimal parameters, and testing by using a test set;
s404: and constructing a PODSDOA-LSSVM fault diagnosis model according to the training test result, diagnosing unknown transformer voiceprint data by using the PODSDOA-LSSVM fault diagnosis model, and outputting a diagnosis result.
Further preferably, the steps of the modified butterfly algorithm are as follows:
step A1: initializing butterfly algorithm search parameters: set the butterfly population quantity asNSetting the maximum iteration number of the algorithm asN 1 Population boundary condition [ 2 ]L b ,U b ]Optimization problem dimensiondim；
Step A2: generating an initial butterfly population according to boundary conditions: generating N by random number in boundary range*dimAn initial butterfly population of a size that is scaled up to 2 by spatially symmetric amplification of the initial populationN*dim；
Step A3: and (3) fitness calculation: calculating the individual fitness of the butterfly of the amplified population according to a fitness criterion function;
step A4: and (3) population recovery: selection by Elite Retention strategyNRecording the individuals with the best fitness as a recovery population, and finding and recording the best individuals of the current recovery population;
step A5: updating the inferior population: selecting two butterfly individuals with the worst fitness, and performing cross processing and mutation operation on the two butterfly individuals;
step A6: and (3) dynamically updating algorithm parameters: updating the sensory modality according to the following formula based on the current number of iterationsβPower index ofaDynamic search switching probabilitypAnd location update operatorw 1 、w 2 ；
Step A7: iterative optimization: if dynamic search switching probabilityp>rand，randGlobally updating the position of the individual for a random number between 0 and 1; if dynamic search switching probabilityp<randLocally updating the position of the individual; updating the global optimum;
step A8: and (3) border crossing checking: checking whether the updated individual exceeds the boundary, and performing boundary correction on the position of a new individual exceeding the boundary;
step A9: judging whether the iteration end condition of the algorithm is met or not at present: if the end condition is not met, the algorithm is switched to the step A5 to be continuously executed; otherwise, outputting the current optimal result, and ending the algorithm.
The invention also provides a transformer core fault voiceprint diagnosis device based on FRCMDE, which comprises a nonvolatile computer storage medium, wherein the computer storage medium stores computer executable instructions, and the computer executable instructions can execute the transformer core fault voiceprint diagnosis method based on FRCMDE.
The invention provides an FRCMDE-based transformer core fault voiceprint diagnosis device which comprises electronic equipment, wherein the electronic equipment comprises: the system comprises one or more processors and a memory, and further comprises an input device and an output device, wherein the processors, the memory, the input device and the output device are connected through a bus or other means, the memory is a nonvolatile computer readable storage medium, the processors execute various functional applications and data processing of the server by running nonvolatile software programs, instructions and modules stored in the memory, namely, the FRCMDE-based transformer core fault voiceprint diagnosis method is realized, the input device receives input digital or character information, and key signal input related to user setting and function control of the FRCMDE-based transformer core fault voiceprint diagnosis device is generated.
The invention has the beneficial effects that: firstly, combining the superiority of SVMD in the aspect of signal processing with the effectiveness of FRCMDE in the aspect of fault feature extraction, effectively filtering noise interference components in signals, and introducing the advantages of fractional calculus into a fine composite multi-scale diffusion entropy so as to improve the sensitivity of entropy features to the sound fault features of a transformer; secondly, constructing an optimal feature subset by utilizing a Fisher ratio, and reducing feature dimensions; the PODSDOA algorithm is used for optimizing the super-parameters of the LSSVM, an improved PODSDOA-LSSVM diagnosis model is constructed, a transformer fault noise signal is diagnosed, and the accuracy of transformer fault diagnosis is effectively improved.
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FIG. 1 is a flow chart of the method of the present invention.
Fig. 2 is a schematic structural diagram of an electronic device.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
As shown in fig. 1, the FRCMDE-based transformer core fault voiceprint diagnosis method of the present embodiment includes the following steps:
s1, carrying out Successive Variational Modal Decomposition (SVMD) on transformer voiceprint data to obtain a plurality of intrinsic modal components (IMF), calculating the kurtosis and energy ratio of each intrinsic modal component, selecting the intrinsic modal components (IMF) containing useful information and carrying out reconstruction;
s2, solving fractional order fine composite multi-scale dispersion entropy (FRCMDE value) of the reconstructed signal in an analysis scale;
s3, removing redundant and invalid parts in fractional order fine composite multi-scale spreading entropy under different scales by using a Fisher ratio, and constructing a feature subset of an optimal scale;
and S4, optimizing the hyperparameters of the LSSVM by using a PODSOA algorithm, constructing a PODSOA-LSSVM fault diagnosis model on the basis of the obtained optimal feature subset, diagnosing the voiceprint data of the unknown transformer and outputting a diagnosis result.
In step S1 of this embodiment, a Successive Variational Modal Decomposition (SVMD) is performed on the transformer voiceprint data to obtain a plurality of intrinsic modal components (IMF), and the intrinsic modal components (IMF) containing useful information are selected and reconstructed by calculating the kurtosis and energy ratio of each intrinsic modal component. The specific process is as follows:
s101: carrying out Successive Variational Modal Decomposition (SVMD) on the voiceprint data of the transformer to obtain a plurality of intrinsic modal components (IMF);
s102: calculating the kurtosis of each intrinsic mode component (IMF), wherein the calculation formula is as follows:
in the formula:y b is a firstiOf the component of the eigenmodebA data value for each location;μ i is as followsiA mean value of the eigenmode components;σ i is a firstiStandard deviation of the eigenmode components;u i is a firstiThe data number of each eigenmode component;
s103: calculating the energy ratio of each intrinsic mode component (IMF), wherein the calculation formula is as follows:
in the formula:e i is a firstiThe energy of each eigenmode component (IMF), M is expressed as the number of the eigenmode components (IMF).
S104: and selecting the eigenmode component containing useful information for signal reconstruction through a kurtosis criterion and an energy ratio criterion.
The specific process of step S2 in this embodiment is as follows:
s201: decomposing the reconstructed signal sequence by SVMDX=[x 1 ,x 2 ,⋯x n ]N is the length X of the signal sequence X n For the nth data, in the RCMDE algorithm (composite multiscale dispersion entropy), the initial point is the difference between the original point and the original point as [1,τ]continuously dividing the signal into lengths of
And averaging each region to obtain a coarse grained sequence. Namely:
in the formula (I), the compound is shown in the specification,
is the sequence after the coarse granulation of the signal sequence X,kis a serial number at the same scale,jthe length of the signal sequence is equal to the number of divided sections, and is also the data length of the coarse grained sequence;
s202: calculating the average of the corresponding scatter pattern probabilities for the coarse grained sequence:
（1）coarse grained sequence using standard normal distribution functionxMapping to [0,1]Mapping sequences within a rangeY＝[y 1 , y 2 ,⋯,y n ]，y n Mapping the signal for the nth; namely that
In the formula (I), the compound is shown in the specification,μ，σfor the expected and standard deviation of the signal sequence X,tin the form of a time, the time,x i decomposes the reconstructed ith signal for SVMD,y i mapping the signal for the ith;
(2) Mapping sequence is converted by linear transformation algorithmYMapping to [1, c ]]Of integers within the range, to obtain a linearly transformed signal, i.e.
In the formula (I), the compound is shown in the specification,roundis a rounding function;cthe number of categories;
is as followsiA linear transformed signal.
(3) To pair
And performing phase space reconstruction, wherein the embedded vector is as follows:
in the formula (I), the compound is shown in the specification,min order to embed the dimension number of the dimension,dfor time delay, each
Mapping to a scatter patternr v0 , v vm1⋯-1 Wherein, in the process,
due to the scattering patternr v0 , v vm1⋯-1 Contains thereinmAn element, each element being taken as [1, c ]]So that the number of all possible scattering patterns isc m 。
(4) Computingc m Each scatter patternr v0 , v vm1⋯-1 Probability of (2)PI.e. by
In the formula (I), the compound is shown in the specification,Num(r v0 , v vm1⋯-1 ) Is composed of
The number of corresponding scatter patterns.
(5) Calculating the average value of the probability of the corresponding scattering mode of the coarse-grained sequences with different scales, namely:
in the formula (I), the compound is shown in the specification,
is a scale ofτFirst ofτThe probability of the corresponding scatter pattern of the coarse grained sequence,
(r v v vm01⋯-1 ) Average probability of the corresponding walking mode of the coarse graining sequence under different scales;
s203: computing fractional order fine composite multiscale dispersion entropy (FRCMDE value) at different scales, i.e.
In the formula (I), the compound is shown in the specification,
namely that
(r v v vm01⋯-1 )，
Is composed ofgammaThe function of the function is that of the function,Ψis composed ofdigammaThe function of the function is that of the function,αis a fractional order factor.
In the present embodiment, it is preferred that,musually, the number of the optical fibers is 2 or 3,ctake [4,8 ]]Time delaydGenerally, 1 is taken.
In this embodiment, the specific process of step S3 is as follows:
s301: computing Fisher ratio, namely, calculating the characteristic vector set formed by fractional order fine composite multi-scale dispersion entropy of transformer voiceprint data
In the formula (I), the compound is shown in the specification,F k() is shown askThe Fisher ratio of the feature vector of the dimension,
is shown askThe inter-class dispersion of the dimensional feature vectors,
denotes the firstkIntra-class dispersion of dimensional feature vectors;
s302: and sorting the Fisher ratios, and selecting the feature subset with the optimal scale.
In this embodiment, the specific process of step S4 is as follows:
s401: dividing the feature subset with the optimal scale into a training set and a testing set;
s402: improved butterfly algorithm (PODSBO) utilizes training set pairs and minPenalty factor for two-times support vector machine (LSSVM)cAnd radial basis inner product function parametersgOptimizing to obtain optimal parameters;
s403: training a Least Squares Support Vector Machine (LSSVM) with optimal parameters, and testing by using a test set;
s404: and constructing a PODSDOA-LSSVM fault diagnosis model according to the training test result, diagnosing unknown transformer voiceprint data by using the PODSDOA-LSSVM fault diagnosis model, and outputting a diagnosis result.
In this embodiment, the improved butterfly algorithm in step S4 is a butterfly algorithm that is combined and optimized based on population optimization strategies such as optimizing an initial population, improving a disadvantaged population, and the like, introducing a dynamic search parameter that can be adaptively adjusted, and introducing a variable weight location update factor strategy, and combines the advantages of the two optimization strategies to form a butterfly algorithm based on population optimization and a dynamic parameter strategy. The steps of the improved butterfly algorithm are as follows:
step A1: initializing butterfly algorithm search parameters: set butterfly population quantity asNSetting the maximum iteration number of the algorithm asN 1 Population boundary condition [ 2 ]L b , U b ]Optimization problem dimensiondim；
Step A2: generating an initial butterfly population according to boundary conditions: generating N by random number in boundary range*dimThe initial butterfly population of the size is expanded to 2 by space symmetryN*dim；
Step A3: and (3) fitness calculation: calculating the individual fitness of the butterfly of the amplified population according to a fitness criterion function;
step A4: and (3) population recovery: selection by Elite Retention strategyNRecording the individuals with the best fitness as a recovery population, and finding and recording the best individuals of the current recovery population;
step A5: updating the inferior population: selecting two butterfly individuals with the worst fitness, and performing cross processing and mutation operation on the butterfly individuals;
step A6: dynamically updating algorithm parameters: updating the sensory modality according to the current iteration number and the following formulaβPower index ofaDynamic search switching probabilitypAnd location update operatorw 1 、w 2 ；
Step A7: iterative optimization: if dynamic search switching probabilityp>rand，randGlobally updating the position of the individual for a random number between 0 and 1; if dynamic search switching probabilityp<randLocally updating the position of the individual; updating the global optimum;
step A8: and (3) border crossing checking: checking whether the updated individual exceeds the boundary, and performing boundary correction on the position of a new individual exceeding the boundary;
step A9: judging whether the iteration end condition of the algorithm is met or not at present: if the end condition is not met, the algorithm is switched to the step A5 to be continuously executed; otherwise, outputting the current optimal result, and ending the algorithm.
The embodiment of the invention also provides a transformer core fault voiceprint diagnosis device based on FRCMDE, which comprises a nonvolatile computer storage medium, wherein the computer storage medium stores computer executable instructions, and the computer executable instructions can execute the transformer core fault voiceprint diagnosis method based on FRCMDE in any embodiment.
The FRCMDE-based transformer core fault voiceprint diagnosis apparatus provided in this embodiment includes an electronic device, and fig. 2 is a schematic structural diagram of the electronic device provided in the embodiment of the present invention, where the electronic device includes: one or more processors 100 and a memory 200, with one processor 100 being an example in fig. 2. The electronic device may further include: an input device 300 and an output device 400. The processor 100, the memory 200, the input device 300 and the output device 400 may be connected by a bus or other means, and fig. 2 illustrates the connection by a bus as an example. The memory 200 is the non-volatile computer-readable storage medium described above. The processor 100 executes various functional applications and data processing of the server by running the nonvolatile software programs, instructions and modules stored in the memory 200, that is, implements the FRCMDE-based transformer core fault voiceprint diagnosis method. The input device 300 may receive input numeric or character information and generate key signal inputs related to user settings and function controls of the FRCMDE-based transformer core fault voiceprint diagnostic device. The output device 400 may include a display device such as a display screen.
The above-described embodiments of the apparatus are merely illustrative, and units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one position, or may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, and not to limit it; although the present 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 solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (7)
1. A transformer core fault voiceprint diagnosis method based on FRCMDE is characterized by comprising the following steps:
s1, carrying out successive variational modal decomposition on transformer voiceprint data to obtain a plurality of intrinsic modal components, calculating the kurtosis and energy ratio of each intrinsic modal component, selecting the intrinsic modal components containing useful information, and reconstructing the intrinsic modal components;
s2, solving a fractional order fine composite multi-scale dispersion entropy of the reconstructed signal in an analysis scale;
s3, removing redundant and invalid parts in fractional order fine composite multi-scale scattered entropies under different scales by using a Fisher ratio, and constructing a feature subset of an optimal scale;
and S4, optimizing the hyperparameters of the LSSVM by using a PODSOA algorithm, constructing a PODSOA-LSSVM fault diagnosis model on the basis of the obtained optimal feature subset, diagnosing the voiceprint data of the unknown transformer and outputting a diagnosis result.
2. The FRCMDE-based transformer core fault voiceprint diagnostic method of claim 1, wherein the specific process of step S2 is as follows:
s201: setting successive variation modal decomposition reconstructed signal sequenceX=[x 1 ,x 2 ,⋯x n ]N is the length X of the signal sequence X n For the nth data, in the composite multi-scale entropy-spread algorithm, the initial points are set to be the values in [1,τ]continuously dividing the signal into lengths ofτAnd averaging each region to obtain a coarse grained sequence;
s202: calculating the average of the corresponding scatter pattern probabilities for the coarse grained sequence:
mapping the coarse grained sequence to [0,1 ] using a standard normal distribution function]Mapping sequences within a rangeY＝[y 1 , y 2 ,⋯,y n ]，y n Mapping the signal for the nth; namely, it is
In the formula (I), the compound is shown in the specification,μ，σfor the expected and standard deviation of the signal sequence X,tin the form of a time, the time,x i the reconstructed ith signal is decomposed for SVMD,y i mapping the signal for the ith;
mapping sequence is converted by linear transformation algorithmYThe mapping is to be made to a1,c]among integers within the range, a linear transformation signal is obtained, i.e.
In the formula (I), the compound is shown in the specification,roundis a rounding function;cis a classCounting;
is as followsiA linear transform signal;
for is to
And performing phase space reconstruction, wherein the embedded vector is as follows:
in the formula (I), the compound is shown in the specification,min order to embed the dimension number, the number of the embedded dimension,dfor time delay, each
Mapping to a scatter patternr v0 , v vm1⋯-1 Wherein, in the process,
due to the scattering patternr v0 , v vm1⋯-1 Contains thereinmAn element, each element being taken as [1, c ]]Of all possible scattering patterns isc m ；
Computingc m Each scatter patternr v0 , v vm1⋯-1 Probability of (2)PNamely:
in the formula (I), the compound is shown in the specification,Num(r v0 , v vm1⋯-1 ) Is composed of
The number of corresponding scattering patterns;
calculating the average value of the probability of the corresponding scattering mode of the coarse graining sequences with different scales, namely:
in the formula (I), the compound is shown in the specification,
is a scaleτFirst ofkThe probability of the corresponding walking pattern of a coarse grained sequence,
(r v v vm01⋯-1 ) Average probability of the corresponding walking mode of the coarse graining sequence under different scales;
s203: calculating fractional order fine composite multi-scale dispersion entropy at different scales, namely:
in the formula (I), the compound is shown in the specification,
namely, it is
(r v v vm01⋯-1 )，
Is composed ofgammaThe function of the function is that of the function,Ψis composed ofdigammaThe function of the function(s) is,αis a fractional order factor.
3. The FRCMDE-based transformer core fault voiceprint diagnostic method of claim 1, wherein the specific process of step S3 is as follows:
s301: calculating a Fisher ratio for a feature vector set formed by fractional order fine composite multi-scale dispersion entropy of transformer voiceprint data, namely:
in the formula (I), the compound is shown in the specification,F k() denotes the firstkThe Fisher ratio of the feature vector of the dimension,
is shown askThe inter-class dispersion of the dimensional feature vectors,
is shown askIntra-class dispersion of dimensional feature vectors;
s302: and sorting the Fisher ratios, and selecting the feature subset with the optimal scale.
4. The FRCMDE-based transformer core fault voiceprint diagnostic method of claim 1, wherein the specific process of step S4 is as follows:
s401: dividing the feature subset with the optimal scale into a training set and a testing set;
s402: improved butterfly algorithm utilizes punishment factors of training set pairs and least square support vector machinecAnd radial basis inner product function parametersgOptimizing to obtain optimal parameters;
s403: training a least square support vector machine with optimal parameters, and testing by using a test set;
s404: and constructing a PODSDOA-LSSVM fault diagnosis model according to the training test result, diagnosing unknown transformer voiceprint data by using the PODSDOA-LSSVM fault diagnosis model, and outputting a diagnosis result.
5. The FRCMDE-based transformer core fault voiceprint diagnostic method of claim 4, wherein the modified butterfly algorithm comprises the steps of:
step A1: butterfly pairInitializing butterfly algorithm search parameters: set butterfly population quantity asNSetting the maximum iteration number of the algorithm asN 1 Population boundary condition [ 2 ]L b , U b ]Optimization problem dimensiondim；
Step A2: generating an initial butterfly population according to boundary conditions: in the boundary range, random number is adopted to generate N*dimAn initial butterfly population of a size that is scaled up to 2 by spatially symmetric amplification of the initial populationN*dim；
Step A3: and (3) fitness calculation: calculating the individual fitness of the butterfly of the amplified population according to a fitness criterion function;
step A4: and (3) population recovery: selection by Elite Retention strategyNRecording the individuals with the best fitness as a recovery population, and finding and recording the best individuals of the current recovery population;
step A5: updating the inferior population: selecting two butterfly individuals with the worst fitness, and performing cross processing and mutation operation on the two butterfly individuals;
step A6: and (3) dynamically updating algorithm parameters: updating the sensory modality according to the current iteration number and the following formulaβPower index ofaDynamic search switching probabilitypAnd location update operatorw 1 、w 2 ；
Step A7: iterative optimization: if dynamic search switching probabilityp>rand，randGlobally updating the position of the individual for a random number between 0 and 1; if dynamic search switching probabilityp<randLocally updating the position of the individual; updating the global optimum;
step A8: and (3) border crossing inspection: checking whether the updated individual exceeds the boundary, and performing boundary correction on the position of a new individual exceeding the boundary;
step A9: judging whether the iteration end condition of the algorithm is met or not at present: if the end condition is not met, the algorithm is switched to the step A5 to be continuously executed; otherwise, outputting the current optimal result, and ending the algorithm.
6. An FRCMDE-based transformer core fault voiceprint diagnostic apparatus, comprising a non-volatile computer storage medium having computer-executable instructions stored thereon, the computer-executable instructions being capable of performing the FRCMDE-based transformer core fault voiceprint diagnostic method of any one of claims 1 to 5.
7. The FRCMDE-based transformer core fault voiceprint diagnostic apparatus of claim 6 comprising an electronic device comprising one or more processors and memory, and further comprising an input device and an output device, wherein the processors, memory, input device and output device are connected by a bus or other means, the memory is a non-volatile computer readable storage medium, the processor executes various functional applications and data processing of the server by running non-volatile software programs, instructions and modules stored in the memory, and the input device receives input digital or character information and generates key signal inputs related to user settings and function control of the FRCMDE-based transformer core fault voiceprint diagnostic apparatus.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116125235A (en) * | 2023-04-14 | 2023-05-16 | 南昌工程学院 | GIS partial discharge fault diagnosis method based on ultrasonic signals |
| CN116430188A (en) * | 2023-06-13 | 2023-07-14 | 南昌工程学院 | Transformer fault voiceprint diagnosis method based on improved white crown chicken optimization algorithm |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109214460A (en) * | 2018-09-21 | 2019-01-15 | 西华大学 | Method for diagnosing fault of power transformer based on Relative Transformation Yu nuclear entropy constituent analysis |
| CN110864902A (en) * | 2019-12-24 | 2020-03-06 | 安徽工业大学 | Rolling bearing early fault detection method based on fractional-order step entropy |
| CN111414893A (en) * | 2020-04-09 | 2020-07-14 | 北华大学 | Rotor fault feature extraction method based on VMD fine composite multi-scale diffusion entropy |
| CN113532829A (en) * | 2021-07-15 | 2021-10-22 | 东北石油大学 | Reciprocating compressor fault diagnosis method based on improved RCMDE |
| CN115062733A (en) * | 2022-08-19 | 2022-09-16 | 江西派源科技有限公司 | Transformer voiceprint fault diagnosis method based on empirical mode decomposition and butterfly algorithm |
| CN115060497A (en) * | 2022-06-10 | 2022-09-16 | 南昌工程学院 | Bearing fault diagnosis method based on CEEMD energy entropy and optimized PNN |
| CN115096590A (en) * | 2022-05-23 | 2022-09-23 | 燕山大学 | Rolling bearing fault diagnosis method based on IWOA-ELM |
| WO2022261805A1 (en) * | 2021-06-15 | 2022-12-22 | 大连理工大学 | Diesel engine gearbox fault diagnosis method |
-
2022
- 2022-12-29 CN CN202211704573.3A patent/CN115688017A/en active Pending
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109214460A (en) * | 2018-09-21 | 2019-01-15 | 西华大学 | Method for diagnosing fault of power transformer based on Relative Transformation Yu nuclear entropy constituent analysis |
| CN110864902A (en) * | 2019-12-24 | 2020-03-06 | 安徽工业大学 | Rolling bearing early fault detection method based on fractional-order step entropy |
| CN111414893A (en) * | 2020-04-09 | 2020-07-14 | 北华大学 | Rotor fault feature extraction method based on VMD fine composite multi-scale diffusion entropy |
| WO2022261805A1 (en) * | 2021-06-15 | 2022-12-22 | 大连理工大学 | Diesel engine gearbox fault diagnosis method |
| CN113532829A (en) * | 2021-07-15 | 2021-10-22 | 东北石油大学 | Reciprocating compressor fault diagnosis method based on improved RCMDE |
| CN115096590A (en) * | 2022-05-23 | 2022-09-23 | 燕山大学 | Rolling bearing fault diagnosis method based on IWOA-ELM |
| CN115060497A (en) * | 2022-06-10 | 2022-09-16 | 南昌工程学院 | Bearing fault diagnosis method based on CEEMD energy entropy and optimized PNN |
| CN115062733A (en) * | 2022-08-19 | 2022-09-16 | 江西派源科技有限公司 | Transformer voiceprint fault diagnosis method based on empirical mode decomposition and butterfly algorithm |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116125235A (en) * | 2023-04-14 | 2023-05-16 | 南昌工程学院 | GIS partial discharge fault diagnosis method based on ultrasonic signals |
| CN116430188A (en) * | 2023-06-13 | 2023-07-14 | 南昌工程学院 | Transformer fault voiceprint diagnosis method based on improved white crown chicken optimization algorithm |
| CN116430188B (en) * | 2023-06-13 | 2023-08-29 | 南昌工程学院 | Transformer fault voiceprint diagnosis method based on improved white crown chicken optimization algorithm |
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