CN111044865A - Partial discharge wavelet denoising method for direct-current gas insulation electrical equipment - Google Patents
Partial discharge wavelet denoising method for direct-current gas insulation electrical equipment Download PDFInfo
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Abstract
The invention discloses a local discharge wavelet denoising method for direct current gas insulation electric equipment, which comprises the following steps: (1) acquiring a partial discharge current pulse signal of a typical insulation defect of the direct-current gas insulation electric equipment; (2) selecting an optimal wavelet by using a dynamic time warping method, determining the number of optimal signal decomposition layers, decomposing the partial discharge current pulse signal through discrete wavelet transformation, and extracting wavelet high-frequency components of each layer and wavelet low-frequency components of the highest layer; (3) processing wavelet high-frequency components of each layer by using an automatic threshold and an intermediate threshold function, removing noise components, and reserving partial discharge current pulse signal components; (4) and combining the denoised high-frequency wavelet components of each layer and the original low-frequency wavelet component of the highest layer through inverse discrete wavelet transform, and reconstructing the denoised partial discharge current pulse signal. The method effectively removes the interference in the direct current partial discharge, improves the detection precision and accuracy, and has simple and quick calculation and easy realization.
Description
Technical Field
The invention relates to a local discharge wavelet denoising method for direct-current gas insulation electric equipment.
Background
The rapid development of high-voltage direct-current power transmission and distribution technology puts higher requirements on the safety and stability of related direct-current gas insulated electrical equipment. Partial discharge generated at the insulation defect of the direct current gas insulation electric equipment causes the gradual degradation of an insulation system of the equipment, and further causes equipment failure and secondary accidents such as fire, casualties and the like. Therefore, detecting partial discharge is an important means for on-line monitoring of the insulation state of the dc gas-insulated electrical equipment, risk assessment and asset management, and is also an effective method for ensuring safe and stable operation of the electrical equipment and the whole electrical power system. However, in the on-site online partial discharge detection process, white noise, discrete spectrum noise, periodic narrow-band noise and aperiodic random pulse noise seriously affect the precision and accuracy of detection, and further affect the risk coefficient evaluation of partial discharge and the identification of subsequent defect modes. Therefore, it is necessary to remove noise from the partial discharge of the dc gas-insulated electrical device, so as to improve the accuracy of online monitoring of the insulation state.
Wavelet transforms can perform analysis and processing of signals in both the time and frequency domains. The wavelet denoising technology has been applied to the partial discharge denoising of the alternating current electrical equipment, however, the mechanisms of the alternating current partial discharge and the direct current partial discharge are different, the wavelet denoising technology of the alternating current partial discharge is not suitable for the direct current partial discharge denoising, and the direct current partial discharge wavelet denoising technology at home and abroad is still in the beginning stage of research at present. In addition, the traditional optimal wavelet selection is based on a correlation coefficient method, in order to overcome the problem that the lengths of partial discharge signals and wavelet functions are different, the method needs to add the steps of signal standardization, resampling, displacement and the like, and the calculation process is complex and time-consuming.
Disclosure of Invention
The technical problem to be solved by the invention is to provide a local discharge wavelet denoising method for a direct current gas insulated electrical device, which can improve the precision and accuracy of the online detection of the local discharge, further improve the accuracy of risk coefficient evaluation, defect mode identification and fault location, can effectively remove various interferences in the local discharge generated by free metal particle insulation defects, improve the detection precision and accuracy of the local discharge, can accurately select and process the optimal wavelet of the direct current local discharge current pulse by an introduced dynamic time warping method, has the characteristics of simple and fast calculation, and has practicability and wide application.
In order to solve the problems, the invention adopts the following technical scheme:
a local discharge wavelet denoising method for direct current gas insulation electric equipment comprises the following steps:
1) acquiring a partial discharge current pulse signal of a typical insulation defect of the direct-current gas insulation electric equipment;
2) selecting an optimal wavelet by using a dynamic time warping method, determining the number of optimal signal decomposition layers, decomposing the partial discharge current pulse signal through discrete wavelet transformation, and extracting wavelet high-frequency components of each layer and wavelet low-frequency components of the highest layer;
3) processing wavelet high-frequency components of each layer by using an automatic threshold and an intermediate threshold function, removing noise components, and reserving partial discharge current pulse signal components;
4) and combining the denoised high-frequency wavelet components of each layer and the original low-frequency wavelet component of the highest layer through inverse discrete wavelet transform, and reconstructing the denoised partial discharge current pulse signal.
Preferably, in the step 1), a free metal particle insulation defect of the dc gas-insulated electrical equipment is simulated, and a partial discharge current pulse signal is obtained by using a high-frequency current method.
Preferably, the step 2) comprises the following steps:
(a) calculating a minimum dynamic time warping path of the partial discharge current pulse signal and the wavelet function by using a dynamic time warping method, and comparing the similarity of the partial discharge current pulse signal and the wavelet function, wherein the DWT is obtained on the basis of the following formula:
wherein X represents the time series of the partial discharge current pulse signal, Y represents the time series of the wavelet function, the wavelet function comprises Daubechies, Biorthogonal, Coiflet and Symlet functions, and w represents the time series of the partial discharge current pulse signalkThe regular path is represented, and the wavelet function corresponding to the minimum dynamic time regular path is the optimal wavelet for analyzing the local discharge current pulse signal of the direct current gas insulation electrical equipment;
(b) the optimal signal decomposition layer number determines the decomposition scale of the partial discharge current pulse signal, and the optimal signal decomposition layer number J is obtained based on the following formula:
wherein L represents the length of the partial discharge current pulse sequence, LWCharacterizing the length of the wavelet function, and characterizing the rounding function by fix;
(c) decomposing the partial discharge current pulse signals layer by layer downwards by using discrete wavelet transform, the optimal wavelet function determined in the step (a) and the optimal signal decomposition layer number determined in the step (b); generating wavelet high-frequency component and wavelet low-frequency component on the first layer, continuously decomposing the wavelet low-frequency component obtained on the previous layer on the next layer, and repeating the process to the highest decomposition layer number; and extracting wavelet high-frequency components of each layer and wavelet low-frequency components of the highest layer.
Preferably, in the local discharge wavelet denoising method for the dc gas insulated electrical device according to the present invention, in step 3), the automatic threshold λ is obtained based on the following formula:
wherein m isjRepresenting the median of the wavelet high-frequency components of j layers, njAnd characterizing the length of the high-frequency component of the wavelet of the j layer.
Preferably, in the wavelet denoising method for partial discharge of dc gas-insulated electrical equipment according to the present invention, in step 3), the intermediate threshold function δ (t) is defined as follows:
where x characterizes the input value and λ characterizes the threshold value.
The invention has the advantages and beneficial effects that:
1. the direct current gas insulation electric equipment partial discharge wavelet denoising method can effectively remove various interferences in partial discharge generated by free metal particle insulation defects, and improve the detection precision and accuracy of the partial discharge.
2. The dynamic time warping method introduced by the invention can accurately select the optimal wavelet for processing the direct current partial discharge current pulse, and has the characteristics of simple and fast calculation.
3. The direct current gas insulation electric equipment partial discharge wavelet denoising method introduced by the invention can be used for improving the accuracy of the risk coefficient evaluation, the defect mode identification and the fault location of the partial discharge.
Drawings
In order to clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, but the protection scope of the present invention is not limited.
Fig. 1 is a flowchart of a local discharge wavelet denoising method for a dc gas insulated electrical device according to an embodiment of the present invention;
FIG. 2 is a schematic structural diagram of a free metal particle insulation defect of a DC gas insulated electrical apparatus utilized in the present invention;
FIG. 3 is a schematic diagram of a partial discharge current pulse signal according to the present invention;
FIG. 4 is a diagram illustrating an optimal wavelet function selection result based on a dynamic time warping method according to the present invention;
FIG. 5 is a schematic diagram of a current pulse sequence containing a disturbance according to the present invention;
FIG. 6 is a schematic diagram of wavelet high-frequency components of each layer after decomposing a noisy partial discharge current pulse signal sequence by using discrete wavelet transform according to the present invention;
FIG. 7 is a schematic diagram of denoising wavelet high-frequency components of each layer by using an automatic threshold and an intermediate threshold function according to the present invention;
FIG. 8 is a schematic diagram of a partial discharge current pulse signal sequence obtained after denoising by the method of the present invention.
Detailed Description
Referring to fig. 1 to 8, a local discharge wavelet denoising method for a dc gas insulated electrical device includes the steps of:
step 1): acquiring a partial discharge current pulse signal of a typical insulation defect of the direct-current gas insulation electric equipment;
step 2): selecting an optimal wavelet by using a dynamic time warping method, determining the number of optimal signal decomposition layers, decomposing the partial discharge current pulse signal through discrete wavelet transformation, and extracting wavelet high-frequency components of each layer and wavelet low-frequency components of the highest layer;
step 3): processing wavelet high-frequency components of each layer by using an automatic threshold and an intermediate threshold function, removing noise components, and reserving partial discharge current pulse signal components;
step 4): and combining the denoised high-frequency wavelet components of each layer and the original low-frequency wavelet component of the highest layer through inverse discrete wavelet transform, and reconstructing the denoised partial discharge current pulse signal.
In step 1), fig. 2 simulates the free metal particle insulation defect that occurs most frequently in a dc gas-insulated electrical device, and is composed of a ball electrode, a bowl electrode, and metal particles placed at the bowl electrode. The radius of the ball electrode is 12.5mm, the height of the bowl electrode is 10mm, the diameter of the bowl electrode is 80mm, the curvature radius of the bowl electrode is 150mm, and the diameter of the metal particle is 1 mm. The above electrode is sealed in the insulator and filled with SF6An insulating gas.
As shown in fig. 3, a high-frequency current method is used to collect a partial discharge current pulse signal.
Further, the step 2) includes the steps of,
(a) calculating a minimum dynamic time warping path of the partial discharge current pulse signal and the wavelet function by using a dynamic time warping method, and comparing the similarity of the partial discharge current pulse signal and the wavelet function, wherein the DWT is obtained on the basis of the following formula:
wherein X represents the time series of the partial discharge current pulse signal, Y represents the time series of the wavelet function, the wavelet function comprises Daubechies, Biorthogonal, Coiflet and Symlet functions, and w represents the time series of the partial discharge current pulse signalkThe regular path is represented, and the wavelet function corresponding to the minimum dynamic time regular path is the optimal wavelet for analyzing the local discharge current pulse signal of the direct current gas insulation electrical equipment;
fig. 4 shows the result of selecting the optimal wavelet function based on the dynamic time warping method, where the wavelet function bior2.6 and the partial discharge current pulse signal have the minimum dynamic time warping path. Therefore, a wavelet function bior2.6 is selected to analyze and process the partial discharge current pulse signal of the direct current gas insulation electrical equipment.
Fig. 5 is a sequence of partial discharge current pulse signals with interference.
(b) The optimal signal decomposition layer number determines the decomposition scale of the partial discharge current pulse signal, and the optimal signal decomposition layer number J is obtained based on the following formula:
wherein L represents the length of the partial discharge current pulse sequence, LWCharacterizing the length of the wavelet function, and characterizing the rounding function by fix;
in the experimental mode, the length of a partial discharge current pulse sequence is 5000, the length of a wavelet function bior2.6 is 13, and the number of optimal signal decomposition layers is calculated to be 8.
(c) Decomposing the partial discharge current pulse signals layer by layer downwards by using discrete wavelet transform, the optimal wavelet function determined in the step (a) and the optimal signal decomposition layer number determined in the step (b); generating wavelet high-frequency component and wavelet low-frequency component on the first layer, continuously decomposing the wavelet low-frequency component obtained on the previous layer on the next layer, and repeating the process to the highest decomposition layer number; and extracting wavelet high-frequency components of each layer and wavelet low-frequency components of the highest layer.
Fig. 6 shows wavelet high-frequency components of each layer after decomposing a noisy partial discharge current pulse signal sequence by using discrete wavelet transform.
Further, in the local discharge wavelet denoising method for the dc gas insulated electrical device according to the present invention, in step 300, the automatic threshold λ is obtained based on the following formula:
wherein m isjRepresenting the median of the wavelet high-frequency components of j layers, njAnd characterizing the length of the high-frequency component of the wavelet of the j layer.
Further, in the method for denoising partial discharge wavelet of dc gas insulated electrical equipment according to the present invention, in step 3), the intermediate threshold function δ (t) is defined as follows:
where x characterizes the input value and λ characterizes the threshold value.
FIG. 7 shows that the wavelet high-frequency components of each layer are denoised by using an automatic threshold and an intermediate threshold function, noise interference components of the wavelet high-frequency components of each layer are removed, and partial discharge current pulse signal components are reserved.
In step 4), the denoised high-frequency wavelet components of each layer and the original low-frequency wavelet component of the highest layer are combined through inverse discrete wavelet transform, and a reconstructed sequence of the partial discharge current pulse signals obtained after denoising is shown in fig. 7.
The above is only a specific embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that are not thought of through the inventive work should be included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope defined by the claims.
Claims (5)
1. A local discharge wavelet denoising method for direct current gas insulation electric equipment is characterized by comprising the following steps: the method comprises the following steps:
1) acquiring a partial discharge current pulse signal of a typical insulation defect of the direct-current gas insulation electric equipment;
2) selecting an optimal wavelet by using a dynamic time warping method, determining the number of optimal signal decomposition layers, decomposing the partial discharge current pulse signal through discrete wavelet transformation, and extracting wavelet high-frequency components of each layer and wavelet low-frequency components of the highest layer;
3) processing wavelet high-frequency components of each layer by using an automatic threshold and an intermediate threshold function, removing noise components, and reserving partial discharge current pulse signal components;
4) and combining the denoised high-frequency wavelet components of each layer and the original low-frequency wavelet component of the highest layer through inverse discrete wavelet transform, and reconstructing the denoised partial discharge current pulse signal.
2. The wavelet denoising method for the partial discharge of the direct current gas insulation electric equipment according to claim 1, wherein: in the step 1), simulating typical insulation defects of free metal particles of the direct current gas insulation electrical equipment, and acquiring a partial discharge current pulse signal by adopting a high-frequency current method.
3. The wavelet denoising method for the partial discharge of the direct current gas insulation electric equipment according to claim 1, wherein: the step 2) comprises the following steps:
(a) calculating a minimum dynamic time warping path of the partial discharge current pulse signal and the wavelet function by using a dynamic time warping method, and comparing the similarity of the partial discharge current pulse signal and the wavelet function, wherein the DWT is obtained on the basis of the following formula:
wherein X represents the time sequence of the partial discharge current pulse signal and Y represents the time of the wavelet functionInter-sequence, wavelet functions include Daubechies, Biorthogonal, Coiflet and Symlet functions, wkThe regular path is represented, and the wavelet function corresponding to the minimum dynamic time regular path is the optimal wavelet for analyzing the local discharge current pulse signal of the direct current gas insulation electrical equipment;
(b) the optimal signal decomposition layer number determines the decomposition scale of the partial discharge current pulse signal, and the optimal signal decomposition layer number J is obtained based on the following formula:
wherein L represents the length of the partial discharge current pulse sequence, LWCharacterizing the length of the wavelet function, and characterizing the rounding function by fix;
(c) decomposing the partial discharge current pulse signals layer by layer downwards by using discrete wavelet transform, the optimal wavelet function determined in the step (a) and the optimal signal decomposition layer number determined in the step (b); generating wavelet high-frequency component and wavelet low-frequency component on the first layer, continuously decomposing the wavelet low-frequency component obtained on the previous layer on the next layer, and repeating the process to the highest decomposition layer number; and extracting wavelet high-frequency components of each layer and wavelet low-frequency components of the highest layer.
4. The wavelet denoising method for the partial discharge of the direct current gas insulation electric equipment according to claim 1, wherein: in the step 3), the automatic threshold λ is obtained based on the following formula:
wherein m isjRepresenting the median of the wavelet high-frequency components of j layers, njAnd characterizing the length of the high-frequency component of the wavelet of the j layer.
5. The wavelet denoising method for the partial discharge of the direct current gas insulation electric equipment according to claim 1, wherein: in the step 3), the intermediate threshold function δ (t) is defined as follows:
where x characterizes the input value and λ characterizes the threshold value.
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