Cable partial discharge detection and identification method and system based on pulse polar coordinate vector

Abstract

The invention relates to a cable partial discharge detection and identification method and system based on a pulse polar coordinate vector. The method comprises the following steps: installing an Internet of Things intelligent sensor for sensing a current pulse signal of a cable grounding lower lead; background noise in the current pulse signal is removed based on wavelet multi-resolution decomposition and an improved wavelet threshold; calculating a polar coordinate vector of the current pulse signal according to the denoised current pulse signal so as to draw a polar coordinate vector domain distribution diagram; and calculating a cross correlation coefficient between the polar coordinate vector domain distribution diagram and polar coordinate vector diagrams of various signal types in a preset sample library so as to determine the signal type of the current current pulse signal. Compared with the prior art, the signal type can be judged without obtaining the power frequency synchronizing signal, hardware investment is saved, and cable insulation state evaluation can be rapidly and effectively achieved.

Description

Technical Field

[0001] The invention relates to the technical field of cable partial discharge detection, and in particular to a cable partial discharge detection and identification method and system based on pulse polar coordinate vectors. Background Art

[0002] Power cables have the advantages of good insulation performance, excellent thermal and mechanical properties, and high power supply reliability. In recent years, they have gradually replaced overhead lines to become the main mode of power transmission in cities, and their proportion in the entire power system transmission line has increased year by year. With the continuous expansion of the scale of the power grid and the continuous improvement of the voltage level, the safe and stable operation of cables is of great significance to ensuring the reliability of power supply. During the manufacturing, transportation, installation and operation of cables, various defects are prone to occur in the cable body, intermediate joints and terminals due to raw materials, production processes, mechanical shock or aging, which can easily cause cable failures under the test voltage or rated voltage. The implementation of cable insulation status assessment based on partial discharge detection can realize cable defect warning in the early stage of failure, which is of great significance to improving the reliability of cable operation.

[0003] At present, partial discharge detection has been widely used in online monitoring and fault diagnosis of cable insulation status. However, in field applications, electromagnetic interference is mutually mixed through coupling, thus affecting the accuracy of discharge signal extraction and cable insulation status assessment. At the same time, the existing partial discharge analysis is mainly based on the phase spectrum method to achieve different noise or discharge type identification. This method requires a stable power frequency trigger signal, but it is difficult to obtain an accurate power frequency trigger signal under strong electromagnetic interference. Summary of the invention

[0004] The purpose of the present invention is to overcome the defects of the above-mentioned prior art and to provide a cable partial discharge detection and identification method and system based on pulse polar coordinate vectors, which can determine the signal type without obtaining the power frequency synchronization signal.

[0005] The purpose of the present invention can be achieved by the following technical solutions: A method for detecting and identifying partial discharge of a cable based on pulse polar coordinate vectors comprises the following steps: Install IoT smart sensors to sense the current pulse signals of the cable grounding lead; Removing background noise from current pulse signals based on wavelet multi-resolution decomposition and improved wavelet threshold; According to the denoised current pulse signal, the polar coordinate vector of the current pulse signal is calculated, thereby drawing a polar coordinate vector domain distribution diagram; The mutual correlation coefficient between the polar coordinate vector domain distribution diagram and the polar coordinate vector diagram of each signal type in the preset sample library is calculated, so as to determine the signal type of the current current pulse signal.

[0006] Furthermore, the IoT smart sensor includes a high-frequency current sensor, a wireless transmission module and a battery module. The high-frequency current sensor is connected to the wireless transmission module for data transmission, and the battery module supplies power to the high-frequency current sensor and the wireless transmission module respectively. The IoT smart sensor is installed at the grounding lower lead of the cable and is used to sense the current pulse signal generated when partial discharge occurs inside the cable.

[0007] Furthermore, the detection frequency range of the IoT smart sensor is 16kHz~50MHz.

[0008] Furthermore, the background noise removal process of the current pulse signal specifically includes: The current pulse signal is decomposed into multiple layers by using discrete wavelet transform, bior6.8 mother wave and multi-resolution decomposition method, generating detailed components of multiple layers and approximate components of the last layer. For the approximate component, an improved wavelet threshold is used for denoising, and the inverse wavelet transform is used to reconstruct the denoised signal.

[0009] Furthermore, the denoising expression of the improved wavelet threshold is: In the formula, is the signal after wavelet threshold processing at time t, is the input value, is the threshold value, is a positive integer, the size of which is determined by the length of the denoised signal and the length of the mother wave. for The symbol function of .

[0010] Furthermore, the sequence expression of the denoised current pulse signal is: In the formula, is the signal sampling index, is the signal sampling length, is the sequence of current pulse signals after denoising; The calculation expression of the polar coordinate vector of the current pulse signal is: vector i ⃗ = ∑ k =1 m =3 s [ i +( k - 1) d ]∙ e k ⃗ , i =1, 2, 3,⋯, N - ( m - 1) d In the formula, is the polar coordinate vector of the current pulse signal under the signal sampling index i, is the signal index interval; is the number of dimensions, is the dimension index, Index for dimension The unit vector of .

[0011] Furthermore, the calculation expression of the mutual correlation coefficient is: r = ∑ i =0 N - 1 [ X i - X ¯ ][ Y i - Y ¯ ] ∑ i =0 N - 1 [ X i - X ¯ ] 2 ∑ i =0 N - 1 [ Y i - Y ¯ ] 2 In the formula, is the mutual correlation coefficient, To calculate the polar coordinate vector domain distribution map, is the polar coordinate vector diagram in the sample library, is the signal average value of the polar coordinate vector domain distribution diagram, is the signal average value of the polar coordinate vector diagram in the sample library, is the signal sampling length.

[0012] Furthermore, the sample library includes polar coordinate vector diagrams of corona discharge, internal discharge, surface discharge, switching interference, background white noise and non-sinusoidal interference.

[0013] Furthermore, the maximum value of the mutual correlation coefficient between the polar coordinate vector domain distribution diagram and the polar coordinate vector diagram of each signal type in the sample library is used as the signal type of the current current pulse signal.

[0014] The present invention also provides a cable partial discharge detection and identification system based on the above-mentioned cable partial discharge detection and identification method based on pulse polar coordinate vectors, comprising: IoT smart sensor, used to sense the current pulse signal of the cable grounding lead; De-noising module, used to remove background noise in current pulse signal based on wavelet multi-resolution decomposition and improved wavelet threshold; A polar coordinate vector domain distribution diagram drawing module is used to calculate the polar coordinate vector of the current pulse signal according to the denoised current pulse signal, so as to draw a polar coordinate vector domain distribution diagram; The signal type judgment module is used to calculate the correlation coefficient between the polar coordinate vector domain distribution diagram and the polar coordinate vector diagram of each signal type in the preset sample library, so as to determine the signal type of the current pulse signal.

[0015] Compared with the prior art, the present invention has the following advantages: (1) The present invention senses the current pulse signal of the cable grounding lead and calculates the polar coordinate vector of the current pulse signal, thereby drawing a polar coordinate vector domain distribution diagram. The polar coordinate vector domain distribution diagram is used to determine the signal type based on the correlation coefficient between the polar coordinate vector domain distribution diagram and the polar coordinate vector diagram of each signal type in a preset sample library. The polar coordinate vector method of the present invention overcomes the shortcomings of the traditional phase spectrum method and can determine the signal type without obtaining the power frequency synchronization signal, thus saving hardware investment, reducing on-site test wiring, and quickly and effectively realizing the cable insulation status evaluation.

[0016] (2) The present invention adopts a high-frequency current method to obtain the local discharge signal inside the cable from the grounded lower lead, which does not affect the normal operation of the cable, reduces the detection risk, and has the advantages of safety, reliability, and easy installation. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A schematic flow chart of a method for detecting and identifying partial discharge of a cable based on pulse polar coordinate vectors provided in an embodiment of the present invention; Figure 2 A schematic diagram of a current pulse signal before denoising provided in an embodiment of the present invention; Figure 3 A schematic diagram of a denoised current pulse signal provided in an embodiment of the present invention; Figure 4 A schematic diagram of a polar coordinate vector domain distribution sample of a discharge signal provided in an embodiment of the present invention; Figure 5 A first schematic diagram of a polar coordinate vector domain distribution sample of a noise signal provided in an embodiment of the present invention; Figure 6 It is a second schematic diagram of a polar coordinate vector domain distribution sample of a noise signal provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0019] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0021] Example 1 like Figure 1 As shown, this embodiment provides a method for detecting and identifying partial discharge of a cable based on a pulse polar coordinate vector, comprising the following steps: S1: Install IoT smart sensors to sense the current pulse signal of the cable grounding lead; S2: Removal of background noise from current pulse signals based on wavelet multi-resolution decomposition and improved wavelet threshold; S3: Calculate the polar coordinate vector of the current pulse signal according to the denoised current pulse signal, thereby drawing a polar coordinate vector domain distribution diagram; S4: Calculate the correlation coefficient between the polar coordinate vector domain distribution diagram and the polar coordinate vector diagram of each signal type in the preset sample library, so as to determine the signal type of the current pulse signal.

[0022] In step S1, the IoT smart sensor includes a high-frequency current sensor, a wireless transmission module and a battery module. The high-frequency current sensor is connected to the wireless transmission module for data transmission, and the battery module supplies power to the high-frequency current sensor and the wireless transmission module respectively. The IoT smart sensor is installed at the grounding lower lead of the cable to sense the high-frequency current pulse signal generated when local discharge occurs inside the cable. The detection frequency range of the IoT smart sensor is 16kHz~50MHz.

[0023] In this embodiment, the transmission impedance of the IoT smart sensor is 16mV / mA ​​and the sensitivity is 10pC.

[0024] In step S2, the background noise removal process of the current pulse signal specifically includes: The current pulse signal is decomposed into 8 layers by using discrete wavelet transform, bior6.8 mother wavelet and multi-resolution decomposition method, generating 8 layers of detailed components and the eighth layer of approximate components. For the approximate components of each layer, an improved wavelet threshold is used for denoising, and the inverse wavelet transform is used to achieve signal reconstruction after denoising.

[0025] The denoising expression of the improved wavelet threshold is: In the formula, is the signal after wavelet threshold processing at time t, is the input value, is the threshold value, is a positive integer, the size of which is determined by the length of the denoised signal and the length of the mother wave. The value of is 3, for The symbol function of .

[0026] like Figure 2 As shown, it is the current pulse signal before denoising in this embodiment; Figure 3 is the current pulse signal after denoising in this embodiment. It can be seen that the current pulse signal after denoising is smoother and a lot of noise is reduced.

[0027] In step S3, the calculation process of calculating the polar coordinate vector domain distribution of the current pulse signal after denoising is as follows: (a) Obtain the denoised signal sequence in, is the signal sampling index, is the signal sampling length, is the sequence of current pulse signals after denoising; (b) Constructing the polar coordinate vector of the current pulse signal vector i ⃗ = ∑ k =1 m =3 s [ i +( k - 1) d ]∙ e k ⃗ , i =1, 2, 3,⋯, N - ( m - 1) d In the formula, is the polar coordinate vector of the current pulse signal under the signal sampling index i, is the signal index interval; is the number of dimensions, is the dimension index, Index for dimension The unit vector of .

[0028] For example, when , When the current pulse signal polar coordinate vector is (c) Draw a polar coordinate vector diagram based on the polar coordinate vector of the current pulse signal.

[0029] In step S4, the mutual correlation coefficient between the signal polar coordinate vector diagram and the sample library polar coordinate vector diagram is calculated to realize signal type recognition. The calculation formula of the mutual correlation coefficient is as follows:

[0030] r = ∑ i =0 N - 1 [ X i - X ¯ ][ Y i - Y ¯ ] ∑ i =0 N - 1 [ X i - X ¯ ] 2 ∑ i =0 N - 1 [ Y i - Y ¯ ] 2 In the formula, is the mutual correlation coefficient, To calculate the polar coordinate vector domain distribution map, is the polar coordinate vector diagram in the sample library, is the signal average value of the polar coordinate vector domain distribution diagram, and the correlation coefficient ranges from 0 to 1. is the signal average value of the polar coordinate vector diagram in the sample library, is the signal sampling length. The sample library includes polar coordinate vector diagrams of corona discharge, internal discharge, surface discharge, switching interference, background white noise and non-sinusoidal interference. The maximum value of the correlation coefficient between the measured polar coordinate vector diagram and the sample library polar coordinate vector diagram is the type of the measured signal.

[0031] like Figure 4 , Figure 5 and Figure 6 As shown, they are schematic diagrams of the polar coordinate vector domain distribution samples of the discharge signal and the polar coordinate vector domain distribution samples of the noise signal.

[0032] Example 2 This embodiment provides a cable partial discharge detection and identification system based on a cable partial discharge detection and identification method based on pulse polar coordinate vectors as in Embodiment 1, comprising: IoT smart sensor, used to sense the current pulse signal of the cable grounding lead; De-noising module, used to remove background noise in current pulse signal based on wavelet multi-resolution decomposition and improved wavelet threshold; A polar coordinate vector domain distribution diagram drawing module is used to calculate the polar coordinate vector of the current pulse signal according to the denoised current pulse signal, so as to draw a polar coordinate vector domain distribution diagram; The signal type judgment module is used to calculate the correlation coefficient between the polar coordinate vector domain distribution diagram and the polar coordinate vector diagram of each signal type in the preset sample library, so as to determine the signal type of the current pulse signal.

[0033] It should be noted that the specific content and beneficial effects of the system of the present application can be found in the above-mentioned method embodiment, which will not be repeated here.

[0034] The preferred specific embodiments of the present invention are described in detail above. It should be understood that a person skilled in the art can make many modifications and changes based on the concept of the present invention without creative work. Therefore, any technical solution that can be obtained by a person skilled in the art through logical analysis, reasoning or limited experiments based on the concept of the present invention on the basis of the prior art should be within the scope of protection determined by the claims.

Claims

1. A method for detecting and identifying partial discharge of cables based on pulse polar coordinate vectors, characterized in that: The following steps are involved: Install IoT smart sensors to sense the current pulse signals of the cable grounding lead; Removing background noise from current pulse signals based on wavelet multi-resolution decomposition and improved wavelet threshold; According to the denoised current pulse signal, the polar coordinate vector of the current pulse signal is calculated, thereby drawing a polar coordinate vector domain distribution diagram; The mutual correlation coefficient between the polar coordinate vector domain distribution diagram and the polar coordinate vector diagram of each signal type in the preset sample library is calculated, so as to determine the signal type of the current current pulse signal.

2. A cable partial discharge detection and identification method based on pulse polar coordinate vector according to claim 1, characterized in that: The IoT smart sensor includes a high-frequency current sensor, a wireless transmission module and a battery module. The high-frequency current sensor is connected to the wireless transmission module for data transmission, and the battery module supplies power to the high-frequency current sensor and the wireless transmission module respectively. The IoT smart sensor is installed at the grounding lower lead of the cable and is used to sense the current pulse signal generated when partial discharge occurs inside the cable.

3. A cable partial discharge detection and identification method based on pulse polar coordinate vector according to claim 1, characterized in that: The detection frequency range of the IoT smart sensor is 16kHz~50MHz.

4. A method for detecting and identifying partial discharge of cables based on pulse polar coordinate vectors according to claim 1, characterized in that: The background noise removal process of the current pulse signal specifically includes: The current pulse signal is decomposed into multiple layers by using discrete wavelet transform, bior6.8 mother wave and multi-resolution decomposition method, generating detailed components of multiple layers and approximate components of the last layer. For the approximate component, an improved wavelet threshold is used for denoising, and the inverse wavelet transform is used to reconstruct the denoised signal.

5. A cable partial discharge detection and identification method based on pulse polar coordinate vector according to claim 4, characterized in that: The denoising expression of the improved wavelet threshold is: , where is the signal after wavelet threshold processing at time t, is the input value, is the threshold value, is a positive integer, the size of which is determined by the length of the denoised signal and the length of the mother wave. for The symbol function of .

6. A cable partial discharge detection and identification method based on pulse polar coordinate vector according to claim 1, characterized in that: The sequence expression of the denoised current pulse signal is: , where is the signal sampling index, is the signal sampling length, is the sequence of current pulse signals after denoising; The calculation expression of the polar coordinate vector of the current pulse signal is: , where is the polar coordinate vector of the current pulse signal under the signal sampling index i, is the signal index interval; is the number of dimensions, is the dimension index, Index for dimension The unit vector of .

7. A method for detecting and identifying partial discharge of cables based on pulse polar coordinate vectors according to claim 1, characterized in that: The calculation expression of the mutual correlation coefficient is: , where is the mutual correlation coefficient, To calculate the polar coordinate vector domain distribution map, is the polar coordinate vector diagram in the sample library, is the signal average value of the polar coordinate vector domain distribution diagram, is the signal average value of the polar coordinate vector diagram in the sample library, is the signal sampling length.

8. The cable partial discharge detection and identification method based on pulse polar coordinate vector according to claim 1 is characterized in that: The sample library includes polar coordinate vector diagrams of corona discharge, internal discharge, surface discharge, switching interference, background white noise and non-sinusoidal interference.

9. A method for detecting and identifying partial discharge of cables based on pulse polar coordinate vectors according to claim 1, characterized in that: The maximum value of the mutual correlation coefficient between the polar coordinate vector domain distribution diagram and the polar coordinate vector diagram of each signal type in the sample library is taken as the signal type of the current current pulse signal.

10. A cable partial discharge detection and identification system based on a cable partial discharge detection and identification method based on pulse polar coordinate vectors as claimed in any one of claims 1 to 9, characterized in that: include: IoT smart sensor, used to sense the current pulse signal of the cable grounding lead; De-noising module, used to remove background noise in current pulse signal based on wavelet multi-resolution decomposition and improved wavelet threshold; A polar coordinate vector domain distribution diagram drawing module is used to calculate the polar coordinate vector of the current pulse signal according to the denoised current pulse signal, so as to draw a polar coordinate vector domain distribution diagram; The signal type judgment module is used to calculate the correlation coefficient between the polar coordinate vector domain distribution diagram and the polar coordinate vector diagram of each signal type in the preset sample library, so as to determine the signal type of the current pulse signal.

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