GIS arcing signal processing method and device

Abstract

The invention relates to a GIS arcing signal processing method and device, and the method comprises the steps: obtaining a GIS arcing signal, and calculating the power spectrum entropy of the arcing signal; performing wavelet packet decomposition on the arcing signal to obtain a multi-layer decomposition signal; calculating the power spectrum entropy of each layer of decomposition signal, and determining the optimal number of decomposition layers according to the power spectrum entropy; reconstructing the multi-layer decomposition signal based on the optimal decomposition layer number to obtain a reconstructed signal; and according to the reconstruction signal, determining the starting and ending time of the arcing signal, and calculating the overtravel of the arcing signal. According to the method, the starting and ending time points of the arcing signal are judged by decomposing and reconstructing the power spectrum of the arcing signal, so that the accuracy and the self-adaptability of GIS arcing signal detection are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of electric power signals, and in particular relates to a method and device for processing GIS arcing signals. Background Art

[0002] Gas-insulated switchgear (GIS) is a key component of power systems, primarily used to separate and isolate electrical equipment. GIS disconnectors are responsible for opening and closing power systems to ensure the proper operation and safe maintenance of power equipment. However, due to their complex mechanical structure and harsh operating environments, GIS disconnectors are prone to structural damage and assembly errors after prolonged use. These issues can lead to improper opening and closing of disconnectors, posing a threat to the safe operation of the power system.

[0003] Direct detection methods typically require installing sensors inside the GIS disconnector to directly obtain the position of the moving and static contacts. However, due to the limited internal space of the GIS disconnector and its operation in a high-voltage, high-current environment, sensor installation and signal extraction are extremely difficult. Furthermore, direct detection methods can compromise the isolation of the disconnector, posing insulation risks and making them unsuitable for industrial applications.

[0004] Existing state perception methods mainly include detection methods based on motor current and detection methods based on vibration signals.

[0005] Detection methods based on motor current: This method determines the status of the disconnector by measuring the current changes of the drive motor. However, the motor current signal contains noise, and the current characteristics of motors of different models are dispersed, resulting in unclear current characteristics. Existing methods rely on physical analysis of the current envelope and threshold judgment, but the setting of the threshold is difficult to quantify directly and requires expert intervention for physical analysis, which is difficult to adapt to the complex environment of industrial sites. Detection methods based on vibration signals: This method determines the status of the disconnector by detecting the vibration signal generated during its operation. However, the vibration signal contains noise, and the natural frequency of the disconnector structure is difficult to match with the vibration frequency under the action of electromagnetic force, resulting in inaccurate detection results.

[0006] Another method for confirming the open and closed positions of GIS disconnectors based on electromagnetic transient signals uses a comparator to remove noise from the signal. However, the comparator's threshold is difficult to set accurately, which can easily filter out valid signals or retain noise, resulting in inaccurate signal timing and affecting the calculation of overtravel and open distance. Summary of the Invention

[0007] In order to improve the accuracy and adaptability of GIS arc signal detection, a first aspect of the present invention provides a GIS arc signal processing method, including: obtaining the GIS arc signal and calculating the power spectrum entropy of the arc signal; performing wavelet packet decomposition on the arc signal to obtain a multi-layer decomposition signal; calculating the power spectrum entropy of each layer of decomposition signal, and determining the optimal number of decomposition layers based on the power spectrum entropy; reconstructing the multi-layer decomposition signal based on the optimal number of decomposition layers to obtain a reconstructed signal; determining the start and end times of the arc signal based on the reconstructed signal, and calculating the overtravel of the arc signal.

[0008] In some embodiments of the present invention, performing wavelet packet decomposition on the arcing signal to obtain a multi-layer decomposition signal includes: performing wavelet packet decomposition on the arcing signal using a DB4 wavelet basis function and a scaling function.

[0009] Furthermore, the calculation of the power spectrum entropy of each layer of decomposition signal and determining the optimal number of decomposition layers based on the power spectrum entropy include: calculating the normalized power spectrum entropy of each layer of decomposition signal based on the coefficients of the layer decomposition signal; and determining the optimal number of decomposition layers based on the normalized power spectrum entropy and the power spectrum entropy of the original signal.

[0010] In some embodiments of the present invention, reconstructing the multi-layer decomposition signal based on the optimal number of decomposition layers to obtain the reconstructed signal includes: selecting one or more corresponding nodes based on the optimal number of decomposition layers; and performing an inverse transformation on the selected node or nodes to obtain the reconstructed signal.

[0011] In some embodiments of the present invention, determining the start and end times of the arcing signal according to the reconstructed signal includes: comparing the reconstructed signal with a preset value to determine the start and end times of the arcing signal.

[0012] In some embodiments of the present invention, the calculating the power spectrum entropy of the arcing signal includes: calculating the power spectrum density of the arcing signal; normalizing the power spectrum density; and calculating the power spectrum entropy based on the normalized power spectrum density.

[0013] The second aspect of the present invention provides a GIS arc signal processing device, including: an acquisition module, used to obtain the arc signal of the GIS and calculate the power spectrum entropy of the arc signal; a calculation module, used to perform wavelet packet decomposition on the arc signal to obtain a multi-layer decomposition signal; calculate the power spectrum entropy of each layer of decomposition signal, and determine the optimal number of decomposition layers based on the power spectrum entropy; a reconstruction module, used to reconstruct the multi-layer decomposition signal based on the optimal number of decomposition layers to obtain a reconstructed signal; and a determination module, used to determine the start and end time of the arc signal based on the reconstructed signal, and calculate the overtravel of the arc signal.

[0014] Furthermore, the reconstruction module includes: a selection unit for selecting one or more corresponding nodes based on the optimal number of decomposition layers; and a reconstruction unit for performing an inverse transformation on the selected node or nodes to obtain a reconstructed signal.

[0015] The third aspect of the present invention provides an electronic device, comprising: one or more processors; a storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the GIS arcing signal processing method provided in the first aspect of the present invention.

[0016] A fourth aspect of the present invention provides a computer-readable medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the method for processing GIS arcing signals provided in the first aspect of the present invention is implemented.

[0017] The beneficial effects of the present invention are: (1) Strong adaptability: Power Spectral Entropy (PSE) is an entropy measurement method for measuring the spectral distribution characteristics of a signal. It calculates the entropy value by performing spectral analysis on the signal. This method can adaptively determine the optimal number of layers based on the spectral characteristics of the signal, does not rely on manually set thresholds, and is adaptable to signals of different types and complexities.

[0018] (2) Better denoising effect: By calculating the power spectral entropy of the wavelet packet coefficients at each layer and comparing it with the power spectral entropy of the original signal, the decomposition of that layer is considered complete when the power spectral entropy of the wavelet packet coefficients at a layer is less than one-third of the original signal. Advantages: Power spectral entropy accurately reflects the spectral characteristics of the signal, selects the optimal number of layers, effectively removes noise, and retains the main components of the signal, improving the accuracy and reliability of signal processing.

[0019] (3) More accurate signal time point extraction: By making the signal purer after denoising, threshold judgment is performed on the start and end time points of the arcing signal, which enables accurate extraction of the signal's time characteristics. This avoids time point detection errors caused by noise interference, ensures the accuracy of overtravel and open distance calculations, and improves the accuracy of GIS disconnector position judgment.

[0020] (4) Improved computational efficiency: Wavelet packet transform has multi-scale analysis capabilities, which can efficiently process signals. That is, by determining the optimal number of layers through power spectrum entropy, the number of unnecessary decomposition layers is reduced, and the efficiency of signal processing is improved, making it suitable for real-time monitoring and analysis scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1Schematic diagram of a basic flow chart of a method for processing GIS arcing signals in some embodiments of the present invention; Figure 2 A schematic diagram of electromagnetic transient generation of a GIS disconnector in some embodiments of the present invention; Figure 3 Schematic diagram showing the comparison of synchronous acquisition of bus voltage waveform and Rogowski coil induced voltage in the method for processing GIS arcing signals in some embodiments of the present invention; Figure 4 Schematic diagram of signal comparison before and after reconstruction and denoising of GIS arcing signals in some embodiments of the present invention; Figure 5 Schematic diagram of the structure of a GIS arcing signal processing device in some embodiments of the present invention; Figure 6 Schematic diagram of the structure of an electronic device in some embodiments of the present invention. DETAILED DESCRIPTION

[0022] The principles and features of the present invention are described below with reference to the accompanying drawings. The examples given are only used to explain the present invention and are not used to limit the scope of the present invention.

[0023] refer to Figures 1 to 3 In the first aspect of the present invention, a method for processing a GIS arcing signal is provided, comprising: S100. acquiring the arcing signal of the GIS and calculating the power spectrum entropy of the arcing signal; S200. performing wavelet packet decomposition on the arcing signal to obtain a multi-layer decomposition signal; calculating the power spectrum entropy of each layer of the decomposition signal, and determining the optimal number of decomposition layers according to the power spectrum entropy; S300. reconstructing the multi-layer decomposition signal based on the optimal number of decomposition layers to obtain a reconstructed signal; S400. determining the start and end time of the arcing signal according to the reconstructed signal, and calculating the overtravel of the arcing signal.

[0024] In step S100 of some embodiments of the present invention, calculating the power spectrum entropy of the arcing signal includes: S101. Calculate the power spectral density of the arcing signal; Specifically, let the original arcing signal be Y ( t ), for the original signal Y ( t ) is Fourier transformed to obtain the power spectral density P(f) : , in, represents the Fourier transform.

[0025] S101. Normalizing the power spectral density; Specifically, the normalized power spectral density for: ; S103. Calculate the power spectrum entropy according to the normalized power spectrum density.

[0026] The calculation formula of power spectrum entropy H is: ; It should be noted that obtaining the arcing signal of the GIS includes: passing through the partial discharge detection port of the GIS disconnector through the installation position of the Rogowski coil. The partial discharge detection port is a non-electromagnetic shielding location, and the discharge process can be detected.

[0027] refer to Figure 3 Rogowski coils can extract electromagnetic transient information during the GIS disconnector discharge process. When a very fast transient overvoltage (VFTO) occurs on the busbar, the induced voltage of the Rogowski coil also induces a pulse voltage at the same time. Similarly, the peak value of the busbar voltage pulse is not strictly proportional to the peak value of the induced voltage of the Rogowski coil. Analyzing the open and closed positions of the GIS disconnector only requires analyzing the presence and termination of arcing signals.

[0028] In step S200 of some embodiments of the present invention, performing wavelet packet decomposition on the arcing signal to obtain a multi-layer decomposition signal includes: performing wavelet packet decomposition on the arcing signal using a DB4 wavelet basis function and a scaling function.

[0029] Specifically, let the wavelet packet basis function be the Daubechies 4 (db4) wavelet basis function. The following is the expression of the Daubechies 4 wavelet basis function: Wavelet basis functions The expression is: , in, h k are the filter coefficients, and for the Daubechies 4 wavelet these are: ; ;

[0030] ; As you can see, the scaling function and its coefficients are often used to generate approximate coefficients in the wavelet transform, which represent the smooth parts of the signal at different scales. The exact form of the scaling function depends on the wavelet basis function used, and each wavelet basis function has its own specific scaling function and coefficients.

[0031] In this embodiment, the scaling function (Scaling Function), the scaling function ϕ(t) The expression is: ; Next, perform wavelet packet decomposition: perform wavelet packet decomposition of the jth layer on the signal Y(t) to obtain the coefficients W j,k ( t ): ; Furthermore, the calculation of the power spectrum entropy of each layer of decomposition signal and determining the optimal number of decomposition layers based on the power spectrum entropy include: calculating the normalized power spectrum entropy of each layer of decomposition signal based on the coefficients of the layer decomposition signal; and determining the optimal number of decomposition layers based on the normalized power spectrum entropy and the power spectrum entropy of the original signal.

[0032] Specifically, for the wavelet packet coefficients of the jth layer W j,k , calculate its power spectrum entropy H j,k : , in, is the normalized power spectral density.

[0033] When the power spectrum entropy of the current layer H j,k Less than the power spectrum entropy of the original signal H When one third of , It is considered that the decomposition is in place and the optimal number of layers is determined .

[0034] Optionally, the wavelet basis function includes one or more of Haar wavelet, Morlet wavelet, Mexican Hat wavelet, Complex Gaussian wavelet or Spline wavelet.

[0035] In some implementations of step S300 of the present invention, reconstructing the multi-layer decomposition signal based on the optimal number of decomposition layers to obtain the reconstructed signal includes: S301. Based on the optimal decomposition level, select one or more corresponding nodes; Specifically, according to the optimal number of layers , select the corresponding node for signal reconstruction.

[0036] S302. Perform inverse transformation on the selected node or multiple nodes to obtain a reconstructed signal.

[0037] Perform inverse transformation on the selected nodes to reconstruct the denoised signal : .

[0038] In step S400 of some embodiments of the present invention, determining the start and end times of the arcing signal according to the reconstructed signal includes comparing the reconstructed signal with a preset value to determine the start and end times of the arcing signal.

[0039] Specifically, the reconstructed signal Perform threshold judgment to detect the start and end time points of the arcing signal t start and t end : , ; Then, perform overtravel calculation: Calculate the overtravel Δt based on the start and end time points of the arcing signal: .

[0040] It can be understood that by adaptively determining the optimal number of layers based on the spectral characteristics of the signal, it does not rely on manually set thresholds and adapts to signals of different types and complexities; by comparing signal reconstructions with the optimal number of layers, the start and end times of the arcing signal can be accurately determined.

[0041] refer to Figure 4 , using the steps of the present invention, the upper figure is the original electromagnetic transient signal obtained, and the lower figure is the reconstructed signal after denoising. It can be seen that the signal is effectively denoised.

[0042] Example 2 refer to Figure 5 According to a second aspect of the present invention, a device 1 for processing arc signals of a GIS is provided, comprising: an acquisition module 11 for acquiring the arc signal of the GIS and calculating the power spectrum entropy of the arc signal; a calculation module 12 for performing wavelet packet decomposition on the arc signal to obtain a multi-layer decomposition signal; calculating the power spectrum entropy of each layer of the decomposition signal, and determining the optimal number of decomposition layers according to the power spectrum entropy; a reconstruction module 13 for reconstructing the multi-layer decomposition signal based on the optimal number of decomposition layers to obtain a reconstructed signal; and a determination module 14 for determining the start and end time of the arc signal according to the reconstructed signal, and calculating the overtravel of the arc signal.

[0043] Furthermore, the reconstruction module 13 includes: a selection unit for selecting one or more corresponding nodes based on the optimal decomposition layer number; a reconstruction unit for performing an inverse transformation on the selected node or nodes to obtain a reconstructed signal.

[0044] Example 3 refer to Figure 6 According to the third aspect of the present invention, an electronic device is provided, comprising: one or more processors; a storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the GIS arcing signal processing method according to the first aspect of the present invention.

[0045] The electronic device 500 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 501, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 502 or a program loaded from a storage device 508 into a random access memory (RAM) 503. Various programs and data required for the operation of the electronic device 500 are also stored in the RAM 503. The processing device 501, the ROM 502, and the RAM 503 are connected to each other via a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0046] Typically, the following devices may be connected to the I / O interface 505: an input device 506 including, for example, a touch screen, a touchpad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 507 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 508 including, for example, a hard disk, etc.; and a communication device 509. The communication device 509 may allow the electronic device 500 to communicate with other devices wirelessly or by wire to exchange data. Figure 6 The electronic device 500 is shown with various devices, but it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed instead. Figure 6 Each block shown in the figure may represent one device, or may represent multiple devices as needed.

[0047] In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication device 509, or installed from the storage device 508, or installed from the ROM 502. When the computer program is executed by the processing device 501, the above-mentioned functions defined in the method of the embodiment of the present disclosure are executed. It should be noted that the computer-readable medium described in the embodiment of the present disclosure can be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to, an electrical connection having one or more conductors, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In embodiments of the present disclosure, a computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In embodiments of the present disclosure, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries computer-readable program code. Such a propagated data signal may take a variety of forms, including, but not limited to, an electromagnetic signal, an optical signal, or any suitable combination thereof. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transfer a program for use by or in conjunction with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wire, optical cable, RF (radio frequency), etc., or any suitable combination thereof.

[0048] The computer-readable medium may be included in the electronic device, or may exist independently without being incorporated into the electronic device. The computer-readable medium carries one or more computer programs, which, when executed by the electronic device, cause the electronic device to: Computer program code for performing the operations of embodiments of the present disclosure may be written in one or more programming languages, or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, Python, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0049] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or a part of code, and the module, program segment, or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should be noted that each box in the block diagram and / or flowchart, and the combination of the boxes in the block diagram and / or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0050] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for processing GIS arcing signals, characterized in that: include: Obtain the arcing signal of GIS and calculate the power spectrum entropy of the arcing signal; Performing wavelet packet decomposition on the arcing signal to obtain a multi-layer decomposition signal; Calculate the power spectrum entropy of each layer of decomposition signal, and determine the optimal number of decomposition layers based on the power spectrum entropy; Based on the optimal number of decomposition layers, the multi-layer decomposition signal is reconstructed to obtain a reconstructed signal; According to the reconstructed signal, the start and end time of the arcing signal are determined, and the overtravel of the arcing signal is calculated.

2. The method for processing GIS arcing signals according to claim 1, characterized in that: The performing wavelet packet decomposition on the arcing signal to obtain a multi-layer decomposition signal comprises: The arcing signal is subjected to wavelet packet decomposition through DB4 wavelet basis function and scaling function.

3. The method for processing GIS arcing signals according to claim 2, characterized in that: The step of calculating the power spectrum entropy of each layer of decomposition signal and determining the optimal number of decomposition layers according to the power spectrum entropy comprises: According to the coefficients of the layer decomposition signal, the normalized power spectrum entropy of each layer decomposition signal is calculated; The optimal number of decomposition layers is determined based on the normalized power spectrum entropy and the power spectrum entropy of the original signal.

4. The method for processing GIS arcing signals according to claim 1, characterized in that: The reconstructing of the multi-layer decomposition signal based on the optimal number of decomposition layers to obtain the reconstructed signal includes: Based on the optimal number of decomposition levels, select one or more corresponding nodes; Perform an inverse transformation on the selected node or multiple nodes to obtain a reconstructed signal.

5. The method for processing GIS arcing signals according to claim 1, characterized in that: Determining the start and end time of the arcing signal according to the reconstructed signal includes: The reconstructed signal is compared with the preset value to determine the start and end time of the arcing signal.

6. The method for processing GIS arcing signals according to claim 1, characterized in that: The calculation of the power spectrum entropy of the arcing signal comprises: Calculate the power spectral density of the arcing signal; Normalizing the power spectral density; The power spectral entropy is calculated based on the normalized power spectral density.

7. A GIS arcing signal processing device, characterized in that: include: An acquisition module is used to acquire the arcing signal of the GIS and calculate the power spectrum entropy of the arcing signal; A calculation module, used for performing wavelet packet decomposition on the arcing signal to obtain a multi-layer decomposition signal; Calculate the power spectrum entropy of each layer of decomposition signal, and determine the optimal number of decomposition layers based on the power spectrum entropy; A reconstruction module, used to reconstruct the multi-layer decomposition signal based on the optimal decomposition layer number to obtain a reconstructed signal; The determination module is used to determine the start and end time of the arcing signal according to the reconstructed signal, and calculate the overtravel of the arcing signal.

8. The GIS arcing signal processing device according to claim 7, characterized in that: The reconstruction module includes: A selection unit, used for selecting one or more corresponding nodes based on the optimal decomposition layer number; The reconstruction unit is used to perform an inverse transformation on a selected node or multiple nodes to obtain a reconstructed signal.

9. An electronic device, comprising: one or more processors; A storage device for storing one or more programs, when the one or more programs are executed by the one or more processors, enables the one or more processors to implement the GIS arcing signal processing method as described in any one of claims 1 to 6.

10. A computer readable medium having a computer program stored thereon, wherein: When the computer program is executed by a processor, the method for processing GIS arcing signals as described in any one of claims 1 to 6 is implemented.

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