Signal synchronous acquisition and measurement embedded device and early warning system

Abstract

The invention discloses an embedded device for synchronous signal acquisition and measurement and an early warning system, comprising a signal acquisition position determination module for determining the installation position of each sensor in the signal acquisition module; a signal acquisition module for acquiring electromagnetic field signals inside a GIS; a signal processing front end for performing signal conditioning on the electromagnetic field signals transmitted by the signal acquisition module, and transmitting the conditioned signals to an analog-to-digital converter; an analog-to-digital converter for converting the conditioned signals from analog signals into digital signals and then transmitting them to a signal measurement module; a signal measurement module for monitoring digital signals based on strong correlation features obtained in advance screening, and obtaining a full time domain waveform diagram including a time domain waveform diagram and a frequency domain waveform diagram of the partial discharge signal when it is determined that a partial discharge signal is monitored, wherein the strong correlation features include time domain strong correlation features and frequency domain strong correlation features; the invention can realize high-precision synchronous detection of GIS partial discharge time domain full waveform data.

Description

Technical Field

[0001] The present invention relates to the technical field of electric power equipment, and in particular to an embedded device for synchronous signal acquisition and measurement and an early warning system. Background Art

[0002] In high-voltage gas insulated switchgear (GIS), partial discharge is a common fault phenomenon that can cause equipment performance degradation, damage, and even accidents. As an important representation of the defect state of GIS equipment, the research on its positioning and measurement technology is an important way to effectively perceive the status of GIS equipment and analyze and warn. Among them, accurate and rapid collection and measurement of partial discharge signals inside GIS is crucial for equipment health status monitoring and fault diagnosis.

[0003] In the related technology, discharge detection is generally based on the high-frequency electromagnetic wave signal (Ultra High Frequency, UHF) inside the GIS. However, the GIS is a fully enclosed structure with a complex internal structure. How to monitor the entire GIS on the basis of using the least sensors has become a major problem in the industry. In addition, most of the existing partial discharge signal acquisition devices use PRPD (Phase Resolved Partial Discharge) discharge spectra and other forms to transmit compressed partial discharge signals to the host computer, and cannot restore the detected high-frequency, complete full-time waveform partial discharge time domain signals to the host computer, and cannot meet the needs of accurate positioning of partial discharge signals inside the GIS. Since the sampling rate is proportional to the positioning accuracy, improving the sampling accuracy is bound to face problems such as insufficient chip computing resources. Therefore, due to the defects of low sampling frequency, weak detection performance, and huge data volume, the existing GIS partial discharge monitoring on the market has problems such as missed reports and high false alarm rates. Therefore, facing the needs of massive UHF time domain full waveform data acquisition, it is urgent to design a multi-channel, high-precision synchronous acquisition and measurement device. Summary of the invention

[0004] The technical problem to be solved by the present invention is how to achieve high-precision synchronous detection of GIS partial discharge time-domain full waveform data.

[0005] The present invention solves the above technical problems by the following technical means:

[0006] The present invention proposes an embedded device for synchronous signal acquisition and measurement, the device comprising a signal acquisition position determination module, a signal acquisition module, a signal processing front end, an analog-to-digital converter and a signal measurement module, wherein:

[0007] The signal acquisition position determination module is used to determine the installation position of each sensor in the signal acquisition module;

[0008] The signal acquisition module is used to collect electromagnetic field signals inside the GIS;

[0009] The signal processing front end is used to perform signal conditioning on the electromagnetic field signal transmitted by the signal acquisition module, and transmit the conditioned signal to the analog-to-digital converter;

[0010] The analog-to-digital converter is used to convert the conditioned signal from an analog signal to a digital signal and then transmit it to the signal measurement module;

[0011] The signal measurement module is used to monitor the digital signal based on the strongly correlated features obtained in advance, and when it is determined that a local discharge signal is monitored, a full time domain waveform diagram including a time domain waveform diagram and a frequency domain waveform diagram of the local discharge signal is obtained, wherein the strongly correlated features include time domain strong correlation features and frequency domain strong correlation features.

[0012] Furthermore, the signal acquisition location determination module includes a topology structure construction unit, a path analysis unit and a location optimization unit, wherein:

[0013] The topology structure building unit is used to model the internal node topology of the GIS and determine the internal topology of the GIS;

[0014] The path analysis unit is used to determine the shortest path between nodes according to the topological structure inside the GIS;

[0015] The position optimization unit is used to obtain a 0-1 planning model for sensor deployment based on the shortest path between nodes, and determine the installation position of sensors for global monitoring.

[0016] Furthermore, the signal processing front end includes amplifying and filtering circuits having the same number as the sensors in the signal acquisition module, and the amplifying and filtering circuits are connected to the sensors in a one-to-one correspondence;

[0017] The amplifying and filtering circuit includes a radio frequency gain circuit, a bandpass filter and a bandstop filter which are connected in sequence, wherein the frequency of the bandpass filter is 300MHz~1500MHz, and the frequency of the bandstop filter is 800MHz~1000MHz.

[0018] Furthermore, the analog-to-digital converter adopts an ADC chip with a data conversion rate of 4 channels at 4 Gsps or 1 channel at 5 Gsps.

[0019] Furthermore, the 0-1 planning model of the sensor layout includes an objective function and constraints, and the location optimization unit includes:

[0020] A function conversion subunit, used for converting the variables in the objective function into spin states to obtain an Ising energy function, wherein the variables are flags indicating whether a sensor is installed on a node;

[0021] A basic solution system solving subunit, used for converting the constraint conditions into constraint equations about variables and finding a basic solution system of the constraint equations;

[0022] The position solving subunit is used to introduce two unitary operators based on the Ising energy function and the basic solution system, and to build a quantum circuit based on the initial state and the unitary operators to solve the sensor installation position.

[0023] Furthermore, the 0-1 planning model of the sensor layout is:

[0024]

[0025] In the formula, and represents a vector of all 1s, express , Represents the propagation path matrix of electromagnetic waves in each segment.

[0026] Furthermore, the function conversion subunit is specifically used for:

[0027] The 0-1 variables in the objective function Convert to spin state ,make , the Ising energy function is obtained as:

[0028]

[0029] In the formula, for The coefficient of the term, is a constant.

[0030] Furthermore, the basic solution is a solving subunit, which is specifically used for:

[0031] The constraints Transformed into , the constraint equations about the variables are obtained as follows:

[0032]

[0033] In the formula, is the constraint matrix.

[0034] Furthermore, the position solving subunit is specifically used for:

[0035] Preparation of initial state , using Hadamard goalkeeper Transformed into a solution of the constraint equation;

[0036] Two unitary operators are introduced:

[0037]

[0038]

[0039]

[0040] Where: For the effect Pauli matrix, , , , is the constraint matrix, for The coefficient of is the Hamiltonian, which is transformed from the objective function required to obtain the optimal solution. is the Hamiltonian with constraints, , are two iteratively optimized parameters found by the gradient descent algorithm. , :

[0041]

[0042] in addition, , is the operator symbol of tensor product, For the qubits, To solve the system vectors, is the total number of quantum bits;

[0043] Will and Act on input separately , specifically by Door and Gate execution, obtain quantum circuit, and apply and Perform quantum evolution and obtain the evolved quantum state ;

[0044] In iteration to When the Hamiltonian of is equal to the minimum energy eigenstate, Convert to variable , determine the sensor installation location.

[0045] Furthermore, the device also includes a feature screening module, which is used to use the time-frequency domain features of the digital signal as nodes of the graph neural network to perform correlation strength screening to obtain the time-domain strong correlation features and the frequency-domain strong correlation features, wherein the lines between the nodes represent the relationship between the features represented by the nodes.

[0046] Furthermore, the time-frequency domain characteristics include margin, skewness, root mean square, mean, variance, peak-to-peak value, shape factor, kurtosis, impulse factor and C index; the frequency domain characteristics include mean frequency, frequency concentration, frequency center, root mean square frequency and main frequency band position change.

[0047] Furthermore, the feature screening module includes:

[0048] Feature map construction unit for complete feature maps in graph neural networks middle, is a node set, is the edge set, let Respectively represent nodes The eigenvectors of the edges, the eigenvectors of the nodes and the state vectors of its surrounding nodes and nodes The feature vectors of surrounding nodes;

[0049] The aggregation update unit is used to aggregate and update the information of input nodes and edges according to the local transfer function of updating the node state, and output the node label. The formula is expressed as:

[0050]

[0051]

[0052] In the formula, is the local transition function for updating the node state, is the local output function, is the state vector learned by the graph neural network iteration, is the node label;

[0053] Iteration unit, used to calculate the association between a node and its adjacent nodes in the continuous iteration of the graph neural network, and obtain the strong correlation features in the time domain and the strong correlation features in the frequency domain

[0054] Furthermore, the iteration unit is specifically used for:

[0055] set up and are the vectors constructed by superimposing the state vectors of all nodes, all output labels, the feature vectors of all nodes and edges, and the feature vectors of all nodes. The formula can be written in a more compact form as follows:

[0056]

[0057]

[0058] in, express No. Iterations, Represented by the eigenvector and the The state vector of the iteration is obtained by the global transfer function The state vector of all nodes in the iteration, and are the global transfer function and the global output function, respectively, and are the local transfer functions of all nodes. and local output function Stacking;

[0059] During the iteration process, nodes with similar states, nodes with complementary states, and the influence of each node on the overall graph neural network are determined. One of the nodes with similar states is selected, nodes with complementary states are merged into one node, and the node with the greatest influence on the overall graph neural network is used as a strong correlation feature.

[0060] Further, the signal measurement module includes a cache unit, a first monitoring unit, a second monitoring unit and a waveform synthesis unit, wherein:

[0061] The cache unit is used to cache the digital signal;

[0062] The first monitoring unit is used to extract the time domain strong correlation feature from the digital signal, determine that a partial discharge signal is monitored based on the time domain strong correlation feature, and output a first time domain waveform diagram during discharge;

[0063] The second monitoring unit is used to extract the frequency domain strong correlation feature from the digital signal, determine that a partial discharge signal is monitored based on the frequency domain strong correlation feature, and output a frequency domain waveform diagram during discharge;

[0064] The waveform synthesis unit is used to obtain a full time domain waveform diagram of the partial discharge signal based on the first time domain waveform diagram and the frequency domain waveform diagram.

[0065] Furthermore, the first monitoring unit is used to analyze the time domain strong correlation feature to determine whether it exceeds a first discharge threshold, and if so, determine that a partial discharge signal is monitored, and if not, determine that no partial discharge signal is monitored;

[0066] The second monitoring unit is used to analyze the frequency domain strong correlation feature to determine whether it exceeds a second discharge threshold, and if so, determine that a partial discharge signal is monitored; otherwise, determine that no partial discharge signal is monitored.

[0067] Furthermore, when no partial discharge signal is detected, the digital signal stored in the cache unit is deleted.

[0068] Furthermore, the second monitoring unit includes:

[0069] A Fourier transform subunit, used for performing at least two fast Fourier transforms on the electromagnetic field signal to obtain frequency domain data of at least two frequency bands;

[0070] The normalization subunit is used to extract the frequency domain strong correlation features from the frequency domain data of each frequency band, and after analyzing and matching the frequency domain strong correlation features, when it is determined that the frequency domain data of each frequency band exceeds the second discharge threshold, the frequency domain waveform corresponding to each frequency band is normalized to obtain a frequency domain waveform diagram during discharge.

[0071] Furthermore, the waveform synthesis unit comprises:

[0072] A waveform conversion subunit, used for converting the frequency domain waveform diagram into a second time domain waveform diagram during discharge;

[0073] The waveform synthesis subunit is used to synthesize the first time domain waveform diagram and the second time domain waveform diagram to obtain a full time domain waveform diagram of the partial discharge signal.

[0074] In addition, the present invention also proposes a multi-structure adaptive GIS field-electric fusion real-time status perception and early warning system, the system includes an industrial computer and the signal synchronization acquisition and measurement embedded device as described above, the industrial computer is connected to the signal synchronization acquisition and measurement embedded device, the signal synchronization acquisition and measurement embedded device is used to collect the time domain full waveform data of GIS local discharge and send the time domain full waveform data to the industrial computer, the industrial computer is used to search for the actual discharge position of the GIS local discharge source based on the time domain full waveform data.

[0075] The advantages of the present invention are:

[0076] (1) The present invention realizes multi-channel acquisition of electromagnetic field signals inside GIS by setting a multi-channel acquisition module, and conditions the acquired GIS internal electric field signals to obtain high-performance electromagnetic field signals. Then, the digital signal converted from the electromagnetic field signal is detected using the strong correlation characteristics obtained by pre-screening as an indicator. When it is determined that a local discharge signal is monitored, the time domain waveform and the frequency domain waveform are respectively obtained according to the time domain strong correlation characteristics and the frequency domain strong correlation characteristics, and the time domain waveform and the frequency domain waveform are integrated to obtain a full time domain waveform. Therefore, the present invention can realize the acquisition of a high-precision full time domain waveform of GIS local discharge.

[0077] (2) The present invention solves the planning model of sensor point distribution by using a quantum approximate optimization algorithm, and can solve the optimal layout of GIS partial discharge measurement points within a polynomial time. When faced with complex scenarios that are almost impossible to calculate with traditional computers, using quantum computing to solve will be faster. However, the probability of obtaining optimal performance by imposing constraints through penalty terms is very small. Therefore, the present invention calculates the basic solution system of constraint conditions and imposes constraints on the easy-to-drive Hamiltonian, which can greatly increase the probability that the final observed calculation results meet the requirements and are more inclined to the optimal solution. Therefore, the present invention can quickly and accurately determine the layout of GIS partial discharge measurement points, so as to detect and locate partial discharge signals inside GIS and eliminate hidden dangers inside GIS in a timely manner.

[0078] (3) The present invention substitutes the time-frequency domain features of multiple electromagnetic field signal detection into the graph neural network, and uses each time-frequency domain feature as a node of the graph neural network. The lines between the nodes represent the association between the features represented by the nodes. The graph neural network is iterated to perform association strength screening to obtain the features with the highest correlation, i.e., the strongly correlated features. Then, the local discharge signal is detected based on the screened strongly correlated features, which reduces the amount of calculation. However, since the correlation between the indicators is taken into account, the accuracy of the local discharge signal detection is not affected. Therefore, the present invention improves the accuracy of the local discharge signal detection while greatly reducing the amount of calculation.

[0079] (4) When the amount of collected signal data is large, if FFT calculation is performed directly, the processing speed is slow due to the large amount of data, which is not conducive to real-time signal detection and processing. Therefore, the present invention adopts array processing, divides the FFT results into at least two frequency bands for parallel processing, and calculates the correlation of the waveforms of at least two frequency bands respectively, which can increase the basis for correlation determination to multiple, thereby greatly increasing the amount of information reflecting the relative deformation of the waveform, and at the same time can fully reflect the changes in the frequency response curve in different frequency intervals due to different degrees of discharge, and finally fuse the waveforms of multiple frequency bands. Therefore, the array processing mechanism of the present invention is a frequency domain segmentation and combination mechanism. Each part in the array only processes a part of the frequency domain, and then fuses them together to restore the complete frequency domain waveform. The mechanism disperses the noise signal superimposed on the frequency domain data to the segmented part to reduce the stacking of noise signals, which will help to achieve high-sensitivity detection of local discharge UHF signals.

[0080] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0081] Figure 1 It is a structural schematic diagram of an embedded device for synchronous signal acquisition and measurement proposed by an embodiment of the present invention;

[0082] Figure 2 It is a principle block diagram of an embedded device for synchronous signal acquisition and measurement in one embodiment of the present invention;

[0083] Figure 3 is a schematic diagram of a calculation flow of a global waveform diagram of a partial discharge signal in one embodiment of the present invention;

[0084] Figure 4 It is a flow chart of implementing a quantum approximate optimization algorithm based on constraints on hetero-driven Hamiltonians in one embodiment of the present invention;

[0085] Figure 5 is a complete feature graph of a graph neural network in one embodiment of the present invention;

[0086] Figure 6 It is a schematic diagram of the neural network iteration process in one embodiment of the present invention. DETAILED DESCRIPTION

[0087] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in combination with the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. 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.

[0088] like Figure 1 As shown, an embodiment of the present invention proposes an embedded device for synchronous signal acquisition and measurement, characterized in that the device includes a signal acquisition position determination module 10, a signal acquisition module 20, a signal processing front end 30, an analog-to-digital converter 40 and a signal measurement module 50, wherein:

[0089] The signal acquisition position determination module 10 is used to determine the installation position of each sensor in the signal acquisition module 20;

[0090] The signal acquisition module 20 is used to collect electromagnetic field signals inside the GIS;

[0091] The signal processing front end 30 is used to perform signal conditioning on the electromagnetic field signal transmitted by the signal acquisition module 20, and transmit the conditioned signal to the analog-to-digital converter 40;

[0092] The analog-to-digital converter 40 is used to convert the conditioned signal from an analog signal to a digital signal and transmit it to the signal measurement module 50;

[0093] The signal measurement module 50 is used to monitor the digital signal based on the strongly correlated features obtained in advance, and when it is determined that a local discharge signal is monitored, a full time domain waveform diagram including a time domain waveform diagram and a frequency domain waveform diagram of the local discharge signal is obtained, wherein the strongly correlated features include time domain strong correlation features and frequency domain strong correlation features.

[0094] This embodiment realizes multi-channel acquisition of electromagnetic field signals inside the GIS by setting up a multi-channel acquisition module, and conditions the acquired GIS internal electric field signals to obtain high-performance electromagnetic field signals. Thereafter, the strong correlation characteristics obtained by pre-screening are used as indicators to detect the digital signals converted from the electromagnetic field signals, and when it is determined that a local discharge signal is monitored, the time domain waveform diagram and the frequency domain waveform diagram are respectively obtained according to the time domain strong correlation characteristics and the frequency domain strong correlation characteristics, and the time domain waveform diagram and the frequency domain waveform diagram are integrated to obtain a full time domain waveform diagram; therefore, the present invention can realize the acquisition of a high-precision full time domain waveform diagram of GIS local discharge.

[0095] As a further preferred technical solution, Figure 2As shown, the signal processing front end 30 includes amplifying and filtering circuits having the same number as the sensors in the signal acquisition module, and the amplifying and filtering circuits are connected to the sensors in a one-to-one correspondence;

[0096] The amplifying and filtering circuit includes a radio frequency gain circuit, a bandpass filter and a bandstop filter which are connected in sequence, wherein the frequency of the bandpass filter is 300MHz~1500MHz, and the frequency of the bandstop filter is 800MHz~1000MHz.

[0097] It should be noted that this embodiment performs bandpass and band-stop filtering according to the effective frequency band and common interference frequency band of the UHF signal in GIS, and is more suitable for UHF signal acquisition in GIS. The set bandpass filter is used to extract useful signals in a specific frequency band and filter out irrelevant interference signals and noise, and a low-noise, wide-band, cascadeable and high-linearity signal amplifier can be connected after the band-stop filter.

[0098] As a preferred technical solution, the analog-to-digital converter 40 uses an ADC chip with a data conversion rate of 4Gsps for 4 channels or 5Gsps for 1 channel. The synchronous acquisition and measurement embedded device set in this embodiment can realize 4GSPS 4-channel high-speed data acquisition and real-time processing, and supports multi-channel sampling, which can realize simultaneous acquisition of 4-channel data, with a signal input range of 1.4V, 12-bit data at a sampling rate of 4GSPS, and at the same time, the signal processing logic with fixed functions is placed in the hardware FPGA to improve the parallel speed of the system and realize multi-channel, high-precision GIS internal electromagnetic field signal acquisition.

[0099] As a further preferred technical solution, the signal measurement module 50 includes a cache unit, a first monitoring unit, a second monitoring unit and a waveform synthesis unit, wherein:

[0100] The cache unit is used to cache the digital signal;

[0101] The first monitoring unit is used to extract the time domain strong correlation feature from the digital signal, determine that a partial discharge signal is monitored based on the time domain strong correlation feature, and output a first time domain waveform diagram during discharge;

[0102] The second monitoring unit is used to extract the frequency domain strong correlation feature from the digital signal, determine that a partial discharge signal is monitored based on the frequency domain strong correlation feature, and output a frequency domain waveform diagram during discharge;

[0103] The waveform synthesis unit is used to obtain a full time domain waveform diagram of the partial discharge signal based on the first time domain waveform diagram and the frequency domain waveform diagram.

[0104] As a further preferred technical solution, the first monitoring unit is used to analyze the time domain strong correlation feature to determine whether it exceeds a first discharge threshold, and if so, determine that a partial discharge signal is monitored, and if not, determine that no partial discharge signal is monitored;

[0105] The second monitoring unit is used to analyze the frequency domain strong correlation feature to determine whether it exceeds a second discharge threshold, and if so, determine that a partial discharge signal is monitored; otherwise, determine that no partial discharge signal is monitored.

[0106] As a further preferred technical solution, when no partial discharge signal is detected, the digital signal stored in the cache unit is deleted.

[0107] It should be noted that the collected time domain data is completely sent to the high-performance storage cache unit (Buffer). If it is determined that no partial discharge UHF signal is detected, the detected time domain data is deleted from the Buffer to release storage space for the next collected time domain data.

[0108] As a further preferred technical solution, the second monitoring unit includes:

[0109] A Fourier transform subunit, used for performing at least two fast Fourier transforms on the electromagnetic field signal to obtain frequency domain data of at least two frequency bands;

[0110] The normalization subunit is used to extract the frequency domain strong correlation features from the frequency domain data of each frequency band, and after analyzing and matching the frequency domain strong correlation features, when it is determined that the frequency domain data of each frequency band exceeds the second discharge threshold, the frequency domain waveform corresponding to each frequency band is normalized to obtain a frequency domain waveform diagram during discharge.

[0111] When the amount of collected signal data is large, if FFT calculation is performed directly, the processing speed is slow due to the large amount of data, which is not conducive to real-time signal detection and processing. Therefore, the present invention adopts array processing, such as Figure 3As shown, the FFT results are divided into four frequency bands for parallel processing (the frequency bands are 200M-525M, 525M-850M, 850M-1175M, and 1175M-1500M, respectively). The correlations on the four frequency bands are calculated respectively, which can increase the basis for correlation determination to multiple, thereby greatly increasing the amount of information reflecting the relative deformation of the waveform, and at the same time fully reflecting the changes in the frequency response curve in different frequency intervals due to different degrees of discharge, and finally the waveforms of multiple frequency bands are fused. Therefore, the array processing mechanism of the present invention is a frequency domain segmentation and combination mechanism, in which each part of the array only processes a part of the frequency domain, and then they are fused together to restore the complete frequency domain waveform. The mechanism disperses the noise signal superimposed on the frequency domain data to the segmented part, reducing the stacking of noise signals, which will help to achieve high-sensitivity detection of local discharge UHF signals.

[0112] As a further preferred technical solution, the waveform synthesis unit includes:

[0113] A waveform conversion subunit, used for converting the frequency domain waveform diagram into a second time domain waveform diagram during discharge;

[0114] The waveform synthesis subunit is used to synthesize the first time domain waveform diagram and the second time domain waveform diagram to obtain a full time domain waveform diagram of the partial discharge signal.

[0115] It should be noted that this embodiment selects Xilinx UltraScale+MPSoC FPGA chip to process digital signals, receives sampling data from analog-to-digital converter ADC based on buffer unit buffer, calculates characteristic indexes of transmitted time domain waveform data, matches data, and converts time domain waveform into frequency domain waveform by FFT, and calculates characteristic indexes after arraying frequency domain waveform, and performs data matching, normalizes the obtained frequency domain waveform, performs IFFT after normalization, obtains time domain waveform, and outputs full waveform data in time domain after synthesizing with waveform graph directly obtained in time domain. This embodiment proposes frequency domain arraying method and real-time digital matching method, develops GIS partial discharge UHF signal synchronous acquisition and measurement embedded device based on ultra-high speed ADC and FPGA architecture, and obtains high-precision full waveform data in time domain of partial discharge UHF signal.

[0116] Furthermore, due to the complex structure of GIS, in order to monitor the entire GIS network using the least sensors, this embodiment further provides a signal acquisition position determination module for quickly determining the location of the GIS partial discharge measurement point, so as to collect the electromagnetic field signal inside the GIS according to the deployed sensors. The signal acquisition position determination module includes a topology structure construction unit, a path analysis unit and a position optimization unit, wherein:

[0117] The topology structure building unit is used to model the internal node topology of the GIS and determine the internal topology of the GIS;

[0118] The path analysis unit is used to determine the shortest path between nodes according to the topological structure inside the GIS;

[0119] The position optimization unit is used to obtain a 0-1 planning model for sensor deployment based on the shortest path between nodes, and determine the installation position of sensors for global monitoring.

[0120] Specifically, the construction process of the 0-1 planning model for sensor deployment is as follows: number each node of the GIS equipment, measure the topological relationship and line length between nodes, and use variables As a sign of whether a node is equipped with a sensor, the measurable segment discharge is defined as the path of the electromagnetic wave generated by the discharge point of any segment to the deployed sensor, which has at least one path passing through the beginning of the segment and one path passing through the end of the segment. As a flag for whether the node has installed the sensor:

[0121]

[0122] In the system The length of a segment is , section start To The shortest distance between the sensors of nodes is , segment end To The shortest distance between the sensors of nodes is , is the actual discharge potential to the beginning of the segment The distance accounts for the length of the segment ratio.

[0123] by Indicates Discharge point of the segment The electromagnetic waves generated reach the node The shortest distance is:

[0124]

[0125] If electromagnetic waves pass through Arrive at The nodes take the shortest time. ,by Indicates The discharge electromagnetic wave on the line reaches the The case where the shortest distance passes through the line endpoint is:

[0126]

[0127] To ensure the line The discharge electromagnetic wave can reach a pair of sensors from both ends of the line. For all lines, this objective function and constraint conditions are met:

[0128]

[0129] The above objective function and constraints are converted into matrix form to establish the 0-1 planning model of sensor deployment:

[0130]

[0131] In the formula, and represents a vector of all 1s, express , Represents the propagation path matrix of electromagnetic waves in each segment.

[0132] Specifically, the formula of the propagation path matrix of the electromagnetic wave in each segment is expressed as:

[0133]

[0134] In the formula, Indicates The discharge electromagnetic wave on the line reaches the nodes, is it through The shortest route.

[0135] It should be noted that is the configuration vector to be solved, As a sign of whether a node is equipped with a sensor, the installation location of the sensor for global monitoring can be obtained.

[0136] As a further preferred technical solution, the location optimization unit includes:

[0137] A function conversion subunit, used for converting the variables in the objective function into spin states to obtain an Ising energy function, wherein the variables are flags indicating whether a sensor is installed on a node;

[0138] A basic solution system solving subunit, used for converting the constraint conditions into constraint equations about variables and finding a basic solution system of the constraint equations;

[0139] The position solving subunit is used to introduce two unitary operators based on the Ising energy function and the basic solution system, and to build a quantum circuit based on the initial state and the unitary operators to solve the sensor installation position.

[0140] As a further preferred technical solution, the function conversion subunit is specifically used for:

[0141] The 0-1 variables in the objective function Convert to spin state ,make , the Ising energy function is obtained as:

[0142]

[0143] In the formula, for The coefficient of the term, is a constant.

[0144] As a further preferred technical solution, the basic solution system solving subunit is specifically used for:

[0145] The constraints Transformed into , the constraint equations about the variables are obtained as follows:

[0146]

[0147] In the formula, is the constraint matrix.

[0148] As a further preferred technical solution, the position solving subunit is specifically used for:

[0149] Preparation of initial state , using Hadamard goalkeeper Transformed into a solution of the constraint equation;

[0150] It should be noted that the initial state Must meet For example, if the feasible solution is [1,0,1], The gate needs to be applied to the 0th and 2nd qubits to generate the initial state. In simple terms, we need to select any feasible solution through door to prepare an initial state.

[0151] Two unitary operators are introduced:

[0152]

[0153]

[0154]

[0155] Where: For the effect Pauli matrix, , , , is the constraint matrix, for The coefficient of is the Hamiltonian, which is transformed from the objective function required to obtain the optimal solution. is the Hamiltonian with constraints, , are two iteratively optimized parameters found by the gradient descent algorithm. ,

[0156]

[0157] in addition, , is the operator symbol of tensor product, For the qubits, To solve the system vectors, is the total number of quantum bits;

[0158] Will and Act on input separately , specifically by Door and Gate execution, obtain quantum circuit, and apply and Perform quantum evolution and obtain the evolved quantum state ;

[0159] In iteration to When the Hamiltonian of is equal to the minimum energy eigenstate, Convert to variable , determine the sensor installation location.

[0160] As a further preferred technical solution, Figure 4 As shown, taking three qubits as an example, the QAOA quantum circuit based on the constraint of the different-driven Hamiltonian is used in this embodiment. After multiple measurements, we can get the loss function The expected value is as follows:

[0161]

[0162] The gradient descent algorithm is used to optimize the parameters and Perform optimization and then use the optimized parameters and Input the quantum computer to prepare the next quantum state:

[0163]

[0164] Where: To prevent optimization steps To optimize the step size is the gradient solver operator.

[0165] After the parameters and Multiple iterations of When the Hamiltonian of is equal to the minimum energy eigenstate, The expected value of reaches the minimum, at this time the final quantum state is measured, through Convert to variable , get the flag of whether the sensor is installed, and find out the installation position of the sensor for global monitoring.

[0166] Furthermore, since there are multiple time-frequency domain indicators for detecting local discharge signals, if all time-frequency domain indicators are calculated to detect local discharge signals, the detection results are theoretically very accurate. However, if the detection and judgment are based on all time-frequency domain indicators, it will take up a large amount of computing resources, and the computing resources of the current hardware cannot be calculated. The accuracy cannot be met by randomly selecting certain features for calculation. This embodiment is designed to use a neural network to screen out strongly correlated features for subsequent local discharge signal detection through a feature screening module, thereby greatly reducing the amount of calculation and improving the accuracy of local discharge signal detection. Specifically, the time-frequency domain features include margin, skewness, root mean square, mean, variance, peak-to-peak value, shape factor, kurtosis, pulse factor and C index; the frequency domain features include mean frequency, frequency concentration, frequency center, root mean square frequency and main frequency band position change. The specific time-frequency domain features for detecting local discharge signals are shown in Table 1:

[0167] Table 1 Proposed time-frequency domain features

[0168]

[0169] Where: is the maximum absolute value (peak value), is the square root amplitude, is the skewness, Time domain waveform data The third-order center distance, Time domain waveform data The standard deviation of is the root mean square, For the Time domain data, is the total number of time domain data points, is the shape factor, For the deviation: is the kurtosis, is the pulse factor, is the time domain waveform data, is the mean frequency, is the amplitude of each frequency on the spectrum, is a fixed coefficient used to adjust the ratio in the formula. is the frequency concentration index 1, is the frequency concentration index 2, is the frequency concentration index 3, is the frequency center index, is the frequency of each point on the spectrum, is the frequency concentration index 4, is the root mean square frequency index, is the main frequency band position change index 1, is the main frequency band position change index 2, is the frequency concentration index 5, is the frequency concentration index 6, is the frequency concentration index 7, is the frequency concentration index 8, For the multiplication sign.

[0170] It should be noted that the indicators listed in Table 1 are common time-frequency domain indicators, some of which contain certain characteristics of UHF and are common UHF signal indicators. These characteristic indicators have more or less information that can be used for UHF signal detection, but this information may be repeated or complementary in multiple features. Therefore, this embodiment uses a graph neural network to screen out indicators with strong correlation among these characteristic indicators.

[0171] As a further preferred technical solution, the feature screening module includes:

[0172] Feature map construction unit for complete feature maps in graph neural networks middle, is a node set, is the edge set, let Respectively represent nodes The eigenvectors of the edges, the eigenvectors of the nodes and the state vectors of its surrounding nodes and nodes The feature vectors of surrounding nodes;

[0173] The aggregation update unit is used to aggregate and update the information of input nodes and edges according to the local transfer function of updating the node state, and output the node label. The formula is expressed as:

[0174]

[0175]

[0176] In the formula, is the local transition function for updating the node state, is the local output function, is the state vector learned by the graph neural network iteration, is the node label;

[0177] The iteration unit is used to calculate the correlation between a node and its adjacent nodes in the continuous iteration of the graph neural network, and obtain the strong correlation features in the time domain and the strong correlation features in the frequency domain.

[0178] As a further preferred technical solution, the iteration unit is specifically used for:

[0179] set up and are the vectors constructed by superimposing the state vectors of all nodes, all output labels, the feature vectors of all nodes and edges, and the feature vectors of all nodes. The formula can be written in a more compact form as follows:

[0180]

[0181]

[0182] in, express No. Iterations, Represented by the eigenvector and the The state vector of the iteration is obtained by the global transfer function The state vector of all nodes in the iteration, and are the global transfer function and the global output function, respectively, and are the local transfer functions of all nodes. and local output function Stacking;

[0183] During the iteration process, nodes with similar states, nodes with complementary states, and the influence of each node on the overall graph neural network are determined. One of the nodes with similar states is selected, nodes with complementary states are merged into one node, and the node with the greatest influence on the overall graph neural network is used as a strong correlation feature.

[0184] It should be noted that the characteristic vector of a node is some inherent state of the node, which will not change during the iteration process of the graph neural network. Specifically in this embodiment, it is some parameter indicators in the time-frequency domain feature calculation formula of the electromagnetic field signal, such as standard deviation, peak value, etc. The characteristic vector of an edge refers to the connection between two nodes. Specifically in this embodiment, it refers to the same parameter indicators or similar processing processes in the calculation formula between nodes. Connecting these similar feature nodes helps the model learn the relationship and mutual influence between these features. The state vector refers to the continuously updated node vector obtained according to the operation during the iteration process; the node label is the target value used for supervised learning tasks. In this embodiment, each node represents a signal feature, and the corresponding label is used to represent the category and state of these signal features.

[0185] This embodiment uses the graph neural network GNN to calculate the connection between a node and its adjacent nodes. Through multiple iterations, the relationship between the adjacent nodes of the adjacent nodes and the current node can also be found, which is very suitable for screening strong correlation features. This embodiment imports all the features of the GIS partial discharge time-frequency domain signal (as shown in Table 1 above) into the graph neural network as nodes in the network, and constructs a complete feature map as shown in Figure 5 As shown, Figure 5 Each node in the ,represents a single feature, and the lines between nodes represent the ,relationship between the features represented by the nodes. Some features are highly ,related, such as and The Chinese Communist Party , while the relationship between some features is weak, so no connection is made. Blank nodes represent features that are not closely related to other features.

[0186] like Figure 6 As shown in the figure, during the iteration process of the graph neural network, the feature vector of the node itself will not change with the network iteration; while the state vector between nodes changes with the iteration of the GNN network. Therefore, the graph neural network can calculate the connection between a node and its adjacent nodes. Through multiple iterations, the connection between the adjacent nodes of the adjacent nodes and the current node can also be found. Through the continuous iteration of the graph neural network, it can be found that the states of some nodes in the graph neural network are becoming more and more similar, the states of some nodes are complementary, and the influence of some nodes on the overall graph neural network becomes smaller. Select one of the similar nodes, combine the complementary nodes into one, and then select the node that has the greatest impact on the overall system, that is, obtain the strong correlation characteristics of the system.

[0187] The graph neural network used in this embodiment constrains the model in advance through the loss function, and finally uses the gradient descent algorithm to minimize the loss, thereby obtaining the optimal parameters of the network model and obtaining a trained graph neural network. This embodiment substitutes the features in Table 1 above into the graph neural network, performs correlation strength screening, and obtains the features with the highest correlation, that is, the results of the strong correlation features are the root mean square, pulse factor and margin index in the time domain, the root mean square frequency index, main frequency band position change index 1 and main frequency band position change index 2 in the frequency domain. Then, the local discharge signal is detected based on these screened strong correlation features. Although the amount of calculation is reduced, the accuracy of the local discharge signal detection is not affected because the correlation between the indicators is considered. Therefore, the present invention improves the accuracy of the local discharge signal detection under the premise of greatly reducing the amount of calculation.

[0188] In addition, the second embodiment of the present invention also proposes a multi-structure adaptive GIS field-electric fusion real-time status perception and early warning system, the system includes an industrial computer and a signal synchronization acquisition and measurement embedded device as described in the first embodiment above, the industrial computer is connected to the signal synchronization acquisition and measurement embedded device, the signal synchronization acquisition and measurement embedded device is used to collect the time domain full waveform data of GIS local discharge and send the time domain full waveform data to the industrial computer, the industrial computer is used to search for the actual discharge position of the GIS local discharge source based on the time domain full waveform data.

[0189] It should be noted that other embodiments or implementation methods of the embedded device for synchronous signal acquisition and measurement of the present invention can refer to the first embodiment mentioned above, which will not be described in detail here.

[0190] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0191] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of the present invention, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0192] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. A signal synchronous acquisition and measurement embedded device, characterized in that: The device comprises a signal acquisition position determination module, a signal acquisition module, a signal processing front end, an analog-to-digital converter and a signal measurement module, wherein: The signal acquisition position determination module is used to determine the installation position of each sensor in the signal acquisition module based on the 0-1 planning model of sensor layout; The signal acquisition module is used to collect electromagnetic field signals inside the GIS; The signal processing front end is used to perform signal conditioning on the electromagnetic field signal transmitted by the signal acquisition module, and transmit the conditioned signal to the analog-to-digital converter; The analog-to-digital converter is used to convert the conditioned signal from an analog signal to a digital signal and then transmit it to the signal measurement module; The signal measurement module is used to monitor the digital signal based on the strong correlation features obtained in advance, and when it is determined that a partial discharge signal is monitored, obtain a full time domain waveform diagram including a time domain waveform diagram and a frequency domain waveform diagram of the partial discharge signal, wherein the strong correlation features include a time domain strong correlation feature and a frequency domain strong correlation feature; The signal measurement module includes a first monitoring unit, a second monitoring unit and a waveform synthesis unit. The first monitoring unit is used to extract the time domain strong correlation feature of the digital signal, determine that a local discharge signal is monitored based on the time domain strong correlation feature, and output a first time domain waveform diagram during discharge; the second monitoring unit is used to extract the frequency domain strong correlation feature of the digital signal, determine that a local discharge signal is monitored based on the frequency domain strong correlation feature, and output a frequency domain waveform diagram during discharge; the waveform synthesis unit is used to obtain a full time domain waveform diagram of the local discharge signal based on the first time domain waveform diagram and the frequency domain waveform diagram.

2. The embedded device for synchronous signal acquisition and measurement as claimed in claim 1, characterized in that: The signal acquisition location determination module includes a topology structure construction unit, a path analysis unit and a location optimization unit, wherein: The topology structure building unit is used to model the internal node topology of the GIS and determine the internal topology of the GIS; The path analysis unit is used to determine the shortest path between nodes according to the topological structure inside the GIS; The position optimization unit is used to obtain a 0-1 planning model for sensor deployment based on the shortest path between nodes, and determine the installation position of sensors for global monitoring.

3. The embedded device for synchronous signal acquisition and measurement as claimed in claim 1, characterized in that: The signal processing front end includes amplifying and filtering circuits having the same number as the sensors in the signal acquisition module, and the amplifying and filtering circuits are connected to the sensors in a one-to-one correspondence; The amplifying and filtering circuit includes a radio frequency gain circuit, a bandpass filter and a bandstop filter which are connected in sequence, wherein the frequency of the bandpass filter is 300MHz~1500MHz, and the frequency of the bandstop filter is 800MHz~1000MHz.

4. The embedded device for synchronous signal acquisition and measurement as claimed in claim 1, characterized in that: The analog-to-digital converter adopts an ADC chip with a data conversion rate of 4 channels at 4 Gsps or 1 channel at 5 Gsps.

5. The embedded device for synchronous signal acquisition and measurement as claimed in claim 2, characterized in that: The 0-1 planning model of the sensor point arrangement includes an objective function and constraints, and the position optimization unit includes: A function conversion subunit, used for converting the variables in the objective function into spin states to obtain an Ising energy function, wherein the variables are flags indicating whether a sensor is installed on a node; A basic solution system solving subunit, used for converting the constraint conditions into constraint equations about variables and finding a basic solution system of the constraint equations; The position solving subunit is used to introduce two unitary operators based on the Ising energy function and the basic solution system, and to build a quantum circuit based on the initial state and the unitary operators to solve the sensor installation position.

6. The embedded device for synchronous signal acquisition and measurement as claimed in claim 5, characterized in that: The 0-1 planning model of the sensor layout is: In the formula, and represents a vector of all 1s, express , Represents the propagation path matrix of electromagnetic waves in each segment, is the configuration vector to be solved, It is the flag of whether the node is equipped with a sensor, and N represents the number of nodes.

7. The embedded device for synchronous signal acquisition and measurement as claimed in claim 5, characterized in that: The function conversion subunit is specifically used for: The 0-1 variables in the objective function Convert to spin state ,make , the Ising energy function is obtained as: In the formula, for The coefficient of the term, is a constant, A flag indicating whether the node has a sensor installed.

8. The embedded device for synchronous signal acquisition and measurement as claimed in claim 6, characterized in that: The basic solution system solving subunit is specifically used for: The constraints Transformed into , the constraint equations about the variables are obtained as follows: In the formula, is the constraint matrix.

9. The embedded device for synchronous signal acquisition and measurement according to claim 7, characterized in that: The position solving subunit is specifically used for: Preparation of initial state , using Hadamard goalkeeper Transformed into a solution of the constraint equation; Two unitary operators are introduced: Where: For the effect Pauli matrix, , , , is the constraint matrix, for The coefficient of is the Hamiltonian, which is transformed from the objective function required to obtain the optimal solution. is the Hamiltonian with constraints, , are two iteratively optimized parameters found by the gradient descent algorithm. , in order to ensure that the Hamiltonian H and the constraint operator C are commutative, , in, , is the operator symbol of tensor product, For the qubits, To solve the system vectors, is the total number of quantum bits; Will and Act on input separately , specifically by Door and Gate execution, obtain quantum circuit, and apply and Perform quantum evolution and obtain the evolved quantum state ; In iteration to When the Hamiltonian of is equal to the minimum energy eigenstate, Convert to variable , determine the sensor installation location.

10. The embedded device for synchronous signal acquisition and measurement according to claim 1, characterized in that: The device also includes a feature screening module, which is used to use the time-frequency domain features of the digital signal as nodes of the graph neural network to perform correlation strength screening to obtain the time-domain strong correlation features and the frequency-domain strong correlation features, wherein the lines between the nodes represent the relationship between the features represented by the nodes.

11. The embedded device for synchronous signal acquisition and measurement according to claim 10, characterized in that: The time-frequency domain features include margin, skewness, root mean square, mean, variance, peak-to-peak value, shape factor, kurtosis, impulse factor and C index; the frequency domain features include mean frequency, frequency concentration, frequency center, root mean square frequency and main frequency band position change.

12. The embedded device for synchronous signal acquisition and measurement according to claim 10, characterized in that: The feature screening module comprises: Feature map construction unit for complete feature maps in graph neural networks middle, is a node set, is the edge set, let Respectively represent nodes The eigenvectors of the edges, the eigenvectors of the nodes and the state vectors of its surrounding nodes and nodes The feature vectors of the surrounding nodes; The aggregation update unit is used to aggregate and update the information of input nodes and edges according to the local transfer function of updating the node state, and output the node label. The formula is expressed as: In the formula, is the local transition function for updating the node state, is the local output function, is the state vector learned by the graph neural network iteration, is the node label; The iteration unit is used to calculate the correlation between a node and its adjacent nodes in the continuous iteration of the graph neural network, and obtain the strong correlation features in the time domain and the strong correlation features in the frequency domain.

13. The embedded device for synchronous signal acquisition and measurement according to claim 12, characterized in that: The iteration unit is specifically used for: set up and are the vectors constructed by superimposing the state vectors of all nodes, all output labels, the feature vectors of all nodes and edges, and the feature vectors of all nodes. The formula can be written in a more compact form as follows: in, express No. Iterations, Represented by the eigenvector and the The state vector of the iteration is obtained by the global transfer function The state vector of all nodes in the iteration, and are the global transfer function and the global output function, respectively, and are the local transfer functions of all nodes. and local output function Stacking; During the iteration process, nodes with similar states, nodes with complementary states, and the influence of each node on the overall graph neural network are determined. One of the nodes with similar states is selected, nodes with complementary states are merged into one node, and the node with the greatest influence on the overall graph neural network is used as a strong correlation feature.

14. The embedded device for synchronous signal acquisition and measurement according to claim 1, characterized in that: The signal measurement module further includes a cache unit for performing cache processing on the digital signal.

15. The embedded device for synchronous signal acquisition and measurement according to claim 14, characterized in that: The first monitoring unit is used to analyze the time domain strong correlation feature to determine whether it exceeds a first discharge threshold, and if so, determine that a partial discharge signal is monitored, and if not, determine that no partial discharge signal is monitored; The second monitoring unit is used to analyze the frequency domain strong correlation feature to determine whether it exceeds a second discharge threshold, and if so, determine that a partial discharge signal is monitored; otherwise, determine that no partial discharge signal is monitored.

16. The embedded device for synchronous signal acquisition and measurement according to claim 14, characterized in that: When no partial discharge signal is detected, the digital signal stored in the cache unit is deleted.

17. The embedded device for synchronous signal acquisition and measurement according to claim 14, characterized in that: The second monitoring unit comprises: A Fourier transform subunit, used for performing at least two fast Fourier transforms on the electromagnetic field signal to obtain frequency domain data of at least two frequency bands; The normalization subunit is used to extract the frequency domain strong correlation features from the frequency domain data of each frequency band, and after analyzing and matching the frequency domain strong correlation features, when it is determined that the frequency domain data of each frequency band exceeds the second discharge threshold, the frequency domain waveform corresponding to each frequency band is normalized to obtain a frequency domain waveform diagram during discharge.

18. The embedded device for synchronous signal acquisition and measurement according to claim 17, characterized in that: The waveform synthesis unit comprises: A waveform conversion subunit, used for converting the frequency domain waveform diagram into a second time domain waveform diagram during discharge; The waveform synthesis subunit is used to synthesize the first time domain waveform diagram and the second time domain waveform diagram to obtain a full time domain waveform diagram of the partial discharge signal.

19. A multi-structure adaptive GIS field-electricity fusion real-time status perception and early warning system, characterized in that: The system includes an industrial computer and a signal synchronous acquisition and measurement embedded device as described in any one of claims 1 to 18, wherein the industrial computer is connected to the signal synchronous acquisition and measurement embedded device, the signal synchronous acquisition and measurement embedded device is used to acquire time-domain full-waveform data of GIS local discharge and send the time-domain full-waveform data to the industrial computer, and the industrial computer is used to search for the actual discharge position of the GIS local discharge source based on the time-domain full-waveform data.

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