GIS equipment monitoring method and system based on multi-channel synchronous sampling

Abstract

The invention relates to the technical field of GIS equipment monitoring, in particular to a GIS equipment monitoring method and system based on multi-channel synchronous sampling, and the method comprises the steps: obtaining a historical partial discharge value, a historical vibration characteristic value, a historical gas category set and a historical temperature monitoring value of GIS equipment through a monitoring device, carrying out the preprocessing of the historical vibration characteristic value, and obtaining the historical temperature monitoring value of the GIS equipment; inputting the historical vibration frequency sequence into a frequency prediction model to obtain a historical predicted frequency sequence; obtaining an actual vibration frequency sequence; calculating a historical predicted difference value by using a difference value calculation formula; identifying a historical gas concentration set; the current temperature change rate is calculated based on the historical temperature monitoring value and the current temperature monitoring value, and whether the GIS equipment is in a normal operation state or not is judged based on the historical partial discharge value, the historical prediction difference value, the historical gas concentration set and the current temperature change rate. According to the invention, the problem of insufficient early warning capability in the monitoring process of the GIS equipment at present can be solved.

Description

Technical Field

[0001] The present invention relates to the technical field of GIS equipment monitoring, and in particular to a GIS equipment monitoring method and system based on multi-channel synchronous sampling. Background Art

[0002] GIS equipment is the core equipment that ensures the reliable transmission and distribution of electric energy in the power system. Its operating status is directly related to the safety and efficiency of the power system. Therefore, it is necessary to monitor the status of GIS equipment to ensure the stable operation of the power system.

[0003] Traditional GIS equipment monitoring methods rely primarily on a few key parameters to assess equipment status. While these methods can reflect the equipment's operating status to a certain extent, their lack of analytical and predictive capabilities for these key parameters prevents them from identifying potential problems in their early stages, increasing operational risks. Consequently, current GIS equipment monitoring suffers from insufficient early warning capabilities. Summary of the Invention

[0004] The present invention provides a GIS equipment monitoring method and system based on multi-channel synchronous sampling, the main purpose of which is to solve the problem of insufficient early warning capability in the current monitoring process of GIS equipment.

[0005] To achieve the above objectives, the present invention provides a GIS equipment monitoring method based on multi-channel synchronous sampling, comprising: receiving a GIS equipment monitoring instruction; confirming an equipment monitoring environment based on the GIS equipment monitoring instruction; wherein the equipment monitoring environment includes: a GIS equipment to be monitored and a monitoring device for monitoring the GIS equipment; the monitoring device includes: a high-frequency current transformer, an acceleration sensor, a gas chromatograph, and a temperature sensor; and obtaining, using the monitoring device, historical partial discharge values, historical vibration characteristic values, historical gas category sets, and historical temperature monitoring values ​​of the GIS equipment. wherein the historical gas category sets include: Gas and Gas; perform a preprocessing operation on the historical vibration characteristic value to obtain a historical vibration frequency sequence, wherein the preprocessing operation refers to performing frequency domain data conversion on the historical vibration characteristic value; input the historical vibration frequency sequence into a pre-constructed frequency prediction model to obtain a historical predicted frequency sequence, wherein the frequency prediction model refers to a calculation model that can predict the vibration frequency sequence of the next unit moment based on the input historical vibration frequency sequence; obtain the actual vibration frequency sequence of the next unit moment, and use the pre-constructed difference calculation formula to calculate the historical predicted difference between the actual vibration frequency sequence and the historical predicted frequency sequence; identify the historical gas concentration set corresponding to the historical gas category set, wherein the historical gas concentration set includes: historical Concentration and History concentration; obtain the current temperature monitoring value, and calculate the current temperature change rate based on the historical temperature monitoring value and the current temperature monitoring value; determine whether the GIS device is in a preset normal operating state based on the historical partial discharge value, the historical prediction difference, the historical gas concentration set and the current temperature change rate; if the GIS device is in a normal operating state, return to the above-mentioned step of receiving the GIS device monitoring instruction; if the GIS device is not in a normal operating state, issue a GIS device abnormality prompt to complete the GIS device monitoring based on multi-channel synchronous sampling.

[0006] Optionally, the use of the monitoring device to obtain the historical partial discharge value, historical vibration characteristic value, historical gas category set and historical temperature monitoring value of the GIS equipment includes: connecting a high-frequency current transformer to the GIS equipment, monitoring the high-frequency current in the GIS equipment at a preset historical unit time through the high-frequency current transformer, and converting the high-frequency current into a historical partial discharge value using the high-frequency current transformer; connecting an acceleration sensor to the GIS equipment, monitoring the acceleration signal generated by the vibration of the GIS equipment at a historical unit time through the acceleration sensor, and converting the acceleration signal into a historical vibration characteristic value using the acceleration sensor; connecting a gas chromatograph to the GIS equipment, and using the gas chromatograph to monitor the gas product category of the GIS equipment at a historical unit time to obtain a historical gas category set; connecting a temperature sensor to the GIS equipment, and using the temperature sensor to monitor the temperature of the GIS equipment at a historical unit time to obtain a historical temperature monitoring value.

[0007] Optionally, the preprocessing operation on the historical vibration eigenvalues ​​to obtain a historical vibration frequency sequence includes: performing a fast Fourier transform on the historical vibration eigenvalues ​​to obtain an initial vibration frequency sequence, wherein the fast Fourier transform refers to converting the historical vibration eigenvalues ​​in the time domain into an initial vibration frequency sequence in the frequency domain; determining a vibration cutoff frequency interval based on a preset vibration frequency range; and performing vibration frequency screening on the initial vibration frequency sequence according to the vibration cutoff frequency interval to obtain a historical vibration frequency sequence.

[0008] Optionally, the process of constructing the frequency prediction model includes: obtaining a plurality of vibration frequency sequence samples; and calculating a kernel function according to the plurality of vibration frequency sequence samples using the following formula: ,in, express The kernel function, Represents the first sample of the i-th vibration frequency sequence The amplitude of the vibration frequency, Represents the first of the input historical vibration frequency sequence The amplitude of the vibration frequency, σ represents the width parameter of the kernel function, express and The square of the Euclidean distance between represents a natural exponential function; obtaining a Lagrange multiplier and a bias vector, and constructing a frequency prediction model based on the kernel function, the Lagrange multiplier and the bias vector.

[0009] Optionally, the frequency prediction model is as follows: ,in, represents the historical vibration frequency sequence of the input, Indicates based on The calculated historical forecast frequency, represents the number of vibration frequency sequence samples in multiple vibration frequency sequence samples, and represents the first and second Lagrange multipliers of the i-th vibration frequency sequence sample in multiple vibration frequency sequence samples, Represents the bias vector.

[0010] Optionally, the method of calculating the historical prediction difference between the actual vibration frequency sequence and the historical prediction frequency sequence using a pre-constructed difference calculation formula includes: normalizing the actual vibration frequency sequence and the historical prediction frequency sequence to obtain a standard vibration frequency sequence and a standard prediction frequency sequence, wherein the normalization process refers to converting the actual vibration frequency sequence and the prediction frequency sequence of different dimensions into a standard vibration frequency sequence and a standard prediction frequency sequence of standardized dimensions; and calculating the historical prediction difference based on the standard vibration frequency sequence and the standard prediction frequency sequence using a difference calculation formula, wherein the difference calculation formula is as follows: ,in, represents the historical forecast difference, Indicates the number of vibration frequency amplitudes in the standard vibration frequency sequence or the standard predicted frequency sequence, Represents the standard vibration frequency sequence The kth vibration frequency amplitude in , Represents the standard prediction frequency series The kth vibration frequency amplitude in , Represents an absolute value symbol. Optionally, the calculating the current temperature change rate based on the historical temperature monitoring value and the current temperature monitoring value includes: calculating the current temperature change rate based on the current temperature monitoring value and the historical temperature monitoring value using a pre-built temperature change rate calculation formula, wherein the temperature change rate calculation formula is as follows: ,in, Indicates the current temperature monitoring value. Represents a historical temperature monitoring value. Optionally, the determining whether the GIS device is in a preset normal operating state based on the historical partial discharge value, the historical prediction difference value, the historical gas concentration set, and the current temperature change rate includes: aggregating the historical partial discharge value, the historical prediction difference value, the historical gas concentration set, and the current temperature change rate to obtain a device monitoring value set; determining whether the device monitoring value set meets a preset monitoring characteristic standard; if the device monitoring value set meets the monitoring characteristic standard, determining that the GIS device is in a normal operating state; if the device monitoring value set does not meet the monitoring characteristic standard, determining that the GIS device is not in a normal operating state.

[0011] Optionally, the determining whether the device monitoring value set meets the preset monitoring characteristic standard includes: obtaining a standard monitoring value set, wherein the standard monitoring value set includes: a standard partial discharge value, a standard prediction difference value, a standard gas concentration set and a standard temperature change rate, and the standard gas concentration set includes: a standard Concentration and standard Concentration; Based on the equipment monitoring value set and the standard monitoring value set, the monitoring difference is calculated using the following formula: ,in, Indicates the monitoring difference, Represents the historical partial discharge value, Indicates the standard partial discharge value, represents the standard prediction error, express Historical gas concentration of the gas, express Standard gas concentration of the gas, express Historical gas concentration of the gas, express Standard gas concentration of the gas, Indicates the standard temperature change rate; determines whether the monitoring difference is greater than the preset monitoring error threshold: if the monitoring difference is greater than the monitoring error threshold, it is determined that the device monitoring value set meets the monitoring characteristic standard; if the monitoring difference is not greater than the monitoring error threshold, it is determined that the device monitoring value set does not meet the monitoring characteristic standard.

[0012] To achieve the above-mentioned objectives, the present invention further provides a GIS equipment monitoring system based on multi-channel synchronous sampling, comprising: a monitoring instruction receiving module for receiving a GIS equipment monitoring instruction and confirming an equipment monitoring environment based on the GIS equipment monitoring instruction, wherein the equipment monitoring environment includes: a GIS equipment to be monitored and a monitoring device for monitoring the GIS equipment, wherein the monitoring device includes: a high-frequency current transformer, an acceleration sensor, a gas chromatograph, and a temperature sensor; and a data monitoring processing module for using the monitoring device to obtain historical partial discharge values, historical vibration characteristic values, historical gas category sets, and historical temperature monitoring values ​​of the GIS equipment, wherein the historical gas category sets include: Gas and Gas, preprocessing operation is performed on the historical vibration characteristic value to obtain a historical vibration frequency sequence, wherein the preprocessing operation refers to frequency domain data conversion of the historical vibration characteristic value; a data monitoring calculation module is used to input the historical vibration frequency sequence into a pre-built frequency prediction model to obtain a historical prediction frequency sequence, wherein the frequency prediction model refers to a calculation model that can predict the vibration frequency sequence of the next unit moment based on the input historical vibration frequency sequence, obtain the actual vibration frequency sequence of the next unit moment, use the pre-built difference calculation formula to calculate the historical prediction difference between the historical vibration frequency sequence and the historical prediction frequency sequence, identify the historical gas concentration set corresponding to the historical gas category set, wherein the historical gas concentration set includes: historical Concentration and History concentration, obtain the current temperature monitoring value, and calculate the current temperature change rate based on the historical temperature monitoring value and the current temperature monitoring value; an equipment status judgment module is used to judge whether the GIS equipment is in a preset normal operating state based on the historical partial discharge value, the historical prediction difference, the historical gas concentration set and the current temperature change rate. If the GIS equipment is in a normal operating state, it returns to the above-mentioned step of receiving the GIS equipment monitoring instruction; if the GIS equipment is not in a normal operating state, it will issue a GIS equipment abnormality prompt.

[0013] In order to solve the above problems, the present invention also provides an electronic device, which includes: a memory storing at least one instruction; and a processor executing the instructions stored in the memory to implement the above-mentioned GIS equipment monitoring method based on multi-channel synchronous sampling.

[0014] In order to solve the above problems, the present invention also provides a computer-readable storage medium, which stores at least one instruction, and the at least one instruction is executed by a processor in an electronic device to implement the above-mentioned GIS equipment monitoring method based on multi-channel synchronous sampling.

[0015] To solve the problems described in the background technology, the present invention first receives a GIS equipment monitoring instruction and confirms the equipment monitoring environment based on the GIS equipment monitoring instruction. The equipment monitoring environment includes: the GIS equipment to be monitored and a monitoring device for monitoring the GIS equipment. The monitoring device includes: a high-frequency current transformer, an acceleration sensor, a gas chromatograph, and a temperature sensor. The monitoring device can monitor various operating data of the GIS equipment and obtain historical partial discharge values, historical vibration characteristic values, historical gas category sets, and historical temperature monitoring values ​​of the GIS equipment. The historical gas category sets include: Gas and Gas, since the original historical vibration characteristic values ​​are in the time domain, it is not conducive to directly analyzing the vibration frequency characteristics of the equipment. Therefore, it is necessary to perform a preprocessing operation on the historical vibration characteristic values, and convert the historical vibration characteristic values ​​into a historical vibration frequency sequence in order to understand the frequency components of the equipment vibration. The preprocessing operation refers to the frequency domain data conversion of the historical vibration characteristic values, and inputting the historical vibration frequency sequence into a pre-built frequency prediction model to obtain a historical predicted frequency sequence. The frequency prediction model refers to a calculation model that can predict the vibration frequency sequence of the next unit moment based on the input historical vibration frequency sequence, and obtain the actual vibration frequency sequence of the next unit moment. In order to quantify the difference between the actual vibration frequency and the predicted vibration frequency, it is necessary to use a pre-built difference calculation formula to calculate the historical predicted difference between the actual vibration frequency sequence and the historical predicted frequency sequence. In order to monitor the operating status of the equipment, it is necessary to identify the historical gas concentration set corresponding to the historical gas category set, wherein the historical gas concentration set includes: historical Concentration and History The concentration is obtained, and the current temperature monitoring value is obtained. The current temperature change rate is calculated based on the historical temperature monitoring value and the current temperature monitoring value. The temperature change rate can intuitively show the energy loss and operating status of the GIS device, making it easier to detect abnormal heating conditions of the GIS device. Because the historical partial discharge value, the historical prediction difference, the historical gas concentration set, and the current temperature change rate reflect the operating status of the GIS device from different angles, it can be determined whether the GIS device is in a preset normal operating state based on the historical partial discharge value, the historical prediction difference, the historical gas concentration set, and the current temperature change rate. If the GIS device is in a normal operating state, the process returns to the above-mentioned step of receiving the GIS device monitoring instruction. If the GIS device is not in a normal operating state, a GIS device abnormality prompt is issued, completing the GIS device monitoring based on multi-channel synchronous sampling. Therefore, the present invention can solve the current problem of insufficient early warning capabilities in the monitoring process of GIS devices. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1A flowchart of a GIS equipment monitoring method based on multi-channel synchronous sampling provided by one embodiment of the present invention; Figure 2 A functional module diagram of a GIS equipment monitoring system based on multi-channel synchronous sampling provided by one embodiment of the present invention; Figure 3 A schematic structural diagram of an electronic device for implementing the GIS equipment monitoring method based on multi-channel synchronous sampling provided by one embodiment of the present invention.

[0017] Description of reference numerals: 1. Electronic device; 10. Processor; 11. Memory; 12. Bus.

[0018] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0019] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0020] The embodiment of the present application provides a GIS device monitoring method based on multi-channel synchronous sampling. The execution subject of the GIS device monitoring method based on multi-channel synchronous sampling includes but is not limited to at least one of the electronic devices such as a server and a terminal that can be configured to execute the method provided by the embodiment of the present application. In other words, the GIS device monitoring method based on multi-channel synchronous sampling can be executed by software or hardware installed on a terminal device or a server device, and the software can be a blockchain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc.

[0021] Reference Figure 1 FIG2 is a flow chart of a GIS device monitoring method based on multi-channel synchronous sampling according to an embodiment of the present invention. In this embodiment, the GIS device monitoring method based on multi-channel synchronous sampling includes: S1, receiving a GIS device monitoring instruction, and confirming the device monitoring environment based on the GIS device monitoring instruction.

[0022] In detail, the equipment monitoring environment includes: GIS equipment to be monitored and a monitoring device for monitoring the GIS equipment, and the monitoring device includes: a high-frequency current transformer, an acceleration sensor, a gas chromatograph and a temperature sensor.

[0023] It should be noted that a GIS equipment monitoring instruction refers to a command issued by a user or system manager to initiate monitoring of GIS equipment. This instruction includes specific requirements for the monitoring task, such as the type of GIS equipment to be monitored, the monitoring cycle, the monitoring duration, and the type of data being monitored. GIS equipment refers to gas-insulated metal-enclosed switchgear used in power systems to control and protect power lines. GIS equipment has advantages such as a small footprint, high safety, and minimal maintenance, and is widely used in power system substations, power plants, and other locations.

[0024] It should be explained that the monitoring device refers to a collection of various instruments and equipment used to obtain data related to the operating status of GIS equipment. In this embodiment, the monitoring device is a high-frequency current transformer, an acceleration sensor, a gas chromatograph, and a temperature sensor. The monitoring device can monitor the relevant operating data of the GIS equipment, providing data support for the status assessment and fault diagnosis of the GIS equipment. The high-frequency current transformer refers to a device used to monitor the high-frequency current signal generated by the partial discharge of the GIS equipment. The acceleration sensor refers to a device that can monitor the acceleration changes caused by the vibration of the GIS equipment and convert it into an electrical signal output. The gas chromatograph refers to an analytical instrument used to monitor the gas composition inside the GIS equipment. The temperature sensor refers to a device used to monitor the temperature of the GIS equipment, which can convert the temperature condition of the GIS equipment into an electrical signal. By monitoring the temperature changes of the GIS equipment, overheating of the GIS equipment can be detected to prevent the GIS equipment from being damaged due to overheating.

[0025] For example, a substation needs to monitor GIS equipment. A GIS monitoring command is issued through the monitoring center's system management program. The command specifies that the GIS equipment to be monitored is the switchgear that connects two transmission lines within the substation. The monitoring cycle is every two minutes for 16 hours. The monitoring data types include partial discharge values, vibration characteristics, gas classification sets, and temperature monitoring values.

[0026] S2. Using the monitoring device, obtain historical partial discharge values, historical vibration characteristic values, historical gas category sets, and historical temperature monitoring values ​​of the GIS equipment, wherein the historical gas category sets include: Gas and gas.

[0027] Specifically, the historical partial discharge value refers to the intensity of partial discharge activity in GIS equipment, as monitored by high-frequency current transformers, at historical unit times. The historical vibration characteristic value refers to the characteristic value of the vibration signal of GIS equipment, as detected by accelerometers, at historical unit times. The historical gas category set refers to the set of specific gas types within GIS equipment, as detected by gas chromatographs, at historical unit times. The historical temperature monitoring value refers to the temperature value of GIS equipment, as detected by temperature sensors, at historical unit times. Gas refers to The equipment may produce sulfur dioxide gas. Gas refers to hydrogen sulfide gas that may be generated by GIS equipment. The historical unit time refers to a historical moment whose time interval with the current moment is a unit time. For example, if the current moment is 7:01, the historical unit time may be 7:00, in which case the unit time is 1 minute.

[0028] It should be understood that the magnitude of historical partial discharge values ​​can reflect whether there are local defects or electric field concentration in the insulating medium of the GIS equipment. The historical vibration characteristic values ​​reflect the mechanical dynamic characteristics of the GIS equipment during operation. By analyzing the vibration signal amplitude, frequency distribution and other characteristics in the historical vibration characteristic values, it can be judged whether the mechanical structure of the GIS equipment is stable (for example, whether there are problems such as loose parts and bearing wear). and Gas is a characteristic gas of GIS equipment failure. Its concentration changes can be used to determine whether the equipment is experiencing internal faults such as overheating and discharge. Historical temperature monitoring values ​​provide a direct indication of equipment heating conditions. Abnormally high temperatures may indicate poor contact or overload in the GIS equipment.

[0029] Furthermore, the use of the monitoring device to obtain the historical partial discharge value, historical vibration characteristic value, historical gas category set and historical temperature monitoring value of the GIS equipment includes: connecting a high-frequency current transformer to the GIS equipment, monitoring the high-frequency current in the GIS equipment at a preset historical unit time through the high-frequency current transformer, and converting the high-frequency current into a historical partial discharge value using the high-frequency current transformer; connecting an acceleration sensor to the GIS equipment, monitoring the acceleration signal generated by the vibration of the GIS equipment at a historical unit time through the acceleration sensor, and converting the acceleration signal into a historical vibration characteristic value using the acceleration sensor; connecting a gas chromatograph to the GIS equipment, and using the gas chromatograph to monitor the gas product category of the GIS equipment at a historical unit time to obtain a historical gas category set; connecting a temperature sensor to the GIS equipment, and using the temperature sensor to monitor the temperature of the GIS equipment at a historical unit time to obtain a historical temperature monitoring value.

[0030] It is understandable that the historical unit moments are usually evenly distributed according to a certain monitoring cycle (such as every 2 minutes, every 5 minutes, etc.) to ensure the continuity and comparability of the monitoring data. When partial discharge of insulation occurs inside the GIS equipment, the high-frequency current generated instantly passes through the high-frequency current transformer and is converted into a small current signal that is easy to collect and analyze. By recording and quantifying the small current signal, the historical partial discharge value is finally obtained, such as: the amplitude of the partial discharge current recorded at a certain time is 50pC. As the internal mechanical structure of the GIS equipment operates during operation, the equipment casing vibrates, and the piezoelectric crystal or piezoresistive element inside the acceleration sensor deforms accordingly. At this time, the acceleration signal generated by the vibration needs to be converted into an electrical signal. After the electrical signal is processed by the acceleration sensor, the historical vibration characteristic value containing information such as vibration frequency and amplitude can be obtained, such as: the vibration acceleration amplitude of the equipment is monitored to be 0.5m / s² at a frequency of 100Hz.

[0031] It should be understood that the gas chromatograph is connected to the gas chamber of the GIS equipment, and can use the difference in the distribution coefficients of different gases between the stationary phase and the mobile phase in the chromatographic column to separate the gas components, and detect the separated gas through the corresponding detector (such as thermal conductivity detector, flame ionization detector, etc.) to determine the components in the gas (such as 、 The types and concentrations of decomposition products (such as chlorine, chlorine, and other decomposition products) are used to obtain a historical gas category set. When the GIS equipment is running, the temperature sensor converts the monitored temperature signal into a historical temperature monitoring value, such as: the monitored GIS equipment temperature is 65°C.

[0032] S3. Preprocessing the historical vibration characteristic values ​​to obtain a historical vibration frequency sequence, and inputting the historical vibration frequency sequence into a pre-built frequency prediction model to obtain a historical prediction frequency sequence.

[0033] Specifically, preprocessing involves converting historical vibration eigenvalues ​​into frequency domain data. A frequency prediction model is a computational model that predicts the frequency sequence for the next unit moment based on the input historical frequency sequence. Frequency domain data conversion involves converting a vibration signal in the time domain into a frequency domain signal.

[0034] It should be noted that the historical vibration frequency sequence refers to the ordered data formed after the historical vibration characteristic values ​​have been preprocessed, which is used to represent the frequency component distribution and amplitude of the GIS equipment vibration signal. The historical predicted frequency sequence refers to the data sequence composed of the predicted values ​​of the vibration frequency and amplitude at the next unit moment output by the model after the historical vibration frequency sequence is input into the frequency prediction model. The unit moment refers to the moment determined by the monitoring cycle during the monitoring process. For example: if the current time is 7:00 and the monitoring cycle is 5 minutes, the unit moment can be 7:05.

[0035] It should be explained that the preprocessing operation on the historical vibration characteristic values ​​to obtain a historical vibration frequency sequence includes: performing a fast Fourier transform on the historical vibration characteristic values ​​to obtain an initial vibration frequency sequence, wherein the fast Fourier transform refers to converting the historical vibration characteristic values ​​in the time domain into an initial vibration frequency sequence in the frequency domain; determining a vibration cutoff frequency interval based on a preset vibration frequency range; and performing vibration frequency screening on the initial vibration frequency sequence according to the vibration cutoff frequency interval to obtain a historical vibration frequency sequence.

[0036] It can be understood that the initial vibration frequency sequence refers to the sequence data containing all frequency components obtained after the historical vibration characteristic values ​​are fast Fourier transformed. The vibration frequency range refers to the frequency interval range that is pre-set based on the design parameters and operating conditions of the GIS equipment to distinguish between the inherent vibration and external interference vibration during normal operation of the equipment. The vibration frequency range reflects the stable vibration frequency limit generated by the mechanical structure and electrical components of the GIS equipment under normal working conditions, and is an important reference for identifying and removing environmental noise and non-operation-related vibrations of the equipment. The vibration cutoff frequency interval refers to the precise frequency interval determined based on the vibration frequency range and used to screen valid vibration frequency data. Vibration frequency screening refers to eliminating low-frequency noise data below the minimum value of the vibration cutoff frequency interval and high-frequency interference data above the maximum value of the vibration cutoff frequency interval in the initial vibration frequency sequence according to the vibration cutoff frequency interval.

[0037] Specifically, the minimum and maximum values ​​of the vibration cutoff frequency interval define a frequency range. Vibration frequency data within this range is considered valid data closely related to the actual operating status of the equipment. Low-frequency data outside the range is typically caused by environmental mechanical interference (such as nearby construction vibration and fan operation vibration) and electromagnetic noise. High-frequency data may be caused by external electromagnetic interference of the equipment and the sensor's own electronic noise. These data are considered interference or invalid data and need to be removed from the initial vibration frequency sequence. For example, if the frequency components of the initial vibration frequency sequence are 50Hz-500Hz and the vibration frequency range of the GIS equipment is set to 100Hz-400Hz, the vibration cutoff frequency interval can be determined to be [100Hz, 400Hz]. In this case, the initial vibration frequency sequence is screened to remove all low-frequency data below 100Hz and high-frequency interference data above 400Hz. The remaining frequency data and their corresponding amplitudes are the historical vibration frequency sequence.

[0038] It should be understood that the process of constructing the frequency prediction model includes: obtaining a plurality of vibration frequency sequence samples; and calculating the kernel function according to the plurality of vibration frequency sequence samples using the following formula: ,in, express The kernel function, Represents the first sample of the i-th vibration frequency sequence The amplitude of the vibration frequency, Represents the first of the input historical vibration frequency sequence The amplitude of the vibration frequency, σ represents the width parameter of the kernel function, express and The square of the Euclidean distance between represents a natural exponential function; obtaining a Lagrange multiplier and a bias vector, and constructing a frequency prediction model based on the kernel function, the Lagrange multiplier and the bias vector.

[0039] It's important to explain that vibration frequency sequence samples refer to the vibration frequency sequences monitored by GIS equipment at different times and under different operating conditions. Each vibration frequency sequence sample contains multiple frequency components and their corresponding amplitudes. The kernel function is used to measure the similarity between a vibration frequency sequence sample and the input historical vibration frequency sequence during the frequency prediction model construction process.

[0040] It can be understood that the Lagrange multiplier refers to an auxiliary parameter introduced based on optimization theory during the construction of the frequency prediction model. The bias vector is a learnable parameter in the frequency prediction model. Its function is to adjust the model's prediction results so that the model better fits the data. During the model calculation process, the bias vector b is added to the result calculated using the kernel function and weighted by the Lagrange multiplier to obtain the final prediction value. By learning from the vibration frequency sequence samples, the model automatically adjusts the value of the bias vector to minimize the prediction error and make the prediction result closer to the actual vibration frequency value.

[0041] Specifically, the frequency prediction model is as follows: ,in, represents the historical forecast frequency series, represents the number of vibration frequency sequence samples in multiple vibration frequency sequence samples, and Represents the two Lagrange multipliers of the i-th vibration frequency sequence sample in multiple vibration frequency sequence samples, It should be understood that after the kernel function is calculated, the Lagrange multiplier will be optimized to minimize the prediction error. In the frequency prediction model, and These two Lagrange multipliers are used to adjust the contribution of different training samples to the prediction results. The values ​​of the Lagrange multipliers can be obtained by optimizing the vibration frequency sequence samples (e.g., through quadratic programming). Lagrange multipliers can improve the frequency prediction model's accuracy in fitting the vibration frequency sequence data, thereby increasing prediction accuracy. The kernel function enables the frequency prediction model to handle nonlinear relationships, improving prediction accuracy. In this embodiment, a radial basis function can be used as the kernel function. This function calculates similarity based on the Euclidean distance between the vibration frequency sequence samples and the historical vibration frequency sequence. The closer the Euclidean distance, the larger the kernel function value and the higher the similarity. For example, there are three vibration sequence samples, the first vibration sequence sample is [0.1, 0.22, 0.35], the second vibration sequence sample is [0.13, 0.27, 0.32], and the third vibration sequence sample is [0.32, 0.16, 0.24]. The input historical vibration frequency sequence is [0.24, 0.13, 0.26], the width parameter is 0.2, and after optimization calculation, the first Lagrange multiplier is [0.203, 0.300, 0.249], the second Lagrange multiplier is [0.050, 0.100, 0.079], and the bias vector is 0.049. The historical predicted frequency sequence obtained by the frequency prediction model is [0.21, 0.17, 0.24].

[0042] S4. Obtain the actual vibration frequency sequence at the next unit time, and use a pre-built difference calculation formula to calculate the historical prediction difference between the actual vibration frequency sequence and the historical prediction frequency sequence.

[0043] It can be understood that the actual vibration frequency sequence refers to the vibration frequency data sequence obtained through measurement at the next unit time, which reflects the actual vibration frequency status of the GIS equipment at that moment. The historical prediction difference refers to the difference between the actual vibration frequency sequence and the historical prediction frequency sequence calculated according to the difference calculation formula. The historical prediction difference can be used to assess the degree of deviation between the actual and predicted values ​​of the GIS equipment vibration frequency, thereby providing a basis for determining whether the equipment is in normal operation.

[0044] In detail, the method of calculating the historical prediction difference between the actual vibration frequency sequence and the historical prediction frequency sequence using a pre-constructed difference calculation formula includes: normalizing the actual vibration frequency sequence and the historical prediction frequency sequence to obtain a standard vibration frequency sequence and a standard prediction frequency sequence, wherein the normalization process refers to converting the actual vibration frequency sequence and the prediction frequency sequence of different dimensions into a standard vibration frequency sequence and a standard prediction frequency sequence of standardized dimensions; and calculating the historical prediction difference based on the standard vibration frequency sequence and the standard prediction frequency sequence using a difference calculation formula, wherein the difference calculation formula is as follows: ,in, represents the historical forecast difference, Indicates the number of vibration frequency amplitudes in the standard vibration frequency sequence or the standard predicted frequency sequence, Represents the standard vibration frequency sequence The kth vibration frequency amplitude in , Represents the standard prediction frequency series The kth vibration frequency amplitude in , It should be noted that the standard vibration frequency sequence refers to the actual vibration frequency sequence after normalization. The standard prediction frequency sequence refers to the historical prediction frequency sequence after normalization.

[0045] For example, if the actual vibration frequency sequence is [0.24, 0.13, 0.26, 0.18, 0.22], and the historical predicted frequency sequence is [0.21, 0.17, 0.24, 0.19, 0.23], after normalization, the standard vibration frequency sequence is [0.8, 0.4, 0.9, 0.6, 0.7], and the standard predicted frequency sequence is [0.7, 0.6, 0.8, 0.7, 0.8], the historical standard deviation value can be calculated as 0.12 using the error calculation formula.

[0046] S5. Identify the historical gas concentration set corresponding to the historical gas category set, wherein the historical gas concentration set includes: historical Concentration and History concentration.

[0047] It should be explained that the historical gas concentration set refers to the historical gas category set monitored by the gas sensor. Gas and A collection of gas concentrations. Concentration refers to the concentration generated by GIS equipment Gas concentration value. History Concentration refers to the concentration generated by GIS equipment The concentration value of the gas.

[0048] Specifically, when monitoring GIS equipment, the gas chromatograph will detect and analyze the gas generated inside the GIS equipment. When the GIS equipment has faults such as aging of the insulation material, partial discharge or overheating, some components in the insulation material will decompose and produce Gas. When certain metal parts inside GIS equipment corrode under certain conditions, or when insulating materials decompose under fault conditions, gas may be generated. Gas. The gas chromatograph will monitor the gas generated in the GIS equipment. Gas and The gas concentration values ​​of the two gases are analyzed and recorded. Concentration and History The concentration analysis can help determine the operating status of GIS equipment. and Gas concentrations are usually at a low level and relatively stable. Concentration or History If the concentration increases abnormally, it indicates that there may be a potential fault inside the GIS equipment. For example: a high historical Concentration indicates that the GIS equipment may have serious partial discharge, arc discharge, etc., and the abnormal history Concentrations of this type indicate that accelerated corrosion of metal components or other failures (e.g., extensive breakdown of insulation) may be occurring.

[0049] S6. Obtain a current temperature monitoring value, and calculate a current temperature change rate based on the historical temperature monitoring value and the current temperature monitoring value.

[0050] Specifically, the current temperature monitoring value refers to the temperature value monitored in real time at the current moment. The current temperature change rate refers to the temperature change rate calculated based on the historical temperature monitoring value and the current temperature monitoring value. It is used to reflect the temperature change of the GIS device between two monitoring times.

[0051] It should be understood that the calculation of the current temperature change rate based on the historical temperature monitoring value and the current temperature monitoring value includes: calculating the current temperature change rate based on the current temperature monitoring value and the historical temperature monitoring value using a pre-built temperature change rate calculation formula, wherein the temperature change rate calculation formula is as follows: ,in, Indicates the current temperature monitoring value. Represents the historical temperature monitoring value. For example, if the historical temperature monitoring value of a GIS device is 50 degrees Celsius and the current temperature monitoring value is 56 degrees Celsius, the current temperature change rate can be calculated as 0.107.

[0052] S7. Determine whether the GIS equipment is in a preset normal operating state based on historical partial discharge values, historical prediction differences, historical gas concentration sets, and current temperature change rates.

[0053] It should be explained that the normal operating state means that all key indicators of the GIS equipment are within the safety threshold range, there are no obvious insulation degradation, partial discharge, overheating and other fault hazards inside the equipment, and the equipment can continuously, stably and reliably complete the working state of power transmission and distribution functions. For example: the amplitude of the partial discharge signal monitored by the GIS equipment during the historical operation is low, and the number of discharges is sparse, and its value is lower than the partial discharge safety threshold set by the industry standard or the equipment manufacturer; the difference between the actual vibration frequency sequence and the historical predicted frequency sequence is small and within the pre-set reasonable error range; the historical Concentration and History The concentrations are maintained at a low level with no obvious upward trend, which is consistent with the concentration characteristics of the gas components inside the normally operating equipment; the current temperature change rate fluctuates within a reasonable range, the temperature changes smoothly, and there is no abnormal rapid heating or cooling phenomenon. The overall temperature is in a controllable and stable state.

[0054] It should be noted that the determination of whether the GIS device is in a preset normal operating state based on historical partial discharge values, historical prediction differences, historical gas concentration sets and current temperature change rates includes: summarizing the historical partial discharge values, historical prediction differences, historical gas concentration sets and current temperature change rates to obtain a device monitoring value set; determining whether the device monitoring value set meets a preset monitoring feature standard; if the device monitoring value set meets the monitoring feature standard, determining that the GIS device is in a normal operating state; if the device monitoring value set does not meet the monitoring feature standard, determining that the GIS device is not in a normal operating state.

[0055] It can be understood that the equipment monitoring value set refers to the historical partial discharge value, historical prediction difference, historical gas concentration set (including: Concentration and The comprehensive data set formed by summarizing the current temperature change rate is used to evaluate the overall operating status of the GIS equipment. For example, when monitoring a GIS equipment, the historical partial discharge value is 10pC, the historical prediction difference is 0.05, and the historical Concentration is 0.1ppm, history The concentration is 0.03ppm and the current temperature change rate is 0.136. These data together constitute the equipment monitoring value set for this monitoring. The monitoring characteristic standard refers to a set of thresholds or standards set in advance to measure whether the GIS equipment is operating normally. It is a comprehensive evaluation basis for measuring whether the GIS equipment is operating normally. For example: the historical partial discharge value must be less than 20pC, the historical prediction difference must not exceed 0.1, and the historical Concentration below 0.5ppm, history The concentration is less than 0.1ppm and the current temperature change rate is within 0.25. By comprehensively considering the difference between each data set of the equipment monitoring set and the corresponding standard value, it can be determined whether the equipment monitoring value set is within the normal range.

[0056] Specifically, the determination of whether the device monitoring value set meets the preset monitoring characteristic standard includes: obtaining a standard monitoring value set, wherein the standard monitoring value set includes: a standard partial discharge value, a standard prediction difference, a standard gas concentration set and a standard temperature change rate, and the standard gas concentration set includes: a standard Concentration and standard Concentration; Based on the equipment monitoring value set and the standard monitoring value set, the monitoring difference is calculated using the following formula: ,in, Indicates the monitoring difference, Represents the historical partial discharge value, Indicates the standard partial discharge value, represents the standard prediction error, express Historical gas concentration of the gas, express Standard gas concentration of the gas, express Historical gas concentration of the gas, express Standard gas concentration of the gas, Indicates the standard temperature change rate; determines whether the monitoring difference is greater than the preset monitoring error threshold: if the monitoring difference is greater than the monitoring error threshold, it is determined that the device monitoring value set meets the monitoring characteristic standard; if the monitoring difference is not greater than the monitoring error threshold, it is determined that the device monitoring value set does not meet the monitoring characteristic standard.

[0057] It should be understood that the standard monitoring value set refers to a set of reference data sets that are pre-set by humans to measure whether the operating data of GIS equipment meets the monitoring characteristic standards. The standard partial discharge value refers to the standard numerical value of the partial discharge signal amplitude that is pre-set by humans under the normal operation of GIS equipment. The standard predicted difference refers to the standard difference between the actual vibration frequency sequence and the predicted vibration frequency sequence that is pre-set by humans under the normal operation of GIS equipment. The standard temperature change rate refers to the standard temperature change rate that is pre-set by humans under the normal operation of GIS equipment. The standard gas concentration set refers to the standard gas concentration set that is pre-set by humans under the normal operation of GIS equipment. and A reference data set for gas concentrations. Concentration index The standard concentration of gas under normal operating conditions of GIS equipment. Concentration index The standard concentration of gas under normal operating conditions of GIS equipment.

[0058] It should be explained that the monitoring difference refers to a comprehensive quantitative indicator obtained by comparing the various monitoring data in the equipment monitoring value set with the corresponding standard values ​​in the standard monitoring value set. The monitoring difference can be used to measure the overall deviation between the actual operating state of the equipment and the ideal normal state. The larger the value, the greater the difference between the equipment operating state and the normal state. The monitoring error threshold refers to a pre-set critical value, which serves as the demarcation point for judging whether the equipment monitoring value set meets the monitoring characteristic standard. When the calculated monitoring difference is greater than the monitoring error threshold, it means that the equipment monitoring value set does not meet the monitoring characteristic standard, the actual operating state of the GIS equipment deviates greatly from the normal state, and is not in normal operating state. When the monitoring difference is not greater than the monitoring error threshold, it means that the equipment monitoring value set meets the monitoring characteristic standard and the GIS equipment is in normal operating state.

[0059] For example, a GIS device is monitored daily, and its historical partial discharge value is 15pC, and the historical prediction difference is 0.15. The historical gas concentration of the gas is 0.8ppm, The historical gas concentration is 0.3ppm, and the current temperature change rate is 0.12. The set standard partial discharge value is 10pC, the standard prediction difference is 0.1, and the standard The concentration is 0.5ppm, standard The concentration is 0.2 ppm, the standard temperature change rate is 0.1, and the monitoring error threshold is 2. Calculation shows that the monitoring difference is 2.3, which is greater than the monitoring error threshold. This indicates that the device monitoring value set of the GIS device does not meet the monitoring characteristic standard and that the GIS device is not operating normally.

[0060] S8. If the GIS device is in normal operation, return to the above step of receiving the GIS device monitoring instruction. If the GIS device is not in normal operation, issue a GIS device abnormality prompt to complete the GIS device monitoring based on multi-channel synchronous sampling.

[0061] Specifically, when the GIS equipment is determined to be operating normally, the monitoring system returns to the initial step of receiving GIS equipment monitoring commands, continuing the monitoring process in a loop. By continuously receiving new monitoring commands, it collects real-time information such as historical partial discharge values, vibration frequency sequences, gas concentration data, and temperature monitoring values. It continuously calculates parameters such as historical prediction differences and current temperature change rates, and repeats the process of determining the equipment's operating status. This enables long-term, dynamic monitoring of the GIS equipment, ensuring timely detection of even the slightest changes in its operating status. If the GIS equipment is determined to be operating abnormally, the monitoring system triggers a GIS equipment abnormality alert. This alert can be provided in various ways, such as through audible and visual alarms issued by the monitoring center's alarm system, or by sending notifications to maintenance personnel via text messages or emails. The alert includes information such as the equipment number, abnormality type (e.g., excessive partial discharge, abnormal gas concentration, excessive temperature change rate), monitoring difference, and specific monitoring data, enabling monitoring and maintenance personnel to quickly understand the equipment's condition.

[0062] To solve the problems described in the background technology, the present invention first receives a GIS equipment monitoring instruction and confirms the equipment monitoring environment based on the GIS equipment monitoring instruction. The equipment monitoring environment includes: the GIS equipment to be monitored and a monitoring device for monitoring the GIS equipment. The monitoring device includes: a high-frequency current transformer, an acceleration sensor, a gas chromatograph, and a temperature sensor. The monitoring device can monitor various operating data of the GIS equipment and obtain historical partial discharge values, historical vibration characteristic values, historical gas category sets, and historical temperature monitoring values ​​of the GIS equipment. The historical gas category sets include: Gas and Gas, since the original historical vibration characteristic values ​​are in the time domain, it is not conducive to directly analyzing the vibration frequency characteristics of the equipment. Therefore, it is necessary to perform a preprocessing operation on the historical vibration characteristic values, and convert the historical vibration characteristic values ​​into a historical vibration frequency sequence in order to understand the frequency components of the equipment vibration. The preprocessing operation refers to the frequency domain data conversion of the historical vibration characteristic values, and inputting the historical vibration frequency sequence into a pre-built frequency prediction model to obtain a historical predicted frequency sequence. The frequency prediction model refers to a calculation model that can predict the vibration frequency sequence of the next unit moment based on the input historical vibration frequency sequence, and obtain the actual vibration frequency sequence of the next unit moment. In order to quantify the difference between the actual vibration frequency and the predicted vibration frequency, it is necessary to use a pre-built difference calculation formula to calculate the historical predicted difference between the actual vibration frequency sequence and the historical predicted frequency sequence. In order to monitor the operating status of the equipment, it is necessary to identify the historical gas concentration set corresponding to the historical gas category set, wherein the historical gas concentration set includes: historical Concentration and History The concentration is obtained, and the current temperature monitoring value is obtained. The current temperature change rate is calculated based on the historical temperature monitoring value and the current temperature monitoring value. The temperature change rate can intuitively show the energy loss and operating status of the GIS device, making it easier to detect abnormal heating conditions of the GIS device. Because the historical partial discharge value, the historical prediction difference, the historical gas concentration set, and the current temperature change rate reflect the operating status of the GIS device from different angles, it can be determined whether the GIS device is in a preset normal operating state based on the historical partial discharge value, the historical prediction difference, the historical gas concentration set, and the current temperature change rate. If the GIS device is in a normal operating state, the process returns to the above-mentioned step of receiving the GIS device monitoring instruction. If the GIS device is not in a normal operating state, a GIS device abnormality prompt is issued, completing the GIS device monitoring based on multi-channel synchronous sampling. Therefore, the present invention can solve the current problem of insufficient early warning capabilities in the monitoring process of GIS devices.

[0063] like Figure 2 , which is a functional module diagram of a GIS equipment monitoring system based on multi-channel synchronous sampling provided by one embodiment of the present invention.

[0064] The GIS equipment monitoring system 100 based on multi-channel synchronous sampling described in the present invention can be installed in an electronic device. Depending on the functionality to be implemented, the GIS equipment monitoring system 100 based on multi-channel synchronous sampling can include a monitoring instruction receiving module 101, a data monitoring processing module 102, a data monitoring calculation module 103, and an equipment status determination module 104. A module, also referred to as a unit, refers to a series of computer program segments that can be executed by an electronic device processor and perform a fixed function, and is stored in the electronic device's memory.

[0065] The monitoring instruction receiving module 101 is used to receive a GIS equipment monitoring instruction and confirm the equipment monitoring environment based on the GIS equipment monitoring instruction, wherein the equipment monitoring environment includes: the GIS equipment to be monitored and a monitoring device for monitoring the GIS equipment, wherein the monitoring device includes: a high-frequency current transformer, an acceleration sensor, a gas chromatograph, and a temperature sensor; the data monitoring processing module 102 is used to use the monitoring device to obtain historical partial discharge values, historical vibration characteristic values, historical gas category sets, and historical temperature monitoring values ​​of the GIS equipment, wherein the historical gas category sets include: Gas and Gas, preprocessing operation is performed on the historical vibration characteristic value to obtain a historical vibration frequency sequence, wherein the preprocessing operation refers to frequency domain data conversion of the historical vibration characteristic value; the data monitoring calculation module 103 is used to input the historical vibration frequency sequence into a pre-constructed frequency prediction model to obtain a historical prediction frequency sequence, wherein the frequency prediction model refers to a calculation model that can predict the vibration frequency sequence of the next unit moment according to the input historical vibration frequency sequence, obtain the actual vibration frequency sequence of the next unit moment, use the pre-constructed difference calculation formula to calculate the historical prediction difference between the historical vibration frequency sequence and the historical prediction frequency sequence, identify the historical gas concentration set corresponding to the historical gas category set, wherein the historical gas concentration set includes: historical Concentration and History concentration, obtain the current temperature monitoring value, and calculate the current temperature change rate based on the historical temperature monitoring value and the current temperature monitoring value; the equipment status judgment module 104 is used to judge whether the GIS equipment is in a preset normal operating state based on the historical partial discharge value, the historical prediction difference, the historical gas concentration set and the current temperature change rate. If the GIS equipment is in a normal operating state, return to the above-mentioned step of receiving the GIS equipment monitoring instruction; if the GIS equipment is not in a normal operating state, issue a GIS equipment abnormality prompt.

[0066] In detail, each module in the GIS equipment monitoring system 100 based on multi-channel synchronous sampling in the embodiment of the present invention adopts the same method as above when in use. Figure 1 The GIS equipment monitoring method based on multi-channel synchronous sampling described in the present invention has the same technical means and can produce the same technical effects, so it will not be repeated here.

[0067] like Figure 3 2 is a schematic diagram of the structure of an electronic device for implementing a GIS equipment monitoring method based on multi-channel synchronous sampling provided by an embodiment of the present invention.

[0068] The electronic device 1 may include a processor 10, a memory 11 and a bus 12, and may also include a computer program stored in the memory 11 and executable on the processor 10, such as a GIS equipment monitoring method program based on multi-channel synchronous sampling.

[0069] The memory 11 includes at least one type of readable storage medium, including flash memory, a mobile hard disk, a multimedia card, a card-type memory (e.g., SD or DX memory), a magnetic memory, a magnetic disk, an optical disk, etc. In some embodiments, the memory 11 may be an internal storage unit of the electronic device 1, such as a mobile hard disk of the electronic device 1. In other embodiments, the memory 11 may also be an external storage device of the electronic device 1, such as a plug-in mobile hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a flash memory card, etc. equipped on the electronic device 1. Furthermore, the memory 11 includes both the internal storage unit of the electronic device 1 and an external storage device. The memory 11 can be used not only to store application software installed on the electronic device 1 and various types of data, such as the code of a GIS equipment monitoring method program based on multi-channel synchronous sampling, but can also be used to temporarily store data that has been output or is about to be output.

[0070] In some embodiments, the processor 10 may be comprised of an integrated circuit, such as a single packaged integrated circuit or a plurality of packaged integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and a combination of various control chips. The processor 10 is the control core (control unit) of the electronic device, connecting the various components of the electronic device using various interfaces and circuits. It executes programs or modules stored in the memory 11 (e.g., a GIS equipment monitoring method program based on multi-channel synchronous sampling) and accesses data stored in the memory 11 to execute various functions and process data.

[0071] The bus 12 may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus. The bus 12 may be divided into an address bus, a data bus, a control bus, etc. The bus 12 is configured to enable communication between the memory 11 and at least one processor 10, etc.

[0072] Figure 3 Only the electronic device with components is shown, and it can be understood by those skilled in the art that Figure 3 The structure shown does not constitute a limitation on the electronic device 1 , and may include fewer or more components than shown in the figure, or combine certain components, or arrange the components differently.

[0073] For example, although not shown, the electronic device 1 may further include a power supply (e.g., a battery) to power various components. Preferably, the power supply may be logically connected to the at least one processor 10 via a power management device, thereby enabling functions such as charge management, discharge management, and power consumption management via the power management device. The power supply may further include any components such as one or more DC or AC power supplies, a recharging device, a power failure detection circuit, a power converter or inverter, and a power status indicator. The electronic device 1 may also include various sensors, Bluetooth modules, Wi-Fi modules, etc., which are not further described here.

[0074] Furthermore, the electronic device 1 may also include a network interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which is generally used to establish a communication connection between the electronic device 1 and other electronic devices.

[0075] Optionally, the electronic device 1 may further include a user interface, which may be a display or an input unit (such as a keyboard). Optionally, the user interface may also be a standard wired interface or a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen. The display may also be appropriately referred to as a display screen or a display unit, and is used to display information processed by the electronic device 1 and to display a visual user interface.

[0076] The GIS equipment monitoring method program based on multi-channel synchronous sampling stored in the memory 11 of the electronic device 1 is a combination of multiple instructions. When running in the processor 10, the program can achieve: receiving a GIS equipment monitoring instruction; confirming an equipment monitoring environment based on the GIS equipment monitoring instruction, wherein the equipment monitoring environment includes: a GIS equipment to be monitored and a monitoring device for monitoring the GIS equipment, wherein the monitoring device includes: a high-frequency current transformer, an acceleration sensor, a gas chromatograph, and a temperature sensor; and obtaining historical partial discharge values, historical vibration characteristic values, historical gas category sets, and historical temperature monitoring values ​​of the GIS equipment using the monitoring device, wherein the historical gas category sets include: Gas and Gas; perform a preprocessing operation on the historical vibration characteristic value to obtain a historical vibration frequency sequence, wherein the preprocessing operation refers to performing frequency domain data conversion on the historical vibration characteristic value; input the historical vibration frequency sequence into a pre-constructed frequency prediction model to obtain a historical predicted frequency sequence, wherein the frequency prediction model refers to a calculation model that can predict the vibration frequency sequence of the next unit moment based on the input historical vibration frequency sequence; obtain the actual vibration frequency sequence of the next unit moment, and use the pre-constructed difference calculation formula to calculate the historical predicted difference between the actual vibration frequency sequence and the historical predicted frequency sequence; identify the historical gas concentration set corresponding to the historical gas category set, wherein the historical gas concentration set includes: historical Concentration and History concentration; obtain the current temperature monitoring value, and calculate the current temperature change rate based on the historical temperature monitoring value and the current temperature monitoring value; determine whether the GIS device is in a preset normal operating state based on the historical partial discharge value, the historical prediction difference, the historical gas concentration set and the current temperature change rate; if the GIS device is in a normal operating state, return to the above-mentioned step of receiving the GIS device monitoring instruction; if the GIS device is not in a normal operating state, issue a GIS device abnormality prompt to complete the GIS device monitoring based on multi-channel synchronous sampling.

[0077] Specifically, the specific implementation method of the processor 10 for the above instructions can refer to Figures 1 to 3 The description of the relevant steps in the corresponding embodiments will not be repeated here.

[0078] Furthermore, if the modules / units integrated into the electronic device 1 are implemented as software functional units and sold or used as independent products, they may be stored in a computer-readable storage medium. The computer-readable storage medium may be volatile or non-volatile. For example, the computer-readable medium may include any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard drive, a magnetic disk, an optical disk, a computer memory, or a read-only memory (ROM).

[0079] The present invention also provides a computer-readable storage medium, wherein the computer program is stored in the computer program. When executed by a processor of an electronic device, the computer program can achieve the following: receiving a GIS equipment monitoring instruction; confirming an equipment monitoring environment based on the GIS equipment monitoring instruction; wherein the equipment monitoring environment includes: a GIS equipment to be monitored and a monitoring device for monitoring the GIS equipment; the monitoring device includes: a high-frequency current transformer, an acceleration sensor, a gas chromatograph, and a temperature sensor; and obtaining, using the monitoring device, historical partial discharge values, historical vibration characteristic values, historical gas category sets, and historical temperature monitoring values ​​of the GIS equipment; wherein the historical gas category sets include: Gas and Gas; perform a preprocessing operation on the historical vibration characteristic value to obtain a historical vibration frequency sequence, wherein the preprocessing operation refers to performing frequency domain data conversion on the historical vibration characteristic value; input the historical vibration frequency sequence into a pre-constructed frequency prediction model to obtain a historical predicted frequency sequence, wherein the frequency prediction model refers to a calculation model that can predict the vibration frequency sequence of the next unit moment based on the input historical vibration frequency sequence; obtain the actual vibration frequency sequence of the next unit moment, and use the pre-constructed difference calculation formula to calculate the historical predicted difference between the actual vibration frequency sequence and the historical predicted frequency sequence; identify the historical gas concentration set corresponding to the historical gas category set, wherein the historical gas concentration set includes: historical Concentration and History concentration; obtain the current temperature monitoring value, and calculate the current temperature change rate based on the historical temperature monitoring value and the current temperature monitoring value; determine whether the GIS device is in a preset normal operating state based on the historical partial discharge value, the historical prediction difference, the historical gas concentration set and the current temperature change rate; if the GIS device is in a normal operating state, return to the above-mentioned step of receiving the GIS device monitoring instruction; if the GIS device is not in a normal operating state, issue a GIS device abnormality prompt to complete the GIS device monitoring based on multi-channel synchronous sampling.

[0080] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, systems and methods can be implemented in other ways. For example, the system embodiments described above are only exemplary, and actual implementations may have other division methods.

[0081] The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed across multiple network elements. Some or all of the modules may be selected to achieve the purpose of the solution of this embodiment according to actual needs.

[0082] In addition, the functional modules in various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or hardware plus software functional modules.

[0083] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0084] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A GIS equipment monitoring method based on multi-channel synchronous sampling, characterized in that: The method includes: receiving a GIS equipment monitoring instruction; confirming an equipment monitoring environment based on the GIS equipment monitoring instruction; wherein the equipment monitoring environment includes: a GIS equipment to be monitored and a monitoring device for monitoring the GIS equipment; the monitoring device includes: a high-frequency current transformer, an acceleration sensor, a gas chromatograph, and a temperature sensor; and obtaining, using the monitoring device, historical partial discharge values, historical vibration characteristic values, historical gas category sets, and historical temperature monitoring values ​​of the GIS equipment; wherein the historical gas category sets include: Gas and Gas; perform a preprocessing operation on the historical vibration characteristic value to obtain a historical vibration frequency sequence, wherein the preprocessing operation refers to performing frequency domain data conversion on the historical vibration characteristic value; input the historical vibration frequency sequence into a pre-constructed frequency prediction model to obtain a historical predicted frequency sequence, wherein the frequency prediction model refers to a calculation model that can predict the vibration frequency sequence of the next unit moment based on the input historical vibration frequency sequence; obtain the actual vibration frequency sequence of the next unit moment, and use the pre-constructed difference calculation formula to calculate the historical predicted difference between the actual vibration frequency sequence and the historical predicted frequency sequence; identify the historical gas concentration set corresponding to the historical gas category set, wherein the historical gas concentration set includes: historical Concentration and History concentration; obtain the current temperature monitoring value, and calculate the current temperature change rate based on the historical temperature monitoring value and the current temperature monitoring value; determine whether the GIS device is in a preset normal operating state based on the historical partial discharge value, the historical prediction difference, the historical gas concentration set and the current temperature change rate; if the GIS device is in a normal operating state, return to the above-mentioned step of receiving the GIS device monitoring instruction; if the GIS device is not in a normal operating state, issue a GIS device abnormality prompt to complete the GIS device monitoring based on multi-channel synchronous sampling.

2. The GIS equipment monitoring method based on multi-channel synchronous sampling according to claim 1 is characterized in that: The method of using the monitoring device to obtain the historical partial discharge value, historical vibration characteristic value, historical gas category set and historical temperature monitoring value of the GIS equipment includes: connecting a high-frequency current transformer to the GIS equipment, monitoring the high-frequency current in the GIS equipment at a preset historical unit time through the high-frequency current transformer, and converting the high-frequency current into a historical partial discharge value using the high-frequency current transformer; connecting an acceleration sensor to the GIS equipment, monitoring the acceleration signal generated by the vibration of the GIS equipment at a historical unit time through the acceleration sensor, and converting the acceleration signal into a historical vibration characteristic value using the acceleration sensor; connecting a gas chromatograph to the GIS equipment, and using the gas chromatograph to monitor the gas product category of the GIS equipment at a historical unit time to obtain a historical gas category set; connecting a temperature sensor to the GIS equipment, and using the temperature sensor to monitor the temperature of the GIS equipment at a historical unit time to obtain a historical temperature monitoring value.

3. The GIS equipment monitoring method based on multi-channel synchronous sampling according to claim 2 is characterized in that: The preprocessing operation on the historical vibration characteristic values ​​to obtain a historical vibration frequency sequence includes: performing a fast Fourier transform on the historical vibration characteristic values ​​to obtain an initial vibration frequency sequence, wherein the fast Fourier transform refers to converting the historical vibration characteristic values ​​in the time domain into an initial vibration frequency sequence in the frequency domain; determining a vibration cutoff frequency interval based on a preset vibration frequency range; and performing vibration frequency screening on the initial vibration frequency sequence according to the vibration cutoff frequency interval to obtain a historical vibration frequency sequence.

4. The GIS equipment monitoring method based on multi-channel synchronous sampling according to claim 3 is characterized in that: The process of constructing the frequency prediction model includes: obtaining multiple vibration frequency sequence samples; and calculating the kernel function according to the multiple vibration frequency sequence samples using the following formula: ,in, express The kernel function, Represents the first sample of the i-th vibration frequency sequence The amplitude of the vibration frequency, Represents the first of the input historical vibration frequency sequence The amplitude of the vibration frequency, σ represents the width parameter of the kernel function, express and The square of the Euclidean distance between represents a natural exponential function; obtaining a Lagrange multiplier and a bias vector, and constructing a frequency prediction model based on the kernel function, the Lagrange multiplier and the bias vector.

5. The GIS equipment monitoring method based on multi-channel synchronous sampling according to claim 4 is characterized in that: The frequency prediction model is as follows: ,in, represents the historical vibration frequency sequence of the input, Indicates based on The calculated historical forecast frequency, represents the number of vibration frequency sequence samples in multiple vibration frequency sequence samples, and represents the first and second Lagrange multipliers of the i-th vibration frequency sequence sample in multiple vibration frequency sequence samples, Represents the bias vector.

6. The GIS equipment monitoring method based on multi-channel synchronous sampling according to claim 5 is characterized in that: The method of calculating the historical prediction difference between the actual vibration frequency sequence and the historical prediction frequency sequence using a pre-constructed difference calculation formula includes: normalizing the actual vibration frequency sequence and the historical prediction frequency sequence to obtain a standard vibration frequency sequence and a standard prediction frequency sequence, wherein the normalization process refers to converting the actual vibration frequency sequence and the prediction frequency sequence of different dimensions into a standard vibration frequency sequence and a standard prediction frequency sequence of standardized dimensions; and calculating the historical prediction difference based on the standard vibration frequency sequence and the standard prediction frequency sequence using a difference calculation formula, wherein the difference calculation formula is as follows: ,in, represents the historical forecast difference, Indicates the number of vibration frequency amplitudes in the standard vibration frequency sequence or the standard predicted frequency sequence, Represents the standard vibration frequency sequence The kth vibration frequency amplitude in , Represents the standard prediction frequency series The kth vibration frequency amplitude in , Indicates the absolute value symbol.

7. The GIS equipment monitoring method based on multi-channel synchronous sampling according to claim 6 is characterized in that: The calculating the current temperature change rate based on the historical temperature monitoring value and the current temperature monitoring value includes: calculating the current temperature change rate based on the current temperature monitoring value and the historical temperature monitoring value using a pre-built temperature change rate calculation formula, wherein the temperature change rate calculation formula is as follows: ,in, Indicates the current temperature monitoring value. Indicates historical temperature monitoring values.

8. The GIS equipment monitoring method based on multi-channel synchronous sampling according to claim 7 is characterized in that: The method of judging whether the GIS device is in a preset normal operating state based on historical partial discharge values, historical prediction differences, historical gas concentration sets and current temperature change rates includes: summarizing the historical partial discharge values, historical prediction differences, historical gas concentration sets and current temperature change rates to obtain a device monitoring value set; judging whether the device monitoring value set meets a preset monitoring feature standard; if the device monitoring value set meets the monitoring feature standard, judging that the GIS device is in a normal operating state; if the device monitoring value set does not meet the monitoring feature standard, judging that the GIS device is not in a normal operating state.

9. The GIS equipment monitoring method based on multi-channel synchronous sampling according to claim 8, characterized in that: The determination of whether the equipment monitoring value set meets the preset monitoring characteristic standard includes: obtaining a standard monitoring value set, wherein the standard monitoring value set includes: a standard partial discharge value, a standard prediction difference value, a standard gas concentration set and a standard temperature change rate, and the standard gas concentration set includes: a standard Concentration and standard Concentration; Based on the equipment monitoring value set and the standard monitoring value set, the monitoring difference is calculated using the following formula: ,in, Indicates the monitoring difference, Represents the historical partial discharge value, Indicates the standard partial discharge value, represents the standard prediction error, express Historical gas concentration of the gas, express Standard gas concentration of the gas, express Historical gas concentration of the gas, express Standard gas concentration of the gas, Indicates the standard temperature change rate; determines whether the monitoring difference is greater than the preset monitoring error threshold: if the monitoring difference is greater than the monitoring error threshold, it is determined that the device monitoring value set meets the monitoring characteristic standard; if the monitoring difference is not greater than the monitoring error threshold, it is determined that the device monitoring value set does not meet the monitoring characteristic standard.

10. A GIS equipment monitoring system based on multi-channel synchronous sampling, characterized in that: The system includes: a monitoring instruction receiving module for receiving a GIS equipment monitoring instruction and confirming an equipment monitoring environment based on the GIS equipment monitoring instruction, wherein the equipment monitoring environment includes: a GIS equipment to be monitored and a monitoring device for monitoring the GIS equipment, wherein the monitoring device includes: a high-frequency current transformer, an acceleration sensor, a gas chromatograph, and a temperature sensor; and a data monitoring processing module for using the monitoring device to obtain historical partial discharge values, historical vibration characteristic values, historical gas category sets, and historical temperature monitoring values ​​of the GIS equipment, wherein the historical gas category sets include: Gas and Gas, preprocessing operation is performed on the historical vibration characteristic value to obtain a historical vibration frequency sequence, wherein the preprocessing operation refers to frequency domain data conversion of the historical vibration characteristic value; a data monitoring calculation module is used to input the historical vibration frequency sequence into a pre-built frequency prediction model to obtain a historical prediction frequency sequence, wherein the frequency prediction model refers to a calculation model that can predict the vibration frequency sequence of the next unit moment based on the input historical vibration frequency sequence, obtain the actual vibration frequency sequence of the next unit moment, use the pre-built difference calculation formula to calculate the historical prediction difference between the historical vibration frequency sequence and the historical prediction frequency sequence, identify the historical gas concentration set corresponding to the historical gas category set, wherein the historical gas concentration set includes: historical Concentration and History concentration, obtain the current temperature monitoring value, and calculate the current temperature change rate based on the historical temperature monitoring value and the current temperature monitoring value; an equipment status judgment module is used to judge whether the GIS equipment is in a preset normal operating state based on the historical partial discharge value, the historical prediction difference, the historical gas concentration set and the current temperature change rate. If the GIS equipment is in a normal operating state, it returns to the above-mentioned step of receiving the GIS equipment monitoring instruction; if the GIS equipment is not in a normal operating state, it will issue a GIS equipment abnormality prompt.

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