Heavy gas action setting method, system, equipment and medium

Abstract

The invention discloses a heavy gas action setting method, system and equipment and a medium. The method comprises the following steps: constructing an experimental platform for simulating an internal fault of a transformer; dynamic parameters of oil flow pressure and flow velocity are collected in real time on the to-be-measured pipeline; acquiring pressure intensity, flow velocity and heavy gas action signals of a gas relay under different fault working conditions; extracting dynamic characteristics of pressure intensity and flow velocity; and establishing a data mapping relationship between the extracted dynamic characteristics and the heavy gas action duration, calculating a correlation coefficient and correlation degree between each characteristic index and the heavy gas action duration based on a primary algorithm, and setting an optimal setting value. The collected experimental data are analyzed through a grey correlation analysis method, and the parameters of the heavy gas action are evaluated according to the analysis result, so that the correlation of each parameter to the heavy gas action can be effectively identified. And the heavy gas action setting value is optimized according to the analysis result, so that the misoperation rate is reduced, and the response speed and the fault recognition capability of the relay are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of heavy gas action setting, and in particular to a heavy gas action setting method, system, equipment and medium. Background Art

[0002] Buchholz relays are key devices in transformer protection systems, with double-float gas relays being the most widely used. However, their operating settings are typically set based on experience, which presents numerous drawbacks. Given the complex and varied nature of transformer fault types, the operating characteristics of heavy gas relays under varying fault intensities are affected by a variety of factors, such as pipeline oil pressure, flow rate, and oil temperature, and the relationships between these factors are complex. Relying on traditional empirical settings makes it difficult to accurately assess the operating characteristics of heavy gas relays. This can lead to misoperation or refusal to operate during actual operation, reducing the reliability of transformer protection and failing to effectively ensure safe and stable operation of the transformer.

[0003] Grey correlation analysis is a mathematical method used to analyze complex multi-factor relationships in a system. It can quantify the correlation between different influencing factors and target parameters, providing a scientific basis for optimizing the relay's operating setting value. Currently, no study has combined the two to analyze the factors influencing the heavy gas operating characteristics of double-float gas relays. Therefore, introducing grey correlation analysis into the study of the heavy gas operating characteristics of double-float gas relays can effectively identify key influencing factors, optimize the relay's operating setting value, improve fault detection accuracy and response speed, and thus enhance the overall performance of the transformer protection system. Summary of the Invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a heavy gas action setting method and system to solve the problem that the traditional gas relay action setting value is set based on experience, does not fully consider the impact of different operating conditions and fault modes on the relay action characteristics, and is difficult to adapt to complex and changeable transformer fault conditions.

[0006] When a transformer fails, the heavy gas operating characteristics are affected by multiple factors such as pipeline oil flow pressure, flow velocity, and oil temperature. These factors have complex relationships, and it is difficult to accurately evaluate their effects on the heavy gas operating characteristics using traditional methods, making it impossible to accurately determine the set values.

[0007] Due to the above reasons, the gas relay is prone to misoperation or refusal to operate, which reduces the reliability of transformer protection and cannot effectively ensure the safe and stable operation of the transformer.

[0008] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0009] In a first aspect, the present invention provides a heavy gas action setting method, comprising:

[0010] Obtained the construction of an experimental platform for simulating internal faults of transformers;

[0011] Collect the dynamic parameters of oil flow pressure and flow rate in real time on the pipeline to be tested;

[0012] Obtain pressure, flow rate and heavy gas action signal of gas relay under different fault conditions;

[0013] Extract dynamic characteristics of pressure and flow rate;

[0014] A data mapping relationship is established between the extracted dynamic features and the duration of heavy gas action. The correlation coefficient and correlation degree between each characteristic index and the duration of heavy gas action are calculated based on the grey correlation analysis method, and the optimal setting value is set.

[0015] As a preferred solution of the heavy gas action setting method of the present invention, the experimental platform for simulating internal faults of transformers includes:

[0016] The experimental platform simulates the pulsating oil flow caused by a severe gas fault in the transformer;

[0017] The instantaneous release of high-pressure gas inside the air cannon generates a pressure wave that pushes the oil flow in the pipeline to impact the baffle of the gas relay, causing the gas relay to operate heavily.

[0018] As a preferred embodiment of the heavy gas action setting method of the present invention, the real-time acquisition of dynamic parameters of oil flow pressure and flow rate on the pipeline to be tested includes:

[0019] The pipeline to be tested is integrated with sensors to collect pressure and speed parameters in real time during the transmission process and pre-process the parameters;

[0020] To provide data support for analyzing the heavy gas signal response characteristics of the gas relay, a complete experimental framework is formed covering the excitation source, transmission medium, core measured components and parameter monitoring module.

[0021] As a preferred solution of the heavy gas action setting method of the present invention, the step of obtaining the pressure, flow rate and heavy gas action signal of the gas relay under different fault conditions includes:

[0022] Conduct multiple experiments, and set up different air cannon excitation pressure groups for gas relay damper action tests;

[0023] Simulate the transient oil flow conditions under different impacts, collect the oil flow pressure, flow velocity and heavy gas signal data in the pipeline under transient oil flow impact, and preprocess and extract dynamic features of the collected experimental data of different transformer fault conditions.

[0024] As a preferred embodiment of the heavy gas action setting method of the present invention, the extraction of dynamic characteristics of pressure and flow rate includes:

[0025] Use time series analysis technology to extract the dynamic characteristics of data and select the statistical characteristics of pressure and velocity in a time period;

[0026] By using the grey correlation analysis method on the collected experimental data, the various parameters affecting the heavy gas action are evaluated and the correlation between different influencing factors and the heavy gas action is quantified.

[0027] As a preferred solution of the heavy gas action setting method of the present invention, the calculation of the correlation coefficient and correlation degree between each characteristic index and the heavy gas action duration based on the grey correlation analysis method includes:

[0028] The grey correlation analysis method was used to analyze the correlation between the maximum pressure, maximum flow rate and excitation pressure and the duration of heavy gas action. The influence of each parameter on the heavy gas action was evaluated according to the size of the correlation.

[0029] As a preferred solution of the heavy gas action setting method of the present invention, wherein: setting the optimal setting value includes:

[0030] Select heavy gas action as reference data, calculate the results, perform dimensionless processing on the selected data, and calculate the correlation degree;

[0031] According to the degree of correlation, the dominance of each influencing factor is determined.

[0032] In a second aspect, the present invention provides a heavy gas action setting system, comprising: an acquisition module for acquiring pipeline parameters and constructing a pipeline model;

[0033] Platform construction module, which builds an experimental platform for simulating transformer internal faults;

[0034] The sensor module collects the dynamic parameters of oil flow pressure and flow rate in real time on the pipeline to be tested;

[0035] Acquisition module, which obtains the pressure, flow rate and heavy gas action signal of the gas relay under different fault conditions;

[0036] Feature extraction module, extracting dynamic features of pressure and flow rate;

[0037] The calculation module establishes a data mapping relationship between the extracted dynamic features and the duration of heavy gas action, calculates the correlation coefficient and correlation degree between each characteristic index and the duration of heavy gas action based on the grey correlation analysis method, and sets the optimal setting value.

[0038] In a third aspect, the present invention provides an electronic device, comprising:

[0039] memory and processor;

[0040] The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the heavy gas action setting method are implemented.

[0041] In a fourth aspect, the present invention provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the heavy gas action setting method.

[0042] Compared with existing technologies, this invention offers the following advantages: It analyzes collected experimental data using gray correlation analysis and, based on the analysis results, evaluates the parameters of the heavy gas operation, effectively identifying the correlations between each parameter and the heavy gas operation. This method uses gray correlation analysis to assess the factors influencing heavy gas operation and effectively identifies the correlations between each parameter. Finally, based on the analysis results, the heavy gas operation setting value is optimized to reduce the false trip rate and improve the relay's response speed and fault identification capabilities. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0044] Figure 1 The figure is a schematic diagram of the overall flow of a heavy gas action setting method according to an embodiment of the present invention.

[0045] Figure 2 This is a diagram of an experimental model of a gas relay's heavy gas signal response according to an embodiment of the present invention.

[0046] Figure 3 This is a design diagram for a heavy gas signal response experiment of a gas relay according to an embodiment of the present invention.

[0047] Figure 4 This is a flowchart of synchronous acquisition of heavy gas signals of a gas relay according to an embodiment of the present invention.

[0048] Figure 5 This is a flow chart for optimizing the heavy gas signal setting value of a gas relay according to an embodiment of the present invention. DETAILED DESCRIPTION

[0049] To make the above-mentioned objects, features, and advantages of the present invention more clearly understood, the following detailed description of the specific embodiments of the present invention is given in conjunction with the accompanying drawings. It is obvious that the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in this field without creative work should fall within the scope of protection of the present invention.

[0050] Example 1, reference Figure 1 , which is an embodiment of the present invention, provides a heavy gas action setting method, comprising:

[0051] S1: Build an experimental platform for simulating transformer internal faults;

[0052] S2: Real-time acquisition of dynamic parameters of oil flow pressure and flow velocity on the pipeline to be tested;

[0053] S3: Obtain the pressure, flow rate and heavy gas action signal of the gas relay under different fault conditions;

[0054] S4: Extract dynamic characteristics of pressure and flow rate;

[0055] S5: Establish a data mapping relationship between the extracted dynamic features and the duration of heavy gas action, calculate the correlation coefficient and correlation degree between each characteristic index and the duration of heavy gas action based on the grey correlation analysis method, and set the optimal setting value.

[0056] This invention provides a method for setting the heavy gas action of a gas relay based on gray correlation analysis, aiming to improve the accuracy and reliability of double-float gas relays in transformer fault detection. By establishing a gray correlation model between the heavy gas action characteristics and the various influencing factors, this method evaluates the correlation between the heavy gas action setting value under different operating conditions and provides a new approach for verifying and correcting gas relay setting values.

[0057] Example 2, reference Figure 1-5 , which is an embodiment of the present invention, provides a heavy gas action setting method based on the above embodiment.

[0058] In the embodiment of the present application, the experimental platform for simulating internal faults of the transformer constructed in step S1 includes 7 major parts: an air cannon, a pulsating flow generating chamber, a pipe to be tested, a gas relay, a bellows, a butterfly valve, and a capsule oil pillow.

[0059] Preferably, the air cannon provides the test platform with the external excitation source required for the test, and uses the instantaneous release of compressed air to simulate the internal fault source of the transformer box; the pulsating flow generating chamber provides the compressed air with expansion work space to stimulate the pulsating oil flow; the pipeline to be tested is used to arrange and install various test sensors, such as pressure sensors and speed sensors; the gas relay is used to detect the light and heavy gas signals generated by the impact of the pulsating flow in the pipeline, simulating the light and heavy gas actions during transformer failure; the bellows is used to connect the pipeline, perform pipeline correction and pipeline vibration suppression; the butterfly valve is used to control the on and off of the pipeline and simulate the change in the cross-sectional area of the pipeline diameter by changing the opening and closing angle; the capsule oil pillow is used to store and replenish oil.

[0060] A simulated transformer fault experimental platform was built, using an air cannon as the excitation source to simulate an internal transformer fault, causing changes in the pipeline oil flow and triggering the double-float gas relay. At the same time, a double-float gas relay heavy gas signal response platform and a transformer capsule oil pillow platform were built, respectively used to monitor the various parameters at the moment of heavy gas action in real time, absorb and compensate for oil flow pulsations, and maintain oil pressure stability. Under different transformer fault conditions, the pipeline oil flow pressure changes were measured based on a PCM300 pressure transmitter (measuring range 0-0.4MPa, resolution 0.001MPa, diaphragm 316s stainless steel). A non-contact ultrasonic flowmeter was preferably used to measure the pipeline flow rate (because its non-contact measurement method does not interfere with the flow field, and the signal processing is stable, accurate, and anti-interference). The heavy gas action signal was measured and output through the gas relay. These data were collected and analyzed using a dynamic signal data acquisition instrument.

[0061] like Figure 2 As shown in the figure, a double-float gas relay heavy gas signal response experimental platform is built. This experimental platform can simulate the pulsating oil flow excited by a heavy gas fault in the transformer. The main principle is: the instantaneous release of high-pressure gas inside the air cannon generates a pressure wave that pushes the pipeline oil flow to impact the gas relay baffle, causing the gas relay to operate with heavy gas.

[0062] In an optional embodiment, the experimental platform for simulating internal transformer faults constructed in step S1 may also include an air cannon using compressed air as power, and utilizing an electromagnetic valve to quickly release compressed gas to generate a pulse oil flow shock, thereby triggering the gas relay to operate, which is suitable for simulating various oil flow fault scenarios with sudden changes in intensity.

[0063] In another optional embodiment, the experimental platform for simulating internal transformer faults constructed in step S1 can also use a servo hydraulic system as an excitation source, adjust the hydraulic shock intensity and frequency through a programmable controller, accurately simulate the oil flow disturbance characteristics inside the transformer, and improve the controllability and repeatability of the fault simulation.

[0064] In the embodiment of the present application, step S2 collects the dynamic parameters of oil flow pressure and flow rate in real time on the pipeline to be tested, including the following steps A1-A2:

[0065] A1: The pipeline to be tested is integrated with sensors to collect pressure and speed parameters during the transmission process in real time and pre-process the parameters;

[0066] A2: Provide data support for analyzing the heavy gas signal response characteristics of the gas relay, forming a complete experimental architecture covering the excitation source, transmission medium, core measured components and parameter monitoring modules.

[0067] like Figure 3 As shown in the figure, a systematic experimental platform for the heavy gas signal response of a gas relay has been constructed. Its main components consist of an air cannon, a pulsating flow chamber, a pipeline, a gas relay, a bellows, a butterfly valve, and a capsule oil conservator. These components are connected in series to form a fluid transmission link. The pipeline module integrates a pressure sensor and a velocity sensor to collect real-time pressure and velocity parameters during transmission, providing data support for analyzing the heavy gas signal response characteristics of the gas relay. This completes the experimental architecture, encompassing the excitation source (air cannon, pulsating flow chamber), the transmission medium (pipeline), the core device under test (the gas relay), and the parameter monitoring module (sensor).

[0068] Common sensor output signals include current and voltage, corresponding to two circuit designs with different ranges. During the design process, the pressure sensor and gas relay output signals were 0-5V voltage values, measuring pipeline pressure and heavy gas signals, respectively. The ultrasonic flow sensor outputs a 4-20mA current signal, measuring pipeline flow velocity. Before using a data acquisition instrument, a conversion circuit was required to convert the current signal into a voltage signal.

[0069] The outputs of the sensors are connected to a dynamic signal data acquisition instrument, which is connected to a computer at a frequency of no less than 10kHz. When the circuit is connected, each sensor begins measuring data. The three sets of signals measured simultaneously are collected and processed by the acquisition instrument, and the signals of each channel are displayed on the computer. The collected data is real-time and synchronous.

[0070] In the embodiment of the present application, in step A1 of step S2, the pipeline to be tested is integrated with a sensor, which collects pressure and speed parameters in real time during the transmission process and pre-processes the parameters, including the following steps:

[0071] like Figure 3As shown, PCM300 pressure transmitter is selected with a measuring range of 0-0.4MPa, a resolution of 0.001MPa, and a diaphragm of 316s stainless steel. It is used to measure the pressure change of the pipeline oil flow during the test; under the premise of ensuring measurement accuracy, non-contact ultrasonic flowmeter is preferably used to measure the pipeline flow velocity. The non-contact measurement method will not affect the flow field. The signal digital processing technology makes the signal measurement more stable and accurate and has a strong anti-interference ability, which can avoid the influence of the measuring device on the surge of the pipeline oil flow, making the measurement result more accurate; the gas relay transmits the heavy gas action signal through the heavy gas line; the pipeline flow velocity, pipeline pressure and the heavy gas signal of the gas relay are collected and analyzed through the dynamic signal data acquisition instrument.

[0072] Step S2 mentions pressure transmitters and ultrasonic flowmeters, both of which are contact or non-contact. Possible alternatives include other non-contact technologies, such as laser Doppler velocimeters, fiber optic sensors, or multi-sensor fusion, including temperature and vibration sensors, to collect more dimensional data and improve monitoring accuracy.

[0073] Preferably, a dynamic signal data acquisition instrument is used for data collection and transmission. Wireless sensor networks, such as Bluetooth, can be considered to achieve wireless networking of sensor nodes and solve the layout limitations of wired connections for distributed monitoring of large transformer groups. Based on the concept of the content of the present invention, many changes can be made in the specific implementation and application scope. As long as these changes do not deviate from the concept of the present invention, they are all within the scope of protection of this patent.

[0074] In the embodiment of the present application, obtaining the pressure, flow rate and heavy gas action signal of the gas relay under different fault conditions in step S3 includes the following steps C1-C2:

[0075] C1: Conduct multiple experiments, setting up different air cannon excitation pressure groups for gas relay damper action tests;

[0076] C2: Simulate transient oil flow conditions under different impacts, collect oil flow pressure, flow velocity, and heavy gas signal data in the pipeline under transient oil flow impact, and preprocess and extract dynamic features of the collected experimental data of different transformer fault conditions.

[0077] Specifically, C1-C2 can be reflected through the following specific implementation process:

[0078] To establish the correlation between the heavy gas operating characteristics and various influencing factors and to evaluate the degree of correlation between the heavy gas operating setting values and various influencing factors under different fault conditions, multiple experiments were conducted, sequentially setting up different air cannon excitation pressure groups to test the gas relay damper operation. These experiments were designed to simulate transient oil flow conditions under different shocks. The oil flow pressure, flow velocity, and heavy gas signal data within the pipeline under transient oil flow shocks were collected. The collected experimental data for different transformer fault conditions was then preprocessed and dynamic feature extraction was performed.

[0079] It should be noted that the experimental data are preprocessed in C2, which is specifically expressed as follows:

[0080] The collected experimental data for different transformer fault conditions was preprocessed, using normalization to bring the pressure and velocity data into the same dimension and numerical range to facilitate subsequent correlation analysis. Time series analysis techniques were used to extract the dynamic characteristics of the data, selecting statistical features of the pressure and velocity over a time period L, such as their mean, standard deviation, and slope.

[0081] The normalization method is used to unify the pressure and velocity data into the same dimension and numerical range to facilitate subsequent correlation analysis.

[0082]

[0083] Where, P norm is the normalized pressure.

[0084]

[0085] Where V norm is the normalized speed.

[0086] It should be noted that the specific process of dynamic feature extraction of data in C2 is as follows:

[0087] The dynamic characteristics of the data are extracted using time series analysis technology, and the statistical characteristics of pressure and velocity in a time period of L are selected.

[0088] Pressure characteristics:

[0089] mean Standard deviation Slope

[0090] Speed characteristics:

[0091] mean Standard deviation Slope

[0092] By applying the grey correlation analysis method to the collected experimental data, we can effectively evaluate the various parameters that affect the heavy gas action and quantify the degree of correlation between different influencing factors and the heavy gas action.

[0093] In the embodiment of the present application, step S4: extracting the dynamic characteristics of pressure and flow rate includes the following steps D1-D2:

[0094] D1: Use time series analysis technology to extract the dynamic characteristics of the data and select the statistical characteristics of pressure and velocity over a period of time;

[0095] D2: By applying the first-level algorithm to the collected experimental data, the various parameters affecting the heavy gas action are evaluated and the degree of correlation between different influencing factors and the heavy gas action is quantified.

[0096] Preferably, in the embodiment of the present application, the primary algorithm in step D2 adopts the grey relational analysis method, which is specifically expressed as follows:

[0097] The grey correlation analysis method is used to analyze the correlation between the maximum pressure, maximum flow rate, and excitation pressure and the duration of heavy gas action. The influence of each parameter on heavy gas action is evaluated by the size of the correlation. First, it is assumed that there are m groups of heavy gas action data, and the heavy gas action is selected as the reference. The calculation results are:

[0098] {x0(j)}={x0(1),x0(2),…,x0(m)}

[0099] Where, j = 1, 2, 3,…, m.

[0100] Perform dimensionless processing on the selected data:

[0101]

[0102] Where, i = 1, 2, 3,…, n.

[0103] Calculate the correlation coefficient ξ i (j):

[0104]

[0105] Where, min{|X0(j)-X i (j)|} is |X0(j)-X i (j)|, max{|X0(j)-X i (j)|} is |X0(j)-X i (j)|The maximum value.

[0106] Calculate the correlation r i :

[0107]

[0108] Correlation r i It reflects the influence of various factors on the duration of heavy gas action. The greater the correlation, the greater the influence, and vice versa.

[0109] In an optional embodiment, the first-level algorithm can also be a correlation analysis method to evaluate the linear relationship between characteristic parameters such as pressure and flow rate and the duration of heavy gas action. The method is simple and intuitive and can quickly screen out key factors that are highly correlated with relay action.

[0110] In an optional embodiment, the primary algorithm may also be principal component analysis (PCA), which is suitable for situations with a large number of parameter dimensions and collinearity between variables. It can extract the main influencing factors through dimensionality reduction, effectively improving the efficiency of feature analysis and the interpretability of the results.

[0111] In the embodiment of this application, Figure 4 In step S5, a data mapping relationship is established between the extracted dynamic features and the duration of the heavy gas action, the correlation coefficient and correlation degree between each characteristic index and the duration of the heavy gas action are calculated based on the grey correlation analysis method, and the optimal setting value is set, which includes the following steps E1-E3:

[0112] E1: Select heavy gas action as reference data, calculate the results, perform dimensionless processing on the selected data, and calculate the correlation degree;

[0113] E2: Determine the dominance of each influencing factor based on the degree of correlation.

[0114] It should be noted that to explore the relationship between the heavy gas activation signal under different transient oil flow shocks, multiple experiments were conducted to collect pipeline oil flow pressure, flow velocity, and heavy gas signals under transient oil flow shocks. A dichotomy method was used to determine the critical excitation pressure for heavy gas activation: 0.109 MPa. When the minimum excitation pressure was greater than 0.109 MPa, the heavy gas did not activate, and the simulated transformer did not experience any internal faults. When the excitation pressure was greater than or equal to 0.109 MPa, the heavy gas activated and cut off the power supply, at which point a serious internal fault occurred in the simulated transformer.

[0115] To further investigate the heavy gas actuation of the BF double-float gas relay under different transient oil flow shocks, ten sets of gas relay damper actuation tests were conducted using air cannon excitation pressures set to 0.105 MPa, 0.108 MPa, 0.109 MPa, 0.11 MPa, 0.105 MPa, 0.12 MPa, 0.125 MPa, 0.13 MPa, 0.135 MPa, and 0.14 MPa, as shown in Table 1. Transient oil flow under different shocks was simulated, and pipeline oil flow pressure, flow velocity, and heavy gas signals were collected under these conditions.

[0116] Table 1

[0117]

[0118] By processing experimental data using grey correlation analysis, we can effectively evaluate key parameters affecting heavy gas operation and quantify the correlation between different influencing factors and heavy gas operation. This patent uses grey correlation analysis to analyze the correlation between pressure and velocity and heavy gas operation, and determines the degree of influence of each influencing factor on heavy gas operation based on the magnitude of the correlation. As shown in Table 2, the influence of heavy gas operation pressure on heavy gas operation is more significant than that of heavy gas operation flow rate.

[0119] Table 2

[0120]

[0121] like Figure 5 As shown, a method for setting the heavy gas action of a gas relay based on the grey correlation analysis method of the present invention comprises the following specific steps:

[0122] Statistically analyze the correlation distribution of the effects of pressure and speed factors on heavy gas operation under different working conditions, and determine the pressure correlation threshold. Pressure correlation threshold To adjust the set value.

[0123] like and The set value is not adjusted. and Then judge the dynamic characteristics of the pressure. If the data fluctuation range is large, the transformer is unstable and the setting value needs to be increased to prevent false operation; if the data fluctuation range is small, the transformer is stable and the setting value needs to be reduced to improve the sensitivity of fault detection. and Then judge the dynamic characteristics of the speed. If the data fluctuates greatly and the transformer operation is unstable, the setting value needs to be increased to prevent false operation; otherwise, the setting value should be reduced to improve the sensitivity of fault detection.

[0124] In summary, this method can accurately identify the impact of oil flow pressure and velocity on relay operation under different operating conditions, enabling scientific setting and dynamic adjustment of setpoints. By constructing an air cannon-pulsating flow-double-float gas relay experimental platform to simulate the internal fault environment of a transformer, the pressure, flow velocity, and heavy gas operation signals under different excitation pressures were collected, their dynamic characteristics were extracted, and a grey correlation analysis method was used to establish a correlation model between parameters and operation duration, quantitatively evaluating the influence of each factor.

[0125] This method realizes the transformation from empirical setting to data-driven setting, and has the advantages of high setting accuracy, strong anti-interference ability, and strong adaptability. It significantly improves the response reliability and action sensitivity of the relay in transformer fault detection.

[0126] Example 3. The above is a schematic scheme for a method for adjusting the heavy gas operation. It should be noted that the technical scheme of this system for adjusting the heavy gas operation is based on the same concept as the technical scheme of the aforementioned method for adjusting the heavy gas operation. For details not described in detail in the technical scheme of the heavy gas operation adjustment system in this embodiment, please refer to the description of the technical scheme of the aforementioned method for adjusting the heavy gas operation.

[0127] This embodiment also provides a heavy gas action setting system, including:

[0128] Platform construction module, which builds an experimental platform for simulating transformer internal faults;

[0129] The sensor module collects the dynamic parameters of oil flow pressure and flow rate in real time on the pipeline to be tested;

[0130] Acquisition module, which obtains the pressure, flow rate and heavy gas action signal of the gas relay under different fault conditions;

[0131] Feature extraction module, extracting dynamic features of pressure and flow rate;

[0132] The calculation module establishes a data mapping relationship between the extracted dynamic features and the duration of heavy gas action, calculates the correlation coefficient and correlation degree between each characteristic index and the duration of heavy gas action based on the grey correlation analysis method, and sets the optimal setting value.

[0133] This embodiment also provides an electronic device suitable for heavy gas action adjustment, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement the heavy gas action adjustment method proposed in the above embodiment.

[0134] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the method for setting the heavy gas action proposed in the above embodiment is implemented.

[0135] The storage medium proposed in this embodiment and the method for implementing heavy gas action setting proposed in the above embodiment belong to the same inventive concept. Technical details not fully described in this embodiment can be referred to the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0136] Through the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented with the help of software and necessary general hardware, and of course can also be implemented by hardware. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which can be stored in a computer-readable storage medium, such as a computer's floppy disk, read-only memory (ROM), random access memory (RAM), flash memory (FLASH), hard disk or optical disk, etc., including a number of instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute the methods of various embodiments of the present invention.

[0137] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to 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, which should all be included in the scope of the claims of the present invention.

Claims

1. Heavy gas action setting method, characterized in that: include: Build an experimental platform for simulating transformer internal faults; Collect the dynamic parameters of oil flow pressure and flow rate in real time on the pipeline to be tested; Obtain pressure, flow rate and heavy gas action signal of gas relay under different fault conditions; Extract dynamic characteristics of pressure and flow rate; A data mapping relationship is established between the extracted dynamic features and the duration of heavy gas action. The correlation coefficient and correlation degree between each characteristic index and the duration of heavy gas action are calculated based on the first-level algorithm, and the optimal setting value is set.

2. The heavy gas action setting method according to claim 1, characterized in that: The experimental platform for simulating internal faults of transformers is constructed, including: The experimental platform simulates the pulsating oil flow caused by a severe gas fault in the transformer; The instantaneous release of high-pressure gas inside the air cannon generates a pressure wave that pushes the oil flow in the pipeline to impact the baffle of the gas relay, causing the gas relay to operate heavily.

3. The heavy gas action setting method according to claim 2, characterized in that: The real-time acquisition of dynamic parameters of oil flow pressure and flow velocity on the pipeline to be tested includes: The pipeline to be tested is integrated with a data acquisition device to collect data parameters in real time during the transmission process and pre-process the parameters; The system provides data support for analyzing the heavy gas signal response characteristics of the gas relay, forming a complete experimental framework covering the excitation source, transmission medium, core measured components and parameter monitoring modules.

4. The heavy gas action setting method according to claim 3, characterized in that: The method of obtaining the pressure, flow rate and heavy gas action signal of the gas relay under different fault conditions includes: Conduct multiple experiments, setting different air cannon excitation pressure groups to perform gas relay damper action tests; Simulate the transient oil flow conditions under different impacts, collect the oil flow pressure, flow velocity and heavy gas signal data in the pipeline under transient oil flow impact, and preprocess and extract dynamic features of the collected experimental data of different transformer fault conditions.

5. The heavy gas action setting method according to claim 4, characterized in that: The dynamic characteristics of the extraction pressure and flow rate include: Use time series analysis technology to extract the dynamic characteristics of data and select the statistical characteristics of pressure and velocity in a time period; By applying a first-level algorithm to the collected experimental data, various parameters affecting the heavy gas action are evaluated, and the degree of correlation between different influencing factors and the heavy gas action is quantified.

6. The heavy gas action setting method according to claim 5, characterized in that: The grey correlation analysis method is used to calculate the correlation coefficient and correlation degree between each characteristic index and the duration of the heavy gas action, including: A first-level algorithm is used to analyze the correlation between the maximum pressure, maximum flow rate, excitation pressure and the duration of heavy gas action, and the influence of each parameter on the heavy gas action is evaluated according to the size of the correlation.

7. The heavy gas action setting method according to claim 6, characterized in that: The setting of the optimal setting value includes: Select heavy gas action as reference data, calculate the results, perform dimensionless processing on the selected data, and calculate the correlation degree; According to the degree of correlation, the dominance of each influencing factor is determined.

8. A heavy gas action setting system, applying the method according to any one of claims 1 to 7, characterized in that: include: Platform construction module, which builds an experimental platform for simulating transformer internal faults; The sensor module collects the dynamic parameters of oil flow pressure and flow rate in real time on the pipeline to be tested; Acquisition module, which obtains the pressure, flow rate and heavy gas action signal of the gas relay under different fault conditions; Feature extraction module, extracting dynamic features of pressure and flow rate; The calculation module establishes a data mapping relationship between the extracted dynamic features and the duration of heavy gas action, calculates the correlation coefficient and correlation degree between each characteristic index and the duration of heavy gas action based on the first-level algorithm, and sets the optimal setting value.

9. An electronic device comprising: memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the heavy gas action setting method described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing computer-executable instructions, wherein the computer-executable instructions, when executed by a processor, implement the steps of the heavy gas action setting method according to any one of claims 1 to 7.

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