Double-float ball gas relay heavy gas action flow rate prediction method

Abstract

The application discloses a double-float-ball gas relay heavy-gas action flow rate prediction method, relates to the technical field of non-electric quantity protection of power transformers, and develops a transient oil flow impact experiment through building a heavy-gas action characteristic test platform, and builds a gas-liquid two-phase flow numerical simulation model verified through the experiment; then, full-condition data coupled with different fault intensities and gas phase fractions are obtained by extending simulation, a three-component prediction model with the gas phase fraction as input and the action flow rate correction coefficient as output is fitted and established; finally, the gas phase fraction is dynamically calculated based on real-time fault electrical parameters of the transformer, the heavy-gas action flow rate under the current condition is predicted by using the model, and real-time misoperation risk assessment is carried out by comparing the measured flow rate, and the protection setting value is dynamically adapted. The application realizes accurate prediction and dynamic protection of the action characteristic of the gas relay, and can significantly improve the reliability of the non-electric quantity protection of the transformer without hardware modification.

Description

Technical Field

[0001] This invention relates to the field of non-electrical quantity protection technology for power transformers, and more specifically to a method for predicting the heavy gas operation velocity of a dual-float gas relay based on gas phase fraction. Background Technology

[0002] A gas relay is a non-electrical protection device for transformers installed on the pipeline between the transformer tank and the conservator. Its reliability directly affects the safe operation of the transformer. Its working principle is as follows: When a minor fault occurs inside the transformer, a small amount of gas generated by the decomposition of the insulating oil accumulates at the top of the relay, causing the oil level to drop and the float ball to sink, triggering a light gas alarm signal. When a serious fault occurs inside the transformer, the electric arc at the fault point causes the insulating oil to vaporize instantaneously, generating a large amount of gas and forming a transient surging oil flow (gas-liquid two-phase flow). This oil flow impacts the relay baffle, causing it to rotate and triggering the heavy gas protection action, cutting off the transformer power supply.

[0003] Currently, the research and application of gas relays for heavy gas protection in the industry mainly suffer from the following defects and shortcomings:

[0004] The factory settings of existing gas relays are mostly calibrated through steady-state oil flow impact tests. However, actual transformer faults generate transient surging oil flows, accompanied by a large amount of fault gas forming a gas-liquid two-phase flow environment. The complex working conditions of the coupling between fault gas and transient oil flow in actual faults are not fully considered, which can easily lead to failure to trip under heavy gas conditions. If the protection device does not trigger a trip because it has not reached the fixed setting value, it can lead to the expansion of faults such as transformer winding burnout and insulation oil deterioration, which can cause significant losses to the enterprise.

[0005] Currently, most related studies only focus on the impact effect of pure oil flow medium on the relay baffle. It has been pointed out that the suddenness of fault excitation creates a significant instantaneous pressure difference between the front and back surfaces of the baffle. The additional impact torque provided by this pressure difference, when superimposed with the torque generated by the kinetic energy of the oil flow, is sufficient to overcome the combined torque of the permanent magnet's magnetic force and the float's buoyancy. This results in the gas relay's setting value under transient oil flow impact being lower than the factory setting value calibrated by the steady-state oil flow impact test.

[0006] However, the above does not consider that faulty gas will alter the density and pressure propagation characteristics of the gas-liquid mixture, leading to a decrease in the buoyancy torque and oil flow impact torque on the baffle. In cases of low fault intensity and long fault duration: a small instantaneous pressure difference results in insufficient additional torque; a high gas fraction reduces the density of the gas-liquid mixture, decreasing the oil flow torque, and the faulty gas also hinders pressure propagation, further reducing the pressure difference. Under this coupled situation, if the total impact torque is less than the resistance torque, there is a risk of the gas relay failing to operate. Blindly lowering the setting value to avoid failure to operate may cause malfunctions under fault-free or low gas content conditions, resulting in unconventional power outages and affecting power supply reliability. Furthermore, on-site maintenance cannot adapt to dynamic conditions at low cost, requiring frequent power outages to replace and adjust the permanent magnet magnetic force and other gas relay hardware, leading to high maintenance costs and impacting power supply continuity.

[0007] Some studies mention that faulty gases may affect the operation of gas relays, but they have not established a quantitative correlation between gas phase fraction and heavy gas operation flow rate through a complete process of "experimental testing-simulation verification-data fitting". Existing optimization methods for setting values ​​mostly focus on adjusting a single parameter or judging a combination of two parameters, neither of which incorporates gas phase fraction as a core consideration factor; at the same time, there is a lack of methods for predicting heavy gas operation flow rate and a failure-to-operate risk assessment system for gas-liquid two-phase flow conditions, making it impossible to identify protection hazards caused by faulty gases in advance.

[0008] Therefore, there is an urgent need to propose a dynamic velocity prediction and protection scheme that can adapt to gas-liquid two-phase flow conditions without hardware modification. Summary of the Invention

[0009] In view of the above problems, this invention is proposed to provide a method for predicting the gas operation velocity of a dual-float gas relay that overcomes or at least partially solves the above problems. This method achieves accurate prediction and dynamic protection of the gas relay's operating characteristics, and can significantly improve the reliability of transformer non-electrical quantity protection without hardware modification.

[0010] To achieve the above objectives, the present invention adopts the following technical solution:

[0011] In a first aspect, embodiments of the present invention provide a method for predicting the heavy gas operation velocity of a dual-float gas relay, comprising:

[0012] S1: Build a heavy gas action characteristic test platform, carry out transient oil flow impact test, simulate the coupled working conditions of different fault intensities and gas phase fractions, and simultaneously collect the oil flow velocity change curve over time.

[0013] S2: Based on the data collected by the test platform, construct a numerical simulation model of gas-liquid two-phase flow;

[0014] S3: Based on the numerical simulation model of gas-liquid two-phase flow, expand the simulation conditions to obtain full-condition heavy gas action simulation flow velocity data under different fault intensities and different gas phase fractions.

[0015] S4: Based on the full-condition heavy gas action simulation flow velocity data, combined with the gas phase fraction, a prediction model for the heavy gas action flow velocity correction coefficient is fitted.

[0016] S5: Calculate the gas phase fraction in real time based on the on-site fault data, and input it into the prediction model to predict the heavy gas action flow rate under the current working condition;

[0017] S6: Based on the predicted heavy gas operating flow rate and the measured flow rate, conduct a risk assessment of heavy gas failure to operate, and dynamically adjust the gas relay protection setting value according to the assessment results.

[0018] Preferably, S2 includes:

[0019] S21 constructs a fluid computational domain model:

[0020] Based on the physical structure of the BF dual-float gas relay, a 1:1 scale fluid computational domain model is established; the fluid computational domain model includes the internal oil flow cavity of the gas relay, as well as the extended inlet section and extended outlet section connected to its inlet and outlet respectively.

[0021] S22 performs mesh generation:

[0022] The fluid computational domain model is discretized using a hybrid mesh generation technique to generate a computational mesh;

[0023] S23 sets the simulation physics model and boundary conditions on the computational grid:

[0024] The fluid medium is set as a two-phase flow of gas and liquid, wherein the liquid phase is insulating oil and the gas phase is fault mixed gas.

[0025] The VOF model was selected to track the interfacial changes between gas and liquid phases.

[0026] The SST k-ω turbulence model was selected to calculate the turbulence characteristics of the flow field;

[0027] The curve of oil flow velocity versus time obtained from the actual measurement of S1 is set as the inlet boundary condition of the computational grid;

[0028] Dynamic meshing technology is enabled, and a combination of Smooth and Remesh algorithms is configured to dynamically update the mesh in the rotating region of the baffle in the computational mesh to simulate the baffle rotation process caused by oil flow impact, thereby obtaining the numerical simulation model of the gas-liquid two-phase flow.

[0029] Preferably, S3 includes:

[0030] S31 defines fault severity levels: fault severity is quantified into at least two levels, including a low fault severity level for simulating minor faults and a high fault severity level for simulating severe faults;

[0031] S32 defines the range of gas phase fraction variation: set a set of representative gas phase fractions to cover the typical fault gas production range from low to high;

[0032] S33 performs full-condition combined simulation: for each defined fault intensity, each set gas phase fraction is coupled sequentially to form multiple combined conditions;

[0033] S34 Extracting Action Flow Rate Data: In the transient simulation results of each of the combined working conditions, monitor the rotation angle of the gas relay baffle. When the rotation angle reaches the heavy gas action trigger threshold, record and extract the oil flow simulation flow rate value corresponding to that moment as the heavy gas action simulation flow rate data under that specific working condition. Collect the heavy gas action simulation flow rate data obtained under all combined working conditions to form the full-condition heavy gas action simulation flow rate data.

[0034] Preferably, the prediction model is:

[0035]

[0036] In the formula, K ( i ) is the correction factor. i For gas phase fraction, S ( i ) is an S-shaped transition term. P ( i ) is a polynomial growth term. T ( i ) is an auxiliary S-shaped enhancement term. α , β , γ These are the weighting coefficients;

[0037]

[0038]

[0039]

[0040] In the formula, k 1 represents the steepness parameter. i 01 The center point of the main threshold. a The coefficient of the quadratic term, b The coefficient of the cubic term, c The coefficient of the fourth term, k 2 is the auxiliary steepness parameter.i 02 This serves as the center point for the auxiliary threshold.

[0041] Preferably, S5 includes:

[0042] S51 collects fault electrical parameters in real time:

[0043] The transformer fault monitoring system acquires dynamic parameters in real time during the fault discharge process, including: the inter-electrode voltage at the fault point. U Discharge current amplitude I and duration of fault discharge t ;

[0044] S52 calculates the fault discharge energy based on the fault electrical parameters. W :

[0045]

[0046] S53 calculates the fault gas quantity based on the fault discharge energy. V 气 :

[0047]

[0048] in, k Indicates the gas production coefficient of insulating oil;

[0049] S54 calculates the gas phase fraction based on the amount of faulty gas:

[0050]

[0051] in, V 油 The initial oil flow volume in the fault area;

[0052] S55 inputs the gas phase fraction into the prediction model to obtain the correction coefficient under the current gas phase fraction, thereby predicting the heavy gas operating velocity under the current operating condition:

[0053]

[0054] in, V st As the baseline motion flow rate, V i This refers to the flow rate of heavy gas under the current operating conditions.

[0055] Preferably, the process of obtaining the reference motion flow rate is as follows:

[0056] From the full-condition heavy gas operation simulation flow rate data, extract the heavy gas operation simulation flow rate values ​​corresponding to low and high fault intensities under fault-free gas conditions, and calculate the baseline operation flow rate based on these values:

[0057]

[0058] in, V 低0% The simulated flow velocity values ​​for heavy gas action under low fault intensity conditions in a fault-free gas environment are given. V 高0% The simulated flow velocity value for heavy gas action corresponding to high fault intensity under fault-free gas conditions.

[0059] Preferably, S6 includes:

[0060] S61: Based on the predicted heavy gas flow rate V i Measured flow velocity V 实测 and baseline motion flow rate V st Calculate the optimization coefficients C :

[0061]

[0062] S62: Determine the optimization coefficient C If the value is less than 1, it is determined that there is a risk of failure to operate due to heavy gas, and S63 is executed; otherwise, the correction coefficient is recalculated.

[0063] S63: Dynamically adjust the setting value of the gas relay protection.

[0064] Preferably, the adjusted gas relay protection setting value is:

[0065]

[0066] in, V 调 This is the adjusted setpoint. This is for a safety margin.

[0067] As can be seen from the above technical solution, compared with the prior art, the present invention discloses a method for predicting the heavy gas action velocity of a dual-float gas relay, which has the following effects:

[0068] (1) A relatively complete technical process of "experimental testing-simulation verification-data fitting" was constructed, which improved the reliability of the prediction model. It can reproduce the coupled working conditions of transient oil flow impact and fault gas presence with different fault intensities and gas phase fractions, and simultaneously collect pressure, flow velocity and action signals to provide real data support for modeling; the supporting numerical simulation method realizes the restoration of the gas-liquid two-phase flow field through the VOF model and the SST k-ω turbulence model. The average error between experiment and simulation is controlled within 3.68%, which solves the problems of lack of reliable verification and insufficient data support in the existing technology.

[0069] (2) A three-component prediction coefficient model is used to predict the flow velocity of heavy gas action, balancing accuracy and engineering practicality. The model is based on fitting experimental and simulation data, clearly defining the quantitative correlation between gas phase fraction and heavy gas action velocity, with a prediction error ≤0.002m / s. Furthermore, the model has a simple structure, requiring only the gas phase fraction to quickly calculate correction coefficients for predicting heavy gas action velocity. It eliminates the need for complex parameter adjustments such as fault intensity, meeting the rapid application needs of on-site maintenance personnel and filling the gap in existing research lacking simplified quantitative prediction tools.

[0070] (3) Realizes dynamic protection with real-time adaptation and early warning, solving the mismatch problem of traditional fixed setting values. The model is pre-fixed in the protection device. By continuously collecting transformer voltage, current and DGA data (dataset analyzing the gas composition content after insulating oil vaporization), the gas phase fraction is calculated in real time and the adaptation setting value is dynamically adjusted, which can effectively avoid the risk of failure to operate. At the same time, there is no need to modify the gas relay hardware. Adaptation can be achieved only through software algorithm upgrades, without affecting the continuity of power supply.

[0071] (4) Establish a full-process risk prevention and control and engineering adaptation mechanism to effectively reduce the risk of failure to operate due to heavy gas. By using the failure to operate risk assessment method, protection hazards can be identified in advance based on the comparison between predicted flow rate and measured flow rate, providing clear guidance for equipment testing; combined with the engineering application steps of the prediction results, the setting value can be dynamically adjusted according to the actual working conditions, avoiding setting value mismatch caused by the influence of fault gas, significantly improving the response timeliness and reliability of transformer heavy gas protection, and reducing the economic losses caused by unconventional power outages.

[0072] (5) It has low cost and strong versatility, and is suitable for the on-site operation and maintenance needs of power systems. The calculation of fault gas volume relies on the voltage and current data of the existing fault monitoring system of the transformer, without the need to install additional gas sensors, thus reducing the cost of engineering modification; the test platform and simulation method can be transferred to the characteristic research of similar double float gas relays, and the three-component model can be adapted to different scenarios by fine-tuning the parameters, providing a standardized technical solution for the optimization of gas relay protection in the power industry, and has broad application value.

[0073] (6) Supports post-fault review and optimization of network-wide protection strategies. By comparing the predicted data of this invention with actual fault data, a standardized database of gas phase fraction and adaptation setting values ​​can be formed, providing a reference for adjusting the protection parameters of transformers of the same type, and enabling fault experience to drive the iteration of non-electrical quantity protection technologies in the power system. Attached Figure Description

[0074] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0075] Figure 1 This is a flowchart of the heavy gas action flow rate prediction method for dual-float gas relays provided in this embodiment of the invention;

[0076] Figure 2 This is a structural diagram of the heavy gas action characteristic testing platform provided in an embodiment of the present invention;

[0077] Figure 3 This is a schematic diagram of the gas relay fluid calculation domain provided in an embodiment of the present invention;

[0078] Figure 4 This is a flowchart of heavy gas action flow rate prediction provided in an embodiment of the present invention;

[0079] Figure 5 This is a diagram illustrating the predicted flow rate of low-fault-intensity heavy gas action provided in an embodiment of the present invention.

[0080] Figure 6 This is a diagram illustrating the effect of predicting the flow rate of high-fault-intensity heavy gas action provided in an embodiment of the present invention. Detailed Implementation

[0081] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0082] This invention discloses a method for predicting the heavy gas operation velocity of a dual-float gas relay, such as... Figure 1 As shown, it includes:

[0083] S1: Build a heavy gas action characteristic test platform, carry out transient oil flow impact test, simulate coupling conditions with different fault intensities and gas phase fractions, and simultaneously collect pipeline pressure, oil flow velocity and heavy gas action signals.

[0084] S2: Construct a numerical simulation model of gas-liquid two-phase flow based on the data collected by the test platform;

[0085] S3: Based on the numerical simulation model of gas-liquid two-phase flow, the simulation conditions are extended to obtain the full-condition heavy gas action simulation flow velocity data under different fault intensities and different gas phase fractions.

[0086] S4: Based on the simulated flow velocity data of heavy gas action under full working conditions, combined with the gas phase fraction, a prediction model for the correction coefficient of heavy gas action flow velocity is obtained by fitting.

[0087] S5: Calculate the gas phase fraction in real time based on on-site fault data, and input it into the prediction model to predict the heavy gas action flow rate under the current working conditions;

[0088] S6: Based on the predicted heavy gas operating velocity and the measured velocity, conduct a risk assessment of heavy gas failure to operate, and dynamically adjust the gas relay protection setting value according to the assessment results.

[0089] The specific implementation process of the method of the present invention is described in detail below.

[0090] Step 1: Build a heavy gas action characteristic test platform and conduct transient oil flow experiments.

[0091] like Figure 2 As shown, the test platform is a dedicated experimental device for the heavy gas action characteristics of the BF double float gas relay. The static connection relationship is as follows: the output end of the air cannon 1 is sealed and connected to the transient oil flow generating chamber 2. The outlet of the transient oil flow generating chamber 2 is connected to the test pipe 3 through a flange. The test pipe 3 is connected in series with the gas relay 4, the bellows 5, and the butterfly valve 6, and the end is connected to the capsule-type oil tank 7. The pressure sensor 8 and the ultrasonic flow velocity sensor 9 are installed at the pressure measuring point and flow velocity measuring point of the test pipe 3 through threaded interfaces, respectively. The signal output ends of the two sensors and the heavy gas action signal output end of the gas relay are all connected to the data acquisition system through shielded cables to form a complete data acquisition link. Air cannon 1 instantly releases compressed air to simulate the internal fault source of a transformer; transient oil flow generating chamber 2 provides space for compressed air to expand and do work, converting gas kinetic energy into fluid kinetic energy, and exciting a transient surging oil flow consistent with the actual fault; bellows 5 is used to connect pipelines, correct deviations, and suppress vibrations, avoiding interference from pipeline vibrations on test data; butterfly valve 6 is used to control the on / off of pipelines; capsule-type oil conservator 7 realizes the functions of oil storage and replenishment, maintaining the stability of the system oil level; the data acquisition system synchronously records pipeline pressure, oil flow velocity, and gas relay action signals, realizing multi-parameter collaborative acquisition.

[0092] In this embodiment, after the experimental system was debugged, the excitation pressure of the air cannon was set to 0.110MPa, 0.115MPa, 0.120MPa, 0.125MPa, and 0.130MPa to simulate internal transformer faults of different fault intensities. The air cannon 1 was activated to release compressed air, generating a surging oil flow in the transient oil flow generating chamber 2, which impacted the gas relay baffle. The data acquisition system simultaneously recorded the curves of oil flow velocity and pipeline oil pressure changes over time, as well as captured the trigger moment of the heavy gas action.

[0093] Step 2: Verify the accuracy of the experiment using numerical simulation.

[0094] like Figure 3 As shown, the fluid computational domain model is established based on the 1:1 solid structure of the BF double-float gas relay. The fluid computational domain includes the internal oil flow cavity of the gas relay, the extended inlet section, and the extended outlet section. The extended section is used to ensure the full development of the fluid and avoid the inlet and outlet effects from affecting the accuracy of the flow field calculation. A hybrid mesh generation technique is adopted, with structured meshes used for key stress areas such as baffles and lower floats in the fluid computational domain model, and unstructured meshes used for other areas. After verification of mesh independence, the mesh density can ensure a balance between computational accuracy and efficiency.

[0095] The simulation was conducted using ANSYS Fluent, with the fluid medium set as a gas-liquid two-phase flow. The liquid phase was insulating oil (density 960 kg / m³, viscosity 0.012 kg / (m³)). The gas phase is a faulty mixed gas (density 0.28 kg / m³, calculated by composition weighting). By default, the float of the gas relay descends, and the upper part is already filled with faulty gas. The VOF model is used to track the gas-liquid two-phase interface, and the SST k-ω turbulence model is used to calculate the flow field characteristics. This model can simultaneously ensure the calculation accuracy of the near-wall and far-field regions. The oil flow velocity change curve collected in step 1 is imported as the inlet boundary condition, and the other boundaries are set as non-slip walls. Dynamic mesh technology is enabled, and the Smooth and Remesh combined algorithm is used to dynamically update the mesh in the baffle rotation area to simulate the rotation process of the baffle after being impacted by the oil flow.

[0096] After the simulation calculation was completed, the simulated flow velocity at the moment of triggering the heavy gas action of the gas relay was extracted and the error was calculated with the actual flow velocity measured in step 1. The average error was 3.68%, which verified the reliability of the numerical simulation model and showed that the model can accurately reproduce the actual gas-liquid two-phase flow field characteristics and the action response of the gas relay.

[0097] Step 3: Expand the simulation conditions to obtain full-condition simulation flow velocity data.

[0098] Based on the numerical simulation model verified in step 2, the simulation conditions are expanded: the fault intensity is divided into low fault intensity (0.110MPa) and high fault intensity (0.130MPa), covering common fault levels in the field;

[0099] Define the range of gas phase fraction variation: Based on the actual diameter of the BF gas relay inlet pipe, calculate the coordinates of the positions 5% to 30% downward along the Y-axis from the upper end of the pipe. In Fluent preprocessing, mark the areas above the coordinates with elements and perform local initialization to fill them with mixed gas, thereby simulating gas phase fractions of 5%, 10%, 15%, 20%, 25%, and 30% in sequence, covering the typical range of fault gas content; other simulation parameters remain consistent with step 2 to ensure the effectiveness of the operating condition comparison.

[0100] For each defined fault intensity, a set gas phase fraction is sequentially coupled to form multiple combined operating conditions. A transient simulation is performed separately for each combined operating condition to capture the oil flow velocity when the baffle rotates to the heavy gas action angle, which is used as the simulated heavy gas action velocity under that condition. Finally, the simulated heavy gas action velocity data for all operating conditions is obtained, providing fundamental data support for subsequent model fitting.

[0101] Step 4: Combine the gas phase fraction to obtain the correction coefficient through fitting. K Three-component prediction model

[0102] ① Determination of reference motion flow rate

[0103] The average values ​​of the simulated flow velocities for heavy gas motion under fault-free gas conditions corresponding to low and high fault intensities were selected from the full-condition heavy gas motion simulation flow velocity data as the baseline motion flow velocity. V st The calculation formula is:

[0104]

[0105] Substitute data V 低0% =0.856m / s, V 高0% =0.835m / s, therefore V st =0.845m / s, this reference flow rate represents the standard flow rate threshold for the gas relay to activate under heavy gas conditions when there is no faulty gas interference.

[0106] ② Three-component prediction model K ( i Fitting

[0107] Gas phase fraction i Independent variable, correction coefficient KAs the dependent variable, calculate the gas phase fractions in the numerical simulation results. K The values ​​are fitted using data regression to obtain a three-component prediction model:

[0108]

[0109] S ( i )for S Transitional term, formula is:

[0110]

[0111] Parameters are obtained through data fitting. k 1=25、 i 01 =0.14.

[0112] P ( i ) represents a polynomial growth term, and the formula is:

[0113]

[0114] Fitted parameters a =0.15、 b =0.8、 c =-0.5.

[0115] T ( i () is the auxiliary S-shaped enhancement term, and the formula is:

[0116]

[0117] Fitted parameters k 2=40、 i 02 =0.22;

[0118] α, β, γ The weighting coefficients are used to fit the results. α =0.08、 β =0.12、 γ =0.05, finally obtaining the complete formula:

[0119]

[0120] ③ Model reliability verification

[0121] Will use K ( i The corrected predicted flow rate for heavy gas operation under low and high fault intensity conditions was compared with the baseline flow rate and the simulated flow rate. The key interval where the gas content was greater than 15% was selected, and the fitting effect was as follows: Figure 5 and Figure 6 As shown, the predicted flow velocity matches the simulation data well under most operating conditions. With a gas content of 20%, the predicted value is completely consistent with the simulation value. Under a low fault intensity with a gas content of 25%, the prediction error is only 0.009 m / s. This prediction model demonstrates good performance in predicting heavy gas flow velocity.

[0122] Step 5: Based on K ( i Prediction of heavy gas action flow velocity

[0123] like Figure 4 As shown, the prediction process uses fault site data as input, and the dynamic prediction process is as follows:

[0124] (1) Calculate the gas phase fraction

[0125] Dynamic fault parameters, such as inter-electrode voltage, are obtained through a transformer fault monitoring system. U Current amplitude I and discharge duration t

[0126] ① Calculate the fault discharge energy W：

[0127]

[0128] ② Calculate the amount of faulty gas V 气 :

[0129]

[0130] In the formula, the gas production coefficient of insulating oil is... k= 70 cm³ / kJ.

[0131] ③ Calculate the gas phase fraction i :

[0132]

[0133] In the formula, V 气 The instantaneous fault gas quantity participating in gas-liquid mixing. V 油 The initial oil flow volume in the fault area; the fault area is defined as the closed section from the transformer tank outlet flange to the gas relay inlet, that is, the critical path through which the gas-liquid mixture directly impacts the gas relay baffle.

[0134] (2) Prediction of heavy gas flow rate

[0135] Will i Substituting the formula of the prediction model constructed in step 4, we obtain the gas phase fraction at the corresponding current time. KValue, through formula

[0136]

[0137] The motion velocity value under the current working condition is corrected to obtain the predicted motion velocity for heavy gas.

[0138] Step 6: Risk Assessment of Refusal to Move

[0139] The measured flow velocity of heavy gas action during actual operation was collected at the site. V 实测 Calculate the optimization coefficients C :

[0140]

[0141] like C If the value is ≥1, then the calculation should be recalculated based on the actual gas relay diameter and the weighting coefficient in the prediction model for changes in operating conditions. K Value; if C If the value is less than 1, a risk of failure to operate is identified, triggering a temporary adjustment instruction for the setpoint value.

[0142]

[0143] In the formula, V 调 The adjusted setpoint, with a safety margin. =5%.

[0144] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

[0145] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for predicting the flow rate of heavy gas action in a dual-float gas relay, characterized in that, include: S1: Build a heavy gas action characteristic test platform, carry out transient oil flow impact test, simulate the coupled working conditions of different fault intensities and gas phase fractions, and simultaneously collect the oil flow velocity change curve over time. S2: Based on the data collected by the test platform, construct a numerical simulation model of gas-liquid two-phase flow; S3: Based on the numerical simulation model of gas-liquid two-phase flow, expand the simulation conditions to obtain full-condition heavy gas action simulation flow velocity data under different fault intensities and different gas phase fractions. S4: Based on the full-condition heavy gas action simulation flow velocity data, combined with the gas phase fraction, a prediction model for the heavy gas action flow velocity correction coefficient is fitted. S5: Calculate the gas phase fraction in real time based on the on-site fault data, and input it into the prediction model to predict the heavy gas action flow rate under the current working condition; S6: Based on the predicted heavy gas operating flow rate and the measured flow rate, conduct a risk assessment of heavy gas failure to operate, and dynamically adjust the gas relay protection setting value according to the assessment results. The prediction model is as follows: In the formula, K ( i ) is the correction factor. i For gas phase fraction, S ( i ) is an S-shaped transition term. P ( i ) is a polynomial growth term. T ( i ) is an auxiliary S-shaped enhancement term. α , β , γ These are the weighting coefficients; In the formula, k 1 represents the steepness parameter. i 01 The center point of the main threshold. a The coefficient of the quadratic term, b The coefficient of the cubic term, c The coefficient of the fourth term, k 2 is the auxiliary steepness parameter. i 02 This serves as the center point for the auxiliary threshold.

2. The method as described in claim 1, characterized in that, S2 includes: S21 constructs a fluid computational domain model: Based on the physical structure of the BF dual-float gas relay, a 1:1 scale fluid computational domain model is established; the fluid computational domain model includes the internal oil flow cavity of the gas relay, as well as the extended inlet section and extended outlet section connected to its inlet and outlet respectively. S22 performs mesh generation: The fluid computational domain model is discretized using a hybrid mesh generation technique to generate a computational mesh; S23 sets the simulation physics model and boundary conditions on the computational grid: The fluid medium is set as a two-phase flow of gas and liquid, wherein the liquid phase is insulating oil and the gas phase is fault mixed gas. The VOF model was selected to track the interfacial changes between gas and liquid phases. The SST k-ω turbulence model was selected to calculate the turbulence characteristics of the flow field; The curve of oil flow velocity versus time obtained from the actual measurement of S1 is set as the inlet boundary condition of the computational grid; Dynamic meshing technology is enabled, and a combination of Smooth and Remesh algorithms is configured to dynamically update the mesh in the rotating region of the baffle in the computational mesh to simulate the baffle rotation process caused by oil flow impact, thereby obtaining the numerical simulation model of the gas-liquid two-phase flow.

3. The method as described in claim 1, characterized in that, S3 includes: S31 defines fault severity levels: fault severity is quantified into at least two levels, including a low fault severity level for simulating minor faults and a high fault severity level for simulating severe faults; S32 defines the range of gas phase fraction variation: set a set of representative gas phase fractions to cover the typical fault gas production range from low to high; S33 performs full-condition combined simulation: for each defined fault intensity, each set gas phase fraction is coupled sequentially to form multiple combined conditions; S34 Extracting Action Flow Rate Data: In the transient simulation results of each of the combined working conditions, monitor the rotation angle of the gas relay baffle. When the rotation angle reaches the heavy gas action trigger threshold, record and extract the oil flow simulation flow rate value corresponding to that moment as the heavy gas action simulation flow rate data under that specific working condition. Collect the heavy gas action simulation flow rate data obtained under all combined working conditions to form the full-condition heavy gas action simulation flow rate data.

4. The method as described in claim 1, characterized in that, S5 includes: S51 collects fault electrical parameters in real time: The transformer fault monitoring system acquires dynamic parameters in real time during the fault discharge process, including: the inter-electrode voltage at the fault point. U Discharge current amplitude I and duration of fault discharge t ; S52 calculates the fault discharge energy based on the fault electrical parameters. W : S53 calculates the fault gas quantity based on the fault discharge energy. V 气 : in, k Indicates the gas production coefficient of insulating oil; S54 calculates the gas phase fraction based on the amount of faulty gas: in, V 油 The initial oil flow volume in the fault area; S55 inputs the gas phase fraction into the prediction model to obtain the correction coefficient under the current gas phase fraction, thereby predicting the heavy gas operating velocity under the current operating condition: in, V st As the baseline motion flow rate, V i This refers to the flow rate of heavy gas under the current operating conditions.

5. The method as described in claim 4, characterized in that, The process for obtaining the baseline motion flow rate is as follows: From the full-condition heavy gas operation simulation flow rate data, extract the heavy gas operation simulation flow rate values ​​corresponding to low and high fault intensities under fault-free gas conditions, and calculate the baseline operation flow rate based on these values: in, V 低0% The simulated flow velocity values ​​for heavy gas action under low fault intensity conditions in a fault-free gas environment are given. V 高0% The simulated flow velocity value for heavy gas action corresponding to high fault intensity under fault-free gas conditions.

6. The method as described in claim 4, characterized in that, S6 includes: S61: Based on the predicted heavy gas flow rate V i Measured flow velocity V 实测 and baseline motion flow rate V st Calculate the optimization coefficients C : S62: Determine the optimization coefficient C If the value is less than 1, it is determined that there is a risk of failure to operate due to heavy gas, and S63 is executed; otherwise, the correction coefficient is recalculated. S63: Dynamically adjust the setting value of the gas relay protection.

7. The method as described in claim 5, characterized in that, The adjusted gas relay protection setting value is: in, V 调 This is the adjusted setpoint. This is for safety margin.

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