A transformer oil level online monitoring method and system based on multi-parameter fusion inversion

Abstract

The application provides a transformer oil level online monitoring method and system based on multi-parameter fusion inversion, which comprises the following steps: obtaining oil temperature signals, oil pressure signals and oil flow rate signals at the oil outlet of the bottom of a transformer; time synchronizing and preprocessing the oil temperature signals, the oil pressure signals and the oil flow rate signals; establishing a temperature compensation physical property model of transformer oil according to the oil temperature signals; establishing an oil flow dynamic pressure correction model according to the oil flow rate signals and the temperature compensation physical property model; constructing a transformer oil level inversion model according to the oil pressure signals, the temperature compensation physical property model and the dynamic pressure correction amount; and performing state correction and residual compensation according to the transformer operating state, historical calibration data and credible oil level samples to obtain a transformer oil level monitoring result. The application solves the technical problems of insufficient reliability of transformer oil level monitoring, insufficient temperature and flow compensation and inconvenience of existing transformer reconstruction and deployment in the prior art.

Description

Technical Field

[0001] This invention relates to the field of transformer condition monitoring and online oil level diagnosis technology, specifically to a transformer oil level monitoring method and system based on multi-parameter fusion inversion. Background Technology

[0002] Transformer oil is a crucial insulating and heat dissipation medium in oil-immersed transformers, and its level directly affects the transformer's insulation performance, heat dissipation capacity, and operational safety. If the transformer oil level is too low, it may lead to partial exposure of windings, core, or leads, resulting in decreased insulation, localized overheating, or even escalation of the fault. Conversely, an abnormally high oil level may indicate abnormal oil replenishment, internal gas accumulation, abnormal temperature rise, or abnormal tank pressure. Therefore, continuous and reliable online monitoring of the transformer oil level is of paramount importance.

[0003] In related technologies, transformer oil levels are typically measured directly using oil tank level gauges, magnetic level gauges, float level gauges, pressure level gauges, or image recognition methods. While these methods can provide oil level information to some extent, they still have limitations. For example, the high installation position of oil tank or top level gauges makes on-site inspection and maintenance inconvenient; float, mechanical linkage, or magnetic level gauge structures are susceptible to jamming, contamination, and aging seals; image recognition methods are sensitive to lighting, viewing angle, dirt, and on-site installation conditions; existing direct pressure level measurement methods only use the pressure at the bottom outlet of the transformer for calculation, but in actual operation, the oil pressure at the bottom outlet of the transformer is not only related to the oil column height but is also affected by factors such as density changes due to transformer oil temperature, oil thermal expansion, cooling system operating status, oil flow velocity changes, and local oil circuit flow resistance. Especially under forced oil circulation, cooler switching, rapid load changes, or significant oil temperature gradients, the relationship between the pressure at the bottom outlet of the transformer and the actual oil level is no longer a simple static pressure linear relationship. Therefore, relying on traditional top oil level gauges may lead to problems such as inconvenient installation and maintenance, high modification costs, and reliability issues due to mechanical structure. If the oil level is calculated based solely on a single pressure signal, errors are likely to occur under conditions of temperature changes or oil flow disturbances.

[0004] In summary, existing technologies suffer from several technical problems, including insufficient reliability of transformer oil level monitoring, inadequate compensation for pressure-based oil level estimation at the transformer bottom outlet, insufficient utilization of oil temperature and flow conditions, high cost of retrofitting existing transformers, and significant errors in oil level inversion under abnormal operating conditions. Summary of the Invention

[0005] This invention solves the above-mentioned technical problems by employing the following technical solution: an online monitoring method for transformer oil level based on multi-parameter fusion inversion, comprising: S1. Obtain oil temperature signal, oil pressure signal and oil flow rate signal at the oil outlet at the bottom of the transformer. Perform time synchronization and preprocessing on the oil temperature signal, oil pressure signal and oil flow rate signal to obtain a temperature, pressure and flow monitoring dataset. S2. Establish a temperature compensation model for transformer oil based on the oil temperature signal to obtain the oil density and viscosity after temperature compensation. S3. Based on the oil flow rate signal and the temperature compensation model, establish an oil flow dynamic pressure correction model to obtain the dynamic pressure correction amount caused by the oil flow rate; S4. Based on the oil pressure signal, the temperature compensation property model, and the dynamic pressure correction, construct a transformer oil level inversion model to obtain the initial oil level inversion value. S5. Based on the transformer operating status and historical calibration data, the initial oil level inversion value is corrected for status and compensated for residuals to obtain the transformer oil level monitoring result. S6. Based on the transformer oil level monitoring results and the temperature, pressure and flow monitoring dataset, determine whether the transformer has abnormal oil level, abnormal oil leakage, abnormal oil replenishment, abnormal oil flow, or abnormal sensor.

[0006] This invention provides a method for inverting oil level by fusing oil temperature, oil pressure, and oil flow velocity at the bottom oil outlet of a transformer. The method corrects for oil density and viscosity by oil temperature, corrects for the effects of dynamic pressure and local flow resistance by oil flow velocity, and provides the main constraint on oil column height by the oil pressure at the bottom oil outlet of the transformer. Thus, online oil level inversion can be achieved without relying on traditional top oil level direct measurement structures, improving the accuracy, reliability, and adaptability of transformer oil level monitoring.

[0007] In a more specific technical solution, in S1, the oil temperature signal, oil pressure signal, and oil flow rate signal are acquired by a sensor installed at the oil outlet at the bottom of the transformer.

[0008] Furthermore, S2 specifically includes: S2.1: The temperature compensation model for transformer oil density is established as follows: In the formula, This refers to the density of the transformer oil after temperature compensation. Reference temperature The density of the oil below, This is the coefficient of thermal expansion of the transformer oil. This is the oil temperature monitoring value at the bottom oil outlet of the transformer.

[0009] S2.2: The temperature compensation model for transformer oil viscosity is established as follows: In the formula, The viscosity of transformer oil after temperature compensation. Reference temperature The viscosity of the oil under the condition, The viscosity temperature coefficient of transformer oil. This is the oil temperature monitoring value at the bottom oil outlet of the transformer.

[0010] Furthermore, in S3, the following method is used to establish a dynamic pressure correction model caused by transformer oil flow: In the formula, This indicates the dynamic pressure correction caused by the oil flow rate. This indicates the oil flow velocity at the bottom outlet of the transformer, which is monitored and obtained by a flow velocity sensor. and The calibration coefficients, which are related to flow resistance characteristics, can be obtained through simulation under laboratory conditions.

[0011] Furthermore, in S4, a dynamic pressure correction model caused by transformer oil flow is established using the following method: In the formula, This represents the initial oil level inversion value. This indicates the oil pressure at the bottom oil outlet of the transformer. Indicates reference pressure. This indicates the pressure zero-point offset or installation height correction item. It represents the acceleration due to gravity.

[0012] Furthermore, in step S5, the corresponding inversion parameter group is selected according to the transformer operating state; the operating state includes at least one of the following: static state, natural oil circulation state, forced oil circulation state, cooler switching state, temperature rise transition state, and abnormal leakage state.

[0013] In a more specific technical solution, in step S5, the transformer oil level monitoring results are obtained by fusing the physical inversion master model and the residual compensation model: In the formula, This indicates the transformer oil level monitoring results. This represents the residual compensation amount obtained based on historical calibration data, reliable oil level samples, or operating status characteristics.

[0014] Furthermore, S6 specifically includes: S6.1: Calculate the expected oil level change due to temperature expansion based on the oil temperature monitoring value at the bottom oil outlet of the transformer. The calculation method is as follows: In the formula, This represents the expected change in oil level caused by temperature changes. This is the temperature-oil level equivalent coefficient.

[0015] The oil level temperature residual is further constructed, and the calculation method is as follows: In the formula, To retrieve the change in oil level, This represents the oil level temperature residual. When the inverted oil level decreases and the oil level temperature residual remains negative, it indicates that the decrease cannot be fully explained by the decrease in oil temperature; when the inverted oil level increases and the oil level temperature residual remains positive, it indicates that the increase exceeds the range that can be explained by the thermal expansion of oil temperature.

[0016] S6.2: The pressure balance residual is constructed in reverse based on the oil level inversion model. The calculation method is as follows: In the formula, This represents the pressure balance residual. If the pressure balance residual consistently exceeds the threshold, it indicates that the bottom oil pressure, bottom oil temperature, bottom oil flow rate, and transformer oil level monitoring results do not meet the physical consistency requirement.

[0017] S6.3: When the transformer oil level monitoring result is lower than the low oil level alarm threshold, or lower than the low oil level warning threshold and the oil level change rate is continuously less than the falling rate threshold, it is determined that there is an abnormality of low oil level; when the transformer oil level monitoring result is higher than the high oil level alarm threshold, or higher than the high oil level warning threshold and the oil level change rate is continuously greater than the rising rate threshold, it is determined that there is an abnormality of high oil level.

[0018] S6.4: When the transformer oil level monitoring result continues to decrease, the oil pressure at the bottom oil outlet of the transformer decreases synchronously, the oil level temperature residual is continuously negative and exceeds the residual threshold, and the oil flow rate at the bottom oil outlet of the transformer does not show a disturbance corresponding to the decrease in oil level, it is determined that there is an abnormal oil leakage.

[0019] S6.5: When the transformer oil level monitoring result continues to rise or rises in a step, the oil pressure at the bottom oil outlet of the transformer rises synchronously, and the oil level temperature residual is continuously positive and exceeds the residual threshold, it is determined that there is an abnormal oil replenishment or an abnormal rise in oil level; if the rise status matches the maintenance record or oil replenishment command, it is marked as a normal oil replenishment event, otherwise it is marked as an unrecorded abnormal oil replenishment.

[0020] S6.6: When the oil flow velocity at the transformer oil outlet exceeds the oil flow velocity threshold range corresponding to the current operating state, or when the correlation between the fluctuation of the transformer oil level monitoring result and the oil flow velocity fluctuation at the bottom oil outlet of the transformer exceeds the correlation threshold, it is determined that there is an abnormal oil flow or an oil flow disturbance.

[0021] S6.7: When the oil pressure signal, oil temperature signal, or oil flow rate signal experiences a sudden change exceeding the physical reach, prolonged stagnation, range exceeding the limit, timestamp asynchrony, or does not meet the preset physical consistency relationship with other monitored quantities, it is determined that there is a sensor malfunction.

[0022] In a more specific technical solution, a transformer oil level monitoring system based on the fusion and inversion of oil temperature, oil pressure, and oil flow rate at the bottom oil outlet of the transformer includes: The transformer bottom oil outlet parameter sensing module is used to acquire the oil temperature signal, oil pressure signal and oil flow rate signal at the oil outlet of the transformer bottom. The data synchronization and preprocessing module is used to perform time synchronization and preprocessing on the oil temperature signal, oil pressure signal and oil flow rate signal. The data synchronization and preprocessing module is connected to the temperature, pressure and flow sensing module at the bottom oil outlet of the transformer. The physical parameter compensation module is used to establish a temperature compensation model based on the oil temperature signal to obtain the oil density and oil viscosity after temperature compensation. The physical parameter compensation module is connected to the data synchronization preprocessing module. A dynamic pressure correction module is used to obtain the dynamic pressure correction amount based on the oil flow rate signal and the temperature compensation property model. The dynamic pressure correction module is connected to the physical parameter compensation module. The oil level inversion module is used to invert the transformer oil level monitoring results based on the oil pressure signal, temperature compensation model and dynamic pressure correction amount. The oil level inversion module is connected to the dynamic pressure correction module. An anomaly diagnosis and alarm module is used to output alarms for abnormal oil level, abnormal oil leakage, abnormal oil replenishment, abnormal oil flow, or abnormal sensor based on the transformer oil level monitoring results and temperature, pressure and flow monitoring dataset. The anomaly diagnosis and alarm module is connected to the oil level inversion module.

[0023] The advantages of this invention are: (1) The present invention uses three types of information—oil temperature, oil pressure, and oil flow rate—at the bottom of the transformer to constrain the oil level inversion, thus avoiding the problem of insufficient compensation for temperature and flow disturbances caused by relying solely on a single pressure signal.

[0024] (2) The present invention establishes an oil density and viscosity compensation model by means of oil temperature, so that the conversion process of pressure at the bottom oil outlet of the transformer to oil level can adapt to the change of transformer oil temperature.

[0025] (3) The present invention establishes a dynamic pressure correction model by means of oil flow rate, which can correct the influence of oil flow on the pressure of the bottom oil outlet of the transformer under natural oil circulation, forced oil circulation or cooler switching.

[0026] (4) This invention improves the adaptability of oil level inversion under different transformer types, different installation locations and different operating conditions by using operation status identification and residual compensation model.

[0027] (5) This invention classifies and judges various anomalies by oil level temperature residual, pressure balance residual and oil flow correlation, which can reduce false alarms caused by temperature expansion and contraction, oil flow disturbance, pressure shock or sensor drift, and improve the accuracy of identifying oil leakage, oil replenishment, oil flow and sensor anomalies. Attached Figure Description

[0028] Figure 1 This is a schematic diagram of the basic steps of the transformer oil level monitoring method according to Embodiment 1 of the present invention; Figure 2 This is a schematic diagram of the transformer oil level monitoring system according to Embodiment 1 of the present invention; Detailed Implementation

[0029] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0030] like Figure 1 As shown, the present invention provides a method for online monitoring of transformer oil level based on multi-parameter fusion inversion, which includes the following basic steps: S1. Obtain oil temperature signal, oil pressure signal and oil flow rate signal at the oil outlet at the bottom of the transformer. Perform time synchronization and preprocessing on the oil temperature signal, oil pressure signal and oil flow rate signal to obtain a temperature, pressure and flow monitoring dataset. S2. Based on the oil temperature signal, establish a temperature-compensated physical property model of transformer oil to obtain the oil density and viscosity after temperature compensation. S3. Based on the oil flow rate signal and the temperature compensation property model, establish an oil flow dynamic pressure correction model to obtain the dynamic pressure correction amount caused by the oil flow rate. S4. Based on the oil pressure signal, the temperature compensation property model, and the dynamic pressure correction, construct a transformer oil level inversion model to obtain the initial oil level inversion value. S5. Based on the transformer operating status, historical calibration data, and reliable oil level samples, the initial oil level inversion value is corrected for status and compensated for residuals to obtain the transformer oil level monitoring results. S6. Based on the transformer oil level monitoring results and the temperature, pressure and flow monitoring dataset, determine whether the transformer has abnormal oil level, abnormal oil leakage, abnormal oil replenishment, abnormal oil flow, or abnormal sensor operation.

[0031] In this embodiment, by arranging oil temperature, oil pressure, and oil flow velocity sensors at the oil outlet at the bottom of the transformer, an oil level inversion link combining temperature compensation, oil flow correction, and state residual compensation is constructed, enabling online monitoring of the transformer oil level without relying on the top oil level direct measurement structure.

[0032] Furthermore, in step S1, the transformer bottom oil outlet temperature, pressure, and flow sensing module can be installed at the transformer bottom oil outlet oil passage, near the oil drain valve, at the transformer bottom oil outlet sampling port, or at the low-level circulating oil passage. The oil temperature signal is used to reflect the oil temperature status at the transformer bottom oil outlet, the oil pressure signal is used to provide the main constraint on the oil column height, and the oil flow rate signal is used to reflect the oil circulation status and dynamic pressure disturbances.

[0033] In this embodiment, the oil temperature signal, oil pressure signal, and oil flow rate signal are processed by timestamp alignment, outlier removal, low-pass filtering, moving average, zero-point calibration, and range normalization to obtain a temperature, pressure, and flow monitoring dataset within the same time window.

[0034] Furthermore, S2 specifically includes: S2.1: The temperature compensation model for transformer oil density is established as follows: In the formula, This refers to the density of the transformer oil after temperature compensation. Reference temperature The density of the oil below, This is the coefficient of thermal expansion of the transformer oil. This is the oil temperature monitoring value at the bottom oil outlet of the transformer.

[0035] S2.2: The temperature compensation model for transformer oil viscosity is established as follows: In the formula, The viscosity of transformer oil after temperature compensation. Reference temperature The viscosity of the oil under the condition, The viscosity temperature coefficient of transformer oil. This is the oil temperature monitoring value at the bottom oil outlet of the transformer.

[0036] Furthermore, in S3, the following method is used to establish a dynamic pressure correction model caused by transformer oil flow: In the formula, This indicates the dynamic pressure correction caused by the oil flow rate. This indicates the oil flow velocity at the bottom outlet of the transformer, which is monitored and obtained by a flow velocity sensor. and The calibration coefficients, which are related to flow resistance characteristics, can be obtained through simulation under laboratory conditions.

[0037] Furthermore, in S4, a dynamic pressure correction model caused by transformer oil flow is established using the following method: In the formula, This represents the initial oil level inversion value. This indicates the oil pressure at the bottom oil outlet of the transformer. Indicates reference pressure. This indicates the pressure zero-point offset or installation height correction item. It represents the acceleration due to gravity.

[0038] Furthermore, S5 uses a fusion of the physical inversion master model and the residual compensation model to obtain the transformer oil level monitoring results: In the formula, This indicates the transformer oil level monitoring results. This represents the residual compensation amount obtained based on historical calibration data, reliable oil level samples, or operating status characteristics.

[0039] In step S6, anomaly diagnosis can be performed based on the consistency between the oil level inversion results and the temperature, pressure and flow monitoring data. The specific steps are as follows: S6.1: Calculate the expected oil level change due to temperature expansion based on the oil temperature monitoring value at the bottom oil outlet of the transformer. The calculation method is as follows: In the formula, This represents the expected change in oil level caused by temperature changes. This is the temperature-oil level equivalent coefficient.

[0040] The oil level temperature residual is further constructed, and the calculation method is as follows: In the formula, To retrieve the change in oil level, This represents the oil level temperature residual. When the inverted oil level decreases and the oil level temperature residual remains negative, it indicates that the decrease cannot be fully explained by the decrease in oil temperature; when the inverted oil level increases and the oil level temperature residual remains positive, it indicates that the increase exceeds the range that can be explained by the thermal expansion of oil temperature.

[0041] S6.2: The pressure balance residual is constructed in reverse based on the oil level inversion model. The calculation method is as follows: In the formula, This represents the pressure balance residual. If the pressure balance residual consistently exceeds the threshold, it indicates that the bottom oil pressure, bottom oil temperature, bottom oil flow rate, and transformer oil level monitoring results do not meet the physical consistency requirement.

[0042] S6.3: When the transformer oil level monitoring result is lower than the low oil level alarm threshold, or lower than the low oil level warning threshold and the oil level change rate is continuously less than the falling rate threshold, it is determined that there is an abnormality of low oil level; when the transformer oil level monitoring result is higher than the high oil level alarm threshold, or higher than the high oil level warning threshold and the oil level change rate is continuously greater than the rising rate threshold, it is determined that there is an abnormality of high oil level.

[0043] S6.4: When the transformer oil level monitoring result continues to decrease, the oil pressure at the bottom oil outlet of the transformer decreases synchronously, the oil level temperature residual is continuously negative and exceeds the residual threshold, and the oil flow rate at the bottom oil outlet of the transformer does not show a disturbance corresponding to the decrease in oil level, it is determined that there is an abnormal oil leakage.

[0044] S6.5: When the transformer oil level monitoring result continues to rise or rises in a step, the oil pressure at the bottom oil outlet of the transformer rises synchronously, and the oil level temperature residual is continuously positive and exceeds the residual threshold, it is determined that there is an abnormal oil replenishment or an abnormal rise in oil level; if the rise status matches the maintenance record or oil replenishment command, it is marked as a normal oil replenishment event, otherwise it is marked as an unrecorded abnormal oil replenishment.

[0045] S6.6: When the oil flow velocity at the transformer oil outlet exceeds the oil flow velocity threshold range corresponding to the current operating state, or when the correlation between the fluctuation of the transformer oil level monitoring result and the oil flow velocity fluctuation at the bottom oil outlet of the transformer exceeds the correlation threshold, it is determined that there is an abnormal oil flow or an oil flow disturbance.

[0046] S6.7: When the oil pressure signal, oil temperature signal, or oil flow rate signal experiences a sudden change exceeding the physical reach, prolonged stagnation, range exceeding the limit, timestamp asynchrony, or does not meet the preset physical consistency relationship with other monitored quantities, it is determined that there is a sensor malfunction.

[0047] This embodiment addresses the scenario of online transformer oil level monitoring. First, oil temperature, oil pressure, and oil flow rate are collected at the oil outlet at the bottom of the transformer. Second, oil temperature is used to correct for oil density and viscosity, and oil flow rate is used to correct for the influence of dynamic pressure. Finally, the oil level is obtained by inversion based on the main constraint of the oil pressure at the bottom of the transformer, and the inversion stability is improved by combining operating status and residual compensation. This method reduces the dependence on the top oil level measurement structure and improves the deployment convenience and reliability of online oil level monitoring for existing transformers.

[0048] Application examples can be applied to oil-immersed power transformers, distribution transformers, reactors, or other power equipment with insulating oil as the medium. The above method can be implemented collaboratively by a temperature, pressure, and current sensing unit at the bottom oil outlet of the transformer, an edge computing unit, a communication unit, and a background monitoring platform.

[0049] like Figure 2 As shown, this invention also proposes a self-recovery system for measurement data of an all-fiber current transformer, the system comprising: The transformer bottom oil outlet parameter sensing module is used to acquire the oil temperature signal, oil pressure signal and oil flow rate signal at the oil outlet of the transformer bottom. The data synchronization and preprocessing module is used to perform time synchronization and preprocessing on the oil temperature signal, oil pressure signal and oil flow rate signal. The data synchronization and preprocessing module is connected to the temperature, pressure and flow sensing module at the bottom oil outlet of the transformer. The physical parameter compensation module is used to establish a temperature compensation model based on the oil temperature signal to obtain the oil density and oil viscosity after temperature compensation. The physical parameter compensation module is connected to the data synchronization preprocessing module. A dynamic pressure correction module is used to obtain the dynamic pressure correction amount based on the oil flow rate signal and the temperature compensation property model. The dynamic pressure correction module is connected to the physical parameter compensation module. The oil level inversion module is used to invert the transformer oil level monitoring results based on the oil pressure signal, temperature compensation model and dynamic pressure correction amount. The oil level inversion module is connected to the dynamic pressure correction module. An anomaly diagnosis and alarm module is used to output alarms for abnormal oil level, abnormal oil leakage, abnormal oil replenishment, abnormal oil flow, or abnormal sensor based on the transformer oil level monitoring results and temperature, pressure and flow monitoring dataset. The anomaly diagnosis and alarm module is connected to the oil level inversion module.

[0050] In one embodiment, the transformer bottom oil outlet parameter sensing module includes: Temperature sensing unit: used to acquire t The oil temperature at the bottom oil outlet of the transformer. ; Pressure sensing unit: used to acquire t The oil pressure at the bottom oil outlet of the transformer. ; Flow rate sensing unit: used to acquire t The oil flow rate at the bottom oil outlet of the transformer at any time. .

[0051] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0052] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0053] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for online monitoring of transformer oil level based on multi-parameter fusion inversion, characterized in that, include: S1. Obtain oil temperature signal, oil pressure signal and oil flow rate signal at the oil outlet at the bottom of the transformer. Perform time synchronization and preprocessing on the oil temperature signal, oil pressure signal and oil flow rate signal to obtain a temperature, pressure and flow monitoring dataset. S2. Based on the oil temperature signal, establish a temperature-compensated physical property model of transformer oil to obtain the oil density and viscosity after temperature compensation. S3. Based on the oil flow rate signal and the temperature compensation property model, establish an oil flow dynamic pressure correction model to obtain the dynamic pressure correction amount caused by the oil flow rate. S4. Based on the oil pressure signal, the temperature compensation property model, and the dynamic pressure correction, construct a transformer oil level inversion model to obtain the initial oil level inversion value. S5. Based on the transformer operating status, historical calibration data, and reliable oil level samples, the initial oil level inversion value is corrected for status and compensated for residuals to obtain the transformer oil level monitoring results. S6. Based on the transformer oil level monitoring results and the temperature, pressure and flow monitoring dataset, determine whether the transformer has abnormal oil level, abnormal oil leakage, abnormal oil replenishment, abnormal oil flow, or abnormal sensor operation.

2. The online transformer oil level monitoring method based on multi-parameter fusion inversion according to claim 1, characterized in that, The oil temperature signal, oil pressure signal, and oil flow rate signal are acquired by a sensor installed at any one of the following locations: the oil outlet at the bottom of the transformer, the sampling port at the oil outlet at the bottom of the transformer, near the drain valve, or in the low-level circulating oil circuit.

3. The online transformer oil level monitoring method based on multi-parameter fusion inversion according to claim 1, characterized in that, The following logic is used to establish a temperature-compensated oil density model: In the formula, This refers to the density of the transformer oil after temperature compensation. Reference temperature The density of the oil below, This is the coefficient of thermal expansion of the transformer oil. This is the oil temperature monitoring value at the bottom oil outlet of the transformer.

4. The online monitoring method for transformer oil level based on multi-parameter fusion inversion according to claim 1, characterized in that, The following logic is used to establish a temperature-compensated oil viscosity model: In the formula, The viscosity of transformer oil after temperature compensation. Reference temperature The viscosity of the oil under the condition, The viscosity temperature coefficient of transformer oil. This is the oil temperature monitoring value at the bottom oil outlet of the transformer.

5. The online monitoring method for transformer oil level based on multi-parameter fusion inversion according to claim 1, characterized in that, The following logic is used to establish the dynamic pressure correction model for oil flow: In the formula, This indicates the dynamic pressure correction caused by the oil flow rate. This indicates the oil flow velocity at the bottom outlet of the transformer, which is monitored and obtained by a flow velocity sensor. and The calibration coefficients, which are related to flow resistance characteristics, can be obtained through simulation under laboratory conditions.

6. The online monitoring method for transformer oil level based on multi-parameter fusion inversion according to claim 1, characterized in that, The following logic is used to construct the transformer oil level inversion model: In the formula, This represents the initial oil level inversion value. This indicates the oil pressure at the bottom oil outlet of the transformer. Indicates reference pressure. This indicates the pressure zero-point offset or installation height correction item. It represents the acceleration due to gravity.

7. The online transformer oil level monitoring method based on multi-parameter fusion inversion according to claim 1, characterized in that, The transformer oil level monitoring results were obtained by fusing the physical inversion master model and the residual compensation model. In the formula, This indicates the transformer oil level monitoring results. This represents the residual compensation amount obtained based on historical calibration data, reliable oil level samples, or operating status characteristics.

8. The online monitoring method for transformer oil level based on multi-parameter fusion inversion according to claim 1, characterized in that, The judgment of transformer oil level events is based on the inversion results of transformer oil temperature, oil pressure, oil flow, and oil level, including: (1) First, the expected change in oil level due to temperature expansion is calculated based on the oil temperature monitoring value at the bottom oil outlet of the transformer. The calculation method is as follows: In the formula, This represents the expected change in oil level caused by temperature changes. This is the temperature-oil level equivalent coefficient; The oil level temperature residual is further constructed, and the calculation method is as follows: In the formula, To retrieve the change in oil level, The residual value is the oil level temperature. When the inverted oil level decreases and the oil level temperature residual value remains negative, it indicates that the decrease cannot be fully explained by the decrease in oil temperature. When the inverted oil level increases and the oil level temperature residual value remains positive, it indicates that the increase exceeds the range of explanation for the thermal expansion of oil temperature. (2) The pressure balance residual is constructed in reverse based on the oil level inversion model. The calculation method is as follows: In the formula, This is the pressure balance residual; if the pressure balance residual continues to exceed the threshold, it indicates that the bottom oil pressure, bottom oil temperature, bottom oil flow rate and transformer oil level monitoring results do not meet the physical consistency relationship. (3) When the transformer oil level monitoring result is lower than the low oil level alarm threshold, or lower than the low oil level warning threshold and the oil level change rate is continuously less than the falling rate threshold, it is determined that there is an abnormality of low oil level; when the transformer oil level monitoring result is higher than the high oil level alarm threshold, or higher than the high oil level warning threshold and the oil level change rate is continuously greater than the rising rate threshold, it is determined that there is an abnormality of high oil level. (4) When the transformer oil level monitoring result continues to decrease, the oil pressure at the bottom oil outlet of the transformer decreases synchronously, the oil level temperature residual is continuously negative and exceeds the residual threshold, and the oil flow rate at the bottom oil outlet of the transformer does not show a disturbance corresponding to the decrease in oil level, it is judged that there is an abnormal oil leakage. (5) When the transformer oil level monitoring result continues to rise or rises in a step, the oil pressure at the bottom oil outlet of the transformer rises synchronously, and the oil level temperature residual is continuously positive and exceeds the residual threshold, it is determined that there is an abnormal oil replenishment or an abnormal rise in oil level; if the rise state matches the maintenance record or oil replenishment command, it is marked as a normal oil replenishment event, otherwise it is marked as an unrecorded abnormal oil replenishment. (6) When the oil flow rate at the transformer oil outlet exceeds the oil flow rate threshold range corresponding to the current operating state, or when the correlation between the fluctuation of the transformer oil level monitoring result and the oil flow rate fluctuation at the bottom oil outlet of the transformer exceeds the correlation threshold, it is determined that there is an abnormal oil flow or an oil flow disturbance. (7) When the oil pressure signal, oil temperature signal or oil flow rate signal shows a sudden change that exceeds the physical reach, a long-term stagnation, a range exceeding the limit, a timestamp that is not synchronized, or does not meet the preset physical consistency relationship with other monitoring quantities, it is determined that there is a sensor abnormality.

9. A transformer oil level monitoring system based on the fusion and inversion of oil temperature, oil pressure, and oil flow rate at the bottom oil outlet of the transformer, characterized in that, The system includes: The transformer bottom oil outlet temperature, pressure and flow sensing module is used to acquire the oil temperature signal, oil pressure signal and oil flow rate signal at the oil outlet of the transformer bottom. The data synchronization preprocessing module is used to perform time synchronization and preprocessing on the oil temperature signal, oil pressure signal and oil flow rate signal; The physical property compensation module is used to establish a temperature-compensated physical property model based on the oil temperature signal to obtain the oil density and viscosity after temperature compensation. The dynamic pressure correction module is used to obtain the dynamic pressure correction amount based on the oil flow rate signal and the temperature compensation property model. The oil level inversion module is used to invert the transformer oil level monitoring results based on the oil pressure signal, temperature compensation property model and dynamic pressure correction. The anomaly diagnosis and alarm module is used to output alarms for abnormal oil level, oil leakage, oil replenishment, oil flow and sensor anomalies based on the transformer oil level monitoring results and temperature, pressure and flow monitoring datasets.

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