An insulation state evaluation method and system of an on-line monitoring device of a voltage transformer

Abstract

The application discloses an insulation state evaluation method of an on-line monitoring device of a voltage transformer, and comprises the following steps: obtaining an original dielectric loss tangent value through a dielectric loss tester, and collecting temperature and relative humidity through an environmental sensor; compensating the collected dielectric loss tangent value for temperature and humidity to obtain a compensated dielectric loss tangent value; performing trend prediction on the compensated dielectric loss tangent value based on an autoregressive integrated moving average model to obtain a predicted value; performing abnormal state determination by comparing the predicted value with a dynamic threshold value; performing risk evaluation by comprehensively considering equipment state parameters, and outputting a corresponding maintenance decision according to the evaluation result; and the application eliminates the interference of environmental factors on the dielectric loss tangent value measurement, improves the measurement accuracy, and discovers the abnormal change trend of the dielectric loss value in advance through trend prediction, thereby realizing early warning.

Description

Technical Field

[0001] This invention relates to the field of power monitoring technology, specifically to a method and system for assessing the insulation status of an online monitoring device for voltage transformers. Background Technology

[0002] A voltage transformer is an electrical device used to measure high voltage. It uses the principle of electromagnetic induction to reduce high voltage to a safe level for measurement by conventional voltmeters or data acquisition systems. An online voltage transformer monitoring device is used to monitor the operating status of a voltage transformer in real time. Because online voltage transformer monitoring devices are exposed to complex electromagnetic environments and dynamically changing temperature and humidity conditions outdoors for extended periods, they are prone to gradual aging of insulation materials and abnormal fluctuations in dielectric loss, which can lead to equipment failure.

[0003] Existing insulation condition assessment methods mainly rely on fixed threshold judgment or single parameter monitoring, which has the following drawbacks:

[0004] First, the dielectric loss tangent (tanδ) is significantly affected by environmental factors such as temperature and humidity. Traditional methods do not consider dynamic compensation for environmental parameters, resulting in large measurement errors. For example, an increase in temperature may cause tanδ to increase artificially, while changes in humidity may introduce nonlinear interference. Fixed thresholds cannot adapt to such fluctuations, easily leading to false alarms or missed alarms.

[0005] Second, insulation degradation is a gradual process. Traditional methods are based on instantaneous values ​​or short-term averages for evaluation, which cannot capture the long-term trend changes in dielectric loss and make it difficult to provide early warnings of potential faults.

[0006] Third, fixed thresholds are usually based on empirical values ​​or equipment factory standards, without taking into account historical equipment operating data and environmental characteristics. This results in the thresholds being out of touch with actual operating conditions and failing to reflect the cumulative effects of equipment aging or environmental changes. Summary of the Invention

[0007] The purpose of this invention is to address the problems existing in the prior art by providing a method and system for assessing the insulation status of an online monitoring device for voltage transformers. This eliminates the interference of environmental factors on the measurement of the dielectric loss tangent, improving measurement accuracy; and enables early warning by predicting abnormal changes in dielectric loss values.

[0008] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0009] A method for assessing the insulation status of an online monitoring device for voltage transformers includes the following steps:

[0010] S1. Obtain the original dielectric loss tangent value tanδ using a dielectric loss tester. rawTemperature T and relative humidity H are collected through environmental sensors;

[0011] S2. Perform temperature and humidity compensation on the collected dielectric loss tangent value to obtain the compensated dielectric loss tangent value tanδ. final ;

[0012] S3. Based on the autoregressive integral moving average model, the trend of the compensated dielectric loss tangent is predicted to obtain the predicted value.

[0013] S4. Determine abnormal states by comparing predicted values ​​with dynamic thresholds.

[0014] S5. Conduct risk assessment based on comprehensive equipment status parameters, and output corresponding maintenance decisions based on the assessment results.

[0015] Furthermore, step S2 includes:

[0016] S2.1, The collected dielectric loss tangent value tanδ raw Temperature compensation is achieved using the following formula:

[0017]

[0018] Where, tanδ comp tanδ is the tangent of the dielectric loss angle after temperature compensation. raw The original dielectric loss tangent value was collected; α i T represents the temperature compensation coefficient determined by fitting experimental data; T is the ambient temperature collected; T ref The preset reference temperature is n; n is the order of the polynomial, which is a positive integer.

[0019] Furthermore, step S2 includes:

[0020] S2.2, the tangent of the dielectric loss angle after temperature compensation (tanδ) comp Humidity correction is achieved using the following formula:

[0021] tanδ final =tanδ comp ·(1+β(HH ref ));

[0022] Where, tanδ final β is the final dielectric loss tangent after temperature and humidity compensation; H is the humidity influence coefficient determined experimentally; H is the ambient humidity collected. ref This is the preset baseline humidity.

[0023] Furthermore, step S3 includes:

[0024] S3.1 Construct the autoregressive integral moving average model ARIMA(1,0,1), with the following formula:

[0025] tanδ t =φ1tanδ t-1 +θ1∈ t-1 +∈ t ;

[0026] Where φ1 is the autoregressive coefficient, representing the linear relationship between the current value and the previous value; θ1 is the moving average coefficient, representing the linear relationship between the current error and the error at the previous time step; ε t The white noise at the current moment has a mean of 0 and a variance of σ. 2 The normal distribution;

[0027] S3.2, Take the N most recent temperature and humidity compensated tangent values ​​of the dielectric loss angle tanδ final denoted as tanδ t-1 ,tanδ t-2 ,...,tanδ t-N The stationarity of the data was confirmed by the Augmented Dickey-Fuller test;

[0028] S3.3 Constructing the likelihood function:

[0029]

[0030] Where, ∈ t =tanδ t -φ1tanδ t-1 -θ1∈ t-1 ;

[0031] The estimated values ​​of φ1 and θ1 are obtained by maximizing lnL using Newton's iterative method. and

[0032] S3.4, The recursive formula for the predicted value is:

[0033] When k=1

[0034]

[0035] When k≥2

[0036]

[0037] Furthermore, step S4 includes:

[0038] S4.1, Take the tangent value of the dielectric loss angle tanδ after N historical temperature and humidity compensations. final denoted as tanδ t-1 ,tanδt-2 ,...,tanδ t-N ;

[0039] S4.2 Calculate the average insulation state level μ of N historical dielectric loss tangent values. t The formula is as follows:

[0040]

[0041] S4.3 Calculate the standard deviation σ of the historical N dielectric loss angle tangent values. t The formula is as follows:

[0042]

[0043] S4.4, By comparing predicted values The rules for determining abnormal states based on dynamic threshold ranges are as follows:

[0044] When the predicted value satisfies

[0045]

[0046] This indicates that the insulation condition is within the normal historical fluctuation range and no intervention is required.

[0047] When the predicted value satisfies

[0048]

[0049] or

[0050]

[0051] This indicates that the insulation condition has significantly deviated from the historical average, triggering a level two alarm, requiring manual inspection and a shorter monitoring cycle;

[0052] When the predicted value satisfies

[0053]

[0054] or

[0055]

[0056] This indicates an extremely abnormal insulation condition. The machine should be stopped immediately for inspection and repair to prevent accidents caused by equipment failure.

[0057] Furthermore, step S5 includes:

[0058] S5.1 Establish a risk assessment model, the formula is as follows:

[0059]

[0060] in, It represents the ratio of the predicted dielectric loss value to the allowable maximum value; It represents the ratio of the operating years to the designed life; It represents the ratio of the historical fault times to the total operating cycle; w1, w2, and w3 are weighting factors and satisfy w1 + w2 + w3 = 1;

[0061] S5.2. Output the maintenance decision according to the risk assessment value R:

[0062] When R ≤ 0.3, the insulation state is good, the predicted value does not reach the warning threshold, and the equipment can continue to operate, and only regular monitoring is required;

[0063] When 0.3 < R ≤ 0.6, there is a potential deterioration trend in the insulation state, manual inspection needs to be arranged, the monitoring cycle is shortened to 50% of the original interval, and key parameters are recorded;

[0064] When R > 0.6, the insulation state of the equipment is seriously abnormal, stop the machine immediately and carry out a comprehensive overhaul, check the cause of the fault, and replace the aging components.

[0065] Furthermore, it also includes:

[0066] S6. Store the collected original data, compensated tangent value of dielectric loss angle, predicted value, abnormal determination result, and maintenance decision in the database, and display the following information in real time through the man-machine interface:

[0067] Real-time monitoring curves of the current temperature T and humidity H;

[0068] Original tangent value of dielectric loss angle tanδ raw And the compensated value tanδ final Historical comparison trend chart;

[0069] Future change curve and confidence interval of the tangent value of dielectric loss angle predicted by the ARIMA model;

[0070] Real-time update status of the dynamic threshold range;

[0071] Grade identification of the risk assessment value R and corresponding maintenance suggestions.

[0072] An insulation state evaluation system for an on-line monitoring device of a voltage transformer, including:

[0073] Data acquisition module, which acquires voltage U2 and current I2 through a secondary-side sensor, obtains the original tangent value of dielectric loss angle tanδ raw , and acquires temperature T and relative humidity H through an environmental sensor;

[0074] Compensation calculation module, which performs temperature and humidity compensation on the acquired tangent value of dielectric loss angle to obtain the compensated tangent value of dielectric loss angle tanδfinal ;

[0075] The trend prediction module uses an autoregressive integral moving average model to predict the trend of the compensated dielectric loss tangent and obtain the predicted value.

[0076] The anomaly detection module determines anomalies by comparing predicted values ​​with dynamic thresholds.

[0077] The risk assessment module performs risk assessments based on comprehensive equipment status parameters and outputs corresponding maintenance decisions based on the assessment results.

[0078] A computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the above-described method steps.

[0079] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method steps described above.

[0080] Compared with the prior art, the beneficial effects of the present invention are:

[0081] 1. Through the above-mentioned temperature and humidity compensation measures, the interference of environmental factors on the measurement of dielectric loss tangent can be accurately eliminated, improving the accuracy of the measurement and providing a reliable data basis for insulation condition assessment;

[0082] 2. By predicting trends, abnormal changes in dielectric loss values ​​can be detected in advance, enabling early warning and providing strong support for preventive maintenance of equipment. This avoids sudden equipment failures and improves equipment reliability and service life.

[0083] 3. The dynamic threshold mechanism avoids false alarms or missed alarms caused by fixed thresholds, improving the accuracy of judgment; the hierarchical early warning mechanism realizes differentiated operation and maintenance strategies, which not only reduces unnecessary maintenance costs, but also enables rapid response to serious anomalies and effectively prevents equipment failure. Attached Figure Description

[0084] 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 some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0085] Figure 1 This is a flowchart illustrating the insulation status assessment method for an online monitoring device for voltage transformers according to this application. Detailed Implementation

[0086] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of the present invention, and 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.

[0087] The sequence number of each step in this application does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0088] In the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only for distinguishing descriptions and should not be construed as indicating or implying relative importance. It should also be understood that although the terms "first," "second," etc., are used in the text to describe various elements in some embodiments of this application, these elements should not be limited by these terms. These terms are merely used to distinguish one element from another. For example, a first table may be named a second table, and similarly, a second table may be named a first table, without departing from the scope of the various described embodiments. Both the first table and the second table are tables, but they are not the same table.

[0089] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0090] A voltage transformer is an electrical device used to measure high voltage. It uses the principle of electromagnetic induction to reduce high voltage to a safe level for measurement by conventional voltmeters or data acquisition systems. An online voltage transformer monitoring device is used to monitor the operating status of a voltage transformer in real time. Because online voltage transformer monitoring devices are exposed to complex electromagnetic environments and dynamically changing temperature and humidity conditions outdoors for extended periods, they are prone to gradual aging of insulation materials and abnormal fluctuations in dielectric loss, which can lead to equipment failure.

[0091] Existing insulation condition assessment methods mainly rely on fixed threshold judgment or single parameter monitoring, which has the following drawbacks:

[0092] First, the dielectric loss tangent (tanδ) is significantly affected by environmental factors such as temperature and humidity. Traditional methods do not consider dynamic compensation for environmental parameters, resulting in large measurement errors. For example, an increase in temperature may cause tanδ to increase artificially, while changes in humidity may introduce nonlinear interference. Fixed thresholds cannot adapt to such fluctuations, easily leading to false alarms or missed alarms.

[0093] Second, insulation degradation is a gradual process. Traditional methods are based on instantaneous values ​​or short-term averages for evaluation, which cannot capture the long-term trend changes in dielectric loss and make it difficult to provide early warnings of potential faults.

[0094] Third, fixed thresholds are usually based on empirical values ​​or equipment factory standards, without taking into account historical equipment operating data and environmental characteristics. This results in the thresholds being out of touch with actual operating conditions and failing to reflect the cumulative effects of equipment aging or environmental changes.

[0095] To address the above technical issues, such as Figure 1 As shown, in the first aspect of this application, an insulation status assessment method for an online monitoring device for a voltage transformer is provided, comprising the following steps S1-S6.

[0096] S1. Obtain the original dielectric loss tangent value tanδ using a dielectric loss tester. raw Temperature (T) and relative humidity (H) are collected using environmental sensors.

[0097] Specifically, a digital dielectric loss tester is used, supporting real-time online measurement with a resolution of no less than 0.001%, to obtain the original dielectric loss tangent value tanδ. raw The measuring electrodes of the dielectric loss tester must be in close contact with the insulating components of the voltage transformer online monitoring device to avoid external electromagnetic interference. Environmental sensors should be installed in a well-ventilated area, away from direct sunlight or localized heat sources.

[0098] The dielectric loss value and environmental parameters are synchronously acquired using a multi-channel data acquisition card, with a sampling frequency ≥1kHz. The raw signal is filtered to eliminate high-frequency noise. The acquired data is transmitted to an industrial control computer via RS485 or Ethernet and stored in a structured database, including timestamps, device IDs, and parameter values.

[0099] S2. Perform temperature and humidity compensation on the collected dielectric loss tangent value to obtain the compensated dielectric loss tangent value tanδ. final .

[0100] In some embodiments, step S2 includes the following steps S2.1-S2.2.

[0101] S2.1, The collected dielectric loss tangent value tanδ raw Temperature compensation is achieved using the following formula:

[0102]

[0103] Where, tanδ comp tanδ is the tangent of the dielectric loss angle after temperature compensation. raw The original dielectric loss tangent value was collected; α i T represents the temperature compensation coefficient determined by fitting experimental data; T is the ambient temperature collected; T ref The preset reference temperature is n; n is the order of the polynomial, which is a positive integer.

[0104] Specifically, in some embodiments, under constant humidity, the dielectric loss values ​​corresponding to different temperatures T are measured, and the temperature compensation coefficient α is solved through regression analysis. i Select the typical temperature during normal equipment operation as the preset reference temperature T. ref .

[0105] The above steps fit the nonlinear relationship between temperature and dielectric loss value using a polynomial model, measure the dielectric loss value corresponding to different temperatures under constant humidity, solve for the temperature compensation coefficient, and correct the original dielectric loss value based on the polynomial expansion of the temperature difference, effectively eliminating the interference of ambient temperature on the measurement of dielectric loss tangent.

[0106] S2.2, the tangent of the dielectric loss angle after temperature compensation (tanδ) comp Humidity correction is achieved using the following formula:

[0107] tanδ final =tanδ comp ·(1+β(HH ref ));

[0108] Where, tanδ final β is the final dielectric loss tangent after temperature and humidity compensation; H is the humidity influence coefficient determined experimentally; H is the ambient humidity collected. ref This is the preset baseline humidity.

[0109] Specifically, in some embodiments, at a constant temperature, the rate of change of dielectric loss value corresponding to different humidity levels H is measured, and the percentage of relative change in dielectric loss value is the humidity influence coefficient β.

[0110] Using a linear correction model, the humidity difference ΔH = HH ref As a scaling factor. When the humidity is higher than the reference value H... ref When the temperature is low, the dielectric loss value is amplified proportionally; conversely, it is reduced, thus eliminating the effect of ambient humidity on the temperature-compensated dielectric loss value tanδ.comp The residual effects.

[0111] For example, the operating environment temperature of a certain voltage transformer online monitoring device is 35℃, T ref The temperature is 25℃ and the humidity is 70%RH. ref =50%).

[0112] Temperature compensation:

[0113] If α1 = 0.002 and α2 = 0.0001, then:

[0114]

[0115] If tanδ is measured raw =0.05, then tanδ comp =0.05-0.03=0.02.

[0116] Humidity correction:

[0117] If β = 0.005, then:

[0118] tanδ final =0.02×(1+0.005×(7050))=0.02×1.1=0.022.

[0119] The above steps use a linear correction model to measure the rate of change of dielectric loss value corresponding to different humidity levels at a constant temperature, determine the humidity influence coefficient, and use the humidity difference as a proportional factor to correct the dielectric loss value after temperature compensation. This further eliminates the residual influence of environmental humidity on dielectric loss value and improves the accuracy of dielectric loss value measurement.

[0120] S3. Based on the autoregressive integral moving average model, the trend of the compensated dielectric loss tangent is predicted to obtain the predicted value.

[0121] In some embodiments, step S3 includes the following steps S3.1-S3.4.

[0122] S3.1 Construct the autoregressive integral moving average model ARIMA(1,0,1), with the following formula:

[0123] tanδ t =φ1tanδ t-1 +θ1∈ t-1 +∈ t ;

[0124] Where φ1 is the autoregressive coefficient, representing the linear relationship between the current value and the previous value; θ1 is the moving average coefficient, representing the linear relationship between the current error and the error at the previous time step; ε tThe white noise at the current moment has a mean of 0 and a variance of σ. 2 The normal distribution;

[0125] S3.2, Take the N most recent temperature and humidity compensated tangent values ​​of the dielectric loss angle tanδ final denoted as tanδ t-1 ,tanδ t-2 ,...,tanδ t-N The stationarity of the data was confirmed by the Augmented Dickey-Fuller test;

[0126] Specifically, assuming the sequence has a unit root, the ADF statistic is calculated and compared with the critical value. If the ADF statistic is less than the critical value, the null hypothesis is rejected, and the data is stationary. If the data is non-stationary, the compensation model needs to be readjusted or differencing needs to be performed.

[0127] S3.3 Constructing the likelihood function:

[0128]

[0129] Among them, the error term is calculated ∈ t =tanδ t -φ1tanδ t-1 -θ1∈ t-1 ;

[0130] The estimated values ​​of φ1 and θ1 are obtained by maximizing lnL using Newton's iterative method. and

[0131] Specifically, set φ1 (0) =0.1, θ1 (0) =0.1. Input data: A sequence of tangent values ​​of dielectric loss angle of length N.

[0132] Calculate the lnL gradient of the likelihood function:

[0133]

[0134] Calculate the Hessian matrix H:

[0135]

[0136] Parameter update, Newton iteration:

[0137]

[0138] Set convergence criteria:

[0139] |lnL (k+1) -ln L (k) |<10 -6

[0140] Alternatively, set a maximum number of iterations.

[0141] After multiple iterations, the output is... and

[0142] S3.4, The recursive formula for the predicted value is:

[0143] When k=1

[0144]

[0145] When k≥2

[0146]

[0147] The above steps construct an ARIMA(1,0,1) model to predict the trend of the dielectric loss tangent after temperature and humidity compensation. First, the ADF test is used to ensure data stationarity. Then, the autoregressive coefficients and moving average coefficients are solved using Newton's iteration method to maximize the likelihood function and optimize the model parameters. A recursive formula is used for prediction, combining the observed values ​​and error terms from the previous time step to generate predicted values ​​for future time steps. This method can effectively capture the temporal characteristics of dielectric loss values, providing a reliable basis for subsequent anomaly detection.

[0148] On the one hand, through rigorous stability testing and parameter optimization, the accuracy of prediction is improved, avoiding the misjudgment problem of traditional fixed threshold methods; on the other hand, the dynamic recursive mechanism can update the prediction results in real time, adapt to changes in equipment status, and provide timely and reliable data support for insulation status assessment, thereby achieving early warning and precise maintenance.

[0149] S4. Determine abnormal states by comparing predicted values ​​with dynamic thresholds.

[0150] In some embodiments, step S4 includes the following steps S4.1-S4.4.

[0151] S4.1, Take the tangent value of the dielectric loss angle tanδ after N historical temperature and humidity compensations. final denoted as tanδ t-1 ,tanδ t-2 ,...,tanδ t-N ;

[0152] S4.2 Calculate the average insulation state level μ of N historical dielectric loss tangent values. t The formula is as follows:

[0153]

[0154] S4.3 Calculate the standard deviation σ of the historical N dielectric loss angle tangent values. t The formula is as follows:

[0155]

[0156] S4.4, By comparing predicted values The rules for determining abnormal states based on dynamic threshold ranges are as follows:

[0157] When the predicted value satisfies

[0158]

[0159] This indicates that the insulation condition is within the normal historical fluctuation range and no intervention is required.

[0160] When the predicted value satisfies

[0161]

[0162] or

[0163]

[0164] This indicates that the insulation condition has significantly deviated from the historical average, triggering a level two alarm, requiring manual inspection and a shorter monitoring cycle;

[0165] When the predicted value satisfies

[0166]

[0167] or

[0168]

[0169] This indicates an extremely abnormal insulation condition. The machine should be stopped immediately for inspection and repair to prevent accidents caused by equipment failure.

[0170] Traditional methods using fixed thresholds fail to reflect the impact of environmental fluctuations such as temperature and humidity on dielectric loss values, easily leading to false alarms. The above steps utilize a dynamic threshold mechanism to determine insulation anomalies. First, the mean and standard deviation of the dielectric loss angle tangent are calculated based on historical data to establish a dynamic threshold range. Then, the predicted values ​​from the ARIMA model are compared with the thresholds, categorized into three levels based on the degree of deviation: normal fluctuation, significant deviation, and extreme anomaly, triggering different levels of maintenance responses accordingly. This method quantifies the degree of anomaly through statistical principles, achieving refined grading and early warning of insulation conditions.

[0171] On the one hand, the dynamic threshold can adapt to environmental changes and equipment aging, avoid false alarms or missed alarms caused by fixed thresholds, and improve the accuracy of judgment; on the other hand, the hierarchical early warning mechanism implements a differentiated operation and maintenance strategy, which not only reduces unnecessary maintenance costs but can also quickly respond to serious anomalies, thereby effectively preventing equipment failures and ensuring the safe and stable operation of the power system.

[0172] S5. Integrate equipment status parameters for risk assessment and output corresponding maintenance decisions according to the assessment results.

[0173] In some embodiments, step S5 includes the following steps S5.1 - S5.2.

[0174] S5.1. Establish a risk assessment model with the following formula:

[0175]

[0176] Where, represents the ratio of the predicted dielectric loss value to the allowable maximum value; represents the ratio of the operating years to the designed life; represents the ratio of the number of historical faults to the total operating cycle; w1, w2, and w3 are weighting factors and satisfy w1 + w2 + w3 = 1;

[0177] S5.2. Output a maintenance decision according to the risk assessment value R:

[0178] When R ≤ 0.3, the insulation state is good, the predicted value does not reach the early warning threshold, and the equipment can continue to operate, and only regular monitoring is required;

[0179] When 0.3 < R ≤ 0.6, there is a potential deterioration trend in the insulation state, manual inspection needs to be arranged, the monitoring cycle is shortened to 50% of the original interval, and key parameters are recorded;

[0180] When R > 0.6, the insulation state of the equipment is seriously abnormal, immediately shut down and conduct a comprehensive overhaul, check the cause of the failure, and replace aging components.

[0181] The above steps establish a risk assessment model through multi-dimensional parameter fusion, comprehensively consider key indicators such as dielectric loss value, operating years, and historical failure rate, and assign different weighting factors to quantify the overall risk level of the equipment insulation state. According to the range of the R value, the system automatically matches differentiated maintenance decisions, from regular monitoring to emergency shutdown, forming a closed-loop management. The model in this embodiment not only covers the current dielectric loss value state but also introduces long-term factors (aging, historical failures), achieving a comprehensive assessment of risks.

[0182] On the one hand, by weighted fusion of multi-source parameters, the limitations of a single indicator are avoided, making the evaluation results more objective and reliable. On the other hand, the hierarchical response mechanism linked with dynamic thresholds not only optimizes the allocation of operation and maintenance resources but also promptly blocks high-risk states, significantly improving the intelligence and economy of equipment management. For example, when the proportion of years of operation is high, an inspection will be triggered even if the dielectric loss value is normal, reflecting the design concept of preventive maintenance.

[0183] S6. Store the collected raw data, the compensated tangent of the dielectric loss angle, the predicted value, the anomaly judgment results, and the maintenance decisions into the database, and display the following information in real time through the human-computer interaction interface:

[0184] Real-time monitoring curves of current temperature T and humidity H;

[0185] Original tangent of the dielectric loss angle tanδ raw With the compensated value tanδ final Historical comparison trend chart;

[0186] The future change curve and confidence interval of the dielectric loss angle tangent predicted by the ARIMA model;

[0187] Real-time update status of dynamic threshold range;

[0188] Risk assessment value R level label and corresponding maintenance recommendations.

[0189] A second aspect of this application provides an insulation condition assessment system for an online monitoring device of a voltage transformer, comprising:

[0190] The data acquisition module obtains the original dielectric loss tangent value tanδ using a dielectric loss tester. raw Temperature T and relative humidity H are collected through environmental sensors;

[0191] The compensation calculation module performs temperature and humidity compensation on the collected dielectric loss tangent value to obtain the compensated dielectric loss tangent value tanδ. final ;

[0192] The trend prediction module uses an autoregressive integral moving average model to predict the trend of the compensated dielectric loss tangent and obtain the predicted value.

[0193] The anomaly detection module determines anomalies by comparing predicted values ​​with dynamic thresholds.

[0194] The risk assessment module performs risk assessments based on comprehensive equipment status parameters and outputs corresponding maintenance decisions based on the assessment results.

[0195] In a third aspect, this application provides a computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program, wherein the computer program, when executed by a processor, implements the above-described method steps.

[0196] In a fourth aspect, this application provides an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method steps described above.

[0197] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for assessing the insulation status of an online monitoring device for voltage transformers, characterized in that, It includes the following steps: S1. Obtain the original dielectric loss tangent value tanδ using a dielectric loss tester. raw Temperature T and relative humidity H are collected through environmental sensors; S2. Perform temperature and humidity compensation on the collected dielectric loss tangent value to obtain the compensated dielectric loss tangent value tanδ. final Step S2 includes: S2.1, The collected dielectric loss tangent value tanδ raw Temperature compensation is achieved using the following formula: ； Where, tanδ comp tanδ is the tangent of the dielectric loss angle after temperature compensation. raw The original dielectric loss tangent value was collected; α i T represents the temperature compensation coefficient determined by fitting experimental data; T is the ambient temperature collected; T ref The preset reference temperature; n is the polynomial order, taking a positive integer; S2.2, the tangent of the dielectric loss angle after temperature compensation (tanδ) comp Humidity correction is achieved using the following formula: ； Where, tanδ final β is the final dielectric loss tangent after temperature and humidity compensation; H is the humidity influence coefficient determined experimentally; H is the ambient humidity collected. ref The preset baseline humidity; S3. Based on the autoregressive integral moving average model, the trend of the compensated dielectric loss tangent is predicted to obtain the predicted value. Step S3 includes: S3.

1. Construct an autoregressive integrated moving average model ARIMA(1,0,1), and the formula is as follows: ； Where ϕ1 is the autoregressive coefficient, representing the linear relationship between the current value and the previous value; θ1 is the moving average coefficient, representing the linear relationship between the current error and the error at the previous time step. The white noise at the current time t has a mean of 0 and a variance of σ. 2 The normal distribution; S3.2, Take the N most recent temperature and humidity compensated tangent values ​​of the dielectric loss angle tanδ final denoted as tanδ t-1 , tanδ t-2 , ...,tanδ t-N The stationarity of the data was confirmed by the Augmented Dickey-Fuller test; S3.

3. Construct a likelihood function: ； in, ; The estimated values ​​of ϕ1 and θ1 are obtained by maximizing lnL using Newton's iterative method. and ; S3.

4. The recurrence formula for the predicted value is: When k = 1, ； When k ≥ 2, ； S4. Determine the abnormal state by comparing the predicted value with the dynamic threshold; S5. Conduct a risk assessment based on the comprehensive equipment status parameters, and output the corresponding maintenance decision according to the assessment result.

2. The insulation status assessment method for an online monitoring device for voltage transformers according to claim 1, characterized in that, Step S4 includes: S4.1, Take the tangent value of the dielectric loss angle tanδ after N historical temperature and humidity compensations. final denoted as tanδ t-1 , tanδ t-2 , ...,tanδ t-N ; S4.2 Calculate the average insulation state level μ of N historical dielectric loss tangent values. t The formula is as follows: ； S4.3 Calculate the standard deviation σ of the historical N dielectric loss angle tangent values. t The formula is as follows: ； S4.4, By comparing predicted values The rules for determining abnormal states based on dynamic threshold ranges are as follows: When the predicted value satisfies ； It indicates that the insulation state is within the normal historical fluctuation range and no intervention is required; When the predicted value satisfies ； or ； It indicates that the insulation state significantly deviates from the historical mean, triggers a secondary alarm, and manual inspection needs to be arranged and the monitoring period should be shortened; When the predicted value satisfies ； or ； It indicates that the insulation state is extremely abnormal, and the machine should be stopped immediately for maintenance to avoid accidents caused by equipment failures.

3. The insulation status assessment method for an online monitoring device for voltage transformers according to claim 1, characterized in that, Step S5 includes: S5.

1. Establish a risk assessment model, and the formula is as follows: ； in, This represents the ratio of the predicted dielectric loss value to the maximum allowable value. This represents the ratio of the service life to the design life. This represents the ratio of the number of historical failures to the total operating cycle; w1, w2, and w3 are weighting factors, and w1 + w2 + w3 = 1. S5.

2. According to the risk assessment value R, output the maintenance decision: When R ≤ 0.3, the insulation state is good, the predicted value does not reach the warning threshold, and the equipment can continue to operate and only needs regular monitoring; When 0.3 < R ≤ 0.6, there is a potential deterioration trend in the insulation state, manual inspection needs to be arranged, the monitoring period should be shortened to 50% of the original interval, and key parameters should be recorded; When R > 0.6, the insulation state of the equipment is seriously abnormal, stop the machine immediately and conduct a comprehensive inspection to find out the cause of the failure and replace the aging components.

4. The insulation status assessment method for an online monitoring device for voltage transformers according to claim 1, characterized in that, It also includes: S6. Store the collected original data, compensated tangent value of the dielectric loss angle, predicted value, abnormal determination result, and maintenance decision in the database, and display the following information in real time through the human-computer interaction interface: Real-time monitoring curves of the current voltage U2, current I2, temperature T, and humidity H; Original tangent of the dielectric loss angle tanδ raw With the compensated value tanδ final Historical comparison trend chart; Future change curve and confidence interval of the tangent value of the dielectric loss angle predicted by the ARIMA model; Real-time update status of the dynamic threshold range; Level identification of the risk assessment value R and corresponding maintenance suggestions.

5. An insulation condition assessment system for an online monitoring device of a voltage transformer, used to implement the assessment method according to any one of claims 1 to 4, characterized in that, It includes: The data acquisition module obtains the original dielectric loss tangent value tanδ using a dielectric loss tester. raw Temperature T and relative humidity H are collected through environmental sensors; The compensation calculation module performs temperature and humidity compensation on the collected dielectric loss tangent value to obtain the compensated dielectric loss tangent value tanδ. final ; The trend prediction module uses an autoregressive integral moving average model to predict the trend of the compensated dielectric loss tangent and obtain the predicted value. ; An abnormal determination module that determines the abnormal state by comparing the predicted value with the dynamic threshold; A risk assessment module that conducts a risk assessment based on the comprehensive equipment status parameters and outputs the corresponding maintenance decision according to the assessment result.

6. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, wherein when the computer program is executed by a processor, the steps of the method described in any one of claims 1 to 4 are implemented.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, the steps of the method described in any one of claims 1 to 4 are implemented.

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