Transformer overload self-diagnosis method based on multiple parameters
By building a transformer overload diagnostic test platform, measuring multiple parameters, eliminating outliers, and calculating discriminant factors, the problem of low overload detection accuracy of transformer is solved, high-precision overload evaluation and real-time monitoring are achieved, and power supply reliability is improved.
Patent Information
- Application Number
- CN202510528364.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-04-25
- Publication Date
- 2025-07-25
AI Technical Summary
In the prior art, transformer overload mainly relies on manual regular inspections, which have strong subjectivity, low detection accuracy, and inability to monitor in real time, which affects the reliability of power supply. Modern power systems have urgent needs for real-time and accurate overload diagnosis technology.
Build a transformer overload diagnostic test platform, measure temperature, noise parameters and dielectric loss factor, eliminate outliers through the Glubbs criterion method, calculate discrimination factors, and judge the transformer overload degree based on multiple parameters.
It realizes high-precision transformer overload assessment, can judge the overload status of the transformer in real time and accurately, and improves power supply reliability.
Smart Images
Figure CN120370072A_ABST
Abstract
Description
Technical Field
[0001] The invention relates to the field of transformer diagnosis, and in particular to a transformer overload self-diagnosis method based on multiple parameters. Background Art
[0002] With the development of science and technology and the progress of the times, people are becoming more and more dependent on electronic devices, and the demand for electricity in various fields of society is also increasing. The promotion of intelligence in all walks of life has undoubtedly increased the load to a greater extent. Therefore, during peak power consumption periods, transformers need to carry loads far exceeding their rated capacity to meet power demand, which makes transformer overload operation more frequent.
[0003] In the early days, transformer overload was mainly prevented by regular manual inspections, which were highly subjective, had low detection accuracy, could not be monitored in real time, and required power outages, which affected the reliability of power supply. Modern power systems have increasingly higher requirements for power supply reliability and power quality, and there is an urgent need for real-time and accurate transformer overload diagnosis technology. Summary of the invention
[0004] In order to solve the above technical problems, the present invention provides a transformer overload self-diagnosis method based on multiple parameters with simple algorithm and high diagnostic accuracy.
[0005] The technical solution of the present invention to solve the above technical problem is: a transformer overload self-diagnosis method based on multiple parameters, comprising the following steps:
[0006] Step 1: Build a transformer overload diagnosis test platform;
[0007] Step 2: Conduct tests based on the transformer overload diagnosis test platform to measure the temperature and noise parameters of the transformer under rated working conditions and overload, and measure the dielectric loss factor through the transformer bushing end screen;
[0008] Step 3: Process the measured parameters and eliminate abnormal parameters in the measured parameters;
[0009] Step 4: Based on the rated working state parameters of the transformer, calculate the discrimination factors when the transformer is in various overload levels;
[0010] Step 5: Determine the transformer overload degree based on the load determination factor and daily operating parameters.
[0011] The above-mentioned multi-parameter based transformer overload self-diagnosis method. In step one, the transformer overload diagnosis test platform includes a dielectric loss monitoring terminal, a first temperature sensor, a second temperature sensor, a first noise sensor, a second noise sensor, a first current transformer, a second current transformer, a third current transformer, the high-voltage side bushing of phase A of the transformer, the high-voltage side bushing of phase B of the transformer, the high-voltage side bushing of phase C of the transformer, the low-voltage side bushing of phase A of the transformer, the low-voltage side bushing of phase B of the transformer, the low-voltage side bushing of phase C of the transformer, the transformer winding and a load regulator;
[0012] The first temperature sensor and the second temperature sensor are symmetrically arranged on the left and right sides of the transformer box body. The first noise sensor and the second noise sensor are symmetrically arranged on the front and back sides of the transformer box body. The lead-out ends of the high-voltage side bushing of phase A of the transformer, the high-voltage side bushing of phase B of the transformer, and the high-voltage side bushing of phase C of the transformer are connected to the busbar of the same voltage level, and the ends are respectively connected to the A, B, and C phases of the high-voltage side of the transformer winding correspondingly; the lead-out ends of the low-voltage side bushing of phase A of the transformer, the low-voltage side bushing of phase B of the transformer, and the low-voltage side bushing of phase C of the transformer are all connected to the load regulator, and the ends are respectively connected to the A, B, and C phases of the low-voltage side of the transformer winding correspondingly. The first current transformer, the second current transformer, and the third current transformer are respectively installed on the bushing end screens of the high-voltage side bushing of phase A of the transformer, the high-voltage side bushing of phase B of the transformer, and the high-voltage side bushing of phase C of the transformer. The signal output ends of the first current transformer, the second current transformer, and the third current transformer are all connected to the dielectric loss monitoring terminal.
[0013] The above-mentioned multi-parameter based transformer overload self-diagnosis method. The specific process of step two is as follows:
[0014] Start the load regulator, adjust the load level to the rated capacity of the transformer, obtain the data of the first temperature sensor and the second temperature sensor, and at the same time obtain the data of the first noise sensor and the second noise sensor. Obtain the dielectric loss factor at the corresponding time through the dielectric loss monitoring terminal. Repeat the test in this way, and successively measure the data when the load level is adjusted to 10%, 20%, and 50% higher than the rated capacity of the transformer. Test within the time that the transformer can withstand during overload to obtain T(i,t), S(i,t), and tanδ(i,t), where T(i,t) represents the temperature of the t-th test when the load level is i, S(i,t) represents the noise of the t-th test when the load level is i, and tanδ(i,t) represents the dielectric loss factor of the t-th test when the load level is i. The total number of tests is m.
[0015] In the above multi-parameter based transformer overload self-diagnosis method, in step 2, i = 0, 1, 2, 3. When i = 0, it means the added load is exactly the rated capacity of the transformer. When i = 1, it means the added load is 10% higher than the rated capacity of the transformer. When i = 2, it means the added load is 20% higher than the rated capacity of the transformer. When i = 3, it means the added load is 50% higher than the rated capacity of the transformer.
[0016] In the above multi-parameter based transformer overload self-diagnosis method, in step 3, using the Grubbs criterion method, the outliers in T(i, t), S(i, t), and tanδ(i, t) are removed. The process is as follows:
[0017] A set B(i, t) is formed by combining the three groups of data. First, the average value and variance of B(i, t) are calculated.
[0018] Calculate the average value W(i, t) and variance s 2 (i, t) of the test data under each load level:
[0019]
[0020] Using the Grubbs criterion method, the critical value G α (m) is obtained, and then the statistic G is calculated:
[0021]
[0022] Compare the calculated statistic G with the critical value G α (m):
[0023] G > G α (m) (4)
[0024] If equation (4) holds, then this value is determined to be an outlier and this value is removed. The data set after removing the outlier is B′(i, t), and the data after removing the outlier is also denoted as T′(i, t), S′(i, t), and tanδ′(i, t).
[0025] In the above multi-parameter based transformer overload self-diagnosis method, the specific process of step 4 is as follows:
[0026] Fit T′(i, t), S′(i, t), and tanδ′(i, t). The fitting formula is:
[0027]
[0028] Z iis the fitting factor, where i = 0, 1, 2, 3. i also represents the load level. The fitting factor Z0 when the load is the rated capacity of the transformer, the discriminant fitting factor Z1 when the load exceeds the rated capacity of the transformer by 10%, the fitting factor Z2 when the load exceeds the rated capacity of the transformer by 20%, and the fitting factor Z3 when the load exceeds the rated capacity of the transformer by 50% are calculated respectively. α, γ, are the parameter weight coefficients of temperature, noise, and dielectric loss factor respectively; T′ max (i, t), T′ min (i, t) are the maximum and minimum values of T′(i, t) respectively. S′ max (i, t), S′ min (i, t) are the maximum and minimum values of S′(i, t) respectively. tanδ′ min (i, t) is the minimum value of tanδ′(i, t);
[0029] The final discriminant factor β i The calculation formula is:
[0030]
[0031] In the above multi-parameter based transformer overload self-diagnosis method, in step five, the discriminant method for the transformer overload degree is as follows:
[0032] If β < β1, the transformer is in normal operation;
[0033] If β1 < β < β2, the transformer is in light overload;
[0034] If β2 < β < β3, the transformer is in medium overload;
[0035] If β3 < β, the transformer is in heavy overload;
[0036] β is the factor fitted from the parameters during daily operation.
[0037] The beneficial effects of the present invention are as follows: The present invention first builds a transformer overload diagnosis test platform; then conducts tests to measure the temperature, noise parameters of the transformer under rated working conditions and overload, and the dielectric loss factor through the end shield of the transformer bushing; then processes the measured parameters to eliminate abnormal parameters in the measured parameters; then, based on the parameters of the transformer under rated working conditions, calculates the discriminant factors for the transformer at various overload degrees respectively; finally, discriminates the overload degree of the transformer based on the load discriminant factors and daily operation parameters. Through the collection and processing of multi-parameters, high-precision diagnosis can be achieved, and the overload degree of the transformer can be effectively evaluated. Description of the Drawings
[0038] Figure 1This is the overall flowchart of the present invention.
[0039] Figure 2 This is the structural schematic diagram of the transformer overload diagnosis test platform of the present invention. Specific embodiments
[0040] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.
[0041] As Figure 1 shown, a multi-parameter-based transformer overload self-diagnosis method includes the following steps:
[0042] Step 1: Build a transformer overload diagnosis test platform.
[0043] As Figure 2 shown, the transformer overload diagnosis test platform includes a dielectric loss monitoring terminal 1, a first temperature sensor 2, a second temperature sensor 3, a first noise sensor 4, a second noise sensor 5, a first current transformer 6, a second current transformer 7, a third current transformer 8, a transformer A-phase high-voltage side bushing 9a, a transformer B-phase high-voltage side bushing 9b, a transformer C-phase high-voltage side bushing 9c, a transformer A-phase low-voltage side bushing 10a, a transformer B-phase low-voltage side bushing 10b, a transformer C-phase low-voltage side bushing 10c, a transformer winding 11, and a load regulator 12;
[0044] The first temperature sensor 2 and the second temperature sensor 3 are symmetrically arranged on the left and right sides of the transformer box 13, the first noise sensor 4 and the second noise sensor 5 are symmetrically arranged on the front and back sides of the transformer box 13, the lead-out ends of the transformer A-phase high-voltage side bushing 9a, the transformer B-phase high-voltage side bushing 9b, and the transformer C-phase high-voltage side bushing 9c are connected to the busbar of the same voltage level, and the ends are respectively connected to the A, B, and C phases of the high-voltage side of the transformer winding 11 correspondingly; the lead-out ends of the transformer A-phase low-voltage side bushing 10a, the transformer B-phase low-voltage side bushing 10b, and the transformer C-phase low-voltage side bushing 10c are all connected to the load regulator 12, and the ends are respectively connected to the A, B, and C phases of the low-voltage side of the transformer winding 11 correspondingly. The first current transformer 6, the second current transformer 7, and the third current transformer 8 are respectively installed on the bushing end shields of the transformer A-phase high-voltage side bushing 9a, the transformer B-phase high-voltage side bushing 9b, and the transformer C-phase high-voltage side bushing 9c, and the signal output ends of the first current transformer 6, the second current transformer 7, and the third current transformer 8 are all connected to the dielectric loss monitoring terminal 1.
[0045] Step 2: Conduct tests based on the transformer overload diagnosis test platform, measure the temperature, noise parameters of the transformer under rated working conditions and overload, and measure the dielectric loss factor through the bushing end shield of the transformer.
[0046] The specific process of the said Step 2 is:
[0047] Start the load regulator, adjust the load level to the rated capacity of the transformer, obtain data from the first temperature sensor and the second temperature sensor, and simultaneously obtain data from the first noise sensor and the second noise sensor, and obtain the dielectric loss factor at the corresponding time through the dielectric loss monitoring terminal, repeat the test, and measure the data when the load level is adjusted to 10%, 20%, and 50% higher than the rated capacity of the transformer in turn, test within the time range that the transformer can withstand when overloaded, and obtain T(i, t), S(i, t), and tanδ(i, t), where T(i, t) represents the temperature of the tth test when the load level is i, S(i, t) represents the noise of the tth test when the load level is i, and tanδ(i, t) represents the dielectric loss factor of the tth test when the load level is i, and the total number of tests is m.
[0048] i=0, 1, 2, 3, i=0 means that the added load is exactly the rated capacity of the transformer, i=1 means that the added load is 10% higher than the rated capacity of the transformer, i=2 means that the added load is 20% higher than the rated capacity of the transformer, and i=3 means that the added load is 50% higher than the rated capacity of the transformer.
[0049] Step 3: Process the measurement parameters and eliminate abnormal parameters.
[0050] Using the Grubbs criterion method, outliers in T(i, t), S(i, t), and tanδ(i, t) are removed. The process is as follows:
[0051] The three sets of data are combined into a set B(i, t), and the mean and variance of B(i, t) are first calculated;
[0052] Calculate the mean value W(i,t) and variance s of the test data under each load level 2 (i,t):
[0053]
[0054] Using the Grubbs criterion method, we get the critical value G α (m), and then calculate the statistic G:
[0055]
[0056] The calculated statistic G and the critical value G α (m) for comparison:
[0057] G>G α (m) (4)
[0058] If Equation (4) holds, then determine that this value is an outlier and eliminate this value. The data set after eliminating the outlier is B′(i,t), and the data after eliminating the outlier is also denoted as T′(i,t), S′(i,t), and tanδ′(i,t).
[0059] Step 4: Based on the rated operating state parameters of the transformer, calculate the discrimination factors for the transformer at various overload levels respectively.
[0060] The specific process of the above Step 4 is as follows:
[0061] Perform fitting on T′(i,t), S′(i,t), and tanδ′(i,t). The fitting formula is:
[0062]
[0063] Z i is the fitting factor, where i = 0, 1, 2, 3, and i also represents the load level. Respectively find the fitting factor Z0 when the load is the rated capacity of the transformer, the discrimination fitting factor Z1 when the load exceeds the rated capacity of the transformer by 10%, the fitting factor Z2 when the load exceeds the rated capacity of the transformer by 20%, and the fitting factor Z3 when the load exceeds the rated capacity of the transformer by 50%. α, γ are the parameter weight coefficients of temperature, noise, and dielectric loss factor respectively; T′ max (i,t), T′ min (i,t) are the maximum and minimum values of T′(i,t) respectively, S′ max (i,t), S′ min (i,t) are the maximum and minimum values of S′(i,t) respectively, and tanδ′ min (i,t) is the minimum value of tanδ′(i,t);
[0064] The final discrimination factor β i The calculation formula is:
[0065]
[0066] Step 5: Based on the discrimination factor and the daily operating parameters, determine the overload level of the transformer.
[0067] The method for determining the overload level of the transformer is as follows:
[0068] If β < β1, then the transformer is in the normal operating state;
[0069] If β1 < β < β2, then the transformer is in the light overload state;
[0070] If β2 < β < β3, then the transformer is in the medium overload state;
[0071] β3
Claims
1. A multi-parameter based self-diagnosis method for transformer overload, characterized in that, It includes the following steps: Step 1: Build a transformer overload diagnosis test platform; Step 2: Conduct tests based on the transformer overload diagnosis test platform, measure the temperature, noise parameters of the transformer under rated working conditions and overload, and measure the dielectric loss factor through the end screen of the transformer bushing; Step 3: Process the measured parameters and eliminate abnormal parameters in the measured parameters; Step 4: Based on the rated working condition parameters of the transformer, calculate the discrimination factors when the transformer is at various overload levels respectively; Step 5: Discriminate the overload level of the transformer based on the load discrimination factor and daily operation parameters.
2. The multi-parameter-based transformer overload self-diagnosis method according to claim 1, characterized in that, In the said Step 1, the transformer overload diagnosis test platform includes a dielectric loss monitoring terminal, a first temperature sensor, a second temperature sensor, a first noise sensor, a second noise sensor, a first current transformer, a second current transformer, a third current transformer, the high-voltage side bushing of phase A of the transformer, the high-voltage side bushing of phase B of the transformer, the high-voltage side bushing of phase C of the transformer, the low-voltage side bushing of phase A of the transformer, the low-voltage side bushing of phase B of the transformer, the low-voltage side bushing of phase C of the transformer, the transformer winding and a load regulator; The first temperature sensor and the second temperature sensor are symmetrically arranged on the left and right sides of the transformer box body, the first noise sensor and the second noise sensor are symmetrically arranged on the front and back sides of the transformer box body, the leading ends of the high-voltage side bushing of phase A of the transformer, the high-voltage side bushing of phase B of the transformer, and the high-voltage side bushing of phase C of the transformer are connected to the busbar of the same voltage level, and the ends are respectively connected to the A, B, and C phases of the high-voltage side of the transformer winding correspondingly; the leading ends of the low-voltage side bushing of phase A of the transformer, the low-voltage side bushing of phase B of the transformer, and the low-voltage side bushing of phase C of the transformer are all connected to the load regulator, and the ends are respectively connected to the A, B, and C phases of the low-voltage side of the transformer winding correspondingly. The first current transformer, the second current transformer, and the third current transformer are respectively installed on the end screens of the high-voltage side bushing of phase A of the transformer, the high-voltage side bushing of phase B of the transformer, and the high-voltage side bushing of phase C of the transformer, and the signal output ends of the first current transformer, the second current transformer, and the third current transformer are all connected to the dielectric loss monitoring terminal.
3. The method for self-diagnosing transformer overload based on multiple parameters according to claim 2, characterized in that, The specific process of the said Step 2 is as follows: Start the load regulator, adjust the load level to the rated capacity of the transformer, obtain the data of the first temperature sensor and the second temperature sensor, and at the same time obtain the data of the first noise sensor and the second noise sensor. Obtain the dielectric loss factor at the corresponding time through the dielectric loss monitoring terminal, repeat the test in this way, and sequentially measure the data when the load level is adjusted to 10%, 20%, and 50% higher than the rated capacity of the transformer. Test within the time that the transformer can withstand during overload to obtain T(i,t), S(i,t), and tanδ(i,t), where T(i,t) represents the temperature of the t-th test when the load level is i, S(i,t) represents the noise of the t-th test when the load level is i, and tanδ(i,t) represents the dielectric loss factor of the t-th test when the load level is i. The total number of tests is m.
4. The multi-parameter based transformer overload self-diagnosis method according to claim 3, characterized in that, In the second step, i = 0, 1, 2, 3. When i = 0, it means the added load is exactly the rated capacity of the transformer. When i = 1, it means the added load is 10% higher than the rated capacity of the transformer. When i = 2, it means the added load is 20% higher than the rated capacity of the transformer. When i = 3, it means the added load is 50% higher than the rated capacity of the transformer.
5. The multi-parameter-based transformer overload self-diagnosis method according to claim 4, wherein In the third step, using the Grubbs criterion method, the outliers in T(i,t), S(i,t), and tanδ(i,t) are removed. The process is as follows: Form a set B(i,t) with the three groups of data, and first calculate the mean and variance of B(i,t). Calculate the average value W(i,t) and variance s 2 (i,t) of the test data for each load level: Using the Grubbs criterion method, the critical value G α (m) is obtained, and then the statistic G is calculated: Compare the calculated statistic G with the critical value G α (m): G > G α (m) (4) If equation (4) holds, then this value is determined to be an outlier and this value is removed. The data set after removing the outlier is B′(i,t), and the data after removing the outlier is also denoted as T′(i,t), S′(i,t), and tanδ′(i,t).
6. The multi-parameter-based transformer overload self-diagnosis method according to claim 5, wherein The specific process of the fourth step is as follows: Fit T′(i,t), S′(i,t), and tanδ′(i,t), and the fitting formula is: Z i is the fitting factor, where i = 0, 1, 2, 3. i also represents the load level. The fitting factor Z0 when the load is the rated capacity of the transformer, the discriminant fitting factor Z1 when the load exceeds the rated capacity of the transformer by 10%, the fitting factor Z2 when the load exceeds the rated capacity of the transformer by 20%, and the fitting factor Z3 when the load exceeds the rated capacity of the transformer by 50% are respectively obtained. α, γ, are the parameter weight coefficients of temperature, noise, and dielectric loss factor respectively; T′ max (i,t) and T′ min (i,t) are the maximum and minimum values of T′(i,t) respectively, and S′ max (i,t) and S′ min (i,t) are the maximum and minimum values of S′(i,t) respectively, and tanδ min (i,t) is the minimum value of tanδ′(i,t); Final discrimination factor β i The calculation formula is as follows:
7. The multi-parameter-based transformer overload self-diagnosis method according to claim 6, wherein In the fifth step, the method for discriminating the overload degree of the transformer is as follows: If β < β1, then the transformer is in a normal operation state; If β1 < β < β2, then the transformer is in a light overload state; If β2 < β < β3, then the transformer is in a medium overload state; If β3 < β, then the transformer is in a heavy overload state; β is the factor fitted from the parameters during daily operation.