Substation oil-filled equipment fault early warning method and device based on data fitting

Abstract

The application discloses a substation oil-filled equipment fault early warning method and device based on data fitting, relates to the technical field of electrical equipment online monitoring and fault diagnosis, and comprises the following steps: obtaining a temperature-pressure curve fitting model by using temperature-pressure data in the initial stage of operation of oil-filled electrical equipment; fitting the temperature-pressure data in the initial stage of operation according to the temperature-pressure curve fitting model to obtain initial state coefficients; fitting the temperature-pressure data during monitoring according to the temperature-pressure curve fitting model to obtain measured state coefficients; obtaining main state coefficients by using the relative change amount between the measured state coefficients and the initial state coefficients; and obtaining the fault defect type of the substation oil-filled electrical equipment according to a typical fault main state coefficient table. The application successfully determines the defect and fault type of the oil-filled electrical equipment by comparing the state coefficients, and early warns the bad working state of the oil-filled electrical equipment.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of substation maintenance, and particularly relates to a substation oil-filled equipment fault early warning method and device based on data fitting. BACKGROUND

[0002] In recent years, China's power grid construction has made remarkable progress, and the scale of the power grid has been increasing, and the voltage level has been gradually improved, which puts forward higher requirements for the operation reliability of main equipment such as power transformers, high-voltage shunt reactors and converter transformers. The quality of the bushing, as an important component of the transformer and the reactor, is crucial and directly affects the safety of the power grid operation. Currently, the defects of the bushing mainly include oil leakage, flashover, abnormal oil dissolved gas, abnormal partial discharge, installation problems, etc.

[0003] Among these main defects, the bushing oil leakage defect is the most common. The bushing is a low-oil device, and if the oil leakage is not treated in time and effectively, it may lead to more serious consequences, directly affecting the safe operation of the transformer, etc.

[0004] Therefore, if the reason for the bushing oil leakage can be monitored, it is of great significance for the reliable operation of the bushing and even the transformer. SUMMARY

[0005] In order to solve the above-mentioned deficiencies in the prior art, the present application provides a substation oil-filled equipment fault early warning method and device based on data fitting.

[0006] The technical scheme provided by the present application is as follows:

[0007] In a first aspect, the present application provides a substation oil-filled equipment fault early warning method based on data fitting, which adopts the following technical scheme,

[0008] The substation oil-filled equipment fault early warning method based on data fitting comprises:

[0009] Obtaining a temperature-pressure curve fitting model by using the temperature-pressure data at the initial stage of operation of the oil-filled electrical equipment;

[0010] Fitting the temperature-pressure data at the initial stage of operation according to the temperature-pressure curve fitting model to obtain an initial state coefficient;

[0011] Fitting the temperature-pressure data during the monitoring period according to the temperature-pressure curve fitting model to obtain a measured state coefficient;

[0012] Obtaining a main state coefficient by using the relative change between the measured state coefficient and the initial state coefficient;

[0013] Obtaining the fault defect type of the substation oil-filled electrical equipment according to a typical fault main state coefficient table.

[0014] As a further technical solution of the present application, the temperature and pressure data of the oil-filled electrical equipment at the initial stage of operation are used to obtain a temperature and pressure curve fitting model; specifically including:

[0015] The collected temperature and pressure data of the oil-filled electrical equipment at the initial stage of operation are fitted to form a temperature and pressure data curve fitting model, and the curve fitting model is a hyperbolic function of formula (1): Temperature pressure data Curve fitting model The curve fitting model is a hyperbolic function of formula (1):

[0016] P = a × e b×t +c × e d×t ; (1)

[0017] a is the state coefficient of the initial liquid level of the insulating oil of the bushing;

[0018] b is the state coefficient of gas expansion;

[0019] c is the state coefficient of the molar mass of the gas in the cavity of the bushing;

[0020] d is the state coefficient of the expansion of the insulating oil of the bushing;

[0021] t is the Celsius temperature;

[0022] P is the pressure of the oil-filled electrical equipment.

[0023] As a further technical solution of the present application, the temperature and pressure data of the oil-filled electrical equipment at the initial stage of operation are used to obtain a temperature and pressure curve fitting model; specifically including:

[0024] The collected temperature and pressure data of the oil-filled electrical equipment at the initial stage of operation are fitted to form a temperature and pressure data curve fitting model, and the curve fitting model is a hyperbolic function of formula (1):

[0025] P = bt 3 +dt z +ct+a (2)

[0026] a is the state coefficient of the initial liquid level of the insulating oil of the bushing;

[0027] b is the state coefficient of gas expansion;

[0028] c is the state coefficient of the molar mass of the gas in the cavity of the bushing;

[0029] d is the state coefficient of the expansion of the insulating oil of the bushing;

[0030] t is the Celsius temperature;

[0031] P is the pressure of the oil-filled electrical equipment.

[0032] ​As a further technical scheme of the present application, the temperature and pressure data in the initial operation period are fitted according to the temperature and pressure curve fitting model to obtain initial state coefficients; specifically, the temperature and pressure data collected in the initial operation period are fitted using the temperature and pressure data curve fitting model to obtain a temperature and pressure curve fitting function with temperature and pressure as variables, and the coefficients in the temperature and pressure curve fitting function at this time are the initial state coefficients.

[0033] As a further technical scheme of the present application, the temperature and pressure data in the monitoring period are fitted according to the temperature and pressure curve fitting model to obtain measured state coefficients; specifically, the temperature and pressure data collected in the continuous operation period are fitted using the temperature and pressure data curve fitting model to obtain a temperature and pressure curve fitting function with temperature and pressure as variables, and the coefficients in the temperature and pressure curve fitting function at this time are the measured state coefficients.

[0034] As a further technical scheme of the present application, the relative change amount between the measured state coefficients and the initial state coefficients is used to obtain main state coefficients; specifically, the measured state coefficients and the initial state coefficients are used to calculate the relative change amount according to formula (3), and the relative change amount greater than 100% is named as the main state coefficient.

[0035]

[0036] wherein Vai is the state coefficient initial value difference of the initial liquid level height of the pipe insulation oil; Vbi is the state coefficient initial value difference of the gas expansion; Vci is the state coefficient initial value difference of the molar mass of the gas in the cavity of the bushing; Vdi is the state coefficient initial value difference of the expansion of the insulation oil of the bushing; a0 is the state coefficient initial value of the initial liquid level height of the bushing insulation oil; b0 is the state coefficient initial value of the gas expansion; c0 is the state coefficient initial value of the molar mass of the gas in the cavity of the bushing; d0 is the state coefficient initial value of the expansion of the insulation oil of the bushing; ai is the actual value of the state coefficient of the initial liquid level height of the pipe insulation oil; bi is the actual value of the state coefficient of the gas expansion; ci is the actual value of the state coefficient of the molar mass of the gas in the cavity of the bushing; di is the actual value of the state coefficient of the expansion of the insulation oil of the bushing.

[0037] As a further technical scheme of the present application, the fault defect type of the oil-filled electrical equipment of the substation is obtained according to the typical fault main state coefficient table; specifically, when the state coefficient initial value difference of the main state coefficient obtained according to the measured state coefficient reaches a set value, the corresponding typical fault main state coefficient table is used to obtain the fault defect type of the oil-filled electrical equipment of the substation.

[0038] The fault defect type includes bushing gas leakage, insulation oil cracking gas, bushing oil leakage, excessive oil injection, and bushing heating.

[0039] In a second aspect, the application further provides a substation oil-filled equipment fault early warning device based on data fitting, comprising:

[0040] An initial state unit obtains a temperature pressure curve fitting model using temperature pressure data in an initial running stage of the oil-filled electrical equipment, and fits the temperature pressure data in the initial running stage according to the temperature pressure curve fitting model to obtain an initial state coefficient;

[0041] A measured state unit fits temperature pressure data in a monitoring period according to the temperature pressure curve fitting model to obtain a measured state coefficient;

[0042] A state relationship unit obtains a main state coefficient using a relative change amount between the measured state coefficient and the initial state coefficient;

[0043] A defect judgment unit obtains a fault defect type of the substation oil-filled electrical equipment according to a typical fault main state coefficient table.

[0044] The application has the following beneficial effects:

[0045] The embodiment of the application obtains a double exponential function model using temperature pressure data in an initial running stage, obtains an initial state coefficient according to initial monitoring data of oil temperature and oil pressure of the oil-filled electrical equipment, obtains a measured state coefficient according to running monitoring data of oil temperature and oil pressure of the oil-filled electrical equipment, successfully judges and determines a defect and a fault type of the oil-filled electrical equipment through comparison of the state coefficients, and early warns a bad working state of the oil-filled electrical equipment. BRIEF DESCRIPTION OF DRAWINGS

[0046] Figure 1 A flow chart of a substation oil-filled equipment fault early warning method based on data fitting is provided for the application;

[0047] Figure 2 A sleeve temperature pressure curve graph of a specific embodiment is provided for the application;

[0048] Figure 3a A normal stage pressure temperature scatter plot of a specific embodiment is provided for the application;

[0049] Figure 3b An abnormal stage pressure temperature scatter plot of a specific embodiment is provided for the application;

[0050] Figure 4a A normal stage sleeve oil temperature pressure measurement graph of a specific embodiment is provided for the application;

[0051] Figure 4b An abnormal stage sleeve oil temperature pressure measurement graph of a specific embodiment is provided for the application;

[0052] Figure 5This is a structural diagram of the fault early warning device for substation oil filling equipment based on data fitting proposed in this invention. Detailed Implementation

[0053] The following will clearly and completely describe the concept, specific structure, and technical effects of the present invention in conjunction with embodiments and accompanying drawings, so as to fully understand the purpose, features, and effects of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, not all of them. Other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are all within the scope of protection of the present invention.

[0054] like Figures 1-5 As shown, it illustrates a specific embodiment of the present invention:

[0055] See Figure 1 This invention provides a fault early warning method for substation oil-filled equipment based on data fitting, comprising:

[0056] Step 101: Using the temperature and pressure data from the initial stage of operation of the oil-filled electrical equipment, obtain a temperature and pressure curve fitting model;

[0057] Step 102: Fit the temperature and pressure data during the initial stage of operation to obtain the initial state coefficients based on the temperature and pressure curve fitting model.

[0058] Step 103: Fit the temperature and pressure data during the monitoring period according to the temperature and pressure curve fitting model to obtain the measured state coefficient.

[0059] Step 104: Obtain the principal state coefficients by using the relative changes between the measured state coefficients and the initial state coefficients;

[0060] Step 105: Based on the table of typical fault main state coefficients, obtain the fault defect types of oil-filled electrical equipment in the substation.

[0061] This invention, through monitoring the oil temperature and pressure of the low-voltage bushings of transformers, determines the type of bushing defect by comparing the actual values ​​with the initial values, thus providing a monitoring basis for transformer bushing monitoring. The oil mass refers to the mass of the insulating oil in the low-voltage bushing, and the fill factor is the ratio of the bushing volume to the insulating oil volume at 25°C, expressed as a percentage.

[0062] In this embodiment of the invention, temperature and pressure data from the initial stage of operation of oil-filled electrical equipment are used to obtain a temperature and pressure curve fitting model; specifically, it includes:

[0063] The temperature and pressure data collected at the initial stage of the operation of the oil-filled electrical equipment are fitted based on fitting principles such as the least square method and the Newton-Gauss method to form a curve fitting model of the temperature and pressure data approximation curve, wherein the fitting curve model can be a double exponential function, a polynomial function, a single exponential function, etc., and the specific design fitting model is used as the criterion.

[0064] For example, for the oil temperature and oil pressure data of a certain 110 kV bushing, the curve fitting model is a double exponential function shown in formula (1):

[0065] P = a × e b×t + c × e d×t (1)

[0066] a is a state coefficient of the initial liquid level height of the bushing insulating oil;

[0067] b is a state coefficient of gas expansion;

[0068] c is a state coefficient of the molar mass of the gas in the cavity of the bushing;

[0069] d is a state coefficient of the expansion of the bushing insulating oil;

[0070] t is the Celsius temperature;

[0071] P is the pressure of the oil-filled electrical equipment.

[0072] The initial state coefficients are obtained by fitting the temperature and pressure data at the initial stage of operation according to the temperature and pressure curve fitting model, which specifically includes: the temperature and pressure data collected at the initial stage of operation are fitted using the curve fitting model to obtain a bivariate function with temperature and pressure as variables, and there are multiple coefficients in the bivariate function other than the variables, which are referred to as initial state coefficients. For example, the oil temperature and oil pressure data of a certain 110 kV bushing during the 168-hour trial operation are fitted using the double exponential function curve fitting model to obtain four coefficients a, b, c, and d, which are set as the initial state coefficients.

[0073] The measured state coefficients are obtained by fitting the temperature and pressure data during the monitoring period according to the temperature and pressure curve fitting model, which specifically includes: the temperature and pressure data collected during the continuous operation are fitted using the curve fitting model to obtain a bivariate function with temperature and pressure as variables, and there are multiple coefficients in the bivariate function other than the variables, which are referred to as measured state coefficients. For example, the oil temperature and oil pressure data of a certain 110 kV bushing during the two-year operation are fitted using the double exponential function curve fitting model to obtain four coefficients a, b, c, and d, which are set as the measured state coefficients.

[0074] In the embodiment of the present application, the relative change amount between the measured state coefficient and the initial state coefficient is used to obtain the main state coefficient, and the main state coefficient is obtained by using the measured state coefficient and the initial state coefficient to calculate the relative change amount according to formula (3), and the relative change amount greater than 100% is named as the main state coefficient.

[0075]

[0076] Wherein, Vai is the state coefficient initial value difference of the initial liquid level height of the pipe insulation oil; Vbi is the state coefficient initial value difference of the gas expansion; Vci is the state coefficient initial value difference of the molar mass of the gas substance in the bushing cavity; Vdi is the state coefficient initial value difference of the expansion of the bushing insulation oil; a0 is the state coefficient initial value of the initial liquid level height of the bushing insulation oil; b0 is the state coefficient initial value of the gas expansion; c0 is the state coefficient initial value of the molar mass of the gas substance in the bushing cavity; d0 is the state coefficient initial value of the expansion of the bushing insulation oil; ai is the state coefficient actual value of the initial liquid level height of the pipe insulation oil; bi is the state coefficient actual value of the gas expansion; ci is the state coefficient actual value of the molar mass of the gas substance in the bushing cavity; di is the state coefficient actual value of the expansion of the bushing insulation oil.

[0077] In the embodiment of the present application, the fault defect type of the oil-filled electrical equipment of the transformer substation is obtained according to the typical fault main state coefficient table, and the temperature and pressure data of the oil-filled equipment under different defect states are obtained through experimental verification and simulation modeling, the temperature and pressure data under each defect are fitted to obtain the main state coefficient under each defect, and the typical fault main state coefficient table is formed. According to the main state coefficient obtained by the measured state coefficient, the typical fault main state coefficient table is used for corresponding, the state coefficient initial value is determined according to the oil quality and the filling coefficient during the hot oil circulation; the state coefficient actual value is determined for the oil quality and the filling coefficient of the fixed interval time; the state coefficient initial value difference is calculated, and the defect type is obtained according to the state coefficient initial value difference.

[0078] In the embodiment of the present application, after modeling and simulation of a certain 110kV bushing, the main state coefficients under the defect faults of oil leakage, local overheating, insulation oil cracking gas and excessive oil injection are obtained by calculation.

[0079] The influence of the initial liquid level height of the insulation oil on the state coefficient and the influence of the molar mass of the gas substance on the state coefficient are obtained to obtain the defect type of the low-voltage bushing.

[0080] A state coefficient criterion table is established for the double exponential function model, specifically including: obtaining the state coefficient of the casing pressure curve through the double exponential curve model for casing pressure at different oil levels and casing pressure of different gas substance molar mass, and forming a state coefficient criterion table according to the state coefficient of the casing pressure curve and casing defects, wherein the casing defects include: casing gas leakage, insulation oil cracking gas, casing oil leakage, excessive casing oil injection, and casing heating.

[0081] The casing pressure at different oil levels is calculated at a temperature of -30 to 70 DEG C, and the state coefficient of the casing pressure curve at different oil levels is obtained according to the double exponential function fitting method, as shown in Table 1. Table 1 shows the effect of the initial liquid level of the insulation oil on the state coefficient. The state coefficient a is greatly affected by the liquid level, and the state coefficient d is basically not affected by the oil level.

[0082] Table 1

[0083] Casing fluid level (m) a b c d 1.0 74.07 x 10 3 ]] 0.0054 1.52 x 10 3 ]] 0.0634 1.5 93.92 x 10 3 ]]> 0.0041 1.70 x 10 3 ]]> 0.0620 2.0 113.8 x 10 3 ]] 0.0033 1.82 x 10 3 ]]> 0.0610 2.5 133.71 x 10 3 ]]> 0.0028 1.89 x 10 3 ]]> 0.0610 3.0 153.71 x 10 3 ]]> 0.0024 1.95 x 10 3 ]] 0.0600 3.5 173.63 x 10 3 ]] 0.0021 1.99 x 10 3 ]] 0.0600 4.0 193.61 x 10 3 ]] 0.0019 2.03 x 10 3 ]] 0.0599 4.5 213.61 x 10 3 ]]> 0.0017 2.06 x 10 3 ]] 0.0598 5.0 233.51×10 0.0015 2.08 x 10 3 ]]> 0.0597

[0084] The casing pressure at different gas substance molar mass is calculated at a temperature of -30 to 70 DEG C, and the state coefficient of the casing pressure curve at different oil levels is obtained according to the double exponential function fitting method, as shown in Table 2. Table 2 shows the effect of the gas substance molar mass on the state coefficient c. The state coefficient c is greatly affected by the gas substance molar mass, and the state coefficient d is basically not affected by the gas substance molar mass.

[0085] Table 2

[0086] Gas substance molar mass (mol) a b c d 0.2 106.71 x 10 3 ]]> 0.0006 443.4 0.0589 0.4 113.42 x 10 3 ]]> 0.0013 850.2 0.0594 0.6 120.13 x 10 3 ]]> 0.0018 1225 0.0599 0.8 126.91 x 10 3 ]]> 0.0023 1572 0.0603 1.0 133.70 x 10 3 ]]> 0.0028 1895 0.0608 1.2 140.51 x 10 3 ]]> 0.0032 2197 0.0612 1.4 147.42 x 10 3 ]]> 0.0036 2481 0.0616 1.6 154.22 x 10 3 ]]> 0.0040 2747 0.0619 1.8 161.11 x 10 3 ]]> 0.0043 3000 0.0623

[0087] According to the effect of the gas substance molar mass and the casing liquid level on the state coefficients a, b, c and d, a defect table as shown in Table 3 can be summarized. Table 3 is a comparison table of casing defects and state coefficients. It is used as a basis for judging the defects of oil-immersed paper casing.

[0088] Table 3

[0089] Defect type Primary condition coefficient Secondary condition coefficient Casing gas leakage b, c, d - Insulating oil cracking gas b, c a Casing oil leakage a b, c Overfilling c d Casing heating c a

[0090] Specifically:

[0091] When b, c and d are the main state coefficients, and the initial value difference of the state coefficients reaches a set value, the diagnosis is casing gas leakage;

[0092] When the initial value difference of the state coefficients b and c reaches a set value, and the initial value difference of the state coefficient a reaches a set value, the diagnosis is insulation oil cracking gas;

[0093] When the initial value difference of the state coefficient of a is the main state coefficient and reaches the set value, and the initial value difference of the state coefficient of b and c reaches the set value, the diagnosis is that the bushing leaks oil;

[0094] When the initial value difference of the state coefficient of c is the main state coefficient and reaches the set value, and the initial value difference of the state coefficient of d reaches the set value, the diagnosis is that the bushing is overfilled with oil;

[0095] When the initial value difference of the state coefficient of c is the main state coefficient and reaches the set value, and the initial value difference of the state coefficient of a reaches the set value, the diagnosis is that the bushing is overheated.

[0096] In the embodiment of the present application, the threshold value of the initial difference value can be set according to the actual situation, and the following threshold value is the preferred scheme of the present application, and the specific setting value of the present application is subject to the on-site design.

[0097] Preferably, when △bi, △ci and △di are all greater than 30%, the diagnosis is that the bushing leaks gas;

[0098] When △ai is greater than 10%, and △bi and △ci are both greater than 30%, the diagnosis is that the insulating oil is cracked to produce gas;

[0099] When △ai is greater than 10%, and △bi and △ci are both greater than 10%, the diagnosis is that the bushing leaks oil;

[0100] When △ci is greater than 30%, and △di is greater than 10%, the diagnosis is that the bushing is overfilled with oil;

[0101] When △ci is greater than 30%, and △di is greater than 10%, the diagnosis is that the bushing is overheated.

[0102] The diagnosis of the first embodiment of the present application is as follows in Table 4: the 750kV Bakui line high resistance B phase neutral point bushing and the 750kV Qucheng line high resistance C phase neutral point bushing are actually diagnosed. In the diagnosis of the 750kV Bakui line high resistance B phase neutral point bushing, the coefficient c changes the most, the coefficient a changes secondly, and the diagnosis result shows that the 750kV Bakui line high resistance B phase neutral point bushing has an overheating defect.

[0103] The normal stage pressure-temperature diagram is as follows Figure 3a , and the abnormal stage pressure-temperature distribution diagram is as follows Figure 3b .

[0104] Table 4

[0105]

[0106] The infrared thermal image of the Bakui line bushing is inspected, and it is found that the hotspot temperature of the bushing terminal is as high as 84℃, and the relative temperature difference is 83.5%, which indicates that the diagnosis result is correct.

[0107] The diagnosis embodiment two of the present application is as shown in Table 5: in the diagnosis of the neutral point bushing of high resistance C phase of 750 kV Qucheng line, the coefficient a has the largest change, the coefficient c has the second largest change, the diagnosis result shows that the neutral point bushing of high resistance C phase of 750 kV Qucheng line has leakage defect. The bushing is supplemented with oil for many times on site, and the oil level and pressure of the bushing do not change significantly, which indicates that the bushing has been connected with the oil chamber of the transformer body. The diagnosis is correct. The scatter plot of the measured value of the oil temperature and pressure of the bushing in the normal stage is as shown in Figure 4a , the scatter plot of the measured value of the oil temperature and pressure of the bushing in the abnormal stage is as shown in Figure 4b .

[0108] Table 5

[0109]

[0110] Another embodiment of the present application takes the fitting curve model as a polynomial function as an example, and adopts the temperature and pressure data of the oil-filled electrical equipment in the initial stage of operation to obtain a temperature and pressure curve fitting model; specifically comprising:

[0111] The temperature and pressure data of the oil-filled electrical equipment in the initial stage of operation are fitted to form a temperature and pressure data curve fitting model, and the curve fitting model is a polynomial function of formula (2):

[0112] P=bt 3 +dt 2 +ct+a (2)

[0113] a is a state coefficient of the initial liquid level height of the bushing insulating oil;

[0114] b is a state coefficient of gas expansion;

[0115] c is a state coefficient of the molar mass of the gas in the cavity of the bushing;

[0116] d is a state coefficient of the expansion of the bushing insulating oil;

[0117] t is the Celsius temperature;

[0118] P is the pressure of the oil-filled electrical equipment.

[0119] In the embodiment of the present application, the fault defect type of the oil-filled electrical equipment of the transformer substation is obtained according to the typical fault main state coefficient table; specifically, the temperature and pressure data of the oil-filled equipment under different defect states are obtained through experimental verification and simulation modeling, the temperature and pressure data under each defect are fitted, the main state coefficient under each defect is obtained, and the typical fault main state coefficient table is formed. According to the main state coefficient obtained by the measured state coefficient, the typical fault main state coefficient table is used for correspondence, the initial value of the state coefficient is determined according to the oil quality and the filling coefficient during the hot oil circulation; the actual value of the state coefficient is determined for the oil quality and the filling coefficient of the fixed interval time; the defect type is obtained by calculating the initial value difference of the state coefficient and judging the initial value difference of the state coefficient.

[0120] At-30℃ to 70℃, the sleeve pressure of different oil surface heights is calculated, and the state coefficient of the sleeve pressure curve of different oil surface heights obtained by the polynomial function fitting method is shown in Table 6. Table 6 shows the influence of the initial liquid level of the insulating oil on the state coefficient; the state coefficient a is greatly affected by the liquid level.

[0121] Table 6

[0122] Fluid level (m) b d c a 1.5 1.425 83.54 2743 2.31 x 10 5 ]]> 2.5 1.425 83.54 2743 2.71 x 10 5 ]] 3.5 1.425 83.54 2743 3.11 x 10 5 ]]> 4.5 1.425 83.54 2743 3.51 x 10 5 ]]> 5.51 1.425 83.54 2743 3.91 x 10 5 ]]

[0123] At-30℃ to 70℃, the sleeve pressure of different oil-filled content is calculated, and the state coefficient of the sleeve temperature pressure curve of different oil-filled content obtained by the polynomial function fitting method is shown in Table 7. Table 7 shows the influence of different oil-filled content on the state coefficient. The state coefficients b, c and d are greatly affected by the molar mass of the gas material.

[0124] Table 7

[0125] Oil filling content (%) b d c a 80 6.76 186.9 1986 2.65 x 10 5 ]]> 85 3.69 258.9 7568 2.62 x 10 5 ]]> 90 4.49 438 13060 2.67 x 10 5 ]] 95 5.81 572.9 16700 2.63 x 10 5 ]]> 97.5 5.03 651.2 18430 2.63 x 10 5 ]]

[0126] At-30℃ to 70℃, the sleeve pressure of different gas precipitation amount is calculated, and the state coefficient of the sleeve temperature pressure curve of different gas precipitation amount obtained by the polynomial function fitting method is shown in Table 8. Table 8 shows the influence of different gas precipitation amount on the state coefficient. The state coefficient a is greatly affected by the molar mass of the gas material.

[0127] Table 8

[0128] Gas evolution amount (mol) b d c a 0.5 0.59 7.11 232 3.02 x 10 5 ]] 1.0 1.17 14.22 464 3.53 x 10 5 ]]> 2.0 2.35 28.44 928 4.57 x 10 5 ]]> 4.0 4.69 56.88 1856 6.64 x 10 5 ]] 8.0 9.38 113.8 3712 10.77 x 10 5 ]]

[0129] According to the influence of the molar mass of the gas material and the sleeve liquid level on a, b, c and d, the defect table shown in Table 9 can be summarized. Table 9 is a comparison table of sleeve defects and state coefficients. And it is used as a basis for judging the defects of oil-immersed paper sleeve.

[0130] Table 9

[0131] Defect type Primary condition coefficient Secondary condition coefficient Casing gas leakage b, d - Insulating oil cracking gas c a Casing oil leakage a - Overfilling d - Casing heating c d

[0132] Specifically,

[0133] When b and d are main state coefficients, and the state coefficient initial value difference reaches a set value, the bushing gas leakage is diagnosed;

[0134] When c is the main state coefficient, and the state coefficient initial value difference reaches a set value, and the state coefficient initial value difference of a reaches a set value, the insulation oil cracking gas is diagnosed;

[0135] When a is the main state coefficient, and the state coefficient initial value difference reaches a set value, the bushing oil leakage is diagnosed;

[0136] When d is the main state coefficient, and the state coefficient initial value difference reaches a set value, the bushing oil injection is diagnosed;

[0137] When c is the main state coefficient, and the state coefficient initial value difference reaches a set value, and the state coefficient initial value difference of d reaches a set value, the bushing heating is diagnosed.

[0138] Taking a three-phase common transformer and three bushings as an example, the initial state coefficients and the measured state coefficients of the three bushings are shown in Table 10. The state coefficient b and the state coefficient c of the B-phase bushing have significant changes. The defect corresponding table is used to diagnose that the B-phase bushing has a heating fault. Through special inspection by the operation and maintenance personnel, the infrared thermal imaging shows that the B-phase bushing has heating, and the overall temperature is 7.5 degrees Celsius higher than that of the other two phases.

[0139] Table 10

[0140]

[0141] The above embodiment introduces the substation oil-filled equipment fault early warning method based on data fitting from the perspective of method. The following embodiment introduces the substation oil-filled equipment fault early warning method based on data fitting from the perspective of device. Details are shown in the following embodiment.

[0142] Referring to Figure 5 Another specific embodiment of the present application is a substation oil-filled equipment fault early warning device based on data fitting, which comprises:

[0143] The curve fitting unit 201 obtains a temperature-pressure curve fitting model by using the temperature-pressure data at the initial stage of the operation of the oil-filled electrical equipment;

[0144] The initial state unit 202 fits the temperature-pressure data at the initial stage of the operation according to the temperature-pressure curve fitting model to obtain the initial state coefficient;

[0145] The measured state unit 203 fits the temperature and pressure data during the monitoring period according to the temperature and pressure curve fitting model to obtain a measured state coefficient;

[0146] The state relationship unit 204 obtains a main state coefficient by using a relative change amount between the measured state coefficient and the initial state coefficient;

[0147] The defect judgment unit 205 obtains a fault defect type of the oil-filled electrical equipment of the substation according to a typical fault main state coefficient table.

[0148] The various changes and specific examples in the system in the foregoing embodiments are also applicable to the data fitting-based substation oil-filled equipment fault early warning device of the present embodiment, and through the foregoing detailed description of the data fitting-based substation oil-filled equipment fault early warning method, those skilled in the art can clearly understand the data fitting-based substation oil-filled equipment fault early warning device in the present embodiment, so as to avoid repetition.

[0149] The present application has been described in detail above, but the present application is not limited to the above-described embodiments, and various changes can be made within the knowledge of those skilled in the art without departing from the spirit of the present application. Many other changes and modifications can be made without departing from the concept and scope of the present application. It should be understood that the present application is not limited to a specific embodiment, and the scope of the present application is defined by the appended claims.

Claims

1. A method for substation oil-filled equipment failure early warning based on data fitting, characterized in that, The application relates to a method for judging the fault defect type of oil-filled electrical equipment in a transformer substation. The method comprises the following steps: a temperature-pressure data curve fitting model is obtained by using the temperature-pressure data of the oil-filled electrical equipment in the initial operation period; initial state coefficients are obtained by fitting the temperature-pressure data in the initial operation period according to the temperature-pressure curve fitting model; measured state coefficients are obtained by fitting the temperature-pressure data in the monitoring period according to the temperature-pressure curve fitting model; main state coefficients are obtained by using the relative change amount between the measured state coefficients and the initial state coefficients; the fault defect type of the oil-filled electrical equipment in the transformer substation is obtained according to a typical fault main state coefficient table. The method comprises the following steps: （1）； （2）； the temperature-pressure data of the oil-filled electrical equipment in the initial operation period are fitted to form a temperature-pressure data curve fitting model, and the curve fitting model is a hyperbolic function of formula (1) or a polynomial function of formula (2): a is an initial liquid level state coefficient of the bushing insulating oil; b is a state coefficient of gas expansion; c is a state coefficient of the molar mass of the gas in the bushing cavity; d is a state coefficient of the expansion of the bushing insulating oil; t is the temperature in Celsius; P is the pressure of the oil-filled electrical equipment; the initial state coefficients are obtained by fitting the temperature-pressure data in the initial operation period according to the temperature-pressure curve fitting model; the measured state coefficients are obtained by fitting the temperature-pressure data in the monitoring period according to the temperature-pressure curve fitting model; the main state coefficients are obtained by using the relative change amount between the measured state coefficients and the initial state coefficients; the fault defect type of the oil-filled electrical equipment in the transformer substation is obtained according to a typical fault main state coefficient table. The fault defect type includes bushing gas leakage, insulating oil cracking gas, bushing oil leakage, excessive oil injection and bushing heating.

2. The data fitting based substation oil-filled equipment failure pre-alarm method of claim 1, wherein, The main state coefficient is obtained by using the relative change amount between the measured state coefficient and the initial state coefficient, and specifically includes: The state coefficients of the bushing pressure curve are obtained by the double exponential curve model according to the state coefficients of the bushing pressure curve and the bushing defects, and a state coefficient criterion table is formed. The relative change amount is calculated according to formula (3) by using the state coefficient and the initial state coefficient, and the state coefficient with a relative change amount greater than 100% is named as the main state coefficient. （3） ； Wherein, Vai is the state coefficient initial value difference of the initial liquid level of the pipe insulation oil; Vbi is the state coefficient initial value difference of the gas expansion; Vci is the state coefficient initial value difference of the molar mass of the gas in the sleeve cavity; Vdi is the state coefficient initial value difference of the expansion of the sleeve insulation oil; a0 is the state coefficient initial value of the initial liquid level of the sleeve insulation oil; b0 is the state coefficient initial value of the gas expansion; c0 is the state coefficient initial value of the molar mass of the gas in the sleeve cavity; d0 is the state coefficient initial value of the expansion of the sleeve insulation oil; ai is the state coefficient actual value of the initial liquid level of the pipe insulation oil; bi is the state coefficient actual value of the gas expansion; ci is the state coefficient actual value of the molar mass of the gas in the sleeve cavity; di is the state coefficient actual value of the expansion of the sleeve insulation oil.

3. A substation oil-filled equipment failure early warning device based on data fitting, characterized in that, The substation oil-filled equipment fault early warning method based on data fitting in any of claims 1-2 comprises: An initial state unit obtains a temperature pressure curve fitting model using temperature pressure data at the initial stage of operation of the oil-filled electrical equipment, and fits the temperature pressure data at the initial stage of operation according to the temperature pressure curve fitting model to obtain an initial state coefficient; A measured state unit fits temperature pressure data during monitoring according to the temperature pressure curve fitting model to obtain a measured state coefficient; A state relationship unit obtains a main state coefficient using a relative change amount between the measured state coefficient and the initial state coefficient; A defect judgment unit obtains a fault defect type of the substation oil-filled electrical equipment according to a typical fault main state coefficient table.

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