CN112255478A - A system for automatic detection and life evaluation of generator stator bar aging state - Google Patents
A system for automatic detection and life evaluation of generator stator bar aging state Download PDFInfo
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Abstract
本发明公开了一种发电机定子线棒老化状态自动检测与寿命评估系统，通过将多组同规格的定子线棒在不同温度、电压的条件下进行加速老化试验，自动采集并储存试验电压、试验温度、蓝光白度值与其相应的时间，利用回归分析得到每个试样的蓝光白度‑时间对数曲线和方程，应用威布尔分布概率理论设定蓝光白度的失效值，并根据失效值和蓝光白度‑时间对数方程确定每个试样的寿命终止时间，最后通过Fallou模型对所得到每个试样的试验电压、试验温度、寿命终止时间进行拟合，得到该规格发电机定子线棒的寿命评估模型，并可根据该模型和待预测试样的运行条件，求出待预测试样的运行寿命，从而使得待预测试样寿命预测更方便，同时预测结果更精准。
The invention discloses an automatic detection and life evaluation system for the aging state of a stator bar of a generator. By performing accelerated aging tests on multiple groups of stator bars of the same specification under different temperature and voltage conditions, the test voltage and Test temperature, blue light whiteness value and its corresponding time, use regression analysis to obtain the blue light whiteness-time logarithmic curve and equation of each sample, apply Weibull distribution probability theory to set the blue light whiteness failure value, and according to the failure value value and the blue light whiteness-time logarithmic equation to determine the end-of-life time of each sample, and finally fit the test voltage, test temperature, and end-of-life time of each sample through the Fallou model to obtain the generator of this specification The life evaluation model of the stator bar can be used to calculate the operating life of the sample to be predicted according to the model and the operating conditions of the sample to be predicted, so that the life prediction of the sample to be predicted is more convenient and the prediction result is more accurate.
Description
Technical Field
The invention relates to the technical field of high-voltage insulating devices, in particular to a system for automatically detecting the aging state of a stator bar of a generator and evaluating the service life of the stator bar of the generator.
Background
The generator stator bar is an important component of the generator stator winding and is one of the main components of the generator. The stator bar is composed of a plurality of strands of copper wires and main insulation, connecting joints are arranged at two ends of the bar, and the insulation quality of the bar is an important index for measuring the insulation technical level of a motor manufacturer.
The aging and the service life of the generator stator bar are one of the focuses of the industry, the research on the service life of the generator stator bar is less, the method is older, and the research focuses on the electrical characteristics, for example, Chinese patent CN201811319841.3 discloses a method for evaluating the insulation aging state of the stator bar by a space charge method, and the method only calculates the service life of the bar by using the relation between voltage and breakdown time, so the method has low accuracy.
In view of the above, the present invention provides a system and a method for automatically detecting an aging state of a generator stator bar and evaluating a lifetime of the generator stator bar, so as to solve the above problems.
Disclosure of Invention
The invention aims to provide a system which can periodically detect important parameters of a generator stator bar according to requirements and automatically evaluate the service life of the generator stator bar when a test cut-off condition is met
The technical scheme adopted by the invention for solving the technical problems is as follows: a generator stator bar aging state automatic detection and life evaluation system comprises a thermostat, a test power module arranged in the thermostat, a data acquisition module for acquiring generator stator bar state parameters to be detected in the thermostat and a life evaluation algorithm module for calculating and evaluating the acquired data;
the data acquisition module is used for acquiring the test voltage, the test temperature, the blue light whiteness value and the corresponding time of the stator bar of the generator to be tested;
the life evaluation algorithm module comprises the following steps:
A. when the accelerated aging test of the generator stator bar reaches a cut-off condition, acquiring all data;
B. calculating the test time corresponding to the blue light whiteness value reaching the failure standard value under the accelerated stress by using the time-blue light whiteness value data of each stator bar test sample;
C. establishing a life prediction model of a generator stator bar;
D. determining parameters of a life prediction model;
carrying out accelerated aging tests on a plurality of groups of generator stator bars with the same specification at different temperatures and voltages, collecting corresponding blue light whiteness values and the accelerated aging duration of each stator bar, and obtaining each parameter of the life prediction model of the generator stator bars in the step 3), thereby obtaining the life evaluation model of the generator stator bars with the specification.
E. Life prediction
And calculating the service life of the stator bar to be predicted according to the running condition of the generator stator bar to be estimated and the obtained stator bar life estimation model.
Further, the method comprises the following steps: in the step B, the specific test time calculation step is,
(1) and performing polynomial fitting on the blue light whiteness value and the logarithm of the test time of each stator bar test sample, wherein the equation fitting form is as follows:
R457＝a+b1 ln(t)+b2 ln2(t)+b3 ln3(t) (1)
wherein R is457Is the blue-light whiteness value, ln (t) is the logarithm of the test time, a, b1、b2、b3Respectively as parameters of a fitting equation;
(2) setting a failure standard value of the whiteness of the blue light according to the failure probability of weibull distribution;
(3) and calculating the failure time t according to the fitted equation and the failure standard value.
Further, the method comprises the following steps: in the step C, a service life prediction model of the stator bar adopts a Fallou model, the service life termination time of a group of test bars is taken as a basis, the test voltage and the test temperature are taken as service life evaluation parameters, and the specific evaluation model is as follows:
wherein A is1、A2、B1、B2The model parameters are determined by the properties of the test material, L is the life of the test sample, U is the test voltage applied to the test sample, and T is the absolute temperature applied to the test sample.
Further, the method comprises the following steps: in step D, the specific parameter determination step is:
(1) carrying out polynomial fitting on the blue light whiteness-aging time logarithm by applying regression analysis to the corresponding blue light whiteness value and the accelerated aging duration time of each stator bar to obtain a change curve of the blue light whiteness value of each stator bar along with the aging time logarithm;
(2) setting a failure standard of a blue light whiteness value, and taking the time reaching the standard value as failure time to obtain the service life of each stator bar;
(3) the test temperature, the test voltage and the service life of each stator bar under the condition are brought into the Fallou model in the step C, and the model is fitted by utilizing regression analysis to obtain a parameter A of the Fallou model1、B1、A2、B2And obtaining a life evaluation model of the generator stator bar with the specification.
Further, the method comprises the following steps: the test cut-off condition was that the blue light whiteness value of the test sample was 36.8% below its initial value.
Further, the method comprises the following steps: the device is characterized by further comprising a human-computer interaction module, wherein the human-computer interaction module is electrically connected with the service life assessment algorithm module, the data acquisition module, the thermostat and the test power module respectively, and is used for setting a data acquisition period, test voltage and temperature of each test sample, displaying a time-blue light whiteness value change curve of each stator bar test sample according to an instruction, eliminating invalid test data in a human-computer interaction mode, and starting the service life assessment module.
Further, the method comprises the following steps: the test power supply module adopts 30 kV-40 kV alternating current variable voltage to provide test voltage for the test sample.
The invention has the beneficial effects that: the system carries out accelerated aging tests on a plurality of groups of stator bars with the same specification under the conditions of different temperatures and voltages, automatically collects and stores test voltages, test temperatures, blue light whiteness values and corresponding time to obtain a life evaluation model of the stator bar of the generator with the specification, and can calculate the operating life of a sample to be predicted according to the model and the operating conditions of the sample to be predicted, so that the life of the sample to be predicted is more conveniently predicted, and the prediction result is more accurate.
Drawings
FIG. 1 is a schematic diagram of a system for automatically detecting the aging state of a generator stator bar and evaluating the service life of the generator stator bar.
FIG. 2 is a diagram showing the effect of curve fitting at 30kV various temperatures.
FIG. 3 is a diagram showing the effect of curve fitting at 35kV various temperatures.
FIG. 4 is a graph showing the effect of curve fitting at 40kV various temperatures.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in detail below. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
As shown in figure 1, the system for automatically detecting the aging state of the generator stator bar and evaluating the service life comprises a constant temperature box, a test power module arranged in the constant temperature box, a data acquisition module for acquiring the state parameters of the generator stator bar to be detected in the constant temperature box and a service life evaluation algorithm module for calculating and evaluating the acquired data, wherein the test power module adopts 30 kV-40 kV alternating current variable voltage to provide test voltage for a test sample, the constant temperature box is used to provide test temperature for the test sample,
the data acquisition module is used for acquiring the test voltage, the test temperature, the blue light whiteness value and the corresponding time of the stator bar of the generator to be tested, and the acquisition of the blue light whiteness value can adopt a blue light whiteness tester;
the life evaluation algorithm module comprises the following steps:
A. when the accelerated aging test of the generator stator bar reaches a cut-off condition, acquiring all data, namely bar test voltage, test temperature and blue light whiteness value, wherein the test cut-off condition is that the blue light whiteness value of a test sample is lower than 36.8% of an initial value, and other settings can be carried out according to different product condition values;
B. calculating the test time corresponding to the blue light whiteness value reaching the failure standard value under the accelerated stress by using the time-blue light whiteness value data of each stator bar test sample, which specifically comprises the following steps:
(1) and performing polynomial fitting on the blue light whiteness value and the logarithm of the test time of each stator bar test sample, wherein the equation fitting form is as follows:
R457＝a+b1 ln(t)+b2 ln2(t)+b3 ln3(t) (1)
wherein R is457Is the blue-light whiteness value, ln (t) is the logarithm of the test time, a, b1、b2、b3Respectively as parameters of a fitting equation;
(2) setting a blue light whiteness failure standard value according to the failure probability of weibull distribution (Weibull distribution), wherein the set value can be 63.2% of the failure probability commonly used by the weibull distribution, and other settings can also be carried out according to the actual situation;
(3) calculating failure time t according to the fitted equation and the failure standard value;
C. establishing a life prediction model of a generator stator bar;
because the generator stator bar can be subjected to various stresses to age in the operation process, wherein voltage and temperature are two main aging stresses, the aging can cause the optical characteristics of main insulation of the stator bar to change, and accordingly the blue light whiteness value is reduced along with the increase of aging time, therefore, the service life prediction model of the stator bar adopts a Fallou model, the time when the blue light whiteness value reaches a failure standard value is taken as the service life end time of the stator bar, the service life end time of a group of test bars is taken as a basis, and the test voltage and the test temperature are taken as service life evaluation parameters, and the specific evaluation model is as follows:
wherein A is1、A2、B1、B2The model parameters are determined by the properties of the test materials, L is the service life of the test sample, U is the test voltage applied to the test sample, T is the absolute temperature applied to the test sample, and the absolute temperature is equal to the temperature plus 273.15 ℃ in centigrade;
D. determining parameters of a life prediction model;
carrying out accelerated aging tests on a plurality of groups of generator stator bar bars with the same specification at different temperatures and voltages, collecting corresponding blue light whiteness values and the accelerated aging duration of each stator bar, and obtaining each parameter of a life prediction model of the generator stator bar in the step 3), thereby obtaining a life evaluation model of the generator stator bar with the specification, wherein the specific parameter determination step is as follows:
(1) carrying out polynomial fitting on the blue light whiteness-aging time logarithm by applying regression analysis to the corresponding blue light whiteness value and the accelerated aging duration time of each stator bar to obtain a change curve of the blue light whiteness value of each stator bar along with the aging time logarithm;
(2) setting a failure standard of a blue light whiteness value, and taking the time reaching the standard value as failure time to obtain the service life of each stator bar;
(3) the test temperature, the test voltage and the service life of each stator bar under the condition are brought into the Fallou model in the step C, and the model is fitted by utilizing regression analysis to obtain a parameter A of the Fallou model1、B1、A2、B2Thereby obtaining a service life evaluation model of the generator stator bar with the specification
E. Life prediction
And calculating the service life of the stator bar to be predicted according to the running condition of the generator stator bar to be estimated and the obtained stator bar life estimation model.
Disclosed below is one example of a specific implementation:
the accelerated aging test is carried out on the generator stator bar products with the same specification of a certain company under three voltages of 30kV, 35kV and 40kV, the set temperature is respectively 140 ℃, 160 ℃ and 180 ℃, the condition of the generator stator bar quitting the test is that the blue light whiteness value is reduced to be less than 36.8 percent of the initial value, and the test results are shown in table 1:
TABLE 1 blue light whiteness value test data for various samples at different voltages/temperatures
The data were fitted using regression analysis and equation 3 to obtain the parameter values shown in table 2, with the fitting effects shown in fig. 2 to 4:
TABLE 2 parameters of the logarithmic curve fit of whiteness-time for each blue light at different voltages/temperatures
According to the failure probability of 63.2% of the weibull distribution function, setting the loss of the blue light whiteness value to be more than 63.2%, namely, the loss is reduced to be less than 36.8% to be the end-of-life time, and the failure standard value is 7.717% when the initial value of the blue light whiteness value is 20.97%. The values were substituted into each fitted equation and the resulting end-of-life times are shown in table 3:
TABLE 3 end Life of the test specimens under the respective test conditions
After the data in Table 3 are obtained, fitting is carried out by using the data in Table 3 and using formula 1 to obtain the parameters A1, A2, B1 and B2 in formula 2 as-2.381, 5841, -0.3098 and 34.11 respectively,
finally, the service life evaluation model of the generator stator bar with the specification is obtained as follows:
wherein L is service life (day/D), U is operating voltage (kV), and T is operating temperature (absolute temperature/K)
If the existing generator needs to continuously work at 120 ℃ and 18kV, and the service life of a stator bar of the generator is obtained, then:
the voltage U of 18kV and the temperature T of 120+273.15K are introduced into the life evaluation model, i.e., equation 3, and the life of the stator bar is obtained to be about 4727.82 days, and about 12.95 years.
The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are only exemplary embodiments of the present invention, and are not intended to limit the present invention, and any modifications, equivalents, improvements and the like made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (7)
1. A generator stator bar aging state automatic detection and life evaluation system is characterized in that: the device comprises a constant temperature box, a test power supply module arranged in the constant temperature box, a data acquisition module for acquiring state parameters of a stator bar of a generator to be tested in the constant temperature box and a service life evaluation algorithm module for calculating and evaluating the acquired data;
the data acquisition module is used for acquiring the test voltage, the test temperature, the blue light whiteness value and the corresponding time of the stator bar of the generator to be tested;
the life evaluation algorithm module comprises the following steps:
A. when the accelerated aging test of the generator stator bar reaches a cut-off condition, acquiring all data;
B. calculating the test time corresponding to the blue light whiteness value reaching the failure standard value under the accelerated stress by using the time-blue light whiteness value data of each stator bar test sample;
C. establishing a life prediction model of a generator stator bar;
D. determining parameters of a life prediction model;
and C, carrying out accelerated aging tests on a plurality of groups of generator stator bars with the same specification at different temperatures and voltages, collecting corresponding blue light whiteness values and the accelerated aging duration of each stator bar, and obtaining each parameter of the life prediction model of the generator stator bars in the step C, so as to obtain a life evaluation model of the generator stator bars with the specification.
E. Life prediction
And calculating the service life of the stator bar to be predicted according to the running condition of the generator stator bar to be estimated and the obtained stator bar life estimation model.
2. An automatic generator stator bar aging status detection and life assessment system as claimed in claim 1, wherein: in the step B, the specific test time calculation step is,
(1) and performing polynomial fitting on the blue light whiteness value and the logarithm of the test time of each stator bar test sample, wherein the equation fitting form is as follows:
R457＝a+b1 ln(t)+b2 ln2(t)+b3 ln3(t) (1)
wherein R is457Is the blue-light whiteness value, ln (t) is the logarithm of the test time, a, b1、b2、b3Respectively as parameters of a fitting equation;
(2) setting a failure standard value of the whiteness of the blue light according to the failure probability of weibull distribution;
(3) and calculating the failure time t according to the fitted equation and the failure standard value.
3. An automatic generator stator bar aging status detection and life assessment system as claimed in claim 2, wherein: in the step C, a service life prediction model of the stator bar adopts a Fallou model, the service life termination time of a group of test bars is taken as a basis, the test voltage and the test temperature are taken as service life evaluation parameters, and the specific evaluation model is as follows:
wherein A is1、A2、B1、B2The model parameters are determined by the properties of the test material, L is the life of the test sample, U is the test voltage applied to the test sample, and T is the absolute temperature applied to the test sample.
4. A generator stator bar aging status automatic detection and life assessment system as defined in claim 3, wherein: in step D, the specific parameter determination step is:
(1) carrying out polynomial fitting on the blue light whiteness-aging time logarithm by applying regression analysis to the corresponding blue light whiteness value and the accelerated aging duration time of each stator bar to obtain a change curve of the blue light whiteness value of each stator bar along with the aging time logarithm;
(2) setting a failure standard of a blue light whiteness value, and taking the time reaching the standard value as failure time to obtain the service life of each stator bar;
(3) each will beThe testing temperature, the testing voltage and the service life of the stator bar under the conditions are brought into the Fallou model in the step C, and the model is fitted by utilizing regression analysis to obtain a parameter A of the Fallou model1、B1、A2、B2And obtaining a life evaluation model of the generator stator bar with the specification.
5. An automatic generator stator bar aging status detection and life assessment system as claimed in claim 1, wherein: the test cut-off condition was that the blue light whiteness value of the test sample was 36.8% below its initial value.
6. An automatic generator stator bar aging status detection and life assessment system as claimed in claim 1, wherein: the device is characterized by further comprising a human-computer interaction module, wherein the human-computer interaction module is electrically connected with the service life assessment algorithm module, the data acquisition module, the thermostat and the test power module respectively, and is used for setting a data acquisition period, test voltage and temperature of each test sample, displaying a time-blue light whiteness value change curve of each stator bar test sample according to an instruction, eliminating invalid test data in a human-computer interaction mode, and starting the service life assessment module.
7. An automatic generator stator bar aging status detection and life assessment system as claimed in claim 1, wherein: the test power supply module adopts 30 kV-40 kV alternating current variable voltage to provide test voltage for the test sample.
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Application publication date: 20210122 |