CN108038795B - Transformer hot spot temperature inversion method and system based on streamline and support vector machine - Google Patents
Transformer hot spot temperature inversion method and system based on streamline and support vector machine Download PDFInfo
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Abstract
The invention discloses a transformer hot spot temperature inversion method and a system based on a streamline and a support vector machine, comprising the following steps of: s1, designing a multi-factor and multi-level training sample set by adopting an orthogonal method; s2, carrying out temperature field simulation on the transformer under the corresponding conditions of each training sample to obtain a temperature field distribution diagram; s3 calculating a corresponding flow chart of each training sample from the temperature field distribution diagram, selecting a main flow line from the flow chart, extracting a flow line temperature change curve from the main flow line, and selecting a temperature measurement point according to the flow line temperature change curve; s4 constructing a test sample set; s5, pre-judging the position of the hot point on the winding of the transformer according to the temperature field distribution pattern; and S6, taking the test sample set as input, and simultaneously performing temperature distribution inversion on the winding where the pre-judging hot point is located by adopting a support vector machine. The method has the advantages of simple calculation, high precision and high efficiency, and can be better applied to engineering practice.
Description
Technical Field
The invention belongs to the technical field of transformer hot spot monitoring, and particularly relates to a transformer hot spot temperature inversion method based on a streamline and a support vector machine.
Background
The transformer is one of the most important devices in the power system, and the number of the transformer is large, the structure is complex, and the reliability and the safety of power supply are directly related. The highest temperature reached by each component in the transformer is the hot spot temperature, and is one of important factors influencing the running state, the physical condition and the insulation life of the transformer. In order to ensure the safety and the high efficiency of the transformer equipment in operation and avoid faults in the operation process, the method is of great importance for the online monitoring of the hot spots of the transformer.
The hot spot measuring method for domestic and foreign transformers mainly comprises a direct measuring method and a thermal simulation method. In the direct measurement method, the optical fiber sensor is embedded on a winding lead to directly obtain the hot spot temperature, but for the transformer in actual operation, the embedding of the sensor can influence the oil flow distribution, and the repeated measurement is needed when the operation working conditions of the transformer are different, so the measurement cost is high; also, due to the uncertainty of the hotspot temperature, the measurement result is not necessarily the hotspot temperature. The thermal simulation method is a simplified form derived from the load guide rule IEC 354, and the winding hot spot temperature is obtained from the measured top layer oil temperature and the temperature rise of the winding relative to the top layer oil temperature, so that the thermal simulation method has large errors. Therefore, in order to monitor the running transformer in real time, the online running hot spot temperature is predicted according to the historical running data of the transformer, the dynamic load is adjusted in time according to the working condition, and the hot spot temperature is inverted by adopting an artificial intelligence method, so that the hot tide of the current research is formed. However, in the intelligent method, the selection of the temperature characteristic quantity lacks the basis of a physical model, the hot spot temperature inversion accuracy is not high, and the calculation speed is slow.
Disclosure of Invention
The invention aims to provide a transformer hot spot temperature inversion method based on a streamline and a support vector machine, which has high precision and efficiency and is feasible.
The invention relates to a transformer hot spot temperature inversion method based on a streamline and a support vector machine, which comprises the following steps:
s1, designing a multi-factor and multi-level training sample set by an orthogonal method according to the actual environment of the transformer by taking the considered environmental factors of the transformer as factors;
s2, carrying out temperature field simulation on the transformer under the corresponding conditions of the training samples to obtain a temperature field distribution diagram reflecting hot spot distribution and oil flow temperature distribution in the transformer;
s3, calculating an oil medium flow curve, namely a flow chart, corresponding to each training sample from the temperature field distribution chart; selecting flow lines which are densely distributed and have consistent flow rules from the flow line diagram as main flow lines; extracting a temperature change curve of an oil medium in the streamline direction from the main streamline, namely a streamline temperature change curve; selecting a streamline point capable of representing the change trend of the streamline temperature change curve according to the streamline temperature change curve, namely a streamline temperature representation point; selecting 1-3 points on a transformer shell, which are closest to a streamline temperature representation point, as temperature measurement points;
s4, obtaining external environment parameters, hot spot temperatures and temperature measuring point temperatures of the transformer under each preset working condition by using a simulation method or a test measurement method, wherein the preset working conditions and the external environment parameters, the hot spot temperatures and the temperature measuring point temperatures corresponding to the preset working conditions form a test sample set;
s5, pre-judging the position of a hot point on a winding of the transformer according to the temperature field distribution pattern obtained in the step S2;
s6 takes the external environment parameters and the temperature of the temperature measuring point in the test sample set as input, and adopts a support vector machine to perform temperature distribution inversion on the winding where the pre-judging hot point is located, wherein the maximum temperature is the temperature of the hot point, and the position where the maximum temperature is located is the position of the hot point.
Further, the considered environmental factors of the transformer include multiple types of low-pressure heat sources, high-pressure heat sources, ambient temperature, upper convection heat transfer coefficients, lower convection heat transfer coefficients and side convection heat transfer coefficients.
Further, the transformer three-dimensional model comprises an iron core, a winding, a structural member, an oil duct and a radiator.
Further, step S2 further includes:
based on a transformer three-dimensional model coupled by multiple physical fields, calculating by using a finite element method or obtaining the loss of the transformer under the corresponding condition of each training sample by using a load test;
and (3) performing temperature field simulation on the transformer under the corresponding condition of each training sample by taking the loss as a heat source and taking the environmental factors of the transformer as input, so as to obtain a temperature field distribution diagram.
Further, the preset working condition comprises one or more of rated load, over charge and under load.
Further, the external environment parameters of the transformer comprise one or more of temperature, wind speed and humidity.
Further, step S5 is specifically: and prejudging the position of the transformer winding with the temperature higher than the preset temperature as the hot spot position.
Further, step S6 further includes:
taking external environment parameters and temperature of temperature measurement points in a test sample set as input of a support vector regression, and selecting different SVR kernel functions to respectively invert the temperature distribution of a winding where a pre-judging hot point is located;
and analyzing the temperature measured value of the winding where the pre-judging hot point is located and the error of the inversion temperature under each training sample, and performing internal cross validation by combining the training samples to determine the optimal SVR kernel function and the optimal optimizing parameter.
And training an SVR kernel function by using the optimal optimization parameters, and inverting the temperature distribution of the winding where the prejudgment hot point is located under each test sample, wherein the maximum temperature is the hot point temperature, and the position where the maximum temperature is located is the hot point position.
The invention relates to a transformer hot spot temperature inversion system based on a streamline and a support vector machine, which comprises:
the first module is used for designing a multi-factor and multi-level training sample set by adopting an orthogonal method according to the actual environment of the transformer by taking the considered environmental factors of the transformer as factors;
the second module is used for carrying out temperature field simulation on the transformer under the conditions corresponding to the training samples to obtain a temperature field distribution diagram reflecting hot spot distribution and oil flow temperature distribution in the transformer;
the third module is used for calculating an oil medium flow curve, namely a flow chart, corresponding to each training sample from the temperature field distribution diagram; selecting flow lines which are densely distributed and have consistent flow rules from the flow line diagram as main flow lines; extracting a temperature change curve of an oil medium in the streamline direction from the main streamline, namely a streamline temperature change curve; selecting a streamline point capable of representing the change trend of the streamline temperature change curve according to the streamline temperature change curve, namely a streamline temperature representation point; selecting 1-3 points on a transformer shell, which are closest to a streamline temperature representation point, as temperature measurement points;
the fourth module is used for obtaining external environment parameters, hot point temperatures and temperature measuring point temperatures of the transformer under each preset working condition by using a simulation method or a test measurement method, and the preset working conditions and the external environment parameters, the hot point temperatures and the temperature measuring point temperatures corresponding to the preset working conditions form a test sample set;
the fifth module is used for pre-judging the position of the hot spot on the winding of the transformer according to the temperature field distribution pattern obtained by the second module;
and the sixth module is used for taking the external environment parameters and the temperature of the temperature measuring point of the test sample set as input, and simultaneously performing temperature distribution inversion on the winding where the pre-judging hot point is located by adopting a support vector machine, wherein the maximum temperature is the temperature of the hot point, and the position where the maximum temperature is located is the position of the hot point.
Compared with the prior art, the invention has the following advantages and beneficial effects:
(1) SVR is selected as a winding hot spot inversion method, and on the basis, a streamline-based shell temperature measuring point selection method is provided to select characteristic quantities with universality, so that the method can be better applied to engineering practice.
(2) The method has the advantages of simple calculation, high precision, high efficiency and feasibility.
Drawings
FIG. 1 is a flow chart of temperature feature point selection;
FIG. 2 is a flow chart of a transformer hot spot temperature inversion method based on a streamline and a support vector machine.
Detailed Description
In order to more clearly illustrate the present invention and/or the technical solutions in the prior art, the following will describe embodiments of the present invention with reference to the accompanying drawings. It is obvious that the drawings in the following description are only some examples of the invention, and that for a person skilled in the art, other drawings and embodiments can be derived from them without inventive effort.
Fig. 1 shows a specific process of a transformer hot spot temperature inversion method based on a streamline and a support vector machine, which can be applied to hot spot temperature inversion of a 10kV voltage transformer, and the specific steps are as follows:
step 1, designing a training sample set.
And designing a multi-factor and multi-level training sample set by adopting an orthogonal method by taking the considered environmental factors of the transformer as factors. In this embodiment, the considered environmental factors include 6 factors, that is, a low-pressure heat source, a high-pressure heat source, an ambient temperature, an upper convective heat transfer coefficient, a lower convective heat transfer coefficient, and a side convective heat transfer coefficient, and a scheme of 6 factors and 5 levels, that is, a training sample, is orthogonally designed according to the actual environment of the transformer, so as to obtain 25 sets of training samples. The orthogonal test tables and training sample sets in this example are shown in tables 1 and 2, respectively.
Table 1 orthogonal test table
TABLE 2 training sample set
And 2, simulating a temperature field of the transformer.
The method further comprises the following steps:
2.1 establishing a multi-physical-field coupled transformer three-dimensional model, wherein the transformer three-dimensional model comprises an iron core, a winding, a structural part, an oil duct and a radiator. And calculating by using a finite element method or obtaining the transformer loss under the corresponding condition of each training sample by using a load test.
And 2.2, performing temperature field simulation on the transformer under the corresponding condition of each training sample by taking loss as a heat source and taking environmental factors of the transformer as input to obtain a temperature field distribution diagram. The temperature field profile is used to describe hot spots inside the transformer and the distribution of oil flow temperatures.
And 3, selecting a temperature measuring point of the transformer shell.
The method further comprises the following steps:
and 3.1 acquiring an oil medium flow curve, namely a flow chart, corresponding to each training sample according to the temperature field distribution diagram corresponding to each training sample, wherein the flow chart is used for describing the flow process of the fluid in the transformer.
And 3.2 according to the fluid analysis result, defining the streamline with dense streamline distribution and consistent flowing rule as a main streamline, and selecting the main streamline from the streamline graph by adopting a manual observation mode.
3.3 extracting the temperature change curve of the oil medium in the streamline direction from the main streamline, namely the streamline temperature change curve.
And 3.4 selecting a streamline point capable of representing the change trend of the streamline temperature change curve according to the streamline temperature change curve, namely a streamline temperature representation point.
And 3.5, selecting 1-3 points closest to the streamline temperature representation point on the transformer shell as temperature measurement points.
And 4, establishing a test sample set.
Under a preset specific working condition, obtaining the external environment parameter, the hot spot temperature and the temperature measuring point temperature of the transformer by a simulation calculation or test measurement method, wherein the specific working condition and the external environment parameter, the hot spot temperature and the temperature measuring point temperature corresponding to the specific working condition form a test sample. The hot spot temperatures in the test sample set can be used to verify the accuracy of the hot spot temperatures obtained by inversion. The test sample set constructed in this example is shown in table 3. The specific working conditions comprise rated load, over charge, under load and the like. The external environmental parameters include temperature, wind speed, humidity, and the like.
TABLE 3 test sample set
And 5, positioning the hot spot.
And (3) according to the temperature field distribution graph obtained in the step (2), prejudging the position of a transformer hot spot on the winding, wherein the prejudging is to prejudge the position of the transformer hot spot, of which the temperature is higher than a preset temperature, on the winding as the position of the hot spot. In the embodiment, the pre-determined hot spot positions are 1-3 layers on the upper part of the winding.
And 6, carrying out inversion by a Support Vector Regression (SVR).
The method further comprises the following steps:
6.1 taking the external environment parameters and the temperature of the temperature measuring points in the test sample set as the input of a support vector regression machine, normalizing the input quantity, and selecting different SVR kernel functions to respectively invert the temperature distribution of the winding where the pre-judging points are located.
6.2, analyzing the temperature measured value of the winding where the pre-judging hot point is located under each training sample and the error of the inversion temperature obtained in the substep 6.1, and determining the optimal SVR kernel function and the optimal optimizing parameter by combining the internal cross validation of the training samples.
6.3 training an SVR kernel function by using the optimal optimization parameters, and inverting the temperature distribution of the winding where the prejudgment hot point is located under each test sample, wherein the maximum temperature is the hot point temperature, and the position of the maximum temperature is the hot point position.
In this embodiment, the measured hot spot temperature value and the inversion value corresponding to each test sample and the error thereof are shown in table 4.
TABLE 4 measured hot-spot temperature values and inversion values corresponding to the test samples and their errors
According to the table 1, the hot spot inversion result and the measured value error of the transformer are within 1 ℃, the method has the characteristics of high precision, simple calculation, high efficiency and feasibility, and solves the problems of high difficulty, low precision and low simulation calculation speed of the temperature field of the conventional hot spot measurement method.
Although the foregoing embodiments have been described in some detail by way of illustration, it will be apparent to those skilled in the art that certain changes and modifications may be made without departing from the spirit and scope of the invention, which is to be limited only by the claims.
Claims (9)
1. The transformer hot spot temperature inversion method based on the streamline and the support vector machine is characterized by comprising the following steps:
s1, designing a multi-factor and multi-level training sample set by an orthogonal method according to the actual environment of the transformer by taking the considered environmental factors of the transformer as factors;
s2, carrying out temperature field simulation on the transformer under the corresponding conditions of the training samples to obtain a temperature field distribution diagram reflecting hot spot distribution and oil flow temperature distribution in the transformer;
s3, calculating an oil medium flow curve, namely a flow chart, corresponding to each training sample from the temperature field distribution chart; selecting flow lines which are densely distributed and have consistent flow rules from the flow line diagram as main flow lines; extracting a temperature change curve of an oil medium in the streamline direction from the main streamline, namely a streamline temperature change curve; selecting a streamline point capable of representing the change trend of the streamline temperature change curve according to the streamline temperature change curve, namely a streamline temperature representation point; selecting 1-3 points on a transformer shell, which are closest to a streamline temperature representation point, as temperature measurement points;
s4, obtaining external environment parameters, hot spot temperatures and temperature measuring point temperatures of the transformer under each preset working condition by using a simulation method or a test measurement method, wherein the preset working conditions and the external environment parameters, the hot spot temperatures and the temperature measuring point temperatures corresponding to the preset working conditions form a test sample set;
s5, pre-judging the position of the hot point on the transformer winding according to the temperature field distribution pattern obtained in the step S2;
s6 takes the external environment parameters and the temperature of the temperature measuring point in the test sample set as input, and adopts a support vector machine to simultaneously perform temperature distribution inversion on the winding position where the pre-judging hot point is located, wherein the maximum temperature is the temperature of the hot point, and the position where the maximum temperature is located is the position of the hot point.
2. The transformer hot spot temperature inversion method based on the streamline and the support vector machine as claimed in claim 1, characterized in that:
the considered environmental factors of the transformer comprise multiple factors of a low-voltage heat source, a high-voltage heat source, an ambient temperature, an upper convection heat transfer coefficient, a lower convection heat transfer coefficient and a side convection heat transfer coefficient.
3. The transformer hot spot temperature inversion method based on the streamline and the support vector machine as claimed in claim 1, characterized in that:
step S2 further includes:
based on a transformer three-dimensional model coupled by multiple physical fields, calculating by using a finite element method or obtaining the loss of the transformer under the corresponding condition of each training sample by using a load test;
and (3) performing temperature field simulation on the transformer under the corresponding condition of each training sample by taking the loss as a heat source and taking the environmental factors of the transformer as input, so as to obtain a temperature field distribution diagram.
4. The transformer hot spot temperature inversion method based on the streamline and the support vector machine as claimed in claim 3, characterized in that:
the transformer three-dimensional model comprises an iron core, a winding, a structural part, an oil duct and a radiator.
5. The transformer hot spot temperature inversion method based on the streamline and the support vector machine as claimed in claim 1, characterized in that:
the preset working condition comprises one or more of rated load, over charge and under load.
6. The transformer hot spot temperature inversion method based on the streamline and the support vector machine as claimed in claim 1, characterized in that:
the external environment parameters of the transformer comprise one or more of temperature, wind speed and humidity.
7. The transformer hot spot temperature inversion method based on the streamline and the support vector machine as claimed in claim 1, characterized in that:
step S5 specifically includes:
and prejudging the position of the transformer winding with the temperature higher than the preset temperature as the hot spot position.
8. The transformer hot spot temperature inversion method based on the streamline and the support vector machine as claimed in claim 1, characterized in that:
step S6 further includes:
taking external environment parameters and temperature of temperature measurement points in a test sample set as input of a support vector regression, and selecting different SVR kernel functions to respectively invert the temperature distribution of a winding where a pre-judging hot point is located;
analyzing the temperature measured value of the winding where the pre-judging hot point is located under each training sample and the error of the inversion temperature, and performing internal cross validation by combining the training samples to determine an optimal SVR kernel function and optimal optimization parameters;
and training an SVR kernel function by using the optimal optimization parameters, and inverting the temperature distribution of the winding where the prejudgment hot point is located under each test sample, wherein the maximum temperature is the hot point temperature, and the position where the maximum temperature is located is the hot point position.
9. Transformer hot spot temperature inversion system based on streamline and support vector machine, characterized by includes:
the first module is used for designing a multi-factor and multi-level training sample set by adopting an orthogonal method according to the actual environment of the transformer by taking the considered environmental factors of the transformer as factors;
the second module is used for carrying out temperature field simulation on the transformer under the conditions corresponding to the training samples to obtain a temperature field distribution diagram reflecting hot spot distribution and oil flow temperature distribution in the transformer;
the third module is used for calculating an oil medium flow curve, namely a flow chart, corresponding to each training sample from the temperature field distribution diagram; selecting flow lines which are densely distributed and have consistent flow rules from the flow line diagram as main flow lines; extracting a temperature change curve of an oil medium in the streamline direction from the main streamline, namely a streamline temperature change curve; selecting a streamline point capable of representing the change trend of the streamline temperature change curve according to the streamline temperature change curve, namely a streamline temperature representation point; selecting 1-3 points on a transformer shell, which are closest to a streamline temperature representation point, as temperature measurement points;
the fourth module is used for obtaining external environment parameters, hot point temperatures and temperature measuring point temperatures of the transformer under each preset working condition by using a simulation method or a test measurement method, and the preset working conditions and the external environment parameters, the hot point temperatures and the temperature measuring point temperatures corresponding to the preset working conditions form a test sample set;
the fifth module is used for pre-judging the position of the hot spot on the winding of the transformer according to the temperature field distribution pattern obtained by the second module;
and the sixth module is used for taking the external environment parameters and the temperature of the temperature measuring point of the test sample set as input, and simultaneously performing temperature distribution inversion on the winding where the pre-judging hot point is located by adopting a support vector machine, wherein the maximum temperature is the temperature of the hot point, and the position where the maximum temperature is located is the position of the hot point.
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