CN117277917A - Temperature monitoring method and system for high-speed magnetic suspension permanent magnet motor - Google Patents
Temperature monitoring method and system for high-speed magnetic suspension permanent magnet motor Download PDFInfo
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- 230000017525 heat dissipation Effects 0.000 claims abstract description 19
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
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- H02K9/06—Arrangements for cooling or ventilating by ambient air flowing through the machine having means for generating a flow of cooling medium with fans or impellers driven by the machine shaft
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02K—DYNAMO-ELECTRIC MACHINES
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Abstract
The invention discloses a temperature monitoring method and a temperature monitoring system for a high-speed magnetic suspension permanent magnet motor, belongs to the technical field of heat dissipation of permanent magnet motors, and is used for solving the technical problems that in the normal operation process of the high-speed magnetic suspension permanent magnet motor, the real-time operation temperature of each component cannot be obtained, the temperature of each component of the motor cannot be monitored in real time, and corresponding cooling measures are taken. The method comprises the following steps: extracting key structures of the high-speed magnetic suspension permanent magnet motor, and constructing a corresponding equivalent heat transfer model; determining real-time loss parameters of all parts according to real-time operation parameters of the high-speed magnetic suspension permanent magnet motor in a preset time period after the motor is started; inputting the real-time loss parameters into an equivalent heat transfer model to obtain real-time temperature predicted values of all parts of the motor in a preset time period; and inputting the real-time temperature predicted value sequence in the preset time period into a pre-trained time sequence temperature prediction model to obtain a temperature advanced predicted value sequence in the next preset time period.
Description
Technical Field
The invention relates to the technical field of heat dissipation of permanent magnet motors, in particular to a temperature monitoring method and system of a high-speed magnetic levitation permanent magnet motor.
Background
With the development of technology, the industry has increasingly high requirements on the rotation speed of the motor. Because of the advantages of high rotating speed, high power density, no excitation loss and the like, the high-speed magnetic levitation permanent magnet motor is gradually and widely focused by the industry, and is applied in a large range. The magnetic suspension bearing adopted in the high-speed magnetic suspension permanent magnet motor enables the rotor to realize stable suspension by means of magnetic force generated by the permanent magnet or the controllable electromagnet. The friction loss between the rotor and the bearing can be greatly reduced by the magnetic suspension technology combining permanent magnets and electromagnetism, so that the rotating speed of the high-speed magnetic suspension permanent magnet motor reaches the speed of a few times or even tens of times of that of a common motor. However, the high rotational speed of the high-speed magnetic levitation permanent magnet motor also results in a significant increase in heat generated by the stator and rotor during operation of the motor. If the temperature of the rotor is too high, irreversible loss of the magnet of the rotor can be caused, the performance of the high-speed magnetic suspension permanent magnet motor is seriously influenced, and the service life and the operation reliability of the high-speed magnetic suspension permanent magnet motor are directly influenced.
Because the internal structure of the motor is complex, for non-rotating parts, the temperature is generally measured in real time by embedding the temperature sensor, but the design cost of the motor is increased by the method, the motor is required to be taken out and disassembled when the temperature sensor is damaged, and the redundant workload is increased by replacing the motor with a new temperature sensor. However, in the case of a rotating member such as a rotor, temperature measurement cannot be performed by embedding a temperature sensor. Therefore, in the normal operation process of the high-speed magnetic suspension permanent magnet motor, most of the modes for acquiring the real-time operation temperature of each component are prediction modes. The network model of the motor is built through some preprocessing software, and temperature estimation is carried out through input parameters, but in the mode, the thermal resistance parameters of all components can change along with the change of the operation working condition and the temperature, errors are accumulated continuously along with time in temperature prediction, the accuracy of temperature prediction is affected finally, and the temperature monitoring effect and the early warning effect on the magnetic suspension permanent magnet motor are further affected and reduced.
Disclosure of Invention
The embodiment of the invention provides a temperature monitoring method and a system for a high-speed magnetic suspension permanent magnet motor, which are used for solving the following technical problems: the existing temperature prediction method of the high-speed magnetic suspension permanent magnet motor has the defects that the working condition and the temperature change bring larger error accumulation, so that the temperature prediction precision is lower and lower along with the time, and the temperature values of all parts of the motor cannot be continuously and effectively predicted.
The embodiment of the invention adopts the following technical scheme:
in one aspect, an embodiment of the present invention provides a method for monitoring a temperature of a high-speed magnetic levitation permanent magnet motor, where the method includes: extracting key structures of the high-speed magnetic suspension permanent magnet motor, and constructing a corresponding equivalent heat transfer model;
determining real-time loss parameters of all components according to the real-time operation parameters of the high-speed magnetic suspension permanent magnet motor in a preset time period after the motor is started;
inputting the real-time loss parameters into the equivalent heat transfer model to obtain real-time temperature predicted values of all parts of the motor in the preset time period;
inputting the real-time temperature predicted value sequence in the preset time period into a pre-trained time sequence temperature prediction model to obtain a temperature advanced predicted value sequence in the next preset time period;
and displaying the real-time temperature predicted value sequence and the temperature advanced predicted value sequence in a central control screen in a curve form, and performing overtemperature early warning.
In a possible implementation manner, the method for constructing the equivalent heat transfer model comprises the steps of extracting key structures of the high-speed magnetic suspension permanent magnet motor, and constructing the equivalent heat transfer model specifically comprising:
extracting a key structure of the high-speed magnetic suspension permanent magnet motor; the key structure comprises a stator core, a rotor core, a winding, a permanent magnet and a bearing;
constructing a plurality of modeling nodes according to the key structure;
determining heat transfer relations among the modeling nodes, and representing heat transfer values among two adjacent modeling nodes through thermal resistance;
constructing an equivalent heat transfer model of the high-speed magnetic suspension permanent magnet motor according to the heat transfer relation and the thermal resistance between every two modeling nodes; wherein the heat source of each modeled node in the equivalent heat transfer model is represented in terms of node loss.
In a possible implementation manner, in a preset time period after the motor is started, determining real-time loss parameters of each component according to the real-time operation parameters of the high-speed magnetic suspension permanent magnet motor specifically includes:
acquiring real-time operation parameters of the high-speed magnetic suspension permanent magnet motor at all moments in the preset time period; wherein the real-time operating parameters include at least: rotor speed, rotor torque, motor input current, and friction torque;
according to the real-time operation parameters, calculating the real-time loss parameters of all the components at each moment in the preset time period; wherein, the real-time loss parameters of each component are as follows: stator core loss, rotor wind friction loss, winding copper loss, permanent magnet eddy current loss, and bearing loss.
In a possible implementation manner, the real-time loss parameter is input into the equivalent heat transfer model to obtain a real-time temperature predicted value of each part of the motor within the preset time period, which specifically includes:
according to the density, the volume and the specific heat capacity of each key structure, calculating the heat capacity parameter of each modeling node in the equivalent heat transfer model;
constructing a system matrix of the equivalent heat transfer model according to the thermal resistance between every two modeling nodes;
constructing an input matrix of the equivalent heat transfer model according to the heat capacity parameters of the modeling nodes;
establishing a heat balance equation of the equivalent heat transfer model according to the system matrix and the input matrix:the method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>The temperature rise state variable of the ith modeling node is obtained; a is the system matrix, B is the input matrix; />A state vector formed for the operating temperature of the ith modeling node; />An input vector formed for the loss parameter of the i-th modeling node; />For the water cooling heat dissipation power at the time t, when the water cooling heat dissipation structure is not opened, the water cooling heat dissipation structure is at the time of +>=1；
Inputting the system matrix, the input matrix and the real-time loss parameters into the heat balance equation to obtain a temperature rise state variable of a key structure corresponding to an ith modeling node at the moment t;
and calculating the real-time temperature predicted value of each key structure at each moment according to the temperature rise state variable of each key structure at each moment.
In a possible embodiment, after calculating the real-time temperature predicted value of each critical structure at each moment according to the temperature rise state variable and the initial temperature of each critical structure at each moment, the method further includes:
according toUpdating the real-time temperature predicted value to obtain an optimal temperature predicted value +.>The method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>For the real-time temperature prediction value at time t, +.>For Kalman gain, ++>For the measurement matrix at time t +.>Is a jacobian matrix;
and updating the real-time temperature predicted value into the optimal temperature predicted value.
In a possible embodiment, before inputting the real-time temperature prediction value sequence within the preset time period into the pre-trained time-series temperature prediction model, the method further comprises:
acquiring operation parameters and corresponding temperature data of each component generated by the high-speed magnetic suspension permanent magnet motor along with the change of operation time under various working conditions, and taking the operation parameters and the corresponding temperature data of each component as a model training data set; wherein the operation parameters comprise real-time water cooling heat dissipation power;
constructing a first time sequence prediction model and a second time sequence prediction model;
according toDetermining the weight of the prediction result of the first time sequence prediction model in the total prediction result>；
Wherein,the value range is [0,1 ] for the weight change rate]；/>A mean square error of a predicted sequence and a real sequence of the first time sequence prediction model is obtained; />A mean square error of a predicted sequence and a real sequence of the second time sequence prediction model is obtained; t represents the current time as t time, and n represents the predicted sequence duration; />The weight of the predicted result of the first time sequence predicted model in the total predicted result is the last moment;
according toDynamically fusing the first time sequence prediction model and the second time sequence prediction model to obtain a time sequence temperature prediction model expression +.>；
Wherein,for the first timing prediction model; />For the second timing prediction model; />The method comprises the steps of inputting data, wherein the input data is a temperature data sequence in a period of time;
and training the time sequence temperature prediction model through the model training data set.
In a possible implementation manner, the real-time temperature predicted value sequence in the preset time period is input into a pre-trained time sequence temperature prediction model to obtain a temperature advanced predicted value sequence in a next preset time period, which specifically includes:
representing the real-time temperature predicted value in the preset time period in a time sequence form to obtain a real-time temperature predicted value sequence;
inputting the real-time temperature predicted value sequence into the time sequence temperature prediction model;
the coding module of the second time sequence prediction model is used for carrying out coding calculation on the real-time temperature prediction value sequence and calculating the mean value and standard deviation of the real-time temperature prediction value sequence;
splicing the output sequence of the decoding module in the second time sequence prediction model with the mean value and the standard deviation, and inputting the spliced output sequence into a full-connection layer for calculation to obtain a prediction result of the second time sequence prediction model;
and obtaining a predicted result of the first time sequence predicted model, and inputting the predicted result of the first time sequence predicted model and the predicted result of the second time sequence predicted model into the time sequence temperature predicted model expression to obtain a temperature advanced predicted value sequence in the next preset time period.
In a possible implementation manner, the real-time temperature predicted value sequence and the temperature advanced predicted value sequence are displayed in a central control screen in a curve form, and overtemperature early warning is performed, which specifically includes:
in a preset time period after the motor is started, predicting a real-time temperature predicted value, namely adding a current time node in a temperature change curve in the central control screen, and displaying the real-time temperature predicted value at a corresponding coordinate; wherein, the abscissa value of the coordinate is the current time node, and the ordinate value is the real-time temperature predicted value;
after the first preset time period is finished, a temperature change curve corresponding to the next preset time period is drawn at one time according to a predicted temperature advance predicted value sequence of the next preset time period;
when the ordinate value of any point in the temperature change curve is higher than a preset temperature threshold, displaying overtemperature early warning prompt information through the central control screen; wherein the preset temperature threshold is less than the demagnetization temperature threshold of the permanent magnet.
In a possible embodiment, after presenting the real-time temperature predictor sequence and the temperature advance predictor sequence in a curved form in a central control screen, the method further comprises:
determining a time node when the longitudinal coordinate value in the temperature change curve reaches a first preset threshold value for the first time in the preset time period;
starting a water cooling structure of the high-speed magnetic suspension permanent magnet motor at the time node, and acquiring water cooling power of the water cooling structure;
substituting the water cooling heat dissipation power into the equivalent heat transfer model to participate in calculation of a real-time temperature predicted value;
if the first difference between the time node and the ending time of the preset time period is smaller than a second preset threshold, calculating a second difference between the second preset threshold and the first difference;
and adding the second difference value on the basis of the preset time period, and updating the duration of the preset time period.
On the other hand, the embodiment of the invention also provides a temperature monitoring system of the high-speed magnetic suspension permanent magnet motor, which comprises:
the model construction module is used for extracting key structures of the high-speed magnetic suspension permanent magnet motor and constructing a corresponding equivalent heat transfer model;
the temperature prediction module is used for determining real-time loss parameters of all components according to the real-time operation parameters of the high-speed magnetic suspension permanent magnet motor in a preset time period after the motor is started; inputting the real-time loss parameters into the equivalent heat transfer model to obtain real-time temperature predicted values of all parts of the motor in the preset time period; inputting the real-time temperature predicted value sequence in the preset time period into a pre-trained time sequence temperature prediction model to obtain a temperature advanced predicted value sequence in the next preset time period;
and the information display and early warning module is used for displaying the real-time temperature predicted value sequence and the temperature advanced predicted value sequence in a central control screen in a curve form and carrying out overtemperature early warning.
Compared with the prior art, the temperature monitoring method and system for the high-speed magnetic suspension permanent magnet motor provided by the embodiment of the invention have the following beneficial effects:
according to the invention, the equivalent model is combined with the time sequence model, the advantages of the equivalent model and the time sequence model on the motor heating temperature prediction are fully exerted, and as the prediction precision of the equivalent model is poorer and worse along with the time, the temperature prediction is carried out by adopting the equivalent model with more accurate prediction results in the initial time stage, then the temperature sequence obtained by prediction is input into the improved time sequence prediction model after the prediction in the initial time stage is completed, and the temperature change in the future time period is predicted through the temperature change in the initial time period, so that all the temperature prediction values are obtained. The algorithm combines the prediction precision advantages of the two models at different time nodes, optimizes and improves the time sequence prediction model, ensures that the temperature change predicted value of each component of the high-speed magnetic suspension permanent magnet motor is in a high-precision prediction state in the whole course, and improves the prediction efficiency and precision.
In addition, the invention carries out over-temperature early warning on the high-speed magnetic suspension permanent magnet motor according to the temperature predicted value, thereby reducing the possibility of over-temperature loss of the high-speed magnetic suspension permanent magnet motor and prolonging the service life of the high-speed magnetic suspension permanent magnet motor. After the heating temperature of a certain part of the high-speed magnetic suspension permanent magnet motor reaches a certain value, the water cooling heat dissipation structure is controlled to be started, and the self-fan cooling structure of the high-speed magnetic suspension permanent magnet motor can be used for cooling before the water cooling heat dissipation structure, so that a part of unnecessary heat dissipation structure energy consumption is saved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required to be used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments described in the present invention, and other drawings may be obtained according to the drawings without inventive effort to those skilled in the art. In the drawings:
FIG. 1 is a flow chart of a temperature monitoring method for a high-speed magnetic levitation permanent magnet motor according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a temperature variation curve according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of a temperature monitoring system of a high-speed magnetic levitation permanent magnet motor according to an embodiment of the present invention.
Reference numerals illustrate:
300: a temperature monitoring system of the high-speed magnetic suspension permanent magnet motor; 310: a model building module; 320: a temperature prediction module; 330: and the information display and early warning module.
Detailed Description
In order to make the technical solution of the present invention better understood by those skilled in the art, the technical solution of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments herein without making any inventive effort, shall fall within the scope of the present invention.
The embodiment of the invention provides a temperature monitoring method of a high-speed magnetic suspension permanent magnet motor, as shown in fig. 1, specifically comprising the following steps of S101-S105:
s101, extracting key structures of the high-speed magnetic suspension permanent magnet motor, and constructing a corresponding equivalent heat transfer model.
Specifically, extracting a key structure of the high-speed magnetic suspension permanent magnet motor; the key structure comprises a stator core, a rotor core, windings, permanent magnets and a bearing. And then constructing a plurality of modeling nodes according to the key structure.
Further, the heat transfer relation among a plurality of modeling nodes is determined, and the heat transfer value between two adjacent modeling nodes is represented by thermal resistance. And then constructing an equivalent heat transfer model of the high-speed magnetic suspension permanent magnet motor according to the heat transfer relation and the thermal resistance between every two modeling nodes. Wherein the heat source of each modeled node in the equivalent heat transfer model is represented by a node loss.
As a possible implementation manner, by analyzing the heat transfer principle inside the high-speed magnetic levitation permanent magnet motor, it is known that the heating part of the high-speed magnetic levitation permanent magnet motor mainly comprises a stator core, a rotor core, a winding, a permanent magnet and a bearing, and the five structures are set as key structures. According to the five key structures, five corresponding modeling nodes are firstly constructed and are respectively a stator core node, a rotor core node, a winding node, a permanent magnet node and a bearing node. And then obtaining the thermal resistance among all the nodes, and connecting the nodes through the thermal resistance to form an equivalent heat transfer network. Wherein, stator core nodes are connected with winding nodes and rotor core nodes, and bearing nodes are connected with rotor core nodes; the rotor core nodes are connected with the permanent magnet nodes.
S102, determining real-time loss parameters of all components according to real-time operation parameters of the high-speed magnetic suspension permanent magnet motor in a preset time period after the motor is started.
Specifically, acquiring real-time operation parameters of the high-speed magnetic suspension permanent magnet motor at each moment in a preset time period; wherein, the real-time operation parameters at least comprise: rotor speed, rotor torque, motor input current, and friction torque.
Further, according to the real-time operation parameters, calculating the real-time loss parameters of all the components at each moment in a preset time period; wherein, the real-time loss parameters of each component are as follows: stator core loss, rotor wind friction loss, winding copper loss, permanent magnet eddy current loss, and bearing loss.
As a feasible implementation mode, the high-speed magnetic suspension permanent magnet motor generates heat, heat sources come from various losses in the motor, and each part can generate a certain degree of loss in the operation process. The loss of the high-speed magnetic suspension permanent magnet motor mainly comprises stator core loss, rotor wind friction loss, winding copper loss, permanent magnet eddy current loss and bearing loss. Wherein stator core losses are due to eddy currents and hysteresis and eddy current parasitic losses. In the high-speed magnetic suspension permanent magnet motor, the rotor surface speed is up to 200m/s, and the loss generated by friction between the rotor surface rotating at high speed and air is large, so that the wind friction loss occupies a large proportion in the total loss, and the loss parameter of the wind friction loss of the rotor is introduced, so that the temperature prediction is more in accordance with the actual heating principle of the high-speed magnetic suspension permanent magnet motor.
S103, inputting the real-time loss parameters into an equivalent heat transfer model to obtain real-time temperature predicted values of all parts of the motor in a preset time period.
Specifically, according to the density, the volume and the specific heat capacity of each key structure, the heat capacity parameter of each modeling node in the equivalent heat transfer model is calculated. And then constructing a system matrix of the equivalent heat transfer model according to the thermal resistance between every two modeling nodes. And finally, constructing an input matrix of the equivalent heat transfer model according to the heat capacity parameters of each modeling node.
Further, according to the system matrix and the input matrix, a heat balance equation of the equivalent heat transfer model is established:the method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>The temperature rise state variable of the ith modeling node is obtained; a is a system matrix, B is an input matrix; />A state vector formed for the operating temperature of the ith modeling node; />An input vector formed for the loss parameter of the i-th modeling node; />For the water cooling heat dissipation power at the time t, when the water cooling heat dissipation structure is not opened,=1。
further, the system matrix, the input matrix and the real-time loss parameters are all input into a heat balance equation, and the temperature rise state variable of the key structure corresponding to the ith modeling node at the moment t is obtained. And calculating the real-time temperature predicted value of each key structure at each moment according to the temperature rise state variable of each key structure at each moment.
As a possible embodiment, according toCalculating the heat capacity C of the ith modeling node i . Wherein (1)>Density of the key structure corresponding to the ith modeling node,/->Key node corresponding to ith modeling nodeVolume of structure->And the specific heat capacity of the key structure corresponding to the ith modeling node. According to the formula, the heat capacity of the bearing, the heat capacity of the stator core, the heat capacity of the rotor core, the heat capacity of the winding and the heat capacity of the permanent magnet are calculated respectively, and a system matrix A is constructed according to the heat capacity parameters and the heat resistance among modeling nodes. Then constructing an input matrix B according to the heat capacity parameters:
；
further according toUpdating the real-time temperature predicted value to obtain an optimal temperature predicted value +.>The method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>For the real-time temperature prediction value at time t, +.>For Kalman gain, ++>For the measurement matrix at time t +.>Is a jacobian matrix. And finally, updating the real-time temperature predicted value into an optimal temperature predicted value. And synchronously identifying and updating the predicted temperatures of all parts of the permanent magnet motor through a Kalman filtering algorithm, so that the prediction accuracy of temperature prediction is improved.
As a possible embodiment, the time node at which the ordinate value in the temperature profile reaches the first preset threshold value for the first time is determined within a preset time period. At the time node, a water cooling structure of the high-speed magnetic suspension permanent magnet motor is started, and water cooling power of the water cooling structure is obtained. Substituting the water cooling heat dissipation power into an equivalent heat transfer model to participate in calculation of a real-time temperature predicted value. If the first difference between the time node and the ending time of the preset time period is smaller than the second preset threshold, a second difference between the second preset threshold and the first difference is calculated. And finally, adding a second difference value on the basis of a preset time period, and updating the duration of the preset time period.
In one embodiment, assume a preset time period [0, T]In, the temperature value reaches a first preset threshold value for the first timeTime node t p Then at t p The water cooling structure is started at any time, and the self-fan cooling in the high-speed magnetic suspension permanent magnet motor is only used for cooling before the water cooling structure is started. After the heat radiation structure is started, according to the actual water cooling heat radiation power, calculating the temperature rise state variable of each modeling node>And a real-time temperature predicted value. At this time, calculate H 1 =T-t p Obtaining the ending time and t of the preset time period p First difference H of time 1 . If H 1 Less than a second preset threshold +.>Then calculate the second preset threshold and t p Is the second difference H of (2) 2 =/>-t p . Finally, adding a second difference value on the basis of the preset time period, and updating the preset time period to be [0, T+H ] 2 ]. The method aims at providing enough time for predicting the temperature of the water cooling structure after the water cooling structure is opened, so that the temperature change of the water cooling structure after the water cooling structure is opened is captured better, and a better learning basis is provided for a time sequence temperature prediction model.
S104, inputting the real-time temperature predicted value sequence in the preset time period into a pre-trained time sequence temperature predicted model to obtain a temperature advanced predicted value sequence in the next preset time period.
Specifically, under various working conditions, operating parameters generated by the high-speed magnetic suspension permanent magnet motor along with the change of the operating time and corresponding temperature data of each component are obtained and used as a model training data set; the operation parameters comprise real-time water cooling heat dissipation power.
Further, a first timing prediction model and a second timing prediction model are constructed. Then according toDetermining the weight of the prediction result of the first time sequence prediction model in the total prediction result>. Wherein (1)>The value range is [0,1 ] for the weight change rate]；/>The mean square error of the predicted sequence and the real sequence of the first time sequence prediction model is obtained; />The mean square error of the predicted sequence and the real sequence of the second time sequence prediction model is obtained; t represents the current time as t time, and n represents the predicted sequence duration; />And for the last moment, the weight of the prediction result of the first time sequence prediction model in the total prediction result.
Further according toDynamically fusing the first time sequence prediction model and the second time sequence prediction model to obtain a time sequence temperature prediction model expression +.>. Finally through model trainingTraining a data set and training a sequential temperature prediction model. In the above, the->A first timing prediction model; />A second time sequence prediction model;for input data, the input data is a sequence of temperature data over a period of time.
As a possible implementation mode, the invention constructs two different time sequence prediction models, wherein the first time sequence prediction model is preferably selected from a regression differential moving average model, and the second time sequence prediction model is preferably selected from a long-period and short-period memory network model. The long-term memory network model has strong learning ability for time sequence data with complex change, but when the long-term memory network model is applied to new data, if the distribution of the new data and the training data are different greatly, the algorithm performance is poor, and the autoregressive differential moving average model has better prediction effect for the data change condition, so that the autoregressive differential moving average model can correct the prediction result of the long-term memory network model. According to the method, two models are predicted simultaneously, and the weights of the two models are dynamically adjusted according to the accuracy of the prediction results of the two models, so that the prediction accuracy of the whole time sequence temperature prediction model is improved.
Further, the real-time temperature predicted value in the preset time period is expressed in a time sequence form, and a real-time temperature predicted value sequence is obtained. And then inputting the real-time temperature predicted value sequence into a time sequence temperature predicted model which is trained well and has the accuracy reaching the standard. And carrying out coding calculation on the real-time temperature predicted value sequence through a coding module of the second time sequence predicted model, and calculating the mean value and standard deviation of the real-time temperature predicted value sequence. And splicing the output sequence of the decoding module in the second time sequence prediction model with the mean value and the standard deviation, and inputting the spliced output sequence into a full-connection layer for calculation to obtain a prediction result of the second time sequence prediction model. And obtaining a predicted result of the first time sequence predicted model, and inputting the predicted result of the first time sequence predicted model and the predicted result of the second time sequence predicted model into a time sequence temperature predicted model expression to obtain a temperature advanced predicted value sequence in the next preset time period.
In one embodiment, if the time sequence prediction model predicts for a long time, serious error accumulation effect will occur, resulting in reduced feasibility and accuracy, so that the prediction duration of each time sequence temperature prediction model is set to 5min, and each prediction needs to input all the predicted temperature sequences before the current time into the time sequence prediction model.
S105, displaying the real-time temperature predicted value sequence and the temperature advanced predicted value sequence in a central control screen in a curve form, and performing overtemperature early warning.
Specifically, in a preset time period after the motor is started, a real-time temperature predicted value is predicted every time, namely a current time node is added in a temperature change curve in a central control screen, and the real-time temperature predicted value is displayed at a corresponding coordinate; wherein, the abscissa value of the coordinate is the current time node, and the ordinate value is the real-time temperature predicted value.
Further, after the first preset time period is finished, a temperature change curve corresponding to the next preset time period is drawn at one time according to a predicted temperature advance predicted value sequence of the next preset time period.
In one embodiment, fig. 2 is a schematic diagram of a temperature change curve provided in an embodiment of the present invention, and as shown in fig. 2, a rectangular coordinate system is determined with time as an abscissa and temperature as an ordinate. And in a preset time period 0-t1, acquiring a real-time temperature predicted value along with the updating of a time node, and displaying the real-time temperature predicted value in a rectangular coordinate system, wherein a temperature change curve (a solid line part before t 1) is gradually prolonged. When the time t1 is reached, a predicted temperature advance predicted value sequence of the next time period is obtained, and the predicted temperature advance predicted value sequence is drawn in a rectangular coordinate system (a dotted line part after t 1) at one time.
As a possible implementation mode, when the ordinate value of any point in the temperature change curve is higher than a preset temperature threshold, displaying overtemperature early warning prompt information through a central control screen; the preset temperature threshold is smaller than the demagnetization temperature threshold of the permanent magnet.
In addition, the embodiment of the invention also provides a temperature monitoring system of the high-speed magnetic suspension permanent magnet motor, as shown in fig. 3, the temperature monitoring system of the high-speed magnetic suspension permanent magnet motor specifically comprises:
the model construction module 310 is used for extracting key structures of the high-speed magnetic suspension permanent magnet motor and constructing a corresponding equivalent heat transfer model;
the temperature prediction module 320 is configured to determine real-time loss parameters of each component according to real-time operation parameters of the high-speed magnetic suspension permanent magnet motor in a preset time period after the motor is started; inputting the real-time loss parameters into the equivalent heat transfer model to obtain real-time temperature predicted values of all parts of the motor in the preset time period; inputting the real-time temperature predicted value sequence in the preset time period into a pre-trained time sequence temperature prediction model to obtain a temperature advanced predicted value sequence in the next preset time period;
and the information display and early warning module 330 is used for displaying the real-time temperature predicted value sequence and the temperature advanced predicted value sequence in a central control screen in a curve form and carrying out over-temperature early warning.
The embodiments of the present invention are described in a progressive manner, and the same and similar parts of the embodiments are all referred to each other, and each embodiment is mainly described in the differences from the other embodiments. In particular, for apparatus, devices, non-volatile computer storage medium embodiments, the description is relatively simple, as it is substantially similar to method embodiments, with reference to the section of the method embodiments being relevant.
The foregoing describes certain embodiments of the present invention. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
The foregoing is merely exemplary of the present invention and is not intended to limit the present invention. Various modifications and changes may be made to the embodiments of the invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the embodiments of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. The temperature monitoring method of the high-speed magnetic suspension permanent magnet motor is characterized by comprising the following steps of:
extracting key structures of the high-speed magnetic suspension permanent magnet motor, and constructing a corresponding equivalent heat transfer model;
determining real-time loss parameters of all components according to the real-time operation parameters of the high-speed magnetic suspension permanent magnet motor in a preset time period after the motor is started;
inputting the real-time loss parameters into the equivalent heat transfer model to obtain real-time temperature predicted values of all parts of the motor in the preset time period;
inputting the real-time temperature predicted value sequence in the preset time period into a pre-trained time sequence temperature prediction model to obtain a temperature advanced predicted value sequence in the next preset time period;
and displaying the real-time temperature predicted value sequence and the temperature advanced predicted value sequence in a central control screen in a curve form, and performing overtemperature early warning.
2. The method for monitoring the temperature of the high-speed magnetic levitation permanent magnet motor according to claim 1, wherein the method is characterized by extracting key structures of the high-speed magnetic levitation permanent magnet motor and constructing a corresponding equivalent heat transfer model, and specifically comprises the following steps:
extracting a key structure of the high-speed magnetic suspension permanent magnet motor; the key structure comprises a stator core, a rotor core, a winding, a permanent magnet and a bearing;
constructing a plurality of modeling nodes according to the key structure;
determining heat transfer relations among the modeling nodes, and representing heat transfer values among two adjacent modeling nodes through thermal resistance;
constructing an equivalent heat transfer model of the high-speed magnetic suspension permanent magnet motor according to the heat transfer relation and the thermal resistance between every two modeling nodes; wherein the heat source of each modeled node in the equivalent heat transfer model is represented in terms of node loss.
3. The method for monitoring the temperature of a high-speed magnetic levitation permanent magnet motor according to claim 1, wherein the determining the real-time loss parameter of each component according to the real-time operation parameter of the high-speed magnetic levitation permanent magnet motor in a preset time period after the motor is started specifically comprises:
acquiring real-time operation parameters of the high-speed magnetic suspension permanent magnet motor at all moments in the preset time period; wherein the real-time operating parameters include at least: rotor speed, rotor torque, motor input current, and friction torque;
according to the real-time operation parameters, calculating the real-time loss parameters of all the components at each moment in the preset time period; wherein, the real-time loss parameters of each component are as follows: stator core loss, rotor wind friction loss, winding copper loss, permanent magnet eddy current loss, and bearing loss.
4. The method for monitoring the temperature of the high-speed magnetic levitation permanent magnet motor according to claim 2, wherein the real-time loss parameter is input into the equivalent heat transfer model to obtain a real-time temperature predicted value of each part of the motor in the preset time period, specifically comprising:
according to the density, the volume and the specific heat capacity of each key structure, calculating the heat capacity parameter of each modeling node in the equivalent heat transfer model;
constructing a system matrix of the equivalent heat transfer model according to the thermal resistance between every two modeling nodes;
constructing an input matrix of the equivalent heat transfer model according to the heat capacity parameters of the modeling nodes;
establishing a heat balance equation of the equivalent heat transfer model according to the system matrix and the input matrix:the method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>The temperature rise state variable of the ith modeling node is obtained; a is the system matrix, B is the input matrix; />A state vector formed for the operating temperature of the ith modeling node; />An input vector formed for the loss parameter of the i-th modeling node; />For the water cooling heat dissipation power at the time t, when the water cooling heat dissipation structure is not opened, the water cooling heat dissipation structure is at the time of +>=1；
Inputting the system matrix, the input matrix and the real-time loss parameters into the heat balance equation to obtain a temperature rise state variable of a key structure corresponding to an ith modeling node at the moment t;
and calculating the real-time temperature predicted value of each key structure at each moment according to the temperature rise state variable of each key structure at each moment.
5. The method according to claim 4, wherein after calculating the real-time temperature predicted value of each critical structure at each moment according to the temperature rise state variable and the initial temperature of each critical structure at each moment, the method further comprises:
according toFurther processing the real-time temperature predicted valueNew, the optimal temperature predicted value +.>The method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>For the real-time temperature prediction value at time t, +.>For Kalman gain, ++>For the measurement matrix at time t +.>Is a jacobian matrix;
and updating the real-time temperature predicted value into the optimal temperature predicted value.
6. A method of monitoring the temperature of a high-speed magnetic levitation permanent magnet machine according to claim 1, wherein prior to inputting the real-time sequence of temperature predictions over the predetermined time period into the pre-trained time-series temperature prediction model, the method further comprises:
acquiring operation parameters and corresponding temperature data of each component generated by the high-speed magnetic suspension permanent magnet motor along with the change of operation time under various working conditions, and taking the operation parameters and the corresponding temperature data of each component as a model training data set; wherein the operation parameters comprise real-time water cooling heat dissipation power;
constructing a first time sequence prediction model and a second time sequence prediction model;
according toDetermining the weight of the prediction result of the first time sequence prediction model in the total prediction result>；
Wherein,the value range is [0,1 ] for the weight change rate]；/>A mean square error of a predicted sequence and a real sequence of the first time sequence prediction model is obtained; />A mean square error of a predicted sequence and a real sequence of the second time sequence prediction model is obtained; t represents the current time as t time, and n represents the predicted sequence duration; />The weight of the predicted result of the first time sequence predicted model in the total predicted result is the last moment;
according toDynamically fusing the first time sequence prediction model and the second time sequence prediction model to obtain a time sequence temperature prediction model expression +.>；
Wherein,for the first timing prediction model; />For the second timing prediction model; />The method comprises the steps of inputting data, wherein the input data is a temperature data sequence in a period of time;
and training the time sequence temperature prediction model through the model training data set.
7. The method for monitoring the temperature of a high-speed magnetic levitation permanent magnet motor according to claim 6, wherein the step of inputting the real-time temperature predicted value sequence in the preset time period into a pre-trained time sequence temperature prediction model to obtain the temperature advanced predicted value sequence in the next preset time period comprises the following steps:
representing the real-time temperature predicted value in the preset time period in a time sequence form to obtain a real-time temperature predicted value sequence;
inputting the real-time temperature predicted value sequence into the time sequence temperature prediction model;
the coding module of the second time sequence prediction model is used for carrying out coding calculation on the real-time temperature prediction value sequence and calculating the mean value and standard deviation of the real-time temperature prediction value sequence;
splicing the output sequence of the decoding module in the second time sequence prediction model with the mean value and the standard deviation, and inputting the spliced output sequence into a full-connection layer for calculation to obtain a prediction result of the second time sequence prediction model;
and obtaining a predicted result of the first time sequence predicted model, and inputting the predicted result of the first time sequence predicted model and the predicted result of the second time sequence predicted model into the time sequence temperature predicted model expression to obtain a temperature advanced predicted value sequence in the next preset time period.
8. The method for monitoring the temperature of a high-speed magnetic levitation permanent magnet motor according to claim 1, wherein the real-time temperature predicted value sequence and the temperature advanced predicted value sequence are displayed in a central control screen in a curve form, and overtemperature early warning is performed, specifically comprising:
in a preset time period after the motor is started, predicting a real-time temperature predicted value, namely adding a current time node in a temperature change curve in the central control screen, and displaying the real-time temperature predicted value at a corresponding coordinate; wherein, the abscissa value of the coordinate is the current time node, and the ordinate value is the real-time temperature predicted value;
after the first preset time period is finished, a temperature change curve corresponding to the next preset time period is drawn at one time according to a predicted temperature advance predicted value sequence of the next preset time period;
when the ordinate value of any point in the temperature change curve is higher than a preset temperature threshold, displaying overtemperature early warning prompt information through the central control screen; wherein the preset temperature threshold is less than the demagnetization temperature threshold of the permanent magnet.
9. A method of monitoring the temperature of a high-speed magnetic levitation permanent magnet machine according to claim 8, wherein after displaying the real-time temperature predicted value sequence and the temperature advanced predicted value sequence in a curved form in a central control screen, the method further comprises:
determining a time node when the longitudinal coordinate value in the temperature change curve reaches a first preset threshold value for the first time in the preset time period;
starting a water cooling structure of the high-speed magnetic suspension permanent magnet motor at the time node, and acquiring water cooling power of the water cooling structure;
substituting the water cooling heat dissipation power into the equivalent heat transfer model to participate in calculation of a real-time temperature predicted value;
if the first difference between the time node and the ending time of the preset time period is smaller than a second preset threshold, calculating a second difference between the second preset threshold and the first difference;
and adding the second difference value on the basis of the preset time period, and updating the duration of the preset time period.
10. A temperature monitoring system for a high-speed magnetic levitation permanent magnet motor, the system comprising:
the model construction module is used for extracting key structures of the high-speed magnetic suspension permanent magnet motor and constructing a corresponding equivalent heat transfer model;
the temperature prediction module is used for determining real-time loss parameters of all components according to the real-time operation parameters of the high-speed magnetic suspension permanent magnet motor in a preset time period after the motor is started; inputting the real-time loss parameters into the equivalent heat transfer model to obtain real-time temperature predicted values of all parts of the motor in the preset time period; inputting the real-time temperature predicted value sequence in the preset time period into a pre-trained time sequence temperature prediction model to obtain a temperature advanced predicted value sequence in the next preset time period;
and the information display and early warning module is used for displaying the real-time temperature predicted value sequence and the temperature advanced predicted value sequence in a central control screen in a curve form and carrying out overtemperature early warning.
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| US20200119625A1 (en) * | 2018-10-12 | 2020-04-16 | Guangzhou Automobile Group Co., Ltd | Method and Apparatus for Determining Motor Temperature, and Storage Medium |
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| CN115313763A (en) * | 2022-08-09 | 2022-11-08 | 重庆大学 | Heat pipe control method for motor of new energy automobile |
| US20230179131A1 (en) * | 2020-07-30 | 2023-06-08 | Huawei Digital Power Technologies Co., Ltd. | Temperature prediction method and apparatus |
| DE102021214517A1 (en) * | 2021-12-16 | 2023-06-22 | Robert Bosch Gesellschaft mit beschränkter Haftung | Method and device for determining a temperature in a component of an electric motor for an electric drive system using machine learning methods |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20200119625A1 (en) * | 2018-10-12 | 2020-04-16 | Guangzhou Automobile Group Co., Ltd | Method and Apparatus for Determining Motor Temperature, and Storage Medium |
| US20230179131A1 (en) * | 2020-07-30 | 2023-06-08 | Huawei Digital Power Technologies Co., Ltd. | Temperature prediction method and apparatus |
| CN112234911A (en) * | 2020-09-21 | 2021-01-15 | 盖耀辉 | Real-time monitoring method and model for temperature of permanent magnet motor rotor |
| DE102021214517A1 (en) * | 2021-12-16 | 2023-06-22 | Robert Bosch Gesellschaft mit beschränkter Haftung | Method and device for determining a temperature in a component of an electric motor for an electric drive system using machine learning methods |
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