CN110007232B - Method and related device for predicting running efficiency of squirrel-cage asynchronous motor - Google Patents
Method and related device for predicting running efficiency of squirrel-cage asynchronous motor Download PDFInfo
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- CN110007232B CN110007232B CN201910434258.5A CN201910434258A CN110007232B CN 110007232 B CN110007232 B CN 110007232B CN 201910434258 A CN201910434258 A CN 201910434258A CN 110007232 B CN110007232 B CN 110007232B
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- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
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Abstract
The application discloses a method for predicting the running efficiency of a squirrel-cage asynchronous motor, which comprises the steps of establishing a squirrel-cage asynchronous motor simulation model; inputting harmonic voltage to the squirrel-cage asynchronous motor simulation model for simulation to obtain simulation data; carrying out BP neural network training based on the simulation data to obtain a BP neural network prediction model; and acquiring actual operation data of the current squirrel-cage asynchronous motor, and inputting the actual operation data into the BP neural network prediction model to obtain the operation efficiency of the current squirrel-cage asynchronous motor. The prediction method can accurately and efficiently predict the operating efficiency of each type of squirrel-cage asynchronous motor, and provides effective reference for the type selection and the use of the squirrel-cage asynchronous motor. The application also discloses a system and a device for predicting the running efficiency of the squirrel-cage asynchronous motor and a computer readable storage medium, which have the technical effects.
Description
Technical Field
The application relates to the technical field of motors, in particular to a method for predicting the running efficiency of a squirrel-cage asynchronous motor; it also relates to a system, a device and a computer readable storage medium for predicting the running efficiency of the squirrel-cage asynchronous motor.
Background
With the continuous development of industrial production, the squirrel-cage asynchronous motor is widely applied due to the advantages of high rotating speed, high efficiency, large speed regulation range and the like. And along with the aggravation of the problems of voltage distortion, harmonic waves and other electric energy quality, the running stability and the running efficiency of the squirrel-cage asynchronous motor are reduced, and particularly in areas with poor electric energy quality, the influence of a power grid on the running stability and the running efficiency of the squirrel-cage asynchronous motor is particularly obvious. In view of the above, predicting the operating efficiency of the squirrel-cage asynchronous motor provides an effective reference for the selection and use of the squirrel-cage asynchronous motor, and thus the technical problem to be solved by those skilled in the art is urgently needed.
Disclosure of Invention
The method aims to provide a method for predicting the operating efficiency of the squirrel-cage asynchronous motor, which can accurately and efficiently predict the operating efficiency of various squirrel-cage asynchronous motors; another object of the present application is to provide a system, an apparatus and a computer readable storage medium for predicting the operating efficiency of a squirrel cage asynchronous motor, all having the above technical effects.
In order to solve the technical problem, the application provides a method for predicting the operating efficiency of a squirrel-cage asynchronous motor, which comprises the following steps:
establishing a squirrel-cage asynchronous motor simulation model;
inputting harmonic voltage to the squirrel-cage asynchronous motor simulation model for simulation to obtain simulation data;
carrying out BP neural network training based on the simulation data to obtain a BP neural network prediction model;
and acquiring actual operation data of the current squirrel-cage asynchronous motor, and inputting the actual operation data into the BP neural network prediction model to obtain the operation efficiency of the current squirrel-cage asynchronous motor.
Optionally, the establishing a squirrel-cage asynchronous motor simulation model includes:
establishing a 2D electromagnetic finite element model of the squirrel-cage asynchronous motor through Maxwell software based on the structural parameters of the squirrel-cage asynchronous motor;
establishing a 3D stator structure model of the squirrel-cage asynchronous motor through Solid works software based on the structural parameters of the squirrel-cage asynchronous motor;
and establishing a coupling model of the electromagnetic field and the structural field of the squirrel-cage asynchronous motor through Workbench software based on the material parameters of the squirrel-cage asynchronous motor.
Optionally, the inputting harmonic voltage into the squirrel-cage asynchronous motor simulation model to perform simulation to obtain simulation data includes:
respectively inputting single harmonic voltage and multiple harmonic voltage to the squirrel-cage asynchronous motor simulation model for simulation to obtain corresponding operation curves; wherein the operating curve comprises: torque curves, electrical power curves, and mechanical power curves;
reading corresponding data on the operating curve when the squirrel-cage asynchronous motor is in a steady state;
and obtaining the torque change value and the motor efficiency value of the squirrel-cage asynchronous motor according to the data.
Optionally, the training the BP neural network based on the simulation data to obtain a BP neural network prediction model includes:
and training the BP neural network optimized by the particle swarm optimization based on the simulation data to obtain the BP neural network prediction model.
Optionally, the training the BP neural network optimized by the particle swarm algorithm based on the simulation data to obtain the BP neural network prediction model includes:
initializing the BP neural network and the particle swarm algorithm;
based on the simulation data after normalization processing, iterative optimization is carried out by using the particle swarm optimization to obtain an optimal threshold and an optimal weight;
and taking the optimal threshold and the optimal weight as initial values of the BP neural network, and training the BP neural network based on the simulation data to obtain the BP neural network prediction model.
In order to solve the above technical problem, the present application further provides a system for predicting the operating efficiency of a squirrel-cage asynchronous motor, including:
the model establishing module is used for establishing a squirrel-cage asynchronous motor simulation model;
the harmonic voltage input module is used for inputting harmonic voltage to the squirrel-cage asynchronous motor simulation model for simulation to obtain simulation data;
the neural network training module is used for carrying out BP neural network training based on the simulation data to obtain a BP neural network prediction model;
and the operation efficiency prediction module is used for acquiring the actual operation data of the current squirrel-cage asynchronous motor and inputting the actual operation data into the BP neural network prediction model to obtain the operation efficiency of the squirrel-cage asynchronous motor.
Optionally, the neural network training module is specifically configured to train the BP neural network optimized by the particle swarm algorithm based on the simulation data to obtain the BP neural network prediction model.
In order to solve the above technical problem, the present application further provides a device for predicting the operating efficiency of a squirrel-cage asynchronous motor, including:
a memory for storing a computer program;
a processor for implementing the steps of the method for predicting the operating efficiency of a squirrel cage asynchronous motor as described in any one of the above when executing said computer program.
In order to solve the above technical problem, the present application further provides a computer readable storage medium, which stores a computer program, and the computer program, when executed by a processor, implements the steps of the method for predicting the operating efficiency of a squirrel cage asynchronous motor as described in any one of the above.
The method for predicting the operating efficiency of the squirrel-cage asynchronous motor comprises the steps of establishing a squirrel-cage asynchronous motor simulation model; inputting harmonic voltage to the squirrel-cage asynchronous motor simulation model for simulation to obtain simulation data; carrying out BP neural network training based on the simulation data to obtain a BP neural network prediction model; and acquiring actual operation data of the current squirrel-cage asynchronous motor, and inputting the actual operation data into the BP neural network prediction model to obtain the operation efficiency of the current squirrel-cage asynchronous motor.
Therefore, according to the method for predicting the operating efficiency of the squirrel-cage asynchronous motor, the simulation model of the squirrel-cage asynchronous motor is established, the harmonic voltage is input to the simulation model of the squirrel-cage asynchronous motor for simulation to obtain a large amount of simulation data, BP neural network training is further performed based on the simulation data, and the BP neural network prediction model suitable for the common squirrel-cage asynchronous motor is obtained.
The system, the device and the computer readable storage medium for predicting the operating efficiency of the squirrel-cage asynchronous motor have the technical effects.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
Fig. 1 is a schematic flowchart of a method for predicting the operating efficiency of a squirrel-cage asynchronous motor according to an embodiment of the present application;
FIG. 2 is a schematic diagram of a system for predicting the operating efficiency of a squirrel-cage asynchronous motor according to an embodiment of the present application;
fig. 3 is a schematic diagram of a device for predicting the operating efficiency of a squirrel-cage asynchronous motor according to an embodiment of the present application.
Detailed Description
The core of the application is to provide a method for predicting the running efficiency of the squirrel-cage asynchronous motor, which can accurately and efficiently predict the running efficiency of various squirrel-cage asynchronous motors; another object of the present application is to provide a system, an apparatus and a computer readable storage medium for predicting the operating efficiency of a squirrel cage asynchronous motor, all having the above technical effects.
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
Referring to fig. 1, fig. 1 is a schematic flowchart illustrating a method for predicting an operating efficiency of a squirrel-cage asynchronous motor according to an embodiment of the present disclosure; with reference to fig. 1, the prediction method includes:
s101: establishing a squirrel-cage asynchronous motor simulation model;
specifically, the method aims to establish a squirrel-cage asynchronous motor simulation model, specifically establish a plurality of squirrel-cage asynchronous motor simulation models with the same capacity under different air gap widths, perform simulation operation by utilizing the squirrel-cage asynchronous motor simulation models to obtain a large amount of simulation data, and provide a data basis for the construction of a BP neural network prediction model.
In a specific embodiment, the establishing a squirrel-cage asynchronous motor simulation model may include: establishing a 2D electromagnetic finite element model of the squirrel-cage asynchronous motor through Maxwell software based on the structural parameters of the squirrel-cage asynchronous motor; establishing a 3D stator structure model of the squirrel-cage asynchronous motor through Solid works software based on the structural parameters of the squirrel-cage asynchronous motor; and establishing a coupling model of an electromagnetic field and a structural field of the squirrel-cage asynchronous motor through Workbench software based on the material parameters of the squirrel-cage asynchronous motor. Specifically, the air gap widths of the squirrel-cage asynchronous motor can be respectively set to be 0.5mm, 1mm and 1.5mm, and a squirrel-cage asynchronous motor simulation model is established through the simulation software.
S102: inputting harmonic voltage to a squirrel-cage asynchronous motor simulation model for simulation to obtain simulation data; specifically, for the 3 rd harmonic:
thus, the magnetomotive force for which 3 rd order harmonic synthesis can be obtained is:
it can be seen that the magnetomotive force synthesized by the harmonic of order 3 is zero, and in the symmetrical squirrel-cage asynchronous motor, the magnetomotive force synthesized by the harmonic of order 3 also has the above properties, that is, the magnetomotive force synthesized by the harmonic of order 3 is zero, for example, the magnetomotive force synthesized by the harmonic of order 9 is zero, and the magnetomotive force synthesized by the harmonic of order 15 is zero.
In addition, it can be found by analysis that the harmonics affecting the squirrel cage asynchronous motor are mainly 5 th harmonic, 7 th harmonic and 11 th harmonic, so the input voltage is determined as follows:
wherein, UHC0、UHC5、UHC7、UHC11The fundamental content, the 5 th harmonic content, the 7 th harmonic content and the 11 th harmonic content, respectively. And harmonic content
In the above formula, i represents a harmonic type, and when i is 7, the corresponding UHC7I.e. 7 th harmonic content, UrmsIs a valid value, UhnIs an n-th harmonic.
And further, inputting harmonic voltage to the squirrel-cage asynchronous motor simulation model under each air gap width according to the determined input voltage for simulation to obtain simulation data.
In a specific embodiment, the inputting of the harmonic voltage to the squirrel-cage asynchronous motor simulation model for simulation to obtain simulation data includes inputting a single harmonic voltage and multiple harmonic voltages to the squirrel-cage asynchronous motor simulation model for simulation to obtain corresponding operation curves; wherein the operating curve comprises: torque curves, electrical power curves, and mechanical power curves; reading corresponding data on a running curve of the squirrel-cage asynchronous motor in a steady state; and obtaining the torque change value and the motor efficiency value of the squirrel-cage asynchronous motor according to the data.
Specifically, a single harmonic voltage, that is, a harmonic voltage including only the fundamental wave content and any one of the above 5 th harmonic content, 7 th harmonic content, and 11 th harmonic content, may be first input to the squirrel cage asynchronous motor simulation model, and if only the fundamental wave content and the 5 th harmonic content are included, the input harmonic voltage is:
of course, in order to obtain a large amount of simulation data, the single harmonic voltage containing the fundamental wave content and the 5 th harmonic content, the single harmonic voltage containing the fundamental wave content and the 7 th harmonic content, and the single harmonic voltage containing the fundamental wave content and the 11 th harmonic content may be respectively input into the squirrel cage asynchronous motor simulation model for simulation, so as to obtain the operation curves under various single harmonic voltages.
And further, as a comparison group, inputting multiple harmonic voltages to the squirrel-cage asynchronous motor simulation model. The multiple harmonic voltage is a harmonic voltage containing a fundamental wave content and a harmonic content in any combination of the above-described 5 th harmonic content, 7 th harmonic content, and 11 th harmonic content, with respect to the single harmonic voltage. For example, including the fundamental content, the 5 th harmonic content, and the 11 th harmonic content, the input harmonic voltage is:
similarly, in order to obtain a large amount of simulation data, harmonic voltages under various combination conditions can be input into a squirrel-cage asynchronous motor simulation model for simulation, and further, operation curves under various multiple harmonic voltages can be obtained.
Further, on the basis of obtaining the operation curves (including a torque curve, an electric power curve and a mechanical power curve) under the single harmonic voltage and the multiple harmonic voltage, corresponding data on the steady-state operation curve of the squirrel-cage asynchronous motor are read, and the torque change value and the motor efficiency value of the squirrel-cage asynchronous motor under the single harmonic voltage and the multiple harmonic voltage are respectively and correspondingly obtained according to the read data.
Specifically, the torque data corresponding to the steady state of the squirrel-cage asynchronous motor is read from the torque curve and is further based on
Obtaining the mean value of the torque
In the above formula, n is the read data volume, for example, the data volume of the torque data of the squirrel-cage asynchronous motor with 1mm air gap width under the single harmonic voltage containing the fundamental wave content and the 5 th harmonic content; t isiThe torque instantaneous value when the squirrel-cage asynchronous motor reaches the steady state is obtained. Further, according to
Calculating to obtain a torque change value delta T of the squirrel-cage asynchronous motor under the current harmonic voltage; wherein, T in the above formulanIs the rated torque. In addition, the corresponding electric power data of the squirrel-cage asynchronous motor in the steady state are read from the electric power curve and are based on
Calculating to obtain the average value of electric power
n is the amount of data read, pjThe instantaneous value of the electric power when the squirrel-cage asynchronous motor reaches the steady state is obtained. And reading the mechanical power data corresponding to the squirrel-cage asynchronous motor in the steady state from the mechanical power curve and according to the mechanical power data
Calculating to obtain the mean value of the mechanical power
n is the amount of data read, pmThe mechanical power instantaneous value when the squirrel-cage asynchronous motor reaches the steady state is obtained. Further, according to
And calculating to obtain the motor efficiency value of the squirrel-cage asynchronous motor.
S103: carrying out BP neural network training based on simulation data to obtain a BP neural network prediction model;
specifically, a BP neural network with three layers (including an input layer, a hidden layer and an output layer) can be constructed in MATLAB software, and then a large amount of simulation data obtained through the steps is utilized to perform BP neural network training to obtain a BP neural network prediction model, and the BP neural network prediction model is subsequently utilized to perform operation efficiency prediction.
In a specific embodiment, the training of the BP neural network based on the simulation data to obtain the BP neural network prediction model includes training of the BP neural network optimized by the particle swarm algorithm based on the simulation data to obtain the BP neural network prediction model.
Specifically, in order to guarantee the training result and improve the prediction accuracy of the BP neural network prediction model, the embodiment optimizes the BP neural network by using the particle swarm algorithm, and then trains the BP neural network optimized by the particle swarm algorithm to obtain the BP neural network prediction model.
In a specific embodiment, the training of the BP neural network optimized by the particle swarm algorithm based on the simulation data to obtain a BP neural network prediction model includes initializing the BP neural network and the particle swarm algorithm; based on the simulation data after the normalization processing, iterative optimization is carried out by utilizing a particle swarm algorithm to obtain an optimal threshold and an optimal weight; and taking the optimal threshold and the optimal weight as initial values of the BP neural network, and training the BP neural network based on simulation data to obtain a BP neural network prediction model.
Specifically, a BP neural network is initialized, and parameters of a particle swarm algorithm (including setting of population scale, evolution times, speed updating parameters, individual extremum and population extremum) are set. And carrying out normalization processing on the simulation data, carrying out particle swarm algorithm iteration optimization based on the simulation data after the normalization processing to generate a fitness curve, judging whether the particle swarm algorithm meets error precision or reaches the maximum iteration times based on the fitness curve, if so, exiting the particle swarm algorithm, taking the weight and the threshold obtained by the iteration at the moment, namely the optimal weight and the optimal threshold as initial values of the BP neural network, and further, training the BP neural network by utilizing the simulation data to obtain a BP neural network prediction model. In addition, partial data can be selected from the simulation data as prediction data, the prediction data is input into a BP neural network after being normalized to obtain prediction output data, and the prediction output data is further subjected to inverse normalization processing.
S104: and acquiring actual operation data of the current squirrel-cage asynchronous motor, and inputting the actual operation data into a BP neural network prediction model to obtain the operation efficiency of the current squirrel-cage asynchronous motor.
Specifically, when the operation efficiency of a certain squirrel cage asynchronous motor, namely the current squirrel cage asynchronous motor, needs to be predicted, the actual operation data of the squirrel cage asynchronous motor, including the harmonic type, the harmonic content, the air gap width and the torque variation value, are firstly obtained and input into the BP neural network prediction model as input quantities, and the output quantity of the BP neural network prediction model is the operation efficiency.
In summary, according to the method for predicting the operating efficiency of the squirrel cage asynchronous motor provided by the application, the simulation model of the squirrel cage asynchronous motor is established, the harmonic voltage is input to the simulation model of the squirrel cage asynchronous motor for simulation to obtain a large amount of simulation data, and then the BP neural network training is performed based on the simulation data to obtain the BP neural network prediction model suitable for the common squirrel cage asynchronous motor, so that when the operating efficiency is predicted, the corresponding operating efficiency can be obtained by inputting the actual operating data of the corresponding squirrel cage asynchronous motor into the BP neural network, the accurate and efficient prediction of the operating efficiency of each type of squirrel cage asynchronous motor is realized, and effective references are provided for the model selection and the use of the squirrel cage asynchronous motor.
The present application also provides a system for predicting the operating efficiency of a squirrel cage asynchronous motor, which is described below and which may be referred to in correspondence with the method described above. Referring to fig. 2, fig. 2 is a schematic diagram of a system for predicting an operating efficiency of a squirrel cage asynchronous motor according to an embodiment of the present application, and with reference to fig. 2, the system includes:
the model building module 10 is used for building a squirrel-cage asynchronous motor simulation model;
the harmonic voltage input module 20 is used for inputting harmonic voltage to the squirrel-cage asynchronous motor simulation model for simulation to obtain simulation data;
the neural network training module 30 is used for carrying out BP neural network training by using simulation data to obtain a BP neural network prediction model;
and the operation efficiency prediction module 40 is used for acquiring actual operation data of the current squirrel-cage asynchronous motor and inputting the actual operation data into the BP neural network prediction model to obtain the operation efficiency of the current squirrel-cage asynchronous motor.
On the basis of the above embodiment, optionally, the neural network training module 30 is specifically configured to train the BP neural network optimized by the particle swarm optimization based on the simulation data to obtain a BP neural network prediction model.
The present application also provides a device for predicting the operating efficiency of a squirrel-cage asynchronous motor, as shown in fig. 3, the device comprising: a memory 11 and a processor 12;
wherein the memory 11 is used for storing a computer program; the processor 12 is arranged to implement the following steps when executing the computer program:
establishing a squirrel-cage asynchronous motor simulation model; inputting harmonic voltage to a squirrel-cage asynchronous motor simulation model for simulation to obtain simulation data; carrying out BP neural network training by utilizing simulation data to obtain a BP neural network prediction model; and acquiring actual operation data of the current squirrel-cage asynchronous motor, and inputting the actual operation data into a BP neural network prediction model to obtain the operation efficiency of the current squirrel-cage asynchronous motor.
For the introduction of the prediction apparatus provided in the present application, please refer to the embodiments of the above method, which is not described herein again.
The present application further provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of:
establishing a squirrel-cage asynchronous motor simulation model; inputting harmonic voltage to a squirrel-cage asynchronous motor simulation model for simulation to obtain simulation data; carrying out BP neural network training by utilizing simulation data to obtain a BP neural network prediction model; and acquiring actual operation data of the current squirrel-cage asynchronous motor, and inputting the actual operation data into a BP neural network prediction model to obtain the operation efficiency of the current squirrel-cage asynchronous motor.
The computer-readable storage medium may include: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
For the introduction of the computer-readable storage medium provided by the present invention, please refer to the above method embodiments, which are not described herein again.
The embodiments are described in a progressive manner in the specification, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. The device, the apparatus and the computer-readable storage medium disclosed in the embodiments correspond to the method disclosed in the embodiments, so that the description is simple, and the relevant points can be referred to the description of the method.
Those of skill would further appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative components and steps have been described above generally in terms of their functionality in order to clearly illustrate this interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in Random Access Memory (RAM), memory, Read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
The method, system, device and computer readable storage medium for predicting the operating efficiency of the squirrel cage asynchronous motor provided by the present application are described in detail above. The principles and embodiments of the present application are explained herein using specific examples, which are provided only to help understand the method and the core idea of the present application. It should be noted that, for those skilled in the art, it is possible to make several improvements and modifications to the present application without departing from the principle of the present application, and such improvements and modifications also fall within the scope of the claims of the present application.
Claims (9)
1. A method for predicting the running efficiency of a squirrel-cage asynchronous motor is characterized by comprising the following steps:
establishing a squirrel-cage asynchronous motor simulation model;
inputting harmonic voltage to the squirrel-cage asynchronous motor simulation model for simulation to obtain simulation data;
carrying out BP neural network training based on the simulation data to obtain a BP neural network prediction model;
and acquiring actual operation data of the current squirrel-cage asynchronous motor, and inputting the actual operation data into the BP neural network prediction model to obtain the operation efficiency of the current squirrel-cage asynchronous motor.
2. The prediction method of claim 1, wherein said establishing a squirrel cage asynchronous motor simulation model comprises:
establishing a 2D electromagnetic finite element model of the squirrel-cage asynchronous motor through Maxwell software based on the structural parameters of the squirrel-cage asynchronous motor;
establishing a 3D stator structure model of the squirrel-cage asynchronous motor through Solid works software based on the structural parameters of the squirrel-cage asynchronous motor;
and establishing a coupling model of the electromagnetic field and the structural field of the squirrel-cage asynchronous motor through Workbench software based on the material parameters of the squirrel-cage asynchronous motor.
3. The prediction method of claim 1, wherein the inputting harmonic voltages into the squirrel-cage asynchronous motor simulation model for simulation to obtain simulation data comprises:
respectively inputting single harmonic voltage and multiple harmonic voltage to the squirrel-cage asynchronous motor simulation model for simulation to obtain corresponding operation curves; wherein the operating curve comprises: torque curves, electrical power curves, and mechanical power curves;
reading corresponding data on the operating curve when the squirrel-cage asynchronous motor is in a steady state;
and obtaining the torque change value and the motor efficiency value of the squirrel-cage asynchronous motor according to the data.
4. The prediction method of claim 1, wherein the training of the BP neural network based on the simulation data to obtain the BP neural network prediction model comprises:
and training the BP neural network optimized by the particle swarm optimization based on the simulation data to obtain the BP neural network prediction model.
5. The prediction method according to claim 4, wherein the training of the BP neural network optimized by the particle swarm optimization based on the simulation data to obtain the BP neural network prediction model comprises:
initializing the BP neural network and the particle swarm algorithm;
based on the simulation data after normalization processing, iterative optimization is carried out by using the particle swarm optimization to obtain an optimal threshold and an optimal weight;
and taking the optimal threshold and the optimal weight as initial values of the BP neural network, and training the BP neural network based on the simulation data to obtain the BP neural network prediction model.
6. A system for predicting the operating efficiency of a squirrel cage asynchronous motor, comprising:
the model establishing module is used for establishing a squirrel-cage asynchronous motor simulation model;
the harmonic voltage input module is used for inputting harmonic voltage to the squirrel-cage asynchronous motor simulation model for simulation to obtain simulation data;
the neural network training module is used for carrying out BP neural network training based on the simulation data to obtain a BP neural network prediction model;
and the operation efficiency prediction module is used for acquiring the actual operation data of the current squirrel-cage asynchronous motor and inputting the actual operation data into the BP neural network prediction model to obtain the operation efficiency of the squirrel-cage asynchronous motor.
7. The prediction system of claim 6, wherein the neural network training module is specifically configured to train the BP neural network optimized by the particle swarm optimization based on the simulation data to obtain the BP neural network prediction model.
8. A device for predicting the operating efficiency of a squirrel-cage asynchronous motor is characterized by comprising:
a memory for storing a computer program;
a processor for implementing the steps of the method for predicting the operating efficiency of a squirrel cage asynchronous motor as claimed in any one of claims 1 to 5 when executing said computer program.
9. A computer-readable storage medium, characterized in that it stores a computer program which, when being executed by a processor, implements the steps of the method for predicting the operating efficiency of a squirrel cage asynchronous machine according to any one of claims 1 to 5.
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Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH05161265A (en) * | 1991-11-28 | 1993-06-25 | Toshiba Corp | Power system monitor |
| WO1997049977A1 (en) * | 1996-06-24 | 1997-12-31 | Arcelik A.S. | Model-based fault detection system for electric motors |
| CN102536774A (en) * | 2011-12-27 | 2012-07-04 | 酒泉钢铁(集团)有限责任公司 | Operation characteristics and operation energy efficiency analyzing device of industrial circulating water pump set |
| CN103344368A (en) * | 2013-06-26 | 2013-10-09 | 华北电力大学 | Squirrel-cage type asynchronous motor energy efficiency online monitoring method based on measurable electrical amount |
| CN104283393A (en) * | 2014-09-25 | 2015-01-14 | 南京工程学院 | Method for optimizing structure parameter of single-winding magnetic suspension switch reluctance machine |
| CN105740562A (en) * | 2016-02-04 | 2016-07-06 | 华北电力科学研究院有限责任公司 | Determination method for no-load characteristic curve of power generator |
| CN107179503A (en) * | 2017-04-21 | 2017-09-19 | 美林数据技术股份有限公司 | The method of Wind turbines intelligent fault diagnosis early warning based on random forest |
| CN108205110A (en) * | 2016-12-20 | 2018-06-26 | 北京金风科创风电设备有限公司 | The detection method and device of generator of wind generating set failure |
| CN108932580A (en) * | 2018-06-05 | 2018-12-04 | 浙江运达风电股份有限公司 | Wind turbines pitch variable bearings wear monitoring and method for early warning based on data modeling |
-
2019
- 2019-05-23 CN CN201910434258.5A patent/CN110007232B/en not_active Expired - Fee Related
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH05161265A (en) * | 1991-11-28 | 1993-06-25 | Toshiba Corp | Power system monitor |
| WO1997049977A1 (en) * | 1996-06-24 | 1997-12-31 | Arcelik A.S. | Model-based fault detection system for electric motors |
| CN102536774A (en) * | 2011-12-27 | 2012-07-04 | 酒泉钢铁(集团)有限责任公司 | Operation characteristics and operation energy efficiency analyzing device of industrial circulating water pump set |
| CN103344368A (en) * | 2013-06-26 | 2013-10-09 | 华北电力大学 | Squirrel-cage type asynchronous motor energy efficiency online monitoring method based on measurable electrical amount |
| CN104283393A (en) * | 2014-09-25 | 2015-01-14 | 南京工程学院 | Method for optimizing structure parameter of single-winding magnetic suspension switch reluctance machine |
| CN105740562A (en) * | 2016-02-04 | 2016-07-06 | 华北电力科学研究院有限责任公司 | Determination method for no-load characteristic curve of power generator |
| CN108205110A (en) * | 2016-12-20 | 2018-06-26 | 北京金风科创风电设备有限公司 | The detection method and device of generator of wind generating set failure |
| CN107179503A (en) * | 2017-04-21 | 2017-09-19 | 美林数据技术股份有限公司 | The method of Wind turbines intelligent fault diagnosis early warning based on random forest |
| CN108932580A (en) * | 2018-06-05 | 2018-12-04 | 浙江运达风电股份有限公司 | Wind turbines pitch variable bearings wear monitoring and method for early warning based on data modeling |
Non-Patent Citations (1)
| Title |
|---|
| 基于逐层预测模型的感应电机效率优化滑模控制;苗敬利 等;《电工技术学报》;20140331;第29卷(第3期);第206-212页 * |
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