CN104598686A - Water pump motor modeling and optimizing method based on electromagnetic calculation and neural network - Google Patents
Water pump motor modeling and optimizing method based on electromagnetic calculation and neural network Download PDFInfo
- Publication number
- CN104598686A CN104598686A CN201510035609.7A CN201510035609A CN104598686A CN 104598686 A CN104598686 A CN 104598686A CN 201510035609 A CN201510035609 A CN 201510035609A CN 104598686 A CN104598686 A CN 104598686A
- Authority
- CN
- China
- Prior art keywords
- motor
- pump
- neural network
- water pump
- power
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 title claims abstract description 74
- 238000013528 artificial neural network Methods 0.000 title claims abstract description 45
- 238000000034 method Methods 0.000 title claims abstract description 43
- 238000004364 calculation method Methods 0.000 title claims abstract description 15
- 238000005457 optimization Methods 0.000 claims abstract description 62
- 238000013461 design Methods 0.000 claims abstract description 36
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 18
- 230000002068 genetic effect Effects 0.000 claims abstract description 11
- 238000002922 simulated annealing Methods 0.000 claims abstract description 11
- 238000012549 training Methods 0.000 claims description 21
- XEEYBQQBJWHFJM-UHFFFAOYSA-N Iron Chemical group [Fe] XEEYBQQBJWHFJM-UHFFFAOYSA-N 0.000 claims description 12
- 238000003062 neural network model Methods 0.000 claims description 11
- 210000002569 neuron Anatomy 0.000 claims description 9
- 238000011478 gradient descent method Methods 0.000 claims description 6
- 238000005259 measurement Methods 0.000 claims description 6
- 230000001537 neural effect Effects 0.000 claims description 4
- RYGMFSIKBFXOCR-UHFFFAOYSA-N Copper Chemical compound [Cu] RYGMFSIKBFXOCR-UHFFFAOYSA-N 0.000 claims description 3
- 230000002146 bilateral effect Effects 0.000 claims description 3
- 239000004020 conductor Substances 0.000 claims description 3
- 229910052802 copper Inorganic materials 0.000 claims description 3
- 239000010949 copper Substances 0.000 claims description 3
- 238000009413 insulation Methods 0.000 claims description 3
- 229910052742 iron Inorganic materials 0.000 claims description 3
- 239000003973 paint Substances 0.000 claims description 3
- 230000000452 restraining effect Effects 0.000 claims description 3
- 238000009394 selective breeding Methods 0.000 claims description 3
- 238000012163 sequencing technique Methods 0.000 claims description 3
- 238000004519 manufacturing process Methods 0.000 description 4
- 238000004458 analytical method Methods 0.000 description 2
- 230000005611 electricity Effects 0.000 description 2
- 238000011056 performance test Methods 0.000 description 2
- 238000005086 pumping Methods 0.000 description 2
- 238000012360 testing method Methods 0.000 description 2
- 238000013459 approach Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000010205 computational analysis Methods 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 230000008878 coupling Effects 0.000 description 1
- 238000010168 coupling process Methods 0.000 description 1
- 238000005859 coupling reaction Methods 0.000 description 1
- 230000007423 decrease Effects 0.000 description 1
- 238000004134 energy conservation Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 239000012530 fluid Substances 0.000 description 1
- 239000007788 liquid Substances 0.000 description 1
- 238000005065 mining Methods 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
Classifications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/80—Technologies aiming to reduce greenhouse gasses emissions common to all road transportation technologies
- Y02T10/82—Elements for improving aerodynamics
Landscapes
- Structures Of Non-Positive Displacement Pumps (AREA)
Abstract
The invention discloses a water pump motor modeling and optimizing method based on electromagnetic calculation and a neural network. And for the pump performance, a BP neural network method is adopted, and a hydraulic model is established to quickly fit a flow field flow state model which is difficult to accurately calculate. And forming a 'and pump integrated model' by combining a motor mechanism model and a hydraulic fitting model. And (3) optimally designing the structural parameters of the motor part by adopting a simulated annealing genetic algorithm as an optimization means. The method has the main functions that on the premise of meeting the power of the input shaft of the water pump, the optimal motor structure design parameters are intelligently searched, the motor performance and the water pump load characteristic are accurately matched, and the allowance coefficient of the power of the matched motor is reduced so as to reduce the power of the matched motor.
Description
Technical field
The present invention relates to pump motor neural network modeling approach field, specifically a kind ofly to calculate and the pump motor modeling and optimization method of neural network based on electromagnetism.
Background technology
Producing a large amount of factory effluents in mine underground mining process, and be attended by a large amount of oozing and gush underground water, in order to ensure to keep the safety in production in mine, with large-scale unwatering pump, these water must be drained into outside well in time reliably.Mine is kept the safety in production for a long time and is required that these unwatering pumps must with the production run continuous service of whole mine, thus, mine drainage pumping system is one of key equipment of Mine Safety in Production guarantee, also be simultaneously one of mine primary electricity using device, the most multipotency of its power consumption accounts for 40% of mine total electricity consumption.The energy-efficient running of unwatering pump to the saving energy and reduce the cost of mine, control cost significant.
Sump pump system is that adapted motor driving water pump vane rotates the structure producing centrifugal force and discharged along pipeline by liquid.The power selection of water pump necessary electromotor is normally multiplied by suitable reserve factor to determine by the shaft power of pumps design operating point.If pump unreasonable allocation, produce the phenomenon of low load with strong power, motor power will can not get effective utilization.Meanwhile, the efficiency of water pump adapted motor self is not high, can produce large energy deadweight loss yet.Therefore the energy saving direction of sump pump system mainly contains the performance optimization of unwatering pump adapted motor, power match two aspects of unwatering pump and adapted motor.
There is following problem in the work at present in sump pump system is energy-conservation:
(1) adopt mechanism model to carry out computational analysis to water pump hydraulic performance, then because the waterpower mechanism model of water pump is a three dimensional non-linear multivariable dynamic model, CFD flow field flow simulation method must be adopted just to carry out analog computation analysis.And the flow field fluid flowing model of water pump effectively cannot be combined with motor electromagnetic model, be unfavorable for the analysis of entire system performance;
(2) Optimization Work of motor is with the performance index of motor self for target, does not investigate the power how reducing adapted motor simultaneously, improves motor performance and water pump load characteristic coupling matching degree.
summary of the inventionthe object of this invention is to provide and a kind ofly to calculate and the pump motor modeling and optimization method of neural network, to solve prior art Problems existing based on electromagnetism.
In order to achieve the above object, the technical solution adopted in the present invention is:
Calculate and the pump motor modeling and optimization method of neural network based on electromagnetism, it is characterized in that: comprise the following steps:
(1), according to the mechanism model of water pump submersible three phase asynchronous motor, motor forward electromagnetic computation program is write;
(2), adopt BP neural net method, data fitting modeling is carried out to water pump hydraulic model;
(3), using the input data of the Output rusults of motor electromagnetic calculating as water pump hydraulic model, in conjunction with the pump integrated model of two parts program forming machine;
(4), reverse matching is carried out to parallel water pump data, the demand data drawn according to the reverse matching of pump performance, the allowance coefficient target of setting and motor performance target, realize being optimized design function to institute's adapted motor, form pump motor reverse optimization system;
(5), adopt Genetic Simulated Annealing Algorithm, the demand data drawn according to the reverse matching of pump performance, the allowance coefficient target of setting and motor performance target, coordinate motor forward electromagnetic computation program, computing machine Automatic Optimal Design is carried out to adapted motor.
Described to calculate based on electromagnetism and the pump motor modeling and optimization method of neural network, it is characterized in that: in described step (1), motor forward electromagnetic computation program:
To the submersible motor of water pump adapted, adopt modelling by mechanism method, computation process sequencing being formed can the motor electromagnetic calculation procedure of independent operating;
The input data of program comprise: output power kW, supply frequency Hz, line voltage V, stator connection, number of poles, pitch, rotor groove number, stator outer interior diameter mm, air gap mm, rotor internal diameter mm, stator slot molded dimension mm, slot shape of rotor size mm), often enclose the number of turn, parallel branch number around radical, slot insulation thickness mm, wire diameter, bilateral paint film mm, the long mm of iron core, stacking factor, coil straight line portion stretch out long mm, end ring mean diameter mm, area mm^2, stray loss kW, iron loss factor, leakage reactance coefficient etc.
Export data as calculated to comprise: efficiency, power factor, nominal torque, torque capacity, detent torque, starting current, copper factor;
On the basis that traditional threephase asynchronous magnetic equivalent circuit method electromagnetism calculates, the design feature that service condition in water and rotor all adopt closed slot is immersed according to submersible motor, on the mechanical loss of motor and the computing method of rotor groove top leakage permeance, have employed the computing method different from common asynchronous moter;
Pump motor mechanical loss is divided into three parts to calculate: the frictional dissipation of the frictional dissipation of rotor and chilled water, the frictional dissipation of thrust bearing and guide bearing;
For the groove top leakage permeance of pump motor rotor closed slot, the method for equivalent slot opening is adopted to calculate.
Described to calculate based on electromagnetism and the pump motor modeling and optimization method of neural network, it is characterized in that: in described step (2), BP neural network:
Set up two kinds of structure proximates, the BP neural network 1 that function is different and BP neural network 2, be respectively used in conjunction with motor forward electromagnetic computation program, the pump integrated model of forming machine, and in conjunction with motor electromagnetic calculation and optimization algorithm, form pump motor reverse optimization design system;
For in conjunction with motor forward electromagnetic computation program, the water pump waterpower BP neural network model 1 of the pump integrated model of forming machine, adopt two-output impulse generator, three-decker, hidden layer 8 neuron, gradient descent method round-off error, training end condition is that training error is less than 2% " architectural feature; with water pump input shaft power, pump efficiency for mode input, export water lift of pump and data on flows;
For in conjunction with motor forward electromagnetic computation program and Genetic Simulated Annealing Algorithm, form the water pump waterpower BP neural network model 2 of pump motor reverse optimization system, adopt triple input single output, three-decker, hidden layer 8 neuron, adopts gradient descent method round-off error, and training end condition is that training error is less than 2% " architectural feature; with pumps design flow, lift, target pump efficiency for input, calculate prediction of output water pump demand shaft power.
Described to calculate based on electromagnetism and the pump motor modeling and optimization method of neural network, it is characterized in that: in described step (3), motor-and-pump integrated model:
After input electric machine structure parameter carries out the calculating of motor forward electromagnetism, the result of calculating is transferred to through actual measurement sample training and the BP neural network 1 of having restrained, finally exports delivery rate, the lift of water pump under current motor power rating;
The input structure parameter that motor forward electromagnetic program uses, and water pump waterpower BP neural network model 1 is used for carrying out the data of neuron training, all leave in file with the form of txt document, program reads automatically;
Motor-and-pump integrated model can independent of pump motor reverse optimization system described in step (4), and isolated operation calculates.
Described to calculate based on electromagnetism and the pump motor modeling and optimization method of neural network, it is characterized in that: in described step (4), pump motor reverse optimization system:
In conjunction with motor forward electromagnetic computation program, BP neural network 2 and Genetic Simulated Annealing Algorithm, form pump motor reverse optimization system;
BP neural network 2 carries out training according to actual measurement sample and after restraining, when input one group of flow, lift and target pump efficiency data, matching is provided the shaft power that water pump needs to input in this case, coordinate artificially given allowance coefficient, water pump can be drawn in this case, the optimum output power that the motor of its adapted should have; Power-optimized designs target using this power as motor, coordinates efficiency, other indexs of torque, carries out pump motor multi-objective optimization design of power; Optimization aim is set as the reserve factor between the efficiency of motor, power factor, torque capacity, detent torque, starting current and pump; Motor optimized variable is chosen: iron core long, every groove conductor number, rotor slot part inside dimension parameter and output rating;
Pump motor multi-objective optimization design of power is generally difficult to find the optimum solution meeting all target calls, then exported as optimum results, for artificial selection using txt form by the noninferior solution meeting part optimization aim obtained when optimizing and stop;
Pump motor reverse optimization system can independent of the described motor-and-pump integrated model of step (3), and isolated operation calculates.
The present invention relates to phase asynchronous submersible motor optimal design and large-scale unwatering pump conservancy property based on neural net model establishing technical field, by calculating water pump adapted motor forward electromagnetism and carrying out neural network matching modeling to water pump hydraulic performance, set up motor-and-pump integrated model.Simultaneously in conjunction with optimized algorithm, form pump motor reverse optimization design system.
The present invention with compared with prior art, its beneficial effect is: the present invention adopts neural network approximating method to carry out modeling to water pump, and institute's Modling model is suitable in conjunction with motor model, carries out analytical calculation to entire system performance.With in conjunction with optimized algorithm, according to the principle of pump power matched, intelligent reverse optimization is carried out to motor construction parameter.
Accompanying drawing explanation
Fig. 1 is the motor-and-pump integrated calculating of the present invention and the logic relation picture of pump motor reverse optimization functional realiey.
Fig. 2 is the BP neural network structure of institute's employing pump hydraulic performance matching in motor-and-pump integrated model.
Fig. 3 by pump motor reverse optimization design system employing parallel water pump data are carried out to the BP neural network structure of reverse matching.
Fig. 4 is the flow process of motor-and-pump integrated normatron pumping system overall performance.
Fig. 5 to be optimized the flow process of calculating for pump motor reverse optimization design system to institute's adapted motor.
Embodiment
Calculate and the pump motor modeling and optimization method of neural network based on electromagnetism, comprise the following steps:
(1), according to the mechanism model of water pump submersible three phase asynchronous motor, motor forward electromagnetic computation program is write;
(2), adopt BP neural net method, data fitting modeling is carried out to water pump hydraulic model;
(3), using the input data of the Output rusults of motor electromagnetic calculating as water pump hydraulic model, in conjunction with the pump integrated model of two parts program forming machine;
(4), to parallel water pump data carry out reverse matching, according to pump power demand, safety allowance coefficient, realize being optimized design function to institute's adapted motor, form pump motor reverse optimization system, as shown in Figure 5;
(5), adopt Genetic Simulated Annealing Algorithm, the demand data drawn according to the reverse matching of pump performance, the allowance coefficient target of setting and motor performance target, coordinate motor forward electromagnetic computation program, computing machine Automatic Optimal Design is carried out to adapted motor.
In step (1), motor forward electromagnetic computation program:
To the submersible motor of water pump adapted, adopt modelling by mechanism method, computation process sequencing being formed can the motor electromagnetic calculation procedure of independent operating;
The input data of program comprise: output power kW, supply frequency Hz, line voltage V, stator connection, number of poles, pitch, rotor groove number, stator outer interior diameter mm, air gap mm, rotor internal diameter mm, stator slot molded dimension mm, slot shape of rotor size mm), often enclose the number of turn, parallel branch number around radical, slot insulation thickness mm, wire diameter, bilateral paint film mm, the long mm of iron core, stacking factor, coil straight line portion stretch out long mm, end ring mean diameter mm, area mm^2, stray loss kW, iron loss factor, leakage reactance coefficient etc.
Export data as calculated to comprise: efficiency, power factor, nominal torque, torque capacity, detent torque, starting current, copper factor;
On the basis that traditional threephase asynchronous magnetic equivalent circuit method electromagnetism calculates, the design feature that service condition in water and rotor all adopt closed slot is immersed according to submersible motor, on the mechanical loss of motor and the computing method of rotor groove top leakage permeance, have employed the computing method different from common asynchronous moter;
Pump motor mechanical loss is divided into three parts to calculate: the frictional dissipation of the frictional dissipation of rotor and chilled water, the frictional dissipation of thrust bearing and guide bearing;
For the groove top leakage permeance of pump motor rotor closed slot, the method for equivalent slot opening is adopted to calculate.
In step (2), BP neural network:
Set up two kinds of structure proximates, the BP neural network 1 that function is different and BP neural network 2, be respectively used in conjunction with motor forward electromagnetic computation program, the pump integrated model of forming machine, and in conjunction with motor electromagnetic calculation and optimization algorithm, form pump motor reverse optimization design system;
For in conjunction with motor forward electromagnetic computation program, the water pump waterpower BP neural network model 1 of the pump integrated model of forming machine, adopt two-output impulse generator, three-decker, hidden layer 8 neuron, gradient descent method round-off error, training end condition is that training error is less than 2% " architectural feature; as shown in Figure 2, with water pump input shaft power, pump efficiency for mode input, export water lift of pump and data on flows;
For in conjunction with motor forward electromagnetic computation program and Genetic Simulated Annealing Algorithm, form the water pump waterpower BP neural network model 2 of pump motor reverse optimization system, adopt triple input single output, three-decker, hidden layer 8 neuron, adopts gradient descent method round-off error, training end condition is that training error is less than 2% " architectural feature; as shown in Figure 3, with pumps design flow, lift, target pump efficiency for input, calculate prediction of output water pump demand shaft power.
In step (3), motor-and-pump integrated model:
After input electric machine structure parameter carries out the calculating of motor forward electromagnetism, the result of calculating is transferred to through actual measurement sample training and the BP neural network 1 of having restrained, finally exports delivery rate, the lift of water pump under current motor power rating, as shown in Figure 4;
The input structure parameter that motor forward electromagnetic program uses, and water pump waterpower BP neural network model 1 is used for carrying out the data of neuron training, all leave in file with the form of txt document, program reads automatically;
Motor-and-pump integrated model can independent of pump motor reverse optimization system in step (4), and isolated operation calculates.
In step (4), the reverse optimization of pump motor shown in Fig. 5 system:
In conjunction with motor forward electromagnetic computation program, BP neural network 2 and Genetic Simulated Annealing Algorithm, form pump motor reverse optimization system;
BP neural network 2 carries out training according to actual measurement sample and after restraining, when input one group of flow, lift and target pump efficiency data, matching is provided the shaft power that water pump needs to input in this case, coordinate artificially given allowance coefficient, water pump can be drawn in this case, the optimum output power that the motor of its adapted should have; Power-optimized designs target using this power as motor, coordinates efficiency, other indexs of torque, carries out pump motor multi-objective optimization design of power; Optimization aim is set as the reserve factor between the efficiency of motor, power factor, torque capacity, detent torque, starting current and pump; Motor optimized variable is chosen: iron core long, every groove conductor number, rotor slot part inside dimension parameter and output rating;
Pump motor multi-objective optimization design of power is generally difficult to find the optimum solution meeting all target calls, then exported as optimum results, for artificial selection using txt form by the noninferior solution meeting part optimization aim obtained when optimizing and stop;
Pump motor reverse optimization system can independent of step (3) motor-and-pump integrated model, and isolated operation calculates.
Calculating and the pump motor modeling and optimization system of neural network based on electromagnetism, by carrying out neural network matching to water pump hydraulic performance to the calculating of water pump adapted motor forward electromagnetism, motor-and-pump integrated model can be set up.Simultaneously in conjunction with optimized algorithm, according to the principle of pump power matched, intelligent reverse optimization is carried out to water pump adapted motor.
Wherein, concrete motor-and-pump integrated modeling process is as follows:
Step 1: the threephase asynchronous machine prototype structure parameter of input object pump system institute adapted, by motor forward electromagnetic computation program, calculates the output parameter such as motor output torque, power, and the state parameter such as electric current, efficiency.
Step 2: according to the hydraulic performance test test data of object pump system, water pump BP neural network model 1 is trained, until neural network has the input-output characteristic consistent with water pump measured data.
Step 3: the result that motor forward electromagnetism calculates is transferred to water pump BP neural network model 1, calculates and exports under current motor power rating, the final delivery rate of pump system, lift performance.Output motor state parameter and water pump partial status parameter simultaneously.
It is as follows that above-mentioned water pump adapted motor carries out intelligent reverse optimization process:
Step 1: according to the hydraulic performance test test data of object pump system, carries out reverse train to BP neural network 2 until convergence.
Step 2: by pumps design flow, lift and target pump efficiency input BP neural network 2, calculate and provide water pump in this flow, lift demand situation, need the shaft power from motor input.
Step 3: each performance arget value of setting Motor Optimizing Design and pump reserve factor decline desired value.
Step 4: electric machine structure parameter is divided into and does not participate in Optimal Parameters and participate in Optimal Parameters two groups.The adjustment of running optimizatin algorithm participates in Optimal Parameters, and cooperation does not participate in Optimal Parameters and forms multiple new design of electrical motor scheme.
Step 5: new for all motors design proposal is transported to motor forward electromagnetic computation program, calculates each scheme output performance.
Step 6: check in all new motor design proposals whether have the scheme meeting all optimization aim.Export this strategy parameter.No, regenerate one group of design of electrical motor scheme according to Genetic Simulated Annealing Algorithm rule.Repeat step 5, optimize end condition until meet.
Claims (5)
1. calculate and the pump motor modeling and optimization method of neural network based on electromagnetism, it is characterized in that: comprise the following steps:
(1), according to the mechanism model of water pump submersible three phase asynchronous motor, motor forward electromagnetic computation program is write;
(2), adopt BP neural net method, data fitting modeling is carried out to water pump hydraulic model;
(3), using the input data of the Output rusults of motor electromagnetic calculating as water pump hydraulic model, in conjunction with the pump integrated model of two parts program forming machine;
(4), to parallel water pump data carry out reverse matching, according to pump power demand, safety allowance coefficient, realize being optimized design function to institute's adapted motor, form pump motor reverse optimization system;
(5), adopt Genetic Simulated Annealing Algorithm, the demand data drawn according to the reverse matching of pump performance, the allowance coefficient target of setting and motor performance target, coordinate motor forward electromagnetic computation program, computing machine Automatic Optimal Design is carried out to adapted motor.
2. according to claim 1ly to calculate and the pump motor modeling and optimization method of neural network based on electromagnetism, it is characterized in that: in described step (1), motor forward electromagnetic computation program:
To the submersible motor of water pump adapted, adopt modelling by mechanism method, computation process sequencing being formed can the motor electromagnetic calculation procedure of independent operating;
The input data of program comprise: output power kW, supply frequency Hz, line voltage V, stator connection, number of poles, pitch, rotor groove number, stator outer interior diameter mm, air gap mm, rotor internal diameter mm, stator slot molded dimension mm, slot shape of rotor size mm), often enclose the number of turn, parallel branch number around radical, slot insulation thickness mm, wire diameter, bilateral paint film mm, the long mm of iron core, stacking factor, coil straight line portion stretch out long mm, end ring mean diameter mm, area mm^2, stray loss kW, iron loss factor, leakage reactance coefficient etc.;
Export data as calculated to comprise: efficiency, power factor, nominal torque, torque capacity, detent torque, starting current, copper factor;
On the basis that traditional threephase asynchronous magnetic equivalent circuit method electromagnetism calculates, the design feature that service condition in water and rotor all adopt closed slot is immersed according to submersible motor, on the mechanical loss of motor and the computing method of rotor groove top leakage permeance, have employed the computing method different from common asynchronous moter;
Pump motor mechanical loss is divided into three parts to calculate: the frictional dissipation of the frictional dissipation of rotor and chilled water, the frictional dissipation of thrust bearing and guide bearing;
For the groove top leakage permeance of pump motor rotor closed slot, the method for equivalent slot opening is adopted to calculate.
3. according to claim 1ly to calculate and the pump motor modeling and optimization method of neural network based on electromagnetism, it is characterized in that: in described step (2), BP neural network:
Set up two kinds of structure proximates, the BP neural network 1 that function is different and BP neural network 2, be respectively used in conjunction with motor forward electromagnetic computation program, the pump integrated model of forming machine, and in conjunction with motor electromagnetic calculation and optimization algorithm, form pump motor reverse optimization design system;
For in conjunction with motor forward electromagnetic computation program, the water pump waterpower BP neural network model 1 of the pump integrated model of forming machine, adopt two-output impulse generator, three-decker, hidden layer 8 neuron, gradient descent method round-off error, training end condition is that training error is less than 2% " architectural feature; with water pump input shaft power, pump efficiency for mode input, export water lift of pump and data on flows;
For in conjunction with motor forward electromagnetic computation program and Genetic Simulated Annealing Algorithm, form the water pump waterpower BP neural network model 2 of pump motor reverse optimization system, adopt triple input single output, three-decker, hidden layer 8 neuron, adopts gradient descent method round-off error, and training end condition is that training error is less than 2% " architectural feature; with pumps design flow, lift, target pump efficiency for input, calculate prediction of output water pump demand shaft power.
4. according to claim 1ly to calculate and the pump motor modeling and optimization method of neural network based on electromagnetism, it is characterized in that: in described step (3), motor-and-pump integrated model:
After input electric machine structure parameter carries out the calculating of motor forward electromagnetism, the result of calculating is transferred to through actual measurement sample training and the BP neural network 1 of having restrained, finally exports delivery rate, the lift of water pump under current motor power rating;
The input structure parameter that motor forward electromagnetic program uses, and water pump waterpower BP neural network model 1 is used for carrying out the data of neuron training, all leave in file with the form of txt document, program reads automatically;
Motor-and-pump integrated model can independent of pump motor reverse optimization system described in step (4), and isolated operation calculates.
5. according to claim 1ly to calculate and the pump motor modeling and optimization method of neural network based on electromagnetism, it is characterized in that: in described step (4), pump motor reverse optimization system:
In conjunction with motor forward electromagnetic computation program, BP neural network 2 and Genetic Simulated Annealing Algorithm, form pump motor reverse optimization system;
BP neural network 2 carries out training according to actual measurement sample and after restraining, when input one group of flow, lift and target pump efficiency data, matching is provided the shaft power that water pump needs to input in this case, coordinate artificially given allowance coefficient, water pump can be drawn in this case, the optimum output power that the motor of its adapted should have; Power-optimized designs target using this power as motor, coordinates efficiency, other indexs of torque, carries out pump motor multi-objective optimization design of power; Optimization aim is set as the reserve factor between the efficiency of motor, power factor, torque capacity, detent torque, starting current and pump; Motor optimized variable is chosen: iron core long, every groove conductor number, rotor slot part inside dimension parameter and output rating;
Pump motor multi-objective optimization design of power is generally difficult to find the optimum solution meeting all target calls, then exported as optimum results, for artificial selection using txt form by the noninferior solution meeting part optimization aim obtained when optimizing and stop;
Pump motor reverse optimization system can independent of the described motor-and-pump integrated model of step (3), and isolated operation calculates.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510035609.7A CN104598686A (en) | 2015-01-24 | 2015-01-24 | Water pump motor modeling and optimizing method based on electromagnetic calculation and neural network |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510035609.7A CN104598686A (en) | 2015-01-24 | 2015-01-24 | Water pump motor modeling and optimizing method based on electromagnetic calculation and neural network |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN104598686A true CN104598686A (en) | 2015-05-06 |
Family
ID=53124466
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201510035609.7A Pending CN104598686A (en) | 2015-01-24 | 2015-01-24 | Water pump motor modeling and optimizing method based on electromagnetic calculation and neural network |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN104598686A (en) |
Cited By (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104933240A (en) * | 2015-06-10 | 2015-09-23 | 中国人民解放军装甲兵工程学院 | Optimum design method for layout of armored vehicle cooling system |
| CN106021695A (en) * | 2016-05-16 | 2016-10-12 | 江苏大学 | Design variable stratification-based motor multi-target optimization design method |
| CN106292337A (en) * | 2016-10-17 | 2017-01-04 | 安徽大学 | Point-to-point motion trajectory planning method for permanent magnet spherical motor based on sinusoidal acceleration function and application thereof |
| CN106681146A (en) * | 2016-12-31 | 2017-05-17 | 浙江大学 | Blast furnace multi-target optimization control algorithm based on BP neural network and genetic algorithm |
| CN107330174A (en) * | 2017-06-21 | 2017-11-07 | 太原科技大学 | A kind of wheel hub motor optimization method based on genetic annealing algorithms |
| CN109727238A (en) * | 2018-12-27 | 2019-05-07 | 贵阳朗玛信息技术股份有限公司 | The recognition methods of x-ray chest radiograph and device |
| CN109829248A (en) * | 2019-03-04 | 2019-05-31 | 苏州尼昂科技有限公司 | Method for determination of performance parameter, device and the electronic equipment of water pump |
| CN110598317A (en) * | 2019-09-10 | 2019-12-20 | 大连理工大学 | Method for constructing shielded nuclear main pump digital prototype by coupling multiple physical fields |
| CN112687351A (en) * | 2021-01-07 | 2021-04-20 | 哈尔滨工业大学 | Method for rapidly predicting microwave electromagnetic performance of composite medium based on genetic algorithm-BP neural network |
| CN114545908A (en) * | 2022-04-28 | 2022-05-27 | 中汽研汽车检验中心（天津）有限公司 | Method for constructing and simulating vehicle hydraulic system model and vehicle simulation system |
| CN114958395A (en) * | 2022-06-02 | 2022-08-30 | 江苏运能能源科技有限公司 | Waste heat utilization method and system for biomass carbonization furnace |
| CN115573926A (en) * | 2022-11-21 | 2023-01-06 | 南京群顶科技股份有限公司 | Machine room water pump energy-saving operation method combining BP neural network fitting characteristic curve |
| CN116094206A (en) * | 2023-03-08 | 2023-05-09 | 四川宜宾力源电机有限公司 | Motor using rotor and gap trend calculation method of rotor and stator |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101567617A (en) * | 2009-03-06 | 2009-10-28 | 北京理工大学 | Optimal design method for structural parameters of cylindrical linear motors |
| CN101957871A (en) * | 2010-06-28 | 2011-01-26 | 江苏方天电力技术有限公司 | Phase advance capability modeling method of synchronous generator based on forward propagation NN (Neural Network) |
| CN102063537A (en) * | 2010-12-28 | 2011-05-18 | 浙江工业大学 | Method for designing single-phase asynchronous machine based on multi-target hybrid simulated annealing algorithm |
| 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 |
-
2015
- 2015-01-24 CN CN201510035609.7A patent/CN104598686A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101567617A (en) * | 2009-03-06 | 2009-10-28 | 北京理工大学 | Optimal design method for structural parameters of cylindrical linear motors |
| CN101957871A (en) * | 2010-06-28 | 2011-01-26 | 江苏方天电力技术有限公司 | Phase advance capability modeling method of synchronous generator based on forward propagation NN (Neural Network) |
| CN102063537A (en) * | 2010-12-28 | 2011-05-18 | 浙江工业大学 | Method for designing single-phase asynchronous machine based on multi-target hybrid simulated annealing algorithm |
| 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 |
Non-Patent Citations (4)
| Title |
|---|
| 任明旭等: ""基于BP神经网络和遗传算法的电动汽车电机优化设计"", 《第十一届沈阳科学学术年会暨中国汽车产业集聚区发展与合作论坛》 * |
| 冯欣南: "《微特电机》", 30 September 1991, 华中理工大学出版社 * |
| 姜锟: ""基于C++/C#的电机优化系统设计"", 《中国优秀硕士学位论文全文数据库 工程科技Ⅱ辑》 * |
| 黄家友等: ""浅谈潜水电动机的型式及设计特点"", 《大电机技术》 * |
Cited By (21)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104933240A (en) * | 2015-06-10 | 2015-09-23 | 中国人民解放军装甲兵工程学院 | Optimum design method for layout of armored vehicle cooling system |
| CN104933240B (en) * | 2015-06-10 | 2018-02-06 | 中国人民解放军装甲兵工程学院 | A kind of Cooling System of Armored Vehicles layout optimization design method |
| CN106021695B (en) * | 2016-05-16 | 2019-04-30 | 江苏大学 | Motor multi-objective optimization design of power method based on design variable layering |
| CN106021695A (en) * | 2016-05-16 | 2016-10-12 | 江苏大学 | Design variable stratification-based motor multi-target optimization design method |
| CN106292337A (en) * | 2016-10-17 | 2017-01-04 | 安徽大学 | Point-to-point motion trajectory planning method for permanent magnet spherical motor based on sinusoidal acceleration function and application thereof |
| CN106681146A (en) * | 2016-12-31 | 2017-05-17 | 浙江大学 | Blast furnace multi-target optimization control algorithm based on BP neural network and genetic algorithm |
| CN107330174B (en) * | 2017-06-21 | 2020-11-17 | 太原科技大学 | Wheel hub motor optimization method based on genetic annealing algorithm |
| CN107330174A (en) * | 2017-06-21 | 2017-11-07 | 太原科技大学 | A kind of wheel hub motor optimization method based on genetic annealing algorithms |
| CN109727238A (en) * | 2018-12-27 | 2019-05-07 | 贵阳朗玛信息技术股份有限公司 | The recognition methods of x-ray chest radiograph and device |
| CN109829248A (en) * | 2019-03-04 | 2019-05-31 | 苏州尼昂科技有限公司 | Method for determination of performance parameter, device and the electronic equipment of water pump |
| CN110598317A (en) * | 2019-09-10 | 2019-12-20 | 大连理工大学 | Method for constructing shielded nuclear main pump digital prototype by coupling multiple physical fields |
| CN110598317B (en) * | 2019-09-10 | 2021-11-02 | 大连理工大学 | Method for constructing shielded nuclear main pump digital prototype by coupling multiple physical fields |
| CN112687351B (en) * | 2021-01-07 | 2023-04-18 | 哈尔滨工业大学 | Method for rapidly predicting microwave electromagnetic performance of composite medium based on genetic algorithm-BP neural network |
| CN112687351A (en) * | 2021-01-07 | 2021-04-20 | 哈尔滨工业大学 | Method for rapidly predicting microwave electromagnetic performance of composite medium based on genetic algorithm-BP neural network |
| CN114545908A (en) * | 2022-04-28 | 2022-05-27 | 中汽研汽车检验中心（天津）有限公司 | Method for constructing and simulating vehicle hydraulic system model and vehicle simulation system |
| CN114545908B (en) * | 2022-04-28 | 2022-07-19 | 中汽研汽车检验中心（天津）有限公司 | Method for constructing and simulating vehicle hydraulic system model and vehicle simulation system |
| CN114958395A (en) * | 2022-06-02 | 2022-08-30 | 江苏运能能源科技有限公司 | Waste heat utilization method and system for biomass carbonization furnace |
| CN114958395B (en) * | 2022-06-02 | 2023-04-18 | 江苏运能能源科技有限公司 | Waste heat utilization method and system for biomass carbonization furnace |
| CN115573926A (en) * | 2022-11-21 | 2023-01-06 | 南京群顶科技股份有限公司 | Machine room water pump energy-saving operation method combining BP neural network fitting characteristic curve |
| CN116094206A (en) * | 2023-03-08 | 2023-05-09 | 四川宜宾力源电机有限公司 | Motor using rotor and gap trend calculation method of rotor and stator |
| CN116094206B (en) * | 2023-03-08 | 2023-06-23 | 四川宜宾力源电机有限公司 | Motor using rotor and gap trend calculation method of rotor and stator |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN104598686A (en) | Water pump motor modeling and optimizing method based on electromagnetic calculation and neural network | |
| CN109492819B (en) | Time optimization partition-based tidal pumping station system variable station number and variable angle day optimization operation method | |
| CN103678783A (en) | Method of optimallying designing closed-winding permanent magnet brushless DC motor | |
| CN104481893B (en) | Optimized water pump energy conservation control method | |
| CN202856673U (en) | Brushless direct current motor rotating speed control system for intelligent water pump based on INGA | |
| CN105971836A (en) | System for lifting water through solar photovoltaic and making redundant electricity get on grid | |
| CN206376832U (en) | Energy-saving control system of beam-pumping unit | |
| CN104270061A (en) | Submersible motor energy-saving control method based on optimal voltage-frequency ratio control | |
| CN110083912A (en) | The optimal waterpower permanent magnet generator optimum design method of annual electricity generating capacity | |
| CN105653812A (en) | Dynamic closing curve optimizing method of guide vanes | |
| CN104716790A (en) | 22KW permanent magnet synchronous motor design method | |
| CN210518154U (en) | Multi-frequency converter single-motor fracturing electric control system | |
| CN108087943B (en) | A kind of central heating method and system | |
| CN103603763A (en) | Bulb tubular turbine for micro-head power generation of water plant | |
| CN210107564U (en) | Intelligent hydraulic balance control system device adopting datum point tracking method | |
| RU88167U1 (en) | SYSTEM OF OPTIMAL CONTROL OF INSTALLATION OF ELECTRIC CENTRIFUGAL PUMP | |
| CN101256212A (en) | Comprehensive analysis testing method for extra-high voltage electric power gold tool | |
| CN111022311B (en) | Photovoltaic water pump control method | |
| Xiang et al. | Study on optimal scheduling pump group based on parallel HQ characteristics and pipe characteristics | |
| CN203744478U (en) | Energy-saving structure | |
| Сінчук et al. | Modeling tools for improving energy efficiency of water drainage complexes at iron ore underground mines | |
| CN105259504A (en) | An energy efficiency scoring method for a three-phase asynchronous motor system | |
| Baranovskyi et al. | Improving the energy efficiency of a mine drainage installation by means of an industrial electric drive | |
| Chulliyev et al. | Analysis of the efficiency of the use of electricity at the pumping station (Ramadan) | |
| CN203515931U (en) | Top pressure power generation device for industrial circulating water system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20150506 |