CN105717452A - Automatic detection method and system of compressor models - Google Patents
Automatic detection method and system of compressor models Download PDFInfo
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- CN105717452A CN105717452A CN201610074390.6A CN201610074390A CN105717452A CN 105717452 A CN105717452 A CN 105717452A CN 201610074390 A CN201610074390 A CN 201610074390A CN 105717452 A CN105717452 A CN 105717452A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/34—Testing dynamo-electric machines
Abstract
The invention belongs to the field of motor control, and especially relates to an automatic detection method and system of compressor models. According to the invention, offline parameters obtained by means of offline identification and a preset error function [delta][sigma](n) of parameters of compressors of known models are utilized to calculate the error value of each parameter, and the model of the current compressor is judged through the error values, so that the working efficiency is improved, the accuracy is improved, the present situation of after-sale service of air conditioners is effectively improved, and the after-sale maintenance efficiency of the air conditioners is improved.
Description
Technical field
The invention belongs to Motor Control Field, particularly relate to automatic testing method and the detection system of a kind of compressor model.
Background technology
Nowadays, convertible frequency air-conditioner technology is growing, and the model of cooler compressor is numerous, and the corresponding parameter of electric machine is also different.How to confirm the model of compressor rapidly and accurately to keep in repair, become air-conditioner a great problem after sale.The method that current market is used mostly is to tear machine open to check compressor famous brand, in order to after having confirmed the model of compressor, selects the corresponding parameter of electric machine to regulate and control by the toggle switch on circuit board.But this kind is torn machine open and checked the famous brand method of compressor, and work efficiency is not high, and because it is main for being artificial manipulation, therefore error rate is relatively larger.
Summary of the invention
In view of this, namely the purpose of the present invention is in that to provide automatic testing method and the detection system of a kind of compressor model, the model determining compressor can be pushed away according to the parameter of electric machine automatically picked out is counter, both improve work efficiency, also improve accuracy, it is possible to be effectively improved the after-sale service present situation of air-conditioner.
First, the automatic testing method of the compressor model that the embodiment of the present invention provides, comprise the steps:
Obtaining the offline parameter of current compressor, described offline parameter includes the resistance of compressor, inductance and back emf coefficient；
Transfer error function Δ Σ (n) of the various compressor parameters of default known models；The parameters that described error function Δ Σ (n) is compressor respectively with the summation of the product of corresponding error coefficient；
The error amount of the offline parameter of current compressor and the various compressor parameters of known models is calculated respectively according to described error function；
Minimum error values is extracted, it is judged that the model determining current compressor is and the compressor model corresponding to described minimum error values from the plurality of error amount.
As preferably, the model judging to determine current compressor be with the step of the compressor model corresponding to described minimum error values after, also include:
Obtain the parameter corresponding to current compressor model determined, control compressor and run according to the parameter of described correspondence.
On the other hand, the embodiment of the present invention also provides for the automatic checkout system of a kind of compressor model, and as improvement, described system includes with lower unit:
Offline parameter acquiring unit, for obtaining the offline parameter of current compressor, described offline parameter includes the resistance of compressor, inductance and back emf coefficient；
Data call unit, for transferring error function Δ Σ (n) of the various compressor parameters of default known models；The parameters that described error function Δ Σ (n) is compressor respectively with the summation of the product of corresponding error coefficient；
Error calculation unit, for calculating the error amount of the offline parameter of current compressor and the various compressor parameters of known models respectively according to described error function；
Model determines unit, for extracting minimum error values from the plurality of error amount, it is judged that the model determining current compressor is and the compressor model corresponding to described minimum error values.
As preferably, described system also includes:
Run control unit, for obtaining the parameter corresponding to current compressor model determined, control compressor and run according to the parameter of described correspondence.
The automatic testing method of the compressor model provided according to embodiments of the present invention and detection system, error function Δ Σ (n) utilizing the various compressor parameters of the offline parameter that off-line identification gets and the known models preset calculates the error amount of parameters, the model of current compressor is judged by error amount, both improve work efficiency, also improve accuracy, can effectively improve the after-sale service present situation of air-conditioner, improve air-conditioning after-sales service efficiency.
Accompanying drawing explanation
Fig. 1 is the flowchart of the automatic testing method of the compressor model that one embodiment of the present invention provides；
Fig. 2 is the structure flow chart of the error coefficient that one embodiment of the invention provides；
Fig. 3 is the structured flowchart of the automatic checkout system of the compressor model that one embodiment of the present invention provides.
Detailed description of the invention
In order to make the purpose of the present invention, technical scheme and advantage clearly understand, below in conjunction with drawings and Examples, the present invention is further elaborated.Should be appreciated that specific embodiment described herein is only in order to explain the present invention, is not intended to limit the present invention.
Fig. 1 is the flowchart of the automatic testing method of the compressor model that one embodiment of the present invention provides；For the ease of illustrating, illustrate only part related to the present embodiment, as shown in the figure:
The automatic testing method of a kind of compressor model, comprises the following steps:
Step S10: obtaining the offline parameter of current compressor, described offline parameter includes the resistance of compressor, inductance and back emf coefficient.
Step S20: transfer error function Δ Σ (n) of the various compressor parameters of default known models；The parameters that described error function Δ Σ (n) is compressor respectively with the summation of the product of corresponding error coefficient.
Step S30: calculate the error amount of the offline parameter of current compressor and the various compressor parameters of known models according to described error function respectively.
Step S40: extract minimum error values from the plurality of error amount, it is judged that the model determining current compressor is and the compressor model corresponding to described minimum error values.
In specific implementation process, first the parameter of current compressor is carried out off-line identification, obtain the offline parameter of current compressor.This offline parameter includes but not limited to the resistance R of compressor, inductance L and back emf coefficient ke.It practice, in implementing process, it is also possible to obtain other parameter further, for instance the magnetic pole logarithm P etc. of current compressor.Such as, every offline parameter of a certain compressor got is: P=2, Ld=18.932243, Lq=27.240234375, ke=0.2053125, R=0.789697265625.
After obtaining offline parameter, be accomplished by transferring error function Δ Σ (n) of the various compressor parameters of default known models, the form of expression of this error function Δ Σ (n) be preferably the parameters of compressor respectively with the summation of the product of corresponding error coefficient.In specific implementation process, it is possible to previously according to the size that compressor performance is affected by parameters, determine the different error coefficient corresponding from the parameters of compressor respectively, further construct error function Δ Σ (n).As a preferred embodiment, including D axle inductance, Q axle inductance, back emf coefficient and resistance for offline parameter, this error function Δ Σ (n) can be expressed as:
Δ Σ (n)=Δ Ld (n) * (δ 1)+Δ Lq (n) * (δ 2)+Δ ke (n) * (δ 3)+Δ R (n) * (δ 4),
Wherein, Ld is D axle inductance, and Lq is Q axle inductance, and ke is back emf coefficient, and R is resistance；Δ x=| (X-Y)/Y | * 100, X are the various compressor parameters of known models, and Y is corresponding offline parameter；N is corresponding compressor model；The error coefficient that δ n is respectively corresponding with parameters, and all δ n's and equal to 1.It is to say, δ 1 is the error coefficient that D axle inductance Ld is corresponding, δ 2 is the error coefficient that Q axle inductance Lq is corresponding, and δ 3 is error coefficient corresponding for back emf coefficient ke, and δ 4 is error coefficient corresponding for resistance R, and δ 1+ δ 2+ δ 3+ δ 4=1.
Here, δ n both can be the numerical value that the significance level that compressor performance is affected by the parameters determined according to experiment effect is preset, it is also possible to builds model beforehand through below scheme as shown in Figure 2 and obtains:
Step S01: calculate the meansigma methods of the parameters of the various compressors of known models respectively；
Step S02: using meansigma methods as datum quantity, calculate the perunit value of parameters；
Step S03: go out the standard deviation of each perunit value described according to described mean value calculation；
Step S04: confirm the error coefficient δ n of each parameter according to the standard deviation of each parameter accounting in the standard deviation sum of all parameters.
Below according to a concrete form, the structure of error coefficient δ n is described in detail.Under indicate the compressor of 10 kinds of different models and the parameter of correspondence thereof, wherein P is magnetic pole logarithm, and Ld is D axle inductance, and Lq is Q axle inductance, and ke is back emf coefficient, and R is resistance.
For the magnetic pole logarithm P=2 of compressor, corresponding compressor model is 1-5.The numerical value of the D axle inductance Ld looking first at the compressor of these 5 kinds of models is respectively gone up shown in table, and the meansigma methods calculating Ld is 15.54, then with this meansigma methods 15.54 for datum quantity, calculates the perunit value of the D axle inductance Ld of the compressor of every kind of model；Further according to standard deviation formula, calculating these 5 standard deviations between perunit value and meansigma methods is 0.24.Same, for Q axle inductance Lq, back emf coefficient ke and resistance R, adopt same procedural model to calculate the standard deviation respectively 0.19,0.26 and 0.23 of correspondence, confirm the error coefficient δ n of each parameter further according to accounting in the standard deviation sum of all parameters of the standard deviation of each parameter.In upper table, δ 1=26.42%, δ 2=20.90%, δ 3=28.09%, δ 4=24.59%.
Therefore, as magnetic pole logarithm P=2, error function Δ Σ (n)=Δ Ld (n) * 26.42%+ Δ Lq (n) * 20.90%+ Δ ke (n) * 28.09%+ Δ R (n) * 24.59%；
Similar, as magnetic pole logarithm P=3, Δ Σ (n)=Δ Ld (n) * 25.15%+ Δ Lq (n) * 21.78%+ Δ ke (n) * 21.70%+ Δ R (n) * 31.37%.
Continue for the aforementioned offline parameter got, calculate the error amount of offline parameter and known various compressor parameters according to above-mentioned error function Δ Σ (n): obtain a result after calculating into:
Δ Σ (1)=7.865841392, Δ Σ (2)=26.29074634, Δ Σ (3)=53.59188252, Δ Σ (4)=9.892413882, Δ Σ (5)=4.604418978.Further, relatively learn that the value of Δ Σ (5) is minimum, being minimum error values, the compressor model of its correspondence is sequence number is the ASM98D1UFZA (ferrite) of " 5 ", therefore judges to determine that the model of current compressor is ASM98D1UFZA (ferrite).So far, it is possible to automatically detect the model of compressor, the after-sale service for air-conditioner improves accuracy and efficiency.
Further, as a preferred embodiment, the model judging to determine current compressor be with the step S40 of the compressor model corresponding to described minimum error values after, also include:
Step S50: obtain the parameter corresponding to current compressor model determined, controls compressor and runs according to the parameter of described correspondence.
The automatic testing method of the compressor model provided according to embodiments of the present invention, error function Δ Σ (n) utilizing the various compressor parameters of the offline parameter that off-line identification gets and the known models preset calculates the error amount of parameters, the model of current compressor is judged by error amount, both improve work efficiency, also improve accuracy, can effectively improve the after-sale service present situation of air-conditioner, improve air-conditioning after-sales service efficiency.
On the other hand, the embodiment of the present invention also provides for the automatic checkout system of a kind of compressor model.Namely Fig. 3 illustrates the structured flowchart of the automatic checkout system of the compressor model that one embodiment of the present invention provides.Same, for the ease of illustrating, illustrate only part related to the present embodiment, as shown in the figure:
A kind of automatic checkout system of compressor model, including with lower unit:
Offline parameter acquiring unit 31, for obtaining the offline parameter of current compressor, described offline parameter includes but not limited to the resistance of compressor, inductance and back emf coefficient；Such as, in another embodiment, described offline parameter can also include magnetic pole logarithm；
Data call unit 32, for transferring error function Δ Σ (n) of the various compressor parameters of default known models；The parameters that described error function Δ Σ (n) is compressor respectively with the summation of the product of corresponding error coefficient；
Error calculation unit 33, for calculating the error amount of the offline parameter of current compressor and the various compressor parameters of known models respectively according to described error function；
Model determines unit 34, for extracting minimum error values from the plurality of error amount, it is judged that the model determining current compressor is and the compressor model corresponding to described minimum error values.
Further, as a preferred embodiment, described system also includes:
Run control unit 35, for obtaining the parameter corresponding to current compressor model determined, control compressor and run according to the parameter of described correspondence.
In specific implementation process, error function Δ Σ (n) that described data call unit 32 is transferred is:
Δ Σ (n)=Δ Ld (n) * (δ 1)+Δ Lq (n) * (δ 2)+Δ ke (n) * (δ 3)+Δ R (n) * (δ 4), wherein, Ld is D axle inductance, Lq is Q axle inductance, ke is back emf coefficient, and R is resistance；Δ x=| (X-Y)/Y | * 100, X are the various compressor parameters of known models, and Y is corresponding offline parameter；N is corresponding compressor model；The error coefficient that δ n is respectively corresponding with parameters.
Further, when implementing, as a preferred embodiment, the error coefficient δ n that described parameters is corresponding obtains respectively through following model construction:
Calculate the meansigma methods of the parameters of the various compressors of known models respectively；
Using meansigma methods as datum quantity, calculate the perunit value of parameters；
The standard deviation of each perunit value described is gone out according to described mean value calculation；
The standard deviation according to each parameter accounting in the standard deviation sum of all parameters confirms the error coefficient δ n of each parameter.
In sum, the automatic testing method of the compressor model provided according to embodiments of the present invention and detection system, error function Δ Σ (n) utilizing the various compressor parameters of the offline parameter that off-line identification gets and the known models preset calculates the error amount of parameters, the model of current compressor is judged by error amount, both improve work efficiency, also improve accuracy, it is possible to effectively improve the after-sale service present situation of air-conditioner, improve air-conditioning after-sales service efficiency.
Those skilled in the art is it can be understood that arrive, for convenience of description and succinctly, only it is illustrated with the division of above-mentioned each functional unit, in practical application, as desired above-mentioned functions distribution can be completed by different functional units, it is divided into different functional units or module, to complete all or part of function described above by the internal structure of described system.Each functional unit in embodiment can be integrated in a processing unit, can also be that unit is individually physically present, can also two or more unit integrated in a unit, above-mentioned integrated unit both can adopt the form of hardware to realize, it would however also be possible to employ the form of SFU software functional unit realizes.It addition, the concrete title of each functional unit is also only to facilitate mutually distinguish, it is not limited to the protection domain of the application.The specific works process of unit in said system, it is possible to reference to the corresponding process in preceding method embodiment, do not repeat them here.
It should be noted that in above-described embodiment, included unit is carry out dividing according to function logic, but is not limited to above-mentioned division, as long as being capable of corresponding function；It addition, the concrete title of each functional unit is also only to facilitate mutually distinguish, it is not limited to protection scope of the present invention.
The foregoing is only presently preferred embodiments of the present invention, not in order to limit the present invention, although the present invention having been carried out explanation in greater detail with reference to previous embodiment, for a person skilled in the art, the technical scheme described in foregoing embodiments still can be modified or wherein portion of techniques feature carries out equivalent replacement by it.All any amendment, equivalent replacement and improvement etc. made within the spirit and principles in the present invention, should be included within protection scope of the present invention.
Claims (10)
1. the automatic testing method of a compressor model, it is characterised in that said method comprising the steps of:
Obtaining the offline parameter of current compressor, described offline parameter includes the resistance of compressor, inductance and back emf coefficient；
Transfer error function Δ Σ (n) of the various compressor parameters of default known models；The parameters that described error function Δ Σ (n) is compressor respectively with the summation of the product of corresponding error coefficient；
The error amount of the offline parameter of current compressor and the various compressor parameters of known models is calculated respectively according to described error function；
Minimum error values is extracted, it is judged that the model determining current compressor is and the compressor model corresponding to described minimum error values from the plurality of error amount.
2. the automatic testing method of compressor model as claimed in claim 1, it is characterised in that the model judging to determine current compressor be with the step of the compressor model corresponding to described minimum error values after, also include:
Obtain the parameter corresponding to current compressor model determined, control compressor and run according to the parameter of described correspondence.
3. the automatic testing method of compressor model as claimed in claim 1, it is characterised in that described error function Δ Σ (n) is:
Δ Σ (n)=Δ Ld (n) * (δ 1)+Δ Lq (n) * (δ 2)+Δ ke (n) * (δ 3)+Δ R (n) * (δ 4),
Wherein, Ld is D axle inductance, and Lq is Q axle inductance, and ke is back emf coefficient, and R is resistance；Δ x=| (X-Y)/Y | * 100, X are the various compressor parameters of known models, and Y is corresponding offline parameter；N is corresponding compressor model；The error coefficient that δ n is respectively corresponding with parameters.
4. the automatic testing method of compressor model as claimed in claim 1, it is characterised in that described offline parameter also includes magnetic pole logarithm.
5. the automatic testing method of compressor model as claimed in claim 3, it is characterised in that the error coefficient δ n that described parameters is corresponding obtains respectively through following model construction:
Calculate the meansigma methods of the parameters of the various compressors of known models respectively；
Using meansigma methods as datum quantity, calculate the perunit value of parameters；
The standard deviation of each perunit value described is gone out according to described mean value calculation；
The standard deviation according to each parameter accounting in the standard deviation sum of all parameters confirms the error coefficient δ n of each parameter.
6. the automatic checkout system of a compressor model, it is characterised in that described system includes with lower unit:
Offline parameter acquiring unit, for obtaining the offline parameter of current compressor, described offline parameter includes the resistance of compressor, inductance and back emf coefficient；
Data call unit, for transferring error function Δ Σ (n) of the various compressor parameters of default known models；The parameters that described error function Δ Σ (n) is compressor respectively with the summation of the product of corresponding error coefficient；
Error calculation unit, for calculating the error amount of the offline parameter of current compressor and the various compressor parameters of known models respectively according to described error function；
Model determines unit, for extracting minimum error values from the plurality of error amount, it is judged that the model determining current compressor is and the compressor model corresponding to described minimum error values.
7. the automatic checkout system of compressor model as claimed in claim 6, it is characterised in that described system also includes:
Run control unit, for obtaining the parameter corresponding to current compressor model determined, control compressor and run according to the parameter of described correspondence.
8. the automatic checkout system of compressor model as claimed in claim 6, it is characterised in that error function Δ Σ (n) that described data call unit is transferred is:
Δ Σ (n)=Δ Ld (n) * (δ 1)+Δ Lq (n) * (δ 2)+Δ ke (n) * (δ 3)+Δ R (n) * (δ 4),
Wherein, Ld is D axle inductance, and Lq is Q axle inductance, and ke is back emf coefficient, and R is resistance；Δ x=| (X-Y)/Y | * 100, X are the various compressor parameters of known models, and Y is corresponding offline parameter；N is corresponding compressor model；The error coefficient that δ n is respectively corresponding with parameters.
9. the automatic checkout system of compressor model as claimed in claim 6, it is characterised in that described offline parameter also includes magnetic pole logarithm.
10. the automatic checkout system of compressor model as claimed in claim 8, it is characterised in that the error coefficient δ n that described parameters is corresponding obtains respectively through following model construction:
Calculate the meansigma methods of the parameters of the various compressors of known models respectively；
Using meansigma methods as datum quantity, calculate the perunit value of parameters；
The standard deviation of each perunit value described is gone out according to described mean value calculation；
The standard deviation according to each parameter accounting in the standard deviation sum of all parameters confirms the error coefficient δ n of each parameter.
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| CN106970606A (en) * | 2017-03-16 | 2017-07-21 | 武汉理工大学 | A kind of automobile air conditioner compressor controller calibration system and method |
| CN108050667A (en) * | 2018-01-09 | 2018-05-18 | 广东美的制冷设备有限公司 | Computational methods, multi-split air conditioner and the storage medium of compressor frequency threshold value |
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| CN109931253A (en) * | 2019-03-29 | 2019-06-25 | 四川虹美智能科技有限公司 | A kind of method and device of the control parameter of determining compressor |
| CN111781497A (en) * | 2020-06-11 | 2020-10-16 | 宁波奥克斯电气股份有限公司 | Method and device for identifying compressor model, maintenance equipment and air conditioner |
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