CN113009333A - Method and device for detecting air gap eccentricity of induction motor rotor - Google Patents

Method and device for detecting air gap eccentricity of induction motor rotor Download PDF

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CN113009333A
CN113009333A CN201911319866.8A CN201911319866A CN113009333A CN 113009333 A CN113009333 A CN 113009333A CN 201911319866 A CN201911319866 A CN 201911319866A CN 113009333 A CN113009333 A CN 113009333A
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equivalent
fault model
rotor
induction motor
air gap
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CN113009333B (en
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刘永强
吴立泉
黄伟钳
王锦锋
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Zhuhai Wanpu Technology Co ltd
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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
    • G01R31/346—Testing of armature or field windings
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
    • G01B7/00—Measuring arrangements characterised by the use of electric or magnetic techniques
    • G01B7/30—Measuring arrangements characterised by the use of electric or magnetic techniques for measuring angles or tapers; for testing the alignment of axes
    • G01B7/31—Measuring arrangements characterised by the use of electric or magnetic techniques for measuring angles or tapers; for testing the alignment of axes for testing the alignment of axes
    • G01B7/312—Measuring arrangements characterised by the use of electric or magnetic techniques for measuring angles or tapers; for testing the alignment of axes for testing the alignment of axes for measuring eccentricity, i.e. lateral shift between two parallel axes

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Abstract

The invention relates to a method and a device for detecting air gap eccentricity of an induction motor rotor, wherein the method comprises the following steps: determining the window length and the sliding window step length of a sliding window; setting cycle identification times; based on the working parameters of the induction motor in the window length corresponding to the sliding window step length, sequentially performing fault model equivalent parameter identification by using an induction motor rotor air gap eccentric fault model and a particle swarm algorithm, and obtaining the fault model equivalent parameter identification value of each identification until the frequency of fault model equivalent parameter identification reaches the cycle identification frequency; and acquiring the condition of the induction motor rotor air gap eccentricity based on the identified fault model equivalent parameter identification values of each time. The scheme for detecting the air gap eccentricity of the induction motor rotor provided by the embodiment of the invention can effectively detect the air gap eccentricity fault of the induction motor rotor, and is easy to identify the characteristics of the air gap eccentricity state of the induction motor rotor by using a parameter identification method.

Description

Method and device for detecting air gap eccentricity of induction motor rotor
Technical Field
The invention belongs to the technical field of motor fault detection, and particularly relates to a method and a device for detecting air gap eccentricity of an induction motor rotor.
Background
Induction motors may cause air gap eccentricity when rotor stiffness is insufficient or bearing wear or mounting errors occur. The induction motor air gap eccentricity can cause the air gap magnetic field to be distorted, various performance indexes of the motor are deteriorated, and in severe cases, a stator and a rotor are sassafras, so that the motor is burnt. Therefore, it is necessary to research the fault of the induction motor and find an effective way for fault diagnosis. Parameter identification is an important method for monitoring the state of the rotor of the induction motor, and the key technology is to accurately establish a state model of the induction motor. The current modeling method for the state of the induction motor mainly treats the motor rotor as three phases in an equivalent manner, but the modeling method for treating the motor rotor as three phases in an equivalent manner is not suitable for modeling the air gap eccentricity fault of the induction motor because the air gap eccentricity state of the induction motor rotor cannot be accurately described.
Disclosure of Invention
The invention provides a method and a device for detecting the air gap eccentricity of an induction motor rotor, aiming at solving the technical problem of effective detection of the air gap eccentricity fault of the induction motor rotor.
According to a first aspect of embodiments of the present invention, a method for detecting an air gap eccentricity of a rotor of an induction motor includes:
determining the window length and the sliding window step length of a sliding window;
setting cycle identification times;
based on the working parameters of the induction motor in the window length corresponding to the sliding window step length, sequentially performing fault model equivalent parameter identification by using an induction motor rotor air gap eccentric fault model and a particle swarm algorithm, and obtaining the fault model equivalent parameter identification value of each identification until the frequency of fault model equivalent parameter identification reaches the cycle identification frequency; and
and acquiring the condition of the induction motor rotor air gap eccentricity based on the fault model equivalent parameter identification value identified each time.
In certain embodiments, the method further comprises: acquiring working parameters of an induction motor; the working parameters of the induction motor comprise the stator voltage and the stator current of the induction motor;
the method for identifying the equivalent parameters of the fault model in sequence by applying the induction motor rotor air gap eccentric fault model and the particle swarm algorithm based on the working parameters of the induction motor in the window length corresponding to the sliding window in sequence by sliding according to the sliding window step length comprises the following steps of: based on the working parameters of the induction motor in the window length corresponding to the sliding window step length, after the working parameters of the induction motor are subjected to coordinate transformation, a fault model equivalent parameter identification is carried out on the vector subjected to the coordinate transformation by using an induction motor rotor air gap eccentric fault model and a particle swarm algorithm in sequence;
wherein a transformation matrix D is used2×3Performing said coordinate transformation to transform a matrix D2×3Comprises the following steps:
Figure BDA0002326837560000021
wherein, theta1Is the synchronous electrical angle.
In some embodiments, the obtaining of the induction machine rotor air gap eccentricity based on the identified fault model equivalent parameter identification values comprises: acquiring an equivalent parameter change curve of the induction motor fault model based on the fault model equivalent parameter identification value identified each time; calculating a fault model equivalent parameter theoretical value corresponding to each identification by using an induction motor rotor air gap eccentric fault model, and acquiring a reference curve of an induction motor fault model equivalent parameter based on the fault model equivalent parameter theoretical value corresponding to each identification; and acquiring the condition of the induction motor rotor air gap eccentricity based on the induction motor fault model equivalent parameter change curve and the corresponding reference curve.
In certain embodiments, the method further comprises: calculating the normal value of the equivalent parameter by using a normal model;
the method for obtaining the equivalent parameter change curve of the fault model of the induction motor based on the fault model equivalent parameter identification value of each identification comprises the following steps: constructing a fault model equivalent parameter change curve based on the fault model equivalent parameter identification value and the equivalent parameter normal value identified each time; and
the method for acquiring the reference curve corresponding to the equivalent parameter of the induction motor fault model based on the theoretical value of the equivalent parameter of the fault model corresponding to each identification comprises the following steps: and constructing a reference curve corresponding to the equivalent parameters of the fault model based on the theoretical values of the equivalent parameters of the fault model corresponding to each identification and the normal values of the equivalent parameters.
In some embodiments, the constructing a fault model equivalent parameter variation curve based on the fault model equivalent parameter identification values and the equivalent parameter normal values identified at each time includes: acquiring an identification value deviation ratio of the equivalent parameters of the fault model based on the identification value of the equivalent parameters of the fault model and the normal value of the equivalent parameters of the fault model identified at each time, wherein the relation between the identification value deviation ratio of the equivalent parameters of the fault model and the initial phase of the input a-phase voltage is used as a change curve of the equivalent parameters of the fault model of the induction motor;
the method for constructing the reference curve corresponding to the equivalent parameter of the fault model based on the theoretical value and the normal value of the equivalent parameter of the fault model corresponding to each identification comprises the following steps: and acquiring a theoretical value deviation ratio of the equivalent parameters of the fault model based on the theoretical value and the normal value of the equivalent parameters of the fault model corresponding to each identification, wherein the relation between the theoretical value deviation ratio of the equivalent parameters of the fault model and the initial phase of the input a-phase voltage is used as a reference curve corresponding to the equivalent parameter change curve of the fault model of the induction motor.
In some embodiments, the obtaining the condition of the induction motor rotor air gap eccentricity based on the induction motor fault model equivalent parameter variation curve and the corresponding reference curve includes: and comparing the degree of engagement between the equivalent parameter change curve of the induction motor fault model and the corresponding reference curve, and judging whether the induction motor generates the rotor air gap eccentricity or not based on the degree of engagement so as to obtain the condition of the rotor air gap eccentricity of the induction motor.
In some embodiments, the induction machine rotor air gap eccentricity fault model comprises a fault model voltage equation that is:
Figure BDA0002326837560000031
in the formula: u. ofds、uqs、udrAnd uqrRespectively are stator d-axis voltage, stator q-axis voltage, rotor d-axis voltage and rotor q-axis voltage; i.e. ids、iqs、idrAnd iqrStator d-axis current, stator q-axis current, rotor d-axis current and rotor q-axis current respectively; r issAnd RrRespectively a stator phase winding resistance and a rotor equivalent resistance of a normal model; l iss11、Ls12And LsΛThe first equivalent inductance of the stator of the fault model, the second equivalent inductance of the stator of the fault model and the third equivalent inductance of the stator of the fault model are respectively; l isr11、Lr12And LrΛThe first equivalent inductance of the rotor of the fault model, the second equivalent inductance of the rotor of the fault model and the third equivalent inductance of the rotor of the fault model are respectively; l ism11、Lm12、Lm21And Lm22Respectively being a first equivalent mutual inductance of the fault model, a second equivalent mutual inductance of the fault model, a third equivalent mutual inductance of the fault model and a fourth equivalent mutual inductance of the fault model; l ismAnd L'mThe first equivalent mutual inductance and the second equivalent mutual inductance of the normal model are respectively; omega1And ωrSynchronous electrical angular velocity and rotor electrical angular velocity; lambdaxIs an intermediate variable; p is a differential operator;
the fault model equivalent parameters include: first equivalent inductance L of stator of fault models11Stator second equivalent inductance L of fault models12First equivalent inductance L of rotor of fault modelr11Rotor second equivalent inductance L of fault modelr12First equivalent mutual inductance L of the fault modelm11Second equivalent mutual inductance L of fault modelm12Third equivalent mutual inductance L of fault modelm21And fourth equivalent mutual inductance L of fault modelm22；
The expression of the equivalent parameters of the fault model comprises the following steps:
Ls11＝Lm(Λx-(1-s)Λpidqs)+Lδs
Ls12＝Lm(Λx+(1-s)Λpiqds)+Lδs
Figure BDA0002326837560000032
Figure BDA0002326837560000033
Lm11＝L'm(Λx-(1-s)Λpidqr)
Lm12＝L'm(Λx+(1-s)Λpiqdr)
Figure BDA0002326837560000041
Figure BDA0002326837560000042
LsΛ＝LmΛx+Lδs
LrΛ＝L'mΛx+Lδr
Figure BDA0002326837560000043
Figure BDA0002326837560000044
Figure BDA0002326837560000045
Figure BDA0002326837560000046
Figure BDA0002326837560000047
Figure BDA0002326837560000048
Figure BDA0002326837560000049
Figure BDA00023268375600000410
in the formula, s is slip ratio, Λ0、Λ1And ΛpIs an intermediate variable, idqs、iqds、idqrAnd iqdrIs an intermediate variable, LδsFor stator phase winding leakage inductance, LδrFor rotor loop leakage inductance, delta is air gap eccentricity, g0Is the effective air gap length of the motor, thetarIs the electrical angle of the rotor, r0The radius of the inner circle of the stator;
the relational expression of the fault model equivalent parameters and the input a-phase voltage initial phase respectively comprises the following steps:
Figure BDA0002326837560000051
Figure BDA0002326837560000052
Figure BDA0002326837560000053
Figure BDA0002326837560000054
Lm11＝L'm(Λx-(1-s)Λpcos(θu-Δθuir)/sin(θu-Δθuir))
Lm12＝L'm(Λx+(1-s)Λpsin(θu-Δθuir)/cos(θu-Δθuir))
Figure BDA0002326837560000055
Figure BDA0002326837560000056
Δθuir＝arctan((γ1γ4-γ3γ2)/(γ1γ3+γ2γ4))
Figure BDA0002326837560000057
Figure BDA0002326837560000058
Figure BDA0002326837560000059
γ4＝sω1RrLm
Lr＝L'm+Lδr
wherein, thetauFor inputting the initial phase of the a-phase voltage, Delta thetauirIn order to represent the difference between the initial phase of the current of the rotor circuit 1 and the initial phase of the phase voltage of the stator a when the axis of the rotor circuit 1 is coincident with the axis of the winding of the phase of the stator a,
Figure BDA00023268375600000510
is the power factor angle, gamma1、γ2、γ3And gamma4Are all intermediate variables, LrThe equivalent inductance of the rotor is the normal model.
In some embodiments, the normal model of the induction motor comprises a normal voltage equation and a normal flux linkage equation for the case that the induction motor does not have the rotor air gap eccentricity;
the normal voltage equation is:
Figure BDA00023268375600000511
the normal flux linkage equation is:
Figure BDA0002326837560000061
the normal model of the induction motor also comprises an expression of the equivalent parameters of the normal model, and the expression of the equivalent parameters of the normal model comprises:
Figure BDA0002326837560000062
Rr＝rr-2rbcosα；
in the formula uds、uqs、udrAnd uqrRespectively are stator d-axis voltage, stator q-axis voltage, rotor d-axis voltage and rotor q-axis voltage; i.e. ids、iqs、idrAnd iqrRespectively stator d-axis current, stator q-axis current and rotor d-axis currentCurrent and rotor q-axis current;
Figure BDA0002326837560000063
and
Figure BDA0002326837560000064
respectively a stator d-axis magnetic flux linkage, a stator q-axis magnetic flux linkage, a rotor d-axis magnetic flux linkage and a rotor q-axis magnetic flux linkage; l issAnd LrStator and rotor equivalent inductances, L, of the normal model, respectivelymAnd L'mFirst and second equivalent mutual inductances, r, of the normal model, respectivelysAnd RrThe stator phase winding resistance and the rotor equivalent resistance of the normal model, omega respectively1And ωrSynchronous and rotor electrical angular velocities, r, respectivelyrAnd rbRotor loop resistance and rotor bar resistance, respectively, alpha is the electrical angle between adjacent rotor loop axes, LmsFor self-inductance of the stator phase winding, LσsFor stator phase winding leakage inductance, LσrFor the leakage inductance of the rotor loop, n is the number of the rotor guide bars of the motor, and P is a differential operator.
According to a second aspect of the embodiments of the present invention, an induction motor rotor air gap eccentricity detecting apparatus includes:
the determining module is used for determining the window length and the sliding window step length of the sliding window;
the setting module is used for setting the cycle identification times;
the identification module is used for sequentially identifying the equivalent parameters of the fault model by applying an induction motor rotor air gap eccentric fault model and a particle swarm algorithm based on the working parameters of the induction motor in the window length corresponding to the sliding window which sequentially slides according to the sliding window step length, and acquiring the identification value of the equivalent parameters of the fault model of each identification until the number of times of parameter identification reaches the number of times of cycle identification; and
and the analysis module is used for acquiring the condition of the air gap eccentricity of the induction motor rotor based on the identified fault model equivalent parameter identification values of each time.
In certain embodiments, the apparatus is for implementing a method as described in any of the preceding.
The embodiment of the invention has the following beneficial effects: the method and the device for detecting the air gap eccentricity of the rotor of the induction motor, provided by the embodiment of the invention, can effectively detect the air gap eccentricity fault of the rotor of the induction motor, and the motor equivalent characteristic parameter for describing the air gap eccentricity state of the rotor of the induction motor has obvious change characteristics, so that the characteristic of the air gap eccentricity state of the rotor of the induction motor can be easily identified by using a parameter identification method.
Drawings
FIG. 1 is a schematic flow chart of a method for detecting an air gap eccentricity of an induction motor rotor according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of an induction motor rotor air gap eccentricity detection apparatus according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to specific embodiments and the accompanying drawings. Those skilled in the art will appreciate that the present invention is not limited to the drawings and the following examples.
The existing modeling method for equivalently processing the motor rotor into three phases is not suitable for fault modeling of the induction motor because the air gap eccentric state of the induction motor rotor cannot be accurately described.
In view of this, embodiments of the present invention provide a scheme for detecting an induction motor rotor air gap eccentricity, which is used for detecting a rotor air gap eccentricity based on a constructed induction motor rotor air gap eccentricity fault model, and can effectively detect an induction motor rotor air gap eccentricity fault, and an equivalent parameter of the fault model for describing the induction motor rotor air gap eccentricity has an obvious variation characteristic, so that a parameter identification method is easily applied to identify a characteristic of an induction motor rotor air gap eccentricity state.
In the embodiment of the present invention, the device or apparatus implementing the above scheme has computing capability, and is capable of performing input and output operations, including but not limited to embedded devices.
Embodiments of the present invention are further described below with reference to the accompanying drawings. FIG. 1 illustrates a flow diagram of a method of induction motor rotor air gap eccentricity detection, which may be implemented in a device having computing capabilities, according to one embodiment of the present invention.
As shown in fig. 1, the method for detecting the air gap eccentricity of the rotor of the induction motor according to the embodiment of the present invention includes:
step 100, obtaining stator voltage and stator current of an induction motor and steady-state rotating speed of the induction motor;
in one embodiment, U may be usedabcRepresenting the stator voltage of the induction machine, can be represented by IabcRepresenting the stator current. In the present embodiment, the steady-state rotation speed of the induction motor is related to the rotor electrical angular velocity, such as the rotor electrical angular velocity ω described laterr，ωr＝2πnrp/60，nrIs the steady-state speed of the induction motor, and P is the pole pair number of the motor (P is lower case, and later mentioned upper case P is a differential operator).
Step 200, determining the window length and the sliding window step length of a sliding window, and setting the cycle identification times;
in this embodiment, the window length is used to represent the length of the sliding window along the time axis, and may represent the data amount or the number of sampling points corresponding to one parameter identification process; the sliding window step size is used for representing the time step of each sliding of the sliding window, and can represent the position relation between adjacent sliding windows relative to the time axis. The sliding window step length can be ldTo indicate. During rotor air gap eccentricity detection, a particle swarm algorithm and a fault model (namely a subsequent induction motor rotor air gap eccentricity fault model) are applied to carry out first parameter identification processing on the initial position of a sliding window based on corresponding data in the window length, then the sliding window slides once according to the sliding window step length, the parameter identification processing is carried out on the current position reached after the sliding window slides once based on the corresponding data in the window length by applying the particle swarm algorithm and the fault model until the sliding frequency reaches the set period identification frequency, at the moment, the total sliding length of the window corresponds to a period, the period refers to the period of sinusoidal voltage and current applied to a motor, for example, the alternating current frequency of China is 50 Hz, namely the period is 0.02 s; then judging the air gap eccentricity of the rotor according to the identification processing results of all the parametersThe case (1). In this regard, a detailed description will follow, and the description is only provided to facilitate an overall understanding of the technical solutions of the present embodiment. The skilled person can understand that, in order to find the condition of the rotor air gap eccentricity in time, the data of the latest period of time can be acquired, and the sliding window technology and the particle swarm algorithm are applied to identify the data based on the fault model. Generally, to ensure the accuracy of the recognition result, the length of the time period should be selected to be at least one period greater than the window length.
In this embodiment, since the sliding window step can carry voltage phase information, the initial phase of the input a-phase voltage can be obtained by the sliding window step. The initial phase of the input a-phase voltage can be thetauAnd (4) showing. As can be known from the following description, the equivalent inductance and the equivalent resistance expression are related to the initial phase of the input a-phase voltage, so that the present embodiment can extract the characteristics of the induction motor with the rotor air gap eccentric fault in the induction motor multi-loop state model through the sliding window technique.
The window length may be determined based on the recognition result accuracy and/or the operation duration, and generally, the longer the window length is, the more beneficial the recognition result accuracy is, but the operation duration may be relatively longer, on the premise that other conditions are not changed. In one embodiment, the window length may be selected to be, for example, 0.3 seconds, 0.5 seconds, 0.6 seconds, 0.8 seconds, 1 second, and so on.
The step length of the sliding window can be determined according to the sampling frequency of an instrument for measuring the voltage and the current of the induction motor, and generally speaking, under the condition that other conditions are not changed, the higher the sampling frequency is, the larger the selectable range of the step length of the sliding window is; conversely, the lower the sampling frequency, the smaller the range over which the sliding window step size can be selected.
Further, the sliding window step size may additionally be determined according to the number of sample points to be defined. Generally speaking, under the condition that other conditions are not changed, the more the number of sampling points to be defined is, the larger the step length of the sliding window is; conversely, the smaller the number of sampling points to be defined, the smaller the sliding window step size.
In one embodiment, the cycle identification number represents the corresponding identification number when the total length of the sliding window sliding reaches one cycle. The cycle identification frequency can be determined jointly according to the calculation speed of one-time identification of the actual terminal, the total identification result and the time and sampling frequency allowed by the judgment result, the cycle identification frequency can be set properly according to a specific application scene, and the sampling frequency limits the settable cycle identification frequency. For example, assuming that the window length is 0.5 seconds, the sampling frequency is 8000Hz, and the period is 0.02 seconds, the number of sampling points per period, that is, 0.02 seconds, is 160, at this time, the sliding window step length may be selected to correspond to 2 sampling points, the sliding window step length is 0.02 × 2 × 1000/160 — 0.25 milliseconds, the initial phase of the input voltage is increased by pi/40 (unit: rad), and the number of cycle identifications per period, that is, 0.02 seconds, is 80; if the sliding window step is selected to correspond to 4 sampling points, the sliding window step is 0.5 ms, the initial phase of the input voltage is increased by pi/20 (unit: rad), and the cycle identification frequency of one cycle is 40 times. In the example given here, the sliding window step size is a fixed step size. In some application scenarios, the sliding window step size may be a variable step size.
And 300, based on the working parameters of the induction motor in the window length corresponding to the sliding window step length, sequentially sliding the sliding window according to the sliding window step length, performing fault model equivalent parameter identification in sequence by using an induction motor rotor air gap eccentric fault model and a particle swarm algorithm, obtaining and storing the fault model equivalent parameter identification value of each identification until the frequency of fault model equivalent parameter identification reaches the cycle identification frequency.
For example, for the current sliding window, transform matrix D is employed2×3Performing coordinate transformation on the obtained stator voltage and stator current of the induction motor corresponding to the window of the current sliding window to obtain transformed vectors, wherein the transformed vectors include stator d-axis voltage, stator q-axis voltage, rotor d-axis voltage, rotor q-axis voltage, stator d-axis current, stator q-axis current, rotor d-axis current and rotor q-axis current, and the corresponding data in the window covers multiple period data, so that the data of the d-axis voltage and the d-axis current include multiple period data, and it can be known from the following description that the corresponding data in the window is transformed to serve as induction motorThe input data (for example, voltage in the model) and the output reference data (for example, current in the model) of the motor rotor air gap eccentric fault model are used, for example, the stator voltage is a 3 × N matrix, wherein N represents the number of corresponding sampling points in a window, and U ═ Na,ub,uc]T，D2×3For the transformation matrix, there is Udq＝D2×3U＝[ud,uq]T(ii) a And performing fault model equivalent parameter identification on the transformed vector in sequence by using an induction motor rotor air gap eccentric fault model and a particle swarm algorithm to obtain an equivalent resistance and an equivalent inductance value, storing the equivalent parameter identification value as the fault model equivalent parameter identification value of the current identification, adding 1 to the identification frequency when the identification frequency does not reach the cycle identification frequency, and continuously performing coordinate transformation, parameter identification and storage processing by using a window after sliding in a sliding step length as a current window until the identification frequency reaches the cycle identification frequency.
Wherein the matrix D is transformed2×3Comprises the following steps:
Figure BDA0002326837560000091
in the transformation matrix, θ1For synchronizing electrical angle, synchronous electrical angle theta1Synchronous electrical angular velocity omega for rotating magnetic field1(synchronous with the air-gap field) times t, i.e. theta1＝ω1t。
Aiming at the condition that the induction motor does not have the eccentricity of the air gap of the rotor, the normal model of the induction motor comprises a normal voltage equation and a normal flux linkage equation, which are respectively as follows:
the normal voltage equation is:
Figure BDA0002326837560000101
the normal flux linkage equation is:
Figure BDA0002326837560000102
the normal model of the induction motor also comprises an expression of the equivalent parameters of the normal model, and the expression of the equivalent parameters of the normal model comprises:
Figure BDA0002326837560000103
Rr＝rr-2rbcosα。
in the formula uds、uqs、udrAnd uqrRespectively are stator d-axis voltage, stator q-axis voltage, rotor d-axis voltage and rotor q-axis voltage; i.e. ids、iqs、idrAnd iqrStator d-axis current, stator q-axis current, rotor d-axis current and rotor q-axis current respectively;
Figure BDA0002326837560000104
and
Figure BDA0002326837560000105
respectively a stator d-axis magnetic flux linkage, a stator q-axis magnetic flux linkage, a rotor d-axis magnetic flux linkage and a rotor q-axis magnetic flux linkage; l iss、LrStator and rotor equivalent inductances, L, of the normal model, respectivelymAnd L'mFirst and second equivalent mutual inductances, r, of the normal model, respectivelys、RrThe stator phase winding resistance and the rotor equivalent resistance of the normal model, omega respectively1、ωrSynchronous and rotor electrical angular velocities, ω, respectively1To the grid frequency f1Related, ω1＝2πf1，rrAnd rbRespectively a rotor loop resistor and each rotor conducting bar resistor,αis the electrical angle, L, between the axes of adjacent rotor circuitsmsFor self-inductance of the stator phase winding, LσsFor stator phase winding leakage inductance, LσrFor the leakage inductance of the rotor loop, n is the number of the rotor guide bars of the motor, and P is a differential operator. Parameters, e.g. r, given in the normal model of the induction machine corresponding to the machine itselfs、rr、rb、Lms、LσsAnd LσrCan be obtained by looking up a relevant manual according to the motor model。
The normal model of the induction machine can be used to determine the equation form of the fault model (e.g., induction machine rotor air gap eccentricity fault model), e.g., a variable that is 0 in the normal model is also correspondingly 0 in the fault model; and the method can also be used for determining the characteristics of equivalent parameters of a fault model when the induction motor is normal so as to be used when needed in the fault model.
In the embodiment, the equation form of the induction motor rotor air gap eccentricity fault model is constructed based on the equation form of the induction motor normal model. The induction motor rotor air gap eccentric fault model comprises a fault model voltage equation, wherein the fault model voltage equation is as follows:
Figure BDA0002326837560000111
in the formula: u. ofds、uqs、udrAnd uqrRespectively are stator d-axis voltage, stator q-axis voltage, rotor d-axis voltage and rotor q-axis voltage; i.e. ids、iqs、idrAnd iqrStator d-axis current, stator q-axis current, rotor d-axis current and rotor q-axis current respectively; r issAnd RrRespectively a stator phase winding resistance and a rotor equivalent resistance of a normal model; l iss11、Ls12And LsΛThe first equivalent inductance of the stator of the fault model, the second equivalent inductance of the stator of the fault model and the third equivalent inductance of the stator of the fault model are respectively; l isr11、Lr12And LrΛThe first equivalent inductance of the rotor of the fault model, the second equivalent inductance of the rotor of the fault model and the third equivalent inductance of the rotor of the fault model are respectively; l ism11、Lm12、Lm21And Lm22Respectively being a first equivalent mutual inductance of the fault model, a second equivalent mutual inductance of the fault model, a third equivalent mutual inductance of the fault model and a fourth equivalent mutual inductance of the fault model; l ismAnd L'mThe first equivalent mutual inductance and the second equivalent mutual inductance of the normal model are respectively; omega1And ωrSynchronous electrical angular velocity and rotor electrical angular velocity; lambdaxIs an intermediate variable; p is a differential operator.
In the embodiment, the induction motor rotor air gap eccentric fault model and the particle swarm algorithm are used for identifying the equivalent parameters of the fault model, and the particle swarm algorithm belongs to the existing mature technology, so detailed description is omitted. When fault model equivalent parameter identification is carried out, the measured stator voltage is used as the input of an induction motor rotor air gap eccentric fault model, the measured stator current is used as a target function adaptive value (namely the output reference data) of the current in the induction motor rotor air gap eccentric fault model, the particle swarm algorithm is used for identifying the parameters in the induction motor rotor air gap eccentric fault model, and therefore the fault model equivalent parameter identification value of each identification is obtained.
Because the stator third equivalent inductance L of the fault model is smaller in eccentricitysΛAnd a rotor third equivalent inductance L of the fault modelrΛStator equivalent inductance L with normal model respectivelysEquivalent inductance L of rotor of normal modelrThe difference is not large, the precision problem of the identification algorithm is added, and the stator third equivalent inductance L of the fault modelsΛAnd a rotor third equivalent inductance L of the fault modelrΛIs not sufficient as a measure for determining whether air gap eccentricity has occurred in the motor, and therefore in one embodiment, the stator third equivalent inductance L of the fault modelsΛAnd a rotor third equivalent inductance L of the fault modelrΛNot as an equivalent parameter to the fault model. In this embodiment, in the above fault model, the fault model equivalent parameters include: first equivalent inductance L of stator of fault models11Stator second equivalent inductance L of fault models12First equivalent inductance L of rotor of fault modelr11Rotor second equivalent inductance L of fault modelr12First equivalent mutual inductance L of the fault modelm11Second equivalent mutual inductance L of fault modelm12Third equivalent mutual inductance L of fault modelm21And fourth equivalent mutual inductance L of fault modelm22. Therefore, the particle swarm algorithm is utilized to identify the equivalent parameters of the fault model in the induction motor rotor air gap eccentric fault model, so as to obtain the equivalent parameters of the fault model at each timeAnd identifying the equivalent parameter identification value of the identified fault model.
In this embodiment, the obtained identification value of the equivalent parameter of the fault model for each identification needs to be combined with the theoretical value of the equivalent parameter of the fault model to obtain the condition of the air gap eccentricity of the rotor of the induction motor.
Wherein, the expression of the equivalent parameters of the fault model comprises:
Ls11＝Lm(Λx-(1-s)Λpidqs)+Lδs
Ls12＝Lm(Λx+(1-s)Λpiqds)+Lδs
Figure BDA0002326837560000121
Figure BDA0002326837560000122
Lm11＝L'm(Λx-(1-s)Λpidqr)
Lm12＝L'm(Λx+(1-s)Λpiqdr)
Figure BDA0002326837560000123
Figure BDA0002326837560000124
LsΛ＝LmΛx+Lδs
LrΛ＝L'mΛx+Lδr
Figure BDA0002326837560000125
Figure BDA0002326837560000126
Figure BDA0002326837560000127
Figure BDA0002326837560000128
Figure BDA0002326837560000129
Figure BDA0002326837560000131
Figure BDA0002326837560000132
Figure BDA0002326837560000133
in the formula, s is slip ratio, Λ0、Λ1And ΛpIs an intermediate variable, idqs、iqds、idqrAnd iqdrIs an intermediate variable, LδsSame as L in the Normal modelσsLeakage inductance of stator phase winding, LδrSame as L in the Normal modelσrLeakage inductance of rotor circuit, delta air gap eccentricity, g0Is the effective air gap length of the motor, thetarIs the electrical angle of the rotor, r0The inner circle radius of the stator.
The relational expression of the fault model equivalent parameters and the input a-phase voltage initial phase respectively comprises the following steps:
Figure BDA0002326837560000134
Figure BDA0002326837560000135
Figure BDA0002326837560000136
Figure BDA0002326837560000137
Lm11＝L'm(Λx-(1-s)Λpcos(θu-Δθuir)/sin(θu-Δθuir))
Lm12＝L'm(Λx+(1-s)Λpsin(θu-Δθuir)/cos(θu-Δθuir))
Figure BDA0002326837560000138
Figure BDA0002326837560000139
Δθuir＝arctan((γ1γ4-γ3γ2)/(γ1γ3+γ2γ4))
Figure BDA00023268375600001310
Figure BDA00023268375600001311
Figure BDA00023268375600001312
γ4＝sω1RrLm
Lr＝L'm+Lδr
wherein, thetauFor inputting the initial phase of the a-phase voltage, Delta thetauirIn order to represent the difference between the initial phase of the current of the rotor circuit 1 and the initial phase of the phase voltage of the stator a when the axis of the rotor circuit 1 is coincident with the axis of the winding of the phase of the stator a,
Figure BDA00023268375600001313
is a power factor angle, which can be obtained according to a motor model inquiry related manual, gamma1、γ2、γ3And gamma4Are all intermediate variables, LrThe equivalent inductance of the rotor is the normal model.
The characteristic signal of the rotor air gap eccentricity is already embodied in the equivalent inductance of the rotor fault model. In the embodiment, a particle swarm algorithm and a sliding window technology are applied, the measured stator current is used as a target function adaptive value, and the induction motor rotor air gap eccentricity fault model provided by the embodiment is used for identifying equivalent parameters of the induction motor fault model, so that the characteristic of the induction motor that the rotor air gap is eccentric can be extracted.
And 400, acquiring the condition of the air gap eccentricity of the induction motor rotor based on the identified fault model equivalent parameter identification values of each time.
In the embodiment of the invention, the identification values of the equivalent parameters of the fault model identified at each time are sequentially output to obtain the change curve of the equivalent parameters of the fault model of the induction motor, and the condition of the air gap eccentricity of the rotor of the induction motor can be obtained based on the change curve of the equivalent parameters of the fault model of the induction motor and the theoretical value of the equivalent parameters of the fault model calculated based on the air gap eccentricity fault model of the rotor of the induction motor. According to the embodiment of the invention, the induction motor rotor air gap eccentricity condition can be monitored by means of four equations of the induction motor rotor air gap eccentricity fault model and eight fault model equivalent parameters. And under the condition that the fault model equivalent parameters are multiple, obtaining the equivalent parameter change curve of the induction motor fault model aiming at each fault model equivalent parameter.
In this embodiment, the obtaining of the induction motor rotor air gap eccentricity based on the identified fault model equivalent parameter identification values includes: acquiring equivalent parameter change curves of all fault models of the induction motor based on the identified equivalent parameter identification values of the fault models of all times; calculating theoretical values of equivalent parameters of each fault model by using an induction motor rotor air gap eccentric fault model, and acquiring a reference curve corresponding to the equivalent parameters of each fault model of the induction motor based on the theoretical values of the equivalent parameters of each fault model; and acquiring the condition of the air gap eccentricity of the rotor of the induction motor based on the equivalent parameter change curve and the corresponding reference curve of each fault model of the induction motor.
In one embodiment, the obtaining the induction motor rotor air gap eccentricity based on the identified fault model equivalent parameter identification values comprises: calculating each equivalent parameter normal value by using a normal model, acquiring each identified fault model equivalent parameter identification value based on each identified fault model equivalent parameter identification value, and calculating each fault model equivalent parameter theoretical value corresponding to each identification by using an induction motor rotor air gap eccentric fault model; constructing equivalent parameter change curves of the fault models based on the equivalent parameter identification values of the fault models and the corresponding equivalent parameter normal values, and constructing reference curves of the equivalent parameters of the fault models based on the theoretical values of the equivalent parameters of the fault models and the corresponding equivalent parameter normal values; and acquiring the condition of the air gap eccentricity of the rotor of the induction motor based on the equivalent parameter change curve and the corresponding reference curve of each fault model of the induction motor.
In an embodiment, the constructing the equivalent parameter variation curve of each fault model based on the equivalent parameter identification value of each fault model and the corresponding equivalent parameter normal value includes: constructing equivalent parameter change curves of the fault models based on the relation between the ratio of the difference value between the equivalent parameter identification value of each fault model and the corresponding equivalent parameter normal value and the initial phase of the input a-phase voltage;
constructing a reference curve of each fault model equivalent parameter based on the theoretical value of each fault model equivalent parameter and the corresponding equivalent parameter normal value comprises the following steps: and constructing a reference curve of each fault model equivalent parameter based on the relation between the ratio of the difference value between the theoretical value of each fault model equivalent parameter and the corresponding equivalent parameter normal value and the initial phase of the input a-phase voltage.
Here, (the recognized value or the theoretical value-normal value)/normal value may be simply referred to as a deviation ratio, (the recognized value-normal value)/normal value may be simply referred to as a recognized value deviation ratio, (the theoretical value-normal value)/normal value may be simply referred to as a theoretical value deviation ratio. As can be known by those skilled in the art, the reference curve of the equivalent parameter of the fault model can characterize the rotor air gap eccentricity fault, so that the rotor eccentricity fault can be obtained by analyzing the reference curve of the equivalent parameter of the fault model, and the analysis mode is not limited to the mode of passing the deviation ratio.
The condition of induction motor rotor air gap eccentricity is obtained based on fault model equivalent parameter identification values of each identification, and the method comprises the following steps: calculating each equivalent parameter normal value by using a normal model, acquiring each identified fault model equivalent parameter identification value based on each identified fault model equivalent parameter identification value, and calculating each fault model equivalent parameter theoretical value corresponding to each identification by using an induction motor rotor air gap eccentric fault model; constructing equivalent parameter change curves of the fault models based on the equivalent parameter identification values of the fault models and the corresponding equivalent parameter normal values, and constructing reference curves of the equivalent parameters of the fault models based on the theoretical values of the equivalent parameters of the fault models and the corresponding equivalent parameter normal values; and comparing the degree of engagement between the equivalent parameter change curve of each fault model and the corresponding reference curve, and judging whether the induction motor generates the rotor air gap eccentricity or not based on the degree of engagement so as to obtain the condition of the rotor air gap eccentricity of the induction motor. In some embodiments, the degree of engagement between each fault model equivalent parameter variation curve and the corresponding reference curve is compared by determining whether the difference between the average deviation ratio of the theoretical value and the average deviation ratio of the identification value is within a set threshold range. It can be understood that the setting of the threshold range is related to the identification precision of the algorithm used, and is also related to the identification conditions of equivalent parameters of different fault models of different types of motors, and the like, and cannot be summarized.
The embodiment of the invention also provides a device for detecting the air gap eccentricity of the rotor of the induction motor, which can be implemented in equipment with computing capability, as shown in fig. 2.
As shown in fig. 2, the device for detecting the air gap eccentricity of the rotor of the induction motor comprises:
the determining module is used for determining the window length and the sliding window step length of the sliding window;
the setting module is used for setting the cycle identification times;
the identification module is used for sequentially identifying the equivalent parameters of the fault model by applying an induction motor rotor air gap eccentric fault model and a particle swarm algorithm based on the working parameters of the induction motor in the window length corresponding to the sliding window which sequentially slides according to the sliding window step length, obtaining and storing the identification value of the equivalent parameters of the fault model identified at each time until the number of times of parameter identification reaches the number of times of period identification;
and the acquisition module is used for acquiring the condition of the air gap eccentricity of the rotor of the induction motor based on the identified fault model equivalent parameter identification values of each time.
The device for detecting the air gap eccentricity of the rotor of the induction motor further comprises an acquisition module used for acquiring the working parameters of the induction motor. In some embodiments, the operating parameters include stator voltage and stator current, and induction machine steady state speed.
For the sake of space saving, the same contents of the induction motor rotor air gap eccentricity detection apparatus of the present embodiment and the aforementioned induction motor rotor air gap eccentricity detection method are not repeated, and those skilled in the art can know the relevant contents of the induction motor rotor air gap eccentricity detection apparatus of the present embodiment by referring to the description of the aforementioned induction motor rotor air gap eccentricity detection method.
As used herein, the term "include" and its various variants are to be understood as open-ended terms, which mean "including, but not limited to. The term "based on" may be understood as "based at least in part on". The term "one embodiment" may be understood as "at least one embodiment". The term "another embodiment" may be understood as "at least one other embodiment".
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The embodiments of the present invention have been described above. However, the present invention is not limited to the above embodiment. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (10)

1. A method for detecting the air gap eccentricity of an induction motor rotor is characterized by comprising the following steps:
determining the window length and the sliding window step length of a sliding window;
setting cycle identification times;
based on the working parameters of the induction motor in the window length corresponding to the sliding window step length, sequentially performing fault model equivalent parameter identification by using an induction motor rotor air gap eccentric fault model and a particle swarm algorithm, and obtaining the fault model equivalent parameter identification value of each identification until the frequency of fault model equivalent parameter identification reaches the cycle identification frequency; and
and acquiring the condition of the induction motor rotor air gap eccentricity based on the fault model equivalent parameter identification value identified each time.
2. The method of claim 1, further comprising: acquiring working parameters of an induction motor; the working parameters of the induction motor comprise the stator voltage and the stator current of the induction motor;
the method for identifying the equivalent parameters of the fault model in sequence by applying the induction motor rotor air gap eccentric fault model and the particle swarm algorithm based on the working parameters of the induction motor in the window length corresponding to the sliding window in sequence by sliding according to the sliding window step length comprises the following steps of: based on the working parameters of the induction motor in the window length corresponding to the sliding window step length, after the working parameters of the induction motor are subjected to coordinate transformation, a fault model equivalent parameter identification is carried out on the vector subjected to the coordinate transformation by using an induction motor rotor air gap eccentric fault model and a particle swarm algorithm in sequence;
wherein a transformation matrix D is used2×3Performing said coordinate transformation to transform a matrix D2×3Comprises the following steps:
Figure FDA0002326837550000011
wherein, theta1Is the synchronous electrical angle.
3. The method of claim 1, wherein obtaining the induction machine rotor air gap eccentricity based on the identified fault model equivalent parameter identification values comprises: acquiring an equivalent parameter change curve of the induction motor fault model based on the fault model equivalent parameter identification value identified each time; calculating a fault model equivalent parameter theoretical value corresponding to each identification by using an induction motor rotor air gap eccentric fault model, and acquiring a reference curve of an induction motor fault model equivalent parameter based on the fault model equivalent parameter theoretical value corresponding to each identification; and acquiring the condition of the induction motor rotor air gap eccentricity based on the induction motor fault model equivalent parameter change curve and the corresponding reference curve.
4. The method of claim 3, further comprising: calculating the normal value of the equivalent parameter by using a normal model;
the method for obtaining the equivalent parameter change curve of the fault model of the induction motor based on the fault model equivalent parameter identification value of each identification comprises the following steps: constructing a fault model equivalent parameter change curve based on the fault model equivalent parameter identification value and the equivalent parameter normal value identified each time; and
the method for acquiring the reference curve corresponding to the equivalent parameter of the induction motor fault model based on the theoretical value of the equivalent parameter of the fault model corresponding to each identification comprises the following steps: and constructing a reference curve corresponding to the equivalent parameters of the fault model based on the theoretical values of the equivalent parameters of the fault model corresponding to each identification and the normal values of the equivalent parameters.
5. The method according to claim 4, wherein the constructing of the fault model equivalent parameter variation curve based on the fault model equivalent parameter identification values and the equivalent parameter normal values identified at each time comprises: acquiring an identification value deviation ratio of the equivalent parameters of the fault model based on the identification value of the equivalent parameters of the fault model and the normal value of the equivalent parameters of the fault model identified at each time, wherein the relation between the identification value deviation ratio of the equivalent parameters of the fault model and the initial phase of the input a-phase voltage is used as a change curve of the equivalent parameters of the fault model of the induction motor;
the method for constructing the reference curve corresponding to the equivalent parameter of the fault model based on the theoretical value and the normal value of the equivalent parameter of the fault model corresponding to each identification comprises the following steps: and acquiring a theoretical value deviation ratio of the equivalent parameters of the fault model based on the theoretical value and the normal value of the equivalent parameters of the fault model corresponding to each identification, wherein the relation between the theoretical value deviation ratio of the equivalent parameters of the fault model and the initial phase of the input a-phase voltage is used as a reference curve corresponding to the equivalent parameter change curve of the fault model of the induction motor.
6. The method of claim 3, wherein the obtaining of the induction machine rotor air gap eccentricity based on the induction machine fault model equivalent parameter variation curve and the corresponding reference curve comprises: and comparing the degree of engagement between the equivalent parameter change curve of the induction motor fault model and the corresponding reference curve, and judging whether the induction motor generates the rotor air gap eccentricity or not based on the degree of engagement so as to obtain the condition of the rotor air gap eccentricity of the induction motor.
7. The method of claim 1, wherein the induction machine rotor air gap eccentricity fault model comprises a fault model voltage equation that is:
Figure FDA0002326837550000021
in the formula: u. ofds、uqs、udrAnd uqrRespectively are stator d-axis voltage, stator q-axis voltage, rotor d-axis voltage and rotor q-axis voltage; i.e. ids、iqs、idrAnd iqrStator d-axis current, stator q-axis current, rotor d-axis current and rotor q-axis current respectively; r issAnd RrRespectively a stator phase winding resistance and a rotor equivalent resistance of a normal model; l iss11、Ls12And LsΛThe first equivalent inductance of the stator of the fault model, the second equivalent inductance of the stator of the fault model and the third equivalent inductance of the stator of the fault model are respectively; l isr11、Lr12And LrΛThe first equivalent inductance of the rotor of the fault model, the second equivalent inductance of the rotor of the fault model and the third equivalent inductance of the rotor of the fault model are respectively; l ism11、Lm12、Lm21And Lm22Respectively being a first equivalent mutual inductance of the fault model, a second equivalent mutual inductance of the fault model, a third equivalent mutual inductance of the fault model and a fourth equivalent mutual inductance of the fault model; l ismAnd L'mThe first equivalent mutual inductance and the second equivalent mutual inductance of the normal model are respectively; omega1And ωrSynchronous electrical angular velocity and rotor electrical angular velocity; lambdaxIs an intermediate variable; p is a differential operator;
the fault model equivalent parameters include: fault modelFirst equivalent inductance L of stators11Stator second equivalent inductance L of fault models12First equivalent inductance L of rotor of fault modelr11Rotor second equivalent inductance L of fault modelr12First equivalent mutual inductance L of the fault modelm11Second equivalent mutual inductance L of fault modelm12Third equivalent mutual inductance L of fault modelm21And fourth equivalent mutual inductance L of fault modelm22；
The expression of the equivalent parameters of the fault model comprises the following steps:
Ls11＝Lm(Λx-(1-s)Λpidqs)+Lδs
Ls12＝Lm(Λx+(1-s)Λpiqds)+Lδs
Figure FDA0002326837550000031
Figure FDA0002326837550000032
Lm11＝L'm(Λx-(1-s)Λpidqr)
Lm12＝L'm(Λx+(1-s)Λpiqdr)
Figure FDA0002326837550000033
Figure FDA0002326837550000034
LsΛ＝LmΛx+Lδs
LrΛ＝L′mΛx+Lδr
Figure FDA0002326837550000035
Figure FDA0002326837550000036
Figure FDA0002326837550000041
Figure FDA0002326837550000042
Figure FDA0002326837550000043
Figure FDA0002326837550000044
Figure FDA0002326837550000045
Figure FDA0002326837550000046
in the formula, s is slip ratio, Λ0、Λ1And ΛpIs an intermediate variable, idqs、iqds、idqrAnd iqdrIs an intermediate variable, LδsFor stator phase winding leakage inductance, LδrFor rotor loop leakage inductance, delta is air gap eccentricity, g0Is the effective air gap length of the motor, thetarIs the electrical angle of the rotor, r0StatorThe radius of the inner circle;
the relational expression of the fault model equivalent parameters and the input a-phase voltage initial phase respectively comprises the following steps:
Figure FDA0002326837550000047
Figure FDA0002326837550000048
Figure FDA0002326837550000049
Figure FDA00023268375500000410
Lm11＝L'm(Λx-(1-s)Λpcos(θu-Δθuir)/sin(θu-Δθuir))
Lm12＝L'm(Λx+(1-s)Λpsin(θu-Δθuir)/cos(θu-Δθuir))
Figure FDA00023268375500000411
Figure FDA00023268375500000412
Δθuir＝arctan((γ1γ4-γ3γ2)/(γ1γ3+γ2γ4))
Figure FDA00023268375500000413
Figure FDA00023268375500000414
Figure FDA00023268375500000415
γ4＝sω1RrLm
Lr＝L′m+Lδr
wherein, thetauFor inputting the initial phase of the a-phase voltage, Delta thetauirIn order to represent the difference between the initial phase of the current of the rotor circuit 1 and the initial phase of the phase voltage of the stator a when the axis of the rotor circuit 1 is coincident with the axis of the winding of the phase of the stator a,
Figure FDA0002326837550000051
is the power factor angle, gamma1、γ2、γ3And gamma4Are all intermediate variables, LrThe equivalent inductance of the rotor is the normal model.
8. The method of claim 5, wherein the normal model of the induction machine comprises a normal voltage equation and a normal flux linkage equation for the case where the induction machine does not exhibit rotor air gap eccentricity;
the normal voltage equation is:
Figure FDA0002326837550000052
the normal flux linkage equation is:
Figure FDA0002326837550000053
the normal model of the induction motor also comprises an expression of the equivalent parameters of the normal model, and the expression of the equivalent parameters of the normal model comprises:
Figure FDA0002326837550000054
Rr＝rr-2rb cosα；
in the formula uds、uqs、udrAnd uqrRespectively are stator d-axis voltage, stator q-axis voltage, rotor d-axis voltage and rotor q-axis voltage; i.e. ids、iqs、idrAnd iqrStator d-axis current, stator q-axis current, rotor d-axis current and rotor q-axis current respectively;
Figure FDA0002326837550000055
and
Figure FDA0002326837550000056
respectively a stator d-axis magnetic flux linkage, a stator q-axis magnetic flux linkage, a rotor d-axis magnetic flux linkage and a rotor q-axis magnetic flux linkage; l issAnd LrStator and rotor equivalent inductances, L, of the normal model, respectivelymAnd L'mFirst and second equivalent mutual inductances, r, of the normal model, respectivelysAnd RrThe stator phase winding resistance and the rotor equivalent resistance of the normal model, omega respectively1And ωrSynchronous and rotor electrical angular velocities, r, respectivelyrAnd rbRotor loop resistance and rotor bar resistance, respectively, alpha is the electrical angle between adjacent rotor loop axes, LmsFor self-inductance of the stator phase winding, LσsFor stator phase winding leakage inductance, LσrFor the leakage inductance of the rotor loop, n is the number of the rotor guide bars of the motor, and P is a differential operator.
9. An induction motor rotor air gap eccentricity detection device, comprising:
the determining module is used for determining the window length and the sliding window step length of the sliding window;
the setting module is used for setting the cycle identification times;
the identification module is used for sequentially identifying the equivalent parameters of the fault model by applying an induction motor rotor air gap eccentric fault model and a particle swarm algorithm based on the working parameters of the induction motor in the window length corresponding to the sliding window which sequentially slides according to the sliding window step length, and acquiring the identification value of the equivalent parameters of the fault model of each identification until the number of times of parameter identification reaches the number of times of cycle identification; and
and the analysis module is used for acquiring the condition of the air gap eccentricity of the induction motor rotor based on the identified fault model equivalent parameter identification values of each time.
10. The apparatus according to claim 9, for implementing the method according to any one of claims 1-8.
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114157214A (en) * 2021-11-19 2022-03-08 华中科技大学 Eccentric fault detection and air gap flux density compensation method for multi-module synchronous motor

Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20130033215A1 (en) * 2011-08-01 2013-02-07 Illinois Institute Of Technology Apparatus and method for permanent magnet electric machine condition monitoring
CN103698699A (en) * 2013-12-06 2014-04-02 西安交通大学 Asynchronous motor fault monitoring and diagnosing method based on model
EP2757384A2 (en) * 2013-01-21 2014-07-23 AREVA GmbH Method and device for detecting faults in an electric machine
WO2014135784A1 (en) * 2013-03-05 2014-09-12 Electricite De France Method for detecting a fault in the eccentricity of a rotor of a rotating electrical machine
CN106680716A (en) * 2017-01-20 2017-05-17 浙江大学 Fault diagnosis method for the permanent magnet motor bearing based on the sensor-less
CN108718169A (en) * 2018-07-03 2018-10-30 广东水利电力职业技术学院（广东省水利电力技工学校） A kind of asynchronous machine grading variable-frequency soft starting device machine control method and system
CN110109015A (en) * 2019-05-31 2019-08-09 中南大学 A kind of asynchronous motor Fault monitoring and diagnosis method based on deep learning
CN110470212A (en) * 2019-07-10 2019-11-19 哈尔滨工业大学（威海） A kind of hub motor dynamic fault of eccentricity detection method

Patent Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20130033215A1 (en) * 2011-08-01 2013-02-07 Illinois Institute Of Technology Apparatus and method for permanent magnet electric machine condition monitoring
EP2757384A2 (en) * 2013-01-21 2014-07-23 AREVA GmbH Method and device for detecting faults in an electric machine
WO2014135784A1 (en) * 2013-03-05 2014-09-12 Electricite De France Method for detecting a fault in the eccentricity of a rotor of a rotating electrical machine
CN103698699A (en) * 2013-12-06 2014-04-02 西安交通大学 Asynchronous motor fault monitoring and diagnosing method based on model
CN106680716A (en) * 2017-01-20 2017-05-17 浙江大学 Fault diagnosis method for the permanent magnet motor bearing based on the sensor-less
CN108718169A (en) * 2018-07-03 2018-10-30 广东水利电力职业技术学院（广东省水利电力技工学校） A kind of asynchronous machine grading variable-frequency soft starting device machine control method and system
CN110109015A (en) * 2019-05-31 2019-08-09 中南大学 A kind of asynchronous motor Fault monitoring and diagnosis method based on deep learning
CN110470212A (en) * 2019-07-10 2019-11-19 哈尔滨工业大学（威海） A kind of hub motor dynamic fault of eccentricity detection method

Non-Patent Citations (4)

* Cited by examiner, † Cited by third party
Title
MA JIRDEHI等: "Parameters estimation of squirrel-cage induction motors using ANN and ANFIS", ALEXANDRIA ENGINEERING JOURNAL, pages 357 - 368 *
SOUALHI, A等: "Detection and Diagnosis of Faults in Induction Motor Using an Improved Artificial Ant Clustering Technique", IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, pages 4053 - 4062 *
吴立泉等: "一种感应电机转子断条早期故障诊断方法", 电机与控制应用, no. 09 *
阳同光;蒋新华;: "基于粒子群优化算法电机转子故障诊断研究", 计算机工程与应用, no. 01 *

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114157214A (en) * 2021-11-19 2022-03-08 华中科技大学 Eccentric fault detection and air gap flux density compensation method for multi-module synchronous motor
CN114157214B (en) * 2021-11-19 2023-10-27 华中科技大学 Eccentric fault detection and air gap flux density compensation method for multi-module synchronous motor

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