Motor fault diagnosis method and motor controller

Abstract

The present application discloses a motor fault diagnosis method and a motor controller. The diagnosis method includes: using a dual-vector model prediction algorithm to calculate the difference between a reference current and a predicted current, and then calculating two voltage vectors and corresponding action times that minimize the evaluation function, obtaining the utilization of each voltage vector under multiple cycles according to their respective duty cycles, grouping the recorded voltage vectors according to their effects on the three-phase current, and then judging the fault condition by establishing a fault index including voltage vector selection and action time, and finally detecting the fault in combination with the distribution of each group of voltage vectors, and accurately locating the fault phase. The present application does not require additional observers and hardware equipment, and can achieve diagnosis by only using the existing states and variables in the dual-vector prediction control loop. It is robust to changes in speed and torque, has low algorithm complexity and is easy to combine with existing control methods, and can achieve online turn-to-turn short-circuit fault monitoring and fault phase identification.

Description

Technical Field

[0001] The present application relates to the technical field of motor detection, and in particular to a motor fault diagnosis method and a motor controller. Background Art

[0002] AC asynchronous motors are economical, practical, low maintenance, highly adaptable, and highly reliable, and are widely used in the industrial sector. With the development of society and the progress of industry, more and more applications have put forward higher requirements for the reliable operation of motors, among which stator faults account for 37% of motor faults, and most stator faults are caused by early turn-to-turn short-circuit faults. Existing stator fault diagnosis methods have many shortcomings, such as difficulty in fault identification, reliance on additional equipment, high cost, and large amount of data calculation. Therefore, it is of great significance to carry out research on online turn-to-turn short-circuit fault diagnosis technology. Summary of the invention

[0003] The main purpose of the present application is to provide a motor fault diagnosis method and a motor controller, aiming to solve the problem of difficulty in diagnosing turn-to-turn short circuit faults in the prior art.

[0004] To achieve the above-mentioned purpose, the present application provides a motor fault diagnosis method, which is applied to a three-phase asynchronous motor, and the method includes: based on the voltage vector of the inverter module in different switching states and the stator current at the previous moment, outputting a predicted current acting on the motor stator at the next moment; based on the predicted current and the magnetic flux of the motor stator at the previous moment, determining the duty cycle of each voltage vector in multiple control cycles; calculating the utilization rate of each voltage vector under multiple control cycles; and monitoring the motor for turn-to-turn short-circuit faults based on the utilization rate of each voltage vector.

[0005] Optionally, the monitoring of the motor for turn-to-turn short-circuit faults based on the utilization rates of each of the voltage vectors includes: constructing a fault index based on the utilization rates of each of the voltage vectors; and determining whether a turn-to-turn short-circuit fault occurs based on a comparison result between the fault index and a preset threshold.

[0006] Optionally, constructing a fault indicator based on the utilization of each voltage vector includes: constructing a fault indicator shown in the following formula (1) based on the utilization of each voltage vector:

[0007] (1)

[0008] Where: Indicates the utilization rate of phase A voltage vector, represents the utilization rate of the B phase voltage vector, Indicates the utilization rate of the C phase voltage vector; n Indicates the number of statistical control cycles; Indicates a fault indicator.

[0009] Optionally, after determining that a turn-to-turn short circuit fault occurs, the method further includes: if the utilization rate of the A phase voltage vector is greater than the utilization rates of the other two phases voltage vectors, then it is determined that a turn-to-turn short circuit fault occurs in phase A; if the utilization rate of the B phase voltage vector is greater than the utilization rates of the other two phases voltage vectors, then it is determined that a turn-to-turn short circuit fault occurs in phase B; if the utilization rate of the C phase voltage vector is greater than the utilization rates of the other two phases voltage vectors, then it is determined that a turn-to-turn short circuit fault occurs in phase C.

[0010] Optionally, the outputting a predicted current acting on the motor stator at the next moment based on the voltage vector of the inverter module in different switching states and the stator current at the previous moment includes: outputting a predicted current acting on the motor stator at the next moment as shown in the following formula (2) based on the voltage vector of the inverter module in different switching states and the stator current at the previous moment:

[0011] (2)

[0012] Where: , They represent the k-th moment α , β The measured value of the stator current of the shaft, , Respectively represent the k+1 moment α , β The predicted stator current of the shaft, T s represents the control cycle, R s is the stator resistance, R r is the rotor resistance, ω represents the rotor speed, L s and L r represent the stator inductance and rotor inductance respectively, L m represents the stator-rotor mutual inductance, T r represents the rotor electromagnetic time constant, σ=1-L m 2 / L s L r Indicates the leakage flux coefficient of the motor; , Respectively represent the k moment α , β The stator flux of the shaft, , Respectively represent the k moment α ,β The stator voltage of the shaft.

[0013] Optionally, determining the duty cycle of each voltage vector in a plurality of control cycles based on the predicted current and the flux linkage of the motor stator at the previous moment includes: constructing an evaluation function shown in the following formula (3) based on the predicted current and the flux linkage of the motor stator at the previous moment:

[0014] (3)

[0015] The evaluation function is used to determine the duty cycle of the dual vector voltage under each voltage vector control in multiple control cycles as shown in the following formula (4):

[0016] (4)

[0017] Where: T s represents the control cycle, t opt1 , t opt2 are the two voltage vector action times respectively, represents the duty cycle of the first voltage vector, Indicates the duty cycle of the second voltage vector; , Respectively α , β The given stator current of the axis, , Respectively represent the k+2 moment α , β The measured value of the stator current of the shaft; , The first voltage vector acts on α , β The current slope of the axis, , The second voltage vector acts on α , β The current slope of the axis; , Respectively represent the k+1 moment α , β The predicted stator current of the shaft, , They represent the k+2 moment respectively. α , β Predicted stator current of the shaft; , Respectively α , β The current residual of the axis; Represents the evaluation function.

[0018] Optionally, the calculating the utilization rate of each voltage vector under multiple control cycles includes: grouping the voltage vectors using the following formula (5):

[0019] The voltage vectors are grouped using the following formula (5):

[0020] (5);

[0021] The utilization rate of each voltage vector under multiple control cycles is calculated using the following formula (6) according to the duty cycle of each voltage vector:

[0022] (6)

[0023] Where: , , , , , Respectively represent the voltage vectors of the inverter in different switching states; , , Respectively represent the utilization of the A-phase, B-phase, and C-phase voltage vectors; , , , They represent the utilization rates of the two voltage vectors in the corresponding control cycle respectively; T s represents the control cycle, t opt1 , t opt2 They are the two voltage vector action times respectively.

[0024] In addition, to achieve the above-mentioned purpose, the present application also provides a motor controller, including: a current prediction control module, which is used to output the predicted current acting on the motor stator at the next moment based on the voltage vector of the inverter module in different switching states and the stator current at the previous moment; a minimization cost function module, which is used to determine the duty cycle of each voltage vector in multiple control cycles based on the predicted current and the magnetic flux of the motor stator at the previous moment; a fault monitoring and diagnosis module, which is used to calculate the utilization rate of each voltage vector under multiple control cycles, and to monitor the motor for turn-to-turn short-circuit faults based on the utilization rate of each voltage vector.

[0025] Optionally, the fault monitoring and diagnosis module is further used to construct a fault index based on the utilization rate of each of the voltage vectors.

[0026] Optionally, the fault monitoring and diagnosis module is also used to determine whether a turn-to-turn short circuit fault occurs based on the comparison result of the fault indicator with a preset threshold value, and if the utilization rate of the A phase voltage vector is greater than the utilization rates of the other two phases voltage vectors, it is determined that a turn-to-turn short circuit fault occurs in phase A; or, if the utilization rate of the B phase voltage vector is greater than the utilization rates of the other two phases voltage vectors, it is determined that a turn-to-turn short circuit fault occurs in phase B; or, if the utilization rate of the C phase voltage vector is greater than the utilization rates of the other two phases voltage vectors, it is determined that a turn-to-turn short circuit fault occurs in phase C.

[0027] The motor fault diagnosis method provided by the present application outputs the predicted current for the stator through the dual vector model, determines the duty cycle of each voltage vector according to the stator current and the stator flux, and then calculates the utilization rate of each voltage vector under multiple control cycles according to the duty cycle of each voltage vector obtained, because when the motor does not fail, the utilization rate of each voltage vector acting on the motor stator is relatively balanced, and when the motor has a turn-to-turn short circuit fault, the motor will mask the fault phase current fault characteristics under the influence of closed-loop control, and the utilization rate of the voltage vector acting on the fault phase will increase significantly, that is, it will be higher than the utilization rate of the voltage vector acting on other normal phases. Therefore, by statistically analyzing the utilization rate of each voltage vector under multiple control cycles, it is possible to detect whether the motor has a turn-to-turn short circuit and accurately identify the fault phase where the turn-to-turn short circuit occurs. The motor fault diagnosis method provided by the present application fully utilizes the existing parameters in the motor control process, reduces the difficulty of extracting fault characteristics by recording the motor switch state under multiple control cycles, achieves the goal of online fault diagnosis without adding additional hardware, and has the advantages of simple structure and easy implementation. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 A flow chart of a motor fault diagnosis method provided according to an embodiment of the present application;

[0029] Figure 2 An equivalent diagram of a three-phase stator winding with a turn-to-turn short circuit fault provided in accordance with an embodiment of the present application;

[0030] Figure 3 A current residual and a corresponding spatial voltage vector distribution diagram provided in an embodiment of the present application;

[0031] Figure 4 A dynamic process diagram of the fault indicator output waveform when a given speed and load are switched provided in an embodiment of the present application;

[0032] Figure 5 Output waveform diagram of the fault indicator and fault location provided in the embodiment of the present application when the A phase fails;

[0033] Figure 6The figure is a structural block diagram of a motor controller according to one embodiment of the present application.

[0034] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0035] It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0036] AC asynchronous motors are economical, practical, low maintenance, highly adaptable, and highly reliable, and are widely used in the industrial sector. With the development of society and the progress of industry, more and more applications have put forward higher requirements for the reliable operation of motors, among which stator faults account for 37% of motor faults, and most stator faults are caused by early turn-to-turn short-circuit faults. Therefore, it is of great significance to carry out research on online turn-to-turn short-circuit fault diagnosis technology.

[0037] In order to achieve effective turn-to-turn short-circuit fault diagnosis, domestic and foreign scholars have proposed many methods, which are mainly divided into three fault diagnosis methods: signal processing, analytical model and artificial intelligence. The signal processing-based method determines whether the motor is faulty by extracting fault features, and the diagnosis result is affected by the external conditions of the motor working condition; the analytical model-based method establishes an accurate motor mathematical model, compares the model prediction output with the actual state sampling value, and uses the residual to achieve motor fault diagnosis, which is low-cost and easy to implement online; the artificial intelligence-based method uses artificial intelligence algorithms to achieve motor fault diagnosis, without the need for precise modeling of the motor, but it is heavily dependent on sample data, the algorithm is complex and the computational burden is large, and the versatility is limited by the motor structure and operating environment of the sample acquisition.

[0038] Finite set model predictive control (FCS-MPC) has the advantages of simple structure and excellent dynamic performance. It has been widely used in the field of motor control. However, since the MPC control strategy is significantly different from the traditional control strategy principle, there are few turn-to-turn short-circuit fault diagnosis methods for MPC systems. To this end, some scholars have proposed using the DC component and harmonic component of the value function in the MPC system for detection, but the fault phase cannot be identified and it is difficult to implement online. Relevant scholars have proposed using the residual between the healthy current and the actual current to construct fault indicators and positioning indicators, but it still has the disadvantages of relying on motor parameters and being unable to accurately locate the fault phase.

[0039] In order to solve the above problems, the present application provides a motor fault diagnosis method. Figure 1 is a flow chart of a motor fault diagnosis method provided according to an embodiment of the present application, the method is applied to a three-phase asynchronous motor, and the method is executed by a motor controller, refer to Figure 1, the motor fault diagnosis method may include the following steps:

[0040] S110, outputting a predicted current acting on the motor stator at the next moment based on the voltage vector of the inverter module in different switching states and the stator current at the previous moment;

[0041] S120, determining a duty cycle of each voltage vector in a plurality of control cycles based on the predicted current and the flux linkage of the motor stator at the previous moment;

[0042] S130, calculating the utilization rate of each voltage vector under multiple control cycles;

[0043] S140: Perform turn-to-turn short-circuit fault monitoring on the motor based on utilization rates of each voltage vector.

[0044] The motor fault diagnosis method provided by the present application outputs the predicted current for the stator through the dual vector model, determines the duty cycle of each voltage vector according to the stator current and the stator flux, and then calculates the utilization rate of each voltage vector under multiple control cycles according to the duty cycle of each voltage vector obtained, because when the motor does not fail, the utilization rate of each voltage vector acting on the motor stator is relatively balanced, and when the motor has a turn-to-turn short circuit fault, the motor will mask the fault phase current fault characteristics under the influence of closed-loop control, and the utilization rate of the voltage vector acting on the fault phase will increase significantly, that is, it will be higher than the utilization rate of the voltage vector acting on other normal phases. Therefore, by statistically analyzing the utilization rate of each voltage vector under multiple control cycles, it is possible to detect whether the motor has a turn-to-turn short circuit and accurately identify the fault phase where the turn-to-turn short circuit occurs. The motor fault diagnosis method provided by the present application fully utilizes the existing parameters in the motor control process, reduces the difficulty of extracting fault characteristics by recording the motor switch state under multiple control cycles, achieves the goal of online fault diagnosis without adding additional hardware, and has the advantages of simple structure and easy implementation.

[0045] Furthermore, the motor fault diagnosis method provided in the present application is controlled by a dual-vector model. Dual vector refers to the simultaneous application of two voltage vectors to control the system in one control cycle. In the dual-vector model, two different voltage vectors are applied to change the system state in one control cycle. These voltage vectors are selected from a finite set of voltage vectors that can be generated by the inverter module. For example, for a three-phase two-level inverter module, there are 8 switching states, corresponding to 6 non-zero voltage vectors and 2 zero voltage vectors. Thanks to the good steady-state performance of the dual-vector control strategy, the fault characteristic changes are more obvious and are robust to speed and load changes.

[0046] The present application method is described in detail below using a three-phase asynchronous motor as an example. It should be understood that the present application method is also applicable to other multi-phase motor systems.

[0047] Among them, step S110 and step S120 are the control process of the motor, step S130 and step S140 are the monitoring process of the motor for turn-to-turn short-circuit faults. The method provided in the embodiment of the present application is to utilize the existing control process of the motor to perform turn-to-turn short-circuit fault monitoring of the motor without adding additional hardware structure, and specifically identify the fault phase, thereby solving the problems of difficulty in fault feature extraction, complex fault phase positioning, and poor robustness in the prior art.

[0048] In step S110 , the motor controller predicts the stator current at the next moment according to the given stator current and the stator current acting on the motor at the previous moment through its built-in current prediction module.

[0049] It can be known that the motor controller includes an inverter module, which is to convert the applied DC voltage into AC voltage and then act on the three-phase stator of the asynchronous motor. Taking the three-phase two-level inverter module as an example, each bridge arm includes an upper bridge arm and a lower bridge arm, and each bridge arm has a switch unit. In this way, the inverter module has six switch units, and the six switch units have 6 valid switch states, that is, there are 6 valid voltage vectors, which can be expressed as follows:

[0050] , , , , , .

[0051] in, Indicates the voltage vector when the upper bridge arm of phase A is turned on and the lower bridge arm is turned off, the upper bridge arm of phase B is turned off and the lower bridge arm is turned on, and the upper bridge arm of phase C is turned off and the lower bridge arm is turned on. V 1.

[0052] In an exemplary embodiment, the motor controller may output a predicted current acting on the motor stator at the next moment as shown in the following formula (2) based on the voltage vector of the inverter module in different switching states and the stator current at the previous moment:

[0053] (2)

[0054] Where: , They represent the k-th moment respectively. α , β The measured value of the stator current of the shaft, , Respectively represent the k+1 moment α , β The predicted stator current of the shaft, Ts represents the control cycle, R s is the stator resistance, R r is the rotor resistance, ω represents the rotor speed, L s and L r represent the stator inductance and rotor inductance respectively, L m represents the stator-rotor mutual inductance, T r represents the rotor electromagnetic time constant, σ=1-L m 2 / L s L r Indicates the leakage flux coefficient of the motor; , Respectively represent the k moment α , β The stator flux of the shaft, , Respectively represent the k moment α , β The stator voltage of the shaft.

[0055] In step S120, the duty cycle of each voltage vector obtained is used to calculate the voltage vector utilization in subsequent steps, and then finally turn-to-turn short circuit fault detection and faulty phase identification are performed according to the utilization of each voltage vector.

[0056] In order to evaluate the pros and cons of different control strategies (i.e., different voltage vector combinations), it is necessary to define an evaluation function, which usually includes tracking error terms (such as current tracking error, speed tracking error) and control quantity change terms (such as switching frequency caused by voltage vector switching, etc.). Then, the optimal two voltage vector combinations and their action times in the current control cycle are determined by minimizing the evaluation function. Since the control variables are discrete finite sets, in this exemplary embodiment, the two voltage vectors that are more easily selected by the evaluation function after current imbalance can be grouped according to the effect of applying each voltage vector on the phase current. Specifically in this exemplary embodiment, step S120 may specifically include the following process:

[0057] S1201, constructing an evaluation function shown in the following formula (3) based on the predicted current and the magnetic flux of the motor stator at the previous moment:

[0058] (3)

[0059] S1202, using the evaluation function to determine the duty cycle of the dual vector voltage in multiple control cycles as shown in the following formula (4):

[0060] (4)

[0061] Where: T s represents the control cycle, t opt1 , t opt2 are the two voltage vector action times respectively, represents the duty cycle of the first voltage vector, Indicates the duty cycle of the second voltage vector;

[0062] , Respectively α , β The given stator current of the axis, , Respectively represent the k+2 moment α , β The measured value of the stator current of the shaft;

[0063] , The first voltage vector acts on α , β The current slope of the axis, , The second voltage vector acts on α , β The current slope of the axis;

[0064] , Respectively represent the k+1 moment α , β The predicted stator current of the shaft, , They represent the k+2 moment respectively. α , β Predicted stator current of the shaft; , Respectively α , β The current residual of the axis; Represents the evaluation function.

[0065] The derivation process of the above formula (3) and formula (4) is introduced below.

[0066] It can be known that, in the absence of a fault, the voltage equation of the asynchronous motor can be expressed as follows:

[0067] (7)

[0068] The flux equation of an asynchronous motor can be expressed as follows:

[0069] (8)

[0070] However, when a motor fails, the mathematical model changes. Assume that the three-phase stator winding of the asynchronous motor is connected in star shape, and the turn-to-turn short circuit fault occurs in the unspecified fault phase. phase fault , and the other two phases are phase health1 as well as phase health2 , the equivalent diagram is as follows Figure 2 As shown. The turn-to-turn short-circuit fault divides the faulty phase winding into two parts, where s1 is the normal part and s2 is the short-circuited part of the faulty phase winding. R f Represents the resistance of the short-circuit loop, is the fault current of the short-circuited turn.

[0071] Then, in the case of a fault, the voltage equation of the asynchronous motor can be expressed as follows:

[0072] (9)

[0073] The flux equation under fault conditions can be expressed as follows:

[0074] (10)

[0075] Where: and 、 and They are Stator voltage and stator current in the coordinate system, for phase fault The electrical angle between phase A and phase A.

[0076] According to the voltage equation shown in equation (9) and the flux equation shown in equation (10), we can get the following equation (11): Current residual in coordinate system , :

[0077] (11)

[0078] The current residuals of different fault phases are given in Table 1 below:

[0079] Table 1

[0080]

[0081] The evaluation function is used to make the stator current track the reference current. In theory, when the motor is in a healthy state α、β The current reference value of the axis at the next moment is approximately equal to the measured value, so the evaluation function can be transformed into a combination of the measured value and the predicted value. The evaluation function when the motor is in a healthy state is expressed as follows:

[0082] (3')

[0083] When a turn-to-turn short-circuit fault occurs, α The current residual on the axis will also be introduced into the evaluation function, and the evaluation function expression under the fault state is obtained as shown in the following formula (3):

[0084] (3)

[0085] In order to better analyze the influence of current residual on the subsequent model predictive control system, the α The axis coincides with the B and C phases, so that the residual current of the B and C phases after transformation only exists in the rotating α The relative positions of the current residuals and voltage vectors of different fault phases after transformation are shown in Figure 3 As shown. At this time, the fault component of the evaluation function can also be regarded as only the α The shaft current residual causes:

[0086] (4')

[0087] Under healthy conditions, the utilization of each effective voltage vector is approximately equal. Assuming that the motor is under open-loop control and a stator short-circuit fault occurs, the fault phase current gradually increases with the degree of the short-circuit fault. The other two phase currents also increase in amplitude due to the influence of magnetic flux coupling, and the phase difference with the fault phase current gradually increases. At this time, the three-phase current is unbalanced. In order to rebalance the three-phase current under fault conditions, the optimal voltage vector selected by the evaluation function changes, and the controller will create a set of unbalanced voltage vectors, that is, the action time of the two voltage vectors in the same control cycle is different.

[0088] Specifically, after obtaining the evaluation function shown in formula (3), the optimal vector is selected through the evaluation function. Then the action time of the two voltage vectors satisfies the following formula:

[0089] (10)

[0090] The motor controller can further determine the action time of the first voltage vector according to the above formula (10), and because the action time of the first voltage vector and the second voltage vector are complementary in one control cycle, the action time of the second voltage vector can be characterized by the control cycle and the action time of the first voltage vector.

[0091] After obtaining the action time of the two voltage vectors t opt1 , t opt2 After that, the motor controller further calculates the duty ratio of the two voltage vectors under n control cycles, as shown in the above formula (4).

[0092] In this exemplary embodiment, a dual vector control strategy is adopted to calculate the action time of two voltage vectors, which can make the voltage vector utilization rate change more obvious in the fault situation, make the fault characteristic change more obvious, and be robust to speed and load changes.

[0093] After obtaining the duty cycle of each voltage vector, in step S130, the motor controller calculates the utilization rate of each voltage vector under multiple control cycles. As described above, the inverter module in the motor controller has the following six effective voltage vectors:

[0094] , , , , , .

[0095] according to t opt1 , t opt2 The time when each voltage vector is selected by the evaluation function under multiple control cycles is obtained, and according to the effect of applying each voltage vector on the phase current, the two voltage vectors that are more easily selected by the evaluation function after the current imbalance occurs are grouped into one group. Specifically, the motor controller can use the following formula (5) to group the voltage vectors:

[0096] (5);

[0097] The controller further calculates the voltage vectors applied in n control cycles, and calculates the utilization rate of each voltage vector according to the duty cycle using the following formula (6):

[0098] (6)

[0099] Where: , , , They respectively represent the utilization rates of the two voltage vectors in the corresponding control cycle.

[0100] In step S140, the motor controller performs turn-to-turn short circuit fault monitoring and fault phase identification on the motor according to the utilization rates of the voltage vectors obtained in the above steps.

[0101] In an exemplary embodiment, step S140 may specifically include the following process:

[0102] S1401, constructing a fault index based on the utilization rate of each voltage vector;

[0103] S1402: Determine whether a turn-to-turn short circuit fault occurs based on a comparison result between the fault indicator and a preset threshold.

[0104] In step S1401, the motor controller may construct a fault indicator as shown in the following formula (1):

[0105] (1)

[0106] Where: Indicates the utilization rate of phase A voltage vector, represents the utilization rate of the B phase voltage vector, Indicates the utilization rate of the C phase voltage vector; n Indicates the number of control cycles; Indicates a fault indicator.

[0107] After a turn-to-turn short-circuit fault occurs, the mathematical model of the motor can be decomposed into two parts: healthy and faulty. The voltage vector applied by the controller to the motor can also be decomposed into two parts. The balanced part will cause a significant imbalance in the amplitude and phase of the three-phase current. In order to eliminate the current residual component of the evaluation function, the utilization of the two voltage vectors corresponding to the electrical position of the fault phase is increased, so that the three-phase current re-tracks the reference current.

[0108] Based on the principle of minimizing the tracking error between the stator current and the reference current, the two voltage vectors of the faulty phase have the best effect in adjusting the faulty phase current, and the frequency of the selected optimal vector is much higher than that of the other two phases. Based on this, in step S1402, if the fault index calculated according to formula (1) is greater than the preset threshold, it can be determined that the motor has a turn-to-turn short circuit fault.

[0109] Furthermore, taking the distribution of voltage vectors recorded in n control cycles as an example, the utilization rate of the voltage vector of phase A, the utilization rate of the voltage vector of phase B, and the utilization rate of the voltage vector of phase C under the healthy state are approximately equal to n / 3. Although there is inherent asymmetry in the actual motor, this asymmetry is negligible compared with the significant change of the fault index under the fault state. The motor controller calculates the fault index according to formula (1). If the calculation result shows that:

[0110] If the utilization rate of the voltage vector of phase A is greater than the utilization rates of the voltage vectors of the other two phases, it is determined that a turn-to-turn short circuit fault occurs in phase A;

[0111] Otherwise, if the utilization rate of the voltage vector of phase B is greater than the utilization rates of the voltage vectors of the other two phases, it is determined that a turn-to-turn short circuit fault occurs in phase B;

[0112] Otherwise, if the utilization rate of the C phase voltage vector is greater than the utilization rates of the other two phase voltage vectors, it is determined that a turn-to-turn short circuit fault occurs in the C phase.

[0113] It can be seen that the motor fault diagnosis method provided in the embodiment of the present application can effectively identify the fault phase because the fault characteristics generated by different fault locations vary differently.

[0114] After recording n control cycles, the fault indicators are calculated for fault monitoring, and then the counter is cleared and recorded again. Although the fault indicators calculated after a certain statistical control cycle are representative, the delay of fault detection can be ignored due to the relatively small control step size.

[0115] It is worth noting that compared with the traditional single vector control that only sends out one vector at a time, the method of the embodiment of the present application adopts dual vector control to reduce the proportion of the balanced part, and the unbalanced utilization rate of the voltage vector is more obvious as a fault characteristic change.

[0116] As a verification, this application has been verified on the MATLAB simulation platform. Figure 4 A dynamic process diagram of the fault indicator output waveform when a given speed and load are switched provided in an embodiment of the present application, Figure 4 The vertical axis is the fault index (per unit value), such as Figure 4 As shown, when the fault indicator is lower than the preset threshold, the diagnosis result is healthy. When the fault indicator is higher than the threshold, the diagnosis result is faulty. When a turn-to-turn short circuit is detected, the fault diagnosis method of the present application is executed to determine the fault phase. The fault location output is divided into three levels: A, B, and C. Figure 4 The dynamic process of the fault indicator when the given speed and load change is given, and it can be seen that the method proposed in this application is robust to speed and torque changes.

[0117] Figure 5The output waveform diagram of the fault indicator and fault location provided in the embodiment of the present application when the A phase fault occurs, Figure 5 From top to bottom, the vertical coordinates of each sub-graph are fault current (A), three-phase voltage vector utilization (unit value), fault index (unit value), fault diagnosis result (unit value), fault location result (unit value), which can be obtained from Figure 5 It can be seen from the simulation results that the method proposed in this application has a good diagnostic response speed and can effectively detect and locate turn-to-turn short circuits.

[0118] In summary, the proposed method for diagnosing turn-to-turn short-circuit faults of asynchronous motors under dual-vector model predictive control based on voltage vector distribution reduces the difficulty of extracting fault features and the computational burden of digital processors. The fault indicators respond quickly and are robust to speed and torque. No additional observers or hardware equipment are required, and turn-to-turn short-circuit faults can be effectively diagnosed and fault phases can be identified.

[0119] Based on the above embodiments, the present application further provides a motor controller. Figure 6 is a structural block diagram of a motor controller according to an embodiment of the present application. Figure 6 middle Indicates the d-axis stator current reference value; Indicates the q-axis stator current reference value; represents the stator current at time k+1; represents the stator flux at time k+1; represents the stator current at time k+2; represents the stator current at time k; , , Respectively represent the utilization of the A-phase, B-phase, and C-phase voltage vectors; Indicates the first voltage vector selected by the evaluation function; The second voltage vector representing the selection of the evaluation function; represents the rotor flux at time k; Indicates the time when two voltage vectors act respectively; , Respectively indicate the corresponding and Utilization of two voltage vectors.

[0120] like Figure 6 As shown, the motor controller may include a current prediction control module, a cost minimization function module and a fault monitoring and diagnosis module, wherein:

[0121] The current prediction control module can be used to output the predicted current acting on the motor stator at the next moment based on the voltage vector of the inverter module in different switching states and the stator current at the previous moment;

[0122] The minimization cost function module can be used to determine the duty cycle of each voltage vector in multiple control cycles based on the predicted current and the flux linkage of the motor stator at the previous moment;

[0123] The fault monitoring and diagnosis module can be used to calculate the utilization rate of each voltage vector under multiple control cycles, and to monitor the motor for turn-to-turn short-circuit faults based on the utilization rate of each voltage vector.

[0124] In addition, the fault monitoring and diagnosis module can also be used to build a fault index based on the utilization of each voltage vector.

[0125] Furthermore, the fault monitoring and diagnosis module can also be used to determine whether a turn-to-turn short-circuit fault occurs based on the comparison result between the fault indicator and the preset threshold value, and if the utilization rate of the A-phase voltage vector is greater than the utilization rates of the other two-phase voltage vectors, it is determined that a turn-to-turn short-circuit fault occurs in the A-phase; or, if the utilization rate of the B-phase voltage vector is greater than the utilization rates of the other two-phase voltage vectors, it is determined that a turn-to-turn short-circuit fault occurs in the B-phase; or, if the utilization rate of the C-phase voltage vector is greater than the utilization rates of the other two-phase voltage vectors, it is determined that a turn-to-turn short-circuit fault occurs in the C-phase.

[0126] It should be understood that the motor controller can execute the motor fault diagnosis method described in any of the above method embodiments, and accordingly, also has the beneficial effects described in any of the above embodiments, which will not be repeated here.

[0127] The above are only preferred embodiments of the present application, and are not intended to limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A motor fault diagnosis method, characterized in that: include: Based on the voltage vector of the inverter module in different switching states and the stator current at the previous moment, the predicted current acting on the motor stator at the next moment is output; Determining the duty cycle of each voltage vector in a plurality of control cycles based on the predicted current and the flux linkage of the motor stator at a previous moment; Calculate the utilization of each voltage vector under multiple control cycles; The motor is monitored for turn-to-turn short circuit faults based on the utilization rates of the voltage vectors.

2. The method according to claim 1, characterized in that The monitoring of the motor for turn-to-turn short circuit faults based on the utilization rate of each voltage vector includes: constructing a fault indicator based on the utilization rate of each of the voltage vectors; Based on the comparison result between the fault indicator and the preset threshold, it is determined whether a turn-to-turn short circuit fault occurs.

3. The method according to claim 2, characterized in that The constructing a fault indicator based on the utilization rate of each of the voltage vectors includes: Based on the utilization rate of each voltage vector, a fault index as shown in the following formula (1) is constructed: （1） Where: Indicates the utilization rate of phase A voltage vector, represents the utilization rate of the B phase voltage vector, Indicates the utilization rate of the C phase voltage vector; n Indicates the number of statistical control cycles; Indicates a fault indicator.

4. The method according to claim 3, characterized in that After determining that an inter-turn short circuit fault occurs, the method further includes: If the utilization rate of the voltage vector of phase A is greater than the utilization rates of the voltage vectors of the other two phases, it is determined that a turn-to-turn short circuit fault occurs in phase A; If the utilization rate of the voltage vector of phase B is greater than the utilization rates of the voltage vectors of the other two phases, it is determined that a turn-to-turn short circuit fault occurs in phase B; If the utilization rate of the C phase voltage vector is greater than the utilization rates of the other two phase voltage vectors, it is determined that a turn-to-turn short circuit fault occurs in the C phase.

5. The method according to claim 1, characterized in that The method outputs a predicted current acting on the stator of the motor at the next moment based on the voltage vector of the inverter module in different switching states and the stator current at the previous moment, including: Based on the voltage vector of the inverter module in different switching states and the stator current at the previous moment, the predicted current acting on the motor stator at the next moment is output as shown in the following formula (2): （2） Where: , They represent the k-th moment α , β The measured value of the stator current of the shaft, , Respectively represent the k+1 moment α , β The predicted stator current of the shaft, T s represents the control cycle, R s is the stator resistance, R r is the rotor resistance, ω represents the rotor speed, L s and L r represent the stator inductance and rotor inductance respectively, L m represents the stator-rotor mutual inductance, T r represents the rotor electromagnetic time constant, σ=1-L m 2 / L s L r Indicates the leakage flux coefficient of the motor; , Respectively represent the k moment α , β The stator flux of the shaft, , Respectively represent the k moment α , β The stator voltage of the shaft.

6. The method according to claim 1, characterized in that The step of determining the duty cycle of each voltage vector in a plurality of control cycles based on the predicted current and the flux linkage of the motor stator at the previous moment includes: Based on the predicted current and the flux of the motor stator at the previous moment, an evaluation function as shown in the following formula (3) is constructed: （3） The evaluation function is used to determine the duty cycle of the dual vector voltage under each voltage vector control in multiple control cycles as shown in the following formula (4): （4） Where: T s represents the control cycle, t opt1 , t opt2 are the two voltage vector action times respectively, represents the duty cycle of the first voltage vector, Indicates the duty cycle of the second voltage vector; , Respectively α , β The given stator current of the axis, , Respectively represent the k+2 moment α , β The measured value of the stator current of the shaft; , The first voltage vector acts on α , β The current slope of the axis, , The second voltage vector acts on α , β The current slope of the axis; , Respectively represent the k+1 moment α , β The predicted stator current of the shaft, , They represent the k+2 moment respectively. α , β Predicted stator current of the shaft; , Respectively α , β The current residual of the axis; Represents the evaluation function.

7. The method according to claim 1, characterized in that The calculating the utilization rate of each voltage vector under a plurality of control cycles includes: The voltage vectors are grouped using the following formula (5): （5）； The utilization rate of each voltage vector under multiple control cycles is calculated using the following formula (6) according to the duty cycle of each voltage vector: （6） Where: , , , , , Respectively represent the voltage vectors of the inverter in different switching states; , , Respectively represent the utilization of the A-phase, B-phase, and C-phase voltage vectors; , , , They represent the utilization rates of the two voltage vectors in the corresponding control cycle respectively; T s represents the control cycle, t opt1 , t opt2 They are the two voltage vector action times respectively.

8. A motor controller, characterized in that: include: A current prediction control module, used for outputting a predicted current acting on the motor stator at the next moment based on the voltage vector of the inverter module in different switching states and the stator current at the previous moment; A cost minimization function module is used to determine the duty cycle of each voltage vector in multiple control cycles based on the predicted current and the flux linkage of the motor stator at the previous moment; The fault monitoring and diagnosis module is used to calculate the utilization rate of each voltage vector under multiple control cycles, and to monitor the motor for turn-to-turn short-circuit faults based on the utilization rate of each voltage vector.

9. The motor controller according to claim 8, characterized in that: The fault monitoring and diagnosis module is further used to: construct a fault index based on the utilization rate of each of the voltage vectors.

10. The motor controller according to claim 9, characterized in that: The fault monitoring and diagnosis module is also used to determine whether a turn-to-turn short circuit fault occurs based on the comparison result between the fault indicator and the preset threshold value, and if the utilization rate of the A-phase voltage vector is greater than the utilization rates of the other two-phase voltage vectors, it is determined that a turn-to-turn short circuit fault occurs in the A-phase; or, if the utilization rate of the B-phase voltage vector is greater than the utilization rates of the other two-phase voltage vectors, it is determined that a turn-to-turn short circuit fault occurs in the B-phase; or, if the utilization rate of the C-phase voltage vector is greater than the utilization rates of the other two-phase voltage vectors, it is determined that a turn-to-turn short circuit fault occurs in the C-phase.

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