High-frequency residual error-characteristic correlation type permanent magnet motor turn-to-turn short circuit fault diagnosis method

Abstract

The invention discloses a high-frequency residual error-feature correlation type permanent magnet motor turn-to-turn short circuit fault diagnosis method, belongs to the technical field of permanent magnet motor fault diagnosis, and aims to solve the problems that fault features are easily interfered by working conditions and slight faults are difficult to diagnose when turn-to-turn short circuit diagnosis is carried out on an existing motor. The method comprises the following steps: step 1, high-frequency signal injection and information acquisition; injecting the high-frequency rotating voltage signal into a motor control system under a natural coordinate system; collecting each phase high-frequency current and each phase high-frequency voltage of the motor through a band-pass filter; 2, subtracting the high-frequency voltage from the healthy voltage, and calculating to obtain a high-frequency voltage residual error of each phase; step 3, obtaining fault characteristic quantity FIn of each phase according to the motor parameters, the high-frequency voltage of each phase and the high-frequency voltage residual error; and 4, judging whether a turn-to-turn short circuit fault and a fault phase occur or not according to the fault characteristic quantity of each phase.

Description

Technical Field

[0001] The invention relates to a high-frequency residual-feature correlation type permanent magnet motor turn-to-turn short circuit fault diagnosis method, belonging to the technical field of permanent magnet motor fault diagnosis. Background Art

[0002] Permanent magnet synchronous motors (PMSMs), with their high efficiency, high power factor, and excellent dynamic performance, have become the preferred drive solution for applications requiring high reliability. However, for drive systems requiring long-term operation under complex operating conditions, stator winding faults are inevitable. These faults can cause current imbalances, localized overheating, and other issues, threatening system stability. Therefore, developing fast and effective stator winding fault diagnosis strategies is crucial for achieving highly reliable electromechanical systems.

[0003] Turn-to-turn short-circuit faults arise from localized failure of the insulation of the winding conductors. Essentially, they form a low-impedance path between adjacent conductors due to insulation aging, mechanical stress, or electrothermal degradation. When a turn-to-turn short-circuit fault occurs, the symmetry of the stator winding is disrupted, and the magnetic circuits corresponding to the healthy and faulty parts of the winding interact with each other, resulting in an uneven magnetic field distribution. This can cause neutral point voltage offset, reduced fault phase voltage, and increased electromagnetic torque harmonic content, leading to increased torque fluctuations and reduced system efficiency. It is important to note that the subtle characteristics of minor faults make them difficult to detect. Failure to detect them in a timely manner can easily lead to irreversible damage, such as permanent demagnetization of the permanent magnets and accelerated melting of the insulation.

[0004] Currently proposed turn-to-turn short-circuit diagnostic methods are ineffective for diagnosing minor faults. These faults have weak electrical signatures and are easily masked by electromagnetic noise, load fluctuations, and other factors, making them difficult to diagnose in their early stages. By the time a fault is detected using conventional methods, the short circuit inside the motor is often already quite severe, missing the optimal maintenance opportunity. Furthermore, the diagnostic results of conventional methods are significantly affected by changes in operating conditions, making it difficult to establish a unified standard for distinguishing the severity of faults. Because the current, voltage, and other parameters required for fault diagnosis are often closely related to operating conditions, motors with the same fault condition may not produce the same diagnostic results when operating at different speeds and torques, thus affecting the determination of the severity of the fault. Currently, most diagnostic methods are tested under fixed operating conditions. However, for motors operating in complex environments and conditions, it is crucial to minimize the impact of operating condition changes on diagnostic results. Summary of the Invention

[0005] In order to solve the problems that the fault characteristics of existing motors are easily disturbed by working conditions and minor faults are difficult to diagnose when diagnosing inter-turn short circuits, the present invention provides a high-frequency residual-feature correlation type permanent magnet motor inter-turn short circuit fault diagnosis method.

[0006] The present invention provides a high-frequency residual-feature correlation method for diagnosing inter-turn short-circuit faults in a permanent magnet motor, the method comprising the following steps:

[0007] Step 1: high-frequency signal injection and information collection step;

[0008] Inject high-frequency rotating voltage signals into the motor control system in the natural coordinate system, where n represents each phase winding of the motor, n = a, b, c...;

[0009] The high-frequency current i of each phase of the motor is collected through a bandpass filter nh and each phase high frequency voltage

[0010] Step 2: High frequency voltage The healthy voltage under the injection of high frequency signal The high-frequency voltage residual of each phase is calculated by subtracting

[0011] Step 3: According to the motor parameters and high-frequency voltage of each phase and high-frequency voltage residual Obtain each phase fault characteristic FI n ;

[0012] Step 4: According to the fault characteristic quantity FI of each phase n Determine whether a turn-to-turn short circuit fault occurs and identify the fault phase.

[0013] Preferably, the process of step 2 is:

[0014] The high-frequency voltage residual of each phase is obtained by the following formula:

[0015]

[0016] Where, Represents the high-frequency voltage residual matrix, which is composed of the high-frequency voltage residual of each phase The N×1 matrix is formed, where N is the number of motor phases;

[0017] Represents the high-frequency voltage matrix, which is composed of the high-frequency voltage of each phase The N×1 matrix formed;

[0018] Represents the high-frequency healthy voltage matrix, which is the healthy voltage injected by each phase at high frequency The N×1 matrix; high-frequency health voltage matrix Estimate as follows:

[0019]

[0020] Where, [Inh ] represents the high-frequency current matrix, and the high-frequency current i of each phase nh The N×1 matrix formed;

[0021] [L s ] represents the inductance matrix of the motor, which is an N×N matrix consisting of self-inductance L and mutual inductance;

[0022] R s Indicates the phase resistance of the stator winding.

[0023] Preferably, the process of step 3 is:

[0024] The motor parameters, high-frequency voltage of each phase and high-frequency voltage residual The input is sent to the observer based on the high-frequency residual-feature correlation analytical model, which uses the least squares method to calculate the fault feature value FI of each phase. n , the equation based on the high-frequency residual-feature correlation analytical model is:

[0025]

[0026] Where R s , L are motor parameters.

[0027] Preferably, in step 4, whether a turn-to-turn short circuit fault occurs is determined based on the fault characteristic quantity of each phase:

[0028] FI n >FI T Indicates that the motor winding has a turn-to-turn short circuit fault, otherwise it is healthy, FI T is the fault threshold.

[0029] Preferably, when an inter-turn short circuit fault occurs, the phase with the largest fault characteristic value is the fault phase.

[0030] Preferably, the phase difference between each phase of the injected high-frequency rotating voltage signal is consistent with the phase difference between the motor windings.

[0031] Preferably, the method is suitable for diagnosing a turn-to-turn short circuit fault in a single-phase winding.

[0032] Preferably, the method is applicable to a structure in which each phase winding of the motor is a coil or a plurality of coils connected in series.

[0033] Beneficial effects of the present invention:

[0034] 1. The high-frequency residual-feature correlation type permanent magnet motor inter-turn short-circuit fault diagnosis method proposed in the present invention solves the problem that minor faults are difficult to diagnose. It can complete the diagnosis of multi-level fault scenarios from minor to severe, and realizes the distinction between similar faults, positioning of the fault phase and judgment of the fault severity with high sensitivity.

[0035] 2. The high-frequency residual-feature correlation type permanent magnet motor inter-turn short-circuit fault diagnosis method proposed in the present invention solves the problem that the fault characteristics are easily disturbed by the working conditions. Under dynamic working conditions where the speed or torque changes, the diagnostic effect is decoupled from the speed, and is less affected by torque changes, with good robustness.

[0036] 3. The high-frequency residual-feature correlation type permanent magnet motor inter-turn short-circuit fault diagnosis method proposed in the present invention is targeted at the operating requirements of complex working conditions. Under complex working conditions with coupled changes in speed and torque, the diagnosis results can still maintain high robustness. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 is an equivalent circuit model of a six-phase motor with an inter-turn short circuit fault according to an embodiment of the present invention;

[0038] Figure 2 This is a control block diagram of a high-frequency voltage injection system for a motor according to an embodiment of the present invention;

[0039] Figure 3 This is a schematic diagram of the fault diagnosis process of the present invention;

[0040] Figure 4 Schematic diagram of the effect of speed change on diagnostic effect in an embodiment of the present invention, wherein Figure 4 (a) shows two conductor short circuit scenarios, which are minor faults. Figure 4 (b) presents two conductor short circuit scenarios, which are serious faults;

[0041] Figure 5 Schematic diagram of the effect of torque change on diagnostic effect in an embodiment of the present invention, where Figure 5 (a) shows two conductor short circuit scenarios, which are minor faults. Figure 5 (b) presents two conductor short circuit scenarios, which are serious faults;

[0042] Figure 6 Schematic diagram of the effect of speed and torque coupling changes on diagnostic results in an embodiment of the present invention, where Figure 6 (a) shows two conductor short circuit scenarios, which are minor faults. Figure 6 (b) shows two conductor short circuit scenarios, which are serious faults. DETAILED DESCRIPTION

[0043] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0044] It should be noted that, in the absence of conflict, the embodiments of the present invention and the features in the embodiments may be combined with each other.

[0045] The present invention will be further described below with reference to the accompanying drawings and specific embodiments, but they are not intended to limit the present invention.

[0046] Specific implementation method 1: Figures 1 to 6 This embodiment describes a high-frequency residual-feature correlation method for diagnosing inter-turn short-circuit faults in a permanent magnet motor, the method comprising the following steps:

[0047] Step 1: high-frequency signal injection and information collection step;

[0048] Inject high-frequency rotating voltage signals into the motor control system in the natural coordinate system, where n represents each phase winding of the motor, n = a, b, c...;

[0049] The high-frequency current i of each phase of the motor is collected through a bandpass filter nh and each phase high frequency voltage

[0050] Step 2: High frequency voltage The healthy voltage under the injection of high frequency signal The high-frequency voltage residual of each phase is calculated by subtracting

[0051] Step 3: According to the motor parameters and high-frequency voltage of each phase and high-frequency voltage residual Obtain each phase fault characteristic FI n ;

[0052] Step 4: Based on the value of the fault characteristic quantity FI of each phase n Determine whether a turn-to-turn short circuit fault occurs and identify the fault phase.

[0053] This method is applicable to a structure where each phase of the motor winding consists of one coil or multiple coils connected in series. This embodiment takes a six-phase permanent magnet synchronous motor A phase winding with an inter-turn short circuit fault as an example. Each phase of the motor winding consists of two coils connected in series. The six phase windings are symmetrically distributed and connected together at a neutral point. Figure 1As shown in FIG. 1 , it is an equivalent circuit model of a turn-to-turn short circuit fault of the motor phase A winding in this embodiment. The inductance of the two coils of phase A is L a1 , L a2 , the phase current is i a , the phase resistance is R a ; The inductance of the two coils of phase B is L b1 , L b2 , the phase current is i b , the phase resistance is R b ; The inductance of the two coils of phase C is L c1 , L c2 , the phase current is i c , the phase resistance is R c ; The inductance of the two coils of phase D is L d1 , L d2 , the phase current is i d , the phase resistance is R d ; The inductance of the two coils of phase E is L e1 , L e2 , the phase current is i e , the phase resistance is R e ; The inductance of the two coils of phase F is L f1 , L f2 , the phase current is i f , the phase resistance is R f ; A phase has a turn-to-turn short circuit, R f is the short-circuit resistance, representing the contact resistance between the short-circuit conductors, i F For the flow through R f of short-circuit current.

[0054] See also Figure 2 As shown in the figure, the high-frequency voltage injection system control block diagram of the motor described in this embodiment is shown. In order to ensure good control effect and easy implementation of the algorithm, the motor is regarded as a system with two sets of three-phase windings, that is, the phases in each set of windings are separated by 120 degrees of electrical angle, where the ABC phases are separated by 120 degrees and the DEF phases are separated by 120 degrees. For the six-motor motor as a whole, the ABCDEF phases are separated by 60 degrees. d = 0. According to the detected six-phase current and the electrical angle θ of the motor, two sets of fundamental currents i in the rotating coordinate system are obtained after coordinate transformation. d1 、i q1 and i d2 、i q2 , respectively with the preset given value I d1_ref , I q1_ref , I d2_ref , I q2_refSince the two sets of windings are not completely independent, they share a neutral point, which is one less degree of freedom than the traditional dual three-phase system, thus increasing the control of zero-sequence current i o1 Constraints can be used to improve system stability and harmonic suppression capabilities. o1 With the preset given value I o1_ref Compare. d1 、i q1 、i o1 and i d2 、i q2 The two sets of differences after comparison are respectively passed through the PI regulator to obtain the given voltage u in the rotating coordinate system. d1 、u d1 and u d2 、u q2 At the same time, according to the electrical angular velocity ω of the motor, a feedforward voltage compensation is added to the control system to achieve decoupling control between the dq axes. In order to obtain the high-frequency component of the motor under fault conditions, a six-phase high-frequency rotating voltage u with a constant frequency and amplitude and a 60° phase difference is injected into the motor control system. ah 、u bh 、u ch 、u dh 、u eh 、u fh .

[0055] See also Figure 3 FIG. 1 is a flow chart of the high-frequency residual-feature correlation type permanent magnet motor turn-to-turn short circuit fault diagnosis method of the present invention, and the specific implementation steps are as follows:

[0056] Step 1: high-frequency voltage injection and information collection step;

[0057] A high-frequency rotating voltage u with a frequency of 1600 Hz, an amplitude of 4 V, and a phase difference of 60° is injected into the motor control system. ah ,u bh ,u ch ,u dh ,u eh ,,u fh The high-frequency current i of each phase injected at high frequency is collected through a second-order bandpass filter. nh (n=a,b,c,d,e,f) and high frequency voltage

[0058] Step 2: High frequency voltage The healthy voltage under the injection of high frequency signal The high-frequency voltage residual of each phase is calculated by subtracting

[0059] The specific process is:

[0060] The high-frequency voltage residual of each phase is obtained by the following formula:

[0061]

[0062] Where, Represents the high-frequency voltage residual matrix, which is composed of the high-frequency voltage residual of each phase The N×1 matrix is formed, where N is the number of motor phases; are the high-frequency voltage residuals of the ABCEDF phases respectively;

[0063] Represents the high-frequency voltage matrix, which is composed of the high-frequency voltage of each phase The N×1 matrix formed;

[0064] are the measured high-frequency voltages of the ABCEDF phases;

[0065] Represents the high-frequency healthy voltage matrix, which is the healthy voltage injected by each phase at high frequency The N×1 matrix formed; are the healthy voltages of the ABCEDF phase under high frequency injection;

[0066] High-Frequency Healthy Voltage Matrix Estimate as follows:

[0067]

[0068] Where, [I nh ] represents the high-frequency current matrix, and the high-frequency current i of each phase nh The N×1 matrix formed;

[0069] [I nh ]=[i ah i bh i ch i dh i eh i fh ] T ,i ah ,i bh ,i ch ,i dh ,i eh ,i fh are the measured high-frequency currents of the ABCEDF phases;

[0070] [L s ] represents the inductance matrix of the motor, which is an N×N matrix consisting of self-inductance L and mutual inductance;

[0071] Where L is the self-inductance, M1, M2, and M3 are the mutual inductances at three different spatial positions in the motor winding;

[0072] R s Indicates the phase resistance of the stator winding.

[0073] Step 3: According to the motor parameters and high-frequency voltage of each phase and high-frequency voltage residual Obtain each phase fault characteristic FI n ;

[0074] The specific process is:

[0075] The motor parameters, high-frequency voltage of each phase and high-frequency voltage residual The input is sent to the observer based on the high-frequency residual-feature correlation analytical model for observation. The observer uses the least squares method to calculate the fault feature value FI of each phase. n , the equation of the high-frequency residual-feature correlation analytical model is:

[0076]

[0077] Where R s , L are motor parameters.

[0078] Calculate the fault feature quantity FI using the high-frequency residual-feature correlation analytical model n The theoretical basis is:

[0079] First, a turn-to-turn short-circuit fault model is adopted that takes into account the series structure of the motor windings and the magnetic coupling effect between the short-circuited and normal parts of the windings. This model directly reflects the relationship between the fault phase voltage and the short-circuit current. It is called the series field-circuit coupling model. The voltage equation of the fault phase in the model is:

[0080]

[0081] Where R f - short-circuit resistance, representing the contact resistance between short-circuited conductors;

[0082] μ——failure rate, which represents the ratio of the number of short-circuited coil turns to the total number of turns in one phase;

[0083] N c ——The number of series coils in each phase winding;

[0084] i F - short-circuit current;

[0085] Since after high frequency excitation injection, the short circuit current iFWill contain components injected at high frequencies:

[0086] i F =i F1 +i Fh =I F1 sin(θ+α1)+I Fh sin(θ h +α h )

[0087] Where i F1 、i Fh ——Short-circuit current at fundamental frequency and injected high frequency respectively;

[0088] I F1 , I Fh ——are the short-circuit current amplitudes at fundamental frequency and injected high frequency respectively;

[0089] θ, θ h ——are the phase angles of short-circuit current at fundamental frequency and injected high frequency, respectively.

[0090] α1, α h ——are the initial phase angles of the short-circuit current at the fundamental frequency and the injected high frequency, respectively.

[0091] Therefore, under high-frequency injection, the fault voltage equation of the series field-circuit coupling model is:

[0092]

[0093] Where ω h ——The electrical angular frequency of the injected high-frequency signal;

[0094] R ISFh ——Equivalent high-frequency short-circuit impedance. After the injection frequency is determined, its size is only related to the fault characteristics.

[0095] Under high frequency injection, based on the turn-to-turn short-circuit fault model of the equivalent circuit and combined with the calculation formula of high-frequency healthy voltage, the voltage equation of the fault phase is obtained as follows:

[0096]

[0097] Based on the above, a method that can reflect the high-frequency voltage residual and fault characteristics (μ, R ISFh ) is a high-frequency residual-feature correlation analytical model of the relationship between , and the model equation is:

[0098]

[0099] Then, based on the high-frequency residual-feature correlation analytical model, the fault feature quantity FI of each phase for turn-to-turn short circuit diagnosis is constructed. n When the motor is in a healthy state, FIn =0; As the severity of the fault increases, R f decreases or μ increases, in either case, FI n will increase. n The definition of is:

[0100]

[0101] Finally, the fault feature quantity FI is obtained by analyzing the mathematical relationship between the residual amount and the fault feature in the high-frequency residual-feature correlation model. n Since both the numerator and denominator of the calculation formula contain sinusoidal quantities, in order to reduce the volatility of the calculation results, the least squares method is used to calculate FI n .

[0102] In step 4, it is determined whether a turn-to-turn short circuit fault occurs based on the fault characteristic of each phase:

[0103] FI n >FI T Indicates that the motor winding has a turn-to-turn short circuit fault, otherwise it is healthy, FI T is the fault threshold, FI T =0.038.

[0104] Each phase FI n Input to the inter-turn short circuit fault diagnosis module to determine whether the motor winding is faulty. If a fault occurs, further locate the fault phase and determine the severity of the fault. If no fault occurs, return to step 1 to continue monitoring.

[0105] According to the fault characteristic quantity FI of each phase n Determine the severity of the fault:

[0106] FI n =(0,0.06) indicates that the motor turn-to-turn short circuit fault is mild;

[0107] FI n =[0.06,0.11) indicates that the motor inter-turn short circuit fault is of medium severity;

[0108] FI n =[0.11,+∞) indicates that the motor inter-turn short circuit fault is serious.

[0109] Taking a specific example to illustrate the specific diagnostic basis of the turn-to-turn short circuit fault diagnosis module: at the same injection frequency, once the fault characteristic quantity FI of a phase n Exceeds the set threshold FI T , it is considered that the motor has a turn-to-turn short circuit fault, and FI a ,FI b ,FIc ,FI d ,FI e ,FI f The phase with the largest fault characteristic value is determined as the fault phase. The larger the fault characteristic value of the fault phase, the more serious the fault.

[0110] like Figure 4 The figure shows the effect of speed change on the diagnostic effect of the present invention, keeping the load torque 10N·m constant and the speed changing within the rated range. n They are the largest among all phases, namely the characteristic value FI of the fault phase. Figure 4 (a) Two conductor short circuit scenarios are minor faults. Figure 4 (b) Two scenarios are serious faults, and the severity of the two faults in each figure is similar. The results show that in this embodiment, the present invention can diagnose minor faults that are difficult to diagnose and distinguish similar faults. The diagnostic effect is almost unaffected by changes in speed conditions.

[0111] like Figure 5 The figure shows the effect of torque change on the diagnostic effect of the present invention. The speed is kept constant at 1000r / min and the load torque changes within the rated range. The FI shown in the figure is the largest among all phases, that is, the characteristic value FI of the fault phase. Figure 5 (a) Two conductor short circuit scenarios are minor faults. Figure 5 (b) Both scenarios are serious faults, and the severity of the two faults in each figure is similar. The results show that in this embodiment, the present invention can diagnose serious faults and distinguish similar faults, and the diagnostic effect is less affected by changes in torque conditions.

[0112] like Figure 6 The figure shows the influence of the coupling change of speed and torque within the rated range on the diagnostic effect of the present invention. The characteristic values FI shown in the figure are the largest among all phases, that is, the characteristic value FI of the fault phase. Figure 6 (a) Two conductor short circuit scenarios are minor faults. Figure 6 (b) Both scenarios are serious faults, and the severity of the two faults in each figure is similar. The results show that in this embodiment, the characteristic value surface of the present invention under complex working conditions is relatively smooth and no aliasing occurs, indicating good robustness of the diagnostic method.

[0113] The high-frequency residual-feature correlation type permanent magnet motor inter-turn short circuit fault diagnosis method described in the present invention is applicable to types of motors including permanent magnet synchronous motors in which all winding coils are in a series structure, and is only applicable to the fault scenario of diagnosing an inter-turn short circuit in a single-phase winding.

[0114] Although the present invention is described herein with reference to specific embodiments, it should be understood that these embodiments are merely illustrative of the principles and applications of the invention. It should be understood that many modifications may be made to the illustrative embodiments, and that other arrangements may be devised, without departing from the spirit and scope of the invention as defined by the appended claims. It should be understood that the various dependent claims and features described herein may be combined in ways other than those described in the original claims. It should also be understood that features described in conjunction with individual embodiments may be used in conjunction with other described embodiments.

Claims

1. A high-frequency residual-feature correlation method for diagnosing inter-turn short-circuit faults in permanent magnet motors, characterized in that: The method comprises the following steps: Step 1: high-frequency signal injection and information collection step; Inject high-frequency rotating voltage signals into the motor control system in the natural coordinate system, where n represents each phase winding of the motor, n = a, b, c...; The high-frequency current i of each phase of the motor is collected through a bandpass filter nh and each phase high frequency voltage Step 2: High frequency voltage The healthy voltage under the injection of high frequency signal The high-frequency voltage residual of each phase is calculated by subtracting Step 3: According to the motor parameters and high-frequency voltage of each phase and high-frequency voltage residual Obtain each phase fault characteristic FI n ; Step 4: According to the fault characteristic quantity FI of each phase n Determine whether a turn-to-turn short circuit fault occurs and identify the fault phase.

2. A high-frequency residual-feature correlation type permanent magnet motor turn-to-turn short circuit fault diagnosis method according to claim 1, characterized in that: The process of step 2 is: The high-frequency voltage residual of each phase is obtained by the following formula: Where, Represents the high-frequency voltage residual matrix, which is composed of the high-frequency voltage residual of each phase The N×1 matrix is formed, where N is the number of motor phases; Represents the high-frequency voltage matrix, which is composed of the high-frequency voltage of each phase The N×1 matrix formed; Represents the high-frequency healthy voltage matrix, which is the healthy voltage injected by each phase at high frequency The N×1 matrix; high-frequency health voltage matrix Estimate as follows: Where, [I nh ] represents the high-frequency current matrix, and the high-frequency current i of each phase nh The N×1 matrix formed; [L s ] represents the inductance matrix of the motor, which is an N×N matrix consisting of self-inductance L and mutual inductance; R s Indicates the phase resistance of the stator winding.

3. A high-frequency residual-feature correlation type permanent magnet motor turn-to-turn short circuit fault diagnosis method according to claim 2, characterized in that: The process of step 3 is: The motor parameters, high-frequency voltage of each phase and high-frequency voltage residual The input is sent to the observer based on the high-frequency residual-feature correlation analytical model, which uses the least squares method to calculate the fault feature value FI of each phase. n , the equation based on the high-frequency residual-feature correlation analytical model is: Where R s , L are motor parameters.

4. A high-frequency residual-feature correlation type permanent magnet motor turn-to-turn short circuit fault diagnosis method according to claim 3, characterized in that: In step 4, the fault characteristic quantity FI of each phase is n Determine whether a turn-to-turn short circuit fault occurs: FI n >FI T Indicates that the motor winding has a turn-to-turn short circuit fault, otherwise it is healthy, FI T is the fault threshold.

5. A high-frequency residual-feature correlation type permanent magnet motor turn-to-turn short circuit fault diagnosis method according to claim 4, characterized in that: When a turn-to-turn short circuit fault occurs, the phase with the largest fault characteristic value is the fault phase.

6. The high-frequency residual-feature correlation type permanent magnet motor turn-to-turn short circuit fault diagnosis method according to claim 1, characterized in that: The phase difference between each phase of the injected high-frequency rotating voltage signal is consistent with the phase difference between the motor windings.

7. The high-frequency residual-feature correlation type permanent magnet motor turn-to-turn short circuit fault diagnosis method according to claim 1, characterized in that: This method is suitable for diagnosing a turn-to-turn short circuit fault in a single-phase winding.

8. The high-frequency residual-feature correlation type permanent magnet motor turn-to-turn short circuit fault diagnosis method according to claim 1, characterized in that: This method is applicable to a structure in which each phase winding of a motor is a coil or multiple coils are connected in series.

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