Permanent magnet synchronous motor fault diagnosis, positioning and classification method and system

Abstract

The invention relates to the technical field of motor fault diagnosis and state monitoring, and particularly discloses a permanent magnet synchronous motor fault diagnosis, positioning and classification method and system, and the diagnosis method and system extract a residual signal through a model predictive control (MPC) strategy, carry out the coordinate transformation of a current residual, extract a DC component, and carry out the fault diagnosis, positioning and classification of a permanent magnet synchronous motor. And whether a turn-to-turn short-circuit fault or a high-resistance connection fault occurs is determined by taking the absolute difference value of the direct-current component as a fault diagnosis index. According to the method, whether the inter-turn short circuit (ITF) fault and the high-resistance connection (HRC) fault occur or not can be judged, the fault occurrence position and the fault type are further determined, high diagnosis, positioning and classification precision and robustness are achieved, and the method can adapt to complex working conditions such as rotating speed, load change and parameter mismatch. In addition, the fault diagnosis and classification process can be efficiently realized on line in real time, the operation is simple and convenient, and the real-time performance is strong.

Description

Technical Field

[0001] The present invention relates to the technical field of motor fault diagnosis and state monitoring, and in particular to a method and system for diagnosing, locating and classifying permanent magnet synchronous motor faults. Background Art

[0002] Permanent magnet synchronous motor (PMSM) has been widely used in industrial drive, transportation, energy and other fields due to its high efficiency, high reliability and high power density. However, as the motor runs longer, the stator winding may fail due to insulation aging, environmental factors and mechanical stress. Inter-Turn Fault (ITF) and High-Resistance Connection (HRC) are the two most common and most harmful types. These faults may cause the motor's operating performance to deteriorate or even cause serious equipment damage, affecting the safety and reliability of the system.

[0003] There are significant differences in the formation mechanism and manifestation of turn-to-turn short-circuit faults and high-resistance connection faults. Turn-to-turn short-circuit faults are usually caused by insulation damage, which are characterized by unbalanced stator current and a sharp increase in heat loss, and may quickly develop into a more serious phase-to-phase short circuit or ground fault. High-resistance connection faults, on the other hand, are manifested as increased resistance at the connection point, causing current fluctuations and voltage anomalies. Since the degree of harm and handling methods of the two types of faults are completely different, fault classification is of great significance in motor maintenance strategies. For example, turn-to-turn short-circuit faults require rapid detection and intervention to avoid fault expansion, while high-resistance connection faults require precise analysis of the changing trend of the fault point to optimize the timing of maintenance.

[0004] At present, the fault detection method based on current signal analysis (MCSA) has become the mainstream technology for motor fault diagnosis because of its simplicity and ease. However, in a closed-loop control system, the controller actively reduces the current imbalance by adjusting the voltage vector, which makes it difficult for the MCSA method to capture effective features in the early fault detection, especially in the early stage of the turn-to-turn short circuit fault. High-resistance connection faults are more difficult to identify quickly and accurately using traditional methods because their characteristic signals are weak and change slowly. In addition, existing methods mostly focus on the accuracy of fault detection, and there is little research on the classification of fault types, which makes it difficult to meet the needs of accurate classification of different faults in practical applications. Summary of the invention

[0005] The present invention provides a permanent magnet synchronous motor fault diagnosis, positioning and classification method and system, which solves the technical problem that it is difficult to effectively capture fault characteristics in a closed-loop control system, the classification capability is insufficient, and the real-time performance and robustness are poor.

[0006] In order to solve the above technical problems, the present invention provides a permanent magnet synchronous motor fault diagnosis method, comprising the steps of:

[0007] A1. Calculate the residual current of the α and β axes by the difference between the predicted current of the α and β axes of the three-phase permanent magnet synchronous motor and the actual measured current of the α and β axes;

[0008] A2, multiply the α and β axis residual currents by themselves and extract their DC components and use the absolute difference of the DC components as a fault diagnosis indicator;

[0009] A3. In a fixed healthy state, 1.5 times the absolute difference of the DC component extracted after the α-axis and β-axis residual currents are multiplied by themselves is used as the fault diagnosis index threshold. The fault diagnosis index is compared with the fault diagnosis index threshold. If the fault diagnosis index is greater than the fault diagnosis index threshold, it is determined that a turn-to-turn short circuit fault or a high resistance connection fault has occurred. Otherwise, it is determined to be normal.

[0010] The present invention also provides a permanent magnet synchronous motor fault diagnosis system, which mainly comprises a residual current acquisition module, a fault diagnosis index calculation module and a fault diagnosis module, which are respectively used to execute steps A1 to A3 in the permanent magnet synchronous motor fault diagnosis method.

[0011] The present invention also provides a permanent magnet synchronous motor fault location method. Based on the permanent magnet synchronous motor fault diagnosis method, the key is that after it is determined in step A3 that a turn-to-turn short circuit fault or a high resistance connection fault has occurred, the diagnosis method comprises the steps of:

[0012] B1. Perform coordinate transformation on the residual current of the α and β axes to rotate them to align with the B phase and the C phase, and obtain the residual current in the coordinate system aligned with the B phase and the C phase;

[0013] B2, the rotated residual current is squared and the DC component is extracted through low-pass filtering, and the DC component of the residual current of the α and β axes is used as the fault location feature;

[0014] B3. Calculate the fault location index based on the fault location characteristics;

[0015] B4. Locate the fault according to the fault location indicators and determine the location where the fault occurred.

[0016] Furthermore, in step B3, the fault location indicator includes:

[0017]

[0018] Among them, FI loc_A It represents the residual current of α and β axes at time k and extracts the DC component through low-pass filtering The difference, FI loc_BThe residual current of phase B after rotation at time k is squared and the DC component is extracted by low-pass filtering. The difference, FI loc_C The C-phase residual current after rotation at time k is squared and the DC component is extracted by low-pass filtering. difference

[0019] Furthermore, the step B4 is specifically as follows:

[0020] If FI loc_A >0 and FI loc_B >0 or FI loc_A <0 and FI loc_B <0, it is determined that the fault occurs in phase C;

[0021] If FI loc_A >0 and FI loc_C <0, it is determined that the fault occurs in phase A;

[0022] If FI loc_A <0 and FI loc_B >0, it is determined that the fault occurs in phase B.

[0023] The present invention also provides a permanent magnet synchronous motor fault locating system, the key of which is that it includes a coordinate transformation module, a fault locating feature calculation module, a fault locating index calculation module, and a fault locating module, which are respectively used to execute steps B1 to B4 in the permanent magnet synchronous motor fault diagnosis method.

[0024] The present invention also provides a permanent magnet synchronous motor fault classification method. Based on the permanent magnet synchronous motor fault diagnosis method, the key is that after it is determined in step A3 that a turn-to-turn short circuit fault or a high resistance connection fault has occurred, the diagnosis method comprises the steps of:

[0025] C1, multiply the α and β axis residual currents with the fault phase current, and extract their DC components through a low-pass filter to obtain classification features;

[0026] C2, calculate the fault classification index based on the classification features;

[0027] C3. Classify the fault according to the fault classification index to determine whether the fault type is a turn-to-turn short circuit fault or a high-resistance connection fault.

[0028] Furthermore, in step C2, the fault classification index is the α and β axis classification features FI cla_α , FI cla_β The difference between FI cla =FI cla_α -FI cla_β .

[0029] Furthermore, the step C3 is specifically as follows: setting a fault classification threshold Thre cla =0, when FI cla >Thre cla When the fault is detected, it is judged as a high-resistance connection fault; otherwise, it is judged as a turn-to-turn short-circuit fault.

[0030] The present invention also provides a permanent magnet synchronous motor fault classification system, the key of which is that it includes a fault classification feature calculation module, a fault classification index calculation module, and a fault classification module, which are respectively used to execute steps C1 to C3 in the permanent magnet synchronous motor fault classification method.

[0031] The present invention provides a permanent magnet synchronous motor fault diagnosis, positioning and classification method and system. The diagnosis method and system extract residual signals through a model predictive control (MPC) strategy, and perform coordinate transformation on the current residual to extract its DC component, and use the absolute difference of the DC component as a fault diagnosis index to determine whether a turn-to-turn short circuit fault or a high resistance connection fault has occurred; after determining that a fault has occurred, the positioning method and system further perform coordinate transformation based on the α and β axis residual currents, align with the B phase and the C phase, obtain fault positioning features of different phases, calculate the fault positioning index, and determine the phase where the fault occurs according to the fault positioning index; the classification method and system multiplies the α and β axis residual currents with the fault phase current and extracts the DC component to obtain classification features, calculates the fault classification index based on the classification features, classifies the fault according to the fault classification index, and determines whether the type of fault is a turn-to-turn short circuit fault or a high resistance connection fault.

[0032] Different from the traditional fault diagnosis method based on current residual, the present invention defines the fault diagnosis index by constructing the difference of the DC component of the current residual, and uses the index to classify the fault. The present invention can determine whether the two types of faults, inter-turn short circuit (ITF) and high resistance connection (HRC) faults, have occurred, and further determine the location and type of the fault. It has high diagnosis, positioning and classification accuracy and robustness, and can adapt to complex working conditions such as speed, load changes and parameter mismatch. In addition, the fault diagnosis and classification process can be implemented online in real time and efficiently, with simple operation and strong real-time performance. The present invention does not require additional observers, hardware or control structure changes, can be easily embedded in existing motor control systems, and has broad industrial application potential. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 is a flow chart of a permanent magnet synchronous motor fault diagnosis method provided by an embodiment of the present invention;

[0034] Figure 2 is a three-phase stator winding diagram of a permanent magnet motor with an inter-turn short circuit fault provided by an embodiment of the present invention;

[0035] Figure 3 It is a three-phase winding diagram of a permanent magnet motor with high resistance connection fault provided by an embodiment of the present invention;

[0036] Figure 4 This is a flow chart of a method for diagnosing, locating and classifying permanent magnet synchronous motor faults provided by an embodiment of the present invention;

[0037] Figure 5 It is a diagram of experimental results of fault diagnosis by speed mutation, load mutation and parameter mismatch test provided by an embodiment of the present invention;

[0038] Figure 6 is a flow chart of a permanent magnet synchronous motor fault locating method provided by an embodiment of the present invention;

[0039] Figure 7 The embodiments of the present invention provide fault location indicators, fault location results and fault current waveform diagrams under different fault types and locations;

[0040] Figure 8 is a flow chart of a permanent magnet synchronous motor fault classification method provided by an embodiment of the present invention;

[0041] Fig. 9 is a schematic diagram of turn-to-turn short circuit faults and classification characteristics provided by an embodiment of the present invention;

[0042] Fig.10 is a schematic diagram of high-resistance connection faults and classification characteristics provided by an embodiment of the present invention;

[0043] Fig.11 It is a diagnostic and classification performance diagram under different working conditions provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0044] The following specifically illustrates the implementation mode of the present invention in conjunction with the accompanying drawings. The embodiments are provided for illustrative purposes only and cannot be understood as limiting the present invention. The accompanying drawings are provided for reference and illustration only and do not constitute a limitation on the scope of patent protection of the present invention, because many changes can be made to the present invention without departing from the spirit and scope of the present invention.

[0045] Example 1

[0046] The permanent magnet synchronous motor fault diagnosis method provided by the embodiment of the present invention is as follows: Figure 1 As shown in the flowchart, the steps include:

[0047] A1. Obtaining residual current: Under the framework of model predictive control (MPC), the residual current of the α and β axes is calculated by the difference between the predicted current of the α and β axes of the three-phase permanent magnet synchronous motor and the actual measured current of the α and β axes;

[0048] A2. Calculate the fault diagnosis index: multiply the residual current of α and β axes by themselves and extract the DC component through low-pass filtering (LPF), and use the absolute difference of the DC component as the fault diagnosis index;

[0049] A3. Perform fault diagnosis: 1.5 times the absolute difference of the DC component extracted after the α-axis and β-axis residual currents are multiplied by themselves in a fixed healthy state is used as the fault diagnosis index threshold. The fault diagnosis index is compared with the fault diagnosis index threshold. If the fault diagnosis index is greater than the fault diagnosis index threshold, it is determined that a turn-to-turn short circuit fault or a high resistance connection fault has occurred. Otherwise, it is determined to be normal.

[0050] Model predictive control (MPC) has been widely used in the field of motor control due to its fast dynamic response, intuitive algorithm and high flexibility. The MPC strategy has the ability to predict and compensate errors, providing an effective means for extracting current residuals. The residual signal based on MPC can not only reflect the health status of the motor, but also capture the characteristic differences between turn-to-turn short circuits and high-resistance connection faults.

[0051] The three-phase stator winding of a permanent magnet motor with an interturn short circuit fault is as follows: Figure 2 As shown. In order to reflect the position of the fault phase, unlike the traditional modeling method, it is not assumed that the turn-to-turn short circuit fault occurs in a specific A, B or C phase, but it is assumed that the fault occurs in the fault phase, and the other two phases are the first normal phase and the second normal phase. The fault phase winding is divided into the normal winding L S1 and short-circuited winding L S2 , short-circuit coefficient μ = l S2 / (l S1 +l S2 ), where l S1 and l S2 They are respectively the normal winding L S1 and short-circuited winding L S1 The length, R f is the short-circuit resistance. The windings of the first normal phase and the second normal phase are represented by L h1 and L h2 ,i ITF_f is the fault current of the fault phase, i ph_f is the normal current of the fault phase, i h1 and i h2 are the currents of the first normal phase and the second normal phase respectively. ij represents the mutual inductance between windings i and j, i,j = S1, S2, h1 and h2 correspond to winding L respectively S1 , L S2 , L h1 and L h2 , and i≠j.

[0052] Under the condition of inter-turn short circuit fault, the voltage equations of the α and β axes of the permanent magnet motor are:

[0053]

[0054] Among them, i α 、i β are the α and β axis currents, R s is the stator resistance, θ e is the electrical angle, Ψ pm is the permanent magnet flux, ω e is the electrical angular velocity, L d , L q They are the d-axis and q-axis inductances, μ represents, θ f is the electrical angle between the fault phase and motor phase A. If the fault occurs in phase A, θ f =0°. When the fault occurs in phase B or phase C, θ f = -120° and θ f =﹣240°.

[0055] Due to the use of MPC control, the predicted current satisfies the voltage equation of a healthy motor. According to the voltage equation at ITF, the residual current of the α and β axes at time k can be obtained by subtracting the predicted current from the measured current:

[0056]

[0057] Permanent magnet motor high resistance connection fault three-phase winding Figure 3 In order to reflect the position of the fault phase, unlike the traditional modeling method, it is not assumed that the high resistance connection fault occurs in a specific A, B or C phase, but it is assumed that the fault occurs in the fault phase, and the other two phases are the first normal phase and the second normal phase. The currents of the fault phase, the first normal phase and the second normal phase are represented by i ph_f 、i h1 、i h2 ,i HRC_f is the fault current under HRC fault. According to the fault mechanism of high resistance connection, the fault phase is equivalent to the series fault resistance ΔR.

[0058] Under high resistance connection fault conditions, the d and q axis voltage equations of the permanent magnet motor are:

[0059]

[0060] i d 、i q They are the d-axis and q-axis currents respectively.

[0061] Due to the use of MPC control, the predicted current satisfies the voltage equation of a healthy motor. According to the voltage equation during HRC, the residual current at time k can be obtained by subtracting the predicted current from the measured current:

[0062]

[0063] Among them, k-1 represents the moment before moment k, T s is the discrete period of the motor equation.

[0064] In order to maintain the consistency of the current residual under HRC and the current residual under ITF, the current residual under d and q axes under HRC is subjected to inverse Park transformation to obtain:

[0065]

[0066] The α and β axis residual currents under HRC and ITF are multiplied by themselves and filtered to obtain the corresponding DC components:

[0067]

[0068] Among them, the parameters customized to simplify the formula form

[0069] By analyzing the above expressions, it can be found that when the motor has the above two faults, the absolute difference between the α and β axis DC components is obviously larger. When the motor is in a healthy state, the absolute difference between the α and β axis DC components is relatively small. Therefore, this embodiment defines the fault diagnosis index as the absolute difference between the α and β axis DC components:

[0070]

[0071] They respectively represent the DC components of the residual currents of the α and β axes of the permanent magnet synchronous motor actually measured at time k.

[0072] By setting the fault diagnosis indicator threshold Thre dec , when FI dec >Thre dec When , it is determined to be a fault, that is, a turn-to-turn short circuit fault or a high resistance connection fault has occurred.

[0073] The specific flow chart of fault diagnosis is as follows: Figure 4 As shown, the DC components of the α and β axis residual currents of phases B and C are and Further obtain the fault diagnosis index FI of phase B and phase C dec_B , FI dec_C , which is similar to the fault diagnosis indicator threshold Thre dec Compare, if it is greater than the threshold Thredec , it is judged as a fault. The judgment order is to first dec and the fault diagnosis indicator threshold Thre dec For comparison, if FI dec >Thre dec It is directly judged as a fault, otherwise FI dec_B and the fault diagnosis indicator threshold Thre dec For comparison, if FI dec_B >Thre dec It is directly judged as a fault, otherwise FI dec_C and the fault diagnosis indicator threshold Thre dec For comparison, if FI dec_C >Thre dec If the fault is positive, it is directly judged as faulty, otherwise it is judged as healthy.

[0074] The robustness of the proposed permanent magnet synchronous motor fault diagnosis method is verified through speed mutation, load mutation and parameter mismatch tests. The relevant experimental results are as follows: Figure 5 As shown, it shows that under different mutation conditions, and FI dec Changes from Figure 5 It can be seen that despite the sudden changes in speed, load and parameters, the diagnostic indicators remain stable and no misjudgment occurs. Especially in the case of a sudden change in speed, the diagnostic indicators remain unchanged, preventing the occurrence of misdiagnosis, proving that the method has strong robustness.

[0075] In summary, the present embodiment provides a permanent magnet synchronous motor fault diagnosis method, which obtains the α and β axis current residual information of the motor through a model predictive control (MPC) framework, performs coordinate transformation on the current residual, extracts its DC component, further defines a fault diagnosis index by constructing the difference of the DC component of the current residual, and uses the index to perform fault diagnosis to determine whether a fault occurs, with a faster diagnostic speed and higher diagnostic accuracy and robustness.

[0076] Example 2

[0077] The present embodiment provides a permanent magnet synchronous motor fault diagnosis system, which includes a residual current acquisition module, a fault diagnosis index calculation module and a fault diagnosis module, which are respectively used to execute steps A1 to A3 in the permanent magnet synchronous motor fault diagnosis method described in Example 1. The specific functions of the residual current acquisition module, the fault diagnosis index calculation module and the fault diagnosis module have been described in detail in Example 1, and will not be repeated in this embodiment. What needs to be emphasized in this embodiment is an objectively existing electronic system that can use the permanent magnet synchronous motor fault diagnosis method described in Example 1 to diagnose permanent magnet synchronous motor faults.

[0078] Example 3

[0079] Based on the determination of the occurrence of a fault in Example 1, it is necessary to further locate the fault. This embodiment provides a method for locating a permanent magnet synchronous motor fault, such as Figure 6 As shown in the flowchart, the steps include:

[0080] B1. Perform coordinate transformation on the residual current of the α and β axes to rotate them to align with the B phase and the C phase, and obtain the residual current in the coordinate system aligned with the B phase and the C phase;

[0081] B2, the rotated residual current is squared and the DC component is extracted through low-pass filtering, and the DC component of the residual current of the α and β axes is used as the fault location feature;

[0082] B3. Calculate the fault location index based on the fault location characteristics;

[0083] B4. Locate the fault according to the fault location indicators and determine the location where the fault occurred.

[0084] The characteristics of different fault locations under ITF and HRC are shown in Table 1.

[0085] Table 1 Fault characteristics of different fault types and fault locations

[0086]

[0087] In order to determine the fault phase, the residual currents of the α and β axes are transformed to rotate them to align with different phases. The residual currents in the α and β coordinate systems are expressed as By performing a rotation transformation using the Euler formula, we can obtain the residual current at time k in the coordinate system aligned with phases B and C:

[0088]

[0089] The rotated residual current is squared and the DC component is extracted through low-pass filtering to obtain the corresponding B-phase and C-phase fault location features:

[0090]

[0091] The residual currents of the α and β axes at time k are multiplied by themselves and the DC components are extracted by low-pass filtering to obtain:

[0092]

[0093] This embodiment defines the fault location index as:

[0094]

[0095] Then combine the fault location indicators of different coordinate systems, such as Figure 4 As shown in the flowchart, the fault location decision logic is as follows:

[0096] If FI loc_A >0 and FI loc_B >0 or FI loc_A <0 and FI loc_B <0, it is determined that the fault occurs in phase C;

[0097] If FI loc_A >0 and FI loc_C <0, it is determined that the fault occurs in phase A;

[0098] If FI loc_A <0 and FI loc_B >0, it is determined that the fault occurs in phase B.

[0099] This logic is based on the residual characteristics of different fault locations and can effectively distinguish the phase where the fault occurs.

[0100] Fault location indicators, fault location results and fault current waveforms under different fault types and locations are shown in Figure 2. Figure 7 As shown, Figure 7 (a) corresponds to the location result of phase A (ITF fault), Figure 7 (b) corresponds to the positioning result of phase C (HRC fault). By simulating the ITF fault of phase A and the HRC fault of phase C, Figure 7 The experimental results show that the fault location index changes suddenly after the fault occurs, and by comparing the fault location index of each phase, the fault phase can be accurately determined. The delay of the positioning process is 63.1ms (ITF) and 329.2ms (HRC), indicating that the method has good real-time performance and positioning accuracy.

[0101] In summary, the present embodiment provides a permanent magnet synchronous motor fault location method, which performs coordinate transformation on the α-axis and β-axis residual currents to obtain the residual current in a coordinate system aligned with the B-phase and the C-phase, and then multiplies the rotated residual current by itself and extracts the DC component through low-pass filtering, and uses the DC component of the α-axis and β-axis residual currents together as fault location features, calculates the fault location index based on the fault location feature, and performs fault location based on the fault location index, so as to accurately determine the location of the inter-turn short circuit (ITF) fault or the high resistance connection (HRC) fault, and experiments have verified the effectiveness and excellence of the present method.

[0102] Example 4

[0103] This embodiment provides a permanent magnet synchronous motor fault location system, which includes a coordinate transformation module, a fault location feature calculation module, a fault location index calculation module, and a fault location module, which are respectively used to execute steps B1 to B4 in the permanent magnet synchronous motor fault diagnosis method described in Example 3. The specific functions of the coordinate transformation module, the fault location feature calculation module, the fault location index calculation module, and the fault location module have been described in detail in Example 3, and will not be repeated in this embodiment. What needs to be emphasized in this embodiment is an objectively existing electronic system that can use the permanent magnet synchronous motor fault location method described in Example 3 to locate the permanent magnet synchronous motor fault.

[0104] Example 5

[0105] On the basis of determining the fault location in Example 3, it is necessary to classify the fault type to determine the type of fault that occurred. This embodiment provides a permanent magnet synchronous motor fault classification method, such as Figure 8 As shown in the flowchart, the method further comprises the steps of:

[0106] C1, multiply the α and β axis residual currents with the fault phase current, and extract their DC components through a low-pass filter to obtain classification features;

[0107] C2, calculate the fault classification index based on the classification features;

[0108] C3. Classify the fault according to the fault classification index to determine whether the fault type is a turn-to-turn short circuit fault or a high-resistance connection fault.

[0109] Fault classification is based on the phase characteristics of the α and β axis residual currents. The classification characteristics are obtained by multiplying the α and β axis residual currents with the fault phase current and extracting their DC components through a low-pass filter:

[0110]

[0111] The fault classification index is defined as the difference of classification features:

[0112]

[0113] Where θ0 is the angle between the fault current and the fault phase current at ITF. Under HRC conditions, FI cla is a positive value; under ITF conditions, FI cla Is a negative value.

[0114] The process of fault classification is as follows Figure 4 As shown, set the fault classification threshold Thre cla =0, when FI cla >Thre cla It is judged as HRC when the value is 0, otherwise it is judged as ITF.

[0115] Related fault classification characteristics are shown in the figure Fig. 9 and Fig.10 , Fig. 9 is the inter-turn short circuit fault and classification characteristics, Fig.10 It is the high resistance connection fault and classification characteristics. Fig. 9 (a) corresponds to the A phase ITF fault, Fig. 9 (b) corresponds to the B phase ITF fault, Fig. 9 (c) corresponds to the C phase ITF fault. Fig.10 (a) corresponds to the A phase HRC fault, Fig.10 (b) corresponds to the B phase HRC fault, Fig.10 (c) corresponds to the C phase HRC fault.

[0116] To further verify the effectiveness of the method, the experiment was carried out in the range of 300 to 600 rpm and 30% to 50% of the rated load. Fig.11 , Fig.11 (a) is the performance of diagnostic indicators under various working conditions. Fig.11 (b) is the performance of classification indicators. Fig.11 The results show that in the healthy state, the diagnostic indicators are almost unaffected by changes in operating conditions, so a fixed threshold can be set. In the ITF case, the diagnostic indicators increase with increasing speed and load; while in the HRC case, the increase in speed weakens the effect of resistance on the total impedance, but the increased phase current enhances the HRC fault characteristics. The experimental results show that this method can still work effectively under various operating conditions.

[0117] In summary, the present embodiment provides a permanent magnet synchronous motor fault classification method, which multiplies the α and β axis residual currents with the fault phase current, and extracts its DC component through a low-pass filter to obtain classification features, and then calculates the fault classification index based on the classification features, and then classifies the fault according to the fault classification index, and determines whether the type of the fault is a turn-to-turn short circuit fault or a high-resistance connection fault, and cooperates with a permanent magnet synchronous motor fault location method provided in Example 3, so that the type of fault and the fault phase can be accurately determined. The experimental results show that this method can not only quickly identify the fault, but also accurately locate the fault phase, and has good real-time performance under both ITF and HRC fault types. Further working condition verification shows that the method can still work stably under different speeds and load conditions, and has strong robustness, and can effectively cope with complex situations such as speed mutations, load changes and parameter mismatches. The proposed method exhibits high precision, low latency and strong robustness under a variety of working conditions, and has a wide range of practical application value.

[0118] Example 6

[0119] This embodiment provides a permanent magnet synchronous motor fault classification system, which includes a fault classification feature calculation module, a fault classification index calculation module, and a fault classification module, which are respectively used to execute steps C1 to C3 in the permanent magnet synchronous motor fault classification method described in Example 5. The specific functions of the fault classification feature calculation module, the fault classification index calculation module, and the fault classification module have been described in detail in Example 5, and will not be repeated in this embodiment. What needs to be emphasized in this embodiment is an objectively existing electronic system that can apply the permanent magnet synchronous motor fault classification method described in Example 5 to classify permanent magnet synchronous motor faults.

[0120] The above embodiments are preferred implementation modes of the present invention, but the implementation modes of the present invention are not limited to the above embodiments. Any other changes, modifications, substitutions, combinations, and simplifications that do not deviate from the spirit and principles of the present invention should be equivalent replacement methods and are included in the protection scope of the present invention.

Claims

1. A permanent magnet synchronous motor fault diagnosis method, characterized in that: Includes steps: A1. Calculate the residual current of the α and β axes by the difference between the predicted current of the α and β axes of the three-phase permanent magnet synchronous motor and the actual measured current of the α and β axes; A2, multiply the α and β axis residual currents by themselves and extract their DC components and use the absolute difference of the DC components as a fault diagnosis indicator; A3. In a fixed healthy state, 1.5 times the absolute difference of the DC component extracted after the α-axis and β-axis residual currents are multiplied by themselves is used as the fault diagnosis index threshold. The fault diagnosis index is compared with the fault diagnosis index threshold. If the fault diagnosis index is greater than the fault diagnosis index threshold, it is determined that a turn-to-turn short circuit fault or a high resistance connection fault has occurred. Otherwise, it is determined to be normal.

2. Permanent magnet synchronous motor fault diagnosis system, characterized in that: It comprises a residual current acquisition module, a fault diagnosis index calculation module and a fault diagnosis module, which are respectively used to execute steps A1 to A3 in the permanent magnet synchronous motor fault diagnosis method according to claim 1.

3. A method for locating a permanent magnet synchronous motor fault, based on the permanent magnet synchronous motor fault diagnosis method according to claim 1, characterized in that: After it is determined in step A3 that a turn-to-turn short circuit fault or a high resistance connection fault has occurred, the diagnostic method comprises the following steps: B1. Perform coordinate transformation on the residual current of the α and β axes to rotate them to align with the B phase and the C phase, and obtain the residual current in the coordinate system aligned with the B phase and the C phase; B2, the rotated residual current is squared and the DC component is extracted through low-pass filtering, and the DC component of the residual current of the α and β axes is used as the fault location feature; B3. Calculate the fault location index based on the fault location characteristics; B4. Locate the fault according to the fault location indicators and determine the location where the fault occurred.

4. The method for locating a permanent magnet synchronous motor fault according to claim 3, characterized in that: In step B3, the fault location indicators include: Among them, FI loc_A It represents the residual current of α and β axes at time k and extracts the DC component through low-pass filtering The difference, FI loc_B The residual current of phase B after rotation at time k is squared and the DC component is extracted by low-pass filtering. The difference, FI loc_C The C-phase residual current after rotation at time k is squared and the DC component is extracted by low-pass filtering. difference.

5. The method for locating a permanent magnet synchronous motor fault according to claim 4, characterized in that: The step B4 is specifically as follows: If FI loc_A >0 and FI loc_B >0 or FI loc_A <0 and FI loc_B <0, it is determined that the fault occurs in phase C; If FI loc_A >0 and FI loc_C <0, it is determined that the fault occurs in phase A; If FI loc_A <0 and FI loc_B >0, it is determined that the fault occurs in phase B.

6. Permanent magnet synchronous motor fault location system, characterized by: It includes a coordinate transformation module, a fault location feature calculation module, a fault location index calculation module, and a fault location module, which are respectively used to execute steps B1 to B4 in the permanent magnet synchronous motor fault diagnosis method described in any one of claims 3 to 5.

7. A permanent magnet synchronous motor fault classification method, based on the permanent magnet synchronous motor fault diagnosis method according to claim 1, characterized in that: After it is determined in step A3 that a turn-to-turn short circuit fault or a high resistance connection fault has occurred, the diagnostic method comprises the following steps: C1, multiply the α and β axis residual currents with the fault phase current, and extract their DC components through a low-pass filter to obtain classification features; C2, calculate the fault classification index based on the classification features; C3. Classify the fault according to the fault classification index to determine whether the fault type is a turn-to-turn short circuit fault or a high-resistance connection fault.

8. The permanent magnet synchronous motor fault classification method according to claim 7 is characterized in that: In step C2, the fault classification index is the α and β axis classification features FI cla_α , FI cla_β The difference between FI cla =FI cla_α -FI cla_β .

9. The permanent magnet synchronous motor fault classification method according to claim 8, characterized in that: The step C3 specifically includes: setting the fault classification threshold Thre cla =0, when FI cla >Thre cla When the fault is detected, it is judged as a high-resistance connection fault; otherwise, it is judged as a turn-to-turn short-circuit fault.

10. Permanent magnet synchronous motor fault classification system, characterized by: It comprises a fault classification feature calculation module, a fault classification index calculation module and a fault classification module, which are respectively used to execute steps C1 to C3 in the permanent magnet synchronous motor fault classification method described in any one of claims 7 to 9.

Images