Method for diagnosing inter-turn short circuit fault of stator winding of salient pole polyphase rectifier generator

Abstract

This application discloses a method for diagnosing inter-turn short-circuit faults in the stator windings of a salient-pole multiphase rectifier generator. Addressing the problems of incomplete feature extraction, poor location accuracy, and large quantitative errors in existing diagnostic methods for non-salient-pole generators due to the uneven air gap and salient-pole effect, this method establishes a complete electromagnetic model of inter-turn short-circuit faults based on the second harmonic distribution characteristics of the air gap magnetic permeability of the salient-pole generator. It derives analytical expressions for the fault components of electrical quantities on the stator side, neutral point, and DC side, extracts a multi-dimensional fault feature set including salient-pole-specific components such as the third harmonic and fourth harmonic components, and combines this with the electrical isolation characteristics of multiple windings to achieve precise location of the fault set and phase. Multiple electromagnetic mechanism-based methods are used to comprehensively assess the severity of the fault. This diagnostic method requires no modification to the generator body and can effectively detect extremely minor faults with a short-circuit turns ratio k=0.01, with a false negative rate of less than 0.1%, significantly improving the operational safety of high-power DC power supply systems.

Description

Technical Field

[0001] This application relates to the field of motor fault diagnosis technology, and in particular to a method for diagnosing inter-turn short-circuit faults in the stator winding of a salient-pole multiphase rectifier generator. Background Technology

[0002] Twelve-phase rectifier generators have the advantages of high output power, low ripple, and strong fault tolerance, and are widely used in core fields such as marine electric propulsion, aerospace, and industrial DC power supply. Stator winding inter-turn short circuits are one of the most common electrical faults in generators, accounting for more than 30% of all generator faults. Initially, the fault only manifests as localized insulation degradation, but if not diagnosed and addressed promptly, it will rapidly develop into phase-to-phase short circuits and ground faults, ultimately leading to generator burnout, power system failure, and significant economic losses and safety accidents.

[0003] In existing technologies, a relatively complete system of inter-turn short-circuit fault diagnosis methods has been established for salient-pole twelve-phase rectifier generators. However, for salient-pole twelve-phase rectifier generators, due to the inherent characteristics of their rotors—protruding magnetic poles, uneven air gaps, and periodic changes in inductance with rotor position—the electromagnetic mechanism of faults differs fundamentally from that of salient-pole generators. Existing methods generally suffer from the following defects: First, feature extraction is incomplete, ignoring the third harmonic and fourth harmonic components generated by the salient-pole effect and only extracting the second harmonic feature shared with salient-pole generators, resulting in low fault detection sensitivity and a tendency to miss faults. Second, fault location accuracy is poor, failing to fully utilize the electrical isolation characteristics of the four sets of Y windings (star-connected stator windings), making it impossible to accurately locate the number of windings and phases where faults occur in parallel systems with multiple windings. Third, severity assessment is inaccurate, lacking quantitative derivation based on electromagnetic mechanisms and relying heavily on empirical formulas, resulting in errors exceeding 20% ​​when the short-circuit turns ratio k < 0.05, which cannot meet practical engineering requirements. Summary of the Invention

[0004] The purpose of this application is to overcome the shortcomings of the prior art and provide a method and system for diagnosing inter-turn short-circuit faults in the stator windings of a salient-pole multiphase rectifier generator based on electromagnetic mechanism derivation. This method breaks through the limitations of the traditional diagnostic framework for salient-pole motors and achieves high-sensitivity detection, accurate location, and high-precision quantitative assessment of faults. To achieve the above objectives, this application adopts the following technical solution.

[0005] In a first aspect, embodiments of this application provide a method for diagnosing inter-turn short-circuit faults in the stator winding of a salient-pole multiphase rectifier generator, including:

[0006] S1. Synchronously acquire the three-phase current of multiple star-connected stator windings of the generator, the neutral point voltage of each winding, the DC side electrical quantities and the rotor position angle signal;

[0007] S2. Based on the second harmonic distribution characteristics of the air gap magnetic permeability of the salient pole motor, an electromagnetic model of the inter-turn short circuit fault of the stator winding is established, and the analytical expressions of the fault components of each electrical quantity on the stator side, neutral point and DC side are derived.

[0008] S3. Based on the fault component analytical expression, extract a multi-dimensional fault feature set containing harmonic components unique to salient pole motors;

[0009] S4. Compare the feature values ​​in the multi-dimensional fault feature set with the preset fault judgment threshold. When the preset judgment condition is met, it is determined that there is a short circuit fault between turns of the stator winding.

[0010] S5. Based on the electrical isolation characteristics of multiple windings and the differences in fault characteristics of each winding, locate the number of faulty windings; based on the three-phase current residual characteristics of the faulty windings, locate the faulty phase.

[0011] S6. Use at least two quantitative calculation methods derived from the electromagnetic model to calculate the short-circuit turns ratio and comprehensively assess the severity of the fault.

[0012] Furthermore, the multi-dimensional fault feature set mentioned in step S3 includes three categories: stator side current features, neutral point voltage features, and DC side electrical quantity features;

[0013] The specific harmonic components of the salient pole motor include the third harmonic component of the stator current, the fourth harmonic component of the dq axis current, the third harmonic component of the neutral point voltage, and the third harmonic component of the DC side voltage.

[0014] Furthermore, the preset fault determination threshold mentioned in step S4 is determined using the 3σ principle, specifically including:

[0015] N sets of feature data are continuously collected under the generator's healthy state. The mean μ and standard deviation σ of each set of features are calculated, and the threshold is set to μ+3σ. The preset judgment condition is that any two or more core features exceed the corresponding threshold at the same time. The core features include at least the neutral point third harmonic voltage, the d-axis second harmonic current, and the DC side second harmonic voltage.

[0016] Furthermore, the specific method for locating the number of faulty windings in step S5 includes:

[0017] Define the characteristic anomaly α for each winding. m The characteristic anomaly degree α m This is the sum of the standardized deviations of multiple core characteristics of the winding; when the characteristic anomaly α of a certain winding... m The anomaly degree α of the remaining windings is greater than the preset anomaly degree threshold. m If all values ​​are less than the preset anomaly threshold, the winding is determined to be a faulty winding.

[0018] Furthermore, the specific method for locating the faulty phase in step S5 includes:

[0019] Calculate the fundamental residual ΔI of the three-phase current in the located faulty winding. Am ΔI Bm ΔI Cm The fundamental residual is the absolute value of the difference between the amplitude of the three-phase fundamental current under fault conditions and the amplitude of the corresponding phase fundamental current under healthy conditions.

[0020] The phase with the largest fundamental residual amplitude is determined to be the fault phase, and ΔI is satisfied. Am ≈∣ΔI Bm +ΔI Cm |

[0021] Furthermore, the quantitative calculation method described in step S6 includes a calculation method based on the DC side second harmonic voltage, a calculation method based on the d-axis second harmonic current, and a calculation method based on the neutral point third harmonic voltage.

[0022] The short-circuit turns ratio is calculated using the three methods described above, and the average of the three is taken as the final short-circuit turns ratio. The fault level is then classified based on the final short-circuit turns ratio.

[0023] Furthermore, when there are two or more sets of windings, the characteristic anomaly α m When all values ​​exceed the preset anomaly threshold, it is determined that multiple windings have experienced inter-turn short-circuit faults simultaneously. For each winding with anomaly exceeding the threshold, a phase-specific location operation is performed, and a fault priority ranking is generated based on the short-circuit turn ratio of each winding. The higher the short-circuit turn ratio, the higher the priority.

[0024] Furthermore, it also includes a dynamic threshold adjustment step, specifically including:

[0025] When the generator is determined to be in a healthy state, the current feature value is added to a sliding window of a preset size, and the mean and standard deviation of the features within the window are calculated in real time to update the fault determination threshold.

[0026] When the change in generator operating conditions exceeds a preset threshold, the sliding window is cleared and health data is re-collected to establish a baseline. The operating conditions include load, speed, and excitation current, and the change in operating conditions is the weighted average of the change rates of each operating condition parameter.

[0027] Secondly, embodiments of this application provide a stator winding inter-turn short-circuit fault diagnosis system for a salient-pole multiphase rectifier generator, comprising:

[0028] The signal acquisition module is used to synchronously acquire the three-phase current of multiple star-connected stator windings of the generator, the neutral point voltage of each winding, the DC side electrical quantities and the rotor position angle signal, and transmit the acquired signals to the data preprocessing module.

[0029] The data preprocessing module is used to filter, denoise, and synchronize the received signal to obtain the preprocessed electrical signal.

[0030] The fault modeling and feature extraction module is used to establish an electromagnetic model of inter-turn short-circuit fault based on the second harmonic distribution characteristics of the air gap magnetic permeability of a salient pole motor, derive the analytical expression of the fault components, and extract a multi-dimensional fault feature set from the preprocessed electrical signal according to the analytical expression of the fault components.

[0031] The fault determination module is used to compare the feature values ​​in the multi-dimensional fault feature set with the preset fault determination threshold. When the preset determination conditions are met, the module outputs a fault presence signal to the fault location module and the severity determination module.

[0032] The fault location module is used to locate the number of faulty windings based on the electrical isolation characteristics of multiple windings and the differences in fault characteristics of each winding, locate the faulty phase based on the three-phase current residual characteristics of the faulty winding, and output the fault location information.

[0033] The severity determination module is used to calculate the short-circuit turns ratio using at least two quantitative calculation methods derived from the electromagnetic model, comprehensively assess the severity of the fault, and output fault level information.

[0034] The early warning output module is used to generate corresponding early warning prompts and maintenance suggestions based on the fault presence signal, fault location information, and fault level information.

[0035] Thirdly, embodiments of this application provide an electronic device, including: one or more processors;

[0036] A memory for storing one or more programs; when the one or more programs are executed by the one or more processors, the one or more processors are able to implement the steps of any of the preceding diagnostic methods.

[0037] Fourthly, embodiments of this application provide a computer-readable medium storing a computer program, which, when executed by a processor, can implement the steps of the diagnostic method described in any of the preceding claims.

[0038] This application discloses a method for diagnosing inter-turn short-circuit faults in the stator windings of a salient-pole multiphase rectifier generator. Addressing the problems of incomplete feature extraction, poor location accuracy, and large quantitative errors in existing diagnostic methods for non-salient-pole generators due to the uneven air gap and salient-pole effect, this method establishes a complete electromagnetic model of inter-turn short-circuit faults based on the second harmonic distribution characteristics of the air gap magnetic permeability of the salient-pole generator. It derives analytical expressions for the fault components of electrical quantities on the stator side, neutral point, and DC side, extracts a multi-dimensional fault feature set including salient-pole-specific components such as the third harmonic and fourth harmonic components, and combines this with the electrical isolation characteristics of multiple windings to achieve precise location of the fault set and phase. Multiple electromagnetic mechanism-based methods are used to comprehensively assess the severity of the fault. This diagnostic method requires no modification to the generator body and can effectively detect extremely minor faults with a short-circuit turns ratio k=0.01, with a false negative rate of less than 0.1%, significantly improving the operational safety of high-power DC power supply systems. Attached Figure Description

[0039] Figure 1 A flowchart illustrating the diagnostic strategy provided in this application embodiment;

[0040] Figure 2 This is a schematic diagram showing the connection between the stator winding and the load of a twelve-phase rectifier generator during normal operation, as provided in an embodiment of this application.

[0041] Figure 3 This is a schematic diagram of an inter-turn short circuit fault in the stator winding of a twelve-phase rectifier generator provided in an embodiment of this application.

[0042] Figure 4 An equivalent schematic diagram of the inter-turn short-circuit fault of the first set of stator windings of a twelve-phase rectifier generator provided in an embodiment of this application;

[0043] Figure 5 This is a schematic diagram of the air gap magnetic permeability distribution of a salient pole motor provided in an embodiment of this application;

[0044] Figure 6 The three-phase current waveform diagram of the inter-turn short circuit Y1 winding of the first set of stator windings of the twelve-phase rectifier generator provided in the embodiments of this application;

[0045] Figure 7 A waveform comparison of A1 phase current before and after inter-turn short circuit of the first set of stator windings of the twelve-phase rectifier generator provided in this application embodiment;

[0046] Figure 8 The waveform diagram of the neutral point voltage of the first set of stator windings of the twelve-phase rectifier generator provided in the embodiments of this application is shown.

[0047] Figure 9 This application provides a spectrum analysis diagram of the inter-turn short-circuit neutral point voltage of the first set of stator windings of a twelve-phase rectifier generator according to an embodiment of the present application.

[0048] Figure 10 A comparison waveform diagram of the output DC voltage of the first set of stator windings of the twelve-phase rectifier generator before and after an inter-turn short circuit is provided in the embodiments of this application;

[0049] Figure 11 The output DC voltage spectrum analysis diagram of the first set of stator windings of the twelve-phase rectifier generator before and after the inter-turn short circuit is provided in the embodiment of this application;

[0050] Figure 12 A waveform comparison of the d-axis current before and after an inter-turn short circuit in the first set of stator windings of a twelve-phase rectifier generator provided in this application embodiment;

[0051] Figure 13 The d-axis current spectrum analysis diagram before and after the inter-turn short circuit of the first set of stator windings of the twelve-phase rectifier generator provided in the embodiments of this application;

[0052] Figure 14 A waveform comparison of the q-axis current before and after an inter-turn short circuit in the first set of stator windings of the twelve-phase rectifier generator provided in this application embodiment;

[0053] Figure 15 The q-axis current spectrum analysis diagram before and after the inter-turn short circuit of the first set of stator windings of the twelve-phase rectifier generator provided in the embodiments of this application;

[0054] Figure 16 This application provides a core flowchart of a method for diagnosing inter-turn short-circuit faults in the stator winding of a salient-pole multiphase rectifier generator.

[0055] Figure 17 A schematic diagram of the module structure of a stator winding inter-turn short circuit fault diagnosis system for a salient-pole multiphase rectifier generator provided in this application embodiment;

[0056] Figure 18 This is a structural block diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0057] To enable those skilled in the art to better understand the technical solutions of this application, exemplary embodiments of this application are described below with reference to the accompanying drawings, including various details of the embodiments of this application to aid understanding. These should be considered merely exemplary. Therefore, those skilled in the art should recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this application. Similarly, for clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description. Unless otherwise specified, the various embodiments of this application and the features within those embodiments can be combined with each other.

[0058] As used herein, the term "and / or" includes any and all combinations of one or more of the associated enumerated entries. The terminology used herein is for describing particular embodiments only and is not intended to limit the application. As used herein, the singular forms "a" and "the" are also intended to include the plural forms, unless the context clearly indicates otherwise. It should also be understood that when the terms "comprising" and / or "made of" are used herein, the presence of the stated feature, integral, step, operation, element, and / or component is specified, but the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or groups thereof is not excluded. Terms such as "connected" or "linked" are not limited to physical or mechanical connections but can include electrical connections, whether direct or indirect.

[0059] Unless otherwise specified, all terms used in this application (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art. It should also be understood that terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant art and this application, and will not be interpreted as having an idealized or overly formal meaning, unless expressly so defined in this application.

[0060] The stator winding inter-turn short-circuit fault diagnosis method for salient-pole multiphase rectifier generators proposed in this application is applicable to all salient-pole multiphase synchronous generators, including but not limited to 4-pole, 6-pole, and 8-pole structures. This diagnostic method is applicable to twelve-phase rectifier systems with different topologies such as four parallel and four series. The feature extraction method can be verified by simulation tools such as MATLAB / Simulink and ANSYS Maxwell.

[0061] One embodiment of this application takes a salient-pole twelve-phase rectifier generator with four poles, four sets of Y-connections, and a spatial electrical angle difference of 15° as the core implementation object. A simulation model is built based on the MATLAB / Simulink and Ansys / Maxwell platforms. The main technical parameters of the motor are shown in Table 1.

[0062] Table 1 Main Technical Parameters of the Motor

[0063]

[0064] refer to Figures 1-17 A method for diagnosing inter-turn short-circuit faults in the stator winding of a salient-pole multiphase rectifier generator mainly includes the following steps.

[0065] S1. Signal Acquisition and Preprocessing. Synchronously acquire the three-phase currents of multiple star-connected stator windings of the generator, the neutral point voltage of each winding, DC side electrical quantities, and rotor position angle signals.

[0066] Specifically, the three-phase stator currents of four sets of Y-windings (star-connected stator windings) are collected simultaneously. , , (m=1,2,3,4, corresponding to the first to fourth windings respectively), neutral point voltage of each winding DC side total voltage DC side total current and rotor position angle The sampling frequency is set to 10 kHz, and the sampling duration is no less than 5 power frequency cycles. In this embodiment, the sampling duration is 10 power frequency cycles (0.2 s) to ensure the accuracy of signal analysis.

[0067] The acquired signals are transmitted to the data preprocessing unit via shielded cables. A Butterworth low-pass filter is used to filter out high-frequency noise above 1kHz, and a unified timestamp is used to achieve synchronous alignment of multi-channel signals to eliminate phase deviation between different sensors.

[0068] S2. Fault Modeling and Characteristic Mechanism Derivation. Based on the second harmonic distribution characteristics of the air gap magnetic permeability of a salient-pole motor, an electromagnetic model of inter-turn short-circuit faults in the stator winding is established, and analytical expressions for the fault components of various electrical quantities on the stator side, neutral point, and DC side are derived. (See schematic diagram of air gap magnetic permeability distribution for reference.) Figure 5 .

[0069] 2.1 System Topology

[0070] The salient-pole twelve-phase rectifier generator system proposed in this embodiment consists of four independent Y-connected stator windings (Y1-Y4) and four parallel three-phase uncontrolled rectifier bridges, as follows: Figure 2 As shown, the four windings are spatially spaced 15° apart electrically. Each winding corresponds to a rectifier bridge, and the DC sides of the four rectifier bridges are connected in parallel to supply power to the load. The core advantage of this topology is that the fundamental magnetomotive forces of the four windings cancel each other out, the inter-winding fundamental mutual inductance is approximately zero, and a fault only affects the winding that experiences a short circuit.

[0071] 2.2 Derivation of the Electromagnetic Fundamentals of Salient-Pole Motors

[0072] The essential difference between salient-pole and non-salient-pole motors lies in the periodic variation of the air gap permeability with rotor position. Let the electrical angle between the rotor d-axis and the A1 phase axis be . ,in, The rotor's electric angular velocity, The initial position angle is given. The electrical angle between any point on the stator circumference and the phase axis A1 is given. Then the air gap permeability at that point can be expanded into a Fourier series. Neglecting the fourth and higher harmonics (whose amplitude is less than 5% of the fundamental frequency), the expression is:

[0073]

[0074] wherein, is the constant component of the air gap permeance, is the second harmonic component of the air gap permeance.

[0075] When α-θ=0 (d-axis position), the permeance is the maximum, which is denoted as ; when α-θ=π / 2 (q-axis position), the permeance is the minimum, which is denoted as . Substituting into the above equation, we can obtain:

[0076]

[0077] According to the definition of inductance (N is the number of winding turns), the expression for the stator phase self-inductance can be obtained:

[0078]

[0079] wherein, is the constant component of the self-inductance, is the second harmonic component of the self-inductance.

[0080] Similarly, the expression for the inter-phase mutual inductance can be obtained:

[0081]

[0082] wherein, is the constant component of the mutual inductance, is the second harmonic component of the mutual inductance, and the negative sign indicates that the mutual inductance flux linkage is in the opposite direction to the self-inductance flux linkage.

[0083] 2.3 Modeling and Derivation of Inter-turn Short Circuit Fault

[0084] When an inter-turn short circuit occurs in phase A1, the complete winding is split into a healthy branch (the number of turns is ) and a faulty branch (the number of turns is ), where k is the short-circuit turn ratio (0<k<1), as shown in Figure 3 and Figure 4 . According to Kirchhoff's current law, the current relationship is:

[0085]

[0086] wherein, is the outlet terminal current of phase A1, is the current of the healthy branch, is the current of the faulty branch, is the fault circulating current.

[0087] Since the inductance is proportional to the square of the number of turns, and the mutual inductance is proportional to the product of the number of turns, the parameters of the two branches can be obtained:

[0088] 1. Self-perception of health pathways:

[0089] 2. Self-inductance of the faulty branch:

[0090] 3. Mutual inductance between the two branches:

[0091] 4. Mutual inductance between the healthy branch and other windings:

[0092] 5. Mutual inductance between the faulty branch and other windings:

[0093] The total self-inductance after connecting the healthy branch and the faulty branch in series The result is consistent with the self-inductance of the complete winding, proving that the parameter derivation is correct.

[0094] 2.4 Derivation of flux linkage and voltage equations under fault conditions

[0095] The flux linkage of any branch is equal to the sum of the flux linkages produced by the currents in all coupled branches, that is:

[0096]

[0097] For the healthy branch of phase A1, its flux linkage equation is:

[0098]

[0099] In the formula, Let be the magnetic flux linkage generated by the rotor winding current in the healthy branch. Substituting the parameters and simplifying, we get:

[0100]

[0101] Similarly, the flux linkage of the faulty branch can be obtained:

[0102]

[0103] According to Faraday's law of electromagnetic induction, the voltage equation for a salient-pole motor is:

[0104]

[0105] In the formula, the first term is the voltage drop across the resistor, the second term is the transformer electromotive force, and the third term is the kinetic electromotive force.

[0106] Substituting the flux linkage equation into the voltage equation and simplifying the results, we obtain the terminal voltage equation for the complete winding of phase A1:

[0107]

[0108] In the formula, Stator phase resistance, This is the neutral point voltage of the first winding. The second item on the right is the voltage under healthy conditions, and the third item is the fault-related additional item. All fault components are proportional to k.

[0109] 2.5 Derivation of Neutral Point Voltage

[0110] Adding the left and right sides of the first set of three-phase voltage equations for windings, the left side is:

[0111]

[0112] During normal operation, the three-phase terminal voltages are symmetrical and their sum is 0.

[0113] Simplify item by item on the right:

[0114] 1. Resistance term: (No neutral current in the Y-connected winding).

[0115] 2. The constant component and second harmonic component of the transformer electromotive force: The sum of the product of the symmetrical three-phase current and the symmetrical inductive component is 0.

[0116] 3. Second harmonic component of the moving electromotive force: After simplification, the third harmonic component is obtained (unique to salient pole).

[0117] 4. Additional fault items: After simplification, the DC component and the third harmonic component are obtained.

[0118] Finally, we obtain the complete expression for the neutral point voltage:

[0119]

[0120] In the formula, This is the DC offset component. The amplitudes of the third harmonic components are both proportional to k. In a salient-pole motor... Therefore Only DC offset exists.

[0121] 2.6 Derivation of Stator Current Components

[0122] The expression for the stator current is obtained by solving the phase voltage equation:

[0123]

[0124] 1. Fundamental component of healthy current , , (excluding k):

[0125]

[0126] In the formula, The phase voltage amplitude, This represents the constant component of the stator winding self-inductance. Under fault-free conditions, the fundamental components of the three-phase currents have equal amplitudes and are 120° out of phase, exhibiting complete symmetry.

[0127] 2. Fault fundamental residual component , , (Including k):

[0128]

[0129] In the formula, This represents the fundamental amplitude of the fault circulating current. The additional fundamental current introduced by the fault causes an imbalance in the three-phase fundamental currents, and... This is a hallmark feature of inter-turn short-circuit faults.

[0130] 3. Third harmonic residual component of the fault , , (Contains k, unique to salient poles):

[0131]

[0132] In the formula, This is the second harmonic component of the stator winding self-inductance. This component is generated by the interaction between the second harmonic component of the moving electromotive force and the fundamental fault circulating current. The three-phase third harmonic residual components have equal amplitudes and the same phase, which is a unique characteristic of inter-turn short circuits in salient pole motors.

[0133] 2.7 Complete Analysis of Each Component of the dq-axis Current

[0134] The Park transformation converts the current in the stationary abc coordinate system into the current in the rotated dq coordinate system. The transformation matrix is:

[0135]

[0136] In the formula, θ=ωt is the angle between the rotor d-axis and the A1 phase axis.

[0137] Substituting the three-phase currents into the Park transformation matrix and performing a complete trigonometric identity transformation, we obtain the complete expression for the dq-axis current of the salient-pole motor:

[0138]

[0139] In the formula, , This refers to the DC component of the dq-axis current. , It is a second harmonic component; , It is a 4th harmonic component (unique to salient poles).

[0140] 2.8 Derivation of DC Side Voltage

[0141] The four sets of windings are connected in parallel on the DC side of the rectifier bridge, and the total DC voltage is the maximum value of the output voltages of the four bridges:

[0142]

[0143] After the fault, the phase voltage of the first winding contains the fundamental positive sequence, fundamental negative sequence, third harmonic, and DC offset components. Since the third harmonic and DC offset components are in phase in the three phases and cancel each other out in the line voltage, the output voltage of the first rectifier bridge only contains the fundamental positive and negative sequence components.

[0144]

[0145] In the formula, The amplitude of the second harmonic component (proportional to k) It is the amplitude of the third harmonic component (unique to salient poles, proportional to k).

[0146] Since the fault only affected the first rectifier bridge, the other three bridges output normal 6-pulse voltages. The Fourier expansion of the total DC voltage is:

[0147]

[0148] In the formula, the 2nd, 3rd, and 12th harmonic components are fault characteristic components, and the 24th harmonic and above are inherent ripple.

[0149] S3. Multi-dimensional fault feature extraction. Based on the analytical expression of the fault components, a multi-dimensional fault feature set containing harmonic components unique to salient-pole motors is extracted.

[0150] Specifically, time-domain analysis, frequency-domain analysis, and Park transform are performed on the preprocessed signal to extract 12 multi-dimensional fault features, including stator current fundamental asymmetry, third harmonic amplitude, dq-axis second / fourth harmonic amplitude, neutral point voltage DC offset / third harmonic amplitude, and DC-side second / third / twelfth harmonic amplitude. These features are categorized into three types:

[0151] 1. Stator-side current characteristics:

[0152] Fundamental asymmetry characteristics: residual fundamental current in the fault phase The asymmetry of the three-phase fundamental wave is significantly increased.

[0153] Third harmonic characteristics: The three-phase third harmonic currents have equal amplitudes and the same phase, and the amplitude is proportional to k.

[0154] dq-axis current characteristics: After Park transformation, the d-axis current exhibits significant second and fourth harmonic components, while the q-axis current exhibits a significant fourth harmonic component. No fourth harmonic component is found in salient-pole motors.

[0155] 2. Neutral point voltage characteristics: The neutral point voltage of a faulty winding exhibits both a DC offset component and a third harmonic component, while a healthy winding does not have this characteristic. The third harmonic component is a unique diagnostic feature of salient-pole motors, possessing strong anti-interference capabilities and unaffected by load changes.

[0156] 3. DC-side voltage and current characteristics: The DC-side voltage and current exhibit 2nd, 3rd, and 12th harmonic components. Among them, the 2nd harmonic component is common to all inter-turn short-circuit faults, the 3rd harmonic component is unique to salient-pole motors, and the 12th harmonic component is unique to four-parallel topologies.

[0157] Among them, the third harmonic component of the stator current, the fourth harmonic component of the dq axis current, the third harmonic component of the neutral point voltage, and the third harmonic component of the DC side voltage are harmonic components unique to salient pole motors.

[0158] Based on the electromagnetic mechanism formula derived above, the theoretical amplitudes of each core fault characteristic when k=0.16 were calculated in advance, as shown in Table 2, which serves as a benchmark for comparing the simulation results.

[0159] Table 2. Theoretical values ​​of core fault characteristics under severe fault condition k=0.16

[0160]

[0161] Refer to the instruction manual. Figures 6-15 The waveform characteristics were analyzed item by item in the simulation results under the severe fault condition of k=0.16 to verify the accuracy of the theoretical derivation. The results are listed in Table 3. Figure 6 The waveform diagram shows the three-phase current of the Y1 winding. Figure 7 The image shows a comparison of the waveforms before and after a current fault in phase A1. Figure 8 This is a waveform diagram of the neutral point voltage. Figure 9 This is a spectrum analysis diagram of the neutral point voltage. Figure 10 The waveforms before and after a DC voltage fault are shown. Figure 11 This is a DC voltage spectrum analysis diagram. Figure 12 The waveforms before and after the d-axis current fault are shown. Figure 13 This is a d-axis current spectrum analysis diagram. Figure 14 The waveforms before and after the q-axis current fault are shown. Figure 15 This is a q-axis current spectrum analysis diagram.

[0162] Table 3 Comparison of Simulation Results and Theoretical Values ​​under Severe Fault Condition k=0.16

[0163]

[0164] S4. Fault Existence Determination. The feature values ​​in the multi-dimensional fault feature set are compared with the preset fault determination threshold. When the preset determination condition is met, it is determined that there is a stator winding inter-turn short circuit fault.

[0165] 4.1 Determination of Static Threshold

[0166] The 3σ principle was used to determine the fault judgment threshold. Under the generator's healthy condition, 100 sets of data were continuously collected, and the mean of each feature was calculated. and standard deviation Set the threshold to The core features and thresholds are as follows:

[0167] Neutral point third harmonic voltage threshold:

[0168] d-axis frequency harmonic current threshold:

[0169] DC-side frequency harmonic voltage threshold:

[0170] When any two core features exceed the threshold simultaneously, an inter-turn short circuit fault is determined to exist, which can reduce the false positive rate to below 0.3%.

[0171] In this embodiment, 20 sets of health data were collected each under generator no-load, 25% load, 50% load, 75% load, and rated load conditions, for a total of 100 sets. The thresholds of the core features were calculated as follows:

[0172] Neutral point third harmonic voltage threshold: 0.12 V

[0173] d-axis second harmonic current threshold: 0.07 A

[0174] DC-side frequency harmonic voltage threshold: 0.06 V

[0175] When a very minor fault occurs with k=0.01, the measured neutral point third harmonic voltage is 0.15 V and the d-axis second harmonic current is 0.09 A. Both core characteristics exceed the threshold at the same time, and the system accurately determines that a fault exists with a false negative rate of less than 0.1%.

[0176] 4.2 Dynamic Threshold Adjustment

[0177] When generator operating conditions (load, speed, excitation current) change significantly, the health baseline characteristic values ​​will drift. This application uses a sliding window method to dynamically update the health baseline thresholds.

[0178] 1. When the generator is determined to be in a healthy state, add the current characteristic value to the sliding window (window size is 100).

[0179] 2. Calculate the mean μ and standard deviation σ of the features within the window in real time, and update the threshold to μ+3σ.

[0180] 3. When the operating conditions change by more than ±20%, clear the sliding window and re-collect data to establish a baseline.

[0181] Operating conditions include load, speed, and excitation current. The magnitude of the change in operating conditions is the weighted average of the rates of change of each parameter, with load rate having a weight of 0.5, speed a weight of 0.3, and excitation current a weight of 0.2. For example, when the generator load rate increases from 50% to 80%, while the speed and excitation current remain constant, the magnitude of the change in operating conditions is (80% - 50%) / 50%. 0.5 = 30%. If the percentage exceeds the 20% threshold, clear the sliding window and re-establish the baseline. This method effectively avoids misjudgments caused by fluctuations in operating conditions, with a system misjudgment rate of less than 0.3% under complex operating conditions.

[0182] S5. Fault Location. Based on the electrical isolation characteristics of multiple windings and the differences in fault characteristics of each winding, locate the number of faulty windings; based on the three-phase current residual characteristics of the faulty winding, locate the faulty phase.

[0183] 5.1 Location of the number of faulty windings

[0184] Since the fundamental magnetomotive forces of the four windings cancel each other out, the inter-winding fundamental mutual inductance is approximately zero. Therefore, the fault only significantly affects the winding where the short circuit occurred. Characteristic anomaly degree is defined as follows:

[0185]

[0186] In the formula, m=1,2,3,4 represents the number of windings. The third harmonic voltage amplitude at the neutral point of the m-th winding is given. This represents the amplitude of the second-harmonic frequency current along the d-axis of the m-th winding. The value is the amplitude of the third harmonic current of the m-th winding stator.

[0187] When a certain set of windings And the other three sets of windings If this occurs, the winding is determined to be a faulty winding.

[0188] In this embodiment, when the first Y1 winding experiences a severe fault with k=0.16, the calculation is as follows:

[0189] α1=2868.5, α2=0.82, α3=0.76, α4=0.85. Only the characteristic anomaly of the Y1 winding is much greater than 5, so the fault was accurately determined to occur in the first winding. The fault location accuracy rate is 100%.

[0190] 5.2 Fault Phase Location

[0191] Calculate the three-phase fundamental current residuals in the faulty winding:

[0192]

[0193] In the formula, , , This represents the fundamental current amplitude under healthy conditions. The phase with the largest residual amplitude is the faulty phase, and it satisfies the following conditions: .

[0194] In this embodiment, under healthy conditions, the fundamental current amplitudes of phases A1, B1, and C1 are all 100 A. Under fault conditions, the measured fundamental current amplitudes of the three phases are 177.30 A, 138.70 A, and 137.90 A, respectively. The calculated values ​​are... =77.30 A, =38.70 A, =37.90 A, The phase A1 was accurately identified as the faulty phase, with a phase location accuracy rate of ≥98%.

[0195] 5.3 Handling simultaneous faults in multiple windings

[0196] When multiple windings experience inter-turn short-circuit faults simultaneously, the characteristic anomalies of all windings will exceed the standard at the same time. In this case, by performing phase-specific location analysis on each abnormal winding, the number and phase of all faulty windings can be determined. This diagnostic method can simultaneously diagnose independent faults in up to four windings, and the location accuracy is not affected by the number of faulty windings. Furthermore, a fault priority ranking is generated based on the short-circuit turns ratio (k) of each winding; a larger k value indicates a higher priority.

[0197] For example, simulating simultaneous faults in winding Y1 (k=0.12) and winding Y3 (k=0.05), the calculated values ​​are α1=2156.2 and α3=928.7, both greater than 5, indicating a simultaneous fault in both windings. The faulty phases are identified as phase B1 of winding Y1 and phase C3 of winding Y3. Based on the k values, the fault priority of winding Y1 is higher than that of winding Y3, providing guidance for tiered maintenance.

[0198] S6. Fault Severity Assessment. At least two quantitative calculation methods based on the electromagnetic model are used to calculate the short-circuit turns ratio and comprehensively assess the fault severity.

[0199] Based on the electromagnetic mechanism, three independent formulas for calculating the short-circuit turns ratio k were derived. The short-circuit turns ratio k was calculated using the above three methods, and the average of the three was taken as the final result to improve the accuracy of quantitative assessment.

[0200] 6.1 Three quantitative calculation methods

[0201] 1. Method 1: Discrimination method based on DC-side second harmonic voltage

[0202] The linear relationship between the DC-side second harmonic voltage amplitude $U_{dc2}$ and k:

[0203]

[0204] In the formula, This represents the fundamental amplitude of the stator phase electromotive force. This is the short-circuit transition resistance. Rearranging, we get:

[0205]

[0206] Applicable scenarios: Online monitoring, no additional sensors required on the stator side.

[0207] 2. Method Two: Judgment Method Based on d-Axis Second Harmony Current

[0208] The amplitude of the d-axis frequency harmonic current Relationship with k:

[0209]

[0210] In the formula, This represents the maximum mutual inductance between the stator and the excitation winding. For excitation current, For d-axis synchronous inductance, Let be the q-axis synchronous inductor. Simplifying, we get:

[0211]

[0212] Applicable scenarios: High-precision offline diagnostics with an error of less than 3%.

[0213] 3. Method 3: Discrimination method based on the third harmonic voltage of the neutral point

[0214] The amplitude of the third harmonic voltage at the neutral point Relationship with k:

[0215]

[0216] In the formula, Let be the amplitude of the fundamental wave of the fault circulation. After simplification, we get:

[0217]

[0218] Applicable scenarios: salient pole motors, with strong anti-interference capabilities.

[0219] 6.2 Fault Level Classification

[0220] Fault levels are classified according to the short-circuit turns ratio k value:

[0221] Minor fault: k < 0.03

[0222] Moderate fault: 0.03 ≤ k < 0.1

[0223] Critical fault: k≥0.1

[0224] In this embodiment, the fault with k=0.16 is quantitatively calculated:

[0225] Method 1:

[0226] Method 2:

[0227] Method 3: Iterative calculation:

[0228] Initially k=0.16, I f1 =483.4 A

[0229] First calculation:

[0230] Second iteration: ,

[0231] Third iteration: If the change is less than 0.001, stop the iteration.

[0232] The calculation results from the three methods were 0.1578, 0.1581, and 0.1585, respectively, with an average of 0.1581. The relative error compared to the actual set k=0.16 was 1.19%, far below the 5% error threshold required by the project. Based on the fault level classification standard, it was determined to be a severe fault, consistent with the actual fault severity. The average error of the quantitative assessment of the short-circuit turns ratio across the entire fault range was ≤1.5%.

[0233] Summary of Fault Diagnosis and Location Verification Results. Based on the above calculation and simulation results, the fault diagnosis and location verification results are summarized in Table 4.

[0234] Table 4. Verification Results of Fault Diagnosis and Location under Severe Fault Conditions (k=0.16)

[0235]

[0236]

[0237] refer to Figure 17 An embodiment of this application also provides a stator winding inter-turn short circuit fault diagnosis system for a salient-pole multiphase rectifier generator, which may specifically include the following modules.

[0238] 1. Signal Acquisition Module: Includes 12 current sensors, 4 voltage sensors, 2 DC-side electrical quantity sensors and 1 rotor position encoder. The sampling frequency is 10 kHz. It is used to synchronously acquire the three-phase stator current of the four sets of Y windings, the neutral point voltage of each set of windings, the total DC-side voltage and current, and the rotor position angle signal, and transmit the acquired signals to the data preprocessing module.

[0239] 2. Data preprocessing module: It has a built-in Butterworth low-pass filter and synchronization alignment unit to filter, denoise and synchronize the received signal to obtain the preprocessed electrical signal.

[0240] 3. Fault Modeling and Feature Extraction Module: Built-in electromagnetic model and feature extraction algorithm verified by MATLAB / Simulink and ANSYS Maxwell. Based on the second harmonic distribution characteristics of the air gap magnetic permeability of the salient pole motor, an electromagnetic model of inter-turn short circuit fault is established, the analytical expression of fault components is derived, and 12 multi-dimensional fault feature sets are extracted from the preprocessed electrical signal according to the analytical expression of fault components.

[0241] 4. Fault Judgment Module: Stores health baseline thresholds, executes 3σ principle dynamic threshold update logic and multi-core feature joint judgment logic, compares the feature values ​​in the multi-dimensional fault feature set with the preset fault judgment threshold, and outputs a fault presence signal to the fault location module and severity judgment module when the preset judgment conditions are met.

[0242] 5. Fault Location Module: Executes fault set number location and phase location algorithms, locates the number of faulty winding sets based on the electrical isolation characteristics of multiple winding sets and the differences in fault characteristics of each winding set, locates the faulty phase based on the three-phase current residual characteristics of the faulty winding set, and outputs fault location information.

[0243] 6. Severity determination module: It has three built-in quantitative calculation methods based on electromagnetic mechanism to calculate the short-circuit turns ratio k and classify the fault level, comprehensively evaluate the severity of the fault and output the fault level information.

[0244] 7. Early warning output module: Displays the fault location, k-value, and fault level through the human-machine interface. Triggers audible and visual alarms in case of severe faults and uploads fault information to the remote operation and maintenance platform to generate corresponding early warning prompts and operation and maintenance suggestions.

[0245] This diagnostic system achieves automated, real-time, and end-to-end diagnosis of inter-turn short-circuit faults in the stator windings of salient-pole multiphase rectifier generators. Data exchange between modules is smooth, with strong logical coherence, and the diagnostic accuracy is consistent with the aforementioned diagnostic methods. No modifications to the generator itself are required; all functions can be performed using existing sensors. It is suitable for high-power DC power supply systems in fields such as marine electric propulsion, aerospace, and industrial DC power supply, significantly reducing maintenance costs and improving system safety and reliability.

[0246] The embodiments of the aforementioned diagnostic methods and the embodiments of the aforementioned diagnostic systems are identical or related in technical concept, and can be referenced and learned from each other in terms of technical details and technical effects, which will not be repeated here.

[0247] Overall, the advantages of this application compared to the prior art include:

[0248] 1. Existing technologies for inter-turn short-circuit diagnosis of salient-pole twelve-phase rectifier generators generally directly adopt the feature extraction framework of non-salient-pole motors, focusing only on fault features common to both types of motors, such as the second harmonic, completely ignoring the third and fourth harmonic components generated by the salient-pole effect. This results in a single dimension of fault features and missing information, making it easy to miss even very minor faults. This application establishes a complete electromagnetic model of inter-turn short-circuit faults based on the second harmonic distribution characteristics of the air gap magnetic permeability of salient-pole motors. It systematically derives the analytical expressions of fault components for various electrical quantities on the stator side, neutral point, and DC side, and extracts a multi-dimensional fault feature set of 12 items, including salient-pole-specific components such as the third harmonic and fourth harmonic components. This comprehensively covers all detectable electrical features generated by the fault, fundamentally solving the problem of missed detection caused by missing features.

[0249] 2. Existing technologies, due to incomplete feature extraction and unreasonable threshold settings, typically only achieve effective detection when the short-circuit turns ratio k ≥ 0.05. Their ability to detect early insulation degradation faults (k < 0.05) is severely insufficient, failing to provide early warnings at the fault's nascent stage and easily leading to rapid fault deterioration. This application, through multi-dimensional feature fusion and dynamic threshold setting based on the 3σ principle, combined with a joint judgment logic of three core features—neutral point third harmonic voltage, d-axis second harmonic current, and DC-side second harmonic voltage—achieves effective detection even in the extremely minor fault stage (short-circuit turns ratio k = 0.01), with a false negative rate of less than 0.1%. This enables timely detection of potential problems in the early stages of insulation degradation, preventing the fault from further developing into phase-to-phase or ground faults.

[0250] 3. Existing technologies do not fully utilize the electrical isolation characteristics of the four Y-windings of a salient-pole twelve-phase rectifier generator, which exhibit mutual cancellation of fundamental magnetomotive forces and near-zero inter-winding fundamental mutual inductance. This makes it impossible to accurately distinguish the number of windings experiencing faults in parallel systems with multiple windings. Phase identification also relies heavily on empirical thresholds, resulting in an accuracy rate generally below 90%. This application defines the characteristic anomaly degree of each winding and achieves 100% accurate fault identification by comparing the standardized characteristic deviation values ​​of the four windings. Simultaneously, based on the unique three-phase current residual distribution pattern of inter-turn short-circuit faults, it calculates the three-phase fundamental current residuals to achieve fault phase identification with an accuracy rate ≥98% and no cross-judgment errors.

[0251] 4. Existing technologies mostly rely on empirical formulas or purely data-driven methods to calculate the short-circuit turns ratio, lacking rigorous electromagnetic mechanism support and exhibiting poor parameter adaptability. In minor fault stages where the short-circuit turns ratio k < 0.05, the error exceeds 20%, failing to provide reliable quantitative basis for operation and maintenance decisions. This application derives three independent quantitative calculation methods based on the fault electromagnetic model: DC-side second harmonic voltage, d-axis second harmonic current, and neutral point third harmonic voltage. These methods are cross-validated from different electrical dimensions, and the average of the three is taken as the final result. The average error of the quantitative assessment across the entire fault range is ≤ 1.5%, far superior to existing technologies, providing accurate quantitative support for tiered operation and maintenance.

[0252] 5. Existing fault judgment thresholds are mostly pre-set fixed values, which cannot adapt to the health benchmark drift caused by changes in generator load, speed, excitation current, and other operating conditions. Under complex operating conditions, the misjudgment rate exceeds 5%, seriously affecting the long-term operational stability of the diagnostic system. This application adopts a sliding window method to dynamically update the health benchmark threshold. When the generator is determined to be in a healthy state, the mean and standard deviation of the features within the window are updated in real time. When the change in operating conditions exceeds ±20%, the window is automatically cleared and data is re-acquired to establish a benchmark. At the same time, the extracted features such as the neutral point third harmonic and the DC side third harmonic are not affected by load fluctuations and excitation current changes. Under complex operating conditions, the system's misjudgment rate is less than 0.3%.

[0253] 6. Existing diagnostic methods require the installation of additional flux linkage, temperature, or vibration sensors on the generator body, which not only increases equipment modification costs and system complexity but may also damage the original generator structure, affecting its operational reliability. Furthermore, these methods are only applicable to generators with specific pole numbers and rectifier topologies, lacking versatility. This application requires no modification to the generator body and utilizes only electrical signals collected by existing conventional sensors to achieve full-function diagnostics including fault detection, location, and severity assessment. It is applicable to salient-pole multiphase synchronous generators with various pole numbers (4, 6, 8, etc.) and different topologies (four parallel, four series, etc.) and can be directly applied to multiple fields such as marine electric propulsion, aerospace, and industrial DC power supply.

[0254] 7. Most existing technologies can only handle inter-turn short circuits in a single winding. When multiple windings fail simultaneously, they cannot accurately distinguish the number of faulty windings or phases, nor can they provide priority guidance for maintenance. This application independently determines the characteristic anomaly degree of each winding, enabling simultaneous diagnosis of independent faults in up to four windings. The location accuracy is unaffected by the number of faulty windings. Furthermore, it automatically generates a fault priority ranking based on the short-circuit turns ratio (k) of each winding; a larger k value indicates higher priority, guiding maintenance personnel to prioritize the handling of severe faults and improving fault handling efficiency.

[0255] Based on the same inventive concept, embodiments of this application also provide an electronic device. Figure 18 This is a structural block diagram of an electronic device provided in an embodiment of this application. Figure 18 As shown in the embodiments of this application, an electronic device includes: one or more processors 101, a memory 102, and one or more I / O interfaces 103. The memory 102 stores one or more programs, which, when executed by the one or more processors, cause the one or more processors to implement any of the diagnostic methods described in the above embodiments; the one or more I / O interfaces 103 are connected between the processor and the memory, configured to enable information interaction between the processor and the memory.

[0256] The processor 101 is a device with data processing capabilities, including but not limited to a central processing unit (CPU); the memory 102 is a device with data storage capabilities, including but not limited to random access memory (RAM, more specifically SDRAM, DDR, etc.), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), and flash memory (FLASH); the I / O interface (read / write interface) 103 is connected between the processor 101 and the memory 102, and can realize information interaction between the processor 101 and the memory 102, including but not limited to a data bus (Bus).

[0257] In some embodiments, the processor 101, memory 102, and I / O interface 103 are interconnected via bus 104, and thus connected to other components of the computing device.

[0258] In some embodiments, the one or more processors 101 include a field-programmable gate array.

[0259] This application also provides a computer-readable medium. The computer-readable medium stores a computer program, which, when executed by a processor, implements the steps of any of the diagnostic methods described in the above embodiments. The computer-readable storage medium may be volatile or non-volatile.

[0260] This application also provides a computer program product, including computer-readable code, or a non-volatile computer-readable storage medium carrying computer-readable code, wherein when the computer-readable code is run in the processor of an electronic device, the processor in the electronic device performs the above-described diagnostic method.

[0261] Those skilled in the art will understand that all or some of the steps, systems, and apparatuses disclosed above, and their functional modules / units, can be implemented as software, firmware, hardware, or suitable combinations thereof. In hardware implementations, the division between functional modules / units mentioned above does not necessarily correspond to the division of physical components; for example, a physical component may have multiple functions, or a function or step may be performed collaboratively by several physical components. Some or all physical components may be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit (ASIC). Such software can be distributed on a computer-readable storage medium, which may include computer storage media (or non-transitory media) and communication media (or transient media).

[0262] As is known to those skilled in the art, the term computer storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information, such as computer-readable program instructions, data structures, program modules, or other data. Computer storage media includes, but is not limited to, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), static random access memory (SRAM), flash memory or other memory technologies, portable compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical disc storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible to a computer. Furthermore, it is known to those skilled in the art that communication media typically contain computer-readable program instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.

[0263] The computer-readable program instructions described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to the computer-readable storage media in the respective computing / processing device.

[0264] The computer program instructions used to perform the operations of this application may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, status setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The computer-readable program instructions may be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuits, such as programmable logic circuits, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), are personalized by utilizing the status information of the computer-readable program instructions. These electronic circuits can execute the computer-readable program instructions to implement various aspects of this application.

[0265] The computer program product described herein can be implemented specifically through hardware, software, or a combination thereof. In one alternative embodiment, the computer program product is specifically embodied in a computer storage medium; in another alternative embodiment, the computer program product is specifically embodied in a software product, such as a software development kit (SDK), etc.

[0266] Various aspects of this application are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-readable program instructions.

[0267] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that, when executed by the processor of the computer or other programmable data processing apparatus, they create means for implementing the functions / actions specified in one or more blocks of the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium that causes a computer, programmable data processing apparatus, and / or other device to operate in a particular manner; thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing aspects of the functions / actions specified in one or more blocks of the flowchart and / or block diagram.

[0268] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to perform the functions / actions specified in one or more boxes of a flowchart and / or block diagram.

[0269] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of an instruction containing one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0270] Exemplary embodiments have been disclosed in this application, and while specific terminology has been used, it is used only and should be interpreted in a general illustrative sense and is not intended to be limiting. In some embodiments, it will be apparent to those skilled in the art that features, characteristics, and / or elements described in conjunction with particular embodiments may be used alone, or in combination with features, characteristics, and / or elements described in conjunction with other embodiments, unless otherwise expressly indicated. Therefore, those skilled in the art will understand that various changes in form and detail may be made without departing from the scope of this application as set forth by the appended claims.

Claims

1. A method for diagnosing inter-turn short-circuit faults in the stator winding of a salient-pole multiphase rectifier generator, characterized in that, include: S1. Synchronously acquire the three-phase current of multiple star-connected stator windings of the generator, the neutral point voltage of each winding, the DC side electrical quantities and the rotor position angle signal; S2. Based on the second harmonic distribution characteristics of the air gap magnetic permeability of the salient pole motor, an electromagnetic model of the inter-turn short circuit fault of the stator winding is established, and the analytical expressions of the fault components of each electrical quantity on the stator side, neutral point and DC side are derived. S3. Based on the fault component analytical expression, extract a multi-dimensional fault feature set containing harmonic components unique to salient pole motors; S4. Compare the feature values ​​in the multi-dimensional fault feature set with the preset fault judgment threshold. When the preset judgment condition is met, it is determined that there is a short circuit fault between turns of the stator winding. S5. Based on the electrical isolation characteristics of multiple windings and the differences in fault characteristics of each winding, locate the number of faulty windings. Based on the three-phase current residual characteristics of the faulty winding, the faulty phase is located; S6. Use at least two quantitative calculation methods derived from the electromagnetic model to calculate the short-circuit turns ratio and comprehensively assess the severity of the fault.

2. The diagnostic method according to claim 1, characterized in that, The multi-dimensional fault feature set mentioned in step S3 includes three categories: stator side current characteristics, neutral point voltage characteristics, and DC side electrical quantity characteristics; The specific harmonic components of the salient pole motor include the third harmonic component of the stator current, the fourth harmonic component of the dq axis current, the third harmonic component of the neutral point voltage, and the third harmonic component of the DC side voltage.

3. The diagnostic method according to claim 1, characterized in that, The preset fault determination threshold mentioned in step S4 is determined using the 3σ principle, specifically including: N sets of feature data are continuously collected under the generator's healthy state. The mean μ and standard deviation σ of each set of features are calculated, and the threshold is set to μ+3σ. The preset judgment condition is that any two or more core features exceed the corresponding threshold at the same time. The core features include at least the neutral point third harmonic voltage, the d-axis second harmonic current, and the DC side second harmonic voltage.

4. The diagnostic method according to claim 1, characterized in that, The specific method for locating the number of faulty windings in step S5 includes: Define the characteristic anomaly α for each winding. m The characteristic anomaly degree α m This is the sum of the standardized deviations of multiple core characteristics of the winding; when the characteristic anomaly α of a certain winding... m The anomaly degree α of the remaining windings is greater than the preset anomaly degree threshold. m If all values ​​are less than the preset anomaly threshold, the winding is determined to be a faulty winding.

5. The diagnostic method according to claim 1, characterized in that, The specific methods for locating the faulty phase in step S5 include: Calculate the fundamental residual ΔI of the three-phase current in the located faulty winding. Am ΔI Bm ΔI Cm The fundamental residual is the absolute value of the difference between the amplitude of the three-phase fundamental current under fault conditions and the amplitude of the corresponding phase fundamental current under healthy conditions. The phase with the largest fundamental residual amplitude is determined to be the fault phase, and ΔI is satisfied. Am ≈∣ΔI Bm +ΔI Cm | 6. The diagnostic method according to claim 1, characterized in that, The quantitative calculation methods described in step S6 include a calculation method based on the DC side second harmonic voltage, a calculation method based on the d-axis second harmonic current, and a calculation method based on the neutral point third harmonic voltage. The short-circuit turns ratio is calculated using the three methods described above, and the average of the three is taken as the final short-circuit turns ratio. The fault level is then classified based on the final short-circuit turns ratio.

7. The diagnostic method according to claim 4, characterized in that, When there are two or more sets of windings, the characteristic anomaly α m When all values ​​exceed the preset anomaly threshold, it is determined that multiple windings have experienced inter-turn short-circuit faults simultaneously. For each winding with anomaly exceeding the threshold, a phase-specific location operation is performed, and a fault priority ranking is generated based on the short-circuit turn ratio of each winding. The higher the short-circuit turn ratio, the higher the priority.

8. The diagnostic method according to claim 3, characterized in that, It also includes a dynamic threshold adjustment step, specifically including: When the generator is determined to be in a healthy state, the current feature value is added to a sliding window of a preset size, and the mean and standard deviation of the features within the window are calculated in real time to update the fault determination threshold. When the change in generator operating conditions exceeds a preset threshold, the sliding window is cleared and health data is re-collected to establish a baseline. The operating conditions include load, speed, and excitation current, and the change in operating conditions is the weighted average of the change rates of each operating condition parameter.

9. A system for diagnosing inter-turn short-circuit faults in the stator winding of a salient-pole multiphase rectifier generator, characterized in that, include: The signal acquisition module is used to synchronously acquire the three-phase current of multiple star-connected stator windings of the generator, the neutral point voltage of each winding, the DC side electrical quantities and the rotor position angle signal, and transmit the acquired signals to the data preprocessing module. The data preprocessing module is used to filter, denoise, and synchronize the received signal to obtain the preprocessed electrical signal. The fault modeling and feature extraction module is used to establish an electromagnetic model of inter-turn short-circuit fault based on the second harmonic distribution characteristics of the air gap magnetic permeability of a salient pole motor, derive the analytical expression of the fault components, and extract a multi-dimensional fault feature set from the preprocessed electrical signal according to the analytical expression of the fault components. The fault determination module is used to compare the feature values ​​in the multi-dimensional fault feature set with the preset fault determination threshold. When the preset determination conditions are met, the module outputs a fault presence signal to the fault location module and the severity determination module. The fault location module is used to locate the number of faulty windings based on the electrical isolation characteristics of multiple windings and the differences in fault characteristics of each winding, locate the faulty phase based on the three-phase current residual characteristics of the faulty winding, and output the fault location information. The severity determination module is used to calculate the short-circuit turns ratio using at least two quantitative calculation methods derived from the electromagnetic model, comprehensively assess the severity of the fault, and output fault level information. The early warning output module is used to generate corresponding early warning prompts and maintenance suggestions based on the fault presence signal, fault location information, and fault level information.

10. A computer-readable medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it can perform the steps of the diagnostic method as described in any one of claims 1 to 8.

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