Permanent magnet generator short circuit fault monitoring and diagnosis method based on diagnosis winding

Abstract

The invention relates to the technical field of generator operation and maintenance, in particular to a permanent magnet generator short circuit fault monitoring and diagnosis method based on a diagnosis winding, which comprises the following steps: acquiring no-load back electromotive force signals in a normal operation state and various typical fault states of a permanent magnet generator, and constructing a normal feature data set and a fault feature data set; acquiring a no-load back electromotive force signal of the permanent magnet generator in the current operation state; comparing the obtained current no-load back electromotive force signal with the normal characteristic data set, and judging the current working state of the permanent magnet generator; if it is judged that the current working state of the permanent magnet generator is in the fault state, the obtained current no-load back electromotive force signal is compared with the fault feature data set. In combination with a data processing algorithm, the diagnosis winding can continuously monitor the operation state of the permanent magnet generator, compares the current operation parameters with the preset reference experiment data, accurately evaluates the health state of the winding, and monitors and diagnoses faults such as turn-to-turn short circuit in real time.

Description

Technical Field

[0001] This application relates to the technical field of generator operation and maintenance, and in particular to a method for monitoring and diagnosing short - circuit faults of a permanent - magnet generator based on a diagnostic winding. Background Art

[0002] Affected by complex working conditions such as overload and impact and harsh working environments, the performance of some structures and components of a permanent - magnet generator will gradually deteriorate, resulting in frequent occurrence of permanent - magnet generator faults. When the response to the faults generated by the permanent - magnet generator is not timely, it will cause huge losses in material and human resources. Therefore, a method capable of real - time monitoring and diagnosing the operating state of a permanent - magnet generator is needed. Summary of the Invention

[0003] Aiming at the deficiencies of the existing technology, this application provides a method for monitoring and diagnosing short - circuit faults of a permanent - magnet generator based on a diagnostic winding. Combining with data - processing algorithms, the diagnostic winding can continuously monitor the operating state of the permanent - magnet generator, compare the current operating parameters with the preset reference experimental data, accurately evaluate the health state of the winding, and real - time monitor and diagnose faults such as turn - to - turn short - circuit.

[0004] The above application objectives of this application are achieved through the following technical solutions: A method for monitoring and diagnosing short - circuit faults of a permanent - magnet generator based on a diagnostic winding, comprising the following steps: Obtain the no - load back - electromotive - force signals of the permanent - magnet generator in the normal operating state and various typical fault states, and construct a normal feature data set and a fault feature data set; Obtain the no - load back - electromotive - force signal of the permanent - magnet generator in the current operating state; Compare the obtained current no - load back - electromotive - force signal with the normal feature data set to judge the current working state of the permanent - magnet generator; If it is determined that the current working state of the permanent - magnet generator is in a fault state, compare the obtained current no - load back - electromotive - force signal with the fault feature data set; Diagnose the fault information of the permanent - magnet generator according to the comparison result between the current no - load back - electromotive - force signal and the fault feature data set, and generate a diagnosis result.

[0005] In summary, this application has the following beneficial technical effects: In the embodiment of the present application, by monitoring the change of the no-load back electromotive force of the winding in real time to judge the change of the internal magnetic field of the motor, and thus judge the operating condition of the generator. When a permanent magnet generator fails, the current back electromotive force characteristic signal of the permanent magnet generator is matched with the fault characteristic data set to obtain the current fault information of the permanent magnet generator in real time, and a real-time diagnostic report is obtained, providing a reliable basis for subsequent maintenance and repair. It realizes the real-time monitoring and acquisition of the working state of the permanent magnet generator, and can output a diagnostic report in real time, significantly improving the comprehensiveness of the monitoring of the working state of the permanent magnet generator, as well as the sensitivity and convenience of the monitoring and judgment of the faults of the permanent magnet generator.

[0006] In the embodiment of the present application, an independent diagnostic winding is used to monitor the no-load back electromotive force of the permanent magnet generator in real time. By directly collecting the electrical parameters inside the motor through the diagnostic winding, the fault characteristics can be extracted more directly and simply without the need to additionally increase observers or sensing devices. The diagnostic winding can rely on the characteristics of the motor body for self-perception and detection, thus greatly simplifying the hardware deployment of fault monitoring and reducing the complexity and cost of the system.

[0007] In the embodiment of the present application, the harmonic frequency components after the Fourier decomposition of the no-load back electromotive force under normal working conditions and various different fault conditions are stored as reference data, and the current harmonic content and harmonic amplitude change measured by the diagnostic winding are compared in real time, so as to quickly and intuitively judge whether there is a turn-to-turn short circuit fault in the winding. Through this frequency-domain analysis method, the occurrence of motor faults can be effectively determined in the shortest time, ensuring the safety and reliability of system operation.

[0008] On the basis of real-time monitoring, the embodiment of the present application further combines fault diagnosis technology. Relying on a pre-established fault database and feature extraction algorithm, it can deeply analyze the occurrence time, severity, fault location and fault type of the fault. By comparing the harmonic characteristics of the no-load back electromotive force with the standard model in the fault database, not only the existence of the fault can be identified, but also the nature of the fault can be accurately diagnosed, generating a detailed diagnostic report to help maintenance personnel quickly locate problems, evaluate the development trend of faults, and formulate maintenance strategies. Description of the Drawings

[0009] Figure 1 is a schematic flow chart of an embodiment of the present application; Figure 2 is a schematic structural diagram of a diagnostic winding according to an embodiment of the present application; Figure 3 is a schematic diagram of the no-load back electromotive force waveforms of diagnostic winding 1 and diagnostic winding 2 during normal operation of the generator according to an embodiment of the present application; Figure 4 is a schematic diagram of the characteristic harmonic content at different fault times of the generator according to an embodiment of the present application; Figure 5 It is a schematic diagram of the characteristic harmonic content at different fault positions of a generator according to an embodiment of the present application; Figure 6 It is a schematic diagram of the characteristic harmonic content at different fault degrees of a generator according to an embodiment of the present application. Specific Embodiments

[0010] The following will further elaborate on the present application in conjunction with the attached Figure 1 - attached Figure 6 drawings for a more detailed description.

[0011] To more clearly understand the technical solutions demonstrated in the embodiments of the present application, first, a brief introduction to permanent magnet generators and their fault causes will be provided.

[0012] Permanent magnet generators, with their advantages such as small volume, low noise, fast dynamic response, high power density, and high transmission system efficiency, are widely used in fields such as aerospace, medical equipment, military, wind power generation systems, and electric vehicle power units, effectively providing stable and reliable power support for various power consumption scenarios. As an emergency high-quality power source for large equipment, the reliability and fault-tolerant operation of permanent magnet generators are crucial for power generation systems. However, affected by complex working conditions such as overload and impact, as well as harsh working environments, the performance of some structures and components will gradually deteriorate, resulting in frequent failures of permanent magnet generators, causing huge losses in material and human resources. Therefore, studying the fault diagnosis of permanent magnet generators is of great theoretical research value and engineering application value for effectively improving the safety and reliability of permanent magnet generators and their drive systems, reducing maintenance time, and improving the system's utilization efficiency.

[0013] The magnetic flux generated by the permanent magnet intersects with the short-circuited turns and generates an overcurrent far greater than the rated current in the short-circuited turns. This overcurrent will cause local overheating of the motor, leading to a sharp deterioration of the insulation of the short-circuited turns and their adjacent conductors. The fault will quickly spread to other turns of the coil and may extend to other phases, thus causing more serious faults such as short-circuits between coils, short-circuits between phases, and short-circuits between phase and ground. Inter-turn short-circuits in the stator winding may also cause irreversible demagnetization of the permanent magnet, having an irreparable impact on the motor in a short period of time. For the built-in inter-turn short-circuit fault diagnosis method based on the positive-sequence third harmonic of the back electromotive force, the diagnostic accuracy is low at low speeds and cannot be used in the low-speed region. In the form of an iterative observer, when the motor changes, it is necessary to recalculate the back electromotive force reference waveform and the inductance matrix.

[0014] There is still room for improvement in the real-time monitoring and diagnosis of short-circuit faults in permanent magnet generators for the above technical solutions, and the sensitivity and convenience of fault monitoring and diagnosis need to be urgently improved.

[0015] In view of the above technical problems, an embodiment of the present application provides a method for monitoring and diagnosing short - circuit faults of a permanent - magnet generator based on a diagnostic winding, including the following steps: S101. Obtain the no - load back - electromotive - force signals of the permanent - magnet generator in the normal operating state and various typical fault states, and construct a normal feature data set and a fault feature data set; S102. Obtain the no - load back - electromotive - force signal of the permanent - magnet generator in the current operating state; S103. Compare the obtained current no - load back - electromotive - force signal with the normal feature data set to judge the current working state of the permanent - magnet generator; S104. If it is determined that the current working state of the permanent - magnet generator is in a fault state, compare the obtained current no - load back - electromotive - force signal with the fault feature data set; S105. According to the comparison result between the current no - load back - electromotive - force signal and the fault feature data set, diagnose the fault information of the permanent - magnet generator and generate a diagnosis result.

[0016] The following is a further introduction in combination with a specific usage scenario.

[0017] First, execute step S101. Obtain the no - load back - electromotive - force signals of the permanent - magnet generator in the normal operating state and various typical short - circuit fault states through the diagnostic winding. Perform Fourier transform on the obtained no - load back - electromotive - force signals, extract their harmonic frequency components as reference data, construct a normal feature data set and a fault feature data set, and store them in the database. The reference data covers harmonic characteristics under different working conditions, speeds, loads, environmental temperatures, etc., providing a comparison reference for fault detection; Specifically, the normal feature data set includes the no - load back - electromotive - force spectrum data of the permanent - magnet generator in the normal operating state, including standard measurement data under various working conditions such as different load conditions, speed changes, and environmental temperature fluctuations, as well as the content of each order of harmonic frequency and its corresponding amplitude change based on Fourier transform.

[0018] The fault feature data set includes the no - load back - electromotive - force spectrum data under different fault operating states, the fault occurrence duration data under different fault operating states, the characteristic data of short - circuit faults occurring in windings at different positions, and the content of each order of harmonic frequency and its corresponding amplitude change based on Fourier transform; the no - load back - electromotive - force spectrum data includes standard measurement data under various working conditions such as different load conditions, speed changes, and environmental temperature fluctuations, and the different fault states include short - circuit fault types such as one - turn winding short - circuit, two - turn winding short - circuit, three - turn winding short - circuit, and four - turn winding short - circuit; Then execute step S102. The diagnostic winding monitors and obtains the no - load back - electromotive - force characteristic signal of the permanent - magnet generator in the current operating state in real time; In step S103, the currently obtained no-load back electromotive force characteristic signal is compared with the normal characteristic data set to determine the current working state of the permanent magnet generator. Specifically, to determine the current working state of the permanent magnet generator, first perform a Fourier transform on the currently obtained no-load back electromotive force characteristic signal to extract the harmonic frequency components. After extracting the harmonic frequency components, based on the harmonic frequency components of the currently obtained no-load back electromotive force signal, obtain the harmonic spectrum of the currently obtained no-load back electromotive force signal. Subsequently, compare the harmonic spectrum of the currently obtained no-load back electromotive force signal with the normal characteristic data set. If the currently obtained no-load back electromotive force signal fits the elements in the normal characteristic data set, it is determined that the permanent magnet generator is working normally. If the currently obtained no-load back electromotive force signal does not fit any of the elements in the normal characteristic data set, it is determined that the permanent magnet generator is in a short-circuit fault state. When it is determined that the permanent magnet generator is in a short-circuit fault state, execute step S104 and compare the currently obtained no-load back electromotive force signal with the fault characteristic data set. Step S105, according to the comparison result between the currently obtained no-load back electromotive force signal and the fault characteristic data set, diagnose the fault information of the permanent magnet generator and generate a diagnosis result. Specifically, the fault characteristic data set includes the no-load back electromotive force spectrum data under different fault operating states, the fault occurrence duration data under different fault operating states, the characteristic data of short-circuit faults occurring in windings at different positions, and the content of each order harmonic frequency and its corresponding amplitude change based on the Fourier transform. In essence, for the back electromotive force characteristic signal collected by the permanent magnet generator in each fault state, after Fourier transform, it corresponds to a certain fault information. The aforementioned fault information includes the type, degree, time, and position of the fault, that is, the fault information is the mapping of the back electromotive force characteristic signal of the permanent magnet generator in the fault state. When it is determined that the permanent magnet generator is in a short-circuit fault state, by comparing the back electromotive force characteristic signal of the permanent magnet generator at this time with the back electromotive force characteristic signal of the elements in the fault characteristic data set, and then by capturing the fault information of the matching elements, the fault diagnosis result of the permanent magnet generator under the current back electromotive force characteristic signal can be identified.

[0019] On this basis, it is also possible to identify whether there is a fault inside the winding of the permanent magnet generator according to the deviation of the harmonic components and harmonic amplitudes in the currently obtained no-load back electromotive force, and then match the frequency deviation in the no-load back electromotive force with the preset fault mode database to diagnose the type of the fault, the time node when the fault occurs, and the trend of fault propagation, and generate a diagnosis report. The diagnosis report includes the number of turns of the faulty winding, the duration of the fault occurrence, and the position where the fault occurs.

[0020] Generally speaking, in the embodiments of the present application, under normal operation and various different fault states, the no-load back electromotive force collected by the diagnostic winding is Fourier decomposed, and the obtained harmonic components and harmonic amplitudes are set as reference experimental data and stored in the database. The reference data covers harmonic characteristics under different working conditions, speeds, loads, ambient temperatures and other factors, providing a comparison reference for fault detection. The diagnostic winding continuously monitors the current no-load back electromotive force, analyzes the frequency components and harmonic contents after real-time Fourier decomposition, and determines whether the current harmonic content and harmonic amplitude are abnormal by dynamically comparing with the reference experimental data, so as to determine whether there is a turn-to-turn short circuit. When an abnormality is detected, the data processing unit combines the fault database to further analyze the type of the fault, the time node of occurrence, the severity of the fault, the fault location and the development trend. The diagnostic system outputs a detailed diagnostic report by comparing with the preset fault modes, including different fault types and degrees such as the number of shorted winding turns, the duration of the short circuit occurrence, and the occurrence location, providing a reliable basis for subsequent maintenance and repair.

[0021] In summary, the embodiments of the present application judge the change of the internal magnetic field of the motor by real-time monitoring the change of the no-load back electromotive force of the diagnostic winding, so as to judge the operating condition of the generator. When a permanent magnet generator fails, the current fault information of the permanent magnet generator is obtained in real time by matching the current back electromotive force characteristic signal of the permanent magnet generator with the fault characteristic data set, and a real-time diagnostic report is obtained, providing a reliable basis for subsequent maintenance and repair. It realizes the real-time monitoring and acquisition of the working state of the permanent magnet generator, and can output a diagnostic report in real time, significantly improving the comprehensiveness of the monitoring of the working state of the permanent magnet generator, as well as the sensitivity and convenience of the monitoring and judgment of the faults of the permanent magnet generator.

[0022] In the embodiments of the present application, the current working state of the permanent magnet generator is judged by collecting the back electromotive force generated during the operation of the permanent magnet generator, which is more direct and effective than traditional mechanical or thermal imaging detection methods, has higher detection accuracy and judgment sensitivity for faults, and the collection of the back electromotive force is not limited by the speed of the permanent magnet generator. Therefore, the technical solution proposed in the embodiments of the present application can cover most of the speed ranges of the permanent magnet generator.

[0023] In the embodiments of the present application, the current no-load electromotive force signal of the permanent magnet generator can also be obtained in real time, and the current no-load electromotive force signal is dynamically compared with the normal characteristic data set and the fault characteristic data set in real time, and the current working state of the generator is output in real time, so as to improve the comprehensiveness of the monitoring of the generator under the operating state.

[0024] In the embodiments of the present application, the processing of back electromotive force data and the subsequent comparison of data can be carried out by a data processing unit. The data processing unit performs Fourier decomposition on the no-load back electromotive force to obtain key harmonic characteristic quantities; the data processing unit transmits the data to a database and compares the real-time data with the preset data; and determines whether the motor has a fault. When it is determined that the motor has a fault, the monitored characteristic harmonics are matched with the preset data to accurately determine the fault type, fault location, and fault degree, so as to realize real-time monitoring and diagnosis of short-circuit faults and precise evaluation of the health status of the permanent magnet generator winding. The data processing unit can be one or more integrated circuits configured to implement the above methods. For example: one or more application specific integrated circuits (ASICs), or one or more digital signal processors (DSPs), or one or more field programmable gate arrays (FPGAs), or a combination of at least two of these integrated circuit forms. When the embodiments of the present application are carried out, the data can be stored in a readable storage medium. The aforementioned computer-readable storage medium includes: various media that can store data such as USB flash drives, mobile hard disks, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical discs.

[0025] In the embodiments of the present application, by arranging a diagnostic winding on the stator winding of the permanent magnet generator, the no-load back electromotive force during the operation of the permanent magnet generator is collected and monitored in real time through the diagnostic winding, and the no-load back electromotive force signals in the normal operation state and various typical fault states of the permanent magnet generator are also collected through the diagnostic winding; The diagnostic winding is used to monitor the no-load back electromotive force, while the main winding is used for normal motor operation and power output. The diagnostic winding can be arranged in two phases on the stator winding of the permanent magnet generator, and the phase difference between the two-phase diagnostic windings is 120 electrical degrees. The diagnostic winding and the main winding are distributed in the same slot. Exemplarily, taking a two-pole and thirty-six-slot permanent magnet generator with a star-connected winding as an example, the motor structure is as Figure 2 shown.

[0026] The diagnostic winding adopts a two-phase structure and is evenly distributed on the stator core. Each phase of the diagnostic winding occupies three slot positions, and the two-phase windings are 120 degrees apart in electrical angle. It can overcome the limitations of single-phase diagnostic windings in detecting inter-turn short-circuit faults. An independent diagnostic winding is set in the stator and rotor windings of the permanent magnet generator, and the no-load back electromotive force in the air gap is monitored in real time by using this winding. Continuously obtain the electromotive force data measured by the diagnostic winding to ensure the accuracy and timeliness of data acquisition. Specifically, with the design of two-phase diagnostic windings, even in the case of a fault symmetric about the two-phase windings, the monitored harmonic frequency components are the same, but due to the electrical position difference of the two-phase windings, the measured harmonic amplitudes will show significant differences. This amplitude difference provides a reliable basis for accurately identifying and distinguishing inter-turn short-circuit fault types, thus significantly improving the accuracy and reliability of fault diagnosis.

[0027] The diagnostic winding has anti-interference ability. Even under harsh conditions such as high temperature, high humidity, strong vibration, and strong electromagnetic interference, it can still ensure the reliability of data and the stability of monitoring. The diagnostic winding and the main winding share the same slot design, which does not affect the normal power generation function of the generator and does not require additional energy-consuming equipment. During the normal operation of the motor, the diagnostic winding maintains a low-power consumption and independent monitoring state, and can quickly respond and diagnose faults when necessary, thus significantly reducing energy waste and improving the overall efficiency of the system. The diagnostic winding can monitor the no-load back electromotive force in real time, directly reflect the change of the motor winding state, respond quickly, and has high sensitivity, which is suitable for the discovery of early faults.

[0028] The specific principle of real-time monitoring of the diagnostic winding can be achieved through the following methods: When a short-circuit fault occurs in the motor, the magnetomotive force generated by the short-circuit winding in the short-circuit slot is Fourier decomposed: In the formula: is the number of turns of the short-circuit coil, is leading by the phase angle, is the space angle, and are the effective values of the short-circuit coil current and the A-phase current.

[0029] Under normal working conditions, the armature magnetomotive force does not contain the third and third-order integer harmonics, and the content of higher harmonics is relatively small. Ignoring the higher harmonics, the armature magnetomotive force can be expressed as: In the formula: is the fundamental magnetomotive force amplitude, is the spatial angle.

[0030] When a fault occurs in the motor, the resultant magnetomotive force generated by the armature in the air gap can be expressed as: In the formula: is the magnetomotive force after the short-circuit fault occurs, is the magnetomotive force of the normal armature winding, is the magnetomotive force generated by the short-circuited winding.

[0031] Then under normal conditions, the air-gap magnetic flux can be expressed as: In the formula is the magnetomotive force of the normal armature winding, is the air-gap magnetic reluctance.

[0032] When a short-circuit fault occurs, the air-gap magnetic flux can be expressed as: When in the normal condition, the no-load back electromotive force induced in the diagnostic winding is: When in the fault condition, the no-load back electromotive force induced in the diagnostic winding is: In the formula: is the frequency of the motor input excitation, is the number of turns of the winding.

[0033] In summary, the no-load back electromotive force induced in the diagnostic winding is different when the motor is running normally and when a short-circuit fault occurs, and its harmonic content and components are different. Therefore, by performing Fourier decomposition on the no-load back electromotive force data collected through real-time monitoring of the diagnostic winding and comparing the harmonic content, the effect of fault diagnosis can be achieved.

[0034] Exemplarily, during actual use, when the motor is running, the no-load back electromotive force waveforms are collected through diagnostic winding 1 and diagnostic winding 2 as Figure 3 shown, Figure 4 The figure shows the characteristic harmonic content of the generator at different fault times, including the characteristic harmonics of the no-load back electromotive force monitored by the two-phase diagnostic windings at different fault occurrence times such as normal, 1 ms, 3 ms, 50 ms, etc. It can be seen from the figure that the characteristic harmonics of the no-load back electromotive force change significantly as the fault occurrence time increases. Using these harmonic contents and amplitudes as data in the database can effectively diagnose whether a fault has occurred and the fault time.

[0035] Such as Figure 5, as shown in the figure are the characteristic harmonic contents at different fault positions of the generator, including the no-load back electromotive force characteristic harmonics monitored by the two-phase diagnostic windings when faults occur in different slots such as normal, 1 slot, 2 slots, 3 slots, etc. It can be seen from the figure that there are obvious differences in the characteristic harmonic contents and amplitudes monitored by the diagnostic windings when faults occur in different slots, and the monitoring results between the two-phase diagnostic windings are different, which is beneficial to distinguish the fault situations symmetrical about the diagnostic windings and can better diagnose the fault positions. Taking these harmonic contents and amplitudes as the data in the database can effectively diagnose whether a fault occurs and the fault position.

[0036] Such as Figure 6 , as shown in the figure are the characteristic harmonic contents at different fault degrees of the generator, including different fault situations such as normal, 2-turn short circuit, 3-turn short circuit, 4-turn short circuit, etc. It can be seen from the figure that there are obvious changes in the characteristic harmonic contents and amplitudes in different fault situations, which can be used to effectively distinguish the fault degrees. Taking these harmonic contents and amplitudes as the data in the database can effectively diagnose whether a fault occurs and the fault degree. The fault monitoring and diagnosis technology based on the diagnostic winding is sensitive and rapid to faults, and can quickly diagnose whether a fault occurs, as well as the fault time, position and degree, effectively reducing the losses and hazards caused by the faults.

[0037] The embodiments of the specific implementation manners are all preferred embodiments of the present application, and do not limit the protection scope of the present application accordingly. Therefore, all equivalent changes made according to the structure, shape and principle of the present application shall be covered within the protection scope of the present application.

Claims

1. A short-circuit fault monitoring and diagnosis method for a permanent magnet generator based on a diagnostic winding, characterized in that: include: Obtain the no-load back-EMF signal of the permanent magnet generator under normal operating conditions and various typical fault conditions, and construct a normal feature data set and a fault feature data set; Obtain the no-load back electromotive force signal of the permanent magnet generator in the current operating state; Compare the acquired current no-load back electromotive force signal with the normal characteristic data set to determine the current working state of the permanent magnet generator; If it is determined that the current working state of the permanent magnet generator is in a fault state, the obtained current no-load back electromotive force signal is compared with the fault characteristic data set; According to the comparison result of the current no-load back electromotive force signal and the fault characteristic data set, the fault information of the permanent magnet generator is diagnosed and the diagnosis result is generated.

2. A permanent magnet generator short circuit fault monitoring and diagnosis method based on diagnostic winding according to claim 1, characterized in that: The step of obtaining the no-load back electromotive force signal of the permanent magnet generator comprises: A two-phase diagnostic winding is arranged on the stator winding of the permanent magnet generator, the phase difference between the two-phase diagnostic winding is 120 degrees, and the diagnostic winding and the main winding are distributed in the same slot; The no-load back electromotive force of the permanent magnet generator during operation is collected and monitored in real time through the diagnostic winding.

3. A permanent magnet generator short circuit fault monitoring and diagnosis method based on diagnostic winding according to claim 2, characterized in that: The constructing of a normal feature data set and a fault feature data set comprises: Perform Fourier transform on the acquired no-load back electromotive force signal to extract its harmonic frequency components; The harmonic frequency components of the permanent magnet generator in normal and fault states are used as benchmark data to construct normal feature data sets and fault feature data sets.

4. A permanent magnet generator short circuit fault monitoring and diagnosis method based on diagnostic winding according to claim 3, characterized in that: The normal feature data set includes: No-load back-EMF spectrum data of permanent magnet generator under normal operating conditions, including standard measurement data under different load conditions, speed changes, and ambient temperature fluctuations; The frequency content of each order of harmonics and its corresponding amplitude changes based on Fourier transform.

5. A permanent magnet generator short circuit fault monitoring and diagnosis method based on diagnostic winding according to claim 4, characterized in that: The fault feature data set includes: No-load back-EMF spectrum data under different fault operating states, the no-load back-EMF spectrum data including standard measurement data under different load conditions, speed changes, and ambient temperature fluctuations, the different fault states including one-turn winding short circuit, two-turn winding short circuit, three-turn winding short circuit, and four-turn winding short circuit fault types; Fault occurrence duration data under different fault operating states; Characteristic data of short-circuit faults occurring in windings at different locations; The frequency content of each order of harmonics and its corresponding amplitude changes based on Fourier transform.

6. A permanent magnet generator short circuit fault monitoring and diagnosis method based on diagnostic winding according to claim 4, characterized in that: The determining of the current working state of the permanent magnet generator includes: Perform Fourier transform on the current no-load back-EMF signal to extract the harmonic frequency components; Based on the extracted harmonic frequency components of the current no-load back-electromotive force signal, a harmonic spectrum of the current no-load back-electromotive force signal is obtained; The harmonic spectrum of the current no-load back-EMF signal is compared with the normal characteristic data set. If the current no-load back-EMF signal fits the elements in the normal characteristic data set, it is determined that the permanent magnet generator is working normally. If the current no-load back-EMF signal does not fit any element in the normal feature data set, it is determined that the permanent magnet generator is in a fault state.

7. A method for monitoring and diagnosing short-circuit faults of a permanent magnet generator based on diagnostic windings according to claim 4, characterized in that: The diagnosing the fault information of the permanent magnet generator and generating a diagnostic report comprises: According to the deviation of harmonic components and harmonic amplitude in the current no-load back electromotive force, it is possible to identify whether there is a fault inside the permanent magnet generator winding; Based on the matching of the frequency deviation in the no-load back electromotive force with the preset fault mode database, the fault type, the time point of the fault occurrence and the trend of fault propagation are diagnosed, and a diagnostic report is generated.

8. A permanent magnet generator short circuit fault monitoring and diagnosis method based on diagnostic winding according to claim 7, characterized in that: The diagnostic report includes the number of faulty winding turns, the duration of the fault, and the location of the fault.

9. The method for monitoring and diagnosing short-circuit faults of a permanent magnet generator based on diagnostic windings according to claim 1 is characterized in that: Also includes: Real-time acquisition of the current no-load electromotive force signal of the permanent magnet generator; The current no-load electromotive force signal is dynamically compared with the normal characteristic data set and the fault characteristic data set in real time.

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