Fault test method, system and equipment suitable for fan converter

Abstract

The invention relates to the technical field of electrical sensor measurement, and provides a fault test method, system and equipment suitable for a fan converter, and the method comprises the steps: collecting current data in an IGBT module of the fan converter, and taking the current data as an original current signal; screening a plurality of dominant frequencies of the original current signal; decomposing the original current signal to obtain a plurality of component signals, and corresponding to each dominant frequency to obtain a plurality of dominant frequency signals; screening to obtain a reference signal; according to the variation trend difference between each component signal and the reference signal, the variation abnormity of each component signal is obtained; analyzing the energy difference between each component signal and the reference signal in the time-frequency energy matrix to obtain the energy difference index of each component signal; obtaining an anomaly index of each component signal; and performing fault detection on the fan converter based on the anomaly index of each component signal. The invention aims to solve the problem that high-frequency electromagnetic noise affects signal performance of a current sampling loop and covers real fault features.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electrical sensor measurement, in particular to a fault test method, system and device suitable for a fan converter. BACKGROUND

[0002] As a core component in wind power generation systems, fan converters are responsible for converting the AC power generated by wind turbines into DC power or synchronous AC power that meets grid standards. Their performance directly affects the efficiency and safety of the entire system. As wind power capacity continues to increase, the risk of converter failure also rises. Common faults include overload, short circuit, temperature abnormalities, and electrical interference. Existing methods combine real-time monitoring, data collection, and intelligent algorithms to monitor the working state of the converter in real time by establishing an operating model. The system collects data such as current, voltage, and temperature using sensors and uses data analysis techniques such as machine learning and signal processing to identify potential fault patterns in real time. In addition, fault test systems often have self-learning capabilities that can optimize fault diagnosis algorithms based on historical fault data to improve fault detection accuracy and response speed.

[0003] In existing technology, the detection of fan converter faults mainly analyzes the changes in the current signal to determine whether there are abnormalities in the operation of the fan converter by analyzing the state change pattern of the current. However, the current of the fan converter exhibits multiple modalities, and the high-frequency electromagnetic noise generated by the IGBT switching action of the converter is coupled into the current sampling loop, resulting in sharp spikes in the current waveform (amplitude can reach more than 20% of the normal value), which masks the true fault characteristics, causing the analysis of the current signal change pattern to be disturbed, and thus affecting the accuracy of the fan converter fault monitoring results. SUMMARY

[0004] The present application provides a fault test method, system and device suitable for a fan converter to solve the problem that existing high-frequency electromagnetic noise can affect the signal performance of the current sampling loop and mask the true fault characteristics. The technical solution adopted is as follows: The present application provides a fault test method suitable for a fan converter, which includes the following steps: Collecting current data in the IGBT module of the fan converter as the original current signal; Analyzing the amplitude of each frequency in the frequency spectrum of the original current signal to select several main frequencies of the original current signal; decomposing the original current signal to obtain several component signals, and corresponding to each main frequency to obtain several main frequency signals; analyzing the difference between each main frequency signal and the original current signal to select a reference signal; Based on the difference in the variation trend between each component signal and the reference signal, the variation anomaly of each component signal is obtained; the energy difference between each component signal and the reference signal in the time-frequency energy matrix is ​​analyzed to obtain the energy difference index of each component signal; and the anomaly index of each component signal is obtained by combining the variation anomaly of the component signals and the energy difference index. Fault detection of wind turbine converters is performed based on the anomaly index of each component signal.

[0005] Optionally, the specific method for analyzing the amplitude characteristics of each frequency in the spectrum of the original current signal and filtering several dominant frequencies of the original current signal includes: Based on the amplitude performance of each frequency in the spectrum of the original current signal and the amplitude difference between adjacent frequencies, the probability of the dominant frequency of each frequency is obtained. The frequencies in the spectrum of the original current signal are arranged in descending order of their dominant frequency probability to obtain a dominant frequency probability sequence. The dominant frequency probability sequence is then clustered by hierarchical clustering to obtain several clusters. The mean of the frequency probabilities of all frequencies in any cluster is obtained as the average frequency probability of that cluster; all frequencies in the cluster with the highest average frequency probability are taken as several frequencies of the original current signal.

[0006] Optionally, the specific method for obtaining the probability of the dominant frequency for each frequency is as follows:

[0007] in, Indicates the first The possible frequencies of the main frequency. This represents the number of frequencies in the spectrum of the original current signal. Indicates the first The amplitude of a frequency in the spectrum Indicates the first The amplitude of a frequency in the spectrum and They represent the first Passing the exam The amplitude of each frequency in the spectrum; Represents the absolute value function. This represents an exponential function with the natural constant as its base.

[0008] Optionally, the specific method for obtaining several main frequency signals includes: A bipartite graph is constructed by taking the frequencies corresponding to each component signal as the left nodes, taking each dominant frequency as the right node, taking the difference between the frequency of the left node and the dominant frequency of the right node as the edge between the left and right nodes, and performing KM matching on the bipartite graph, with the matching rule being that the shorter the edge value, the more matching. The matched frequency and the corresponding component signal of each dominant frequency are obtained, and the component signal corresponding to the matched frequency of each dominant frequency is taken as the dominant frequency signal of each dominant frequency.

[0009] Optionally, the analysis of the difference between each dominant frequency signal and the original current signal to obtain the reference signal includes the specific method that: For the first dominant frequency signal, the standard deviation of all amplitudes thereof is obtained as the amplitude standard deviation of the first dominant frequency signal, and the amplitude standard deviation of the original current signal is obtained; and the DTW distance between the first dominant frequency signal and the original current signal is obtained; the reference prominence of the first dominant frequency signal is calculated. The calculation method of the reference prominence of the first dominant frequency signal is:

[0010] Wherein, represents the dominant frequency possibility of the dominant frequency corresponding to the first dominant frequency signal, represents the DTW distance between the first dominant frequency signal and the original current signal L, represents the amplitude standard deviation of the first dominant frequency signal, represents the amplitude standard deviation of the original current signal L; represents the absolute value function, represents the exponential function with the natural constant as the base; The reference prominence of each dominant frequency signal is obtained, and the dominant frequency signal corresponding to the maximum reference prominence is taken as the reference signal.

[0011] Optionally, the method for obtaining the change abnormality of each component signal includes the specific method that: The reference signal is segmented into a plurality of data segments according to the window length, any data segment of the reference signal is linearly fitted by the least square method, and the fitting slope is obtained as the change slope of the data segment; the first component signal is segmented into a plurality of data segments according to the window length, and the change slope of each data segment is obtained; the change abnormality of the first component signal is calculated. The calculation method of the change abnormality of the first component signal is:

[0012] Wherein, This indicates the number of data segments in the reference signal and each component signal; Indicates reference signal The Middle The slope of the change in the data segment; Indicates the first of the component signals The slope of the change in the data segment; This represents the absolute value function.

[0013] Optionally, the specific method for obtaining the energy difference index of each component signal includes: By using short-time Fourier transform, the reference signal and the... The time-frequency energy matrix is ​​obtained from each component signal. Energy difference index of component signals The calculation method is as follows:

[0014] in, This indicates the number of time points corresponding to the reference signal and each component signal; Indicates reference signal The time-frequency energy matrix of the first Elements at each point in time; Indicates the first The time-frequency energy matrix of the reference signal of the first line Elements at each point in time; This represents the absolute value function.

[0015] Optionally, the specific method for obtaining the anomaly index of each component signal includes: Using the reference weight as the weight for anomalies and the difference weight as the weight for the energy difference index, the results are obtained by applying the first... The weighted sum of the variation anomalies and energy difference coefficients of the component signals is used as the result of the first weighted sum. Anomaly index of component signals.

[0016] This invention also proposes a fault testing system suitable for wind turbine converters, the system comprising: The current signal acquisition module is used to acquire current data from the IGBT module of the wind turbine converter and use it as the raw current signal. The current signal analysis module is used to analyze the amplitude performance of each frequency in the spectrum of the original current signal, and to filter out several main frequencies of the original current signal; to decompose the original current signal into several component signals, and to correspond them with each main frequency to obtain several main frequency signals; to analyze the differences between each main frequency signal and the original current signal, and to filter out reference signals. According to the difference between the change trend of each component signal and the reference signal, the change abnormality of each component signal is obtained; the energy difference index of each component signal is obtained by analyzing the energy difference between each component signal and the reference signal in the time-frequency energy matrix; and the abnormality index of each component signal is obtained by comprehensively considering the change abnormality and the energy difference index of the component signal. The converter fault detection module is used for detecting the fault of the fan converter based on the abnormality index of each component signal.

[0017] The application also provides a fault test device suitable for the fan converter, which comprises a memory, a processor and a computer program stored in the memory and running on the processor, and the processor implements the steps of the above method when executing the computer program.

[0018] The application has the following beneficial effects: the amplitude analysis in the spectrum of the original current signal is used to screen the main frequency in each frequency, and the component signals obtained by EMD decomposition are used to obtain a plurality of main frequency signals, so that the main frequency signals can reflect a plurality of change modes of the IGBT module, and the reference signal is screened and obtained based on the similar relationship between the overall change trend of the main frequency signal and the original current signal, the amplitude fluctuation and the possibility of the main frequency, so that the regular change trend of the original current signal can be reflected without being affected by high-frequency noise, and the abnormality analysis and comparison of the subsequent component signals can be provided; the local change trend of each component signal and the reference signal is compared, and the analysis between the time-frequency energy matrices is performed based on the reference signal, the local change trend can reflect the stability of the local change slope and the distribution rule of the extreme points, the micro waveform distortion caused by the IGBT fault can be accurately captured, and the energy abnormality concentration or loss in the time domain caused by the fault can be reflected by the time-frequency energy matrix, so that the abnormality index of the component signal can be quantitatively and comprehensively obtained; the current signal in the IGBT module of the fan converter is abnormally recognized, the influence of the high-frequency noise coupling current loop caused by the switching action is reduced, the waveform change caused by various faults in the current signal is analyzed, the fault monitoring of various current change modes is performed, and finally the accurate fault test of the fan converter is realized. BRIEF DESCRIPTION OF DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description only show some embodiments of the application, and for those skilled in the art, other drawings can also be obtained without creative labor.

[0020] Figure 1 The flowchart of the fault test method suitable for the fan converter is provided in an embodiment of the application. Figure 2 A structure block diagram of a fault test system suitable for a fan converter is provided for another embodiment of the present application. DETAILED DESCRIPTION

[0021] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0022] Please refer to Figure 1 which shows a flow chart of a fault test method suitable for a fan converter provided by an embodiment of the present application, and the method comprises the following steps: Step S001, collecting current data in an IGBT module of a fan converter as original current signals.

[0023] The purpose of the present embodiment is to identify abnormalities in the current by monitoring the current in the IGBT switch of the fan converter, so as to judge the fault of the fan converter, and therefore, the current data needs to be collected first.

[0024] Specifically, by means of a Hall effect closed-loop sensor, the present embodiment selects a closed-loop Hall current sensor (precision ±0.2%, bandwidth ≥200 kHz) to be directly connected into the output loop of the IGBT module, to measure the collector current (±2000A range) in real time, and to reduce heat loss through the primary perforation design; at the same time, to capture the IGBT switch transient (≤2μs), the response time of the sensor needs to be <100ns, and the magnetic shield shell needs to suppress 25 kHz switch electromagnetic interference (EMI), so as to collect the current data in the IGBT module in real time, and the sampling time interval is determined by the Hall current sensor, which will not be described herein again, and the current data constitutes the original current signals.

[0025] It should be noted that the fan converter is the core component of the wind power generation system, mainly bearing the key functions of power conversion and grid-connected control. Its role is to convert the unstable AC power output by the wind turbine (such as variable frequency power of 0~50Hz) into stable AC power (such as 50Hz / 380V or 690V) that matches the grid through power electronic conversion such as rectification and inversion, while achieving maximum power point tracking (MPPT) to improve power generation efficiency, and having low voltage ride through (LVRT), harmonic suppression and other grid adaptability functions to ensure safe and efficient grid operation of the wind turbine generator; The fan converter may occur a variety of faults during operation, mainly including IGBT module faults (such as switch open circuit / short circuit, abnormal driving signal), resulting in current waveform distortion or overcurrent burnout.

[0026] It should be further noted that the IGBT module is the core power switching device of the fan converter, and its faults mainly include switch open circuit, short circuit and abnormal driving signal; Open circuit fault will cause the interruption of current path, resulting in the loss of output current or three-phase imbalance (such as more than 50% reduction of current in a phase); Short circuit fault will cause instantaneous overcurrent (up to 10 times the rated value), which will burn out the module if the protection is not timely; Abnormal driving signal (such as pulse loss or timing error) will cause IGBT switch to be out of synchronization, resulting in output voltage total harmonic distortion (THD) exceeding 5% or even causing bridge arm shoot-through; By analyzing the current data and extracting the waveform characteristics, the IGBT module fault can be monitored. When the IGBT has an open circuit fault, the three-phase current will be obviously unbalanced, and the current data will have obvious distortion characteristics; Short circuit fault is characterized by instantaneous overcurrent (and high-frequency oscillation harmonic); Therefore, by analyzing the changes of current data, the state of the current IGBT module can be determined, and whether the IGBT module has an abnormality can be analyzed.

[0027] Step S002, analyze the amplitude of each frequency in the frequency spectrum of the original current signal, and select several main frequencies of the original current signal; decompose the original current signal to obtain several component signals, and obtain several main frequency signals corresponding to each main frequency; analyze the difference between each main frequency signal and the original current signal, and select a reference signal.

[0028] It should be noted that when analyzing the current data, the current data presents various change modes, and different change modes present different data characteristics. In order to accurately analyze the current data, the frequency spectrum of the original current data is obtained by Fourier transform, and then the main frequency fluctuation range of the current data is obtained; The main frequency usually shows a large amplitude in the frequency spectrum, and the amplitude difference between the main frequencies is small, so as to quantize the main frequency possibility of each signal in the frequency spectrum, and cluster based on the main frequency possibility, and select the main frequency based on the clustering result. After EMD decomposition of the original current signal, the main frequency signal in the component signal is provided as a basis.

[0029] Preferably, in one embodiment of the present application, the amplitude performance of each frequency in the spectrum of the original current signal is analyzed, and several main frequencies of the original current signal are screened out, including the specific method as follows: The Fourier transform is performed on the original current signal to obtain the spectrum of the original current signal, the spectrum contains several frequencies, and the amplitude of each frequency in the spectrum is obtained, then the calculation method of the main frequency possibility of the first frequency is as follows:

[0030] Wherein, represents the number of frequencies in the spectrum of the original current signal, represents the amplitude of the first frequency in the spectrum, represents the amplitude of the first frequency in the spectrum, represents the amplitude of the first frequency in the spectrum, represents the amplitude of the first frequency in the spectrum, represents the amplitude of the first frequency in the spectrum, represents the amplitude of the first frequency in the spectrum; represents the absolute value function, represents the exponential function with the natural constant as the base, the present embodiment adopts the model to present the inverse proportional relationship and the normalization processing, is the input of the model, and the implementer can set the inverse proportional function and the normalization function according to the actual situation. It should be noted that the larger the amplitude of the current frequency in the entire spectrum of the signal, the greater the possibility of the corresponding main frequency, and the smaller the amplitude difference between the current frequency and the adjacent frequency, the greater the proportion of the current frequency and the adjacent frequency in the original current signal, and the more likely to be the main frequency signal.

[0031] Further, the frequencies in the spectrum of the original current signal are arranged in descending order of the main frequency possibility, and the main frequency possibility sequence is obtained, the main frequency possibility sequence is clustered by hierarchical clustering, and several one-dimensional clustering clusters are obtained, the present embodiment sets the clustering maturity to 2, and finally several clusters are obtained; the mean value of the main frequency possibility of all frequencies in any cluster is obtained as the average main frequency possibility of the cluster; all frequencies in the cluster with the maximum average main frequency possibility are taken as the several main frequencies of the original current signal.

[0032]

[0033] ​​​​​It needs to be further explained that the main frequency is caused by the fluctuation of the current signal under various disturbances. The more complex the frequency change is, the more change patterns of the current signal are. In the process of running the fan converter, the power change of the IGBT module is caused by various factors, which will exist in the current data in various states. Therefore, in order to accurately analyze the current change state, the causes of the current change need to be analyzed. In order to analyze the current signal under the interference of various factors, a plurality of IMF components are obtained by EMD decomposition. Different IMF components represent the change state of signals of different frequencies. The component signal decomposed earlier in the EMD decomposition process has a higher frequency, and the possibility of being a noise signal is greater. It is more likely to be high-frequency electromagnetic noise generated by the switching action of the converter IGBT. Therefore, the main frequency and the IMF component need to be corresponded to obtain the main frequency signal and further analyze the difference between the original current signal to obtain a reference signal that highlights the change trend of the original current signal.

[0034] Preferably, in an embodiment of the present application, the original current signal is decomposed to obtain a plurality of component signals, and a plurality of main frequency signals are obtained by corresponding the main frequencies. The specific method includes: The original current signal is decomposed by EMD to obtain a plurality of IMF components, each IMF component being a component signal. Each component signal corresponds to a frequency. The frequencies of the left nodes and the main frequencies of the right nodes are constructed into a bipartite graph. The difference between the frequencies of the left nodes and the main frequencies of the right nodes (the absolute value of the difference between the frequencies) is taken as the edge between the left and right nodes. The bipartite graph is matched by KM. The matching rule is that the shorter the edge value is, the more matched it is. Then the matched frequencies and the corresponding component signals of the main frequencies are obtained. The component signals corresponding to the frequencies matched by the main frequencies are taken as the main frequency signals of the main frequencies. It is particularly noted that the number of left and right nodes in the bipartite graph may be different, i.e. the number of main frequencies and the number of component signals are different. If the number of main frequencies is greater than the number of component signals, only the main frequency signals of the successfully matched main frequencies are obtained. If the number of component signals is greater than the number of main frequencies, only the component signals whose frequencies are successfully matched are taken as the corresponding main frequency signals.

[0035] It needs to be noted that after obtaining the main frequency signal, a reference signal that can better represent the change trend of the original current signal is needed. On the basis of analyzing the similarity relationship between the main frequency signal and the overall change trend of the original current signal, the main frequency possibility and the difference between the main frequency signal and the original current signal in the amplitude fluctuation are further combined. The greater the main frequency possibility is, the more original current signal characteristics the main frequency signal contains, the more similar the overall change trend is, and the smaller the difference in amplitude fluctuation is. The main frequency signal can better reflect the regular change trend of the original current signal and is not affected by high-frequency noise. The possibility of being a reference signal for subsequent comparison and analysis is greater.

[0036] Preferably, in one embodiment of the present application, the difference between each main frequency signal and the original current signal is analyzed to screen the reference signal, including the specific method as follows: For the first main frequency signal, the standard deviation of all amplitudes is obtained as the amplitude standard deviation of the first main frequency signal, and the amplitude standard deviation of the original current signal is obtained; and the DTW distance between the first main frequency signal and the original current signal is obtained; the reference prominence of the first main frequency signal is calculated. The calculation method of the reference prominence of the first main frequency signal is as follows:

[0037] Wherein, represents the main frequency possibility of the first main frequency signal corresponding to the main frequency, represents the DTW distance between the first main frequency signal and the original current signal L, represents the amplitude standard deviation of the first main frequency signal, represents the amplitude standard deviation of the original current signal L; represents the absolute value function, represents the exponential function with the natural constant as the base, and the present embodiment adopts model to present the inverse proportional relationship and normalization processing, is the input of the model, and the implementer can set the inverse proportional function and the normalization function according to the actual situation.

[0038] Further, according to the above method, the reference prominence of each main frequency signal is obtained, and the main frequency signal corresponding to the maximum reference prominence is taken as the reference signal.

[0039] Thus, through the amplitude analysis in the frequency spectrum of the original current signal, the main frequency in each frequency is screened, and the component signals obtained by EMD decomposition are correspondingly obtained to reflect the various change modes of the IGBT module through the main frequency signals. Based on the similar relationship between the overall change trend of the main frequency signal and the original current signal, the amplitude fluctuation and the main frequency possibility, the reference signal is screened and obtained to regularly reflect the regular change trend of the original current signal without being affected by high-frequency noise, thereby providing a basis for subsequent component signal abnormality analysis and comparison.

[0040] Step S003, obtaining the change abnormality of each component signal according to the difference in change trend between each component signal and the reference signal; analyzing the energy difference in the time-frequency energy matrix between each component signal and the reference signal to obtain an energy difference index of each component signal; and comprehensively obtaining an abnormality index of each component signal by combining the change abnormality and the energy difference index of the component signal.

[0041] It should be noted that the IGBT module is a core power switching device, and its switching action directly affects the waveform characteristics of the output current. When the IGBT is working normally, the local fluctuation trend of the original current signal (such as the rising and falling slope performance, the distribution of extreme points, etc.) will show regular changes and be consistent with the reference signal. Once a fault (such as open circuit, short circuit or abnormal driving) occurs, the regularity will be destroyed, causing the local change trend to deviate significantly.

[0042] It should be further noted that the IGBT operates at a fixed switching frequency (such as 2 kHz) under PWM (pulse width modulation) driving, and the current signal is as follows: when the IGBT is turned on, the current rises linearly with the DC bus voltage, and the slope is determined by the load inductance L and the voltage difference; when the IGBT is turned off, the current flows through the freewheeling diode, and the slope is reversed; therefore, different change modes will be shown in the decomposed signal, i.e., each component signal. By calculating the change relationship between the reference signal and each component signal, the closer the overall change trend formed by the local fluctuation trend, the more consistent the overall trend, so as to quantify the change abnormality.

[0043] Preferably, in an embodiment of the present application, the change abnormality of each component signal is obtained according to the difference in change trend between each component signal and the reference signal, and the specific method comprises: The window length is preset, and the window length is described by taking 5 as an example in this embodiment. The reference signal is segmented by the window length to obtain a plurality of data segments, each of which contains 5 data points in the reference signal, and there is no overlap between the data segments. It should be particularly noted that if the number of remaining data points is insufficient to form a data segment, the remaining data points are directly taken as a data segment for subsequent processing. Any data segment of the reference signal is linearly fitted by the least square method, and the fitting slope is obtained as the change slope of the data segment. Similarly, a plurality of data segments of the first component signal are obtained according to the window length, and the change slope of each data segment is obtained. The change abnormality of the first component signal is calculated according to the following formula:

[0044] Wherein, ​​The number of data segments in each of the reference signal and the component signals (the reference signal also belongs to the component signals, and the number of data segments is the same when each component signal is equal in length) is represented. The reference signal is represented The change slope of the th data segment is represented. The change slope of the th data segment in the th component signal is represented. The absolute value function is represented.

[0045] It should be noted that the ratio is obtained by accumulating the difference between the change slopes of the same data segments and the cumulative sum of the change slope values, and the closer the ratio is to 0, the closer the change slopes of the same data segments are, and the higher the corresponding trend consistency is, and the smaller the change abnormality of the corresponding component signal is. The difference between the change slopes increases due to the current mutation caused by the IGBT fault, and the closer the ratio is to 1, the greater the change abnormality is.

[0046] It should be further noted that the microscopic waveform distortion caused by the IGBT fault can be accurately captured by analyzing the stability and extreme point distribution of the local change slope of the current signal, and then the difference between the slopes (change trends) of the actual current signal (each component signal) and the reference signal in the local time window is quantified to determine whether the IGBT is working normally.

[0047] Preferably, in an embodiment of the present application, the energy difference between each component signal and the reference signal in the time-frequency energy matrix is analyzed to obtain an energy difference index of each component signal, and the specific method includes: It should be noted that when the IGBT fails (such as open circuit, short circuit, or drive abnormality), the energy of the current signal will change significantly in the time-frequency domain. For open circuit failure, the energy is concentrated in the low frequency (because the high-frequency switching action component is missing); for short circuit failure, the energy is suddenly enhanced in the high frequency band (because of transient overcurrent and resonance oscillation); for drive abnormality, abnormal peak values appear in the switching frequency sideband (such as 2 kHz ± 50 Hz); therefore, the distribution between the component signal and the reference signal in the time domain and the frequency domain is analyzed again, and based on the time-frequency energy matrix, the energy difference between the reference signal and the component signal in the time domain is analyzed under the condition that the frequency domain of the component signal and the reference signal is fixed. The time-frequency energy matrix represents the energy density of the signal at time and frequency In the fault detection of the fan converter, the time-frequency energy matrix reveals how the energy of the current signal dynamically distributes with time and frequency, and quantifies the intensity of each time component, and the fault will cause abnormal concentration or loss of energy in a specific time period at a fixed frequency band.

[0048] Specifically, the short-time Fourier transform is used to obtain the reference signal and the The time-frequency energy matrix is ​​obtained from each component signal, then the... Energy difference index of component signals The calculation method is as follows:

[0049] in, This indicates the reference signal and the number of time points corresponding to each component signal (if each component signal is of equal length, the number of time points is equal). Indicates reference signal In the time-frequency energy matrix, the first Elements at each time point (the frequency of the reference signal is fixed, and the elements are determined according to the time); Indicates the first The time-frequency energy matrix of the reference signal of the first line Elements at each point in time; This represents the absolute value function.

[0050] It should be noted that the sum of the accumulated energy differences between the component signal and the reference signal in the main frequency band is used. The larger the sum of the differences, the greater the energy shift caused by the fault. The total energy of the reference signal in the main frequency band is then normalized.

[0051] Preferably, in one embodiment of the present invention, the abnormality index of each component signal is obtained by combining the abnormality of the component signal changes and the energy difference index, including the following specific method: For the Single component signal, preset reference weight This embodiment adopts To narrate; to As the difference weight, the reference weight is used as the weight for the anomaly of change, and the difference weight is used as the weight for the energy difference index, through the first... The weighted sum of the variation anomalies and energy difference coefficients of the component signals is used as the result of the first weighted sum. Anomaly index of component signals.

[0052] It should be noted that, since the dispersion and concentration of energy are more significant in indicating the degree of fault manifestation than regular trends, more weight is assigned to the energy difference index to obtain the anomaly index of the component signals.

[0053] At this point, on the basis of the reference signal, the local change trend of each component signal and the reference signal is compared, and the analysis between the time-frequency energy matrixes. The local change trend can reflect the stability of the local change slope and the distribution law of the extreme point, accurately capture the micro waveform distortion caused by the IGBT fault, and the time-frequency energy matrix reflects the energy abnormal concentration or dissipation in the time domain caused by the fault. Therefore, the abnormality index of the component signal is quantified.

[0054] Step S004, based on the abnormality index of each component signal, the fan converter is detected for fault.

[0055] Specifically, the IGBT module based on the fault threshold value is set, the open circuit threshold value is set to 0.8, the short circuit threshold value is set to 0.5, and the drive abnormal threshold value is set to 0.3; for any one component signal, if the abnormality index is greater than the open circuit threshold value, the fan converter has an open circuit fault of the IGBT module, if the abnormality index is in the range of , the fan converter has a short circuit fault of the IGBT module, if the abnormality index is in the range of , the fan converter has a drive signal abnormality fault, and if the abnormality index is less than or equal to the drive abnormal threshold value, the component signal corresponding to the current change mode has no fault.

[0056] Further, for each component signal of the original current signal, at least one component signal has an abnormality index in the threshold range under each fault, so that the fan converter has a corresponding fault, and if the abnormality index of all component signals is less than or equal to the drive abnormal threshold value, the fan converter has no fault.

[0057] It should be noted that each component signal reflects a variety of current change modes of the original current signal corresponding to the IGBT module, and the abnormality index of each current change mode is judged to comprehensively detect the fault of the fan converter.

[0058] At this point, by identifying the abnormality of the current signal in the IGBT module of the fan converter, the influence of the high-frequency noise coupling current loop generated by the switching action is reduced, so that the waveform change caused by various faults in the current signal is analyzed, the fault of each current change mode is monitored, and finally the accurate fault test of the fan converter is realized.

[0059] Please refer to Figure 2 , which shows the structure block diagram of the fault test system for the fan converter provided by another embodiment of the application, and the system comprises: The current signal acquisition module 101 acquires the current data in the IGBT module of the fan converter and serves as the original current signal. The current signal analysis module 102: analyze the amplitude performance of each frequency in the spectrum of the original current signal, screen several main frequencies of the original current signal; decompose the original current signal to obtain several component signals, and correspond to each main frequency to obtain several main frequency signals; analyze the difference performance of each main frequency signal and the original current signal, and screen to obtain a reference signal; According to the difference in the change trend of each component signal and the reference signal, the change abnormality of each component signal is obtained; analyzing the energy difference of each component signal and the reference signal in the time-frequency energy matrix, obtaining the energy difference index of each component signal; comprehensively considering the change abnormality and the energy difference index of the component signal, obtaining the abnormality index of each component signal; The converter fault detection module 103: based on the abnormality index of each component signal, the fan converter is detected for fault.

[0060] Another embodiment of the present application provides a fault test device suitable for a fan converter, comprising a memory, a processor and a computer program stored in the memory and running on the processor, wherein the processor executes the computer program to realize the above method steps S001 to S004.

[0061] The above is only the preferred embodiment of the present application, and is not used to limit the present application, any modification, equivalent replacement, improvement, etc. within the principle of the present application, should be included in the protection scope of the present application.

Claims

1. A method of fault testing suitable for use in a fan inverter, characterised in that, The method comprises the following steps: Collecting current data in the fan converter IGBT module as a raw current signal; Analyzing the amplitude of each frequency in the spectrum of the raw current signal, and screening several main frequencies of the raw current signal; decomposing the raw current signal to obtain several component signals, and corresponding to each main frequency to obtain several main frequency signals; analyzing the difference between each main frequency signal and the raw current signal, and screening to obtain a reference signal; According to the difference between the change trend of each component signal and the reference signal, the change abnormality of each component signal is obtained; analyzing the energy difference between each component signal and the reference signal in the time-frequency energy matrix, obtaining the energy difference index of each component signal; comprehensively considering the change abnormality and the energy difference index of the component signal, obtaining the abnormality index of each component signal; Based on the abnormality index of each component signal, the fan converter is detected for fault.

2. The method for fault testing of a variable frequency drive for a fan as claimed in claim 1, wherein, The specific method for analyzing the amplitude of each frequency in the spectrum of the raw current signal and screening several main frequencies of the raw current signal comprises: Based on the amplitude of each frequency in the spectrum of the raw current signal and the amplitude difference between adjacent frequencies, the main frequency possibility of each frequency is obtained; Arranging each frequency in the spectrum of the raw current signal according to its main frequency possibility from large to small to obtain a main frequency possibility sequence, and clustering the main frequency possibility sequence by hierarchical clustering to obtain several clusters; Obtaining the mean value of the main frequency possibility of all frequencies in any cluster as the average main frequency possibility of the cluster; all frequencies in the cluster with the maximum average main frequency possibility are regarded as several main frequencies of the raw current signal.

3. The method for fault testing of a variable frequency drive for a fan as claimed in claim 2, wherein, The specific method for obtaining the main frequency possibility of each frequency comprises: ； wherein represents the dominant frequency likelihood of the th frequency, represents the number of frequencies in the spectrum of the original current signal, represents the amplitude of the th frequency in the spectrum, represents the amplitude of the th frequency in the spectrum, and represent the amplitude of the th and th frequency in the spectrum, respectively; represents the absolute value function, represents the exponential function with base of the natural constant.

4. The method for fault testing of a variable frequency drive for a fan as defined in claim 1, wherein, The specific method for obtaining several main frequency signals comprises: A bipartite graph is constructed by taking the frequencies corresponding to each component signal as left nodes and taking each main frequency as a right node, taking the difference between the frequencies of the left nodes and the main frequencies of the right nodes as the edges between the left and right nodes, and performing KM matching on the bipartite graph, the matching rule being that the shorter the edge value is, the more matched it is, obtaining the matched frequencies and corresponding component signals for each main frequency, and taking the component signals corresponding to the frequencies matched by each main frequency as the main frequency signals of each main frequency.

5. The method for fault testing of a variable frequency drive for a fan as defined in claim 1, wherein, The specific method for analyzing the difference between each main frequency signal and the raw current signal and screening to obtain a reference signal comprises: For the first order dominant frequency signal, the standard deviation of all amplitudes is obtained as the amplitude standard deviation of the first order dominant frequency signal, and the amplitude standard deviation of the original current signal is obtained; and the DTW distance between the first order dominant frequency signal and the original current signal is obtained; the reference saliency of the first order dominant frequency signal is calculated as follows: ； wherein, represents the fundamental frequency of the fundamental frequency signal, represents the fundamental frequency of the DTW distance between the fundamental frequency signal and the original current signal L, represents the standard deviation of the amplitude of the fundamental frequency signal; represents the absolute value function, represents the exponential function with base of the natural constant; Obtaining the reference prominence of each main frequency signal, and taking the main frequency signal corresponding to the maximum reference prominence as the reference signal.

6. The method for fault testing of a variable frequency drive for a fan as claimed in claim 1, wherein, The specific method for obtaining the change abnormality of each component signal comprises: The reference signal is segmented by window length to obtain a plurality of data segments, any data segment of the reference signal is linearly fitted by least square method, and a fitting slope is obtained as a change slope of the data segment; the change slope of the first data segment is taken as a first change slope of the first component signal. The component signal is segmented according to the window length to obtain a plurality of data segments, and the change slopes of the data segments are obtained; the change slope of the first data segment is taken as a first change slope of the first component signal. The change abnormality of the component signal is calculated by the following formula: ​ ； wherein, denotes the number of data segments in the reference signal and in each component signal, respectively; denotes the reference signal the change slope of the th data segment in the reference signal; denotes the change slope of the th data segment in the th component signal; denotes the absolute value function.

7. The method for fault testing of a variable frequency drive for a fan as defined in claim 1, wherein, The specific method for obtaining the energy difference index of each component signal comprises: The time-frequency energy matrixes of the reference signal and the first component signal are obtained respectively by short-time Fourier transform. The time-frequency energy matrixes of the reference signal and the first component signal are obtained respectively by short-time Fourier transform. The energy difference index of the first component signal is calculated by the following formula: The energy difference index of the first component signal is calculated by the following formula: ； wherein, denotes the number of time points corresponding to the reference signal and the respective component signal; denotes the reference signal at the element of the time-frequency energy matrix at the time point; denotes the element of the time-frequency energy matrix of the reference signal at the time point; denotes the absolute value function.

8. The method for fault testing of a variable frequency drive for a fan as defined in claim 1, wherein, The specific method for obtaining the abnormality index of each component signal comprises: The reference weight is used as the weight of the change abnormality, the difference weight is used as the weight of the energy difference index, and the weighted sum of the change abnormality and the energy difference coefficient of the m-th component signal is obtained as the abnormality index of the m-th component signal. The weighted sum of the change abnormality and the energy difference coefficient of the m-th component signal is obtained as the abnormality index of the m-th component signal. The weighted sum of the change abnormality and the energy difference coefficient of the m-th component signal is obtained as the abnormality index of the m-th component signal.

9. A fault testing system for a fan inverter, characterized by, The system comprises: A current signal acquisition module for collecting current data in the fan converter IGBT module as a raw current signal; A current signal analysis module for analyzing the amplitude of each frequency in the spectrum of the raw current signal, and screening several main frequencies of the raw current signal; decomposing the raw current signal to obtain several component signals, and corresponding to each main frequency to obtain several main frequency signals; analyzing the difference between each main frequency signal and the raw current signal, and screening to obtain a reference signal; According to the difference between the change trend of each component signal and the reference signal, the change abnormality of each component signal is obtained; the energy difference index of each component signal is obtained by analyzing the energy difference between each component signal and the reference signal in the time-frequency energy matrix; and the abnormality index of each component signal is obtained by comprehensively considering the change abnormality and the energy difference index of each component signal. The converter fault detection module is configured to perform fault detection on the fan converter based on the abnormality index of each component signal.

10. A fault testing device for a fan inverter, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, The processor implements the steps of the method for testing the fault of the fan converter according to any one of claims 1-8 when executing the computer program.

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