A Robust Diagnosis Method and Device for Open-Circuit Faults of a Grid-Connected Three-Phase Inverter

Abstract

The present application provides a robust diagnostic method and device for open - circuit faults of a grid - connected three - phase inverter, which relates to the technical field of fault diagnosis. Among them, the method includes: determining the fault characteristics of a robust operating condition, and calculating the detectable degree of various faults during operation under different measurement conditions; determining the robustness of the faults to measurement noise, and screening to obtain recommended measurement conditions; combining the recommended measurement conditions with the historical operation data of the grid - connected three - phase inverter in different fault states to obtain training fault characteristics for model training; training a model according to the training fault characteristics; collecting real - time operation data in the grid - connected three - phase inverter, and extracting real - time fault characteristics for fault detection according to the real - time operation data; inputting the real - time fault characteristics into a fault classifier to obtain a diagnostic result. Through the fault detectability, the robustness of the fault characteristics to sensor noise is analyzed, thereby optimizing the design of the measurement conditions and improving the accuracy of fault diagnosis.

Description

Technical Field

[0001] The present application relates to the technical field of fault diagnosis, and particularly relates to a robust diagnosis method and device for open - circuit faults of a grid - connected three - phase inverter. Background Art

[0002] As the core part of main power electronic devices and various new - energy power generation devices and drive systems, the safety and reliability of inverters are related to the safety and stability of the entire system. As one of the main application directions of inverter devices, the safe and stable operation of grid - connected inverters has an important impact on improving the reliability of the entire converter system. Research shows that due to the influence of many factors, inverters are prone to failures. In particular, the damage of power semiconductor switching devices accounts for 38% of inverter failures. Therefore, the fault diagnosis of power switching tubes of grid - connected inverters has very important significance and value. However, in the related technology, the fault diagnosis of power switching tubes of grid - connected inverters is affected by various factors, resulting in a low accuracy of fault diagnosis and affecting the safe operation of power switching tubes of grid - connected inverters. Summary of the Invention

[0003] The present application aims to solve at least one of the technical problems in the related technology to some extent.

[0004] To this end, the first object of the present application is to propose a robust diagnosis method for open - circuit faults of a grid - connected three - phase inverter.

[0005] The second object of the present application is to propose a device.

[0006] The third object of the present application is to propose an electronic device.

[0007] The fourth object of the present application is to propose a computer - readable storage medium.

[0008] The fifth object of the present application is to propose a computer program product.

[0009] To achieve the above object, the first - aspect embodiment of the present application proposes a robust diagnosis method for open - circuit faults of a grid - connected three - phase inverter, including:

[0010] Determine the fault characteristics of the robust operating conditions, and calculate the detectable degree of various faults during the operation of the grid - connected three - phase inverter under different measurement conditions;

[0011] Determine the robustness degree of the faults to measurement noise according to the detectable degree of the faults, and screen out the recommended measurement conditions according to the robustness degree;

[0012] Obtain the training fault characteristics for model training according to the recommended measurement conditions and the historical operation data of the grid - connected three - phase inverter in different fault states.

[0013] Train a model based on the training fault features to obtain a fault classifier;

[0014] Collect real-time operation data in a grid-connected three-phase inverter, and extract real-time fault features for fault detection according to the real-time operation data;

[0015] Input the real-time fault features into the fault classifier to obtain a diagnosis result.

[0016] Optionally, the calculation formula for the relative detectability of the fault is:

[0017]

[0018]

[0019] where and are fault conditions, is the fault and 's relative detectability, and is the total set of the fault features, represents the probability distribution of the features corresponding to the fault , represents the probability distribution of the features corresponding to the fault , where , is the kernel function, is 's m-th sample, is 's n-th sample, is 's m-th sample, is 's n-th sample, is 's total number of samples, is 's total number of samples.

[0020] Optionally, the calculation formula for the robustness of the fault to measurement noise is:

[0021]

[0022] where is the robustness of the fault to measurement noise, , , represents the probability distribution of the features corresponding to the fault under the condition of containing noise Indicates a fault in the presence of noise The probability distribution of the corresponding features

[0023] Optionally, the recommended measurement conditions screened according to the robustness include:

[0024] Obtain the noise robustness corresponding to each signal-to-noise ratio in different time windows, and determine the signal-to-noise ratio range where the robustness corresponding to different time windows is greater than the preset threshold;

[0025] Take the time window corresponding to the maximum signal-to-noise ratio range that meets the robustness threshold as the recommended measurement condition.

[0026] Optionally, the training fault features obtained by combining the recommended measurement conditions with the historical operation data of the grid-connected three-phase inverter in different fault states include:

[0027] Extract the fault features from the historical operation data, and generate the training fault features in combination with the time window in the recommended measurement conditions.

[0028] To achieve the above object, an embodiment of the second aspect of the present application proposes a robust diagnosis device for open-circuit faults of a grid-connected three-phase inverter, including:

[0029] The first calculation module is used to determine the fault features of the robust operating conditions and calculate the detectable degree of various faults during the operation of the grid-connected three-phase inverter under different measurement conditions;

[0030] The condition screening module is used to determine the robustness of the fault to measurement noise according to the detectable degree of the fault, and screen the recommended measurement conditions according to the robustness;

[0031] The first acquisition module is used to combine the recommended measurement conditions with the historical operation data of the grid-connected three-phase inverter in different fault states to obtain training fault features for model training;

[0032] The training module is used to train the model according to the training fault features to obtain a fault classifier;

[0033] The second acquisition module is used to acquire the real-time operation data in the grid-connected three-phase inverter, and extract the real-time fault features for fault detection according to the real-time operation data;

[0034] The classification module is used to input the real-time fault features into the fault classifier to obtain a diagnosis result.

[0035] To achieve the above object, an embodiment of the third aspect of the present application proposes an electronic device, including: a processor, and a memory communicatively connected to the processor;

[0036] The memory stores computer-executable instructions;

[0037] The processor executes the computer-executable instructions stored in the memory to implement the method according to any one of the first aspect.

[0038] To achieve the above object, an embodiment of the fourth aspect of the present application provides a computer-readable storage medium, in which computer-executable instructions are stored, and when the computer-executable instructions are executed by a processor, they are used to implement the method according to any one of the first aspect.

[0039] To achieve the above object, an embodiment of the fifth aspect of the present application provides a computer program product, and when the computer program is executed by a processor, it implements the method according to any one of the first aspect.

[0040] The robust diagnosis method, device, electronic device and storage medium for open-circuit faults of a grid-connected three-phase inverter provided by the present application analyze the robustness of fault characteristics to sensor noise through fault detectability, thereby optimizing the design of measurement conditions and improving the accuracy of fault diagnosis. The fault characteristics obtained after optimizing the measurement conditions enhance the robustness of the diagnosis method to sensor noise.

[0041] Additional aspects and advantages of the present application will be given in part in the following description, become apparent in part from the following description, or be learned through the practice of the present application. Description of the Drawings

[0042] The above and / or additional aspects and advantages of the present application will become apparent and easy to understand from the following description of the embodiments in conjunction with the drawings, where:

[0043] Figure 1 is a schematic flowchart of a robust diagnosis method for open-circuit faults of a grid-connected three-phase inverter provided by an embodiment of the present application;

[0044] Figure 2 is a schematic diagram of the topology structure of a grid-connected three-phase inverter;

[0045] Figure 3 is a unified circuit model of faults of IGBTs;

[0046] Figure 4 is a flowchart of fault diagnosis;

[0047] Figure 5 is a schematic structural diagram of a robust diagnosis device for open-circuit faults of a grid-connected three-phase inverter provided by an embodiment of the present application. Detailed Embodiments

[0048] Embodiments of the present application will be described in detail below. Examples of the embodiments are shown in the accompanying drawings, where the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the accompanying drawings are exemplary and are intended to explain the present application and should not be construed as a limitation to the present application.

[0049] As the core part of main power electronic devices and various new energy power generation devices and drive systems, the safety and reliability of inverters are related to the safety and stability of the entire system. As one of the main application directions of inverter devices, the safe and stable operation of grid-connected inverters has an important impact on improving the reliability of the entire conversion system. Research shows that due to the influence of many factors, inverters are prone to failures. In particular, the damage of power semiconductor switching devices accounts for 38% of inverter failures. Therefore, the fault diagnosis of power switching tubes in grid-connected inverters has very important significance and value.

[0050] Insulated gate bipolar transistor (IGBT) is the most commonly used power switching device in inverters. Due to the large electrothermal stress it bears for a long time, it is extremely prone to failures, and IGBT failures mainly manifest as open-circuit failures and short-circuit failures. Currently, there are very mature technologies to quickly cut off short-circuit failures. However, after an open-circuit failure occurs, the inverter can still operate for a short time. If not handled in time, it will cause secondary failures. Therefore, researching the open-circuit fault diagnosis method for IGBTs in grid-connected inverters has great significance.

[0051] Existing fault diagnosis technologies for power switching tubes can be roughly divided into model-based, signal-based, and data-driven fault diagnosis methods. The accuracy of model-based fault diagnosis methods highly depends on the accuracy and detail of the circuit model, but constructing such a circuit model is very complex and time-consuming. Signal-based fault diagnosis methods generally have problems such as difficult threshold setting, the need for prior fault knowledge, being affected by load fluctuations (based on current signals), and increasing system costs (based on voltage signals). Compared with the above two methods, data-driven fault diagnosis methods do not require too in-depth understanding of the system and mainly rely on the data itself. The quality of the data largely determines the accuracy of the diagnosis method. This method only uses data to train the diagnosis model and then conducts fault diagnosis by establishing a classifier. Therefore, it has strong adaptability and has received extensive attention.

[0052] However, in order to improve the fault diagnosis ability of grid-connected inverter power switching tubes based on data-driven methods, existing research mainly focuses on the improvement and optimization of specific diagnostic algorithms, and there is less research on fault detectability. However, only when a fault can be diagnosed does the improvement and optimization of the fault diagnosis algorithm make sense. Therefore, blindly designing a fault diagnosis algorithm without analyzing fault detectability will surely consume manpower and material resources and is very likely to achieve unsatisfactory results. As the basis of fault diagnosis, fault detectability analyzes the attributes that characterize the fault diagnosis ability of the system from an essential level. Its research provides new ideas for improving fault diagnosis ability and is of great significance. However, there are few reports on the analysis and research of fault detectability for the fault diagnosis problem of grid-connected inverter power switching tubes.

[0053] Therefore, for the open-circuit fault of IGBT, this paper studies a fault diagnosis method that is robust to operating conditions and sensor noise. Through a fault detectability analysis method of a model, fault characteristics that are robust to operating conditions are determined and an optimal measurement condition is determined. Specifically, through the fault detectability analysis theory and a fault state equation model, fault characteristics that can be robust to changes in operating conditions are obtained; through the calculation of fault detectability analysis, the influence of sensor noise on fault detectability is analyzed, and then an optimal time window for fault diagnosability is obtained, so as to optimize the design of measurement conditions.

[0054] A robust operating condition refers to a working state in which the system can still maintain stable operation and meet performance requirements in the face of various uncertainty factors (such as parameter changes, external disturbances, model errors, etc.). This kind of operating condition emphasizes the reliability and stability of the system in a complex environment, ensuring that the basic functions can still be maintained in the worst case.

[0055] In a three-phase grid-connected inverter system, the output power of the grid-connected inverter is different under different operating conditions. The robust operating condition refers to the operating condition in which the fault characteristics are not affected under different inverter output powers.

[0056] The embodiment of the present application provides a robust diagnosis method for open-circuit faults of a three-phase grid-connected inverter. Figure 1 It is a schematic flowchart of a robust diagnosis method for open-circuit faults of a three-phase grid-connected inverter provided by an embodiment of the present application. As Figure 1 shown, the method includes the following steps:

[0057] Step 101, determine the fault characteristics of the robust operating condition, and calculate the detectable degree of various faults during the operation of the three-phase grid-connected inverter under different measurement conditions;

[0058] Step 102, determine the robustness degree of the fault to measurement noise according to the detectable degree of the fault, and screen and obtain recommended measurement conditions according to the robustness degree.

[0059] Step 103: Obtain training fault features for model training based on the recommended measurement conditions and historical operation data of the grid-connected three-phase inverter under different fault states.

[0060] Step 104: Train a model based on the training fault features to obtain a fault classifier.

[0061] Step 105: Collect real-time operation data of the grid-connected three-phase inverter, and extract real-time fault features for fault detection based on the real-time operation data.

[0062] Step 106: Input the real-time fault features into the fault classifier to obtain a diagnostic result.

[0063] The state-space expression of a discrete-time system is

[0064] (1)

[0065] where and are the state variable, input signal, actuator fault signal, and output signal respectively. The vector represents the sensor measurement noise signal. , , , , , and are unknown constant real matrices with appropriate dimensions.

[0066] We introduce the following notations:

[0067] (2)

[0068] (3)

[0069] (4)

[0070] There is In the following research, can be , , , and ; denotes , , , and ; and are integers; denotes the time window length; while denotes the number of test data.

[0071] Using the above symbols, (1) can be written as:

[0072] (5)

[0073] where , , , , .

[0074] has the following form

[0075]

[0076] Introduce the superscript to represent the data under the -th fault condition, where and is a positive integer. In particular, represents the fault-free condition.

[0077] Therefore, according to the introduced superscript, the expression corresponding to the -th fault condition can be obtained from (5):

[0078] (6)

[0079] represents the left null space of , and there is . By left-multiplying both sides of (6) by to eliminate the state term, we can obtain (7):

[0080] (7)

[0081] (8)

[0082] Use (8) to represent the time distribution of the -th fault condition, where represents the value of the fault vector at the discrete time point under the -th fault condition. From equations (7 - 8), we can obtain (9):

[0083] (9)

[0084] As can be seen from (9), the dynamic behavior of the system is a random probability distribution described based on input and output data, and this distribution is only affected by the fault vector and the noise vector.

[0085] If the difference between the random probability distributions of the system dynamic behaviors corresponding to two fault conditions is greater, it indicates that it is easier to isolate these two faults. That is, the fault detectability can be analyzed through the difference between the probability distributions.

[0086] Figure 2 It is a schematic diagram of the grid-connected three-phase inverter topology. As Figure 2 shown, the grid-connected three-phase inverter is the DC-side voltage; are 6 power switching tubes; is the filter inductor; is the equivalent resistance; is the grid-side voltage; are the three-phase currents.

[0087] Figure 3 It is the unified circuit model of the IGBT fault. As Figure 3 shown, since the open-circuit fault of the IGBT will affect the output voltage of the bridge arm, this paper uses the form of superposition controlled source to represent the unified circuit model of the IGBT fault.

[0088] Among them, respectively represent the voltage between two points under normal conditions, respectively represent the change amount introduced to the voltage of the corresponding two points during the fault.

[0089] Based on the above unified circuit model of the fault and combined with Kirchhoff's law, the state equation model of the fault can be obtained as:

[0090] (10)

[0091] Among them:

[0092] represents the current path.

[0093] Taking the state variables , the input signal , the output signal , the IGBT fault signal

[0094] , the sensor sampling noise signal .

[0095] According to the above preliminary knowledge, the expression of the dynamic behavior of the grid-connected three-phase inverter can be obtained as:

[0096] (11)

[0097] Among them, is the left null space of, and there is . , , .

[0098]

[0099]

[0100] is the sampling step, is the identity matrix of.

[0101] Let (12)

[0102] According to (11), it can be known that the fault feature is only affected by the fault vector and the noise vector, so it is robust to changes in the system operating conditions.

[0103] Optionally, the calculation formula for the relative detectability degree of the fault is:

[0104]

[0105] (13)

[0106] Among them, and are the fault conditions, is the and relative detectability degree of the fault, and are the total sets of the fault features, represents the probability distribution of the features corresponding to the fault , represents the probability distribution of the features corresponding to the fault , where , is the kernel function, is the m-th sample in, is the n-th sample in, is the m-th sample in, is The nth sample in is the sum of samples in is the sum of samples in

[0107] As can be seen from (12), the fault feature is a random vector that satisfies a certain probability distribution. We can use the probability distribution differences of the features of different fault types to define the fault detectability. The reason for this definition is that by analyzing the probability distributions of different fault features, the differences between them can be determined. And the more obvious the difference, the easier it is to isolate these two faults.

[0108] The calculation expression of the detectability can be:

[0109] (14)

[0110] where , is the kernel function in the reproducing kernel Hilbert space.

[0111] Optionally, the calculation formula for the robustness of the fault to measurement noise is:

[0112] (15)

[0113] where is the robustness of the fault to measurement noise, , , represents the probability distribution of the feature corresponding to the fault under the condition of containing noise, represents the probability distribution of the feature corresponding to the fault under the condition of containing noise.

[0114] If , it means that the noise reduces the detection ability, and the larger the value, the more the reduction. If , it means that the noise improves the detection ability.

[0115] Optionally, the step 102 screens and obtains the recommended measurement conditions according to the robustness, including:

[0116] Obtain the noise robustness corresponding to each signal-to-noise ratio in different time windows, and determine the signal-to-noise ratio range where the robustness corresponding to different time windows is greater than the preset threshold;

[0117] Take the time window corresponding to the maximum signal-to-noise ratio range that meets the robustness threshold as the recommended measurement condition.

[0118] In this embodiment, to ensure the robustness of fault diagnosis against measurement noise as much as possible, the greater the robustness of the fault characteristics against measurement noise, the better. Therefore, by enumerating different signal-to-noise ratios and time window lengths, the robustness of the fault characteristics under each corresponding condition against measurement noise can be obtained. Furthermore, a three-dimensional graph can be obtained. Through this graph, we can select the time window according to the robustness of the fault characteristics against measurement noise. The robustness corresponding to this time window has good robustness within a large signal-to-noise ratio range.

[0119] Optionally, the step 103 of obtaining the training fault characteristics according to the recommended measurement conditions and the historical operation data of the grid-connected three-phase inverter in different fault states includes:

[0120] Extracting the fault characteristics from the historical operation data and generating the training fault characteristics in combination with the time window in the recommended measurement conditions.

[0121] Figure 4 is a flowchart of fault diagnosis. As Figure 4 shown, the main processes include offline training and online diagnosis. In the offline training stage, first, using the preliminary knowledge and the fault state equation model, the fault characteristics of the robust operating conditions are obtained. Then, through the fault detectability, the analysis of the robustness of the characteristics against measurement noise is carried out, and then the measurement conditions are optimized. The data collected under different fault states are processed through the optimization of the measurement conditions and the extraction of the characteristics of the robust operating conditions, so as to form the fault characteristics of the robust operating conditions and noise. These characteristics are used for model training to obtain the fault classifier. In the online diagnosis stage, combining the real-time collected data and using the fault characteristics extracted in this paper, input them into the fault classifier to form the diagnosis result.

[0122] In a possible embodiment, the simulation parameters of the grid-connected three-phase inverter are set as follows:

[0123] Three-phase power grid: 220V 50Hz, sampling frequency: 100K, switching frequency: 10K, R = 0.3Ω, L = 8e-3H, U DC = 400V

[0124] Optimized measurement conditions:

[0125] Experimental purpose: To form a three-dimensional graph (signal-to-noise ratio, time window length, robustness of fault characteristics against measurement noise)

[0126] Experimental steps:

[0127] ① Through simulation, collect the data of the fault modes respectively (the corresponding coefficient matrices have been calculated)

[0128] ②Enumerate different time windows and calculate the

[0129] ③Enumerate different signal-to-noise ratios. Taking different time windows as known conditions, calculate the

[0130] ④According to (14) and (15), calculate the

[0131] ⑤Taking the time window as the axis, the signal-to-noise ratio as the axis, and the degree as the axis, a three-dimensional graph is formed

[0132] Operating condition robustness experiment

[0133] To verify that the extracted fault features are for robust operating conditions. The grid voltage is sequentially changed from 220V to (for example, 230V, 240V... 300V), and the at this time is collected as the test set, and the average fault diagnosis accuracy of several experiments is statistically calculated.

[0134] Under the grid voltage fluctuation condition, the diagnostic method formed based on the fault features extracted in this paper is robust for the fault diagnosis of the system.

[0135] The DC voltage is sequentially changed from 400V to (for example, 410V, 420V... 500V), and the at this time is collected as the test set, and the average fault diagnosis accuracy of several experiments is statistically calculated. Under the DC side voltage fluctuation condition, the diagnostic method formed based on the fault features extracted in this paper is robust for the fault diagnosis of the system.

[0136] Sensor noise robustness experiment

[0137] To verify the robustness of the extracted fault features to sensor noise under the recommended measurement conditions. Gaussian white noise with a certain signal-to-noise ratio is superimposed on the test set, and the average fault diagnosis accuracy of several experiments is statistically calculated. Under the recommended measurement conditions, the diagnostic method formed based on the fault features extracted in this paper is robust for the fault diagnosis of the system to sensor noise.

[0138] To implement the above embodiments, the present application also proposes a robust diagnostic device for open-circuit faults of a grid-connected three-phase inverter. Figure 5 This is a schematic structural diagram of a robust diagnostic device for open-circuit faults of a grid-connected three-phase inverter provided by an embodiment of the present application. As Figure 5 shown, the device includes:

[0139] The first calculation module 510 is configured to determine the fault characteristics of the robust operating conditions and calculate the detectable degree of various faults during the operation of the grid-connected three-phase inverter under different measurement conditions;

[0140] The condition screening module 520 is configured to determine the robustness degree of the fault to measurement noise according to the detectable degree of the fault, and screen and obtain the recommended measurement conditions according to the robustness degree;

[0141] The first acquisition module 530 is configured to obtain the training fault characteristics for model training according to the recommended measurement conditions in combination with the historical operation data of the grid-connected three-phase inverter in different fault states;

[0142] The training module 540 is configured to train a model according to the training fault characteristics to obtain a fault classifier;

[0143] The second acquisition module 550 is configured to acquire the real-time operation data in the grid-connected three-phase inverter, and extract the real-time fault characteristics for fault detection according to the real-time operation data;

[0144] The classification module 560 is configured to input the real-time fault characteristics into the fault classifier to obtain a diagnosis result.

[0145] To implement the above embodiments, the present application also provides an electronic device, including: a processor, and a memory communicatively connected to the processor; the memory stores computer execution instructions; the processor executes the computer execution instructions stored in the memory to implement the method provided in the foregoing embodiments.

[0146] To implement the above embodiments, the present application also provides a computer-readable storage medium, in which computer execution instructions are stored, and when the computer execution instructions are executed by a processor, they are used to implement the method provided in the foregoing embodiments.

[0147] To implement the above embodiments, the present application also provides a computer program product, including a computer program, and when the computer program is executed by a processor, it implements the method provided in the foregoing embodiments.

[0148] The collection, storage, use, processing, transmission, provision, and disclosure of the user's personal information involved in the present application and other processing are all in compliance with the provisions of relevant laws and regulations and do not violate public order and good customs.

[0149] It should be noted that personal information from users should be collected for legal and reasonable purposes and not shared or sold outside of such legal uses. In addition, such collection / sharing should be carried out after obtaining the informed consent of the users, including but not limited to notifying the users to read the user agreement / user notice before using the function and signing an agreement / authorization including authorizing the relevant user information. In addition, any necessary steps should be taken to defend and safeguard access to such personal information data and ensure that others with access to the personal information data comply with their privacy policies and procedures.

[0150] This application is expected to provide an implementation where users can selectively block the use or access of personal information data. That is, this disclosure is expected to provide hardware and / or software to prevent or block access to such personal information data. Once the personal information data is no longer needed, the risk can be minimized by restricting data collection and deleting the data. In addition, when applicable, personal identifiers are removed from such personal information to protect the privacy of the users.

[0151] In the description of the foregoing embodiments, the descriptions referring to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples", etc. mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of this application. In this specification, the schematic expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0152] In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" can explicitly or implicitly include at least one of such features. In the description of this application, the meaning of "a plurality" is at least two, such as two, three, etc., unless otherwise specifically and clearly defined.

[0153] Any process or method description shown in the flowchart or described in other ways herein can be understood as representing a module, segment, or part of code including one or more executable instructions for implementing a customized logic function or process, and the scope of the preferred implementation of this application includes additional implementations, where the functions can be executed in a manner not shown or discussed, including in a substantially simultaneous manner according to the involved functions or in a reverse order, which should be understood by those skilled in the art to which the embodiments of this application belong.

[0154] The logic and / or steps represented in the flowchart or otherwise described herein can, for example, be considered as a definable sequence list of executable instructions for implementing logical functions, which can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device), or used in conjunction with these instruction execution systems, apparatus, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device. More specific examples (non-exhaustive list) of computer-readable media include the following: an electrical connection portion having one or more wirings (electronic device), a portable computer diskette (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpretation, or otherwise processing as appropriate, and then storing it in a computer memory.

[0155] It should be understood that various parts of the present application can be implemented by hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application-specific integrated circuits having suitable combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc.

[0156] Those of ordinary skill in the art of this technology can understand that all or part of the steps carried by the method of implementing the above embodiments can be completed by instructing relevant hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiment.

[0157] In addition, each functional unit in various embodiments of the present application may be integrated into one processing module, or each unit may exist physically alone, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. When the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0158] The above-mentioned storage medium may be a read-only memory, a magnetic disk, an optical disc, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present application. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present application.

Claims

1. A robust diagnosis method for open circuit fault of a grid-connected three-phase inverter, characterized in that: The following steps are involved: Determine the fault characteristics of the robust operating condition, and calculate the detectability of various faults during the operation of the grid-connected three-phase inverter under different measurement conditions, wherein the robust operating condition is a condition where the fault characteristics are not affected under different inverter output powers; Determining the robustness of the fault to measurement noise according to the detectability of the fault, and screening and obtaining recommended measurement conditions according to the robustness; According to the recommended measurement conditions and in combination with historical operation data of the grid-connected three-phase inverter under different fault conditions, training fault features for model training are obtained; Training a model according to the training fault features to obtain a fault classifier; Collecting real-time operating data from the grid-connected three-phase inverter, and extracting real-time fault features for fault detection based on the real-time operating data; Inputting the real-time fault features into the fault classifier to obtain a diagnosis result; The calculation formula of the detectability of the fault is: in, and For fault conditions, For failure and The relative detectability of and is the total set of fault characteristics, Indications and faults The corresponding probability distribution of the features, Indications and faults The corresponding probability distribution of the features, where , is the kernel function, for The mth sample in for The nth sample in for The mth sample in for The nth sample in for The sum of the samples in for The sum of the samples in .

2. The method according to claim 1, characterized in that: The calculation formula of the robustness of the fault to measurement noise is: in, is the robustness of the fault to measurement noise, , , Indicates a fault in the presence of noise The probability distribution of the corresponding feature, Indicates a fault in the presence of noise The probability distribution of the corresponding feature.

3. The method according to claim 2, characterized in that The step of screening and obtaining the recommended measurement conditions according to the robustness level includes: Obtain the noise robustness corresponding to different time windows at each signal-to-noise ratio, and determine the signal-to-noise ratio range in which the robustness corresponding to different time windows is greater than a preset threshold; The time window corresponding to the maximum signal-to-noise ratio range that meets the robustness threshold is used as the recommended measurement condition.

4. The method according to claim 3, characterized in that The training fault features are obtained according to the recommended measurement conditions combined with the historical operation data of the grid-connected three-phase inverter under different fault states, including: Fault features are extracted from the historical operation data, and the training fault features are generated in combination with the time window in the recommended measurement condition.

5. A robust diagnostic device for open circuit fault of a grid-connected three-phase inverter, characterized in that: include: A first calculation module is used to determine the fault characteristics of the robust operating condition and calculate the detectability of various faults during the operation of the grid-connected three-phase inverter under different measurement conditions; A condition screening module, used to determine the robustness of the fault to measurement noise according to the detectability of the fault, and to screen and obtain recommended measurement conditions according to the robustness; A first acquisition module is used to obtain training fault features for model training based on the recommended measurement conditions combined with historical operation data of the grid-connected three-phase inverter under different fault states; A training module, used for training a model according to the training fault features to obtain a fault classifier; A second acquisition module is used to collect real-time operation data in the grid-connected three-phase inverter, and extract real-time fault features for fault detection based on the real-time operation data; A classification module, used for inputting the real-time fault characteristics into the fault classifier to obtain a diagnosis result; The calculation formula of the detectability of the fault is: in, and For fault conditions, For failure and The relative detectability of and is the total set of fault characteristics, Indications and faults The corresponding probability distribution of the features, Indications and faults The corresponding probability distribution of the features, where , is the kernel function, for The mth sample in for The nth sample in for The mth sample in for The nth sample in for The sum of the samples in for The sum of the samples in .

6. An electronic device, characterized in that: include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the method according to any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 4 when executed by a processor.

8. A computer program product, characterized in that The invention comprises a computer program, which implements the method according to any one of claims 1 to 4 when being executed by a processor.

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