MMC submodule IGBT open-circuit fault diagnosis method based on improved density clustering algorithm

Abstract

The invention relates to an MMC (Modular Multilevel Converter) sub-module IGBT (Insulated Gate Bipolar Translator) open-circuit fault diagnosis method based on an improved density clustering algorithm, and belongs to the field of power electronic equipment. The method comprises the following steps: extracting capacitor voltage of each sub-module in the modular multilevel converter, and performing discrete processing and Fourier transform to obtain a capacitor voltage frequency spectrum; extracting feature vectors including a frequency mean value, a root-mean-square frequency, a standard deviation frequency and a center frequency according to the capacitor voltage spectrum; carrying out fault classification on the feature vector corresponding to each sub-module by adopting a density clustering algorithm improved based on a GOOSE optimization algorithm; and judging the sub-module corresponding to the output result of the noise points in the improved density clustering algorithm as an open-circuit fault, and judging the fault type according to the position of a device in the sub-module. According to the method, the fault sub-module is judged by using the DBSCAN algorithm improved by the GOOSE optimization algorithm, multiple fault points can be diagnosed, the diagnosis precision is high, and the real-time performance is high.

Description

Technical Field

[0001] The invention belongs to the field of power electronic equipment and relates to an MMC submodule IGBT open circuit fault diagnosis method based on an improved density clustering algorithm. Background Art

[0002] Modular Multilevel Converter (MMC), as a new topology of voltage source converter, has been widely used in high voltage and high power occasions due to its high modularity, high output level, low switching frequency, and good output voltage waveform. MMC is composed of a large number of sub-modules in cascade, and each sub-module uses insulated gate bipolar transistors (IGBT) and diodes as commutation devices. However, since MMC contains a large number of sub-modules, its failure rate also increases, and the difficulty of fault detection increases. Therefore, fault diagnosis technology for MMC is particularly important.

[0003] The faults of IGBT devices are mainly divided into open circuit and short circuit faults. Among them, short circuit faults can cause a large current to flow through the bridge arm in a short period of time, causing great damage to the entire MMC system. However, in actual applications, IGBT devices usually integrate short circuit protection functions, which can quickly eliminate short circuit faults. Compared with short circuit faults, open circuit faults are not as significant, which may cause the device to be in an open circuit fault state for a long time without being detected, causing the system to have problems such as overvoltage and overcurrent, which greatly damages the entire MMC system. At present, for the diagnosis of open circuit faults of submodules, some scholars use artificial neural network methods and use a large amount of data to train algorithms to achieve diagnostic effects. However, the algorithm complexity is high and the diagnostic speed is slow; some scholars use the influence of faults on the bridge arm voltage value to perform fault diagnosis. The method is simple and the diagnostic speed is fast, but it can only diagnose single submodule faults. Therefore, realizing rapid diagnosis of multiple faults in submodules is of great significance to the safety and stability of MMC systems. Summary of the invention

[0004] In view of this, the purpose of the present invention is to provide an MMC sub-module IGBT open circuit fault diagnosis method based on an improved density clustering algorithm, combined with device fault feature extraction and fault classification method, to achieve MMC sub-module open circuit fault diagnosis.

[0005] In order to achieve the above object, the present invention provides the following technical solutions:

[0006] A method for diagnosing an open-circuit fault of an MMC submodule IGBT based on an improved density clustering algorithm comprises the following steps:

[0007] S1, extracting the capacitor voltage of each submodule in the modular multilevel converter, and obtaining the capacitor voltage spectrum after discrete processing and Fourier transformation;

[0008] S2, extracting a feature vector including a frequency mean, a root mean square frequency, a standard deviation frequency and a center frequency according to the capacitor voltage spectrum;

[0009] S3, using the density clustering algorithm based on GOOSE improvement to classify the feature vector corresponding to each submodule;

[0010] S4. The submodule corresponding to the output result of the improved density clustering algorithm with noise points is determined as an open circuit fault, and the fault type is determined according to the device position in the submodule.

[0011] Further, in step S1, the capacitor voltage v of each submodule SM(i) is extracted from the modular multilevel converter MMC. ci , the capacitor voltage v ci Discrete processing is performed to obtain the discrete capacitor voltage U c (n), for discrete capacitor voltage U c (n) After fast Fourier transform, the capacitor voltage spectrum c(k) is obtained, k = 1, 2, ..., M, M is the number of spectrum lines, and f c (k) represents the corresponding frequency value.

[0012] Further, in step S2, the characteristic vector F is extracted from the capacitor voltage spectrum c(k). C :

[0013] F C =(F c1 ,F c2 ,F c3 ,F c4 )

[0014] Among them, F c1 ,F c2 ,F c3 ,F c4 is the extracted feature quantity, and its corresponding calculation methods are:

[0015]

[0016]

[0017]

[0018]

[0019] In the above formula, F c1 ,F c2 ,F c3 ,F c4 They represent the frequency mean, root mean square frequency, standard deviation frequency, and center frequency respectively.

[0020] Further, in step S3, a density clustering algorithm based on the GOOSE optimization algorithm is used to cluster the feature vector F C Each sample in is classified, which includes the following steps:

[0021] S31, the feature vector F corresponding to each submodule C As data to be classified;

[0022] S32, using GOOSE optimization algorithm to optimize the neighborhood radius ε R and density threshold ρ minp Search for the best parameters;

[0023] S33, the feature vector F corresponding to the submodule C Select a sample point from the optimal area radius ε R and density threshold ρ minp Determine each sample point as a core point, boundary point or noise point, and then complete the classification, where

[0024] For a sample point t, t∈F C , then first calculate the neighborhood radius ε of the sample point t R The number of samples in N R (t), and determine the neighborhood radius ε R The number of samples in N R (t)≥ρ minp Whether it is satisfied, if it is satisfied, the sample point t is the core point; if not, it is further judged whether the sample point t falls within the neighborhood radius ε of another core point R If it falls within, the sample t is a boundary point, otherwise the sample point t is a noise point.

[0025] Further, in step S32, the neighborhood radius ε is optimized using the GOOSE optimization algorithm. R and density threshold ρ minp The process is:

[0026] S321, first initialize the population position X gi , that is, randomly select a group (ε R ,ρ minp ), and specify the search space range and the maximum number of iterations Z m , if the initial position of the population exceeds the search space, it is reinitialized;

[0027] S322, evaluate the fitness G of each goose position ai And compare to obtain the best fitness G abest and the corresponding optimal position X abestAnd save it, where the development and exploration stages are allocated by introducing a random variable rd, where 0≤rd≤1. If rd≥0.5, it enters the development stage, otherwise it enters the exploration stage, and two other random variables po and co are introduced to select the formula, and their values ​​are between 0 and 1, where co should also be less than the specified value ≥, otherwise α is assigned to co.

[0028] Further, during the development phase, the weight of the stone under the goose's feet in the current iteration is calculated using the following formula:

[0029] W s =rand([w1,w2],1,1)

[0030] Where w1 and w2 are the estimated ranges of the stone weight, and then the time T required for the stone to fall to the ground is calculated. e1 The time it takes for the sound of the stone hitting the ground to propagate to other individuals T e2 :

[0031] T e1 =rand(1,D)

[0032] T e2 =rand(1,D)

[0033] Where D is the dimension of each iteration, then the total time T for the sound to propagate to each individual in a single iteration is e and average time T av for:

[0034]

[0035]

[0036] The distance S that sound travels a and the distance S between the two bodies g As follows, where the distance between individuals is half the distance that sound travels:

[0037] S a =υ a ·T e2

[0038]

[0039] where υ a is the speed of sound in air.

[0040] If variables po and W s If they are greater than the specified values ​​a and b respectively, the falling speed of the stone υ is calculated by the following formula s And update the goose's position:

[0041]

[0042]

[0043] Where g is the acceleration due to gravity.

[0044] If variables po and W s If they are less than the specified values ​​a and b respectively, the falling speed of the stone υ is calculated by the following formula s And update the goose's position:

[0045]

[0046]

[0047] Where, X a(it+1) Represents the updated position of the goose.

[0048] Furthermore, in the search phase, the sound propagation time T in each iteration is compared. e And find the shortest time M e , and introduce a new variable β, the formula is as follows:

[0049]

[0050] Where lp is the current iteration number, M x is the total number of iterations, and the position of the goose is updated according to the following formula:

[0051] X a(it+1) =rand(1,D)·(M e β)+X abest

[0052] Repeat the above steps until the maximum number of iterations is reached or the preset conditions are met, and the algorithm outputs the optimal position X abest is the optimal parameter.

[0053] Further, in step S4, if there are noise points in the results output by the improved density clustering algorithm, it is determined that the corresponding submodule has an open circuit fault, and according to the different device locations of the open circuit fault, it is divided into device Tu fault, device Tl fault, and simultaneous fault of devices Tu and Tl.

[0054] The beneficial effects of the present invention are:

[0055] The MMC submodule open circuit fault diagnosis method proposed in the present invention achieves efficient and accurate identification of MMC submodule open circuit faults by cleverly integrating the advantages of the GOOSE optimization algorithm and DBSCAN. The introduction of the GOOSE optimization algorithm significantly improves the speed and accuracy of data processing, so that the fault diagnosis process can quickly respond to changes in the system state and meet application scenarios with high real-time requirements. The application of the DBSCAN algorithm, by virtue of its powerful clustering analysis capability, effectively distinguishes the data features of normal and faulty submodules, and can accurately locate each fault point one by one even in the complex situation where multiple fault points exist at the same time, greatly improving the accuracy of diagnosis.

[0056] The present invention does not need to rely on the installation of additional hardware sensors, thus avoiding the problems of increased costs and increased system complexity. At the same time, it is not limited by changes in specific system parameters and exhibits good adaptability and robustness, which means that no matter how the size and structure of the power system changes, the method can maintain stable diagnostic performance. This innovative idea that does not rely on precise mathematical models has greatly broadened the scope of application of fault diagnosis technology, reduced the difficulty of implementation, and benefited various power electronic devices from large power grids to distributed energy systems.

[0057] In summary, the MMC submodule open circuit fault diagnosis method proposed in the present invention has high accuracy, strong real-time performance, low cost and wide applicability.

[0058] Other advantages, objectives and features of the present invention will be described in the following description to some extent, and to some extent, will be obvious to those skilled in the art based on the following examination and study, or can be taught from the practice of the present invention. The objectives and other advantages of the present invention can be realized and obtained through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention will be described in detail below in conjunction with the accompanying drawings, wherein:

[0060] Figure 1 It is a schematic diagram of the overall process of the open circuit fault diagnosis method of the present invention;

[0061] Figure 2 It is a schematic diagram of the fault classification process of the improved density clustering algorithm of the present invention;

[0062] Figure 3 It is a fault diagnosis simulation waveform diagram of an open circuit of device Tu in SM1 of the MMC simulation model under the embodiment;

[0063] Figure 4It is a fault diagnosis simulation waveform diagram of an open circuit between the device Tu in SM1 and the device T1 in SM2 of the MMC simulation model under the embodiment;

[0064] Figure 5 It is a fault diagnosis simulation waveform diagram of the device Tu and the device T1 in SM1 of the MMC simulation model under the embodiment being open at the same time. DETAILED DESCRIPTION

[0065] The following describes the embodiments of the present invention by specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments only illustrate the basic concept of the present invention in a schematic manner, and the following embodiments and features in the embodiments can be combined with each other without conflict.

[0066] Among them, the drawings are only used for illustrative explanations, and they only represent schematic diagrams rather than actual pictures, and should not be understood as limitations on the present invention. In order to better illustrate the embodiments of the present invention, some parts of the drawings may be omitted, enlarged or reduced, and do not represent the size of actual products. For those skilled in the art, it is understandable that some well-known structures and their descriptions in the drawings may be omitted.

[0067] The same or similar numbers in the drawings of the embodiments of the present invention correspond to the same or similar parts; in the description of the present invention, it should be understood that if the terms "upper", "lower", "left", "right", "front", "rear", etc. indicate the orientation or position relationship, they are based on the orientation or position relationship shown in the drawings, which is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operate in a specific orientation. Therefore, the terms describing the position relationship in the drawings are only used for illustrative purposes and cannot be understood as limiting the present invention. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to specific circumstances.

[0068] See also Figure 1 to Figure 5 , which is a MMC sub-module IGBT open circuit fault diagnosis method based on improved density clustering algorithm.

[0069] Example

[0070] This embodiment is based on building a three-phase 6-level MMC simulation model in Simulink, and proposes a specific implementation method of an MMC sub-module IGBT open circuit fault diagnosis method based on an improved density clustering algorithm, such as Figure 1 As shown, it includes the following steps:

[0071] S1, extracting the capacitor voltage of each submodule in the modular multilevel converter, and obtaining the capacitor voltage spectrum after discrete processing and Fourier transformation;

[0072] S2, extracting a feature vector including a frequency mean, a root mean square frequency, a standard deviation frequency and a center frequency according to the capacitor voltage spectrum;

[0073] S3, using the density clustering algorithm based on GOOSE improvement to classify the feature vector corresponding to each submodule;

[0074] S4. The submodule corresponding to the output result of the improved density clustering algorithm with noise points is determined as an open circuit fault, and the fault type is determined according to the device position in the submodule.

[0075] In step S1, the capacitor voltage v of each submodule SM(i) is extracted from the actual modular multilevel converter MMC. ci In this embodiment, the capacitor voltage is extracted from the constructed MMC simulation model. ci Discrete processing is performed to obtain the discrete capacitor voltage U c (n), for discrete capacitor voltage U c (n) After performing fast Fourier transform (FFT), the capacitor voltage spectrum c(k) is obtained, where k = 1, 2, ..., M, where M is the number of spectrum lines, and f c (k) is the corresponding frequency value.

[0076] In step S2, the frequency mean F is selected from the capacitor voltage spectrum c(k). c1 , RMS frequency F c2 , standard deviation frequency F c3 and the center of gravity frequency F c4 is the feature quantity, and all the feature quantities are combined into a feature vector F C , where the corresponding feature quantity is calculated as follows:

[0077]

[0078]

[0079]

[0080]

[0081] Then the eigenvector F C It is expressed as:

[0082] F C =(F c1 ,F c2 ,Fc3 ,F c4 )

[0083] In step S3, the feature vector F C As the data to be classified by the algorithm, the improved density clustering algorithm DBSCAN based on the GOOSE optimization algorithm is used for fault classification; in the traditional density clustering algorithm, there is a sample t, t∈F C And in a certain neighborhood radius ε R In it, there are several sample quantities. If the number of samples N R (t)≥ρ minp When , the sample t is called the core point, ρ minp is the density threshold set. If the number of samples in the neighborhood of sample t is less than the density threshold, but it is in the neighborhood of a core point, it is called a boundary point. A point that is neither a core point nor a boundary point is a noise point. In the present invention, the neighborhood radius ε is optimized by the GOOSE algorithm. R and density threshold ρ minp Specifically, Figure 2 As shown, the improved density clustering algorithm DBSCAN based on the GOOSE optimization algorithm of the present invention performs fault classification, including the following steps:

[0084] S31, the feature vector F corresponding to each submodule C As data to be classified;

[0085] S32, using GOOSE optimization algorithm to optimize the neighborhood radius ε R and density threshold ρ minp Search for the best parameters;

[0086] S33, the feature vector F corresponding to the submodule C Select a sample point from the optimal area radius ε R and density threshold ρ minp Determine each sample point as a core point, boundary point or noise point to complete the classification.

[0087] In step S32, the neighborhood radius ε is optimized using the GOOSE optimization algorithm. R and density threshold ρ minp The process is:

[0088] S321, first initialize the population position X gi , that is, randomly select a group (ε R ,ρ minp ), and specify the search space range and the maximum number of iterations Z m , if the initial position of the population exceeds the search space, it is reinitialized;

[0089] S322, evaluate the fitness G of each goose position ai And compare to obtain the best fitness G abest and the corresponding optimal position X abest And save it, where the development and exploration stages are allocated by introducing a random variable rd, where 0≤rd≤1. If rd≥0.5, it enters the development stage, otherwise it enters the exploration stage, and another two random variables po and co are introduced to select the formula, and their values ​​are between 0 and 1, where co should also be less than a certain specified value α, otherwise α is assigned to co.

[0090] During the development phase, the weight of the stone under the goose's feet in the current iteration is calculated using the following formula:

[0091] W s =rand([w1,w2],1,1)

[0092] Where w1 and w2 are the estimated ranges of the stone weight, and then the time T required for the stone to fall to the ground is calculated. e1 The time it takes for the sound of the stone hitting the ground to propagate to other individuals T e2 :

[0093] T e1 =rand(1,D)

[0094] T e2 =rand(1,D)

[0095] Where D is the dimension of each iteration, then the total time T for the sound to propagate to each individual in a single iteration is e and average time T av for:

[0096]

[0097]

[0098] The distance S that sound travels a and the distance S between the two bodies g As follows, where the distance between individuals is half the distance that sound travels:

[0099] S a =v a ·T e2

[0100]

[0101] where υ a is the speed of sound in air.

[0102] If variables po and W s If they are greater than a certain specified value a and b respectively, the falling speed of the stone υ can be calculated by the following formula s And update the goose's position:

[0103]

[0104]

[0105] Where g is the acceleration due to gravity.

[0106] If variables po and W s If they are less than a certain specified value a and b respectively, the falling speed v of the stone can be calculated by the following formula s And update the goose's position:

[0107]

[0108]

[0109] In the search phase, the sound propagation time T in each iteration is compared e And find the shortest time M e , and introduce a new variable β, the formula is as follows:

[0110]

[0111] Where lp is the current iteration number, M x is the total number of iterations. Then the position of the goose can be updated according to the following formula:

[0112] X a(it+1) =rand(1,D)·(M e β)+X abest

[0113] Repeat the above steps until the maximum number of iterations is reached or the preset conditions are met, and the algorithm outputs the optimal position X abest is the optimal parameter.

[0114] In step S4, if there are noise points in the results output by the improved density clustering algorithm, it is determined that the corresponding submodule has an open circuit fault, and according to the different device locations of the open circuit fault, it is divided into three categories: upper switch tube Tu fault of the submodule, lower switch tube Tl fault, and simultaneous fault of devices Tu and Tl, which are represented by fault signals 1, 2, and 3 respectively.

[0115] In this embodiment, a three-phase 6-level MMC simulation model is built, and the MMC submodules are analyzed using the improved density clustering algorithm. The first submodule Tu of the upper bridge arm is set to have an open circuit fault, and the algorithm is used to perform open circuit fault diagnosis. The fault diagnosis simulation waveform is as follows Figure 3As shown, the fault time is 0.263s, the diagnosis time is 0.267s, and the time taken is 4.1ms.

[0116] Set the upper bridge arm SM1's Tu and SM2's Tl to have an open circuit fault, and perform fault diagnosis. The simulation waveform is as follows Figure 4 As shown, the fault time of SM1 is 0.263s, the diagnosis time is 0.267s, and the time taken is 4.2ms; the fault time of SM2 is 0.320s, the diagnosis time is 0.324s, and the time taken is 4.6ms.

[0117] Set Tu and Tl of the upper bridge arm SM1 to fail at the same time, perform fault diagnosis, and the simulation waveform is as follows Figure 5 As shown, the fault time is 0.275s, the diagnosis time is 0.279s, and the time taken is 4.5ms.

[0118] In summary, the proposed method can effectively diagnose the open circuit fault of the sub-module IGBT, has a fast judgment speed and can diagnose multiple fault points.

[0119] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the purpose and scope of the technical solution, which should be included in the scope of the claims of the present invention.

Claims

1. A method for diagnosing open-circuit faults of MMC submodule IGBTs based on an improved density clustering algorithm, characterized in that: It includes the following steps: S1, extracting the capacitor voltage of each submodule in the modular multilevel converter, and obtaining the capacitor voltage spectrum after discrete processing and Fourier transformation; S2, extracting a feature vector including a frequency mean, a root mean square frequency, a standard deviation frequency and a center frequency according to the capacitor voltage spectrum; S3, using the density clustering algorithm improved based on the GOOSE optimization algorithm to classify the feature vector corresponding to each submodule; S4. The submodule corresponding to the output result of the improved density clustering algorithm with noise points is determined as an open circuit fault, and the fault type is determined according to the device position in the submodule.

2. The MMC submodule IGBT open circuit fault diagnosis method based on improved density clustering algorithm according to claim 1, characterized in that: In step S1, the capacitor voltage v of each submodule SM(i) is extracted from the modular multilevel converter MMC. ci , the capacitor voltage v ci Discrete processing is performed to obtain the discrete capacitor voltage U c (n), for discrete capacitor voltage U c (n) After fast Fourier transform, the capacitor voltage spectrum c(k) is obtained, k = 1, 2, ..., M, M is the number of spectrum lines, and f c (k) represents the corresponding frequency value.

3. The MMC submodule IGBT open circuit fault diagnosis method based on improved density clustering algorithm according to claim 2, characterized in that: In step S2, the characteristic vector F is extracted from the capacitor voltage spectrum c(k). C : F C =(F c1 ,F c2 ,F c3 ,F c4 ) Among them, F c1 ,F c2 ,F c3 ,F c4 is the extracted feature quantity, and its corresponding calculation methods are: In the above formula, F c1 ,F c2 ,F c3 ,F c4 They represent the frequency mean, root mean square frequency, standard deviation frequency, and center frequency respectively.

4. The MMC submodule IGBT open circuit fault diagnosis method based on improved density clustering algorithm according to claim 3, characterized in that: In step S3, a density clustering algorithm based on the GOOSE optimization algorithm is used to cluster the feature vector F C Each sample in is classified, which includes the following steps: S31, the feature vector F corresponding to each submodule C As data to be classified; S32, using GOOSE optimization algorithm to optimize the neighborhood radius ε R and density threshold ρ minp Search for the best parameters; S33, the feature vector F corresponding to the submodule C Select a sample point from the optimal area radius ε R and density threshold ρ minp Determine each sample point as a core point, boundary point or noise point, and then complete the classification, where For a sample point t, t∈F C , then first calculate the neighborhood radius ε of the sample point t R The number of samples in N R (t), and determine the neighborhood radius ε R The number of samples in N R (t)≥ρ minp Whether it is satisfied, if it is satisfied, the sample point t is the core point; if not, it is further judged whether the sample point t falls within the neighborhood radius ε of another core point R If it falls within, the sample t is a boundary point, otherwise the sample point t is a noise point.

5. The MMC submodule IGBT open circuit fault diagnosis method based on improved density clustering algorithm according to claim 4, characterized in that: In step S32, the neighborhood radius ε is optimized using the GOOSE optimization algorithm. R and density threshold ρ minp The process is: S321, first initialize the population position X gi , that is, randomly select a group (ε R ,ρ minp ), and specify the search space range and the maximum number of iterations Z m , if the initial position of the population exceeds the search space, it is reinitialized; S322, evaluate the fitness G of each goose position ai And compare to obtain the best fitness G abest and the corresponding optimal position X abest And save it, where the development and exploration stages are allocated by introducing a random variable rd, where 0≤rd≤1. If rd≥0.5, it enters the development stage, otherwise it enters the exploration stage, and two other random variables po and co are introduced to select the formula, and their values ​​are between 0 and 1, where co should also be less than the specified value α, otherwise α is assigned to co.

6. The MMC submodule IGBT open circuit fault diagnosis method based on improved density clustering algorithm according to claim 5, characterized in that: During the development phase, the weight of the stone under the goose's feet in the current iteration is calculated using the following formula: W s =rand([w1,w2],1,1) Where w1 and w2 are the estimated ranges of the stone weight, and then the time T required for the stone to fall to the ground is calculated. e1 The time it takes for the sound of the stone hitting the ground to propagate to other individuals T e2 : T e1 =rand(1,D) T e2 =rand(1,D) Where D is the dimension of each iteration, then the total time T for the sound to propagate to each individual in a single iteration is e and average time T av for: The distance S that sound travels a and the distance S between the two bodies g As follows, where the distance between individuals is half the distance that sound travels: S a =υ a ·T e2 where υ a is the speed of sound in air; If the variables po and W s If they are greater than the specified values ​​a and b respectively, the falling speed of the stone υ is calculated by the following formula s And update the goose's position: Where g is the acceleration due to gravity; If the variables po and W s If they are less than the specified values ​​a and b respectively, the falling speed of the stone υ is calculated by the following formula s And update the goose's position: Where, X a(it+1) Represents the updated position of the goose.

7. The MMC submodule IGBT open circuit fault diagnosis method based on improved density clustering algorithm according to claim 6, characterized in that: In the search phase, the sound propagation time T in each iteration is compared e And find the shortest time M e , and introduce a new variable β, the formula is as follows: Where lp is the current iteration number, M x is the total number of iterations, and the position of the goose is updated according to the following formula: X a(it+1) =rand(1,D)·(M e ·β)+X abest Repeat the above steps until the maximum number of iterations is reached or the preset conditions are met, and the algorithm outputs the optimal position X abest is the optimal parameter.

8. The MMC submodule IGBT open circuit fault diagnosis method based on improved density clustering algorithm according to claim 5, characterized in that: In step S4, if there are noise points in the results output by the improved density clustering algorithm, it is determined that the corresponding submodule has an open circuit fault, and according to the different device locations of the open circuit fault, it is divided into device Tu fault, device Tl fault, and simultaneous fault of devices Tu and Tl.

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