CN115308640B - MMC sub-module open-circuit fault positioning method based on data mining - Google Patents
MMC sub-module open-circuit fault positioning method based on data mining Download PDFInfo
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Abstract
The invention discloses a data mining-based MMC submodule open-circuit fault positioning method, and relates to the technical field of modularized multi-level converters. The invention is quick toThe submodule for rapidly detecting and positioning open-circuit faults in the MMC system is used as a target, one bridge arm is provided with N submodules, and the self-adaptive parameter cut-off distance D is automatically generated by using a cut-off average method according to the actual capacitance voltage value of each submodule at each sampling moment c The method comprises the steps of carrying out a first treatment on the surface of the Then, a Gaussian kernel method is used for generating local density rho, further, local distance delta is obtained, average local distance AVE of each sub-module is calculated according to the local density and the local distance, and the AVE with the largest value is judged to be an abnormal value; if the AVE of a certain sub-module continuously displays abnormality in three sampling periods, the corresponding sub-module is an abnormal sub-module. According to the invention, a complex mathematical model is not required to be constructed, an additional sensor is not required, and a fault threshold value is not required to be manually set, so that the fault sub-module can be positioned and cleared in time, and the operation reliability of the MMC is improved.
Description
Technical Field
The invention relates to the technical field of modularized multi-level converters, in particular to an MMC submodule open-circuit fault positioning method based on data mining
Background
Modular Multilevel Converters (MMCs) have become the first-choice converter topology of a flexible direct current transmission system, and the first-choice converter topology is formed by cascading a plurality of submodules (Sub-modules, SMs) with the same structure, and faults of power switching devices (IGBTs) of the MMC submodules can be divided into open-circuit faults and short-circuit faults, the IGBT short-circuit faults can be identified through conventional overvoltage detection, overcurrent detection and other diagnostic methods, and the IGBT open-circuit faults have no obvious fault characteristics under some working conditions and are difficult to find in time without specific diagnostic methods. Therefore, in order to ensure safe and reliable operation of the MMC, it is important to research the open-circuit fault characteristics of the IGBT in the MMC sub-module and propose an effective diagnosis and positioning method of the open-circuit fault of the IGBT.
In the prior art, there are various methods for detecting and positioning open faults of MMC submodules, and the methods are roughly divided into three types: 1) An additional sensor based approach; 2) A mathematical model-based approach; 3) A machine learning based method. However, the method increases the construction cost of the converter, and a complex mathematical model needs to be constructed, and the threshold value is usually required to be set manually, so that the design difficulty and the running cost of the system are increased, but the implementation difficulty is large in most cases, and the construction cost and the running cost are greatly increased.
Disclosure of Invention
The invention aims to provide a data mining-based MMC submodule open circuit fault positioning method for solving the problems in the background technology.
In order to achieve the above purpose, the present invention provides the following technical solutions: an MMC submodule open circuit fault positioning method based on data mining comprises the following steps:
in each fault detection period delta t, the capacitance voltage values of N sub-modules on a single bridge arm are updated and sampled, and according to the capacitance voltage sets U= { U of the N sub-modules c1 ,u c2 ,u c2 ,···,u cN Obtaining an indication coordinate set I U ＝{1,2,3,···,N}；
Acquiring capacitance voltage values actually acquired by N submodules in a single bridge arm at each sampling moment to obtain a self-adaptive parameter cut-off distance D c ；
Obtaining average local distance AVE of ith sub-module in N sub-modules on single bridge arm i ：
Wherein ρ is i Is the local density, delta, of the ith sub-module i Is the local distance of the ith sub-module
And defining the average local distance AVE with the largest value in N sub-modules on a single bridge arm as an abnormality, and if the average local distance AVE of a certain sub-module is the largest value in the N sub-modules AVE in three continuous sampling periods, the corresponding sub-module is the failure sub-module.
According to one aspect of the invention, the adaptive parameter truncates distance D c The acquisition method of (1) is as follows:
eliminating the maximum value and the minimum value of the capacitor voltage in N sub-modules of a single bridge arm, and processing the capacitor voltage of the remaining N-2 sub-modules as follows:
where MSE u Average capacitance voltage value representing remaining N-2 sub-modules, u cj Is the minimum value of the capacitance voltage in N sub-modules, u ck Is the maximum value of the capacitor voltage in the N sub-modules.
Cut-off distance D c Can be expressed as:
wherein u is ci Is the capacitance voltage value of the ith sub-module
Further, the local density ρ of the ith sub-module of the N sub-modules on a single leg i ：
Firstly, calculating a distance distribution matrix D according to capacitance voltage values of N sub-modules N×N ：
D N×N ＝{Dist(i,j)|1≤i≤N,1≤j≤N}
Wherein the method comprises the steps of
Dist(i,j)＝|u ci -u cj |
u ci 、u cj The capacitance voltage value of the ith sub-module and the capacitance voltage value of the jth sub-module are respectively, dist (i, j) is u ci And u cj Euclidean distance between them.
Reacquiring u ci Local density ρ of (2) i ：
Further, the local distance delta of the ith sub-module i ：
Wherein the method comprises the steps of
Wherein ρ is i 、ρ k The local density of the ith sub-module and the local density of the kth sub-module, respectively
According to another aspect of the present invention, there is provided a modular multilevel converter, including 6 bridge arms, each bridge arm is composed of a plurality of sub-modules with identical structures and bridge arm inductances, the sub-modules adopt a half-bridge structure, and each sub-module includes a power switch device, a diode electrically connected to the power switch device, and a dc capacitor electrically connected to the power switch device.
According to a further aspect of the invention, the invention also relates to a flexible direct current transmission system, the modular multilevel converter being a converter element of the flexible direct current transmission system, and the flexible direct current transmission system further comprising a converter unlock controller which unlocks the modular multilevel converter by means of a direct voltage.
According to yet another aspect of the present invention, there is provided an apparatus comprising one or more processors, a memory for storing one or more programs that, when executed by the one or more processors, cause the one or more processors to operate a data mining-based MMC sub-module open fault localization method.
The invention has at least the following beneficial effects:
the open-circuit fault positioning method for the MMC submodule based on data mining does not need an additional sensor, does not increase the construction cost of the modularized multi-level converter, is easy to implement in the existing modularized multi-level converter system, has strong practicability, does not need to construct a complex mathematical model during implementation, is only based on the difference of capacitance-voltage change between the fault submodule and the normal submodule, utilizes the correlation of data characteristics to develop fault diagnosis research, has less calculated data quantity, greatly reduces the operation cost, does not need to change the operation state of the MMC system, such as introducing circulation, therefore, the operation performance of the MMC system is not influenced, and can be applied to the MMC system under any working condition, thereby further improving the practicability.
Of course, it is not necessary for any one product to practice the invention to achieve all of the advantages set forth above at the same time.
Drawings
FIG. 1 is a schematic flow chart of the overall method of the present invention;
FIG. 2 is a topology block diagram of a three-phase MMC and submodules according to the present invention.
Detailed Description
The following description of the technical solutions in the embodiments of the present disclosure will be made clearly and fully with reference to the accompanying drawings in the embodiments of the present disclosure, and it is apparent that the described embodiments are only some embodiments of the present disclosure, not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments in this disclosure without inventive faculty, are intended to fall within the scope of this disclosure.
The invention provides a data mining-based MMC submodule open-circuit fault positioning method, wherein an MMC topological structure consists of six bridge arms, and each bridge arm comprises N identical submodules and a bridge arm inductor L as shown in figure 2 s The submodules adopt a half-bridge structure, and each submodule consists of two power switches T 1 、T 2 Two diodes D 1 、D 2 And a DC capacitor.
According to the method shown in fig. 1, the method for locating the open circuit fault of the MMC submodule based on data mining comprises the following steps:
in each fault detection period delta t (20 ms), the capacitance voltage values of N submodules on a single bridge arm are updated, and according to the capacitance voltage set U= { U c1 ,u c2 ,u c2 ,···,u cN Obtaining an indication coordinate set I U The ratio of = {1,2,3, the number of the components in the solution is }, the indication coordinate set is mainly used for solving the local distances of N sub-modules;
acquiring capacitance voltage values actually acquired by N submodules in a single bridge arm at each sampling moment, and obtaining self-adaptive parameter cutoff distance D by using a cutoff average method c ；
Generating local density rho by using a Gaussian kernel method, further obtaining local distance delta, and obtaining average local distance AVE of each sub-module according to the local density and the local distance;
the average local distance AVE with the largest value in N sub-modules on a single bridge arm is abnormal, and if the AVE of one sub-module is the largest value in N sub-modules AVE in three continuous sampling periods, the corresponding sub-module is the failure sub-module.
The adaptive parameter cut-off distance D c The acquisition of (1) comprises the steps of:
eliminating the maximum value and the minimum value of the capacitance voltages in N sub-modules in a single bridge arm, and processing the capacitance voltages of the remaining N-2 sub-modules as follows:
wherein u is cj Is the minimum value of the capacitance voltage in N sub-modules, u ck Is the maximum value of the capacitor voltage in the N sub-modules.
Cut-off distance D c Can be expressed as:
wherein u is ci Is the capacitance voltage value of the ith sub-module.
Further, a distance distribution matrix D is calculated according to the capacitance voltage values of the N sub-modules N×N The method of (2) is as follows:
D N×N ＝{Dist(i,j)|1≤i≤N,1≤j≤N}
wherein, the liquid crystal display device comprises a liquid crystal display device,
Dist(i,j)＝|u ci -u cj |
u ci 、u cj the capacitance voltage value of the ith sub-module and the capacitance voltage value of the jth sub-module are respectively, dist (i, j) is u ci And u cj Euclidean distance between them.
Calculating the local density ρ of the ith sub-module i ：
On the other hand, the local distance delta of the ith sub-module i The calculation formula of (2) is as follows:
wherein the method comprises the steps of
Wherein ρ is i 、ρ k The local density of the i-th sub-module and the local density of the k-th sub-module, respectively.
From this, it can be obtained that the average local distance AVE of the ith sub-module i The calculation method comprises the following steps:
wherein ρ is i Is the local density, delta, of the ith sub-module i Is the local distance of the ith sub-module.
It should be noted that the invention also provides a modularized multi-level converter, which comprises 6 bridge arms, each bridge arm is formed by cascading a plurality of sub-modules with the same structure, each sub-module adopts a half-bridge structure, and each sub-module comprises a power switch device, a diode electrically connected to the power switch device and a direct current capacitor electrically connected to the power switch device.
Further, the invention also relates to a flexible direct current transmission system, which applies the modularized multi-level converter as a converter element, and further comprises a converter unlocking controller, wherein the converter unlocking controller unlocks the modularized multi-level converter through direct current voltage.
In another aspect, the present invention also provides an apparatus, including one or more processors, a memory for storing one or more programs, which when executed by the one or more processors, cause the one or more processors to operate a data mining-based MMC submodule open fault positioning method
In particular, the invention is especially suitable for MMC systems, and compared with the traditional sub-module open-circuit fault positioning method, the invention does not need to construct a complex mathematical model, does not need to use an additional sensor or manually set a fault threshold value, can position and clear the fault sub-module in time, and improves the operation reliability of the modularized multi-level converter.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
The specific meaning of the above terms in the present invention can be understood by those of ordinary skill in the art according to the specific circumstances. When an element is referred to as being "mounted," "secured" or "disposed" on another element, it can be directly on the other element or intervening elements may also be present. When an element is referred to as being "connected" to another element, it can be directly connected to the other element or intervening elements may also be present. The terms "vertical," "horizontal," "upper," "lower," "left," "right," and the like are used herein for illustrative purposes only and do not denote a single embodiment.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
In the description of the present specification, the descriptions of the terms "one embodiment," "example," "specific example," and the like, mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present disclosure. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
Claims (7)
1. The MMC submodule open circuit fault positioning method based on data mining is characterized by comprising the following steps of:
at each failure detection period deltatUpdating on a single armNThe capacitance voltage value of the sub-module is sampled according toNIndividual sub-module capacitor voltage set
Get the indicated coordinate set +.>
；
Obtaining a single bridge armNThe submodule acquires the capacitance voltage value actually acquired at each sampling moment to obtain the self-adaptive parameter cut-off distanceD c ；
Adaptive parameter cutoff distanceD c The acquisition steps of (a) are as follows:
wherein the method comprises the steps ofMSE u Representing the remainderNThe average capacitance voltage value of 2 sub-modules,u cj is thatNThe minimum value of the capacitor voltage in the sub-module,u ck is thatNA maximum value of the capacitor voltage in the sub-module;
wherein the method comprises the steps ofu ci Is the firstiCapacitance voltage value in the sub-module;
Wherein the method comprises the steps ofρ i Is the firstiThe local density of the sub-modules is,δ i is the local distance of the ith sub-module;
first, theiLocal density of sub-modulesρ i The acquisition steps of (a) are as follows:
according toNDistance distribution matrix for capacitance voltage value calculation of submoduleD N×N ：
Wherein the method comprises the steps of
Wherein the method comprises the steps ofu ci 、u cj Respectively the firstiCapacitance-voltage value and first of sub-modulesjThe capacitance-voltage value of the sub-module,Dist(i,j) Is thatu ci Andu cj a Euclidean distance between them;
calculation ofu ci Is of the local density of (2)ρ i ：
First, theiLocal distance of individual sub-modulesδ i The calculation formula of (2) is as follows:
wherein the method comprises the steps of
Wherein the method comprises the steps ofρ i 、ρ k Respectively the firstiLocal density and first of sub-moduleskLocal density of sub-modules;
defining on a single armNAverage local distance of greatest value in sub-modulesAVEIf the average local distance of a certain sub-module is abnormalAVEThree consecutive sampling periods areNAnd the corresponding sub-module is a failure sub-module if the sub-module AVE is maximum.
2. The modular multilevel converter based on data mining is characterized by comprising 6 bridge arms, each bridge arm is formed by cascading a plurality of submodules with the same structure, and the open-circuit fault positioning method of the submodules uses the open-circuit fault positioning method of the MMC submodules based on data mining.
3. A modular multilevel converter based on data mining according to claim 2, wherein: the sub-modules are half-bridge type.
4. A modular multilevel converter based on data mining according to claim 2, wherein: the submodule comprises a power switching device, a diode electrically connected to the power switching device and a direct current capacitor electrically connected to the power switching device.
5. A flexible direct current transmission system comprising a modular multilevel converter according to any of claims 2-4 as a converter element.
6. A flexible direct current transmission system as claimed in claim 5, wherein: the system comprises a converter unlocking controller, wherein the converter unlocking controller unlocks the modularized multi-level converter through direct current voltage.
7. An apparatus, comprising:
one or more processors;
a memory for storing one or more programs;
the one or more programs, when executed by the one or more processors, cause the one or more processors to implement a data mining-based MMC sub-module open fault localization method as recited in claim 1.
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