Method and device for identifying dominant inducements in multi-device harmonic coupling process

Abstract

The invention provides a dominant inducement identification method and device for a multi-device harmonic coupling process, which can be applied to the technical field of power distribution networks. The method comprises the following steps: constructing a plurality of reference models of each converter device under a plurality of working conditions based on monitoring data of each converter device; screening characteristic sub-harmonics based on the plurality of reference models of each converter device under the plurality of working conditions to obtain a plurality of characteristic sub-harmonics of each converter device under the plurality of working conditions; determining harmonic coupling leading equipment of the equipment group under each characteristic subharmonic combination corresponding to the working condition combination based on the plurality of characteristic subharmonics of the equipment group under the working condition represented by the working condition combination and a reference model of the equipment group under the working condition represented by the working condition combination; and based on harmonic coupling leading equipment of the equipment group under each characteristic sub-harmonic combination corresponding to the working condition combination, obtaining a leading inducement of the equipment group under the working condition combination.

Description

Technical Field

[0001] The present invention relates to the technical field of power distribution networks, and more specifically, to a method and device for identifying a dominant cause of a multi-device harmonic coupling process. Background Art

[0002] Currently, more and more distributed renewable energy, electric vehicles, flexible loads and other types of sources and loads are connected to the power system through advanced power electronic conversion devices such as inverters and converters. During operation, these devices will inevitably inject a large amount of harmonics into the power grid due to their inherent nonlinear characteristics and pulse width modulation strategies. The harmonics emitted by these multi-harmonic sources interact and couple with each other, which aggravates the uncertainty of power system analysis and makes it difficult to characterize the harmonic emission level.

[0003] The existing harmonic source analysis methods are more suitable for traditional single harmonic sources, and are difficult to meet the requirements of harmonic characteristic analysis under the interaction of multiple types of equipment. They have limited applicability, and the harmonic coupling process of multiple devices is not clearly characterized, the dominant cause of coupling is not clear, and the degree of harmonic coupling is unclear, making it difficult to find the source of harmonic control problems. Summary of the invention

[0004] In view of this, the present invention provides a method and apparatus for identifying a dominant cause of a multi-device harmonic coupling process.

[0005] One aspect of the present invention provides a method for identifying the dominant cause of a multi-device harmonic coupling process, comprising: for multiple converters, based on the monitoring data of each converter, constructing multiple benchmark models of each converter under multiple operating conditions; based on the multiple benchmark models of each converter under multiple operating conditions, screening characteristic subharmonics to obtain multiple characteristic subharmonics of each converter under the multiple operating conditions; for each operating condition combination of each device group, based on the multiple characteristic subharmonics of the device group under the operating conditions represented by the operating condition combination and the benchmark model of the device group under the operating conditions represented by the operating condition combination, determining the harmonic coupling dominant device of the device group under each characteristic subharmonic combination corresponding to the operating condition combination; based on the harmonic coupling dominant device of the device group under each characteristic subharmonic combination corresponding to the operating condition combination, obtaining the dominant cause of the device group under the operating condition combination.

[0006] According to an embodiment of the present invention, based on the monitoring data of each converter device, multiple benchmark models of each converter device under multiple operating conditions are constructed, including: performing fuzzy mean clustering based on the active power data of each converter device to obtain multiple operating conditions when each converter device is independently connected to the distribution network; based on the harmonic current data of each converter device, using a Gaussian mixture model to fit the harmonic emission level of each converter device under multiple operating conditions to obtain multiple benchmark models of each converter device under multiple operating conditions.

[0007] According to an embodiment of the present invention, the above-mentioned fuzzy mean clustering is performed based on the active power data of each of the above-mentioned converter devices to obtain multiple operating conditions when each of the above-mentioned converter devices is independently connected to the above-mentioned distribution network, including: when the number of operating conditions of each of the above-mentioned converter devices is a preset value, fuzzy mean clustering is performed based on the active power data of each of the above-mentioned converter devices to obtain multiple cluster centers corresponding to the above-mentioned preset value; clustering evaluation is performed on the multiple cluster centers corresponding to the above-mentioned preset value based on the variance ratio criterion to obtain evaluation coefficients corresponding to the above-mentioned preset value; based on the evaluation coefficients corresponding to each of the above-mentioned preset values, a target preset value is determined from the multiple preset values; based on the multiple cluster centers corresponding to the target preset value, multiple operating conditions of each of the above-mentioned converter devices when it is independently connected to the above-mentioned distribution network are obtained.

[0008] According to an embodiment of the present invention, based on the harmonic current data of each of the above-mentioned converter devices, a Gaussian mixture model is used to fit the harmonic emission level of each of the above-mentioned converter devices under multiple working conditions, so as to obtain multiple benchmark models of each of the above-mentioned converter devices under multiple working conditions, including: for each working condition of each of the above-mentioned converter devices, the target harmonic current data associated with the above-mentioned working condition is determined from the harmonic current data of each of the above-mentioned converter devices; based on the above-mentioned target harmonic current data, the initial Gaussian mixture probability model is iteratively optimized with the maximum likelihood function as the optimization target, so as to obtain the benchmark model of the above-mentioned converter device under the above-mentioned working condition, wherein the above-mentioned maximum likelihood function is determined based on the above-mentioned initial Gaussian mixture probability model.

[0009] According to an embodiment of the present invention, the above-mentioned determination of the harmonic coupling dominant device of the above-mentioned device group in each characteristic subharmonic combination corresponding to the above-mentioned working condition combination based on the multiple characteristic subharmonics of the above-mentioned device group under the working condition represented by the above-mentioned working condition combination and the benchmark model of the above-mentioned device group under the working condition represented by the above-mentioned working condition combination includes: determining the difference measure of the above-mentioned device group under the above-mentioned working condition combination based on the multiple characteristic subharmonics of the above-mentioned device group under the working condition represented by the above-mentioned working condition combination and the benchmark model of the above-mentioned device group under the working condition represented by the above-mentioned working condition combination; determining the harmonic coupling dominant device of the above-mentioned device group in each characteristic subharmonic combination corresponding to the above-mentioned working condition combination based on the difference measure of the above-mentioned device group under the above-mentioned working condition combination.

[0010] According to an embodiment of the present invention, the above-mentioned determination of the difference measure of the above-mentioned device group under the above-mentioned working condition combination based on the multiple characteristic subharmonics of the above-mentioned device group under the working condition represented by the above-mentioned working condition combination and the benchmark model of the above-mentioned device group under the working condition represented by the above-mentioned working condition combination includes: determining the benchmark model probability distribution of each of the two target converter devices in the above-mentioned device group based on the benchmark model of each of the two target converter devices in the above-mentioned device group under the working condition represented by the above-mentioned working condition combination; determining the harmonic probability distribution of each of the two target converter devices in the above-mentioned device group based on the multiple characteristic subharmonics of each of the two target converter devices in the above-mentioned device group under the working condition represented by the above-mentioned working condition combination; performing divergence calculation based on the benchmark model probability distribution and harmonic probability distribution of each of the two target converter devices in the above-mentioned device group to obtain the measure value corresponding to the multiple characteristic subharmonics of each of the two target converter devices in the above-mentioned device group under the working condition represented by the above-mentioned working condition combination; determining the difference measure of the above-mentioned device group under the above-mentioned working condition combination based on the measure value corresponding to the multiple characteristic subharmonics of each of the two target converter devices in the above-mentioned device group under the working condition represented by the above-mentioned working condition combination.

[0011] According to an embodiment of the present invention, the above-mentioned determination of the harmonic coupling dominant device of the above-mentioned device group under each characteristic subharmonic combination corresponding to the above-mentioned working condition combination based on the difference measurement of the above-mentioned device group under the above-mentioned working condition combination includes: for each characteristic subharmonic combination, determining the measurement value corresponding to the above-mentioned characteristic subharmonic combination from the difference measurement of the above-mentioned device group under the above-mentioned working condition combination; calculating the causal coefficient of the above-mentioned device group based on the measurement value corresponding to the above-mentioned characteristic subharmonic combination; determining the harmonic coupling dominant device of the above-mentioned device group under the above-mentioned characteristic subharmonic combination based on the causal coefficient of the above-mentioned device group.

[0012] According to an embodiment of the present invention, the above-mentioned harmonic coupling dominant device based on the above-mentioned equipment group in each characteristic subharmonic combination corresponding to the above-mentioned working condition combination obtains the dominant inducement of the above-mentioned equipment group in the above-mentioned working condition combination, including: determining multiple dominant harmonic combinations based on the harmonic coupling dominant device of the above-mentioned equipment group in each characteristic subharmonic combination corresponding to the above-mentioned working condition combination; for each dominant harmonic combination, performing information gain calculation based on the above-mentioned first characteristic subharmonic and the above-mentioned at least one second characteristic subharmonic to obtain the information gain of each of the above-mentioned at least one second characteristic subharmonic relative to the above-mentioned first characteristic subharmonic; determining the dominant characteristic subharmonic of the above-mentioned equipment group in each dominant harmonic combination corresponding to the above-mentioned working condition combination based on the information gain of each of the above-mentioned at least one second characteristic subharmonic relative to the above-mentioned first characteristic subharmonic, so as to obtain the dominant inducement of the above-mentioned equipment group in the above-mentioned working condition combination.

[0013] According to an embodiment of the present invention, the information gain calculation based on the first characteristic subharmonic and the at least one second characteristic subharmonic to obtain the information gain of the dominant harmonic combination includes: obtaining the overall entropy corresponding to the first characteristic subharmonic and the overall entropy corresponding to each of the at least one second characteristic subharmonic based on the harmonic probability distribution corresponding to the first characteristic subharmonic and the harmonic probability distribution corresponding to each of the at least one second characteristic subharmonic respectively; calculating the conditional entropy of each second characteristic subharmonic relative to the first characteristic subharmonic based on the overall entropy corresponding to the first characteristic subharmonic and the overall entropy corresponding to each of the at least one second characteristic subharmonic; calculating the information gain of each of the at least one second characteristic subharmonic relative to the first characteristic subharmonic based on the conditional entropy of each of the at least one second characteristic subharmonic relative to the first characteristic subharmonic.

[0014] Another aspect of the present invention provides a method and apparatus for identifying the dominant cause of a multi-device harmonic coupling process, comprising: a construction module for constructing, for multiple converter devices, multiple benchmark models of each converter device under multiple operating conditions based on monitoring data of each converter device; a screening module for screening characteristic subharmonics based on the multiple benchmark models of each converter device under multiple operating conditions to obtain multiple characteristic subharmonics of each converter device under the multiple operating conditions; a dominant device determination module for, for each operating condition combination of each device group, screening the characteristic subharmonics based on the multiple characteristic subharmonics of the device group under the operating conditions represented by the operating condition combination and the characteristic subharmonics of the device group under the operating condition combination. A benchmark model under the operating conditions represented by the combination is used to determine the harmonic coupling dominant device of the above-mentioned device group under each characteristic subharmonic combination corresponding to the above-mentioned operating condition combination, wherein each device group includes two target converter devices, each operating condition combination of each device group includes one operating condition of each of the two target converter devices in the device group, and each characteristic subharmonic combination corresponding to the above-mentioned operating condition combination includes any characteristic subharmonic of each target converter device in the above-mentioned device group under the operating conditions represented by the operating condition combination; a dominant inducement determination module is used to obtain the dominant inducement of the above-mentioned device group under the above-mentioned operating condition combination based on the harmonic coupling dominant device of the above-mentioned device group under each characteristic subharmonic combination corresponding to the above-mentioned operating condition combination.

[0015] Another aspect of the present invention provides an electronic device, comprising: one or more processors; and a memory for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the method described above.

[0016] Another aspect of the present invention provides a computer-readable storage medium storing computer-executable instructions, which are used to implement the above method when executed.

[0017] Another aspect of the present invention provides a computer program product, the computer program product comprising computer executable instructions, and the instructions are used to implement the above method when executed.

[0018] According to an embodiment of the present invention, by constructing a benchmark model of multiple converter devices to accurately characterize the harmonic characteristics of the converter devices under different operating conditions, the benchmark models under different operating conditions are used to screen characteristic subharmonics to effectively identify characteristic subharmonics that have a significant impact on the harmonic coupling process. In addition, based on the benchmark models of multiple converter devices and the characteristic subharmonics with significant impact, the harmonic interaction coupling relationship of multiple converter devices under different operating condition combinations is analyzed to identify the dominant devices in the harmonic coupling process under different operating condition combinations, and determine the dominant characteristic subharmonics, thereby realizing the identification of key influencing factors of the harmonic coupling process of multiple devices, and improving the accuracy of harmonic tracing and the accuracy of harmonic power flow calculation. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The above and other objects, features and advantages of the present invention will become more apparent through the following description of the embodiments of the present invention with reference to the accompanying drawings, in which:

[0020] Figure 1 A flow chart of a method for identifying a dominant cause of a multi-device harmonic coupling process according to an embodiment of the present invention is shown.

[0021] Figure 2 A method for determining the information gain of a dominant harmonic combination according to an embodiment of the present invention is shown.

[0022] Figure 3 A schematic diagram of a method for identifying a dominant cause of a multi-device harmonic coupling process according to a specific embodiment of the present invention is shown.

[0023] Figure 4 A block diagram of a method and apparatus for identifying dominant causes of a multi-device harmonic coupling process according to an embodiment of the present invention is shown.

[0024] Figure 5 A block diagram of an electronic device suitable for implementing a method for identifying a dominant cause of a multi-device harmonic coupling process according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0025] Below, embodiments of the present invention will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the present invention. In the following detailed description, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of embodiments of the present invention. However, it is apparent that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of known structures and technologies are omitted to avoid unnecessary confusion of concepts of the present invention.

[0026] The terms used herein are only for describing specific embodiments and are not intended to limit the present invention. The terms "comprise", "include", etc. used herein indicate the existence of the features, steps, operations and / or components, but do not exclude the existence or addition of one or more other features, steps, operations or components.

[0027] All terms (including technical and scientific terms) used herein have the meanings commonly understood by those skilled in the art unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification and should not be interpreted in an idealized or overly rigid manner.

[0028] When using expressions such as "at least one of A, B, and C, etc.", they should generally be interpreted according to the meaning of the expression commonly understood by those skilled in the art (for example, "a system having at least one of A, B, and C" should include but is not limited to a system having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, C, etc.).

[0029] Driven by the dual goals of "dual carbon" and the strategy of "building a new power system", the dual-high characteristics of the power system have gradually become prominent. Through advanced power electronic conversion devices such as inverters and converters, distributed new energy, electric vehicles, flexible loads and other types of sources are cleverly connected to the power system. However, during operation, these devices will inevitably inject a large amount of harmonics into the power grid due to their inherent nonlinear characteristics and pulse width modulation (PWM) strategies. The harmonics emitted by these multiple harmonic sources interact and couple with each other, exacerbating the uncertainty of power system analysis and making it difficult to characterize the harmonic emission level. In addition, the interactive coupling of multiple types of harmonic sources makes the harmonic spectrum more complex.

[0030] Traditional harmonic modeling methods are often based on the assumption of a single harmonic source and ignore the impact of multi-source coupling, resulting in a decrease in the accuracy of harmonic prediction and analysis, and the problem of unclear harmonic control objects is becoming increasingly prominent.

[0031] In the face of this problem, existing harmonic coupling analysis methods, such as the harmonic state space (HSS) theory and the harmonic transfer function matrix model, both focus on multi-converter grid-connected systems. Although these two harmonic coupling analysis methods have revealed the harmonic coupling mechanism in multi-converter grid-connected systems to a certain extent, and comprehensively considered the impact of various control links of the system on the state variables and the cascade and parallel relationship of the converters, they still focus on a specific system structure and require the topology and control parameters of the converter to be known. However, since these internal structures and working principles may involve commercial secrets or technical protection, the topology of the converter is generally unknown. In addition, the computational complexity of the HSS model and its high sensitivity to changes in control parameters also pose considerable challenges to its promotion in practical applications.

[0032] In summary, the existing harmonic source analysis methods are more suitable for traditional single harmonic sources, and are difficult to meet the requirements of harmonic characteristic analysis under the interaction of multiple types of equipment. They have limited applicability, and the harmonic coupling process of multiple devices is not clearly characterized, the dominant cause of coupling is unclear, and the degree of harmonic coupling is unclear, which makes it difficult to find the source of harmonic control problems.

[0033] An embodiment of the present invention provides a method for identifying the dominant cause of a multi-device harmonic coupling process, by constructing a benchmark model of multiple converter devices to accurately characterize the harmonic characteristics of the converter devices under different operating conditions, and using the benchmark models under different operating conditions to screen characteristic subharmonics to effectively identify characteristic subharmonics that have a significant impact on the harmonic coupling process. In addition, based on the benchmark models of multiple converter devices and the characteristic subharmonics with significant impact, the harmonic interaction coupling relationship of multiple converter devices under different operating condition combinations is analyzed to identify the dominant device of the harmonic coupling process under different operating condition combinations, and determine the dominant characteristic subharmonics, thereby realizing the identification of key influencing factors of the multi-device harmonic coupling process, and improving the accuracy of harmonic tracing and the accuracy of harmonic power flow calculation.

[0034] Specifically, an embodiment of the present invention provides a method for identifying the dominant cause of a multi-device harmonic coupling process, including: for each operating condition combination of each device group, based on multiple characteristic subharmonics of the device group under the operating conditions represented by the operating condition combination and a benchmark model of the device group under the operating conditions represented by the operating condition combination, determining the respective harmonic coupling dominant devices of the device group under each of the multiple characteristic subharmonic combinations corresponding to the operating condition combination; any characteristic subharmonic of each target converter device under the operating conditions represented by the operating condition combination; based on the respective harmonic coupling dominant devices of the device group under each of the multiple characteristic subharmonic combinations corresponding to the operating condition combination, obtaining the dominant inducement of the device group under the operating condition combination.

[0035] Figure 1A flow chart of a method for identifying a dominant cause of a multi-device harmonic coupling process according to an embodiment of the present invention is shown.

[0036] like Figure 1 As shown, the method includes operations S101 to S104.

[0037] In operation S101 , for a plurality of converter devices, based on monitoring data of each converter device, a plurality of benchmark models of each converter device under a plurality of working conditions are constructed.

[0038] According to an embodiment of the present invention, for a plurality of converter devices in a distribution network, monitoring data is collected when each converter device is independently connected to the distribution network.

[0039] According to an embodiment of the present invention, in a distribution network, the harmonic emission level of each converter device is different under different working conditions. Therefore, for each converter device, a corresponding benchmark model is constructed based on its monitoring data under different working conditions. These benchmark models should be able to reflect the harmonic emission characteristics of each converter device under different working conditions. Among them, the monitoring data of each converter device when it is connected to the distribution network alone may include at least active power data, harmonic current data, and voltage data.

[0040] In operation S102, characteristic subharmonics are screened based on a plurality of reference models of each converter device under a plurality of operating conditions to obtain a plurality of characteristic subharmonics of each converter device under a plurality of operating conditions.

[0041] According to the embodiment of the present invention, each converter device has a reference model under each operating condition, and each reference model will obtain a characteristic subharmonic.

[0042] According to an embodiment of the present invention, based on a benchmark model of each converter device under multiple operating conditions, the expected value of the probability density distribution of each harmonic is calculated, and the expected value of the probability density distribution of each harmonic is compared with a set threshold to screen out multiple characteristic harmonics of each converter device under different operating conditions.

[0043] In operation S103, for each operating condition combination of each equipment group, based on multiple characteristic subharmonics of the equipment group under the operating conditions represented by the operating condition combination and a benchmark model of the equipment group under the operating conditions represented by the operating condition combination, a harmonic coupling dominant device of the equipment group under each characteristic subharmonic combination corresponding to the operating condition combination is determined.

[0044] According to an embodiment of the present invention, each device group includes two target converter devices, each target converter device has multiple operating conditions, and multiple operating conditions of the two target converter devices in each device group are combined in pairs to obtain multiple operating condition combinations of the device group. Each operating condition combination of the device group includes one operating condition of each of the two target converter devices, and multiple characteristic subharmonic combinations corresponding to the operating condition combination include one characteristic subharmonic of each of the two target converter devices.

[0045] According to an embodiment of the present invention, for the two target converter devices included in each device group, since the device group is easily interactively coupled when connected to the power distribution network at the same time, the harmonic emission level of the device group is affected. Therefore, based on the benchmark model of the device group under the working condition represented by the working condition combination, the changes of the two target converter devices under multiple working conditions when connected to the power distribution network can be analyzed, and at the same time, based on the multiple characteristic subharmonics of the device group under the working condition represented by the working condition combination, the coupling effect of the characteristic subharmonics between the two target converter devices can be analyzed.

[0046] According to an embodiment of the present invention, based on the changes in multiple operating conditions when two target converter devices are connected to a distribution network and the coupling effect of characteristic subharmonics between the two target converter devices, the harmonic coupling dominant devices of the two target converter devices in multiple characteristic subharmonic combinations corresponding to the operating condition combinations are determined during the coupling process.

[0047] In operation S104, based on the harmonic coupling dominant device of the device group under each characteristic subharmonic combination corresponding to the operating condition combination, a dominant inducement of the device group under the operating condition combination is obtained.

[0048] According to an embodiment of the present invention, based on the harmonic coupling dominant device of the equipment group under each characteristic subharmonic combination corresponding to the operating condition combination, the characteristic subharmonic that occupies the dominant influence is determined and used as the key influencing factor in the harmonic coupling process of the equipment group under the operating condition combination.

[0049] Based on this, the embodiment of the present invention constructs a benchmark model of multiple converter devices to accurately characterize the harmonic characteristics of the converter devices under different operating conditions, and uses the benchmark models under different operating conditions to screen characteristic subharmonics to effectively identify characteristic subharmonics that have a significant impact on the harmonic coupling process. In addition, based on the benchmark models of multiple converter devices and the characteristic subharmonics with significant impact, the harmonic interaction coupling relationship of multiple converter devices under different operating conditions is analyzed to identify the dominant devices in the harmonic coupling process under different operating conditions, and determine the dominant characteristic subharmonics, thereby realizing the identification of key influencing factors of the harmonic coupling process of multiple devices, and improving the accuracy of harmonic tracing and the accuracy of harmonic power flow calculation.

[0050] According to an embodiment of the present invention, based on the monitoring data of each converter device, multiple benchmark models of each converter device under multiple operating conditions of the converter device are constructed, including: performing fuzzy mean clustering based on the active power data of each converter device to obtain multiple operating conditions of each converter device when it is independently connected to the distribution network; and based on the harmonic current data of each converter device, using a Gaussian mixture model to fit the harmonic emission level of the converter device under multiple operating conditions of the converter device to obtain multiple benchmark models of each converter device under multiple operating conditions of the converter device.

[0051] According to an embodiment of the present invention, based on the active power data of each converter device within a preset time length, a fuzzy mean clustering algorithm can be used to divide the operating conditions of the converter device when it is independently connected to the distribution network, so as to obtain multiple operating conditions of each converter device when it is independently connected to the distribution network.

[0052] According to an embodiment of the present invention, based on the harmonic current data of each converter device within a preset time length, a Gaussian mixture model is used to perform harmonic source probability modeling on multiple operating conditions of each converter device, and the harmonic emission levels of each converter device under its multiple operating conditions are fitted based on the parameters of the harmonic current probability distribution, thereby obtaining respective benchmark models of each converter device under its multiple operating conditions.

[0053] According to an embodiment of the present invention, fuzzy mean clustering is performed based on the active power data of each converter device to obtain multiple operating conditions when each converter device is independently connected to the distribution network, including: when the number of operating conditions of each converter device is a preset value, fuzzy mean clustering is performed based on the active power data of each converter device to obtain multiple cluster centers corresponding to the preset value; clustering evaluation is performed on the multiple cluster centers corresponding to the preset value based on the variance ratio criterion to obtain evaluation coefficients corresponding to the preset value; based on the evaluation coefficients corresponding to each of the multiple preset values, a target preset value is determined from the multiple preset values; based on the multiple cluster centers corresponding to the target preset value, multiple operating conditions of each converter device when it is independently connected to the distribution network are obtained.

[0054] According to an embodiment of the present invention, the Fuzzy C-Means (FCM) clustering algorithm is a clustering method based on fuzzy logic, which can minimize an objective function, which is the sum of the weighted distances of all data points to their corresponding cluster centers. However, the FCM clustering algorithm is sensitive to parameter selection, especially the initial membership selection. Different initial selections may lead to different clustering results, which increases the instability of the algorithm. Therefore, the present invention adopts the variance ratio criterion and uses the Calinski-Harabasz index to evaluate the clustering effect to determine the number of optimal working conditions, thereby improving the FCM clustering algorithm. The higher the Calinski-Harabasz index, the better the clustering effect, that is, the greater the separation between clusters and the higher the compactness within the cluster. Among them, the objective function of the FCM clustering algorithm is as follows:

[0055] (1);

[0056] Where N represents the number of active power data, K represents the number of clusters, that is, the number of operating conditions of the converter when it is connected to the distribution network alone, and x i Indicates the i-th active power data, u ij is the membership of the i-th active power data to the j-th cluster, c j represents the cluster center, that is, the working condition of the converter device, and b represents the fuzziness degree. In a specific embodiment of the present invention, the fuzziness degree can be configured as b=2.

[0057] According to a specific embodiment of the present invention, the active power data of the converter device is input into the fuzzy C-means clustering model, and the initial clustering center is set And the number of clusters K for clustering initialization.

[0058] In this specific embodiment, for each active power data And each cluster , update its membership and cluster center. The update method is as follows:

[0059] (2);

[0060] (3);

[0061] In the formula, x n Indicates the nth active power data. i Represents the weighted average of all active power data belonging to cluster j.

[0062] In this specific embodiment, since each cluster center It can be used to represent various working conditions of the converter equipment. Therefore, when the preset value of the number of working conditions of the converter equipment is 2, 3, ..., 10, the number of clusters K is set to 2, 3, ..., 10. For each K value, its corresponding membership degree and cluster center are repeatedly updated until the convergence condition is reached.

[0063] In this specific embodiment, based on the variance ratio criterion, cluster evaluation is performed on multiple cluster centers corresponding to each K value, and the Calinski-Harabasz index under the clustering result corresponding to each K value is calculated to obtain the evaluation coefficient corresponding to each K value. The calculation formula of the Calinski-Harabasz index is as follows:

[0064] (4);

[0065] Where CH represents the Calinski-Harabasz index, tr(B K ) represents the trace of the cluster scatter matrix, that is, the sum of the diagonal elements of the matrix; tr(W K ) represents the trace of the scatter matrix within the cluster, that is, the compactness within the cluster.

[0066] In this specific embodiment, since the larger the Calinski-Harabasz index is, the better the clustering effect is. Therefore, based on formula (4), the K value with the largest Calinski-Harabasz index is selected as the optimal number of clusters, that is, as the target preset value. Based on the optimal number of clusters and its corresponding cluster centers, the final clustering results are output, that is, multiple operating conditions of the converter when it is connected to the distribution network alone.

[0067] Based on this, an embodiment of the present invention uses the Calinski-Harabasz index to improve the fuzzy mean clustering algorithm, so that the algorithm can automatically determine the optimal number of clusters, thereby optimally dividing the operating conditions of each converter device when it is individually connected to the distribution network, thereby avoiding the subjectivity and uncertainty of manual selection.

[0068] According to an embodiment of the present invention, based on the harmonic current data of each converter device, a Gaussian mixture model is used to fit the harmonic emission level of each converter device under multiple operating conditions, so as to obtain multiple benchmark models of the converter device under multiple operating conditions, including: for each operating condition of each converter device, determining target harmonic current data associated with the operating condition from the harmonic current data of each converter device; and based on the target harmonic current data, iteratively optimizing the initial Gaussian mixture probability model with the maximum likelihood function as the optimization target, so as to obtain a benchmark model of the converter device under each operating condition.

[0069] According to an embodiment of the present invention, for each operating condition of the converter device obtained after optimal division, the target harmonic current data associated with each operating condition is screened out from the harmonic current data of the converter device, and a Gaussian mixture model is constructed using the target harmonic current data to serve as a reference model under the operating condition. Specifically, the expression of the initial Gaussian mixture probability model is as follows:

[0070] (5);

[0071] (6);

[0072] In the formula, represents the initial Gaussian mixture probability model under the target harmonic current data; L represents the number of probability sub-distributions of harmonic current data; Parameters representing the probability sub-distribution of the lth harmonic current data; Represents the weight coefficient of the probability sub-distribution of the lth harmonic current data. The Gaussian distribution representing the probability sub-distribution of the lth harmonic current data.

[0073] According to an embodiment of the present invention, in formula (5) and formula (6), the parameters to be solved include In order to solve these parameters so that the probability of the Gaussian mixture model generating data under these parameters is maximized, the expectation maximization (EM) iterative algorithm can be used to iteratively optimize the initial Gaussian mixture probability model with the maximum likelihood function as the optimization goal. Specifically, the probability density of each target harmonic current data under each Gaussian distribution can be calculated, and the posterior probability can be calculated based on the weights of these probability densities and distributions; based on the posterior probability, the parameters to be solved can be updated using methods such as weighted averages and weighted covariance matrices. In order to ensure that the iteration does not cause the maximum likelihood function to fall into a local maximum, the initial value can be given multiple times for iteration. By repeating the above steps, until the parameter to be solved is less than the predetermined threshold, or the maximum number of iterations is reached, a set of iteration results that maximizes the maximum likelihood function is used as the final parameters to obtain the benchmark model of the converter under each working condition.

[0074] Based on this, an embodiment of the present invention uses a Gaussian mixture model to model and analyze the harmonic emission levels under multiple optimal operating conditions, so as to accurately fit the harmonic emission characteristics of the converter equipment under different operating conditions, so as to determine the key factors affecting the power quality, which is conducive to timely discovering changes in the operating status of the converter equipment and providing strong support for equipment maintenance and fault warning.

[0075] According to an embodiment of the present invention, based on a plurality of characteristic subharmonics of a device group under the working conditions represented by the working condition combination and a benchmark model of the device group under the working conditions represented by the working condition combination, respective harmonic coupling dominant devices of the device group under the plurality of characteristic subharmonic combinations corresponding to the working condition combination are determined, including: determining a difference measure of the device group under the working condition combination based on a plurality of characteristic subharmonics of the device group under the working conditions represented by the working condition combination and a benchmark model of the device group under the working conditions represented by the working condition combination; determining a harmonic coupling dominant device of the device group under each characteristic subharmonic combination corresponding to the working condition combination based on the difference measure of the device group under the working condition combination.

[0076] According to an embodiment of the present invention, based on multiple characteristic subharmonics of a device group under the working conditions represented by the working condition combination and a benchmark model of the device group under the working conditions represented by the working condition combination, a difference measure of the device group under the working condition combination is determined, including: determining the benchmark model probability distribution of each of the two target converter devices in the device group based on the benchmark models of each of the two target converter devices in the device group under the working conditions represented by the working condition combination; determining the harmonic probability distribution of each of the two target converter devices in the device group based on the multiple characteristic subharmonics of each of the two target converter devices in the device group under the working conditions represented by the working condition combination; performing divergence calculation based on the benchmark model probability distribution and harmonic probability distribution of each of the two target converter devices in the device group to obtain measure values ​​corresponding to the multiple characteristic subharmonics of each of the two target converter devices in the device group under the working conditions represented by the working condition combination; determining the difference measure of the device group under the working condition combination based on the measure values ​​corresponding to the multiple characteristic subharmonics of the two target converter devices under the working conditions represented by the working condition combination.

[0077] According to a specific embodiment of the present invention, based on multiple characteristic subharmonics and a benchmark model of two target converter devices of each device group under the operating conditions represented by the operating condition combination, the difference in the probability distribution of the characteristic subharmonic current of each target converter device is calculated to characterize the harmonic interaction coupling process of each device group.

[0078] Specifically, based on the benchmark models of the target converter devices included in the device group under the working conditions characterized by the working condition combination, the respective benchmark model probability distributions are determined. Under the same working condition combination, the probability distributions of multiple characteristic subharmonics of each target converter device are measured and recorded. Divergence such as the Kelly divergence (Kullback-Leibler divergence, KL) is used to quantify the difference between the benchmark model probability distribution of each target converter device and the probability distribution of the characteristic subharmonics. Based on the divergence metric, the overall difference metric of the device group under the working condition combination can be comprehensively evaluated.

[0079] According to an embodiment of the present invention, the probability distribution expression of the benchmark model after the target converter device is connected to the distribution network is as follows:

[0080] (7);

[0081] In the formula, represents the probability distribution of the benchmark model of Y equipment connected to the distribution network under the tth working condition combination, Y represents the name of the equipment, It represents the hth harmonic current, where h represents the harmonic order.

[0082] According to an embodiment of the present invention, traversing The probability distribution expression of the characteristic subharmonics after the target converter equipment is connected to the distribution network under the Jth operating condition combination is as follows:

[0083] (8);

[0084] In the formula, It represents the probability distribution of characteristic subharmonics when device Y is connected to the distribution network under the rth operating condition combination.

[0085] According to an embodiment of the present invention, the expression of the Kelly divergence (Kullback-Leibler divergence, KL) is as follows:

[0086] (9);

[0087] In the formula, It represents the Kelly dispersion of the h-order harmonic current of the Y device under the r-th operating condition combination, that is, the difference coefficient between the probability distribution of the baseline model of the Y device under the r-th operating condition combination and the probability distribution of the h-order harmonic current, A measure of the difference between the baseline model probability distribution of device Y and the probability distribution of the characteristic subharmonics.

[0088] According to a specific embodiment of the present invention, a device group includes a charging pile A and a distributed photovoltaic B, wherein the charging pile A has m operating conditions and the distributed photovoltaic B has n operating conditions, then the device group has Based on the combination of charging pile A and distributed photovoltaic B The harmonic emission level of the equipment group is analyzed by using the respective benchmark models under the working conditions represented by the working condition combinations. Assuming that charging pile A has 3 working conditions and distributed photovoltaic B has 4 working conditions, if it is in the first working condition combination (i.e. charging pile A is in the first working condition and distributed photovoltaic B is in the first working condition), the characteristic harmonics of charging pile A include the 3rd, 7th and 9th harmonic currents, and the characteristic harmonics of distributed photovoltaic B include the 5th, 7th, 9th and 13th harmonic currents, respectively. The probability distribution expressions of the benchmark models of charging pile A and distributed photovoltaic B under the first working condition are as follows:

[0089] (10);

[0090] (11);

[0091] In the formula, represents the probability distribution of the benchmark model of charging pile A connected to the distribution network under the first operating condition combination, represents the probability sub-distribution of the benchmark model of charging pile A connected to the distribution network under the first operating condition combination, represents the probability distribution of the benchmark model of distributed photovoltaic B connected to the distribution network under the first operating condition combination, represents the probability sub-distribution of the benchmark model of distributed photovoltaic B connected to the distribution network under the first operating condition combination; in formula (10), the hth harmonic current The harmonic order h=3, 7, 9, in formula (11), the hth harmonic current The harmonic orders are h=5, 7, 9, 13.

[0092] In this specific embodiment, the probability distribution expressions of the characteristic subharmonics of the charging pile A and the distributed photovoltaic B under the first working condition are as follows:

[0093] (12);

[0094] (13);

[0095] In the formula, represents the probability distribution of the characteristic subharmonics of charging pile A connected to the distribution network under the first operating condition combination, represents the probability sub-distribution of the hth harmonic when charging pile A is connected to the distribution network under the first working condition combination, represents the probability distribution of characteristic subharmonics of distributed photovoltaic B connected to the distribution network under the first working condition combination, represents the probability sub-distribution of the hth harmonic when the distributed photovoltaic B is connected to the distribution network under the first working condition combination. In formula (12), the hth harmonic current The harmonic order h=3, 7, 9. In formula (13), the hth harmonic current The harmonic orders are h=5, 7, 9, 13.

[0096] In this specific embodiment, the KL divergence expression shown in formula (9) is used to measure the difference coefficients of the probability distribution of characteristic subharmonics corresponding to charging pile A and distributed photovoltaic B respectively. , the specific expression is as follows:

[0097] (14);

[0098] (15);

[0099] In formula (14), represents the probability sub-distribution of the benchmark model of charging pile A connected to the distribution network under the hth harmonic current of the first operating condition combination, where ;In formula (15), represents the probability sub-distribution of the benchmark model of distributed photovoltaic B connected to the distribution network under the hth harmonic current of the first operating condition combination, where .

[0100] In this specific embodiment, the difference measures of charging pile A and distributed photovoltaic B under the first working condition combination can be expressed as:

[0101] (16);

[0102] (17);

[0103] In the formula, represents the difference measure of charging pile A under the first working condition combination, It represents the difference measure of distributed photovoltaic B under the first working condition combination. Similarly, it can be calculated based on formula (10) to formula (17) to obtain The difference measurement of charging pile A and distributed photovoltaic B under different working conditions.

[0104] According to an embodiment of the present invention, based on the difference measurement of the equipment group under the working condition combination, the harmonic coupling dominant device of the equipment group under each characteristic subharmonic combination corresponding to the working condition combination is determined, including: for each characteristic subharmonic combination, determining the measurement value corresponding to the characteristic subharmonic combination from the difference measurement of the equipment group under the working condition combination; calculating the causal coefficient of the equipment group based on the measurement value corresponding to the characteristic subharmonic combination; and determining the harmonic coupling dominant device of the equipment group under the characteristic subharmonic combination based on the causal coefficient of the equipment group.

[0105] According to an embodiment of the present invention, the characteristic subharmonic combination can be obtained by combining the characteristic subharmonics of each device group under different working condition combinations. For example, under the first working condition combination, the characteristic subharmonics of charging pile A are 3rd, 7th, and 9th harmonic currents, and the characteristic subharmonics of distributed photovoltaic B are 5th, 7th, 9th, and 13th harmonic currents, then the characteristic subharmonic combination includes 12 combinations.

[0106] According to an embodiment of the present invention, for each characteristic subharmonic combination, a causal inference method can be used to calculate the causal coefficient under each characteristic subharmonic combination based on the difference measurement value corresponding to the characteristic subharmonic combination of the equipment group under multiple working condition combinations, and identify the harmonic coupling dominant equipment. Among them, the causal coefficient can be used to characterize the coupling relationship of the equipment group under different working condition combinations under each characteristic subharmonic combination. Specifically, the expression of the causal coefficient is as follows:

[0107] (18);

[0108] In the formula, represents the causal coefficient under the rth operating condition combination, It represents the difference between the h1th harmonic of the Y1 equipment and the benchmark model under the rth operating condition combination, It represents the difference between the h2 harmonic of the Y2 equipment under the rth operating condition combination and the benchmark model. It represents the entropy difference between the benchmark models corresponding to their respective characteristic subharmonics of the Y1 device and the Y2 device under the rth operating condition combination.

[0109] According to a specific embodiment of the present invention, when the equipment group includes a charging pile A and a distributed photovoltaic B, the causal coefficients of the charging pile A and the distributed photovoltaic B under each characteristic harmonic combination are calculated based on formula (18). Taking the third harmonic of the charging pile A and the fifth harmonic of the distributed photovoltaic B as examples, the calculation formula is as follows:

[0110] (19);

[0111] In the formula, It represents the causal coefficient of charging pile A being the third harmonic and distributed photovoltaic B being the fifth harmonic under the first operating condition combination. This causal coefficient can be used to reflect the mutual influence relationship between charging pile A being the third harmonic and distributed photovoltaic B being the fifth harmonic.

[0112] According to an embodiment of the present invention, when the causal coefficient is greater than 0, it means that the harmonics in the distributed photovoltaic B have a greater impact on the harmonic emission of the charging pile A. Therefore, the distributed photovoltaic B can be taken as the cause, and the harmonic emission level of the charging pile A can be taken as the result. In this case, the distributed photovoltaic B is dominant in the coupling process. When the causal coefficient is less than 0, it means that the harmonics in the charging pile A have a greater impact on the harmonic emission of the distributed photovoltaic B. Therefore, the charging pile A can be taken as the cause, and the harmonic emission level of the distributed photovoltaic B can be taken as the result. In this case, the charging pile A is dominant in the coupling process. Therefore, based on the causal coefficient of the device group, the harmonic coupling dominant device of the device group under the characteristic subharmonic combination can be determined.

[0113] Based on this, an embodiment of the present invention measures the difference of the equipment group under multiple working condition combinations to accurately determine the corresponding harmonic coupling dominant equipment under each characteristic subharmonic combination. It not only considers the performance of the equipment group under the overall working condition based on the difference measurement, but also analyzes the influence of the coupling relationship on the equipment group under each characteristic subharmonic combination based on the causal coefficient of the equipment group, thereby realizing the accurate identification of the harmonic coupling dominant equipment.

[0114] According to an embodiment of the present invention, based on the harmonic coupling dominant device of the equipment group in each characteristic subharmonic combination corresponding to the operating condition combination, the dominant inducement of the equipment group under the operating condition combination is obtained, including: determining a plurality of dominant harmonic combinations based on the harmonic coupling dominant device of the equipment group in each characteristic subharmonic combination corresponding to the operating condition combination; for each dominant harmonic combination, performing information gain calculation based on the first characteristic subharmonic and at least one second characteristic subharmonic to obtain the information gain of at least one second characteristic subharmonic relative to the first characteristic subharmonic; determining the respective dominant characteristic subharmonics of the equipment group in the plurality of dominant harmonic combinations corresponding to the operating condition combination based on the information gain of at least one second characteristic subharmonic relative to the first characteristic subharmonic, and obtaining the dominant inducement of the equipment group under the operating condition combination.

[0115] According to an embodiment of the present invention, based on the harmonic coupling dominant devices of the device group under multiple characteristic subharmonic combinations corresponding to the operating condition combination, for example, a harmonic combination with a high degree of coupling and a greater impact on the performance of the device group can be used as a dominant harmonic combination. Each dominant harmonic combination includes a first characteristic subharmonic of a first converter device in the device group and at least one second characteristic subharmonic of a second converter device in the device group.

[0116] According to an embodiment of the present invention, in at least one characteristic subharmonic combination consisting of the first characteristic subharmonic of a converter device and at least one second characteristic subharmonic of a second converter device, the second converter device is used as a harmonic coupling leading device. For example, when the 3rd harmonic of device A is the result, the 5th, 7th, and 13th harmonic currents of device B are the cause. Since device B includes the factors that have the main influence, device B is used as the harmonic coupling leading device.

[0117] According to an embodiment of the present invention, the information gain method is used to calculate the information gain of each second characteristic subharmonic relative to the first characteristic subharmonic, wherein the information gain can be used to reflect how much the information of the second characteristic subharmonic can reduce the uncertainty about the first characteristic subharmonic.

[0118] According to an embodiment of the present invention, for each dominant harmonic combination, the information gain of each second characteristic subharmonic relative to the first characteristic subharmonic is compared, and the second characteristic subharmonic with the largest information gain is selected as the dominant characteristic subharmonic of the dominant harmonic combination.

[0119] According to an embodiment of the present invention, the analysis results of multiple dominant harmonic combinations and their dominant characteristic subharmonics are combined to comprehensively determine the dominant inducement of the equipment group under the working condition combination. The dominant inducement may include, for example, specific harmonic frequencies, interactions between equipment, changes in working conditions, etc.

[0120] Figure 2 A method for determining the information gain of a dominant harmonic combination according to an embodiment of the present invention is shown.

[0121] like Figure 2 As shown, the method for determining the information gain of the dominant harmonic combination includes operations S201 to S203.

[0122] In operation S201, a total entropy corresponding to the first characteristic subharmonic and a total entropy corresponding to at least one second characteristic subharmonic are obtained based on the harmonic probability distribution corresponding to the first characteristic subharmonic and the harmonic probability distribution corresponding to at least one second characteristic subharmonic respectively.

[0123] According to an embodiment of the present invention, the overall entropy corresponding to the harmonic probability distribution corresponding to the first characteristic harmonic and the harmonic probability distribution corresponding to each of the plurality of second characteristic harmonics are calculated respectively by using the definition and calculation formula of entropy. The overall entropy can be used to reflect the uncertainty or confusion of the harmonic probability distribution. Specifically, the expression of the overall entropy is as follows:

[0124] (20);

[0125] In the formula, It represents the overall entropy corresponding to the probability sub-distribution of the hth harmonic when the Y device is connected to the distribution network under the rth operating condition combination; It represents the probability sub-distribution of the hth harmonic when the Y device is connected to the distribution network under the rth operating condition combination.

[0126] In operation S202 , conditional entropy of each second characteristic subharmonic relative to the first characteristic subharmonic is calculated based on the overall entropy corresponding to the first characteristic subharmonic and the overall entropy corresponding to each of the at least one second characteristic subharmonic.

[0127] According to an embodiment of the present invention, for each second characteristic subharmonic, it is necessary to determine the conditional probability distribution of the second characteristic subharmonic value under the condition of a given first characteristic subharmonic value. Using the definition and calculation formula of conditional entropy, the conditional entropy of each second characteristic subharmonic relative to the first characteristic subharmonic is calculated based on the conditional probability distribution. Among them, the conditional entropy can be used to reflect the reduction in the uncertainty or confusion of the second characteristic subharmonic value under the condition of a given first characteristic subharmonic value. Specifically, the expression of conditional entropy is as follows:

[0128] (twenty one);

[0129] In the formula, represents the conditional entropy of each second characteristic subharmonic relative to the first characteristic subharmonic; represents the harmonic probability sub-distribution under the first characteristic harmonic; represents the harmonic probability sub-distribution under the second characteristic harmonic; represents the harmonic probability sub-distribution of the h2 harmonic of the Y2 equipment under the rth operating condition combination. Therefore, formula (21) can be expressed as the influence of the harmonic probability sub-distribution under the first characteristic harmonic under the harmonic probability sub-distribution under the second characteristic harmonic, and the degree of influence is the conditional entropy.

[0130] In operation S203 , based on the conditional entropy of the at least one second characteristic subharmonic relative to the first characteristic subharmonic, the information gain of the at least one second characteristic subharmonic relative to the first characteristic subharmonic is calculated.

[0131] According to an embodiment of the present invention, information gain can be obtained based on the difference between the overall entropy and the conditional entropy. The information gain of each second characteristic subharmonic relative to the first characteristic subharmonic is calculated as follows:

[0132] (twenty two);

[0133] In the formula, Represents the information gain of each second characteristic subharmonic relative to the first characteristic subharmonic.

[0134] According to an embodiment of the present invention, by comparing the information gains of different second characteristic subharmonics relative to the first characteristic subharmonic, the second characteristic subharmonic with a larger information gain has a greater influence on the first characteristic subharmonic. Therefore, the second characteristic subharmonic with the largest information gain can be considered as the dominant characteristic subharmonic.

[0135] According to a specific embodiment of the present invention, when the equipment group includes a charging pile A and a distributed photovoltaic B, when the third harmonic in the charging pile A is the result and the fifth, seventh and thirteenth harmonic currents in the distributed photovoltaic B are the cause, the main influencing factors in the distributed photovoltaic B can be determined by formula (20) to formula (22), so as to clarify the dominant harmonic current in the distributed photovoltaic B that affects the third harmonic in the charging pile A.

[0136] Specifically, based on the above formula (20), the total entropy of the 3rd harmonic current in the charging pile A and the total entropy of the 5th, 7th, 9th, and 13th harmonic current in the distributed photovoltaic B can be calculated respectively, and the calculation method is as follows:

[0137] (twenty three);

[0138] (twenty four);

[0139] In the formula, It represents the total entropy corresponding to the third harmonic probability sub-distribution of charging pile A connected to the distribution network under the first working condition combination; represents the probability sub-distribution of the third harmonic when charging pile A is connected to the distribution network under the first working condition combination; , , , They represent the total entropy corresponding to the 5th, 7th, 9th and 13th harmonic probability sub-distributions of distributed photovoltaic B connected to the distribution network under the first working condition combination; , , , They respectively represent the 5th, 7th, 9th and 13th harmonic probability sub-distributions of distributed photovoltaic B connected to the distribution network under the first operating condition combination.

[0140] In this specific embodiment, based on the above formula (21), the conditional entropy of the 5th, 7th, 9th, and 13th harmonics of the distributed photovoltaic B relative to the 3rd harmonic of the charging pile A can be calculated respectively, and the calculation method is as follows:

[0141] (25);

[0142] In the formula, , , , They represent the conditional entropy of the 5th, 7th, 9th and 13th harmonics of distributed photovoltaic B relative to the 3rd harmonic of charging pile A respectively.

[0143] In this specific embodiment, based on the above formula (22), the information gain caused by the 5th, 7th, 9th and 13th harmonics of distributed photovoltaic B for the 3rd harmonic of charging pile A can be calculated respectively, and the calculation method is as follows:

[0144] (26);

[0145] In the formula, , , , They respectively represent the information gains caused by the 5th, 7th, 9th and 13th harmonics of distributed photovoltaic B for the 3rd harmonic of charging pile A.

[0146] In this specific embodiment, the information gain method is used to determine the dominant characteristic harmonic in the coupling process. The larger the value of the information gain, the more information about the charging pile A is obtained through a certain characteristic harmonic in the distributed photovoltaic B, that is, the greater the impact of the harmonic on the charging pile A.

[0147] Based on this, the embodiments of the present invention can accurately identify which devices have significant harmonic coupling relationships by analyzing the harmonic coupling dominant devices of the equipment group under a specific operating condition combination; in addition, by calculating the information gain, it can quantitatively evaluate the degree of influence of each second characteristic harmonic on the first characteristic harmonic, thereby determining the dominant characteristic harmonic, and combining the analysis results of the dominant characteristic harmonic to reveal the dominant cause of the equipment group under a specific operating condition combination, providing strong support for equipment fault troubleshooting and optimal design.

[0148] Figure 3 A schematic diagram of a method for identifying a dominant cause of a multi-device harmonic coupling process according to a specific embodiment of the present invention is shown.

[0149] like Figure 3 As shown, the method for identifying the dominant cause of the multi-device harmonic coupling process can include four stages: data collection, benchmark model establishment, dominant device identification, and dominant cause identification.

[0150] In the data collection stage of this specific embodiment, the power quality monitoring terminal can be used to collect 30 days of harmonic current amplitude and phase angle and active power data. In this stage, abnormal data can also be filtered out, and for missing data, a suitable interpolation method should be used to process it to ensure data integrity.

[0151] In the benchmark model establishment stage of this specific embodiment, the operating conditions of each converter device are divided based on the harmonics and highway data of each converter device to obtain the number of optimal conditions. The harmonic emission level of each converter device under multiple conditions is fitted using a Gaussian mixture model to obtain the initial benchmark model of each converter device under multiple conditions. Based on the initial benchmark model of each converter device under multiple conditions, the characteristic subharmonics are screened to obtain the harmonic current benchmark model of each converter device.

[0152] In the dominant device identification stage of this specific embodiment, based on the harmonic current benchmark model of each converter device, the characteristic subharmonic difference measurement of the device group under multiple working condition combinations is determined. Based on the characteristic subharmonic difference measurement, the causal coefficient of the device group is calculated and used as a causal indicator. The interaction effect is inferred through the causal indicator to obtain the positive and negative conditions of the causal coefficient, thereby determining the dominant device of the coupling process of the device group.

[0153] In the dominant cause identification stage of this specific embodiment, based on the causal relationship of the characteristic subharmonics of the multi-converter device, the characteristic subharmonic overall entropy and the characteristic subharmonic conditional entropy are calculated to quantify the degree of interaction. In addition, the information gain under the coupling of the multi-converter device is calculated by the characteristic subharmonic overall entropy and the characteristic subharmonic conditional entropy, so as to perform the dominant current analysis based on the information gain under the coupling of the multi-converter device.

[0154] According to a specific embodiment of the present invention, the dominant inducement identification method of the multi-device harmonic coupling process proposed by the present invention can effectively characterize the dominant device in the harmonic coupling process of the multi-converter device, and then clarify the dominant inducement in the coupling process. The comparison results of the method proposed by the present invention and the existing harmonic interaction model are shown in Table 1.

[0155] Table 1

[0156]

[0157] As shown in Table 1, the harmonic state space model and the harmonic transfer function model are mainly based on network parameters. The coupling relationship between multiple devices is clarified by deriving the transfer matrix or transfer function between the harmonics of each device. They belong to mechanism analysis modeling and have high requirements for parameter selection. Moreover, the above two methods only describe the coupling strength in the harmonic interaction process through the derived coupling coefficient, and do not specify the impact of specific harmonics in the coupling process. In the actual operation of the power system, since not all nodes are observable nodes, many parameters are unknown in the mechanism analysis process, which further increases the difficulty of harmonic interaction analysis and has limited applicability. The method proposed in the present invention is based on data-driven, and the dominant device in the coupling process of multiple converters is clarified through causal judgment. The dominant harmonics in the coupling process are further identified based on the information gain method, and the harmonic control object is clarified, which is more applicable.

[0158] Figure 4 A block diagram of a method and apparatus for identifying dominant causes of a multi-device harmonic coupling process according to an embodiment of the present invention is shown.

[0159] like Figure 4 As shown, the dominant inducement identification method and apparatus for the multi-device harmonic coupling process includes a construction module 410 , a screening module 420 , a dominant device determination module 430 and a dominant inducement determination module 440 .

[0160] The construction module 410 is used to construct, for each of the multiple converter devices, multiple benchmark models under multiple working conditions based on the monitoring data of each converter device.

[0161] The screening module 420 is used to screen characteristic subharmonics based on multiple benchmark models of each converter device under multiple working conditions, so as to obtain multiple characteristic subharmonics of each converter device under multiple working conditions.

[0162] The dominant device determination module 430 is used to determine, for each operating condition combination of each device group, the harmonic coupling dominant device of the device group under each characteristic subharmonic combination corresponding to the operating condition combination based on multiple characteristic subharmonics of the device group under the operating condition represented by the operating condition combination and a benchmark model of the device group under the operating condition represented by the operating condition combination.

[0163] The dominant inducement determination module 440 is used to obtain the dominant inducement of the equipment group under the operating condition combination based on the harmonic coupling dominant equipment of the equipment group under each characteristic subharmonic combination corresponding to the operating condition combination.

[0164] According to the embodiments of the present invention, any one or more of the modules, submodules, units, and subunits, or at least part of the functions of any one of them can be implemented in one module. According to the embodiments of the present invention, any one or more of the modules, submodules, units, and subunits can be split into multiple modules for implementation. According to the embodiments of the present invention, any one or more of the modules, submodules, units, and subunits can be at least partially implemented as hardware circuits, such as field programmable gate arrays (FPGAs), programmable logic arrays (PLAs), systems on chips, systems on substrates, systems on packages, application specific integrated circuits (ASICs), or can be implemented by hardware or firmware in any other reasonable way of integrating or packaging the circuit, or by any one of the three implementation methods of software, hardware, and firmware, or by a proper combination of any of them. Alternatively, according to the embodiments of the present invention, one or more of the modules, submodules, units, and subunits can be at least partially implemented as computer program modules, and when the computer program modules are run, the corresponding functions can be executed.

[0165] For example, any multiple of the construction module 410, the screening module 420, the leading device determination module 430, and the leading inducement determination module 440 can be combined in one module / unit / sub-unit for implementation, or any one of the modules / units / sub-units can be split into multiple modules / units / sub-units. Alternatively, at least part of the functions of one or more of these modules / units / sub-units can be combined with at least part of the functions of other modules / units / sub-units and implemented in one module / unit / sub-unit. According to an embodiment of the present invention, at least one of the construction module 410, the screening module 420, the leading device determination module 430, and the leading inducement determination module 440 can be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on a chip, a system on a substrate, a system on a package, an application specific integrated circuit (ASIC), or can be implemented by hardware or firmware such as any other reasonable way of integrating or packaging the circuit, or implemented in any one of the three implementation methods of software, hardware, and firmware, or in any appropriate combination of any of them. Alternatively, at least one of the construction module 410, the screening module 420, the dominant device determination module 430 and the dominant inducement determination module 440 may be at least partially implemented as a computer program module, and when the computer program module is executed, the corresponding function may be performed.

[0166] It should be noted that the dominant inducement identification device part of the multi-device harmonic coupling process in the embodiment of the present invention corresponds to the dominant inducement identification method part of the multi-device harmonic coupling process in the embodiment of the present invention. The description of the dominant inducement identification device part of the multi-device harmonic coupling process specifically refers to the dominant inducement identification method part of the multi-device harmonic coupling process, which will not be repeated here.

[0167] Figure 5 A block diagram of an electronic device suitable for implementing a method for identifying a dominant cause of a multi-device harmonic coupling process according to an embodiment of the present invention is shown. Figure 5 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0168] like Figure 5As shown, the electronic device according to an embodiment of the present invention includes a processor 501, which can perform various appropriate actions and processes according to the program stored in the read-only memory ROM 502 or the program loaded from the storage part 508 to the random access memory RAM 503. The processor 501 may include, for example, a general-purpose microprocessor (such as a CPU), an instruction set processor and / or a related chipset and / or a special-purpose microprocessor (for example, an application-specific integrated circuit (ASIC)), etc. The processor 501 may also include an on-board memory for caching purposes. The processor 501 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present invention.

[0169] In RAM 503, various programs and data required for the operation of the electronic device are stored. The processor 501, ROM 502 and RAM 503 are connected to each other via a bus 504. The processor 501 performs various operations of the method flow according to the embodiment of the present invention by executing the programs in ROM 502 and / or RAM 503. It should be noted that the program can also be stored in one or more memories other than ROM 502 and RAM 503. The processor 501 can also perform various operations of the method flow according to the embodiment of the present invention by executing the programs stored in the one or more memories.

[0170] According to an embodiment of the present invention, the electronic device may further include an input / output (I / O) interface 505, which is also connected to the bus 504. The electronic device may further include one or more of the following components connected to the input / output (I / O) interface 505: an input portion 506 including a keyboard, a mouse, etc.; an output portion 507 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage portion 508 including a hard disk, etc.; and a communication portion 509 including a network interface card such as a LAN card, a modem, etc. The communication portion 509 performs communication processing via a network such as the Internet. A drive 510 is also connected to the input / output (I / O) interface 505 as needed. A removable medium 511, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 510 as needed, so that a computer program read therefrom is installed into the storage portion 508 as needed.

[0171] According to an embodiment of the present invention, the method flow according to an embodiment of the present invention can be implemented as a computer software program. For example, an embodiment of the present invention includes a computer program product, which includes a computer program carried on a computer-readable storage medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 509, and / or installed from the removable medium 511. When the computer program is executed by the processor 501, the above-mentioned functions defined in the system of the embodiment of the present invention are executed. According to an embodiment of the present invention, the system, equipment, device, module, unit, etc. described above can be implemented by a computer program module.

[0172] The present invention also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiment; or may exist independently without being assembled into the device / apparatus / system. The above computer-readable storage medium carries one or more programs, and when the above one or more programs are executed, the method according to the embodiment of the present invention is implemented.

[0173] According to an embodiment of the present invention, the computer-readable storage medium may be a non-volatile computer-readable storage medium. For example, it may include but is not limited to: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present invention, the computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, an apparatus or a device.

[0174] For example, according to an embodiment of the present invention, the computer-readable storage medium may include the ROM 502 and / or the RAM 503 described above and / or one or more memories other than the ROM 502 and the RAM 503 .

[0175] An embodiment of the present invention also includes a computer program product, which includes a computer program, which contains program code for executing the method provided by the embodiment of the present invention. When the computer program product runs on an electronic device, the program code is used to enable the electronic device to implement the method for identifying the dominant cause of the multi-device harmonic coupling process provided by the embodiment of the present invention.

[0176] When the computer program is executed by the processor 501, the above functions defined in the system / device of the embodiment of the present invention are executed. According to the embodiment of the present invention, the system, device, module, unit, etc. described above can be implemented by a computer program module.

[0177] In one embodiment, the computer program may rely on tangible storage media such as optical storage devices, magnetic storage devices, etc. In another embodiment, the computer program may also be transmitted and distributed in the form of signals on a network medium, and downloaded and installed through the communication part 509, and / or installed from the removable medium 511. The program code contained in the computer program may be transmitted using any appropriate network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0178] According to an embodiment of the present invention, the program code for executing the computer program provided by the embodiment of the present invention can be written in any combination of one or more programming languages. Specifically, these computing programs can be implemented using high-level process and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, Java, C++, python, "C" language or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, partially on the remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (e.g., using an Internet service provider to connect through the Internet).

[0179] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present invention. In this regard, each box in the flowchart or block diagram may represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of the code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box may also occur in an order different from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions. It can be understood by those skilled in the art that the features recorded in the various embodiments of the present invention can be combined and / or combined in various ways, even if such a combination or combination is not explicitly recorded in the present invention. In particular, without departing from the spirit and teachings of the present invention, the features recorded in the various embodiments of the present invention can be combined and / or combined in various ways. All such combinations and / or combinations fall within the scope of the present invention.

[0180] The embodiments of the present invention are described above. However, these embodiments are only for the purpose of illustration, and are not intended to limit the scope of the present invention. Although each embodiment is described above, it does not mean that the measures in each embodiment cannot be used in combination advantageously. Without departing from the scope of the present invention, those skilled in the art may make various substitutions and modifications, which should all fall within the scope of the present invention.

Claims

1. A method for identifying the dominant cause of multi-device harmonic coupling process, characterized in that: The method for identifying the dominant cause of the multi-device harmonic coupling process includes: For a plurality of converter devices, based on monitoring data of each converter device, construct a plurality of benchmark models of each converter device under a plurality of working conditions; Based on a plurality of benchmark models of each converter device under a plurality of working conditions, characteristic subharmonics are screened to obtain a plurality of characteristic subharmonics of each converter device under the plurality of working conditions; For each operating condition combination of each device group, based on a plurality of characteristic subharmonics of the device group under the operating condition represented by the operating condition combination and a benchmark model of the device group under the operating condition represented by the operating condition combination, determine the harmonic coupling dominant device of the device group under each characteristic subharmonic combination corresponding to the operating condition combination, wherein each device group includes two target converter devices, each operating condition combination of each device group includes one operating condition of each of the two target converter devices in the device group, and each characteristic subharmonic combination corresponding to the operating condition combination includes any characteristic subharmonic of each target converter device in the device group under the operating condition represented by the operating condition combination; Based on the harmonic coupling dominant device of the equipment group under each characteristic subharmonic combination corresponding to the operating condition combination, the dominant inducement of the equipment group under the operating condition combination is obtained.

2. The method for identifying the dominant cause of the multi-device harmonic coupling process according to claim 1 is characterized in that: The monitoring data is collected when each of the current conversion devices is individually connected to the power distribution network, and the monitoring data includes active power data and harmonic current data; The step of constructing multiple benchmark models of each converter device under multiple working conditions based on the monitoring data of each converter device includes: Performing fuzzy mean clustering based on the active power data of each converter device to obtain multiple operating conditions of each converter device when it is independently connected to the distribution network; Based on the harmonic current data of each converter device, a Gaussian mixture model is used to fit the harmonic emission level of each converter device under multiple working conditions to obtain multiple benchmark models of each converter device under multiple working conditions.

3. The method for identifying the dominant cause of the multi-device harmonic coupling process according to claim 2 is characterized in that: The fuzzy mean clustering is performed based on the active power data of each converter device to obtain multiple operating conditions of each converter device when it is independently connected to the distribution network, including: When the number of operating conditions of each converter device is a preset value, fuzzy mean clustering is performed based on the active power data of each converter device to obtain a plurality of cluster centers corresponding to the preset value, wherein the cluster centers represent the operating conditions of each converter device; Performing cluster evaluation on a plurality of cluster centers corresponding to the preset value based on a variance ratio criterion to obtain an evaluation coefficient corresponding to the preset value; determining a target preset value from the plurality of preset values ​​based on evaluation coefficients corresponding to the plurality of preset values ​​respectively; Based on a plurality of cluster centers corresponding to the target preset value, a plurality of operating conditions of each converter device when it is independently connected to the power distribution network are obtained.

4. The method for identifying the dominant cause of the multi-device harmonic coupling process according to claim 2, characterized in that: The method of fitting the harmonic emission level of each converter device under multiple working conditions using a Gaussian mixture model based on the harmonic current data of each converter device to obtain multiple benchmark models of each converter device under multiple working conditions includes: For each operating condition of each of the current conversion devices, determining target harmonic current data associated with the operating condition from the harmonic current data of each of the current conversion devices; Based on the target harmonic current data, the initial Gaussian mixture probability model is iteratively optimized with the maximum likelihood function as the optimization target to obtain a benchmark model of each converter device under the working condition, wherein the maximum likelihood function is determined based on the initial Gaussian mixture probability model.

5. The method for identifying the dominant cause of the multi-device harmonic coupling process according to claim 1, characterized in that: The method of determining the harmonic coupling dominant device of the device group under each characteristic subharmonic combination corresponding to the operating condition combination based on a plurality of characteristic subharmonics of the device group under the operating condition represented by the operating condition combination and a benchmark model of the device group under the operating condition represented by the operating condition combination comprises: Determine a difference measure of the device group under the operating condition combination based on a plurality of characteristic subharmonics of the device group under the operating condition represented by the operating condition combination and a benchmark model of the device group under the operating condition represented by the operating condition combination; Based on the difference measure of the device group under the operating condition combination, the harmonic coupling dominant device of the device group under each characteristic subharmonic combination corresponding to the operating condition combination is determined.

6. The method for identifying the dominant cause of multi-device harmonic coupling process according to claim 5, characterized in that: The determining of the difference measure of the device group under the operating condition combination based on a plurality of characteristic subharmonics of the device group under the operating condition represented by the operating condition combination and a benchmark model of the device group under the operating condition represented by the operating condition combination comprises: Determining a probability distribution of a benchmark model of each of the two target converter devices in the device group based on a benchmark model of each of the two target converter devices in the device group under the operating condition represented by the operating condition combination; Determining the harmonic probability distribution of each of the two target converter devices in the device group based on a plurality of characteristic subharmonics of each of the two target converter devices in the device group under the operating condition represented by the operating condition combination; Based on the respective baseline model probability distribution and harmonic probability distribution of the two target converter devices in the device group, a divergence calculation is performed to obtain measurement values ​​corresponding to a plurality of characteristic subharmonics of the respective two target converter devices in the device group under the operating conditions represented by the operating condition combination; Based on the metric values ​​corresponding to the multiple characteristic subharmonics of each of the two target converter devices under the operating condition represented by the operating condition combination, a difference metric of the device group under the operating condition combination is determined.

7. The method for identifying the dominant cause of multi-device harmonic coupling process according to claim 5, characterized in that: The determining, based on the difference measure of the device group under the operating condition combination, the harmonic coupling dominant device of the device group under each characteristic subharmonic combination corresponding to the operating condition combination comprises: For each characteristic subharmonic combination, determining a metric value corresponding to the characteristic subharmonic combination from the difference metric of the equipment group under the operating condition combination; Calculating a causal coefficient of the device group based on a metric value corresponding to the characteristic subharmonic combination; Based on the causal coefficient of the device group, a harmonic coupling dominant device of the device group under the characteristic subharmonic combination is determined.

8. The method for identifying the dominant cause of the multi-device harmonic coupling process according to claim 1, characterized in that: The obtaining of the dominant inducement of the device group under the working condition combination based on the harmonic coupling dominant device of the device group under each characteristic subharmonic combination corresponding to the working condition combination includes: Determine a plurality of dominant harmonic combinations based on the harmonic coupling dominant device of the device group under each characteristic subharmonic combination corresponding to the operating condition combination, wherein each of the dominant harmonic combinations includes a first characteristic subharmonic of a first converter device in the device group and at least one second characteristic subharmonic of a second converter device in the device group, and in at least one characteristic subharmonic combination consisting of the first characteristic subharmonic of the first converter device and at least one second characteristic subharmonic of the second converter device, the second converter device serves as a harmonic coupling dominant device; For each dominant harmonic combination, information gain calculation is performed based on the first characteristic subharmonic and the at least one second characteristic subharmonic to obtain the information gain of each of the at least one second characteristic subharmonic relative to the first characteristic subharmonic; Based on the information gain of the at least one second characteristic subharmonic relative to the first characteristic subharmonic, the dominant characteristic subharmonic of the equipment group in each dominant harmonic combination corresponding to the operating condition combination is determined to obtain the dominant inducement of the equipment group under the operating condition combination.

9. The method for identifying the dominant cause of the multi-device harmonic coupling process according to claim 8, characterized in that: The calculating of information gain based on the first characteristic subharmonic and the at least one second characteristic subharmonic to obtain the information gain of the dominant harmonic combination includes: Obtaining a total entropy corresponding to the first characteristic subharmonic and a total entropy corresponding to each of the at least one second characteristic subharmonic based on the harmonic probability distribution corresponding to the first characteristic subharmonic and the harmonic probability distribution corresponding to each of the at least one second characteristic subharmonic; Based on the overall entropy corresponding to the first characteristic subharmonic and the overall entropy corresponding to each of the at least one second characteristic subharmonic, a conditional entropy of each second characteristic subharmonic relative to the first characteristic subharmonic is calculated; Based on the conditional entropy of each of the at least one second characteristic subharmonic relative to the first characteristic subharmonic, the information gain of each of the at least one second characteristic subharmonic relative to the first characteristic subharmonic is calculated.

10. A device for identifying the dominant cause of a multi-device harmonic coupling process, characterized in that: The dominant cause identification device of the multi-device harmonic coupling process includes: A construction module, for constructing, for each of the plurality of converter devices, a plurality of benchmark models of the converter devices under a plurality of working conditions based on monitoring data of each converter device; A screening module, configured to screen characteristic subharmonics based on a plurality of reference models of each converter device under a plurality of working conditions, to obtain a plurality of characteristic subharmonics of each converter device under the plurality of working conditions; A leading device determination module is used to determine, for each operating condition combination of each device group, a harmonic coupling leading device of the device group under each characteristic subharmonic combination corresponding to the operating condition combination based on a plurality of characteristic subharmonics of the device group under the operating condition represented by the operating condition combination and a benchmark model of the device group under the operating condition represented by the operating condition combination, wherein each device group includes two target converter devices, each operating condition combination of each device group includes one operating condition of each of the two target converter devices in the device group, and each characteristic subharmonic combination corresponding to the operating condition combination includes any characteristic subharmonic of each target converter device in the device group under the operating condition represented by the operating condition combination; The dominant inducement determination module is used to obtain the dominant inducement of the equipment group under the operating condition combination based on the harmonic coupling dominant equipment of the equipment group under each characteristic subharmonic combination corresponding to the operating condition combination.

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