A distributed photovoltaic inverter monitoring method and system

Abstract

The present application relates to the field of distributed photovoltaic inverter monitoring technology, and specifically to a distributed photovoltaic inverter monitoring method and system, the method comprising: determining the electrical coupling characteristic value between any two power generation nodes at the current moment based on the similarity and difference of voltage fluctuations between different nodes; determining the node association degree of each power generation node set at the current moment based on the degree of mutual influence between different power generation nodes; monitoring the voltage frequency of the power generation node based on the electrical coupling characteristic value between each power generation node and the central power generation node of the power generation node set to which it belongs, the voltage frequency of each power generation node, and the node association degree of the power generation node set to which each power generation node belongs. The present application enhances the monitoring sensitivity of the distributed photovoltaic inverter to the frequency deviation at the power generation node by comprehensively analyzing the degree of mutual influence between different power generation nodes and the similarity of voltage fluctuations.

Description

Technical Field

[0001] This application relates to the technical field of distributed photovoltaic inverter monitoring, and particularly to a distributed photovoltaic inverter monitoring method and system. Background Art

[0002] Due to the characteristics of cleanliness, renewability, and easy deployment, distributed photovoltaic power generation is gradually becoming an important part of the energy supply. Affected by nonlinear devices in the power generation system, power transmission and distribution system, and load power consumption system, the voltage frequency at the power generation node will have small fluctuations, resulting in a frequency deviation between it and the grid rated frequency, which affects the power quality and grid stability. By real-time monitoring the voltage frequency at the power generation node and using a photovoltaic inverter to control the node voltage, the frequency deviation at the power generation node can be reduced, and the power quality of the grid can be improved.

[0003] In a distributed photovoltaic power generation system on the factory roof, there is a high degree of electrical coupling between different factory buildings in the factory and different power generation nodes on the same factory roof, causing the voltages of multiple power generation nodes to be superimposed on each other, enhancing the influence between power generation nodes with different frequency deviations. Currently, when using a photovoltaic inverter to monitor the voltage frequencies of different power generation nodes in a distributed photovoltaic power generation system on the factory roof, the high degree of electrical coupling between the power generation nodes on the factory roof is not considered, and the influence between power generation nodes with different voltage frequencies is ignored, resulting in a decrease in the monitoring sensitivity of the distributed photovoltaic inverter to the frequency deviation at the power generation node. Summary of the Invention

[0004] To solve the above technical problems, the purpose of this application is to provide a distributed photovoltaic inverter monitoring method and system, and the specific technical solutions adopted are as follows:

[0005] In the first aspect, an embodiment of this application provides a distributed photovoltaic inverter monitoring method, which includes the following steps:

[0006] Use a distributed photovoltaic inverter to obtain the voltages of all acquisition moments of each power generation node in the distributed photovoltaic power generation system within a preset time period before the current moment, and the voltage frequencies of each power generation node at the current moment;

[0007] Based on the distribution of all voltages of each power generation node before the current moment, determine all types of voltage vectors of each power generation node at the current moment. Based on the similarity between any two power generation nodes for each type of voltage vector, determine the voltage fluctuation similarity between any two power generation nodes at the current moment, and combine the differences in the frequencies of all voltages between any two power generation nodes before the current moment to determine the electrical coupling eigenvalue between any two power generation nodes at the current moment;

[0008] Cluster all power generation nodes based on the electrical coupling eigenvalue to obtain all power generation node sets and their central power generation nodes, as well as the membership degrees between all power generation nodes and each central power generation node. Combine the electrical coupling eigenvalues between the central power generation node and all other power generation nodes within each power generation node set to determine the node correlation degree of each power generation node set at the current moment;

[0009] Based on the electrical coupling eigenvalues between each power generation node and the other power generation nodes in its affiliated power generation node set, as well as the voltage frequencies of each power generation node, determine the compensation coefficients of each power generation node at the current moment; Based on the voltage frequencies of each power generation node and the node correlation degrees of the power generation node sets to which each power generation node belongs, determine the compensation frequencies of each power generation node at the current moment, and monitor the voltage frequencies of the power generation nodes.

[0010] Preferably, the method for determining all types of voltage vectors of each power generation node at the current moment is as follows:

[0011] Arrange the voltages at all acquisition times of each power generation node before the current moment in ascending order of voltage magnitude. The voltages between the minimum value and the first quartile, between the first quartile and the median, between the median and the third quartile, and between the third quartile and the maximum value in the arrangement result are respectively formed into the first, second, third, and fourth sequences;

[0012] Calculate the mean values of all voltages in the four sequences respectively and form the voltage mean vector of each power generation node at the current moment; Calculate the variances of all voltages in the four sequences respectively and form the voltage variance vector of each power generation node at the current moment. All types of voltage vectors of each power generation node include the voltage mean vector and the voltage variance vector.

[0013] Preferably, the method for determining the voltage fluctuation similarity between any two power generation nodes at the current moment is as follows:

[0014] At the current moment, analyze the similarity of each type of voltage vector between any two power generation nodes. The voltage fluctuation similarity between any two power generation nodes at the current moment is the result of the fusion of the similarities of all types of voltage vectors at the current moment.

[0015] Preferably, the method for determining the electrical coupling eigenvalue between any two power generation nodes at the current moment is as follows:

[0016] Take the voltages at all acquisition times of each power generation node before the current moment as the input of the time-frequency conversion algorithm, and the output spectrum is the spectrum of each power generation node at the current moment;

[0017] Analyze the difference between the spectra of any two power generation nodes at the current moment, and use the ratio of the voltage fluctuation similarity between any two power generation nodes at the current moment to the difference as the electrical coupling eigenvalue between any two power generation nodes at the current moment.

[0018] Preferably, obtaining all power generation node sets and their central power generation nodes, as well as the membership degrees between all power generation nodes and each central power generation node, includes:

[0019] Take all power generation nodes as the input of the fuzzy C-means clustering algorithm, where the electrical coupling eigenvalue is used as the clustering distance of the fuzzy C-means clustering algorithm. Change the number of clustering clusters multiple times and output the clustering clusters under different settings of the total number of clustering clusters and the membership degrees between all power generation nodes and each clustering center;

[0020] The set quantity excellence degree when the total number of clustering clusters is K The expression is: ; In the formula, represents the membership degree between the power generation node n in the k-th clustering cluster and the clustering center of the k-th clustering cluster when the total number of clustering clusters is K; represents the number of all power generation nodes in the k-th clustering cluster when the total number of clustering clusters is K; K represents the total number of clustering clusters;

[0021] Within the preset total number of clustering clusters interval, take all the clustering clusters output by the fuzzy C-means clustering algorithm when the set quantity excellence degree is the largest, the clustering centers of the clustering clusters, and the membership degrees between all power generation nodes and each clustering center as the power generation node set, the central power generation node of the power generation node set, and the membership degrees between all power generation nodes and each central power generation node, respectively.

[0022] Preferably, the expression of the node correlation degree of each power generation node set at the current moment is: ; In the formula, represents the node correlation degree of the power generation node set i at the current moment; represents the electrical coupling eigenvalue between the j-th power generation node in the power generation node set i and the central power generation node of the power generation node set i at the current moment; represents the average value of the membership degrees between the j-th power generation node in the power generation node set i and the central power generation nodes of all other power generation node sets at the current moment; represents the membership degree between the j-th power generation node in the power generation node set i and the central power generation node of the power generation node set i at the current moment; represents the number of all power generation nodes in the power generation node set i at the current moment; represents the exponential function with the natural constant as the base.

[0023] Preferably, the expression for the compensation coefficient of each power generation node at the current moment is: ; where represents the compensation coefficient of power generation node j at the current moment; represents the electrical coupling eigenvalue between power generation node j and the q-th power generation node in the power generation node set where it is located; , respectively represent the voltage frequency of power generation node j and the voltage frequency of the q-th power generation node in the power generation node set where power generation node j is located at the current moment; represents the maximum value among the electrical coupling eigenvalues between power generation node j and all power generation nodes in the power generation node set where it is located at the current moment; represents the total number of power generation nodes in the power generation node set where power generation node j is located.

[0024] Preferably, the expression for the compensation frequency of each power generation node at the current moment is: ; where represents the compensation frequency of power generation node j at the current moment; represents the node correlation degree of the power generation node set where power generation node j is located at the current moment; norm( ) represents the normalization function.

[0025] Preferably, the monitoring of the voltage frequency of the power generation node includes:

[0026] If the compensation frequency of the power generation node at the current moment exceeds the preset frequency range, then the voltage frequency of this power generation node is abnormal; otherwise, the voltage frequency of this power generation node is normal.

[0027] In a second aspect, the embodiments of the present application further provide a distributed photovoltaic inverter monitoring system, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of the above-mentioned distributed photovoltaic inverter monitoring method according to any one of the above.

[0028] The present application has at least the following beneficial effects:

[0029] In this application, by analyzing the similarity and difference of voltage fluctuations between different nodes, an electrical coupling eigenvalue is constructed. The beneficial effect is that it can exclude the voltage frequency deviation caused by the state of factory equipment and the power grid structure, thereby improving the accuracy of voltage frequency monitoring. By analyzing the degree of mutual influence between power generation nodes, a node correlation degree is constructed. The beneficial effect is that it helps to distinguish the electrical coupling degree of different power generation node sets, thereby accurately judging the influence between power generation nodes with different voltage frequencies and improving the detection sensitivity. By comprehensively considering the electrical coupling eigenvalue and the node correlation degree, a compensation coefficient and a compensation frequency are calculated. The beneficial effect is that it can reduce the influence between power generation nodes with different voltage frequencies and improve the monitoring accuracy and sensitivity of the distributed photovoltaic inverter to the frequency deviation at the power generation node. By comprehensively considering the degree of mutual influence between different power generation nodes and the similarity of voltage fluctuations between different power generation nodes, the stability of the voltage frequency at the power generation node can be accurately monitored, and the monitoring sensitivity of the distributed photovoltaic inverter to the frequency deviation at the power generation node is enhanced. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present application or the prior art, the drawings required for the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0031] Figure 1 It is a flowchart of the steps of a method for monitoring a distributed photovoltaic inverter provided by an embodiment of the present application;

[0032] Figure 2 It is a schematic diagram of the process of obtaining a power generation node set provided by an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0033] In order to further elaborate on the technical means and effects adopted by the present application to achieve the intended invention purpose, the following, in conjunction with the drawings and preferred embodiments, details the specific implementation manners, structures, features, and effects of a method and system for monitoring a distributed photovoltaic inverter proposed according to the present application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0034] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which this application belongs.

[0035] The following specifically describes the specific solutions of a distributed photovoltaic inverter monitoring method and system provided by the present application in conjunction with the accompanying drawings.

[0036] Please refer to Figure 1 , which shows a flowchart of the steps of a distributed photovoltaic inverter monitoring method provided by an embodiment of the present application. The method includes the following steps:

[0037] Step S1: Use a distributed photovoltaic inverter to obtain the voltages of all collection moments of each power generation node in the distributed photovoltaic power generation system within a preset duration before the current moment, and the voltage frequencies of each power generation node at the current moment.

[0038] Install a distributed photovoltaic inverter at each photovoltaic power generation node to monitor and adjust the AC voltage incorporated into the power grid. Use the distributed photovoltaic inverter to obtain the voltages of all collection moments of each power generation node in the distributed photovoltaic power generation system within a preset duration before the current moment, and the voltage frequencies of each power generation node at the current moment, where the collection frequency of the voltage is F.

[0039] It should be noted that the values of the preset duration t and the collection frequency F are both artificially set. In this embodiment, the value of the preset duration t is 1 s, and the value of the collection frequency F is 1024 Hz. Implementations can also be set according to specific situations by themselves, and this embodiment does not make special restrictions.

[0040] Step S2: Based on the distribution of all voltages of each power generation node before the current moment, determine all types of voltage vectors of each power generation node at the current moment. Based on the similarity between each type of voltage vector between any two power generation nodes, determine the voltage fluctuation similarity between any two power generation nodes at the current moment, and combine the differences of all voltages between any two power generation nodes in the frequency domain before the current moment to determine the electrical coupling eigenvalue between any two power generation nodes at the current moment.

[0041] The installation positions of distributed photovoltaic inverters on the roofs of factory buildings are relatively compact, and their power grid structures are relatively simple. The degree of electrical coupling between different power generation nodes is relatively high, and the mutual influence of node voltages is relatively large. When monitoring the voltage frequency of the power grid where the distributed photovoltaic inverter monitoring node is incorporated into the power grid, the grid voltage collected is often the result of the superposition of voltages of different nodes, resulting in an error between the measured value of the voltage frequency and the actual voltage frequency of the distributed photovoltaic inverter incorporated into the power grid.

[0042] Since the power grid structure between different power generation nodes in the distributed photovoltaic power generation system on the roof of the factory building is relatively simple, for nodes with closer electrical distances, the superposition of their node voltages is more serious, and the fluctuations of voltage amplitudes are closer, reflecting a greater degree of electrical coupling between the two.

[0043] Therefore, by analyzing the similarity of voltage fluctuations between any two power generation nodes in the time domain and frequency domain, the electrical coupling eigenvalue between any two power generation nodes at the current moment is determined to characterize the electrical coupling degree between different power generation nodes. Specifically:

[0044] (1) Based on the distribution of all voltages of each power generation node before the current moment, all types of voltage vectors of each power generation node at the current moment are determined.

[0045] To obtain the voltage fluctuation characteristics, the voltages of all acquisition moments of each power generation node before the current moment are arranged in ascending order of voltage magnitude. All voltages between the minimum value and the first quartile, all voltages between the first quartile and the median, all voltages between the median and the third quartile, and all voltages between the third quartile and the maximum value in the arrangement result are respectively formed into the first, second, third, and fourth sequences;

[0046] Calculate the mean value of all voltages in the four sequences respectively and form the voltage mean vector of each power generation node at the current moment; calculate the variance of all voltages in the four sequences respectively and form the voltage variance vector of each power generation node at the current moment.

[0047] All types of voltage vectors include the voltage mean vector and the voltage variance vector.

[0048] (2) Based on the similarity of each type of voltage vector between any two power generation nodes, the voltage fluctuation similarity between any two power generation nodes at the current moment is determined.

[0049] Affected by the working state of factory building equipment, the voltage fluctuations of the AC voltage at the power generation node are different in different stages of its AC cycle. The same factory building or adjacent factory buildings usually have similar production schedules, their equipment working states are close, and at the same time, the electrical distance of the distributed PV inverter is small. At this time, the amplitude changes of the AC voltage at the power generation node are closer. The voltage mean vector reflects the change in the average voltage amplitude of the AC voltage in different stages of the AC cycle, and the voltage variance reflects the fluctuation degree of the voltage amplitude in the corresponding voltage interval in different stages of the AC voltage cycle. When the similarity of the voltage mean vector and the voltage variance vector of two power generation nodes is greater, the changes in the average voltage amplitude and the voltage fluctuation degree of the node voltage in different stages of the AC cycle are closer, and the voltage fluctuation similarity is greater.

[0050] At the current moment, analyze the similarity of each type of voltage vector between any two power generation nodes. The voltage fluctuation similarity between any two power generation nodes at the current moment is the result of the fusion of the similarities of all types of voltage vectors at the current moment. The specific implementation process is as follows:

[0051] Analyze the similarity between the mean voltage vectors of any two power generation nodes, which is denoted as the first similarity between any two power generation nodes;

[0052] Analyze the similarity between the voltage variance vectors of any two power generation nodes, which is denoted as the second similarity between any two power generation nodes;

[0053] The voltage fluctuation similarity between any two power generation nodes is the result of the fusion of the first similarity and the second similarity between any two power generation nodes.

[0054] It should be noted that there are many methods to measure the similarity between vectors. In this embodiment, the cosine similarity of each type of voltage vector between any two power generation nodes is calculated to measure the similarity of the voltage vectors between any two power generation nodes. Implementers can also use the reciprocal of the Euclidean distance or other methods to measure the similarity between vectors. Regarding the selection of methods to measure the similarity between vectors, this embodiment does not make special restrictions.

[0055] Among them, the calculation steps of the cosine similarity are well-known technologies, and the specific calculation process will not be elaborated here.

[0056] It should be understood that fusion refers to the result of combining two or more indicators through positive fusion, that is, combining two or more indicators through addition, multiplication, etc., in order to obtain a comprehensive indicator, so as to more comprehensively and accurately evaluate a certain phenomenon or problem. This fusion method is not limited to simple arithmetic operations, but can also include more complex statistical models and analysis methods. Implementers can choose according to specific circumstances, and this embodiment does not make special restrictions.

[0057] Preferably, in this embodiment, the voltage fluctuation similarity between any two power generation nodes is the sum of the first similarity and the second similarity between any two power generation nodes; in actual application processes, as other implementation methods, the voltage fluctuation similarity between any two power generation nodes is the exponential function value with the natural constant as the base and the sum of the first similarity and the second similarity between any two power generation nodes as the independent variable.

[0058] (3) Based on the voltage fluctuation similarity between any two power generation nodes at the current moment, and in combination with the differences of all voltages between any two power generation nodes in the frequency domain before the current moment, determine the electrical coupling eigenvalue between any two power generation nodes at the current moment.

[0059] Considering that there are various high-power production equipment in the factory connected to the power grid at the same time, the clutter generated by the operation of the equipment will be transmitted along the power grid to different power generation nodes, and superimposed on the AC voltage incorporated into the power grid by the distributed photovoltaic inverter, aggravating the harmonic pollution, and further increasing the deviation of the node voltage frequency.

[0060] Therefore, in order to exclude the interference of the coupling phenomenon between power generation nodes on the voltage frequency monitoring of power generation nodes, by analyzing the differences in the frequency domain of all voltages between any two power generation nodes and combining the voltage similarity fluctuation degree, the electrical coupling eigenvalue between any two power generation nodes at the current moment is determined, specifically as follows:

[0061] Taking the voltages at all acquisition moments of each power generation node before the current moment as the input of the time-frequency conversion algorithm, the frequency spectra of each power generation node are output. Due to the large-scale production of the factory, the production operations carried out in adjacent workshops are similar, the clutter frequencies generated by the equipment are close, and at the same time, the electrical distance between the power generation nodes at adjacent workshops is small, and their electrical coupling degree is high. The more the clutter of the same frequency is superimposed, so the closer the node frequency spectrum distribution is.

[0062] It should be noted that there are many commonly used time-frequency conversion algorithms. In this embodiment, the Fourier transform is used to obtain the frequency spectrum of the voltage in the frequency domain. Implementers can also use wavelet transform or other time-frequency conversion algorithms. There is no special limitation on the selection of the time-frequency conversion algorithm in this embodiment.

[0063] Among them, the Fourier transform is a well-known technology in the field of signal processing, and the specific process of converting time-domain data to the frequency domain will not be elaborated here.

[0064] Furthermore, analyze the differences between the frequency spectra of any two power generation nodes at the current moment, and take the ratio of the voltage fluctuation similarity between any two power generation nodes at the current moment to the difference as the electrical coupling eigenvalue between any two power generation nodes at the current moment.

[0065] It should be noted that the specific process of measuring the differences between the frequency spectra of any two power generation nodes at the current moment is as follows: calculate the DTW distance between all amplitudes in the frequency spectra of any two power generation nodes at the current moment, and use the DTW distance to reflect the differences between all amplitudes of any two power generation nodes. Implementers can also use other methods to measure the differences between data groups, such as Euclidean distance and Manhattan distance. There is no special limitation on the selection of the method for measuring the differences between data groups in this embodiment.

[0066] Among them, the calculation steps of the DTW distance are well-known technologies, and the specific calculation process will not be elaborated here.

[0067] It can be understood from the electrical coupling eigenvalue between any two power generation nodes at the current moment that due to the large-scale production of the factory, the production operations in adjacent workshops are similar, the clutter frequencies generated by the equipment are close, and at the same time, the distance between the power generation nodes in adjacent workshops is small, so their electrical coupling degree is high, and the more clutter of the same frequency is superimposed, so the node spectrum distribution is closer. The DTW distance reflects the difference between the spectra of any two power generation nodes. The smaller the DTW distance between the spectrum sequences of two power generation nodes, the greater the electrical coupling degree, that is, the greater the electrical coupling eigenvalue. In addition, the greater the voltage fluctuation similarity, the greater the electrical coupling degree of the power generation node, that is, the greater the electrical coupling eigenvalue, indicating that the mutual influence degree between the voltage frequencies of the two power generation nodes is greater; conversely, if the DTW distance between the spectrum sequences of the two power generation nodes is smaller, it indicates that the electrical coupling degree is smaller, that is, the electrical coupling eigenvalue is smaller. And the smaller the voltage fluctuation similarity, the smaller the electrical coupling degree of the power generation node, that is, the smaller the electrical coupling eigenvalue, indicating that the mutual influence degree between the voltage frequencies of the two power generation nodes is smaller.

[0068] Step S3: Cluster all power generation nodes based on the electrical coupling eigenvalue to obtain all power generation node sets and their central power generation nodes, as well as the membership degrees between all power generation nodes and each central power generation node, and combine the electrical coupling eigenvalues between the central power generation node and all other power generation nodes within each power generation node set to determine the node correlation degree of each power generation node set at the current moment.

[0069] There are significant differences in the electrical coupling degrees of power generation nodes between different workshops in the factory, and there are significant differences in the influence on the node voltage frequency. Due to the changes in the production tasks of different workshops in the factory at different times, the loads at the power generation nodes are constantly changing, and thus the mutual influence degree between the voltage frequencies of different power generation nodes is constantly changing. Therefore, it is necessary to accurately determine the number of power generation nodes in order to obtain the clustering results of power generation nodes that conform to the production changes of the factory.

[0070] Therefore, by analyzing the correlation between different power generation nodes and clustering all power generation nodes in combination with the clustering algorithm, the optimal power generation set under the optimal partitioning method is obtained, and the node correlation degree of the power generation node set is determined in combination with the electrical coupling eigenvalue. Specifically:

[0071] (1) Take all power generation nodes as the input of the fuzzy C-means clustering algorithm, where the electrical coupling eigenvalue is used as the clustering distance of the fuzzy C-means clustering algorithm, change the number of clustering clusters multiple times, and output the clustering clusters and the membership degrees between all power generation nodes and each clustering center under different settings of the total number of clustering clusters;

[0072] Among them, the fuzzy C-means clustering algorithm is a well-known technology, and the specific principle process of its implementation of clustering will not be elaborated.

[0073] (2) Further, based on the membership relationship between the power generation nodes and the clustering clusters under different clustering methods, a set of power generation nodes is determined, specifically as follows:

[0074] The set quantity excellence degree when the total number of clustering clusters is K The expression is: ; In the formula, represents the membership degree between the power generation node n in the k-th clustering cluster and the clustering center of the k-th clustering cluster when the total number of clustering clusters is K; represents the number of all power generation nodes in the k-th clustering cluster when the total number of clustering clusters is K; K represents the total number of clustering clusters.

[0075] It can be understood from the set quantity excellence degree that the objective function value output by the fuzzy C-means clustering algorithm reflects the electrical distance between nodes within different sets of the clustering output, and the membership degree between the power generation node and the clustering center reflects the electrical coupling degree between the power generation node and the clustering center. When the objective function value output by the fuzzy C-means clustering algorithm is smaller and the membership degree between the power generation node and the clustering center is larger, the degree of fit between each clustering cluster after clustering with this total number of clustering clusters and the working condition distribution of different power generation nodes in the current factory building photovoltaic power generation system is higher, and the obtained set quantity excellence degree is larger;

[0076] On the contrary, when the objective function value output by the fuzzy C-means clustering algorithm is smaller and the membership degree between the power generation node and the clustering center is smaller, the degree of fit between each clustering cluster after clustering with this total number of clustering clusters and the working condition distribution of different power generation nodes in the current factory building photovoltaic power generation system is lower, and the obtained set quantity excellence degree is smaller.

[0077] Further, within the preset total number of clustering clusters interval, all the clustering clusters output by the fuzzy C-means clustering algorithm when the set quantity excellence degree is the largest, the clustering centers of the clustering clusters, and the membership degrees between all the power generation nodes and each clustering center are respectively used as the set of power generation nodes, the central power generation nodes of the set of power generation nodes, and the membership degrees between all the power generation nodes and each central power generation node.

[0078] It should be noted that the value of the preset total number of clustering clusters interval is set artificially. In this embodiment, the total number of clustering clusters interval is [2, 20], that is, an integer is sequentially selected from it as the number of the total number of clustering clusters. For example, the number of clustering clusters in the fuzzy C-means clustering algorithm is set to 2, or the number of clustering clusters in the fuzzy C-means clustering algorithm is set to 3, that is, from 2 to 20, and the clustering effects under different settings of the total number of clustering clusters are analyzed respectively, so as to select the optimal clustering method. The implementer can also set the value range of the preset total number of clustering clusters interval according to the specific situation, and this embodiment does not make special restrictions.

[0079] Preferably, the schematic diagram of the process for obtaining the set of power generation nodes provided in this embodiment is asFigure 2 as shown

[0080] Furthermore, based on all power generation node sets and their central power generation nodes, as well as the membership degrees between all power generation nodes and each central power generation node, and combining the electrical coupling eigenvalues between the central power generation node and all other power generation nodes within each power generation node set, the node correlation degree of each power generation node set at the current moment is determined, specifically as follows:

[0081] The node correlation degree of power generation node set i has the following expression: ; where represents the electrical coupling eigenvalue between the j-th power generation node in power generation node set i and the central power generation node of power generation node set i at the current moment; represents the average value of the membership degrees between the j-th power generation node in power generation node set i and the central power generation nodes of all other power generation node sets at the current moment; represents the membership degree between the j-th power generation node in power generation node set i and the central power generation node of power generation node set i at the current moment; represents the number of all power generation nodes in power generation node set i at the current moment; represents the exponential function with the natural constant as the base.

[0082] It can be understood from the node correlation degree of each power generation node set at the current moment that when the electrical coupling eigenvalue between the central node and the other nodes is smaller, and if the difference between the membership degree between the power generation node and the central power generation node of its own power generation node set and the average value of the membership degrees between the power generation node and the central power generation nodes of all other power generation node sets is larger, that is is smaller, then the node correlation degree of the corresponding power generation node set is smaller, indicating that the overall mutual influence degree of the power generation nodes within the current power generation node set is smaller;

[0083] On the contrary, when the electrical coupling eigenvalue between the central node and the other nodes is larger, and if the difference between the membership degree between the power generation node and the central power generation node of its own power generation node set and the average value of the membership degrees between the power generation node and the central power generation nodes of all other power generation node sets is smaller, that is is smaller, then the node correlation degree of the corresponding power generation node set is larger, indicating that the overall mutual influence degree of the power generation nodes within the current power generation node set is larger.

[0084] Step S4: Determine the compensation coefficient of each power generation node at the current moment based on the electrical coupling eigenvalue between each power generation node and the remaining power generation nodes in its affiliated power generation node set, and the voltage frequency of each power generation node; determine the compensation frequency of each power generation node at the current moment based on the voltage frequency of each power generation node and the node correlation degree of the power generation node set to which each power generation node belongs, and monitor the voltage frequency of the power generation node.

[0085] To improve the monitoring sensitivity and accuracy of the distributed photovoltaic inverter for the frequency deviation at the power generation node, the voltage frequency of the power generation node is corrected by analyzing the electrical coupling eigenvalue between the power generation node and the central power generation node. Specifically:

[0086] The compensation coefficient of power generation node j at the current moment has the following expression: ; where represents the electrical coupling eigenvalue between power generation node j and the q-th power generation node in its power generation node set at the current moment; , respectively represent the voltage frequency of power generation node j and the voltage frequency of the q-th power generation node in the power generation node set where power generation node j is located at the current moment; represents the maximum value among the electrical coupling eigenvalues between power generation node j and all power generation nodes in its power generation node set at the current moment; represents the total number of power generation nodes in the power generation node set where power generation node j is located.

[0087] It can be understood from the compensation coefficient of each power generation node at the current moment that the greater the electrical coupling eigenvalue between two power generation nodes in the power generation node set and the greater the frequency difference, the greater the mutual influence degree of the voltage frequencies between the two, and the greater the absolute value of the compensation coefficient. When the frequency of the surrounding power generation nodes is higher than that of the current node, it will increase the measured frequency of the current node. To obtain the accurate actual frequency, the contribution of this node to calculating the compensation coefficient of the current node is negative at this time. At the same time, when the frequency of the surrounding nodes is lower than that of the current node, it will decrease the measured frequency of the current node. To obtain the accurate actual frequency, the contribution of this node to calculating the compensation coefficient of the current node is positive at this time to reduce its influence on the current node.

[0088] Furthermore, based on the voltage frequency of each power generation node and the node correlation degree of the power generation node set to which each power generation node belongs, determine the compensation frequency of each power generation node at the current moment. Specifically:

[0089] The compensation frequency of power generation node j has the following expression: ; where represents the compensation frequency of power generation node j at the current moment; It represents the node correlation degree of the power generation node set where the power generation node j is located at the current moment; norm( ) represents the normalization function.

[0090] It can be understood from the compensation frequencies of each power generation node at the current moment that the compensation coefficient represents the magnitude and sign of the frequency compensation for the node voltage frequency, and when the node correlation degree of the power generation node set is larger, the correlation degree of voltage changes between different power generation nodes within the set is larger, and the impact on the voltage frequency is greater; conversely, when the node correlation degree of the power generation node set is smaller, the correlation degree of voltage changes between different power generation nodes within the set is larger, and the impact on the voltage frequency is greater.

[0091] If the compensation frequency of the power generation node at the current moment exceeds the preset frequency range, then the voltage frequency of this power generation node is abnormal; conversely, if the compensation frequency of the power generation node at the current moment is within the preset frequency range, then the voltage frequency of this power generation node is normal.

[0092] It should be noted that the value of the preset frequency range is set artificially. In this embodiment, the value of the preset frequency range is [49.5, 50.5], and the implementer can also set it according to the specific situation by himself / herself. This embodiment does not make special restrictions.

[0093] Based on the same inventive concept as the above method, the embodiment of the present application also provides a distributed photovoltaic inverter monitoring system, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any one of the above-mentioned distributed photovoltaic inverter monitoring methods.

[0094] It should be noted that: the above sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above describes specific embodiments of this specification. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0095] Each embodiment in this specification is described in a progressive manner. The same or similar parts between each embodiment can be referred to each other, and the key point of each embodiment is to illustrate the differences from other embodiments.

[0096] The above are only the preferred embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the principle of the present application shall be included in the protection scope of the present application.

Claims

1. A distributed photovoltaic inverter monitoring method, characterized in that: The method comprises the following steps: The distributed photovoltaic inverter is used to obtain the voltage of each power generation node in the distributed photovoltaic power generation system at all acquisition times within a preset time period before the current moment, as well as the voltage frequency of each power generation node at the current moment; The voltages of all the collected moments of each power generation node before the current moment are arranged in ascending order according to the voltage magnitude, and the arrangement results are divided into four sequences; the means of all the voltages in the four sequences are calculated respectively, and they form the voltage mean vector of each power generation node at the current moment; the variances of all the voltages in the four sequences are calculated respectively, and they form the voltage variance vector of each power generation node at the current moment, and all the class voltage vectors of each power generation node include the voltage mean vector and the voltage variance vector, based on the similarity of each class voltage vector between any two power generation nodes, the voltage fluctuation similarity between any two power generation nodes at the current moment is determined, and combined with the differences in all the voltages between any two power generation nodes in the frequency domain before the current moment, the electrical coupling characteristic value between any two power generation nodes at the current moment is determined; Based on the electrical coupling eigenvalues, all power generation nodes are clustered to obtain the set of all power generation nodes and their central power generation nodes, as well as the membership between all power generation nodes and each central power generation node. In addition, the node association degree of each power generation node set at the current moment is determined by combining the electrical coupling eigenvalues ​​between the central power generation node and all other power generation nodes in each power generation node set. Based on the electrical coupling characteristic value between each power generation node and the other power generation nodes in the power generation node set to which it belongs, and the voltage frequency of each power generation node, the compensation coefficient of each power generation node at the current moment is determined; based on the voltage frequency of each power generation node and the node correlation degree of the power generation node set to which each power generation node belongs, the compensation frequency of each power generation node at the current moment is determined, and the voltage frequency of the power generation node is monitored; The method for determining the voltage fluctuation similarity between any two power generation nodes at the current moment is: At the current moment, the similarity of each type of voltage vector between any two power generation nodes is analyzed, and the voltage fluctuation similarity between any two power generation nodes at the current moment is the result of the fusion of the similarities of all types of voltage vectors at the current moment.

2. A distributed photovoltaic inverter monitoring method as claimed in claim 1, characterized in that: The method for determining the electrical coupling characteristic value between any two power generation nodes at the current moment is: The voltage of each power generation node at all acquisition moments before the current moment is used as the input of the time-frequency conversion algorithm, and the output spectrum is used as the spectrum of each power generation node at the current moment; The difference between the frequency spectra of any two power generation nodes at the current moment is analyzed, and the ratio of the voltage fluctuation similarity between any two power generation nodes at the current moment to the difference is used as the electrical coupling characteristic value between any two power generation nodes at the current moment.

3. A distributed photovoltaic inverter monitoring method as claimed in claim 1, characterized in that: The obtaining of the set of all power generation nodes and their central power generation nodes, and the membership between all power generation nodes and each central power generation node, includes: All power generation nodes are used as the input of the fuzzy C-means clustering algorithm, where the electrical coupling eigenvalue is used as the clustering distance of the fuzzy C-means clustering algorithm, the number of clusters is changed multiple times, and the clusters under different total number of clusters are set and the membership between all power generation nodes and each cluster center is output; The excellence of the number of clusters when the total number of clusters is K The expression is: ; In the formula, It represents the degree of membership between the power generation node n in the kth cluster and the cluster center of the kth cluster when the total number of clusters is K; It represents the number of all power generation nodes in the kth cluster when the total number of clusters is K; K represents the total number of clusters; Within the preset total number of clusters, all clusters output by the fuzzy C-means clustering algorithm when the set quantity excellence is maximum, the cluster centers of the clusters, and the membership degrees between all power generation nodes and each cluster center are respectively used as the power generation node set, the central power generation node of the power generation node set, and the membership degrees between all power generation nodes and each central power generation node.

4. A distributed photovoltaic inverter monitoring method as claimed in claim 1, characterized in that: The expression of the node association degree of each power generation node set at the current moment is: ; In the formula, Represents the node association degree of the power generation node set i at the current moment; represents the electrical coupling characteristic value between the jth power generation node in the power generation node set i and the central power generation node in the power generation node set i at the current moment; It represents the mean of the membership between the jth power generation node in the power generation node set i and the central power generation node of all other power generation node sets at the current moment; represents the membership degree between the jth power generation node in the power generation node set i and the central power generation node in the power generation node set i at the current moment; Represents the number of all power generation nodes in the power generation node set i at the current moment; Represents an exponential function with a natural constant as base.

5. A distributed photovoltaic inverter monitoring method as claimed in claim 4, characterized in that: The expression of the compensation coefficient of each power generation node at the current moment is: ; In the formula, represents the compensation coefficient of power generation node j at the current moment; represents the electrical coupling characteristic value between the power generation node j and the qth power generation node in the power generation node set to which it belongs at the current moment; , They represent the voltage frequency of the power generation node j at the current moment and the voltage frequency of the qth power generation node in the power generation node set where the power generation node j is located; It represents the maximum value of the electrical coupling characteristic values ​​between the power generation node j and all the power generation nodes in the power generation node set to which it belongs at the current moment; Represents the total number of power generation nodes in the power generation node set where power generation node j is located.

6. A distributed photovoltaic inverter monitoring method as claimed in claim 5, characterized in that: The expression of the compensation frequency of each power generation node at the current moment is: ; In the formula, represents the compensation frequency of power generation node j at the current moment; It represents the node association degree of the power generation node set where the power generation node j is located at the current moment; norm( ) represents the normalization function.

7. A distributed photovoltaic inverter monitoring method as claimed in claim 1, characterized in that: The monitoring of the voltage frequency of the power generation node includes: If the compensation frequency of the power generation node at the current moment exceeds the preset frequency range, the voltage frequency of the power generation node is abnormal; otherwise, the voltage frequency of the power generation node is normal.

8. A distributed photovoltaic inverter monitoring system, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the distributed photovoltaic inverter monitoring method as described in any one of claims 1 to 7 are implemented.

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