Distributed photovoltaic inverter monitoring method and system

Abstract

The invention relates to the technical field of distributed photovoltaic inverter monitoring, in particular to a distributed photovoltaic inverter monitoring method and system, and the method comprises the steps: determining an electrical coupling characteristic value between any two power generation nodes at the current moment based on the voltage fluctuation similarity and difference between different nodes; determining the node association degree of each power generation node set at the current moment based on the mutual influence degree between different power generation nodes; and 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 the power generation node 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. According to the method, the mutual influence degree and the voltage fluctuation similarity between different power generation nodes are comprehensively analyzed, so that the monitoring sensitivity of the distributed photovoltaic inverter on the frequency deviation at the power generation nodes is enhanced.

Description

Technical Field

[0001] The present application relates to the technical field of distributed photovoltaic inverter monitoring, and in particular to a distributed photovoltaic inverter monitoring method and system. Background Art

[0002] Distributed photovoltaic power generation is becoming an important part of energy supply due to its clean, renewable and easy-to-deploy characteristics. Affected by nonlinear devices in the power generation system, transmission and distribution system and load power system, the voltage frequency at the power generation node will fluctuate slightly, resulting in a frequency deviation from the rated frequency of the power grid, affecting the power quality and the stability of the power grid. By real-time monitoring of the voltage frequency at the power generation node and using photovoltaic inverters to control the node voltage, the frequency deviation at the power generation node can be reduced and the power quality of the power grid can be improved.

[0003] In the distributed photovoltaic power generation system on the roof of the factory building, there is a high degree of electrical coupling between different factory buildings in the factory and different power generation nodes on the roof of the same factory building, which causes the voltages of multiple power generation nodes to overlap with each other, and enhances the influence between power generation nodes with different frequency deviations. At present, when using photovoltaic inverters to monitor the voltage and frequency of different power generation nodes in the distributed photovoltaic power generation system on the roof of the factory building, the high degree of electrical coupling between the power generation nodes on the roof of the factory building is not taken into account, and the influence between the power generation nodes with different voltage and frequency 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] In order to solve the above technical problems, the purpose of this application is to provide a distributed photovoltaic inverter monitoring method and system, and the technical solutions adopted are as follows: In a first aspect, an embodiment of the present application provides a distributed photovoltaic inverter monitoring method, the method comprising 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; Based on the distribution of all voltages of each power generation node before the current moment, all class voltage vectors of each power generation node at the current moment are determined, 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 difference of all 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 rest of the 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.

[0005] Preferably, the method for determining all class voltage vectors of each power generation node at the current moment is: Arrange the voltages of all the collected moments of each power generation node before the current moment in ascending order, and form the first, second, third, and fourth sequences respectively from the arrangement results including all the voltages between the minimum value and the first quartile, all the voltages between the first quartile and the median, all the voltages between the median and the third quartile, and all the voltages between the third quartile and the maximum value; The means of all voltages in the four sequences are calculated respectively, and the voltage mean vector of each power generation node at the current moment is formed; the variances of all voltages in the four sequences are calculated respectively, and the voltage variance vector of each power generation node at the current moment is formed. All class voltage vectors of each power generation node include the voltage mean vector and the voltage variance vector.

[0006] Preferably, 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.

[0007] Preferably, 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.

[0008] Preferably, obtaining 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.

[0009] Preferably, 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.

[0010] Preferably, 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.

[0011] Preferably, 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.

[0012] Preferably, 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.

[0013] In a second aspect, an embodiment of the present application further provides a distributed photovoltaic inverter monitoring system, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein when the processor executes the computer program, the steps of any one of the above-mentioned distributed photovoltaic inverter monitoring methods are implemented.

[0014] This application has at least the following beneficial effects: This application constructs an electrical coupling characteristic value by analyzing the similarity and difference of voltage fluctuations between different nodes, and its beneficial effect is that it can eliminate the voltage frequency deviation caused by the factory equipment status and the grid structure, thereby improving the accuracy of voltage frequency monitoring; this application constructs a node correlation by analyzing the degree of mutual influence between power generation nodes, and its beneficial effect is that it helps to distinguish the degree of electrical coupling of different power generation node sets, so as to accurately judge the influence between power generation nodes with different voltage frequencies and improve the detection sensitivity; this application calculates the compensation coefficient and compensation frequency by combining the electrical coupling characteristic value and the node correlation, and its 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. This application can accurately monitor the stability of the voltage frequency at the power generation node by combining the degree of mutual influence between different power generation nodes and the similarity of voltage fluctuations between different power generation nodes, and enhances the monitoring sensitivity of the distributed photovoltaic inverter to the frequency deviation at the power generation node. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] 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 use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0016] Figure 1 A flowchart of a distributed photovoltaic inverter monitoring method provided in one embodiment of the present application; Figure 2 A schematic diagram of a process for obtaining a power generation node set provided in one embodiment of the present application. DETAILED DESCRIPTION

[0017] In order to further explain the technical means and effects adopted by the present application to achieve the predetermined invention purpose, the following is a detailed description of the distributed photovoltaic inverter monitoring method and system proposed in the present application, its specific implementation method, structure, features and effects, in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0018] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0019] The following is a detailed description of a distributed photovoltaic inverter monitoring method and system provided by the present application in conjunction with the accompanying drawings.

[0020] See also Figure 1 , which shows a flow chart of a distributed photovoltaic inverter monitoring method provided by an embodiment of the present application, the method comprising the following steps: Step S1: using a distributed photovoltaic inverter 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.

[0021] A distributed photovoltaic inverter is installed at each photovoltaic power generation node to monitor and adjust the AC voltage connected to the power grid. The distributed photovoltaic inverter is used to obtain the voltage of each power generation node in the distributed photovoltaic power generation system at all collection times within a preset time before the current moment, as well as the voltage frequency of each power generation node at the current moment, where the voltage collection frequency is F.

[0022] It should be noted that the values ​​of the preset time length t and the acquisition frequency F are both manually set. In this embodiment, the value of the preset time length t is 1s, and the value of the acquisition frequency F is 1024Hz. They can also be set according to specific circumstances. This embodiment does not impose any special restrictions.

[0023] 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 of each type of voltage vector between any two power generation nodes, determine the similarity of voltage fluctuations between any two power generation nodes at the current moment, and combined with the difference in frequency domain of all voltages between any two power generation nodes before the current moment, determine the electrical coupling characteristic value between any two power generation nodes at the current moment.

[0024] The distributed photovoltaic inverters on the roof of the factory building are installed in a relatively compact position, with a relatively simple grid structure, a high degree of electrical coupling between different power generation nodes, and a large mutual influence between node voltages. When the distributed photovoltaic inverter monitors the voltage frequency of the node connected to the grid, the collected grid voltage is often the result of the superposition of voltages at different nodes, resulting in an error between the measured value of the voltage frequency and the actual voltage frequency of the distributed photovoltaic inverter connected to the grid.

[0025] Since the grid structure between different power generation nodes in the distributed photovoltaic power generation system on the roof of the factory building is relatively simple, the closer the electrical distance between the nodes, the more serious the superposition of the node voltages and the closer the fluctuations in the voltage amplitudes, reflecting a greater degree of electrical coupling between the two.

[0026] Therefore, by analyzing the similarity of voltage fluctuations between any two power generation nodes in the time domain and frequency domain, the electrical coupling characteristic value between any two power generation nodes at the current moment is determined to characterize the degree of electrical coupling between different power generation nodes. Specifically: (1) Based on the distribution of all voltages of each power generation node before the current moment, determine all class voltage vectors of each power generation node at the current moment.

[0027] In order to obtain the voltage fluctuation characteristics, the voltages of all the collection moments of each power generation node before the current moment are arranged in ascending order according to the voltage magnitude, and all the voltages between the minimum value and the first quartile, all the voltages between the first quartile and the median, all the voltages between the median and the third quartile, and all the voltages between the third quartile and the maximum value in the arrangement results are respectively formed into the first, second, third, and fourth sequences; The means of all voltages in the four sequences are calculated respectively, and the voltage mean vector of each power generation node at the current moment is formed; the variances of all voltages in the four sequences are calculated respectively, and the voltage variance vector of each power generation node at the current moment is formed.

[0028] All class voltage vectors include voltage mean vector and voltage variance vector.

[0029] (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.

[0030] Affected by the working state of the factory equipment, the fluctuation of the AC voltage at the power generation node at different stages of its AC cycle is different. The same factory or adjacent factories usually have similar production schedules, their equipment working states are similar, and the electrical distance of the distributed photovoltaic inverters is small. At this time, the amplitude change of the AC voltage at the power generation node is closer. The voltage mean vector reflects the change in the average voltage amplitude of the AC voltage at different stages of the AC cycle, and the voltage variance reflects the fluctuation degree of the voltage amplitude in the corresponding voltage interval at different stages of the AC voltage cycle. When the similarity of the voltage mean vector and the voltage variance vector of the two power generation nodes is greater, the changes in the average voltage amplitude and voltage fluctuation degree of the node voltage at different stages of the AC cycle are closer, and the voltage fluctuation similarity is greater.

[0031] At the current moment, the similarity of each type of voltage vector between any two power generation nodes is analyzed. The voltage fluctuation similarity between any two power generation nodes at the current moment is the result of the similarity fusion of all types of voltage vectors at the current moment. The specific implementation process is: Analyze the similarity between the voltage mean vectors of any two power generation nodes, which is recorded as the first similarity between any two power generation nodes; Analyze the similarity between the voltage variance vectors of any two power generation nodes, which is recorded as the second similarity between any two power generation nodes; The voltage fluctuation similarity between any two power generation nodes is a result of merging the first similarity and the second similarity between any two power generation nodes.

[0032] It should be noted that there are many methods for measuring 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 vector between any two power generation nodes. The implementer may also use the inverse of the Euclidean distance or other methods for measuring the similarity between vectors. This embodiment does not impose any special restrictions on the selection of the method for measuring the similarity between vectors.

[0033] The steps for calculating the cosine similarity are well-known techniques, and the specific calculation process will not be described in detail.

[0034] 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 by adding or multiplying them together to obtain a comprehensive indicator, so as to more comprehensively and accurately evaluate a phenomenon or problem. This fusion method is not limited to simple arithmetic operations, but can also include more complex statistical models and analysis methods, which can be selected by the implementer according to the specific situation, and this embodiment does not impose any special restrictions.

[0035] Preferably, in the present 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, as other implementation methods, the voltage fluctuation similarity between any two power generation nodes is an exponential function value with a 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.

[0036] (3) Based on the similarity of voltage fluctuations between any two power generation nodes at the current moment and combined with the differences in the frequency domain of all voltages between any two power generation nodes before the current moment, the electrical coupling characteristic value between any two power generation nodes at the current moment is determined.

[0037] Considering that there are multiple high-power production equipment in the factory connected to the grid at the same time, the noise generated by the operation of the equipment will be transmitted along the grid to different power generation nodes, and superimposed on the AC voltage connected to the grid by the distributed photovoltaic inverter, aggravating the harmonic pollution and further increasing the deviation of the node voltage frequency.

[0038] Therefore, in order to eliminate the interference of the coupling phenomenon between power generation nodes on the voltage frequency monitoring of power generation nodes, the electrical coupling characteristic value between any two power generation nodes at the current moment is determined by analyzing the difference of all voltages between any two power generation nodes in the frequency domain and combining the voltage similarity fluctuation degree, which is specifically: 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 spectrum of each power generation node is output. Due to the large-scale production of the factory, the production operations in adjacent workshops are similar, and the clutter frequencies generated by the equipment are close. At the same time, the electrical distance between the power generation nodes in adjacent workshops is small, and the degree of electrical coupling is high. The more clutter of the same frequency is superimposed, the closer the distribution of the node spectrum is.

[0039] It should be noted that there are many commonly used time-frequency conversion algorithms. In this embodiment, Fourier transform is used to obtain the spectrum of voltage in the frequency domain. The implementer may also use wavelet transform or other time-frequency conversion algorithms. This embodiment does not impose any special restrictions on the selection of time-frequency conversion algorithms.

[0040] Among them, Fourier transform is a well-known technology in the field of signal processing, and the specific process of converting time domain data into frequency domain will not be repeated here.

[0041] Furthermore, 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.

[0042] It should be noted that the specific process of measuring the difference between the frequency spectra of any two power generation nodes at the current moment is: calculating the DTW distance between all amplitudes in the frequency spectra of any two power generation nodes at the current moment, and reflecting the difference in all amplitudes between any two power generation nodes through the DTW distance. The implementer may also use other methods for measuring the difference between data groups, such as Euclidean distance and Manhattan distance. Regarding the selection of the method for measuring the difference between data groups, this embodiment does not impose any special restrictions.

[0043] The calculation steps of the DTW distance are well-known techniques, and the specific calculation process will not be described in detail.

[0044] According to the electrical coupling characteristic value between any two power generation nodes at the current moment, it can be understood that 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 the distance between the power generation nodes in adjacent workshops is small, and the degree of electrical coupling is high. The more clutter of the same frequency is superimposed, the closer the distribution of the node spectrum is. The DTW distance reflects the difference between the spectrums of any two power generation nodes. The smaller the DTW distance of the spectrum sequences of the two power generation nodes, the greater the degree of electrical coupling, that is, the greater the electrical coupling characteristic value. In addition, the greater the similarity of voltage fluctuations, the greater the degree of electrical coupling of the power generation nodes, that is, the greater the electrical coupling characteristic value, indicating that the mutual influence of the voltage frequency between the two power generation nodes is greater; conversely, if the DTW distance of the spectrum sequences of the two power generation nodes is smaller, it means that the degree of electrical coupling is smaller, that is, the electrical coupling characteristic value is smaller. And the smaller the similarity of voltage fluctuations, the smaller the degree of electrical coupling of the power generation nodes, that is, the smaller the electrical coupling characteristic value, indicating that the mutual influence of the voltage frequency between the two power generation nodes is smaller.

[0045] Step S3: Cluster all power generation nodes based on the electrical coupling eigenvalues ​​to obtain all power generation node sets and their central power generation nodes, as well as the membership 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 in each power generation node set to determine the node association degree of each power generation node set at the current moment.

[0046] There are large differences in the degree of electrical coupling between power generation nodes in different factory buildings, and the impact on the node voltage frequency is also quite different. Due to the changes in production tasks of different factory buildings at different times, the load at the power generation nodes is constantly changing, and the mutual influence of voltage and frequency between 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 changes in factory production.

[0047] 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 division method is obtained, and the node correlation of the power generation node set is determined in combination with the electrical coupling eigenvalue. Specifically: (1) 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 output as well as the membership degree between all power generation nodes and each cluster center; Among them, the fuzzy C-means clustering algorithm is a well-known technology, and the specific principle process of implementing clustering is not repeated here.

[0048] (2) Further, based on the affiliation between power generation nodes and clusters under different clustering methods, the power generation node set is determined, specifically: 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.

[0049] According to the excellence of the number of clusters, it can be understood that the objective function value output by the fuzzy C-means clustering algorithm reflects the electrical distance between nodes in different clusters of the clustering output, and the membership between the power generation node and the cluster center reflects the degree of electrical coupling between the power generation node and the cluster center. When the objective function value output by the fuzzy C-means clustering algorithm is smaller and the membership between the power generation node and the cluster center is larger, the higher the degree of fit between each cluster after clustering with the total number of clusters and the distribution of working conditions of different power generation nodes in the current factory photovoltaic power generation system, the greater the excellence of the number of clusters obtained; On the contrary, when the objective function value output by the fuzzy C-means clustering algorithm is smaller and the degree of membership between the power generation node and the cluster center is smaller, the degree of fit between the clusters after clustering with the total number of clusters and the distribution of working conditions of different power generation nodes in the current factory photovoltaic power generation system is lower, and the obtained set quantity excellence is smaller.

[0050] Furthermore, 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.

[0051] It should be noted that the value of the preset total number of clusters interval is artificially set. In this embodiment, the total number of clusters interval is [2, 20], that is, integers are selected in sequence as the total number of clusters. For example, the number of clusters in the fuzzy C-means clustering algorithm is set to 2, or the number of clusters in the fuzzy C-means clustering algorithm is set to 3, that is, 2 is taken from 2 to 20, and the clustering effects under different total number of clusters settings 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 clusters interval according to the specific situation, and this embodiment does not impose any special restrictions.

[0052] Preferably, the schematic diagram of the process of obtaining the power generation node set provided in this embodiment is as follows: Figure 2 shown.

[0053] Furthermore, based on all power generation node sets and their central power generation nodes, as well as the membership between all power generation nodes and each central power generation node, and combined with the electrical coupling characteristic value between the central power generation node and all other power generation nodes in each power generation node set, the node association degree of each power generation node set at the current moment is determined, specifically: Node association degree of power generation node set i The expression is: ; In the formula, 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.

[0054] According to the node association degree of each power generation node set at the current moment, it can be understood that when the electrical coupling characteristic value between the central node and the remaining nodes is smaller, and if the difference between the membership degree between the power generation node and the central power generation node of the power generation node set to which it belongs and the mean value of the membership degree between the power generation node and the central power generation node of all other power generation node sets is larger, that is, The smaller it is, the smaller the node correlation of the corresponding power generation node set is, which means that the overall mutual influence of the power generation nodes in the current power generation node set is smaller; On the contrary, when the electrical coupling characteristic value between the central node and the rest of the nodes is larger, and if the difference between the membership between the power generation node and the central power generation node of its power generation node set and the mean of the membership between the power generation node and the central power generation node of all other power generation node sets is smaller, that is, The smaller it is, the greater the node correlation of the corresponding power generation node set, which means that the overall mutual influence of the power generation nodes in the current power generation node set is greater.

[0055] Step S4: Based on the electrical coupling characteristic value between each power generation node and the remaining power generation nodes in the power generation node set to which it belongs, and the voltage frequency of each power generation node, determine the compensation coefficient 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, determine the compensation frequency of each power generation node at the current moment, and monitor the voltage frequency of the power generation node.

[0056] In order to improve the monitoring sensitivity and accuracy of the distributed photovoltaic inverter to the frequency deviation at the power generation node, the voltage frequency of the power generation node is corrected by analyzing the electrical coupling characteristic value between the power generation node and the central power generation node. Specifically: The compensation coefficient of power generation node j at the current moment The expression is: ; In the formula, 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.

[0057] According to the compensation coefficient of each power generation node at the current moment, it can be understood that the larger the electrical coupling characteristic value between two power generation nodes in the power generation node set and the larger the frequency difference, the greater the mutual influence of the voltage frequency between the two, and the larger the absolute value of the compensation coefficient. If the frequency of the surrounding power generation nodes is higher than that of the current node, the measurement frequency of the current node will be increased. In order to obtain the accurate actual frequency, the contribution of this node to the calculation of the compensation coefficient of the current node is negative. At the same time, if the frequency of the surrounding nodes is lower than that of the current node, the measurement frequency of the current node will be reduced. In order to obtain the accurate actual frequency, the contribution of this node to the calculation of the compensation coefficient of the current node is positive to reduce its influence on the current node.

[0058] Furthermore, based on 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 compensation frequency of each power generation node at the current moment is determined, specifically: Compensation frequency of power generation node j The expression 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.

[0059] According to the compensation frequency of each power generation node at the current moment, it can be understood that the compensation coefficient represents the magnitude and positive and negative of the frequency compensation for the node voltage frequency, and when the node correlation of the power generation node set is greater, the correlation degree of the voltage changes between different power generation nodes in the set is greater, and the impact on the voltage frequency is greater; conversely, when the node correlation of the power generation node set is smaller, the correlation degree of the voltage changes between different power generation nodes in the set is greater, and the impact on the voltage frequency is greater.

[0060] 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; conversely, if the compensation frequency of the power generation node at the current moment is within the preset frequency range, the voltage frequency of the power generation node is normal.

[0061] It should be noted that the value of the preset frequency interval is manually set. In this embodiment, the value of the preset frequency interval is [49.5, 50.5]. The implementer can also set it by himself according to the specific situation. This embodiment does not impose any special restrictions.

[0062] Based on the same inventive concept as the above method, an 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, and when the processor executes the computer program, the steps of any one of the above-mentioned distributed photovoltaic inverter monitoring methods are implemented.

[0063] It should be noted that the above sequence of the embodiments of the present application is for description only and does not represent the advantages and disadvantages of the embodiments. The above is a description of a specific embodiment of this specification. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0064] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0065] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present application should 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; Based on the distribution of all voltages of each power generation node before the current moment, all class voltage vectors of each power generation node at the current moment are determined, 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 difference of all 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 rest of the 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.

2. A distributed photovoltaic inverter monitoring method as claimed in claim 1, characterized in that: The method for determining all class voltage vectors of each power generation node at the current moment is: Arrange the voltages of all the collected moments of each power generation node before the current moment in ascending order, and form the first, second, third, and fourth sequences respectively from the arrangement results including all the voltages between the minimum value and the first quartile, all the voltages between the first quartile and the median, all the voltages between the median and the third quartile, and all the voltages between the third quartile and the maximum value; The means of all voltages in the four sequences are calculated respectively, and the voltage mean vector of each power generation node at the current moment is formed; the variances of all voltages in the four sequences are calculated respectively, and the voltage variance vector of each power generation node at the current moment is formed. All class voltage vectors of each power generation node include the voltage mean vector and the voltage variance vector.

3. A distributed photovoltaic inverter monitoring method as claimed in claim 1, characterized in that: 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.

4. 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.

5. 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.

6. 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.

7. A distributed photovoltaic inverter monitoring method as claimed in claim 6, 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.

8. A distributed photovoltaic inverter monitoring method as claimed in claim 7, 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; 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.

9. 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.

10. 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 a distributed photovoltaic inverter monitoring method as described in any one of claims 1-9 are implemented.

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