An abnormal monitoring method and system for the power supply line of a new energy photovoltaic power station

Abstract

The present invention relates to the field of new energy power generation stations, and relates to a method and system for abnormally monitoring the power supply line of a new energy photovoltaic power station, including: determining the first electrical data of the first line, the target environmental cluster to which the first line belongs, and the target node cluster to which the first line belongs according to the first line to be detected; determining the electrical correction factor of the first line according to the target environmental cluster and the first electrical data corresponding to the first line; determining the electrical difference value of the first line according to the electrical correction factor and the first electrical data; performing abnormal analysis on the first line according to the electrical difference value in the target node cluster to determine the power supply abnormality index of the first line; and determining whether the first line is an abnormal line according to the power supply abnormality index. The present invention realizes the effective identification of abnormal lines by analyzing the change characteristics of the electrical data of lines of the same type and at the same stage under similar environmental conditions, and improves the accuracy of abnormal monitoring of power supply lines.

Description

Technical Field

[0001] The present invention relates to the technical field of new energy power stations, and particularly relates to a method and system for abnormal monitoring of power supply lines in a new energy photovoltaic power station. Background Art

[0002] A photovoltaic power station converts solar energy into electrical energy through solar photovoltaic modules, and transmits the electrical energy to the power grid through systems such as a busbar trunking unit, an inverter, a power distribution cabinet, and a transformer. However, photovoltaic power stations face problems such as environmental changes and equipment aging, which lead to frequent equipment failures, resulting in waste of clean energy and increased maintenance costs. Therefore, it is crucial to monitor the power supply lines of photovoltaic power stations.

[0003] Currently, the methods for abnormal monitoring of photovoltaic power generation systems can be roughly divided into two types: The first method focuses on direct fault location and detection at the level of photovoltaic modules; the second method analyzes and detects abnormal states within the system by constructing electrical and environmental data models of the photovoltaic power generation system. Although the detection method based on a physical model is favored for its high accuracy and real-time performance, this method has certain limitations in adapting to different equipment parameters and changing environmental conditions. On the other hand, the method based on a statistical model performs well in terms of adaptability to different environments. However, due to the mutual correlation and complexity between photovoltaic power generation and factors such as meteorological conditions and solar radiation intensity, the abnormal detection accuracy of this method may be affected. Therefore, the research and improvement of stable monitoring and abnormal detection methods for the power supply lines of photovoltaic power stations have become urgent problems to be solved in the current photovoltaic power generation field. Summary of the Invention

[0004] In order to solve the technical problems of inaccurate and unstable circuit monitoring of a photovoltaic power station, the purpose of the present invention is to provide a method and system for abnormal monitoring of power supply lines in a new energy photovoltaic power station, and the specific technical solutions adopted are as follows:

[0005] In a first aspect, an embodiment of the present invention provides a method for abnormal monitoring of power supply lines in a new energy photovoltaic power station, which is applied to an abnormal monitoring system for power supply lines in a new energy photovoltaic power station. The method includes:

[0006] According to a first line to be detected, determine first electrical data of the first line, a target environmental cluster to which the first line belongs, and a target node cluster to which the first line belongs;

[0007] According to the target environmental cluster and the first electrical data corresponding to the first line, determine an electrical correction factor of the first line;

[0008] According to the electrical correction factor and the first electrical data, determine an electrical difference value of the first line;

[0009] In the target node cluster, perform anomaly analysis on the first line according to the electrical difference value to determine the power supply anomaly index of the first line;

[0010] According to the power supply anomaly index, determine whether the first line is an abnormal line.

[0011] In a second aspect, an embodiment of the present invention provides a power supply line anomaly monitoring system for a new energy photovoltaic power station, including a memory and a processor, the memory is connected to the processor, the processor is used to execute one or more computer programs stored in the memory, and when the processor executes the one or more computer programs, the power supply line anomaly monitoring system for the new energy photovoltaic power station realizes the power supply line anomaly monitoring method for the new energy photovoltaic power station as described in the first aspect.

[0012] In a third aspect, an embodiment of the present invention provides a power supply line anomaly monitoring device for a new energy photovoltaic power station, which is applied to the power supply line anomaly monitoring system of the new energy photovoltaic power station. The device includes:

[0013] A determination unit for determining the first electrical data of the first line, the target environment cluster to which the first line belongs, and the target node cluster to which the first line belongs according to the first line to be detected;

[0014] The determination unit is further configured to determine the electrical correction factor of the first line according to the target environment cluster and the first electrical data corresponding to the first line;

[0015] The determination unit is further configured to determine the electrical difference value of the first line according to the electrical correction factor and the first electrical data;

[0016] An analysis unit for performing anomaly analysis on the first line according to the electrical difference value in the target node cluster to determine the power supply anomaly index of the first line;

[0017] The determination unit is further configured to determine whether the first line is an abnormal line according to the power supply anomaly index.

[0018] In a fourth aspect, an electronic device is provided, including a memory and a processor, the memory is connected to the processor, the processor is used to execute one or more computer programs stored in the memory, and when the processor executes the one or more computer programs, the electronic device realizes the power supply line anomaly monitoring method for the new energy photovoltaic power station as described in the first aspect.

[0019] In a fifth aspect, a computer-readable storage medium is provided. The computer-readable storage medium stores a computer program, and the computer program includes program instructions that, when executed by a processor, cause the processor to execute the method for monitoring abnormal power supply lines of a new energy photovoltaic power station as described in the first aspect.

[0020] The present invention has the following beneficial effects: By analyzing the characteristics of electrical data changes of the same type and same stage lines under similar environmental conditions, the present invention effectively identifies abnormal lines, avoids relying solely on single-dimensional or single-line data to judge the electrical state, thereby reducing the one-sidedness of data analysis and increasing the accuracy of the entire system monitoring; further, by constructing an electrical correction factor for the first line, the present invention can make fine adjustments when analyzing electrical data differences, not only considering the influence of factors such as the service life and maintenance conditions of the line on data changes, but also effectively avoiding data inconsistencies caused by inherent differences between lines, reducing false detection cases caused by data errors; and by analyzing the corrected electrical data differences, the present invention can more accurately judge the consistency of electrical data between similar lines, accurately locate abnormal lines, and improve the accuracy of abnormal power supply line monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0022] Figure 1 It is a schematic framework diagram of a system for monitoring abnormal power supply lines of a new energy photovoltaic power station provided by an embodiment of the present invention;

[0023] Figure 2 It is a schematic flowchart of a method for monitoring abnormal power supply lines of a new energy photovoltaic power station provided by an embodiment of the present invention;

[0024] Figure 3 It is a schematic structural diagram of a device for monitoring abnormal power supply lines of a new energy photovoltaic power station provided by an embodiment of the present invention;

[0025] Figure 4 It is a schematic structural diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0026] To further elaborate on the technical means and effects adopted by the present invention to achieve the intended invention purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation manner, structure, features, and effects of a power supply line abnormal monitoring method and system for a new energy photovoltaic power station proposed according to the present invention. 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.

[0027] 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 the present invention belongs.

[0028] The following specifically describes the specific solutions of a power supply line abnormal monitoring method and system for a new energy photovoltaic power station provided by the present invention in conjunction with the accompanying drawings.

[0029] Please refer to Figure 1 , which shows a schematic framework diagram of a power supply line abnormal monitoring system for a new energy photovoltaic power station provided by an embodiment of the present invention.

[0030] The power supply line abnormal monitoring system 10 for a new energy photovoltaic power station provided by the present invention includes an environmental sensor 20, a power sensor 30, and a plurality of electrical devices 40.

[0031] Among them, the environmental sensor 20 is used to continuously monitor the environmental parameters of the new energy photovoltaic power station, including wind direction, wind speed, ambient temperature, atmospheric pressure, light intensity, solar radiation parameters, etc., and transmit the monitored environmental data to the data processing module.

[0032] Among them, the power sensor 30 is used to continuously detect the DC current value, voltage value, power value during the operation of the photovoltaic array in the electrical device, as well as the input and output voltages, currents, and powers of each busbar box and inverter, and transmit the collected electrical data to the data processing module.

[0033] Among them, the plurality of electrical devices 40 include a solar photovoltaic module, a DC busbar module, a DC-AC inverter module, and a voltage transformation module. The solar photovoltaic module converts solar energy into DC electrical energy. The DC busbar module connects the photovoltaic modules in series to form a photovoltaic string and then connects them in parallel to the photovoltaic busbar lightning protection box. The DC-AC inverter module and the voltage transformation module convert DC electricity into AC electricity and supply it to the power grid.

[0034] Among them, the power supply line abnormal monitoring system 10 for a new energy photovoltaic power station further includes a data processing module.

[0035] Among them, the environmental sensor 20 and the power sensor 30 are respectively communicatively connected to the data processing module, so that the data processing module can receive environmental data and electrical data for comprehensive analysis and processing.

[0036] Among them, the data processing module includes: a data analysis unit for preprocessing and analyzing the received environmental data and electrical data, and calculating the predicted values of the data of each node; an anomaly detection unit for identifying anomalies in the power supply lines of the photovoltaic power station according to the results of the data analysis unit, including abnormal lines or node devices; an alarm notification unit for sending an alarm to the staff and providing anomaly information for timely maintenance or adjustment when an anomaly is detected; the data processing module can also predict the future operating state of the photovoltaic power station according to the changing trends of the environmental data and electrical data, providing decision-making support for the maintenance and optimization of the power station.

[0037] It can be seen that the abnormal monitoring of the power supply lines of the new energy photovoltaic power station works in cooperation through the above components, obtaining and processing the environmental data and electrical data of the new energy photovoltaic power station in real time, and comprehensively monitoring in combination with the operating states of multiple electrical devices 40. The data processing module will analyze these data, calculate the predicted values of the electrical data, and identify abnormal lines or devices by comparing the actual data, improving the operating efficiency and safety of the photovoltaic power station.

[0038] Please refer to Figure 2 , which shows a schematic flowchart of the method for abnormal monitoring of the power supply lines of the new energy photovoltaic power station provided by an embodiment of the present invention. The method includes the following steps:

[0039] S10. Determine the first electrical data of the first line, the target environmental cluster to which the first line belongs, and the target node cluster to which the first line belongs according to the first line to be detected.

[0040] Among them, the first line is a cable or electrical connection path connecting different nodes in each power supply circuit. The first line may include an input node and an output node. The input node is one end of the line, connected to a power source (such as the output end of a photovoltaic module); the output node is the other end of the line, connected to a load (such as the input end of an inverter) or other electrical devices.

[0041] Among them, the first electrical data refers to the electrical parameters of the first line to be detected, including voltage, current, power, etc. This data is collected in real time by electrical sensors and is used to analyze the operating state and performance of the line.

[0042] Among them, the target environmental cluster refers to finding the corresponding environmental cluster according to the environmental data of the first line to be detected. This process includes dividing the environmental data into different clusters and determining which cluster the environmental data of the first line belongs to. This can group the lines into groups with similar environmental conditions for environmental-related analysis.

[0043] Specifically, the environmental cluster refers to an environmental group obtained by performing clustering analysis on all data points according to environmental data (such as wind speed, temperature, light intensity, etc.). With the help of DBSCAN or other clustering algorithms, the environmental data is divided into multiple clusters, and each cluster represents a set of data points with similar environmental conditions.

[0044] Among them, the target node cluster refers to finding the electrical node cluster where the first line is located according to the node electrical data at both ends of the first line to be detected. By clustering the electrical data, it is determined which specific node cluster the nodes of this line belong to, so as to group it into a group with similar electrical characteristics.

[0045] Specifically, the node cluster is an electrical equipment type group formed by clustering analysis of node electrical data (such as K-means and DBSCAN clustering). Each node cluster contains nodes with similar electrical characteristics and represents devices of the same type or function.

[0046] It can be seen that in this embodiment, it is ensured that the first line to be detected is accurately classified into the corresponding environmental and node type groups for more accurate anomaly detection and analysis.

[0047] In one embodiment, the abnormal monitoring system for the power supply line of the new energy photovoltaic power station includes environmental sensors, electrical sensors, and multiple electrical devices. Before determining the first electrical data of the first line, the target environmental cluster to which the first line belongs, and the target node cluster to which the first line belongs according to the first line to be detected, the method further includes: collecting data of the multiple electrical devices by the environmental sensors and the electrical sensors to obtain the electrical data and environmental data of each power supply circuit corresponding to the multiple electrical devices; screening each power supply circuit according to the electrical data of each power supply circuit to obtain at least one group of power supply circuits of the same type; determining the target node cluster to which the first line belongs in the at least one group of power supply circuits of the same type; dividing the environmental data of each power supply circuit based on the same moment as the basis to obtain multiple data points, and each data point is a set of environmental data corresponding to each moment; performing a first preset clustering process on the multiple data points to obtain multiple environmental clusters, and each environmental cluster includes multiple data points in a similar environment; determining the target environmental cluster to which the first line belongs in the multiple environmental clusters.

[0048] Among them, the environmental data may include, but are not limited to, temperature, humidity, wind speed, light intensity, etc., which are obtained by environmental sensors monitoring the environmental variables around the photovoltaic power station.

[0049] Among them, the electrical data may include, but are not limited to, current, voltage, power, etc., which are obtained by electrical sensors monitoring the operation data of electrical equipment such as busbar boxes and inverters.

[0050] Among them, when collecting data from the multiple electrical devices, the collection points are the photovoltaic array and the locations of each busbar box and inverter, and the electrical data monitoring points are the locations of each electrical device, and the input and output data of the electrical devices are collected. The preset solar radiation parameter and the environmental data collection frequency are 1 time / minute, and the collection frequency of various electrical data is 1 time / second. The units of all the collected data are in the International System of Units.

[0051] Among them, when the electrical equipment at both ends of two lines is the same for the same type of power supply circuit, the tasks undertaken by these two power supply lines are the same, that is, such two lines are of the same type. That is, the electrical data input and output by these lines and electrical equipment are similar.

[0052] Among them, in the selected same type of power supply circuits, further determine the specific target node cluster to which the first line belongs, that is, determine the node clustering to which the first line belongs. The determination of the target node cluster helps to classify the first line into a group of lines of similar types, providing a basis for subsequent anomaly detection and data analysis.

[0053] Among them, for all the environmental data in the photovoltaic power station, at each moment, the collected environmental data (such as temperature, humidity, light intensity, etc.) constitute a data point. Each data point represents the environmental state at a time point. The set of environmental data at all time points constitutes a multi-dimensional data space, where each dimension corresponds to an environmental data. Therefore, if the temperature monitored at a certain moment is 20°C, the humidity is 50%, and the air quality index is 100, then the coordinates of this data point in the three-dimensional space are (20, 50, 100).

[0054] Among them, before performing the first preset clustering processing on multiple data points, the collected environmental data can be processed, such as cleaning (removing outliers or missing values), normalization, etc., to ensure the quality and consistency of the data.

[0055] Among them, the first preset clustering can be DBSCAN clustering. DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is a density-based clustering algorithm that can identify clusters of any shape separated by high-density regions.

[0056] Among them, for the first preset clustering process, the preset minimum radius can be set to 3 and the minimum number to 9. Here, the "minimum radius" refers to the minimum number of neighbor points (excluding the point itself) that need to be found within the neighborhood of a certain point to form a dense area; the "minimum number" refers to the minimum number of data points (including the point itself) required to form a valid cluster. Based on the above preset parameters, the DBSCAN clustering algorithm is executed on multiple data points in the multi-dimensional data space; the algorithm will cluster the data points into one or more clusters according to the density connectivity between the data points.

[0057] Among them, each environmental cluster contains numerous data points composed of similar environmental conditions. That is, among the numerous data points within the same environmental cluster, the corresponding environments are relatively more similar, which is denoted as a similar environment.

[0058] Among them, according to the environmental data of the first line, its corresponding environmental cluster is found. Since the environmental data of the first line has been divided into multiple data points at a certain specific moment and processed by DBSCAN clustering, these data points will be assigned to the corresponding environmental clusters. Therefore, the environmental cluster where the first line is located can be determined and regarded as the target environmental cluster. The purpose of doing this is to compare the operating environment of the first line with the environments of other lines for more accurate anomaly detection and data analysis.

[0059] It can be seen that in this embodiment, by comprehensively considering environmental data and electrical data and combining the data collection of environmental sensors and electrical sensors, the system can accurately identify and screen out the same type of power supply circuits. It can not only eliminate the influence of environmental changes on electrical data but also accurately determine the electrical data differences between the same type of lines; using a density-based clustering method (such as DBSCAN) to process environmental data can effectively group the time points with similar environmental conditions into the same cluster. By performing such clustering processing on environmental data, noise interference can be reduced, making the analysis of environmental data more effective and stable.

[0060] In one embodiment, screening the power supply circuits according to the electrical data of the power supply circuits to obtain at least one set of power supply circuits of the same type includes: obtaining power supply nodes in the power supply circuits to obtain a plurality of power supply nodes; obtaining electrical data corresponding to each power supply node in the plurality of power supply nodes; performing a second preset clustering process on each power supply node according to the electrical data corresponding to each power supply node to obtain first node clusters of multiple electrical equipment types, each first node cluster including a plurality of power supply nodes of the same electrical equipment type; connecting the two end nodes of each power supply circuit in the power supply circuits to obtain connection lines of the power supply circuits; performing the first preset clustering process on the connection lines of the power supply circuits with the first node clusters of the multiple electrical equipment types as the clustering space dimension to obtain second node clusters of multiple line types, each second node cluster including a plurality of power supply lines of the same line type; determining each second node cluster as a set of power supply circuits of the same type to obtain at least one set of power supply circuits of the same type.

[0061] Among them, the power supply node is a connection point in the electrical equipment, and electric energy is transmitted to the equipment through these nodes.

[0062] Among them, the electrical data corresponding to each power supply node are the voltage, current, power, etc. of each node. These data are measured by electrical sensors and are used to evaluate the performance and status of the nodes.

[0063] Among them, the second preset clustering can be K-means clustering, with a preset K = 7, which means that the preset nodes are divided into 7 categories, and each category corresponds to a device type or functional module. This K value is usually based on prior knowledge or experience. For example, there are 7 different module types in a photovoltaic power station. Randomly select K nodes as the initial cluster centers. These centers will represent the initial positions of each cluster. Assign each node to the cluster with the nearest cluster center. Recalculate the center point of each cluster, that is, the feature mean of all nodes in the cluster. These new center points will be used as the cluster centers for the next round of clustering, and repeat the previous steps until the cluster centers no longer change significantly (or reach the preset number of iterations). At this time, each node is assigned to a cluster, and the center point of each cluster represents the average feature of the nodes in the cluster. The final result is that all nodes are divided into 7 categories, and each category represents a device type or devices with similar electrical characteristics.

[0064] Among them, through K-means clustering, all nodes in the photovoltaic power station can be divided into multiple categories (clusters) according to their electrical characteristics. These categories correspond to different types of devices or functional modules, which is to classify the nodes according to their electrical characteristics and lay a foundation for subsequent line analysis and anomaly detection.

[0065] Among them, after different types of nodes are identified, these nodes are further analyzed and connected to identify the same type of circuit. Therefore, a second clustering is required, that is, taking the first node cluster of the multiple electrical equipment types as the clustering space dimension, performing the first preset clustering process on the connections of the multiple power supply circuits to obtain a second node cluster of multiple circuit types.

[0066] Among them, the two end nodes of each power supply circuit are physically or logically connected to form a connection line, and these connection lines represent the actual power supply path, and one connection line represents one circuit.

[0067] Among them, the DBSCAN clustering algorithm is used to process with the first node cluster of the electrical equipment type as the clustering space dimension, that is, considering the clusters where the input end and output end nodes of each power supply line connection are located. The DBSCAN algorithm is density-based, and the clustering parameters include the minimum radius (such as 1) and the minimum number of points (such as 3). Through clustering analysis, multiple "second node clusters" are formed, and each cluster includes multiple power supply lines of the same circuit type.

[0068] Among them, the second node cluster is a group of power supply lines with similar electrical characteristics, that is, multiple power supply lines of the same type.

[0069] It can be seen that in this embodiment, by clustering the electrical data of each power supply node, the nodes can be divided into different types. Then, by connecting the nodes to form circuit connection lines and further clustering analysis of these connection lines, the circuits can be classified into different types of power supply lines, which can effectively divide the circuits into groups with similar electrical characteristics, thereby helping to accurately identify and manage various power supply lines in the photovoltaic power station.

[0070] S20. Determine the electrical correction factor of the first circuit according to the target environment cluster and the first electrical data corresponding to the first circuit.

[0071] Among them, the electrical correction factor represents the possible difference situation between the first circuit at the current moment and other circuits, and the other circuits are any circuit in the same target environment cluster and the same target node cluster.

[0072] Among them, the specific implementation steps of S20 can be shown in the following embodiments and will not be repeated here.

[0073] In one embodiment, determining the electrical correction factor of the first line according to the clustering of the target environment and the first electrical data corresponding to the first line includes: determining the electrical change value corresponding to the first line under the clustering of the target environment according to the clustering of the target environment; determining an electrical difference index according to the first electrical data of the first line, where the electrical difference index represents the electrical difference between the first line and any line in the target node cluster; calculating according to the electrical change value and the electrical difference index according to a first preset formula to obtain the electrical correction factor of the first line.

[0074] Among them, in the process of determining the electrical change value corresponding to the first line under the clustering of the target environment according to the clustering of the target environment, it usually reflects the influence of the current environment on the line performance based on the influence of environmental factors and the comparison with historical data of the same type of lines.

[0075] Among them, the electrical difference index reflects the performance difference between the first line and other lines under the same or similar environmental conditions, and is usually obtained based on historical data statistics and comparison.

[0076] Among them, the first preset formula can be as follows:

[0077] ;

[0078] represents the electrical correction factor of the th item of the electrical data of the first line at the current moment, and is used to describe the possible difference situation between the target line and other lines at the current moment.

[0079] represents the th item of the electrical data of the first line, which is the theoretical electrical change value under the current environment; represents the th item of the electrical data, which is the electrical difference index between the line

[0080] and other lines of the same type; that is, there is a certain difference between the first line and the lines of the same type historically, and this difference has a certain stability. According to the electrical data at the current moment and this difference, the possible difference situation at the current moment is calculated.

[0081] It can be seen that in this embodiment, by determining the electrical change value under the target environmental cluster, the electrical performance of the first line under specific environmental conditions can be more accurately evaluated, avoiding the interference of environmental factors on data; the calculation of the electrical difference index can objectively reflect the electrical difference between the first line and other lines of the same type in the target node cluster, helping to identify performance deviations or potential problems; by using the calculation of the electrical correction factor, the electrical data of the first line can be corrected, eliminating the inconsistency caused by environmental changes, thereby improving the accuracy and reliability of the data.

[0082] In one embodiment, the determining the electrical change value corresponding to the first line under the target environmental cluster according to the target environmental cluster includes: randomly selecting a second line in the target node cluster, where the second line belongs to the target environmental cluster; obtaining the second electrical data of the second line at a first moment; and calculating the first moment and the second electrical data according to a second preset formula to obtain the electrical change value corresponding to the first line under the target environmental cluster.

[0083] Among them, randomly select a line (referred to as the second line) from the target node cluster, and the second line belongs to the target environmental cluster. This selection is because when the environments are similar, the changes in the electrical data and the like corresponding to the same type of lines are also similar. Therefore, the theoretical change situation in the current environment can be further obtained, that is, the electrical change value corresponding to the first line under the target environmental cluster.

[0084] Among them, the above first moment can be any moment, and there is no unique limitation here.

[0085] Among them, the second electrical data may include input electrical data and output electrical data.

[0086] Among them, the second preset formula can be expressed as follows:

[0087] ;

[0088] represents the theoretical electrical change value of the th item of electrical data of the first line in the current environment, and is used to judge and analyze the working states of the target line and the lines in the current environment.

[0089] In the formula, and respectively represent the output and input th item of electrical data of the line at a moment similar to the th moment, where the line is any line of the same type as the target line; Indicates the time difference between the similar environment at a certain moment and the current moment, which is used here to describe the degree of reference between two pieces of data, that is, it characterizes the wear condition of the corresponding line during this period; Indicates to calculate the weighted mean of the corresponding differences of different lines, and the weight is , because each line has multiple segments of data, and there is a certain time interval between each segment of data and the current time. During this period, the line will experience a certain degree of wear. Therefore, the time difference is used to perform weighted averaging on it.

[0090] Among them, if = 0 indicates that the two moments are the same moment. Therefore, when selecting the first moment, avoid selecting the same time as the current moment to ensure that is always a positive value and large enough to avoid the problem of the denominator being 0. The calculation of the time difference needs to be cautious to ensure that the time point interval is reasonable and can effectively reflect the impact of data changes.

[0091] It can be seen that through the above process in this embodiment, the operation changes of the power supply line under specific environmental conditions can be monitored and evaluated more accurately, potential abnormal problems can be discovered in a timely manner, and a scientific basis is provided for maintaining and adjusting the stable operation of the power system.

[0092] In one embodiment, determining the electrical difference index according to the first electrical data of the first line includes: randomly selecting a second line in the target node cluster, and the second line belongs to the target environment cluster; respectively performing data processing on the first electrical data of the first line and the second electrical data of the second line to obtain the first change difference factor of the first line and the second change difference factor of the second line; obtaining the total number of power supply lines in the target node cluster; calculating the total number of power supply lines, the first change difference factor, and the second change difference factor according to the third preset formula to obtain the electrical difference index.

[0093] Among them, the first electrical data may include input electrical data and output electrical data; the second electrical data may include input electrical data and output electrical data.

[0094] Among them, in the process of respectively performing data processing on the first electrical data of the first line and the second electrical data of the second line, the following preset formula can be used for processing:

[0095] ;

[0096] In the formula, Indicates at the moment the line both ends of the Change difference factor of electrical data items and respectively represent the th electrical data of the input and output of line at time ; For positive proportional normalization.

[0097] That is, data processing is performed on the first electrical data of the first line to obtain the first change difference factor of the first line, which is expressed by the formula:

[0098] ;

[0099] wherein, represents the first change difference factor of the th electrical data at both ends of the first line at time ; and respectively represent the th electrical data of the input and output of the first line at time ;

[0100] Among them, data processing is performed on the second electrical data of the second line to obtain the second change difference factor of the second line, which is expressed by the formula:

[0101] ;

[0102] wherein, represents the second change difference factor of the th electrical data at both ends of the second line at time ; and respectively represent the th electrical data of the input and output of the first line at time ;

[0103] Among them, the total number of power supply lines is the number of all power supply lines in the current target node cluster.

[0104] Among them, the third preset formula can be expressed as follows:

[0105] ;

[0106] represents the first line on the th electrical data The electrical difference index from other circuits of the same type is used to describe the relative differences in aging, lifespan, etc. of the first circuit compared to other circuits of the same type.

[0107] In the formula, and respectively represent the change difference factors of the th electrical data at both ends of the first circuit and the second circuit at time ; represents the total number of circuits that are of the same type as the first circuit ; represents the time length between time and the current time; is used to calculate the weighted mean of the data change differences at different times, with the weight being . This is to eliminate data errors caused by replacement and repair of other circuits, etc., and also to reduce the occurrence of over - correction caused by data errors during subsequent corrections.

[0108] Among them, and both represent times, but they can be different times. The selection of times is arbitrary. Therefore, if = 0, it means the two times are the same. Therefore, when selecting the first time, avoid choosing the same time as the current time to ensure that is always positive and large enough to avoid the problem of the denominator being 0. The calculation of the time difference needs to be cautious to ensure that the time point intervals are reasonable and can effectively reflect the impact of data changes.

[0109] Among them, represents the total number of circuits that are of the same type as the first circuit ; is the number of other circuits that have the same electrical characteristics and operating conditions as the first circuit . These circuits are used for comparison to evaluate the change of the electrical data and the aging degree of the first circuit . When is 0, it actually means there are no available circuits of the same type for comparison. In this case, the formula (the calculation of the electrical difference index between the first circuit and other circuits of the same type) cannot be executed because at least one circuit of the same type is required for comparative analysis. To avoid the situation where is 0, it can be extended to other types of circuits or historical data can be used for estimation to increase the number of comparison samples, thereby avoiding the situation where is 0 or directly set Set to the default value, and the specific avoidance method is not uniquely limited here.

[0110] It can be seen that in this embodiment, for analyzing different lines, their lifetimes and maintenance conditions are different, and the corresponding data changes are also different. According to the above process, the operating states of different lines can be effectively compared to identify potential abnormal conditions.

[0111] S30. Determine the electrical difference value of the first line according to the electrical correction factor and the first electrical data.

[0112] Among them, the first electrical data includes input data and output data.

[0113] Among them, the electrical difference value represents the difference between the first line in a certain electrical data and other similar lines, that is, the electrical difference between the first line and any line in the target node cluster in the first electrical data.

[0114] In one embodiment, the determining the electrical difference value of the first line according to the electrical correction factor and the first electrical data includes: randomly selecting a second line in the target node cluster, and the second line belongs to the target environment cluster; according to the fourth preset formula, calculate the electrical correction factor, the first electrical data, and the second electrical data of the second line to obtain the electrical difference value of the first line, and the electrical difference value represents the electrical difference between the first line and any line in the target node cluster in the first electrical data.

[0115] Among them, the fourth preset formula is as follows:

[0116] ;

[0117] Represents the first line And the second line In the Item electrical data, used to describe the electrical difference between the current line and the same type of line in a certain electrical data, that is, the abnormal degree of the current line in a certain aspect.

[0118] In the formula, And Respectively represent the first line And the second line The Item electrical data at the input end; And Respectively represent the first line And the second line The Item electrical data at the output end; where the first line is the current line to be analyzed, and the second line is any line of the same type as the current line; represents the absolute value function, which is used to describe the difference between corresponding electrical data; represents the line the th electrical data item of the line has an electrical correction factor at the current moment; is used for direct proportional normalization.

[0119] Among them, and are the first electrical data of the first line, that is, the first electrical data includes input data and output data.

[0120] Among them, and are the second electrical data of the second line, that is, the second electrical data includes input data and output data.

[0121] It can be seen that in this embodiment, by calculating the electrical correction factor and combining the information of the target environment clustering, the electrical data of the first line can be adjusted more precisely, considering the influence of environmental factors and historical data, and the accuracy of the data is improved.

[0122] S40. In the target node cluster, perform anomaly analysis on the first line according to the electrical difference value, and determine the power supply anomaly index of the first line.

[0123] Among them, the power supply anomaly index, as the result of the anomaly analysis, provides a quantitative standard for evaluating whether there are performance problems in the line.

[0124] In one embodiment, the performing anomaly analysis on the first line according to the electrical difference value in the target node cluster and determining the power supply anomaly index of the first line includes: obtaining the total number of power supply lines and the total electrical data in the target node cluster; according to the fifth preset formula, calculating the electrical difference value, the total number of power supply lines and the total electrical data to obtain the power supply anomaly index of the first line.

[0125] Among them, the specific process of the total number of power supply lines in the target node cluster can be to first determine the range of the target node cluster, usually obtained through the aforementioned clustering analysis process; finally, within the target node cluster, count the total number of power supply lines belonging to the cluster.

[0126] Among them, the specific process of the total electrical data in the target node cluster can be to first collect the electrical data of all power supply lines within the target node cluster. These data may include parameters such as voltage, current, power, etc. of each line. Finally, these data are summarized to obtain the total electrical data of all lines within the cluster.

[0127] Among them, the total electrical data represents the total number of items for monitoring electrical data at each node. For example, the number of types of electrical data monitored at each node (such as voltage, current, power, etc.).

[0128] Among them, the fifth preset formula is as follows:

[0129] ;

[0130] represents the current first line The power supply anomaly index is used to describe the power supply anomaly index of the current first line in terms of various electrical data compared with lines of the same type.

[0131] In the formula, represents the first line and the second line at the th item of electrical data, the electrical difference; represents the total electrical data at each node; represents that within the target node cluster, all lines of the same type as the first line

[0132] Among them, because represents that within the target node cluster, all lines of the same type as the first line

[0133] It can be seen that in this embodiment, by counting the total number of power supply lines in the target node cluster, the overall scale and distribution of lines of the same type can be comprehensively understood. Further, through detailed electrical data summarization, more accurate fault detection and anomaly analysis can be carried out. If an anomaly is found in the same target node cluster, the problem can be located more quickly and targeted repairs can be made.

[0134] S50. Determine whether the first line is an abnormal line according to the power supply anomaly index.

[0135] Among them, an abnormal line means that there may be some operation problems with this line, and further detection, repair or optimization is required.

[0136] Among them, in this solution, all the connections of multiple power supply circuits can be traversed, that is, all the power supply lines are traversed for anomaly detection, avoiding the problem of missed detection.

[0137] Among them, the line information marked as abnormal can be entered into the data management module in the system. This information should include detailed data such as line number, abnormal index value, detection time, and possible abnormal description. Further, on the user interface of the system, the abnormal lines are highlighted with obvious colors or marks so that the operation and maintenance personnel can quickly identify and locate them.

[0138] Optionally, through the notification function in the system, a notification of the maintenance task is automatically generated and sent to the relevant maintenance personnel or teams; the notification content should include the detailed information of the abnormal line, the location where the abnormality occurs, the abnormal index, and the specific matters that need to be checked.

[0139] In one embodiment, determining whether the first line is an abnormal line according to the power supply abnormal index includes: judging whether the power supply abnormal index is greater than a preset power supply abnormal index; if the power supply abnormal index is greater than or equal to the preset power supply abnormal index, determining that the first line is an abnormal line; or, if the power supply abnormal index is less than the preset power supply abnormal index, determining that the first line is a non-abnormal line.

[0140] Among them, the preset power supply abnormal index is used to evaluate the severity of the power supply abnormal index and can be set to 0.5, which is not uniquely limited here.

[0141] Among them, a non-abnormal line indicates that the current operating condition of this line is good and no further intervention is required.

[0142] It can be seen that in this embodiment, the power supply lines in the power station can be effectively monitored and managed, potential power supply problems can be discovered and solved in time, so as to ensure the stable operation and power supply safety of the power station.

[0143] The present invention has the following beneficial effects: By analyzing the electrical data change characteristics of the same type and same stage lines under similar environmental conditions, the present invention effectively identifies abnormal lines, avoiding judging the electrical state only relying on single-dimensional or single-line data, thereby reducing the one-sidedness of data analysis and increasing the accuracy of the entire system monitoring. Further, by constructing an electrical correction factor for the first line, the present invention can make fine adjustments when analyzing electrical data differences, not only considering the influence of factors such as the service life and maintenance conditions of the line on data changes, but also effectively avoiding data inconsistency caused by inherent differences between lines, reducing misdetection cases caused by data errors. And by using the corrected electrical data differences for analysis, the present invention can more accurately judge the consistency of electrical data between similar lines, accurately locate abnormal lines, and improve the accuracy of abnormal monitoring of power supply lines.

[0144] It should be noted that: The above sequence of embodiments of the present invention is only for description and does not represent the superiority or inferiority of the embodiments. The processes depicted in the 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.

[0145] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between each embodiment can be referred to each other. Each embodiment focuses on the differences from other embodiments.

[0146] As another aspect of the embodiments of the present application, the embodiments of the present application provide an abnormal monitoring device for power supply lines of a new energy photovoltaic power station. Among them, the abnormal monitoring device for power supply lines of a new energy photovoltaic power station can be a software module. The software module includes several instructions, which are stored in a memory. A processor can access this memory and call the instructions for execution to complete the abnormal monitoring method for power supply lines of a new energy photovoltaic power station described in the above various embodiments.

[0147] See Figure 3 , Figure 3 is a schematic structural diagram of an abnormal monitoring device for power supply lines of a new energy photovoltaic power station provided by an embodiment of the present application. As Figure 3 shown, the abnormal monitoring device 300 for power supply lines of a new energy photovoltaic power station includes:

[0148] A determination unit 301, configured to determine the first electrical data of the first line, the target environmental cluster to which the first line belongs, and the target node cluster to which the first line belongs according to the first line to be detected;

[0149] The determining unit 301 is further configured to determine an electrical correction factor of the first line according to the target environment cluster and the first electrical data corresponding to the first line;

[0150] The determining unit 301 is further configured to determine an electrical difference value of the first line according to the electrical correction factor and the first electrical data;

[0151] The analysis unit 302 is configured to perform anomaly analysis on the first line according to the electrical difference value in the target node cluster to determine a power supply anomaly index of the first line;

[0152] The determining unit 301 is further configured to determine whether the first line is an abnormal line according to the power supply anomaly index.

[0153] The present invention has the following beneficial effects: By analyzing the electrical data change characteristics of the same type and same stage lines under similar environmental conditions, the present invention effectively identifies abnormal lines, avoids judging the electrical state only relying on a single dimension or single line data, thereby reducing the one-sidedness of data analysis and increasing the accuracy of the entire system monitoring; further, by constructing an electrical correction factor for the first line, the present invention can make fine adjustments when analyzing electrical data differences, not only considering the influence of factors such as the service life and maintenance conditions of the line on data changes, but also effectively avoiding data inconsistencies caused by inherent differences between lines, reducing misdetection cases caused by data errors; and by using the corrected electrical data differences for analysis, the present invention can more accurately judge the consistency of electrical data between similar lines, accurately locate abnormal lines, and improve the accuracy of power supply line anomaly monitoring.

[0154] In an embodiment, in the determining the electrical correction factor of the first line according to the target environment cluster and the first electrical data corresponding to the first line, the determining unit 301 is further configured to: determine an electrical change value corresponding to the first line under the target environment cluster according to the target environment cluster; determine an electrical difference index according to the first electrical data of the first line, where the electrical difference index represents the electrical difference between the first line and any line in the target node cluster; calculate according to the electrical change value and the electrical difference index according to a first preset formula to obtain the electrical correction factor of the first line.

[0155] In one embodiment, in the step of clustering according to the target environment to determine the electrical change value corresponding to the first line under the target environment clustering, the determining unit 301 is further configured to: arbitrarily select a second line in the target node cluster, where the second line belongs to the target environment cluster; obtain second electrical data of the second line at a first moment; calculate the first moment and the second electrical data according to a second preset formula to obtain the electrical change value corresponding to the first line under the target environment cluster.

[0156] In one embodiment, in the step of determining the electrical difference index according to the first electrical data of the first line, the determining unit 301 is further configured to: arbitrarily select a second line in the target node cluster, where the second line belongs to the target environment cluster; respectively perform data processing on the first electrical data of the first line and the second electrical data of the second line to obtain a first change difference factor of the first line and a second change difference factor of the second line; obtain the total number of power supply lines in the target node cluster; calculate the total number of power supply lines, the first change difference factor, and the second change difference factor according to a third preset formula to obtain the electrical difference index.

[0157] In one embodiment, in the step of determining the electrical difference value of the first line according to the electrical correction factor and the first electrical data, the determining unit 301 is further configured to: arbitrarily select a second line in the target node cluster, where the second line belongs to the target environment cluster; calculate the electrical correction factor, the first electrical data, and the second electrical data of the second line according to a fourth preset formula to obtain the electrical difference value of the first line, where the electrical difference value represents the electrical difference between the first line and any line in the target node cluster in the first electrical data.

[0158] In one embodiment, in the step of performing anomaly analysis on the first line according to the electrical difference value in the target node cluster to determine the power supply anomaly index of the first line, the analysis unit 302 is further configured to: obtain the total number of power supply lines and the total electrical data in the target node cluster; calculate the electrical difference value, the total number of power supply lines, and the total electrical data according to a fifth preset formula to obtain the power supply anomaly index of the first line.

[0159] In one embodiment, in determining whether the first line is an abnormal line according to the power supply abnormality index, the determining unit 301 is further configured to: determine whether the power supply abnormality index is greater than a preset power supply abnormality index; if the power supply abnormality index is greater than or equal to the preset power supply abnormality index, determine that the first line is an abnormal line; or, if the power supply abnormality index is less than the preset power supply abnormality index, determine that the first line is a non-abnormal line.

[0160] In one embodiment, in the power supply line abnormality monitoring system of the new energy photovoltaic power station, which includes an environmental sensor, an electrical sensor, and a plurality of electrical devices, before determining the first electrical data of the first line, the target environmental cluster to which the first line belongs, and the target node cluster to which the first line belongs according to the first line to be detected, the determining unit 301 is further configured to: collect data on the plurality of electrical devices by using the environmental sensor and the electrical sensor, to obtain the electrical data and environmental data of each power supply circuit corresponding to the plurality of electrical devices; screen the power supply circuits according to the electrical data of each power supply circuit, to obtain at least one group of power supply circuits of the same type; determine the target node cluster to which the first line belongs from the at least one group of power supply circuits of the same type; divide the environmental data of each power supply circuit based on the same moment as a division basis, to obtain a plurality of data points, where each data point is an environmental data set corresponding to each moment; perform a first preset clustering process on the plurality of data points, to obtain a plurality of environmental clusters, and each of the environmental clusters includes a plurality of data points in a similar environment; determine the target environmental cluster to which the first line belongs from the plurality of environmental clusters.

[0161] In one embodiment, in screening the power supply circuits according to the electrical data of each power supply circuit to obtain at least one group of power supply circuits of the same type, the determining unit 301 is further configured to: obtain the power supply nodes in each power supply circuit, to obtain a plurality of power supply nodes; obtain the electrical data corresponding to each power supply node in the plurality of power supply nodes; perform a second preset clustering process on each power supply node according to the electrical data corresponding to each power supply node, to obtain a first node cluster of a plurality of electrical device types, and each of the first node clusters includes a plurality of power supply nodes of the same electrical device type; connect the two end nodes of each power supply circuit in each power supply circuit, to obtain the connection lines of the plurality of power supply circuits; perform the first preset clustering process on the connection lines of the plurality of power supply circuits with the first node cluster of the plurality of electrical device types as the clustering space dimension, to obtain a second node cluster of a plurality of line types, and each of the second node clusters includes a plurality of power supply lines of the same line type; determine each of the second node clusters as a group of power supply circuits of the same type, to obtain at least one group of power supply circuits of the same type.

[0162] It should be noted that the above abnormal power supply line monitoring device for a new energy photovoltaic power station can execute the abnormal power supply line monitoring method for a new energy photovoltaic power station provided by the embodiments of the present application, and has corresponding functional modules and beneficial effects for executing the method. For technical details not described in detail in the embodiments of the abnormal power supply line monitoring device for a new energy photovoltaic power station, reference can be made to the abnormal power supply line monitoring method for a new energy photovoltaic power station provided by the embodiments of the present application.

[0163] See Figure 4 , Figure 4 is a schematic structural diagram of an electronic device provided by an embodiment of the present application. As Figure 4 shown, the electronic device 400 includes a processor 401 and a memory 402. The processor 401 is communicatively connected to the memory 402.

[0164] The processor 401 is configured to support the electronic device to execute the corresponding functions in the abnormal power supply line monitoring method for a new energy photovoltaic power station in the above method embodiment. The processor 401 may be a central processing unit (CPU), a network processor (NP), a hardware chip, or any combination thereof. The above hardware chip may be an application specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The above PLD may be a complex programmable logic device (CPLD), a field-programmable gate array (FPGA), a generic array logic (GAL), or any combination thereof.

[0165] Specifically, the processor 401 may include a sending card, a receiving card, and a driving chip.

[0166] The memory 402 is used to store program codes and the like. The memory 402 may include volatile memory (VM), such as random access memory (RAM); the memory 402 may also include non-volatile memory (NVM), such as read-only memory (ROM), flash memory, hard disk drive (HDD) or solid-state drive (SSD); the memory 402 may further include a combination of the above types of memories.

[0167] An embodiment of the present application further provides a computer-readable storage medium, which stores a computer program, and the computer program includes program instructions. When the program instructions are executed by a computer, the computer is caused to execute the abnormal monitoring method for the power supply line of the new energy photovoltaic power station as described in the foregoing embodiment.

[0168] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above methods. Among them, the storage medium can be a magnetic disk, an optical disc, a read-only memory (ROM) or a random access memory (RAM), etc.

Claims

1. A method for monitoring abnormality of power supply lines of a new energy photovoltaic power station, characterized in that: A power supply line abnormality monitoring system applied to a new energy photovoltaic power station, the method comprising: Determine, according to a first line to be detected, first electrical data of the first line, a target environment cluster to which the first line belongs, and a target node cluster to which the first line belongs; determining an electrical correction factor of the first circuit according to the target environment cluster and first electrical data corresponding to the first circuit; determining an electrical difference value of the first circuit according to the electrical correction factor and the first electrical data; In the target node cluster, performing an abnormality analysis on the first line according to the electrical difference value to determine a power supply abnormality index of the first line; Determining whether the first line is an abnormal line according to the power supply abnormality indicator; The step of determining an electrical correction factor of the first circuit according to the target environment cluster and first electrical data corresponding to the first circuit includes: Determining, according to the target environment cluster, an electrical change value corresponding to the first circuit under the target environment cluster; determining an electrical difference index according to first electrical data of the first line, the electrical difference index representing an electrical difference between the first line and any line in the target node cluster; According to a first preset formula, the electrical change value and the electrical difference index are calculated to obtain an electrical correction factor of the first circuit; The determining, according to the target environment cluster, an electrical change value corresponding to the first circuit under the target environment cluster includes: In the target node cluster, randomly select a second line, where the second line belongs to the target environment cluster; Acquire second electrical data of the second circuit at a first moment; Calculating the first moment and the second electrical data according to a second preset formula to obtain an electrical change value corresponding to the first line under the target environment cluster; The step of determining an electrical difference index according to the first electrical data of the first circuit includes: In the target node cluster, randomly select a second line, where the second line belongs to the target environment cluster; Performing data processing on the first electrical data of the first circuit and the second electrical data of the second circuit respectively to obtain a first change difference factor of the first circuit and a second change difference factor of the second circuit; Obtaining the total number of power supply lines in the target node cluster; According to a third preset formula, the total number of power supply lines, the first change difference factor and the second change difference factor are calculated to obtain an electrical difference index; The first preset formula is: Indicates the first line No. The electrical correction factor of the electrical data at the current moment, Indicates the first line No. Theoretical electrical change value of the electrical data under the current environment, Indicated in Item Electrical Data on Line Electrical difference indicators with other similar lines; The second preset formula is: Indicates the first line No. Theoretical electrical change value of the electrical data under the current environment, and Respectively represent Lines in similar environments at different times The output and input of Item electrical data, including line For the target line Any line of the same type; Indicates The time difference between the similar environment at the moment and the current moment, It means to find the weighted mean of the differences corresponding to different lines, and the weight is ; The third preset formula is: Indicated in Item electrical data on the first circuit Electrical difference indicators compared with other similar lines, and Respectively represent the first line With the second line At the moment At both ends of the line The variation factor of the electrical data, Indicates that the first line The total number of lines of the same type, Indicates time The length of time since the current moment; It means to find the weighted mean of the difference in data changes at different times, and the weight is .

2. The method for monitoring abnormality of power supply lines of a new energy photovoltaic power station according to claim 1, characterized in that: The step of determining the electrical difference value of the first circuit according to the electrical correction factor and the first electrical data includes: In the target node cluster, randomly select a second line, where the second line belongs to the target environment cluster; According to a fourth preset formula, the electrical correction factor, the first electrical data, and the second electrical data of the second line are calculated to obtain an electrical difference value of the first line, wherein the electrical difference value represents an electrical difference between the first line and any line in the target node cluster in the first electrical data; The fourth preset formula is: Indicates the first line With the second line In the Electrical differences in electrical data, and Respectively represent the first line With the second line The input terminal Item electrical data; and Respectively represent the first line With the second line The output Item electrical data; the first line The current line to be analyzed, the second line Any line of the same type as the current line; represents the absolute value function, Indicates line No. The electrical correction factor of the electrical data at the current moment; Used for normalization of positive proportion.

3. The method for monitoring abnormality of power supply lines of a new energy photovoltaic power station according to claim 1, characterized in that: The step of performing an abnormality analysis on the first line according to the electrical difference value in the target node cluster to determine a power supply abnormality index of the first line includes: Obtaining the total number of power supply lines and total electrical data in the target node cluster; According to a fifth preset formula, the electrical difference value, the total number of power supply lines and the total electrical data are calculated to obtain a power supply abnormality index of the first line; The fifth preset formula is: Indicates the current first line Power supply abnormality indicators, Indicates the first line With the second line In the Electrical differences in electrical data items; Indicates the total electrical data at each node; Indicates that within the target node cluster, all nodes with the first link is the total number of lines of the same type.

4. The method for monitoring abnormality of power supply lines of a new energy photovoltaic power station according to claim 1, characterized in that: The determining, according to the power supply abnormality indicator, whether the first line is an abnormal line includes: Determining whether the power supply abnormality index is greater than a preset power supply abnormality index; If the power supply abnormality index is greater than or equal to the preset power supply abnormality index, determining that the first line is an abnormal line; or, If the power supply abnormality index is less than the preset power supply abnormality index, the first line is determined to be a non-abnormal line.

5. The method for monitoring abnormality of power supply lines of a new energy photovoltaic power station according to claim 1, characterized in that: The power supply line abnormality monitoring system of the new energy photovoltaic power station includes an environmental sensor, an electrical sensor and a plurality of electrical devices. Before determining the first electrical data of the first line, the target environment cluster to which the first line belongs and the target node cluster to which the first line belongs according to the first line to be detected, the method further includes: Collecting data of the plurality of electrical devices according to the environmental sensor and the electrical sensor to obtain electrical data and environmental data of each power supply circuit corresponding to the plurality of electrical devices; Screening the power supply circuits according to the electrical data of the power supply circuits to obtain at least one group of power supply circuits of the same type; In the at least one group of power supply circuits of the same type, determining a target node cluster to which the first line belongs; Based on the same time as the division basis, the environmental data of each power supply circuit is divided into data to obtain multiple data points, each data point being a set of environmental data corresponding to each time; Performing a first preset clustering process on the multiple data points to obtain multiple environmental clusters, each of the environmental clusters including multiple data points in a similar environment; Among the multiple environment clusters, determine a target environment cluster to which the first line belongs.

6. The method for monitoring abnormality of power supply lines of a new energy photovoltaic power station according to claim 5, characterized in that: The step of screening the power supply circuits according to the electrical data of the power supply circuits to obtain at least one group of power supply circuits of the same type includes: Acquire power supply nodes in each power supply circuit to obtain multiple power supply nodes; Acquire electrical data corresponding to each of the plurality of power supply nodes; According to the electrical data corresponding to each power supply node, a second preset clustering process is performed on each power supply node to obtain a plurality of first node clusters of electrical device types, each of the first node clusters including a plurality of power supply nodes of the same electrical device type; Connecting the two end nodes of each power supply circuit in the power supply circuits to obtain connection lines of multiple power supply circuits; Taking the first node clusters of the multiple electrical equipment types as the clustering space dimension, performing the first preset clustering process on the connections of the multiple power supply circuits to obtain second node clusters of multiple line types, each of the second node clusters including multiple power supply lines of the same line type; Each of the second node clusters is determined to be a group of power supply circuits of the same type, thereby obtaining at least one group of power supply circuits of the same type.

7. A power supply line abnormality monitoring system for a new energy photovoltaic power station, characterized in that: It includes a memory and a processor, the memory is connected to the processor, the processor is used to execute one or more computer programs stored in the memory, and when the processor executes the one or more computer programs, the power supply line abnormality monitoring system of the new energy photovoltaic power station implements the power supply line abnormality monitoring method of the new energy photovoltaic power station as described in any one of claims 1-6.

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