Power grid line loss intelligent diagnosis and visualization method based on multi-source data acquisition

Abstract

The present application relates to the technical field of three-dimensional visualization, in particular to a power grid line loss intelligent diagnosis and visualization method based on multi-source data acquisition, obtaining sensor data grouping and sorting, detecting power fluctuation synchronicity, extracting change trend, analyzing power difference and node trend, marking abnormal path, generating line loss atlas, positioning abnormal node coordinate level, mapping topological graph adjustment style, and generating visualization layer result. The present application efficiently analyzes multi-dimensional data of a feeder line, captures power fluctuation and abnormality, identifies abnormal nodes in combination with power difference of adjacent nodes, analyzes fluctuation rate to reveal line loss area and structural abnormality, calibrates abnormal position by using three-dimensional coordinates, realizes spatial positioning and risk assessment, clearly displays operation state by three-dimensional layer, improves monitoring accuracy, helps to quickly identify high-risk area by path connection and node positioning, optimizes operation and maintenance efficiency and diagnosis comprehensiveness, and provides accurate basis for power grid management.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of three-dimensional visualization, and in particular to a power grid line loss intelligent diagnosis and visualization method based on multi-source data acquisition. BACKGROUND

[0002] The technical field of three-dimensional visualization includes technical directions such as spatial data modeling, three-dimensional scene construction, and dynamic interactive presentation. In this field, data mapping relationships under a three-dimensional coordinate system are established to convert abstract data into visualized entities with spatial dimensions. Combined with lighting rendering, perspective transformation, and interactive operation techniques, three-dimensional space representation of complex system operation states is achieved. In power grid monitoring, it is mainly applied to device topology structure display, energy flow path tracking, and abnormal state spatial positioning.

[0003] Among them, the power grid line loss intelligent diagnosis and visualization method based on multi-source data acquisition refers to the construction of a three-dimensional topological mapping model of power grid line loss characteristics through multi-dimensional data fusion of distribution transformer terminals, smart meters, and line loss monitoring devices. This method uses a data fusion engine to realize multi-dimensional feature correlation of current voltage phase angle difference, power factor fluctuation, and temperature and humidity environmental parameters. Through a line loss gradient coloring algorithm of the three-dimensional space coordinate system, a spatial distribution thermal map of line loss values is established. Topological node dynamic calibration technology is used to realize three-dimensional spatial positioning of abnormal line loss areas, and finally a layered rendering power grid line loss three-dimensional visualization diagnosis atlas is formed.

[0004] The existing technology mainly relies on multi-source data integration and simple topology structure display in power grid line loss monitoring, and can perform preliminary monitoring. However, when faced with complex power grid topology and a large amount of data, there are limitations. It lacks in-depth identification of power grid feeder line power fluctuations and node differences, and fails to effectively mine the details of power fluctuations and node relationships, making it difficult to accurately locate abnormal nodes. In addition, there is a lack of line loss positioning method based on three-dimensional space coordinates, which cannot visually display the correlation and abnormal state between power grid nodes. The monitoring result is usually a static topology graph, which lacks spatial hierarchy and operability. The technology cannot respond to changes in power grid structure in a timely manner, making it difficult to discover abnormal areas in sudden line loss, affecting problem solving speed. The existing technology lacks sufficient visualization and interactivity, affecting the accuracy and timeliness of diagnosis. SUMMARY

[0005] To solve the technical problems existing in the prior art, the embodiments of the present application provide a power grid line loss intelligent diagnosis and visualization method based on multi-source data acquisition. The technical solution is as follows:

[0006] The power grid line loss intelligent diagnosis and visualization method based on multi-source data acquisition comprises the following steps:

[0007] S1: acquire each feeder sensor data, group by number and arrange in time sequence, detect power fluctuation synchronization, extract change trend, and obtain feeder power fluctuation sequence result;

[0008] S2: call the feeder power fluctuation sequence result, analyze the difference between adjacent feeder node power input and output, compare the power difference value with the node sequence number change trend, mark the nodes with inconsistent fluctuation trend as path abnormal, and obtain the feeder line loss mutation marking result;

[0009] S3: extract the path abnormal nodes in the feeder line loss mutation marking result, calculate the power change rate difference value, extract the alternating fluctuation node group, form the path closure atlas, and generate the line loss structure abnormal path atlas result;

[0010] S4: extract the topology number and position code of the abnormal nodes in the line loss structure abnormal path atlas result, hang the nodes to the space coordinates, label the hierarchical distribution, output the position and level information, and obtain the three-dimensional structure risk positioning information result;

[0011] S5: based on the three-dimensional structure risk positioning information result, map the abnormal nodes to the topology structure graph, adjust the rendering style, determine the layer identification combined with the spatial position, complete the node and path visual expression, and generate the power grid line loss visualization layer result.

[0012] As a further scheme of the present application, the feeder power fluctuation sequence result includes voltage data, current data, power factor data, conductor temperature rise data and loop length data, the feeder line loss mutation marking result includes adjacent feeder node power input value, adjacent feeder node power output value, power difference value change trend, node sequence number change trend and path abnormal node, the line loss structure abnormal path atlas result includes path abnormal node set, low-voltage access point, terminal metering device, branch bus and path node connection sequence, the three-dimensional structure risk positioning information result includes feeder topology number, distribution level identification, geographic position code, spatial coordinate position and node level information, and the power grid line loss visualization layer result includes feeder path topology structure graph, terminal load unit distribution graph, branch structure level graph, node and path visual expression and layer identification form.

[0013] As a further scheme of the present application, the feeder power fluctuation sequence result acquisition step is:

[0014] S101: acquire voltage, current, power factor, conductor temperature rise and loop length sensor data corresponding to each feeder number of the distribution unit, establish data grouping index according to feeder number, horizontally splice different sensor data under the same number according to collection time stamp, and generate feeder time sequence data set;

[0015] S102: Based on the feeder timing data set, arrange each feeder data group in ascending order of timestamp, extract the power values at adjacent time points to form a difference sequence, set a power fluctuation threshold range, identify the starting time point of the section continuously exceeding the threshold, and generate a synchronous fluctuation time marker group;

[0016] S103: Call the synchronous fluctuation time marker group, intercept the power data sequence of each time section corresponding to the feeder, calculate the linear regression slope of the power value in each time section, concatenate the sections with absolute slope values exceeding the set reference value in time sequence, and generate a feeder power fluctuation sequence result.

[0017] As a further scheme of the present application, the acquisition step of the feeder line loss mutation marker result is:

[0018] S201: Call the input and output power value sequences of adjacent feeder nodes in the feeder power fluctuation sequence result, calculate the input-output difference value according to the time point alignment, and generate a feeder power difference sequence by counting the absolute value of the power difference of each feeder node pair.

[0019] S202: Extract the moving average of the difference value in the continuous time window in the feeder power difference sequence, calculate the Pearson correlation coefficient of the increasing trend of the adjacent feeder node sequence number and the change trend of the difference value, and generate a trend difference identifier.

[0020] S203: According to the trend difference identifier, screen the node pairs with a Pearson correlation coefficient below a set threshold, mark the node with a larger sequence number in the node pair as a path abnormal node, and generate a feeder line loss mutation marker.

[0021] As a further scheme of the present application, the acquisition step of the line loss structure abnormal path map result is:

[0022] S301: Call the set of path abnormal nodes in the feeder line loss mutation marker, match the low-voltage access point coordinates, terminal metering device numbers, and branch bus connection relationships in the distribution network topology structure, and generate an abnormal node topology association table.

[0023] S302: Based on the abnormal node topology association table, extract the power values of each abnormal node in three consecutive time periods, calculate the absolute difference value of the power change rate of adjacent periods, and generate a node power fluctuation gradient.

[0024] S303: According to the node power fluctuation gradient, identify the node group in which the fluctuation direction continuously occurs twice, construct the conduction path between nodes according to the connection order of the distribution network, and generate a line loss structure abnormal path map.

[0025] As a further scheme of the present application, the acquisition step of the three-dimensional structure risk positioning information result is:

[0026] S401: Call the path exception node in the line loss structure exception path atlas, extract the corresponding feeder topology number and power distribution level identifier, match the geographic coordinate coding in the power grid GIS system, and generate an exception node space attribute table;

[0027] S402: Based on the exception node space attribute table, the node geographic coordinates are converted into X / Y / Z axis values in a three-dimensional coordinate system according to the feeder voltage level division hierarchy, and a node space coordinate mapping set is generated;

[0028] S403: According to the node space coordinate mapping set, the vector relationship of the connection path between nodes is established in the three-dimensional coordinate system, the corresponding power distribution level identifier of each node is superimposed, and three-dimensional structure risk positioning information is generated.

[0029] As a further scheme of the present application, the acquisition step of the power grid line loss visualization layer result is:

[0030] S501: Call the node coordinates and level identifier in the three-dimensional structure risk positioning information, match the corresponding node position in the feeder path topology structure graph, set the red dashed line channel rendering style according to the path connection state, and generate an abnormal path rendering base map;

[0031] S502: Based on the abnormal path rendering base map, superimpose the device point data of the end load unit distribution graph, assign different shape layer identifiers according to the spatial position of the node, and generate a composite space identification layer;

[0032] S503: Integrate the composite space identification layer and the hierarchical division data of the branch structure level graph, uniformly adjust the transparency and superposition order of each layer, and generate a power grid line loss visualization layer.

[0033] The technical scheme provided by the embodiment of the present application has at least the following beneficial effects:

[0034] In the present application, by acquiring multi-dimensional data of power grid feeders and efficiently integrating and analyzing, power grid system power fluctuation and abnormal change are accurately captured, feeder power change trend is monitored in real time, abnormal nodes are identified in combination with adjacent node power difference, node power fluctuation rate is further analyzed, and potential areas and structure abnormalities of line loss of the power grid are revealed. The innovation lies in that the abnormal areas are accurately marked by using a three-dimensional space coordinate system through the association of nodes and feeder paths, line loss space positioning and risk assessment are realized, the three-dimensional layer structure display makes the power grid operation state and abnormal areas intuitive and clear, and the monitoring and maintenance efficiency and accuracy are improved. Accurate node positioning and path connection help power grid managers quickly locate potential high-risk areas, timely response measures reduce losses and optimize operation and maintenance efficiency, and the overall processing logic optimization improves the flexibility and comprehensiveness of power grid line loss diagnosis, and provides accurate basis for power grid operation and maintenance. BRIEF DESCRIPTION OF DRAWINGS

[0035] Figure 1 for the method of the present application;

[0036] Figure 2 for the feeder power fluctuation sequence result acquisition flowchart of the present application;

[0037] Figure 3 for the feeder line loss mutation marker result acquisition flowchart of the present application;

[0038] Figure 4 for the line loss structure abnormal path map result acquisition flowchart of the present application;

[0039] Figure 5 for the three-dimensional structure risk positioning information result acquisition flowchart of the present application;

[0040] Figure 6 for the power grid line loss visualization layer result acquisition flowchart of the present application. DETAILED DESCRIPTION

[0041] The technical solutions in the present application will be described below with reference to the drawings.

[0042] In the embodiments of the present application, the words such as "example", "for example" are used to represent an example, illustration or description. Any embodiment or design scheme described as "example" in the present application should not be interpreted as more preferred or more advantageous than other embodiments or design schemes. Rather, the word "example" is intended to present the concept in a specific manner. In addition, in the embodiments of the present application, the meaning expressed by "and / or" can be both, or can be one of the two.

[0043] In the embodiments of the present application, "image" and "picture" can be used interchangeably at times. It should be pointed out that when the distinction is not emphasized, the meanings expressed are consistent. "Of", "corresponding" and "corresponding" can be used interchangeably at times. It should be pointed out that when the distinction is not emphasized, the meanings expressed are consistent.

[0044] In the embodiments of the present application, sometimes the subscript such as W1 can be written in the form of non-subscript such as W1. When the distinction is not emphasized, the meanings expressed are consistent.

[0045] In order to make the technical problems, technical solutions and advantages of the present application clearer, specific embodiments will be described in detail below with reference to the drawings.

[0046] Please refer to Figure 1The application provides a technical solution: an intelligent diagnosis and visualization method for power grid line loss based on multi-source data acquisition, including the following steps:

[0047] S1: Obtain the voltage, current, power factor, conductor temperature rise, and loop length sensor data of each feeder in the distribution unit, group them according to the feeder number, splice the data in the same group and arrange them in chronological order, detect the synchronization of power fluctuation sections, extract the power change trend of continuous sections, and obtain the feeder power fluctuation sequence result;

[0048] S2: Call the power input and output value sequences of each adjacent feeder node in the feeder power fluctuation sequence result, calculate the power input and output difference of adjacent nodes respectively, compare the power difference trend, check the correspondence between the node number trend and the power difference trend, judge whether the fluctuation trend is consistent, mark the nodes with inconsistent trend as path abnormal nodes, and obtain the feeder line loss mutation marking result;

[0049] S3: Extract the path abnormal node set in the feeder line loss mutation marking result, locate the low-voltage access point, terminal metering device and branch bus associated with the feeder path node, calculate the power change rate difference of the path abnormal node based on the power change trend of the path abnormal node, compare the power change trend and power change rate relationship before and after the path abnormal node, extract the path node group with alternating fluctuation trend, form the path closure atlas of the path node connection order, and generate the line loss structure abnormal path atlas result;

[0050] S4: Extract the feeder topology number, distribution level identifier, and geographic location code of the path abnormal node in the line loss structure abnormal path atlas result, and perform hierarchical division according to the feeder structure, map the nodes to the spatial coordinate system position, label the corresponding hierarchical distribution position of the path association relationship, output the position and hierarchical information of the node in the spatial coordinate system, and obtain the three-dimensional structure risk positioning information result;

[0051] S5: According to the three-dimensional structure risk positioning information result, map the path abnormal node to the feeder path topology structure diagram, the terminal load unit distribution diagram and the branch structure level diagram, adjust the channel rendering style according to the path connection state in the atlas, determine the layer identifier form according to the spatial position of the node, complete the visual expression of the nodes and paths in the three-dimensional structure, and generate the power grid line loss visualization layer result.

[0052] The feeder power fluctuation sequence result includes voltage data, current data, power factor data, conductor temperature rise data and loop length data, the feeder line loss mutation marker result includes adjacent feeder node power input value, adjacent feeder node power output value, power difference change trend, node serial number change trend and path abnormal node, the line loss structure abnormal path atlas result includes path abnormal node set, low-voltage access point, terminal metering device, branch bus and path node connection sequence, the three-dimensional structure risk positioning information result includes feeder topology number, power distribution level identifier, geographic location code, spatial coordinate position and node level information, and the power grid line loss visualization layer result includes feeder path topology structure diagram, terminal load unit distribution diagram, branch structure level diagram, node and path visual expression and layer identifier form.

[0053] Referring to Figure 2 The acquisition step of the feeder power fluctuation sequence result is as follows:

[0054] S101: Obtain voltage, current, power factor, conductor temperature rise and loop length sensor data corresponding to each feeder number of the power distribution unit, establish a data grouping index according to the feeder number, horizontally splice different sensor data under the same number according to the collection time stamp, and generate a feeder time sequence data set;

[0055] The sensor data of two feeders numbered F01 and F02 at two consecutive time points (T1 = 2025-07-04 10:00:00, T2 = 2025-07-04 10:00:05) are obtained from each data acquisition terminal of the power distribution unit. The AC voltage effective value of feeder F01 at time T1 is 220.1V, the current effective value is 15.2A, the power factor is 0.98, and the difference between the conductor temperature and the ambient temperature is 25.1°C. The physical length of the loop is 15.5 meters. The corresponding values ​​at time T2 are 220.3V, 15.8A, 0.98, 25.9℃, and 15.5 meters. The corresponding values ​​of feeder F02 at time T1 are 219.9V, 18.1A, 0.99, 28.3℃, and 21.8 meters. The corresponding values ​​at time T2 are 220.0V, 18.3A, 0.99, 28.6℃, and 21.8 meters. A data index structure with the feeder number as the unique key value is established. Specifically, an entry is created with the key string 'F01', whose value points to a data set. Another entry is then created for feeder 'F02'. Five different types of sensor data, including voltage, current, power factor, conductor temperature rise, and loop length, collected from the same feeder number are associated and merged horizontally based on their shared acquisition timestamps. For example, for feeder F01, its five data values ​​at timestamp T1 (220.1V, 15.2A, 0.98, 25.1°C, and 15.5m) are retrieved to form a data row. Then, its five data values ​​at timestamp T2 (220.3V, 15.8A, 0.98, 25.9°C, and 15.5m) are retrieved to form the next row. The same operation is performed on feeder F02. The result is a structured data table with time as the vertical sequence and different sensor data as the horizontal fields. Each row represents a complete status snapshot of a specific feeder at a specific point in time, generating a feeder time series dataset.

[0056] S102: Based on the feeder time series data set, arrange each feeder data group in ascending order by timestamp, extract power values ​​at adjacent time points to form a difference sequence, set a power fluctuation threshold range, identify the starting time points of the sections that continuously exceed the threshold, and generate a synchronous fluctuation time mark group;

[0057] Based on the feeder time series data set, the power value of each feeder at each time stamp is first calculated. The power value P is obtained by the following formula: , where U is the effective value of voltage, I is the effective value of current, The power factor is calculated by taking feeder F01 at time T1 as an example and substituting the data into the calculation:

[0058] P(F01,T1)=220.1V*15.2A*0.98=3277.5W;

[0059] Likewise:

[0060] P(F01,T2)=220.3V*15.8A*0.98=3411.0W;

[0061] P(F02,T1)=219.9V*18.1A*0.99=3937.6W;

[0062] P(F02,T2)=220.0V*18.3A*0.99=3985.4W;

[0063] Then, the data of each feeder is arranged in ascending order according to the timestamps T1, T2, …, Tn, and then the power values of adjacent two time points are extracted to construct a power difference value sequence , the calculation method is , for example, the power difference value of feeder F01 at T2 time is , the power difference value of feeder F02 at T2 time is , then, a fluctuation threshold for judging whether the power fluctuation is severe is set , the setting of the threshold refers to the average power of the feeder in a natural day and the standard deviation of the power value , the specific setting formula is , assuming that the of feeder F01 is calculated by calling historical data is 3100W, is 50W, then the fluctuation threshold of feeder F02 is 3800W, is 60W, then the fluctuation threshold , then, the of each feeder is compared with the of each feeder at each time point, if there is a time point , at which more than a preset number (for example, 2) of feeders simultaneously satisfy the condition , and this state still continues at the next time point , the starting time point is recorded, assuming that at a subsequent time point T5, it is calculated that , , and at T6 time, both of them still exceed the respective threshold, then T5 time is identified as a starting point of synchronous fluctuation, and a synchronous fluctuation time marker group is generated.

[0064] S103: call the synchronous fluctuation time marker group, intercept the power data sequence of each time section of the feeder, calculate the linear regression slope of the power value in each time section, concatenate the sections with the absolute value of the slope exceeding the set reference value in time order, and generate a feeder power fluctuation sequence result;

[0065] The generated set of synchronous fluctuation time markers is invoked, assuming that it contains a starting time point T5, and the duration of the fluctuation section is determined, which is retrieved from T5 backward until the first time point T8 is found at which the power difference of all involved feeders (F01 and F02) falls back to their respective fluctuation thresholds The fluctuation section is thus determined as [T5, T8] with T5 = 10:01:00, T6 = 10:01:05, T7 = 10:01:10, T8 = 10:01:15, and the power data sequence of feeder F01 within this [T5, T8] section is intercepted, assuming its power values are respectively:

[0066] P(F01, T5) = 3500 W;

[0067] P(F01, T6) = 3820 W;

[0068] P(F01, T7) = 4150 W;

[0069] P(F01, T8) = 4500 W;

[0070] The linear regression slope of the power values within this section is calculated, with the time point sequence quantized as independent variable x, i.e. x = {1, 2, 3, 4}, and the power value sequence as dependent variable y, i.e. y = {3500, 3820, 4150, 4500}, and the calculation formula of the slope m is where n is the number of data points, here n = 4, and the data is substituted for calculation:

[0071] , ;

[0072] ;

[0073] ;

[0074] Substituting the formula gives , which indicates that the power increases by an average of 333 W per 5-second time step, and a slope reference value is set, which is referenced to the technical specification of the server device power supply unit (PSU) mounted, which usually does not allow the power change rate to exceed a certain value, assuming that the value is 100 W per second, and considering that the time step in this example is 5 seconds, the slope reference value is obtained, and the absolute value of the calculated slope is compared with the reference value , because ​So the power fluctuation of F01 in the segment [T5, T8] does not exceed the benchmark, the segment data is not selected, if the absolute value of the slope calculated by another segment exceeds 500, the complete power data sequence of the segment (such as [P(Ts), …, P(Te)]) is taken as an element, and all other segment data that meet the conditions are arranged in time sequence to generate the feeder power fluctuation sequence result.

[0075] Please refer to Figure 3 The acquisition step of the feeder line loss mutation mark result is:

[0076] S201: Call the adjacent feeder node power input value and output value sequence in the feeder power fluctuation sequence result, align the input and output difference values according to the time point, and calculate the power difference absolute value of each feeder node pair to generate the feeder power difference sequence.

[0077] Call the feeder power fluctuation sequence result, which contains the feeder data with rapid power value change in a specific time segment, for example, select the power fluctuation sequence of feeder F01 in time segment [T5, T8], and determine that there are three monitoring nodes connected in sequence on the power transmission path of the feeder, denoted as F01-N1, F01-N2, and F01-N3, where F01-N1 is the input node, F01-N2 is the intermediate node, and F01-N3 is the output node, forming two adjacent node pairs (F01-N1, F01-N2) and (F01-N2, F01-N3). The power input value sequence of F01-N1 node is called from the fluctuation sequence, and the power values at T5, T6, T7, and T8 are:

[0078] Pin(N1)={3500.0W,3820.0W,4150.0W,4500.0W};

[0079] At the same time, the power input value sequence of downstream node F01-N2 is obtained, which is equal to the output value of the upstream node pair, and the sequence is:

[0080] Pin(N2)={3465.0W,3778.0W,4100.0W,4441.5W};

[0081] And the power input value sequence of F01-N3 is:

[0082] Pin(N3)={3430.7W,3740.2W,3978.0W,4385.9W};

[0083] Align these sequences accurately by time point, calculate the input and output power difference value of the first node pair (F01-N1, F01-N2) at each time, which is calculated as ;

[0084] At T5, the difference value is ;

[0085] At T6, the difference value is ;

[0086] At T7, the difference value is ;

[0087] At T8, the difference value is ;

[0088] The same difference value calculation is performed on the second node pair (F01-N2, F01-N3):

[0089] At T5, the difference value is ;

[0090] At T6, the difference value is ;

[0091] At T7, the difference value is ;

[0092] At T8, the difference value is ;

[0093] The absolute values of these calculated power difference values are then extracted, since all calculation results are positive values, the absolute value is itself. These difference values are organized according to node pairs and time order, and finally a structured data containing the power loss of each node pair at consecutive time points is obtained, generating a feeder power difference sequence.

[0094] S202: Extract the moving average of the difference values in the continuous time window in the feeder power difference sequence, calculate the Pearson correlation coefficient of the increasing trend of the adjacent feeder node sequence number and the change trend of the difference values, and generate a trend difference identifier;

[0095] The feeder power difference sequence is extracted, and the difference value sequence of the node pair (F01-N2, F01-N3) {34.3W, 37.8W, 122.0W, 55.6W} is taken as an example. A continuous time window with a width of 3 time points is set, and the moving average of the difference values in the window is calculated. For the first window starting at T5, the average is , and for the second window starting at T6, the average is , then, to determine whether the line loss distribution of the entire feeder is uniform at a certain time, the data at T7 is selected, at which time the power difference value of the node pair (F01-N1, F01-N2) is 50.0 W, the power difference value of the node pair (F01-N2, F01-N3) is 122.0 W, and assuming that the feeder has a subsequent node pair (F01-N3, F01-N4), the power difference value of the node pair at T7 is 52.0 W, thereby forming two sequences for correlation analysis, the first sequence being an increasing sequence of adjacent feeder node numbers, i.e., the sequence of the first, second, and third line segment numbers X = {1, 2, 3}, and the second sequence being a sequence of power difference values of the corresponding line segments at T7 Y = {50.0, 122.0, 52.0}, the Pearson correlation coefficient r of the two sequences is calculated, and the calculation formula is where n is the number of data pairs, n = 3 here, x is the node number, and y is the power difference value. First, the cumulative values are calculated:

[0096] , , ;

[0097] ;

[0098] ;

[0099] The values are substituted into the formula:

[0100] ;

[0101] The calculated correlation coefficient r = 0.024 is taken as the trend difference identifier of the feeder at T7.

[0102] S203: According to the trend difference identifier, the node pair with a Pearson correlation coefficient below a set threshold value is screened, and the node with a larger number in the node pair is marked as a path abnormal node, and a feeder line loss mutation marker is generated;

[0103] According to the trend difference identifier, i.e., the Pearson correlation coefficient r value, a correlation coefficient threshold value for judging trend consistency is set The threshold value is set by referring to the results of historical data analysis on multiple feeders in a healthy operating state. Analysis shows that the line loss distribution of healthy feeders is uniform, and the r value is usually stable above 0.8. To leave enough judgment margin, the threshold value is set to The trend difference identifier r = 0.024 of the feeder F01 calculated at T7 in the previous paragraph is compared with the threshold value, and since , the correlation coefficient value is lower than the set threshold, so the feeder F01 is screened out at T7 time exists line loss distribution anomaly, need to further locate the problem node, call the power difference sequence Y = {50.0W, 122.0W, 52.0W} for calculating r value, check the value in the sequence, calculate the average value of the sequence And standard deviation , find the data point deviating from the average value the most, where the difference between 122.0W and the average value is , much larger than the difference between other data points and the average value, the difference value 122.0W corresponds to the second node pair (F01-N2, F01-N3), according to the processing rule, mark the node with larger serial number in the node pair, that is, F01-N3 node, as the potential path abnormal node, record the unique identifier "F01-N3" of the node, and generate the feeder line loss mutation mark.

[0104] Please refer to Figure 4 , the acquisition steps of line loss structure abnormal path atlas result are as follows:

[0105] S301: call the path abnormal node set in the feeder line loss mutation mark, match the low-voltage access point coordinates, terminal metering device number and branch bus connection relationship in the power distribution network topology structure, and generate an abnormal node topology association table;

[0106] The feeder line loss mutation mark set is called, which contains node identifiers identified as path anomalies. For example, the set is {"F01-N3", "F02-N5"}. For each abnormal node in the set, a query and match is performed in the pre-built distribution network topology database. The database uses the node identifier as the primary key and stores the physical and electrical connection information of each node. For the node "F01-N3", a matching operation is performed, and the corresponding low-voltage access point three-dimensional geographic coordinates are extracted from the database (longitude: 121.4503, latitude: 31.2215, floor: 15.0 meters). The unique serial number of the terminal metering device directly associated with it is "METER-SN-987654", and its connection relationship on the branch bus is recorded as the upstream node "F01-N2", the downstream node "F01-N3". The upstream node "DP-05-Panel-A" is matched with the same matching process for another node "F02-N5" in the set. The query results in its coordinates (longitude: 121.4511, latitude: 31.2209, floor: 28.0 meters), the associated metering equipment number is "METER-SN-987712", and the connection relationship is the upstream node "F02-N4" and the downstream node "DP-11-Panel-B". These discrete information queried and matched are structured and integrated to create a data entry for each abnormal node. Each entry contains five fields: node identifier, access point coordinates, equipment number, upstream connection point, and downstream connection point. The data entries of all abnormal nodes are combined into a tabular data structure to generate an abnormal node topology association table.

[0107] S302: Based on the abnormal node topology association table, extract the power values ​​of each abnormal node for three consecutive time periods, calculate the absolute value of the difference between the power change rates of adjacent periods, and generate the node power fluctuation gradient;

[0108] Based on the abnormal node topology association table, extract the identifiers of all abnormal nodes, such as "F01-N3", and retrieve the power input values ​​of the node in four consecutive time periods, namely T7, T8, T9, and T10, from the feeder time series data set. Assume that the power value at time T7 is The power value is 3978.0W, which comes from the calculation in the previous step. The power value at time T8 is , the power value at T9 is , the power value at time T10 is , the time interval of each time period For 5 seconds, first calculate the power change rate between adjacent cycles. The change rate of the first time period (T7 to T8) is , the rate of change of the second time period (T8 to T9) , the rate of change of the third time period (T9 to T10) Then, the absolute value of the difference between these consecutive change rates is calculated, which is the node power fluctuation gradient G, and the calculation formula is , applied to this example, the fluctuation gradient at T9 is , and the fluctuation gradient at T10 is Store the series of gradient values calculated for each abnormal node at each time point to generate the node power fluctuation gradient.

[0109] S303: According to the node power fluctuation gradient, identify the node group in which the fluctuation direction continuously occurs twice, and construct the conduction path between nodes according to the connection order of the power distribution network to generate the line loss structure abnormal path atlas;

[0110] According to the node power fluctuation gradient data and the continuous power change rate sequence obtained in the gradient calculation process, for example, the change rate sequence of node "F01-N3" is {81.58, -7.18, 9.0}, the sign of each value in the sequence is judged, and the sign sequence is {positive, negative, positive}. Check if the sign sequence has a continuous two-time direction reversal, that is, find the pattern of "{positive, negative, positive}" or "{negative, positive, negative}". The rate sign sequence of node "F01-N3" {positive, negative, positive} conforms to this pattern, so node "F01-N3" is marked as a fluctuation reversal node at time T10. Assuming that another abnormal node "F01-N7" on the same feeder F01 is also identified as a fluctuation reversal node in the same time period through the same process, these identified nodes "F01-N3" and "F01-N7" form a node group. Then call the abnormal node topology association table to find the electrical connection path between the two nodes. The table records that the downstream of "F01-N3" is "DP-05-Panel-A", and it is assumed that through the query of the topology data, it is known that "DP-05-Panel-A" is finally connected to the upstream node of "F01-N7", thereby constructing a conduction path. The path is represented in the form of an ordered list of node identifiers, for example, ["F01-N3", "DP-05-Panel-A", "F01-N7"]. All identified conduction paths composed of fluctuation reversal nodes are collected, each path is logically connected in a graphical manner, where the nodes are the vertices of the graph, and the electrical connection relationship is the directed edge. Finally, all these paths are integrated to generate the line loss structure abnormal path atlas.

[0111] Please refer to Figure 5 The steps for obtaining the three-dimensional structure risk positioning information result are:

[0112] S401: Call the path exception node in the line loss structure exception path atlas, extract the corresponding feeder topology number and distribution level identifier, match the geographic coordinate coding in the power grid GIS system, and generate an exception node spatial attribute table;

[0113] Call the line loss structure exception path atlas, which contains a conductive path composed of path exception nodes ["F01-N3", "DP-05-Panel-A", "F01-N7"]. First, extract the first exception node "F01-N3" in the path, parse its feeder topology number "F01" from the identifier, and access the pre-stored equipment account database to query the distribution level identifier of "F01-N3" as "L2-FP15-RMU03". This identifier represents that this node is located at the second distribution level, the 15th floor, and the 03rd ring network cabinet. Then, use the node identifier "F01-N3" as a query key to match in the enterprise-level power grid geographic information system (GIS) database. The power grid GIS system binds the physical location of each electrical node with a unique geographic coordinate coding in advance. Assume that the geographic coordinate coding of node "F01-N3" returned by the GIS system is "BDG-A-F15-ZNE-012", which indicates that the device is located in the 12th grid in the east zone of the 15th floor of building A. Perform the same operation on the second node "DP-05-Panel-A" in the path. Its node identifier parses no feeder topology number, and the distribution level identifier is "L3-FP15-DB05A", representing the third distribution, the 15th floor, and the 05th distribution box A phase. Its GIS coordinate coding is "BDG-A-F15-ZNC-045". Perform the operation on the third node "F01-N7". Its feeder topology number is "F01", and the distribution level identifier is "L2-FP20-RMU01", representing the second distribution, the 20th floor, and the 01st ring network cabinet. Its GIS coordinate coding is "BDG-A-F20-ZNW-003". Integrate the feeder topology number, distribution level identifier, and geographic coordinate coding extracted for each node to create a table structure with node identifier as primary key. For example, the first row of data is node "F01-N3", feeder topology "F01", level identifier "L2-FP15-RMU03", and coordinate coding "BDG-A-F15-ZNE-012". The subsequent rows sequentially record the information of other nodes in the path to generate the exception node spatial attribute table.

[0114] S402: Based on the exception node spatial attribute table, divide the level structure according to the feeder voltage grade, convert the node geographic coordinates to X / Y / Z axis values in the three-dimensional coordinate system, and generate a node spatial coordinate mapping set;

[0115] Based on the abnormal node space attribute table, first of all, all the nodes in the table are divided into hierarchical according to their associated feeder voltage level, and the voltage level of the feeder topology number "F01" is queried in the power grid parameter database. The query result is 380V, which belongs to low-voltage distribution network, so all the nodes in the path ["F01-N3", "DP-05-Panel-A", "F01-N7"] are divided into "low-voltage" hierarchical structure, then the geographic coordinate code of each node is converted into specific numerical value under three-dimensional Cartesian coordinate system, this conversion process needs to set a coordinate system origin (0, 0, 0), the ground edge of the southwest corner of building A is defined as the origin, and the main facade of the building is parallel to the X axis, the depth direction is parallel to the Y axis, and the vertical height is the Z axis. For node "F01-N3", its GIS code "BDG-A-F15-ZNE-012" and hierarchical identifier "L2-FP15-RMU03" provide positioning information, among which the floor information "F15" is used to calculate the Z axis coordinate, assuming that the standard floor height is 4.0 meters, and the equipment is installed at a height of 1.5 meters from the ground, then the Z axis coordinate , the area code "ZNE-012" (east grid 12) is converted to specific X, Y coordinates by querying the floor plan grid map, assuming that it is converted to , therefore the spatial coordinates of node "F01-N3" are (50.5, 12.3, 57.5), the same conversion is performed for node "DP-05-Panel-A", the floor is 15, the Z axis coordinate , the area code "ZNC-045" (middle grid 45) is converted to , the spatial coordinates are (25.1, 15.8, 57.2), and the conversion is performed for node "F01-N7", the floor is 20, the Z axis coordinate , the area code "ZNW-003" (west grid 03) is converted to , the spatial coordinates are (5.2, 22.4, 77.5), and these node identifiers and their corresponding three-dimensional coordinate values are organized in the form of key-value pairs to generate the node space coordinate mapping set.

[0116] S403: According to the node space coordinate mapping set, the vector relationship of the connection path between nodes is established in the three-dimensional coordinate system, and the distribution hierarchical identifier corresponding to each node is superimposed to generate three-dimensional structure risk positioning information;

[0117] According to the node space coordinate mapping set, the mapping set contains the specific three-dimensional coordinates of each node on the path, for example, the coordinates of "F01-N3" , the coordinates of "DP-05-Panel-A" , and the coordinates of "F01-N7" In the defined three-dimensional coordinate system, the vector relationship of the connecting path between the two adjacent nodes on the path is established. First, the path segment vector from node "F01-N3" to "DP-05-Panel-A" is calculated The calculation method is the end point coordinate minus the start point coordinate, that is This vector indicates that the first path segment extends 25.4 meters in the negative direction of the X axis, 3.5 meters in the positive direction of the Y axis, and 0.3 meters in the negative direction of the Z axis. Then the second path segment vector from node "DP-05-Panel-A" to "F01-N7" is calculated , This vector indicates that the path segment extends 19.9 meters in the negative direction of the X axis, 6.6 meters in the positive direction of the Y axis, and 20.3 meters in the positive direction of the Z axis. Then, each path segment vector is superimposed and associated with the power distribution level identifier corresponding to its start point and end point. For the vector The level identifier of the start point "F01-N3" is "L2-FP15-RMU03", and the level identifier of the end point "DP-05-Panel-A" is "L3-FP15-DB05A". These information is integrated into a structured data object which contains the start point, end point, three-dimensional coordinates and level information of the path segment. This operation is performed on all path segments, and they are combined into an ordered list. Finally, a composite information body describing the precise direction, position and device level of the abnormal path in three-dimensional space is obtained, and three-dimensional structure risk positioning information is generated.

[0118] Please refer to Figure 6 The acquisition steps of the power grid line loss visualization layer result are as follows:

[0119] S501: Call the node coordinates and level identifiers in the three-dimensional structure risk positioning information, match the corresponding node positions in the feeder path topology structure diagram, set the red dashed channel rendering style according to the path connection state, and generate an abnormal path rendering base map;

[0120] The three-dimensional structure risk location information is called, which includes the abnormal conduction path ["F01-N3", "DP-05-Panel-A", "F01-N7"] and the three-dimensional coordinates and distribution level identification of each node. The pre-built feeder path topology diagram displayed in a three-dimensional vector model is loaded. In the three-dimensional model, a spatial index query is performed based on the coordinate value (50.5, 12.3, 57.5) of the node "F01-N3". The geometric object representing the node in the model is located and obtained. The same node position matching operation is performed on the coordinates (25.1, 15.8, 57.2) of "DP-05-Panel-A" and the coordinates (5.2, 22.4, 77.5) of "F01-N7" in the path. After confirming that all nodes on the path are in the topology diagram, the node position matching operation is performed. After finding the corresponding geometric objects in the image, the preset connection relationship between these objects is retrieved, that is, the line segments or curve geometries representing the physical cables or busbars. For each retrieved line segment geometry representing the path connection status, its rendering attribute parameters are modified. Specifically, the RGB value of the color attribute is set to (255,0,0), the line type attribute is set to "dashed line", and the dashed line style parameter is set to an array consisting of two numbers [15,8], where the first number 15 represents that the length of the dashed line segment is 15 pixels, and the second number 8 represents that the interval between the line segments is 8 pixels. At the same time, the line width attribute is set to 4 pixels. This process does not change the original geometric data of the topological structure diagram, but only applies a set of independent visual style rules to the specified path part to generate an abnormal path rendering base map.

[0121] S502: Rendering a base map based on the abnormal path, overlaying the equipment point data of the terminal load unit distribution map, assigning layer identifiers of different shapes according to the spatial positions of the nodes, and generating a composite spatial identification layer;

[0122] Based on the abnormal path rendering base map, call an independent distribution map data file that records the information of all terminal load units. The file contains the point information of each electrical equipment. For example, there is a record in the data file, whose equipment type is "precision air conditioning", equipment number is "AC-15-02", and spatial position coordinates are (26.0, 14.5, 56.0). The equipment type of another record is "server cabinet", equipment number is "RACK-15-E-04", and spatial position coordinates are (50.0, 11.8, 56.0). These equipment point data are superimposed on the abnormal path rendering base map as new layer elements. For each newly superimposed equipment point, according to the value of its equipment type field, it is assigned from a predefined mapping table. A specific three-dimensional geometric figure is used as its layer identifier. The setting rule of the mapping table is: if the device type is "server cabinet", a cube figure with dimensions of (1.0, 0.6, 2.0) is assigned; if the device type is "precision air conditioner", a cylinder figure with dimensions of (1.2, 1.2, 1.8) is assigned; if the device type is "head cabinet", a quadrangular pyramid figure with a bottom surface of a square with a side length of 1.0 meter and a height of 2.2 meters is assigned. According to this rule, a cube is instantiated at the coordinates (50.0, 11.8, 56.0) and a cylinder is instantiated at the coordinates (26.0, 14.5, 56.0). All these geometric figure sets generated according to the rule are composed into a new layer to generate a composite space identification layer.

[0123] S503: Integrate the hierarchical division data of the composite space identification layer and the branch structure hierarchical layer, uniformly adjust the transparency and superposition order of each layer, and generate a power grid line loss visualization layer;

[0124] The composite space identification layer is integrated, the layer includes the red dotted line abnormal path and the special shape identifier representing various load devices, a division data describing the hierarchical relationship of the power grid branch structure is called, the data gives each electrical node a hierarchical identifier such as "L2-FP15-RMU03" or "L3-FP15-DB05A", for each visible element in the composite space identification layer, the transparency parameter of the rendering and the superposition order parameter determining the front and back occlusion relationship are uniformly adjusted, the basis for the adjustment is the hierarchical division data associated with the element, the specific adjustment rules are set as follows: the layer identifier of the model representing the building wall, floor and other basic structure is set to 0, the transparency Alpha value is set to 0.15, the layer identifier of the model representing the three-level power distribution equipment (the hierarchical identifier starts with L3) is set to 10, the Alpha value is set to 0.7, the layer identifier of the model representing the two-level power distribution equipment (the hierarchical identifier starts with L2) is set to 20, the Alpha value is set to 0.8, the Z-Index of the red dotted line channel representing the abnormal path is set to 50, the Alpha value is set to 0.9, the Z-Index of the layer identifier representing the terminal load unit directly associated with the abnormal path (for example, "RACK-15-E-04" adjacent to the coordinates) is set to 51, the Alpha value is set to 1.0, according to the rules, the system will draw layer by layer in the order of Z-Index value from small to large, the layer with large value will cover the layer with small value, and different transparency settings allow the covered layer information to be visible, finally all layer parameters are set and integrated, and the power grid line loss visualization layer is generated.

[0125] The above merely illustrates the specific implementation of the present application, but the protection scope of the present application is not limited to this, any skilled person in the technical field can easily think of changes or replacements within the technical range disclosed by the present application, which should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method for intelligent diagnosis and visualization of power line losses based on multi-source data acquisition, characterized in that: The following steps are involved: S1: Obtain the sensor data of each feeder, group them by number and arrange them in time sequence, detect the synchronization of power fluctuations, extract the change trend, and obtain the feeder power fluctuation sequence results; S2: Call the feeder power fluctuation sequence results, analyze the difference between the power input and output of adjacent feeder nodes, compare the power difference with the node sequence number change trend, mark the nodes with inconsistent fluctuation trends as path anomalies, and obtain the feeder line loss mutation marking results; S3: extracting abnormal nodes in the feeder line loss mutation mark result, calculating the power change rate difference, extracting the alternating fluctuation node group, forming a path closure graph, and generating a line loss structure abnormal path graph result; S4: extracting the topological number and position code of the abnormal node in the abnormal path map of the line loss structure, attaching the node to the spatial coordinate, marking the hierarchical distribution, outputting the position and hierarchical information, and obtaining the three-dimensional structure risk positioning information result; S5: Based on the three-dimensional structure risk location information results, map abnormal nodes to the topological structure diagram, adjust the rendering style, determine the layer identification based on the spatial position, complete the visual expression of nodes and paths, and generate the power grid line loss visualization layer result.

2. The method for intelligent diagnosis and visualization of power grid line losses based on multi-source data acquisition according to claim 1 is characterized in that: The feeder power fluctuation sequence results include voltage data, current data, power factor data, conductor temperature rise data and loop length data; the feeder line loss mutation marking results include adjacent feeder node power input values, adjacent feeder node power output values, power difference change trends, node sequence number change trends and path abnormal nodes; the line loss structure abnormal path map results include path abnormal node sets, low-voltage access points, terminal metering equipment, branch buses and path node connection sequences; the three-dimensional structure risk positioning information results include feeder topology numbers, distribution level identifiers, geographic location codes, spatial coordinate positions and node level information; the power grid line loss visualization layer results include feeder path topology diagrams, terminal load unit distribution diagrams, branch structure level diagrams, node and path visual expressions and layer identification forms.

3. The method for intelligent diagnosis and visualization of power grid line losses based on multi-source data acquisition according to claim 1 is characterized in that: The steps for obtaining the feeder power fluctuation sequence result are: S101: Obtain the voltage, current, power factor, conductor temperature rise, and loop length sensor data corresponding to each feeder number of the distribution unit, establish a data group index based on the feeder number, and horizontally splice different sensor data with the same number based on the acquisition timestamp to generate a feeder time series data set; S102: Based on the feeder time series data set, arrange each feeder data group in ascending order by timestamp, extract power values ​​at adjacent time points to form a difference sequence, set a power fluctuation threshold range, identify the starting time points of sections that continuously exceed the threshold, and generate a synchronous fluctuation time mark group; S103: Call the synchronous fluctuation time mark group, intercept the power data sequence of the corresponding time segment of each feeder, calculate the linear regression slope of the power value in each time segment, and connect the segments whose absolute value of the slope exceeds the set reference value in series in chronological order to generate the feeder power fluctuation sequence result.

4. The method for intelligent diagnosis and visualization of power grid line losses based on multi-source data acquisition according to claim 1 is characterized in that: The steps for obtaining the feeder line loss mutation marking result are as follows: S201: Calling the power input value and output value sequence of adjacent feeder nodes in the feeder power fluctuation sequence result, calculating the input and output differences by aligning the time points, and counting the absolute value of the power difference of each feeder node pair to generate a feeder power difference sequence; S202: extracting a moving average of difference values ​​in a continuous time window in the feeder power difference sequence, calculating a Pearson correlation coefficient between an increasing trend of sequence numbers of adjacent feeder nodes and a trend of difference value changes, and generating a trend difference identifier; S203: Filtering node pairs whose Pearson correlation coefficients are lower than a set threshold according to the trend difference identifier, marking the node with a larger sequence number in the node pair as a path abnormal node, and generating a feeder line loss mutation mark.

5. The method for intelligent diagnosis and visualization of power grid line losses based on multi-source data acquisition according to claim 1 is characterized in that: The steps for obtaining the abnormal path map result of the line loss structure are as follows: S301: Calling the path abnormal node set in the feeder line loss mutation mark, matching the low-voltage access point coordinates, terminal metering equipment numbers and branch bus connection relationships in the distribution network topology structure, and generating an abnormal node topology association table; S302: Based on the abnormal node topology association table, extract the power values ​​of each abnormal node for three consecutive time periods, calculate the absolute value of the difference between the power change rates of adjacent periods, and generate the node power fluctuation gradient; S303: Based on the node power fluctuation gradient, identify the node group whose fluctuation direction reverses twice in succession, construct the inter-node conduction path according to the connection order of the distribution network, and generate a line loss structure abnormal path map.

6. The method for intelligent diagnosis and visualization of power grid line losses based on multi-source data acquisition according to claim 1, characterized in that: The steps for obtaining the three-dimensional structure risk positioning information result are: S401: Calling the abnormal path node in the abnormal path map of the line loss structure, extracting the corresponding feeder topology number and distribution level identifier, matching the geographic coordinate code in the power grid GIS system, and generating an abnormal node spatial attribute table; S402: Based on the abnormal node spatial attribute table, the hierarchical structure is divided according to the feeder voltage level, the node geographic coordinates are converted into X / Y / Z axis values ​​in a three-dimensional coordinate system, and a node spatial coordinate mapping set is generated; S403: According to the node space coordinate mapping set, a vector relationship of the connection path between nodes is established in a three-dimensional coordinate system, and the distribution level identifier corresponding to each node is superimposed to generate three-dimensional structural risk positioning information.

7. The method for intelligent diagnosis and visualization of power grid line losses based on multi-source data acquisition according to claim 1, characterized in that: The steps for obtaining the grid line loss visualization layer result are as follows: S501: Calling the node coordinates and level identifiers in the three-dimensional structure risk location information, matching the corresponding node positions in the feeder path topology structure diagram, setting the red dotted line channel rendering style according to the path connection status, and generating an abnormal path rendering base map; S502: Rendering a base map based on the abnormal path, superimposing the equipment point data of the terminal load unit distribution map, assigning layer identifiers of different shapes according to the spatial positions of the nodes, and generating a composite spatial identification layer; S503: Integrate the hierarchical division data of the composite space identification layer and the branch structure level map, uniformly adjust the transparency and superposition order of each layer, and generate a power grid line loss visualization layer.

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