A Big Data-Based Intelligent Analysis Method for Transformer Line Loss

Abstract

This invention relates to the field of power analysis technology, specifically to a big data-based intelligent analysis method for transformer substation line loss. The method includes: collecting daily power information of transformer substations and their served users; uploading power connection information between transformer substations and users; constructing a power network topology based on the power connection information; creating upper and lower level data pools based on the power network topology; and storing the daily power information of transformer substations and their served users using the upper and lower level data pools. Based on the power network topology, nodes are selected within the power network topology. This invention analyzes the real-time power transmission loss of transformer substations and their served users through the collection of daily power information, simultaneously constructs a power network topology, and, combined with the power transmission loss analysis results, captures the power transmission path with the highest power transmission loss in the power network topology.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power analysis, in particular to a transformer area line loss intelligent analysis method based on big data. BACKGROUND

[0002] Transformer area line loss refers to the loss of electric energy from the output end of the transformer to the user end within the transformer area transformer power supply range. It includes the copper loss and iron loss of the transformer itself, as well as the loss caused by the line resistance. Reasonable selection of transformer capacity, optimization of transformer area line layout, improvement of power factor and other measures can effectively reduce the transformer area transformer line loss, improve the power supply efficiency and quality, and ensure the economic operation of the power system.

[0003] The invention patent with application number 202110231367.4 discloses a transformer area line loss data deep mining analysis method based on big data, which is characterized by the following steps: S1, obtaining the historical electric power data of the electric meter in the transformer area, and the electric power characteristic attributes of the electric meter in the transformer area; S2, integrating the historical electric power data according to different busbars, and calculating the busbar electric power line loss according to different types of line loss models; S3, classifying the transformer area according to the electric power characteristic attributes of the transformer area where the busbar is located, and training the transformer area line loss fitting model combined with the busbar electric power line loss in the transformer area; S4, calculating the real-time electric quantity imbalance rate of each busbar, and judging the transformer area where the fault busbar is located, obtaining the electric power characteristic attributes of the current transformer area as the input of the transformer area line loss fitting model, calculating the current busbar electric power prediction line loss, and judging the line loss type.

[0004] The application aims to solve the problem that in the existing low-voltage distribution network transformer area line loss analysis and calculation method, with the progress of on-site electric energy collection technology of transformer substations and user terminals and the popularization of smart meters, although the transformer area line loss index data is easy to collect, the collected data of electric energy metering devices is massive, the wiring method is complex, and the fault types are various, relying only on traditional on-site investigation will cause a sharp increase in workload, and there is a lack of targeted line loss data deep analysis method.

[0005] On the other hand, with the change and addition of electric circuit of users in the transformer area, the difficulty of analyzing the transformer area line loss also increases, and the existing transformer area line loss analysis technology needs a long time of debugging and correction to collect data suitable for the new analysis scene.

[0006] Therefore, a transformer area line loss intelligent analysis method based on big data is proposed. SUMMARY

[0007] In view of the above-mentioned shortcomings of the prior art, the present application provides a transformer area line loss intelligent analysis method based on big data, which solves the technical problems proposed in the above background.

[0008] To achieve the above object, the present application is realized by the following technical solutions:

[0009] A transformer area line loss intelligent analysis method based on big data, comprising:

[0010] Collecting daily power information of transformer area transformers and their service power users, uploading power connection information between transformer area transformers and power users, and constructing a power network topology based on the power connection information;

[0011] Creating a superior-inferior data pool according to the power network topology, and storing daily power information of transformer area transformers and their service power users by using the superior-inferior data pool;

[0012] Based on the power network topology, selecting nodes in the power network topology to analyze power transmission loss of daily power information corresponding to the transformer area transformers or power users corresponding to the selected nodes;

[0013] Traversing the power network topology to obtain all power transmission paths in the power network topology, and cumulatively calculating the total power transmission loss of each power transmission path;

[0014] Each time the operation of selecting nodes in the power network topology is performed, the number of selected nodes is two groups, the selected nodes are connected to each other, and the selected nodes are adjacent nodes. After the nodes are selected, the corresponding daily power information in the corresponding data pool is further retrieved based on the selected nodes, and the power transmission loss analysis is performed again;

[0015] The logic of the power transmission loss analysis is as follows:

[0016]

[0017] In the formula: ΔP TQ is the transformer area transformer power transmission loss; U1 and I1 are the high-voltage side voltage and current; U2 and I2 are the low-voltage side voltage and current; is the high-voltage side power factor; is the low-voltage side power factor; P N is the rated power of the transformer area transformer; P CuN is the actual copper loss; P Fe is the core loss; ΔP YH is the power user power transmission loss; R is the resistance of the circuit where the power user is located; n is the total amount of daily power information of the power user; P(i) is the power in the i-th group of daily power information; U(i) is the voltage in the i-th group of daily power information; Δt is the time interval between two adjacent continuously collected groups of daily power information;

[0018] wherein the core loss P FeThe daily power information of the transformer is collected by the user terminal, and the daily power information of the transformer is collected based on a specified frequency and continuously collected, and the power transmission loss ΔP of the transformer in the transformer area is calculated TQ and the power transmission loss ΔP of the user YH The corresponding data stored in the data pool is used to calculate the corresponding data stored in the data pool.

[0019] Capture the total power transmission loss of the largest group of power transmission paths.

[0020] The cable transmission loss of each node in the power network topology is traversed, and each node is arranged in descending order according to the cable transmission loss of each node.

[0021] The transformer service cable network state message is generated, and the user terminal is fed back.

[0022] The content of the transformer service cable network state message includes: power network topology, cable network topology, power transmission loss of each node in the cable network topology, power transmission path with the largest power transmission loss in the power network topology, and overall health of the transformer service power network.

[0023] Further, the daily power information of the transformer includes: input voltage, output voltage, high-voltage side current, low-voltage side current, power factor, active power, reactive power, operating noise audio, and load rate; and the daily power information of the user includes: cumulative power consumption, real-time power, load curve, and real-time voltage.

[0024] The power connection information between the transformer and the user, i.e. the position coordinates, is uploaded in pairs to the same coordinate system, and each pair of uploaded position coordinates is connected in the coordinate system to obtain a group of line segments. The combination of the line segments obtained by connecting the position coordinates corresponding to all power connection information in the coordinate system is the power network topology.

[0025] Further, the daily power information of the transformer includes: input voltage, output voltage, high-voltage side current, low-voltage side current, power factor, active power, reactive power, operating noise audio, and load rate; and the daily power information of the user includes: cumulative power consumption, real-time power, load curve, and real-time voltage.

[0026] The power connection information between the transformer and the user, i.e. the position coordinates, is uploaded in pairs to the same coordinate system, and each pair of uploaded position coordinates is connected in the coordinate system to obtain a group of line segments. The combination of the line segments obtained by connecting the position coordinates corresponding to all power connection information in the coordinate system is the power network topology.

[0027] After the power network topology is constructed, the power network topology is further traversed to capture all nodes in the power network topology;

[0028] A set of data pools, denoted as a first-level data pool, is created to store daily power information corresponding to nodes representing substation transformers in the power network topology;

[0029] A set of data pools, denoted as a second-level data pool, is created to store daily power information corresponding to nodes that are a group of nodes away from the nodes representing substation transformers in the power network topology;

[0030] A set of data pools, denoted as a third-level data pool, is created to store daily power information corresponding to nodes that are a group of nodes away from the nodes representing substation transformers in the power network topology;

[0031] This process is repeated until daily power information corresponding to substation transformers and power users in the power network topology is stored in the data pool;

[0032] Among them, the daily power information of the substation transformer and its service power users is collected in real time, and is updated and stored in the data pool in real time.

[0033] Further, when the daily power information of the substation transformer and its service power users is stored in the data pool, a line graph representing the change of each daily power information is generated based on each daily power information at the same time, and the daily power information represented in the line graph representing the change of each daily power information is updated synchronously with the information updated and stored in the data pool.

[0034] Further, the substation transformer power transmission loss ΔP TQ After being obtained, a correction operation is further performed to correct the result as the final output of the substation transformer power transmission loss ΔP TQ The correction logic of the substation transformer power transmission loss ΔP TQ is represented as:

[0035]

[0036] In the formula, ΔP TQ ′ is the corrected substation transformer power transmission loss; I is the average value of the transformer operating noise audio intensity; I0 is the standard audio intensity of the transformer operating noise; F is the main frequency of the transformer operating noise audio; F0 is the standard main frequency value of the transformer operating noise audio; k1 and k2 are adjustment parameters; and k3 is a correction coefficient;

[0037] Among them, the adjustment parameters k1 and k2 and the correction coefficient k3 are defined by the system end user, the sum of k1 and k2 is one, and both are greater than zero, and k3 ∈ (0, 1).

[0038] Further, all power transmission paths in the power network topology are retrieved based on any one of the BFS algorithm or the DFS algorithm, and the total power transmission loss of the power transmission paths is calculated by the following formula:

[0039]

[0040] In the formula, χ(q) is the total power transmission loss of the power transmission path q; m q is a set of all nodes on the power transmission path q; is the modified substation transformer power transmission loss or the power user power transmission loss corresponding to the jth node;

[0041] Based on the above formula, the total power transmission loss of all power transmission paths in the power network topology is calculated.

[0042] Further, after the creation of the descendingly arranged node queue in the power network topology and the capture of the group of power transmission paths with the highest total power transmission loss, the overall health of the power network served by the substation transformer is analyzed based on the node queue and the group of power transmission paths with the highest total power transmission loss.

[0043] After the creation of the descendingly arranged node queue in the power network topology and the capture of the group of power transmission paths with the highest total power transmission loss, the overall health of the power network served by the substation transformer is analyzed based on the node queue and the group of power transmission paths with the highest total power transmission loss.

[0044] Compared with the known prior art, the technical scheme provided by the present application has the following beneficial effects:

[0045] The present application provides a substation line loss intelligent analysis method based on big data. In the execution process, the real-time power transmission loss of the substation transformer and the power users served by the substation transformer is analyzed through the daily power information collection of the substation transformer and the power users, and a power network topology is constructed synchronously. The power transmission path with the largest power transmission loss in the power network topology is captured by combining the power transmission loss analysis result, and the nodes representing the substation transformer and the power users in the power network topology are descendingly arranged based on the cable transmission loss of each node, so that the comprehensive analysis of the power transmission path with the largest power transmission loss is performed in descending order, thereby obtaining the health of the power network served by the substation transformer. The method brings comprehensive and effective daily management data reference for the management end user of the substation power network, and completely separates the operation and line loss related analysis of the substation power network, thereby avoiding the debugging caused by the power change and addition of the power users in the substation. BRIEF DESCRIPTION OF DRAWINGS

[0046] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed in the embodiments or prior art description will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor on the basis of these drawings.

[0047] Figure 1 A flowchart of a kind of intelligent analysis method of line loss of transformer area based on big data. DETAILED DESCRIPTION

[0048] In order to make the purpose, technical solutions and advantages of the embodiments of the present application more clear, the technical solutions in the embodiments of the present application will be clearly and completely described below in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are some embodiments of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0049] The present application will be further described below in combination with embodiments.

[0050] Embodiment 1:

[0051] A kind of intelligent analysis method of line loss of transformer area based on big data in this embodiment, as shown in Figure 1 It includes:

[0052] Collecting daily power information of transformer area transformer and its service power users, uploading power connection information between transformer area transformer and power users, constructing power network topology based on power connection information, creating upper and lower data pools according to power network topology, and storing daily power information of transformer area transformer and its service power users by applying upper and lower data pools;

[0053] Based on the power network topology, selecting nodes in the power network topology, and analyzing power transmission loss based on the daily power information corresponding to the transformer area transformer or power user corresponding to the selected nodes;

[0054] Traversing the power network topology, obtaining all power transmission paths in the power network topology, and cumulatively calculating the total power transmission loss of each power transmission path;

[0055] Each time the operation of selecting nodes in the power network topology is performed, the number of selected nodes is two groups, the selected nodes are connected to each other, and the selected nodes are adjacent nodes. After the nodes are selected, the corresponding daily power information in the corresponding data pool is further retrieved based on the selected nodes, and the power transmission loss analysis is performed again;

[0056] The logic for power transmission loss analysis is as follows:

[0057]

[0058] Where: ΔP TQ U1 and I1 represent the power transmission loss of the transformer in the distribution area; U1 and I1 represent the voltage and current on the high-voltage side; U2 and I2 represent the voltage and current on the low-voltage side. The power factor on the high-voltage side; The low-voltage side power factor; P N P represents the rated power of the transformer in the distribution area. CuN This represents the actual copper loss; P Fe For core loss; ΔP YH R is the power transmission loss of the electricity user; R is the resistance of the circuit where the electricity user is located; n is the total amount of daily electricity information of the electricity user; P(i) is the power in the i-th group of daily electricity information; U(i) is the voltage in the i-th group of daily electricity information; Δt is the time interval between two consecutively collected groups of daily electricity information.

[0059] Among them, the core loss P Fe Customized by the user, daily power information is collected continuously at a specified frequency, and the power transmission loss ΔP of the transformer in the distribution area is recorded. TQ and power transmission loss ΔP for electricity users YH The calculation is based on the latest stored corresponding data in the data pool;

[0060] Transformer power transmission loss ΔP TQ After obtaining the value, a correction operation is performed, and the correction result is used as the final output of the transformer power transmission loss ΔP. TQ Transformer power transmission loss ΔP TQ The correction logic is expressed as follows:

[0061]

[0062] Where: ΔP TQ ′ represents the corrected power transmission loss of the transformer in the distribution area; I represents the average audio intensity of the transformer operating noise; I0 represents the standard audio intensity of the transformer operating noise; F represents the main frequency of the transformer operating noise; F0 represents the standard main frequency value of the transformer operating noise; k1 and k2 are adjustment parameters; k3 is the correction coefficient;

[0063] Among them, the adjustment parameters k1, k2 and the correction coefficient k3 are defined by the system user. The sum of k1 and k2 is one and both are greater than zero, and k3 ∈ (0, 1).

[0064] All power transmission paths in the power network topology are retrieved based on any one of the BFS algorithm or the DFS algorithm, and the total power transmission loss of the power transmission path is calculated by the following formula:

[0065]

[0066] In the formula, χ(q) is the total power transmission loss of the power transmission path q; m q is the set of all nodes on the power transmission path q; is the corrected power transmission loss of the substation transformer or the power user corresponding to the jth node;

[0067] Based on the above formula, the total power transmission loss of all power transmission paths in the power network topology is calculated.

[0068] Capture the group of power transmission paths with the highest total power transmission loss; traverse the cable transmission loss of each node in the power network topology, and arrange the nodes in descending order according to the cable transmission loss of each node;

[0069] Generate a substation transformer service power network status message and feed back to the user end;

[0070] The contents of the substation transformer service cable network status message include: power network topology, power transmission loss of each node in the cable network topology, power transmission path with the maximum power transmission loss in the power network topology, and overall health of the substation transformer service power network.

[0071] In this embodiment, through the execution of the method in the above embodiment, real-time power transmission loss analysis and monitoring services are brought to the power network served by the substation transformer, and effective daily management data reference is provided for the power network background management user;

[0072] In the execution process of the method, the power transmission loss analysis logic of both the substation transformer and the power user is limited at the same time, and the power transmission loss of the substation transformer is corrected based on the vibration audio of the substation transformer, which further refines the data applied in the execution process of the above method steps. At the same time, the method further proposes a health evaluation of the substation transformer service power network, which is further provided as a data reference to the power network background management user, ensuring that the power network is managed based on the power network background management user, and is more long-term and stable in operation, providing power distribution services.

[0073] Embodiment 2:

[0074] The daily power information of the transformer in the transformer area includes: input voltage, output voltage, high-voltage side current, low-voltage side current, power factor, active power, reactive power, operating noise frequency, load rate, and the daily power information of the power user includes: cumulative power consumption, real-time power, load curve, real-time voltage;

[0075] The power connection information between the transformer in the transformer area and the power user, that is, the position coordinates, is uploaded in pairs to the same coordinate system, and each pair of uploaded position coordinates is connected in the coordinate system to obtain a group of line segments. The combination of the position coordinates corresponding to all the power connection information in the coordinate system is the power network topology.

[0076] After the power network topology is constructed, the power network topology is further traversed to capture all the nodes in the power network topology.

[0077] A group of data pools is created, denoted as a first-level data pool, for storing the daily power information corresponding to the nodes representing the transformer in the transformer area in the power network topology.

[0078] A group of data pools is created, denoted as a second-level data pool, for storing the daily power information corresponding to the nodes 0 groups of nodes representing the transformer in the transformer area in the power network topology.

[0079] A group of data pools is created, denoted as a third-level data pool, for storing the daily power information corresponding to the nodes 1 groups of nodes representing the transformer in the transformer area in the power network topology.

[0080] By analogy, until the daily power information of all nodes in the power network topology corresponding to the transformer and the power user is stored in the data pool.

[0081] Among them, the daily power information of the transformer and its service power user is collected in real time, and is stored in the data pool in real time.

[0082] Through the above setting, the construction logic of the power network topology and the construction logic of the data pool for storing the daily power information are further limited, which provides necessary data support for the method in embodiment 1 when performing power transmission loss analysis, and ensures that the power transmission loss analysis result is stable output.

[0083] As shown in Figure 1 When the daily power information of the transformer and its service power user is stored in the data pool, a line graph representing the change of each item of daily power information is generated based on each item of daily power information respectively, and the daily power information represented in the line graph representing the change of each item of daily power information is updated synchronously with the information stored in the data pool.

[0084] Through the above setting, it is ensured that the daily information in the power network where the transformer of the transformer area is located can be updated synchronously in each application stage in the method in Embodiment 1 after being collected.

[0085] Embodiment 3:

[0086] After the node queue in the descendingly arranged power network topology is completed and the highest group of power transmission paths with total power transmission loss is captured, the overall health of the power network served by the transformer of the transformer area is further analyzed based on the node queue and the highest group of power transmission paths with total power transmission loss.

[0087] The analysis logic of the overall health of the power network served by the transformer of the transformer area is represented as:

[0088]

[0089] In the formula, W is the performance value of the overall health of the power network served by the transformer of the transformer area; g(a all ∩b x％ ) is the number of the same nodes in the node set in the highest group of power transmission paths with total power transmission loss and the x% nodes in the node queue; is the total number of nodes in the highest group of power transmission paths with total power transmission loss;

[0090] Wherein, the value of x% is defined by the user end, and the initial default value of x% is Q -1 100%, Q is the number of power users in the power network served by the transformer of the transformer area, and the larger the performance value W of the overall health of the power network served by the transformer of the transformer area, the healthier the overall health of the power network served by the transformer of the transformer area, and vice versa.

[0091] Through the above setting, the analysis logic of the overall health of the power network served by the transformer of the transformer area is further limited, and based on the analysis of the overall health of the power network served by the transformer of the transformer area, data reference is provided for the background management user of the power network served by the transformer of the transformer area, so that the corresponding power network maintenance decision can be made more effectively.

[0092] To sum up, in the method in the above embodiment, during the execution, the real-time power transmission loss of the transformer and the power users is analyzed by collecting the daily power information of the transformer and the power users served by the transformer, the power transmission path with the largest power transmission loss in the power network topology is captured by synchronously constructing the power network topology combined with the analysis result of the power transmission loss, and each group of nodes representing the transformer and the power users in the power network topology is arranged in descending order based on the cable transmission loss of each node, so that the power transmission path with the largest power transmission loss is comprehensively analyzed in descending order, the health of the transformer serving the power network is obtained, the user of the transformer power network management end is brought comprehensive and effective daily management data reference, and the operation of the transformer power network and the line loss related analysis are completely separated by the method, and the debugging caused by the power change and the addition of the power users in the transformer is avoided.

[0093] The above embodiments are only used to illustrate the technical solutions of the present application, but not limit the present application; although the present application is described in detail with reference to the foregoing embodiments, those skilled in the art should understand that the technical solutions recorded in the foregoing embodiments can be modified, or some technical features can be replaced by equivalents; and these modifications or replacements will not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A big data-based transformer area line loss intelligent analysis method, characterized in that, The method comprises the following steps: Collecting daily power information of the transformer and the power users served by the transformer, uploading power connection information between the transformer and the power users, constructing a power network topology based on the power connection information, creating upper and lower data pools according to the power network topology, and storing the daily power information of the transformer and the power users served by the transformer by using the upper and lower data pools; Based on the power network topology, selecting nodes in the power network topology to analyze power transmission loss based on the daily power information corresponding to the transformer or the power users corresponding to the selected nodes; Each time the operation of selecting nodes in the power network topology is performed, the number of selected nodes is two groups, the selected nodes are connected to each other, and the selected nodes are adjacent nodes. After the nodes are selected, the corresponding daily power information of the selected nodes in the corresponding data pool is further retrieved, and the power transmission loss analysis is further performed; The logic of the power transmission loss analysis is as follows: wherein: ΔP TQ is the power transmission loss of the transformer in the transformer area; U1 and I1 are the voltage and current at the high-voltage side; and U2 and I2 are the voltage and current at the low-voltage side; is the power factor at the high-voltage side; is the power factor at the low-voltage side; P N is the rated power of the transformer in the transformer area; P CuN is the actual copper loss; P Fe is the iron core loss; ΔP YH is the power transmission loss of the power user; R is the resistance of the circuit in which the power user is located; and n is the total amount of daily power information of the power user. P(i) is the power in the i-th group of daily power information; U(i) is the voltage in the i-th group of daily power information; and Δt is the time interval between two adjacent groups of daily power information collected continuously. Wherein, the core loss P Fe Defined by the user end, the daily power information collection is based on continuous collection at a specified frequency, and the power transmission loss ΔP of the transformer in the district TQ And the power transmission loss ΔP of the power user YH Based on the corresponding data stored in the latest data pool Traverse the power network topology to obtain all power transmission paths in the power network topology, and cumulatively calculate the total power transmission loss of each power transmission path; Capture the group of power transmission paths with the highest total power transmission loss, traverse the cable transmission loss of each node in the power network topology, and arrange the nodes in descending order according to the cable transmission loss of each node; Generate a transformer service power network status message and feed back to the user end. 2.The big data-based transformer area line loss intelligent analysis method according to claim 1, characterized in that, The daily power information of the transformer includes input voltage, output voltage, high-voltage side current, low-voltage side current, power factor, active power, reactive power, operating noise audio, and load rate. The daily power information of the power users includes cumulative power consumption, real-time power, load curve, and real-time voltage. The power connection information between the transformer and the power users is the position coordinates. When uploading, the position coordinates are uploaded continuously in pairs to the same coordinate system. Each group of uploaded position coordinates are connected to each other in the coordinate system to obtain a group of line segments. The combination of the line segments obtained by connecting the position coordinates corresponding to all power connection information in the coordinate system is the power network topology. 3.The big data-based transformer area line loss intelligent analysis method according to claim 2, characterized in that, After the power network topology is constructed, the power network topology is further traversed to capture all nodes in the power network topology. A group of data pools, denoted as first-level data pools, are created to store the daily power information corresponding to the nodes representing the transformer in the power network topology. A group of data pools, denoted as second-level data pools, are created to store the daily power information corresponding to the nodes 0 groups of nodes away from the nodes representing the transformer in the power network topology. A group of data pools, denoted as third-level data pools, are created to store the daily power information corresponding to the nodes 1 group of nodes away from the nodes representing the transformer in the power network topology. Similarly, the daily power information of all nodes in the power network topology corresponding to the transformer and the power users is stored in the data pool. The daily power information of the transformer and the users served by the transformer is collected in real time and stored in the data pool. 4.The method of claim 1, wherein, The daily power information of the transformer and the users served by the transformer is collected in real time and stored in the data pool.

5. The big data-based transformer area line loss intelligent analysis method according to claim 1, characterized in that, The power transmission loss ΔP of the transformer in the transformer area TQ After the calculation, a correction operation is further performed to correct the result as the final output of the power transmission loss ΔP of the transformer in the transformer area TQ , the correction logic of the power transmission loss ΔP of the transformer in the transformer area TQ is represented as: where: ΔP TQ is the corrected power transmission loss of the transformer in the transformer area I is the average value of the transformer operating noise audio intensity; I0 is the transformer operating noise standard audio intensity; F is the transformer operating noise audio main frequency; F0 is the transformer operating noise audio standard main frequency value; k1 and k2 are adjustment parameters; and k3 is a correction coefficient. The adjustment parameters k1 and k2 and the correction coefficient k3 are defined by the system user, the sum of k1 and k2 is one, and both are greater than zero, and k3∈(0, 1). 6.The big data-based transformer area line loss intelligent analysis method according to claim 1, characterized in that, The power transmission paths in the power network topology are searched and obtained based on any one of the BFS algorithm or the DFS algorithm, and the total power transmission loss of the power transmission paths is calculated by the following formula: wherein: χ(q) is the aggregate power transmission loss for power transmission path q; m q is the set of all nodes on power transmission path q; is the modified substation transformer power transmission loss or consumer power transmission loss corresponding to the jth node. Based on the above formula, the total power transmission loss of all power transmission paths in the power network topology is calculated.

7. The big data-based transformer area line loss intelligent analysis method according to claim 1, characterized in that, After the node queue in the descendingly arranged power network topology is created and the highest group of power transmission paths in the total power transmission loss is captured, the overall health of the transformer service power network is further analyzed based on the node queue and the highest group of power transmission paths in the total power transmission loss. 8.The big data-based transformer area line loss intelligent analysis method according to claim 7, characterized in that, The analysis logic of the overall health of the transformer service power network is represented as: In the formula, W is the overall health performance value of the transformer service power network of the transformer district; g(a all ∩b x％ ) is the number of the same nodes in the node set and the node queue of the top x% nodes in the highest group of power transmission path loss aggregation; is the total number of nodes in the highest group of power transmission path loss aggregation; Wherein, the x% value is defined by the user terminal, and the x% value is initially defaulted as Q -1 100%, Q is the number of power users in the transformer service power network, and the greater the overall health performance value W of the transformer service power network, the healthier the overall transformer service power network, and vice versa, indicating that the overall transformer service power network is less healthy. 9.The method of claim 1, wherein, The content of the transformer service power network state message includes: the power network topology, the power transmission loss of each node in the power network topology, the power transmission path with the maximum power transmission loss in the power network topology, and the overall health of the transformer service power network.

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