District line loss intelligent analysis method based on big data

Abstract

The invention relates to the technical field of electric power analysis, in particular to an intelligent analysis method for transformer area line loss based on big data, and the method comprises the steps: collecting daily electric power information of a transformer area and a service electricity user, uploading the electric power connection information between the transformer area and the electricity user, constructing an electric power network topology based on the electric power connection information, and analyzing the line loss of the transformer area. Creating a superior and subordinate data pool according to the power network topology, and storing the daily power information of the area transformer and the service power users thereof by using the superior and subordinate data pool; on the basis of the power network topology, nodes are selected in the power network topology, daily power information of the transformer area and service power users of the transformer area is collected, real-time power transmission loss of the transformer area and the power users is analyzed, the power network topology is synchronously constructed, and the power transmission loss analysis result is combined, so that the power transmission loss of the transformer area and the service power users is analyzed. And capturing the power transmission path with the maximum power transmission loss in the power network topology.

Description

Technical Field

[0001] This invention relates to the field of power analysis technology, specifically to a method for intelligent analysis of transformer substation line losses based on big data. Background Technology

[0002] Transformer line loss refers to the loss of electrical energy from the transformer output to the user within the transformer's power supply range. This includes the transformer's own copper and iron losses, as well as losses caused by line resistance. By rationally selecting transformer capacity, optimizing transformer line layout, and improving the power factor, transformer line loss can be effectively reduced, power supply efficiency and quality improved, and the economical operation of the power system ensured.

[0003] The invention patent application number 202110231367.4 discloses a method for in-depth mining and analysis of line loss data in transformer substations based on big data. The method comprises the following steps: S1, acquiring historical power data from electricity meters in the transformer substation and the power characteristic attributes of the substation where the electricity meters are located; S2, integrating the historical power data according to different buses, and calculating the bus power loss according to different types of line loss models; S3, classifying the transformer substations according to the power characteristic attributes of the substations where the buses in S2 are located, and training the substation line loss fitting model based on the power loss of each bus in the substation; S4, calculating the real-time power imbalance rate of each bus, determining the substation where the faulty bus is located, acquiring the current power characteristic attributes of the substation as input to the substation line loss fitting model, calculating the current bus power predicted line loss, and determining the line loss type.

[0004] The application aims to address the problem that "in the existing methods for analyzing and calculating line losses in low-voltage distribution network areas, with the advancement of on-site power energy collection technologies such as substations and user terminals, and the widespread use of smart meters, although it is easy to collect data on line loss indicators, the data collected by power metering devices is massive, the wiring methods are complex, and the types of faults are numerous. Relying solely on traditional on-site investigations would lead to a sharp increase in workload, and there is a lack of targeted methods for in-depth analysis of line loss data."

[0005] On the other hand, with the changes and additions of power user circuits in the transformer substation, the analysis of line loss in the substation also becomes more difficult. Existing line loss analysis technology for transformer substations requires a long period of debugging and calibration when power user circuits in the transformer substation are changed or added, so that the data can be applied to the new analysis scenario.

[0006] To address this, a big data-based intelligent analysis method for transformer substation line loss is proposed. Summary of the Invention

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

[0008] To achieve the above objectives, the present invention provides the following technical solution:

[0009] A big data-based intelligent analysis method for transformer substation line loss includes:

[0010] Collect daily power information of transformers in the distribution area and the power users they serve, upload power connection information between transformers in the distribution area and power users, and construct power network topology based on power connection information;

[0011] Create upper and lower level data pools based on the power network topology, and use the upper and lower level data pools to store the daily power information of the transformer area and the power users it serves;

[0012] Based on the power network topology, nodes are selected in the power network topology to analyze power transmission losses corresponding to the daily power information of the selected transformer area or power user.

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

[0014] Each time the operation of selecting a node in the power network topology is performed, the number of nodes selected is two groups, the selected nodes are interconnected, and the selected nodes are adjacent nodes. After the node is selected, the corresponding daily power information is retrieved from its corresponding data pool based on the selected node, and then the power transmission loss analysis is performed.

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

[0016]

[0017] 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.

[0018] Among them, the core loss P FeCustomized by the user, 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 distribution area is also considered. TQ and power transmission loss ΔP for electricity users YH The calculation is based on the latest stored corresponding data in the data pool;

[0019] Capture the power transmission path with the highest total power transmission loss;

[0020] Traverse the cable transmission loss of each node in the power network topology and sort the nodes in descending order according to their cable transmission loss.

[0021] Generate power network status messages for the transformer substation and send them back to the user terminal;

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

[0023] Furthermore, the daily power information of the transformer substation includes: input voltage, output voltage, high-voltage side current, low-voltage side current, power factor, active power, reactive power, operating noise level, and load factor. The daily power information of the electricity user includes: cumulative electricity consumption, real-time power, load curve, and real-time voltage.

[0024] The power connection information between the transformer and the user is the location coordinates. When the power connection information between the transformer and the user is uploaded, the location coordinates are continuously uploaded to the same coordinate system in pairs. Each set of uploaded location coordinates is connected to each other in the coordinate system to obtain a set of line segments. The combination of the line segments obtained by connecting the location coordinates of all power connection information in the coordinate system is the power network topology.

[0025] Furthermore, the daily power information of the transformer substation includes: input voltage, output voltage, high-voltage side current, low-voltage side current, power factor, active power, reactive power, operating noise level, and load factor. The daily power information of the electricity user includes: cumulative electricity consumption, real-time power, load curve, and real-time voltage.

[0026] The power connection information between the transformer and the power user is the location coordinates. When the power connection information between the transformer and the power user is uploaded, the location coordinates are continuously uploaded to the same coordinate system in pairs. Each set of uploaded location coordinates is connected to each other in the coordinate system to obtain a set of line segments. The combination of line segments obtained by connecting the location coordinates of all power connection information in the coordinate system is the power network topology.

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

[0028] Create a set of data pools, denoted as the first-level data pool, to store the daily power information corresponding to the nodes representing transformer substations in the power network topology;

[0029] Create a set of data pools, denoted as the secondary data pool, to store the daily power information corresponding to the nodes of node group 0, which represents the distance to the transformer area in the power network topology.

[0030] Create a set of data pools, denoted as the third-level data pool, to store the daily power information corresponding to the nodes of the first group of nodes in the power network topology, which represent the distance to the transformer area.

[0031] This process continues until the daily power information of all nodes in the power network topology, corresponding to transformer substations and electricity users, is stored in the data pool.

[0032] Among them, the daily power information of the transformers in the distribution area and the electricity users they serve is collected in real time and updated and stored in the data pool in real time.

[0033] Furthermore, when the daily power information of the transformer substation and its served electricity users is stored in the data pool, line graphs representing the changes of each daily power information are generated synchronously based on each daily power information. The daily power information represented in the line graphs representing the changes of each daily power information is updated synchronously with the information stored in the data pool.

[0034] Furthermore, the power transmission loss ΔP of the transformer in the distribution area 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 The power transmission loss ΔP of the transformer in the distribution area TQ The correction logic is expressed as:

[0035]

[0036] 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;

[0037] 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).

[0038] Furthermore, all power transmission paths in the power network topology are retrieved using either the BFS or DFS algorithm. The total power transmission loss of each power transmission path is calculated using the following formula:

[0039]

[0040] In the formula: χ(q) represents the total power transmission loss along power transmission path q; m q Let q be the set of all nodes on the power transmission path q; For each j-th node, the corrected power transmission loss of the transformer substation or the power transmission loss of the electricity user is denoted as .

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

[0042] Furthermore, after the node queues in the descending power network topology are created and the group of power transmission paths with the highest total power transmission loss is captured, the overall health of the power network served by the transformer substation is further analyzed based on the node queues and the group of power transmission paths with the highest total power transmission loss.

[0043] After the node queues in the descending power network topology are created and the power transmission path with the highest total power transmission loss is captured, the overall health of the power network served by the transformer substation is further analyzed based on the node queues and the power transmission path with the highest total power transmission loss.

[0044] Compared with known public technologies, the technical solution provided by this invention has the following beneficial effects:

[0045] This invention provides a big data-based intelligent analysis method for transformer substation line loss. During execution, this method collects daily power information from transformer substations and their served users, analyzes real-time power transmission losses, and simultaneously constructs a power network topology. Based on the power transmission loss analysis results, it captures the power transmission path with the highest power transmission loss within the topology. Furthermore, it sorts the nodes representing transformer substations and users in the topology in descending order based on their respective cable transmission losses. This descending order, combined with the analysis of the power transmission path with the highest power transmission loss, provides a comprehensive assessment of the health of the power network served by the transformer substations. This provides comprehensive and effective daily management data for users managing the power network in the substation area. Moreover, this method completely separates the operation of the power network from line loss analysis, avoiding debugging issues caused by power changes or additions to the power supply in the substation area. Attached Figure Description

[0046] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0047] Figure 1 This is a flowchart illustrating a method for intelligent analysis of transformer substation line loss based on big data. Detailed Implementation

[0048] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0049] The present invention will be further described below with reference to embodiments.

[0050] Example 1:

[0051] This embodiment presents a method for intelligent analysis of transformer substation line loss based on big data, such as... Figure 1 As shown, it includes:

[0052] Collect daily power information of transformers in the distribution area and the power users they serve, upload power connection information between transformers in the distribution area and power users, construct a power network topology based on the power connection information, create upper and lower level data pools according to the power network topology, and use the upper and lower level data pools to store the daily power information of transformers in the distribution area and the power users they serve.

[0053] Based on the power network topology, nodes are selected in the power network topology to analyze power transmission losses corresponding to the daily power information of the selected transformer area or power user.

[0054] Traverse the power network topology, obtain all power transmission paths in the power network topology, and cumulatively calculate the total power transmission loss of each power transmission path;

[0055] Each time the operation of selecting a node in the power network topology is performed, the number of nodes selected is two groups, the selected nodes are interconnected, and the selected nodes are adjacent nodes. After the node is selected, the corresponding daily power information is retrieved from its corresponding data pool based on the selected node, and then the power transmission loss analysis is performed.

[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:

[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 using either the BFS or DFS algorithm. The total power transmission loss of each path is calculated using the following formula:

[0065]

[0066] In the formula: χ(q) represents the total power transmission loss along power transmission path q; m q Let q be the set of all nodes on the power transmission path q; For each j-th node, the corrected power transmission loss of the transformer substation or the power transmission loss of the electricity user is denoted as .

[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 power transmission path with the highest total power transmission loss; traverse the cable transmission loss of each node in the power network topology and sort the nodes in descending order based on their cable transmission loss.

[0069] Generate power network status messages for the transformer substation and send them back to the user terminal;

[0070] The content of the cable network status message for the transformer service area includes: power network topology, power transmission loss at each node in the cable network topology, power transmission path in the power network topology with the greatest power transmission loss, and the overall health of the power network served by the transformer service area.

[0071] In this embodiment, by executing the method described in the above embodiments, real-time power transmission loss analysis and monitoring services are provided to the power network served by the transformer substation, and effective daily management data references are provided to the power network backend management users.

[0072] During the execution of this method, the power transmission loss analysis logic for both the transformer substation and the power user is defined. Furthermore, the power transmission loss of the transformer substation is corrected based on the vibration audio of the transformer substation, providing further refinement of the data used in the above method steps. This method also proposes a health assessment of the power network served by the transformer substation, which is then provided as a data reference to the power network backend management user. This ensures that the power network, based on the management of the power network backend user, operates more stably and provides distribution services in a longer period.

[0073] Example 2:

[0074] The daily power information of the transformer in the distribution area includes: input voltage, output voltage, high-voltage side current, low-voltage side current, power factor, active power, reactive power, operating noise level, and load factor. The daily power information of the electricity users includes: cumulative power consumption, real-time power, load curve, and real-time voltage.

[0075] The power connection information between the transformer and the power user is the location coordinates. When the power connection information between the transformer and the power user is uploaded, the location coordinates are continuously uploaded to the same coordinate system in pairs. Each set of uploaded location coordinates is connected to each other in the coordinate system to obtain a set of line segments. The combination of line segments obtained by connecting the location coordinates of all power connection information in the coordinate system is the power network topology.

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

[0077] Create a set of data pools, denoted as the first-level data pool, to store the daily power information corresponding to the nodes representing transformer substations in the power network topology;

[0078] Create a set of data pools, denoted as the secondary data pool, to store the daily power information corresponding to the nodes of node group 0, which represents the distance to the transformer area in the power network topology.

[0079] Create a set of data pools, denoted as the third-level data pool, to store the daily power information corresponding to the nodes of the first group of nodes in the power network topology, which represent the distance to the transformer area.

[0080] This process continues until the daily power information of all nodes in the power network topology, corresponding to transformer substations and electricity users, is stored in the data pool.

[0081] Among them, the daily power information of the transformers in the distribution area and the electricity users they serve is collected in real time and updated and stored in the data pool in real time.

[0082] The above settings further define the construction logic of the power network topology and the construction logic of the data pool used to store daily power information, providing necessary preliminary data support for the method in Example 1 to perform power transmission loss analysis, and ensuring stable output of power transmission loss analysis results.

[0083] like Figure 1 As shown, when the daily power information of the transformer and its served electricity users is stored in the data pool, line graphs representing the changes of each daily power information are generated synchronously based on each daily power information. The daily power information represented in the line graphs representing the changes of each daily power information is updated synchronously with the information stored in the data pool.

[0084] Through the above settings, it is ensured that the daily load information of the power network where the transformer is located can be synchronously updated in each application stage of the method in Example 1 after collection.

[0085] Example 3:

[0086] After the node queues in the descending order of the power network topology are created and the power transmission path with the highest total power transmission loss is captured, the overall health of the power network served by the transformer substation is further analyzed based on the node queues and the power transmission path with the highest total power transmission loss.

[0087] The analytical logic for the overall health of the power network served by the transformer substation is expressed as follows:

[0088]

[0089] Where: W is the overall health performance value of the power network served by the transformer in the distribution area; g(a all ∩b x％ ) represents the set of nodes in the power transmission path with the highest total power transmission loss and the number of identical nodes among the top x% of nodes in the node queue; This represents the total number of nodes in the power transmission path with the highest total power transmission loss.

[0090] The value of x% is defined by the user and initially defaults to Q. -1 100%, Q is the number of electricity users in the power network served by the transformer substation. The larger the overall health performance value W of the power network served by the transformer substation, the healthier the power network is, and vice versa.

[0091] The above settings further define the analysis logic for the overall health of the power network served by the transformer substation. Based on the analysis of the overall health of the power network served by the transformer substation, data references are provided for the back-end management users of the power network served by the transformer substation, enabling them to make more effective corresponding power network maintenance decisions.

[0092] In summary, the method described in the above embodiments, during execution, analyzes the real-time power transmission loss of transformers and their served users by collecting daily power information. Simultaneously, it constructs a power network topology and, based on the power transmission loss analysis results, captures the power transmission path with the highest power transmission loss within the topology. Furthermore, it sorts the nodes representing transformers and users in the topology in descending order based on their respective cable transmission losses. This descending order, combined with a comprehensive analysis of the power transmission path with the highest power transmission loss, provides the health of the power network served by the transformers. This offers comprehensive and effective daily management data for users managing the power network. Moreover, this method completely separates the operation of the power network from line loss analysis, avoiding debugging issues caused by power changes or additions to the network.

[0093] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions will not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for intelligent analysis of line loss in substations based on big data, characterized in that: include: Collect daily power information of transformers in the substation area and the power users they serve, upload the power connection information between transformers in the substation area and power users, build the power network topology based on the power connection information, create upper and lower data pools according to the power network topology, and use the upper and lower data pools to store the daily power information of transformers in the substation area and the power users they serve; Based on the power network topology, a node is selected in the power network topology to analyze the power transmission loss based on the daily power information corresponding to the transformer or power user in the selected node; Traversing the power network topology, obtaining all power transmission paths in the power network topology, and accumulating and calculating the total power transmission loss of each power transmission path; Capture a 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 sort each node in descending order according to the cable transmission loss of each node; Generate a status message of the transformer serving the power network in the substation area and feedback it to the user end.

2. According to the big data-based intelligent analysis method for line loss in substations according to claim 1, it is characterized in that: The daily power information of the transformer in the substation includes: input voltage, output voltage, high-voltage side current, low-voltage side current, power factor, active power, reactive power, operating noise audio, load rate, and the daily power information of the electricity user includes: cumulative power consumption, real-time power, load curve, and real-time voltage; The power connection information between the substation transformer and the power user is the location coordinates. When uploading the power connection information between the substation transformer and the power user, the location coordinates are continuously uploaded to the same coordinate system in groups of two. Each group of uploaded location 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 location coordinates corresponding to all power connection information in the coordinate system is the power network topology.

3. According to the big data-based intelligent analysis method for line loss in substations according to claim 2, it is 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; Create a set of data pools, referred to as the primary data pool, for storing daily power information corresponding to nodes representing transformers in the power network topology; Create a group of data pools, recorded as secondary data pools, for storing daily power information corresponding to nodes of group 0 nodes of nodes representing transformers in the power network topology; Create a set of data pools, recorded as three-level data pools, for storing daily power information corresponding to nodes of a group of nodes of a node representing a transformer in a power network topology; And so on, until all nodes in the power network topology corresponding to the daily power information of the transformers and power users are stored in the data pool; Among them, the daily power information of the substation transformers and the electricity users they serve is collected in real time and updated and stored in the data pool in real time.

4. According to the big data-based intelligent analysis method for line loss in substations according to claim 1, it is characterized in that: When the daily power information of the substation transformer and the electricity users it serves is stored in the data pool, a line graph representing the changes of each item of daily power information is generated synchronously based on the daily power information. The daily power information represented in the line graph representing the changes of each item of daily power information is updated synchronously with the information stored in the data pool.

5. The method for intelligent line loss analysis based on big data according to claim 1 is characterized in that: Each time the operation of selecting a node 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 node is selected, the corresponding daily power information is further retrieved from the corresponding data pool based on the selected node, and then the power transmission loss analysis is performed; The logic of the power transmission loss analysis is: Where: ΔP TQ is the power transmission loss of the transformer in the substation area; U1 and I1 are the voltage and current on the high-voltage side; U2 and I2 are the voltage and current on the low-voltage side; is the power factor on the high voltage side; is the power factor on the low voltage side; P N P is the rated power of the transformer in the area; CuN is the actual copper loss; P Fe is the core loss; ΔP YH is the power transmission loss of the power user; 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 ith group of daily power information; U(i) is the voltage in the ith group of daily power information; Δt is the time interval between two groups of daily power information collected consecutively; Among them, the core loss P Fe Customized by the user end, the daily power information collection is based on the specified frequency and continuous collection, the power transmission loss of the transformer in the substation area ΔP TQ And the power transmission loss of electricity users ΔP YH The query is based on the latest corresponding data stored in the data pool.

6. The method for intelligent line loss analysis based on big data according to claim 1 is characterized in that: The power transmission loss of the transformer in the substation area ΔP TQ After the calculation, a correction operation is further performed, and the correction result is used as the final output of the transformer power transmission loss ΔP TQ , the transformer power transmission loss ΔP TQ The correction logic is expressed as: Where: ΔP TQ ′ is the corrected power transmission loss of the transformer in the substation area; I is the average audio intensity of transformer operation noise; I0 is the standard audio intensity of transformer operation noise; F is the main audio frequency of transformer operation noise; F0 is the standard main frequency value of transformer operation noise audio; k1 and k2 are adjustment parameters; k3 is the correction coefficient; Among them, the adjustment parameters k1, k2 and the correction coefficient k3 are customized by the system end user, the sum of k1 and k2 is one, and both are greater than zero, k3∈(0,1).

7. The method for intelligent analysis of line loss in substations based on big data according to claim 1, characterized in that: All power transmission paths in the power network topology are retrieved based on either the BFS algorithm or the DFS algorithm, and the total power transmission loss of the power transmission path is calculated by the following formula: Where: χ(q) is the total power transmission loss of 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 transformer in the substation or the power transmission loss of the power user corresponding to each j-th node; Among them, based on the above formula, the total power transmission loss of all power transmission paths in the power network topology is calculated.

8. The method for intelligent analysis of line loss in a substation area based on big data according to claim 1, characterized in that: After the node queue in the descending power network topology is created and the group of power transmission paths with the highest total power transmission losses are captured, the overall health of the power network served by the substation transformer is further analyzed based on the node queue and the group of power transmission paths with the highest total power transmission losses.

9. The method for intelligent analysis of line loss in a substation area based on big data according to claim 8, characterized in that: The analysis logic of the overall health of the power network served by the transformer in the substation is expressed as: Where: W is the overall health performance value of the power network served by the transformer in the substation; g(a all ∩b x％ ) is the number of nodes that are the same as the node set in the power transmission path with the highest total power transmission loss and the number of nodes in the first x% of the node queue; The total amount of nodes in the power transmission path with the highest power transmission loss is summed up; The value of x% is defined by the user, and the initial default value of x% is Q -1 100%, Q is the number of electricity users in the power network served by the substation transformer. The larger the overall health performance value W of the power network served by the substation transformer is, the healthier the power network served by the substation transformer is as a whole. Conversely, the unhealthier the power network served by the substation transformer is as a whole.

10. The method for intelligent analysis of line loss in substations based on big data according to claim 1, characterized in that: The content of the status message of the power network served by the substation transformer includes: the power network topology, the power transmission loss of each node in the power network topology, the power transmission path in the power network topology with the largest power transmission loss, and the overall health of the power network served by the substation transformer.

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