A method, device, electronic device and storage medium for automatically locating high-loss transmission area

Abstract

The present invention relates to the field of data processing, and provides a method, device, electronic device and storage medium for automatic positioning of high-loss areas in a substation. This application facilitates targeted processing of electricity consumption data by acquiring real-time electricity consumption data of each area in the substation and topological structure information of the substation, and determines high-loss areas in the substation based on correlation relationships between real-time electricity consumption data and topological structure information of each area, thereby solving the problem that the substation has a high line loss rate but cannot quickly locate the high-loss area, and improving the working efficiency of the system; by acquiring relevant data of the substation and analyzing the relevant data to determine the target factors corresponding to the high-loss area, analyzing the causes of abnormal line loss rates, and avoiding affecting the inspection and optimization efficiency of the substation; by analyzing the correlation between the target factors to determine the target optimization strategy for the high-loss area, and optimizing the high-loss area according to the target optimization strategy, the optimization efficiency of the substation can be improved.

Description

Technical Field

[0001] The present invention relates to the field of data processing, and in particular to a method, device, electronic equipment and storage medium for automatically locating high-loss stations. Background Art

[0002] Currently, power grid companies are crucial for obtaining real-time electricity usage data from users within their substations to understand their electricity usage and develop appropriate management strategies. Typically, due to the large number of users within a substation, substation terminals communicate with the master station's front-end processor via wireless public networks. This results in slow data collection and makes it difficult to ensure data integrity. Processing the collected data as a whole significantly increases the complexity.

[0003] Currently, substations still face the problem of high line loss rates but an inability to quickly locate high-loss areas, impacting system efficiency. Furthermore, because current substation topology identification relies primarily on manual identification, it suffers from low efficiency and prone to errors. Furthermore, there is a lack of effective methods to analyze the causes of anomalies, resulting in low accuracy in locating high-loss areas. Summary of the Invention

[0004] In view of this, an embodiment of the present invention provides a method, device, electronic device and storage medium for automatic positioning of high-loss areas in a substation to solve the problems of low recognition efficiency and easy errors in current methods, as well as the lack of effective methods to analyze the causes of abnormalities, resulting in low accuracy in positioning high-loss areas.

[0005] In a first aspect, an embodiment of the present invention provides a method for automatically locating high-loss transmission lines in a transformer area, the method comprising:

[0006] Obtaining real-time power consumption data for each area in the substation and topological information of the substation;

[0007] Determine the high-loss area in the substation area based on the real-time power consumption data of each area and the topological structure information;

[0008] Acquiring relevant data of the substation area, and analyzing the relevant data to determine target factors corresponding to the high-loss area;

[0009] The correlation between the target factors is analyzed to determine the target optimization strategy for the high-damage area, and the high-damage area is optimized according to the target optimization strategy.

[0010] Furthermore, the correlating the real-time power consumption data of each area and the topology information to determine the high-loss area in the substation area includes:

[0011] The real-time electricity consumption data is used to classify each area to obtain category information corresponding to the area.

[0012] Constructing an association relationship between the category information corresponding to each area and the topological structure information;

[0013] Searching for target category information whose occurrence number is greater than a preset number based on the association relationship;

[0014] The electric energy loss amount corresponding to the target category information is calculated, and if the electric energy loss amount is greater than a preset loss amount, the area corresponding to the target category information is determined as a high-loss area.

[0015] Furthermore, clustering the real-time electricity consumption data of each region to obtain clustering results, and determining category information corresponding to each region based on the clustering results, includes:

[0016] Clustering is performed based on the real-time electricity consumption data of each area to obtain clustering results;

[0017] The category information corresponding to each region is determined based on the clustering result.

[0018] Furthermore, after correlating the real-time power consumption data of each area with the topology information to determine the high-loss area in the substation area, the method further includes:

[0019] Obtaining a geographical location corresponding to the high-loss area and first loss data;

[0020] Importing the geographical location corresponding to the high-loss area and the first loss data into a substation loss map, and using the first loss data to determine a rendering style corresponding to the high-loss area;

[0021] The high-loss area in the substation loss map is rendered according to the rendering style to obtain a rendered substation loss map, and the rendered substation loss map is displayed.

[0022] Furthermore, the obtaining of relevant data of the substation area and analyzing the relevant data to determine target factors corresponding to the high-loss area include:

[0023] Obtaining preset indicators associated with the substation area and obtaining relevant data corresponding to each preset indicator, wherein the relevant data includes: operating environment data, equipment status data, and user type data;

[0024] Analyze the impact of the relevant data corresponding to each of the preset indicators on the high-damage area;

[0025] The preset indicator whose influence degree is greater than the preset influence degree is used as the target factor.

[0026] Furthermore, analyzing the correlation between the target factors to determine the target optimization strategy for the high-damage area includes:

[0027] Analyze the degree of correlation between each of the target factors according to a preset correlation analysis method;

[0028] constructing a candidate factor group using the two target factors whose correlation degree is greater than a first threshold;

[0029] Determining a first factor group and a second factor group based on the candidate factor group, wherein the correlation degree between two target factors in the first factor group is greater than the first threshold and less than a second threshold, and the correlation degree between two target factors in the second factor group is greater than the second threshold;

[0030] Obtaining a first optimization strategy corresponding to the first factor group and a second optimization strategy corresponding to the second factor group;

[0031] Calculating a first evaluation value corresponding to the first optimization strategy and a second evaluation value corresponding to the second optimization strategy;

[0032] A first optimization strategy in which the first evaluation value is higher than a preset evaluation value and a second optimization strategy in which the second evaluation value is higher than a preset evaluation value are used as the target optimization strategy.

[0033] Furthermore, after optimizing the high-loss area according to the target optimization strategy, the method further includes:

[0034] detecting second loss data of the high-loss area;

[0035] Comparing the second loss data with the historical loss data of the high-loss area to obtain a comparison result;

[0036] The target optimization strategy is updated according to the comparison result.

[0037] In a second aspect, an embodiment of the present invention provides a high-loss automatic positioning device for a station area, the device comprising:

[0038] An acquisition module is used to acquire real-time power consumption data of each area in the substation and topological structure information of the substation;

[0039] A first analysis module is configured to determine a high-loss area in the transformer area based on correlation between the real-time power consumption data of each area and the topological structure information;

[0040] A second analysis module is used to obtain relevant data of the substation area and analyze the relevant data to determine the target factors corresponding to the high-loss area;

[0041] A processing module is used to analyze the correlation between the target factors to determine the target optimization strategy for the high-damage area, and optimize the high-damage area according to the target optimization strategy.

[0042] In a third aspect, an embodiment of the present invention provides a computer device comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, computer instructions being stored in the memory, and the processor executing the method of the first aspect or any corresponding embodiment thereof by executing the computer instructions.

[0043] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to enable a computer to execute the method of the first aspect or any corresponding embodiment thereof.

[0044] The present application obtains the real-time electricity consumption data of each area in the substation and the topological structure information of the substation, so as to facilitate the targeted processing of the electricity consumption data, and determines the high-loss area in the substation based on the correlation between the real-time electricity consumption data of each area and the topological structure information, thereby solving the problem that the substation has a high line loss rate but cannot quickly locate the high-loss area, thereby improving the working efficiency of the system; by obtaining the relevant data of the substation and analyzing the relevant data to determine the target factors corresponding to the high-loss area, the reasons for the abnormal line loss rate are analyzed to avoid affecting the inspection and optimization efficiency of the substation; by analyzing the correlation between the target factors to determine the target optimization strategy for the high-loss area, and optimizing the high-loss area according to the target optimization strategy, the optimization efficiency of the substation can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0046] Figure 1 is a flow chart of a method for automatically locating high-loss transmission areas according to some embodiments of the present invention;

[0047] Figure 2 is a flow chart of a method for automatically locating high-loss transmission areas according to other embodiments of the present invention;

[0048] Figure 3 1 is a flow chart of a method for automatically locating high-loss transmission lines in a substation according to some further embodiments of the present invention;

[0049] Figure 4 This is a structural block diagram of a high-loss automatic positioning device for a substation according to an embodiment of the present invention;

[0050] Figure 5 Schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0051] To make the purpose, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making creative efforts shall fall within the scope of protection of the present invention.

[0052] According to an embodiment of the present invention, a method, device, electronic device and storage medium for automatic positioning of high-loss substations are provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0053] In this embodiment, a method for automatically locating high-loss transformers is provided. Figure 1 FIG. 1 is a flow chart of a method for automatically locating high-loss transmission area according to an embodiment of the present invention. Figure 1 As shown, the process includes the following steps:

[0054] Step S11: acquiring real-time power consumption data of each area in the substation and topological structure information of the substation.

[0055] In the embodiments of the present application, a substation refers to the power supply range of a transformer in an electric power system, typically consisting of one or more transformers and their associated equipment. Electricity meters and related sensors are installed in each area of ​​the substation to measure real-time electricity usage data. The meters can record information such as electricity consumption and power, while the sensors can monitor parameters such as current and voltage. The meters and sensors are connected to a data concentrator or gateway. Real-time electricity usage data is transmitted to a server via wired or wireless communication. For example, Ethernet, wireless communication (such as Wi-Fi, LoRaWAN, etc.), or power line communication (PLC). Ensure the stability and security of data transmission. Real-time electricity usage data is received on the server. A database management system can be used to organize and store the data, and data analysis and processing tools can be used to process and analyze the data. In addition, substation topology information can be obtained through field surveys, wiring drawings, or other relevant documents. Topology information can be combined with real-time electricity usage data for more in-depth analysis and management.

[0056] Step S12: Correlation is performed based on the real-time power consumption data and topological structure information of each area to determine the high-loss area in the substation area.

[0057] In the embodiment of the present application, the high-loss area in the substation is determined based on the real-time power consumption data and topological structure information of each area. Figure 2 As shown, the following steps A1-A4 are included:

[0058] Step A1: Classify each area using real-time electricity consumption data to obtain category information corresponding to the area.

[0059] Specifically, clustering is performed according to the real-time electricity consumption data of each area to obtain clustering results, and category information corresponding to each area is determined based on the clustering results, including: clustering is performed according to the real-time electricity consumption data of each area to obtain clustering results; and category information corresponding to each area is determined based on the clustering results.

[0060] Real-time electricity usage data includes information such as electricity consumption, usage time, and peak usage. Clustering algorithms are used to process this data, grouping similar areas together to form different clustering results. Clustering algorithms can determine the method and number of clusters based on data characteristics and similarity metrics. After obtaining the clustering results, each cluster needs to be analyzed and interpreted to determine the category information corresponding to each area. For example, this can be achieved by observing cluster characteristics, comparing differences between different clusters, and combining business knowledge and experience. Category information can be defined based on factors such as electricity usage patterns, electricity behavior, and electricity demand, such as residential, commercial, and industrial areas.

[0061] For example, the K-Means algorithm is used to cluster real-time electricity consumption data. First, the number of clusters needs to be determined (the K value is selected). The data can be clustered into three categories: peak electricity consumption area, valley electricity consumption area, and stable electricity consumption area. Representative data points including peak, valley, and stable areas are selected as the initial centroids. Based on the Euclidean distance or other similarity measure between the sample and the centroid, the sample is assigned to the category to which the nearest centroid belongs, and the centroid is updated for each category. Convergence is determined by judging whether the centroid has changed or whether the clustering error is less than a threshold. The clustering results are analyzed, and the real-time electricity consumption data corresponding to each cluster area is marked as a peak electricity consumption area, a valley electricity consumption area, or a stable electricity consumption area.

[0062] It's important to note that clustering results group areas with similar real-time electricity usage data, and different groups are highly diverse. Each group represents a category of information, which can be identified and named based on the characteristics of the data within the group, such as peak electricity usage areas, off-peak electricity usage areas, and stable electricity usage areas. For example, using a city's electricity usage data as an example, the clustering results might divide the city into different categories, such as commercial areas, industrial areas, and residential areas.

[0063] Through these two steps, regions can be classified based on the similarities in their real-time electricity usage data, and the category information for each region can be determined. This analysis can help power companies better understand the electricity usage characteristics and needs of different regions, thereby optimizing power supply and management strategies.

[0064] Step A2: constructing an association relationship between the category information and topology structure information corresponding to each area.

[0065] Specifically, a correlation will be established between the category information and topology information corresponding to each area. Category information can include the area's function, usage pattern, and type of electrical equipment, while topology information can include the area's location in the power network and its connectivity. By establishing this correlation, we can better understand the electricity usage characteristics and power flow of each area.

[0066] Step A3: searching for target category information whose occurrence times are greater than a preset number based on the association relationship.

[0067] Specifically, based on the established association, target category information that appears more than a preset number of times is searched. This means finding category information that appears frequently under specific conditions. These frequently appearing category information may be associated with high losses because they represent common power usage patterns or device types that may result in more power losses. For example, if a certain type of electrical equipment is frequently used in a certain area and appears more than a preset number of times compared to other areas, then this type of equipment may be considered as target category information.

[0068] In step A4, the power loss amount corresponding to the target category information is calculated, and if the power loss amount is greater than a preset loss amount, the area corresponding to the target category information is determined as a high-loss area.

[0069] Specifically, for the determined target category information, the corresponding power loss is calculated. The power loss can be obtained by analyzing power system operating data, using power metering equipment, etc. If the power loss corresponding to the target category information is greater than a preset loss, the corresponding area is determined to be a high-loss area.

[0070] The present embodiment analyzes real-time electricity usage data to categorize each region and establishes a correlation between category information and topology information. This allows for a better understanding of electricity usage in different regions, thereby optimizing electricity management strategies and improving energy efficiency. Secondly, by searching for target category information that appears more than a preset number of times and calculating the corresponding energy loss, high-loss areas can be accurately identified.

[0071] Step S13: Obtain relevant data of the substation area, and analyze the relevant data to determine the target factors corresponding to the high-loss area.

[0072] In the embodiment of the present application, relevant data of the substation is obtained, and the relevant data is analyzed to determine the target factors corresponding to the high-loss area, such as Figure 3 As shown, the following steps B1-B3 are included:

[0073] Step B1: obtaining preset indicators associated with the substation area, and obtaining relevant data corresponding to each preset indicator, wherein the relevant data includes: operating environment data, equipment status data, and user type data.

[0074] Specifically, preset indicators refer to specific performance or characteristics related to the substation. These indicators can be used to evaluate the operating status, energy efficiency, reliability and other aspects of the substation. For each preset indicator, it is necessary to obtain relevant data for analysis and evaluation. The relevant data may include the following aspects: ① Operating environment data: the environmental conditions of the substation, such as temperature, humidity, altitude, etc. These factors may affect the performance and reliability of the equipment. ② Equipment status data: operating status information of various equipment in the substation, such as oil temperature, voltage, current, etc. of the transformer, as well as fault information, maintenance records, etc. of other equipment. ③ User type data: the type and load characteristics of users in the substation, such as industrial users, commercial users, residential users, etc. Different types of users may have different electricity usage behaviors and needs.

[0075] By collecting and analyzing this relevant data, we can comprehensively evaluate the performance of the substation and take appropriate measures to optimize its operation and management, improving energy efficiency and power supply reliability. For example, if the voltage in a substation is found to be unstable, the power quality can be improved by adjusting the transformer tap or installing reactive power compensation equipment. If the load in a substation is found to be too heavy, measures such as load shifting or equipment upgrades can be taken to reduce the load pressure.

[0076] Step B2: Analyze the impact of relevant data corresponding to each preset indicator on the high-damage area.

[0077] Specifically, analyze the impact of the relevant data corresponding to each preset indicator on the high-damage areas. In this step, the relevant data for each preset indicator needs to be deeply analyzed to determine the degree of their impact on the high-damage areas. The impact can be measured through various methods, such as statistical analysis, data mining, and machine learning algorithms.

[0078] For example: ① Data Correlation Analysis: Calculate the correlation coefficient between each preset indicator and high-loss areas. The correlation coefficient indicates the strength of the linear relationship between the indicator and the high-loss area. A high correlation may indicate that the indicator has a significant impact on the high-loss area. The Pearson correlation coefficient can be used when calculating the correlation coefficient between the preset indicator and the high-loss area. The Pearson correlation coefficient is a value between -1 and 1 that measures the strength of the linear relationship between variables. If there is a perfect positive correlation between the two variables, the correlation coefficient is 1; if there is a perfect negative correlation between the two variables, the correlation coefficient is -1; if there is no linear relationship between the two variables, the correlation coefficient is 0. A positive correlation coefficient indicates a positive correlation between the preset indicator and the high-loss area; that is, as the preset indicator increases, the likelihood of the high-loss area also increases. A negative correlation coefficient indicates a negative correlation between the preset indicator and the high-loss area; that is, as the preset indicator increases, the likelihood of the high-loss area decreases. A correlation coefficient close to 0 indicates no linear relationship between the preset indicator and the high-loss area.

[0079] ② Regression Analysis: Build a regression model using each pre-set indicator as the independent variable and the high-damage area as the dependent variable. Through regression analysis, we can determine the impact of each indicator on the high-damage area and obtain the corresponding regression coefficient. In this model, each pre-set indicator serves as the independent variable and the high-damage area serves as the dependent variable. Specifically, we first need to collect relevant data, including the values ​​of each pre-set indicator and information about the corresponding high-damage area. This data is then input into the regression model for data analysis and processing. Through regression analysis, we can determine the impact of each indicator on the high-damage area, which can be reflected by the regression coefficient. The regression coefficient represents the corresponding change in the dependent variable for each unit change in the independent variable. A positive regression coefficient indicates a positive correlation between the independent and dependent variables—that is, an increase in the independent variable leads to an increase in the dependent variable. A negative regression coefficient indicates a negative correlation between the independent and dependent variables—that is, an increase in the independent variable leads to a decrease in the dependent variable.

[0080] In step B3, a preset indicator having an influence degree greater than a preset influence degree is used as a target factor.

[0081] Specifically, since the impact of each preset indicator on the high-damage area has been determined, this step requires screening out those indicators with an impact greater than the threshold from the numerous preset indicators based on the pre-set impact threshold. These indicators are considered to be target factors that have a significant impact on the high-damage area. The method for determining the preset impact threshold can be selected based on specific circumstances. This can be based on a comprehensive consideration of experience, business domain knowledge, data analysis results, etc. Generally speaking, the threshold setting should be able to distinguish between indicators that have a significant impact on the high-damage area and indicators with a smaller impact.

[0082] Step S14: Analyze the correlation between target factors to determine the target optimization strategy for the high-damage area, and optimize the high-damage area according to the target optimization strategy.

[0083] In an embodiment of the present application, the correlation relationship between the target factors is analyzed to determine the target optimization strategy for the high-loss area, including: analyzing the correlation degree between the target factors according to a preset correlation analysis method; constructing a candidate factor group using two target factors with a correlation degree greater than a first threshold; determining a first factor group and a second factor group based on the candidate factor group, wherein the correlation degree between the two target factors in the first factor group is greater than the first threshold and less than the second threshold, and the correlation degree between the two target factors in the second factor group is greater than the second threshold; obtaining a first optimization strategy corresponding to the first factor group and a second optimization strategy corresponding to the second factor group; calculating a first evaluation value corresponding to the first optimization strategy and a second evaluation value corresponding to the second optimization strategy; and using the first optimization strategy with a first evaluation value higher than the preset evaluation value and the second optimization strategy with a second evaluation value higher than the preset evaluation value as target optimization strategies.

[0084] Specifically, correlation analysis is a data analysis method used to study the relationships between variables. In this step, using pre-defined correlation analysis methods, such as correlation analysis and linear regression, each target factor is analyzed to calculate the degree of correlation between them. This degree of correlation is typically expressed using metrics such as the correlation coefficient and the coefficient of determination. Correlation analysis can help understand the interrelationships between target factors, providing a basis for subsequent factor group construction and optimization strategies.

[0085] Next, based on the correlation analysis, two target factors with a correlation greater than a first threshold are selected to form a candidate factor group. The first threshold is a pre-set value used to screen for pairs of target factors with strong correlations. By constructing a candidate factor group, the optimization scope can be further narrowed and optimization efficiency improved.

[0086] Next, within the candidate factor group, a first factor group and a second factor group are further determined. The correlation between the two target factors in the first factor group is greater than the first threshold but less than the second threshold; the correlation between the two target factors in the second factor group is greater than the second threshold. The second threshold is also a pre-set value used to classify relationships of varying degrees of correlation. By determining factor groups, the target factors can be divided into different subsets, allowing for the adoption of different optimization strategies for each subset.

[0087] Next, corresponding optimization strategies are obtained for the first and second factor groups, respectively. These optimization strategies can be optimization algorithms based on mathematical models or heuristic methods based on experience or expert knowledge. Obtaining these optimization strategies provides a basis for subsequent evaluation value calculations and the determination of target optimization strategies. Next, a first evaluation value corresponding to the first optimization strategy and a second evaluation value corresponding to the second optimization strategy are calculated. An evaluation value is a metric used to assess the quality of an optimization strategy and can be an evaluation value of an objective function, a cost function, or other factors. By calculating these evaluation values, the effectiveness of each optimization strategy can be quantified, providing a basis for determining the target optimization strategy.

[0088] Ultimately, the first optimization strategy with a first evaluation value higher than a preset evaluation value and the second optimization strategy with a second evaluation value higher than a preset evaluation value are selected as the target optimization strategy. The preset evaluation value is a pre-set value used to determine whether the optimization strategy meets the requirements. By determining the target optimization strategy, the optimal optimization solution can be screened, providing guidance for practical applications.

[0089] The present application obtains the real-time electricity consumption data of each area in the substation and the topological structure information of the substation, so as to facilitate the targeted processing of the electricity consumption data, and determines the high-loss area in the substation based on the correlation between the real-time electricity consumption data of each area and the topological structure information, thereby solving the problem that the substation has a high line loss rate but cannot quickly locate the high-loss area, thereby improving the working efficiency of the system; by obtaining the relevant data of the substation and analyzing the relevant data to determine the target factors corresponding to the high-loss area, the reasons for the abnormal line loss rate are analyzed to avoid affecting the inspection and optimization efficiency of the substation; by analyzing the correlation between the target factors to determine the target optimization strategy for the high-loss area, and optimizing the high-loss area according to the target optimization strategy, the optimization efficiency of the substation can be improved.

[0090] In an embodiment of the present application, after optimizing the high-loss area according to the target optimization strategy, the method also includes: detecting second loss data of the high-loss area; comparing the second loss data with the historical loss data of the high-loss area to obtain a comparison result; and updating the target optimization strategy based on the comparison result.

[0091] Specifically, the second loss data is obtained by monitoring high-loss areas after optimizing them according to the target optimization strategy. This includes information on energy consumption, material consumption, and production efficiency. Second loss data can be detected using sensors, monitoring equipment, data analysis tools, and other tools.

[0092] Historical loss data refers to records of losses in high-loss areas over a period of time. This data provides information on loss patterns and trends, helping to identify anomalies or opportunities for improvement. Compare secondary loss data to historical loss data to identify significant differences or changes.

[0093] The comparison results may include the following aspects: Trend of loss increase or decrease:

[0094] If the second loss data is higher or lower than the historical average, the direction and magnitude of the loss change can be evaluated.

[0095] Loss fluctuation: Compare the fluctuation of the second loss data with the historical data to understand the stability of the loss.

[0096] Outliers: Detects unusual loss values ​​that differ significantly from historical data and may require further investigation and resolution.

[0097] Based on the comparison results, the target optimization strategy can be adjusted and updated. A target optimization strategy is a specific measure and action plan developed to reduce losses in high-loss areas. ① Target improvement measures are determined for trends of increasing or decreasing losses. Examples include equipment maintenance, parameter adjustments, and operational improvements. ② For situations with large fluctuations in losses, consider implementing measures to stabilize equipment operations, such as optimizing control algorithms, strengthening monitoring, or adding backup equipment. ③ Conduct in-depth analysis of outliers, identify the root cause, and implement measures to resolve the issue. Furthermore, establish an anomaly monitoring and alarm mechanism to promptly detect and address similar situations.

[0098] Based on this, new goals and indicators are set based on the comparison results and the updated strategy to monitor the effectiveness of the optimization strategy. The implementation of the optimization strategy is regularly evaluated, and necessary adjustments and improvements are made based on the actual results.

[0099] In an embodiment of the present application, after determining the high-loss area in the substation based on the correlation between the real-time electricity consumption data and topological structure information of each area, the method also includes: obtaining the geographical location and first loss data corresponding to the high-loss area; importing the geographical location and first loss data corresponding to the high-loss area into the substation loss map, and using the first loss data to determine the rendering style corresponding to the high-loss area; rendering the high-loss area in the substation loss map according to the rendering style to obtain a rendered substation loss map, and displaying the rendered substation loss map.

[0100] Specifically, it is first necessary to determine the geographical location information of the high-loss area, such as longitude and latitude or address. At the same time, it is also necessary to obtain the first loss data related to the area, which may be some numerical values ​​or indicators of energy loss. Import the obtained geographical location and loss data into the substation loss map. This can be achieved by associating or overlaying the data with the map. Then, based on the value or range of the first loss data, determine the rendering style of the high-loss area on the map. For example, colors, icons or other visual methods can be used to represent different degrees of loss. Render the high-loss area on the map according to the determined rendering style. This will display the high-loss area on the map in a specific way, so that it is easier for users to identify and analyze. Finally, the rendered substation loss map is displayed to the user. Users can intuitively understand the location and loss situation of the high-loss area by viewing the map.

[0101] For example, consider loss data for a power substation, including the location and loss amount of each area. You want to create a substation loss map to highlight areas with higher losses. First, obtain the latitude and longitude coordinates and corresponding loss data for all high-loss areas. Then, import this data into the substation loss map. Next, determine a rendering style based on the loss amount. For example, divide the loss amount into three levels: low-loss areas are represented in green, medium-loss areas in yellow, and high-loss areas in red. Finally, render the high-loss areas according to the determined rendering style to create a rendered substation loss map, which is then displayed to the user. The map allows users to clearly see the distribution of high-loss areas and take appropriate measures to reduce losses. Such a substation loss map can help power managers quickly locate problem areas, make informed decisions, and improve energy efficiency and system reliability.

[0102] This embodiment also provides a high-loss automatic positioning device for a substation, which is used to implement the above-mentioned embodiments and preferred implementations. Details already described will not be repeated here. As used below, the term "module" may refer to a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementation using hardware, or a combination of software and hardware, is also possible and contemplated.

[0103] This embodiment provides a high-loss automatic positioning device for a station area, such as Figure 4 Shown, including:

[0104] An acquisition module 41 is used to acquire real-time power consumption data of each area in the substation and topological structure information of the substation;

[0105] The first analysis module 42 is used to determine the high-loss area in the substation area based on the correlation between the real-time power consumption data and the topological structure information of each area;

[0106] The second analysis module 43 is used to obtain relevant data of the substation area and analyze the relevant data to determine the target factors corresponding to the high-loss area;

[0107] The processing module 44 is used to analyze the correlation between the target factors to determine the target optimization strategy for the high-damage area, and optimize the high-damage area according to the target optimization strategy.

[0108] In this embodiment of the present application, the first analysis module 42 is configured to classify each region using real-time power consumption data to obtain category information corresponding to the region. A correlation is established between the category information corresponding to each region and the topology information. Based on the correlation, target category information is searched for that has appeared more than a preset number of times. The power loss corresponding to the target category information is calculated, and if the power loss exceeds the preset loss, the region corresponding to the target category information is determined to be a high-loss region.

[0109] In the embodiment of the present application, the first analysis module 42 is configured to cluster the real-time electricity consumption data of each region to obtain a clustering result; and determine the category information corresponding to each region based on the clustering result.

[0110] In an embodiment of the present application, the device also includes: a display module for obtaining the geographical location corresponding to the high-loss area and the first loss data; importing the geographical location corresponding to the high-loss area and the first loss data into the substation loss map, and using the first loss data to determine the rendering style corresponding to the high-loss area; rendering the high-loss area in the substation loss map according to the rendering style to obtain the rendered substation loss map, and displaying the rendered substation loss map.

[0111] In an embodiment of the present application, the second analysis module 43 is used to obtain preset indicators associated with the substation and obtain relevant data corresponding to each preset indicator, wherein the relevant data includes: operating environment data, equipment status data, and user type data; analyze the degree of influence of the relevant data corresponding to each preset indicator on the high-loss area; and use the preset indicator with an influence greater than the preset influence as the target factor.

[0112] In an embodiment of the present application, the processing module 44 is used to analyze the degree of correlation between each target factor according to a preset correlation analysis method; construct a candidate factor group using two target factors with a correlation degree greater than a first threshold; determine a first factor group and a second factor group based on the candidate factor group, wherein the degree of correlation between the two target factors in the first factor group is greater than the first threshold and less than the second threshold, and the degree of correlation between the two target factors in the second factor group is greater than the second threshold; obtain a first optimization strategy corresponding to the first factor group and a second optimization strategy corresponding to the second factor group; calculate a first evaluation value corresponding to the first optimization strategy and a second evaluation value corresponding to the second optimization strategy; and use the first optimization strategy with a first evaluation value higher than the preset evaluation value and the second optimization strategy with a second evaluation value higher than the preset evaluation value as target optimization strategies.

[0113] In an embodiment of the present application, the device further includes: a detection module for detecting second loss data of the high-loss area; comparing the second loss data with historical loss data of the high-loss area to obtain a comparison result; and updating the target optimization strategy according to the comparison result.

[0114] See also Figure 5 , Figure 5 is a structural diagram of a computer device provided by an optional embodiment of the present invention, such as Figure 5 As shown, the computer device includes: one or more processors 10, memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components utilize different buses to communicate with each other and can be installed on a common mainboard or installed in other ways as needed. The processor can process the instructions executed in the computer device, including instructions stored in the memory or on the memory to display the graphical information of the GUI on an external input / output device (such as, a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Equally, multiple computer devices can be connected, and each device provides part of the necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 5 A processor 10 is taken as an example.

[0115] The processor 10 may be a central processing unit, a network processor, or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit, a programmable logic device, or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic, or any combination thereof.

[0116] The memory 20 stores instructions that can be executed by at least one processor 10, so that the at least one processor 10 executes the method shown in the above embodiment.

[0117] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created based on the use of a computer device for displaying a small program landing page, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely located relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof.

[0118] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0119] The computer device further includes a communication interface 30 for the computer device to communicate with other devices or a communication network.

[0120] The embodiment of the present invention also provides a computer-readable storage medium. The above-mentioned method according to the embodiment of the present invention can be implemented in hardware, firmware, or implemented as a computer code that can be recorded in a storage medium, or implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and downloaded through a network and will be stored in a local storage medium, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state drive, etc.; further, the storage medium can also include a combination of the above-mentioned types of memory. It can be understood that a computer, a processor, a microprocessor controller or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor or hardware, the method shown in the above embodiment is implemented.

[0121] Although the embodiments of the present invention have been described with reference to the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention. Such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A method for automatically locating high-loss transformers in a transformer area, characterized in that: The method comprises: Obtaining real-time power consumption data for each area in the substation and topological information of the substation; Determine the high-loss area in the substation area based on the real-time power consumption data of each area and the topological structure information; Acquiring relevant data of the substation area, and analyzing the relevant data to determine target factors corresponding to the high-loss area; Analyzing the correlation between the target factors to determine a target optimization strategy for the high-damage area, and optimizing the high-damage area according to the target optimization strategy; The analysis of the correlation between the target factors to determine the target optimization strategy for the high-damage area includes: analyzing the degree of correlation between the target factors according to a preset correlation analysis method; constructing a candidate factor group using two target factors with a correlation degree greater than a first threshold, wherein the first threshold is used to screen out target factor pairs with stronger correlation; determining a first factor group and a second factor group based on the candidate factor group, wherein the degree of correlation between the two target factors in the first factor group is greater than the first threshold and less than the second threshold, and the degree of correlation between the two target factors in the second factor group is greater than the second threshold, wherein the second threshold is used to divide correlation relationships of different degrees; obtaining a first optimization strategy corresponding to the first factor group and a second optimization strategy corresponding to the second factor group; calculating a first evaluation value corresponding to the first optimization strategy and a second evaluation value corresponding to the second optimization strategy; using the first optimization strategy with the first evaluation value higher than the preset evaluation value and the second optimization strategy with the second evaluation value higher than the preset evaluation value as the target optimization strategy, wherein the optimization strategy includes an optimization algorithm based on a mathematical model and a heuristic method based on experience or expert knowledge.

2. The method according to claim 1, characterized in that The determining of the high-loss area in the substation area based on the correlation between the real-time power consumption data of each area and the topology information includes: The real-time electricity consumption data is used to classify each area to obtain category information corresponding to the area. Constructing an association relationship between the category information corresponding to each area and the topological structure information; Searching for target category information whose occurrence number is greater than a preset number based on the association relationship; The electric energy loss amount corresponding to the target category information is calculated, and if the electric energy loss amount is greater than a preset loss amount, the area corresponding to the target category information is determined as a high-loss area.

3. The method according to claim 2, characterized in that The clustering is performed according to the real-time electricity consumption data of each area to obtain a clustering result, and the category information corresponding to each area is determined based on the clustering result, including: Clustering is performed based on the real-time electricity consumption data of each area to obtain clustering results; The category information corresponding to each region is determined based on the clustering result.

4. The method according to claim 1, wherein After determining the high-loss area in the substation area by correlating the real-time power consumption data of each area with the topology information, the method further includes: Obtaining a geographical location corresponding to the high-loss area and first loss data; Importing the geographical location corresponding to the high-loss area and the first loss data into a substation loss map, and using the first loss data to determine a rendering style corresponding to the high-loss area; The high-damage area in the high-damage map is rendered according to the rendering style to obtain a rendered high-damage map, and the rendered high-damage map is displayed.

5. The method according to claim 1, wherein The obtaining of relevant data of the station area and analyzing the relevant data to determine target factors corresponding to the high-loss area includes: Obtaining preset indicators associated with the substation area and obtaining relevant data corresponding to each preset indicator, wherein the relevant data includes: operating environment data, equipment status data, and user type data; Analyze the impact of the relevant data corresponding to each of the preset indicators on the high-damage area; The preset indicator whose influence degree is greater than the preset influence degree is used as the target factor.

6. The method according to claim 1, characterized in that After optimizing the high-loss area according to the target optimization strategy, the method further includes: detecting second loss data of the high-loss area; Comparing the second loss data with the historical loss data of the high-loss area to obtain a comparison result; The target optimization strategy is updated according to the comparison result.

7. A high-loss automatic positioning device for a station area, characterized in that: The device comprises: An acquisition module is used to acquire real-time power consumption data of each area in the substation and topological structure information of the substation; A first analysis module is configured to determine a high-loss area in the transformer area based on correlation between the real-time power consumption data of each area and the topological structure information; A second analysis module is used to obtain relevant data of the substation area and analyze the relevant data to determine the target factors corresponding to the high-loss area; a processing module, configured to analyze the correlation between the target factors to determine a target optimization strategy for the high-damage area, and optimize the high-damage area according to the target optimization strategy; The processing module is used to analyze the degree of correlation between each of the target factors according to a preset correlation analysis method; construct a candidate factor group using the two target factors with a correlation degree greater than a first threshold, wherein the first threshold is used to screen out target factor pairs with stronger correlation; determine a first factor group and a second factor group based on the candidate factor group, wherein the degree of correlation between the two target factors in the first factor group is greater than the first threshold and less than the second threshold, and the degree of correlation between the two target factors in the second factor group is greater than the second threshold, wherein the second threshold is used to divide correlation relationships of different degrees; obtain a first optimization strategy corresponding to the first factor group and a second optimization strategy corresponding to the second factor group; calculate a first evaluation value corresponding to the first optimization strategy and a second evaluation value corresponding to the second optimization strategy; use the first optimization strategy with the first evaluation value higher than the preset evaluation value and the second optimization strategy with the second evaluation value higher than the preset evaluation value as the target optimization strategy, wherein the optimization strategy includes an optimization algorithm based on a mathematical model and a heuristic method based on experience or expert knowledge.

8. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the method according to any one of claims 1 to 6 by executing the computer instructions.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the method according to any one of claims 1 to 6.

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