A method and system for intelligent analysis and optimization decision-making of comprehensive line loss rate in transformer substations

Abstract

This invention provides an intelligent analysis and optimization decision-making method and system for the comprehensive line loss rate of distribution transformer areas. First, it acquires and preprocesses relevant data on the comprehensive line loss rate of distribution transformer areas. Then, it constructs a regression model between the comprehensive line loss rate of the distribution transformer area and the power supply volume of the area, the network architecture of each power supply station, and the line loss monitoring mode of each power supply station, representing the power grid of the distribution transformer area as a graph, with eigenvalues ​​serving as independent variables in the regression model. Next, it estimates and optimizes the model, ultimately identifying the key factors affecting the high line loss rate as the root causes. Using equipment technology, personnel, management, and environment as entry points, it draws a cause-effect correlation diagram to determine the end factors causing the root causes. It employs multivariate correlation and complex function goodness-of-fit analysis to determine the contributing factors affecting the root causes. Based on the determined contributing factors, it formulates and implements corresponding countermeasures. This invention comprehensively integrates various relevant data, effectively reducing the comprehensive line loss rate of distribution transformer areas.

Description

Technical Field

[0001] This invention relates to the field of comprehensive line loss rate data processing for transformer substations, and specifically to an intelligent analysis and optimization decision-making method and system for comprehensive line loss rate of transformer substations. Background Technology

[0002] In modern power systems, distribution transformers (DTMs) serve as the basic units of power distribution, and their operational efficiency and energy loss have a crucial impact on the economy and reliability of the entire power system. The comprehensive line loss rate of a DTM is a key indicator measuring the degree of energy loss during transmission within the DTM, directly affecting the operating costs and energy utilization efficiency of power companies. With the continuous growth of electricity demand and the increasingly severe energy supply situation, reducing the comprehensive line loss rate of DTMs has become a critical issue that the power industry urgently needs to address.

[0003] Traditional transformer substation line loss analysis often relies on limited basic data, such as simple electricity meter readings and some equipment parameters. It lacks comprehensive data collection and integration for numerous factors closely related to line loss, such as personnel operation behavior, management strategies, and environmental factors. This inadequate data utilization prevents a comprehensive and accurate understanding of the root causes of line loss, limiting the depth and breadth of line loss analysis.

[0004] Given the many limitations of traditional methods for analyzing and processing the comprehensive line loss rate of distribution transformer areas, there is an urgent need for a new data processing method that can comprehensively integrate various relevant data to effectively reduce the comprehensive line loss rate of distribution transformer areas and improve the operating efficiency and economic benefits of the power system. Summary of the Invention

[0005] The technical problem to be solved by this invention is to provide an intelligent analysis and optimization decision-making method and system for the comprehensive line loss rate of transformer substations, which addresses the shortcomings of existing technologies, effectively solves the problem of high comprehensive line loss rate of transformer substations, improves the utilization efficiency of power resources, and provides strong support for the sustainable development of the power industry.

[0006] To solve the above-mentioned technical problems, the technical solution of the present invention is as follows:

[0007] A method for intelligent analysis and optimization decision-making of comprehensive line loss rate in transformer substations, comprising the following steps:

[0008] Step S1: Obtain relevant data on the comprehensive line loss rate of the distribution area from the multi-source database of the power company, including the power supply volume of the distribution area, the power loss data, and various factors affecting the comprehensive line loss rate of the distribution area. These factors involve the power supply volume of the distribution area, the network architecture of each power supply station, and the line loss monitoring mode of each power supply station. The obtained data is then preprocessed.

[0009] Step S2: Using a multiple linear regression analysis algorithm, construct a regression model between the comprehensive line loss rate of the distribution area and the power supply of the distribution area, the network architecture of each power supply station, and the line loss monitoring mode of each power supply station. Represent the power grid of the distribution area as a graph, and use the eigenvalues ​​of the Laplace matrix of the graph as independent variables of the regression model. Then, perform parameter estimation and model optimization on the model, and finally identify the key factors affecting the high line loss rate as the root cause.

[0010] Step S3: From the many causes of the high overall line loss rate in the transformer area, take equipment technology, personnel, management and environment as the starting point, and use the method of drawing a cause analysis correlation diagram to identify the root cause of the problem from these many causes;

[0011] Step S4: Using multivariate correlation and goodness-of-fit analysis of complex functions, identify the key factors affecting the problem from the end factors identified in Step S3 that caused the problem.

[0012] Step S5: Based on the identified causes of the problems, formulate and implement corresponding countermeasures.

[0013] Preferably, in step S2, the parameters of the regression model are estimated using a gradient descent-based optimization algorithm, and the model parameters are optimized using cross-validation technology; the AIC and BIC information criteria are used to evaluate the quality of the model, obtain the optimal model structure, and then identify the key factors affecting the high line loss rate as the root cause.

[0014] Preferably, step S3 specifically includes the following steps: S3-1, taking the identified root cause of the high overall line loss rate as the core, constructing a multi-layered causal relationship network; this network includes three layers of nodes, the first layer of nodes being the four main categories of factors: equipment technology, personnel, management, and environment; the second layer of nodes being the sub-factors under each main factor; and the third layer of nodes being the end factors under each sub-factor; by establishing a potential relationship model between each factor, comprehensively considering the multi-level causal relationship from the main factor to the end factor, determining the path coefficients between nodes in the network as connection weights; S3-2, based on the multi-layered causal relationship network, using a network... The network analysis algorithm calculates the importance score of each terminal factor; based on the importance score, those terminal factors considered to be critical in affecting the overall line loss rate of the transformer area are selected as key focus objects; S3-3, then the synergistic effect between these key focus terminal factors and other terminal factors is analyzed. Using the principal component analysis method, multiple related terminal factors are transformed into a few unrelated principal components. Based on the comprehensive impact of these principal components on the overall line loss rate, the terminal factors corresponding to the principal components that are significantly correlated with the line loss rate are identified as those terminal factors that cannot be further subdivided and directly or indirectly affect the line loss rate.

[0015] Preferably, step S4 specifically includes the following steps: S4-1, for each determined end factor, a partial correlation analysis method is used to calculate the partial correlation coefficient between the end factor and the power loss while controlling for the influence of other factors; then, a complex function fitting analysis is performed on the end factor and the power loss, and a non-parametric regression method is used for fitting to calculate the goodness of fit between the two; S4-2, taking into account the partial correlation coefficient and goodness of fit between each determined end factor and the power loss, as well as the importance score of the end factor in the causal relationship network, a multi-index comprehensive evaluation method is used to determine the key factors affecting the problem.

[0016] Preferably, in step S4-2, firstly, the partial correlation coefficient and goodness of fit of each determined end factor with the lost electricity, as well as the importance score of the end factor in the causal relationship network, are used as evaluation indicators to construct an evaluation matrix. Then, the evaluation matrix is ​​normalized to obtain a normalized matrix. Based on the normalized matrix, the distance between each end factor and the positive ideal solution and the negative ideal solution is calculated. Finally, based on these two distances, the relative proximity of each end factor is calculated, and the end factor with the larger relative proximity is selected as the factor affecting the crux of the problem.

[0017] Preferably, in step S5, the countermeasures for the technical factors of the equipment include: 1) Precise modeling and intelligent control: Constructing a coupled model of key equipment parameters and multiple influencing factors to obtain the intrinsic relationship between equipment operating characteristics and line loss, and then using an adaptive adjustment device to optimize the equipment operating parameters in real time. At the same time, an Internet of Things monitoring system is constructed to collect equipment operating data in real time, and predictive control algorithms are used to optimize the equipment operating parameters in advance; 2) Equipment upgrade: Using new energy-saving equipment and optimizing the equipment structure to reduce the inherent losses of the equipment itself; 3) Load balancing optimization: Dividing the power grid of the distribution area into multiple load balancing areas, setting up intelligent agents in each area to manage the load, and these intelligent agents interact and make collaborative decisions based on the load status of each node in the area and the load information of adjacent areas to realize the dynamic distribution of power within and between areas.

[0018] Preferably, in step S5, the countermeasures for personnel-related factors include: 1) Inspection optimization: establishing an inspection reliability model based on stochastic process theory, determining model parameters using historical data and Bayesian estimation methods, and then designing a dynamic inspection plan based on risk assessment. This plan comprehensively considers the importance of equipment, historical failure rate data, and environmental factors. It uses a combination of fault tree analysis and failure mode and impact analysis to perform risk scoring on equipment to determine inspection priorities and cycles, and employs dynamic programming algorithms to optimize inspection routes; 2) Based on inspection optimization, improving the reliability of inspection work through the optimized inspection plan and routes; 3) Training... Training optimization: Construct an operational error probability model, linking the probability of operational errors with personnel training level, work pressure, and operational process complexity. Then, develop a personalized training system, using machine learning algorithms to develop targeted training content and methods for each employee based on their skill gaps and historical operational error data; 4) Operational process optimization: Follow human-computer interaction design principles to simplify, standardize, and visualize the operation interface and process, reduce operational complexity, and minimize human error; At the same time, establish an operational error early warning mechanism to monitor the operational behavior data of staff in real time, use anomaly detection algorithms to promptly identify potential operational error risks, and provide real-time reminders and guidance.

[0019] Preferably, in step S5, the countermeasures for management factors include: 1) Maintenance plan optimization: Constructing a full lifecycle maintenance optimization model for equipment, using the Weibull distribution failure rate function to describe equipment failure patterns, and comprehensively considering maintenance costs, reliability recovery factors, and remaining life extension factors, with the goal of minimizing the full lifecycle cost of equipment, and using intelligent optimization algorithms to solve for the optimal maintenance strategy; 2) Information management: Based on the maintenance plan optimization, establishing an equipment maintenance management information system based on blockchain technology to record maintenance information throughout the equipment's lifecycle, and introducing smart contract technology to achieve automated triggering and execution of maintenance tasks; 3) Based on the dynamic optimization of distribution area load balancing, establishing a load balancing effect evaluation index system, including load balancing degree index and line loss index, and continuously adjusting the optimization strategy through the evaluation of actual operating data and simulation results using a feedback control mechanism.

[0020] Preferably, in step S5, the countermeasures for environmental factors include: 1) Thermal effect response: Constructing a line temperature field distribution model, using a two-dimensional heat conduction equation and a multi-layer medium heat conduction model for overhead lines and cable lines respectively, comprehensively considering current heat effects, solar radiation heat, air convection heat dissipation, and boundary conditions in the model, and solving the temperature field distribution using the finite element analysis method; and using new heat dissipation materials and structures, applying them to the surface of lines and equipment; 2) Based on the thermal effect response, establishing a dynamic adjustment system for line operating parameters based on meteorological forecasts, using meteorological data prediction models to predict meteorological parameters such as air temperature and solar radiation, combined with... Temperature field distribution model predicts line resistance change trend, and power system reactive power compensation and voltage regulation device is used to adjust line operating voltage and reactive power in advance to compensate for the increase in line loss caused by increased resistance; 3) Severe weather protection: construct mechanical response model and reliability assessment model of power supply facilities under severe weather, and optimize power supply facility design standards according to the meteorological disaster characteristics of different regions, and adopt targeted disaster resistance design measures; 4) Early warning and emergency response: establish severe weather early warning and emergency response system, use meteorological monitoring network and disaster early warning model to predict the time, intensity and impact range of severe weather in advance, and activate emergency response plan in a timely manner.

[0021] A smart analysis and optimization decision-making system for comprehensive line loss rate of transformer substations, the system comprising:

[0022] The data acquisition and preprocessing module is used to obtain data related to the comprehensive line loss rate of distribution transformer areas from the multi-source databases of power companies. This includes data on the power supply volume of distribution transformer areas, power loss data, and various factors affecting the comprehensive line loss rate of distribution transformer areas. These factors involve the power supply volume of distribution transformer areas, the network architecture of each power supply station, and the line loss monitoring mode of each power supply station. The module also performs preprocessing on the acquired data.

[0023] The root cause identification module uses a multiple linear regression analysis algorithm to construct a regression model between the overall line loss rate of a distribution area and the power supply of the distribution area, the network architecture of each power supply station, and the line loss monitoring mode of each power supply station. The power grid of the distribution area is represented as a graph, and the eigenvalues ​​of the Laplace matrix of the graph are also used as independent variables of the regression model. Then, the model is parameter estimated and optimized, and finally the key factors affecting the high line loss rate are identified as the root causes.

[0024] Key Factor Identification Module: This module is used to identify the root causes of high overall line loss rate in transformer substations by focusing on equipment technology, personnel, management, and environment, and by drawing a cause-effect relationship diagram.

[0025] The root cause identification module is used to identify the root causes affecting the problem from the end factors that cause the problem by employing multivariate correlation and complex function fit goodness-of-fit analysis methods.

[0026] Countermeasures module: Based on the identified root causes of the problems, formulate and implement corresponding countermeasures.

[0027] The beneficial effects of this invention are:

[0028] 1) Multi-source data integration: This invention can widely collect various types of data related to the comprehensive line loss rate of transformer substations, covering four major areas: equipment technology, personnel, management, and environment. In terms of equipment technology, detailed data such as line parameters and transformer performance can be obtained; in terms of personnel, inspection records and operational status are available; in terms of management, maintenance plans and equipment procurement information are included; and environmental data involves meteorological conditions and geographic information. Simultaneously, power loss data is also incorporated. This integration of multi-source data provides rich material for comprehensive and in-depth analysis of the line loss rate, avoiding biased analysis due to data gaps.

[0029] 2) Layered Processing Architecture: The data processing adopts a layered structure, starting from data collection and preprocessing, to building a multi-layered causal network, and then proceeding to the selection and ranking of end factors, multivariate correlation and goodness-of-fit analysis of complex functions, as well as the identification of key factors and the formulation of countermeasures. Each layer is interconnected and progressive, with the previous layer providing the foundation and basis for the next, and the next layer deepening and applying the previous layer, forming a complete and systematic data processing chain. This ensures that the analysis and decision-making regarding line loss rate are not isolated point operations, but rather a comprehensive and in-depth systematic consideration.

[0030] 3) Construction of a Multi-Level Causal Network: By constructing a multi-level causal network, the complex causal structure affecting the overall line loss rate of a transformer substation is analyzed in depth. From the four major categories of macro-level factors to specific end-point factors, the hierarchical relationships and causal transmission paths between each factor are clearly identified. Further subdivisions are made under the equipment technology category, including line factors and transformer factors. The degree of conductor aging within the line factors also affects the line loss rate. This multi-level causal network construction can accurately pinpoint the position and role of each factor in the overall line loss rate impact system, improving the accuracy of causal relationship mining.

[0031] 4) Synergistic Effect Analysis: When determining the connection weights between nodes in the network and establishing potential relationship models, the synergistic effects between various factors should be fully considered, including the synergy between primary and secondary factors, as well as the synergy among secondary factors themselves. Ambient temperature and equipment aging may jointly affect the line loss rate. By quantifying the connection weights of this synergistic effect, the impact of multiple factors combined on the line loss rate can be more accurately grasped, avoiding the errors caused by simple single-factor analysis, thus providing strong support for accurately determining the factors affecting the line loss rate.

[0032] 5) Application of Intelligent Algorithms: Intelligent algorithms are used in the screening and ranking of end-factors to calculate importance scores based on the interrelationships between factors, thereby identifying key end-factors. Partial correlation analysis and other methods are employed in multivariate correlation and complex function fit goodness-of-fit analysis to accurately assess the correlation between end-factors and power loss while controlling for the influence of other factors. The application of these intelligent algorithms improves the scientific rigor and accuracy of the analysis and reduces the bias of subjective human judgment.

[0033] 6) Dynamic Optimization Decision-Making: When formulating countermeasures based on in-depth data analysis, dynamic optimization can be achieved. In terms of equipment technology, intelligent control measures can dynamically adjust equipment operating parameters based on real-time data and model predictions; in terms of management, maintenance plan optimization can dynamically determine maintenance strategies based on equipment status and operating environment. This dynamic optimization decision-making capability enables the management of the overall line loss rate of the distribution area to adapt to constantly changing actual conditions, improve the effectiveness and timeliness of optimization decisions, reduce the line loss rate, and enhance the overall efficiency of the distribution area's operation. Attached Figure Description

[0034] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0035] To facilitate understanding of the present invention, the invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. Those skilled in the art should understand that the embodiments described are merely illustrative of the invention and should not be considered as specific limitations thereof.

[0036] In the following description of the embodiments, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods are omitted so as not to obscure the description of this application with unnecessary detail.

[0037] It should be understood that, when used in this specification and the appended claims, the term "comprising" indicates the presence of a described feature, integral, step, operation, element, and / or component, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or collections thereof. It should also be understood that, as used in this specification and the appended claims, the term "and / or" refers to any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0038] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."

[0039] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance. References to "one embodiment" or "some embodiments" in this application mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0040] Example 1:

[0041] like Figure 1 As shown, this invention provides an intelligent analysis and optimization decision-making method for the comprehensive line loss rate of transformer substations, comprising the following steps:

[0042] Step S1: Obtain relevant data on the comprehensive line loss rate of the distribution area from the multi-source database of the power company, including the power supply volume of the distribution area, the power loss data, and various factors affecting the comprehensive line loss rate of the distribution area. These factors involve the power supply volume of the distribution area, the network architecture of each power supply station, and the line loss monitoring mode of each power supply station. The obtained data is then preprocessed.

[0043] Data on transformer substations exhibiting abnormal comprehensive line losses over n consecutive months (3-6 months) was obtained from multi-source databases of power companies. This included: 1) Power supply data for the substations, accurate to the hour, to capture the potential impact of daily electricity fluctuations on line losses; 2) Network architecture data for each power supply station, including power supply area and transformer capacity for calculating power supply capacity per square meter, as well as line topology information such as node connections and branch lengths, to construct a more accurate power grid model for subsequent analysis; 3) Line loss monitoring mode data for each power supply station, such as monitoring frequency and data acquisition errors; 4) Comprehensive line loss rate data for the substations, recording changes in line loss rates at different times of the day; 5) Equipment technology: detailed parameters of various equipment, such as resistivity curves of different conductor types as a function of temperature, and parameter sequences affecting losses from transformer core material characteristics, etc. 6) Personnel: Staff shift schedule data (recording each staff member's shift time and task allocation), training record data (including training course content, training duration, training assessment results, etc.); 7) Management: Equipment purchase contract data (including equipment purchase price, supplier information, warranty terms, etc., used to analyze the long-term impact of equipment cost and quality on line loss), maintenance plan execution log (recording the actual execution time, maintenance content, maintenance personnel, etc. of each maintenance); 8) Environment: Hourly meteorological data from meteorological stations within the transformer area, including temperature, humidity, wind speed, etc., as well as geographical information data, such as altitude (affecting air insulation performance and thus line loss); 9) Power loss data, calculated in detail according to different voltage levels and power consumption types (such as residential power consumption, industrial power consumption, etc.).

[0044] Data preprocessing includes the following operations: 1) Using data cleaning algorithms to identify and correct abnormal data based on the statistical characteristics and physical constraints of the data; 2) Performing data standardization and normalization to map data of different magnitudes to specific intervals.

[0045] For power supply data, if the data at a certain moment exceeds i times the standard deviation of the historical average for the same period (i can be set according to the data distribution characteristics, such as i=3), it is marked as an anomaly and corrected. The correction method can be a weighted smoothing algorithm based on adjacent data. For power supply data, logarithmic transformation is used for normalization; for proportional data such as line loss rate, linear normalization to the [0,1] interval can be used.

[0046] Step S2: Using a multiple linear regression analysis algorithm, construct a regression model between the comprehensive line loss rate of the distribution area and the power supply of the distribution area, the network architecture of each power supply station, and the line loss monitoring mode of each power supply station. Represent the power grid of the distribution area as a graph, and use the eigenvalues ​​of the Laplace matrix of the graph as independent variables of the regression model. Then, perform parameter estimation and model optimization on the model, and finally identify the key factors affecting the high line loss rate as the root cause.

[0047] In this step, the parameters of the regression model are estimated using a gradient descent-based optimization algorithm, and the model parameters are optimized using cross-validation. The AIC and BIC information criteria are used to evaluate the quality of the model, obtain the optimal model structure, and then identify the key factors affecting the high line loss rate as the root cause.

[0048] Step S3: From the many causes of the high overall line loss rate in the transformer area, take equipment technology, personnel, management and environment as the starting point, and use the method of drawing a cause analysis correlation diagram to identify the root cause of the problem from these many causes.

[0049] Step S4: Using multivariate correlation and goodness-of-fit analysis of complex functions, identify the key factors affecting the problem from the end factors identified in Step S3 that caused the problem.

[0050] Step S5: Based on the identified causes of the problems, formulate and implement corresponding countermeasures.

[0051] Example 2:

[0052] Step S3 specifically includes the following steps:

[0053] S3-1, taking the identified root cause of the high overall line loss rate as the core, constructs a multi-layered causal relationship network. This network includes three layers of nodes. The first layer consists of four main factors: equipment technology, personnel, management, and environment. The second layer consists of sub-factors under each main factor. The third layer consists of end factors under each sub-factor. By establishing a potential relationship model between each factor, and comprehensively considering the multi-level causal relationship from the main factor to the end factor, the path coefficients between nodes in the network are determined as connection weights.

[0054] The first layer of nodes consists of four main categories of factors: equipment technology, personnel, management, and environment. These main factors may be interrelated and provide a classification framework for the second layer of factors. The second layer of nodes consists of sub-factors under each main factor. These are more specific factors than the main factors, such as line factors and transformer factors under the equipment technology category. These sub-factors are related to each other and to the main factors of the first layer. For example, the main factors under the equipment technology category (including line parameters and transformer performance) and the main factors under the personnel category (including inspection quality and operating skills) may influence each other. The third layer of nodes consists of end factors under each sub-factor. End factors are further subdivisions of the sub-factors of the second layer. They are closely connected to the factors above. For example, the end factors under the sub-factor of line parameters (including conductor resistance and line length) are affected by the macro category of equipment technology and the sub-factor of line parameters. At the same time, these end factors also interact with each other.

[0055] The purpose of determining connection weights is to quantify the strength of the causal relationship between these factors. By establishing a latent relationship model, all factors in the entire multi-layered causal network, including the complex relationships between factors at different levels, can be considered simultaneously to comprehensively and accurately determine connection weights, thereby better revealing the causal relationship of abnormal comprehensive line loss rates in transformer substations. The aforementioned latent relationship model can adopt a structural equation model. 1) For the connection weights between major factors, the interaction between them is considered at the macro level. For example, there may be a relationship between equipment technology and management factors: management strategies (including equipment procurement and maintenance plans) will affect the performance of equipment technology, thus creating a certain correlation weight between these two major factors. 2) For the connection weights between major factors and end factors, the causal transmission from the upper-level major factors to the specific end factors is considered. For example, between the major factor of equipment technology and the end factor of conductor aging, the connection weight can be determined by analyzing how equipment technical characteristics (including conductor materials and installation processes) lead to conductor aging. 3) The connection weights between end factors are also important. For example, conductor aging and line joint quality will affect each other. Aging conductors may make the joints more prone to problems. The strength of this relationship also needs to be reflected through connection weights.

[0056] S3-2, based on a multi-layered causal network, uses a network analysis algorithm to calculate the importance score of each terminal factor; based on the importance score, those terminal factors considered critical in affecting the overall line loss rate of the transformer area are selected as key focus areas. A variant of the PageRank algorithm can be used for the network analysis algorithm.

[0057] These key analytical objects are considered relatively important factors in terms of their individual impact on line loss rate. Analyzing the synergistic effects of these key objects with other end-point factors can provide a deeper understanding of the line loss rate problem, because in the actual operating environment of a transformer substation, multiple end-point factors are intertwined and influence each other. For example, if the degree of conductor aging is a high-scoring end-point factor, it may synergize with other end-point factors (including line insulation performance and ambient humidity) to jointly affect the line loss rate.

[0058] S3-3, then analyze the synergistic effect between these key end factors and other end factors. Using principal component analysis, multiple related end factors are transformed into a few unrelated principal components. Based on the comprehensive impact of these principal components on the overall line loss rate, the end factors corresponding to the principal components that are significantly correlated with the line loss rate are identified as those end factors that cannot be further subdivided and directly or indirectly affect the line loss rate.

[0059] By analyzing synergistic effects, we can identify the end factors that cannot be further subdivided and that directly or indirectly affect the line loss rate. These ultimately identified end factors are key targets for subsequent correlation analysis, goodness-of-fit analysis, and factor identification, because they are recognized as fundamental factors that truly have a substantial impact on the line loss rate after comprehensively considering their individual importance and synergistic effects.

[0060] Principal Component Analysis (PCA) is used to transform multiple related end factors into a few uncorrelated principal components. This is achieved through linear transformation, projecting the original data from the end factors into a new coordinate system, maximizing the variance of the data in the new coordinate system. The specific steps are as follows: 1) First, construct the end factor data matrix and standardize it to eliminate differences caused by factors such as units of measurement, ensuring that each factor has equal importance in the analysis; 2) Calculate the covariance matrix of the data matrix. The elements in the covariance matrix represent the covariance between any two end factors. A larger covariance between two end factors indicates a stronger correlation; 3) Solve for the eigenvalues ​​and corresponding eigenvectors of the covariance matrix. The magnitude of the eigenvalues ​​reflects the amount of information contained in the corresponding principal component, and the eigenvectors represent the projection direction of the original end factors onto the principal components; 4) Select the principal components, i.e., sort them according to the magnitude of the eigenvalues, and select the top k principal components whose cumulative contribution rate reaches a certain proportion (e.g., 80% or 85%); 5) By analyzing the relationship between these principal components and the line loss rate, the final end factors are determined. If a principal component is significantly correlated with the line loss rate, then the original terminal factors constituting this principal component are likely key factors that directly or indirectly affect the line loss rate. These original terminal factors may not be further subdivided, and their combined impact on the line loss rate is reflected in the form of principal components.

[0061] While conducting data analysis, it's also necessary to combine the actual physical meaning and power business knowledge to determine the final end-factors. For example, from a physical perspective, if conductor resistance is a direct factor affecting line loss rate, and data analysis reveals its correlation with other factors and its contribution to principal components aligns with its physical function, then conductor resistance can be identified as an end-factor that cannot be further subdivided and directly affects the line loss rate. From a business perspective, if the inspection cycle, as an end-factor, shows an indirect correlation with the line loss rate in data analysis—for example, by affecting the timeliness of equipment fault detection—and in actual transformer area management operations, it is a relatively independent operational factor that cannot be further subdivided, then it can also be identified as an end-factor that indirectly affects the line loss rate.

[0062] For the initially identified end factors, the Granger causality test is used to verify whether a genuine causal relationship exists between these end factors and the line loss rate. The basic principle of the Granger causality test is that if the past value of a variable X has a significant predictive power for the current value of another variable Y, then variable X can be said to be a Granger cause of variable Y. In practical applications, the line loss rate is used as variable Y, and each initially identified end factor is used as variable X. Statistical tests are then used to determine whether a causal relationship exists. Only those end factors that pass the causal relationship verification can be ultimately identified as end factors that cannot be further subdivided and directly or indirectly affect the line loss rate.

[0063] Example 3:

[0064] Step S4 specifically includes the following steps:

[0065] S4-1. For each determined end factor, partial correlation analysis is used to calculate the partial correlation coefficient between the end factor and the power loss while controlling for the influence of other factors. Then, a complex function fitting analysis is performed on the end factor and the power loss, and a non-parametric regression method is used to fit the data and calculate the goodness of fit between the two.

[0066] To analyze the partial correlation coefficient between conductor aging and power loss, it is necessary to control for the influence of environmental factors such as temperature and humidity, as well as other equipment factors. Considering that there may be complex nonlinear and non-monotonic relationships between end factors and power loss, and that simple linear models cannot accurately describe this relationship, a complex function fitting analysis is performed for the relationship between each specific end factor and power loss.

[0067] S4-2. Taking into account the partial correlation coefficient and goodness of fit of each determined end factor with the lost electricity, as well as the importance score of the end factor in the causal relationship network, a multi-index comprehensive evaluation method is used to determine the key factors affecting the problem. The TOPSIS method can be used for this multi-index comprehensive evaluation.

[0068] In step S4-2, firstly, the partial correlation coefficient and goodness of fit of each determined end factor with the lost electricity, as well as the importance score of the end factor in the causal relationship network, are used as evaluation indicators to construct an evaluation matrix. Then, the evaluation matrix is ​​normalized to obtain a normalized matrix. Based on the normalized matrix, the distance between each end factor and the positive ideal solution (the maximum value of each indicator) and the negative ideal solution (the minimum value of each indicator) is calculated. Finally, based on these two distances, the relative proximity of each end factor is calculated, and the end factor with the larger relative proximity is selected as the factor affecting the crux of the problem.

[0069] Example 4:

[0070] In step S5, the countermeasures for the technical factors of the equipment include: 1-1) Precise modeling and intelligent control: Based on multidisciplinary theories such as electromagnetic fields, heat conduction, and electromagnetism, a coupled model of key equipment parameters (such as line impedance, transformer loss, etc.) and various influencing factors (including environmental factors, operating conditions, etc.) is constructed to obtain the intrinsic relationship between equipment operating characteristics and line loss. Then, an adaptive adjustment device is used to optimize the equipment operating parameters in real time. At the same time, an Internet of Things monitoring system is constructed to collect equipment operating data (including line temperature, current, transformer load) in real time, and predictive control algorithms are used to optimize the equipment operating parameters in advance.

[0071] The line impedance model comprehensively considers variables such as temperature, conductor material, and current density, while the transformer loss model covers factors such as load characteristics, core material, and winding structure. Intelligent conductors with automatic resistivity adjustment based on environmental changes are employed, and their material composition and structural parameters are determined through optimization algorithms to achieve stable line impedance and reduce line loss fluctuations. Simultaneously, an IoT monitoring system is constructed to collect real-time equipment operating data and dynamically adjust the line power factor through reactive power compensation and voltage regulation devices to reduce power loss. This series of intelligent control measures aims to reduce power loss and represents a practical application of the precise modeling results, enabling equipment to operate more intelligently and efficiently, thereby reducing the overall line loss rate of the distribution area.

[0072] 1-2) Equipment Upgrades: Adopting new energy-saving equipment and optimizing equipment structure design reduces inherent losses. Selecting new core materials such as amorphous alloys for transformer manufacturing, which inherently have low loss characteristics, is beneficial. Simultaneously, utilizing advanced technologies such as finite element analysis to optimize the transformer core and winding structure reduces no-load losses and additional losses at the hardware level—a strategy directly targeting the performance improvement of the equipment itself.

[0073] 1-3) Load Balancing Optimization: The power grid area is divided into multiple load balancing zones. Intelligent agents are deployed in each zone to manage the load. These agents can interact and make collaborative decisions based on the load status of each node within the zone and the load information of adjacent zones, achieving dynamic power distribution within and between zones. This ensures that transformers operate within their high-efficiency load range, thereby optimizing load distribution and reducing line losses. This is a strategy from the perspective of power grid system operation and management that reduces line losses by optimizing load distribution.

[0074] Countermeasures targeting personnel-related factors include: 2-1) Inspection optimization: From the perspective of inspection plans and routes, firstly, an inspection reliability model based on stochastic process theory is established. Historical data and Bayesian estimation methods are used to determine model parameters to accurately assess the reliability of inspections. Then, a dynamic inspection plan based on risk assessment is designed. This plan comprehensively considers the importance of equipment, historical failure rate data, and environmental factors. Through a combination of fault tree analysis and failure mode and effect analysis, risk scores are performed on equipment to determine inspection priorities and cycles. Furthermore, dynamic programming algorithms are used to optimize inspection routes. The goal is to minimize inspection time and cost while ensuring that high-risk equipment is inspected in a timely manner, ensuring stable equipment operation, and reducing the increase in line losses caused by untimely inspections.

[0075] 2-2) Building upon optimized inspection practices, the reliability of inspection work is improved through optimized inspection plans and routes. The core of this approach is to ensure that inspections are carried out effectively according to plan, promptly identifying potential equipment problems and reducing equipment failures and increased line losses due to untimely inspections. Optimized inspection routes and schedules allow inspection personnel to check equipment more efficiently, ensuring stable equipment operation. This directly contributes to improving the reliability of inspection work in ensuring normal equipment operation and reducing line losses.

[0076] 2-3) Training Optimization: Construct an operational error probability model based on human factors engineering and behavioral psychology theories, linking the operational error probability with factors such as personnel training level, work pressure, and operational process complexity. For example, a logistic regression model can be used to quantify the relationship between these factors. Then, develop a personalized training system, using machine learning algorithms such as cluster analysis and decision tree algorithms, to develop targeted training content and methods for each employee based on their skill gaps and historical operational error data. This will improve employees' operational skills and safety awareness, reduce operational errors caused by insufficient personnel operation level, and thus reduce line losses.

[0077] 2-4) Operation process optimization: Following the principles of human-computer interaction design, simplify, standardize, and visualize the operation interface and process to reduce operational complexity and human error; at the same time, establish an operation error early warning mechanism to monitor the operation behavior data of staff in real time (including operation speed and operation sequence), and use anomaly detection algorithms such as outlier detection based on statistical models or abnormal behavior recognition based on deep learning to promptly identify potential operation error risks and provide real-time reminders and guidance, thereby preventing the adverse impact of operation errors on line loss from the perspective of operation process.

[0078] Countermeasures targeting management factors include: 3-1) Maintenance plan optimization: Constructing a full lifecycle maintenance optimization model for equipment based on reliability theory and lifecycle cost analysis. Using the Weibull distribution failure rate function to describe equipment failure patterns, and comprehensively considering maintenance costs (including periodic maintenance costs and fault repair costs), reliability recovery factors, and remaining life extension factors, the optimal maintenance strategy is solved using intelligent optimization algorithms such as genetic algorithms and particle swarm optimization, with the goal of minimizing the equipment's full lifecycle cost. This includes determining maintenance time points and maintenance types, thereby achieving scientific planning of equipment maintenance work, making equipment maintenance more rational and efficient, and reducing line losses caused by improper maintenance. 3-2) Information management: Building upon maintenance plan optimization, establishing an equipment maintenance management information system based on blockchain technology. Utilizing the distributed ledger, immutability, and traceability characteristics of blockchain, recording maintenance information throughout the equipment's lifecycle, including maintenance plans, execution records, and equipment failure history, improving the transparency and credibility of equipment maintenance information, and facilitating supervision and decision-making by management departments. Simultaneously, smart contract technology is introduced to automate the triggering and execution of maintenance tasks. For example, when equipment reaches a scheduled maintenance time or a specific fault signal occurs, the maintenance process is automatically initiated, maintenance personnel are notified, and tasks are assigned. This improves the timeliness and standardization of maintenance work, ensures the equipment is in good operating condition, and reduces the risk of line loss. These two measures complement each other, and the optimized maintenance plan can be better implemented through the information management system. For instance, when equipment reaches a scheduled maintenance time or a specific fault signal occurs, the maintenance process is automatically initiated, maintenance personnel are notified, and tasks are assigned, ensuring the timeliness and efficiency of maintenance work and reducing the risk of line loss due to improper equipment maintenance.

[0079] 3-3) Based on the dynamic optimization of load balancing in transformer substations, a load balancing effect evaluation index system is established, including load balancing degree indices (such as those calculated based on node load variance and average load) and line loss indices (such as those calculated considering line resistance and current squares). Through evaluation of actual operating data and simulation results, the optimization strategy is continuously adjusted using a feedback control mechanism. When the load balancing degree index or line loss index of a certain area is found to be too high, the agent will promptly adjust the load distribution strategy, increasing or decreasing the power transmission between that area and adjacent areas. This is a dynamic and continuous improvement process to achieve better load balancing and reduced line loss.

[0080] Countermeasures to address environmental factors include: 4-1) Thermal effect mitigation: Constructing a line temperature field distribution model based on heat conduction theory and material physical properties. Two-dimensional heat conduction equations and multi-layered medium heat conduction models are used for overhead lines and cable lines respectively. The models comprehensively consider current heat effects, solar radiation heat, air convection heat source terms, and boundary conditions. The temperature field distribution is solved using finite element analysis to accurately grasp the temperature changes of the line under different environmental conditions. Furthermore, new heat dissipation materials and structures, such as high thermal conductivity heat dissipation coatings and special heat dissipation fin structures, are applied to the surface of lines and equipment to increase heat dissipation area and improve heat dissipation efficiency, mitigating thermal effects from a physical perspective and reducing the impact of high temperatures on line losses. 4-2) Building upon thermal effect mitigation, establishing a dynamic adjustment system for line operating parameters based on meteorological forecasts. Meteorological data prediction models (such as deep learning-based weather forecast models) are used to predict meteorological parameters such as air temperature and solar radiation. Combined with the temperature field distribution model, the trend of line resistance changes is predicted. Power system reactive power compensation and voltage regulation devices are used to adjust the line operating voltage and reactive power in advance to compensate for the increase in line losses caused by increased resistance. Simultaneously, a voltage stability analysis model is used to ensure that voltage adjustments remain within the equipment's allowable range, guaranteeing the safe and stable operation of the line. Parameters are dynamically adjusted to reduce line losses. These two strategies work together: first addressing thermal effects, and then dynamically adjusting parameters based on the impact of these effects, thereby reducing line losses.

[0081] 4-3) Severe Weather Protection: Based on structural mechanics and reliability engineering theories, construct mechanical response models and reliability assessment models for power supply facilities under severe weather conditions (such as wind, rain, snow, and lightning). For tower structures, use finite element analysis to calculate stress-strain distribution under wind load, and assess tower reliability and remaining life based on material strength characteristics and fatigue life theory. For equipment such as transformers, consider the impact of lightning strikes and rainwater erosion on insulation performance, and establish insulation aging models and failure probability models to provide a scientific basis for facility protection and renovation. Simultaneously, optimize power supply facility design standards according to the characteristics of meteorological disasters in different regions, and adopt targeted disaster-resistant design measures, such as improving the wind resistance of towers and installing wind-resistant insulators in coastal areas, and strengthening transformer lightning protection in areas prone to lightning. 4-4) Early Warning and Emergency Response: Establish a severe weather early warning and emergency response system, utilizing meteorological monitoring networks and disaster early warning models to predict the timing, intensity, and impact range of severe weather in advance, and promptly activate emergency response plans. Upon receiving early warning information, promptly activate emergency response plans, including equipment power outage avoidance strategies and advance deployment of repair personnel and materials. For example, cutting off power to potentially affected lines and equipment before a strong typhoon hits can reduce losses, while quickly organizing emergency repairs after the typhoon restores power, shortening outage time and reducing line losses and power outage losses caused by severe weather. These two countermeasures work together; protective measures can reduce damage to power supply facilities caused by severe weather, while early warning and emergency measures can effectively respond before and after severe weather occurs, reducing the adverse effects of line losses and power outages.

[0082] Example 5:

[0083] A smart analysis and optimization decision-making system for comprehensive line loss rate of transformer substations, the system comprising:

[0084] Data acquisition and preprocessing module: Used to acquire data related to the comprehensive line loss rate of the transformer area from the multi-source database of the power company and to preprocess the acquired data; the relevant data includes the power supply of the transformer area, the power loss data, and various factors that affect the comprehensive line loss rate of the transformer area. These factors involve the power supply of the transformer area, the network architecture of each power supply station, and the line loss monitoring mode of each power supply station.

[0085] The root cause identification module uses a multiple linear regression analysis algorithm to construct a regression model between the overall line loss rate of a distribution area and the power supply of the distribution area, the network architecture of each power supply station, and the line loss monitoring mode of each power supply station. The power grid of the distribution area is represented as a graph, and the eigenvalues ​​of the Laplace matrix of the graph are also used as independent variables of the regression model. Then, the model is parameter estimated and optimized, and finally the key factors affecting the high line loss rate are identified as the root causes.

[0086] Key Factor Identification Module: This module is used to identify the root causes of high overall line loss rate in transformer substations by focusing on equipment technology, personnel, management, and environment, and by drawing a cause-effect relationship diagram.

[0087] The root cause identification module is used to identify the root causes affecting the problem from the end factors that cause the problem by employing multivariate correlation and complex function fit goodness-of-fit analysis methods.

[0088] Countermeasures module: Based on the identified root causes of the problems, formulate and implement corresponding countermeasures.

[0089] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0090] In various embodiments, the hardware implementation of the technology can directly utilize existing smart devices, including but not limited to industrial control computers, PCs, smartphones, handheld devices, and floor-standing devices. Its input device preferably uses an on-screen keyboard, its data storage and computing modules utilize existing memory, calculators, and controllers, its internal communication modules utilize existing communication ports and protocols, and its remote communication utilizes existing GPRS networks, the World Wide Web, etc.

[0091] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0092] In the embodiments provided by this invention, it should be understood that the disclosed apparatus / terminal devices and methods can be implemented in other ways. For example, the apparatus / terminal device embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the shown or discussed mutual couplings or direct couplings or communication connections may be through some interfaces; indirect couplings or communication connections between devices or units may be electrical, mechanical, or other forms. Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, i.e., they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0093] In the various embodiments of this invention, the functional units can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated units can be implemented in hardware or as software functional units. If the integrated module / unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of this invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc.

[0094] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for intelligent analysis and optimization decision-making of comprehensive line loss rate in transformer substations, characterized in that, The method includes the following steps: Step S1: Obtain relevant data on the comprehensive line loss rate of the distribution area from the multi-source database of the power company, including the power supply volume of the distribution area, the power loss data, and various factors affecting the comprehensive line loss rate of the distribution area. These factors involve the power supply volume of the distribution area, the network architecture of each power supply station, and the line loss monitoring mode of each power supply station. The obtained data is then preprocessed. Step S2: Using a multiple linear regression analysis algorithm, construct a regression model between the comprehensive line loss rate of the distribution area and the power supply of the distribution area, the network architecture of each power supply station, and the line loss monitoring mode of each power supply station. Represent the power grid of the distribution area as a graph, and use the eigenvalues ​​of the Laplace matrix of the graph as independent variables of the regression model. Then, perform parameter estimation and model optimization on the model, and finally identify the key factors affecting the high line loss rate as the root cause. Step S3: From the many causes of the high overall line loss rate in the transformer area, take equipment technology, personnel, management and environment as the starting point, and use the method of drawing a cause analysis correlation diagram to identify the root cause of the problem from these many causes; Step S4: Using multivariate correlation and goodness-of-fit analysis of complex functions, identify the key factors affecting the problem from the end factors identified in Step S3 that caused the problem. Step S5: Based on the identified causes of the problems, formulate and implement corresponding countermeasures.

2. The intelligent analysis and optimization decision-making method for comprehensive line loss rate of transformer substations according to claim 1, characterized in that, In step S2, the parameters of the regression model are estimated using a gradient descent-based optimization algorithm, and the model parameters are optimized using cross-validation technology. The AIC and BIC information criteria are used to evaluate the quality of the model, obtain the optimal model structure, and then identify the key factors affecting the high line loss rate as the root cause.

3. The intelligent analysis and optimization decision-making method for comprehensive line loss rate of transformer substations according to claim 1, characterized in that, Step S3 specifically includes the following steps: S3-1, taking the identified root cause of the high overall line loss rate as the core, constructing a multi-layered causal relationship network; this network includes three layers of nodes, the first layer of nodes being the four main categories of factors: equipment technology, personnel, management, and environment; the second layer of nodes being the sub-factors under each main factor; and the third layer of nodes being the end factors under each sub-factor; by establishing a potential relationship model between each factor, comprehensively considering the multi-level causal relationship from the main factor to the end factor, determining the path coefficients between nodes in the network as connection weights; S3-2, based on the multi-layered causal relationship network, using network analysis... The algorithm calculates the importance score of each terminal factor; based on the importance score, those terminal factors considered to be critical in affecting the overall line loss rate of the transformer area are selected as key focus objects; S3-3, then the synergistic effect between these key focus terminal factors and other terminal factors is analyzed. Using principal component analysis, multiple related terminal factors are transformed into a few unrelated principal components. Based on the comprehensive impact of these principal components on the overall line loss rate, the terminal factors corresponding to the principal components that are significantly correlated with the line loss rate are identified as those terminal factors that cannot be further subdivided and directly or indirectly affect the line loss rate.

4. The intelligent analysis and optimization decision-making method for comprehensive line loss rate of transformer substations according to claim 1, characterized in that, Step S4 specifically includes the following steps: S4-1, for each determined end factor, a partial correlation analysis method is used to calculate the partial correlation coefficient between the end factor and the power loss while controlling for the influence of other factors; then, a complex function fitting analysis is performed on the end factor and the power loss, and a non-parametric regression method is used for fitting to calculate the goodness of fit between the two; S4-2, taking into account the partial correlation coefficient and goodness of fit of each determined end factor and the power loss, as well as the importance score of the end factor in the causal relationship network, a multi-index comprehensive evaluation method is used to determine the key factors affecting the problem.

5. The intelligent analysis and optimization decision-making method for comprehensive line loss rate of transformer substations according to claim 4, characterized in that, In step S4-2, the partial correlation coefficient and goodness of fit of each determined end factor with the lost power, as well as the importance score of the end factor in the causal relationship network, are first used as evaluation indicators to construct an evaluation matrix. Then, the evaluation matrix is ​​normalized to obtain a normalized matrix. Based on this normalized matrix, the distance between each terminal factor and the positive and negative ideal solutions is calculated; Finally, based on these two distances, the relative proximity of each end factor is calculated, and the end factor with the greater relative proximity is selected as the factor affecting the crux of the problem.

6. The intelligent analysis and optimization decision-making method for comprehensive line loss rate of transformer substations according to claim 1, characterized in that, In step S5, the countermeasures for the technical factors of the equipment include: 1) Precise modeling and intelligent control: Constructing a coupled model of key equipment parameters and multiple influencing factors to obtain the intrinsic relationship between equipment operating characteristics and line loss, and then using an adaptive adjustment device to optimize the equipment operating parameters in real time. At the same time, an Internet of Things monitoring system is constructed to collect equipment operating data in real time, and predictive control algorithms are used to optimize equipment operating parameters in advance; 2) Equipment upgrade: Using energy-saving equipment and optimizing the equipment structure to reduce the inherent losses of the equipment itself; 3) Load balancing optimization: Dividing the power grid of the distribution area into multiple load balancing areas, setting up intelligent agents in each area to manage the load. These intelligent agents interact and make collaborative decisions based on the load status of each node in the area and the load information of adjacent areas to realize the dynamic distribution of power within and between areas.

7. The intelligent analysis and optimization decision-making method for comprehensive line loss rate of transformer substations according to claim 1, characterized in that, In step S5, the countermeasures for personnel-related factors include: 1) Inspection optimization: Establishing an inspection reliability model based on stochastic process theory, determining model parameters using historical data and Bayesian estimation methods, and then designing a dynamic inspection plan based on risk assessment. This plan comprehensively considers the importance of equipment, historical failure rate data, and environmental factors. It uses a combination of fault tree analysis and failure mode and effects analysis to perform risk scoring on equipment to determine inspection priorities and cycles, and employs dynamic programming algorithms to optimize inspection routes; 2) Based on inspection optimization, improving the reliability of inspection work through the optimized inspection plan and routes; 3) Training optimization. 4) Operational process optimization: Following the principles of human-computer interaction design, simplify, standardize, and visualize the operation interface and process to reduce operational complexity and human error; at the same time, establish an operational error early warning mechanism to monitor the operation behavior data of staff in real time, use anomaly detection algorithms to promptly discover potential operational error risks, and provide real-time reminders and guidance.

8. The intelligent analysis and optimization decision-making method for comprehensive line loss rate of transformer substations according to claim 1, characterized in that, In step S5, the countermeasures for management factors include: 1) Maintenance plan optimization: Constructing a full lifecycle maintenance optimization model for equipment, using the Weibull distribution failure rate function to describe equipment failure patterns, and comprehensively considering maintenance costs, reliability recovery factors, and remaining life extension factors, with the goal of minimizing the full lifecycle cost of equipment, and using intelligent optimization algorithms to solve for the optimal maintenance strategy; 2) Information management: Based on the maintenance plan optimization, establishing an equipment maintenance management information system based on blockchain technology to record maintenance information throughout the equipment's lifecycle, and introducing smart contract technology to achieve automated triggering and execution of maintenance tasks; 3) Based on the dynamic optimization of distribution area load balancing, establishing a load balancing effect evaluation index system, including load balancing degree index and line loss index, and continuously adjusting the optimization strategy through the evaluation of actual operating data and simulation results using a feedback control mechanism.

9. The intelligent analysis and optimization decision-making method for comprehensive line loss rate of transformer substations according to claim 1, characterized in that, In step S5, the countermeasures for environmental factors include: 1) Thermal effect response: Constructing a line temperature field distribution model, using a two-dimensional heat conduction equation and a multi-layer medium heat conduction model for overhead lines and cable lines respectively, comprehensively considering current heat effects, solar radiation heat, air convection heat dissipation, and boundary conditions in the model, and solving the temperature field distribution using the finite element analysis method; and using heat dissipation materials and structures, applying them to the surface of the lines and equipment; 2) Based on the thermal effect response, establishing a dynamic adjustment system for line operating parameters based on meteorological forecasts, using meteorological data prediction models to predict air temperature and solar radiation meteorological parameters, combined with temperature field distribution... 3) Severe weather protection: Construct a mechanical response model and a reliability assessment model for power supply facilities under severe weather conditions. At the same time, optimize the design standards of power supply facilities according to the characteristics of meteorological disasters in different regions and adopt targeted disaster resistance design measures. 4) Early warning and emergency response: Establish a severe weather early warning and emergency response system. Utilize meteorological monitoring networks and disaster early warning models to predict the time, intensity and scope of severe weather in advance and activate emergency response plans in a timely manner.

10. A smart analysis and optimization decision-making system for comprehensive line loss rate of transformer substations, characterized in that, The system includes: The data acquisition and preprocessing module is used to obtain data related to the comprehensive line loss rate of distribution transformer areas from the multi-source databases of power companies. This includes data on the power supply volume of distribution transformer areas, power loss data, and various factors affecting the comprehensive line loss rate of distribution transformer areas. These factors involve the power supply volume of distribution transformer areas, the network architecture of each power supply station, and the line loss monitoring mode of each power supply station. The module also performs preprocessing on the acquired data. The root cause identification module uses a multiple linear regression analysis algorithm to construct a regression model between the overall line loss rate of a distribution area and the power supply of the distribution area, the network architecture of each power supply station, and the line loss monitoring mode of each power supply station. The power grid of the distribution area is represented as a graph, and the eigenvalues ​​of the Laplace matrix of the graph are also used as independent variables of the regression model. Then, the model is parameter estimated and optimized, and finally the key factors affecting the high line loss rate are identified as the root causes. Key Factor Identification Module: This module is used to identify the root causes of high overall line loss rate in transformer substations by focusing on equipment technology, personnel, management, and environment, and by drawing a cause-effect relationship diagram. The root cause identification module is used to identify the root causes affecting the problem from the end factors that cause the problem by employing multivariate correlation and complex function fit goodness-of-fit analysis methods. Countermeasures module: Based on the identified root causes of the problems, formulate and implement corresponding countermeasures.

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