Distribution network resource utilization diagnostic analysis system and method based on power supply grid

Abstract

The invention discloses a distribution network resource utilization diagnostic analysis system and method based on a power supply grid, and relates to the field of power resource utilization, the system comprises a data acquisition module, a power supply grid division module, a resource evaluation module, a diagnostic analysis module and a visualization module; the data acquisition module performs data collection on the power distribution network, including a power supply, a load, a storage and a transmission line; the power supply grid division module divides the data through a preset rule and transmits the preset rule to the resource evaluation module; the resource evaluation module performs resource evaluation on the data, and evaluation standards are set as different standards according to different grids; after receiving the evaluation data, the diagnostic analysis module analyzes each power supply grid to obtain a reason for abnormal utilization of the current distribution network resources; and the visualization module is used for carrying out visualization display on the system. Through multi-source data acquisition, power supply grid division and diagnosis analysis processes, the accuracy and intelligence of distribution network resource utilization diagnosis are improved.

Description

Technical Field

[0001] The present invention relates to the field of power resource distribution, and particularly to a diagnosis and analysis system and method for distribution network resource utilization based on a power supply grid. Background Art

[0002] With the continuous growth of power demand and the large-scale access of distributed energy resources, the structure and operation characteristics of modern distribution networks have become increasingly complex. The traditional distribution network management method has been difficult to meet the requirements of efficient, reliable, and sustainable power supply. Therefore, accurately diagnosing and analyzing the utilization of distribution network resources has become an important issue faced by the power industry. The scale of the distribution network is constantly expanding, covering a wide area, including numerous substations, lines, and users, increasing the complexity of the distribution network. The electricity demand varies greatly in different regions, and the load characteristics are complex and diverse, with both large-capacity stable loads of industrial users and loads with obvious peak-valley changes of residential users. At the same time, the access of distributed energy makes the power source distribution more dispersed, and its intermittency and volatility bring many challenges to power balance, voltage regulation, and protection control of the distribution network. For example, solar power generation is affected by weather. When there is sufficient sunlight during the day, the output is large, which may cause local line overload, while when there is no sunlight at night, the output is zero, and it may also cause the idleness of distribution network resources. The limitations of the traditional management method are becoming more and more apparent. The previous distribution network management mainly relied on manual experience and simple monitoring means, lacking a comprehensive, real-time, and accurate understanding of the utilization status of distribution network resources. The data collection is insufficient, unable to deeply cover every user and device, and the data collection frequency is insufficient, unable to timely reflect the rapid changes in the operation state of the distribution network. For example, the traditional electricity meter collects data at long intervals, making it difficult to capture the changes in users' electricity consumption behavior in a short time, which is not conducive to accurate load forecasting and resource allocation. In terms of resource assessment, there is a lack of systematic and scientific methods, and only a few indicators are relied on for simple judgment, making it difficult to comprehensively evaluate the rationality and efficiency of distribution network resource utilization. During the diagnosis and analysis process, the positioning and cause analysis of problems are not accurate and in-depth enough to effectively guide the optimized operation of the distribution network.

[0003] Nowadays, the demand for efficient management is urgent. In order to improve the power supply reliability of the distribution network, reduce energy losses, improve power quality, and achieve the optimal allocation and efficient utilization of distribution network resources, an advanced technical means is needed to monitor, accurately evaluate, and deeply analyze the utilization of distribution network resources in real time. By comprehensively collecting and deeply analyzing the operation data of the distribution network, problems such as resource idleness, overload, large energy losses, and poor power quality can be discovered in time, and corresponding measures can be taken for optimization and improvement.

[0004] Chinese Patent CN117578608A discloses a method for calculating and executing the adjustable resource capacity of distributed power sources in a distribution network. In this invention, new controllable resources connected to the DMS system, including distributed power sources, distributed energy storage, controllable loads, and interruptible loads, etc., are centrally uploaded to the control cloud. The algorithm for calculating the adjustable resource capacity of distributed power sources in the distribution network is improved. According to the topological relationship, the sources, loads, and storages in the distribution network are centrally allocated to give full play to their adjustable capabilities. Finally, the DMS system obtains the capacity of adjustable and controllable resources based on real-time data analysis. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a more accurate and intelligent distribution network resource utilization system.

[0006] To solve the above technical problem, the technical solution adopted by the present invention is:

[0007] A distribution network resource utilization diagnosis and analysis system based on a power supply grid, characterized by comprising:

[0008] A data acquisition module: acquiring distribution network resource-related data from multiple data sources, where the data sources include smart meters, substation monitoring systems, and distributed energy resource monitoring devices;

[0009] A power supply grid division module: dividing the distribution network into multiple power supply grids according to a predetermined rule, where the predetermined rule includes dividing based on geographical location, voltage level, or user type;

[0010] A resource evaluation module: receiving the information from the data acquisition module and the power supply grid division module, further processing the information collected in each grid to obtain specific indicators of the distribution network resources and evaluating them;

[0011] A diagnosis and analysis module: according to the evaluation results of the resource evaluation module, through diagnosis and analysis, obtaining the causes of abnormal situations in the resource evaluation, and at the same time outputting the specific power supply grid, problem type, problem cause, and related indicators where the abnormal situation occurs;

[0012] A visualization display module: used to visually display the diagnosis and analysis results, and the display forms include maps and charts, where the charts include bar charts and pie charts.

[0013] Preferably, the frequency of data acquisition by the data acquisition module is set according to the data source type and data importance. Among them, the frequency of the smart meter collecting user electricity consumption data is once every 15 minutes, the frequency of the substation monitoring system collecting transformer data is once every 5 minutes, and the frequency of the distributed energy resource monitoring device collecting data is real-time or once every 1 minute.

[0014] Preferably, the resource evaluation module calculates and evaluates the collected data, and evaluates the utilization of distribution network resources in each power supply grid, including evaluating line load rate, transformer utilization rate, distributed energy access ratio, line load imbalance degree, transformer overload duration, power output volatility of power supply, load peak-valley difference rate, energy self-sufficiency rate, number of voltage fluctuations, integral of frequency deviation, spare capacity adequacy, and line N-1 passing rate.

[0015] Preferably, for different power supply grids, the setting criteria for the distribution network resource allocation are affected by multiple factors, including: division rules, time periods, and seasons.

[0016] Preferably, the diagnosis and analysis module obtains information related to abnormal utilization of distribution network resources by vectorizing the evaluation data in the power supply grid, forming a matrix with multiple grids, and analyzing the matrix. The specific process is as follows:

[0017] 1) Construct a multi-dimensional vector, and normalize the evaluation data according to the power supply grid to construct an evaluation information vector with all data values between 0 and 1.

[0018] 2) Complement zeros to the vector and construct a matrix. Fill in the missing information in the vector with 0, and combine the multi-dimensional vectors of each power supply grid to form an evaluation information matrix.

[0019] 3) Perform eigenvalue decomposition analysis and singular value decomposition analysis on the evaluation information matrix.

[0020] 4) Through the analysis results, combined with the output information of the resource evaluation module, obtain the problems, and output the specific power supply grid, problem type, problem cause, and relevant indicators where abnormal conditions occur.

[0021] Preferably, the visualization display module can generate a variety of intuitive charts, such as a bar chart to show the comparison of resource utilization rates of each power supply grid, a line chart to reflect the change trend of power quality at different time periods, and a pie chart to present the allocation ratio of various resources. Through the interactive interface, users can screen specific area, time period, and indicator data as needed to achieve in-depth data mining and precise decision-making support.

[0022] The present invention also discloses a method for diagnosing and analyzing the utilization of distribution network resources based on a power supply grid, which is completed relying on the above diagnosis and analysis system, and includes the following steps:

[0023] 1) Data collection stage: Collect multi-source information of the monitored distribution network.

[0024] 2) Power supply grid division stage: Divide the detected power grid into power supply grids according to preset rules.

[0025] 3) Resource evaluation stage: Process the information collected in each grid and evaluate the utilization of distribution network resources within the grid;

[0026] 4) Diagnostic analysis stage: Conduct diagnostic analysis on the utilization of distribution network resources according to different grids.

[0027] The beneficial effects of adopting the above technical solutions are as follows:

[0028] The data acquisition module in the present invention collects data from multiple data sources, monitors the data of the power source, load, energy storage, and transmission of the distribution network in an all-round manner, and ensures the data support for subsequent system analysis.

[0029] The power supply grid division proposed in the present invention divides the power consumption area according to different electricity consumption habits and demands, and uses different distribution network resource evaluation criteria for different power consumption groups, which is conducive to the full utilization of distribution network resources.

[0030] The diagnostic analysis module in the present invention integrates and deeply analyzes the data through a proposed diagnostic analysis method by matrix analysis method, and can jointly analyze different evaluation data in the power supply grid and different power supply grids, which is conducive to the comprehensive utilization of the resources of the entire distribution network. Description of the Drawings

[0031] The present invention will be further described in detail below in conjunction with the drawings and specific embodiments.

[0032] Figure 1 It is a schematic flow chart of a distribution network resource utilization diagnostic analysis system based on a power supply grid proposed by the present invention;

[0033] Figure 2 It is a bar chart representation of the substation load rate in a single power supply grid by the visualization module in the present invention;

[0034] Figure 3 It is a bar chart representation of the connection rate and N-1 rate of the transmission line in a single power supply grid by the visualization module in the present invention. Specific Embodiments

[0035] To make the above objects, features, and advantages of the present invention more obvious and understandable, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings and specific embodiments in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0036] Embodiment 1

[0037] A method for diagnosing and analyzing the utilization of distribution network resources based on a power supply grid, including a data collection stage, a power supply grid division stage, a resource evaluation stage, and a diagnosis and analysis stage;

[0038] 1) Data collection stage

[0039] During the data collection process, to ensure the reliability of the data, multi-source data collection is carried out, including smart meters, substation monitoring systems, and distributed energy resource monitoring devices, etc., to ensure the depth and breadth of data collection, and to comprehensively monitor the power consumption, power supply, and transmission processes in the distribution supply system; at the same time, high-speed and stable transmission channels are used for information collection and transmission to minimize transmission errors as much as possible.

[0040] 2) Power supply grid division stage

[0041] The detected power grid is divided into power supply grids according to preset rules. Rules based on geographical location, voltage level, or user type, etc. are used for division. During the division process, it is necessary to ensure that each grid has a relatively clear boundary and a clear identifier for subsequent management and analysis. After division, the power supply grids are numbered to ensure the convenience of viewing by management personnel.

[0042] 3) Resource evaluation stage

[0043] The data collected daily in each grid is processed. The processing methods include data cleaning and repair, and then specific indicators of distribution network resources are calculated, including outgoing line load rate, transformer utilization rate, and other indicators, to comprehensively evaluate the utilization of distribution network resources in each power supply grid.

[0044] 4) Diagnosis and analysis stage

[0045] The information after resource evaluation enters the diagnosis and analysis stage. Through a multi-dimensional vectorization method for analyzing the utilization of distribution network resources proposed by us, the multi-dimensional vectorization processing of the evaluation information is carried out. The specific process is as follows.

[0046] First of all, we first need to convert various information obtained from the resource evaluation module into the form of multi-dimensional vectors, including various evaluation indicators such as line load rate, transformer utilization rate, and distributed energy access ratio. For each power supply grid at a specific moment, a vector is formed. However, the values and ranges of these indicators vary greatly, which is not conducive to later settlement. Therefore, these values are processed and after normalization, the range of all values enters between 0 and 1, which is equivalent to unifying the weights of the data.

[0047] The second step, the process of filling zeros for the data vectors of abnormal parts and constructing matrices

[0048] In the data during the resource evaluation phase, there may be missing data due to communication failures. For such data, zero-padding operations are performed to ensure the consistency of the vector dimensions. Then, the evaluation vectors of each power supply grid are combined into an evaluation matrix. After that, the evaluation matrix is analyzed, including two aspects. One is eigenvalue decomposition. According to the magnitude and distribution of the eigenvalues, the overall characteristics of the distribution network resource utilization are analyzed. If the first few eigenvalues are large and account for a relatively high proportion of the sum of all eigenvalues, it indicates that there are a few main patterns or factors dominating the distribution network resource utilization. By analyzing the corresponding eigenvectors, the relationships between these dominant factors and various evaluation indicators can be understood. Another analysis method is singular value decomposition. By analyzing the magnitude and changing trend of the singular values, the coupling relationships between different power supply grids and between various evaluation indicators can be discovered. When the left and right singular vectors corresponding to a certain singular value have relatively large coefficients on certain power supply grids and certain evaluation indicators respectively, it can be judged that these power supply grids have strong correlations on these evaluation indicators.

[0049] Finally, further inferences are made on the results of the diagnostic analysis. The range of the power supply grid where the problem occurs and the main evaluation indicators related to the problem are located through the matrix analysis results. This can more accurately determine the location of the distribution network resource utilization problem and avoid misjudgment and missed judgment. A detailed diagnostic report is generated using the matrix analysis results to explain the causes and scope of influence of the problem, providing a scientific basis for subsequent improvement measures.

[0050] Embodiment 2

[0051] Based on the method of the above embodiment, as Figure 1 ,the present invention proposes a diagnostic analysis system for distribution network resource utilization based on power supply grids, including a data acquisition module, a power supply grid division module, a resource evaluation module, a diagnostic analysis module, and a visualization module.

[0052] Embodiment 3

[0053] The data acquisition module collects real-time data of the power supply network. Through multi-source data acquisition, the data acquisition module conducts sufficient data monitoring on the monitored power supply network in terms of breadth and depth, providing data support for subsequent operations. Among them, multiple data sources include smart meters, substation monitoring devices, and distributed energy resource monitoring devices.

[0054] Smart meters collect users' power consumption data, including basic electrical quantity information such as users' real-time power consumption, power usage, voltage, and current. These data can reflect the power consumption habits of electricity customers and can deeply cover every user, enhancing the vertical depth of subsequent data. At the same time, since the power consumption situation of a single user is relatively stable in a short period, to reduce data redundancy, the sampling frequency of smart meters is set to once every 15 minutes. The transformer data collected by the substation monitoring system covers information such as the oil temperature, oil level, winding temperature, load current, and load voltage of the transformer. Through these data, the operation status of the transformer can be monitored to determine whether there are abnormal situations such as overload and overheating. Transformers are key equipment in the distribution network, and their operation status is also relatively changeable, and parameter fluctuations may occur in a short period. Therefore, a collection frequency of once every 5 minutes is adopted. The information collected by distributed energy resource monitoring devices includes information such as the output power, power generation efficiency, and energy storage status of distributed energy. These status information play a key role in the energy allocation work in the distribution network. Considering the intermittent and volatile characteristics of solar energy, wind energy, etc. in distributed energy, which will change multiple times in a short period, real-time collection or high-frequency collection once every 1 minute is used for data collection to provide sufficient data support for subsequent distribution network resource evaluation and utilization.

[0055] Furthermore, the information collected by the data collection module is sent to the power supply grid division module, which divides the distribution network into multiple power supply grids according to predetermined rules. The division rules need to be flexibly subdivided according to the situation.

[0056] One of the partitioning rules is based on geographical location. The distribution network can be divided into different grids according to administrative regions, urban blocks, or geographical coordinate ranges, etc. This partitioning method is beneficial for targeted analysis and management of distribution network resources in specific regions and is convenient for combining with local actual electricity consumption demands and energy distribution situations. Partitioning based on voltage levels is also a commonly used method. There are differences in operating characteristics, load capacities, and resource allocations among distribution networks with different voltage levels. Dividing the distribution network into multiple power supply grids according to voltage levels can better analyze and evaluate the utilization of distribution network resources in areas with different voltage levels. For example, the utilization characteristics of resources in grids with different voltage levels such as 10 kV and 35 kV can be studied separately. The user type is also an important basis for partitioning power supply grids. Different types of users, such as industrial users, commercial users, and residential users, can be divided into different power supply grids. Because the electricity consumption demands, electricity consumption patterns, and requirements for power quality of different types of users are different. For example, industrial users usually have large electricity consumption, relatively stable loads, but have high requirements for power quality, while residential users' electricity consumption has obvious peak and valley characteristics and is relatively sensitive to fluctuations in power quality. Through this partitioning method, the distribution network resource demands of different user groups can be analyzed and met more accurately. Cross-using the above partitioning methods in the power grid to be monitored divides the power supply network into characteristic power supply grids. For the convenience of managing the partitioned power supply grids, each grid is given a characteristic number. Through the identifier, the system can quickly locate and query the parameter information of a specific grid, improving the operating efficiency of the system and the convenience of management. The parameters can also be integrated into the identifier, enabling managers to quickly understand the parameter information of the power supply grid according to the identifier.

[0057] Example 4

[0058] The resource evaluation module receives the information in the data collection module and organizes and preprocesses the data according to the aforementioned power supply grids. For the collected raw data, considering problems such as data errors, missing values, and outliers, for example, during the communication transmission of the data collected by smart meters, individual time points or individual data may be missing due to transmission failures, and during the data collection process of substation monitoring systems, sensors may malfunction and result in outliers. In such cases, data repair and cleaning are required. Specifically, methods such as data interpolation and outlier substitution are used to handle data missing and outlier problems. After preliminary cleaning and repair, further calculations are performed on the data, including line load rate, transformer utilization rate, distributed energy access ratio, line load imbalance degree, transformer overload duration, power output volatility rate, load peak-valley difference rate, energy self-sufficiency rate, voltage fluctuation times, frequency deviation integral, spare capacity adequacy, and line N-1 passing rate. Specifically, the line load rate is obtained by the ratio of the actual load of the line to the additional load of the line; the transformer utilization rate is obtained by the ratio of the actual used capacity of the transformer to the rated capacity of the transformer; the distributed energy access ratio is obtained by the ratio of the distributed energy access capacity to the total energy capacity; the line load imbalance degree is calculated by first calculating the absolute value of the difference between the load of each line and the average load, then finding the average value of these absolute differences, and then dividing by the average load; the transformer overload duration is recorded through the time stamps of data monitoring; the number of breaker operations is counted by the technology of the collected data; the power output volatility rate is calculated by first calculating the fluctuation values of the power output at different times, then finding the average value of these fluctuation values, and then dividing by the average output to obtain the volatility rate; the load peak-valley difference rate is obtained by the ratio of the peak-valley difference value of the load to the average load; the energy self-sufficiency rate is determined by the self-supplied energy amount of the distribution network and the energy demand of the distribution network; the voltage fluctuation times are obtained by counting the number of times the voltage data fluctuation exceeds the set threshold; the frequency deviation integral is obtained by integrating the frequency deviation over a fixed time window; the spare capacity adequacy is obtained by calculating the ratio of the spare capacity to the required spare capacity; the line N-1 passing rate is obtained by regularly conducting N-1 simulated fault tests on the line and counting the ratio of the number of times the line can pass normally when an N-1 fault occurs to the total number of tests. The calculated data is evaluated and analyzed to comprehensively evaluate the utilization of distribution network resources within each power supply grid. For example, by comparing with the set threshold or reasonable range, it is judged whether the utilization of distribution network resources is reasonable and efficient, and whether there are problems such as resource idling, overload, large energy loss, and poor power quality. Finally, the evaluation information is transmitted to the diagnosis and analysis module, and the diagnosis and analysis module analyzes to obtain the distribution network problems and cause analysis.

[0059] Embodiment 5

[0060] After the diagnostic analysis module receives the evaluation data, it further analyzes the reasons for anomalies in the evaluation data. As one of the core development items of this research project, a multi-dimensional vectorization analysis method is proposed in this module, and the improved method is as follows.

[0061] I. Multi-dimensional vectorization of evaluation information

[0062] 1. Vector construction principle

[0063] Based on the various indicators calculated by the resource evaluation module, a multi-dimensional vector is constructed. Suppose the evaluation indicators include line load rate LR, transformer utilization rate TR, distributed energy access ratio DERP, line load imbalance degree LUD, transformer overload duration TOD, power output volatility POV, load peak-valley difference rate LVDR, energy self-sufficiency rate ESR, voltage fluctuation count VFC, frequency deviation integral FDI, reserve capacity adequacy RCA, and line N-1 passing rate LNP, etc. The values of these indicators for each power supply grid at a certain moment are combined into a multi-dimensional vector.

[0064]

[0065] 2. Data standardization processing

[0066] Since the dimensions and numerical ranges of the various indicators may be different, in order to facilitate subsequent analysis, the data in the vector is standardized. The normalization method is adopted. For example, for indicator i, let its minimum value be m i , and the maximum value be M i . The minimum and maximum values are selected according to the divided grid, and the preset thresholds and reasonable ranges of each parameter are adaptively changed. Then the standardized indicator value

[0067]

[0068] where x i is the original indicator value. In this way, all indicator values are mapped to the interval [0,1], making different indicators have the same importance weight in the vector.

[0069] II. Vector zero-padding and matrix construction

[0070] 1. Vector zero-padding

[0071] When performing zero-padding on vectors, in order to enable vectors from different power supply grids to form a matrix with a unified dimension, zero-padding operations are carried out for cases where the vector dimension is insufficient. In actual use, in the event of special circumstances, such as at a certain moment, a certain power supply grid is unable to obtain the value of the distributed energy access ratio (DERP) due to reasons such as failures of distributed energy access devices. Then, in the resource evaluation module, corrections are made through data preprocessing. When constructing the vector, this position is filled with 0. The zero-padding operation can transmit the fault information during data transmission, ensuring both the executability of subsequent operations and the transmission of fault information.

[0072] 2. Matrix construction method

[0073] Suppose there are n power supply grids. After zero-padding operations, the vector dimension of each power supply grid is d, where d is the number of the above evaluation indicators. Then, an n×d matrix A is constructed, where each row of the matrix represents the evaluation information vector of a power supply grid. That is

[0074]

[0075] where is the evaluation information vector of the i-th power supply grid.

[0076] III. Matrix analysis

[0077] 1. Eigenvalue decomposition analysis

[0078] Perform eigenvalue decomposition on the constructed matrix A, that is

[0079] A = UΛU T

[0080] where U is the eigenvector matrix and Λ is a diagonal matrix, and the elements on the diagonal are the eigenvalues λ i . Analyze the overall characteristics of the distribution network resource utilization based on the magnitude and distribution of the eigenvalues. If the first few eigenvalues are large and account for a relatively high proportion of the sum of all eigenvalues, it indicates that there are a few main patterns or factors dominating the distribution network resource utilization. By analyzing the corresponding eigenvectors, the relationships between these dominant factors and various evaluation indicators can be understood.

[0081] Calculate the contribution rate of the eigenvalues

[0082]

[0083] The index information contained in the eigenvector corresponding to the eigenvalue with a larger contribution rate has a more important impact on the distribution network resource utilization status. When the coefficients of the line load rate (LR) and the transformer utilization rate (TR) in a certain eigenvector are large and the contribution rate of this eigenvalue is high, it can be inferred that in the current distribution network, the operating states of the load and the transformer play a key role in the overall resource utilization.

[0084] 2. Singular Value Decomposition Aided Analysis

[0085] Perform singular value decomposition on matrix A simultaneously

[0086] A = U s ΣV s T

[0087] where U s and V s are orthogonal matrices, Σ is a diagonal matrix, and the elements on the diagonal are singular values σ i . Singular value decomposition can further reveal the internal structure of the matrix and the correlation between data. By analyzing the magnitude and trend of singular values, the coupling relationship between different power supply grids and various evaluation indicators can be discovered.

[0088] Calculate the relative magnitude of singular values:

[0089]

[0090] where σ 1 is the largest singular value, and the part with relatively large singular values corresponds to the direction where the data in the matrix changes significantly. For example, when the left and right singular vectors corresponding to a certain singular value have relatively large coefficients in certain power supply grids and certain evaluation indicators, it can be judged that there is a strong correlation between these power supply grids in these evaluation indicators. At this time, the reasons for the common problems or characteristics of these power supply grids in resource utilization problems tend to be the same.

[0091] IV. Output of Diagnostic Analysis Results

[0092] 1. Problem Identification and Location

[0093] Based on the results of eigenvalue decomposition and singular value decomposition, combined with pre-set thresholds or rules, identify the problems existing in the utilization of distribution network resources. When it is found that the coefficient of the overload duration of the transformer in the eigenvector corresponding to a certain eigenvalue exceeds the threshold, and singular value analysis shows that this eigenvector has a large projection on certain power supply grids, it can be judged that there are transformer overload problems in these power supply grids.

[0094] Locate the range of power supply grids where the problem occurs and the main evaluation indicators related to the problem through the matrix analysis results. This can more accurately determine the location of the distribution network resource utilization problem and avoid misjudgment and missed judgment.

[0095] 2. Cause Inference and Explanation

[0096] Further analyze the relationships among various indicators in the eigenvector and singular vector to infer the causes of problems. For example, if it is found that the line load rate LR and the distributed energy access ratio DERP show a negative correlation in a certain eigenvector, and there are problems with unreasonable resource utilization in the power supply grid corresponding to this eigenvalue, then it can be speculated that the improper matching between distributed energy access and line load may be one of the causes of the problem.

[0097] Generate a detailed diagnostic report using the matrix analysis results to explain the causes and scope of influence of the problems, providing a scientific basis for subsequent improvement measures. The report should include the types of problems, such as overload and resource idleness, as well as the power supply grid where the problem occurs, abnormal conditions of relevant evaluation indicators, and possible cause analysis, etc., enabling managers to comprehensively understand the utilization status of distribution network resources and make reasonable decisions.

[0098] Example 6

[0099] Finally, visualize the key data in the system through the visualization module. The specific visualization method can further utilize the geographic information system and combine it with the on-site 3D model to more intuitively display the division of the power supply grid. Use different identifiers to label the distributed energy, substations, and users within the power supply grid to facilitate managers to understand and operate the distribution network information within the power supply grid. At the same time, display various data parameters using charts, and display specific indicators through charts such as bar charts and pie charts, such as Figure 2 、 3 It is possible to display charts for individual grids and individual information. At the same time, through the interactive interface, users can filter specific area, time period, and indicator data as needed to achieve in-depth data mining and precise decision support.

[0100] Example 7

[0101] Additionally, in another important technical development branch of this research project, we also proposed another new analysis method for the diagnostic analysis module, which is as follows:

[0102] Active power and reactive power are important parameters in the power grid system. The two have an intuitive manifestation in the effective utilization of power grid energy. For active power and reactive power, first, we perform two-dimensional processing on the power grid power supply network, establish a coordinate system, and vectorize them. In a power supply grid, there are multiple power sources, load nodes, and multiple connecting lines. For the active power on each line, its direction on the two-dimensional plane can be determined according to the coordinates of its two end nodes. For the direction of reactive power, it is necessary to calculate the phase difference between the current and voltage between nodes by combining the topological structure of the power grid to determine the direction of reactive power in different regions. After determining the direction, the power value is used as the modulus of the power vector, and the values of active power and reactive power can be directly obtained through power measurement equipment.

[0103] Plot the active power vector with the coordinate position of each node in the power supply grid, as well as the determined direction and magnitude of the active power. In this way, an active power vector field covering the entire power supply area is formed. In this vector field, there is an active power vector at each node, whose direction points to the direction of power transmission, and the length represents the magnitude of the active power. Using the same method, a reactive power vector field is established.

[0104] Based on the two-dimensional representation, use P(x,y) to represent the active power component in the x direction, and Q(x,y) to represent the reactive power component in the y direction. By using the method of data interpolation, the collected discrete data is converted into continuous data to obtain the continuous functions P(x,y) and Q(x,y).

[0105] Calculate the line integral of the functions P(x,y) and Q(x,y) on the boundary line L of the two-dimensional power supply grid. The specific process is to first differentiate the boundary curve L to further convert the line integral into a definite integral, and then use the method of numerical integration to calculate the definite integral, and the value of the line integral can be approximately obtained.

[0106] Furthermore, calculate the partial derivatives of the two functions. Similarly, use the numerical differentiation method to calculate the partial derivatives, and use the central difference method to obtain an approximate value. The ratio of the difference in function values to twice the step size can obtain an approximate partial derivative value. Then, according to the partial derivative values, perform a regional integration on the area covered by the power supply grid. The calculation process can be approximated as dividing the power supply grid into many small rectangular regions. For each small rectangular region, calculate the value of the representative point in the region multiplied by the area of the small rectangular region, and finally add them up to obtain an approximate result of the regional integration. Given the large area of the power region, all approximate results are valid.

[0107] In the boundary area where the power supply grid is connected to the external power grid, the curve integral value is relatively large, indicating that the power exchange between this area and the external power grid is frequent; in the area with more distributed energy access, the area integral value shows a large change, indicating that the resource distribution in this area changes relatively violently. By calculating the curve integral and area integral of the function, the health status of the power system can be grasped more comprehensively. Considering the actual use, it is found that the power exchange intensity between the commercial area and the industrial area and the external power grid is relatively high, and the resource distribution change index in the area is relatively large, which is consistent with the high load demand and distributed energy access in these areas. Or when problems such as frequent power exchange between grids or uneven resource distribution are found, the above results can be used to better judge and formulate control strategies.

[0108] The foregoing description of the disclosed embodiments enables those skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but will be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A distribution network resource utilization diagnosis and analysis system based on power supply grid, characterized in that: include: Data collection module: collects distribution network resource related data from multiple data sources, including smart meters, substation monitoring systems, and distributed energy resource monitoring equipment; Power supply grid division module: divides the distribution network into multiple power supply grids according to predetermined rules, wherein the predetermined rules include division based on geographical location, voltage level or user type; Resource evaluation module: receives information from the data acquisition module and the power grid division module, further processes the information collected in each grid, obtains specific indicators of distribution network resources and evaluates them; Diagnosis and analysis module: Based on the evaluation results of the resource evaluation module, the causes of abnormal situations in resource evaluation are obtained through diagnostic analysis, and the specific power supply grid, problem type, cause of the problem and related indicators where the abnormal situation occurs are output; Visual display module: used to display the diagnostic analysis results in an intuitive manner, and the display forms include maps and charts, and the charts include bar charts and pie charts.

2. A distribution network resource utilization diagnosis and analysis system based on a power supply grid according to claim 1, characterized in that: The frequency of data collection by the data acquisition module is set according to the data source type and data importance, where the smart meter collects user electricity consumption data once every 15 minutes, the substation monitoring system collects transformer data once every 5 minutes, and the distributed energy resource monitoring equipment collects data in real time or once every 1 minute.

3. A distribution network resource utilization diagnosis and analysis system based on power supply grid according to claim 1, characterized in that: The resource assessment module calculates, evaluates and analyzes the collected data to evaluate the utilization of distribution network resources in each power supply grid, including evaluating the line load rate, transformer utilization rate, distributed energy access ratio, line load imbalance, transformer overload duration, power output fluctuation rate, load peak-to-valley difference rate, energy self-sufficiency rate, voltage fluctuation times, frequency deviation integral, spare capacity margin and line N-1 pass rate.

4. A distribution network resource utilization diagnosis and analysis system based on a power supply grid according to claim 1, characterized in that: The resource assessment module sets the standard for allocating distribution network resources for different power grids, which is affected by multiple factors, including: division rules, time periods, and seasons.

5. The power grid-based distribution network resource utilization diagnosis and analysis system according to claim 1, characterized in that: The diagnostic analysis module vectorizes the evaluation data in the power grid, forms a matrix with multiple grids, and obtains information related to abnormal utilization of distribution network resources by means of matrix analysis. The specific process is as follows: 1) Construct a multidimensional vector, normalize the evaluation data according to the power grid and construct an evaluation information vector with all data values ​​between 0 and 1; 2) Vector zero filling and matrix construction: fill the missing information in the vector with 0, and combine the multi-dimensional vectors of each power grid to form an evaluation information matrix; 3) Perform eigenvalue decomposition analysis and singular value decomposition analysis on the evaluation information matrix; 4) By analyzing the results and combining them with the output information of the resource assessment module, the problem is obtained, and the specific power supply grid where the abnormal situation occurs, the problem type, the cause of the problem, and related indicators are output.

6. According to the distribution network resource utilization diagnosis and analysis system based on the power supply grid described in claim 1, its visualization display module can generate a variety of intuitive charts, including a bar chart showing the resource utilization comparison of each power supply grid, a line chart reflecting the trend of power quality changes in different time periods, and a pie chart showing the allocation ratio of various resources. Through the interactive interface, users can filter specific areas, time periods and indicator data as needed to achieve deep data mining and accurate decision support.

7. A method for diagnosing and analyzing the utilization of distribution network resources based on a power grid, the method being accomplished by means of a diagnostic analysis system according to any one of claims 1 to 6, characterized in that: The steps include: 1) Data collection stage: collect multi-source information on the monitoring distribution network; 2) Power supply grid division stage: divide the detection power grid into power supply grids according to preset rules; 3) Resource evaluation stage: Process the collected information in each grid and evaluate the utilization of distribution network resources in the grid; 4) Diagnostic analysis phase: Diagnose and analyze the utilization of distribution network resources according to different grids.

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