A power grid equipment status and data management system

Abstract

The present invention provides a power grid equipment status and data management system, belonging to the technical field of power system monitoring. A power grid equipment status and data management system includes: a data acquisition module: collecting operation-related data of each device in the power grid and related data of the power grid; a data processing module: constructing several initial autonomous layers of the power grid based on the operation-related data of each device in the power grid; a dynamic adjustment module: adjusting the initial autonomous layers based on the operation data of each device in the power grid after the initial autonomous layer division; a status determination module: obtaining the real-time operation data of each device in the power grid based on the adjusted initial autonomous layers, and then generating a health index of each device in the power grid; a risk determination module: determining the risk level of each device based on the health index of each device in the power grid and the real-time operation data of each device, and then generating a risk determination report. The safety and reliability of the operation of power grid equipment are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power system monitoring, and particularly to a power grid equipment status and data management system. Background Art

[0002] With the continuous expansion of the power grid scale and the increase in equipment complexity, how to effectively manage the operating status and data of power grid equipment has become an important issue in power grid operation and maintenance.

[0003] In the prior art, the status monitoring and data management of power grid equipment mainly rely on independent monitoring systems, which usually obtain the basic operating data of power grid equipment through fixed acquisition devices. However, there are problems of slow response to power grid state changes and difficulty in dynamically adjusting the correlation between different devices, resulting in low accuracy of equipment status assessment and risk prediction. For example, some existing systems only rely on a preset threshold alarm mechanism and cannot adjust the power grid management strategy in real time according to the operating data of the equipment.

[0004] Therefore, the present invention provides a power grid equipment status and data management system. Summary of the Invention

[0005] The present invention provides a power grid equipment status and data management system, which can make the health status assessment of power grid equipment more accurate by constructing an initial autonomous layer and dynamically adjusting based on real-time operating data, and provide a highly targeted risk level. By introducing a health index and an autonomous layer, the management of power grid equipment becomes more intelligent and efficient, and potential risks can be detected in a timely manner and corresponding reports can be generated, thereby improving the safety and reliability of the operation of power grid equipment. It solves the problems of lagging response, lack of dynamic adjustment and insufficient accuracy in the status monitoring and management of power grid equipment in the prior art.

[0006] The present invention provides a power grid equipment status and data management system, including:

[0007] A data acquisition module: collecting the operation-related data of each device in the power grid and the related data of the power grid based on a preset acquisition device;

[0008] A data processing module: constructing several initial autonomous layers of the power grid based on the operation-related data of each device in the power grid;

[0009] A dynamic adjustment module: obtaining the operation data of each device in the power grid after the initial autonomous layer division, and adjusting the initial autonomous layer based on the operation data of all devices in the power grid;

[0010] A status determination module: obtaining the real-time operation data of each device in the power grid based on the adjusted initial autonomous layer, and then generating a health index of each device in the power grid in combination with a preset algorithm;

[0011] Risk determination module: Determine the risk level of each device based on the health index of each device in the power grid and the real-time operation data of each device, and then generate a risk determination report.

[0012] The present invention provides a power grid equipment status and data management system. The data acquisition module includes

[0013] Device acquisition unit: Based on a preset sensor network, collect the initial operation status data of each device in the power grid;

[0014] Node acquisition unit: Based on preset monitoring devices, collect the load conditions of each node in the power grid;

[0015] Historical acquisition unit: Based on the historical operation data of the power grid, obtain the historical load conditions of each node in the power grid.

[0016] The present invention provides a power grid equipment status and data management system. The data processing module includes:

[0017] First scoring unit: Determine the initial status score of each device in the power grid based on the initial operation status data of each device in the power grid;

[0018] ; where is the initial status score of the kth device in the power grid, is the total number of the initial operation status data of the kth device in the power grid, is the historical operation reliability factor of the kth device in the power grid, is the environmental adaptation factor of the kth device, is the operation angle of the kth device in the power grid, is the measurement value of the gth initial operation status data of the kth device in the power grid, is the weight of the gth initial operation status data of the kth device in the power grid, is the operation time decay factor of the kth device in the power grid, is the cumulative operation time of the kth device in the power grid, is the absolute value of the difference between the initial load and the rated load of the kth device in the power grid, is the rated load of the kth device in the power grid, is the relative importance coefficient of the kth device in the power grid, is the maintenance frequency coefficient of the kth device in the power grid, is the designed maintenance cycle of the kth device in the power grid

[0019] Second scoring unit: Determine the load score of each node in the power grid based on the load condition of each node in the power grid and the historical load condition;

[0020] ; where, is the load score of the q-th node in the power grid, is the maximum allowable load of the q-th node in the power grid, is the rated load of the q-th node in the power grid, is the weight of the maximum allowable load of the q-th node in the power grid, is the load of the q-th node at the historical time point t in the power grid, is the total number of time points of the historical load record of the q-th node in the power grid, the weight of the historical load of the q-th node in the power grid, is the historical load fluctuation factor of the q-th node in the power grid, is the average value of the historical load of the q-th node in the power grid;

[0021] Device determination unit: Determine several associated devices corresponding to each node in the power grid based on the initial state score of each device in the power grid and the load score of each node in the power grid in combination with a preset analysis algorithm;

[0022] Network construction unit: Construct a node network based on all nodes in the power grid and several associated devices corresponding to each node;

[0023] Initial partitioning unit: Analyze the node network and partition the initial autonomous layer based on the analysis result. Each autonomous layer includes several nodes and the devices associated with each node.

[0024] The present invention provides a power grid equipment status and data management system. The initial partitioning unit includes:

[0025] Partition determination sub-unit: Form a node partition corresponding to each node based on the range where each node and the associated devices are located;

[0026] Module optimization sub-unit: Move the nodes and the devices associated with the nodes in each node partition to the node partitions of adjacent nodes and determine the modularity of each node in the adjacent partitions. When the modularity reaches the maximum, stop and determine the node partition of the adjacent node where the stop is located as the adjacent partition;

[0027] Partition formation sub-unit: Each node forms a super node partition with the nodes and the devices corresponding to the nodes in the adjacent partition where it stops;

[0028] Partition optimization subunit: Move several nodes in each supernode partition to adjacent supernode partitions and determine the modularity of each supernode partition in the adjacent supernode partitions until the modularity reaches the maximum value;

[0029] First optimization subunit: Repeat the above steps until the modularity of each supernode partition no longer increases, then stop the iteration, and thus form several comprehensive node partitions;

[0030] Second optimization subunit: Optimize all comprehensive node partitions based on a preset splitting algorithm, and determine the initial autonomous layer based on the optimized comprehensive node partitions.

[0031] The present invention provides a power grid equipment status and data management system, and the second optimization subunit includes:

[0032] Betweenness optimization block: Gradually delete the edge with the largest betweenness based on the betweenness of each edge in each comprehensive node partition. At the same time, determine the modularity of each comprehensive node partition during the process of deleting edges;

[0033] Optimization control block: Determine the change rate of the modularity of each comprehensive node partition during the process of deleting edges. When the change rate of the modularity is greater than the preset change rate threshold, stop deleting, and thus form the optimized comprehensive node partitions.

[0034] The present invention provides a power grid equipment status and data management system, and the dynamic adjustment module includes:

[0035] First scoring unit: Obtain the operation data of each device in the power grid after the initial autonomous layer is divided, and thus determine the first status score of each device in the power grid;

[0036] Redivision unit: Redivide the initial autonomous layer based on the status scores of all devices in the power grid and the load scores of each node in the power grid in combination with a preset analysis algorithm;

[0037] Optimized division unit: Optimize the initial autonomous layer after redivision based on a preset optimization algorithm, and thus determine several adjusted autonomous layers.

[0038] The present invention provides a power grid equipment status and data management system, and the risk determination module includes:

[0039] Risk determination unit: Determine several corresponding risk factors of each device based on the health index of the devices in the power grid and the real-time operation data of each device;

[0040] Index determination unit: Determine the risk index of each device based on several corresponding risk factors, health index, and real-time operation data of each device;

[0041] ; wherein, is the risk index of the k-th device, is the i-th risk factor of the k-th device, is the weight of the i-th risk factor of the k-th device, is the number of risk factors of the k-th device, is the health index of the k-th device, is the health index weight coefficient of the k-th device, is the weight coefficient of the influence coefficient of the real-time operation data of the k-th device;

[0042] wherein, is the influence coefficient of the real-time operation data of the k-th device:

[0043] ; wherein, is the actual measured value of the j-th real-time operation data of the k-th device, is the preset reference value of the j-th real-time operation data of the k-th device, is the preset maximum allowable deviation threshold of the j-th real-time operation data of the k-th device, is the weight of the j-th real-time operation data of the k-th device, is the number of real-time operation data of the k-th device;

[0044] Report generation unit: Determine the risk level of each device based on the risk index of each device, and generate a risk report corresponding to each device based on the preset risk level - risk report database.

[0045] The present invention provides a power grid equipment status and data management system, and risk factors, including: risk factors related to equipment performance, risk factors related to operating environment, risk factors related to operation and use, risk factors related to design and manufacturing, and risk factors related to external environment.

[0046] The power grid equipment status and data management system provided by the present invention can make the assessment of the health status of power grid equipment more accurate by constructing an initial autonomous layer and dynamically adjusting based on real-time operation data, and provide a highly targeted risk level. By introducing a health index and an autonomous layer, the management of power grid equipment becomes more intelligent and efficient, can timely discover potential risks and generate corresponding reports, thereby improving the safety and reliability of the operation of power grid equipment. It solves the problems of lag in the status monitoring and management response of power grid equipment, lack of dynamic adjustment, and insufficient accuracy in the prior art. Description of the Drawings

[0047] To more clearly illustrate the technical solutions in the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0048] Figure 1 It is a schematic structural diagram of a power grid equipment status and data management system provided by an embodiment of the present invention. Detailed implementation manners

[0049] To make the objectives, technical solutions, and advantages of the present invention clearer, the following will clearly and completely describe the technical solutions in the present invention in conjunction with the drawings in the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present invention.

[0050] Embodiment 1

[0051] As Figure 1 shown, a power grid equipment status and data management system provided by an embodiment of the present invention includes:

[0052] Data acquisition module: Based on a preset acquisition device, it acquires the operation-related data of each device in the power grid and the relevant data of the power grid.

[0053] Data processing module: Based on the operation-related data of each device in the power grid, it constructs several initial autonomous layers of the power grid.

[0054] Dynamic adjustment module: It obtains the operation data of each device in the power grid after the initial autonomous layer division, and adjusts the initial autonomous layer based on the operation data of all devices in the power grid.

[0055] Status determination module: Based on the adjusted initial autonomous layer, it obtains the real-time operation data of each device in the power grid, and then generates the health index of each device in the power grid in combination with a preset algorithm.

[0056] Risk determination module: Based on the health index of each device in the power grid and the real-time operation data of each device, it determines the risk level of each device, and then generates a risk determination report.

[0057] In this embodiment, the preset acquisition device refers to the devices pre-installed in the power grid for acquiring the operation data of each device in the power grid and the overall state of the power grid, such as sensors, smart meters, monitoring devices, etc. For example, in a substation, the preset acquisition devices include temperature sensors, current sensors, smart meters, and voltage monitors, which are used to monitor the operation state data such as the temperature, current, and voltage of the transformer in real time;

[0058] In this embodiment, the operation-related data refers to the specific operation situation data of the power grid devices. The operation-related data includes real-time operation data, historical reference data, and standard reference data of the devices, such as voltage, current, temperature, etc. The related data of the power grid refers to the overall operation status of the entire power grid, such as frequency, total power, load distribution, etc. For example, the operation-related data of a certain substation includes that the current load of the transformer is 600A and the voltage is 110kV; the related data of the power grid includes that the current frequency of the entire power grid is 50Hz and the total power output is 500MW.

[0059] In this embodiment, the initial autonomous layer is a multi-layer structure established based on the operation data of each device in the power grid. Each autonomous layer contains a group of power grid devices with similar or interrelated functions, which is convenient for independent operation analysis and control. For example, an initial autonomous layer can be composed of transformers, distribution cabinets, and switch devices located in the same geographical area, and these devices are jointly responsible for the power transmission and distribution of a local power grid.

[0060] In this embodiment, for the operation data of each device in the power grid after the initial autonomous layer is divided, the initial autonomous layer is adjusted after the initial autonomous layer is divided. The system will continuously acquire the real-time operation data of each device in each autonomous layer, and dynamically adjust the structure of the autonomous layer and the attribution of the devices based on the changes in these data to optimize the operation of the power grid. If in a certain initial autonomous layer, the load of a group of devices suddenly increases while the load in another autonomous layer decreases, the dynamic adjustment module will reallocate some devices with heavy loads to another autonomous layer with lighter loads to balance the load.

[0061] In this embodiment, the preset algorithm is the algorithm used in the system to calculate and analyze the health status of power grid devices. The algorithm will combine the real-time operation data, historical data, and other factors of the devices to calculate the health index of the devices. It is an algorithm based on support vector machine (SVM) or neural network, which is used to analyze data such as the current and temperature of the transformer to judge whether its operation is abnormal and generate a health index;

[0062] In this embodiment, the health index of each device in the power grid refers to the evaluation value of the health status of each device in the power grid, usually represented by a percentage or a grade, which reflects whether the current working state of the device is normal or there is a potential failure risk. For example, the health index of a certain transformer is 85%, indicating that its working state is good, but there is a slight overheating phenomenon and it needs to be regularly inspected and maintained.

[0063] In this embodiment, the risk level of each device is a device risk category determined based on the health index of the device and real-time operation data, usually divided into levels such as low, medium, and high, which is used to indicate the possibility of the device failing and its severity. For example, the risk level of a high-voltage circuit breaker is evaluated as "high" because its health index is 45% and there are abnormal current fluctuations, which may pose a risk of poor contact or internal damage;

[0064] In this embodiment, the risk judgment report is a report generated by the system based on the risk level of each device, which includes the risk assessment results of each device, potential problem analysis, and maintenance suggestions for the management personnel to refer to and take corresponding preventive measures. For example, the risk judgment report shows that the No. 4 transformer of a certain substation needs emergency maintenance because its risk level is "high", its health index is 35%, and there are abnormal voltage fluctuations and temperature increases.

[0065] The beneficial effects of the above technical solution are as follows: By constructing the initial autonomous layer and dynamically adjusting based on real-time operation data, it is possible to make the health status evaluation of power grid equipment more accurate and provide a highly targeted risk level. Introducing the health index and the autonomous layer makes the management of power grid equipment more intelligent and efficient, can timely detect potential risks and generate corresponding reports, thereby improving the safety and reliability of the operation of power grid equipment, and solving the problems of lagging response in the state monitoring and management of power grid equipment, lack of dynamic adjustment, and insufficient accuracy in the prior art.

[0066] Embodiment 2

[0067] A power grid equipment status and data management system provided by an embodiment of the present invention, the data acquisition module includes:

[0068] Device acquisition unit: Based on a preset sensor network, collect the initial operation state data of each device in the power grid;

[0069] Node acquisition unit: Based on preset monitoring devices, collect the initial load conditions of each node in the power grid;

[0070] Historical acquisition unit: Based on the historical operation data of the power grid, obtain the historical load conditions of each node in the power grid.

[0071] In this embodiment, the preset sensor network refers to a series of sensor devices pre-deployed in the power grid for real-time collection of the operating states of various devices in the power grid. These sensors are installed on devices such as transformers, circuit breakers, and transmission lines to form a network that can cover the key nodes and devices of the power grid. For example, a preset sensor network may include temperature sensors, current sensors, and voltage sensors installed on a power transformer to monitor the temperature, current, and voltage changes of the transformer.

[0072] In this embodiment, the initial operating state data refers to the basic data collected at the initial stage of the operation of power grid devices, including information such as current, voltage, and power at the start of the device. For example, when a transformer in a certain substation starts up, its initial operating state data may include an input voltage of 220 kV, an output power of 50 MW, and the oil temperature and load current after startup.

[0073] In this embodiment, the preset monitoring devices refer to devices used to monitor the operation of power grid nodes, usually including cameras, intelligent monitoring systems, or other sensing devices. These devices collect information such as the load and flow of the nodes for convenient real-time management and scheduling. For example, the monitoring devices installed at the nodes of a transmission line can monitor the voltage and current load conditions of the nodes in real time. If the load of a certain node suddenly increases, the monitoring device can immediately feedback to the control center.

[0074] In this embodiment, the initial load condition refers to the load amount borne by each node in the power grid at the start of system operation. The load condition includes the magnitude of the current passing through the node, power demand, etc., which are important parameters for evaluating the operation status of the power grid. For example, the initial load condition of a certain substation may be: the total current passing through the station is 500 A, the voltage is 110 kV, and the power demand is 55 MW.

[0075] In this embodiment, the historical load condition refers to the load change data of a certain power grid node over a past period of time, which is used to analyze the long-term trend of power grid operation, evaluate the health status of equipment, and predict future load demands. For example, the historical load condition of a certain transmission line in the past month shows that the load current reaches 800 A during the morning peak period and only 400 A during the night valley period. These data can be used for power grid dispatching and optimal configuration.

[0076] The beneficial effects of the above technical solutions are as follows: Through device and node data collection, and the historical load acquisition mechanism, the all-round dynamic monitoring of the power grid device status and historical data traceability are realized. Based on the sensor network and monitoring devices, not only can the operating state of the device be obtained in real time, but also the load management can be optimized by combining historical data, significantly improving the safety and efficiency of power grid operation, reducing the fault risk, and at the same time providing a basis for predictive maintenance, thereby realizing the autonomous adjustment and management of the smart grid.

[0077] Example 3

[0078] A power grid equipment status and data management system provided by an embodiment of the present invention, the data processing module includes:

[0079] The first scoring unit: determining the initial state score of each device in the power grid based on the initial operation state data of each device in the power grid;

[0080] ; where, is the initial state score of the k-th device in the power grid, is the total number of initial operation state data of the k-th device in the power grid, is the historical operation reliability factor of the k-th device in the power grid, is the environmental adaptation factor of the k-th device, is the operation angle of the k-th device in the power grid, is the measurement value of the g-th initial operation state data of the k-th device in the power grid, is the weight of the g-th initial operation state data of the k-th device in the power grid, is the operation time decay factor of the k-th device in the power grid, is the cumulative operation time of the k-th device in the power grid, is the absolute value of the difference between the initial load and the rated load of the k-th device in the power grid, is the rated load of the k-th device in the power grid, is the relative importance coefficient of the k-th device in the power grid, is the maintenance frequency coefficient of the k-th device in the power grid, is the designed maintenance cycle of the k-th device in the power grid;

[0081] The second scoring unit: determining the load score of each node in the power grid based on the load condition and historical load condition of each node in the power grid;

[0082] ; where, is the load score of the q-th node in the power grid, is the maximum allowable load of the q-th node in the power grid, is the rated load of the q-th node in the power grid, is the weight of the maximum allowable load of the q-th node in the power grid, is the load of the q-th node at the historical time point t in the power grid, is the total number of time points of the historical load record of the q-th node in the power grid, the weight of the historical load of the q-th node in the power grid, is the historical load fluctuation factor of the q-th node in the power grid, is the historical load average value of the q-th node in the power grid;

[0083] Device determination unit: Based on the initial state scores of each device in the power grid and the load scores of each node in the power grid, combined with a preset analysis algorithm, determine several associated devices corresponding to each node in the power grid;

[0084] Network construction unit: Based on all the nodes in the power grid and several associated devices corresponding to each node, construct a node network;

[0085] Initial division unit: Analyze the node network, and based on the analysis results, divide the initial autonomous layer, and each autonomous layer includes several nodes and the devices associated with each node.

[0086] In this embodiment, the historical operation reliability factor reflects the past performance and reliability of the device, and is usually determined according to indicators such as the failure rate or the mean time between failures (MTBF);

[0087] In this embodiment, the environmental adaptation factor reflects the physical angle of the device in space. Especially in devices that are sensitive to position or angle, this parameter may affect its performance;

[0088] In this embodiment, the operation angle reflects the physical angle of the device in space. Especially in devices that are sensitive to position or angle, this parameter may affect its performance;

[0089] In this embodiment, the difference between the initial load and its rated load represents the deviation between the current operating load of the device and the design standard. The closer the load is to the rated value, the smaller the deviation;

[0090] In this embodiment, the relative importance coefficient reflects the importance of the device in the entire power grid system. The higher the importance coefficient, the more critical the device is in the system;

[0091] In this embodiment, the maintenance frequency coefficient represents the actual maintenance frequency of the device. If the device has a high maintenance frequency, usually its state will be better.

[0092] In this embodiment, the rated load of the node is the design load capacity of the node, which is used to measure the maximum load that the node can withstand under ideal conditions.

[0093] In this embodiment, the historical complexity factor refers to the degree of change in the load (power demand or usage) of a power grid node within a historical time range, which is used to quantify and reflect the fluctuation of the node's load between different time points. The greater the load fluctuation, the higher the fluctuation factor; conversely, the smaller the fluctuation, the lower the factor.

[0094] In this embodiment, based on the initial state scores of each device in the power grid and the load scores of each node in the power grid, combined with a preset analysis algorithm, several associated devices corresponding to each node in the power grid are determined. By comprehensively considering the initial state scores of the devices and the load scores of the nodes, the devices that each node should be associated with are determined. Suppose the load score of a certain node is 75 points, and this node needs to be associated with devices whose state scores are higher than 80. Through algorithm calculation, 3 transformers (with state scores of 85, 90, and 95 respectively) are determined as the associated devices of this node, which can optimize the load distribution and ensure the stable operation of the node.

[0095] In this embodiment, the node network is the devices and nodes in the power grid, and the edges represent the connection relationships and mutual influences between the devices and nodes.

[0096] The beneficial effects of the above technical solution are as follows: By comprehensively considering the device state scores and node load scores, combined with a preset algorithm, nodes and devices are accurately matched, an optimized node network is constructed, and the autonomous layer is further divided. Starting from the non-linear complex relationship between power grid devices and nodes, the self-adaptability and operation efficiency of the power grid are improved, the optimized management of the autonomous layer is realized, more stable and flexible power grid control is brought, load fluctuations and equipment failure risks are reduced, and the reliability and maintenance efficiency of the power grid are significantly improved.

[0097] Embodiment 4

[0098] An initial partitioning unit of a power grid device state and data management system provided by an embodiment of the present invention includes:

[0099] Partition determination subunit: Based on the ranges where each node and its corresponding associated devices are located, a node partition corresponding to each node is formed;

[0100] Module optimization subunit: Move the nodes and the devices associated with the nodes in each node partition to the node partitions of adjacent nodes and determine the modularity of each node in the adjacent partitions. When the modularity reaches the maximum, stop and determine the node partition of the adjacent node where it stops as the adjacent partition;

[0101] Partition formation subunit: Each node forms a super node partition with the nodes and the devices corresponding to the nodes in the adjacent partition where it stops;

[0102] Partition optimization subunit: Move several nodes in each super node partition to the adjacent super node partitions and determine the modularity of each super node partition in the adjacent super node partitions until the modularity reaches the maximum value;

[0103] First optimization subunit: Repeat the above steps until the modularity of each super node partition no longer increases, then stop the iteration, and thus form several comprehensive node partitions;

[0104] The second optimization subunit: Optimize all comprehensive node partitions based on a preset splitting algorithm, and determine the initial autonomous layer based on the optimized comprehensive node partitions.

[0105] In this embodiment, the range where each node and its corresponding associated device are located refers to the distribution area of a node in the power grid and its associated power grid devices in the physical space or logical network. This range determines the influence range of the interaction between the node and the device. Suppose a node controls multiple power transformers, then the range of this node and its associated devices is the area where these transformers are located;

[0106] In this embodiment, node partitioning is to divide the nodes and their associated devices in the power grid into different groups according to geography or function. Each group corresponds to a certain area or specific function in the power grid. If a power grid has several power supply stations, and each power supply station is responsible for a specific area, these power supply stations and the area devices they are responsible for can be divided into different node partitions.

[0107] In this embodiment, adjacent nodes refer to other nodes in the power grid that are close to a certain node and have direct or indirect connections with it. If one node controls a substation and another controls a power plant next to it, these two nodes are adjacent nodes;

[0108] In this embodiment, modularity is an index used to measure the quality of the node partitioning structure in the power grid, which evaluates the difference between the connection density inside the node partition and the connection sparsity between different partitions. If the nodes within a certain partition are closely connected and have fewer connections with external partitions, then the modularity of this partition is higher;

[0109] In this embodiment, adjacent partitions refer to other partitions that are close to a certain node or partition in the power grid and have more connections. If one partition covers the power system of a certain city, and the adjacent partition manages the power supply of the peripheral area of the city, these two partitions are adjacent partitions.

[0110] In this embodiment, a super node partition is a larger partition formed by multiple nodes and their associated devices, which is used to simplify the management and optimization of the power grid structure. Each super node represents a community composed of multiple original nodes. If there are multiple small node partitions in a certain area originally, these partitions are combined into a large super node partition after optimization for more efficient management.

[0111] In this embodiment, a comprehensive node partition is a node partition after multiple optimizations and iterations, which reaches the best modularity and becomes a stable partition structure in the power grid. For example, a stable comprehensive partition formed after repeated adjustment and optimization of multiple adjacent partitions is responsible for managing a large range of power equipment.

[0112] In this embodiment, the preset splitting algorithm refers to an algorithm that further splits or adjusts certain node partitions according to a predetermined rule, and is used to optimize the structure of the partitions. For example, if a power partition is too large and inconvenient to manage, the system uses the splitting algorithm to divide it into two, forming two smaller partitions for better management.

[0113] In this embodiment, determining the initial self based on the optimized comprehensive node partition is to form an autonomous layer of the power grid on the basis of the optimized comprehensive node partition, ensuring that each autonomous layer can independently handle management and control tasks. For example, after multiple optimizations, a large-area power system is divided into multiple autonomous layers, and each autonomous layer contains several nodes and can independently complete power dispatching and maintenance.

[0114] The beneficial effects of the above technical solution are as follows: By combining node partitioning with modularity optimization, it innovatively realizes the efficient partition management of the power grid equipment status and data management, dynamically adjusts the attribution of nodes and equipment, enables the modularity of each partition to reach the maximum value, improves the autonomy and stability of power grid management, further optimizes the comprehensive node partition through the splitting algorithm to form an autonomous layer, realizes the precision and intelligence of power grid status management, reduces manual intervention, and achieves efficient self-optimization.

[0115] Embodiment 5

[0116] A power grid equipment status and data management system provided by an embodiment of the present invention, a second optimization subunit, includes:

[0117] Betweenness optimization block: Gradually delete the edge with the largest betweenness based on the betweenness of each edge in each comprehensive node partition. At the same time, determine the modularity of each comprehensive node partition during the process of deleting the edge;

[0118] Optimization control block: Determine the change rate of the modularity of each comprehensive node partition during the process of deleting the edge. When the change rate of the modularity is greater than the preset change rate threshold, stop deleting, and then form the optimized comprehensive node partition.

[0119] In this embodiment, the betweenness of an edge (a concept in graph theory, which represents the proportion of the number of paths connected by this edge in a graph (such as a power grid network) to all the shortest paths. The betweenness of an edge reflects the importance and influence degree of this edge on the connection of nodes in the network. The greater the betweenness, the greater the influence of this edge on the network structure. Suppose in a power grid network diagram, there is an edge A - B connecting two key nodes. If many shortest paths must pass through this edge to connect other nodes, then the betweenness of this edge will be very high. Deleting this edge may significantly affect the connectivity of the network.

[0120] In this embodiment, the preset change rate threshold is a value set by the system to measure the sensitivity of the modularity change. The modularity change rate refers to the change speed of the modularity during the optimization process. When the modularity change rate exceeds this preset threshold, it means that the change of the modularity has been too fast or unstable. Therefore, the current operation should be stopped to prevent adverse effects on the system stability. If the set preset change rate threshold is 0.05 (i.e., 5%), when a certain edge is deleted and the modularity change rate reaches or exceeds 5%, it indicates that the current operation has too much impact on the system structure. At this time, the optimization control block will stop further deleting edges to protect the overall stability of the system.

[0121] The beneficial effects of the above technical solution are as follows: By gradually deleting the edges with the largest betweenness through the betweenness optimization block and under the monitoring of the optimization control block, using the modularity change rate as the stop criterion, the adaptive optimization of the comprehensive node partition is realized, avoiding excessive or insufficient edge deletion operations, ensuring the stability of the power grid structure and the maximization of the modularity, and improving the accuracy and flexibility of the power grid equipment status management.

[0122] Embodiment 6

[0123] A power grid equipment status and data management system provided by an embodiment of the present invention, a dynamic adjustment module, includes:

[0124] The first scoring unit: Obtain the operation data of each device in the power grid after the initial autonomous layer division, and then determine the first status score of each device in the power grid;

[0125] The re-partitioning unit: Re-partition the initial autonomous layer based on the first status scores of all devices in the power grid and the load scores of each node in the power grid in combination with a preset analysis algorithm;

[0126] The optimized partitioning unit: Optimize the initial autonomous layer after re-partitioning based on a preset optimization algorithm, and then determine several adjusted autonomous layers.

[0127] In this embodiment, the first status score is a score calculated based on the operation data of each device in the power grid, used to evaluate the current operation status of the device, reflecting the health status, performance performance and possible problems of the device. If the operation data of a certain transformer shows that its temperature and load are stable within the normal range, it may get a higher first status score, indicating that the device is in good condition. On the contrary, if the operation data of a certain transmission line shows that the current is too high and there are abnormal fluctuations, its first status score may be lower, indicating that attention or maintenance is required;

[0128] In this embodiment, the preset analysis algorithm is a set of pre-set rules or methods for analyzing and processing the status scores of power grid devices and the node load scores, so as to determine how to re-divide the initial autonomous layer, including technologies such as data clustering, threshold judgment, and pattern recognition. For example, a preset analysis algorithm may combine the first status scores of each device and the load scores of nodes to identify areas in the power grid with high load and poor health conditions. Based on these identification results, the algorithm can re-divide the autonomous layers of these areas to improve the operation efficiency and security of the power grid.

[0129] In this embodiment, the preset optimization algorithm is a pre-designed algorithm for further optimizing the autonomous layer structure on the basis of the preliminary division, including genetic algorithms, simulated annealing, particle swarm optimization, etc., for improving the modularity or other performance indicators of the autonomous layer. Suppose that in the preliminary division, there are multiple small-scale sub-regions in the autonomous layer of a certain area. The preset optimization algorithm can further optimize the merging or splitting of these sub-regions to form a more balanced and stable adjusted autonomous layer;

[0130] In this embodiment, the adjusted autonomous layer refers to the autonomous layer structure after re-division and optimization, aiming to better adapt to the dynamic changes of the power grid. The formation of the adjusted autonomous layer means that the regional division of the power grid has been updated according to real-time operation data and optimization algorithms to ensure the stability and efficiency of the system. For example, in the case of a change in the power grid load, the adjusted autonomous layer may merge areas with higher loads with adjacent areas with lower loads to disperse the load pressure and optimize the overall resource allocation and management efficiency of the power grid.

[0131] The beneficial effects of the above technical solutions are as follows: By dynamically adjusting the division of the autonomous layer of power grid devices, the comprehensive evaluation and optimized management of the status and load of power grid devices are realized. Combining the device status scores and node load scores, through preset analysis and optimization algorithms, the autonomous layer is re-divided and optimized, thereby improving the operation efficiency and stability of the power grid, enhancing the ability of equipment fault prediction and prevention, realizing the dynamic allocation of power grid resources, and reducing the overall operation risk.

[0132] Embodiment 7

[0133] A power grid device status and data management system provided by an embodiment of the present invention, a risk determination module, includes:

[0134] Risk determination unit: Determine a corresponding number of risk factors for each device based on the health index of the devices in the power grid and the real-time operation data of each device;

[0135] Index determination unit: Determine the risk index of each device based on the corresponding number of risk factors, health index of each device, and the real-time operation data of each device:

[0136] ; where is the risk index of the k-th device, is the i-th risk factor of the k-th device, is the weight of the i-th risk factor of the k-th device, is the number of risk factors of the k-th device, is the health index of the k-th device, is the weight coefficient of the health index of the k-th device, is the weight coefficient of the influence coefficient of the real-time operation data of the k-th device;

[0137] where is the influence coefficient of the real-time operation data of the k-th device:

[0138] ; where is the actual measured value of the j-th real-time operation data of the k-th device, is the preset reference value of the j-th real-time operation data of the k-th device, is the preset maximum allowable deviation threshold of the j-th real-time operation data of the k-th device, is the weight of the j-th real-time operation data of the k-th device, is the number of real-time operation data of the k-th device;

[0139] Report generation unit: Determine the risk level of each device based on the risk index of each device, and generate a risk report corresponding to each device based on a preset risk level - risk report database.

[0140] In this embodiment, the risk index of each device is a comprehensive indicator, representing the potential risk level of a certain device in the power grid. By combining multiple risk factors, health index, and real-time operation data of the device, it is calculated using a weighted formula. The higher the risk index, the greater the risk of the device, and measures may need to be taken for prevention or maintenance. For example, if the health index of a certain transformer is low (indicating its poor condition), and its load current frequently exceeds the safe range (abnormal real-time operation data), these factors result in a high risk index. This indicates that the transformer may be about to fail and requires priority maintenance;

[0141] In this embodiment, the preset maximum allowable deviation threshold is a preset standard value used to measure the degree of deviation of the real-time operating data of the device from the normal value. If the actual data of the device deviates from this threshold, there may be risks or abnormal conditions. For example, the normal temperature of a certain motor is 60°C, and the preset maximum allowable deviation threshold is 10°C. If the real-time measured temperature of this motor reaches 72°C, exceeding the allowable deviation value (60°C + 10°C), it is considered that the device has an overheating risk and cooling or shutdown measures may need to be taken immediately.

[0142] In this embodiment, the preset risk level - risk report database is a database that stores the risk levels of devices and the corresponding risk reports, including the mapping relationship between the device risk index and the risk level, as well as the detailed report templates preset for different risk levels. When the risk index of a certain device is calculated, the system generates a corresponding risk report according to its corresponding risk level. Assume that the risk levels are divided into three levels: low, medium, and high. The risk index of a certain substation device is relatively high, so it is classified as the "high risk" level. The system selects the standard report template for the high risk level from the database and generates a risk report containing the specific fault risks, influence scope, and recommended maintenance measures of this device.

[0143] The beneficial effects of the above technical solution are as follows: By comprehensively analyzing the health index, risk factors, and real-time operating data of the device, a multi-level risk determination model is constructed to dynamically evaluate the risk level of the device, and customized risk reports can also be generated. This method provides more refined device status monitoring and risk prediction capabilities, effectively improves the safety management level of the power grid, and reduces the impact of potential faults on the overall operation of the power grid.

[0144] Embodiment 8

[0145] A power grid device status and data management system provided by an embodiment of the present invention, risk factors, including: risk factors related to device performance, risk factors related to the operating environment, risk factors related to operation and use, risk factors related to design and manufacturing, and risk factors related to the external environment.

[0146] In this embodiment, the risk factors related to device performance include: failure rate: the frequency of failures of the device or system, aging degree: the service life and wear condition of the device, maintenance record: the frequency, quality, and historical record of device maintenance, operating time: the continuous operating time of the device, which is usually proportional to the failure probability;

[0147] In this embodiment, the risk factors related to the operating environment are temperature: the impact of ambient temperature on the device, extreme temperatures may cause a decline in device performance or failure; humidity: high humidity may cause corrosion or short - circuit of internal components of the device; vibration: the vibration condition of the environment where the device is located, excessive vibration may cause damage to the mechanical structure of the device; dust: the accumulation of dust in the air may affect the heat dissipation of the device and the normal operation of electronic components;

[0148] In this embodiment, the risk factors related to operation and use include: the experience and skills of the operator: the technical level and experience of the operator directly affect the safe operation of the device; usage frequency: the usage frequency and load condition of the device, excessive use may cause early wear of the device; incorrect operation: the risks caused by incorrect operation, including setting errors or improper operation; over - load operation: the device operates at or near its maximum load for a long time, which may increase the risk of failure;

[0149] In this embodiment, the risk factors related to design and manufacturing include: design defects: the defects left in the design stage of the device, which may cause the device to behave abnormally or fail in some cases,

[0150] material quality: the quality problems of the materials used in the device manufacturing process, inferior materials may cause the shortening of the device life or the decline of performance; process problems: the process problems that occur during the manufacturing process, such as welding defects, assembly errors, etc.;

[0151] In this embodiment, the risk factors related to the external environment include: power quality: the stability of the power supply system, voltage fluctuations or short - term power outages may affect the device; network environment: for networked devices, factors such as network stability, latency, and packet loss will affect the operation of the device; natural disasters: natural disasters such as earthquakes, floods, and lightning strikes may directly affect the operation of the device.

[0152] The beneficial effects of the above - mentioned technical solutions are: by comprehensively introducing risk factors in multiple dimensions such as device performance, operating environment, usage behavior, design and manufacturing, and external environment, a comprehensive risk assessment model is established, which can more accurately and meticulously identify potential risks of power grid devices, enhance the accuracy of risk prediction and fault prevention, and significantly improve the safety and stability of power grid devices.

[0153] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present invention, not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A power grid equipment status and data management system, characterized in that: include: Data acquisition module: collects operation-related data of each device in the power grid and related data of the power grid based on the preset acquisition device; Data processing module: builds several initial autonomous layers of the power grid based on the operation-related data of each device in the power grid; Dynamic adjustment module: obtains the operating data of each device in the power grid after the initial autonomous layer division, and adjusts the initial autonomous layer based on the operating data of all devices in the power grid; Status determination module: Based on the adjusted initial autonomous layer, it obtains the real-time operation data of each device in the power grid, and then generates the health index of each device in the power grid in combination with the preset algorithm; Risk determination module: Determines the risk level of each device based on the health index of each device in the power grid and the real-time operation data of each device, and then generates a risk determination report; The data processing module includes: A first scoring unit: determining an initial status score of each device in the power grid based on initial operating status data of each device in the power grid; ;in, Score the initial state of the kth device in the power grid, is the total number of initial operating status data of the kth device in the power grid, is the historical operation reliability factor of the kth device in the power grid, is the environmental adaptability factor of the kth device, is the operating angle of the kth device in the power grid, is the g-th initial operating status data measurement value of the k-th device in the power grid, is the weight of the g-th initial operating status data of the k-th device in the power grid, is the operating time attenuation factor of the kth device in the power grid, is the cumulative operating time of the kth device in the power grid, is the absolute value of the difference between the initial load of the kth device in the grid and its rated load, is the rated load of the kth device in the power grid, is the relative importance coefficient of the kth device in the power grid, is the maintenance frequency coefficient of the kth device in the power grid, is the designed maintenance period of the kth device in the power grid; A second scoring unit: determining a load score of each node in the power grid based on the load condition of each node in the power grid and the historical load condition; ;in, is the load score of the qth node in the power grid, is the maximum allowable load of the qth node in the power grid, is the rated load of the qth node in the power grid, is the weight of the maximum allowable load of the qth node in the power grid, is the load of the qth node in the power grid at the historical time point t, is the total number of time points of historical load records of the qth node in the power grid, The weight of the historical load of the qth node in the power grid, is the historical load fluctuation factor of the qth node in the power grid, is the historical load average of the qth node in the power grid; Device determination unit: determines a number of associated devices corresponding to each node in the power grid based on the initial state score of each device in the power grid and the load score of each node in the power grid combined with a preset analysis algorithm; Network construction unit: constructs a node network based on all nodes in the power grid and several associated devices corresponding to each node; Initial division unit: Analyze the node network and divide the initial autonomous layer based on the analysis results. Each autonomous layer contains several nodes and devices associated with each node.

2. A power grid equipment status and data management system according to claim 1, characterized in that: Data acquisition module, including Equipment collection unit: Based on the preset sensor network, it collects the initial operating status data of each device in the power grid; Node collection unit: collects the load conditions of each node in the power grid based on the preset monitoring equipment; History acquisition unit: obtains the historical load conditions of each node in the power grid based on the historical operation data of the power grid.

3. A power grid equipment status and data management system according to claim 1, characterized in that: Initial division units include: Partition determination subunit: forming a node partition corresponding to each node based on the range where each node and the corresponding associated device are located; Module optimization subunit: Move the nodes and devices associated with each node partition to the node partition of the adjacent node and determine the modularity of each node in the adjacent partition. When the modularity reaches the maximum, the node will stop and the node partition of the adjacent node where it stops will be determined as the adjacent partition. Partition formation subunit: each node forms a super node partition with the nodes in the adjacent partition where it stays and the devices corresponding to the nodes; Partition optimization subunit: moves several nodes of each supernode partition to adjacent supernode partitions and determines the modularity of each supernode partition in the adjacent supernode partition until the modularity reaches the maximum value; First optimization subunit: repeat the above steps until the modularity of each super node partition no longer increases, then stop the iteration, and then form several comprehensive node partitions; The second optimization subunit: optimizes all integrated node partitions based on a preset splitting algorithm, and determines an initial autonomous layer based on the optimized integrated node partitions.

4. A power grid equipment status and data management system according to claim 3, characterized in that: The second optimization subunit comprises: Betweenness optimization block: based on the betweenness of each edge in each integrated node partition, the edge with the largest betweenness is gradually deleted. At the same time, the modularity of each integrated node partition is determined in the process of deleting edges; Optimization control block: Determine the change rate of the modularity of each integrated node partition during the edge deletion process. When the change rate of the modularity is greater than the preset change rate threshold, stop the deletion to form an optimized integrated node partition.

5. A power grid equipment status and data management system according to claim 1, characterized in that: Dynamic adjustment modules, including: A first scoring unit: acquiring operation data of each device in the power grid after the initial autonomous layer division, and then determining a first state score of each device in the power grid; Re-dividing unit: re-dividing the initial autonomous layer based on the first state scores of all devices in the power grid and the load score of each node in the power grid combined with a preset analysis algorithm; Optimizing division unit: Optimizing the initial autonomous layer after redivision based on a preset optimization algorithm, and then determining a number of adjusted autonomous layers.

6. A power grid equipment status and data management system according to claim 1, characterized in that: Risk determination module, including: Risk determination unit: determines a number of corresponding risk factors for each device based on the health index of the devices in the power grid and the real-time operation data of each device; Index determination unit: determines the risk index of each device based on the corresponding risk factors, health index and real-time operation data of each device: ;in, is the risk index of the kth device, is the i-th risk factor of the k-th device, is the weight of the i-th risk factor of the k-th device, is the number of risk factors of the kth device, is the health index of the kth device, is the health index weight coefficient of the kth device, is the weight coefficient of the real-time operation data impact coefficient of the kth device; in, is the real-time operation data impact coefficient of the kth device: ;in, is the actual measured value of the jth real-time operation data of the kth device, is the preset reference value of the jth real-time operation data of the kth device, is the preset maximum allowable deviation threshold of the jth real-time operation data of the kth device, is the weight of the jth real-time running data of the kth device, is the number of real-time operation data of the kth device; Report generating unit: determines the risk level of each device based on the risk index of each device, and generates a risk report corresponding to each device based on a preset risk level-risk report database.

7. A power grid equipment status and data management system according to claim 6, characterized in that: Risk factors include: risk factors related to equipment performance, risk factors related to the operating environment, risk factors related to operation and use, risk factors related to design and manufacturing, and risk factors related to the external environment.

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