Power grid equipment disconnection classification method and system based on multi-dimensional data analysis

Abstract

The invention discloses a power grid equipment disconnection classification method and system based on multi-dimensional data analysis, and relates to the technical field of power grid data processing, and the method comprises the steps: monitoring whether a disconnection condition occurs in a target power grid through topological structure information, determining a disconnection type, laying a solid foundation for the subsequent sub-network island risk assessment, and improving the reliability of the sub-network island risk assessment. And the reliability of subsequent evaluation and analysis is ensured. The island risk of each subnet is evaluated according to the topological structure diagrams of the subnets, the splitting types and the data of the power grid, similar indexes of classification standards are generated in combination with the stability indexes of the subnets and the collaborative indexes between the devices, and the similar indexes are defined according to the island risks of the subnets and the conditions of the devices. And adaptability and reliability of subnet classification are improved. And resource configuration strategies are generated for the classified subnets, and different resource configuration strategies are formulated and allocated for the subnets under different conditions, so that the operation reliability of the subnet equipment after splitting is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid data processing, and in particular to a power grid equipment decomposition and classification method and system based on multi-dimensional data analysis. Background Art

[0002] Grid equipment de-gridding, usually referred to as "de-gridding or de-coupling", refers to a measure to cut off the electrical connection between generators and other parts of the power system, or between one part of the system and other parts of the system, and decompose them into independent and unconnected parts when the power system is disturbed and its stability is destroyed, and the synchronization between the generator and other parts of the power system is lost and cannot be restored, so as to prevent the accident from expanding and causing serious consequences. De-gridding is an important means to ensure the safe and stable operation of the power system.

[0003] In the prior art, after the power grid equipment is decoupled, many subnets may be generated. Since the specific equipment conditions of each subnet are relatively complex, the subnets cannot be accurately and adaptively classified, resulting in poor operating stability of subsequent subnet power grid equipment.

[0004] Therefore, how to improve the accuracy and adaptability of classification between subnets after decoupling is a technical problem that needs to be solved. Summary of the invention

[0005] The purpose of the present invention is to solve the problem of low accuracy and adaptability of classification between subnets after decoupling in the prior art, and propose a network decoupling classification method for power grid equipment based on multidimensional data analysis, which includes: Acquire the topological structure information of the target power grid, monitor whether a disconnection occurs in the target power grid through the topological structure information, and when a disconnection occurs in the target power grid, obtain the operation records before and after the disconnection of the target power grid, and determine the type of disconnection according to the operation records before and after the disconnection of the target power grid; Obtain basic equipment information of the target power grid, determine the location of the decoupling point based on the operation records and decoupling type before and after the decoupling of the target power grid, build the topological structure diagram of all subnets after the decoupling of the target power grid based on the topological structure information of the target power grid, basic equipment information and the location of the decoupling point, and analyze the topological structure diagram of each subnet by the decoupling type to evaluate the islanding risk of each subnet; Obtain the device operation information of each device before and after decoupling in the subnet topology diagram, and analyze the device operation information of each device to evaluate the stability index of each device and the coordination index between devices. Determine the stability index of the subnet based on the stability index of the device, and generate the similarity index of the subnet by combining the stability index of the subnet, the coordination index between devices and the island risk. All subnets in the target power grid are classified according to their similarity indicators, and resource allocation strategies are generated for the classified subnets to optimize the stability of equipment operation in the target power grid.

[0006] In some embodiments of the present application, whether a disconnection situation occurs in the target power grid is monitored by using topology information, including: The standard topology of the target power grid is constructed according to the topology information of the target power grid, and the nodes, lines and devices in the target power grid are traversed at a fixed period to construct the real-time topology of the target power grid, and the difference between the standard topology and the real-time topology of the target power grid is compared to determine the topology difference degree; Acquire the real-time key grid parameters of each node on the standard topology of the target grid, and determine the splitting matching degree through the real-time key grid parameters of each node on the standard topology and the preset splitting characteristic mode; The topology difference and split matching degree are combined to monitor whether splitting occurs in the target power grid.

[0007] In some embodiments of the present application, the type of disconnection is determined according to the operation records before and after the target power grid is disconnected, including: Determine the decoupling time node, and convert the operation records in a period of time before and after the decoupling time node into an operation timeline. The operation timeline is a horizontal axis listed in chronological order, and all operation events are marked on the horizontal axis; The operation timeline includes two types of timelines: a protection operation timeline and a non-protection operation timeline. The time scales of the protection operation timeline and the non-protection operation timeline are aligned. The protection operation timeline and the non-protection operation timeline are compared and integrated to determine the type of decoupling.

[0008] In some embodiments of the present application, the location of the disconnection point is determined according to the operation records and disconnection type before and after the target power grid is disconnected, including: The types of decoupling include active decoupling and passive decoupling; For active disconnection, the disconnection point is determined according to the non-protection operation timeline of the target power grid; For passive disconnection, topology analysis is performed based on the difference between the standard topology and the real-time topology of the target power grid and the protection operation timeline to screen out a set of disconnection point locations. The changes in key parameters of the power grid before and after disconnection at each disconnection point in the set of disconnection point locations are analyzed to determine the disconnection point location.

[0009] In some embodiments of the present application, the island risk of each subnet is evaluated by analyzing the topological structure diagram of each subnet by the type of decomposition, including: Identify key nodes and key lines in the topology diagram of each subnet, evaluate the scale index, load index, type and number of power sources of the subnet through key nodes and key lines, and evaluate the power redundancy of the subnet in combination with the type and number of power sources; The islanding risk level of the subnet is assessed based on the subnet's scale index, load index, power redundancy, and type of decoupling, and the islanding risk situation of the subnet is described by the islanding risk level; ; in, For the The island risk level of each subnet, For the The risk conversion coefficient of the load index of each subnet, For the The load index of each subnet, For the The risk conversion factor of the power redundancy of each subnet, For the Power redundancy of each subnet, For the The size index of each subnet, is a constant determined by the type of solution, is a preset constant, and [] is a rounding symbol.

[0010] In some embodiments of the present application, the device operation information of each device is analyzed to evaluate the stability index of each device and the coordination index between the devices, including: The device operation information of each device before and after the decoupling in the subnet topology structure diagram is divided into the device operation information of the complete period before and after the decoupling and the device operation information of the period after the decoupling according to the time node; Calculate the fluctuation of each operating index of the equipment based on the equipment operation information of the complete period before and after the decoupling, calculate the real-time quantity of each operating index of the equipment based on the equipment operation information of the period after the decoupling, obtain the failure rate of each equipment, and determine the stability index of the equipment by combining the fluctuation of the equipment's operating index, the real-time quantity of the operating index and the failure rate; ; in, For the The stability index of each device, For the The number of running indicators in the device, For the Device The stable weight of the operating indicators, For the Device The real-time quantity of the operating indicators, For the Device The fluctuation of the operating indicators, is the preset constant, For the The failure rate of each device, For the The failure stability influence coefficient of each device, For the The failure stability impact coefficient obtained by mapping the failure rate of each device; The coordination information between the devices is screened out from the device operation information before and after the devices are decoupled, and the coordination indicators between the devices are determined based on the coordination information.

[0011] In some embodiments of the present application, the coordination index between devices is determined according to the coordination information, including: The coefficient of variation of each collaborative indicator is calculated, and the coefficient of variation is integrated according to multiple devices and multiple collaborative indicator categories. The integrated coefficient of variation is used to describe the collaborative indicators between the devices in the subnet.

[0012] In some embodiments of the present application, similar indicators of the subnet are generated by combining the stability index of the subnet, the coordination index between devices and the island risk, including: According to the island risk level, subnets of the same level are planned into the same subnet set. The stability index difference between each subnet in the subnet set and the coordination index difference between devices are calculated. Similarity index is generated based on the stability index difference and the coordination index difference between devices. ; in, To the same subnet set subnet and Similarity index between subnets, is the island risk coefficient corresponding to the island risk level of the subnet set, is the similarity weight of the difference in stability index of the subnet, For the same subnet set subnet and The difference in stability index between subnets is is the similarity weight of the difference in the coordination index between the devices in the subnet, For the same subnet set subnet and The difference in coordination indicators between devices in different subnets.

[0013] Correspondingly, the present application also provides a grid equipment classification system based on multi-dimensional data analysis, including: A monitoring module is used to obtain topological information of the target power grid, monitor whether a disconnection occurs in the target power grid through the topological information, obtain operation records before and after the disconnection of the target power grid when the disconnection occurs in the target power grid, and determine the type of disconnection according to the operation records before and after the disconnection of the target power grid; The evaluation module is used to obtain basic equipment information of the target power grid, determine the location of the decoupling point according to the operation records and decoupling type of the target power grid before and after decoupling, build the topological structure diagram of all subnets after decoupling of the target power grid based on the topological structure information of the target power grid, basic equipment information and the location of the decoupling point, and analyze the topological structure diagram of each subnet by the decoupling type to evaluate the islanding risk of each subnet; A generation module is used to obtain the device operation information of each device before and after the decoupling in the subnet topology diagram, and analyze the device operation information of each device to evaluate the stability index of each device and the coordination index between devices, determine the stability index of the subnet through the stability index of the device, and generate the similarity index of the subnet by combining the stability index of the subnet, the coordination index between devices and the island risk; The classification module is used to classify all subnets in the target power grid according to the similarity indicators of the subnets, and generate resource allocation strategies for the classified subnets to optimize the stability of equipment operation in the target power grid.

[0014] Compared with the prior art, the present invention has the following beneficial effects: 1. Use topology information to monitor whether a disconnection occurs in the target power grid and determine the type of disconnection, laying a solid foundation for subsequent subnet island risk assessment and ensuring the reliability of subsequent assessment and analysis.

[0015] 2. Evaluate the island risk of each subnet based on the subnet topology diagram, the type of decoupling, and the data of the power grid. Then, combine the stability index of the subnet and the coordination index between the devices to generate similar indicators of the classification standard. Define similar indicators based on the island risk of the subnet and the equipment situation, and improve the adaptability and reliability of subnet classification. Generate resource allocation strategies for the classified subnets, and formulate and allocate different resource allocation strategies for subnets in different situations, so as to ensure the reliability of subnet equipment operation after decoupling. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A schematic diagram of a flow chart of a method for classifying power grid equipment based on multi-dimensional data analysis proposed by the present invention; Figure 2 This is a structural schematic diagram of a power grid equipment decomposition and classification system based on multi-dimensional data analysis proposed by the present invention. DETAILED DESCRIPTION

[0017] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0018] Reference Figure 1 , a grid equipment classification method based on multidimensional data analysis, comprising the following steps: Step S101, obtaining the topological structure information of the target power grid, monitoring whether a disconnection occurs in the target power grid through the topological structure information, and when a disconnection occurs in the target power grid, obtaining the operation records before and after the disconnection of the target power grid, and determining the type of disconnection according to the operation records before and after the disconnection of the target power grid.

[0019] In this embodiment, the topological structure information of the target power grid includes power grid nodes, lines, equipment types, capacity, etc. The types of power grid decoupling include active decoupling and passive decoupling. Passive decoupling has the following reasons: accidents in power grid tie lines, tie transformers or busbars: when key tie lines, tie transformers or busbars in the power grid fail, in order to prevent the spread of the fault, the system will automatically decoupling the affected parts of the power grid from other parts. Overload tripping: when some parts of the power grid trip due to overload, it may cause power grid decoupling. Protection malfunction tripping: due to the malfunction of the protection device, unnecessary tripping of the power grid equipment may occur, thereby causing decoupling. Active decoupling includes the following reasons: to eliminate power grid oscillation: when the power grid oscillates, in order to stabilize the operation of the power grid, the operator may manually decoupling the power grid. Low-frequency, low-voltage decoupling device automatically decoupling the power grid: when the frequency or voltage of the power grid drops to a certain level, the low-frequency, low-voltage decoupling device will automatically operate to decoupling the power grid to prevent system collapse.

[0020] In some embodiments of the present application, whether a disconnection situation occurs in the target power grid is monitored by using topology information, including: The standard topology of the target power grid is constructed according to the topology information of the target power grid, and the nodes, lines and devices in the target power grid are traversed at a fixed period to construct the real-time topology of the target power grid, and the difference between the standard topology and the real-time topology of the target power grid is compared to determine the topology difference degree; Acquire the real-time key grid parameters of each node on the standard topology of the target grid, and determine the splitting matching degree through the real-time key grid parameters of each node on the standard topology and the preset splitting characteristic mode; The topology difference and split matching degree are combined to monitor whether splitting occurs in the target power grid.

[0021] In this embodiment, topology search is a search technology that applies artificial intelligence to analyze and identify the structure of the power grid in the field of power grids. It constructs a real-time topology diagram of the power grid by traversing and searching each node, line, and device in the power grid. The currently constructed real-time topology diagram is compared with historical data or a preset normal topology (standard topology). If significant differences are found between the two, these differences are quantified, such as changes in the connection method of certain nodes or lines.

[0022] The preset disconnection characteristic mode is trained and constructed based on the key parameters of the power grid during the historical disconnection of the power grid. In the case of disconnection, the key parameters of the power grid may be as follows: Frequency deviation: The frequency of different parts of the grid begins to differ significantly, which may be caused by load changes, improper adjustment of generator output or changes in the grid structure. Frequency deviation is an early warning sign of decoupling.

[0023] Phase difference increases: In the power grid, the voltage and current phases of different nodes should remain consistent or have a small difference. If the phase difference starts to increase, it may mean that some parts of the power grid are losing synchronization, which is an important feature of desynchronization.

[0024] Voltage fluctuations: The stability of grid voltage is the basis for the normal operation of the grid. If the voltage starts to fluctuate significantly, it may be caused by sudden load changes, generator failures or grid structure problems. Voltage fluctuations are also an indicator of the risk of disconnection.

[0025] Abnormal power flow distribution: The power flow (i.e. the direction and size of power flow) in the power grid should follow the predetermined path and distribution. If the power flow distribution is abnormal, such as some lines are overloaded while other lines are lightly loaded, it may mean that there is a problem with the structure or operation of the power grid, which is also a manifestation of the risk of decoupling.

[0026] It should be noted that the key parameters of the power grid are different from the equipment operating parameters under the subsequent power grid. The power grid is a complex system composed of multiple devices and lines, and the key parameters of the power grid usually refer to the parameters that can reflect the overall operating status and characteristics of the power grid, such as voltage, current, frequency and phase angle. These parameters are the basis for the stable operation of the power grid and an important basis for grid status monitoring and fault diagnosis. The "parameters of each device under the power grid" focus more on describing the operating status and characteristics of specific equipment in the power grid, such as the voltage, current, power factor, etc. of the equipment. Although these parameters are also important information in the operation of the power grid, they more reflect the operating status of a single device rather than the overall status of the power grid.

[0027] In some embodiments of the present application, the type of disconnection is determined according to the operation records before and after the target power grid is disconnected, including: Determine the decoupling time node, and convert the operation records in a period of time before and after the decoupling time node into an operation timeline. The operation timeline is a horizontal axis listed in chronological order, and all operation events are marked on the horizontal axis; The operation timeline includes two types of timelines: a protection operation timeline and a non-protection operation timeline. The time scales of the protection operation timeline and the non-protection operation timeline are aligned. The protection operation timeline and the non-protection operation timeline are compared and integrated to determine the type of decoupling.

[0028] In this embodiment, the protection operation timeline is the action record of the protection device (such as circuit breaker, relay, etc.) in the power grid, and the non-protection operation timeline is the power grid operation record including but not limited to switch operation, equipment switching, load adjustment, etc. The type of disconnection is determined by combining the operation records on the protection operation timeline and the non-protection operation timeline, whether it is active disconnection or passive disconnection.

[0029] Step S102, obtain basic equipment information of the target power grid, determine the location of the decoupling point according to the operation records and decoupling type before and after the target power grid is decoupled, and build a topological structure diagram of all subnets after the target power grid is decoupled based on the topological structure information, basic equipment information and decoupling point location of the target power grid. Analyze the topological structure diagram of each subnet by the decoupling type to evaluate the islanding risk of each subnet.

[0030] In this embodiment, for active disconnection, the disconnection point is usually known. Active disconnection is a disconnection measure taken by the grid dispatcher based on the system operation status and prediction to prevent a large-scale power outage that may occur in the system. For passive disconnection, the disconnection point is usually unknown. Passive disconnection is due to a fault or abnormality in the grid, which causes the system to disconnect automatically or forcibly.

[0031] In this embodiment, island risk refers to the risk brought by the island phenomenon. The island (subnet) phenomenon refers to the situation where the energy storage system, distributed power source or a subnet and the local load spontaneously form an independent power supply area when the power supply of the power grid is interrupted due to a fault or other external factors. In this isolated "island", the power supply continues to supply power to the load, but is disconnected from the main grid. The risk lies in the stability or uncertainty of the self-sufficient power supply area that the main grid cannot control. Generally speaking, the island risk corresponding to active decoupling is smaller than the island risk corresponding to passive decoupling. Island risk may bring the following hazards: 1. Power quality degradation: The voltage and frequency within the island may be out of control, resulting in a serious degradation of power quality, which in turn affects the normal operation of the load equipment.

[0032] 2. Impaired grid stability: Island operation may also cause current surges and phase asynchrony when the grid is reconnected to the grid, causing damage to grid equipment and affecting the overall stability and security of the grid.

[0033] 3. Equipment damage and economic losses: The islanding effect may cause abnormal voltage and frequency in the system, causing damage to the equipment. At the same time, islanding operation will lead to energy waste and load imbalance, causing economic losses.

[0034] In some embodiments of the present application, the location of the disconnection point is determined according to the operation records and disconnection type before and after the target power grid is disconnected, including: The types of decoupling include active decoupling and passive decoupling; For active disconnection, the disconnection point is determined according to the non-protection operation timeline of the target power grid; For passive disconnection, topology analysis is performed based on the difference between the standard topology and the real-time topology of the target power grid and the protection operation timeline to screen out a set of disconnection point locations. The changes in key parameters of the power grid before and after disconnection at each disconnection point in the set of disconnection point locations are analyzed to determine the disconnection point location.

[0035] In this embodiment, for active uncoupling, the specific uncoupling point is obtained according to the system preset algorithm and strategy and is known. For passive uncoupling, the range of uncoupling points is narrowed down in combination with the topological structure of the power grid and the configuration of the protection device, and the possible uncoupling points are combined to form a uncoupling point position set. The uncoupling point position is comprehensively judged in combination with the records on the protection operation timeline and the changes in the key parameters of the power grid before and after uncoupling. The changes in the key parameters of the power grid (frequency deviation changes, phase difference changes, voltage fluctuation changes, etc. can all reflect the uncoupling situation).

[0036] Step S103, obtaining the device operation information of each device before and after the decoupling in the subnet topology diagram, and analyzing the device operation information of each device to evaluate the stability index of each device and the coordination index between devices, determining the stability index of the subnet through the stability index of the device, and generating the similarity index of the subnet by combining the stability index of the subnet, the coordination index between devices and the island risk.

[0037] In this embodiment, the device operation information includes voltage, current, temperature, etc. The coordination index between a single device and multiple devices in a subnet is determined based on the device operation information, and the two together constitute similar indicators.

[0038] In some embodiments of the present application, the island risk of each subnet is evaluated by analyzing the topological structure diagram of each subnet by the type of decomposition, including: Identify key nodes and key lines in the topology diagram of each subnet, evaluate the scale index, load index, type and number of power sources of the subnet through key nodes and key lines, and evaluate the power redundancy of the subnet in combination with the type and number of power sources; The islanding risk level of the subnet is assessed based on the subnet's scale index, load index, power redundancy, and type of decoupling, and the islanding risk situation of the subnet is described by the islanding risk level; ; in, For the The island risk level of each subnet, For the The risk conversion coefficient of the load index of each subnet, For the The load index of each subnet, For the The risk conversion factor of the power redundancy of each subnet, For the Power redundancy of each subnet, For the The size index of each subnet, is a constant determined by the type of solution, is a preset constant, and [] is a rounding symbol.

[0039] In this embodiment, the scale index, load index, type and quantity of power sources of the subnet are evaluated through key nodes and key lines. In the topological structure diagram of the subnet, key nodes (such as power points, load centers, etc.) and key lines (such as transmission corridors, important connecting lines, etc.) are identified. The size of the subnet directly affects its ability to operate in an isolated island. Smaller subnets may be easier to maintain stable operation after decoupling, while larger subnets may face greater challenges. The load index is the load stability obtained by combining the type, distribution, and change law of the load, and analyzing the type of power source in the subnet, such as traditional thermal power, hydropower, nuclear power, wind power, photovoltaic power, etc. There are differences in the stability and reliability of different types of power sources in isolated island operation. The redundancy of the power source in the subnet is evaluated in combination with the type and quantity of power sources, that is, the ability of the subnet to maintain stable operation after losing part of the power source. The higher the power redundancy, the lower the risk of subnet island operation.

[0040] In this embodiment, is a constant determined by the type of decoupling. The constant corresponding to active decoupling is greater than the constant corresponding to passive decoupling, so that The value is smaller. It represents the correction of the subnet size index requirement to the sum of load and power redundancy.

[0041] In some embodiments of the present application, the device operation information of each device is analyzed to evaluate the stability index of each device and the coordination index between the devices, including: The device operation information of each device before and after the decoupling in the subnet topology structure diagram is divided into the device operation information of the complete period before and after the decoupling and the device operation information of the period after the decoupling according to the time node; Calculate the fluctuation of each operating index of the equipment based on the equipment operation information of the complete period before and after the decoupling, calculate the real-time quantity of each operating index of the equipment based on the equipment operation information of the period after the decoupling, obtain the failure rate of each equipment, and determine the stability index of the equipment by combining the fluctuation of the equipment's operating index, the real-time quantity of the operating index and the failure rate; ; in, For the The stability index of each device, For the The number of running indicators in the device, For the Device The stable weight of the operating indicators, For the Device The real-time quantity of the operating indicators, For the Device The fluctuation of the operating indicators, is the preset constant, For the The failure rate of each device, For the The failure stability influence coefficient of each device, For the The failure stability impact coefficient obtained by mapping the failure rate of each device; The coordination information between the devices is screened out from the device operation information before and after the devices are decoupled, and the coordination indicators between the devices are determined based on the coordination information.

[0042] In this embodiment, the operating index fluctuation refers to a value describing the index fluctuation obtained based on the rate of change within the period of time, and the operating index real-time value is a relatively stable value determined based on the average value, maximum value and mode within the period of time. It indicates the correction of the real-time quantity of the operating index by the fluctuation of the operating index. The stability index of the equipment is the product of the average value of the sum of the comprehensive influence quantities of the equipment operating index and the failure stability influence coefficient.

[0043] In some embodiments of the present application, the coordination index between devices is determined according to the coordination information, including: The coefficient of variation of each collaborative indicator is calculated, and the coefficient of variation is integrated according to multiple devices and multiple collaborative indicator categories. The integrated coefficient of variation is used to describe the collaborative indicators between the devices in the subnet.

[0044] In this embodiment, the coordination indicators include but are not limited to the following: Communication delay: The delay time of information transmission between devices.

[0045] Synchronization accuracy: The degree of synchronization between states or operations of devices.

[0046] Response speed: How quickly one device responds to a request or command from another device.

[0047] Error rate: The error rate of information transmission or operation between devices.

[0048] Load balancing: The degree of workload balance among devices.

[0049] The degree of coordination between devices is measured by calculating the coefficient of variation of each indicator data. The smaller the coefficient of variation, the higher the degree of coordination between devices. Multiple indicators and multiple devices are integrated to determine the coordination indicators between devices in the subnet.

[0050] In some embodiments of the present application, similar indicators of the subnet are generated by combining the stability index of the subnet, the coordination index between devices and the island risk, including: According to the island risk level, subnets of the same level are planned into the same subnet set. The stability index difference between each subnet in the subnet set and the coordination index difference between devices are calculated. Similarity index is generated based on the stability index difference and the coordination index difference between devices. ; in, To the same subnet set subnet and Similarity index between subnets, is the island risk coefficient corresponding to the island risk level of the subnet set, is the similarity weight of the difference in stability index of the subnet, For the same subnet set subnet and The difference in stability index between subnets is is the similarity weight of the difference in the coordination index between the devices in the subnet, For the same subnet set subnet and The difference in coordination indicators between devices in different subnets.

[0051] In this embodiment, subnets are classified according to similar indicators in the same subnet set. It is the island risk coefficient corresponding to the island risk level of the subnet set. Different island risk levels correspond to different island risk coefficients.

[0052] Step S104, classifying all subnets in the target power grid according to similarity indicators of the subnets, and generating resource allocation strategies for the classified subnets, so as to optimize the operation stability of the equipment in the target power grid.

[0053] In this embodiment, resource configuration strategies are generated according to the specific characteristics of different types of subnets. The resource configuration strategies include specific combinations of different directions such as equipment upgrade, backup power configuration, line transformation, and network security protection.

[0054] Compared with the prior art, the present invention has the following beneficial effects: 1. Use topology information to monitor whether a disconnection occurs in the target power grid and determine the type of disconnection, laying a solid foundation for subsequent subnet island risk assessment and ensuring the reliability of subsequent assessment and analysis.

[0055] 2. Evaluate the island risk of each subnet based on the subnet topology diagram, the type of decoupling, and the data of the power grid. Then, combine the stability index of the subnet and the coordination index between the devices to generate similar indicators of the classification standard. Define similar indicators based on the island risk of the subnet and the equipment situation, and improve the adaptability and reliability of subnet classification. Generate resource allocation strategies for the classified subnets, and formulate and allocate different resource allocation strategies for subnets in different situations, so as to ensure the reliability of subnet equipment operation after decoupling.

[0056] Through the description of the above implementation methods, those skilled in the art can clearly understand that the present invention can be implemented by hardware, or by software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the present invention can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.), including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each implementation scenario of the present invention.

[0057] Correspondingly, the present application also provides a network classification system for power grid equipment based on multi-dimensional data analysis, such as Figure 2 As shown, including, A monitoring module is used to obtain topological information of the target power grid, monitor whether a disconnection occurs in the target power grid through the topological information, obtain operation records before and after the disconnection of the target power grid when the disconnection occurs in the target power grid, and determine the type of disconnection according to the operation records before and after the disconnection of the target power grid; The evaluation module is used to obtain basic equipment information of the target power grid, determine the location of the decoupling point according to the operation records and decoupling type of the target power grid before and after decoupling, build the topological structure diagram of all subnets after decoupling of the target power grid based on the topological structure information of the target power grid, basic equipment information and the location of the decoupling point, and analyze the topological structure diagram of each subnet by the decoupling type to evaluate the islanding risk of each subnet; A generation module is used to obtain the device operation information of each device before and after the decoupling in the subnet topology diagram, and analyze the device operation information of each device to evaluate the stability index of each device and the coordination index between devices, determine the stability index of the subnet through the stability index of the device, and generate the similarity index of the subnet by combining the stability index of the subnet, the coordination index between devices and the island risk; The classification module is used to classify all subnets in the target power grid according to the similarity indicators of the subnets, and generate resource allocation strategies for the classified subnets to optimize the stability of equipment operation in the target power grid.

[0058] Those skilled in the art will appreciate that the accompanying drawings are merely schematic diagrams of a preferred implementation scenario, and the modules or processes in the accompanying drawings are not necessarily required for implementing the present invention.

[0059] Those skilled in the art will appreciate that the modules in the system in the implementation scenario can be distributed in the system of the implementation scenario according to the implementation scenario description, or can be changed accordingly and located in one or more systems different from the implementation scenario. The modules in the above implementation scenario can be combined into one module, or can be further split into multiple submodules.

[0060] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A method for classifying power grid equipment based on multidimensional data analysis, characterized in that: include, Acquire the topological structure information of the target power grid, monitor whether a disconnection occurs in the target power grid through the topological structure information, and when a disconnection occurs in the target power grid, obtain the operation records before and after the disconnection of the target power grid, and determine the type of disconnection according to the operation records before and after the disconnection of the target power grid; Obtain basic equipment information of the target power grid, determine the location of the decoupling point based on the operation records and decoupling type before and after the decoupling of the target power grid, build the topological structure diagram of all subnets after the decoupling of the target power grid based on the topological structure information of the target power grid, basic equipment information and the location of the decoupling point, and analyze the topological structure diagram of each subnet by the decoupling type to evaluate the islanding risk of each subnet; Obtain the device operation information of each device before and after decoupling in the subnet topology diagram, and analyze the device operation information of each device to evaluate the stability index of each device and the coordination index between devices. Determine the stability index of the subnet based on the stability index of the device, and generate the similarity index of the subnet by combining the stability index of the subnet, the coordination index between devices and the island risk. All subnets in the target power grid are classified according to their similarity indicators, and resource allocation strategies are generated for the classified subnets to optimize the stability of equipment operation in the target power grid.

2. The method for classifying power grid equipment based on multidimensional data analysis according to claim 1, characterized in that: Use topology information to monitor whether the target grid is disconnected, including: The standard topology of the target power grid is constructed according to the topology information of the target power grid, and the nodes, lines and devices in the target power grid are traversed at a fixed period to construct the real-time topology of the target power grid, and the difference between the standard topology and the real-time topology of the target power grid is compared to determine the topology difference degree; Acquire the real-time key grid parameters of each node on the standard topology of the target grid, and determine the splitting matching degree through the real-time key grid parameters of each node on the standard topology and the preset splitting characteristic mode; The topology difference and split matching degree are combined to monitor whether splitting occurs in the target power grid.

3. The method for classifying power grid equipment based on multidimensional data analysis according to claim 1, characterized in that: And determine the type of disconnection based on the operation records before and after the target power grid is disconnected, including: Determine the decoupling time node, and convert the operation records in a period of time before and after the decoupling time node into an operation timeline. The operation timeline is a horizontal axis listed in chronological order, and all operation events are marked on the horizontal axis; The operation timeline includes two types of timelines: a protection operation timeline and a non-protection operation timeline. The time scales of the protection operation timeline and the non-protection operation timeline are aligned. The protection operation timeline and the non-protection operation timeline are compared and integrated to determine the type of decoupling.

4. The method for classifying power grid equipment based on multidimensional data analysis according to claim 2 or 3, characterized in that: Determine the location of the disconnection point based on the operation records and disconnection type before and after the target grid is disconnected, including: The types of decoupling include active decoupling and passive decoupling; For active disconnection, the disconnection point is determined according to the non-protection operation timeline of the target power grid; For passive disconnection, topology analysis is performed based on the difference between the standard topology and the real-time topology of the target power grid and the protection operation timeline to screen out a set of disconnection point locations. The changes in key parameters of the power grid before and after disconnection at each disconnection point in the set of disconnection point locations are analyzed to determine the disconnection point location.

5. The method for classifying power grid equipment based on multidimensional data analysis according to claim 1, characterized in that: Assess the islanding risk of each subnet by analyzing the topology diagram of each subnet by type of decomposition, including: Identify key nodes and key lines in the topology diagram of each subnet, evaluate the scale index, load index, type and number of power sources of the subnet through key nodes and key lines, and evaluate the power redundancy of the subnet in combination with the type and number of power sources; The islanding risk level of the subnet is assessed based on the subnet's scale index, load index, power redundancy, and type of decoupling, and the islanding risk situation of the subnet is described by the islanding risk level; ； in, For the The island risk level of each subnet, For the The risk conversion coefficient of the load index of each subnet, For the The load index of each subnet, For the The risk conversion factor of the power redundancy of each subnet, For the Power redundancy of each subnet, For the The size index of each subnet, is a constant determined by the type of solution, is a preset constant, and [] is a rounding symbol.

6. The method for classifying power grid equipment based on multidimensional data analysis according to claim 5, characterized in that: And analyze the equipment operation information of each device to evaluate the stability index of each device and the coordination index between devices, including, The device operation information of each device before and after the decoupling in the subnet topology structure diagram is divided into the device operation information of the complete period before and after the decoupling and the device operation information of the period after the decoupling according to the time node; Calculate the fluctuation of each operating index of the equipment based on the equipment operation information of the complete period before and after the decoupling, calculate the real-time quantity of each operating index of the equipment based on the equipment operation information of the period after the decoupling, obtain the failure rate of each equipment, and determine the stability index of the equipment by combining the fluctuation of the equipment's operating index, the real-time quantity of the operating index and the failure rate; ； in, For the The stability index of each device, For the The number of running indicators in the device, For the Device The stable weight of the operating indicators, For the Device The real-time quantity of the operating indicators, For the Device The fluctuation of the operating indicators, is the preset constant, For the The failure rate of each device, For the The failure stability influence coefficient of each device, For the The failure stability impact coefficient obtained by mapping the failure rate of each device; The coordination information between the devices is screened out from the device operation information before and after the devices are decoupled, and the coordination indicators between the devices are determined based on the coordination information.

7. The method for classifying power grid equipment based on multidimensional data analysis according to claim 6, characterized in that: Determine the coordination indicators between devices based on the coordination information. include, The coefficient of variation of each collaborative indicator is calculated, and the coefficient of variation is integrated according to multiple devices and multiple collaborative indicator categories. The integrated coefficient of variation is used to describe the collaborative indicators between the devices in the subnet.

8. The method for classifying power grid equipment based on multidimensional data analysis according to claim 7, characterized in that: Combine the stability index of the subnet, the coordination index between devices and the island risk to generate the similarity index of the subnet. include, According to the island risk level, subnets of the same level are planned into the same subnet set. The stability index difference between each subnet in the subnet set and the coordination index difference between devices are calculated. Similarity index is generated based on the stability index difference and the coordination index difference between devices. ； in, To the same subnet set subnet and Similarity index between subnets, is the island risk coefficient corresponding to the island risk level of the subnet set, is the similarity weight of the difference in stability index of the subnet, For the same subnet set subnet and The difference in stability index between subnets is is the similarity weight of the difference in the coordination index between the devices in the subnet, For the same subnet set subnet and The difference in coordination indicators between devices in different subnets.

9. A network classification system for power grid equipment based on multi-dimensional data analysis, characterized in that: include, A monitoring module is used to obtain topological information of the target power grid, monitor whether a disconnection occurs in the target power grid through the topological information, obtain operation records before and after the disconnection of the target power grid when the disconnection occurs in the target power grid, and determine the type of disconnection according to the operation records before and after the disconnection of the target power grid; The evaluation module is used to obtain basic equipment information of the target power grid, determine the location of the decoupling point according to the operation records and decoupling type of the target power grid before and after decoupling, build the topological structure diagram of all subnets after decoupling of the target power grid based on the topological structure information of the target power grid, basic equipment information and the location of the decoupling point, and analyze the topological structure diagram of each subnet by the decoupling type to evaluate the islanding risk of each subnet; A generation module is used to obtain the device operation information of each device before and after the decoupling in the subnet topology diagram, and analyze the device operation information of each device to evaluate the stability index of each device and the coordination index between devices, determine the stability index of the subnet through the stability index of the device, and generate the similarity index of the subnet by combining the stability index of the subnet, the coordination index between devices and the island risk; The classification module is used to classify all subnets in the target power grid according to the similarity indicators of the subnets, and generate resource allocation strategies for the classified subnets to optimize the stability of equipment operation in the target power grid.

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