An intensive wind-solar-storage base equipment state real-time monitoring and fault early warning system

Abstract

The present application relates to the technical field of equipment state monitoring and fault early warning, in particular to an intensive wind-solar-storage base equipment state real-time monitoring and fault early warning system, wherein a device fault diffusion path analysis module simulates the diffusion path of a single-point device based on a wind-solar-storage base equipment directed graph model, and assigns a probability weight to the device nodes in the simulated diffusion path; the actual influence probability corresponding to each diffused device node is dynamically calculated based on the redundancy design of the device and the fault data in the historical data. The present application dynamically adjusts the propagation probability weight of the device fault based on the redundancy design, thereby reducing the false alarm rate of the fault early warning; the present application automatically labels the key path device nodes closest to the fault device and having a physical isolation function, thereby providing the user with the optimal isolation position of the fault; and provides data support for subsequent analysis of the influence range corresponding to the fault device and configuration of the repair priority list at the current time.

Description

Technical Field

[0001] This invention relates to the field of equipment status monitoring and fault early warning technology, specifically to a real-time monitoring and fault early warning system for equipment status in an integrated wind, solar and energy storage base. Background Technology

[0002] With the large-scale development of renewable energy sources such as wind and solar power, intensive integrated wind-solar-storage bases (including wind farms, photovoltaic power stations, and energy storage systems) have become the mainstream construction model. These bases are characterized by their large scale, numerous equipment (wind turbines, photovoltaic modules, inverters, converters, transformers, battery clusters / stacking, etc.), wide geographical distribution (often located in remote and harsh environments), and tight system coupling (wind / solar / storage operate in synergy; equipment failures not only lead to power generation losses but may also trigger chain reactions and even safety accidents, such as energy storage fires, causing significant economic losses and safety risks). Therefore, the current monitoring and safety management of equipment status in intensive integrated wind-solar-storage bases faces enormous challenges.

[0003] Due to the large number of devices in intensive integrated wind, solar, and energy storage bases, traditional sampling monitoring cannot achieve comprehensive coverage of all devices, leaving many devices in an "invisible" state and making it difficult to detect potential faults in a timely manner. At the same time, existing integrated wind, solar, and energy storage bases suffer from severe data silos, with each subsystem typically using independent monitoring systems (SCADA) with different data formats and communication protocols, lacking a unified platform for correlation analysis and collaborative diagnosis. The coupled operational status between wind, solar, and energy storage is difficult to assess effectively. In other words, existing monitoring technologies suffer from numerous problems such as incomplete coverage, data fragmentation, diagnostic lag, weak predictive capabilities, and high costs, making it difficult to meet the urgent needs of large-scale bases for equipment status awareness, accurate fault early warning, and optimized operation and maintenance efficiency.

[0004] Therefore, how to achieve full coverage and real-time status monitoring and early warning of faults for large-scale, cross-type wind, solar, and energy storage equipment, and ensure the efficient, stable, and safe operation of the equipment, has become an urgent problem to be solved in the industry. Summary of the Invention

[0005] The purpose of this invention is to provide a real-time monitoring and fault early warning system for the equipment status of an integrated wind, solar and energy storage base, so as to solve the problems mentioned in the background art.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a real-time monitoring and fault early warning system for equipment status in an intensive wind, solar and energy storage base, the system comprising: an equipment association map construction module, an equipment fault propagation path analysis module, a fault impact analysis module, and a fault maintenance list early warning module;

[0007] The equipment association graph construction module uses wind, solar and energy storage base equipment as nodes and constructs a directed graph model of wind, solar and energy storage base equipment based on the physical connection relationship or logical dependency relationship between the equipment.

[0008] The equipment failure propagation path analysis module simulates the propagation path of a single point device based on the directed graph model of wind, solar and energy storage base equipment, and assigns probability weights to the equipment nodes in the simulated propagation path. Based on the redundant design of the equipment and the fault data in historical data, it dynamically calculates the actual impact probability of each propagating equipment node.

[0009] The fault impact analysis module takes the faulty equipment as the central node and obtains the impact range corresponding to the faulty equipment based on the fault propagation path analysis results of the equipment fault propagation path analysis module.

[0010] The fault maintenance list early warning module obtains the status monitoring results of the wind, solar and energy storage base equipment at the current time, and assesses the fault risk coefficient of each wind, solar and energy storage base equipment; it configures the maintenance priority list at the current time based on the constructed directed graph model of the wind, solar and energy storage base equipment; and it constructs fault early warning feedback information based on the maintenance priority list at the current time and the impact range corresponding to each maintenance equipment.

[0011] Furthermore, in the directed graph model of the wind-solar-storage base equipment, the edge connecting any two connected nodes corresponds to the physical connection relationship, energy transmission relationship, or logical dependency relationship between the two nodes; the direction of the edge connecting the corresponding two nodes is the same as the direction of energy transmission or control logic between the corresponding two nodes.

[0012] The wind-solar-storage base equipment in this invention includes wind turbines, photovoltaic modules, inverters, converters, transformers, battery clusters, upstream circuit breakers, and disconnect switches. This invention strengthens the coupling relationship between equipment in the wind, solar, and storage subsystems by establishing physical connections, energy transmission relationships, or logical dependencies between equipment nodes in the wind-solar-storage base. Furthermore, through a directed graph model of the wind-solar-storage base equipment, it maps the fault propagation relationships between equipment in the wind, solar, and storage subsystems, effectively reducing the occurrence of data silos in intensive wind-solar-storage bases.

[0013] Furthermore, the equipment fault propagation path analysis module includes a propagation path simulation unit and a probability weight allocation unit;

[0014] The diffusion path simulation unit simulates the failure diffusion path of a single point device based on a directed graph model of wind, solar and energy storage base equipment.

[0015] The probability weight allocation unit is used to assign probability weights to the equipment nodes in the fault propagation path simulated by the propagation path simulation unit, and dynamically calculate the actual impact probability of each propagating equipment node based on the redundant design of the equipment and the fault data in the historical data.

[0016] Furthermore, in the process of simulating the fault propagation path of a single point device based on the directed graph model of wind, solar and energy storage base equipment, the diffusion path simulation unit needs to prioritize marking the critical path device nodes in the fault propagation path; in the process of marking the critical path device nodes in the fault propagation path, the shortest path from the fault device node to the root device node in the directed graph model of wind, solar and energy storage base equipment is calculated, and the first device node with physical isolation function in the obtained shortest path is marked as the corresponding critical path device node.

[0017] In this invention, the critical path device node is the smallest device node that cuts off the spread of the fault, including the upstream circuit breaker node or the disconnecting switch node. The selection of the critical path device node not only provides users with an auxiliary reference for the optimal location of fault isolation, but also provides a basis for subsequent steps to analyze the impact range of the faulty device and configure the maintenance priority list for the current time.

[0018] Furthermore, in the process of allocating probability weights to equipment nodes in the fault propagation path simulated by the diffusion path simulation unit, the equipment node pointed to by the edge connection between two connected equipment nodes in the directed graph model of the wind, solar and energy storage base equipment is regarded as the downstream equipment node of the other equipment node. If the downstream equipment node has a multi-path redundancy design, the probability weight of the fault propagation to the corresponding downstream equipment node is reduced to one-tenth of the original value of the number of redundant paths. The original probability weight of the fault propagation to the corresponding downstream equipment node is equal to the product of the ratio of the statistical frequency of the fault occurrence of the downstream equipment node in the corresponding fault propagation path to the total statistical frequency of the corresponding fault propagation path in historical data and 100%. For critical path equipment nodes without redundancy, the corresponding probability weight is preset to 1.

[0019] This invention takes into account the redundancy design of downstream equipment nodes, and then dynamically adjusts the propagation probability weight of equipment failures to reduce the false alarm rate of fault warnings and ensure the accuracy of the constructed directed graph model of wind, solar and energy storage base equipment.

[0020] Furthermore, in the process of obtaining the impact range corresponding to the faulty equipment, the fault impact analysis module extracts each equipment node involved in the fault propagation path of the faulty equipment in the directed graph model of the wind, solar and energy storage base equipment, as well as each equipment node involved in the path segment from the corresponding critical path equipment node to the faulty equipment node in the shortest path from the faulty equipment node to the root equipment node. The sum of the extracted equipment nodes is used as the impact range corresponding to the faulty equipment. The fault propagation direction of two adjacent equipment nodes in the fault propagation path is the same as the direction of the corresponding edge connection line between the two nodes in the directed graph model of the wind, solar and energy storage base equipment.

[0021] Furthermore, the fault repair list early warning module includes an equipment fault risk coefficient assessment unit, a repair priority list construction unit, and a fault early warning feedback information construction unit;

[0022] The equipment failure risk coefficient assessment unit obtains the status monitoring results of the wind, solar and energy storage base equipment at the current time and assesses the failure risk coefficient of each wind, solar and energy storage base equipment.

[0023] The maintenance priority list construction unit configures the maintenance priority list for the current time based on the constructed directed graph model of wind, solar and energy storage base equipment.

[0024] The fault early warning feedback information construction unit constructs fault early warning feedback information based on the current maintenance priority list and the impact range of each maintenance equipment.

[0025] Furthermore, the equipment failure risk coefficient assessment unit obtains the current operating status of the wind, solar, and energy storage base equipment through sensors. This operating status includes the time interval since the last maintenance of the corresponding wind, solar, and energy storage base equipment. The calculation formula for the failure risk coefficient of the wind, solar, and energy storage base equipment is as follows:

[0026] Hb=Gi{Ti} / GSi

[0027] Where Hb represents the fault risk coefficient of the b-th wind, solar and energy storage base equipment; Ti represents the operating status of the i-th wind, solar and energy storage base equipment at the current time; Gi{Ti} represents the number of faults in the historical fault data of the i-th wind, solar and energy storage base equipment where the interval between the corresponding fault time and the last maintenance time is less than or equal to Ti; and GSi represents the total number of historical faults of the i-th wind, solar and energy storage base equipment.

[0028] Furthermore, during the process of configuring the maintenance priority list for the current time, the maintenance priority list construction unit obtains the fault risk coefficient of each wind, solar and energy storage base equipment, and combines it with the impact range corresponding to the corresponding wind, solar and energy storage base equipment as faulty equipment in the directed graph model of wind, solar and energy storage base equipment, calculates the maintenance demand assessment value corresponding to each wind, solar and energy storage base equipment, and sorts each wind, solar and energy storage base equipment in descending order of the corresponding maintenance demand assessment value to obtain the maintenance priority list for the current time.

[0029] In this invention, the maintenance priority list at the current time changes dynamically based on the status monitoring results of each equipment node at different times; the purpose of constructing the maintenance priority list is not only to provide feedback on equipment maintenance information, but also to provide early warning of equipment failure risks.

[0030] The calculation formulas involved in calculating the maintenance requirements assessment value for each wind, solar, and energy storage base equipment are as follows:

[0031] Mb=Hb·τ b

[0032] Where Mb represents the assessed maintenance requirement value corresponding to the b-th wind, solar and energy storage base equipment; τ b This represents the fault propagation coefficient corresponding to the b-th wind-solar-storage base equipment;

[0033]

[0034] Nb represents the number of device nodes belonging to the fault propagation path corresponding to the b-th wind, solar, and energy storage base equipment within the impact range of the b-th wind, solar, and energy storage base equipment; PL (b，f) This represents the cumulative product of the probability weights of each device node included in the shortest path from the f-th device node to the node to which the b-th wind, solar and energy storage base device belongs within the impact range of the b-th wind, solar and energy storage base device.

[0035] Compared with the prior art, the beneficial effects achieved by the present invention are:

[0036] (1) This invention constructs a directed graph model of wind, solar and energy storage base equipment, integrates the physical connection relationship, energy transmission relationship or logical dependency relationship between equipment, and for the first time supports cross-equipment fault propagation analysis of photovoltaic strings, energy storage battery clusters, wind turbines and other equipment;

[0037] (2) The present invention dynamically adjusts the propagation probability weight of equipment faults based on redundancy design, thereby reducing the false alarm rate of fault warning;

[0038] (3) The present invention automatically marks the critical path equipment nodes that are closest to the faulty equipment and have physical isolation function, providing users with the optimal isolation location for the fault; and provides data support for subsequent analysis of the impact range of the faulty equipment and configuration of the maintenance priority list for the current time;

[0039] (4) Based on the directed graph model of wind, solar and energy storage base equipment and the current time status monitoring results of wind, solar and energy storage base equipment, this invention introduces the concept of maintenance demand assessment value, realizes the quantification of the degree of equipment maintenance demand (fault early warning demand) of wind, solar and energy storage base equipment, dynamically constructs the maintenance priority list at the current time, and completes the generation and management of fault early warning feedback information. Attached Figure Description

[0040] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0041] Figure 1 This is a schematic diagram of the structure of an intensive wind, solar and energy storage base equipment real-time monitoring and fault early warning system according to the present invention. Detailed Implementation

[0042] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0043] Please see Figure 1 The present invention provides a technical solution: This embodiment provides a real-time monitoring and fault early warning system for the status of equipment in an intensive wind, solar and energy storage base. The system includes: an equipment association map construction module, an equipment fault propagation path analysis module, a fault impact analysis module, and a fault maintenance list early warning module.

[0044] The equipment association graph construction module uses wind, solar and energy storage base equipment as nodes and constructs a directed graph model of wind, solar and energy storage base equipment based on the physical connection relationship or logical dependency relationship between the equipment.

[0045] In this embodiment, the wind-solar-storage base equipment includes wind turbines, photovoltaic modules, inverters, converters, transformers, battery clusters, upstream circuit breakers, and disconnect switches; the edge connection between any two connected nodes in the directed graph model of the wind-solar-storage base equipment corresponds to the physical connection relationship, energy transmission relationship, or logical dependency relationship between the two nodes; the edge connection between the corresponding two nodes points in the same direction as the energy transmission direction or control logic direction between the corresponding two nodes.

[0046] In this embodiment, inverter failures can automatically affect multiple photovoltaic strings (the number of photovoltaic strings affected in this embodiment is 20). Compared with the single-point fault alarm method in traditional methods, it can accurately quantify power generation loss (such as a reduction of 200kW output).

[0047] In this embodiment, the construction of the directed graph model of the wind, solar and energy storage base equipment can intuitively reflect the fault propagation path of the wind, solar and energy storage base equipment, realize effective management of early warning of equipment faults, and at the same time, effectively strengthen the coupling relationship between the wind, solar and energy storage base equipment. Moreover, the fault propagation process does not reflect the same fault type, but rather that the fault of a certain equipment may cause another related equipment to also fail (it may be the same fault type as the previous equipment, or it may be different from the previous equipment's fault type), such as: inverter failure → photovoltaic shutdown → energy storage overload.

[0048] The equipment failure propagation path analysis module simulates the propagation path of a single point device based on the directed graph model of wind, solar and energy storage base equipment, and assigns probability weights to the equipment nodes in the simulated propagation path. Based on the redundant design of the equipment and the fault data in historical data, it dynamically calculates the actual impact probability of each propagating equipment node.

[0049] The equipment fault propagation path analysis module includes a propagation path simulation unit and a probability weight allocation unit;

[0050] The diffusion path simulation unit simulates the failure diffusion path of a single point device based on a directed graph model of wind, solar and energy storage base equipment.

[0051] In the process of simulating the fault propagation path of a single point device based on the directed graph model of wind, solar and energy storage base equipment, the diffusion path simulation unit needs to prioritize marking the critical path device nodes in the fault propagation path; in the process of marking the critical path device nodes in the fault propagation path, the shortest path from the fault device node to the root device node in the directed graph model of wind, solar and energy storage base equipment is calculated, and the first device node with physical isolation function in the obtained shortest path is marked as the corresponding critical path device node.

[0052] In this example, the critical path device node is the smallest device node that cuts off the spread of the fault, including the upstream circuit breaker node or the disconnector node. The selection of the critical path device node can effectively reduce the fault isolation time, shortening it from 30 minutes of manual troubleshooting to 3 seconds, effectively reducing unplanned downtime by about 40%.

[0053] The probability weight allocation unit is used to allocate probability weights to the equipment nodes in the fault propagation path simulated by the propagation path simulation unit, and dynamically calculate the actual impact probability of each propagating equipment node based on the redundant design of the equipment and the fault data in the historical data.

[0054] In the process of allocating probability weights to equipment nodes in the fault propagation path simulated by the diffusion path simulation unit, the equipment node pointed to by the edge connecting two connected equipment nodes in the directed graph model of wind, solar and energy storage base equipment is regarded as the downstream equipment node of the other equipment node. If the downstream equipment node has a multi-path redundancy design, the probability weight of the fault propagation to the corresponding downstream equipment node is reduced to one-tenth of the original value of the number of redundant paths. The original probability weight of the fault propagation to the corresponding downstream equipment node is equal to the product of the ratio of the statistical frequency of the fault occurrence of the downstream equipment node in the corresponding fault propagation path to the total statistical frequency of the corresponding fault propagation path in historical data and 100%. For critical path equipment nodes without redundancy, the corresponding probability weight is preset to 1.

[0055] The redundant design used in this embodiment includes dual PCS and a backup cooling pump.

[0056] In real-world scenarios, the probability of thermal runaway of energy storage batteries can be reduced from a fixed value of 1 to 0.8 (due to the effect of redundant cooling), effectively reducing ineffective shutdowns by 30%.

[0057] The fault impact analysis module takes the faulty equipment as the central node and obtains the impact range corresponding to the faulty equipment based on the fault propagation path analysis results of the equipment fault propagation path analysis module.

[0058] In the process of obtaining the impact range of the faulty equipment, the fault impact analysis module extracts each equipment node involved in the fault propagation path of the faulty equipment in the directed graph model of the wind, solar and energy storage base equipment, as well as each equipment node involved in the path segment from the critical path equipment node to the faulty equipment node in the shortest path from the faulty equipment node to the root equipment node. The sum of the extracted equipment nodes is taken as the impact range of the corresponding faulty equipment. The fault propagation direction of two adjacent equipment nodes in the fault propagation path is the same as the direction of the edge connection between the corresponding two nodes in the directed graph model of the wind, solar and energy storage base equipment.

[0059] The fault maintenance list early warning module obtains the status monitoring results of the wind, solar and energy storage base equipment at the current time, and assesses the fault risk coefficient of each wind, solar and energy storage base equipment; it configures the maintenance priority list at the current time based on the constructed directed graph model of the wind, solar and energy storage base equipment; and it constructs fault early warning feedback information based on the maintenance priority list at the current time and the impact range corresponding to each maintenance equipment.

[0060] The fault repair list early warning module includes an equipment fault risk coefficient assessment unit, a maintenance priority list construction unit, and a fault early warning feedback information construction unit.

[0061] The equipment failure risk coefficient assessment unit obtains the status monitoring results of the wind, solar and energy storage base equipment at the current time and assesses the failure risk coefficient of each wind, solar and energy storage base equipment.

[0062] The equipment failure risk coefficient assessment unit obtains the current operating status of the wind, solar, and energy storage base equipment through sensors. This operating status includes the time interval since the last maintenance of the corresponding wind, solar, and energy storage base equipment. The calculation formula for the failure risk coefficient of the wind, solar, and energy storage base equipment is as follows:

[0063] Hb=Gi{Ti} / GSi

[0064] Where Hb represents the fault risk coefficient of the b-th wind, solar and energy storage base equipment; Ti represents the operating status of the i-th wind, solar and energy storage base equipment at the current time; Gi{Ti} represents the number of faults in the historical fault data of the i-th wind, solar and energy storage base equipment where the interval between the corresponding fault time and the last maintenance time is less than or equal to Ti; and GSi represents the total number of historical faults of the i-th wind, solar and energy storage base equipment.

[0065] The maintenance priority list construction unit configures the maintenance priority list for the current time based on the constructed directed graph model of wind, solar and energy storage base equipment.

[0066] During the process of configuring the maintenance priority list for the current time, the maintenance priority list construction unit obtains the fault risk coefficient of each wind, solar and energy storage base equipment, and calculates the maintenance demand assessment value corresponding to each wind, solar and energy storage base equipment when it is a faulty equipment in the directed graph model of wind, solar and energy storage base equipment. The equipment is then sorted in descending order of the corresponding maintenance demand assessment value to obtain the maintenance priority list for the current time.

[0067] The calculation formulas involved in calculating the maintenance requirements assessment value for each wind, solar, and energy storage base equipment are as follows:

[0068] Mb=Hb·τ b

[0069] Where Mb represents the assessed maintenance requirement value corresponding to the b-th wind, solar and energy storage base equipment; τ b This represents the fault propagation coefficient corresponding to the b-th wind-solar-storage base equipment;

[0070]

[0071] Nb represents the number of device nodes belonging to the fault propagation path corresponding to the b-th wind, solar, and energy storage base equipment within the impact range of the b-th wind, solar, and energy storage base equipment; PL (b，f) This represents the cumulative product of the probability weights of each device node included in the shortest path from the f-th device node to the node to which the b-th wind, solar and energy storage base device belongs within the impact range of the b-th wind, solar and energy storage base device.

[0072] The fault early warning feedback information construction unit constructs fault early warning feedback information based on the current maintenance priority list and the impact range of each maintenance equipment.

[0073] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0074] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A real-time monitoring and fault early warning system for equipment status in an integrated wind, solar, and energy storage base, characterized in that, The system includes: an equipment association map construction module, an equipment fault propagation path analysis module, a fault impact analysis module, and a fault repair list early warning module; The equipment association graph construction module uses wind, solar and energy storage base equipment as nodes and constructs a directed graph model of wind, solar and energy storage base equipment based on the physical connection relationship or logical dependency relationship between the equipment. The equipment failure propagation path analysis module simulates the propagation path of a single point device based on the directed graph model of wind, solar and energy storage base equipment, and assigns probability weights to the equipment nodes in the simulated propagation path. Based on the redundant design of the equipment and the fault data in historical data, it dynamically calculates the actual impact probability of each propagating equipment node. The fault impact analysis module takes the faulty equipment as the central node and obtains the impact range corresponding to the faulty equipment based on the fault propagation path analysis results of the equipment fault propagation path analysis module. The fault maintenance list early warning module obtains the status monitoring results of the wind, solar and energy storage base equipment at the current time, and assesses the fault risk coefficient of each wind, solar and energy storage base equipment; it configures the maintenance priority list at the current time based on the constructed directed graph model of the wind, solar and energy storage base equipment; and it constructs fault early warning feedback information based on the maintenance priority list at the current time and the impact range corresponding to each maintenance equipment. The fault repair list early warning module includes an equipment fault risk coefficient assessment unit, a maintenance priority list construction unit, and a fault early warning feedback information construction unit. The equipment failure risk coefficient assessment unit obtains the status monitoring results of the wind, solar and energy storage base equipment at the current time and assesses the failure risk coefficient of each wind, solar and energy storage base equipment. The maintenance priority list construction unit configures the maintenance priority list for the current time based on the constructed directed graph model of wind, solar and energy storage base equipment. The fault early warning feedback information construction unit constructs fault early warning feedback information based on the current maintenance priority list and the impact range of each maintenance equipment. The equipment failure risk coefficient assessment unit obtains the current operating status of the wind, solar, and energy storage base equipment through sensors. This operating status includes the time interval since the last maintenance of the corresponding wind, solar, and energy storage base equipment. The calculation formula for the failure risk coefficient of the wind, solar, and energy storage base equipment is as follows: ； Where Hb represents the failure risk coefficient of the b-th wind, solar and energy storage base equipment; Ti represents the operating status of the i-th wind, solar and energy storage base equipment at the current time; Gi{Ti} represents the number of failures in the historical failure data of the i-th wind, solar and energy storage base equipment where the interval between the corresponding failure time and the last maintenance time is less than or equal to Ti; and GSi represents the total number of historical failures of the i-th wind, solar and energy storage base equipment. During the process of configuring the maintenance priority list for the current time, the maintenance priority list construction unit obtains the fault risk coefficient of each wind, solar and energy storage base equipment, and calculates the maintenance demand assessment value corresponding to each wind, solar and energy storage base equipment when it is a faulty equipment in the directed graph model of wind, solar and energy storage base equipment. The equipment is then sorted in descending order of the corresponding maintenance demand assessment value to obtain the maintenance priority list for the current time. The calculation formulas involved in calculating the maintenance requirements assessment value for each wind, solar, and energy storage base equipment are as follows: ； Where Mb represents the assessed maintenance requirement value corresponding to the b-th wind, solar and energy storage base equipment; τ b This represents the fault propagation coefficient corresponding to the b-th wind-solar-storage base equipment; ； Nb represents the number of device nodes belonging to the fault propagation path corresponding to the b-th wind, solar, and energy storage base equipment within the impact range of the b-th wind, solar, and energy storage base equipment; PL （b，f） This represents the cumulative product of the probability weights of each device node included in the shortest path from the f-th device node to the node to which the b-th wind, solar and energy storage base device belongs within the impact range of the b-th wind, solar and energy storage base device.

2. The integrated wind, solar, and energy storage base equipment real-time monitoring and fault early warning system according to claim 1, characterized in that: In the directed graph model of the wind, solar and energy storage base equipment, the edge connecting any two connected nodes corresponds to the physical connection relationship, energy transmission relationship or logical dependency relationship between the two nodes; the direction of the edge connecting the corresponding two nodes is the same as the energy transmission direction or control logic direction between the corresponding two nodes.

3. The integrated wind, solar, and energy storage base equipment real-time monitoring and fault early warning system according to claim 1, characterized in that: The equipment fault propagation path analysis module includes a propagation path simulation unit and a probability weight allocation unit; The diffusion path simulation unit simulates the failure diffusion path of a single point device based on a directed graph model of wind, solar and energy storage base equipment. The probability weight allocation unit is used to assign probability weights to the equipment nodes in the fault propagation path simulated by the propagation path simulation unit, and dynamically calculate the actual impact probability of each propagating equipment node based on the redundant design of the equipment and the fault data in the historical data.

4. The integrated wind-solar-storage base equipment real-time monitoring and fault early warning system according to claim 3, characterized in that: In the process of simulating the fault propagation path of a single point device based on the directed graph model of wind, solar and energy storage base equipment, the diffusion path simulation unit needs to prioritize marking the critical path device nodes in the fault propagation path. In the process of marking the critical path device nodes in the fault propagation path, the shortest path from the faulty device node to the root device node in the directed graph model of wind, solar and energy storage base equipment is calculated, and the first device node with physical isolation function in the obtained shortest path is marked as the corresponding critical path device node.

5. The integrated wind-solar-storage base equipment real-time monitoring and fault early warning system according to claim 4, characterized in that: In the process of allocating probability weights to equipment nodes in the fault propagation path simulated by the diffusion path simulation unit, the equipment node pointed to by the edge connecting two connected equipment nodes in the directed graph model of wind, solar and energy storage base equipment is regarded as the downstream equipment node of the other equipment node. If the downstream equipment node has a multi-path redundancy design, the probability weight of the fault propagation to the corresponding downstream equipment node is reduced to one-tenth of the original value of the number of redundant paths. The original probability weight of the fault propagation to the corresponding downstream equipment node is equal to the product of the ratio of the statistical frequency of the fault occurrence of the downstream equipment node in the corresponding fault propagation path to the total statistical frequency of the corresponding fault propagation path in historical data and 100%. For critical path equipment nodes without redundancy, the corresponding probability weight is preset to 1.

6. The integrated wind-solar-storage base equipment real-time monitoring and fault early warning system according to claim 1, characterized in that: In the process of obtaining the impact range of the faulty equipment, the fault impact analysis module extracts each equipment node involved in the fault propagation path of the faulty equipment in the directed graph model of the wind, solar and energy storage base equipment, as well as each equipment node involved in the path segment from the critical path equipment node to the faulty equipment node in the shortest path from the faulty equipment node to the root equipment node. The sum of the extracted equipment nodes is taken as the impact range of the corresponding faulty equipment. The fault propagation direction of two adjacent equipment nodes in the fault propagation path is the same as the direction of the edge connection between the corresponding two nodes in the directed graph model of the wind, solar and energy storage base equipment.

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