Power grid cascading failure deduction and elastic recovery collaborative decision-making method and system

Abstract

The invention provides a power grid cascading failure deduction and elastic recovery collaborative decision-making method and system, and the method comprises the steps: constructing a state model of each power grid element in extreme weather, and obtaining the state transition probability of each power grid element in continuous time according to the state model; according to the state transition probability, constructing a system cascading failure evolution model under the extreme weather, and according to the system cascading failure evolution model, calculating to obtain a line real-time outage probability under the wind power integration scene; taking the minimum load shedding amount as a target, and constructing an optimal power flow model according to the real-time outage probability of the line, so as to simulate a derating operation response process after a power grid fault, and obtaining a load shedding strategy of the system in each fault state; and based on the load reduction strategy and the system topology constraint, establishing a post-disaster emergency recovery model, and determining a fault recovery time sequence and a load recovery sequence. According to the invention, the risk perception capability, decision scientificity and recovery efficiency of the power grid in extreme events can be obviously improved.

Description

Technical Field

[0001] This invention relates to the field of power grid fault decision-making technology, and in particular to a collaborative decision-making method and system for power grid cascading fault simulation and resilient recovery. Background Technology

[0002] The current power grid exposes two major technical bottlenecks under extreme weather and complex operating scenarios: First, the fault prediction mechanism has a fundamental flaw. Traditional methods rely on static fault databases and preset scenarios, which cannot capture the dynamic time-varying characteristics of component failure rates under extreme weather conditions. For example, when typhoon paths deviate or the scope of ice storms expands, traditional models cannot correct component failure probabilities in real time, resulting in significant deviations in cascading fault path predictions. Second, recovery decisions lack flexible coordination capabilities. Existing technologies adopt a separate strategy of "fault repair - load restoration," which does not consider the dynamic coupling relationship between fault propagation and load reduction. For example, in the case of multi-line cascading faults, traditional methods may prioritize repairing non-critical lines while neglecting support for important load nodes, leading to the risk of secondary power outages during the recovery process. Summary of the Invention

[0003] The purpose of this invention is to provide a collaborative decision-making method and system for power grid cascading fault simulation and resilient recovery. This aims to solve the problem that traditional fault recovery technologies rely on static fault databases and preset scenarios, which cannot capture the dynamic time-varying characteristics of component failure rates under extreme weather conditions, leading to the risk of secondary power outages during the recovery process.

[0004] In a first aspect, the present invention provides a method for collaborative decision-making on power grid cascading fault prediction and resilient recovery, the method comprising:

[0005] A state model of each power grid component under extreme weather conditions is constructed, and the state transition probability of each power grid component in continuous time is obtained based on the state model.

[0006] Based on the state transition probability, a system cascading failure evolution model under extreme weather conditions is constructed, and the real-time outage probability of the line under the wind power grid connection scenario is calculated based on the system cascading failure evolution model.

[0007] With the goal of minimizing the load shedding amount, and based on the real-time outage probability of the line, an optimal power flow model is constructed to simulate the derated operation response process after a power grid fault, and to obtain the load reduction strategy of the system under each fault state.

[0008] Based on the load reduction strategy and system topology constraints, a post-disaster emergency recovery model is established to determine the fault repair sequence and load recovery sequence.

[0009] In some embodiments, the step of constructing state models for each power grid element under extreme weather conditions and obtaining the state transition probabilities of each power grid element over continuous time based on the state models includes:

[0010] The operating state of the power grid components at time t is obtained after being affected by extreme weather and power flow shift at various times, and the final operating state of the power grid components is defined based on the operating state.

[0011] The state transition probability of a power grid element is defined based on its final operating state at consecutive times.

[0012] In some embodiments, the method further includes:

[0013] The state transition probability of the system in continuous time is calculated based on the state transition probability of the power grid components in continuous time.

[0014] In some embodiments, the step of constructing a system cascading failure evolution model under extreme weather conditions based on the state transition probability, and calculating the real-time outage probability of the line in a wind power grid-connected scenario based on the system cascading failure evolution model includes:

[0015] The active power of the line is obtained and compared with the line's rated power and maximum transmission power, respectively. The probability of line overload outage is obtained based on the comparison results.

[0016] The active power probability density function of the line is obtained by random power flow calculation, and the real-time outage probability of the line is obtained based on the line overload outage probability and the line active power probability density function.

[0017] In some embodiments, the step of constructing an optimal power flow model based on the real-time outage probability of the line, with the objective of minimizing the load shedding amount, includes:

[0018] With the goal of minimizing the sum of load shedding at all nodes, a weighting coefficient is introduced, and an optimal power flow model is constructed based on the real-time outage probability of the line.

[0019] Establish constraints on the optimal power flow model, including nodal power balance equations, upper and lower limits of line transmission power, load shedding range, and upper and lower limits of generator output.

[0020] In some embodiments, in the post-disaster emergency recovery model, the transmission line fault repair time follows a log-normal distribution, and its probability density function is determined by the mean and variance parameters, which are used to simulate the randomness of the repair time and determine the repair sequence.

[0021] Secondly, the present invention provides a collaborative decision-making system for power grid cascading fault prediction and resilient recovery, the system comprising:

[0022] The state model construction module is used to construct state models of various power grid components under extreme weather conditions, and to obtain the state transition probabilities of each power grid component over continuous time based on the state models.

[0023] The probability calculation module is used to construct a system cascading failure evolution model under extreme weather conditions based on the state transition probability, and to calculate the real-time outage probability of the line under the wind power grid connection scenario based on the system cascading failure evolution model.

[0024] The power flow model construction module is used to construct an optimal power flow model with the goal of minimizing the load shedding amount and based on the real-time outage probability of the line, so as to simulate the derated operation response process after the power grid fault and obtain the load reduction strategy of the system under each fault state.

[0025] The recovery model construction module is used to establish a post-disaster emergency recovery model based on the load reduction strategy and system topology constraints, and to determine the fault repair sequence and load recovery sequence.

[0026] In some embodiments, the state model building module is further configured to:

[0027] The operating state of the power grid components at time t is obtained after being affected by extreme weather and power flow shift at various times, and the final operating state of the power grid components is defined based on the operating state.

[0028] The state transition probability of a power grid element is defined based on its final operating state at consecutive times.

[0029] Thirdly, the present invention provides a storage medium that stores one or more programs, which, when executed by a processor, implement the above-mentioned collaborative decision-making method for power grid cascading fault simulation and resilient recovery.

[0030] Fourthly, the present invention provides an electronic device, the electronic device comprising a memory and a processor, wherein:

[0031] The memory is used to store computer programs;

[0032] When the processor executes the computer program stored in the memory, it implements the above-mentioned collaborative decision-making method for power grid cascading fault simulation and resilient recovery.

[0033] Compared with the prior art, the present invention has the following advantages:

[0034] This invention introduces dual state variables of extreme weather and power flow transfer to construct a dynamic state model of power grid components, reflecting the interaction between environmental factors and power grid operating status in real time, thus solving the problem of poor adaptability of traditional methods to dynamic environments. In the probabilistic extrapolation stage, a system cascading fault evolution model is constructed based on the dynamic transfer probability of components. The line outage probability is updated in real time through stochastic power flow calculation, accurately capturing the dynamic propagation process of faults and avoiding prediction bias caused by static models. In the recovery optimization stage, an optimal power flow model is constructed with the goal of minimizing load shedding. Dynamic repair timing and load recovery sequences are formulated in combination with system topology constraints, thereby significantly improving the power grid's risk perception capability, decision-making scientificity, and recovery efficiency in extreme events. Attached Figure Description

[0035] Figure 1 This is a flowchart of a collaborative decision-making method for power grid cascading fault simulation and resilient recovery proposed in an embodiment of the present invention;

[0036] Figure 2 This is a schematic diagram of the structure of a collaborative decision-making system for power grid cascading fault simulation and resilient recovery proposed in an embodiment of the present invention. Detailed Implementation

[0037] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are only some embodiments of the present invention, 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. Unless otherwise defined, the technical or scientific terms used herein should have the ordinary meaning understood by those skilled in the art. The terms "comprising" and similar expressions used herein mean that the element or object preceding the word covers the element or object listed after the word and its equivalents, but does not exclude other elements or objects.

[0038] like Figure 1 As shown, an embodiment of the present invention proposes a collaborative decision-making method for power grid cascading fault prediction and resilient recovery. This method includes steps S101 to S104, wherein:

[0039] Step S101: Construct state models of various power grid components under extreme weather conditions, and obtain the state transition probabilities of each power grid component in continuous time based on the state models;

[0040] It should be noted that under extreme weather conditions such as typhoons, any factor, including severe weather and power flow shifts, may cause grid components to fail, thereby altering the system state. Therefore, it is first necessary to obtain the operating state of the grid components at time t after being affected by extreme weather and power flow shifts at various times, and define the final operating state of the grid components based on these operating states. The state transition probabilities of the grid components are then defined based on their final operating states at consecutive times. Specifically, a state model is constructed using the following formula:

[0041] ;

[0042] in, Let represent the operating state of power grid component l at time t, where 1 indicates normal operation of the component and 0 indicates a fault in the component. , These represent the operating states of power grid components at time t after being affected by extreme weather and power flow shifts.

[0043] As typhoons develop and move, they impact power transmission lines and wind turbines within their affected area, resulting in system state changes that are both temporal and random. By discretizing the continuous-time system state changes, the fault evolution process can be simulated. Furthermore, since the system state depends only on the state at the previous moment, the system state transition process can be considered a discrete-time Markov process, and the transition probability between two adjacent time periods can be expressed as:

[0044] ;

[0045] in, This indicates the state of the system at time t. Let t be the set of power grid components that may fail at time t. Indicates the operating status of the component from Transferred to The probability of.

[0046] Specifically, the state transition probability of the system over continuous time is calculated based on the state transition probability of the power grid components over continuous time, as follows:

[0047] ;

[0048] in, The operating state of the power grid components at time t from Transferred to The probability, Let l be the operating state of component l at time t+1. , These represent the probabilities of disaster-related failures and cascading failures, respectively. , These represent the time scales corresponding to the development of disaster-induced faults and cascading faults at time t.

[0049] In summary, by integrating the state transition probabilities of individual components, a system-level dynamic state transition model is constructed, enabling cross-level extrapolation from component failure to system-wide cascading failure. This model can capture the collaborative evolution process of multi-component cascading failures in real time, avoiding prediction biases caused by neglecting inter-component coupling relationships in traditional methods, and providing a more accurate dynamic assessment for system-level failure prediction. Furthermore, by defining the operational state variables of components under the dual influence of extreme weather and tidal current shifts, the transition relationship between normal and fault states of components is formulaically described. Simultaneously, by introducing time-scale parameters for disaster-induced failure probability and cascading failure probability, the state transition probabilities of components are calculated separately, addressing the problem of poor adaptability to dynamic environments in traditional methods.

[0050] Step S102: Construct a system cascading failure evolution model under extreme weather conditions based on the state transition probability, and calculate the real-time outage probability of the line under the wind power grid connection scenario based on the system cascading failure evolution model;

[0051] System state changes caused by various disturbances such as line outages and wind power output fluctuations due to typhoon weather are mainly reflected in power flow changes. Power flow shifts and inrushes can potentially trigger cascading failures. Therefore, the active power of the line is first obtained, and then compared with the line's rated power and maximum transmission power. Based on the comparison results, the probability of line overload outage is obtained. In some embodiments, the probability of line overload outage can be represented by a piecewise function.

[0052] ;

[0053] in, This indicates the probability of line outage due to overload. To protect against the probability of latent faults, This refers to the active power of the line. , These are the line's rated power and maximum transmission power, respectively.

[0054] When considering the uncertainty of wind farm output and the impact of wind turbine failures, the line power flow calculated by stochastic power flow is no longer a constant value, but a probability distribution of line power flow. Therefore, it needs to be obtained through convolution. Combining the line outage probability and the power flow probability density function, the real-time line outage probability in the wind power grid-connected scenario is obtained as follows:

[0055] ;

[0056] in, Let be the probability density function of active power of the line obtained through stochastic power flow calculation. This represents the real-time probability of line outages.

[0057] By introducing a dual calculation mechanism of line overload outage probability and protection latent fault probability, and combining it with the line active power probability density function obtained by stochastic power flow calculation, the risk of line outage under wind power grid connection scenario is assessed in real time. This solves the problem of insufficient adaptability of traditional methods to the volatility of new energy sources, and provides key support for the accurate prediction of fault evolution path.

[0058] Step S103: With the goal of minimizing the load shedding amount, and based on the real-time outage probability of the line, construct an optimal power flow model to simulate the derating operation response process after a power grid fault, and obtain the load reduction strategy of the system under each fault state.

[0059] During the fault evolution process, after multiple faults occur, the transmission system needs to reschedule generators or even perform emergency load shedding to meet the static safety conditions of the system. In transmission system risk assessment, optimal power flow models based on DC power flow are often used to quickly calculate group adjustments and load shedding amounts. In this step, an optimal power flow model is constructed with the objective of minimizing the sum of load shedding amounts at all nodes, and constraints are established regarding this optimal power flow model. These constraints include node power balance equations, upper and lower limits of line transmission power, load shedding ranges, and upper and lower limits of generator output, as shown in the following equations:

[0060] ;

[0061] in, Let N be the load shedding amount at node i, and N be the total number of nodes. Let be the sum of the output of all generators at node i. Let i be the original load power. Let i be the phase difference between node i and node j. Let be the reactance of line ij. Let be the transmission power of line ij. This is the upper limit of transmission power. , These are the engine's minimum and maximum technical power outputs, respectively. The real-time outage probability weights for the lines connected to node i. It is a positive coefficient. Let be the real-time outage probability of the line at time t. Let i be the set of lines connected to node i.

[0062] By minimizing load shedding and employing node phase difference constraints and dynamic transmission power adjustment mechanisms, the derating operation process after a grid fault is optimized. This approach comprehensively considers generator output, original load power, and transmission power limitations, avoiding oscillations during the recovery process caused by local optima, significantly reducing load losses under fault conditions, and ensuring the stability of the grid during derating operation.

[0063] Step S104: Based on the load reduction strategy and system topology constraints, establish a post-disaster emergency recovery model and determine the fault repair sequence and load recovery sequence.

[0064] The main task during the power system recovery phase is to repair faulty components and then restore load supply as quickly as possible. For transmission line fault repair, to ensure the safety of repair personnel, maintenance is only scheduled to begin after the typhoon has ceased affecting the outage lines, and it is assumed that sufficient maintenance resources can simultaneously repair multiple faulty components. In the post-disaster emergency recovery model, the transmission line fault repair time follows a log-normal distribution, with its probability density function determined by the mean and variance parameters. This probability density function is used to simulate the randomness of the repair time and determine the repair sequence. That is, the recovery time T after a non-transient fault outage of a transmission line approximately follows a log-normal distribution. Therefore, the log-normal distribution can be used as an approximate model to measure the line recovery time. The repair time of each faulty line is obtained through random sampling, and its probability density function can be expressed as:

[0065] ;

[0066] in, , Here, represents the mean and variance of the recovery time distribution, respectively, and T represents the recovery time after a non-transient fault outage of the transmission line. is the probability density distribution function.

[0067] In summary, based on the stochastic characteristics of transmission line fault repair time simulated by the log-normal distribution, and combined with system topology constraints and load priority ranking, a dynamic repair sequence and load recovery sequence are formulated. For example, critical lines connecting important load nodes are repaired first, and loads of different priorities are restored in stages, ensuring the efficiency and stability of the recovery process. This model solves the risk of secondary power outages caused by the rigid repair sequence in traditional methods, and improves the flexibility of post-disaster recovery.

[0068] Based on the aforementioned collaborative decision-making method for power grid cascading fault simulation and resilient recovery, a dynamic state model of power grid components is constructed by introducing dual state variables of extreme weather and power flow transfer. This model reflects the interaction between environmental factors and power grid operating status in real time, solving the problem of poor adaptability of traditional methods to dynamic environments. In the probabilistic simulation stage, a system cascading fault evolution model is constructed based on the dynamic transfer probability of components. The line outage probability is updated in real time through stochastic power flow calculation, accurately capturing the dynamic propagation process of faults and avoiding prediction biases caused by static models. In the recovery optimization stage, an optimal power flow model is constructed with the goal of minimizing load shedding. Dynamic repair timing and load recovery sequences are formulated in conjunction with system topology constraints, thereby significantly improving the power grid's risk perception capability, decision-making scientificity, and recovery efficiency in extreme events.

[0069] like Figure 2 As shown, one embodiment of the present invention also proposes a substation unmanned aerial vehicle (UAV) inspection system based on multimodal data fusion, the system comprising:

[0070] The data acquisition module 10 is used to control the UAV equipped with a visible light camera, an infrared thermal imager and a lidar to fly along a preset inspection path to simultaneously acquire visible light images, infrared thermal images and laser point cloud data of the substation, and record the spatial attitude information of each frame of data.

[0071] The registration module 20 is used to perform cross-modal registration of the visible light image, infrared thermal image and laser point cloud data, so as to map the texture information of the visible light image and the temperature information of the infrared thermal image to a unified three-dimensional spatial model, and construct a three-dimensional fusion model of the substation with texture, temperature and geometric attributes.

[0072] The feature extraction module 30 is used to identify and segment independent substation equipment units in the real-world 3D fusion model of the substation. For each equipment unit, multi-dimensional features are extracted from 3D geometry, surface texture and temperature field respectively.

[0073] The monitoring execution module 40 is used to compare the multidimensional features with the equipment health benchmark model pre-set in the substation digital twin in real time. By analyzing the feature deviation, it obtains the mechanical stability, electrical insulation status and thermal operation status of the diagnostic equipment and generates evaluation results.

[0074] In another aspect, the present invention also proposes a storage medium on which one or more programs are stored, which, when executed by a processor, implement the above-mentioned collaborative decision-making method for power grid cascading fault simulation and resilient recovery.

[0075] In another aspect, the present invention also proposes an electronic device, including a memory and a processor, wherein the memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory, so as to realize the above-mentioned collaborative decision-making method for power grid cascading fault simulation and resilient recovery.

[0076] Those skilled in the art will understand that the logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can mean any means that can contain stored, communicated, propagated, or transmitted programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0077] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0078] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0079] While embodiments of the present invention have been described in detail above, it will be apparent to those skilled in the art that various modifications and variations can be made to these embodiments. However, it should be understood that such modifications and variations fall within the scope and spirit of the invention as set forth in the claims. Furthermore, the invention described herein may have other embodiments and can be implemented or carried out in various ways.

Claims

1. A collaborative decision-making method for power grid cascading fault prediction and resilient recovery, characterized in that, The method includes: A state model of each power grid component under extreme weather conditions is constructed, and the state transition probability of each power grid component in continuous time is obtained based on the state model. Based on the state transition probability, a system cascading failure evolution model under extreme weather conditions is constructed, and the real-time outage probability of the line under the wind power grid connection scenario is calculated based on the system cascading failure evolution model. With the goal of minimizing the load shedding amount, and based on the real-time outage probability of the line, an optimal power flow model is constructed to simulate the derated operation response process after a power grid fault, and to obtain the load reduction strategy of the system under each fault state. Based on the load reduction strategy and system topology constraints, a post-disaster emergency recovery model is established to determine the fault repair sequence and load recovery sequence.

2. The power grid cascading fault simulation and resilient recovery collaborative decision-making method according to claim 1, characterized in that, The steps of constructing state models for various power grid components under extreme weather conditions and obtaining the state transition probabilities of each power grid component over continuous time based on the state models include: The operating state of the power grid components at time t is obtained after being affected by extreme weather and power flow shift at various times, and the final operating state of the power grid components is defined based on the operating state. The state transition probability of a power grid element is defined based on its final operating state at consecutive times.

3. The collaborative decision-making method for power grid cascading fault simulation and resilient recovery according to claim 2, characterized in that, The method further includes: The state transition probability of the system in continuous time is calculated based on the state transition probability of the power grid components in continuous time.

4. The power grid cascading fault simulation and resilient recovery collaborative decision-making method according to claim 3, characterized in that, The steps of constructing a system cascading failure evolution model under extreme weather conditions based on the state transition probability, and calculating the real-time outage probability of the power line in the wind power grid-connected scenario based on the system cascading failure evolution model, include: The active power of the line is obtained and compared with the line's rated power and maximum transmission power, respectively. The probability of line overload outage is obtained based on the comparison results. The active power probability density function of the line is obtained by random power flow calculation, and the real-time outage probability of the line is obtained based on the line overload outage probability and the line active power probability density function.

5. The collaborative decision-making method for power grid cascading fault simulation and resilient recovery according to claim 1, characterized in that, The steps of constructing an optimal power flow model based on the real-time outage probability of the line, with the objective of minimizing the load shedding amount, include: With the goal of minimizing the sum of load shedding at all nodes, a weighting coefficient is introduced, and an optimal power flow model is constructed based on the real-time outage probability of the line. Establish constraints on the optimal power flow model, including nodal power balance equations, upper and lower limits of line transmission power, load shedding range, and upper and lower limits of generator output.

6. The collaborative decision-making method for power grid cascading fault simulation and resilient recovery according to claim 1, characterized in that, In the aforementioned post-disaster emergency recovery model, the repair time for transmission line faults follows a log-normal distribution. Its probability density function is determined by the mean and variance parameters, and is used to simulate the randomness of repair time and determine the repair sequence.

7. A collaborative decision-making system for power grid cascading fault prediction and resilient recovery, characterized in that, The system includes: The state model construction module is used to construct state models of various power grid components under extreme weather conditions, and to obtain the state transition probabilities of each power grid component over continuous time based on the state models. The probability calculation module is used to construct a system cascading failure evolution model under extreme weather conditions based on the state transition probability, and to calculate the real-time outage probability of the line under the wind power grid connection scenario based on the system cascading failure evolution model. The power flow model construction module is used to construct an optimal power flow model with the goal of minimizing the load shedding amount and based on the real-time outage probability of the line, so as to simulate the derated operation response process after the power grid fault and obtain the load reduction strategy of the system under each fault state. The recovery model construction module is used to establish a post-disaster emergency recovery model based on the load reduction strategy and system topology constraints, and to determine the fault repair sequence and load recovery sequence.

8. The power grid cascading fault simulation and resilient recovery collaborative decision-making system according to claim 1, characterized in that, The state model construction module is also used for: The operating state of the power grid components at time t is obtained after being affected by extreme weather and power flow shift at various times, and the final operating state of the power grid components is defined based on the operating state. The state transition probability of a power grid element is defined based on its final operating state at consecutive times.

9. A storage medium, characterized in that, The storage medium stores one or more programs that, when executed by a processor, implement the collaborative decision-making method for power grid cascading fault simulation and resilient recovery as described in any one of claims 1-6.

10. An electronic device comprising a memory and a processor, wherein: The memory is used to store computer programs; When the processor executes the computer program stored in the memory, it implements the collaborative decision-making method for power grid cascading fault simulation and resilient recovery as described in any one of claims 1-6.

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