Metering transformer stationing method fusing dynamic vulnerability assessment and multi-objective optimization

Abstract

The invention discloses a metering transformer distribution method fusing dynamic vulnerability assessment and multi-objective optimization, and discloses a device and a medium of the metering transformer distribution method fusing dynamic vulnerability assessment and multi-objective optimization. The metering transformer stationing method integrating dynamic vulnerability assessment and multi-objective optimization comprises the following steps: firstly, establishing a node dynamic load model, and quantifying node importance under disaster cascading failure through topology betweenness centrality and real-time load weight; secondly, constructing a disaster recovery distribution point multi-target optimization model, taking minimization of deployment cost and maximization of fault coverage rate as core targets, embedding dynamic response constraints such as communication delay and routing inspection paths, and forcibly meeting budget limitation and key node full coverage requirements; a genetic-greedy hybrid algorithm (GA-Greedy) is further designed, global search and local disaster resistance optimization strategies are combined, equipment deployment is guided by calculating a coverage rate improvement value of node unit cost, and the solving efficiency is remarkably improved.

Description

Technical Field

[0001] This invention relates to the fields of power system automation and new energy measurement technology, and in particular to a method for the placement of metering transformers that integrates dynamic vulnerability assessment and multi-objective optimization. Background Technology

[0002] As global climate change intensifies, extreme natural disasters such as floods are increasingly damaging power systems, leading to large-scale power outages and significant economic losses. Taking the devastating "7.20" flood in Zhengzhou in 2021 as an example, the city's power distribution network suffered from widespread "metering blind spots" due to the submersion of critical distribution facilities. This severely hampered fault location and rapid power restoration, resulting in power outages exceeding 72 hours and direct economic losses amounting to billions of yuan. As core equipment for real-time monitoring of power grid operation, the proper placement of metering transformers in disaster recovery scenarios directly determines fault response efficiency and the reliability of power restoration.

[0003] However, traditional methods for planning metering transformers in distribution networks are mainly based on static load distribution and fixed disaster level assumptions, making it difficult to cope with dynamic disasters and neglecting multi-objective mixed constraints, thus having certain limitations. Current research on power grid disaster recovery planning mainly relies on complex network theory to calculate betweenness centrality or integrate multi-dimensional indicators to quantify power grid vulnerability. However, most existing models ignore the real-time impact of the dynamic propagation process of disasters on the importance of nodes, making it difficult to accurately reflect the changes in key nodes during disasters. Multi-objective optimization algorithms are often used to balance cost and coverage, or robust optimization is introduced to cope with uncertainty. Summary of the Invention

[0004] This invention aims to address the limitations of existing technologies in optimizing the placement of instrument transformers for disaster recovery scenarios, both domestically and internationally. Furthermore, current placement strategies often fail to consider dynamic load fluctuations and real-time fault isolation requirements, resulting in poor placement rationality and redundancy. To address these issues, this invention provides a disaster recovery placement method for metering instrument transformers that integrates dynamic vulnerability assessment and multi-objective optimization. This method aims to overcome the challenges posed by traditional static planning methods in handling dynamic disaster propagation and the coupling of multi-objective constraints, thereby improving the fault response capability and power supply recovery resilience of distribution networks under extreme disasters.

[0005] This invention also proposes a system with the above-mentioned method for disaster recovery deployment of metering transformers that integrates dynamic vulnerability assessment and multi-objective optimization.

[0006] The metering transformer placement method according to a first aspect of the present invention, which integrates dynamic vulnerability assessment and multi-objective optimization, is characterized by comprising the following steps:

[0007] The intensity of the disaster and the probability of equipment failure are quantified by the logistic function; the node importance index is defined by combining topological betweenness centrality and real-time load weight; and a dynamic node dynamic load model is constructed based on the above failure probability to calculate the load rate of each node and dynamically update the node importance index.

[0008] A dynamic response time quantification model is constructed, with minimizing deployment cost and maximizing fault coverage as the dual objective functions, and budget constraints, critical node coverage, and full node coverage constraints are imposed.

[0009] A genetic-greedy hybrid algorithm is designed to solve the node placement scheme based on the dynamic node vulnerability model and dynamic response time quantization model mentioned above.

[0010] The metering transformer deployment method integrating dynamic vulnerability assessment and multi-objective optimization according to embodiments of the present invention has at least the following beneficial effects: First, the metering transformer deployment method integrating dynamic vulnerability assessment and multi-objective optimization provided by the present invention establishes a node dynamic load model, quantifying the node importance under cascading disaster failures through topological betweenness centrality and real-time load weights; second, it constructs a disaster recovery deployment multi-objective optimization model, with minimizing deployment costs and maximizing fault coverage as the core objectives, embedding dynamic response constraints such as communication delay and inspection paths, and forcibly satisfying budget constraints and the requirement for full coverage of key nodes; further, it designs a genetic-greedy hybrid algorithm (GA-Greedy), combining global search and local disaster mitigation optimization strategies, guiding equipment deployment by calculating the coverage improvement value per unit cost of nodes, significantly improving solution efficiency.

[0011] According to some embodiments of the present invention, the mapping relationship between the node water accumulation depth and the failure probability satisfies:

[0012]

[0013] Among them, h i Let be the water depth at node i; h0 be the waterproofing threshold for node i, which can be set to h0 = 1; k is the fitting coefficient, which is set to 1.5.

[0014] According to some embodiments of the present invention, the node importance index I v satisfy:

[0015]

[0016] Where L(v) is the load weight of node v; α and β are weight coefficients, α = 0.6 and β = 0.4;

[0017] C B (v) represents the betweenness centrality of node v, satisfying:

[0018]

[0019] Where, σ st σ is the number of shortest paths from node s to t; st (v) represents the number of paths that pass through node v.

[0020] According to some embodiments of the present invention, in the steps of quantifying disaster intensity and equipment failure probability through a logistic function; defining node importance indicators by combining topological betweenness centrality and real-time load weights; and constructing a dynamic node dynamic load model based on the above failure probabilities to calculate the load rate of each node and dynamically update the node importance indicators:

[0021] In the node dynamic load model, when a disaster causes node failure, if node i fails due to the disaster, then its load L... i When reassignment to point j is required, the following conditions must be met:

[0022]

[0023] Among them, L i ,L j Let i be the load at point i and j; F be the set of failed nodes; A be the load at point j. ij Let Γ(i) be the line admittance from node i to node j; Γ(i) be the set of neighbors of node i.

[0024] Due to node failures in disaster scenarios, the update formula for importance indicators satisfies:

[0025]

[0026] Among them, I v (i,0) represents the importance index of the initial state of each node; L base,i The baseline load for node i is γ = 0.3.

[0027] According to some embodiments of the present invention, the objective function of the dynamic response time quantization model includes:

[0028] Minimize deployment cost objective function:

[0029]

[0030] Where, x i Indicates whether node i has deployed a metering transformer, x i ∈{0,1}; This represents the equipment and maintenance costs of the i-th node;

[0031] Objective function to maximize fault coverage:

[0032]

[0033] Where, δ i For indicator functions;

[0034]

[0035] x j Indicates whether node j has deployed a mutual inductor.

[0036] According to some embodiments of the present invention, the constraints of the dynamic response time quantization model include:

[0037] Configure budget constraints:

[0038] C total ≤B max

[0039] Among them, B max To allocate budget;

[0040] Critical node coverage constraints:

[0041] There are some important nodes in the entire power distribution network system, and the node importance index I i A value ≥0.7 indicates that the node is important.

[0042]

[0043] This means that nodes whose importance exceeds the threshold of 0.7 at any point during the disaster's propagation must be covered;

[0044] Node full coverage constraint:

[0045] To ensure that every node in the network is directly or indirectly covered by a device, it is necessary to ensure that node i itself has a device deployed, or at least one neighboring node has a device deployed:

[0046]

[0047] According to some embodiments of the present invention, the genetic-greedy hybrid algorithm includes:

[0048] Latin hypercube sampling was used to generate the initial population to ensure uniform coverage of the solution space;

[0049] Combining the dual objective functions of minimizing deployment cost and maximizing fault coverage, a multi-objective optimization fitness function is designed to evaluate the performance of each individual on the fitness function;

[0050] Genetic operations are performed using simulated binary crossover and polynomial mutation operators;

[0051] For each generation of the optimal solution set, a greedy correction is performed. The undeployed nodes are traversed, and the ratio of the number of newly observable nodes to the cost of the transformer is calculated. The nodes with the highest ratio are deployed first until the budget is exhausted.

[0052] Non-dominant ranking is performed by combining the parent and offspring populations, and the top 50% of elite individuals are retained for the next generation.

[0053] The iteration terminates when the improvement rate of the solution set is less than 1% for 5 consecutive generations.

[0054] A metering transformer deployment device integrating dynamic vulnerability assessment and multi-objective optimization according to a second aspect embodiment of the present invention is characterized in that it comprises:

[0055] The node vulnerability module is used to quantify disaster intensity and equipment failure probability through logistic functions; it combines topological betweenness centrality and real-time load weights to define node importance indicators, and constructs a dynamic node dynamic load model based on the above failure probabilities to calculate the load rate of each node and dynamically update the node importance indicators.

[0056] The disaster recovery deployment module is used to build a dynamic response time quantification model, with the dual objective function of minimizing deployment cost and maximizing fault coverage, and imposes budget constraints, critical node coverage, and full node coverage constraints.

[0057] The genetic algorithm solution module is used to design a genetic-greedy hybrid algorithm to solve the node placement scheme based on the dynamic node vulnerability model and dynamic response time quantization model mentioned above.

[0058] According to some embodiments of the present invention, the mapping relationship between the node water accumulation depth and the failure probability satisfies:

[0059]

[0060] Among them, h i Let be the water depth at node i; h0 be the waterproofing threshold for node i, which can be set to h0 = 1; k is the fitting coefficient, which is set to 1.5.

[0061] According to some embodiments of the present invention, the node importance index I v satisfy:

[0062]

[0063] Where L(v) is the load weight of node v; α and β are weight coefficients, α = 0.6 and β = 0.4;

[0064] C B (v) represents the betweenness centrality of node v, satisfying:

[0065]

[0066] Where, σ st σ is the number of shortest paths from node s to t; st(v) represents the number of paths that pass through node v.

[0067] According to some embodiments of the present invention, in the node vulnerability module:

[0068] In the node dynamic load model, when a disaster causes node failure, if node i fails due to the disaster, then its load L... i When reassignment to point j is required, the following conditions must be met:

[0069]

[0070] Among them, L i ,L j Let i be the load at point i and j; F be the set of failed nodes; A be the load at point j. ij Let Γ(i) be the line admittance from node i to node j; Γ(i) be the set of neighbors of node i.

[0071] Due to node failures in disaster scenarios, the update formula for importance indicators satisfies:

[0072]

[0073] Among them, I v (i,0) represents the importance index of the initial state of each node; L base,i The baseline load for node i is γ = 0.3.

[0074] According to some embodiments of the present invention, the objective function of the dynamic response time quantization model includes:

[0075] Minimize deployment cost objective function:

[0076]

[0077] Where, x i Indicates whether node i has deployed a metering transformer, x i ∈{0,1}; This represents the equipment and maintenance costs of the i-th node;

[0078] Objective function to maximize fault coverage:

[0079]

[0080] Where, δ i For indicator functions;

[0081]

[0082] x j Indicates whether node j has deployed a mutual inductor.

[0083] According to some embodiments of the present invention, the constraints of the dynamic response time quantization model include:

[0084] Configure budget constraints:

[0085] C total ≤B max

[0086] Among them, B max To allocate budget;

[0087] Critical node coverage constraints:

[0088] There are some important nodes in the entire power distribution network system, and the node importance index I i A value ≥0.7 indicates that the node is important.

[0089]

[0090] This means that nodes whose importance exceeds the threshold of 0.7 at any point during the disaster's propagation must be covered;

[0091] Node full coverage constraint:

[0092] To ensure that every node in the network is directly or indirectly covered by a device, it is necessary to ensure that node i itself has a device deployed, or at least one neighboring node has a device deployed:

[0093]

[0094] According to some embodiments of the present invention, the genetic algorithm solution module includes:

[0095] The population initialization element can generate an initial population using Latin hypercube sampling, ensuring uniform coverage of the solution space.

[0096] The fitness evaluation element can integrate the dual objective functions of minimizing deployment cost and maximizing fault coverage to design a multi-objective optimized fitness function and evaluate the performance of each individual on the fitness function.

[0097] Genetic manipulation elements are capable of performing genetic operations using simulated binary crossover and polynomial mutation operators;

[0098] The greedy local optimization element can greedily correct the optimal solution set of each generation, traverse the undeployed nodes, calculate the ratio of the number of newly observable nodes to the cost of the current transformer, and prioritize the deployment of the nodes with the highest ratio until the budget is exhausted;

[0099] The elite retention strategy element can combine the parent and offspring populations to perform non-dominant ranking, retaining the top 50% of elite individuals into the next generation;

[0100] The termination condition element terminates the iteration when the improvement rate of the solution set is less than 1% for 5 consecutive generations.

[0101] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0102] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the description of the embodiments taken in conjunction with the following drawings, in which:

[0103] Figure 1 This is a schematic diagram illustrating the steps of the metering transformer deployment method that integrates dynamic vulnerability assessment and multi-objective optimization according to an embodiment of the present invention.

[0104] Figure 2 This is a structural block diagram of a metering transformer deployment device that integrates dynamic vulnerability assessment and multi-objective optimization according to an embodiment of the present invention.

[0105] Figure 3 This is a network topology diagram of an IEEE 33-node distribution network system according to an embodiment of the present invention;

[0106] Figure 4 This is a schematic diagram illustrating the deployment and coverage of instrument transformers under four levels of disaster according to an embodiment of the present invention;

[0107] Figure 5 This is a schematic diagram illustrating the convergence performance of the genetic greedy algorithm under different disaster levels in an embodiment of the present invention. Detailed Implementation

[0108] Embodiments of the present invention are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0109] In the description of this invention, it should be understood that the orientation descriptions, such as up, down, front, back, left, right, etc., are based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limiting this invention.

[0110] In the description of this invention, "several" means one or more, "more than" means two or more, "greater than," "less than," and "exceeding" are understood to exclude the stated number, while "above," "below," and "within" are understood to include the stated number. The use of "first" and "second" in the description is merely for distinguishing technical features and should not be construed as indicating or implying relative importance, or implicitly indicating the number of indicated technical features, or implicitly indicating the order of the indicated technical features.

[0111] In the description of this invention, unless otherwise explicitly defined, terms such as "set up," "install," and "connect" should be interpreted broadly, and those skilled in the art can reasonably determine the specific meaning of the above terms in this invention in conjunction with the specific content of the technical solution.

[0112] Example 1

[0113] There is a lack of domestic and international research on the optimization of instrument transformer deployment in disaster recovery scenarios, and existing deployment strategies often fail to consider dynamic load fluctuations and real-time fault isolation requirements, resulting in poor deployment rationality and redundancy. This invention provides a disaster recovery deployment strategy for metering instrument transformers that integrates dynamic vulnerability assessment and multi-objective optimization. It aims to address the challenges of traditional static planning methods in dealing with the dynamic propagation of disasters and the coupling of multi-objective constraints, thereby improving the fault response capability and power supply recovery resilience of the distribution network under extreme disasters.

[0114] Embodiments of the present invention provide a method for deploying metering transformers that integrates dynamic vulnerability assessment and multi-objective optimization, such as... Figure 1 As shown, the method includes the following steps:

[0115] Step S100: Quantify the disaster intensity and equipment failure probability through the logistic function; combine topological betweenness centrality and real-time load weight to define the node importance index, and construct a dynamic node dynamic load model based on the above failure probability to calculate the load rate of each node and dynamically update the node importance index.

[0116] A disaster level classification, power grid topology vulnerability assessment, and dynamic load-disaster coupling model were constructed to provide data support for optimizing power grid deployment strategies. First, based on historical disaster data and combined with meteorological and geological parameters, the level standards for flood disasters were defined, as shown in Table 1.

[0117] Table 1 Classification of Flood Disaster Levels

[0118]

[0119] Assume that the water depth is the same at all distribution network nodes when a disaster occurs. To quantify the mapping relationship between the occurrence of disasters at various levels and node failure, an equation corresponding to the disaster intensity and equipment failure probability is established. The failure probability P of node i is... f (i) can be represented as:

[0120]

[0121] Among them, h i Let be the water depth at node i; h0 be the waterproofing threshold for node i, which can be set to h0 = 1; k is the fitting coefficient, which is set to 1.5.

[0122] Based on the combination of topology and functional attributes, a node importance index I is defined. v :

[0123]

[0124] Where L(v) is the load weight of node v; α and β are weight coefficients, α = 0.6 and β = 0.4; C B (v) represents the betweenness centrality of node v:

[0125]

[0126] Where, σ st σ is the number of shortest paths from node s to t; st (v) represents the number of paths that pass through node v.

[0127] Considering the cascading effects of disasters, such as load shifting caused by substation outages, it is necessary to establish a dynamic load model L(t) for nodes during the disaster process, as well as a node overload failure model caused by load changes at each node, with the time of the disaster occurrence defined as t=0.

[0128] (1) Node dynamic load model L(t)

[0129] The node baseline load L under normal operating conditions base,i Used as the initial value for L(t).

[0130] When a disaster causes a node to fail, its original load needs to be transferred to an adjacent node. Let node i fail due to a disaster, then its load L... i Load needs to be reallocated. The load allocation rule allocates the load of the failed node according to the admittance weight of the power grid topology.

[0131]

[0132] Where F is the set of failed nodes; A ij Let Γ(i) be the line admittance from node i to node j; let Γ(i) be the set of neighbors of node i.

[0133] (2) Dynamic update mechanism

[0134] For each surviving node j, calculate its line load rate.

[0135]

[0136] Among them, L rated This is the maximum load that the node can withstand. If node j fails, and the load rate of its adjacent lines exceeds 90% of the threshold, it will trigger the downstream node to fail due to overload.

[0137] (3) Dynamically update node importance index I v

[0138] Because nodes fail in disaster scenarios, the importance indicators of each surviving node need to be recalculated. The formula for dynamically updating the node importance indicators is as follows:

[0139]

[0140] Among them, I v (i,0) represents the importance index of the initial state of each node; L base,i The baseline load for node i is γ = 0.3.

[0141] Step S200: Construct a dynamic response time quantification model with the dual objective functions of minimizing deployment cost and maximizing fault coverage, and impose constraints on budget, critical node coverage, and full node coverage.

[0142] In disaster recovery scenarios, quickly locating faults and restoring power are core requirements for minimizing economic losses. This paper proposes a dynamic response time quantification model that comprehensively considers communication latency, inspection path planning, and real-time data processing capabilities, and embeds it into a multi-objective optimization framework.

[0143] The objective function is designed as follows:

[0144] (1) Minimize deployment costs

[0145] Assuming there are N nodes in the target distribution network, and considering the node failure problem in the disaster recovery scenario, the node failure probability formula (1) is introduced into the cost function. During optimization, nodes with low failure probability are selected first. Then, its deployment cost objective function can be described as:

[0146]

[0147] Where, x i Indicates whether node i has deployed a metering transformer, x i ∈{0,1}; This represents the equipment and maintenance costs of the i-th node.

[0148] (2) Maximize fault coverage

[0149] Node i can deploy a current transformer to observe itself and its neighboring nodes. Combining the node importance index function in equation (2), the function for maximizing fault coverage of the target distribution network can be expressed as:

[0150]

[0151] Where, δ i For indicator functions:

[0152]

[0153] x j Indicates whether node j has deployed a mutual inductor.

[0154] Constraints:

[0155] (1) Configure budget constraints

[0156] In practical engineering applications, there is usually a maximum project budget, and the total amount must be controlled to be less than the budget value.

[0157] C total ≤B max (10)

[0158] Among them, B max To allocate a budget.

[0159] (2) Critical node coverage constraints

[0160] There are some important nodes in the entire distribution network system. The node importance evaluation index is determined by equation (2). Based on historical data, when the I of node i... i A value of ≥0.7 indicates that the node is an important node.

[0161]

[0162] This means that nodes whose importance exceeds the threshold of 0.7 at any point during the spread of the disaster must be covered.

[0163] (3) Node full coverage constraint

[0164] To ensure that every node in the network is directly or indirectly covered by a device, it is necessary to ensure that node i itself has a device deployed, or at least one neighboring node has a device deployed:

[0165]

[0166] Step S300: Design a genetic-greedy hybrid algorithm to solve the node placement scheme based on the above dynamic node vulnerability model and dynamic response time quantization model.

[0167] Step 1: Population Initialization

[0168] Latin hypercube sampling is used to generate the initial population, ensuring uniform coverage of the solution space. Each chromosome is encoded as an N-dimensional binary vector, where N is the number of nodes and x... i =1 indicates that node i deploys a mutual inductor and prioritizes generating candidate solutions in areas with a high incidence of historical disasters.

[0169] Step 2: Fitness Assessment

[0170] Combining the deployment cost minimization objective of Equation (7) and the fault coverage maximization objective of Equation (8), a multi-objective optimization fitness function is designed to evaluate the performance of each individual on the fitness function. The expression of the multi-objective optimization fitness function is shown in Equation (13).

[0171] F=ω1C total +ω2(1-R observability (13)

[0172] Step 3: Genetic manipulation

[0173] In the genetic algorithm parameter settings, simulated binary crossover (SBX) and a polynomial mutation operator are used for genetic operations. The crossover probability p... c Set to 0.8; mutation probability p m It is 1 / N.

[0174] Step 4: Greedy Local Optimization

[0175] For each generation of the optimal solution set, a greedy correction is performed. The undeployed nodes are traversed, and the ratio of the number of newly observable nodes to the cost of the transformer is calculated. The nodes with the highest ratio are deployed first until the budget is exhausted.

[0176]

[0177] Where, ΔR observability (i) represents the change in fault coverage of the system with the current transformer deployed at node i. (1-P) f (i) represents the effective probability of the i-th node.

[0178] Choose ρ i The highest-level node is included in the deployment plan to balance coverage improvement with disaster recovery costs.

[0179] Step 5: Elite Retention Strategy

[0180] Non-dominated ranking is performed by combining the parent and offspring populations, and the top 50% of elite individuals are retained for the next generation.

[0181] Step 6: Termination Condition

[0182] The iteration terminates when the improvement rate of the solution set is less than 1% for five consecutive generations.

[0183] Example 2

[0184] Another embodiment of the present invention provides a metering transformer deployment device that integrates dynamic vulnerability assessment and multi-objective optimization, such as... Figure 2 As shown, the device 20 includes:

[0185] The node vulnerability module 201 is used to quantify the disaster intensity and equipment failure probability through a logistic function; it combines topological betweenness centrality and real-time load weight to define a node importance index, and constructs a dynamic node dynamic load model based on the above failure probability to calculate the load rate of each node and dynamically update the node importance index.

[0186] The disaster recovery deployment module 202 is used to build a dynamic response time quantification model, with the dual objective function of minimizing deployment cost and maximizing fault coverage, and imposes budget constraints, critical node coverage, and full node coverage constraints.

[0187] The genetic algorithm solution module 203 is used to design a genetic-greedy hybrid algorithm to solve the node placement scheme based on the above dynamic node vulnerability model and dynamic response time quantization model.

[0188] The metering transformer deployment device integrating dynamic vulnerability assessment and multi-objective optimization in this embodiment can execute the metering transformer deployment method integrating dynamic vulnerability assessment and multi-objective optimization provided in this application embodiment. The implementation principle is similar and will not be described again here.

[0189] Example 3

[0190] To verify the validity of this application, the proposed hybrid algorithm was tested using an Electronic Voltage Transformer (EVT) system in a flood disaster scenario via an IEEE 33-node system. The deployment costs, fault coverage, and dynamic response performance under different disaster levels and budget constraints were compared and analyzed. The network topology of the IEEE 33-node distribution network system is shown below. Figure 3 As shown.

[0191] Considering extreme disaster preparedness scenarios, the maximum rainfall is taken into account for different levels of flood disasters. Specifically, for a level 1 disaster, the water depth is 0.5m, and according to equation (1), the node failure probability is 37.754%; for a level 2 disaster, the water depth is 1m, and the node failure probability is 50%; for a level 3 disaster, the water depth is 1.5m, and the node failure probability is 62.246%; for a level 4 disaster, the water depth is assumed to be 2m, and the node failure probability is 73.106%. The simulation environment and parameter design are shown in Table 2. Regarding the simulation of generating the initial failed node, this part uses the Monte Carlo method to simulate whether the node fails.

[0192] Table 2 Summary of Optimization Results for Multi-Level Disasters

[0193]

[0194] To visually demonstrate the optimization effect of the proposed genetic-greedy hybrid algorithm in flood disaster scenarios, this experiment verifies the stability and effectiveness of the algorithm in random disaster scenarios by calculating the optimal deployment locations for no disaster and four levels of disaster.

[0195] Figure 2 The diagram illustrates the optimal allocation and deployment of instrument transformers under four disaster levels using the algorithm proposed in this paper. When the disaster level is raised to level three, the instrument transformer layout tends to converge towards the main lines, and the system robustness is enhanced by increasing the redundancy configuration of key nodes. Figure 3 Comparative analysis shows that the genetic greedy hybrid algorithm reduces the number of convergence generations by 38.6% compared with the traditional genetic algorithm in the level 4 disaster scenario, and the fluctuation range of the objective function value is controlled within ±2.5%, which verifies the stability of the algorithm under complex constraints.

[0196] Table 2 shows the nonlinear impact of disaster level on coverage. An increase in disaster level causes the disaster coverage to drop from 89.5% to 70.3%, a decrease of 19.2%. At low disaster levels, the coverage decrease is gradual; however, at high disaster levels, the coverage drops sharply. Under high disaster levels, the probability of node failure increases significantly, triggering a cascading failure, leading to the failure of critical observation nodes and disrupting the network's observability structure.

[0197] The strategy proposed in this invention can maintain a disaster coverage rate of more than 70% under low to medium disaster levels, meeting the needs of rapid power restoration; however, under level 4 extreme disasters, the coverage rate is greatly reduced, and dynamic emergency monitoring methods need to be combined to ensure the safety of the distribution network.

[0198] Another embodiment of the present invention provides a computer-readable storage medium storing computer-executable instructions for performing the above-described... Figure 1 The method for deploying metering transformers integrates dynamic vulnerability assessment and multi-objective optimization.

[0199] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0200] It will be understood by those skilled in the art that all or some of the steps and systems in the methods disclosed above can be implemented as software, firmware, hardware, and suitable combinations thereof. Some or all of the physical components can be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit. Such software can be distributed on a computer-readable medium, which can include computer storage media (or non-transitory media) and communication media (or transient media). As is known to those skilled in the art, the term computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disc (DVD) or other optical disc storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible to a computer. Furthermore, as is known to those skilled in the art, communication media typically contain computer-readable instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.

[0201] The above is a detailed description of the preferred embodiments of this application. However, this application is not limited to the above embodiments. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of this application. All such equivalent modifications or substitutions are included within the scope defined by the claims of this application.

Claims

1. A method for deploying metering transformers that integrates dynamic vulnerability assessment and multi-objective optimization, characterized in that, Includes the following steps: The intensity of a disaster and the probability of equipment failure are quantified using a logistic function. By combining topological betweenness centrality and real-time load weight, a node importance index is defined, and a dynamic node dynamic load model is constructed based on the above failure probability to calculate the load rate of each node and dynamically update the node importance index. A dynamic response time quantification model is constructed, with minimizing deployment cost and maximizing fault coverage as the dual objective functions, and budget constraints, critical node coverage, and full node coverage constraints are imposed. A genetic-greedy hybrid algorithm is designed to solve the node placement scheme based on the dynamic node vulnerability model and dynamic response time quantization model mentioned above.

2. The method according to claim 1, characterized in that, The mapping relationship between the water accumulation depth at the nodes and the failure probability satisfies: Among them, h i Let be the water depth at node i; h0 be the waterproofing threshold for node i, which can be set to h0 = 1; k is the fitting coefficient, which is set to 1.

5.

3. The method according to claim 1, characterized in that, The node importance index I v satisfy: Where L(v) is the load weight of node v; α and β are weight coefficients, α = 0.6 and β = 0.4; C B (v) represents the betweenness centrality of node v, satisfying: Where, σ st σ is the number of shortest paths from node s to t; st (v) represents the number of paths that pass through node v.

4. The method according to claim 1, characterized in that, The steps described include: quantifying disaster intensity and equipment failure probability using a logistic function; defining node importance indices by combining topological betweenness centrality and real-time load weights; and constructing a dynamic node load model based on the aforementioned failure probabilities to calculate the load rate of each node and dynamically update the node importance indices. In the node dynamic load model, when a disaster causes node failure, if node i fails due to the disaster, then its load L... i When reassignment to point j is required, the following conditions must be met: Among them, L i ,L j Let i be the load at point i and j; F be the set of failed nodes; A be the load at point j. ij Let Γ(i) be the line admittance from node i to node j; Γ(i) be the set of neighbors of node i. Due to node failures in disaster scenarios, the update formula for importance indicators satisfies: Among them, I v (i,0) represents the importance index of the initial state of each node; L base,i The baseline load for node i is γ = 0.

3.

5. The method according to claim 1, characterized in that, The objective function of the dynamic response time quantization model includes: Minimize deployment cost objective function: Where, x i Indicates whether node i has deployed a metering transformer, x i ∈{0,1}; This represents the equipment and maintenance costs of the i-th node; Objective function to maximize fault coverage: Where, δ i For indicator functions; x j Indicates whether node j has deployed a mutual inductor.

6. The method according to claim 5, characterized in that, The constraints of the dynamic response time quantization model include: Configure budget constraints: C total ≤B max Among them, B max To allocate budget; Critical node coverage constraints: There are some important nodes in the entire power distribution network system, and the node importance index I i A value ≥0.7 indicates that the node is important. This means that nodes whose importance exceeds the threshold of 0.7 at any point during the disaster's propagation must be covered; Node full coverage constraint: To ensure that every node in the network is directly or indirectly covered by a device, it is necessary to ensure that node i itself has a device deployed, or at least one neighboring node has a device deployed:

7. The method according to claim 1, characterized in that, The genetic-greedy hybrid algorithm includes: Latin hypercube sampling was used to generate the initial population to ensure uniform coverage of the solution space; Combining the dual objective functions of minimizing deployment cost and maximizing fault coverage, a multi-objective optimization fitness function is designed to evaluate the performance of each individual on the fitness function; Genetic operations are performed using simulated binary crossover and polynomial mutation operators; For each generation of the optimal solution set, a greedy correction is performed. The undeployed nodes are traversed, and the ratio of the number of newly observable nodes to the cost of the transformer is calculated. The nodes with the highest ratio are deployed first until the budget is exhausted. Non-dominant ranking is performed by combining the parent and offspring populations, and the top 50% of elite individuals are retained for the next generation. The iteration terminates when the improvement rate of the solution set is less than 1% for 5 consecutive generations.

8. A metering transformer deployment device integrating dynamic vulnerability assessment and multi-objective optimization, characterized in that, include: The node vulnerability module is used to quantify the intensity of a disaster and the probability of equipment failure through a logistic function. By combining topological betweenness centrality and real-time load weight, a node importance index is defined, and a dynamic node dynamic load model is constructed based on the above failure probability to calculate the load rate of each node and dynamically update the node importance index. The disaster recovery deployment module is used to build a dynamic response time quantification model, with the dual objective function of minimizing deployment cost and maximizing fault coverage, and imposes budget constraints, critical node coverage, and full node coverage constraints. The genetic algorithm solution module is used to design a genetic-greedy hybrid algorithm to solve the node placement scheme based on the dynamic node vulnerability model and dynamic response time quantization model mentioned above.

9. The apparatus according to claim 8, characterized in that, The mapping relationship between the water accumulation depth at the nodes and the failure probability satisfies: Among them, h i Let be the water depth at node i; h0 be the waterproofing threshold for node i, which can be set to h0 = 1; k is the fitting coefficient, which is set to 1.

5.

10. The apparatus according to claim 8, characterized in that, The node importance index I v satisfy: Where L(v) is the load weight of node v; α and β are weight coefficients, α = 0.6 and β = 0.4; C B (v) represents the betweenness centrality of node v, satisfying: Where, σ st σ is the number of shortest paths from node s to t; st (v) represents the number of paths that pass through node v.

11. The apparatus according to claim 8, characterized in that, In the node vulnerability module: In the node dynamic load model, when a disaster causes node failure, if node i fails due to the disaster, then its load L... i When reassignment to point j is required, the following conditions must be met: Among them, L i ,L j Let i be the load at point i and j; F be the set of failed nodes; A be the load at point j. ij Let Γ(i) be the line admittance from node i to node j; Γ(i) be the set of neighbors of node i. Due to node failures in disaster scenarios, the update formula for importance indicators satisfies: Among them, I v (i,0) represents the importance index of the initial state of each node; L base,i The baseline load for node i is γ = 0.

3.

12. The apparatus according to claim 8, characterized in that, The objective function of the dynamic response time quantization model includes: Minimize deployment cost objective function: Where, x i Indicates whether node i has deployed a metering transformer, x i ∈{0,1}; This represents the equipment and maintenance costs of the i-th node; Objective function to maximize fault coverage: Where, δ i For indicator functions; x j Indicates whether node j has deployed a mutual inductor.

13. The apparatus according to claim 12, characterized in that, The constraints of the dynamic response time quantization model include: Configure budget constraints: C total ≤B max Among them, B max To allocate budget; Critical node coverage constraints: There are some important nodes in the entire power distribution network system, and the node importance index I i A value ≥0.7 indicates that the node is important. This means that nodes whose importance exceeds the threshold of 0.7 at any point during the disaster's propagation must be covered; Node full coverage constraint: To ensure that every node in the network is directly or indirectly covered by a device, it is necessary to ensure that node i itself has a device deployed, or at least one neighboring node has a device deployed:

14. The apparatus according to claim 8, characterized in that, The genetic algorithm solution module includes: The population initialization element can generate an initial population using Latin hypercube sampling, ensuring uniform coverage of the solution space. The fitness evaluation element can integrate the dual objective functions of minimizing deployment cost and maximizing fault coverage to design a multi-objective optimized fitness function and evaluate the performance of each individual on the fitness function. Genetic manipulation elements are capable of performing genetic operations using simulated binary crossover and polynomial mutation operators; The greedy local optimization element can greedily correct the optimal solution set of each generation, traverse the undeployed nodes, calculate the ratio of the number of newly observable nodes to the cost of the current transformer, and prioritize the deployment of the nodes with the highest ratio until the budget is exhausted; The elite retention strategy element can combine the parent and offspring populations to perform non-dominant ranking, retaining the top 50% of elite individuals into the next generation; The termination condition element terminates the iteration when the improvement rate of the solution set is less than 1% for 5 consecutive generations.

15. A computer-readable storage medium, characterized in that, The device stores computer-executable instructions for performing the method of any one of claims 1 to 7.

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