A power communication routing optimization method considering fault risk

Abstract

This invention discloses a power communication routing optimization method considering fault risk. The method establishes a primary and backup routing model for the communication system; correlates the importance of load control services in the power system with the information flow, proposing a communication link importance evaluation index; then, combined with a conditional risk measurement model, it proposes a power communication routing optimization method; it treats communication service losses in each fault scenario as risk values, aiming to minimize the losses of important services across all fault scenarios, and considers constraints such as node constraints, bandwidth constraints, and routing constraints to solve for the primary and backup routing paths of the power communication network. This invention flexibly formulates communication routing paths based on changes in the fault area, and the route selection can proactively avoid fault risk areas, effectively reducing load losses during fault processes; it can be used to formulate power communication routing schemes, improving the resilience of power communication systems in responding to faults.

Description

Technical Field

[0001] This invention relates to the field of electrical engineering, and more particularly to a power communication routing optimization method that takes into account fault risk. Background Technology

[0002] With global climate change, extreme weather events such as typhoons and torrential rains are becoming more frequent. These extreme weather events can damage power and communication transmission lines, affecting the delivery of electricity and information. The mode of electricity transmission is determined by load demand, and all power transmission lines have power flow distribution; while the mode of information routing is determined by scheduling algorithms, with information flow selecting certain communication links for transmission. Because communication links have redundancy, the system can dynamically allocate bandwidth resources according to the needs of communication services. However, traditional scheduling algorithms are pre-set before system operation and do not consider the impact of the external environment during faults. In the face of extreme weather, the power cyber-physical system can take control measures based on meteorological data and other forecast information, changing the transmission mode of information routing and implementing targeted fault defense. When information flow transmits load control services, it includes the target location of control nodes and the corresponding load adjustment amount. Traditional communication optimization methods follow the principle of fairness in the communication process, ignoring the differences between information flows. However, due to the diversity and complexity of real-world scenarios, different power services have different levels of importance and priority, and important power services and peripheral power services have different reliability requirements. In the event of a major power outage, communication systems may struggle to guarantee the integrity of all information flows, necessitating the prioritization of routing paths for more critical information flows. In power transmission networks, services such as relay protection, precise load control, and dispatch automation are all related to load regulation. Therefore, it is necessary to assess the importance of information flows by considering the load regulation in each fault scenario and establishing a power communication routing optimization method that takes into account fault risk. Summary of the Invention

[0003] To address the shortcomings of existing technologies, this invention proposes a power communication routing optimization method that considers fault risk. This method can be used to determine the paths of primary and backup routes, thereby improving the fault defense capability of power communication networks.

[0004] The objective of this invention is achieved through the following technical solution:

[0005] A power communication routing optimization method considering fault risk includes the following steps:

[0006] (1) Establish a primary and backup routing model for the communication system; establish the connection relationship between each control substation and the control center in the communication system using the primary and backup routing method. The control center transmits load control instructions to each control substation through the routing path, and then the communication equipment sends them to the load terminal to execute the load shedding command; the primary and backup routing synchronizes working information. When the primary route of the control substation fails, the backup route is immediately activated to take over the services of the primary route.

[0007] (2) Propose an evaluation index for the importance of communication links; correlate the priority of communication services on the communication links with the load adjustment amount to quantify the importance of each link;

[0008] (3) Based on step (1), establish the fault model and uncertainty set of the communication system; when the power communication system suffers a physical attack in an extreme fault, the communication link also fails accordingly; use the fault model to determine the location of the fault link; when the primary and backup routes fail simultaneously, the communication service is lost.

[0009] (4) Propose an optimization model for power communication routing; combine the fault model and uncertainty set established in step (3) with the communication link importance assessment index proposed in step (2), and reduce the link service correlation in high-risk areas by adjusting the selection of primary and backup routing paths; then use the solver to solve the optimization model, that is, obtain the optimal path of the primary and backup routes for each communication service, and the optimization is completed.

[0010] Furthermore, in step (1), the expression for the primary / backup routing model is as follows:

[0011]

[0012] In the formula, X k The main routing matrix, Y k This is a backup routing matrix, where k is the service number, k = 1, 2, ..., K, and K is the total number of services. i and j are both link nodes. The values ​​of each element in the matrix are as follows:

[0013]

[0014]

[0015] Furthermore, in step (2), when a major failure occurs, the main information flow indicators in the load control service are defined as follows:

[0016]

[0017] In the formula, I k Let be the associated load of information flow k, represent the load adjustment amount of node k, S be the set of fault scenarios, s be the scenario number, and ρ be the load of information flow k. s Let be the probability of scenario s occurring. Let k be the load adjustment amount transmitted by information flow k in scenario s; based on the primary and backup routing model, the importance of all services transmitted by each communication link is obtained by associating the load amount, and the communication link importance evaluation index has the following expression:

[0018]

[0019] In the formula, T represents transpose, and matrix M represents the link service correlation degree.

[0020] Furthermore, in step (3), the expression for the fault model is as follows:

[0021]

[0022] In the formula, Z is an n×n fault matrix. When link (i, j) is interrupted, the elements in the matrix are 1; otherwise, the elements in the matrix are 0. The discrimination method is represented by the sum of all elements after the Hadamard product of the primary / backup routing model and the fault model matrix, with the following uncertainty set:

[0023]

[0024]

[0025] In the formula, r k A binary variable used to determine whether the information flow is interrupted.

[0026] Furthermore, in step (4), by adjusting the selection of primary and backup routing paths, the correlation of link services in high-risk areas is reduced. The optimization model is as follows:

[0027]

[0028] In the formula, I k R represents the associated load of information flow k, where K is the total number of information services, and R is the load of information flow k. ij Indicates the reliability of the link; x ij k and y ij k These are elements in the primary routing matrix and the backup routing matrix, respectively. δ and ξ are parameters used to set priorities. δ ensures that the optimized routing method first minimizes the load loss risk in severe scenarios, and secondly minimizes the link service correlation. ξ ensures that the solution first selects the optimal path for the primary route, and then allocates backup route paths. CvaR is the conditional risk value, and its expression and constraints are as follows:

[0029]

[0030]

[0031] In the formula, β is the confidence level, α and η s For the introduced variable, ρ s Let N be the probability of scenario s. s For the number of fault scenarios, The binary variable is used to determine whether the information flow k is interrupted in scenario s. This represents the load adjustment amount that information flow k transmits in scenario s.

[0032] The beneficial effects of this invention are as follows:

[0033] (1) The communication routing optimization method can comprehensively consider the impact of the fault area location and the importance of information flow, and transfer the information flow with higher importance to the link with higher reliability, so as to avoid the fault area as much as possible.

[0034] (2) Compared with the traditional shortest path routing method, the present invention can adaptively adjust the routing method according to the changes in the faulty and dangerous area, prioritize the protection of the transmission effectiveness of important services, and provide a flexible control strategy to improve the system's resilience.

[0035] (3) Compared with the traditional shortest path routing method, the optimization strategy of this invention can ensure that information reaches the target substation as much as possible. Combining the CVaR optimization model to reduce the failure risk of the worst scenario can effectively reduce the load loss of the power system during faults and improve the system's resilience. Attached Figure Description

[0036] Figure 1 This is a schematic diagram of the framework of the present invention;

[0037] Figure 2 This is a flowchart of the method of the present invention;

[0038] Figure 3 This is a schematic diagram of the power communication network and high-risk areas in an embodiment of the present invention;

[0039] Figure 4 This is a distribution diagram of link service correlation under different routing methods when Region I fails in an embodiment of the present invention; Figure 4 (a) in the figure is a distribution diagram of link service correlation under the shortest path routing method when there is a failure in region I. Figure 4 (b) in the figure is a distribution diagram of link service correlation under the optimized routing method when there is a failure in area I;

[0040] Figure 5 This is a distribution diagram of link service correlation under different routing methods when there is a failure in Region II in this embodiment of the invention; Figure 5 (a) in the figure is a distribution diagram of link service correlation under the shortest path routing method when there is a failure in Region II. Figure 5(b) Distribution of link service correlation under optimized routing method. Detailed Implementation

[0041] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. The specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0042] This invention protects the security of information transmission by assessing the risk of loss of different communication services during extreme weather and optimizing routing paths in power communication networks. Figure 1 This is a block diagram of a power communication routing optimization method considering typhoon risk in an embodiment of the present invention. See [link / reference]. Figure 2 The present invention provides a power communication routing optimization method considering fault risk, the optimization method comprising the following steps:

[0043] (1) Establish a primary and backup routing model for the communication system.

[0044] Specifically, a "1+1" primary and backup routing method is used to establish the connection between each control substation and the control center in the communication system. The control center transmits load control commands to each control substation through the routing path, and then the communication equipment sends them to the load terminals to execute load shedding commands. The primary and backup routes synchronize their working information. When the primary route of a control substation fails, the backup route immediately takes over the services of the primary route. The expression for the primary and backup routing model is as follows:

[0045]

[0046] In the formula, X k The main routing matrix, Y k This is a backup routing matrix, where k is the service number, k = 1, 2, ..., K, and K is the total number of services. i and j are both link nodes. The values ​​of each element in the matrix are as follows:

[0047]

[0048]

[0049] Routing nodes in a communication system can be categorized into three types based on the direction of information flow: source nodes, relay nodes, and terminal nodes. Source nodes receive only incoming information and are typically control center nodes; terminal nodes receive only outgoing information and are information command receiving nodes; relay nodes receive both incoming and outgoing information and connect source nodes and terminal nodes to form a complete routing path. Communication routing in a communication system should meet the following constraints:

[0050] (a) Source node constraints

[0051]

[0052] In the formula, s k For the source node of information service k, {j:(s k ,j)∈e} represents the relationship with the source s k A set of directly connected nodes. The source node only sends information, and both the primary and backup routes have an outgoing degree of 1.

[0053] (b) Relay node constraints

[0054]

[0055] In the formula, i is the relay node of information service k, and {j:(i,j)∈e} represents the set of nodes directly connected to node i. Regardless of whether the service passes through a relay node, the out-degree and in-degree of the information flow k at that node are equal.

[0056] (c) End node constraints

[0057]

[0058] In the formula, d k For the terminal node of information service k, {j:(d k ,j)∈e} represents the connection with the terminal node d k A set of directly connected end nodes. End nodes only receive information, and both primary and backup routes have an in-degree of 1.

[0059] (d) Non-overlapping primary and backup routes constraint

[0060]

[0061] To enhance the reliability of communication routes, the links of primary and backup routes do not overlap, and the primary and backup route paths are traversed at most once on the same link.

[0062] (e) Link bandwidth constraints

[0063]

[0064] In the formula, c m The rated optical path of the link. When various information flow services are transmitted simultaneously, they occupy the optical resources of the communication system and must meet the optical path constraints of the link. The rated optical path of the link depends on the number of available connections in the established optical cable path.

[0065] (2) Propose an evaluation index for the importance of communication links.

[0066] Specifically, the priority of communication services on a link is correlated with load adjustment, quantifying the importance of each link, intuitively reflecting the distribution of important information flows in the network, and displaying the path selection for information transmission under different routing methods. In this invention, the communication service is the load control command from the control center to each node, and the information flow includes the target location of the control node and the corresponding load adjustment. In the event of a major failure, the main indicators of the information flow in the load control service are defined as follows:

[0067]

[0068] In the formula, I k Let be the associated load of information flow k, represent the load adjustment amount of node k, S be the set of fault scenarios, s be the scenario number, and ρ be the load of information flow k. s Let be the probability of scenario s occurring. Let k be the load adjustment amount transmitted by information flow k in scenario s. Based on the primary and backup routing model matrix in step (1), the importance of all services transmitted by each communication link is represented by the associated load amount as follows:

[0069]

[0070] In the formula, T represents the transpose, and matrix M is the link service correlation degree, used to evaluate the importance of information transmitted by the communication link. Since the communication system uses a full-duplex channel, the services transmitted on the same communication link include bidirectional information flows, which are represented by the sum of the matrix and the transpose matrix.

[0071] (3) Establish the fault model and uncertainty set of the communication system. When the power communication system suffers a physical attack during an extreme fault, the communication links also fail accordingly. The fault model matrix is ​​used to determine the location of the faulty links. When the primary and backup routes fail simultaneously, communication services are lost. When the communication system suffers m link failures (m>2), the primary and backup routes for communication services may be interrupted simultaneously. The fault model matrix is ​​represented as follows:

[0072]

[0073] In the formula, Z is an n×n fault matrix. When link (i, j) is interrupted, the elements in the matrix are 1; otherwise, the elements in the matrix are 0. The faulty line is represented in the primary / backup model matrix established in step (1). The discrimination method is represented by the sum of all elements after the Hadamard product of the primary / backup routing matrix and the fault matrix. The uncertainty set is as follows:

[0074]

[0075]

[0076] In the formula, r kThis is a binary variable used to determine whether the information flow is interrupted. Since the above formula is a non-linear equation, it is difficult to solve directly in the model, but it can be equivalently transformed into the following linear equation form:

[0077]

[0078]

[0079]

[0080]

[0081]

[0082] In the formula, M is a large number. and This is a 0-1 integer variable. Due to the random disconnection of multiple links in the fault matrix, there may be multiple points of interruption in the primary and backup paths of the same information flow. This will be achieved through M. and Convert to a binary integer variable, when When, the main route of information flow k is interrupted; when At that time, the backup route for information flow k is interrupted.

[0083] (4) A power communication routing optimization model based on the Conditional Value at Risk (CVaR) model is proposed. Combining the fault model and uncertainty set established in step (3) and the communication link importance assessment index proposed in step (2), the routing optimization model aims to reduce the correlation of link services in high-risk areas by adjusting the selection of primary and backup routing paths. Considering routing constraints and CVaR constraints, the optimization model is solved using the CPLEX solver, thus obtaining the optimal primary and backup paths for each communication service, and the optimization is complete.

[0084] Specifically, the routing optimization model aims to reduce the correlation of link services in high-risk areas by selecting primary and backup routing paths. Its optimization objective is:

[0085]

[0086] In the formula, I k R represents the associated load of information flow k, where K is the total number of information services, and R is the load of information flow k. ij Indicates the reliability of the link; x ij k and y ij kThese are elements in the primary and backup routing matrices, respectively. δ and ξ are two smaller numbers used to set priorities. A smaller δ ensures that the optimized routing method first minimizes the load loss risk in severe scenarios, and secondly minimizes the link service correlation. A smaller ξ ensures that the solution first selects the optimal path for the primary route, and then allocates backup routes. CvaR is the conditional risk value, with the following expression and constraints:

[0087]

[0088]

[0089] In the formula, β is the confidence level, α and η s For the introduced variable, ρ s Let N be the probability of scenario s. s For the number of fault scenarios, The binary variable is used to determine whether the information flow k is interrupted in scenario s. Let k be the load adjustment amount transmitted by information flow k in scenario s. In addition to the model constraints of CvaR, the information node constraints, primary and backup route non-overlapping constraints, link bandwidth constraints in step (1), and the fault scenario generation constraints in step (3) and the optimization objective together constitute a hybrid linear integer programming model.

[0090] Example power communication networks and fault areas, such as Figure 3 As shown, the model is solved for the scenarios where faults occur in region I and region II respectively, and the optimized routing routes are obtained. The optimization results of the communication routes are as follows. Figure 4 and Figure 5 As shown, Figure 4 (a) in the figure is a distribution diagram of link service correlation under the shortest path routing method when there is a failure in region I. Figure 4 (b) in the figure is a distribution diagram of link service correlation under the optimized routing method when there is a failure in area I; Figure 5 (a) in the figure is a distribution diagram of link service correlation under the shortest path routing method when there is a failure in Region II. Figure 5 (b) The distribution diagram of link service correlation under the optimized routing method. The width and color intensity of each side in the diagram represent the service correlation of the link; the wider the width and the lighter the color, the more important the communication service of the link. The optimization results show that after the routing improvement, the information flow can bypass the high-risk fault area and transfer to the safe area, and be allocated to more reliable links as much as possible, ensuring the security of information transmission. Compared with the traditional shortest path routing method, the routing optimization method proposed in this invention can adaptively adjust the routing method according to the changes in the fault-prone area, prioritize the protection of the transmission effectiveness of important services, and reduce the impact of different extreme weather on the power communication system.

Claims

1. A power communication routing optimization method considering fault risk, characterized in that, Includes the following steps: (1) Establish a primary and backup routing model for the communication system; establish the connection relationship between each control substation and the control center in the communication system using the primary and backup routing method. The control center transmits load control instructions to each control substation through the routing path, and then the communication equipment sends the instructions to the load terminal to execute the load shedding command. The primary and backup routes synchronize their working information. When the primary route of the control substation fails, the backup route is immediately activated to take over the services of the primary route. (2) Propose an evaluation index for the importance of communication links; The priority of communication services on the communication link is correlated with the load adjustment amount to quantify the importance of each link; (3) Based on step (1), establish the fault model and uncertainty set of the communication system; when the power communication system suffers a physical attack in an extreme fault, the communication link also fails accordingly; use the fault model to determine the location of the fault link, and when the primary and backup routes fail at the same time, the communication service is lost; (4) Propose an optimization model for power communication routing; combine the fault model and uncertainty set established in step (3) with the communication link importance assessment index proposed in step (2), and reduce the link service correlation in high-risk areas by adjusting the selection of primary and backup routing paths; then use the solver to solve the optimization model, that is, obtain the optimal path of the primary and backup routes for each communication service, and the optimization is completed. The optimization model for reducing link service correlation in high-risk areas by adjusting the selection of primary and backup routing paths is as follows: ； In the formula, I k For information flow k The associated load, K For the total number of information services, R ij Indicates the reliability of the link; x ij k and y ij k These are elements in the primary routing matrix and the backup routing matrix, respectively. and This is a parameter used to set the priority. The optimized routing method should first minimize the risk of load loss in severe scenarios, and secondly minimize the correlation between links and services. The solution first selects the optimal path for the primary route, and then allocates alternative routes. CvaR is the conditional risk value, and its expression and constraints are as follows: ； ； In the formula, β For confidence level, α and η s For the introduced variables, ρ s For the scene s The probability, N s For the number of fault scenarios, For information flow k In the scene s A binary variable used to determine whether an interruption has occurred. For information flow k In the scene s The load adjustment amount transmitted in the middle.

2. The power communication routing optimization method considering fault risk according to claim 1, characterized in that, In step (1), the expression for the primary and backup routing model is as follows: ； In the formula, X k The main routing matrix, Y k As a backup routing matrix, k For business number, k =1,2,…, K ,in K For the total number of business transactions, i , j All are link nodes; the values ​​of each element in the matrix are as follows: ； 。 3. The power communication routing optimization method considering fault risk according to claim 1, characterized in that, In step (2), when a major failure occurs, the main information flow indicators in the load control service are defined as follows: ； In the formula, I k For information flow k The associated load, representing the node k Load adjustment amount, S A collection of fault scenarios. s Number the scene. r s For the scene s The probability of occurrence For information flow k In the scene s The load adjustment amount transmitted in the middle; based on the primary and backup routing model, the importance of all services transmitted on each communication link is obtained by associating the load amount. The communication link importance evaluation index has the following expression: ； In the formula, T represents the transpose, and the matrix is... M This refers to the relevance of the link services.

4. The power communication routing optimization method considering fault risk according to claim 1, characterized in that, In step (3), the expression for the fault model is as follows: ； In the formula, Z for The fault matrix, when the link ( i , j When an interruption occurs, the elements in the matrix are 1; otherwise, the elements are 0. The determination method is represented by the sum of all elements after the Hadamard product of the primary / backup routing model and the fault model matrix, with the following uncertainty set: ； ； In the formula, A binary variable used to determine whether the information flow is interrupted.

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