Power distribution network system risk coupling quantitative analysis method and system

Abstract

The invention discloses a power distribution network system risk coupling quantitative analysis method and system, and the method comprises the steps: collecting the operation data of a power distribution network system, and building an information physical fusion system model; determining a potential risk type of the information system, and identifying and quantifying the risk of the information system; establishing an information attack model, and defining a system risk coupling degree and a node risk coupling degree; and performing simulation analysis on the risk coupling degree, and calculating the risk coupling degree of the system and the node under different attack types to complete the risk analysis of the power distribution network system. According to the power distribution network system risk coupling quantitative analysis method provided by the invention, the system risk coupling degree index directly and clearly reflects the risk superposition effect of the information system and the power system under the information attack; the negative influence of the integrity attack on the power distribution network is greater than that of the availability attack, so that the information attack should be emphatically prevented in a power distribution network information system; the security configuration scheme based on the node risk coupling degree can effectively reduce the system risk.

Description

Technical Field

[0001] The present invention relates to the technical field of power system engineering, and in particular to a method and system for quantitatively analyzing risk coupling of a distribution network system. Background Art

[0002] With the promotion and application of communication, computer and control technologies in distribution networks, traditional distribution networks are gradually evolving into CPDS, which is a deep integration of physical systems and information systems. In CPDS, information systems and physical systems are heterogeneous systems with complex interactive relationships. The energy flow in the physical system changes continuously under the monitoring of the information system, and its research focus is on the time domain variation characteristics of the energy flow; the information system monitors, analyzes and controls the operating status of the physical system, and its focus is on the conversion relationship of discrete events. The information system improves the distribution network's ability to perceive the system status, improves the stability of power supply, and optimizes energy utilization efficiency. However, information system risks can be transmitted to the physical system through the dynamic interaction of information flow and energy flow, thereby affecting the stable operation of the distribution network and may even cause chain failures. Therefore, it is necessary to clarify the causes of CPDS risks and their transmission methods, and analyze the risk superposition effect of power systems and information systems under information-physical coupling.

[0003] At present, many scholars have conducted research on the security risks of power systems under information threats. The existing technology proposes a CPDS vulnerability assessment method under a distributed collaborative control mode, which evaluates the vulnerability of distributed energy in the system by analyzing the power shortage caused by attack events involving multiple components. However, the above quantitative assessment methods often ignore the possible failures in the physical system and focus on the research from the perspective of information system risk transmission or the impact of abnormal information system status on the power system. . Summary of the invention

[0004] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0005] In view of the above-mentioned problems, the present invention is proposed.

[0006] Therefore, the technical problem solved by the present invention is: how to analyze the information risks faced by the distribution network, quantify their importance from the perspective of the risk gain brought by the abnormal state of information nodes to the system, and identify the weak links of the system.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions: a method for quantitative analysis of distribution network system risk coupling, comprising:

[0008] Collect distribution network system operation data and establish a cyber-physical fusion system model;

[0009] Determine the potential risk types of information systems, identify and quantify information system risks;

[0010] Establish an information attack model and define the system risk coupling degree and node risk coupling degree;

[0011] A simulation analysis is conducted on the risk coupling degree. Under different attack types, the risk coupling degree of the system and nodes is calculated to complete the risk analysis of the distribution network system.

[0012] As a preferred solution of the method for quantitative analysis of distribution network system risk coupling described in the present invention, wherein: the establishment of the information-physical fusion system model includes that the information-physical fusion system model CPDS includes an information system and an electric power physical system;

[0013] The power physical system includes traditional primary equipment and new energy equipment such as photovoltaics, wind power, and energy storage equipment; the information system includes a control center, a communication network, and intelligent terminal devices; the control center is the core of the information system, which analyzes the power system status data transmitted from the communication network and issues control instructions to achieve control and optimization of different distribution network scenarios.

[0014] As a preferred solution of the distribution network system risk coupling quantitative analysis method described in the present invention, the potential risk types of the information system are determined, including information attacks on the distribution network including active attacks, passive attacks, adjacent attacks, insider attacks and software and hardware assembly attacks; from the aspect of attack effects, the attack types are divided into integrity attacks and availability attacks.

[0015] As a preferred solution of the method for quantitative analysis of distribution network system risk coupling described in the present invention, the identification and quantification of information system risks include: based on the analysis of potential risk types, it is found that an availability attack on the RTU will cause it to refuse to execute instructions from the control center; if the RTU refuses service due to the information attack when a fault occurs, the fault line will be in a power outage state, and the fault range will be expanded to the downstream, upstream and adjacent feeder branches of the feeder;

[0016] The impact of availability attacks on the downstream area of ​​the fault is expressed as:

[0017]

[0018] in, represents the impact of the failure of the i-th section feeder on the downstream power supply area under information attack; L e represents the load power on the e-th section feeder; irepresents the failure rate of the i-th section feeder; represents the set of downstream feeder segments of the i-th feeder segment; represents the failure probability of the feeder e transfer path. If there is no transferable line downstream of feeder e, then P Tie =1, otherwise the value is equal to the probability of the corresponding tie switch failing to operate; represents the probability that all switches on the path from feeder i to feeder e refuse to operate due to an attack; S i,e represents the set of terminal devices corresponding to all switches on the path from feeder i to feeder e; represents the probability of terminal device k failing due to availability attack; d mttr Mean time to repair, including manual inspection, network forensics, and fault repair; d rst Indicates the remote control switch time;

[0019] Availability attacks cause the fault range to expand to the upstream power supply area, including the adjacent branch feeders of the fault;

[0020] The formula for the impact of availability attacks on the upstream area of ​​the fault is expressed as:

[0021]

[0022] in, represents the impact of the failure of the i-th feeder section on the upstream power supply area under information attack; the set of upstream feeder sections of the i-th feeder section;

[0023] The impact of availability attack on the fault adjacent branch feeder is expressed as:

[0024]

[0025] in, represents the impact of the failure of the i-th section feeder on the adjacent power supply area under information attack; represents the set of adjacent branch feeders of the i-th section feeder; represents the probability that all switches within the range of the i-th feeder to the adjacent feeder m branch refuse to operate due to an attack; represents the set of all feeder segments on adjacent feeder m; It represents the terminal equipment set corresponding to all switches within the range of the i-th feeder to the adjacent feeder m branch; represents the probability that all switches upstream of feeder f on the feeder branch refuse to operate due to an attack; represents the set of terminal devices corresponding to all switches upstream of feeder f on the feeder branch; the risk formula of the distribution network under availability attack is expressed as:

[0026]

[0027] Wherein, B represents the set of all feeder segments.

[0028] As a preferred solution of the method for quantitative analysis of distribution network system risk coupling according to the present invention, wherein: the establishment of the information attack model includes that the impact of the integrity attack on the downstream feeder area of ​​the attacked device terminal can be expressed as:

[0029]

[0030] in, represents the impact of the failure of the jth terminal device on the downstream power supply area under information attack; represents the availability of the upstream feeder area of ​​the jth terminal device; represents the probability that the jth terminal device is attacked and all the terminal devices in its upstream feeder area are operating normally; represents the set of all feeders in the upstream area of ​​terminal device j; represents the set of all feeders in the downstream area of ​​terminal device j; represents the set of all terminal devices in the upstream feeder area of ​​terminal device j; represents the probability of integrity attack leading to failure of terminal device j;

[0031] The impact of integrity attack on fault recovery in fault state can be expressed as:

[0032]

[0033] in, represents the impact of failure recovery of the i-th section feeder under information attack; represents the probability that any terminal device in the downstream area of ​​feeder h fails due to an attack; Represents the set of all terminal devices downstream of the feeder; represents the probability of terminal device j failing due to information attack;

[0034] The risk of distribution network under integrity attack can be expressed as:

[0035]

[0036] Wherein, S represents the set of all terminal devices.

[0037] As a preferred solution of the method for quantitative analysis of distribution network system risk coupling described in the present invention, the system risk coupling degree and the node risk coupling degree include that in CPDS, not only the influence of line failure and equipment failure of the distribution network should be considered, but also the direct and indirect influence caused by the stable operation of the power grid should be considered; then the CPDS risk formula is expressed as:

[0038] R CPDS =RP +R C→P

[0039] Among them, R CPBS represents the risk of distribution network considering information attack; R P represents the risk of traditional distribution network, that is, the risk of distribution network without considering information attack; R C→P It represents the risk gain brought by information attack to the distribution network;

[0040] The system risk coupling degree is used to characterize the risk gain brought to the power system by the abnormal state of the information system under the state of information-physical coupling; the system risk coupling degree formula is expressed as:

[0041]

[0042] in, represents the system risk coupling degree, and its value range is [0, +∞]. The system risk coupling degree is proportional to the risk gain of information threat to the power system. 0, indicating that the information threat will not affect the operation of the distribution network;

[0043] Taking the distribution network under information attack as the research object, the influence of availability attack and integrity attack on the fault handling process of centralized FA system is analyzed; the coupling degree between systems under availability attack and integrity attack is expressed as:

[0044]

[0045] in, Indicates the system risk coupling degree under availability attack; represents the system risk coupling degree under integrity attack; R p It is obtained by setting the success probability of information attack to 0, that is, information attack will not affect the fault handling process of FA system;

[0046] Node risk coupling degree is used to characterize the risk gain brought to the power system by the abnormal state of information nodes under the state of information-physical coupling;

[0047] The node risk coupling degree in the distribution network information system can be expressed as:

[0048]

[0049] in, represents the node coupling degree of information node j; represents the impact of abnormal state of information node j on the distribution network, and its value range is [0, +∞]; if If it is 0, it means that the abnormal status of the node will not affect the operation of the distribution network.

[0050] As a preferred solution of the method for quantitatively analyzing the risk coupling of the distribution network system described in the present invention, the simulation analysis for the risk coupling degree includes that when the information attacks the CPDS, the nodes in the information system are independent of each other, and the attack on a single node will not affect the operating status of other nodes. The gain degree of the risk of the distribution network caused by the information attack on the node is quantified, and all attack events involving the node need to be considered;

[0051] The proportional allocation method is used to allocate the damage caused by attacks involving multiple target nodes. The damage caused by information attacks involving multiple nodes is allocated to different nodes according to the weight coefficients of different nodes. The update formula is expressed as:

[0052]

[0053] in, represents the set of all nodes containing target node j in the information attack event; represents the probability of attack event k occurring; S k represents the set of all target nodes in attack event k; S\S k Indicates that it belongs to the set S and does not belong to the set S k A collection of nodes; represents the probability of failure of information node z due to information attack; represents the risk-gain ratio of node j in attack event k; c v represents the proportional allocation coefficient of information node ν; represents the risk gain caused by attack event k to the power system; Indicates the harm to the power system caused by the attack event;

[0054] Combined with the analysis scenario, the node coupling degree under availability attack and integrity attack can be obtained; under availability attack, represents the probability that the zth RTU fails due to an attack represents the damage caused by attack event k to the power system; under integrity attack, That is, the probability that the zth RTU fails due to an integrity attack Indicates that integrity attack event k causes harm to the power system.

[0055] A distribution network system risk coupling quantitative analysis system using any method as described in the present invention, wherein:

[0056] The cyber-physical fusion system module establishes and maintains the cyber-physical fusion system model of the distribution network; the data collection submodule collects the operating data of the power physical system and the information system, including equipment status, load data, and communication data; the model building submodule establishes the CPDS model that includes the power physical system and the information system; the power physical system includes traditional primary equipment, new energy equipment, and energy storage equipment;

[0057] Information system risk identification and quantification module, which identifies and quantifies the potential risks of information systems and classifies attack types into integrity attacks and availability attacks based on the attack effects;

[0058] Information attack model module, which builds information attack model and calculates the risk coupling degree of system and nodes;

[0059] The simulation analysis module performs simulation verification and result analysis on the distribution network system, verifies the rationality of the proposed risk coupling index and calculation method through simulation results, analyzes the simulation results, and evaluates the effectiveness of different protection measures in reducing system risks.

[0060] A computer device comprises: a memory and a processor; the memory stores a computer program, wherein: the processor implements the steps of any one of the methods of the present invention when executing the computer program.

[0061] A computer-readable storage medium stores a computer program, wherein: when the computer program is executed by a processor, the steps of any one of the methods of the present invention are implemented.

[0062] Beneficial effects of the present invention: Compared with traditional risk assessment indicators, the distribution network system risk coupling quantitative analysis method provided by the present invention can more directly and clearly reflect the risk superposition effect of the information system and the power system under information attacks; the negative impact of integrity attacks on the distribution network is greater than that of availability attacks, so the distribution network information system should focus on preventing such information attacks; the impact of both attack methods on the distribution network increases with the increase of the system automation level, which shows that although informatization enhances the automation level of the system, information risks will also be more likely to affect the distribution network with the increase of the automation level; the security configuration scheme based on node risk coupling can effectively reduce system risks. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them:

[0064] Figure 1 An overall flow chart of a method for quantitatively analyzing risk coupling of a distribution network system provided by the first embodiment of the present invention;

[0065] Figure 2 A diagram showing the impact of integrity attacks on power supply restoration in a distribution network system risk coupling quantitative analysis method provided by the first embodiment of the present invention;

[0066] Figure 3 An IEEE33 node system diagram of a distribution network system risk coupling quantitative analysis method provided by the second embodiment of the present invention;

[0067] Figure 4 A RTU risk coupling degree diagram under integrity attack of a distribution network system risk coupling quantitative analysis method provided by the second embodiment of the present invention;

[0068] Figure 5 A RTU risk coupling degree diagram under availability attack after security configuration of a distribution network system risk coupling quantitative analysis method provided by the second embodiment of the present invention. DETAILED DESCRIPTION

[0069] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0070] Example 1

[0071] Reference Figure 1 , is an embodiment of the present invention, and provides a distribution network system risk coupling quantitative analysis method, comprising:

[0072] S1: Collect distribution network system operation data and establish an information-physical fusion system model.

[0073] Furthermore, the establishing of the information-physical fusion system model includes that the information-physical fusion system model CPDS includes an information system and an electric-physical system.

[0074] Furthermore, the power physical system includes traditional primary equipment and new energy equipment such as photovoltaics, wind power, and energy storage equipment; the information system includes a control center, a communication network, and intelligent terminal devices; the control center is the core of the information system, which analyzes the power system status data transmitted from the communication network and issues control instructions to achieve control and optimization of different distribution network scenarios.

[0075] It should be noted that the FA system has high requirements for the real-time transmission of data. Protection methods such as data verification that require a lot of computing time may have a negative impact on the operation of the distribution network. Smart terminal devices in the distribution network usually have limited computing resources, storage resources, and communication resources, which will limit the information protection methods that can be applied to the device, such as encryption protocols and data verification. Therefore, the improvement of the information protection capabilities of terminal devices often requires the overall upgrade of software and hardware equipment.

[0076] S2: Determine the potential risk types of information systems, identify and quantify information system risks.

[0077] Furthermore, the potential risk types of the information system determined include: information attacks on the distribution network include active attacks, passive attacks, proximity attacks, insider attacks and software and hardware installation attacks; based on the attack effect analysis, the attack types are divided into integrity attacks and availability attacks.

[0078] Furthermore, the identification and quantification of information system risks include: based on the analysis of potential risk types, it is found that an availability attack on the RTU will cause it to refuse to execute instructions from the control center; if the RTU refuses service due to an information attack when a fault occurs, the faulty line will be in a power outage state, and the fault range will be expanded to the downstream, upstream and adjacent feeder branches of the feeder.

[0079] Furthermore, the impact of availability attacks on the downstream area of ​​the fault is expressed as:

[0080]

[0081] in, represents the impact of the failure of the i-th section feeder on the downstream power supply area under information attack; L e represents the load power on the e-th section feeder; i represents the failure rate of the i-th section feeder; represents the set of downstream feeder segments of the i-th feeder segment; represents the failure probability of the feeder e transfer path. If there is no transferable line downstream of feeder e, then P Tie =1, otherwise the value is equal to the probability of the corresponding tie switch failing to operate; represents the probability that all switches on the path from feeder i to feeder e refuse to operate due to an attack; S i,e represents the set of terminal devices corresponding to all switches on the path from feeder i to feeder e; represents the probability of terminal device k failing due to availability attack; d mttr Mean time to repair, including manual inspection, network forensics, and fault repair; d rst Indicates the remote control on / off time.

[0082] Furthermore, the availability attack causes the fault range to expand to the upstream power supply area of ​​the fault, including the adjacent branch feeders of the fault.

[0083] Furthermore, the impact of availability attacks on the upstream area of ​​the fault can be expressed as:

[0084]

[0085] in, It represents the impact of the failure of the i-th feeder section on the upstream power supply area under information attack; the set of upstream feeder sections of the i-th feeder section.

[0086] Furthermore, the impact of availability attack on the fault adjacent branch feeder is expressed as:

[0087]

[0088] in, represents the impact of the failure of the i-th section feeder on the adjacent power supply area under information attack; represents the set of adjacent branch feeders of the i-th section feeder; represents the probability that all switches within the range of the i-th feeder to the adjacent feeder m branch refuse to operate due to an attack; represents the set of all feeder segments on adjacent feeder m; It represents the terminal equipment set corresponding to all switches within the range of the i-th feeder to the adjacent feeder m branch; represents the probability that all switches upstream of feeder f on the feeder branch refuse to operate due to an attack; represents the set of terminal devices corresponding to all switches upstream of feeder f on the feeder branch; the risk formula of the distribution network under availability attack is expressed as:

[0089]

[0090] Wherein, B represents the set of all feeder segments.

[0091] Furthermore, the establishment of the information attack model includes that the impact of the integrity attack on the downstream feeder area of ​​the attacked device terminal can be expressed as:

[0092]

[0093] in, represents the impact of the failure of the jth terminal device on the downstream power supply area under information attack; represents the availability of the upstream feeder area of ​​the jth terminal device; represents the probability that the jth terminal device is attacked and all the terminal devices in its upstream feeder area are operating normally; represents the set of all feeders in the upstream area of ​​terminal device j; represents the set of all feeders in the downstream area of ​​terminal device j; represents the set of all terminal devices in the upstream feeder area of ​​terminal device j; represents the probability that an integrity attack causes the failure of terminal device j.

[0094] Furthermore, the impact of integrity attack on fault recovery in a fault state can be expressed as:

[0095]

[0096] in, represents the impact of failure recovery of the i-th section feeder under information attack; represents the probability that any terminal device in the downstream area of ​​feeder h fails due to an attack; Represents the set of all terminal devices downstream of the feeder; It represents the probability that the information attack causes the terminal device j to fail.

[0097] Furthermore, the risk of distribution network under integrity attack can be expressed as:

[0098]

[0099] Wherein, S represents the set of all terminal devices.

[0100] It should be noted that integrity attacks on terminal devices can cause the corresponding switches to malfunction. If the switch malfunctions due to an information attack on the corresponding RTU, it will not only cause a power outage in the normal operating distribution network, but may also affect the power transfer path in the fault state, thereby expanding the scope of the power outage. When an integrity attack causes the RTU corresponding switch to malfunction, no matter what state the line downstream of the switch is in, its downstream feeder will be in a power outage state due to the loss of connection with the upper power point.

[0101] S3: Establish an information attack model and define the system risk coupling degree and node risk coupling degree.

[0102] Furthermore, the system risk coupling degree and node risk coupling degree include that in CPDS, not only the impact of line failure and equipment failure of the distribution network should be considered, but also the direct and indirect impact caused by the stable operation of the power grid should be considered; then the CPDS risk formula is expressed as:

[0103] R CPDS =R P +R C→P

[0104] Among them, R CPBSrepresents the risk of distribution network considering information attack; R P represents the risk of traditional distribution network, that is, the risk of distribution network without considering information attack; R C→P It represents the risk gain brought by information attack to the distribution network.

[0105] Furthermore, Figure 2 As shown in the figure, when a line fault occurs in the distribution network, if there is a transfer path in the downstream feeder area, the control center will isolate the fault area and transfer the load in the downstream non-fault area to the adjacent feeder. In this process, if the RTU of the faulty downstream feeder malfunctions due to information attack, it may affect the transfer and recovery process of the load in the non-fault area, thereby expanding the scope of power outage caused by the fault. Under the condition of reliable information system, if B4 fails, the control center will disconnect switches S3 and S4 to isolate the fault, close switch S10 to transfer the load in areas B5 to B7 to the adjacent feeder. When B4 fails, if R6 is attacked by information and causes switch S6 to trip, the transfer and recovery path of the non-fault area will be blocked, and only the load on B7 in all downstream areas of S4 can be successfully transferred to the adjacent feeder.

[0106] Furthermore, the system risk coupling degree is used to characterize the risk gain brought to the power system by the abnormal state of the information system under the state of information-physical coupling; the system risk coupling degree formula is expressed as:

[0107]

[0108] in, represents the system risk coupling degree, and its value range is [0, +∞]. The system risk coupling degree is proportional to the risk gain of information threat to the power system. When it is 0, it means that the information threat will not affect the operation of the distribution network.

[0109] Furthermore, the distribution network under information attack is taken as the research object to analyze the impact of availability attack and integrity attack on the fault handling process of the centralized FA system; the coupling degree between systems under availability attack and integrity attack is expressed as:

[0110]

[0111] in, Indicates the system risk coupling degree under availability attack; represents the system risk coupling degree under integrity attack; R p It is obtained by setting the success probability of information attack to 0, that is, information attack will not affect the fault handling process of FA system.

[0112] Furthermore, the node risk coupling degree is used to characterize the risk gain brought to the power system by the abnormal state of the information node under the information-physical coupling state.

[0113] The node risk coupling degree in the distribution network information system can be expressed as:

[0114]

[0115] in, represents the node coupling degree of information node j; represents the impact of abnormal state of information node j on the distribution network, and its value range is [0, +∞]; if If it is 0, it means that the abnormal status of the node will not affect the operation of the distribution network.

[0116] It should be noted that, on the one hand, the terminal equipment monitors the operating status of the power system and uploads it to the control center through the communication network. On the other hand, the control center analyzes and issues control instructions based on the received data, and the system operator can observe and control the power system through the human-machine interface. When the information system operates normally, the control center can timely and accurately perceive problems such as line faults and voltage over-limit in the power system, and formulate treatment plans or take regulatory measures to ensure the safe and efficient operation of the system. When the data collection, transmission and control processes in the information system are affected, the control center may not be able to effectively monitor and control the power system, thereby deteriorating the system operation status. For example, the monitoring data is tampered with, resulting in a misjudgment of the control center, or the terminal equipment cannot execute the control center's instructions, causing the scope of the fault to expand.

[0117] S4: Perform simulation analysis on the risk coupling degree, calculate the risk coupling degree of the system and nodes under different attack types, and complete the risk analysis of the distribution network system.

[0118] Furthermore, the simulation analysis for risk coupling degree includes that when information attacks CPDS, the nodes in the information system are independent of each other, and the attack on a single node will not affect the operating status of other nodes. The degree of gain of the risk of the distribution network caused by the information attack on the node needs to be quantified, and all attack events involving nodes need to be considered.

[0119] Furthermore, the proportional sharing method is used to share the damage caused by attacks involving multiple target nodes. The damage caused by information attacks involving multiple nodes is shared among different nodes according to the weight coefficients of different nodes. The update formula is expressed as:

[0120]

[0121] in, represents the set of all nodes containing target node j in the information attack event; represents the probability of attack event k occurring; S k represents the set of all target nodes in attack event k; S\S k Indicates that it belongs to the set S and does not belong to the set S k A collection of nodes; represents the probability of failure of information node z due to information attack; represents the risk-gain ratio of node j in attack event k; c v represents the proportional allocation coefficient of information node ν; represents the risk gain caused by attack event k to the power system; Indicates the damage to the power system caused by the attack event.

[0122] Furthermore, by combining the analysis scenarios, the node coupling degree under availability attack and integrity attack can be obtained; under availability attack, represents the probability that the zth RTU fails due to an attack represents the damage caused by attack event k to the power system; under integrity attack, That is, the probability that the zth RTU fails due to an integrity attack Indicates that integrity attack event k causes harm to the power system.

[0123] It should be noted that availability attack refers to the attacker's malicious intrusion that destroys the terminal equipment's state monitoring and information transmission capabilities, affecting the safe and stable operation of the distribution network by affecting the observability and controllability of the control center. The typical method of this type of attack is denial of service attack, that is, the attacker sends a large number of forged data packets to the communication network, making the target communication link or terminal equipment unavailable. According to the different levels of availability attacks in the information system, they can be divided into attacks on the physical layer, MAC layer, network and transport layer, and application layer. This paper mainly analyzes information attacks on the application layer, that is, by exhausting the communication bandwidth or computing power of the target RTU in the distribution network, making it unable to respond to the instructions of the control center. When the RTU is in an unavailable state, it cannot execute the control instructions issued by the master station, which will indirectly affect the processing process of line faults in the distribution network. When there is no fault in the distribution network, the unavailability of the RTU caused by the availability attack will not affect the user. When a fault occurs in the distribution network, the RTU denial of service will affect the fault handling process of the FA system, resulting in an expansion of the fault range.

[0124] On the other hand, this embodiment also provides a distribution network system risk coupling quantitative analysis system, which includes:

[0125] The cyber-physical fusion system module establishes and maintains the cyber-physical fusion system model of the distribution network; the data collection submodule collects the operating data of the power physical system and the information system, including equipment status, load data, and communication data; the model building submodule establishes the CPDS model that includes the power physical system and the information system; the power physical system includes traditional primary equipment, new energy equipment, and energy storage equipment.

[0126] The information system risk identification and quantification module identifies and quantifies the potential risks of the information system, and divides the attack types into integrity attacks and availability attacks based on the attack effects.

[0127] The information attack model module establishes the information attack model and calculates the risk coupling degree of the system and nodes.

[0128] The simulation analysis module performs simulation verification and result analysis on the distribution network system, verifies the rationality of the proposed risk coupling index and calculation method through simulation results, analyzes the simulation results, and evaluates the effectiveness of different protection measures in reducing system risks.

[0129] If the above functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0130] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0131] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0132] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0133] Example 2

[0134] Reference Figure 3-5 , which is an embodiment of the present invention, provides a distribution network system risk coupling quantitative analysis method. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0135] IEEE33 node system such as Figure 3 As shown in the figure, the control center algorithm is a centralized FA algorithm, which can locate, isolate and restore line faults in the feeder area. The case contains 32 switches, each of which is monitored by RTU and communicates with the control center through the communication network. The feeder failure rate is 0.05 times / (km·a), and the FA system action time and the average repair time of the fault are set to 5 minutes and 4 hours respectively. The probability of the terminal device causing the switch to refuse to operate due to availability attacks is set to 6%. The probability of the terminal device causing the switch to malfunction due to integrity attacks is set to 0.06 per ten thousand. The proportional weights of all terminal devices are set to 1.

[0136] like Figure 4 As shown in the figure, the RTU risk coupling degree corresponding to the switch on the feeder outlet side under integrity attack is high, and the RTU coupling degree is positively correlated with the load downstream of its corresponding switch.

[0137] In order to analyze the impact of information attacks on system coupling, the distribution network under availability attacks and integrity attacks was simulated respectively. The results are shown in Table 1.

[0138] Table 1 System risk coupling degree under two types of information attacks

[0139] Attack Types No information attack Consider information attacks System risk coupling Availability Attacks 10.0625 10.4572 0.0392 Integrity Attacks 10.0625 11.6770 0.1604

[0140] The simulation results show that compared with the system risk without considering information attack, the system risk under availability attack and integrity attack has increased, and the risk increased by 0.3947MWh and 1.6144MWh respectively. This shows that although the FA system helps to reduce the power outage time caused by line faults, the impact of information attack on it cannot be ignored, especially under the trend of deep integration of information system and physical system in future power system. The system risk coupling degree under availability attack and integrity attack is 0.0392 and 0.1604 respectively. The system risk coupling degree under availability attack is less than that under integrity attack, which is about 4 times that of the former. This is mainly because the switch refusal caused by availability attack may only lead to the expansion of fault scope when the line fails, while the switch misoperation caused by integrity attack may cause power outage or expand the fault impact scope in both normal and faulty state of power system.

[0141] Under integrity attacks, the risk coupling of RTUs with security configurations is significantly reduced, while the coupling of other RTUs in the system remains almost unchanged. Under this attack, the switch misoperation caused by a single device being attacked can cause a power outage on the downstream load without the need to coordinate with other device failures. For the distribution network, the probability of being in a normal state is much higher than the probability of a faulty state. Therefore, although the probability of failure caused by integrity information attacks is much lower than that of availability information attacks, this type of information attack has a greater impact on the distribution network.

[0142] like Figure 5 As shown in the figure, the risk coupling degree of the RTU with security configuration under availability attack is reduced to 0. This shows that security configuration can effectively improve the ability of RTU to resist information attack. In addition, except for the RTU with security configuration, the risk coupling degree of other RTUs in the system has been reduced to a certain extent. This is mainly because under this type of attack, an attack event may include multiple target devices as attacked targets. When one of the target devices is securely configured, the probability of the attack event is reduced to 0, which in turn affects the coupling degree of all devices included in the attack event.

[0143] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A quantitative analysis method for risk coupling of distribution network system, characterized in that: include: Collect distribution network system operation data and establish a cyber-physical fusion system model; Determine the potential risk types of information systems, identify and quantify information system risks; Establish an information attack model and define the system risk coupling degree and node risk coupling degree; A simulation analysis is conducted on the risk coupling degree. Under different attack types, the risk coupling degree of the system and nodes is calculated to complete the risk analysis of the distribution network system.

2. The method for quantitatively analyzing the risk coupling of a distribution network system according to claim 1, characterized in that: The establishing of the information-physical fusion system model includes: the information-physical fusion system model CPDS includes an information system and an electric power-physical system; The power physical system includes traditional primary equipment and photovoltaic, wind power new energy equipment and energy storage equipment; the information system includes the control center, communication network and intelligent terminal equipment; the control center is the core of the information system, which analyzes the power system status data transmitted from the communication network and issues control instructions to achieve control and optimization of different distribution network scenarios.

3. The method for quantitatively analyzing the risk coupling of a distribution network system according to claim 2, characterized in that: The potential risk types of the information system determined include: information attacks on the distribution network include active attacks, passive attacks, proximity attacks, insider attacks, and software and hardware installation attacks; Based on the analysis of attack effects, attack types are divided into integrity attacks and availability attacks.

4. The method for quantitatively analyzing the risk coupling of a distribution network system according to claim 3, characterized in that: The identification and quantification of information system risks include: based on the analysis of potential risk types, it is found that an availability attack on the RTU will cause it to refuse to execute instructions from the control center; if the RTU refuses service due to an information attack when a fault occurs, the fault line will be in a power outage state, and the fault range will be extended to the downstream, upstream and adjacent feeder branches of the feeder; The impact of availability attacks on the downstream area of ​​the fault is expressed as: in, represents the impact of the failure of the i-th section feeder on the downstream power supply area under information attack; L e represents the load power on the e-th section feeder; i represents the failure rate of the i-th section feeder; represents the set of downstream feeder segments of the i-th feeder segment; represents the failure probability of the feeder e transfer path. If there is no transferable line downstream of feeder e, then P Tie =1, otherwise the value is equal to the probability of the corresponding tie switch failing to operate; represents the probability that all switches on the path from feeder i to feeder e refuse to operate due to an attack; S i,e represents the set of terminal devices corresponding to all switches on the path from feeder i to feeder e; represents the probability of terminal device k failing due to availability attack; d mttr Mean time to repair, including manual inspection, network forensics, and fault repair; d rst Indicates the remote control switch time; Availability attacks cause the fault range to expand to the upstream power supply area, including the adjacent branch feeders of the fault; The formula for the impact of availability attacks on the upstream area of ​​the fault is expressed as: in, represents the impact of the failure of the i-th feeder section on the upstream power supply area under information attack; the set of upstream feeder sections of the i-th feeder section; The impact of availability attack on the fault adjacent branch feeder is expressed as: in, represents the impact of the failure of the i-th section feeder on the adjacent power supply area under information attack; represents the set of adjacent branch feeders of the i-th section feeder; represents the probability that all switches within the range of the i-th feeder to the adjacent feeder m branch refuse to operate due to an attack; represents the set of all feeder segments on adjacent feeder m; It represents the terminal equipment set corresponding to all switches within the range of the i-th feeder to the adjacent feeder m branch; represents the probability that all switches upstream of feeder f on the feeder branch refuse to operate due to an attack; represents the set of terminal devices corresponding to all switches upstream of feeder f on the feeder branch; the risk formula of the distribution network under availability attack is expressed as: Wherein, B represents the set of all feeder segments.

5. The method for quantitatively analyzing the risk coupling of a distribution network system according to claim 4, characterized in that: The establishment of the information attack model includes that the impact of the integrity attack on the downstream feeder area of ​​the attacked device terminal can be expressed as: in, represents the impact of the failure of the jth terminal device on the downstream power supply area under information attack; represents the availability of the upstream feeder area of ​​the jth terminal device; represents the probability that the jth terminal device is attacked and all the terminal devices in its upstream feeder area are operating normally; represents the set of all feeders in the upstream area of ​​terminal device j; represents the set of all feeders in the downstream area of ​​terminal device j; represents the set of all terminal devices in the upstream feeder area of ​​terminal device j; represents the probability of integrity attack leading to failure of terminal device j; The impact of integrity attack on fault recovery in fault state can be expressed as: in, represents the impact of failure recovery of the i-th section feeder under information attack; represents the probability that any terminal device in the downstream area of ​​feeder h fails due to an attack; Represents the set of all terminal devices downstream of the feeder; represents the probability of terminal device j failing due to information attack; The risk of distribution network under integrity attack can be expressed as: Wherein, S represents the set of all terminal devices.

6. The method for quantitatively analyzing the risk coupling of a distribution network system according to claim 5, characterized in that: The system risk coupling degree and node risk coupling degree include that in CPDS, not only the impact of line failure and equipment failure of the distribution network should be considered, but also the direct and indirect impact caused by the stable operation of the power grid should be considered; then the CPDS risk formula is expressed as: R CPDS =R P +R C→P Among them, R CPBS represents the risk of distribution network considering information attack; R P represents the risk of traditional distribution network, that is, the risk of distribution network without considering information attack; R C→P It represents the risk gain brought by information attack to the distribution network; The system risk coupling degree is used to characterize the risk gain brought to the power system by the abnormal state of the information system under the state of information-physical coupling; the system risk coupling degree formula is expressed as: in, represents the system risk coupling degree, and its value range is [0, +∞]. The system risk coupling degree is proportional to the risk gain of information threat to the power system. 0, indicating that the information threat will not affect the operation of the distribution network; Taking the distribution network under information attack as the research object, the influence of availability attack and integrity attack on the fault handling process of centralized FA system is analyzed; the coupling degree between systems under availability attack and integrity attack is expressed as: in, Indicates the system risk coupling degree under availability attack; represents the system risk coupling degree under integrity attack; R p It is obtained by setting the success probability of information attack to 0, that is, information attack will not affect the fault handling process of FA system; Node risk coupling degree is used to characterize the risk gain brought to the power system by the abnormal state of information nodes under the state of information-physical coupling; The node risk coupling degree in the distribution network information system can be expressed as: in, represents the node coupling degree of information node j; represents the impact of abnormal state of information node j on the distribution network, and its value range is [0, +∞]; if If it is 0, it means that the abnormal status of the node will not affect the operation of the distribution network.

7. The method for quantitatively analyzing the risk coupling of a distribution network system according to claim 6, characterized in that: The simulation analysis for risk coupling degree includes that when information attacks CPDS, the nodes in the information system are independent of each other, and the attack on a single node will not affect the operating status of other nodes. The gain degree of the risk of the distribution network caused by the information attack on the node is quantified, and all attack events involving nodes need to be considered; The proportional allocation method is used to allocate the damage caused by attacks involving multiple target nodes. The damage caused by information attacks involving multiple nodes is allocated to different nodes according to the weight coefficients of different nodes. The update formula is expressed as: in, represents the set of all nodes containing target node j in the information attack event; represents the probability of attack event k occurring; S k represents the set of all target nodes in attack event k; S\S k Indicates that it belongs to the set S and does not belong to the set S k A collection of nodes; represents the probability of failure of information node z due to information attack; represents the risk-gain ratio of node j in attack event k; c v represents the proportional allocation coefficient of information node ν; represents the risk gain caused by attack event k to the power system; Indicates the harm to the power system caused by the attack event; Combined with the analysis scenario, the node coupling degree under availability attack and integrity attack can be obtained; under availability attack, represents the probability that the zth RTU fails due to an attack represents the damage caused by attack event k to the power system; under integrity attack, That is, the probability that the zth RTU fails due to an integrity attack Indicates that integrity attack event k causes harm to the power system.

8. A distribution network system risk coupling quantitative analysis system using the method according to any one of claims 1 to 7, characterized in that: The cyber-physical fusion system module establishes and maintains the cyber-physical fusion system model of the distribution network; The data collection submodule collects the operation data of the power physical system and information system, including equipment status, load data, and communication data; Model building submodule, building CPDS model including power physical system and information system; power physical system, including traditional primary equipment, new energy equipment and energy storage equipment; Information system risk identification and quantification module, which identifies and quantifies the potential risks of information systems and classifies attack types into integrity attacks and availability attacks based on the attack effects; Information attack model module, which builds information attack model and calculates the risk coupling degree of system and nodes; The simulation analysis module performs simulation verification and result analysis on the distribution network system, verifies the rationality of the proposed risk coupling index and calculation method through simulation results, analyzes the simulation results, and evaluates the effectiveness of different protection measures in reducing system risks.

9. A computer device comprising: A memory and a processor; the memory stores a computer program, wherein the processor implements the steps of the method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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