Cooperative network attack method and device for load frequency control system under wind power intervention, medium and product

Abstract

The invention discloses a collaborative network attack method and device for a load frequency control system under wind power intervention, a medium and a product, and relates to the field of wind power generation control, and the method comprises the steps: building a deviation model, and constructing a load frequency control model under wind power intervention in a target regional power grid; defining a data relationship and constraint information, and establishing an attack vector generation model; establishing an optimization model; determining an attack vector by adopting the optimization model, and tampering the parameter information of the attack target area by adopting the attack vector to obtain tampering information; and tampering information is injected into a load frequency control system to realize network attack, and the LFC center determines a regional control error according to a measured value and executes a control strategy to adjust the output of a generator. According to the application, the vulnerability of network security and possible challenges in the development process of the novel power system can be revealed, so that reference significance is provided for power system constructors to design more comprehensive security defense strategies, and the overall security of the system is improved.

Description

Technical Field

[0001] The present application relates to the field of wind power generation control, and in particular to a method, device, medium and product for coordinated network attack on load frequency control systems with wind power intervention. Background Art

[0002] At present, new power systems are developing rapidly, the proportion of new energy installed capacity continues to increase, and sensor technology and power communication management technology have achieved efficient information flow. However, due to the inherent low inertia and weak damping characteristics of new energy, system regulation has become more difficult. In addition, sensors and communication links are vulnerable to cyber attacks, which poses a challenge to the safe and stable operation of power systems.

[0003] Among the new energy sources, wind power generation accounts for a high proportion. Due to the relatively simple control structure and the lack of effective attack detection methods, wind farms are vulnerable to malicious network attacks, resulting in abnormal operation or failure. In addition, wind turbines will switch control strategies in different wind speed areas, and this switching mechanism is easily exploited by attackers. Attackers can interfere with the control strategy switching of wind turbines by tampering with the rotor speed, thereby disrupting their normal operation. The uncertainty of wind energy will inevitably affect the frequency stability of the interconnected power system after grid connection. Therefore, it is crucial to analyze the characteristics and hazards of possible network attacks and improve the network security resilience of the load frequency control (LFC) system under wind power intervention. The known network attack methods mainly propose network attacks on the load frequency control or wind turbine control devices of conventional power systems, among which the representative attack is the false data injection attack. This attack affects the area control error (ACE) by tampering with the measurement data of the tie line power in the power system, thereby interfering with the control commands issued by the LFC center and making the grid frequency unstable. According to the different ways of injecting attack signals, false data injection attacks can be divided into scaling attacks, ramp attacks, sinusoidal attacks, random attacks and other types.

[0004] Based on the above description, although it can provide a certain basis for improving the resilience of the power system, it only considers the situation where a single attack type acts on the traditional power system, and only targets the interconnection line power measurement data flowing in the information layer, which misleads the LFC control module to generate erroneous control commands, thereby indirectly affecting the system frequency stability. Furthermore, a compound network attack that directly affects the system frequency at the physical level and masks physical changes at the information level may cause the system to become rapidly unstable and unable to recover in time, causing greater harm to the system stability. Based on this, the known network attack methods cannot provide comprehensive reference significance for security defense strategies, which leads to the inability to guarantee the overall security of the system. Summary of the invention

[0005] The purpose of this application is to provide a collaborative network attack method, equipment, medium and product for load frequency control systems with wind power intervention, which can reveal the network security vulnerabilities and possible challenges faced during the development of new power systems, thereby providing reference for power system construction personnel to design more comprehensive security defense strategies and improve the overall security of the system.

[0006] To achieve the above objectives, this application provides the following solutions:

[0007] In a first aspect, the present application provides a collaborative network attack method for a load frequency control system under wind power intervention, comprising:

[0008] Obtain parameter information of the target area, and establish a deviation model in combination with the power generation characteristics of the wind turbine; the deviation model includes: regional frequency deviation, inter-regional tie line power deviation and regional control deviation; the parameter information includes wind turbine rotor speed and tie line power measurement information;

[0009] Based on the deviation model, a load frequency control model under wind power intervention in the target area power grid is constructed;

[0010] The attacker invades the sensor communication channel, collects the parameter information of the attacker during the invasion, and clarifies the data relationship and constraint information based on the parameter information of the attacker during the invasion, so as to establish an attack vector generation model;

[0011] Defining a collaborative network attack model based on the load frequency control model, and establishing an optimization model in combination with the attack vector generation model;

[0012] The optimization model is used to determine an attack vector, and the attack vector is used to tamper with parameter information of the attack target area to obtain tampered information; the attack vector is a bias applied to the parameter information;

[0013] The tampered information is injected into the load frequency control system to achieve a network attack. The LFC center determines the regional control error based on the measured value and executes the control strategy to adjust the generator output.

[0014] Optionally, the regional frequency deviation is expressed as:

[0015]

[0016] Where Δf i represents the frequency deviation of region i, is the load damping coefficient, is the generator moment of inertia, is the output power deviation of the energy unit, is the load deviation, ΔP tie,i is the power deviation of the inter-regional tie line, is the fan output power deviation.

[0017] Optionally, the inter-regional tie line power deviation is expressed as:

[0018]

[0019] In the formula, is the power deviation of the inter-regional tie line, represents the tie line power flow deviation between area i and area j, T ij represents the synchronization coefficient between region i and region j, Δf i represents the frequency deviation of region i, Δf j represents the frequency deviation of region j.

[0020] Optionally, the regional control deviation is expressed as:

[0021] ACE i =β i Δf i +ΔP tie,i ;

[0022] In the formula, ACE i represents the control deviation of region i, β i represents the frequency deviation gain of region i, Δf i represents the frequency deviation of region i, ΔP tie,i is the power deviation of the inter-regional interconnection line.

[0023] Optionally, the attacker invades the sensor communication channel, collects parameter information when the attacker invades, and clarifies data relationship and constraint information based on the parameter information when the attacker invades, including:

[0024] Parse the communication data of the communication protocol between the VSWT cabin control cabinet and the tower control cabinet, and extract the position and value of the fan rotor speed in the communication data packet to determine the numerical constraint of the fan rotor speed;

[0025] The communication data of the line power sensor is collected, and the tie line power data in the communication data of the line power sensor is extracted to determine the range constraint of the inter-region tie line power.

[0026] Optionally, the numerical constraint of the fan rotor speed is expressed as:

[0027] ω r,min ≤ω r ≤ω r,max ;

[0028] The range constraint of the inter-regional tie line power is expressed as:

[0029] ΔP tie,i,min ≤ΔP tie,i ≤ΔP tie,i,max ;

[0030] In the formula, ω r represents the fan rotor angular velocity, ω r,min represents the fan rotor angular velocity ω r The lower limit of ω r,max represents the fan rotor angular velocity ω r The upper limit of ΔP tie,i is the power deviation of the inter-regional tie line, ΔP tie,i,min Indicates the power deviation of the regional tie line ΔP tie,i The lower limit of ΔP tie,i,max Indicates the power deviation of the regional tie line ΔP tie,i upper limit.

[0031] Optionally, the optimization model is a two-layer optimization model; the optimization model is expressed as:

[0032]

[0033] stΔP tie,i,min ≤ΔP tie,i +ΔP≤ΔP tie,i,max

[0034] Δω=δ s ω r +δ r t,t∈L a ;

[0035] ω r,min ≤ω r +Δω≤ω r,max

[0036]

[0037] In the formula, represents the actual control error in region i after tampering with the fan rotor angular velocity, ACE i ' represents the control error of area i after collaborative tampering of the tie line power, that is, the control error of area i observed by the LFC center; ACE i represents the control error of area i when it is normal and not attacked; β i represents the frequency deviation gain of region i, is the generator moment of inertia, ω r represents the fan rotor angular velocity, δ s represents the scaling attack parameter for the wind turbine rotor angular velocity, δ rrepresents the ramp attack parameter for the angular velocity of the wind turbine rotor, t is the current moment, T g Represents the generator torque, ΔP tie,i is the power deviation of the inter-regional tie line, ΔP tie,i,min Indicates the power deviation of the regional tie line ΔP tie,i The lower limit of ΔP tie,i,max Indicates the power deviation of the regional tie line ΔP tie,i The upper limit of Δω represents the bias applied to the original fan rotor angular velocity, ω r,min represents the fan rotor angular velocity ω r The lower limit of ω r,max represents the fan rotor angular velocity ω r The upper limit, L a represents the feasible attack period, ΔP represents the bias applied to the original tie-line power measurement, and ΔP′ represents the bias applied to the final tie-line power measurement.

[0038] In a second aspect, the present application provides a computer device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of any one of the above-mentioned collaborative network attack methods against a load frequency control system with wind power intervention.

[0039] In a third aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any of the above-mentioned methods for collaborative network attack on a load frequency control system with wind power intervention.

[0040] In a fourth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of any of the above-mentioned collaborative network attack methods for load frequency control systems with wind power intervention.

[0041] According to the specific embodiments provided in this application, this application has the following technical effects:

[0042] The present application provides a collaborative network attack method, equipment, medium and product for load frequency control systems with wind power intervention. The attack vector is determined according to the optimization model. When parameter information is tampered with, it can not be detected by the control center, and it can have an impact on the load frequency control system. If it is detected, the purpose of tampering cannot be achieved. This can reveal the vulnerability of network security and the challenges that may be faced during the development of new power systems, thereby providing reference for power system construction personnel to design more comprehensive security defense strategies and improve the overall security of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0044] Figure 1 A flowchart of a coordinated network attack method for a load frequency control system with wind power intervention provided in one embodiment of the present application;

[0045] Figure 2 A flowchart of a coordinated network attack on a load frequency control system with wind power intervention provided in an embodiment of the present application;

[0046] Figure 3 A schematic diagram of an LFC model of a power system with wind power intervention provided in an embodiment of the present application;

[0047] Figure 4 A schematic diagram of a power system communication model with wind power intervention is provided for an embodiment of the present application;

[0048] Figure 5 Provide an optimization model schematic diagram for an embodiment of the present application;

[0049] Figure 6 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0050] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0051] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0052] The embodiment of the present application provides a collaborative network attack method for a load frequency control system under wind power intervention, such as Figure 1 Shown include:

[0053] Step 100: Obtain parameter information of the target area, and establish a deviation model in combination with the power generation characteristics of the wind turbine. The deviation model includes: regional frequency deviation, inter-regional tie line power deviation, and regional control deviation. The parameter information includes wind turbine rotor speed and tie line power measurement information.

[0054] Step 101: construct a load frequency control model under wind power intervention in the target area power grid based on the deviation model.

[0055] Step 102: The attacker invades the sensor communication channel, collects parameter information when the attacker invades, and clarifies data relationship and constraint information based on the parameter information when the attacker invades, so as to establish an attack vector generation model.

[0056] Step 103: Define a collaborative network attack model based on the load frequency control model, and establish an optimization model in combination with the attack vector generation model.

[0057] Step 104: using the optimization model to determine the attack vector, and using the attack vector to tamper with the parameter information of the attack target area to obtain tampered information. The attack vector is a bias applied to the parameter information.

[0058] Step 105: Inject the tampered information into the load frequency control system to implement a network attack. The LFC center determines the regional control error based on the measured value and executes the control strategy to adjust the generator output. This attack will cause the LFC center to be unable to detect the change in the wind turbine output, but in fact the frequency of the load frequency control system will fluctuate, which will cause the load frequency control system to become unstable.

[0059] In another exemplary embodiment of the present application, step 100 mainly involves obtaining information such as various parameters of the target area, and deriving the relationship between the regional frequency deviation and the power deviation of the inter-regional tie line in combination with the power generation characteristics of the wind turbine.

[0060] Step 1001: Taking the analysis of the variable speed wind turbine (VSWT) transmission system model as an example, a relationship between the wind turbine rotor speed and the wind turbine output power is formed, which is expressed as:

[0061] P w =ω r T g .

[0062] Where P w represents the fan output power, ω r represents the fan rotor angular velocity, T g Indicates the generator torque.

[0063] Step 1002: According to the composition of the generator sets in the region, derive the regional frequency deviation formula, which is expressed as:

[0064]

[0065] Where Δf i represents the frequency deviation of region i, is the load damping coefficient, is the generator moment of inertia, is the output power deviation of the energy unit, is the load deviation, is the fan output power deviation. ΔP tie,i is the power deviation of the inter-regional tie line, expressed as:

[0066]

[0067] In the formula, is the power deviation of the inter-regional tie line, represents the tie line power flow deviation between area i and area j, T ij represents the synchronization coefficient between region i and region j, Δf i represents the frequency deviation of region i, Δf j represents the frequency deviation of region j.

[0068] Step 1003: To facilitate the implementation of automatic control and further reflect the deviation of power generation, the regional control deviation (ACE) should be calculated, which is expressed as:

[0069] ACE i =β i Δf i +ΔP tie,i .

[0070] In the formula, ACE i represents the control deviation of region i, β i represents the frequency deviation gain of region i.

[0071] In another exemplary embodiment of the present application, the implementation process of step 102 can be described as follows:

[0072] Step 1021: Utilizing the vulnerability of the communication protocol between the VSWT cabin control cabinet and the tower control cabinet, the communication data is parsed, the position and value information of the fan rotor speed in the data packet is extracted, and its numerical constraints (i.e., the numerical constraints of the fan rotor speed) are further analyzed, which are expressed as:

[0073] ω r,min ≤ω r ≤ω r,max .

[0074] Among them, ω r,min With ω r,max They are respectively the fan rotor angular velocity ω r lower and upper limits.

[0075] Step 1022: Collect the communication data of the line power sensor, extract the tie line power data, and clarify the normal range of the tie line power. Based on this, the range constraint of the inter-region tie line power is expressed as:

[0076] ΔP tie,i,min ≤ΔP tie,i ≤ΔP tie,i,max .

[0077] Among them, ΔP tie,i,min With ΔP tie,i,max They are the inter-regional tie line power deviation ΔP tie,i lower and upper limits.

[0078] In another exemplary embodiment of the present application, the implementation process of step 103 and step 104 can be described as follows:

[0079] Step 1: Based on the formula derived in step 101 and the data constraints analyzed in step 102, the optimization goal is to maximize the actual ACE as much as possible while making the ACE obtained by the control center close to the normal value to ensure that the tampered measurement data is not detected by the control center.

[0080] Step 2: Define the attack vector a = [Δω, ΔP] T , the bias applied to the original fan rotor angular velocity is as follows:

[0081] Δω=δ s ω r +δ r t,t∈L a .

[0082] Where Δω represents the bias imposed by the original fan rotor angular velocity, δ s , δ r They represent the scaling attack and ramp attack parameters for the angular velocity of the fan rotor, t is the current moment, L a Indicates the feasible attack period.

[0083] By combining the scaling attack and ramp attack parameters, false data with regular changes based on the original sensor signal can be generated. The output power deviation value of the wind turbine caused by this bias should be expressed as:

[0084] ΔP′ w =ΔP w +ΔωT g .

[0085] In the formula, ΔP w With ΔP w 'represent the fan output power deviation before and after the attack respectively.

[0086] After tampering with the fan rotor angular velocity, the actual ACE should be:

[0087]

[0088] In the formula, It represents the actual control error in region i after tampering with the angular velocity of the fan rotor. i This is the control error of area i when it is normally not attacked.

[0089] From the attacker's perspective, the difference between the actual ACE and the normal ACE (i.e., the ACE value when not attacked) should be maximized.

[0090] Step 3: In order to keep the ACE observed by the LFC center stable, the tie line power flow measurement should also be tampered with at the same time. The tie line power flow deviation after tampering is expressed as:

[0091] ΔP′ tie,i =ΔP tie,i +ΔP.

[0092] Where ΔP′ tie,i It represents the tie line power flow deviation after tampering, and ΔP represents the bias applied to the tie line power measurement.

[0093] Step 4: After injecting the above attack vector, the ACE obtained by the LFC center will be represented as:

[0094]

[0095] Where Δf i ' and ΔP ti ' e,i They represent the frequency deviation and tie line power deviation after the attack respectively.

[0096] Considering the concealment of the attack, the difference between the ACE obtained by the LFC center and the normal ACE should be minimized.

[0097] Step 5: Analyze the physical quantity constraints involved in the attack model and establish the optimization model as a two-layer optimization model, expressed as:

[0098]

[0099] stΔP tie,i,min ≤ΔP tie,i +ΔP≤ΔP tie,i,max

[0100] Δω=δ s ω r +δ r t,t∈L a .

[0101] ωr,min ≤ω r +Δω≤ω r,max

[0102]

[0103] Where ΔP′ represents the bias ultimately applied to the tie-line power measurement.

[0104] The optimization variables of this optimization model are {δ s ,δ r ,ΔP}, the attack vector a of the collaborative network can be calculated by the optimization model.

[0105] In another exemplary embodiment of the present application, the implementation process of step 105 can be described as follows:

[0106] Step 1051: When time t is in the feasible attack period L a When , the attack vector calculated in step 104 is injected into the system sensor, which is expressed as:

[0107]

[0108]

[0109] Where a1(t) and a2(t) represent the first and second terms of the attack vector a, respectively.

[0110] Step 1052: After injecting the first item a1 of the attack vector a, the wind turbine will switch to the wrong control strategy, which may cause problems such as overload of the wind turbine transmission system and reduced power generation efficiency. At the same time, the second item a2 injected into the attack vector a tampers the tie line power, making the ACE observed by the LFC center within the normal range, which hides the scaling attack and the ramp attack to a certain extent.

[0111] In another exemplary embodiment of the present application, Figure 2 As shown, the implementation method of the coordinated network attack method for the load frequency control system under wind power intervention provided by the present application can be described as follows:

[0112] 1): The attacker should obtain information such as various parameters of the target area, such as power generation structure, topology, etc., to facilitate the subsequent construction of the attack model.

[0113] 2): Combined with the power generation characteristics of different types of units, the output of traditional energy units is distinguished from the output of wind turbines, and a load frequency control model is constructed in the region.

[0114] 3): The attacker exploits the vulnerability of the communication link to collect measurement data, clarify the upper and lower limits and relationships of relevant physical quantities, and provide constraints and other information for the construction of attack vectors.

[0115] 4): Based on the wind turbine model and the constructed LFC model, an optimization model of the attack vector is constructed, and the attack vector is derived from it. Among them, the items of the attack vector are the tampered values ​​of the wind turbine rotor speed and the tie line power.

[0116] 5): Inject the calculated attack vectors into different sensors or communication channels respectively to interfere with the controller's information perception ability.

[0117] 6): The LFC center calculates ACE based on the tampered measurement values, and the result is close to the normal ACE, but in fact the fan output and system frequency fluctuate.

[0118] In another exemplary embodiment of the present application, based on Figure 3 The LFC model of the power system with wind power intervention is shown. Based on the above description, the implementation process of the coordinated network attack method for the load frequency control system with wind power intervention provided by this application can be described as follows:

[0119] 1): Traditional energy units output stable and reliable power according to the predetermined plan, and their power deviation is expressed as ΔP gi The wind turbine adopts a high-speed or low-speed control strategy according to the wind speed, so as to generate the largest possible power output at different wind speed values. The power deviation is represented by ΔP wi The load in the area consumes the power provided by the generator set, and the deviation of consumption is expressed as ΔP Li When the power generation in a region is unbalanced with the load demand, the power generation will be coordinated through the tie line. For a single region, the deviation value of power exchange between regions is ΔP tie,i express.

[0120] 2): The frequency deviation is related to the power deviation in the area, expressed as Δf i The tie line power is related to the frequency deviation and synchronization coefficient of each area. In order to regulate the load frequency in the area, the regional control error is calculated to indicate the power balance, which can simultaneously reflect the deviation of the frequency and the tie line exchange power.

[0121] 3): LFC center uses the system control strategy based on the calculated ACE i Generate control signals, set reference values ​​for conventional energy units, and regulate frequencies within the region.

[0122] In another exemplary embodiment of the present application, based on Figure 4 The power system communication model with wind power intervention is shown. Based on the above description, the implementation process of the coordinated network attack method for the load frequency control system with wind power intervention provided by this application can be described as follows:

[0123] 1): An attacker can exploit the vulnerability of the communication protocol between the nacelle control cabinet and the tower control cabinet in the wind turbine to inject false data into the communication link and tamper with the wind turbine rotor speed information transmitted therein. The wrong rotor speed information will be transmitted to the tower controller, resulting in wrong control commands, which may lead to incorrect switching of different control strategies.

[0124] 2): Attackers can access the power system flow measurement values ​​through malware and other means, inject false data into the communication link transmitted to the LFC center, tamper with the regional interconnection line flow information, and conceal the actual inter-regional flow status.

[0125] In another exemplary embodiment of the present application, based on Figure 5 The optimization model shown, based on the above description, the implementation process of the collaborative network attack method for the load frequency control system under wind power intervention provided by this application can be described as follows:

[0126] 1): The generation of attack vectors is modeled as a two-level optimization problem, where the goal of the upper level problem is to generate attack parameters with the greatest possible attack effect, including scaling attack parameters and ramp attack parameters. The goal of the lower level problem is related to the upper level decision variables.

[0127] 2): Considering the concealment of the attack, the interconnection line flow data should be tampered with in a coordinated manner to minimize the difference between the tampered state and the normal state, while ensuring that the tampered flow value does not exceed the limit.

[0128] In summary, this application uses the vulnerability of the wind turbine communication link, combines scaling attacks with ramp attacks, tampering with the wind turbine rotor speed measurement value, and collaboratively modifies the regional interconnection line power measurement value to cover up the impact of the attack, which can damage the frequency stability of the power system under wind power access. The method provided in this application uses the power system with wind power access as the attack background, reveals the vulnerability of the wind turbine network security, and can provide reference for the network security construction of the power system with wind power intervention.

[0129] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 6As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store collaborative network attack data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a collaborative network attack method for a load frequency control system under wind power intervention is implemented.

[0130] Those skilled in the art will understand that Figure 6 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components. In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0131] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0132] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0133] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0134] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0135] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0136] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0137] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A collaborative network attack method for a load frequency control system under wind power intervention, characterized in that: The collaborative network attack method for the load frequency control system under wind power intervention includes: Obtain parameter information of the target area, and establish a deviation model in combination with the power generation characteristics of the wind turbine; the deviation model includes: regional frequency deviation, inter-regional tie line power deviation and regional control deviation; the parameter information includes wind turbine rotor speed and tie line power measurement information; Based on the deviation model, a load frequency control model under wind power intervention in the target area power grid is constructed; The attacker invades the sensor communication channel, collects the parameter information of the attacker during the invasion, and clarifies the data relationship and constraint information based on the parameter information of the attacker during the invasion, so as to establish an attack vector generation model; Defining a collaborative network attack model based on the load frequency control model, and establishing an optimization model in combination with the attack vector generation model; The optimization model is used to determine an attack vector, and the attack vector is used to tamper with parameter information of the attack target area to obtain tampered information; the attack vector is a bias applied to the parameter information; The tampered information is injected into the load frequency control system to achieve a network attack. The LFC center determines the regional control error based on the measured value and executes the control strategy to adjust the generator output.

2. The coordinated network attack method for load frequency control systems with wind power intervention according to claim 1 is characterized in that: The regional frequency deviation is expressed as: Where Δf i represents the frequency deviation of region i, is the load damping coefficient, is the generator moment of inertia, is the output power deviation of the energy unit, is the load deviation, ΔP tie,i is the power deviation of the inter-regional tie line, is the fan output power deviation.

3. The coordinated network attack method for load frequency control systems with wind power intervention according to claim 1 is characterized in that: The inter-regional tie line power deviation is expressed as: In the formula, is the power deviation of the inter-regional tie line, represents the tie line power flow deviation between area i and area j, T ij represents the synchronization coefficient between region i and region j, Δf i represents the frequency deviation of region i, Δf j represents the frequency deviation of region j.

4. The coordinated network attack method for load frequency control systems with wind power intervention according to claim 1 is characterized in that: The regional control deviation is expressed as: ACE i =b i ·Δf i +ΔP tie,i ; In the formula, ACE i represents the control deviation of region i, β i represents the frequency deviation gain of region i, Δf i represents the frequency deviation of region i, ΔP tie,i is the power deviation of the inter-regional interconnection line.

5. The coordinated network attack method for load frequency control systems with wind power intervention according to claim 1 is characterized in that: The attacker invades the sensor communication channel, collects the parameter information when the attacker invades, and clarifies the data relationship and constraint information based on the parameter information when the attacker invades, including: Parse the communication data of the communication protocol between the VSWT cabin control cabinet and the tower control cabinet, and extract the position and value of the fan rotor speed in the communication data packet to determine the numerical constraint of the fan rotor speed; The communication data of the line power sensor is collected, and the tie line power data in the communication data of the line power sensor is extracted to determine the range constraint of the inter-region tie line power.

6. The coordinated network attack method for load frequency control systems with wind power intervention according to claim 5 is characterized in that: The numerical constraint of the fan rotor speed is expressed as: oh r,min ≤ω r ≤ω r,max ; The range constraint of the inter-regional tie line power is expressed as: ΔP tie,i,min ≤ΔP tie,i ≤ΔP tie,i,max ; In the formula, ω r represents the fan rotor angular velocity, ω r,min represents the fan rotor angular velocity ω r The lower limit of ω r,max represents the fan rotor angular velocity ω r The upper limit of ΔP tie,i is the power deviation of the regional tie line, ΔP tie,i,min Indicates the power deviation of the regional tie line ΔP tie,i The lower limit of ΔP tie,i,max Indicates the power deviation of the regional tie line ΔP tie,i upper limit.

7. The coordinated network attack method for load frequency control system under wind power intervention according to claim 1 is characterized in that: The optimization model is a two-layer optimization model; the optimization model is expressed as: In the formula, represents the actual control error in region i after tampering with the fan rotor angular velocity, ACE i ' represents the control error of area i after collaborative tampering of the tie line power, that is, the control error of area i observed by the LFC center; ACE i represents the control error of area i when it is normal and not attacked; β i represents the frequency deviation gain of region i, M ti is the generator moment of inertia, ω r represents the fan rotor angular velocity, δ s represents the scaling attack parameter for the wind turbine rotor angular velocity, δ r represents the ramp attack parameter for the angular velocity of the wind turbine rotor, t is the current moment, T g Represents the generator torque, ΔP tie,i is the power deviation of the regional tie line, ΔP tie,i,min Indicates the power deviation of the regional tie line ΔP tie,i The lower limit of ΔP tie,i,max Indicates the power deviation of the regional tie line ΔP tie,i The upper limit of Δω represents the bias applied to the original fan rotor angular velocity, ω r,min represents the fan rotor angular velocity ω r The lower limit of ω r,max represents the fan rotor angular velocity ω r The upper limit, L a represents the feasible attack period, ΔP represents the bias applied to the original tie-line power measurement, and ΔP′ represents the bias applied to the final tie-line power measurement.

8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the collaborative network attack method against a load frequency control system under wind power intervention as described in any one of claims 1-7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the coordinated network attack method for a load frequency control system under wind power intervention described in any one of claims 1 to 7 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the coordinated network attack method for a load frequency control system under wind power intervention described in any one of claims 1 to 7 is implemented.

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