A method, device, medium and product for a coordinated cyber attack on a load frequency control system under wind power intervention

Abstract

This application discloses a collaborative network attack method, device, medium, and product targeting a load frequency control system with wind power intervention, relating to the field of wind power generation control. The method includes: establishing a deviation model, constructing a load frequency control model for the target area power grid with wind power intervention; clarifying data relationships and constraint information, and establishing an attack vector generation model; establishing an optimization model; using the optimization model to determine the attack vector, and using the attack vector to tamper with the parameter information of the target area to obtain tampered information; injecting the tampered information into the load frequency control system to achieve a network attack; the LFC center determines the regional control error based on the measured values ​​and executes the control strategy to adjust the generator output. This application can reveal the vulnerability of network security and the potential challenges faced by new power systems during their development, thus providing a reference for power system construction personnel to design more comprehensive security defense strategies and improving the overall system security.

Description

Technical Field

[0001] This application relates to the field of wind power generation control, and in particular to a method, device, medium and product for cooperative network attacks against load frequency control systems with wind power intervention. Background Technology

[0002] Currently, new power systems are developing rapidly, the proportion of installed capacity from new energy sources continues to increase, and sensor technology and power communication management technology have enabled efficient information flow. However, due to the inherent low inertia and weak damping characteristics of new energy sources, system regulation has become more difficult. Furthermore, sensors and communication links are vulnerable to cyberattacks, posing challenges to the safe and stable operation of the power system.

[0003] Wind power accounts for a significant proportion of new energy sources. Due to their relatively simple control structures and lack of effective attack detection methods, wind farms are vulnerable to malicious cyberattacks, leading to operational anomalies or malfunctions. Furthermore, wind turbines switch control strategies under different wind speed zones, a mechanism easily exploited by attackers. Attackers can interfere with the control strategy switching of wind turbines by tampering with rotor speed, thereby disrupting their normal operation. The uncertainty of wind energy inevitably affects the frequency stability of the interconnected power system after grid connection. Therefore, analyzing the characteristics and harms of potential cyberattacks and improving the cybersecurity resilience of load frequency control (LFC) systems with wind power integration is crucial. Known cyberattack methods primarily target load frequency control or wind turbine control devices in conventional power systems, with spoofing attacks being a representative example. This attack manipulates the measurement data of tie-line power in the power system, affecting the area control error (ACE), thereby interfering with control commands issued by the LFC center and causing grid frequency instability. Depending on the method of injecting the attack signal, spoofing attacks can be categorized into scaling attacks, ramp attacks, sinusoidal attacks, and random attacks.

[0004] Based on the above description, while it can provide some basis for improving the resilience of power systems, it only considers the case of a single attack type operating in a traditional power system, and only targets the power measurement data flowing through tie lines in the information layer. This misleads the LFC control module, generating erroneous control commands and indirectly affecting system frequency stability. Furthermore, composite network attacks that directly affect system frequency at the physical level while simultaneously masking physical changes at the information level can lead to rapid system instability and an inability to recover in a timely manner, posing a greater threat to system stability. Therefore, known network attack methods cannot provide comprehensive guidance for security defense strategies, ultimately resulting in a failure 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, device, medium, and product for load frequency control systems with wind power intervention. This can reveal the network security vulnerabilities and potential challenges of new power systems during their development, thereby providing reference for power system builders to design more comprehensive security defense strategies and improving the overall security of the system.

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

[0007] Firstly, this application provides a cooperative network attack method targeting a load frequency control system with wind power intervention, including:

[0008] The parameter information of the target area is obtained, and a deviation model is established 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 aforementioned deviation model, a load frequency control model for wind power intervention in the target area power grid is constructed.

[0010] Attackers intrude into sensor communication channels, collect parameter information during the intrusion, and clarify data relationships and constraints based on the parameter information during the intrusion, in order to establish an attack vector generation model;

[0011] Based on the load frequency control model, a cooperative network attack model is defined, and an optimization model is established in conjunction with the attack vector generation model.

[0012] The attack vector is determined using the optimization model, and the attack vector is used to tamper with the parameter information of the target region to obtain the 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] In the formula, Δf i This represents the frequency deviation in region i. This is the load damping coefficient. The moment of inertia of the generator. For the deviation of the power output of the energy unit, For load deviation, ΔP tie,i For inter-regional tie line power deviation, This refers to the deviation in the output power of the fan.

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

[0018]

[0019] In the formula, For inter-regional tie line power deviation, T represents the power flow deviation of the tie line between region i and region j. ij Δf represents the synchronization coefficient between region i and region j. i Δf represents the frequency deviation in region i. j This 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 β represents the control deviation in region i. i Δf represents the frequency deviation gain in region i. i ΔP represents the frequency deviation in region i. tie,i This refers to the power deviation of inter-regional tie lines.

[0023] Optionally, the attacker intrudes into the sensor communication channel, collects parameter information during the intrusion, and clarifies data relationships and constraints based on this parameter information, including:

[0024] The communication data of the communication protocol between the VSWT nacelle control cabinet and the tower control cabinet is analyzed, and the position and value of the wind turbine rotor speed in the communication data packet are extracted to determine the numerical constraints of the wind turbine 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 tie line power between areas.

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

[0027] ω r,min ≤ω r ≤ω r,max ;

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

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

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

[0031] Optionally, the optimization model is a two-level 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, ACE represents the actual control error in region i after the fan rotor angular velocity was altered. i 'Indicates the control error of region i after coordinated alteration of tie-line power, i.e., the control error of region i as observed by the LFC center; ACE i β represents the control error of region i when it is normal and unattacked; i This represents the frequency deviation gain in region i. Let ω be the moment of inertia of the generator. r δ represents the angular velocity of the fan rotor. s δ represents the scaling attack parameter for the wind turbine rotor angular velocity. rThis represents the ramp attack parameters for the angular velocity of the wind turbine rotor, where t is the current time, and T is the value of T. g The generator torque is represented by ΔP. tie,i For the power deviation of inter-regional tie lines, ΔP tie,i,min Indicates the power deviation ΔP between regional tie lines tie,i The lower limit, ΔP tie,i,max Indicates the power deviation ΔP between regional tie lines tie,i The upper limit, Δω represents the bias applied to the original fan rotor angular velocity, ω r,min Represents the angular velocity ω of the fan rotor r The lower limit, ω r,max Represents the angular velocity ω of the fan rotor r The upper limit, L a The ΔP represents the feasible attack period, ΔP represents the bias applied to the original tie-line power measurement, and ΔP′ represents the final bias applied to the tie-line power measurement.

[0038] In a second aspect, this application provides a computer device, including: 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 the cooperative network attack method against the load frequency control system under wind power intervention as described above.

[0039] Thirdly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the cooperative network attack method against a load frequency control system with wind power intervention as described above.

[0040] Fourthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the cooperative network attack method against a load frequency control system with wind power intervention as described above.

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

[0042] This application provides a collaborative network attack method, device, medium, and product for load frequency control systems with wind power intervention. The attack vector is determined based on an optimization model. When tampering with parameter information, it can be done without being detected by the control center and can affect the load frequency control system. If detected, the tampering purpose cannot be achieved. This reveals the vulnerability of network security in the development of new power systems and the challenges they may face. It provides a reference for power system builders to design more comprehensive security defense strategies and improves the overall security of the system. Attached Figure Description

[0043] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0044] Figure 1 A flowchart illustrating a collaborative network attack method for a load frequency control system with wind power intervention, provided as an embodiment of this application;

[0045] Figure 2 A flowchart of a cooperative network attack against a load frequency control system with wind power intervention is provided as an embodiment of this application.

[0046] Figure 3 A schematic diagram of an LFC (Low Current Factorization) model of a power system with wind power intervention provided in an embodiment of this application;

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

[0048] Figure 5 An optimized model schematic diagram is provided for one embodiment of this application;

[0049] Figure 6 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0050] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0051] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0052] The cooperative network attack method for load frequency control systems with wind power intervention provided in this application embodiment, such as Figure 1 The following are included:

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

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

[0055] Step 102: The attacker intrudes into the sensor communication channel, collects the parameter information during the intrusion, and clarifies the data relationships and constraints based on the parameter information during the intrusion, in order to establish an attack vector generation model.

[0056] Step 103: Define a cooperative network attack model based on the load frequency control model, and establish an optimization model by combining the attack vector generation model.

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

[0058] Step 105: 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 values ​​and executes the control strategy to adjust the generator output. This attack causes the LFC center to be unable to detect changes in turbine output; however, the actual frequency of the load frequency control system will fluctuate, leading to instability of the load frequency control system.

[0059] In another exemplary embodiment of this application, step 100 mainly involves acquiring information such as various parameters of the target area, and deriving the relationship between regional frequency deviation and inter-regional tie-line power deviation based on the wind turbine generation characteristics. Wherein:

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

[0061] P w =ω r T g .

[0062] In the formula, P w Indicates the output power of the fan, ω r T represents the angular velocity of the fan rotor. g This indicates the generator torque.

[0063] Step 1002: Based on the generator set composition within the region, derive the regional frequency deviation formula, which is expressed as:

[0064]

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

[0066]

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

[0068] Step 1003: To facilitate the implementation of automated control and further reflect deviations in power generation, the area control deviation (ACE) should be calculated, expressed as:

[0069] ACE i =β i ·Δf i +ΔP tie,i .

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

[0071] In another exemplary embodiment of this 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 nacelle control cabinet and the tower control cabinet, parse the communication data, extract the position and value information of the wind turbine rotor speed in the data packets, and further analyze its numerical constraints (i.e., the numerical constraints of the wind turbine rotor speed), expressed as:

[0073] ω r,min ≤ω r ≤ω r,max .

[0074] Where, ω r,min With ω r,max These are the angular velocities ω of the wind turbine rotor. r The lower and upper limits.

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

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

[0077] Wherein, ΔP tie,i,min With ΔP tie,i,max These represent the power deviation ΔP between regional tie lines. tie,i The lower and upper limits.

[0078] In another exemplary embodiment of this application, the implementation process of steps 103 and 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, clarify the optimization objective, namely, to maximize the actual ACE as much as possible, while making the ACE obtained by the control center close to the normal value, so as 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] In the formula, Δω represents the bias applied to the original fan rotor angular velocity, and δ s δ r These represent the scaling attack and ramp attack parameters targeting the wind turbine rotor angular velocity, respectively, where t is the current time, and L... a Indicates a feasible attack period.

[0083] By combining scaling attack and ramp attack parameters, spurious data that varies regularly based on the original sensor signal can be generated. The resulting bias in the wind turbine output power deviation should be expressed as:

[0084] ΔP′ w =ΔP w +ΔωT g .

[0085] In the formula, ΔP w With ΔP w 'These represent the deviations in wind turbine output power before and after the attack.

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

[0087]

[0088] In the formula, This represents the actual control error in region i after the fan rotor angular velocity was altered. ACE i This represents the control error of region i when it is normal and unattacked.

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

[0090] Step 3: To keep the ACE observed by the LFC center stable, the tie-line power flow measurement should also be modified. The modified tie-line power flow deviation is expressed as:

[0091] ΔP′ tie,i =ΔP tie,i +ΔP.

[0092] In the formula, ΔP′ tie,i This indicates the altered tie-line power flow deviation, where 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] In the formula, Δf i 'With ΔP ti ' e,i These represent the frequency deviation and the tie-line power deviation after the attack, respectively.

[0096] Considering the stealth 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 constraints involved in the attack model, and establish an optimization model as a two-layer optimization model, represented 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] In the formula, ΔP′ represents the bias applied to the final tie-line power measurement.

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

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

[0106] Step 1051: When time t is within the feasible attack period L a Within this timeframe, the attack vector calculated in step 104 is injected into the system sensors, represented as:

[0107]

[0108]

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

[0110] Step 1052: After injecting the first term a1 of attack vector a, the wind turbine will switch to an incorrect control strategy, which may lead to problems such as overload of the wind turbine drive system and reduced power generation efficiency. At the same time, the second term a2 of attack vector a modifies the tie-line power, so that the ACE observed by the LFC center is within the normal range, thus hiding the scaling attack and the ramp attack to some extent.

[0111] In another exemplary embodiment of this application, such as Figure 2 As shown, the implementation method of the cooperative network attack method for load frequency control systems with wind power intervention provided in this application can be described as follows:

[0112] 1) Attackers should obtain information such as various parameters of the target area, such as power generation structure and topology, to facilitate the subsequent construction of attack models.

[0113] 2): Combining the power generation characteristics of different types of units, the output of traditional energy units and wind turbines are distinguished, and a regional load frequency control model is constructed.

[0114] 3): Attackers exploit the vulnerabilities in communication links 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 optimized model for the attack vector is constructed, from which the attack vector is derived. The terms of the attack vector represent the altered values ​​of the wind turbine rotor speed and the tie-line power, respectively.

[0116] 5): The calculated attack vectors are injected into different sensors or communication channels to interfere with the controller's information perception capabilities.

[0117] 6): The LFC center calculates the ACE based on the tampered measurement values, and the result is similar to the normal ACE. However, in reality, the turbine output and system frequency both fluctuate.

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

[0119] 1) Traditional energy units output stable and reliable power according to a predetermined plan, and the power deviation is denoted by ΔP. gi The wind turbine employs a high-speed or low-speed control strategy based on wind speed to generate the maximum possible power output at different wind speeds. The power deviation is denoted by ΔP. wi The load within the area consumes the power provided by the generator set; the deviation in consumption is represented by ΔP. Li Indicated. When there is an imbalance between power generation and load demand within a region, power generation will be coordinated through tie lines. For a single region, the deviation in power exchange between regions is denoted by ΔP. tie,i express.

[0120] 2): Frequency deviation is related to the power deviation within the region, 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 within the area, the area control error is calculated to represent the power balance situation, and can simultaneously reflect the deviation of frequency and tie-line exchange power.

[0121] 3): The LFC center, based on the system control strategy and the calculated ACE, i It generates control signals, sets reference values ​​for traditional energy units, and regulates the frequency within the region.

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

[0123] 1) Attackers can exploit vulnerabilities in the communication protocol between the nacelle control cabinet and the tower control cabinet in a wind turbine to inject false data into the communication link, tampering with the transmitted turbine rotor speed information. Incorrect rotor speed information will be transmitted to the tower controller, generating incorrect control commands, which may lead to incorrect switching between different control strategies.

[0124] 2) Attackers can access power flow measurements through malware and other means, inject false data into the communication link transmitted to the LFC center, tamper with regional tie line power flow information, and cover up the actual inter-regional power flow status.

[0125] In another exemplary embodiment of this application, based on such Figure 5 Based on the above description and the optimization model shown, the implementation process of the cooperative network attack method for load frequency control systems with wind power intervention provided in this application can be described as follows:

[0126] 1) The generation of attack vectors is modeled as a two-level optimization problem. The objective of the upper-level problem is to generate attack parameters that maximize the attack effect, including scaling attack parameters and ramp attack parameters. The objective of the lower-level problem is related to the decision variables of the upper-level problem.

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

[0128] In summary, this application exploits the vulnerabilities in wind turbine communication links, combining scaling and ramp attacks to tamper with wind turbine rotor speed measurements and coordinates modifications to regional tie-line power measurements to mask the attack's impact. This can disrupt the frequency stability of power systems with wind power integration. The method provided in this application, using a wind-integrated power system as the attack context, reveals the cybersecurity vulnerabilities of wind turbine units and can offer valuable insights for the construction of cybersecurity in power systems with wind power integration.

[0129] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 6As shown, the computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores data related to collaborative network attacks. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communicating with external terminals via a network connection. When executed by the processor, the computer program implements a collaborative network attack method targeting a load frequency control system with wind power intervention.

[0130] Those skilled in the art will understand that Figure 6 The structures shown are merely block diagrams of some structures related to the present application and do not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than shown in the figures, or combine certain components, or have different component arrangements. In an exemplary embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0131] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0132] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[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, data stored, data displayed, 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 the relevant data must comply with relevant regulations.

[0134] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this 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 can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0135] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0136] The technical features of the above embodiments can be combined in any way. For the sake of brevity, 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 document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A cooperative network attack method for load frequency control systems with wind power intervention, characterized in that, The cooperative network attack method targeting the load frequency control system under wind power intervention includes: The parameter information of the target area is obtained, and a deviation model is established 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 aforementioned deviation model, a load frequency control model for wind power intervention in the target area power grid is constructed. Attackers intrude into sensor communication channels, collect parameter information during the intrusion, and clarify data relationships and constraints based on the parameter information during the intrusion, in order to establish an attack vector generation model; Based on the load frequency control model, a cooperative network attack model is defined, and an optimization model is established in conjunction with the attack vector generation model. The attack vector is determined using the optimization model, and the attack vector is used to tamper with the parameter information of the target region to obtain the 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 load frequency control center determines the regional control error based on the tie-line power measurement information and executes the control strategy to adjust the generator output.

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

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

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

5. The method for coordinated network attacks against load frequency control systems with wind power intervention as described in claim 1, characterized in that, An attacker compromises the sensor's communication channel, collects parameter information during the intrusion, and clarifies data relationships and constraints based on this parameter information, including: The communication data of the communication protocol between the nacelle control cabinet and the tower control cabinet of the variable speed wind turbine is analyzed, and the position and value of the wind turbine rotor speed in the communication data packet are extracted to determine the numerical constraints of the wind turbine 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 tie line power between areas.

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

7. The method for coordinated network attacks against load frequency control systems with wind power intervention as described in claim 1, characterized in that, The optimization model is a two-level optimization model; the optimization model is expressed as follows: In the formula, ACE represents the actual control error in region i after the fan rotor angular velocity was altered. i 'Indicates the control error of region i after coordinated alteration of tie-line power, i.e., the control error of region i as observed by the load frequency control center; ACE i β represents the control error of region i when it is normal and unattacked; i This represents the frequency deviation gain in region i. Let ω be the moment of inertia of the generator. r δ represents the angular velocity of the fan rotor. s δ represents the scaling attack parameter for the wind turbine rotor angular velocity. r This represents the ramp attack parameters for the angular velocity of the wind turbine rotor, where t is the current time, and T is the value of T. g Represents generator torque, ΔP tie,i For the power deviation of inter-regional tie lines, ΔP tie,i,min Indicates the power deviation ΔP between regional tie lines tie,i The lower limit, ΔP tie,i,max Indicates the power deviation ΔP between regional tie lines tie,i The upper limit, Δω represents the bias applied to the original fan rotor angular velocity, ω r,min Represents the angular velocity ω of the fan rotor r The lower limit, ω r,max Represents the angular velocity ω of the fan rotor r The upper limit, L a The ΔP represents the feasible attack period, ΔP represents the bias applied to the original tie-line power measurement, and ΔP′ represents the final bias applied to the 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, characterized in that the processor executes the computer program to implement the cooperative network attack method for a load frequency control system with 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 executed by a processor, the computer program implements the cooperative network attack method for a load frequency control system with wind power intervention as described in any one of claims 1-7.

10. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the cooperative network attack method for a load frequency control system with wind power intervention as described in any one of claims 1-7.

Images