Power system frequency anti-interference regulation and control method and device under multiple attacks of deterministic network

Abstract

The invention provides a power system frequency anti-interference regulation and control method and device under multiple attacks of a deterministic network. The method comprises the following steps: acquiring multiple groups of frequency regulation and control related parameters of an area to be controlled; inputting the regional frequency deviation data and the regional power deviation data in each group of frequency regulation related parameters into an adaptive network attack detection system to obtain estimated regional control error data; the adaptive network attack detection system is obtained by training a neural fuzzy system physical model by using pre-collected sample data under multiple network attacks; according to the actual area control error data and the estimated area control error data of the multiple groups of frequency regulation and control related parameters, calculating a root-mean-square error; and according to the root-mean-square error and a preset range, determining a mitigation region control error from the actual region control error and the estimated region error. According to the method, multiple attacks are comprehensively considered, new attacks can be rapidly detected, and the power adjustment efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the field of power systems, and in particular to a method and device for frequency anti-disturbance control of power systems under multiple attacks of deterministic networks. Background Art

[0002] Advances in computer technology and communication technology have promoted the development of cyber-physical systems, and traditional power systems are shifting towards smart power systems. Most smart power systems are connected to the control center through deterministic network channels to regulate important information. However, the transmission of important data in the communication link is vulnerable to various network attacks, including denial of service attacks, false data injection attacks, and delay attacks, which poses a challenge to the frequency regulation of the power system. Power system frequency regulation is essential to maintaining grid frequency and minimizing tie line power deviations, and adjusts generator output power by obtaining data from the grid. When a smart power system is attacked by a network, it will not only seriously affect the system frequency, but also the stability and economic operation of the power system. This is because frequency deviations may trigger various protection measures, resulting in service interruptions, infrastructure damage, and even large-scale power outages. Therefore, power system frequency control is essential to enhancing network security in the power system.

[0003] At present, most power system frequency control only considers a single time-delay attack or a single denial of service attack, and there is no solution to solve multiple attacks at the same time. In addition, existing control technologies, such as event triggering mechanisms or detection methods based on load forecasting, provide effective detection technologies, but require accurate system models and a large amount of calculations, and have significant detection delays. In addition, different attacks have different impacts on the power system, and a more specific evaluation of multiple network attacks is needed, as well as consideration of the actual real-time load disturbance of the power system and the nonlinearity of the system. Summary of the invention

[0004] The existing technology does not comprehensively consider the frequency control of the power system in the event of a cyber attack. In addition, the existing power control has the problems of large amount of calculation and detection delay, and cannot quickly detect cyber attacks.

[0005] In order to solve the above technical problems, the first aspect of the present invention provides a method for frequency anti-disturbance control of a power system under multiple attacks of a deterministic network, comprising:

[0006] Acquire multiple sets of frequency control related parameters of the area to be controlled, wherein each set of frequency control related parameters includes regional frequency deviation data, regional power deviation data and actual regional control error data;

[0007] Input the regional frequency deviation data and regional power deviation data in each group of frequency control related parameters into the adaptive network attack detection system to obtain estimated regional control error data; wherein the adaptive network attack detection system uses the sample data collected in advance under various network attacks to train the physical model of the neural fuzzy system; the sample data of each region includes the regional frequency deviation, the regional power deviation and the regional control error; the physical model of the neural fuzzy system is a combination of fuzzy logic and neural network;

[0008] Calculate the root mean square error based on actual regional control error data and estimated regional control error data of multiple sets of frequency control related parameters;

[0009] If the RMS error is within the preset range, no attack is detected and the actual area control error is used as the mitigation area control error; if the RMS error exceeds the preset range, an attack exists and the estimated area control error is used as the mitigation area control error;

[0010] Regulating power system frequency by mitigating regional control errors.

[0011] As a further embodiment of the present invention, an adaptive network attack detection system is established based on the pre-collected sample data of each area under various network attacks and the physical model of the neural fuzzy system, including:

[0012] The following fuzzy rules are established:

[0013]

[0014] Among them, x1 and x2 are the regional frequency deviation and regional power deviation respectively. and are the antecedents of interval type-2 fuzzy sets, Z is the output of fuzzy logic, i.e., the estimated regional control error, k j1 , k j2 and l j is the adaptive parameter of the fuzzy system, j=1,2,...,N is the number of fuzzy rules, R (j) is the jth fuzzy rule;

[0015] According to fuzzy rules, construct the physical model of neuro-fuzzy system;

[0016] Divide the sample data collected in advance under various network attacks into a training sample set and a test sample set;

[0017] The training sample set is used to train the physical model of the neuro-fuzzy system to obtain an adaptive network attack detection system;

[0018] The adaptive network attack detection system is tested using test samples. If the test passes, the training ends; if the test fails, the physical model of the neuro-fuzzy system is adjusted and the training and testing process is repeated.

[0019] As a further embodiment of the present invention, the process of determining sample data under various network attacks includes:

[0020] Pre-constructing a regional control error model under various network attacks, wherein the regional control error model is a function of regional frequency deviation, regional power deviation and attack parameters;

[0021] According to multiple groups of actual regional frequency deviations and regional power deviations, multiple groups of sample data under various network attacks are obtained by using the regional control error models under various network attacks.

[0022] As a further embodiment of the present invention, the network attack includes: false data injection attack, scaling attack, denial of service attack and delay attack.

[0023] As a further embodiment of the present invention, the regional control error model under the false data injection attack includes:

[0024] The control error model of the frequency measurement data of region i under the attack of false data injection is:

[0025]

[0026] in, is the regional control error of region i at time t under attack, β i is the frequency offset factor of region i, (Δf i +ψ a1 )(t) is the frequency deviation of area i at time t under attack, ψ a1 Inject attack signals for false data, a ij is the capacity factor between region i and region j, ΔP tie,ij (t) is the power deviation of the tie line between area i and area j at time t, t is the instantaneous time, and m is the total number of controlled areas;

[0027] The control error model of the power measurement data between area i and area k under the virtual data injection attack is:

[0028]

[0029] Where Δf i (t) is the frequency deviation of region i at time t, a ik is the capacity factor between region i and region k, (ΔP tie,ik +ψ a2)(t) is the power deviation of the tie line between area i and area k at time t under attack, ψ a2 Inject attack signals into false data;

[0030] The control error model of the control error data of region i under virtual data attack is:

[0031]

[0032] Among them, ACE i (t) is the regional control error of region i at time t, ψ a3 Inject attack signals into false data.

[0033] As a further embodiment of the present invention, the regional control error model under the zoom attack includes:

[0034] The control error model of the frequency measurement data of region i under scaling attack is:

[0035]

[0036] in, is the regional control error of region i at time t under attack, β i is the frequency offset factor of region i, (Δf i ×λ a1 )(t) is the frequency deviation of area i at time t under attack, λ a1 To scale the attack signal, a ij is the capacity factor between region i and region j, ΔP tie,ij (t) is the power deviation of the tie line between area i and area j at time t, t is the instantaneous time, and m is the total number of controlled areas;

[0037] The control error model of the power measurement data between area i and area k under scaling attack is:

[0038]

[0039] Where Δf i (t) is the frequency deviation of region i at time t, a ik is the capacity factor between region i and region k, (ΔP tie,ik ×λ a2 )(t) is the power deviation of the tie line between area i and area k at time t under attack, λ a2 To scale the attack signal;

[0040] The control error model of the control error data of region i under scaling attack is:

[0041]

[0042] Among them, ACE i (t) is the regional control error of region i at time t, λ a3 To zoom in on the attack signal.

[0043] As a further embodiment of the present invention, the regional control error model under the denial of service attack includes:

[0044]

[0045] Where n is the number of cycles, T is one cycle of interference, and T off Free time for interference, is the minimum idle time of interference, and its range is within each cycle of the interference signal.

[0046] Interval [(n-1)T, (n-1)T+T off ] is the idle time of the interference signal, indicating that the denial of service attack is unsuccessful, and the frequency measurement data of the area and the power measurement data of the area received by the controller are correct;

[0047] Interval [(n-1)T+T off ,nT] is the active interval of the interference signal, indicating that the denial of service attack is activated and the controller does not get any information.

[0048] As a further embodiment of the present invention, the regional control error model under the delay attack includes:

[0049] The control error model of the frequency measurement data of area i under the time delay attack is:

[0050]

[0051] in, is the regional control error of region i at time t under attack, β i is the frequency offset factor of region i, Δf i (tt d ) is the frequency deviation of area i at time t under attack, t d is the delayed attack signal, a ij is the capacity factor between region i and region j, ΔP tie,ij (t) is the power deviation of the tie line between area i and area j at time t, t is the instantaneous time, and m is the total number of controlled areas;

[0052] The control error model of the power measurement data between area i and area k under delay attack is:

[0053]

[0054] Where Δf i (t) is the frequency deviation of region i at time t, a ik is the capacity factor between region i and region k, ΔP tie,ik (tt d ) is the power deviation of the contact line between area i and area k at time t under attack, t d is the delayed attack signal;

[0055] The control error model of the control error data of area i under the time delay attack is:

[0056]

[0057] Among them, ACE i (tt d ) is the region i in tt d The regional control error at time t d It is the delayed attack signal.

[0058] As a further embodiment of the present invention, regulating the power system frequency by alleviating the regional control error includes:

[0059] The control error of the mitigation area is input into a pre-built dual-loop enhanced active disturbance rejection controller to obtain a control variable of the speed regulator, and the control variable is sent to the speed regulator of the area to be controlled to adjust the frequency of the power system of the area to be controlled;

[0060] The dual-loop enhanced active disturbance rejection controller is constructed using a regional power system model with disturbances and nonlinearity.

[0061] As a further embodiment of the present invention, the dual-loop enhanced active disturbance rejection controller construction process includes:

[0062] A regional power system model with disturbance and nonlinearity is constructed as follows:

[0063]

[0064] Where y is the area control error ACE of area i, is the first derivative of y, is the second derivative of y, u is the control output of the speed regulator, is the speed regulator output rate coefficient, d is the external disturbance, and f is the overall system nonlinearity;

[0065] According to the regional power system model, a state equation of the regional power system is constructed, wherein the state quantities in the state equation are y, f;

[0066] According to the state equation of the regional power system, the active disturbance rejection state observer of the regional power system is constructed as follows:

[0067]

[0068] in,

[0069] is the estimated value of y, is the observed value of the system state, L0=[ξ1,ξ2,ξ3] T is the observer gain;

[0070] According to the active disturbance rejection state observer of the regional power system, the bandwidth method is used to solve the observer gain in the steady state.

[0071] According to the observer gain and the regional power system model in the steady state, the feedforward control loop L1 and the feedback control loop L2 expressed by the following formula are determined;

[0072]

[0073] Among them, u1 is the control quantity of the feedforward control loop L1, and u2 is the control quantity of the feedback control loop L2. is the speed regulator output rate coefficient, is the observed value of the overall system nonlinearity, k ti , k i Represents the integral gain, η1 and η2 are the proportional control parameter and the derivative control parameter respectively, η1=ω c 2 ,η2=2ω c ,ω c is the bandwidth of the state error feedback control law, s is the Laplace operator in the complex frequency domain;

[0074] According to the feedforward control loop L1 and the feedback control loop L2, the dual-loop enhanced anti-disturbance control controller expressed by the following formula is determined:

[0075] U(s)=u2(u1+ε o -γ);

[0076] Among them, ε o is the predetermined error limit, and γ is the frequency deviation feedback signal output by the generator.

[0077] A second aspect of the present invention provides a power system frequency anti-disturbance control device under multiple attacks of a deterministic network, comprising:

[0078] A parameter acquisition unit, used to acquire multiple groups of frequency control related parameters of the area to be controlled, wherein each group of frequency control related parameters includes regional frequency deviation data, regional power deviation data and actual regional control error data;

[0079] The detection unit is used to input the regional frequency deviation data and regional power deviation data in each group of frequency control related parameters into the adaptive network attack detection system to obtain estimated regional control error data; wherein, the adaptive network attack detection system uses the sample data collected in advance under various network attacks to train the physical model of the neural fuzzy system; the sample data of each region includes the regional frequency deviation Δf i (t), regional power deviation ΔP tie,ij (t) and area control error ACE i (t); The physical model of the neuro-fuzzy system is a combination of fuzzy logic and neural network;

[0080] A calculation unit, used for calculating a root mean square error according to actual regional control error data and estimated regional control error data of multiple sets of frequency control related parameters;

[0081] A judgment unit, configured to: if the root mean square error is within a preset range, no attack is detected, and the actual area control error is used as the mitigation area control error; if the root mean square error exceeds the preset range, there is an attack, and the estimated area control error is used as the mitigation area control error;

[0082] A control unit is used to regulate the frequency of the power system by mitigating the regional control error.

[0083] A third aspect of the present invention 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 implements the method described in any of the foregoing embodiments when executing the computer program.

[0084] A fourth aspect of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor of a computer device, the method described in any of the above embodiments is implemented.

[0085] A fifth aspect of the present invention provides a computer program product, which includes a computer program. When the computer program is executed by a processor of a computer device, the method described in any of the above embodiments is implemented.

[0086] The method and device for anti-disturbance control of power system frequency under various attacks of deterministic networks provided by the present invention can, by pre-establishing an adaptive network attack detection system, predict estimated regional control error data under various network attacks based on actual regional frequency deviation data and regional power deviation data of the controlled area, calculate the root mean square error according to actual regional control error data and estimated regional control error data of multiple groups of frequency control related parameters, determine whether the controlled area is attacked according to the root mean square error and a preset range, and when not attacked, use the actual regional control error to control the power system frequency of the controlled area; when attacked, use the predicted estimated regional control error to control the power system frequency of the controlled area, thereby preventing the power system frequency control from being attacked by various network attacks and quickly adjusting the power when attacked.

[0087] In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are specifically cited below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0088] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. 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 paying creative work.

[0089] Figure 1 A flow chart of the training process of the adaptive network attack detection system according to an embodiment of the present invention is shown;

[0090] Figure 2 A flow chart of a method for frequency anti-disturbance control of a power system under multiple attacks on a deterministic network according to an embodiment of the present invention is shown;

[0091] Figure 3 A flowchart showing a process of constructing a dual-loop enhanced active disturbance rejection controller according to an embodiment of the present invention is shown;

[0092] Figure 4 A structural diagram of a power system frequency anti-disturbance control device under multiple deterministic network attacks according to an embodiment of the present invention is shown;

[0093] Figure 5 A structural diagram of a computer device according to an embodiment of the present invention is shown.

[0094] Description of the accompanying symbols:

[0095] 401. Parameter acquisition unit;

[0096] 402. Detection unit;

[0097] 403, computing unit;

[0098] 404, judging unit;

[0099] 405. Control unit;

[0100] 502. Computer equipment;

[0101] 504, processor;

[0102] 506. Memory;

[0103] 508, driving mechanism;

[0104] 510, input / output module;

[0105] 512. Input devices;

[0106] 514. Output device;

[0107] 516. Presentation equipment;

[0108] 518. Graphical user interface;

[0109] 520, network interface;

[0110] 522, communication link;

[0111] 524. Communication bus. DETAILED DESCRIPTION

[0112] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0113] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, device, product or equipment that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or equipment.

[0114] This specification provides method operation steps as described in the embodiments or flow charts, but more or fewer operation steps may be included based on routine or non-creative work. The order of steps listed in the embodiments is only one way of executing the order of many steps and does not represent the only execution order. When the system or device product is executed in practice, it can be executed in the order of the method shown in the embodiments or the drawings or in parallel.

[0115] It should be noted that the data involved in this application (including but not limited to data used for analysis, stored data, displayed data, etc.) are all information and data authorized by the user or fully authorized by all parties, and the acquisition, transmission, storage, use and processing of relevant data must comply with relevant laws, regulations and standards of relevant countries and regions.

[0116] It should be noted that in the embodiments of the present application, certain software, components, models and other existing solutions in the industry may be mentioned, and they should be regarded as exemplary. Their purpose is only to illustrate the feasibility of implementing the technical solution of the present application, but it does not mean that the applicant has or will necessarily use the solution.

[0117] The existing technology does not comprehensively consider the frequency control of the power system under the situation of network attacks. In addition, the existing power control has the problem of large amount of calculation and detection delay, and cannot quickly detect network attacks. Based on this, in one embodiment of the present invention, an adaptive network attack detection system capable of identifying regional control errors under various network attacks is pre-established, and the adaptive network attack detection system is used to identify whether the regional power system is under attack, and then the power system frequency is controlled according to the identification result, which can prevent the power system frequency control from being attacked by various network attacks, and quickly adjust the power when attacked.

[0118] Specifically, in one embodiment of the present invention, a method for training an adaptive network attack detection system is provided, such as Figure 1 As shown, including:

[0119] Step 101, establish the following fuzzy rules:

[0120]

[0121] Among them, x1 and x2 are the regional frequency deviation and regional power deviation respectively. and are the antecedents of interval type-2 fuzzy sets, Z is the output of fuzzy logic, i.e., the estimated regional control error, k j1 , k j2 and l j is the adaptive parameter of the fuzzy system, j = 1, 2, ..., N is the number of fuzzy rules, R (j)is the jth fuzzy rule. In specific implementation, the number of fuzzy rules can be set according to actual conditions, and the present invention does not limit its specific value.

[0122] Since interval type-2 fuzzy sets retain the ability of type-2 fuzzy sets to handle high uncertainty and have the advantages of less parameter design and simple calculation, this embodiment uses interval type-2 fuzzy logic to design an adaptive network attack detection system.

[0123] In the specific implementation, in order to simplify the amount of calculation, a single-value fuzzifier and product reasoning are used to calculate the activation interval of each fuzzy rule input vector, specifically:

[0124]

[0125] g j (x) and Indicates the upper and lower bounds of the activation interval; and They are the lower membership function and the upper membership function respectively. The upper and lower membership functions are expressed by Gaussian functions as follows:

[0126]

[0127]

[0128] The defuzzified output of the interval type-2 fuzzy logic system is:

[0129]

[0130] Among them, the interval value g l (x) and g r (x) is reduced by the Karnik-Mendel (KM) algorithm. The KM algorithm solves g j (x) and Activate the interval set to iteratively calculate the center point interval value g l (x) and g r (x).

[0131] Step 102: construct a physical model of the neuro-fuzzy system according to the fuzzy rules.

[0132] The physical model of the neuro-fuzzy system is a combination of fuzzy logic and neural network. i , power deviation ΔP tie,ij As the input of the adaptive network attack detection system, the area control error ACE is the output.

[0133] Step 103, divide the pre-collected sample data under various network attacks into a training sample set and a test sample set.

[0134] In specific implementation, the sample data collected in advance under network attacks can be the actual collected data, including the frequency deviation Δf under various network attacks. i , power deviation ΔP tie,ij And the corresponding ACE, and the frequency deviation Δf without network attack i , power deviation ΔP tie,ij And the corresponding ACE.

[0135] Taking into account the problem of small sample size of attack data, attack sample data can also be determined based on the attack model, thereby improving the accuracy of the adaptive network attack detection system in determining the true ACE (not attacked / attacked).

[0136] Step 104, using the training sample set to train the physical model of the neuro-fuzzy system to obtain an adaptive network attack detection system.

[0137] Step 105, using the test sample to test the adaptive network attack detection system, if the test passes, the training ends; if the test fails, the physical model of the neuro-fuzzy system is adjusted, and the training and testing process is repeated.

[0138] During specific implementation, whether the test passes can be determined by the detection accuracy of the adaptive network attack detection system.

[0139] The adaptive network attack detection system trained in this embodiment can predict the predicted ACE based on historical network attacks. During implementation, if the error between the ACE predicted by the adaptive network attack detection system and the actually measured ACE is within a preset range, it means that there is no new attack. If the error between the ACE predicted by the adaptive network attack detection system and the actually measured ACE exceeds the preset range, it means that there is a new attack.

[0140] In a specific embodiment of the present invention, the training sample data is loaded into the physical model of the neural fuzzy system in MATLAB during the training phase to generate an adaptive network attack detection system. Subsequently, during the testing phase, the test sample data is input into the adaptive network attack detection system to verify the detection performance. The following are the steps for designing the adaptive network attack detection system:

[0141] (1) The frequency deviation Δf is obtained through application and testing on the actual power system i , power deviation ΔP tie,ij and area control error ACE. Then Δf i and ΔP tie,ijAs input, the corresponding ACE is used as output, and the data is divided into training sample data and test sample data in a ratio of 80:20, where the test sample data and training sample data are used for training and testing of the adaptive network attack detection system, respectively.

[0142] (2) For training, load the training sample data into the neuro-fuzzy system physical model in Matlab. To generate the adaptive network attack detection system, select the following grid partition: MF = 10, MF type = triangle. Output: MFtype = constant. Select the following parameters to train the adaptive network attack detection system: Optimization method = hybrid, error tolerance = 0, epochs = 10. After the parameters are set, the adaptive network attack detection system is trained.

[0143] (3) During testing, the test sample data is loaded into the physical model of the neuro-fuzzy system to obtain the average test error of the test data. Reducing the test error allows for more accurate training.

[0144] In one embodiment of the present invention, the process of determining sample data under various network attacks includes:

[0145] Pre-constructing a regional control error model under various network attacks, wherein the regional control error model is a function of regional frequency deviation, regional power deviation and attack parameters;

[0146] According to multiple groups of actual regional frequency deviations and regional power deviations, multiple groups of sample data under various network attacks are obtained by using the regional control error models under various network attacks.

[0147] Specifically, network attacks include: false data injection attacks, scaling attacks, denial of service attacks, and delay attacks.

[0148] In order to ensure the power deviation ΔP in different areas of the power system tie,ij (t) and frequency deviation Δf i (t) is kept within the safe range. For the i-th control area, at time t, the area control error (ACE) can be expressed as:

[0149]

[0150] Among them, ACE i (t) is the control error of region i at time t, β i is the frequency offset factor of region i, Δf i (t) is the frequency deviation of region i at time t, coefficient a ij is the capacity factor between region i and region j, ΔPtie,ij (t) is the tie line power deviation between area i and area j, t is the instantaneous time, and m is the total number of controlled areas.

[0151] Δf i (t) = f i (t)-f ref ;

[0152] ΔP tie,ij (t) = P tie,ij (t)-P ref ;

[0153] Among them, f ref , P ref Preset frequency and power for the power system; f i (t), P tie,ij (t) is the actual frequency of region i at time t and the power deviation between region i and region j, which are collected by the phasor measurement unit.

[0154] In smart power systems, f i , P tie,ij and ACE i It is the most important parameter for frequency control. Due to the vulnerability of the communication path between the phasor measurement unit and the control center and control area, an attacker can i and P tie,ij Data uploaded to the control center or ACE transmitted from the control center to the control area i Data can be tampered with, interfered with, or even delayed. In all cases, ACE i Eventually, they will be affected, causing the controller in the control area to send incorrect setting instructions to the speed regulator, thereby disrupting the normal operation of the entire power system. We analyze the impact of different network attacks on the power system and establish regional control errors under different network attacks. The mathematical model is as follows:

[0155] (1) The regional control error model under false data injection attack includes:

[0156] The control error model of the frequency measurement data of region i under the attack of false data injection is:

[0157]

[0158] in, is the regional control error of region i at time t under attack, β i is the frequency offset factor of region i, (Δf i +ψ a1 )(t) is the frequency deviation of area i at time t under attack, ψ a1Inject attack signals for false data, a ij is the capacity factor between region i and region j, ΔP tie,ij (t) is the power deviation of the tie line between area i and area j at time t, t is the instantaneous time, and m is the total number of controlled areas;

[0159] The control error model of the power measurement data between area i and area k under the virtual data injection attack is:

[0160]

[0161] Where Δf i (t) is the frequency deviation of region i at time t, a ik is the capacity factor between region i and region k, (ΔP tie,ik +ψ a2 )(t) is the power deviation of the tie line between area i and area k at time t under attack, ψ a2 Inject attack signals into false data;

[0162] The control error model of the control error data of region i under virtual data attack is:

[0163]

[0164] Among them, ACE i (t) is the regional control error of region i at time t, ψ a3 Inject attack signals into false data.

[0165] In specific implementation, ψ a1 , ψ a2 and ψ a3 Can be a ramp, pulse or random signal.

[0166] (2) The regional control error model under scaling attack includes:

[0167] The control error model of the frequency measurement data of region i under scaling attack is:

[0168]

[0169] in, is the regional control error of region i at time t under attack, β i is the frequency offset factor of region i, (Δf i ×λ a1 )(t) is the frequency deviation of area i at time t under attack, λ a1 To scale the attack signal, a ij is the capacity factor between region i and region j, ΔP tie,ij(t) is the power deviation of the tie line between area i and area j at time t, t is the instantaneous time, and m is the total number of controlled areas;

[0170] The control error model of the power measurement data between area i and area k under scaling attack is:

[0171]

[0172] Where Δf i (t) is the frequency deviation of region i at time t, a ik is the capacity factor between region i and region k, (ΔP tie,ik ×λ a2 )(t) is the power deviation of the tie line between area i and area k at time t under attack, λ a2 To scale the attack signal;

[0173] The control error model of the control error data of region i under scaling attack is:

[0174]

[0175] Among them, ACE i (t) is the regional control error of region i at time t, λ a3 To zoom in on the attack signal.

[0176] (3) The regional control error model under denial of service attack includes:

[0177]

[0178] Where n is the number of cycles, T is one cycle of interference, and T off Free time for interference, is the minimum idle time of interference, and its range is within each cycle of the interference signal.

[0179] Interval [(n-1)T, (n-1)T+T off ] is the idle time of the interference signal, indicating that the denial of service attack is unsuccessful, and the frequency measurement data of the area and the power measurement data of the area received by the controller are correct;

[0180] Interval [(n-1)T+T off ,nT] is the active interval of the interference signal, indicating that the denial of service attack is activated and the controller does not get any information.

[0181] (4) The regional control error model under time-delay attack includes:

[0182] The control error model of the frequency measurement data of area i under the time delay attack is:

[0183]

[0184] in, is the regional control error of region i at time t under attack, β i is the frequency offset factor of region i, Δf i (tt d ) is the frequency deviation of area i at time t under attack, t d is the delayed attack signal, a ij is the capacity factor between region i and region j, ΔP tie,ij (t) is the power deviation of the tie line between area i and area j at time t, t is the instantaneous time, and m is the total number of controlled areas;

[0185] The control error model of the power measurement data between area i and area k under delay attack is:

[0186]

[0187] Where Δf i (t) is the frequency deviation of region i at time t, a ik is the capacity factor between region i and region k, ΔP tie,ik (tt d ) is the power deviation of the contact line between area i and area k at time t under attack, t d is the delayed attack signal;

[0188] The control error model of the control error data of area i under the time delay attack is:

[0189]

[0190] Among them, ACE i (tt d ) is the region i in tt d The regional control error at time t d It is the delayed attack signal.

[0191] Among them, when frequency regulation is attacked by time delay, the optimal controller built for normal settings will deviate from the optimal due to system delays, and the system may become unstable.

[0192] After the adaptive network attack detection system, frequency anti-interference control can be performed based on the adaptive network attack detection system, specifically, Figure 2 As shown in the figure, the frequency anti-disturbance control method of the power system under various attacks of the deterministic network includes:

[0193] Step 201, obtaining multiple sets of frequency control related parameters of the area to be controlled.

[0194] Among them, each group of frequency control related parameters includes regional frequency deviation data, regional power deviation data and actual regional control error data.

[0195] Step 202: input the regional frequency deviation data and the regional power deviation data in each set of frequency control related parameters into the adaptive network attack detection system to obtain estimated regional control error data.

[0196] Among them, the adaptive network attack detection system uses pre-collected sample data under various network attacks to train the physical model of the neuro-fuzzy system; the sample data of each area includes regional frequency deviation, regional power deviation and regional control error; the physical model of the neuro-fuzzy system is a combination of fuzzy logic and neural network.

[0197] Step 203, calculating a root mean square error according to the actual regional control error data and the estimated regional control error data of the multiple sets of frequency control related parameters.

[0198] The root mean square error calculation formula is as follows:

[0199]

[0200] Step 204: If the RMS error is within a preset range, no attack is detected, and the actual area control error is used as the mitigation area control error.

[0201] In a specific implementation, the preset range is, for example, 2.28×10 -4 .

[0202] Step 205: If the RMS error exceeds a preset range, an attack occurs, and the estimated regional control error is used as the mitigation regional control error.

[0203] In the case of a denial of service attack, the ACE signal from the network communication is unavailable, in the case of a false data injection attack, the ACE signal is distorted, and in the case of a delay attack, the ACE signal is delayed. In all three cases, the RMSE is greater than the fault tolerance diagnostic range, indicating that an attack has occurred.

[0204] Step 206: regulating the power system frequency by mitigating the regional control error.

[0205] In one embodiment of the present invention, in order to improve the accuracy of power system frequency control, the above step 206 uses the mitigation area control error to control the power system frequency, including:

[0206] The control error of the mitigation area is input into a pre-built dual-loop enhanced active disturbance rejection controller to obtain a control variable of the speed regulator, and the control variable is sent to the speed regulator of the area to be controlled to adjust the frequency of the power system of the area to be controlled;

[0207] The dual-loop enhanced active disturbance rejection controller is constructed using a regional power system model with disturbances and nonlinearity.

[0208] This embodiment can avoid the influence of inherent nonlinear limitations of the power system and uncertain load fluctuations on power regulation, can effectively offset the frequency deviation of the power system, and optimally distribute the power of the power system.

[0209] In one embodiment of the present invention, Figure 3 As shown in Figure 1, the construction process of the dual-loop enhanced active disturbance rejection controller includes:

[0210] Step 301, construct a regional power system model with disturbance and nonlinearity represented by the following formula:

[0211]

[0212] Where y is the area control error ACE of area i, is the first derivative of y, is the second derivative of y, u is the control output of the speed regulator, is the speed regulator output rate coefficient, d is the external disturbance, and f is the overall system nonlinearity.

[0213] Step 302: construct a state equation of the regional power system according to the regional power system model.

[0214] Among them, the state quantities in the state equation are y, f. Specifically, the state equation of the power system in region i can be expressed as:

[0215]

[0216] Where x1=y, x3=f is the expansion state of the system; is the differential of the system expansion state.

[0217] Step 303: construct an active disturbance rejection state observer for the regional power system represented by the following formula based on the state equation of the regional power system:

[0218]

[0219] in,

[0220] is the estimated value of y, is the observed value of the system state, L0=[ξ1,ξ2,ξ3] T is the observer gain.

[0221] The ADRC state observer can also be rewritten as:

[0222]

[0223] Step 304: According to the active disturbance rejection state observer of the regional power system, the observer gain in the steady state is solved by using a bandwidth method.

[0224] In order to simplify the adjustment process of the parameters of the ADRC state observer, this paper introduces the bandwidth method to adjust the parameters, and the state variable error is Then the error derivative can be written as:

[0225]

[0226] By selecting a suitable observation gain matrix L0, the matrix A0-L0C0 is stable and e→0, so that The condition for the stability of the matrix A0-L0C0 is that the roots of its characteristic polynomial all have negative real parts. Further, the expression of the matrix A-LC can be written as:

[0227]

[0228] The characteristic polynomial of the matrix A-LC is:

[0229]

[0230] According to the core idea of ​​the bandwidth method, the poles of the characteristic polynomial are configured at -ω0, that is:

[0231] (s+ω0) 3 =s 3 +3ω0s 2 +3ω0 2 s+ω0 3

[0232] Where ω0 is the bandwidth of the ADRS state observer; s is the root of the characteristic polynomial of the system.

[0233] The gains of the ADRC state observer can be obtained as follows:

[0234]

[0235] From the above analysis, we can conclude that the root of the characteristic polynomial is -ω0. As long as ω0 is greater than 0, the roots of the characteristic polynomial all have negative real parts. This makes the matrix A-LC stable, and e→0, that is,

[0236] So we can get:

[0237]

[0238] in, express f(x1(t),x2(t),d(t)) can be estimated, which includes the cumulative effects of uncertain models and external disturbances, that is, the real-time impact of system disturbances.

[0239] Step 305, according to the observer gain in the steady state and the regional power system model, determine the feedforward control loop L1 and the feedback control loop L2 represented by the following formula;

[0240]

[0241] Among them, u1 is the control quantity of the feedforward control loop L1, and u2 is the control quantity of the feedback control loop L2. is the speed regulator output rate coefficient, is the observed value of the overall system nonlinearity, k ti , k i Represents the integral gain, η1 and η2 are the proportional control parameter and the derivative control parameter respectively, η1=ω c 2 ,η2=2ω c ,ω c is the bandwidth of the state error feedback control law, and s is the Laplace operator in the complex frequency domain, which is used to represent the frequency domain characteristics of the dynamic system.

[0242] Step 306: According to the feedforward control loop L1 and the feedback control loop L2, a dual-loop enhanced active disturbance rejection controller represented by the following formula is determined:

[0243] U(s)=u2(u1+ε o -γ);

[0244] Among them, ε o is the predetermined error limit, and γ is the frequency deviation feedback signal output by the generator.

[0245] In the designed dual-loop enhanced ADRC, only ω0 and ω c Two parameters need to be adjusted, namely the bandwidth of the ADRS and the bandwidth of the state error feedback control law. In order to maximize the performance of the controller, the controller parameters are then adjusted using optimization methods.

[0246] The basic particle swarm optimization algorithm has the advantages of fewer parameters and a simple solution process. However, it is easy to fall into the defect of local optimal solution due to its slow convergence speed. In view of the defects of the basic particle swarm algorithm, the adaptive particle swarm algorithm is used to optimize the parameters of the dual-loop active disturbance rejection controller. The adaptive particle swarm algorithm combines the fitness function state in the algorithm iteration process to adaptively adjust the inertia weight value. Its main characteristics can be expressed as:

[0247]

[0248]

[0249] in, is the average fitness function value of the population at the kth iteration; is the fitness function value of the i-th particle at the k-th iteration; is the inertia weight value of the i-th particle at the k-th iteration; ω min ,ω max are the minimum and maximum values ​​of the inertia weight respectively; is the minimum fitness function value of the population at the kth iteration. Based on this, the particle position and velocity update expressions of the adaptive particle swarm algorithm can be written as:

[0250]

[0251] in, is the position of the i-th particle at the k-th iteration; is the speed of the ith particle in the kth iteration; c1 is the individual optimal learning factor, which is used to track the current optimal position of the ith particle; c2 is the global optimal learning factor, which is used to track the current global optimal position of the entire community; r1 and r2 are random numbers distributed in the interval [0, 1]; is the optimal solution for individual particles; gbest k The optimal solution for the whole group.

[0252] The main task of the power system speed regulator is to track the command quickly and accurately. The control system designed based on the time multiplied absolute error integral criterion has a small overshoot and the system response speed is also relatively fast. Therefore, the error evaluation function of the active disturbance rejection controller is designed as the fitness function in the adaptive particle swarm optimization algorithm, and its expression is:

[0253]

[0254] After determining the controller parameters and fitness function that need to be adjusted, the adaptive particle swarm optimization algorithm is used to optimize the parameters of the active disturbance rejection controller.

[0255] The specific optimization process of the adaptive particle swarm optimization algorithm mainly includes five modules: initialization module, inertia weight adaptation module, particle update module, optimal particle selection module and result output. The specific steps are as follows:

[0256] Step 1) Initialization module: First, set the parameters of the adaptive particle swarm algorithm, including population size, number of iterations, search space dimension, optimal learning factor, and inertia weight; then initialize the population and select the global optimal particle;

[0257] Step 2) Inertia weight adaptive module: adaptively adjust the inertia weight according to the fitness function of the particle and the average fitness function of the population;

[0258] Step 3) Particle update module: update the speed and position of particles;

[0259] Step 4) Optimal particle selection module: adjust individual optimal particles and global optimal particles according to particle fitness function;

[0260] Step 5) Result output module: responsible for determining the iteration termination conditions and outputting the optimization results.

[0261] Based on the same inventive concept, the present invention also provides a power system frequency anti-disturbance control device under multiple attacks of a deterministic network, as described in the following embodiments. Since the principle of solving the problem by the power system frequency anti-disturbance control device under multiple attacks of a deterministic network is similar to the power system frequency anti-disturbance control method under multiple attacks of a deterministic network, the implementation of the power system frequency anti-disturbance control device under multiple attacks of a deterministic network can refer to the power system frequency anti-disturbance control method under multiple attacks of a deterministic network, and the repeated parts will not be repeated.

[0262] Specifically, Figure 4 As shown, the power system frequency anti-disturbance control device under multiple attacks of deterministic network includes:

[0263] The parameter acquisition unit 401 is used to acquire multiple groups of frequency control related parameters of the area to be controlled, wherein each group of frequency control related parameters includes regional frequency deviation data, regional power deviation data and actual regional control error data;

[0264] The detection unit 402 is used to input the regional frequency deviation data and regional power deviation data in each group of frequency control related parameters into the adaptive network attack detection system to obtain estimated regional control error data; wherein, the adaptive network attack detection system uses the sample data collected in advance under various network attacks to train the neural fuzzy system physical model; the sample data of each region includes the regional frequency deviation Δf i (t), regional power deviation ΔP tie,ij (t) and area control error ACE i (t); The physical model of the neuro-fuzzy system is a combination of fuzzy logic and neural network;

[0265] A calculation unit 403 is used to calculate a root mean square error according to actual regional control error data and estimated regional control error data of multiple sets of frequency control related parameters;

[0266] The judgment unit 404 is configured to: if the root mean square error is within a preset range, no attack is detected, and the actual area control error is used as the mitigation area control error; if the root mean square error exceeds the preset range, there is an attack, and the estimated area control error is used as the mitigation area control error;

[0267] The control unit 405 is used to control the power system frequency by using the regional control error mitigation. The specific implementation process of controlling the power system frequency by using the regional control error mitigation can refer to the above embodiment, which will not be described in detail here.

[0268] The present invention proposes a detection algorithm based on a neuro-fuzzy physical model to protect the power system frequency control from a variety of network attacks without limiting their duration and location, and quickly identify and control them when an attack occurs. At the same time, the present invention also proposes a dual-loop enhanced anti-disturbance method with feedforward compensation to solve the inherent nonlinear limitations and uncertain load fluctuations of the system, which can effectively offset the frequency deviation of the power system and optimally distribute the power of the power system.

[0269] In one embodiment of the present invention, a computer device is provided, such as Figure 5 As shown, the computer device 502 may include one or more processors 504, such as one or more central processing units (CPUs), each of which may implement one or more hardware threads. The computer device 502 may also include any memory 506, which is used to store any kind of information such as code, settings, data, etc. Non-limitingly, for example, the memory 506 may include any one or more combinations of the following: any type of RAM, any type of ROM, flash memory device, hard disk, optical disk, etc. More generally, any memory may use any technology to store information. Further, any memory may provide volatile or non-volatile retention of information. Further, any memory may represent a fixed or removable component of the computer device 502. In one case, when the processor 504 executes an associated instruction stored in any memory or a combination of memories, the computer device 502 may perform any operation of the associated instruction. The computer device 502 also includes one or more drive mechanisms 508 for interacting with any memory, such as a hard disk drive mechanism, an optical disk drive mechanism, etc.

[0270] The computer device 502 may also include an input / output module 510 (I / O) for receiving various inputs (via input devices 512) and for providing various outputs (via output devices 514). A specific output mechanism may include a presentation device 516 and an associated graphical user interface 518 (GUI). In other embodiments, the input / output module 510 (I / O), input device 512, and output device 514 may not be included, and the computer device 502 may be used as a computer device in a network. The computer device 502 may also include one or more network interfaces 520 for exchanging data with other devices via one or more communication links 522. One or more communication buses 524 couple the components described above together.

[0271] The communication link 522 may be implemented in any manner, for example, through a local area network, a wide area network (e.g., the Internet), a point-to-point connection, etc., or any combination thereof. The communication link 522 may include any combination of hardwired links, wireless links, routers, gateway functions, name servers, etc., governed by any protocol or combination of protocols.

[0272] An embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above method are executed.

[0273] An embodiment of the present invention further provides a computer-readable instruction, wherein when a processor executes the instruction, the program therein causes the processor to execute the method described in any of the aforementioned embodiments.

[0274] It should be understood that in various embodiments of the present invention, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0275] It should also be understood that in the embodiments of the present invention, the term "and / or" is only a description of the association relationship of the associated objects, indicating that three relationships may exist. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in the present invention generally indicates that the associated objects before and after are in an "or" relationship.

[0276] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the present invention can be implemented by electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0277] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0278] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, or it can be an electrical, mechanical or other form of connection.

[0279] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the embodiments of the present invention.

[0280] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0281] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of 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 and other media that can store program codes.

[0282] The present invention uses specific embodiments to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. A method for frequency anti-disturbance control of power system under multiple attacks of deterministic network, characterized in that: include: Acquire multiple sets of frequency control related parameters of the area to be controlled, wherein each set of frequency control related parameters includes regional frequency deviation data, regional power deviation data and actual regional control error data; Input the regional frequency deviation data and regional power deviation data in each group of frequency control related parameters into the adaptive network attack detection system to obtain estimated regional control error data; wherein the adaptive network attack detection system uses the sample data collected in advance under various network attacks to train the physical model of the neural fuzzy system; the sample data of each region includes the regional frequency deviation, the regional power deviation and the regional control error; the physical model of the neural fuzzy system is a combination of fuzzy logic and neural network; Calculate the root mean square error based on actual regional control error data and estimated regional control error data of multiple sets of frequency control related parameters; If the RMS error is within the preset range, the actual regional control error is used as the mitigation regional control error; if the RMS error exceeds the preset range, the estimated regional control error is used as the mitigation regional control error; Regulating power system frequency by mitigating regional control errors.

2. The method according to claim 1, characterized in that Based on the pre-collected sample data of various regions under various network attacks and the physical model of the neuro-fuzzy system, an adaptive network attack detection system is established, including: The following fuzzy rules are established: Among them, x1 and x2 are the regional frequency deviation and regional power deviation respectively. and are the antecedents of interval type-2 fuzzy sets, Z is the output of fuzzy logic, i.e., the estimated regional control error, k j1 , k j2 and l j is the adaptive parameter of the fuzzy system, j=1,2,...,N is the number of fuzzy rules, R (j) is the jth fuzzy rule; According to fuzzy rules, construct the physical model of neuro-fuzzy system; Divide the sample data collected in advance under various network attacks into a training sample set and a test sample set; The training sample set is used to train the physical model of the neuro-fuzzy system to obtain an adaptive network attack detection system; The adaptive network attack detection system is tested using test samples. If the test passes, the training ends; if the test fails, the physical model of the neuro-fuzzy system is adjusted and the training and testing process is repeated.

3. The method according to claim 1, characterized in that The process of determining sample data under various network attacks includes: Pre-constructing a regional control error model under various network attacks, wherein the regional control error model is a function of regional frequency deviation, regional power deviation and attack parameters; According to multiple groups of actual regional frequency deviations and regional power deviations, multiple groups of sample data under various network attacks are obtained by using the regional control error models under various network attacks.

4. The method according to claim 1, characterized in that The network attacks include: false data injection attacks, scaling attacks, denial of service attacks and delay attacks.

5. The method according to claim 4, characterized in that The regional control error model under the false data injection attack includes: The control error model of the frequency measurement data of region i under the attack of false data injection is: in, is the regional control error of region i at time t under attack, β i is the frequency offset factor of region i, (Δf i +ψ a1 )(t) is the frequency deviation of region i at time t under attack, ψa1 is the attack signal injected with false data, a ij is the capacity factor between region i and region j, ΔP tie,ij (t) is the power deviation of the tie line between area i and area j at time t, t is the instantaneous time, and m is the total number of controlled areas; The control error model of the power measurement data between area i and area k under the virtual data injection attack is: Where Δf i (t) is the frequency deviation of region i at time t, a ik is the capacity factor between region i and region k, (ΔP tie,ik +ψ a2 )(t) is the power deviation of the tie line between area i and area k at time t under attack, ψ a2 Inject attack signals into false data; The control error model of the control error data of region i under virtual data attack is: Among them, ACE i (t) is the regional control error of region i at time t, ψ a3 Inject attack signals into false data.

6. The method according to claim 4, characterized in that The regional control error model under the scaling attack includes: The control error model of the frequency measurement data of region i under scaling attack is: in, is the regional control error of region i at time t under attack, β i is the frequency offset factor of region i, (Δf i ×λ a1 )(t) is the frequency deviation of area i at time t under attack, λ a1 To scale the attack signal, a ij is the capacity factor between region i and region j, ΔP tie,ij (t) is the power deviation of the tie line between area i and area j at time t, t is the instantaneous time, and m is the total number of controlled areas; The control error model of the power measurement data between area i and area k under scaling attack is: Where Δf i (t) is the frequency deviation of region i at time t, a ik is the capacity factor between region i and region k, (ΔP tie,ik ×λ a2 )(t) is the power deviation of the tie line between area i and area k at time t under attack, λ a2 To scale the attack signal; The control error model of the control error data of region i under scaling attack is: Among them, ACE i (t) is the regional control error of region i at time t, λ a3 To zoom in on the attack signal.

7. The method according to claim 4, characterized in that The regional control error model under the denial of service attack includes: Where n is the number of cycles, T is one cycle of interference, and T off Free time for interference, is the minimum idle time of interference, and its range is within each cycle of the interference signal. Interval [(n-1)T, (n-1)T+T off ] is the idle time of the interference signal, indicating that the denial of service attack is unsuccessful, and the frequency measurement data of the area and the power measurement data of the area received by the controller are correct; Interval [(n-1)T+T off ,nT] is the active interval of the interference signal, indicating that the denial of service attack is activated and the controller does not get any information.

8. The method according to claim 4, characterized in that The regional control error model under the time delay attack includes: The control error model of the frequency measurement data of area i under the time delay attack is: in, is the regional control error of region i at time t under attack, β i is the frequency offset factor of region i, Δf i (tt d ) is the frequency deviation of area i at time t under attack, t d is the delayed attack signal, a ij is the capacity factor between region i and region j, ΔP tie,ij (t) is the power deviation of the tie line between area i and area j at time t, t is the instantaneous time, and m is the total number of controlled areas; The control error model of the power measurement data between area i and area k under delay attack is: Where Δf i (t) is the frequency deviation of region i at time t, a ik is the capacity factor between region i and region k, ΔP tie,ik (tt d ) is the power deviation of the contact line between area i and area k at time t under attack, t d is the delayed attack signal; The control error model of the control error data of area i under the time delay attack is: Among them, ACE i (tt d ) is the region i in tt d The regional control error at time t d It is the delayed attack signal.

9. The method according to claim 1, characterized in that Regulating power system frequency by mitigating regional control errors, including: The control error of the mitigation area is input into a pre-built dual-loop enhanced active disturbance rejection controller to obtain a control variable of the speed regulator, and the control variable is sent to the speed regulator of the area to be controlled to adjust the frequency of the power system of the area to be controlled; The dual-loop enhanced active disturbance rejection controller is constructed using a regional power system model with disturbances and nonlinearity.

10. The method according to claim 9, characterized in that The dual-loop enhanced active disturbance rejection controller construction process includes: A regional power system model with disturbance and nonlinearity is constructed as follows: Where y is the area control error ACE of area i, is the first derivative of y, is the second derivative of y, u is the control output of the speed regulator, is the speed regulator output rate coefficient, d is the external disturbance, and f is the overall system nonlinearity; According to the regional power system model, a state equation of the regional power system is constructed, wherein the state quantities in the state equation are y, f; According to the state equation of the regional power system, the active disturbance rejection state observer of the regional power system is constructed as follows: in, C0=[1 0 0]; is the estimated value of y, is the observed value of the system state, L0=[ξ1,ξ2,ξ3] T is the observer gain; According to the active disturbance rejection state observer of the regional power system, the bandwidth method is used to solve the observer gain in the steady state. According to the observer gain and the regional power system model in the steady state, the feedforward control loop L1 and the feedback control loop L2 expressed by the following formula are determined; Among them, u1 is the control quantity of the feedforward control loop L1, and u2 is the control quantity of the feedback control loop L2. is the speed regulator output rate coefficient, is the observed value of the overall system nonlinearity, k ti , k i Represents the integral gain, η1 and η2 are the proportional control parameter and the derivative control parameter respectively, η1=ω c 2 ,η2=2ω c ,ω c is the bandwidth of the state error feedback control law, s is the Laplace operator in the complex frequency domain; According to the feedforward control loop L1 and the feedback control loop L2, the dual-loop enhanced anti-disturbance control controller expressed by the following formula is determined: U(s)=u2(u1+ε o -c); Among them, ε o is the predetermined error limit, and γ is the frequency deviation feedback signal output by the generator.

11. A frequency anti-disturbance control device for a power system under multiple attacks of a deterministic network, characterized in that: include: A parameter acquisition unit, used to acquire multiple groups of frequency control related parameters of the area to be controlled, wherein each group of frequency control related parameters includes regional frequency deviation data, regional power deviation data and actual regional control error data; The detection unit is used to input the regional frequency deviation data and regional power deviation data in each group of frequency control related parameters into the adaptive network attack detection system to obtain estimated regional control error data; wherein, the adaptive network attack detection system uses the sample data collected in advance under various network attacks to train the physical model of the neural fuzzy system; the sample data of each region includes the regional frequency deviation Δf i (t), regional power deviation ΔP tie,ij (t) and area control error ACE i (t); The physical model of the neuro-fuzzy system is a combination of fuzzy logic and neural network; A calculation unit, used for calculating a root mean square error according to actual regional control error data and estimated regional control error data of multiple sets of frequency control related parameters; A judgment unit, configured to use the actual regional control error as the mitigation regional control error if the root mean square error is within a preset range; and use the estimated regional control error as the mitigation regional control error if the root mean square error exceeds the preset range; A control unit is used to regulate the frequency of the power system by mitigating the regional control error.

12. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 10 is implemented.

13. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor of a computer device, the method according to any one of claims 1 to 10 is implemented.

14. A computer program product, comprising a computer program, characterized in that: When the computer program is executed by a processor of a computer device, the method according to any one of claims 1 to 10 is implemented.

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