Power system dynamic event trigger load frequency control method under Dos attack and random time delay

Abstract

The invention relates to the technical field of power system control, solves the problems of network attack, limited bandwidth and transmission delay of a power system in a signal transmission process and conservative DET parameter selection and controller design, and particularly relates to a power system dynamic event triggered load frequency control method under Dos attack and random delay. Comprising obtaining a continuous time system parameter matrix; setting an event triggering threshold function; state parameters facing Dos attack and random time delay conditions are set; constructing a state feedback controller; and collaborative design optimization is carried out by applying a GA optimization algorithm. Compared with a traditional control method, the method has the advantages that on the premise of ensuring the stability of the system, by optimizing event triggering conditions, the communication requirement and bandwidth occupation are remarkably reduced, the utilization efficiency of communication resources is improved, network congestion and delay possibly caused by frequent communication are reduced, and the service life of the system is prolonged. And the operation efficiency of the whole power system is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power system control, and in particular to a method for controlling load frequency of a power system triggered by dynamic events under DoS attacks and random time delays. Background Art

[0002] As modern power systems continue to grow in scale and complexity, ensuring their stable operation has attracted the attention of experts and scholars. Load frequency control (LFC) is a key technology for ensuring stable power system operation. This technology maintains system frequency stability by regulating power generation, while ensuring that the power exchange between interconnecting lines in different control areas meets predetermined values. In LFC power systems, the introduction of networked control technology can lead to problems such as cyberattacks, communication delays, and signal attenuation. Achieving stable system operation despite these network issues remains a significant challenge. Summary of the Invention

[0003] In response to the shortcomings of the existing technology, the present invention provides a dynamic event-triggered load frequency control method for power systems under DoS attacks and random delays, which solves the problems of network attacks, limited bandwidth and transmission delay in the signal transmission process of power systems, as well as the conservative DET parameter selection and controller design.

[0004] To solve the above technical problems, the present invention provides the following technical solution: a method for controlling load frequency in a power system under dynamic event triggering under DoS attack and random time delay, the method comprising the following steps:

[0005] S1. Obtaining operating parameters of the LFC power system and obtaining a continuous-time system parameter matrix of a state-space model of the LFC power system based on the operating parameters;

[0006] S2. Obtain the DET parameters of the dynamic event trigger mechanism and set an event trigger threshold function between the sensor and the controller to solve the bandwidth limitation problem based on the DET parameters;

[0007] S3. Establish state parameters for the LFC power system to withstand DoS attacks and random delays during information transmission, including the attack duration and maximum number of DoS attacks, as well as the probability density function describing the signal delay distribution.

[0008] S4. Construct a state feedback controller based on the continuous-time system parameter matrix, event trigger threshold function, and state parameters, and design the matrix inequality of the state feedback controller;

[0009] S5. Obtain the population initialization range of the GA optimization algorithm, and apply the GA optimization algorithm to collaboratively design the DET parameters and the gain matrix of the state feedback controller;

[0010] S6. Substitute the obtained gain matrix into the state feedback controller to stabilize the LFC power system.

[0011] Furthermore, the operating parameters of the LFC power system include the generator damping coefficient D, speed drop R, turbine time constant T ch , regulator time constant T g , generator inertia moment M, frequency deviation coefficient β, time synchronization coefficient T, the LFC power system state space model is:

[0012]

[0013] In the above formula, x(t) is the state of the system at time t; represents the future system state; y(t) is the system measurement output; u(t) is the system control input; w(t) represents the external disturbance.

[0014] Furthermore, the expressions of the continuous-time system parameter matrices A, B, C, and F are:

[0015] A=[A ij ] 2×2

[0016] B=diag{B1(t),B2(t)}

[0017] C=diag{C1(t),C2(t)}

[0018] F=diag{F1(t),F2(t)}

[0019] in,

[0020]

[0021]

[0022] Where T represents transpose; represents the regulator time constant of control area i; β i represents the frequency deviation coefficient of control area i; M i represents the generator inertia moment of control area i; T ij is the time synchronization coefficient of control areas i and j; R i Indicates that the speed of control area i decreases; represents the turbine time constant of control region i; represents the regulator time constant of control area i; D irepresents the generator damping coefficient of control area i.

[0023] Furthermore, the expression of the event triggering threshold function is:

[0024] s k+1 =min{t>s k |e T (t)Ωe(t)≥δ r (t)x T (s k )Ωx(s k )}

[0025] Where s k+1 The next trigger moment; k is the latest triggering moment; t represents the current time; Ω is the weight matrix; e(t) = x(t) - x(s k ) is the error, x(t) is the current state; x(s k ) is the state at the latest triggering moment; e T (t) and x T (s k ) represent e(t) and x(s k ) is the transpose; min represents the minimum value; δ r (t) is the event triggering dynamic item parameter.

[0026] Furthermore, the expressions for the attack duration and maximum number of attacks of the DoS attack are:

[0027]

[0028] in, Indicates the maximum number of attacks in the time period (t1, t2); Indicates the duration of the attack in the time period (t1, t2); n0 and n a is a parameter related to the number of attacks; and is a parameter related to the attack duration;

[0029] The probability density distribution function of the delay distribution is:

[0030] ρ(λ)=630λe -25λ

[0031] Where λ is the delay value in the range [0, 0.3].

[0032] Furthermore, the state feedback controller related to the Dos attack signal is:

[0033] u(t)=ζ(t)Kx(s k),t∈[t k ,t k+1 )

[0034] Where u(t) is the state feedback controller; ζ(t) is the DoS attack signal, which is 0 when the attack occurs and 1 when the attack occurs; K is the gain matrix of the state feedback controller; x(s k ) represents the system status at the latest triggering moment, s k is the latest triggering moment after considering the time delay; t∈[t k ,t k+1 ) indicates the current time at t k to t k+1 The value within.

[0035] Furthermore, the matrix inequality of the state feedback controller is:

[0036]

[0037] in:

[0038]

[0039] Where, He(·) represents (·) plus its transpose. The symbol appears in other places of the present invention to represent the same meaning; a and β are parameters generated in the derivation process; c2 is a parameter related to the system performance index; v1 and v2 are parameters generated in the scaling process; Ω is the weight matrix of DET; n a and It is a parameter related to Dos attack; P N are the variables in the Lyapunov function; I is the identity matrix of appropriate dimensions; O is the zero matrix of appropriate dimensions; τ m is the maximum delay; γ is the system performance index; b1X T With b2X T are variables introduced in the proof process, b1 and b2 are given scalars, and X T are unknown parameters to be determined; P, U, Q, R, and S are variables in the Lyapunov function; represents the Kronecker product between two matrices; F(0) and F(-τ m ) is the value produced by the derivation process; is a matrix with appropriate dimensions; δ0 is the DET parameter; A, B, F are system matrices; K is the controller gain; is the augmented matrix of the unit matrix and the zero matrix; diag represents the diagonal matrix; Representation matrix Croneck product with matrix R; Representation matrix Kronecker product with matrix I.

[0040] Furthermore, in step S5, the specific process includes the following steps:

[0041] S51. Perform contract transformation on the matrix inequality of the state feedback controller to obtain a linear matrix inequality, namely:

[0042] right and Multiply the front and back of and its transpose;

[0043] right Multiply the front and back and its transpose, we get:

[0044]

[0045] in:

[0046]

[0047] Where, are all matrices obtained by contract transformation;

[0048] S52, by solving the linear matrix inequality obtained in S51, and by K=L(V T ) -1 Get the gain matrix K of the state feedback controller;

[0049] S53, encoding the DET parameters and the gain matrix K together, and setting the population search range;

[0050] S54, randomly generating an initial population based on the initialization range obtained in S53;

[0051] S55. Use the LMI tool to solve the linear matrix inequality group for each individual. If there is a solution, calculate the corresponding fitness function value; if there is no solution, assign a sufficiently large value to the fitness value of the individual;

[0052] S56. Execute GA optimization operations of mutation, inheritance, selection, and optimal value retention. If the maximum number of iterations is reached, save the gain matrix K and DET parameters of the optimal individual obtained during the iteration process. Otherwise, return to step S55.

[0053] By means of the above technical solution, the present invention provides a method for controlling load frequency of a power system under dynamic event triggering under DoS attack and random time delay, which has at least the following beneficial effects:

[0054] 1. The present invention applies dynamic event-triggered load frequency control to the power system. Compared with traditional control methods, this method significantly reduces communication requirements and bandwidth usage by optimizing event triggering conditions while ensuring system stability, thereby alleviating the communication burden. This dynamic event triggering mechanism can dynamically adjust the trigger threshold based on the actual operating status of the system, so that control signals are sent only when necessary, rather than at fixed time intervals. This design not only improves the utilization efficiency of communication resources, but also helps reduce network congestion and delays that may be caused by frequent communications, thereby improving the operating efficiency of the entire power system.

[0055] 2. This invention considers the potential network attacks and delays that the LFC power system may be subject to when transmitting data over the network. DoS attack signals are incorporated into the design of the state feedback controller, enabling it to maintain stable system operation even under attack. Furthermore, a probability density function is used to describe the delay distribution, effectively utilizing delay information.

[0056] 3. The collaborative design of DET parameters and state feedback controller in this invention not only reduces conservatism, but also further improves the closed-loop system Performance. By introducing the GA optimization algorithm, the nonlinear constraints contained in the conditions can be efficiently solved, thereby obtaining the optimal DET parameters and controller gains within the current search range.

[0057] 4. The present invention combines the control method triggered by dynamic events and uses genetic algorithms for DET parameter selection and controller optimization to achieve the control effect of reducing the use of network communication resources, responding to Dos attacks and transmission delays while ensuring stable operation of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0059] Figure 1 For the present invention;

[0060] Figure 2 Schematic diagram of the framework of the LFC power system in the present invention;

[0061] Figure 3 is a state trajectory value curve diagram of the LFC power system in the present invention;

[0062] Figure 4 This is a schematic diagram of event triggering represented by the event trigger threshold function in the present invention;

[0063] Figure 5 It is an iterative curve diagram of the γ value obtained using the GA optimization algorithm in the present invention;

[0064] Figure 6 This is a random delay distribution diagram with probability density in the present invention. DETAILED DESCRIPTION

[0065] To make the above-mentioned objectives, features, and advantages of the present invention more clearly understood, the present invention is further described below in detail with reference to the accompanying drawings and specific embodiments. This will enable a full understanding of how this application uses technical means to solve technical problems and achieve technical effects, and to implement the invention accordingly.

[0066] In practical applications, network access to power systems inevitably exposes them to network attacks, limited bandwidth, and communication delays. To address bandwidth limitations, the present invention establishes a dynamic event-triggered (DET) mechanism between sensors and controllers. Based on DET's characteristics, signal transmission is permitted only when the signal meets the triggering conditions, thus avoiding unnecessary information transmission. This method can significantly reduce the use of limited network resources and maintain system stability even when transmitting only a small amount of information. Network attacks and transmission delays are also unavoidable during information transmission in LFC power systems. To mitigate Denial-of-Service (DoS) attacks, the present invention incorporates DoS attack signals into the controller design process to mitigate the impact of attacks on the system. Delay is a common phenomenon in information transmission. Many researchers typically consider delay to have upper and lower bounds or classify it as long or short to reduce the conservatism of delay modeling. These approaches ignore the fact that delay distribution conforms to a certain probability density distribution. Therefore, the present invention describes its distribution characteristics by considering that delay conforms to a probability density distribution.

[0067] In most dynamic event triggering (DET) design studies, the dynamic term parameters are typically pre-determined. This leads to a certain degree of conservatism in parameter selection. Therefore, the present invention incorporates a genetic algorithm (GA) to collaboratively design controller gains and DET parameters. This combination not only reduces conservatism but also significantly improves the overall performance and stability of the system.

[0068] Furthermore, this embodiment proposes a method for dynamic event-triggered load frequency control of power systems under Dos attacks and random delays. By using the dynamic event-triggered load frequency control method and using a genetic algorithm (GA) to collaboratively design the DET parameters and the state feedback controller, the LFC power system can achieve rapid response, bandwidth saving, and resistance to Dos attacks and communication delays. Figure 1 As shown, the method includes the following steps:

[0069] S1. Obtain operating parameters of the LFC power system, and obtain continuous-time system parameter matrices A, B, C, and F of the LFC power system state-space model based on the operating parameters. In this embodiment, the operating parameters can be used to determine the control regions in the LFC power system, thereby further determining the LFC power system state-space model. In this embodiment, there are two control regions, namely, region 1 and region 2.

[0070] Specifically, the operating parameters of the LFC power system include the generator damping coefficient D, speed drop R, turbine time constant T ch , regulator time constant T g , generator inertia moment M, frequency deviation coefficient β, time synchronization coefficient T. In this embodiment, the operating parameters of the LFC power system are as follows:

[0071] Region 1:

[0072] D=0.6,R=0.05,T ch =0.5, T g =0.2, M=10, β=20.6, T=1 / π.

[0073] Region 2:

[0074] D=0.9,R=0.0625,T ch =0.6, T g =0.3, M=8, β=16.9, T=1 / π.

[0075] From this, the state space model of the LFC power system can be obtained, as shown in Figure 2 The figure shows the framework diagram of the LFC power system studied in this embodiment.

[0076]

[0077] In the above formula, x(t) is the state of the system at time t; represents the future system state; y(t) is the system measurement output; u(t) is the system control input; w(t) represents the external disturbance, here set A, B, C, and F are the continuous-time system parameter matrices, and the expression is:

[0078] A=[A ij ] 2×2

[0079] B=diag{B1(t),B2(t)}

[0080] C=diag{C1(t),C2(t)}

[0081] F=diag{F1(t),F2(t)}

[0082] in,

[0083]

[0084]

[0085] The symbols i and j above represent different control areas, and in this embodiment, the values are 1 and 2.

[0086] Where T represents transpose; represents the regulator time constant of control area i; β i represents the frequency deviation coefficient of control area i; M i represents the generator inertia moment of control area i; T ij is the time synchronization coefficient of control areas i and j; R i Indicates that the speed of control area i decreases; represents the turbine time constant of control region i; represents the regulator time constant of control area i; D i Represents the generator damping coefficient of control area i. Since this embodiment is a LFC power system with dual control areas. At this time, A ii Possible value A 11 and A 22 , A ij Possible value A 12 and A 21 . And A=[A ij ] 2×2 It contains A 11 、A 22 、A 12 、A 21 .

[0087] S2. Obtain the DET parameters c,δ0,σ,n of the dynamic event trigger mechanism, and set the event trigger threshold function s between the sensor and the controller to solve the bandwidth limitation problem based on the DET parameters c,δ0,σ,n k+1 .like Figure 4As shown, the designed DET mechanism can effectively reduce data transmission. In this embodiment, the initial values of the given DET parameters are: c=0.0005, δ0=0.02, σ=0.9, and n=1.

[0088] Design the event trigger threshold function according to the given DET parameters, namely:

[0089] s k+1 =min{t>s k |e T (t)Ωe(t)≥δ r (t)x T (s k )Ωx(s k )}

[0090] Where s k+1 The next trigger moment; k is the latest triggering moment; t represents the current time; Ω is the weight matrix; e(t) = x(t) - x(s k ) is the error, x(t) is the current state; x(s k ) is the state at the latest triggering moment; e T (t) and x T (s k ) represent e(t) and x(s k ) is the transpose; min represents the minimum value; δ r (t) is the event triggering dynamic item parameter.

[0091] S3. Establish state parameters in the LFC power system to withstand DoS attacks and random delays during information transmission, including the attack duration and maximum number of DoS attacks, as well as the probability density function ρ(λ) describing the signal delay distribution. Consider the LFC power system facing network attacks and signal delays during information transmission. Since network attacks are energy-limited in practice, their attack duration and number of attacks are set. In order to more accurately describe the delay distribution, the delay distribution is described using a given probability density function, as follows: Figure 6 As shown, the distribution of delay with probability density is shown.

[0092] Considering that the DoS attack energy is limited, the parameters related to the attack duration and number of attacks are set, namely:

[0093]

[0094] in, Indicates the maximum number of attacks in the time period (t1, t2); Indicates the duration of the attack in the time period (t1, t2); n0 and na is a parameter related to the number of attacks; and is a parameter related to the attack duration;

[0095] In this embodiment, the parameters related to the attack duration and the number of attacks are set to the following values: n0=1, n a =3, t1=0, t2=30,

[0096] Set the probability density distribution function of the delay distribution to:

[0097] ρ(λ)=630λe -25λ

[0098] Where λ is the delay value in the range [0, 0.3].

[0099] The rest of the delay-related parameters are set as follows:

[0100]

[0101] S4. A state feedback controller is constructed based on the continuous-time system parameter matrix, the event trigger threshold function, and the state parameters, and a matrix inequality of the state feedback controller is designed. This embodiment constructs a state feedback controller related to the DoS attack signal based on the DoS attack in the LFC power system, namely:

[0102] u(t)=ζ(t)Kx(s k ),t∈[t k ,t k+1 )

[0103] Where u(t) is the state feedback controller; ζ(t) is the DoS attack signal, which is 0 when the attack occurs and 1 when the attack occurs; K is the gain matrix of the state feedback controller; x(s k ) represents the system status at the latest triggering moment, s k is the latest triggering moment after considering the time delay; t∈[t k ,t k+1 ) indicates the current time at t k to t k+1 The value within.

[0104] The matrix inequality for designing the state feedback controller is:

[0105]

[0106] in:

[0107]

[0108]

[0109] Where, He(·) represents (·) plus its transpose. The symbol appears in other places of the present invention to represent the same meaning; α and β are parameters generated in the derivation process; c2 is a parameter related to the system performance index; v1 and v2 are parameters generated in the scaling process; Ω is the weight matrix of DET; n a and It is a parameter related to Dos attack; P N are the variables in the Lyapunov function; I is the identity matrix of suitable dimensions; o is the zero matrix of suitable dimensions; τ m is the maximum delay; γ is the system performance index; b1X T With b2X T are variables introduced in the proof process, b1 and b2 are given scalars, and X T are unknown parameters to be determined; P, U, Q, R, and S are variables in the Lyapunov function; represents the Croneck product between two matrices, f(λ) is a given function, f T (λ) represents the transpose of f(λ), f(λ) is a parameter related to the delay, represents the pair f(λ)f T (λ) from -τ m Integrate to 0 and then invert; F(0) and F(-τ m ) is the value produced by the derivation process; is a matrix with appropriate dimensions; δ0 is the DET parameter; A, B, F are system matrices; K is the controller gain; is the augmented matrix of the unit matrix and the zero matrix; diag represents the diagonal matrix; Representation matrix Croneck product with matrix R; Representation matrix Kronecker product with matrix I. Due to the coupling term and is nonlinear (the parameters of the state feedback controller exist in and The LMI tool cannot be used to solve the unknown variables in the state feedback controller. When the parameters of the state feedback controller are given a priori by the GA optimization algorithm, the above matrix inequalities are all linear and can be solved using the LMI tool.

[0110] S5. Obtain the population initialization range of the GA optimization algorithm, and apply the GA optimization algorithm to collaboratively design the DET parameters c, δ0, σ and the gain matrix K of the state feedback controller. As a preferred implementation of step S5, the specific process includes the following steps:

[0111] S51. Perform contract transformation on the matrix inequality of the state feedback controller to obtain a linear matrix inequality, namely:

[0112] right and Multiply the front and back of and its transpose;

[0113] right Multiply the front and back and its transpose, we get:

[0114]

[0115] in:

[0116]

[0117]

[0118] Where, are matrices obtained by contract transformation, and the remaining parameters have the same meanings as in S4. In this embodiment, it is necessary to obtain the minimum γ value, which is used as the optimization target of GA.

[0119] S52, by solving the linear matrix inequality obtained in S51, and by K=L(V T ) -1 Get the gain matrix K of the state feedback controller;

[0120] S53, encode the DET parameters c, δ0, σ, n and the gain matrix K together, and set the population search range;

[0121] S54, randomly generating an initial population based on the initialization range obtained in S53;

[0122] S55. Use the LMI tool to solve the linear matrix inequality group for each individual. If there is a solution, calculate the corresponding fitness function value; if there is no solution, assign a sufficiently large value to the fitness value of the individual;

[0123] S56, perform GA optimization operations of mutation, inheritance, selection, and optimal value retention. If the maximum number of iterations is reached, save the optimal individual gain matrix K and DET parameters c, δ0, σ, n obtained during the iteration process. Otherwise, return to step S55. Figure 5 As shown in the figure, it is a schematic diagram of the iterative results of the GA optimization algorithm. As the number of iterations increases, the LFC power system The value of the performance parameter γ becomes smaller and smaller, and after about 15 generations of iteration, its value reaches the optimal value under the current conditions.

[0124] S6. Substitute the obtained gain matrix K into the state feedback controller to stabilize the LFC power system. Figure 3 As shown, Figure 3 (a) and Figure 3 (b) shows the failure of the open-loop system state trajectory to converge to zero, while the closed-loop system state trajectory converges to zero. In the figure, x1-x10 represent the 10 state components in the dual-region LFC power system, and Dos represents a DoS attack. This demonstrates that the state feedback controller designed in this application can stabilize the system under DoS attacks and random delays.

[0125] Those skilled in the art will appreciate that all or part of the steps in the above-mentioned embodiment methods can be accomplished by instructing the relevant hardware through a program. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0126] The above embodiments provide a detailed introduction to the present invention. Specific examples are used herein 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 ideas. At the same time, for those skilled in the art, according to the ideas of the present invention, there may be changes in the specific implementation methods and application scopes. In summary, the contents of this specification should not be understood as limiting the present invention.

Claims

1. A method for controlling load frequency in a power system under dynamic event triggering under DoS attack and random time delay, characterized in that: The method comprises the following steps: S1. Obtaining operating parameters of the LFC power system and obtaining a continuous-time system parameter matrix of a state-space model of the LFC power system based on the operating parameters; S2. Obtain the DET parameters of the dynamic event trigger mechanism and set an event trigger threshold function between the sensor and the controller to solve the bandwidth limitation problem based on the DET parameters; S3. Establish state parameters for the LFC power system to withstand DoS attacks and random delays during information transmission, including the attack duration and maximum number of DoS attacks, as well as the probability density function describing the signal delay distribution. S4. Construct a state feedback controller based on the continuous-time system parameter matrix, event trigger threshold function, and state parameters, and design the matrix inequality of the state feedback controller; S5. Obtain the population initialization range of the GA optimization algorithm, and apply the GA optimization algorithm to collaboratively design the DET parameters and the gain matrix of the state feedback controller; S6. Substitute the obtained gain matrix into the state feedback controller to stabilize the LFC power system.

2. The power system dynamic event triggered load frequency control method according to claim 1, characterized in that: The operating parameters of the LFC power system include the generator damping coefficient D, speed drop R, turbine time constant T ch , regulator time constant T g , generator inertia moment M, frequency deviation coefficient β, time synchronization coefficient T, the LFC power system state space model is: In the above formula, x(t) is the state of the system at time t; represents the future system state; y(t) is the system measurement output; u(t) is the system control input; w(t) represents the external disturbance.

3. The power system dynamic event triggered load frequency control method according to claim 1, characterized in that: The expressions of the continuous-time system parameter matrices A, B, C, and F are: A=[A ij ] 2×2 B=diag{B1(t),B2(t)} C=diag{C1(t),C2(t)} F=diag{F1(t),F2(t)} in, Where, represents transpose; represents the regulator time constant of control area i; β i represents the frequency deviation coefficient of control area i; M i represents the generator inertia moment of control area i; T ij is the time synchronization coefficient of control areas i and j; R i Indicates that the speed of control area i decreases; represents the turbine time constant of control region i; represents the regulator time constant of control area i; D i represents the generator damping coefficient of control area i.

4. The method for controlling load frequency triggered by dynamic events in a power system according to claim 1, characterized in that: The expression of the event trigger threshold function is: Where s k+1 The next trigger moment; k is the latest triggering moment; t represents the current time; Ω is the weight matrix; e(t) = x(t) - x(s k ) is the error, x(t) is the current state; x(s k ) is the state at the latest triggering moment; and They represent e(t) and x(s k ) is the transpose; min represents the minimum value; δ r (t) is the event triggering dynamic item parameter.

5. The power system dynamic event triggered load frequency control method according to claim 4, characterized in that: The expressions of the attack duration and maximum attack times of the DoS attack are: in, Indicates the maximum number of attacks in the time period (t1, t2); Indicates the duration of the attack in the time period (t1, t2); n0 and n a is a parameter related to the number of attacks; and is a parameter related to the attack duration; The probability density distribution function of the delay distribution is: p(λ)=630λe -25λ Where λ is the delay value in the range [0, 0.3].

6. The method for controlling load frequency triggered by dynamic events in a power system according to claim 1, characterized in that: The state feedback controller related to the Dos attack signal is: u(t)=ζ(t)Kx(s k ),t∈[t k ,t k+1 ) Where u(t) is the state feedback controller; ζ(t) is the DoS attack signal, which is 0 when the attack occurs and 1 when the attack occurs; K is the gain matrix of the state feedback controller; x(s k ) represents the system status at the latest triggering moment, s k is the latest triggering moment after considering the time delay; t∈[t k ,t k+1 ) indicates the current time at t k to t k+1 The value within.

7. The method for controlling load frequency triggered by dynamic events in a power system according to claim 6, characterized in that: The matrix inequality of the state feedback controller is: Ω-v1I<0 v2I-P N <0 in: e1=[OOOI -IO] Where, He(·) represents (·) plus its transpose. The symbol appears in other places of the present invention to represent the same meaning; a and β are parameters generated in the derivation process; c2 is a parameter related to the system performance index; v1 and v2 are parameters generated in the scaling process; Ω is the weight matrix of DET; n a and It is a parameter related to Dos attack; P N are the variables in the Lyapunov function; I is the identity matrix of appropriate dimensions; O is the zero matrix of appropriate dimensions; τ m is the maximum delay; γ is the system performance index; and are variables introduced in the proof process, b1 and b2 are given scalars, are unknown parameters to be determined; P, U, Q, R, and S are variables in the Lyapunov function; represents the Kronecker product between two matrices; F(0) and F(-τ m ) is the value produced by the derivation process; is a matrix with appropriate dimensions; δ0 is the DET parameter; A, B, F are system matrices; K is the controller gain; is the augmented matrix of the unit matrix and the zero matrix; diag represents the diagonal matrix; Representation matrix Croneck product with matrix R; Representation matrix Kronecker product with matrix I.

8. The power system dynamic event-triggered load frequency control method according to claim 7, characterized in that: In step S5, the specific process includes the following steps: S51. Perform contract transformation on the matrix inequality of the state feedback controller to obtain a linear matrix inequality, namely: right and Multiply the front and back of and its transpose; right Multiply the front and back and its transpose, we get: in: Where, V and L are matrices obtained by contract transformation; S52, by solving the linear matrix inequality obtained in S51, and by Get the gain matrix K of the state feedback controller; S53, encoding the DET parameters and the gain matrix K together, and setting the population search range; S54, randomly generating an initial population based on the initialization range obtained in S53; S55. Use the LMI tool to solve the linear matrix inequality group for each individual. If there is a solution, calculate the corresponding fitness function value; if there is no solution, assign a sufficiently large value to the fitness value of the individual; S56. Execute GA optimization operations of mutation, inheritance, selection, and optimal value retention. If the maximum number of iterations is reached, save the gain matrix K and DET parameters of the optimal individual obtained during the iteration process. Otherwise, return to step S55.

Images