A wind power system frequency controller to cope with cyber attacks

Abstract

The present invention belongs to the field of power system frequency stability control and specifically provides a wind power system frequency controller for responding to cyber attacks. This invention employs a dynamic event-triggered quantization control strategy (DETS). By adding auxiliary dynamic variables to the event triggering conditions to effectively extend the threshold function, and using a logarithmic quantizer for quantization measurement, the quantized values are constrained within the quantizer sector boundaries, enabling the system to better maintain stability and network resources. Furthermore, a random deception attack model with a malicious tolerance boundary is considered to improve security. Ultimately, this invention maintains system stability in response to cyber attacks while simultaneously improving computing power.

Description

Technical Field

[0001] The present invention belongs to the field of power system frequency stability control, and specifically provides a wind power system frequency controller for responding to network attacks. Background Art

[0002] As energy supply issues become increasingly severe and the ecological environment deteriorates, clean energy sources such as solar power, wind power, and hydropower are gaining increasing attention. With the continuous reduction in wind turbine manufacturing costs, the gradual decline in fossil fuels, and the increasing cost of extraction, wind power has shown strong development potential. Driven by the rapid growth of wind power efficiency and the need to minimize carbon emissions, countries and regions around the world have accelerated the deployment of wind power facilities over the past decade, and the Chinese government has also given equal attention to the wind power sector.

[0003] In the early stages of wind power system development, its scale was small and the control requirements were low. However, with the continuous development of electricity and technology, the scale of wind power systems has continued to expand. In addition, due to the characteristics of decentralized distribution and intermittent output, wind power energy cannot be centralized and large-scaled for power generation like traditional energy. In order to overcome this problem, we need to rebuild the wind power system. Therefore, multi-regional interconnected wind power systems came into being, and the degree of interconnection between regions has also been increasing. At present, the power grid has formed a large-scale multi-regional interconnected wind power system. There are many advantages to the interconnection of power systems: 1) More reasonable and economical development of primary energy, complementary advantages of water and fire resources, and integration of new energy into the power grid, which not only solves the problem of unbalanced energy and load distribution, but also gives full play to their role; 2) Reduction of standby capacity. Through the interconnection of power systems, whether in normal operation or in case of failure, each region can use interconnection lines for support, thereby reducing both maintenance standby capacity and accident standby capacity; 3) Reduction of the total load peak of the system. When the load is disturbed, the interconnected power systems can support each other in a short time and perform peak-shifting adjustments, thereby reducing the peak load and reducing the total installed capacity of the regional power grid; 4) Improvement of the safety and reliability of the power system. Since the capacity of the interconnected power system is increased, some failures have less impact on the system, and the probability of failure is relatively small. Moreover, they can support each other, thus improving safety and reliability; 5) Improvement of the economy of the power system operation. Since the power supply costs of different regions may be different, the power generation costs of power plants in energy-rich areas are lower. It can be seen from this that through regional interconnection, economic dispatch of electricity can be achieved to obtain economic benefits. Multi-regional interconnected wind power systems can also unify widely distributed wind power stations to achieve efficient and flexible utilization of decentralized energy. It can not only coordinate and provide users with large-scale electricity through grid control, but also independently control the operation of individual wind power systems to directly provide electricity to users. However, the access of wind power energy from multiple regions to the interconnected wind power system will bring great impact and challenges to traditional wind power systems.

[0004] Frequency is a key indicator of wind power system operational quality and safety. Imbalances between power generation and consumption can severely impact power system stability. Load frequency control (LFC), also known as secondary regulation, utilizes the system's active power to restore system frequency. LFC's two primary functions are maintaining system frequency and ensuring that power exchange between systems remains within normal limits. Consequently, LFC analysis and design have garnered significant attention over the past decade. With the rapid development of networks, large amounts of data are exchanged through limited communication channels. LFC schemes inevitably face communication and computational challenges, with increased computation exacerbating power consumption. To address this issue, sampled data control (SDC) reduces data transmission, requiring only valid information at the instant of sampling. Within SDC, periodic event-triggered mechanisms have been proposed, ensuring control performance with minimal data. However, even when the system is stable, a significant amount of signal transmission using SDC is still required, wasting limited communication resources. Therefore, designing a suitable LFC approach that minimizes transmission waste while maintaining adequate performance remains a challenging problem.

[0005] Furthermore, in networked wind power systems, measurement information and control commands are typically transmitted via communication networks, making malicious cyberattacks a critical concern. For example, cyberattacks can directly alter sensor or transmission data, inject false information into the communication network, and alter control signals, essentially changing them to their opposite sign. However, existing research on deception attack models has not fully captured the malicious intent of attack strategies. Summary of the Invention

[0006] The present invention aims to address the numerous problems existing in the prior art and provide a wind power system frequency controller that is resistant to cyberattacks. This invention proposes a dynamic event-triggered quantization control strategy (DETS). This strategy effectively extends the threshold function by adding auxiliary dynamic variables to the event triggering conditions. It also employs a logarithmic quantizer for quantization measurement, limiting the quantized value to within the quantizer sector boundaries. This strategy not only maintains system stability while conserving network resources, but also improves computing power through quantization measurement. Furthermore, a random deception attack model with a tolerable limit for malicious intent is considered to enhance security.

[0007] To achieve the above object, the technical solution adopted by the present invention is:

[0008] A wind power system frequency controller for responding to network attacks, characterized in that the controller is represented as:

[0009]

[0010] Where k represents a discrete time variable, represents the power input of the controller, β(k) is the Bernoulli random variable, and K is the control matrix of the controller;

[0011] Y(k) and Θ(k) represent the range of the strength of the deception attack;

[0012] q(x(k s ))=col[q(x1(k s )),...,q(x N (k s )],

[0013] u0 is the quantization level of the logarithmic quantizer, ρ represents the quantization density of the logarithmic quantizer, and q is the logarithmic quantization value with density ρ; x i (k s ) represents the state variable of the i-th control area at the triggering time k s , i=1,2,...,N, N is the total number of control areas in the wind power system;

[0014] β i is the system frequency response coefficient of the i-th control area;

[0015] is the sampling time of the deception attack, η s is the time delay from sensor to actuator.

[0016] Furthermore, the triggering time k s satisfy:

[0017]

[0018] Among them, k s+1 Indicates the next trigger moment;

[0019] ψ(x(k s ),e(k),σ)=e T (k)Φe(k)-σx T (k s )Φx(k s ), Φ is the trigger matrix, σ is the preset constant: 0<σ<1; e(k)=x(k)-x(k s ), x(k s )=col[x1(k s ),...,x N (k s )];

[0020] α(k) is the dynamic parameter used in the event-triggered system:

[0021] α(k)=ζα(k-1)-χψ(x(k s ),e(k),σ)

[0022] Wherein, ζ and χ are preset positive real numbers, and satisfy ζ>χ.

[0023] Based on the above technical solution, the beneficial effects of the present invention are:

[0024] The present invention provides a wind power system frequency controller for responding to network attacks. The controller adopts a dynamic event-triggered quantization control strategy (DETS). By adding auxiliary dynamic variables to the event triggering conditions to effectively expand the threshold function, and using a logarithmic quantizer for quantization measurement, the quantization value is limited within the quantizer sector boundary, so that the system can better maintain stability and network resources. Furthermore, a random deception attack model with a malicious tolerable boundary is considered to improve security. Ultimately, the present invention has the advantages of maintaining system stability when responding to network attacks while improving computing power. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 Schematic diagram of the structure of the discrete interconnected wind power generation system LFC model in the present invention.

[0026] Figure 2 This is a system representation diagram of the dynamic time-triggered quantization control strategy in the present invention.

[0027] Figure 3 This is a LFC model diagram of the interconnected wind power generation system with network delay in the present invention.

[0028] Figure 4 This is a state response diagram of the LFC model of the interconnected wind power generation system in Example 1 of the present invention.

[0029] Figure 5 This is an event trigger sequence diagram of the LFC model of the interconnected wind power generation system in Example 1 of the present invention.

[0030] Figure 6 This is a trajectory diagram of a deception attack on the LFC model of the interconnected wind power generation system in Example 1 of the present invention.

[0031] Figure 7 This is a state response diagram of the LFC model of the interconnected wind power generation system in Example 2 of the present invention.

[0032] Figure 8 This is an event trigger sequence diagram of the LFC model of the interconnected wind power generation system in Example 2 of the present invention.

[0033] Figure 9 It is the control input of the LFC model of the interconnected wind power generation system in Example 2 of the present invention. DETAILED DESCRIPTION

[0034] In order to make the purpose, technical solutions and beneficial effects of the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and embodiments.

[0035] The present invention proposes a new dynamic event-triggered quantitative load frequency control method considering network attacks for multi-region interconnected wind power systems subject to network attacks, which mainly includes:

[0036] 1. Establish a unified model for interconnected wind power systems, treating wind power variations as load fluctuations and fully mobilizing the regulation capabilities of conventional units to control frequency instability caused by load variations and random wind fluctuations;

[0037] 2. Based on dynamic event triggering and quantization, the LFC design method adds auxiliary dynamic variables to the event trigger conditions to expand the threshold function range and limit the quantized value to the sector boundary of the quantizer. This not only enables better quantization, but also maintains system stability while saving network resources.

[0038] 3. Based on dynamic event triggering and quantification, taking into account the impact of attacks and time delays, a random deception attack model is proposed. This model not only describes the deception attack on the communication mechanism, but also reflects the malicious intention of tampering with data, thereby improving security and reliability.

[0039] Specifically:

[0040] 1. Establish a wind power system model

[0041] The present invention constructs a discrete interconnected wind power generation system LFC model, such as Figure 1 shown; according to Figure 1 The logical relationship between the transfer function and the variables shown in the figure, the state space model of the i-th control area considering wind power can be expressed as:

[0042]

[0043] Where i represents the i-th region of the multi-region wind power system, represents the total communication power deviation, Respectively represent the changes in turbine mechanical output, load, valve position deviation, governor output, and wind turbine output power. represents the power input to the controller, ΔP wind represents the power output of the wind turbine, Δf i 、 The derivative of M i 、D i、T ij are the inertia constant, load damping coefficient, and synchronization coefficient of the i-th control area, respectively. R i are the turbine time constant, governor time constant, wind turbine time constant, and governor droop characteristics of the i-th control region respectively;

[0044] The following vector is further defined to represent the state variables:

[0045]

[0046] y i =col[ACE i ,Δf i ]

[0047]

[0048]

[0049] Among them, ACE i is the regional control error, expressed as β i is the system frequency response coefficient of the i-th control area;

[0050] Then the state space expression of the i-th control area of the wind power system is obtained:

[0051]

[0052] in,

[0053] The present invention defines h as the sampling period, h>0, and the periodic sampling sequence is Given; at each sampling time In the above equation, the discrete state space model of a region can be obtained as follows:

[0054]

[0055] in,

[0056] Further definition:

[0057] x(k)=col[x1(k),…,x N (k)],u(k)=col[u1(k),…,u N (k)],y(k)=col[y1(k),...,y N (k)],w(k)=col[w1(k),...,wN (k)], h = 1, the discrete state space expression of the multi-area power system containing N areas is expressed as:

[0058]

[0059] in,

[0060] 2. Dynamic event-triggered quantitative control strategy

[0061] The traditional periodic event trigger mechanism is expressed as:

[0062] k s+1 =min{k>k s ∣ψ(x(k s ),e(k),σ)>0} (6)

[0063] Among them, k s Indicates the current triggering moment, k s+1 Indicates the next trigger moment, k=k s +lh, k∈[k s ,k s+1 ); e(k)=x(k)-x(k s ),

[0064] ψ(x(k s ),e(k),σ)=e T (k)Φe(k)-σx T (k s )Φx(k s ), Φ is the trigger matrix, σ is the preset constant: 0<σ<1;

[0065] In the above method, when the system is stable, there are still many data signals transmitted on the network; in order to solve the over-transmission problem, such as Figure 2 As shown, the present invention proposes a dynamic event-triggered transmission mechanism:

[0066]

[0067] Among them, α(k) is the dynamic parameter used in the event-triggered system, which is described as:

[0068] α(k)=ζα(k-1)-χψ(x(k s ),e(k),σ) (8)

[0069] Where ζ and χ are positive real numbers and satisfy ζ>χ. In order to ensure normal information transmission within a limited bandwidth, the control signal is quantized by a logarithmic quantizer. A logarithmic quantizer is designed:

[0070]

[0071] Among them, u0 is the quantization level, ρ represents the quantization density; q is the logarithmic quantization value of density ρ, q(x(k s ))=col[q(x1(k s )),...,q(x N (k s )];

[0072] If equation (7) is valid, the transmission signal x(k) at time k is logarithmized, i.e., q(x(k))=q(x(k s ), the sensor immediately releases the data packet and transmits it to the controller through the communication network. Then the controller design is:

[0073]

[0074] Where K is the control matrix of the controller;

[0075] Therefore, the quantization signal of the present invention is designed based on event trigger conditions. That is, by using quantitative measurements of the state to update the dynamic event trigger conditions, and by adjusting the density of the quantization levels, the quantization error near the origin can be flexibly adjusted to stabilize the system; at the same time, as the state error changes, the corresponding event trigger parameters also change accordingly to better preserve bandwidth resources.

[0076] 3. Random Deception Attack Model Considering Time Delay

[0077] like Figure 3 The figure shows the process of signal transmission. It can be seen that during the signal transmission process, it is not only affected by the interval of the event trigger k, but also by the time delay;

[0078] The present invention designs a random deception attack from sensor to actuator guaranteed by Bernoulli random variable β(k), and designs a dynamic event trigger mechanism based on quantization; since the signal to be modified is the trigger output value, is the sampling time of the deception attack, is the next sampling moment of the deception attack, the deception attack model and the control signal considering the deception attack are as follows:

[0079]

[0080]

[0081] in, The strength of the deception attack represented by Y(k) and Θ(k) is limited to a certain range:

[0082] Y(k)Y T (k)≤λ -1 Q -1 ,Θ(k)Θ T (k)≤v -1 Q -1 (12)

[0083] Where λ,v are constants and satisfy λ,v>0, and the diagonal matrix Q>0 is a matrix used to limit the range of the attack signal;

[0084] Considering the influence of time-varying delay in signal transmission, it is assumed that the state under spoofing attack is at time Sampling, and the next update is at time Arrival, η s represents the time delay of the entire communication network from sensor to actuator, and satisfies η s ≤η M , η M is the maximum time lag; thus, the final control method is obtained:

[0085]

[0086] definition From this, the variable time-delay dynamic event triggering equation of the multi-regional power system quantitative LFC can be obtained as follows:

[0087]

[0088] in, τ(k) is the time-varying delay, τ M is the upper bound of the time-varying lag;

[0089] In the present invention, there is no continuous attack on the wind power generation system, and its characteristics can be characterized by β(k) in formula (11); considering that the consumption of attack energy limits the strength of the attack signal, parameters λ and v are used to characterize the strength of the deception attack.

[0090] In order to demonstrate the rationality and effectiveness of the controller proposed in the present invention, two embodiments are provided below:

[0091] Example 1

[0092] Assume the average probability of a deception attack is ( represents the average value of β(k), deception attack parameters λ = 20, v = 10; solve to obtain the controller feedback gain K and trigger parameter Φ of the control strategy with deception attack in this embodiment, as well as the controller feedback gain Kn and trigger parameter Φ without deception attackn , simulate the LFC model of the wind power system, and the state response is as follows Figure 4 As shown in the figure above (the system with deception attack, the system without deception attack), the event trigger sequence is as follows Figure 5 As shown in the figure (the upper figure shows a system without deception attack, and the lower figure shows a system with deception attack), the trajectory change diagram of network attack is as follows Figure 6 As shown in the figure, it can be seen that the deception attack causes the system state to fluctuate, and the controller based on dynamic event triggering and quantization strategy proposed in the present invention makes all states close to the equilibrium point, which shows the effectiveness of the controller.

[0093] Example 2

[0094] Assume that the communication delay is τ m =1 and τ M =3; Based on the existing fixed threshold trigger strategy (FETS), static trigger strategy (SETS) and the dynamic event triggered quantitative control strategy (DETS) proposed in this invention, the corresponding trigger parameters Φ and feedback gain K are obtained respectively; the wind power system LFC model is simulated, and the corresponding state is as follows Figure 7 As shown in (DETS, FETS, SETS from top to bottom), the event trigger sequence is as follows Figure 8 As shown in (DETS, FETS, SETS are represented from top to bottom), the control input is shown in 9 (DETS, FETS, SETS are represented from top to bottom); Figure 7 It can be seen that the frequency polarization amplitude under the dynamic trigger control of the present invention is small, which proves the effectiveness of the present invention in suppressing disturbances. Figure 8 It can be seen that the value of the trigger sequence under the dynamic trigger control of the present invention is smaller, which means that the system can achieve better control effect by releasing fewer data packets, indicating that the present invention can better reduce data transmission in the power system and save network communication resources; Figure 9 It can be seen that the control input fluctuation of the present invention is small, and the damage to the hardware facilities of the power system is small.

[0095] The above description is only a specific embodiment of the present invention. Any feature disclosed in this specification, unless otherwise stated, can be replaced by other equivalent or alternative features with similar purposes; all disclosed features, or all steps in the methods or processes, except for mutually exclusive features and / or steps, can be combined in any way.

Claims

1. A wind power system frequency controller for responding to network attacks, characterized in that: The controller is represented as: Where k represents a discrete time variable, represents the power input of the controller, β(k) is the Bernoulli random variable, and K is the control matrix of the controller; Y(k) and Θ(k) represent the range of the strength of the deception attack; q(x(k s ))=col[q(x1(k s )),...,q(x N (k s )], u0 is the quantization level of the logarithmic quantizer, ρ represents the quantization density of the logarithmic quantizer, and q is the logarithmic quantization value with density ρ; x i (k s ) represents the state variable of the i-th control area at the triggering time k s , i=1,2,...,N, N is the total number of control areas in the wind power system; β i is the system frequency response coefficient of the i-th control area; is the sampling time of the deception attack, η s is the time delay from sensor to actuator.

2. The wind power system frequency controller for resisting network attacks according to claim 1, characterized in that: Trigger time k s satisfy: Among them, k s+1 Indicates the next trigger moment; ψ(x(k s ),e(k),σ)=e T (k)Φe(k)-σx T (k s )Φx(k s ), Φ is the trigger matrix, σ is the preset constant: 0<σ<1; e(k)=x(k)-x(k s ), x(k s )=col[x1(k s ),...,x N (k s )]; α(k) is the dynamic parameter used in the event-triggered system: α(k)=ζα(k-1)-χψ(x(k s ),e(k),σ) Wherein, ζ and χ are preset positive real numbers, and satisfy ζ>χ.

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