A wind power system control method and controller for responding to DoS network attacks

Abstract

This invention belongs to the field of wind power system control technology and discloses a wind power system control method and controller to cope with DoS network attacks. It constructs a novel adaptive event triggering mechanism algorithm with memory characteristics. When a hacker launches a DoS attack on an interconnected wind power system through the communication network, the controller signal is not updated, and the communication triggering mechanism does not generate a new trigger moment. When the DoS attack interference signal is turned off, the value of the state variable changes, and the introduced adaptive threshold dynamically adjusts with the error. Simultaneously, the memory parameter introduces a time delay into the triggering mechanism. This invention proposes a new event triggering mechanism algorithm. The introduced adaptive threshold dynamically adjusts with the error, while the memory parameter introduces a time delay into the triggering mechanism. The combined effect of these two factors achieves system stability requirements while reducing data transmission and saving bandwidth. It has good adaptability and strong robustness and can be applied to various industrial applications.

Description

Technical Field

[0001] This invention belongs to the field of wind power system control technology, and in particular relates to a wind power system control method and controller for dealing with DoS network attacks. Background Technology

[0002] To meet the growing global demand for wind energy, double-fed induction generators (DFIGs) are used. DFIGs have gained widespread application due to their advantages such as low configuration cost, strong active and reactive power control capabilities, and high environmental adaptability. Meanwhile, DFIG-based power systems have also seen significant development over the past decade, with various control methods proposed, such as sliding mode control, proportional-integral regulator-based control, and control to improve inter-regional oscillation damping.

[0003] In the early stages of power system development, the scale was small and control requirements were low. However, with the continuous development of electricity and technology, the scale of power systems has been expanding. Furthermore, wind power, due to its dispersed distribution and intermittent output, cannot be generated on a large scale like traditional energy sources. To overcome this problem, it is necessary to rebuild the power system, thus giving rise to multi-regional interconnected wind power systems. The degree of interconnection between regions has also been increasing, and the power grid has now formed a large-scale multi-regional interconnected power system. The interconnection of power systems has many advantages, such as: 1) More rational and economical development of primary energy sources, achieving complementary advantages of hydropower and thermal power resources, and also enabling the integration of new energy sources into the grid. This solves the problem of unbalanced energy and load distribution and fully utilizes their potential. 2) Reduced reserve capacity. Through power system interconnection, regions can support each other using tie lines during normal operation or faults, thereby reducing both maintenance and contingency reserve capacity. 3) Reduced total system load peak. When load disturbances occur, interconnected power systems can provide short-term mutual support and perform peak-shaving adjustments, thereby reducing the peak load and lowering the total installed capacity of the regional power grid. 4) Improved power system safety and reliability: Due to the increased capacity of interconnected power systems, some faults have a smaller impact on the system, and the probability of faults is relatively low. Furthermore, mutual support is possible, thus improving safety and reliability. 5) Enhanced power system operation economy: Since power supply costs may vary across regions, power plants in energy-rich areas have lower generation costs. Therefore, regional interconnection enables economic dispatch of electricity, resulting in economic benefits. Multi-regional interconnected wind power systems can unify widely distributed wind power stations, achieving efficient and flexible utilization of decentralized energy. This system can provide large-scale electricity to users through grid control or independently control individual wind power systems to directly supply electricity to users. However, the integration of multi-regional wind power energy into the interconnected wind power system will bring significant impact and challenges to traditional wind power systems.

[0004] In modern linear frequency regulation (LFM) technology, feedback control is widely used to maintain power system stability. Traditional feedback control loops typically include modules such as sensors, controllers, actuators, and communication channels. With the development of computer technology, the operation of controllers, actuators, and communication channels has gradually shifted to digital platforms. Data sampling is a crucial step for the controller to obtain digital signals. Among the methods for acquiring data sampling, linear frequency regulation based on periodic triggering (PTM) has received increasing attention in recent years for its application in system stability. This transmission strategy periodically samples the system state and transmits the sampled data packets to the controller at constant sampling intervals, making it relatively stable compared to the traditional triggering mechanism PTM. However, the non-selective transmission of PTM results in the transmission of a large amount of redundant data, leading to excessive occupancy of the transmission channel and communication overload. This drawback is particularly prominent in large-scale, multi-region interconnected power systems.

[0005] DoS attacks refer to the deliberate exploitation of vulnerabilities in network protocol implementations or the brutal depletion of the target's resources through brute force. The aim is to render the target computer or network unable to provide normal service or resource access, causing the target system to stop responding or even crash. Because interconnected power systems typically utilize open communication networks to connect adjacent areas, many control signals in interconnected wind power systems are transmitted through these networks. The Network Control System (NCS) faces threats from multiple directions, and these threats evolve over time. Cyberattacks attempt to find weaknesses in the system to render computers or networks unable to provide normal service, affecting the normal operation of the entire interconnected wind power system. Therefore, network security issues in wind power systems should be given high priority.

[0006] DoS attacks work by exploiting system vulnerabilities to disrupt network services, preventing computers and networks from providing normal services and causing significant losses to users. Currently, there are three common DoS attack methods: (1) exploiting software flaws; (2) exploiting protocol vulnerabilities; and (3) competing for resources. These attacks are low-cost but highly aggressive and destructive. Hackers use these attacks to continuously damage computers and networks, causing significant negative impacts on users.

[0007] In recent years, numerous research findings have emerged considering malicious DoS network attacks. Some scholars have studied the use of periodic triggering mechanisms in interconnected wind power systems when DoS attacks are taken into account. To ensure stable system operation under DoS attacks, some scholars have proposed an event-triggered control method based on sampled data. Furthermore, considering unknown external disturbances, some experts have discussed combining event-triggered and time-triggered methods under DoS attacks, designing non-periodic event-triggered control methods. However, none of them have considered adaptive event-triggered mechanism algorithms with memory characteristics.

[0008] Based on the above analysis, the problems and shortcomings of the existing technology are as follows:

[0009] (1) At present, the access of wind power energy in multiple regions to the interconnected wind power system will bring great impact and challenges to the traditional wind power system, and make the wind power system face the risk of DoS attack.

[0010] (2) None of the existing wind power system control methods for dealing with DoS network attacks involve an adaptive event triggering mechanism algorithm with memory characteristics.

[0011] (3) Existing event triggering mechanisms typically sample and transmit system states periodically at constant sampling intervals, which consumes a large amount of communication bandwidth and increases the computer burden.

[0012] (4) The existing event triggering mechanism cannot adjust the event triggering strategy according to the activation and sleep state of the DoS attack, and cannot guarantee the stability of the system. Summary of the Invention

[0013] For existing multi-region interconnected wind power systems susceptible to non-periodic denial-of-service attacks, this invention provides a wind power system control method and controller to cope with DoS network attacks, and particularly relates to a new adaptive event-triggered proportional-integral-derivative (PID) load frequency control strategy with memory characteristics.

[0014] This invention is implemented as follows: a wind power system control method for responding to DoS network attacks, the wind power system control method for responding to DoS network attacks includes:

[0015] A novel adaptive event triggering mechanism algorithm with memory characteristics is constructed. When a hacker launches a DoS attack on the interconnected wind power system through the communication network, the controller signal is not updated, and the communication triggering mechanism does not generate a new trigger moment. However, when the DoS attack interference signal is turned off, the value of the state variable changes, the introduced adaptive threshold is dynamically adjusted with the error, and the memory parameter introduces a time delay to the triggering mechanism.

[0016] Furthermore, the wind power system control method for responding to DoS network attacks includes the following steps:

[0017] Step 1: Adjust the system frequency and control the load frequency;

[0018] Step 2: Establish a DFIG-based wind farm and perform wind power system modeling;

[0019] Step 3: Construct an adaptive event triggering mechanism with memory characteristics;

[0020] Step 4: Determine the DoS attack pattern and design a new controller.

[0021] Furthermore, the load frequency control in step one is a control method that adjusts the system frequency to reach the rated value or maintains the regional tie-line switching power at the planned value. When the active power generation and consumption in the system are unbalanced, a frequency deviation Δf is caused. The load characteristics and the active power changes generated by the generator's rotating energy absorb Δf, which is called the static frequency characteristic K of the load. D When Δf exceeds the governor dead zone, the generator set adjusts its output according to its respective droop rate R to suppress the frequency deviation Δf, which is called the generator's static frequency characteristic K. G The system reaches a new equilibrium, achieving a single frequency adjustment.

[0022] In secondary frequency regulation, the control deviation ACE of each region is calculated. Each region receives the adjustment amount from the load frequency controller, and the generator output is adjusted according to the adjustment amount to restore the frequency and tie-line exchange power to the specified values, achieving minute-level accuracy. When the interconnected power system is running, primary and secondary frequency regulation are interconnected, and the change in active power output of the generator at any given time is the sum of the active power output from primary and secondary frequency regulation. Load frequency control, through secondary frequency regulation, controls the control deviation ACE of the controlled region to achieve dynamic stability of the system.

[0023] Furthermore, the wind power system modeling in step two includes:

[0024] The simplified power system, with the voltage and flux linkage equations established, is as follows:

[0025]

[0026] In the formula, R is the resistance, L represents the generator's self-inductance and mutual inductance, and i represents the stator and rotor currents dq-axis, respectively. ds i qs and i dr i qr V ds and V qs The dq axis represents the stator voltage, V dr and V qrThe d and q axes represent the rotor voltage, respectively; s represents the rotor slip, and w represents the rotor voltage. s This indicates the air gap magnetic flux density of the generator; the subscripts s and r represent the stator and rotor, respectively.

[0027] The wind farm equations based on DFIG are established as follows:

[0028]

[0029] In the formula, X2=[1 / R r ], T1=[L0 / (w s R s )],X3=(L m / L ss M w For the constant inertia of the wind turbine, L ss =L s +L m L0 = [L rr +(L 2 m / L ss )], L rr =L r +L m L r L m This represents the stator, rotor, and magnetizing inductor.

[0030] A dynamic model of the i-th control region of a wind farm based on DFIG is established. Based on the logical relationship between the transfer function and variables, the following equation is derived:

[0031]

[0032] Define new state variables and obtain the state-space expression of a DFIG-type wind power generation system with interconnected multiple regions:

[0033]

[0034] Memory-based PID controller:

[0035]

[0036] In the formula, Kp represents the gain coefficient of the proportional controller, K I K represents the gain coefficient of the integral controller. D Let represent the gain coefficient of the integral-differential controller; introduce a memory parameter 0≤a(t)≤a in the controller, and the derivative satisfies the following inequality:

[0037] 0≤μ1≤à(t)≤μ2.

[0038] Furthermore, the construction of the adaptive event triggering mechanism with memory characteristics in step three includes:

[0039] The typical event triggering mechanism (ETM) is as follows:

[0040]

[0041] In the formula, It is a transmission instant sequence that satisfies the ETM condition; and It is an integer sequence; h is the sampling interval of the sensor;

[0042] Φ is the threshold parameter, and Φ is the positive definite weighting matrix to be designed.

[0043] Constructing an adaptive event triggering mechanism AMETM with memory properties:

[0044]

[0045] In the formula, an adaptive threshold parameter σ(i) is introduced. l h)>0, implemented as follows:

[0046]

[0047] In the formula, σ∈(0,1) are the upper and lower limits of the adaptive threshold.

[0048] If α(t) = 0 and υ = 0, the adaptive event-triggered mechanism AMETM simplifies to ETM; σ(i) is introduced. l h), σ(i) l h) Adaptively adjust based on system fluctuations. When the system is unstable, adjust to a smaller σ(i) l h) to obtain a higher communication frequency; when system fluctuations are small, σ(i) l h) is increased, and the transmission frequency is reduced.

[0049] Furthermore, the determination of the DoS attack mode in step four includes:

[0050] DoS attacks based on aperiodic interference signals that disrupt digital communication in a network, with the following channel trigger signals:

[0051]

[0052] in, This represents the current DoS attack cycle number; T>0 indicates the DoS attack's action cycle. X represents the length of the sleep period of a DoS attack in the nth action cycle; DoS (t) = 1 indicates that the system has suffered a DoS attack, XDoS (t) = 0 indicates that the system has not been subjected to a DoS attack.

[0053] Two DoS attack states are defined as follows:

[0054] This represents the sleep cycle of the nth action cycle;

[0055] This represents the attack cycle of the nth action cycle.

[0056] if If the sampled control data is successfully transmitted to the controller, the controller input u(t) of the actuator is updated in a timely manner; otherwise, the transmission will be rejected and the update of the controller input will be blocked.

[0057] When a non-periodic DoS attack is activated, the event is triggered momentarily as follows:

[0058]

[0059] In the formula, t k,n h represents the k-th transmission instant in the n-th interval; the value of k represents the number of trigger instants within the n-th period of the DoS attack; k n It is the maximum number of instants triggered within the interval of the nth DoS attack.

[0060] A new controller design method is proposed by combining AMETM and consideration of DoS attacks with PID controllers.

[0061] When s = 1, ..., k n -1, When, define T s,n =[t s,n h,t s+1,n h) represents the holding interval of the zero-order hold (ZOH). Under a DoS attack, the control input is written as:

[0062]

[0063] In the formula, Indicates the holding interval of the zero-order hold; middle:

[0064]

[0065]

[0066] Divided into the following sub-intervals:

[0067]

[0068] in Represented as:

[0069]

[0070]

[0071] for Indicate i l,n h = t k,n h+lh,η0(t)=ti l,n h,η(t)=η0(t)+α(t). The time-varying delay η(t) satisfies:

[0072]

[0073] According to the definition: e k,n (t)=x(i l,n h)-x(t k,n h) and e k,n (t-α(t))=x(i l,n h-α(t))-x(t k,n h-α(t)), the controller input is modified to:

[0074]

[0075] The dynamic model of the multi-regional interconnected power system of the DFIG wind farm based on AMETM is obtained and formalized as follows:

[0076]

[0077] y(t) = Cx(t).

[0078] The gain expression is solved as follows:

[0079]

[0080] The gain K is obtained using relational expressions and the YALMIP toolbox in MATLAB. P K I and K D Then, the gain is substituted into the established system model to verify the effectiveness of the designed controller.

[0081] Another object of the present invention is to provide a wind power system controller for responding to DoS network attacks using the aforementioned wind power system control method for responding to DoS network attacks, wherein the wind power system controller for responding to DoS network attacks includes:

[0082] The load frequency control module is used to adjust the system frequency to reach the rated value or maintain the area tie line switching power at the planned value, thereby realizing load frequency control.

[0083] The wind power system modeling module is used to establish a simplified power system with voltage and flux linkage equations and a DFIG-based wind farm, thereby constructing a wind power system model.

[0084] An adaptive event triggering module is used to introduce an adaptive threshold parameter σ(i) l h)>0, thereby constructing an adaptive event triggering mechanism with memory characteristics;

[0085] The controller design module is used to determine the DoS attack mode and design a new controller based on DoS attacks caused by aperiodic interference signals that block digital communication.

[0086] Another object of the present invention is to provide a computer device including a memory and a processor, the memory storing a computer program, which, when executed by the processor, causes the processor to perform the steps of the wind power system control method for responding to DoS network attacks.

[0087] Another object of the present invention is to provide a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the steps of the wind power system control method for responding to DoS network attacks.

[0088] Another objective of this invention is to provide an information data processing terminal, which is used to implement the wind power system controller for responding to DoS network attacks.

[0089] Based on the above technical solutions and the technical problems solved, the advantages and positive effects of the technical solution to be protected by this invention are as follows:

[0090] Due to limited computer bandwidth and resources, many researchers have proposed Event Triggered Mechanisms (ETMs) for linear frequency regulation schemes in multi-region interconnected power systems to reduce bandwidth utilization while maintaining control performance. Compared to traditional control mechanisms, ETMs determine whether the currently sampled data will be used for system stability control at each sampling period based on various proposed event triggering conditions. Under these event triggering conditions, a constant threshold parameter is always maintained to limit redundant data transmission. Experimental results show that compared to PTM control, the execution frequency of the ETM-based control process is ultimately reduced, thus significantly saving communication resources and satisfying the stability requirements of the modeled closed-loop system. However, even when the system is stable, a significant amount of data transmission still occupies the communication channel.

[0091] To further reduce communication overhead, we adopted an event-triggered mechanism with dynamic threshold parameters, namely the Adaptive Event Triggered Mechanism (AETM). The threshold parameters adaptively adjust based on the continuously transmitted data volume to ensure high transmission efficiency. Subsequently, to further ensure system performance, we incorporated the state and error parameters of the interconnected wind power system into the adaptive threshold parameter design strategy. Based on this, we designed an event-triggered mechanism with memory characteristics (METM), which shortens response fluctuations by using historical state transmission data packets to achieve higher control performance. Finally, we integrated the adaptive threshold parameters and the memory function into a single adaptive event-triggered mechanism with memory characteristics (AMETM).

[0092] This invention provides a novel adaptive event triggering mechanism algorithm with memory characteristics. When a hacker launches a DoS attack on an interconnected wind power system through a communication network, the controller signal will not be updated, and the communication triggering mechanism will not generate a new trigger moment. However, when the DoS attack interference signal is turned off, the value of the state variable changes significantly. At this time, the introduced adaptive threshold will be dynamically adjusted with the error change. At the same time, the memory parameter introduces a time delay to the triggering mechanism. The combined effect of the two makes the system tend to stabilize.

[0093] A PID controller comprises proportional, integral, and derivative components. Compared to simple proportional controllers, proportional-integral controllers, and proportional-derivative controllers, the three components of a PID controller work together to enable faster, more stable, and more accurate dynamic processes, resulting in better control performance. Compared to memoryless controllers, memory-based PID controllers contain a memory of state variables in their control signal. When the control signal cannot be updated normally due to network attacks or transmission delays, memory-based PID controllers can achieve better control performance, exhibiting better adaptability and stronger robustness, making them applicable to various industrial applications.

[0094] The wind power system control method for dealing with DoS network attacks provided by this invention proposes a new event-triggered mechanism algorithm, which reduces data transmission and saves bandwidth while meeting system stability requirements.

[0095] (1) Due to the dispersed distribution and intermittent output of wind power energy in my country, it is impossible to generate electricity on a centralized, large scale like traditional energy sources. To overcome this problem, it is necessary to rebuild the power system, thus giving rise to multi-regional interconnected wind power systems. However, many control signals of multi-regional interconnected wind power systems are transmitted through communication networks, which inevitably makes them vulnerable to network attacks. This invention can effectively counter DoS attacks, maintain system stability, save a large amount of computer resources, and can be widely applied in my country's wind power sector to obtain significant economic benefits.

[0096] (2) This invention primarily considers random DoS attacks. Due to energy limitations, the DoS attack models commonly used by researchers in the industry are constrained; they often limit the attack cycle and frequency in the construction of mathematical models. However, in actual production, the randomness and uncertainty of network attacks often make it impossible for the system to predict its actual parameters such as the attack cycle in advance. To improve the practical application value of this research, unlike commonly used DoS attack models, the DoS attack in this invention only gives upper and lower bounds to the length of the dormant interval, allowing DoS attacks to be generated randomly within a relatively wide range. Under severe DoS attack conditions, the stability and bandwidth utilization of the system in this invention are superior.

[0097] (3) To save communication resources, this invention introduces system error, its derivative and integral, and adaptively adjusted threshold parameters into the event triggering mechanism. The commonly used event triggering mechanism has the following characteristics: 1) It is only related to the system error or system state at the current moment; 2) The threshold parameter is a constant or adopts different dynamic laws. In comparison, the AMETM proposed in this invention can reduce bandwidth occupation, reduce communication burden, reduce frequency fluctuations, and ensure system performance. Attached Figure Description

[0098] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the embodiments of the present invention will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0099] Figure 1 This is a flowchart of a wind power system control method for responding to DoS network attacks provided in an embodiment of the present invention;

[0100] Figure 2 This is a block diagram of a multi-regional interconnected wind power system based on DFIG provided in an embodiment of the present invention;

[0101] Figure 3 This is a graph showing the H∞ exponent change under a DoS network attack, provided in an embodiment of the present invention.

[0102] Figure 4 This is a schematic diagram of the non-periodic nature of DoS network attacks provided in the embodiments of the present invention;

[0103] Figure 5 This is a schematic diagram of the data sampling interval under a DoS network attack provided in an embodiment of the present invention;

[0104] Figure 6 This is a diagram showing the dynamic adjustment of σ between [0.02, 0.1] provided in an embodiment of the present invention;

[0105] Figure 7 The embodiment of the present invention provides a setting σ = 0.04 to reflect σ(i) l h) Effect diagram on data sampling;

[0106] Figure 8 This is a system state response diagram provided in an embodiment of the present invention. Detailed Implementation

[0107] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0108] To address the problems existing in the prior art, this invention provides a wind power system control method and controller for responding to DoS network attacks. The invention will be described in detail below with reference to the accompanying drawings.

[0109] like Figure 1 As shown, the wind power system control method for responding to DoS network attacks provided in this embodiment of the invention includes the following steps:

[0110] S101, Adjust the system frequency and perform load frequency control;

[0111] S102, Establish a DFIG-based wind farm and perform wind power system modeling;

[0112] S103, Construct an adaptive event triggering mechanism with memory characteristics;

[0113] S104, Identify DoS attack patterns and design a new controller.

[0114] As a preferred embodiment, the wind power system control method for responding to DoS network attacks provided by this invention specifically includes the following steps:

[0115] 1. Load frequency control

[0116] Load frequency control is a control method that adjusts the system frequency to reach the rated value or maintains the area tie-line exchange power at the planned value. Specifically, when the generation and consumption of active power in the system are unbalanced, causing a frequency deviation Δf, the load characteristics and the changes in active power generated by the generator's rotating energy first absorb Δf. This phenomenon is called the static frequency characteristic K of the load. D When Δf exceeds the governor dead zone, the generator set adjusts its output according to its respective droop rate (R) to suppress the frequency deviation Δf. This phenomenon is called the generator's static frequency characteristic K. GAt this point, the system reaches a new equilibrium. This process is called primary frequency regulation, which is differential regulation and must be adjusted using secondary frequency regulation. Secondary frequency regulation first calculates the control deviation (ACE) for each region. Each region obtains the adjustment amount based on the load frequency controller, and adjusts the generator output to restore the frequency and tie-line power exchange to the specified values. This process takes minutes. In interconnected power systems, primary and secondary frequency regulation are interconnected. The change in active power output from the generator at any given time is the sum of the active power output from primary and secondary frequency regulation. Load frequency control mainly achieves dynamic stability by controlling the control deviation (ACE) of the controlled region through secondary frequency regulation.

[0117] 2. Wind power system modeling

[0118] First, this invention establishes a simplified power system based on the voltage and flux linkage equations, as follows:

[0119]

[0120] In the formula, R is the resistance, L represents the generator's self-inductance and mutual inductance, and i represents the stator and rotor currents dq-axis, respectively. ds i qs and i dr i qr V ds and V qs The dq axis represents the stator voltage, V dr and V qr The d and q axes represent the rotor voltage, respectively. The rotor slip is represented by s, and w... s This represents the air gap magnetic flux density of the generator. The subscripts 's' and 'r' represent the stator and rotor, respectively.

[0121] The wind farm equations based on DFIG are established as follows:

[0122]

[0123] In the formula, X2=[1 / R r ], T1=[L0 / (w s R s )],X3=(L m / L ss M w For the constant inertia of the wind turbine, L ss =L s +L m L0 = [L rr +(L 2 m / L ss )], L rr =L r +L mL r L m This represents the stator, rotor, and magnetizing inductor.

[0124] At this point, the present invention begins to establish the wind power system model required by the present invention. First, the present invention establishes a dynamic model of the i-th control region of the wind farm based on DFIG, as follows: Figure 2 As shown.

[0125] according to Figure 2 The logical relationship between the transfer function and the variables in the equation can be derived from equation (3):

[0126]

[0127] In the following work, this invention defines new state variables to obtain the state-space expression of a DFIG-type wind power generation system with multiple interconnected regions:

[0128]

[0129] Note 1: This invention proposes a suitable controller to achieve the control requirements. The following describes a memory-based PID controller:

[0130]

[0131] In the formula, Kp represents the gain coefficient of the proportional controller, K I K represents the gain coefficient of the integral controller. D Let represent the gain coefficient of the integral-differential controller. A memory parameter 0 ≤ a(t) ≤ a is introduced into the controller. Its derivative satisfies the following inequality:

[0132] 0≤μ1≤à(t)≤μ2

[0133] Note 2: A PID controller includes proportional, integral, and derivative components. Compared to simple proportional controllers, proportional-integral controllers, and proportional-derivative controllers, the three components of a PID controller work together to make the dynamic process faster, more stable, and more accurate, resulting in better control performance. Compared to controllers without memory, memory-based PID control signals contain memory of state variables. When the control signal cannot be updated normally due to network attacks or transmission delays, memory-based PID controllers can achieve better control performance, have better adaptability and stronger robustness, and can be applied to various industrial applications.

[0134] 3. An adaptive event triggering mechanism with memory characteristics

[0135] The general event triggering mechanism (ETM) is as follows:

[0136]

[0137] In the formula, It is a transmission instant sequence that satisfies the ETM condition. and It is an integer sequence; h is the sampling interval of the sensor.

[0138] Φ is the threshold parameter, and Φ is the positive definite weighting matrix to be designed.

[0139] The commonly used periodic triggering mechanism periodically samples the system state at a constant sampling interval and transmits the sampled data packets to the controller. Compared with the traditional triggering mechanism, it only triggers when the state variable meets the specific condition in (6), thereby reducing the communication burden and saving communication bandwidth. However, when the system is stable, a lot of sampled data is still transmitted through the communication network. Therefore, this invention proposes an adaptive event triggering mechanism (AMETM) with memory characteristics.

[0140]

[0141] The most important aspect of this invention is the introduction of an adaptive threshold parameter σ(i) l h)>0 is implemented as follows:

[0142]

[0143] In the formula, σ∈(0,1) are the upper and lower limits of the adaptive threshold.

[0144] Note 3: If α(t) = 0 and υ = 0, then the AMETM proposed in this invention can be simplified to ETM. Compared with the constant threshold parameter in equation (6), σ(i) is introduced in equation (7). l h), σ(i) l h) can be adaptively adjusted based on system fluctuations. When the system is unstable, it can be adjusted to a smaller σ(i) accordingly. l h), to obtain a higher communication frequency. Similarly, when the system fluctuation is small, σ(i) l h) is increased, causing the transmission frequency to decrease. This is achieved by adjusting σ(i). l The value of h) can further reduce the occupancy rate of communication channels and save computer resources.

[0145] 4. DoS attack mode

[0146] This invention addresses a type of DoS attack that disrupts digital communication in a network, specifically a non-periodic interference signal. The trigger signal for its channel is as follows:

[0147]

[0148] in, This represents the current DoS attack cycle number; T>0 indicates the DoS attack's action cycle. X represents the length of the "sleep" period of the DoS attack in the nth action cycle. DoS (t) = 1 indicates that the system has suffered a DoS attack, X DoS (t) = 0 indicates that the system has not been subjected to a DoS attack.

[0149] This invention defines two DoS attack states as follows:

[0150] This represents the "sleep" cycle of the nth action cycle;

[0151] This represents the "attack" cycle of the nth action cycle.

[0152] if The sampled control data can be successfully transmitted to the controller, and the actuator's controller input u(t) will be updated in a timely manner. Otherwise, the transmission will be rejected, and the update of the controller input will be blocked.

[0153] When a non-periodic DoS attack is activated, the event is triggered momentarily as follows:

[0154]

[0155] In the formula, t k,n h represents the k-th transmission instant in the n-th interval. The value of k represents the number of trigger instants within the n-th cycle of the DoS attack. n It is the maximum number of instants triggered within the interval of the nth DoS attack.

[0156] 5. Proposed Control Methods

[0157] To integrate AMETM and the consideration of DoS attacks into the PID controller, this invention proposes a new controller design method.

[0158] When s = 1, ..., k n -1, When, define T s,n =[t s,n h,t s+1,n h) represents the holding interval of the zero-order hold (ZOH). Under a DoS attack, the control input can be written as:

[0159]

[0160] In the formula, This indicates the holding interval of the zero-order hold (ZOH). in:

[0161]

[0162] It can be divided into several sub-intervals as follows:

[0163]

[0164] in It can be represented as:

[0165]

[0166] for Indicate i l,n h = t k,n h+lh,η0(t)=ti l,n h,η(t)=η0(t)+α(t). The time-varying delay η(t) satisfies:

[0167]

[0168] At the same time, according to the definition: e k,n (t)=x(i l,n h)-x(t k,n h) and e k,n (t-α(t))=x(i l,n h-α(t))-x(t k,n h-α(t)), the controller input formula (11) can be modified as follows:

[0169]

[0170] Substituting equation (16) into equation (4) based on the above description, the present invention can obtain the desired dynamic model of the multi-region interconnected power system of the DFIG wind farm based on AMETM, which can be formalized as:

[0171]

[0172] y(t) = Cx(t).

[0173] The gain expression is solved as follows:

[0174]

[0175] The gain K is obtained using relation (18) and the YALMIP toolbox in MATLAB. P K I and K D Then, the gain can be substituted into the previously established system model to verify that the controller designed in this invention is effective.

[0176] like Figure 3As shown, when subjected to a DoS network attack, the controller can achieve H∞ exponential stability of the multi-region interconnected DFIG power system, thus achieving the energy balance objective in the system's control goals.

[0177] Figure 4 This indicates the non-periodic nature of DoS attacks; each state variable of a DoS attack changes as it switches between sleep and attack intervals.

[0178] like Figure 5 As shown, the transmission of sampled data will occur once at the beginning of each action interval, preceded by a relatively long release interval. Simultaneously, a large amount of sampled data will be transmitted after this point.

[0179] The wind power system controller for responding to DoS network attacks provided in this embodiment of the invention includes:

[0180] The load frequency control module is used to adjust the system frequency to reach the rated value or maintain the area tie line switching power at the planned value, thereby realizing load frequency control.

[0181] The wind power system modeling module is used to establish a simplified power system with voltage and flux linkage equations and a DFIG-based wind farm, thereby constructing a wind power system model.

[0182] An adaptive event triggering module is used to introduce an adaptive threshold parameter σ(i) l h)>0, thereby constructing an adaptive event triggering mechanism with memory characteristics;

[0183] The controller design module is used to determine the DoS attack mode and design a new controller based on DoS attacks caused by aperiodic interference signals that block digital communication.

[0184] Due to its dispersed distribution and intermittent output, wind power cannot be generated in a centralized, large-scale manner like traditional energy sources. To overcome this problem, it is necessary to rebuild the power system, and thus multi-regional interconnected wind power systems have emerged.

[0185] Multi-regional interconnected wind power systems are extremely large and complex operating systems, often relying on communication networks to process vast and dispersed energy control information. Ensuring stable system operation and preventing DoS attacks is therefore crucial. Traditional controllers are prone to vulnerabilities when controlling such complex wind power systems, making them vulnerable to DoS attacks, consuming significant computer resources, causing control system instability, and resulting in substantial economic losses. However, system controllers employing an Adaptive Event Triggering Mechanism (AMETM) with memory characteristics can utilize historical states and adaptive thresholds to continuously adjust system states, maintaining stability and effectively controlling the smooth and efficient operation of the power system. AMETM also plays a positive role in improving bandwidth utilization and efficiently using computer resources. Whether adjusting sampled data or responding to network attacks, AMETM helps the power system rationally utilize bandwidth, conserve computer resources, and ensure the high-efficiency operation of the entire interconnected power system's communication network. Meanwhile, the application of AMETM in the control of interconnected wind power systems can further improve the controllability of the power system. Its memory and adaptive characteristics enable reasonable sampling and transmission of relevant information data, which can effectively control various risks and problems in the communication network of the power system. This helps the wind power system to control the risks of the communication network within a reasonable range, which is very beneficial for the control of the wind power system and the reduction of the degree of risk.

[0186] Given AMETM's numerous advantages in the control of interconnected wind power systems, wind power companies should pay close attention to the application of its controllers. Meanwhile, interconnected wind power systems demand high levels of technical expertise and operational skills from personnel. This necessitates wind power companies prioritizing the training of professional technicians, improving their knowledge base and operational proficiency. This will enable technicians to promptly identify and resolve network threats to the power system, thereby ensuring comprehensive management and control of the entire interconnected wind power system and further promoting power security.

[0187] The parameters of the state-space expression (4) of the wind farm system based on DFIG are shown in the following table:

[0188]

[0189] The AMETM proposed in this invention is shown in (7). Compared with the general event triggering mechanism, AMETM (7) introduces a dynamic threshold parameter σ(i). l h) and memory parameter α(t).

[0190] Introducing dynamic threshold parameter σ(i) l The case of h) is as follows:

[0191] According to (8), σ(i) lh) It dynamically adjusts according to changes in error to save transmission resources. Figure 6 In this example, we set σ to be dynamically adjusted between [0.02, 0.1]. It can be seen that when the state fluctuations are large and the system error is large, σ(i) l h) is small and constantly changing; when the system tends to stabilize and the system error is small, σ(i) l h) tends to the upper bound of σ and remains constant.

[0192] In addition, we Figure 7 In the middle, σ is set to 0.04 to reflect σ(i l h) Impact on data sampling. As shown in the figure, the number of triggers increases significantly, and the settling time becomes longer, reflecting a deterioration in system stability.

[0193] The case of introducing the memory parameter α(t) is as follows: The memory parameter α(t) introduces a time delay into the dynamic matrix time model. Compared with ETM(6), the existence of historical states allows the system (4) to achieve stability more effectively. To prove this, the memory parameter α(t) is removed, and the system state response is as follows. Figure 8 As shown in the figure. In this case, the system has a long settling time and poor stability performance.

[0194] Combining the two scenarios above, it can be verified that the proposed AMETM can make DFIG-based wind power systems more stable and reduce transmission resource waste.

[0195] It should be noted that embodiments of the present invention can be implemented in hardware, software, or a combination of both. The hardware portion can be implemented using dedicated logic; the software portion can be stored in memory and executed by a suitable instruction execution system, such as a microprocessor or dedicated-design hardware. Those skilled in the art will understand that the above-described devices and methods can be implemented using computer-executable instructions and / or included in processor control code, for example, such code provided on a carrier medium such as a disk, CD, or DVD-ROM, a programmable memory such as read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The devices and modules of the present invention can be implemented by hardware circuitry such as very large-scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field-programmable gate arrays, programmable logic devices, etc., or by software executed by various types of processors, or by a combination of the above-described hardware circuitry and software, such as firmware.

[0196] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications, equivalent substitutions, and improvements made by those skilled in the art within the scope of the technology disclosed in the present invention, and within the spirit and principles of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A wind power system control method for responding to DoS network attacks, characterized in that, The wind power system control method for responding to DoS network attacks includes: A novel adaptive event triggering mechanism algorithm with memory characteristics is constructed. When a hacker launches a DoS attack on the interconnected wind power system through the communication network, the controller signal is not updated and the communication triggering mechanism does not generate a new trigger moment. However, when the DoS attack interference signal is turned off, the value of the state variable changes, the introduced adaptive threshold is dynamically adjusted with the error, and the memory parameter introduces a time delay to the triggering mechanism. The wind power system control method for responding to DoS network attacks includes the following steps: Step 1: Adjust the system frequency and control the load frequency; Step 2: Establish a DFIG-based wind farm and perform wind power system modeling; Step 3: Construct an adaptive event triggering mechanism with memory characteristics; Step 4: Determine the DoS attack pattern and design a new controller; The determination of the DoS attack mode in step four includes: Two DoS attack states are defined as follows: This represents the sleep cycle of the nth action cycle; This represents the attack cycle of the nth action cycle; if If the sampled control data is successfully transmitted to the controller, the controller input u(t) of the actuator is updated in a timely manner; otherwise, the transmission will be rejected and the update of the controller input will be blocked. A new controller design method is proposed by combining AMETM and consideration of DoS attacks with PID controllers. A dynamic model of the multi-regional interconnected power system of the DFIG wind farm based on AMETM is constructed; the gain K is obtained through relational expressions and the YALMIP toolbox in MATLAB. P K I and K D Then, the gain is substituted into the established system model to verify the effectiveness of the designed controller.

2. The wind power system control method for responding to DoS network attacks as described in claim 1, characterized in that, The load frequency control in step one is a control method that adjusts the system frequency to reach the rated value or maintains the regional tie-line switching power at the planned value. When the active power generation and consumption in the system are unbalanced, a frequency deviation Δf is caused. The load characteristics and the active power changes generated by the generator's rotating energy absorb Δf, which is called the static frequency characteristic K of the load. D When Δf exceeds the governor dead zone, the generator set adjusts its output according to its respective droop rate R to suppress the frequency deviation Δf, which is called the generator's static frequency characteristic K. G The system reaches a new equilibrium, achieving a single frequency modulation. In secondary frequency regulation, the control deviation ACE of each region is calculated. Each region obtains the adjustment amount based on the load frequency controller. The generator output is adjusted by adjusting the adjustment amount to restore the frequency and tie-line exchange power to the specified value, achieving minute-level control. When the interconnected power system is running, primary and secondary frequency regulation are interconnected. The change in active power output of the generator at any time is the sum of the active power output of primary and secondary frequency regulation. Load frequency control controls the control deviation ACE of the control region through secondary frequency regulation to achieve dynamic stability of the system.

3. The wind power system control method for responding to DoS network attacks as described in claim 1, characterized in that, The wind power system modeling in step two includes: The simplified power system, with the voltage and flux linkage equations established, is as follows: In the formula, R is the resistance, L represents the generator's self-inductance and mutual inductance, and i represents the stator and rotor currents dq-axis, respectively. ds i qs and i dr i qr V ds and V qs The dq axis represents the stator voltage, V dr and V qr The d and q axes represent the rotor voltage, respectively; s represents the rotor slip, and w represents the rotor voltage. s This represents the air gap magnetic flux density of the generator; the subscripts s and r represent the stator and rotor, respectively. The wind farm equations based on DFIG are established as follows: In the formula, X2=[1 / R r ], T1=[L0 / (w s R s )],X3=(L m / L ss M w For the constant inertia of the wind turbine, L ss =L s +L m L0 = [L rr +(L 2 m / L ss )], L rr =L r +L m L r L m Indicates the stator, rotor, and magnetizing inductor; A dynamic model of the i-th control region of a wind farm based on DFIG is established. Based on the logical relationship between the transfer function and variables, the following equation is derived: Define new state variables and obtain the state-space expression of a DFIG-type wind power generation system with interconnected multiple regions: Memory-based PID controller: In the formula, Kp represents the gain coefficient of the proportional controller, K I K represents the gain coefficient of the integral controller. D Let represent the gain coefficient of the integral-differential controller; introduce a memory parameter 0≤a(t)≤a in the controller, and the derivative satisfies the following inequality: 0≤μ1≤à(t)≤μ2.

4. The wind power system control method for responding to DoS network attacks as described in claim 1, characterized in that, The construction of the adaptive event triggering mechanism with memory characteristics in step three includes: The typical event triggering mechanism (ETM) is as follows: In the formula, It is a transmission instant sequence that satisfies the ETM condition; and It is an integer sequence; h is the sampling interval of the sensor; Φ is the threshold parameter, and Φ is the positive definite weighting matrix to be designed. Constructing an adaptive event triggering mechanism AMETM with memory properties: In the formula, an adaptive threshold parameter σ(i) is introduced. l h)>0, implemented as follows: In the formula, σ∈(0,1) are the upper and lower limits of the adaptive threshold; If α(t) = 0 and υ = 0, the adaptive event-triggered mechanism AMETM simplifies to ETM; σ(i) is introduced. l h), σ(i) l h) Adaptively adjust based on system fluctuations; when the system is unstable, adjust to a smaller σ(i) l h) to obtain a higher communication frequency; when system fluctuations are small, σ(i) l h) is increased, and the transmission frequency is reduced.

5. The wind power system control method for responding to DoS network attacks as described in claim 1, characterized in that, The determination of the DoS attack mode in step four includes: DoS attacks based on aperiodic interference signals that disrupt digital communication in a network, with the following channel trigger signals: in, This represents the current DoS attack cycle number; T>0 indicates the DoS attack action cycle. X represents the length of the sleep period of a DoS attack in the nth action cycle; DoS (t) = 1 indicates that the system has suffered a DoS attack, X DoS (t) = 0 indicates that the system has not suffered a DoS attack; Two DoS attack states are defined as follows: This represents the sleep cycle of the nth action cycle; This represents the attack cycle of the nth action cycle; if If the sampled control data is successfully transmitted to the controller, the controller input u(t) of the actuator is updated in a timely manner; otherwise, the transmission will be rejected and the update of the controller input will be blocked. When a non-periodic DoS attack is activated, the event is triggered momentarily as follows: In the formula, t k,n h represents the k-th transmission instant in the n-th interval; the value of k represents the number of trigger instants within the n-th period of the DoS attack; k n It is the maximum number of instantaneous events triggered within the interval of the nth DoS attack; A new controller design method is proposed by combining AMETM and consideration of DoS attacks with PID controllers. When s = 1, ..., k n -1, When, define T s,n =[t s,n h,t s+1,n h), representing the holding interval of the zero-order hold (ZOH); under a DoS attack, the control input is written as: In the formula, Indicates the holding interval of the zero-order hold; middle: Divided into the following sub-intervals: in Represented as: for Indicate i l,n h = t k,n h+lh,η0(t)=ti l,n h,η(t)=η0(t)+α(t); the time-varying delay η(t) satisfies: According to the definition: e k,n (t)=x(i l,n h)-x(t k,n h) and e k,n (t-α(t))=x(i l,n h-α(t))-x(t k,n h-α(t)), the controller input is modified to: The dynamic model of the multi-regional interconnected power system of the DFIG wind farm based on AMETM is obtained and formalized as follows: y(t) = Cx(t). The gain expression is solved as follows: The gain K is obtained using relational expressions and the YALMIP toolbox in MATLAB. P K I and K D Then, the gain is substituted into the established system model to verify the effectiveness of the designed controller.

6. A wind power system controller for responding to DoS network attacks, employing the wind power system control method for responding to DoS network attacks as described in any one of claims 1 to 5, characterized in that, The wind power system controller for responding to DoS network attacks includes: The load frequency control module is used to adjust the system frequency to reach the rated value or maintain the area tie line switching power at the planned value, thereby realizing load frequency control. The wind power system modeling module is used to establish a simplified power system with voltage and flux linkage equations and a DFIG-based wind farm, thereby constructing a wind power system model. An adaptive event triggering module is used to introduce an adaptive threshold parameter σ(i) l h)>0, thereby constructing an adaptive event triggering mechanism with memory characteristics; The controller design module is used to determine the DoS attack mode and design a new controller based on DoS attacks caused by aperiodic interference signals that block digital communication.

7. A computer device, characterized in that, The computer device includes a memory and a processor. The memory stores a computer program, which, when executed by the processor, causes the processor to perform the steps of the wind power system control method for responding to DoS network attacks as described in any one of claims 1 to 5.

8. A computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the steps of the wind power system control method for responding to DoS network attacks as described in any one of claims 1 to 5.

9. An information data processing terminal, characterized in that, The information data processing terminal is used to implement the wind power system controller for responding to DoS network attacks as described in claim 6.

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