CN114244605B - Load frequency control method and system considering network attack and time-varying delay - Google Patents
Load frequency control method and system considering network attack and time-varying delay Download PDFInfo
- Publication number
- CN114244605B CN114244605B CN202111543191.2A CN202111543191A CN114244605B CN 114244605 B CN114244605 B CN 114244605B CN 202111543191 A CN202111543191 A CN 202111543191A CN 114244605 B CN114244605 B CN 114244605B
- Authority
- CN
- China
- Prior art keywords
- control
- deviation
- attack
- power system
- controller
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L63/00—Network architectures or network communication protocols for network security
- H04L63/14—Network architectures or network communication protocols for network security for detecting or protecting against malicious traffic
- H04L63/1441—Countermeasures against malicious traffic
- H04L63/1466—Active attacks involving interception, injection, modification, spoofing of data unit addresses, e.g. hijacking, packet injection or TCP sequence number attacks
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
- H02J13/00004—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by the power network being locally controlled
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L63/00—Network architectures or network communication protocols for network security
- H04L63/14—Network architectures or network communication protocols for network security for detecting or protecting against malicious traffic
- H04L63/1441—Countermeasures against malicious traffic
- H04L63/1458—Denial of Service
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L63/00—Network architectures or network communication protocols for network security
- H04L63/20—Network architectures or network communication protocols for network security for managing network security; network security policies in general
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S40/00—Systems for electrical power generation, transmission, distribution or end-user application management characterised by the use of communication or information technologies, or communication or information technology specific aspects supporting them
- Y04S40/20—Information technology specific aspects, e.g. CAD, simulation, modelling, system security
Landscapes
- Engineering & Computer Science (AREA)
- Computer Security & Cryptography (AREA)
- Computer Hardware Design (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Power Engineering (AREA)
- Feedback Control In General (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The invention relates to a load frequency control method and a load frequency control system considering network attack and time-varying delay. The method comprises the following characteristics: carrying out region division on the power system; constructing a load frequency control model according to the electric signals in each control area and corresponding transmission data; constructing a controller of the power system according to the load frequency control model; obtaining control input considering false data injection attack and denial of service attack; determining a dynamic state space model of the power system according to a control input considering a false data injection attack and a denial of service attack and a controller; and determining parameters of the dynamic state space model by using a Lyapunov stability theory and a linear matrix inequality, and further performing load frequency control on the power system by using the dynamic state space model with the determined parameters. The invention realizes the accurate control of the load frequency control under the influence of network attack and time-varying time lag.
Description
Technical Field
The invention relates to the technical field of load frequency control of power systems, in particular to a load frequency control method and system considering network attack and time-varying delay.
Background
The introduction of information and communication technology has led to the further development of the power grid into Cyber Physical Systems (CPS). Load Frequency Control (LFC) plays a role in adjusting system Frequency and tie line power in the CPS, and is important to stable operation and Control of the system. In the LFC scheme, the frequency and power measurements of the generators within each control area are transmitted to a control center over a communications network. However, LFCs are compromised by the wide spread of time delays and network attacks in communication networks.
In many physical processes, time delays are widely present in signal transmission, measurement and control. The delay is inherently LFC in nature, and when the measurement signal is transmitted in the communication channel, it inevitably introduces time delay and affects the behavior of the whole power system, and a delay exceeding the delay margin leads to an unstable frequency response. While there is a constant delay in conventional dedicated communication channels, future use of open communication networks will introduce time-varying delays, and the control of the system is also more challenging.
The vulnerability of information transmission makes the measured value possibly be tampered, various network attacks are injected into a transmission channel of the LFC system, and a control strategy based on error information influences the stable operation of a power system, so that the harmfulness of the network attacks on the LFC is not negligible. Among the complex network attacks, false Data Injection Attack (FDIA) and denial of service (DoS) attacks are two practical and common Attack behaviors for power systems, and especially FDIA behavior concealment is not easy to detect; in addition, the randomness of the FDIA and DoS attacks further threatens the stability control of the system.
In order to stabilize LFC systems, a lot of research has been conducted on time delay and controller design of power systems under attack. For the problem of time delay, jiang et al have studied the time-lag stability with invariable and time-varying delay in the LFC scheme; xia et al propose a disturbance estimation method for designing a power system load frequency controller for random time lags; lou et al developed a mechanism to evaluate and mitigate the effects of latency; zhai et al consider the time-varying delay problem in developing a finite time control method. In summary, much of the existing research has focused on the design of various controllers with constant delay, time varying delay, and random delay. For power system network attacks, fang et al have studied power system controller designs with DoS attacks and spoofing attacks; pang et al studied the event-triggered control of LFC under malicious DoS attacks; deng et al propose a distributed elastic control method for CPS under DoS attack; chen et al devised a distributed load frequency controller and dynamic event triggering mechanism to resist a degree of DoS and spoofing attacks. Therefore, the existing work basically considers the robust controller of the LFC scheme under the network attack.
However, the above LFC scheme researches only one-sided on the influence of delay or network attack on the system, and lacks comprehensive consideration. The actual power system is complex and there is a possibility that a delay and a network attack may occur simultaneously, which has prompted the present invention. It needs to be supplemented that most of the current LFC scheme researches for time delay focus on single-delay power system stability analysis and controller design, leaving technical gaps of multiple time-varying delay situations; furthermore, most of the existing studies are dedicated to the detection and evaluation of FDIA, lacking a study reference in stability analysis.
Disclosure of Invention
The invention aims to provide a load frequency control method and a load frequency control system considering network attack and time-varying time delay, and the accurate control of the load frequency control under the influence of the network attack and the time-varying time delay is realized.
In order to achieve the purpose, the invention provides the following scheme:
a load frequency control method considering network attacks and time-varying delay comprises the following steps:
carrying out region division on the power system; acquiring an electric signal in each control area by using a sensor and acquiring transmission data in each control area by using a phasor measurement unit;
constructing a load frequency control model according to the electric signals in each control area and corresponding transmission data;
constructing a controller of the power system according to the load frequency control model;
obtaining a control input considering a false data injection attack and a denial of service attack;
determining a dynamic state space model of the power system from the control input and the controller that considers the spurious data injection attack and the denial of service attack;
and determining parameters of the dynamic state space model by utilizing a Lyapunov stability theory and a linear matrix inequality, and further performing load frequency control on the power system by utilizing the dynamic state space model with the determined parameters.
Optionally, the constructing a load frequency control model according to the electrical signal and the corresponding transmission data in each control region specifically includes the following formula:
wherein, Δ f i Indicating a frequency deviation, D i Representing the damping coefficient, M, of the generator i Representing generator moment of inertia, Δ P tie-i Representing the active power deviation, Δ P, of the link mi Representing the deviation, Δ P, of the mechanical power output of the generator di Representing load disturbance, Δ P vi Indicating the deviation of the valve position, T ij Representing the crossline synchronisation coefficient, T ti Representing the turbine time constant, T gi Representing the generator time constant, d j (t) denotes the time varying delay of the control input, ACE i Indicating the regional control deviation, beta i Representing a frequency offset factor, τ j (t) represents the remote signal time varying delay, t represents time, j represents a region not equal to i,
the derivative of the frequency deviation is indicated.
Optionally, the constructing a controller of the power system according to the load frequency control model specifically includes:
using formulas
Determining a controller of the power system;
where u (t) represents the control input vector, K/K Pi /K Ii All represent the PI controller gain, u i (t) represents a control input vector of the i-th zone, y (t) represents a measurement signal, y (t) = Cx (t), x (t) represents a state variable vector, the state variable vector is frequency deviation, tie-line active power deviation, generator mechanical power output deviation, valve position deviation and zone control deviation, and C represents a correlation matrix.
Optionally, the obtaining takes into account control inputs of a spurious data injection attack and a denial of service attack, and specifically includes:
using formulas
Determining a control input that accounts for spurious data injection attacks and denial of service attacks;
where β (t) is a bernoulli random variable, f (y (t)) represents an attack vector constructor, and K represents a controller gain matrix.
Optionally, the determining a dynamic state space model of the power system according to the control input considering the spurious data injection attack and the denial of service attack and the controller specifically includes:
using a formula
Determining a dynamic state space model;
wherein A, B and A dj 、B dj E each represents a correlation matrix, n represents the number of control regions, and τ j (t) and d j (t) each represents a time-varying delay, ψ (t) represents an initial state variable vector, one continuous vector when t ∈ - τ,0, and w (t) represents an external disturbance input.
A load frequency control system that accounts for network attacks and time-varying delays, comprising:
the data acquisition module is used for carrying out region division on the power system; acquiring an electric signal in each control area by using a sensor and acquiring transmission data in each control area by using a phasor measurement unit;
the load frequency control model building module is used for building a load frequency control model according to the electric signals in each control area and corresponding transmission data;
the controller building module of the power system is used for building a controller of the power system according to the load frequency control model;
the actual control input acquisition module is used for acquiring control input considering false data injection attack and denial of service attack;
a dynamic state space model determination module for determining a dynamic state space model of the power system according to a control input considering a false data injection attack and a denial of service attack and the controller;
and the load frequency control module is used for determining the parameters of the dynamic state space model by utilizing the Lyapunov stability theory and the linear matrix inequality, and further performing load frequency control on the power system by utilizing the dynamic state space model with the determined parameters.
Optionally, the load frequency control model building module specifically includes the following formula:
wherein, Δ f i Represents a frequency deviation, D i Representing the damping coefficient, M, of the generator i Representing generator moment of inertia, Δ P tie-i Representing the active power deviation, Δ P, of the link mi Representing the deviation, Δ P, of the mechanical power output of the generator di Representing load disturbance, Δ P vi Indicating the deviation of the valve position, T ij Representing the link synchronization coefficient, T ti Representing the turbine time constant, T gi Representing the generator time constant, d j (t) represents the time varying delay of the control input, ACE i Indicating the regional control deviation, beta i Representing a frequency offset factor, τ j (t) represents the time-varying delay of the remote signal, t represents time, j represents a region unequal to i,
the derivative of the frequency deviation is indicated.
Optionally, the controller building module of the power system specifically includes:
a controller building unit of the power system for utilizing the formula
Determining a controller of the power system;
where u (t) represents the control input vector, K/K Pi /K Ii All represent the PI controller gain, u i (t) control of the i-th areaThe system comprises an input vector, y (t) represents a measurement signal, y (t) = Cx (t), x (t) represents a state variable vector, the state variable vector comprises frequency deviation, tie line active power deviation, generator mechanical power output deviation, valve position deviation and zone control deviation, and C represents a correlation matrix.
Optionally, the actual control input obtaining module specifically includes:
an actual control input determination unit for using a formula
Determining a control input that accounts for spurious data injection attacks and denial of service attacks;
where β (t) is a bernoulli random variable, f (y (t)) represents an attack vector constructor, and K represents a controller gain matrix.
Optionally, the dynamic state space model determining module specifically includes:
a dynamic state space model determination unit for utilizing a formula
Determining a dynamic state space model;
wherein, A, B and A dj 、B dj E represents a correlation matrix, n represents the number of control regions, and τ j (t) and d j (t) each represents a time-varying delay, ψ (t) represents an initial state variable vector, one continuous vector when t ∈ - τ,0, and w (t) represents an external disturbance input.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
the load frequency control method and the system considering network attack and time-varying delay, provided by the invention, are used for carrying out regional division on a power system; acquiring an electric signal in each control area by using a sensor and acquiring transmission data in each control area by using a phasor measurement unit; constructing a load frequency control model according to the electric signals in each control area and corresponding transmission data; constructing a controller of the power system according to the load frequency control model; obtaining a control input considering a false data injection attack and a denial of service attack; determining a dynamic state space model of the power system according to a control input considering a false data injection attack and a denial of service attack and a controller; and determining parameters of the dynamic state space model by utilizing a Lyapunov stability theory and a linear matrix inequality, and further performing load frequency control on the power system by utilizing the dynamic state space model with the determined parameters. Analyzing a state space equation aiming at the influence of the concealment, randomness and uncertainty of network attack and multi-time-varying delay on the stability of a system; and secondly, parameters of the controller are obtained by utilizing a Lyapunov stability theory and a Linear Matrix Inequality (LMI), so that the exponential mean square stability of a closed-loop system under the specified performance is ensured, and the accurate control of the LFC scheme under the influence of network attack and time-varying time lag is realized.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
Fig. 1 is a schematic flow chart of a load frequency control method considering network attack and time-varying delay according to the present invention;
fig. 2 is a two-area four-machine power system LFC control scheme including communication delay and network attack according to the present invention.
Fig. 3 is a dynamic model of the ith control region of the multi-region LFC scheme of the present invention.
FIG. 4 shows the values of the Bernoulli random variable β (t) and the DoS attack signature in the present invention.
FIG. 5 is a pole-zero distribution plot for four control schemes of the present invention.
FIG. 6 is a distribution diagram of pole-zero distributions for four control schemes using the Pade' approximant model, according to the present invention.
Fig. 7 is a graph of frequency deviation for four control schemes according to the present invention.
Fig. 8 is a plot of the active power deviation of the tie line for four control schemes of the present invention.
FIG. 9 is a graph of the deviation of the mechanical output power of the generator for four control schemes in accordance with the present invention
Fig. 10 is a graph of valve position deviation for four control schemes according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention aims to provide a load frequency control method and a load frequency control system considering network attack and time-varying time delay, and the accurate control of the load frequency control under the influence of the network attack and the time-varying time delay is realized.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
Fig. 1 is a schematic flow chart of a load frequency control method considering network attack and time-varying delay provided by the present invention, and as shown in fig. 1, the load frequency control method considering network attack and time-varying delay provided by the present invention includes:
s101, carrying out region division on the power system; acquiring an electric signal in each control area by using a sensor and acquiring transmission data in each control area by using a phasor measurement unit; the sensor uploads collected signals (such as frequency, active power and the like) to local control, the phasor measurement unit uploads collected data to a control center through a communication network, and the control center transmits an instruction to the motor;
s102, constructing a load frequency control model according to the electric signals in each control area and corresponding transmission data;
s102 specifically includes the following formula:
wherein, Δ f i Represents a frequency deviation, D i Representing the damping coefficient, M, of the generator i Representing generator moment of inertia, Δ P tie-i Representing the active power deviation, Δ P, of the link mi Representing the deviation, Δ P, of the mechanical power output of the generator di Representing load disturbance, Δ P vi Indicating the deviation of the valve position, T ij Representing the crossline synchronisation coefficient, T ti Representing the turbine time constant, T gi Representing the generator time constant, d j (t) denotes the time varying delay of the control input, ACE i Indicating the regional control deviation, beta i Representing a frequency offset factor, τ j (t) represents the time-varying delay of the remote signal, t represents time, j represents a region unequal to i,
the derivative of the frequency deviation is indicated.
S103, constructing a controller of the power system according to the load frequency control model;
s103 specifically comprises the following steps:
using formulas
Determining a controller of the power system;
wherein u (t) represents the control input vector, K/K Pi /K Ii All represent the PI controller gain, u i (t) represents a control input vector of the i-th zone, y (t) represents a measurement signal, y (t) = Cx (t), x (t) represents a state variable vector, the state variable vector is frequency deviation, tie line active power deviation, generator mechanical power output deviation, valve position deviation and zone control deviation, and C represents a correlation matrix.
Let x i (t)＝[Δ fi ΔP tie-i ΔP mi ΔP vi ∫ ACE i ] T Then, then
Wherein r is i In order to reduce the speed of the vehicle,
A＝[A ij ] n×n ，B＝diag{B 1 ，B 2 ，...，B n }，C＝diag{C 1 ，C 2 ，...，C n }，E＝diag{E 1 ，E 2 ，...，E n }；
u(t)＝[u 1 (t) u 2 (t) ... u n (t)] T ；
K＝diag{K 1 ，K 2 ，...，K n }，K i ＝[K Pi K Ii ]；
s104, acquiring control input considering false data injection attack and denial of service attack;
s104 specifically comprises the following steps:
using formulas
Determining a control input that accounts for a false data injection attack and a denial of service attack;
wherein, beta (t) is Bernoulli random variable, f (y (t)) represents attack vector constructor, and K represents controller gain matrix.
FDIA is initiated by an attack vector construction algorithm that injects false data into the attack area in the power system with incomplete network information. The measurement signal is y (t), and the malicious measurement signal can be expressed as a nonlinear function f (y (t)), wherein f (·) represents an attack vector construction algorithm. The input of the algorithm comes from the phasor measurement unit and the output of the algorithm is submitted to the control center. The network attack can be launched in a continuous or random mode to obtain various attack effects, and a Bernoulli random variable is introduced into the attack, so that the statistical property of the attack is ensured. Thus, the measurements submitted to the controller are as follows:
the bernoulli random variable β (t) embeds the random features of FDIA. When β (t) =1, an attack occurs,
when β (t) =0, the attack has not occurred, the controller receives the correct information,
DoS attack: the invention adopts the periodic DoS attack which is initiated by a periodic interference signal and can be expressed as follows:
the random nature of DoS attacks comes from the sleep time of the jammer. When T ∈ [ T ] on ，T off ]When the attack occurs, the controller receives:
when in use
When the attack does not occur, the information of the controller is correctly received,
and (3) establishing a dynamic state space model by considering the characteristics of FDIA, doS attack and multi-time-varying delay.
S105, determining a dynamic state space model of the power system according to the control input considering the false data injection attack and the denial of service attack and the controller;
s105 specifically comprises the following steps:
using a formula
Determining a dynamic state space model;
wherein, A and B、A dj 、B dj E represents a correlation matrix, n represents the number of control regions, and τ j (t) and d j (t) each represents a time-varying delay, ψ (t) represents an initial state variable vector, one continuous vector when t ∈ - τ,0, and w (t) represents an external disturbance input.
τ j (t) and d j (t) satisfies the following conditions, respectively:
τ and d each represents τ j (t) and d j (ii) an upper bound of (t),
respectively represent tau j (t) and d j Rate of change of (t), μ j 、λ j Respectively represent t j (t)、
The upper bound, and the rate of change are all constants.
And S106, determining parameters of the dynamic state space model by utilizing the Lyapunov stability theory and the linear matrix inequality, and further performing load frequency control on the power system by utilizing the dynamic state space model with the determined parameters.
Fig. 2 is a real LFC power system considering network attack and communication delay, which is a two-Area (Area 1, area 2) four-motor (G1, G2, G3, G4) power system, and it includes an overall control architecture combining local control and remote control; the sensors upload collected signals (such as frequency, active power and the like) to a local controller, the phasor measurement unit uploads collected data to a control center through a communication network, the control center transmits commands to the motor, and network attack and time delay occur in the process of remote control communication data transmission. In the multi-zone LFC scheme proposed by the present invention, the dynamic control model of the ith control zone is shown in fig. 3, where j represents a zone unequal to i.
The specific derivation process is as follows:
firstly, defining a Lyapunov functional, and establishing an exponential stability condition by using a theorem 1; transforming the inequality into the form of a linear matrix inequality using lemma 2; verify that the system has H according to lemma 3 ∞ Performance, thus yields the theoretical LMI (8). The required reasoning and theory 1 is now given.
Introduction 1: for a given scalar α > 0 and ε > 0, if
2, leading: for a given matrix
And S 12 If it is determined that
The following is concluded to be true:
(1)S 11 ＜0，
(2)S 22 ＜0，
and 3, introduction: for all non-zeros
And specifying a scalar γ > 0, if
Then the dynamic state space model under zero initial condition ψ (t) =0 has H ∞ And (4) performance.
And 4, introduction: for symmetric matrix
And full rank matrix
Its singular decomposition W = UW 0 V, wherein U T ＝U -1 ,V T ＝V -1 ，
Is a rectangular diagonal matrix with positive real numbers on the diagonals, there is a matrix X such that PW = WX, if and only if P has positive real numbers
In the form of (1), wherein
Theory 1: for a given scalar τ j ＞0，
δ＞0，μ j ＞0，λ j > 0, gamma > 0, controller gain matrix K and positive integer n, the dynamic state space model is exponential mean square stable and has H for any time varying delay and satisfying network attacks ∞ Performance, if there is a positive symmetric matrix P = P T ＞0，U＝U T ＞0，
And is provided with
wherein the content of the first and second substances,
Ψ 13 ＝[PB dj KC] 1×n ，
Ψ 16 ＝τδ(BKCF-BKC) T ，Ψ 17 ＝[τ j PA dj ] 1×n ，
Ψ 22 ＝diag{[-(1-μ j )Q j ]}，
Ψ 33 ＝diag{[-(1-λ j )R j ]}，
Ψ 77 ＝diag{[τ j P]}；
Theory 2: for a given scalar τ j ＞0,
δ＞0,μ j ＞0,λ j > 0, gamma > 0 and a positive integer n, the dynamic state space model is numerically mean square stable and has H for any time-varying delay and network attack ∞ Performance, if there is a positive symmetric matrix X = X T ＞0,
And matrix Y of suitable dimensions satisfies the following LMI:
wherein the content of the first and second substances,
compared with the prior art, the invention has the beneficial effects that:
(1) Three random factors affecting power system stability are considered and modeled, including multiple time-varying delays, covert FDIA, and malicious DoS attacks
(2) Time-varying delay, hidden FDIA and malicious DoS attacks are introduced into the LFC system, and a new delay attack multi-region power system model is constructed for research.
(3) On the basis of a new model, the model is obtained by utilizing the Lyapunov stability theory
H ∞ Of exponential stability of performanceAnd (4) carrying out sufficient conditions. The results show that the proposed LFC scheme is a significant improvement in both frequency and time domain.
The invention is explained in further detail below with reference to the drawings and examples:
s1, parameters of a three-region power system are shown in a table 1; the parameters required for the controller are shown in table 2. The total simulation duration is set to 10 seconds, and a disturbance w (t) with Δ t =0.2s is added from 1 second, assuming that w (t) is:
TABLE 1
TABLE 2
The initial conditions were:
ψ 1 (t)＝[0.02 -0.09 0.04 -0.05 0.008] T ；
ψ 2 (t)＝[-0.02 -0.04 0.05 -0.05 -0.01] T ；
ψ 3 (t)＝[0.04 -0.05 0.04 -0.015 0.015] T ；
DoS attack time in each period satisfies the following constraint:
under these conditions, the bernoulli variable β (t) and the DoS attack signal indicating the occurrence of FDIA are shown in fig. 4 (a) and (b), respectively.
S2, calculating the gain K = diag { K }of the controller 1 ，K 2 ，K 3 Therein of
K 1 ＝[-0.0466，-0.0076]，
K 2 ＝[-0.0656，-0.0095]，K 3 ＝[-0.0693，-0.0114]To demonstrate the effectiveness of the proposed controller, the LFC system zero-point diagram for the four control schemes is shown in fig. 5, compared to existing controllers. It can be seen that the poles of the scheme without the controller are greater than zero, indicating that the system without the controller is unstable due to network attacks and time delays. All poles of the first controller, the second controller and the proposed controller are less than zero, which indicates that the stability of the multi-zone power system can be ensured under the controller. Furthermore, enlarging the lower left corner of the picture, it can be seen that the pole of the proposed control scheme is closer to zero than the other two controllers and therefore also has better performance.
S3, to further analyze the performance of these controllers in the frequency domain, the time delay is approximated using the Pade approximation, and then the pole-zero of the system is recalculated, as shown in FIG. 6. It can be seen that most poles are close to zero after the pade approximation. However, the no control scheme still has some poles greater than zero, indicating that the no control system is unstable; enlarging the picture it can be seen that the performance of the controllers one and two is better than before, while the pole closest to zero is obtained by the proposed controller. In addition, 0-10s simulation data are decomposed by using a Prony method, and model damping and characteristic values s = -sigma +/-j omega are dominated d Coefficient and natural frequency ω n Prony result of (5). Therefore, all controllers suppress power oscillation, and the anti-interference performance is improved; due to sigma PC At the maximum, the proposed controller has the shortest transient time, i.e. has better control performance.
And S4, analyzing the time domain performance of the product in order to further verify the superiority of the product. The frequency derivatives Δ f for the three control regions are shown in fig. 7. It can be seen that due to network attacks and time delays, the system cannot be stable without control; in addition, the system is stable at 4-6s under controller two, while controller one and the proposed controlThe controller can be stable before 3s, and the overshoot of the proposed method is less than controller one. Active power deviation delta P of junctor of three control areas tie And the mechanical output deviation delta P of the generator m And valve position deviation Δ P v As shown in fig. 8, 9 and 10, respectively.
The invention provides a load frequency control system considering network attack and time-varying delay, which comprises:
the data acquisition module is used for carrying out region division on the power system; acquiring an electric signal in each control area by using a sensor and acquiring transmission data in each control area by using a phasor measurement unit;
the load frequency control model building module is used for building a load frequency control model according to the electric signals in each control area and the corresponding transmission data;
the controller construction module of the power system is used for constructing a controller of the power system according to the load frequency control model;
the actual control input acquisition module is used for acquiring control input considering false data injection attack and denial of service attack;
the dynamic state space model determining module is used for determining a dynamic state space model of the power system according to the control input considering the false data injection attack and the denial of service attack and the controller;
and the load frequency control module is used for determining the parameters of the dynamic state space model by utilizing the Lyapunov stability theory and the linear matrix inequality, and further performing load frequency control on the power system by utilizing the dynamic state space model with the determined parameters.
The load frequency control model building module specifically comprises the following formula:
wherein, Δ f i Indicating a frequency deviation, D i Representing the damping coefficient, M, of the generator i Representing generator moment of inertia, Δ P tie-i Representing the active power deviation, Δ P, of the link mi Representing the deviation, Δ P, of the mechanical power output of the generator di Representing load disturbance, Δ P vi Indicating the deviation of the valve position, T ij Representing the crossline synchronisation coefficient, T ti Representing the turbine time constant, T gi Representing the generator time constant, d j (t) denotes the time varying delay of the control input, ACE i Indicating the regional control deviation, beta i Representing the frequency offset factor, τ j (t) represents the time-varying delay of the remote signal, t represents time, j represents a region unequal to i,
the derivative of the frequency deviation is indicated.
The controller building module of the power system specifically comprises:
a controller building unit of the power system for utilizing the formula
Determining a controller of the power system;
where u (t) represents the control input vector, K/K Pi /K Ii All represent the PI controller gain, u i (t) control input vector for ith area, y (t) measurement signal, y: (a), (b), (c), (d), and (d)t) = Cx (t), x (t) denotes the state variable vector, which is the frequency deviation, the tie-line active power deviation, the generator mechanical power output deviation, the valve position deviation and the zone control deviation, C denotes the correlation matrix.
The actual control input acquisition module specifically comprises:
an actual control input determination unit for using a formula
Determining a control input that accounts for a false data injection attack and a denial of service attack;
where β (t) is a bernoulli random variable, f (y (t)) represents an attack vector constructor, and K represents a controller gain matrix.
The dynamic state space model determining module specifically includes:
a dynamic state space model determination unit for utilizing a formula
Determining a dynamic state space model;
wherein A, B and A dj 、B dj E represents a correlation matrix, n represents the number of control regions, and τ j (t) and d j (t) each represents a time-varying delay, ψ (t) represents an initial state variable vector, one continuous vector when t ∈ - τ,0, and w (t) represents an external disturbance input.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. For the system disclosed by the embodiment, the description is relatively simple because the system corresponds to the method disclosed by the embodiment, and the relevant points can be referred to the description of the method part.
The principle and the embodiment of the present invention are explained by applying specific examples, and the above description of the embodiments is only used to help understanding the method and the core idea of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.
Claims (8)
1. A load frequency control method considering network attack and time-varying delay is characterized by comprising the following steps:
carrying out region division on the power system; acquiring an electric signal in each control area by using a sensor and acquiring transmission data in each control area by using a phasor measurement unit;
constructing a load frequency control model according to the electric signals in each control area and corresponding transmission data;
constructing a controller of the power system according to the load frequency control model;
obtaining control input considering false data injection attack and denial of service attack;
determining a dynamic state space model of the power system according to a control input considering a false data injection attack and a denial of service attack and a controller;
determining parameters of a dynamic state space model by utilizing a Lyapunov stability theory and a linear matrix inequality, and further performing load frequency control on the power system by utilizing the dynamic state space model with the determined parameters;
the method for constructing the load frequency control model according to the electric signals in each control area and the corresponding transmission data specifically comprises the following formulas:
wherein, Δ f i Indicating a frequency deviation, D i Representing the damping coefficient, M, of the generator i Representing generator moment of inertia, Δ P tie-i Representing the active power deviation, Δ P, of the link mi Representing the deviation, Δ P, of the mechanical power output of the generator di Representing load disturbance, Δ P vi Indicating the deviation of the valve position, T ij Denotes the tie-line synchronization coefficient, t ti Representing the turbine time constant, T gi Representing the generator time constant, d j (t) denotes the time varying delay of the control input, ACE i Indicating the regional control deviation, beta i Representing a frequency offset factor, τ j (t) represents the time-varying delay of the remote signal, t represents time, j represents a region unequal to i,
the derivative of the frequency deviation is indicated.
2. The method for controlling the load frequency considering the cyber attack and the time-varying delay according to claim 1, wherein the constructing the controller of the power system according to the load frequency control model specifically includes:
using formulas
Determining a controller of the power system;
where u (t) represents the control input vector, K/K Pi /K Ii All represent the PI controller gain, u i (t) representing the i-th areaControl input vector, y (t) represents the measurement signal, y (t) = Cx (t), x (t) represents the state variable vector, which is the frequency deviation, the tie line active power deviation, the generator mechanical power output deviation, the valve position deviation and the zone control deviation, and C represents the correlation matrix.
3. The method as claimed in claim 2, wherein the obtaining of the control input considering the false data injection attack and the denial of service attack specifically includes:
using formulas
Determining a control input that accounts for a false data injection attack and a denial of service attack;
where β (t) is a bernoulli random variable, f (y (t)) represents an attack vector constructor, and K represents a controller gain matrix.
4. The method as claimed in claim 3, wherein the determining the dynamic state space model of the power system according to the control input considering the spurious data injection attack and the denial of service attack and the controller specifically comprises:
using formulas
Determining a dynamic state space model;
wherein A, B and A dj 、B dj E each represents a correlation matrix, n represents the number of control regions, and τ j (t) and d j (t) each represents a time-varying delay, ψ (t) represents an initial state variable vector, one continuous vector when t ∈ - τ,0, and w (t) represents an external disturbance input.
5. A load frequency control system considering network attacks and time-varying delays, comprising:
the data acquisition module is used for carrying out region division on the power system; acquiring an electric signal in each control area by using a sensor and acquiring transmission data in each control area by using a phasor measurement unit;
the load frequency control model building module is used for building a load frequency control model according to the electric signals in each control area and corresponding transmission data;
the controller building module of the power system is used for building a controller of the power system according to the load frequency control model;
the actual control input acquisition module is used for acquiring control input considering false data injection attack and denial of service attack;
the dynamic state space model determining module is used for determining a dynamic state space model of the power system according to the control input considering the false data injection attack and the denial of service attack and the controller;
the load frequency control module is used for determining parameters of the dynamic state space model by utilizing the Lyapunov stability theory and the linear matrix inequality, and further performing load frequency control on the power system by utilizing the dynamic state space model with the determined parameters;
the load frequency control model building module specifically comprises the following formula:
wherein, Δ f i Represents a frequency deviation, D i Representing the damping coefficient, M, of the generator i Representing generator moment of inertia, Δ P tie-i Represents the active power deviation, Δ P, of the tie line mi Representing the deviation, Δ P, of the mechanical power output of the generator di Representing load disturbance, Δ P vi Indicating the deviation of the valve position, T ij Representing the crossline synchronisation coefficient, T ti Representing the turbine time constant, T gi Representing the generator time constant, d j (t) represents the time varying delay of the control input, ACE i Indicating the regional control deviation, beta i Representing a frequency offset factor, τ j (t) represents the remote signal time varying delay, t represents time, j represents a region not equal to i,
the derivative of the frequency deviation is indicated.
6. The load frequency control system considering network attacks and time-varying delay according to claim 5, wherein the controller constructing module of the power system specifically comprises:
a controller building unit of the power system for utilizing the formula
Determining a controller of the power system;
wherein u (t) represents the control input vector, K/K Pi /K Ii All represent the PI controller gain, u i (t) represents a control input vector of the i-th zone, y (t) represents a measurement signal, y (t) = Cx (t), x (t) represents a state variable vector, and the state variable vector represents frequency deviation, active power deviation of a tie line, and generator machineryPower output deviation, valve position deviation, and zone control deviation, C represents the correlation matrix.
7. The system according to claim 6, wherein the actual control input obtaining module specifically includes:
an actual control input determination unit for utilizing the formula
Determining a control input that accounts for spurious data injection attacks and denial of service attacks;
where β (x) is a bernoulli random variable, f (y (t)) represents an attack vector constructor, and K represents a controller gain matrix.
8. The load frequency control system according to claim 7, wherein the dynamic state space model determining module specifically comprises:
a dynamic state space model determination unit for utilizing a formula
Determining a dynamic state space model;
wherein A, B and A dj 、B dj E represents a correlation matrix, n represents the number of control regions, and τ j (t) and d j (t) each represents a time-varying delay, ψ (t) represents an initial state variable vector, one continuous vector when t ∈ - τ,0, and w (t) represents an external disturbance input.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111543191.2A CN114244605B (en) | 2021-12-16 | 2021-12-16 | Load frequency control method and system considering network attack and time-varying delay |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111543191.2A CN114244605B (en) | 2021-12-16 | 2021-12-16 | Load frequency control method and system considering network attack and time-varying delay |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114244605A CN114244605A (en) | 2022-03-25 |
| CN114244605B true CN114244605B (en) | 2022-10-11 |
Family
ID=80757316
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111543191.2A Active CN114244605B (en) | 2021-12-16 | 2021-12-16 | Load frequency control method and system considering network attack and time-varying delay |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114244605B (en) |
Families Citing this family (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116455074B (en) * | 2023-04-19 | 2024-02-20 | 贵州电网有限责任公司 | Data processing method and device applied to power grid dispatching and electronic equipment |
| CN116760032A (en) * | 2023-08-18 | 2023-09-15 | 成都大学 | Design method for integral event triggering switching load frequency controller |
| CN117596077A (en) * | 2024-01-18 | 2024-02-23 | 国网冀北电力有限公司 | Method, device and equipment for detecting network attack of frequency control power system |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111988303A (en) * | 2020-08-17 | 2020-11-24 | 南京邮电大学 | False data injection attack detection method suitable for load frequency control of power system |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113315129B (en) * | 2021-04-27 | 2022-08-23 | 浙江工业大学 | Method for controlling load frequency of multi-region power system under DoS attack and deception attack |
| CN113268731B (en) * | 2021-05-13 | 2022-03-01 | 北京航空航天大学杭州创新研究院 | Estimation method for false data attack of load frequency control system |
-
2021
- 2021-12-16 CN CN202111543191.2A patent/CN114244605B/en active Active
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111988303A (en) * | 2020-08-17 | 2020-11-24 | 南京邮电大学 | False data injection attack detection method suitable for load frequency control of power system |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114244605A (en) | 2022-03-25 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN114244605B (en) | Load frequency control method and system considering network attack and time-varying delay | |
| Sargolzaei et al. | Delayed inputs attack on load frequency control in smart grid | |
| Luo et al. | Interval observer-based detection and localization against false data injection attack in smart grids | |
| Yang et al. | Event-based distributed state estimation under deception attack | |
| Choi et al. | Ramp-induced data attacks on look-ahead dispatch in real-time power markets | |
| Zhang et al. | Stability analysis and controller design of a wide-area time-delay system based on the expectation model method | |
| Liu et al. | Modeling and distributed gain scheduling strategy for load frequency control in smart grids with communication topology changes | |
| CN113741309B (en) | Dual-dynamic event trigger controller model design method based on observer | |
| CN110161882A (en) | A kind of fault detection method of the networked system based on event trigger mechanism | |
| Wang et al. | Distributed estimation of power system oscillation modes under attacks on GPS clocks | |
| CN110865616A (en) | Design method of event trigger zone memory DOF controller under random FDI attack | |
| CN114268424B (en) | Detection method for hidden network attack in electric automobile load frequency control system | |
| Tahoun et al. | Secure control design for nonlinear cyber–physical systems under DoS, replay, and deception cyber-attacks with multiple transmission channels | |
| Rahimi et al. | Effect of communication time-delay attacks on the performance of automatic generation control | |
| Shen et al. | Controller design for network‐based Markovian jump systems with unreliable communication links | |
| Zhao et al. | Control-theory based security control of cyber-physical power system under multiple cyber-attacks within unified model framework | |
| Lin et al. | Estimator for multirate sampling systems with multiple random measurement time delays | |
| Zhang et al. | On the impact of measurement errors on power system automatic generation control | |
| Beura et al. | Implementation of novel reduced-order h∞ filter for simultaneous detection and mitigation of fdi-attacks in agc systems | |
| Jafari et al. | Optimal false data injection attacks against power system frequency stability | |
| Song et al. | Observer-based output feedback control for networked systems with dual-channel event-triggered sampling and quantization | |
| Wang et al. | Zonotopic fault detection observer design for discrete‐time systems with adaptively adjusted event‐triggered mechanism | |
| Li et al. | Secure state estimation for complex networks with multi-channel oriented round robin protocol | |
| CN113625647A (en) | Nonlinear system event driver and DOFSS controller joint design method | |
| CN110794811B (en) | Safety control method of networked motion control system with quantification |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |