CN114244605A - 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 PDF

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CN114244605A
CN114244605A CN202111543191.2A CN202111543191A CN114244605A CN 114244605 A CN114244605 A CN 114244605A CN 202111543191 A CN202111543191 A CN 202111543191A CN 114244605 A CN114244605 A CN 114244605A
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control
attack
deviation
time
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CN114244605B (en
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李坚
黄琦
张光斗
鹿超群
白明金
蔡东升
胡维昊
张真源
易建波
孙敏
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University of Electronic Science and Technology of China
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    • 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

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  • General Engineering & Computer Science (AREA)
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  • Signal Processing (AREA)
  • Power Engineering (AREA)
  • Supply And Distribution Of Alternating Current (AREA)
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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 is characterized by comprising the following steps: 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 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 invention realizes the accurate control of the load frequency control under the influence of network attack and time-varying time lag.

Description

Load frequency control method and system considering network attack and time-varying delay
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, the secure operation of LFCs is threatened by the time delays and network attacks that are widespread 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 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 wrong information influences the stable operation of the 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) Attack are two practical and common Attack behaviors for power systems, and especially FDIA behavior concealment is not easily detectable; 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 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 are focused on the stability analysis and controller design of a single-delay power system, leaving a technical gap of a plurality of 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 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 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:
Figure BDA0003414923450000031
Figure BDA0003414923450000032
Figure BDA0003414923450000033
Figure BDA0003414923450000034
Figure BDA0003414923450000035
wherein, Δ fiIndicating a frequency deviation, DiRepresenting the damping coefficient, M, of the generatoriRepresenting generator moment of inertia, Δ Ptie-iRepresenting the active power deviation, Δ P, of the linkmiRepresenting the deviation, Δ P, of the mechanical power output of the generatordiRepresenting load disturbance, Δ PviIndicating the deviation of the valve position, TijRepresenting the crossline synchronisation coefficient, TtiRepresenting the turbine time constant, TgiRepresenting the generator time constant, dj(t) denotes the time varying delay of the control input, ACEiIndicating the regional control deviation, betaiRepresenting a frequency offset factor, τj(t) represents the time-varying delay of the remote signal, t represents time, j represents a region unequal to i,
Figure BDA0003414923450000036
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
Figure BDA0003414923450000037
Determining a controller of the power system;
wherein u (t) represents a control input vector, K/KPi/KIi`All represent the PI controller gain, ui(t) control input vector of the ith area, y (t) measurement signal, y (t) Cx (t), and x (t) state variable vector including frequency deviation, active power deviation of tie line and mechanical power output deviation of generatorDifference, load disturbance and zone control deviation, C denotes the correlation matrix.
Optionally, the obtaining control input considering a false data injection attack and a denial of service attack specifically includes:
using formulas
Figure BDA0003414923450000041
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 construction function, and K represents 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 formulas
Figure BDA0003414923450000042
Determining a dynamic state space model;
wherein, A, B, Adj，BdjE represents a correlation matrix, n represents the number of control regions, and τj(t) and dj(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 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.
Optionally, the load frequency control model building module specifically includes the following formula:
Figure BDA0003414923450000051
Figure BDA0003414923450000052
Figure BDA0003414923450000053
Figure BDA0003414923450000054
Figure BDA0003414923450000055
wherein, Δ fiIndicating a frequency deviation, DiRepresenting the damping coefficient, M, of the generatoriRepresenting generator moment of inertia, Δ Ptie-iRepresenting the active power deviation, Δ P, of the linkmiRepresenting the deviation, Δ P, of the mechanical power output of the generatordiRepresenting load disturbance, Δ PviIndicating the deviation of the valve position, Tij tableIndicating the crossline synchronization coefficient, TtiRepresenting the turbine time constant, TgiRepresenting the generator time constant, dj(t) denotes the time varying delay of the control input, ACEiIndicating the regional control deviation, betaiRepresenting a frequency offset factor, τj(t) represents the time-varying delay of the remote signal, t represents time, j represents a region unequal to i,
Figure BDA0003414923450000056
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
Figure BDA0003414923450000061
Determining a controller of the power system;
wherein u (t) represents a control input vector, K/KPi/KIiAll represent the PI controller gain, ui(t) represents a control input vector of the i-th area, y (t) represents a measurement signal, y (t) is cx (t), x (t) represents a state variable vector, the state variable vector comprises frequency deviation, active power deviation of a connecting line, mechanical power output deviation of a generator, load disturbance and area 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
Figure BDA0003414923450000062
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 construction function, and K represents controller gain matrix.
Optionally, the dynamic state space model determining module specifically includes:
a dynamic state space model determination unit for utilizing a formula
Figure BDA0003414923450000063
Determining a dynamic state space model;
wherein, A, B, Adj、BdjE represents a correlation matrix, n represents the number of control regions, and τj(t) and dj(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 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 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 the stability of a system by multi-time-varying delay; secondly, parameters of the controller are obtained by utilizing a Lyapunov stability theory and a Linear Matrix Inequality (LMI), the exponential mean square stability of a closed-loop system under the specified performance is guaranteed, and the accurate control of the LFC scheme under the influence of network attack and time-varying time lag is realized.
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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 pole-zero distribution plot for four control schemes using the Pade approximation model of 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 mechanical output power deviation 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 commands 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:
Figure BDA0003414923450000081
Figure BDA0003414923450000082
Figure BDA0003414923450000091
Figure BDA0003414923450000092
Figure BDA0003414923450000093
wherein, Δ fiIndicating a frequency deviation, DiRepresenting the damping coefficient, M, of the generatoriRepresenting generator moment of inertia, Δ Ptie-iRepresenting the active power deviation, Δ P, of the linkmiRepresenting the deviation, Δ P, of the mechanical power output of the generatordiRepresenting load disturbance, Δ PviIndicating the deviation of the valve position, Tij denotes a tie-line synchronization coefficient, TtiRepresenting the turbine time constant, TgiRepresenting the generator time constant, dj(t) denotes the time varying delay of the control input, ACEiDenotes the zone control deviation,. beta.i denotes the frequency offset factor,. tau.j(t) represents the time-varying delay of the remote signal, t represents time, j represents a region unequal to i,
Figure BDA0003414923450000094
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
Figure BDA0003414923450000095
Determining a controller of the power system;
wherein u (t) represents a control input vector, K/KPi/KIiAll represent the PI controller gain, ui(t) represents a control input vector of the i-th area, y (t) represents a measurement signal, y (t) is cx (t), x (t) represents a state variable vector, the state variable vector comprises frequency deviation, active power deviation of a connecting line, mechanical power output deviation of a generator, load disturbance and area control deviation, and C represents a correlation matrix.
Let xi(t)＝[Δfi ΔPtie-i ΔPmi ΔPvi ∫ACEi]TThen, then
Figure BDA0003414923450000096
Wherein r isiFor speed reduction, A ═ Aij]n×n，B＝diag{B1，B2，...，Bn}，C＝diag{C1，C2，...，Cn}，E＝diag{E1，E2，...，En}.；
Figure BDA0003414923450000101
Figure BDA0003414923450000102
Figure BDA0003414923450000103
Figure BDA0003414923450000104
Figure BDA0003414923450000105
Figure BDA0003414923450000106
u(t)＝[u1(t) u2(t) ... un(t)]T；
K＝diag{K1，K2，...，Kn}，Ki＝[KPi KIi]；
S104, acquiring control input considering false data injection attack and denial of service attack;
s104 specifically comprises the following steps:
using formulas
Figure BDA0003414923450000107
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 construction function, 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 non-linear function f (y (t)), wherein f (· 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:
Figure BDA0003414923450000111
the bernoulli random variable β (t) embeds the random features of FDIA. When β (t) ═ 1, an attack occurs,
Figure BDA0003414923450000112
when β (t) ═ 0, the attack has not occurred, the controller receives the correct information,
Figure BDA0003414923450000113
DoS attack: the invention adopts a periodic DoS attack which is initiated by a periodic interference signal and can be expressed as follows:
Figure BDA0003414923450000114
the random nature of DoS attacks comes from the sleep time of the jammer. When T ∈ [ T ]on，Toff]When the attack occurs, the controller receives:
Figure BDA0003414923450000115
when in use
Figure BDA0003414923450000116
When the attack does not occur, the information of the controller is correctly received,
Figure BDA0003414923450000117
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 formulas
Figure BDA0003414923450000121
Determining a dynamic state space model;
wherein, A, B, Adj、BdjE represents a correlation matrix, n represents the number of control regions, and τj(t) and dj(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.
τj(t) and dj(t) satisfies the following conditions, respectively:
Figure BDA0003414923450000122
τ and d each represents τj(t) and dj(ii) an upper bound of (t),
Figure BDA0003414923450000123
respectively represent tauj(t) and djRate of change of (t), μj、λjRespectively represent
Figure BDA0003414923450000124
Upper bound, upper bound and rate of change ofAre 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 (Area1, Area2) four-motor (G1, G2, G3, G4) power system, and it contains 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 lemma and theory 1 are now given.
Introduction 1: for a given scalar α > 0 and ε > 0, if
Figure BDA0003414923450000131
Then the zero solution of the dynamic state space model is exponentially stable in the mean square sense.
2, leading: for a given matrix
Figure BDA0003414923450000132
And S12If, if
Figure BDA0003414923450000133
The following is concluded to be true:
(1)S11＜0，
Figure BDA0003414923450000134
(2)S22＜0，
Figure BDA0003414923450000135
introduction and management3: for all non-zeros
Figure BDA0003414923450000136
And specifying a scalar γ > 0, if
Figure BDA0003414923450000137
Then the dynamic state space model under the zero initial condition ψ (t) ═ 0 has H∞And (4) performance.
And (4) introduction: for symmetric matrix
Figure BDA0003414923450000138
And full rank matrix
Figure BDA0003414923450000139
Its singular decomposition W ═ UW0V, wherein UT＝U-1，VT＝V-1，
Figure BDA00034149234500001310
Is a rectangular diagonal matrix with positive real numbers on the diagonals, there being a matrix X such that PW is WX, if and only if P has
Figure BDA00034149234500001311
In the form of (1), wherein
Figure BDA00034149234500001312
Theory 1: for a given scalar τj＞0，
Figure BDA0003414923450000141
δ＞0，μj＞0，λj> 0, γ > 0, controller gain matrix K and positive integer n, the dynamic state space model is exponential mean square stable and has H ∞ performance for any time varying delay and satisfying network attacks if a positive symmetric matrix P ═ P is presentT＞0，U＝UT＞0，
Figure BDA0003414923450000142
And is
Figure BDA0003414923450000143
(j ═ 1, 2,. n) satisfies the following LMI:
Figure BDA0003414923450000144
wherein the content of the first and second substances,
Figure BDA0003414923450000145
Figure BDA0003414923450000146
Ψ13＝[PBdjKC]1×n，
Figure BDA0003414923450000147
Ψ16＝τδ(BKCF-BKC)T，Ψ17＝[τjPAdj]1×n，
Figure BDA0003414923450000148
Ψ33＝diag{[-(1-λj)Rj]}，
Figure BDA0003414923450000149
Ψ77＝diag{[τjP]}；
theorem 1 gives the exponential mean square stability and H of the dynamic state space model given the controller gain matrix K∞Sufficient condition of performance. In addition, to design and ensure H∞The parameters of the controller, the regional environment, and theory 2.
Theory 2: for a given scalar τj＞0，
Figure BDA00034149234500001410
δ＞0，μj＞0，λj> 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 any, of a positively symmetric matrix
X＝XT＞0，
Figure BDA0003414923450000151
(j ═ 1, 2,. said., n) and a matrix Y of suitable dimensions satisfy the following LMI:
Figure BDA0003414923450000152
wherein the content of the first and second substances,
Figure BDA0003414923450000153
Figure BDA0003414923450000154
Figure BDA0003414923450000155
Figure BDA0003414923450000156
Figure BDA0003414923450000157
compared with the prior art, the invention has the beneficial effects that:
(1) three random factors affecting the stability of the power system 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 method obtains the H-containing model by utilizing the Lyapunov stability theory∞Adequate conditions for the exponential stability of the properties. 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 the three-region power system are shown in table 1; the parameters required for the controller are shown in table 2. The total simulation time is set to 10 seconds, and a disturbance w (t) with Δ t equal to 0.2s is added from 1 second, assuming that w (t) is:
Figure BDA0003414923450000161
TABLE 1
Figure BDA0003414923450000162
TABLE 2
Figure BDA0003414923450000163
The initial conditions were:
Figure BDA0003414923450000164
ψ1(t)＝[0.02 -0.09 0.04 -0.05 0.008]T；
ψ2(t)＝[-0.02 -0.04 0.05 -0.05 -0.01]T；
ψ3(t)＝[0.04 -0.05 0.04 -0.015 0.015]T；
DoS attack time in each period satisfies the following constraint:
Figure BDA0003414923450000171
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 of the controller to be diag { K ═ diag {1，K2，K3Therein of
K1＝[-0.0466，-0.0076]，K2＝[-0.0656，-0.0095]，K3＝[-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 solution without controller are larger than zero, indicating that the system without control 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 of simulation data were decomposed using the Prony method, leading model damping, eigenvalues s- σ ± j ωdCoefficient and natural frequency ωrProny result of (5). It can be seen that all controllers are suppressing power oscillation and have anti-interference performanceThe improvement is also obtained; due to sigmaPCAt 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 sample in order to further verify the superiority of the sample. 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 controller can be stable 3s ago, and the overshoot of the proposed method is smaller than controller one. Active power deviation delta P of junctor of three control areastieAnd the mechanical output deviation delta P of the generatormAnd valve position deviation Δ PvAs 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 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:
Figure BDA0003414923450000181
Figure BDA0003414923450000182
Figure BDA0003414923450000183
Figure BDA0003414923450000191
Figure BDA0003414923450000192
wherein, Δ fiIndicating a frequency deviation, DiRepresenting the damping coefficient, M, of the generatoriRepresenting generator moment of inertia, Δ Ptie-iRepresenting the active power deviation, Δ P, of the linkmiRepresenting the deviation, Δ P, of the mechanical power output of the generatordiRepresenting load disturbance, Δ PviIndicating the deviation of the valve position, TijRepresenting the crossline synchronisation coefficient, TtiRepresenting the turbine time constant, TgiRepresenting the generator time constant, dj(t) denotes the time varying delay of the control input, ACEiIndicating the regional control deviation, betaiRepresenting a frequency offset factor, τj(t) represents the time-varying delay of the remote signal, t represents time, j represents a region unequal to i,
Figure BDA0003414923450000193
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
Figure BDA0003414923450000194
Determining a controller of the power system;
wherein u (t) represents a control input vector, K/KPi/KIiAll represent the PI controller gain, ui(t) represents a control input vector of the i-th area, y (t) represents a measurement signal, y (t) is cx (t), x (t) represents a state variable vector, the state variable vector comprises frequency deviation, active power deviation of a connecting line, mechanical power output deviation of a generator, load disturbance and area control deviation, and C represents a correlation matrix.
The actual control input obtaining module specifically comprises:
an actual control input determination unit for using a formula
Figure BDA0003414923450000195
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 construction function, and K represents controller gain matrix.
The dynamic state space model determining module specifically includes:
a dynamic state space model determination unit for utilizing a formula
Figure BDA0003414923450000201
Determining a dynamic state space model;
wherein, A, B, Adj、BdjE represents a correlation matrix, n represents the number of control regions, and τj(t) and dj(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 method part for description.
The principles and embodiments of the present invention have been described herein using specific examples, which are provided only to help understand the method and the core concept 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 (10)

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;
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.
2. The method for controlling the load frequency according to claim 1, wherein the method for controlling the load frequency considering network attacks and time-varying delays constructs a load frequency control model according to the electrical signals and corresponding transmission data in each control area, and specifically comprises the following formula:
Figure FDA0003414923440000011
Figure FDA0003414923440000012
Figure FDA0003414923440000013
Figure FDA0003414923440000014
Figure FDA0003414923440000015
wherein, Δ fiIndicating a frequency deviation, DiRepresenting the damping coefficient, M, of the generatoriRepresenting generator moment of inertia, Δ Ptie-iRepresenting the active power deviation, Δ P, of the linkmiRepresenting the deviation, Δ P, of the mechanical power output of the generatordiRepresenting load disturbance, Δ PviIndicating the deviation of the valve position, TijRepresenting the crossline synchronisation coefficient, TtiRepresenting the turbine time constant, TgiRepresenting the generator time constant, dj(t) denotes the time varying delay of the control input, ACEiIndicating the regional control deviation, betaiRepresenting a frequency offset factor, τj(t) represents the time-varying delay of the remote signal, t represents time, j represents a region unequal to i,
Figure FDA0003414923440000023
the derivative of the frequency deviation is indicated.
3. The method according to claim 2, wherein the constructing the controller of the power system according to the load frequency control model specifically includes:
using formulas
Figure FDA0003414923440000021
Determining a controller of the power system;
wherein u (t) represents a control input vector, K/KPi/KIiAll represent the PI controller gain, ui(t) represents a control input vector of the i-th area, y (t) represents a measurement signal, y (t) is cx (t), x (t) represents a state variable vector, the state variable vector comprises frequency deviation, active power deviation of a connecting line, mechanical power output deviation of a generator, load disturbance and area control deviation, and C represents a correlation matrix.
4. The method as claimed in claim 3, wherein the obtaining of the control input considering the false data injection attack and the denial of service attack specifically comprises:
using formulas
Figure FDA0003414923440000022
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 construction function, and K represents controller gain matrix.
5. The method for controlling the load frequency considering the cyber attack and the time-varying delay according to claim 4, wherein the determining the dynamic state space model of the power system according to the control input considering the dummy data injection attack and the denial of service attack and the controller specifically comprises:
using formulas
Figure FDA0003414923440000031
Determining a dynamic state space model;
wherein, A, B, Adj、BdjE represents a correlation matrix, n represents the number of control regions, and τj(t) and dj(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.
6. A load frequency control system that considers 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 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.
7. The load frequency control system considering cyber attack and time-varying delay according to claim 6, wherein the load frequency control model constructing module specifically includes the following formula:
Figure FDA0003414923440000041
Figure FDA0003414923440000042
Figure FDA0003414923440000043
Figure FDA0003414923440000044
Figure FDA0003414923440000045
wherein, Δ fiIndicating a frequency deviation, DiRepresenting the damping coefficient, M, of the generatoriRepresenting generator moment of inertia, Δ Ptie-iRepresenting the active power deviation, Δ P, of the linkmiRepresenting the deviation, Δ P, of the mechanical power output of the generatordiRepresenting load disturbance, Δ PviIndicating the deviation of the valve position, TijRepresenting the crossline synchronisation coefficient, TtiRepresenting the turbine time constant, TgiRepresenting the generator time constant, dj(t) denotes the time varying delay of the control input, ACEiIndicating the regional control deviation, betaiRepresenting a frequency offset factor, τj(t) represents the time-varying delay of the remote signal, t represents time, j represents a region unequal to i,
Figure FDA0003414923440000047
the derivative of the frequency deviation is indicated.
8. The load frequency control system considering network attack and time-varying delay according to claim 7, wherein the controller building module of the power system specifically comprises:
a controller building unit of the power system for utilizing the formula
Figure FDA0003414923440000046
Determining a controller of the power system;
wherein u (t) represents a control input vector, K/KPi/KIiAll represent the PI controller gain, ui(t) represents a control input vector of the i-th area, y (t) represents a measurement signal, y (t) is cx (t), x (t) represents a state variable vector, the state variable vector comprises frequency deviation, active power deviation of a connecting line, mechanical power output deviation of a generator, load disturbance and area control deviation, and C represents a correlation matrix.
9. The system according to claim 8, wherein the actual control input obtaining module specifically includes:
an actual control input determination unit for using a formula
Figure FDA0003414923440000051
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 construction function, and K represents controller gain matrix.
10. The system according to claim 9, wherein the dynamic state space model determining module specifically comprises:
a dynamic state space model determination unit for utilizing a formula
Figure FDA0003414923440000052
Determining a dynamic state space model;
wherein, A, B, Adj、BdjE represents a correlation matrix, n represents the number of control regions, and τj(t) and dj(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.
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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116455074A (en) * 2023-04-19 2023-07-18 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 (3)

* Cited by examiner, † Cited by third party
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
CN113268731A (en) * 2021-05-13 2021-08-17 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
CN113315129A (en) * 2021-04-27 2021-08-27 浙江工业大学 Method for controlling load frequency of multi-region power system under DoS attack and deception attack

Patent Citations (3)

* Cited by examiner, † Cited by third party
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
CN113315129A (en) * 2021-04-27 2021-08-27 浙江工业大学 Method for controlling load frequency of multi-region power system under DoS attack and deception attack
CN113268731A (en) * 2021-05-13 2021-08-17 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

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
A.MORATTAB: "decentralized model predictive load-frequency control for deregulated power systems in a tough situation", 《2011 IEEE TRONDHEIM POWERTECH》 *

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116455074A (en) * 2023-04-19 2023-07-18 贵州电网有限责任公司 Data processing method and device applied to power grid dispatching and electronic equipment
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

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