CN113949064A - Load frequency fault-tolerant control system and method based on reverse behavior reconstruction - Google Patents

Load frequency fault-tolerant control system and method based on reverse behavior reconstruction Download PDF

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CN113949064A
CN113949064A CN202111300633.0A CN202111300633A CN113949064A CN 113949064 A CN113949064 A CN 113949064A CN 202111300633 A CN202111300633 A CN 202111300633A CN 113949064 A CN113949064 A CN 113949064A
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CN113949064B (en
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薛飞
李宏强
李旭涛
李�浩
张迪
杨挺
张爽
田蓓
马鑫
张汉花
任勇
焦龙
杨慧彪
吴玫蓉
韩旭涛
唐子慧
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Tianjin University
Electric Power Research Institute of State Grid Ningxia Electric Power Co Ltd
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Electric Power Research Institute of State Grid Ningxia Electric Power Co Ltd
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
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Abstract

A load frequency fault-tolerant control system based on reverse behavior reconstruction comprises an information acquisition module: and acquiring and sensing the operating parameters of the controlled system by using a sensor arranged on the controlled system, and generating corresponding controlled system output quantity y' (t) according to the received operating parameters. Aiming at the predicted information/physical fault, the controller library module designs a corresponding controller to ensure the dynamic performance of a fault system to form a controller library, wherein the number of controllers in the basic controller library is M + N; the reverse behavior reconstruction module reversely reconstructs the dynamic behavior of the controlled system in a parallel mode according to the output y '(t) of the controlled system, the control instruction u' (t) and the controller library and obtains reconstructed reference input
Figure DDA0003338245010000011
The performance index calculation module inputs the output y' (t) of the controlled system and the reconstructed reference
Figure DDA0003338245010000012
Calculating the root mean square error to obtain a performance index MSE; the controller selection module selects a controller input corresponding to the minimum performance index MSE. The invention also provides a load frequency fault-tolerant control method based on reverse behavior reconstruction.

Description

Load frequency fault-tolerant control system and method based on reverse behavior reconstruction
The technical field is as follows:
the application relates to the technical field of active fault-tolerant control of load frequency of an interconnected power grid, in particular to a load frequency fault-tolerant control system and method based on reverse behavior reconstruction.
Background art:
the installed capacity of new energy which is continuously connected to the grid in a novel electric power system is obvious, and various dominant and recessive faults in the system are increased due to randomness and uncertainty of the installed capacity, so that the active power output of a power grid at a sending end is obviously reduced, and the frequency is obviously deviated from a rated operation point. In addition, as the degree of power grid informatization is continuously improved, various uncertain factors (such as time delay, packet loss and hacking) in the network transmission process bring new potential safety hazards to various real-time control systems. Load Frequency Control (LFC) exchanges power through a control area link, and realizes stable control between a system frequency input deviation and a controller output control quantity.
Comprehensive sensing and real-time stable control of the running state are basic requirements of various real-time control systems in the power internet of things. However, the tight coupling between the information subsystem and the physical subsystem causes malicious network attacks and sudden physical failures, which result in structural changes of the power internet of things, and such uncertainty violates the assumption of small-range fluctuation of parameters in the robust control theory. The traditional fault-tolerant control strategy based on hardware redundancy or fault identification needs to depend on accurate prior controlled system parameters and a large number of operating state parameters, so that the risk of mismatching between a controller and a controlled system exists, and fault tolerance failure is caused.
The invention content is as follows:
accordingly, there is a need for a load frequency fault tolerant control system based on reverse behavior reconstruction that does not rely on fault identification.
There is also a need to provide a method for load frequency fault-tolerant control based on reverse behavior reconstruction that does not rely on fault identification.
A load frequency fault-tolerant control system based on reverse behavior reconstruction comprises an information acquisition module, a controller library module, a reverse behavior reconstruction module, a performance index calculation module and a controller selection module, wherein the information acquisition module comprises a processor, a processor module, a memory module and a controller selection module:
the information acquisition module: and acquiring and sensing the operating parameters of the controlled system by using a sensor arranged on the controlled system, and generating corresponding controlled system output quantity y' (t) according to the received operating parameters.
The controller library module: aiming at the predicted information/physical fault, designing a corresponding controller to ensure the dynamic performance of a fault system and forming a basic controller library, wherein the number of controllers in the basic controller library is M + N, and the controller library is sigmac＝{Σc1,Σc2,…,Σc(M+N)}, controller library ΣcThe method comprises the steps that a controller set designed for a specific operation scene is represented, M means that a controlled system has M normal working scenes, and N means that the controlled system has N typical fault scenes;
a reverse behavior reconstruction module: according to the output quantity y '(t) of the controlled system, the control command u' (t) and the existing controller library sigmacReversely reconstructing the dynamic behavior of the controlled system in a parallel mode and obtaining the reconstructed reference input
Figure BDA0003338244990000023
Reconstructed reference input
Figure BDA0003338244990000024
Asymptotically converges to the ideal reference input r (t).
A performance index calculation module: the output quantity y' (t) of the controlled system and the reconstructed reference input
Figure BDA0003338244990000025
Calculating the root mean square error to obtain a performance index MSE;
a controller selection module: and selecting the controller input corresponding to the minimum performance index MSE, thereby realizing zero trial and error one-time accurate switching.
A load frequency fault-tolerant control method based on reverse behavior reconstruction comprises the following steps:
aiming at the predicted information/physical fault, designing a corresponding controller to ensure the dynamic performance of a fault system and forming a basic controller library, wherein the number of controllers in the basic controller library is M + N, and the order of sigma isc＝{Σc1,Σc2,…,Σc(M+N)Denotes a controller designed for a specific operation scenarioThe method comprises the steps that a set is formed, wherein M means that a controlled system has M normal working scenes, and N means that the controlled system has N typical fault scenes;
receiving operation parameters of a sensed controlled system acquired and sensed by a sensor, and generating a corresponding controlled system output quantity y' (t) according to the received operation parameters;
receiving a control instruction u' (t) generated by a control center according to the operation parameters of the system to be controlled acquired and sensed by a sensor;
according to the output quantity y '(t) of the controlled system, the control command u' (t) and the existing controller library sigmacReversely reconstructing the dynamic behavior of the controlled system in a parallel mode and obtaining the reconstructed reference input
Figure BDA0003338244990000021
Reconstructed reference input
Figure BDA0003338244990000022
Asymptotically converges to the ideal reference input r (t);
the output quantity y' (t) of the controlled system and the reconstructed reference input
Figure BDA0003338244990000031
Calculating the root mean square error to obtain a performance index MSE;
and selecting the controller input corresponding to the minimum performance index MSE, thereby realizing zero trial and error one-time accurate switching.
In the load frequency fault-tolerant control system and method based on reverse behavior reconstruction, a reverse behavior reconstruction strategy is provided, a corresponding controller is designed aiming at predicted information/physical faults to ensure the dynamic performance of a fault system, prior fault information does not need to be acquired, and only the input and output measured values of the system are relied on, so that fault tolerance on the faults can be realized as far as possible, and better performance is provided for the stability control and the system stable operation of an information/physical tightly-coupled power internet of things under the complex and various fault conditions.
Description of the drawings:
FIG. 1 is a functional block diagram of a load frequency fault-tolerant control system based on reverse behavior reconstruction;
FIG. 2 is a block diagram of a load frequency fault tolerant control system based on reverse behavior reconstruction;
FIG. 3 is a schematic diagram of a controller library framework;
FIG. 4 is a three-zone interconnect power system architecture diagram;
FIG. 5 is a diagram illustrating normal communication delays of Area 1-Area 3;
FIG. 6 is a communication delay when Area 2 is under a delay attack;
FIG. 7 is an Area 1-Area 3 load fluctuation;
FIG. 8 is Δ f for different fault scenarios1And Δ Ptie1The mean square error of (d).
In the figure: the load frequency fault-tolerant control system based on the reverse behavior reconstruction comprises a load frequency fault-tolerant control system 10 based on the reverse behavior reconstruction, an information acquisition module 20, a controller library module 30, a reverse behavior reconstruction module 40, a performance index calculation module 50 and a controller selection module 60.
The specific implementation mode is as follows:
the application aims at providing a load frequency fault-tolerant control system independent of fault identification aiming at power internet of things information/physical faults, and the core idea is that a most matched controller is put into a controller loop in a zero trial and error mode. First, a corresponding controller is designed for the predicted information/physical fault to ensure the dynamic performance of the fault system, and a basic controller library is formed. And then, designing a controller switching strategy, and putting the optimal controller into the system according to a performance evaluation result to ensure the safe and stable operation of the system. The following detailed description of embodiments of the present invention is provided in connection with the accompanying drawings and examples. The following examples are intended to illustrate the present application but are not intended to limit the scope of the present application.
Referring to fig. 1 to fig. 3, the load frequency fault-tolerant control system 10 based on reverse behavior reconstruction includes an information acquisition module 20, a controller library module 30, a reverse behavior reconstruction module 40, a performance index calculation module 50, and a controller selection module 60:
the information acquisition module 20: and acquiring and sensing the operating parameters of the controlled system by using a sensor arranged on the controlled system, and generating corresponding controlled system output quantity y' (t) according to the received operating parameters.
Controller library module 30: aiming at the predicted information/physical fault, designing a corresponding controller to ensure the dynamic performance of a fault system and forming a basic controller library, wherein the number of controllers in the basic controller library is M + N, and the controller library is sigmac＝{Σc1,Σc2,…,Σc(M+N)}, controller library ΣcThe method comprises the steps that a controller set designed for a specific operation scene is represented, M means that a controlled system has M normal working scenes, and N means that the controlled system has N typical fault scenes;
the reverse behavior reconstruction module 40: according to the output quantity y '(t) of the controlled system, the control command u' (t) and the existing controller library sigmacReversely reconstructing the dynamic behavior of the controlled system in a parallel mode and obtaining the reconstructed reference input
Figure BDA0003338244990000041
Reconstructed reference input
Figure BDA0003338244990000042
Asymptotically converges to the ideal reference input r (t). In the reconstruction process, the preset controllers do not need to be switched into the control loop in sequence, and the dynamic behaviors of all the preset controllers in the controller library can be reconstructed only by the control parameters and the control instructions of the control center in any detection time period, so that a foundation is laid for performance evaluation of the controllers.
Performance index calculation module 50: the output quantity y' (t) of the controlled system and the reconstructed reference input
Figure BDA0003338244990000043
Calculating the root mean square error to obtain a performance index MSE; wherein the mean square error MSE is defined as the reconstructed reference input
Figure BDA0003338244990000044
A measure of the degree of difference between two variables of the output y' (t) of the system to be controlled, i.e.
Figure BDA0003338244990000045
The MSE index is a numerically accurate representation of the control deviation of the various controllers without information ambiguity. Because the control target of the real-time control system is the controlled system output y '(t) and the ideal reference input r (t) is tracked in real time, such as the state quantities of the rotating speed, the frequency f and the like of the generator, and for the actual controlled system, the control target is the area control error ACE which is 0, namely r (t) -y' (t) ═ 0, therefore, the reconstructed reference input is adopted
Figure BDA0003338244990000057
The MSE index of the deviation from the controlled system output y' (t) is used as a measure of the dynamic performance of the controller.
The controller selection module 60: and selecting the controller input corresponding to the minimum performance index MSE, thereby realizing zero trial and error one-time accurate switching. For example, based on the calculated performance index { MSE }1,MSE2,…,MSEM+NAnd selecting the most matched controller to be put into the control loop in a zero trial and error mode, namely selecting the preset controller with the minimum performance index MSE, namely
Figure BDA0003338244990000051
Wherein, the controller set in the basic controller library is obtained by the following method:
1) establishing a real-time control system model of information/physical faults:
giving a controlled object model in a controlled system under a normal condition:
Figure BDA0003338244990000052
wherein x ∈ Rp、u∈Rq、ω∈RrAnd y ∈ RvRespectively, state variable, input variable, external disturbance variable and outputThe output variable, matrix A, B, H, C, D is an adaptive matrix.
Modeling according to the expected fault effect in the controlled system:
(1) fault type modeling
A. Modeling data type faults, wherein under the faults, the following relation is satisfied between actual control quantity/measurement and reality:
control amount: u. off(t)＝ΓPΓCu(t)
Measuring quantity: y isf(t)＝ΨCΨPy(t)
In the formula, the subscript f represents the fault condition,
Figure BDA0003338244990000053
representing an actuator physical fault matrix;
Figure BDA0003338244990000054
representing a network fault matrix between the controller and the actuator;
Figure BDA0003338244990000055
representing a network information fault matrix between the sensor and the controller;
Figure BDA0003338244990000056
representing a sensor physical failure matrix;
B. modeling aging type fault, let tauscRepresenting the network delay, τ, from sensor to controllercaFor network delay from a controller to an actuator in a controlled system, a control instruction sent by the controller is u '(t), a received output quantity of the controlled system is y' (t), and the control instruction u (t) actually received by the actuator in the controlled system and a control instruction provided by a control center and the actual output quantity y (t) of the controlled system meet the requirements
u(t)＝u'(t+τca)
y'(t)＝y(t+τsc)
Further, the pure time-lag link is approximated by adopting the Pade approximation technology, and the state space after approximation is expressed as
Figure BDA0003338244990000061
Figure BDA0003338244990000062
In the formula, xca(t)、xsc(t) is an intermediate variable introduced by the approximation of Pade, Ak,Bk,Ck,DkAre respectively as
Figure BDA0003338244990000063
Figure BDA0003338244990000064
Where k is { sc, ca }, lkTo approximate the order, aiAnd bi(i＝1,2,…,lk) To approximate the coefficients, given by:
Figure BDA0003338244990000065
C. component fault modeling is carried out, and under the fault, the original operation structure of a controlled system is obviously changed and is expressed as abnormal state space description, namely
Figure BDA0003338244990000066
In the formula, a ', B ', H ', C ', and D ' represent controlled system matrices after the occurrence of the failure.
(2) Information/physics hybrid fault modeling
Based on mathematical modeling of three types of faults, let augmented vector X be [ X, X ═ Xca,xsc]TEstablishing electric power internet of things real-time control system considering information/physical mixed faultsThe system state equation is
Figure BDA0003338244990000067
In the formula, Af＝A'，Bf＝B'ΓPΓC，Hf＝H'，Cf＝ΨCΨPC'，Df＝ΨCΨPD'ΓPΓC；
2) The basic controller library paradigm design method comprises the following steps:
the real-time control system state equation has model uncertainty, so the controller design must have excellent robustness to the model uncertainty, and the application refers to the hybrid H2/H∞The method carries out the design of a basic controller to realize the robust control of the known faults;
considering that the controlled system has M normal working scenes and N typical fault scenes, the number of controllers in the basic controller library is M + N, so thatc＝{Σc1,Σc2,…,Σc(M+N)Denotes a set of controllers designed for a particular operating scenario, having the following general form:
Figure BDA0003338244990000071
wherein j is 1,2, …, M + N, xuj(t) intermediate variables introduced by the controller, Auj,Buj,Cuj,DujIs a controller system matrix.
Further, the present application also provides a load frequency fault-tolerant control method based on reverse behavior reconstruction, which includes the following steps:
step S300, aiming at the predicted information/physical fault, designing a corresponding controller to ensure the dynamic performance of the fault system, and forming a basic controller library, wherein the number of controllers in the basic controller library is M + N, and the order of sigma is adoptedc＝{Σc1,Σc2,…,Σc(M+N)Denotes for a specific operational scenarioThe method comprises the steps that a designed controller set is provided, wherein M means that a controlled system has M normal working scenes, and N means that the controlled system has N typical fault scenes;
step S303, receiving operation parameters of the controlled system acquired and sensed by the sensor, and generating a corresponding controlled system output quantity y' (t) according to the received operation parameters;
step S305, receiving a control instruction u' (t) generated by the control center according to the operation parameters of the system to be controlled acquired and sensed by the sensor;
step S307, the output y '(t) of the controlled system, the control command u' (t) and the existing controller library ΣcReversely reconstructing the dynamic behavior of the controlled system in a parallel mode and obtaining the reconstructed reference input
Figure BDA0003338244990000072
Reconstructed reference input
Figure BDA0003338244990000073
Asymptotically converges to the ideal reference input r (t);
step S309, performing root mean square error calculation on the output quantity y' (t) of the controlled system and the reconstructed reference input to obtain a performance index MSE;
and step S311, selecting the controller corresponding to the minimum performance index MSE to be switched, so that zero trial and error one-time accurate switching is realized.
Wherein, for the predicted information/physical fault, corresponding controllers are designed to ensure the dynamic performance of the fault system, and a basic controller library is formed, wherein the number of controllers in the basic controller library is M + N, and let Σc＝{Σc1,Σc2,…,Σc(M+N)The method comprises the following steps of (1) representing a controller set designed for a specific operation scene, wherein M means that a controlled system has M normal working scenes, and N means that the controlled system has N typical fault scenes, and the steps are as follows:
1) establishing a real-time control system model of information/physical faults:
giving a controlled object model in a controlled system under a normal condition:
Figure BDA0003338244990000081
wherein x ∈ Rp、u∈Rq、ω∈RrAnd y ∈ RvState variables, input variables, external disturbance variables, and output variables, respectively, and the matrix A, B, H, C, D is a dimensional matrix.
Modeling according to the expected fault effect in the controlled system:
(1) fault type modeling
A. Modeling data type faults, wherein under the faults, the following relation is satisfied between actual control quantity/measurement and reality:
control amount: u. off(t)＝ΓPΓCu(t)
Measuring quantity: y isf(t)＝ΨCΨPy(t)
In the formula, the subscript f represents the fault condition,
Figure BDA0003338244990000082
representing an actuator physical fault matrix;
Figure BDA0003338244990000083
representing a network fault matrix between the controller and the actuator;
Figure BDA0003338244990000084
representing a network information fault matrix between the sensor and the controller;
Figure BDA0003338244990000085
representing a sensor physical failure matrix;
B. modeling aging type fault, let tauscRepresenting the network delay, τ, from sensor to controllercaFor network delay from the controller to the actuator in the controlled system, the control instruction sent by the controller is u '(t), the output quantity of the controlled system received is y' (t), the control instruction u (t) provided by the control center and actually received by the actuator in the controlled system, and the actual system of the controlled systemThe output margin y (t) satisfies
u(t)＝u'(t+τca)
y'(t)＝y(t+τsc)
Further, the pure time-lag link is approximated by adopting the Pade approximation technology, and the state space after approximation is expressed as
Figure BDA0003338244990000086
Figure BDA0003338244990000087
In the formula, xca(t)、xsc(t) is an intermediate variable introduced by the approximation of Pade, Ak,Bk,Ck,DkAre respectively as
Figure BDA0003338244990000091
Figure BDA0003338244990000092
Where k is { sc, ca }, lkTo approximate the order, aiAnd bi(i＝1,2,…,lk) To approximate the coefficients, given by:
Figure BDA0003338244990000093
C. component fault modeling is carried out, and under the fault, the original operation structure of a controlled system is obviously changed and is expressed as abnormal state space description, namely
Figure BDA0003338244990000094
In the formula, A ', B ', H ', C ' and D ' represent controlled system matrixes after the faults occur;
(2) information/physics hybrid fault modeling
Based on mathematical modeling of three types of faults, let augmented vector X be [ X, X ═ Xca,xsc]TThe state equation of the real-time control system of the power internet of things considering the information/physical mixed fault is established as
Figure BDA0003338244990000095
In the formula, Af＝A'，Bf＝B'ΓPΓC，Hf＝H'，Cf＝ΨCΨPC'，Df＝ΨCΨPD'ΓPΓC；
2) The basic controller library paradigm design method comprises the following steps:
the real-time control system state equation has model uncertainty, so the controller design must have excellent robustness to the model uncertainty, and the application refers to the hybrid H2/H∞The method carries out the design of a basic controller to realize the robust control of the known faults;
considering that the controlled system has M normal working scenes and N typical fault scenes, the number of controllers in the basic controller library is M + N, so thatc＝{Σc1,Σc2,…,Σc(M+N)Denotes a set of controllers designed for a particular operating scenario, having the following general form:
Figure BDA0003338244990000096
wherein j is 1,2, …, M + N, xuj(t) intermediate variables introduced by the controller, Auj,Buj,Cuj,DujIs a controller system matrix;
wherein "is based on the controlled system output y '(t), the control command u' (t) and the existing controller library ΣcIn a parallel mannerReversely reconstructing the dynamic behavior of the controlled system and obtaining the reconstructed reference input
Figure BDA0003338244990000101
Reconstructed reference input
Figure BDA0003338244990000102
The step of asymptotically converging on the ideal reference input r (t) is specifically as follows:
obtaining the dynamic behavior of the real-time control system:
defining a signal vector s (t) [ [ ω (t), r (t), y '(t), u' (t)]TBased on the Laplace transform and polynomial matrix elementary row transform method, the dynamic behavior of the controlled system and the controller can be described as
The controlled system:
BP＝{s＝(r,y′,u′)T|[-Np2(ξ)0Dp(ξ)-Np1(ξ)]s(t)＝0}
a controller:
Bcj＝{s＝(r,y′,u′)T|[0Ncj(ξ)-Ncj(ξ)-Dcj(ξ)]s(t)＝0}
where xi ═ d/dt, Np1(ξ)、Np2(ξ)、Dp(ξ)、Ncj(ξ)、Dcj(xi) is an adaptive polynomial matrix;
the controlled object and the controller can be known to pass through the common variable by the two formulas
Figure BDA0003338244990000103
Figure BDA0003338244990000104
Interconnection, dynamic behavior of controlled objects can be controlled by coupling variables [ r (t), y '(t), u' (t) to the controller]TInterconnection is realized;
according to the dynamic behavior model of the controlled system, the controller sigmacjDynamic behavior B in an arbitrary detection periodcjComplete reconstruction can be achieved only by y '(t) and u' (t), without accurate a priori system operating parameters and fault information; order to
Figure BDA0003338244990000105
Satisfy for reconstructed controller dynamic behavior
Figure BDA0003338244990000106
In the formula (I), the compound is shown in the specification,
Figure BDA0003338244990000107
is sigmacjReconstructed reference input, if ∑cjReconstructing the reference input for the controller matching the current operating state of the controlled system
Figure BDA0003338244990000108
Asymptotically converges to the ideal reference input r (t);
wherein, the output quantity y' (t) of the system to be controlled and the reconstructed reference input are
Figure BDA0003338244990000109
The method comprises the following steps of calculating a root mean square error to obtain a performance index MSE';
the output quantity y' (t) of the controlled system and the reconstructed reference input
Figure BDA00033382449900001010
Calculating the root mean square error to obtain a performance index MSE; wherein the mean square error MSE is defined as the reconstructed reference input
Figure BDA00033382449900001011
A measure of the degree of difference between two variables of the output y' (t) of the system to be controlled, i.e.
Figure BDA0003338244990000111
The MSE index is a numerically accurate representation of the control deviation of the various controllers without information ambiguity. Wherein due to real timeThe control target of the control system is that the controlled system output y '(t) tracks ideal reference input r (t) in real time, such as state quantities of generator rotating speed, frequency f and the like, and for the actual controlled system, the control target is that the regional control error ACE is 0, namely r (t) -y' (t) ═ 0, so that the reconstructed reference input is adopted
Figure BDA0003338244990000114
The MSE index of the deviation between the MSE index and the output y' (t) of the controlled system is used as a measure index of the dynamic performance of the controller;
the method comprises the following steps of selecting a controller input corresponding to the minimum performance index MSE so as to realize zero trial and error one-time accurate switching:
according to the performance index { MSE) obtained by calculation1,MSE2,…,MSEM+NAnd selecting the most matched controller to be put into the control loop in a zero trial and error mode, namely selecting the preset controller with the minimum performance index MSE, namely
Figure BDA0003338244990000112
In the load frequency fault-tolerant control system and method based on reverse behavior reconstruction, a reverse behavior reconstruction strategy is provided, a corresponding controller is designed aiming at predicted information/physical faults to ensure the dynamic performance of a fault system, prior fault information does not need to be acquired, and only the input and output measured values of the system are relied on, so that fault tolerance on the faults can be realized as far as possible, and better performance is provided for the stability control and the system stable operation of an information/physical tightly-coupled power internet of things under the complex and various fault conditions.
The following examples illustrate the effect of the reconstruction-AFTC strategy of the present application:
in the application, the load frequency control of a three-region interconnected power system shown in fig. 4 is taken as a research object to carry out simulation experiment, the power grid frequency can be obtained by a synchronous Phasor Measurement Unit (PMU) and uploaded to a control center through a communication network, meanwhile, a control instruction of the control center is sent to a synchronous generator set through the communication network, the active power supply and demand balance of the power system under the rated frequency is maintained by adjusting the active power of the synchronous generator set in real time, simulation parameters are shown in table 1, and simulation experiment parameters in table 1
Figure BDA0003338244990000113
Figure BDA0003338244990000121
Consider that only a single fault occurs for each zone, lasting 50 s. Let normal communication delay of Area 1-Area 3, Area 2 network attack delay, and Area 1-Area 3 load step fluctuation be as shown in fig. 5, 6, and 7, respectively. Table 2 shows possible operation scenarios of the three-region interconnection system, which are 8 types. The adopted comparison method comprises the following steps: a passive fault-tolerant control (PFTC) strategy that is asymptotically stable for all fault situations and an active fault-tolerant control strategy based on fault identification (fault identification-AFTC) are considered. To compare the advantages of the proposed fault-tolerant strategy with the existing fault-tolerant strategies, the Mean Square Error (MSE) of the fault situation compared to the normal situation is calculated and quantified, as shown in fig. 8.
TABLE 2 possible operational scenarios for interconnected power systems (Single failure in each zone)
Figure BDA0003338244990000122
As can be seen from FIG. 8, when the PFTC strategy is adopted, Δ f1And Δ Ptie1Is generally larger than AFTC, only slightly better than AFTC strategy in failure cases 2 and 6 (Δ f in the remaining cases)1And Δ Ptie1Root mean square error is at least 18.40% and 220% higher). The performance index of the proposed behavior reconstruction-AFTC strategy is approximately close to the control performance of the fault identification-AFTC strategy, but the proposed scheme only depends on the input/output quantity of the controlled system when selecting the best matching controller without depending on the precise input/output quantityThe prior system parameter knowledge can achieve the similar fault-tolerant effect, so the practicability is stronger.

Claims (8)

1. A load frequency fault-tolerant control system based on reverse behavior reconstruction is characterized in that: the system comprises an information acquisition module, a controller library module, a reverse behavior reconstruction module, a performance index calculation module and a controller selection module:
the information acquisition module: acquiring and sensing the operating parameters of the controlled system by using a sensor arranged on the controlled system, and generating corresponding controlled system output quantity y' (t) according to the received operating parameters;
the controller library module: aiming at the predicted information/physical fault, designing a corresponding controller to ensure the dynamic performance of a fault system and forming a basic controller library, wherein the number of controllers in the basic controller library is M + N, and the controller library is sigmac＝{Σc1,Σc2,…,Σc(M+N)}, controller library ΣcThe method comprises the steps that a controller set designed for a specific operation scene is represented, M means that a controlled system has M normal working scenes, and N means that the controlled system has N typical fault scenes;
a reverse behavior reconstruction module: according to the output quantity y '(t) of the controlled system, the control command u' (t) and the existing controller library sigmacReversely reconstructing the dynamic behavior of the controlled system in a parallel mode and obtaining the reconstructed reference input
Figure FDA0003338244980000012
Reconstructed reference input
Figure FDA0003338244980000013
Asymptotically converges to the ideal reference input r (t);
a performance index calculation module: the output quantity y' (t) of the controlled system and the reconstructed reference input
Figure FDA0003338244980000014
Calculating the root mean square error to obtain a performance index MSE;
a controller selection module: and selecting the controller input corresponding to the minimum performance index MSE, thereby realizing zero trial and error one-time accurate switching.
2. The load frequency fault tolerant control system based on reverse behavior reconstruction as claimed in claim 1, wherein: the controller set in the basic controller library is obtained by:
1) establishing a real-time control system model of information/physical faults:
giving a controlled object model in a controlled system under a normal condition:
Figure FDA0003338244980000011
wherein x ∈ Rp、u∈Rq、ω∈RrAnd y ∈ RvState variables, input variables, external disturbance variables and output variables, respectively, and the matrix A, B, H, C, D is an adaptive matrix;
modeling according to the expected fault effect in the controlled system:
(1) fault type modeling
A. Modeling data type faults, wherein under the faults, the following relation is satisfied between actual control quantity/measurement and reality:
control amount: u. off(t)＝ΓPΓCu(t)
Measuring quantity: y isf(t)＝ΨCΨPy(t)
In the formula, subscript f represents a failure state, ΓPDiag (γ P1, γ P2, …, γ Pp), representing the actuator physical fault matrix; gamma-shapedCA diag (γ C1, γ C2, …, γ Cp) represents a network fault matrix between the controller and the actuator; ΨCDiag (ψ C1, ψ C2, …, ψ Cq) representing a network information failure matrix between the sensor and the controller; ΨPDiag (ψ P1, ψ P2, …, ψ Pq) representing a sensor physical failure matrix;
B. modeling aging type fault, let tauscMesh representing sensor to controllerCollateral delay, τcaFor network delay from a controller to an actuator in a controlled system, a control instruction sent by the controller is u '(t), a received output quantity of the controlled system is y' (t), and the control instruction u (t) sent by a control center and the actual output quantity y (t) of the controlled system, which are actually received by the actuator in the controlled system, satisfy
u(t)＝u'(t+τca)
y'(t)＝y(t+τsc)
Further, the pure time-lag link is approximated by adopting the Pade approximation technology, and the state space after approximation is expressed as
Figure FDA0003338244980000021
Figure FDA0003338244980000022
In the formula, xca(t)、xsc(t) is an intermediate variable introduced by the approximation of Pade, Ak,Bk,Ck,DkAre respectively as
Figure FDA0003338244980000023
Figure FDA0003338244980000024
Where k is { sc, ca }, lkTo approximate the order, aiAnd bi(i＝1,2,…,lk) To approximate the coefficients, given by:
Figure FDA0003338244980000025
C. component fault modeling is carried out, and under the fault, the original operation structure of a controlled system is obviously changed and is expressed as abnormal state space description, namely
Figure FDA0003338244980000031
In the formula, A ', B ', H ', C ' and D ' represent controlled system matrixes after the faults occur;
(2) information/physics hybrid fault modeling
Based on mathematical modeling of three types of faults, let augmented vector X be [ X, X ═ Xca,xsc]TThe state equation of the real-time control system of the power internet of things considering the information/physical mixed fault is established as
Figure FDA0003338244980000032
In the formula, Af＝A'，Bf＝B'ΓPΓC，Hf＝H'，Cf＝ΨCΨPC'，Df＝ΨCΨPD'ΓPΓC；
2) The basic controller library paradigm design method comprises the following steps:
the real-time control system state equation has model uncertainty, so the controller design must have excellent robustness to the model uncertainty, and the application refers to the hybrid H2/H∞The method carries out the design of a basic controller to realize the robust control of the known faults;
considering that the controlled system has M normal working scenes and N typical fault scenes, the number of controllers in the basic controller library is M + N, so thatc＝{Σc1,Σc2,…,Σc(M+N)Denotes a set of controllers designed for a particular operating scenario, having the following general form:
∑cj:
Figure FDA0003338244980000033
wherein j is 1,2, …, M + N, xuj(t) intermediate variables introduced by the controller, Auj,Buj,Cuj,DujIs a controller system matrix.
3. The reverse behavior reconstruction based load frequency fault tolerant control system according to claim 1 or 2, characterized in that: mean square error MSE is defined as the reconstructed reference input
Figure FDA0003338244980000035
A measure of the degree of difference between two variables of the output y' (t) of the system to be controlled, i.e.
Figure FDA0003338244980000034
The MSE index is a numerically accurate representation of the control deviation of the various controllers without information ambiguity.
4. A load frequency fault-tolerant control method based on reverse behavior reconstruction comprises the following steps:
aiming at the predicted information/physical fault, designing a corresponding controller to ensure the dynamic performance of a fault system and forming a basic controller library, wherein the number of controllers in the basic controller library is M + N, and the order of sigma isc＝{Σc1,Σc2,…,Σc(M+N)The method comprises the following steps that a controller set designed aiming at a specific operation scene is represented, M means that a controlled system has M normal working scenes, and N means that the controlled system has N typical fault scenes;
receiving operation parameters of a sensed controlled system acquired and sensed by a sensor, and generating a corresponding controlled system output quantity y' (t) according to the received operation parameters;
receiving a control instruction u' (t) generated by a control center according to the operation parameters of the system to be controlled acquired and sensed by a sensor;
according to the output quantity y '(t) of the controlled system, the control command u' (t) and the existingController library ΣcReversely reconstructing the dynamic behavior of the controlled system in a parallel mode and obtaining the reconstructed reference input
Figure FDA0003338244980000041
Reconstructed reference input
Figure FDA0003338244980000042
Asymptotically converges to the ideal reference input r (t);
the output quantity y' (t) of the controlled system and the reconstructed reference input
Figure FDA0003338244980000043
Calculating the root mean square error to obtain a performance index MSE;
and selecting the controller input corresponding to the minimum performance index MSE, thereby realizing zero trial and error one-time accurate switching.
5. The method as claimed in claim 4, wherein the "corresponding controllers are designed to ensure the dynamic performance of the failed system for the predicted information/physical failure, and a basic controller library is formed, wherein the number of controllers in the basic controller library is M + N, such that Σc＝{Σc1,Σc2,…,Σc(M+N)The method comprises the following steps of (1) representing a controller set designed for a specific operation scene, wherein M means that a controlled system has M normal working scenes, and N means that the controlled system has N typical fault scenes, and the steps are as follows:
1) establishing a real-time control system model of information/physical faults:
giving a controlled object model in a controlled system under a normal condition:
Figure FDA0003338244980000044
wherein x ∈ Rp、u∈Rq、ω∈RrAnd y ∈ RvAre respectively provided withState variables, input variables, external disturbance variables and output variables, and the matrix A, B, H, C, D is an adaptive matrix;
modeling according to the expected fault effect in the controlled system:
(1) fault type modeling
A. Modeling data type faults, wherein under the faults, the following relation is satisfied between actual control quantity/measurement and reality:
control amount: u. off(t)＝ΓPΓCu(t)
Measuring quantity: y isf(t)＝ΨCΨPy(t)
In the formula, subscript f represents a failure state, ΓPDiag (γ P1, γ P2, …, γ Pp), representing the actuator physical fault matrix; gamma-shapedCA diag (γ C1, γ C2, …, γ Cp) represents a network fault matrix between the controller and the actuator; ΨCDiag (ψ C1, ψ C2, …, ψ Cq) representing a network information failure matrix between the sensor and the controller; ΨPDiag (ψ P1, ψ P2, …, ψ Pq) representing a sensor physical failure matrix;
B. modeling aging type fault, let tauscRepresenting the network delay, τ, from sensor to controllercaFor network delay from a controller to an actuator in a controlled system, a control instruction sent by the controller is u '(t), a received output quantity of the controlled system is y' (t), and the control instruction u (t) sent by a control center and the actual output quantity y (t) of the controlled system, which are actually received by the actuator in the controlled system, satisfy
u(t)＝u'(t+τca)
y'(t)＝y(t+τsc)
Furthermore, the pure time-lag link is approximated by adopting the Pade approximation technology, and the state space after approximation is expressed as
Figure FDA0003338244980000051
Figure FDA0003338244980000052
In the formula, xca(t)、xsc(t) is an intermediate variable introduced by the approximation of Pade, Ak,Bk,Ck,DkAre respectively as
Figure FDA0003338244980000053
Figure FDA0003338244980000054
Where k is { sc, ca }, lkTo approximate the order, aiAnd bi(i＝1,2,…,lk) To approximate the coefficients, given by:
Figure FDA0003338244980000061
C. component fault modeling is carried out, and under the fault, the original operation structure of a controlled system is obviously changed and is expressed as abnormal state space description, namely
Figure FDA0003338244980000062
In the formula, A ', B ', H ', C ' and D ' represent controlled system matrixes after the faults occur;
(2) information/physics hybrid fault modeling
Based on mathematical modeling of three types of faults, let augmented vector X be [ X, X ═ Xca,xsc]TThe state equation of the real-time control system of the power internet of things considering the information/physical mixed fault is established as
Figure FDA0003338244980000063
In the formula, Af＝A'，Bf＝B'ΓPΓC，Hf＝H'，Cf＝ΨCΨPC'，Df＝ΨCΨPD'ΓPΓC；
2) The basic controller library paradigm design method comprises the following steps:
the real-time control system state equation has model uncertainty, so the controller design must have excellent robustness to the model uncertainty, and the application refers to the hybrid H2/H∞The method carries out the design of a basic controller to realize the robust control of the known faults;
considering that the controlled system has M normal working scenes and N typical fault scenes, the number of controllers in the basic controller library is M + N, so thatc＝{Σc1,Σc2,…,Σc(M+N)Denotes a set of controllers designed for a particular operating scenario, having the following general form:
∑cj:
Figure FDA0003338244980000064
wherein j is 1,2, …, M + N, xuj(t) intermediate variables introduced by the controller, Auj,Buj,Cuj,DujIs a controller system matrix.
6. The load frequency fault-tolerant control method based on reverse behavior reconstruction as claimed in claim 5, wherein the control command u '(t) is based on the output y' (t) of the controlled system and the existing controller library ΣcReversely reconstructing the dynamic behavior of the controlled system in a parallel mode and obtaining the reconstructed reference input
Figure FDA0003338244980000065
Reconstructed reference input
Figure FDA0003338244980000066
Asymptotic convergenceThe ideal reference input r (t) comprises the following steps:
obtaining the dynamic behavior of the real-time control system:
defining a signal vector s (t) [ [ ω (t), r (t), y '(t), u' (t)]TBased on the Laplace transform and polynomial matrix elementary row transform method, the dynamic behavior of the controlled system and the controller can be described as
The controlled system:
BP＝{s＝(r,y′,u′)T|[-Np2(ξ)0Dp(ξ)-Np1(ξ)]s(t)＝0}
a controller:
Bcj＝{s＝(r,y′,u′)T|[0Ncj(ξ)-Ncj(ξ)-Dcj(ξ)]s(t)＝0}
where xi ═ d/dt, Np1(ξ)、Np2(ξ)、Dp(ξ)、Ncj(ξ)、Dcj(xi) is an adaptive polynomial matrix;
the controlled object and the controller can be known to pass through the common variables [ r (t), y' (t),
Figure FDA0003338244980000071
interconnection, dynamic behavior of controlled objects can be controlled by coupling variables [ r (t), y '(t), u' (t) to the controller]TInterconnection is realized;
according to the dynamic behavior model of the controlled system, the controller sigmacjDynamic behavior B in an arbitrary detection periodcjComplete reconstruction can be achieved only by y '(t) and u' (t), without accurate a priori system operating parameters and fault information; order to
Figure FDA0003338244980000072
Satisfy for reconstructed controller dynamic behavior
Figure FDA0003338244980000073
In the formula (I), the compound is shown in the specification,
Figure FDA0003338244980000074
is sigmacjReconstructed reference input, if ∑cjReconstructing the reference input for the controller matching the current operating state of the controlled system
Figure FDA0003338244980000075
Asymptotically converges to the ideal reference input r (t).
7. The load frequency fault-tolerant control method based on reverse behavior reconstruction as claimed in claim 6, characterized in that: "output y' (t) of system to be controlled, reconstructed reference input
Figure FDA0003338244980000076
The method comprises the following steps of calculating a root mean square error to obtain a performance index MSE';
using reconstructed reference input
Figure FDA0003338244980000077
The mean square error MSE index between the output y '(t) of the controlled system and the output y' (t) of the controlled system is used as a measurement index, and the mean square error MSE is defined as a reconstructed reference input
Figure FDA0003338244980000078
A measure of the degree of difference between two variables of the output y' (t) of the system to be controlled, i.e.
Figure FDA0003338244980000079
The MSE index is a numerically accurate representation of the control deviation of the various controllers without information ambiguity.
8. The load frequency fault-tolerant control method based on reverse behavior reconstruction as claimed in claim 7, characterized in that: the step of selecting the controller investment corresponding to the minimum performance index MSE so as to realize zero trial and error one-time accurate switching specifically comprises the following steps:
according to the performance index { MSE) obtained by calculation1,MSE2,…,MSEM+NAnd selecting the most matched controller to be put into the control loop in a zero trial and error mode, namely selecting the preset controller with the minimum performance index MSE, namely
Figure FDA0003338244980000081
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