A dual-observation model optimization control method for LFC power system based on event triggering

Abstract

The present invention discloses a dual-observation model optimization control method for an LFC power system based on event triggering. The method firstly performs classified control design for the possible matching interference and mismatching interference of the LFC system with multiple faults and multiple disturbances based on the mathematical model established by the inverse Laplace transform method. Due to the unknowability of the state and the interference, a new dual-observation modeling strategy is constructed at the same time. Then, the advantages of event triggering to reduce the channel transmission bandwidth and the advantages of performance optimization control are considered to save cost resources. This method is not only applicable to non-ideal systems such as multiple faults, multiple disturbances, uncertain nonlinearity, etc., but can also realize optimization control on the basis of robust control, thereby reducing the waste of resources.

Description

Technical Field

[0001] The present invention relates to a control method for an LFC power system, and in particular to a dual-observation model optimization control method for an LFC power system based on event triggering. Background Art

[0002] LFC is one of the core components of the power system. Traditional LFC technology uses dedicated lines for dedicated transmission, which has high maintenance costs and poor flexibility. If the fluctuation is large, the power generation equipment and power consumption devices will be severely damaged, and even the power system will be paralyzed. The further development of power system technology requires new forms of updating and iteration. The new LFC system needs to support the decentralization of the power system control process through the network architecture.

[0003] The design methods applied to LFC power systems mainly include network control and robust control. In order to coordinate the occupancy of network bandwidth and reduce the realization of data redundancy function, the LFC power system must also be able to observe the real-time data of internal states and the robustness to multiple faults and disturbances. Therefore, it is necessary to use relevant experience and theoretical knowledge to study new control algorithms related to robust optimization, online observation, etc. Summary of the invention

[0004] The purpose of this invention is to propose a dual-observation model optimization control method for LFC power system based on event triggering, which can effectively utilize relevant experience and theoretical knowledge, and achieve good control and observation effects with the least possible resource cost when there are multiple faults and disturbances in the system.

[0005] The specific technical solution of the present invention is as follows: A dual-observation model optimization control method for an LFC power system based on event triggering comprises the following steps:

[0006] The dual-observation model optimization control method of the LFC power system based on event triggering is characterized in that the control system model is established by using the Laplace inverse transform method:

[0007]

[0008] In the formula, The unknown and difficult to collect related state vectors of the LFC power system during the modeling process. is the control input transmitted from the controller end channel of the LFC power system to the actuator, d(t) is the matching interference of the external excitation source with unknown signals and nonlinear terms that can be superimposed on the controller end, w(t) is the unknown mismatching interference caused by the external environment or wave frequency disorder on the actuator end, is the output value that the sensor can receive, ΔA(t) is the unknown bounded system error matrix model set due to parameter uncertainty in the modeling process, A, B, C, G are the system matrices obtained by establishing a mathematical model for the LFC power system, where:

[0009]

[0010] Among them, the rotational inertia coefficient M g ; Power group damping coefficient E s ; Speed ​​regulator time constant T g ; Turbine time constant T ch ; Frequency offset constant s μ ; Governor droop characteristic R g ;

[0011] Classify and handle multiple faults and multiple disturbances:

[0012] ① For the matching interference of external excitation sources with unknown signals and nonlinear terms that can be superimposed on the controller side, it is proposed to design and establish an observation model for dynamic compensation. The external excitation sources with unknown signals and nonlinear terms are as follows:

[0013]

[0014] in, It is a system matrix obtained by establishing a mathematical model for multiple external excitation sources (i = 1, 2, ... N) with unknown signals and nonlinear terms, and meets the following relevant conditions:

[0015] And η(t) is unknown;

[0016] (2) The external incentive system established is considerable;

[0017] The disturbance observer that establishes the reconstruction model for the matching disturbance caused by multiple external excitation sources is as follows:

[0018]

[0019] in, is the observer gain of multiple disturbance observers, α i (t) is an intermediate variable generated by time difference of multiple reconstructed models. Since multiple disturbance observers are designed to observe multiple external excitation original systems, it is impossible to achieve a consistent solution while solving the observer gain. In order to reduce the computational workload, the models of many external excitation original systems and observer systems are unified, that is, augmented and expanded, which will produce the following conditions:

[0020] ⑴

[0021] ⑵

[0022] ⑶

[0023] ⑷

[0024] ⑸

[0025] ⑹

[0026] ② For the unknown mismatching disturbance caused by external environment or wave frequency disorder at the actuator end, the H∞ performance is considered, and the output response obtained under the influence of the mismatching disturbance is adjustable, that is, within a certain H∞ index;

[0027] Continue to design a state observer, but in the dual observation model, the disturbance observation value can be introduced into the state observer to make the state observation value closer to the actual value of the internal state of the power system. The model of the state observer is as follows:

[0028]

[0029] in: is the controller gain of the state observer. The observation value obtained by the state observer is introduced into the event generator. When the event trigger mechanism containing the threshold is met, the data is updated and transmitted, and the discrete observation state value is introduced into the network port for use by unnecessary external systems. The event trigger mechanism is as follows:

[0030]

[0031] Where Φ is the weighted matrix of event-triggered control, μ is the event trigger threshold that constrains data transmission, and k is the trigger sequence. In addition, H2 optimization items can be added during design to optimize the control method.

[0032]

[0033] Analyze the effectiveness of the design method:

[0034] C001: Establish an error system for the designed observer and controller:

[0035] C002: To establish an error system, the internal state of the control system must first be able to converge;

[0036] C003:

[0037] C004: Establish an error system and then satisfy the requirement that the observation errors of multiple disturbance observers can converge;

[0038] C005:

[0039]

[0040] C006: Unify the error system model of many disturbance observers;

[0041] C007:

[0042] C008: Establish the error system so that the final state observer can converge the observation error of the unknown internal state of the power system;

[0043] C009:

[0044] C010: Establish an error system for the designed observer and controller:

[0045] C011: in

[0046] C012:

[0047] C013: and

[0048] C014: Select the appropriate Lyapunov functional as follows:

[0049] C015:

[0050] C016:

[0051] C017: Comprehensively considering the control effect, observation effect and optimization effect, the following inequality is satisfied:

[0052] C018:

[0053] C019: Among them, is the measured output of the error system;

[0054] C020: If the system is to satisfy the control effect, observation effect and optimization effect, the controller gain and observer gain need to be feasible solutions of the following LMI:

[0055] C021:

[0056] C022: Among them,

[0057] C023:

[0058] C024: Among them, and By solving the LMI in B004, we can obtain the controller gain and observer gain that satisfy various effects.

[0059] C025: Therefore, according to C018, C021, and C024, we can get the relevant proof to obtain the stability of the error system under the double observation model;

[0060] C026: According to C025, the convergence of the power system state and the convergence of the observation error can be guaranteed;

[0061] C027: According to C026, it can be proved that the designed controller and observer can effectively achieve control effect, observation effect and optimization effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 is a flow chart of a method according to an embodiment of the present invention;

[0063] Figure 2 The state response diagram of the first state of processing multiple external interference excitation sources by the method proposed by the present invention is shown in the embodiment;

[0064] Figure 3 The state response diagram of the second state of processing multiple external interference excitation sources by the method proposed by the present invention is an embodiment;

[0065] Figure 4 The state response diagram of the third state of processing multiple external interference excitation sources by the method proposed by the present invention is shown in the embodiment;

[0066] Figure 5 The interference observation diagram of the first external interference excitation source using the method proposed by the present invention is shown in the embodiment;

[0067] Figure 6 The interference observation diagram of the second external interference excitation source using the method proposed by the present invention is shown in the embodiment;

[0068] Figure 7 The interference observation diagram of the third external interference excitation source using the method proposed by the present invention is shown in the embodiment;

[0069] Figure 8 The embodiment adopts the execution interval diagram of the event triggering in the method proposed by the present invention;

[0070] Fig. 9 The trigger error and observation error response diagram of the embodiment using the method proposed by the present invention; DETAILED DESCRIPTION

[0071] The present invention is further explained below in conjunction with specific embodiments. It should be understood that these embodiments are only used to illustrate the present invention and are not used to limit the scope of the present invention. After reading the present invention, various equivalent forms of modifications to the present invention by those skilled in the art all fall within the scope defined by the claims attached to this application.

[0072] like Figure 1 As shown, a dual-observation model optimization control method for an LFC power system based on event triggering includes the following steps:

[0073] Step 1: Set various parameters:

[0074] Moment of inertia M g =0.1667; Power group damping coefficient E s =0.0085; speed regulator time constant T g =0.08; turbine time constant T ch =0.3; frequency offset constant s μ =0.425; speed regulator droop characteristic R g =2.

[0075] Step 2: monitor the sampled observation status in real time, and introduce the network port to the observation status that meets the event trigger mechanism;

[0076] Step 3: If the state observation value of the LFC power system is found to not meet the event triggering condition during the power grid monitoring process, the channel state value of the last trigger will be transmitted;

[0077] Step 4: Repeat steps 2 and 3, and continue to operate after the power system is stable;

[0078] Here is a real case:

[0079] Figure 1 is a flow chart of a method according to an embodiment of the present invention; Figure 2-4 The state response diagram of the first to third states of processing multiple external interference excitation sources by the method proposed by the present invention is shown in the embodiment; Figure 5-7 The interference observation diagrams of the first to third external interference excitation sources using the method proposed by the present invention are shown in the embodiment; Figure 8 The embodiment adopts the execution interval diagram of the event triggering in the method proposed by the present invention;

[0080] Fig. 9 The trigger error and observation error response diagram of the embodiment using the method proposed by the present invention;

[0081] It can be seen from the figure that the designed method can obtain relatively satisfactory control and observation performance.

[0082] References

[0083] [1]Yao X,Lian Y,Park J H.Disturbance-observer-based event-triggeredcontrol for semi-Markovianjump nonlinear systems[J].Applied Mathematics andComputation,2019,363:124597.

[0084] [2]Liu L,Chen M,Li T,et al.Composite Anti-Disturbance Reference ModelL 2-L∞L_∞Control for Helicopter Slung Load System[J].Journal ofIntelligent&Robotic Systems,2021,102:1-21.

Claims

1. A dual-observation model optimization control method for LFC power system based on event triggering, characterized in that: The following steps are involved: Establish the control system model of LFC power system by using inverse Laplace transform method; Classify and handle multiple faults and multiple disturbances, and use different methods to suppress the impact of disturbances on the system; Establish a dual observation model to observe the unknown state and unknown disturbance of the power system; Designed with H2 optimized event-triggered controller to effectively reduce network port transmission bandwidth; Use actual examples to simulate and verify the effectiveness of the control method; Among them, the control system model established by Laplace inverse transform is: In the formula, The unknown and difficult to collect related state vectors of the LFC power system during the modeling process. is the control input transmitted from the controller end channel of the LFC power system to the actuator, d(t) is the matching interference of the external excitation source with unknown signals and nonlinear terms that can be superimposed on the controller end, w(t) is the unknown mismatching interference caused by the external environment or wave frequency disorder on the actuator end, is the output value that the sensor can receive, ΔA(t) is the unknown bounded system error matrix model set due to parameter uncertainty in the modeling process, A, B, C, G are the system matrices obtained by establishing a mathematical model for the LFC power system, in: Among them, the rotational inertia coefficient M g ; Power group damping coefficient E s ; Speed ​​regulator time constant T g ; Turbine time constant T ch ; Frequency offset constant s μ ; Thermal turbine participation factor R g ; Among them, the multi-fault and multi-disturbance situations are classified and processed: ① For the matching interference of external excitation sources with unknown signals and nonlinear terms that can be superimposed on the controller side, it is proposed to design and establish an observation model for dynamic compensation. The external excitation sources with unknown signals and nonlinear terms are as follows: in, It is the system matrix obtained by establishing a mathematical model for multiple external excitation sources (i = 1, 2, ... N) with unknown signals and nonlinear terms, and meets the following relevant conditions: ⑴ And η(t) is unknown; (2) The external incentive system established is considerable; The disturbance observer that establishes the reconstruction model for the matching disturbance caused by multiple external excitation sources is as follows: in, is the observer gain of multiple disturbance observers, α i (t) is an intermediate variable generated by time difference of multiple reconstructed models. Since multiple disturbance observers are designed to observe multiple external excitation original systems, it is impossible to achieve a consistent solution while solving the observer gain. In order to reduce the computational workload, the models of many external excitation original systems and observer systems are unified, that is, augmented and expanded, which will produce the following conditions: ⑴ ⑵ ⑶ ⑷ ⑸ ⑹ ② For the unknown mismatching disturbance caused by external environment or wave frequency disorder at the actuator end, the H∞ performance is considered, and the output response obtained under the influence of the mismatching disturbance is adjustable, that is, within a certain H∞ index; Continue to design a state observer, but in the dual observation model, the disturbance observation value can be introduced into the state observer to make the state observation value closer to the actual value of the internal state of the power system. The model of the state observer is as follows: in: is the controller gain of the state observer. The observation value obtained by the state observer is introduced into the event generator. When the event trigger mechanism containing the threshold is met, the data is updated and transmitted, and the discrete observation state value is introduced into the network port for use by unnecessary external systems. The event trigger mechanism is as follows: Where Φ is the weighted matrix of event-triggered control, μ is the event trigger threshold that constrains data transmission, and k is the trigger sequence. In addition, H2 optimization items can be added during design to optimize the control method. Among them, the effectiveness of the design method is analyzed: B001: Establish an error system for the designed observer and controller: in, and B002: Select the appropriate Lyapunov functional as follows: in B003: Comprehensively considering the control effect, observation effect and optimization effect, the following inequality is satisfied: in, is the measured output of the error system; B004: If the system is to satisfy the control effect, observation effect and optimization effect, the controller gain and observer gain need to be feasible solutions of the following LMI: in, and Π 25 =y1CG; P 37 =μΦ+Q; P 77 =-Φ+μΦ+Q; B005: Among them, and By solving the LMI in B004, we can obtain the controller gain and observer gain that satisfy various effects. B006: Therefore, according to B003, B004, and B005, we can get the relevant proof to obtain the stability of the error system under the double observation model; B007: According to B006, the convergence of the power system state and the convergence of the observation error can be guaranteed; B008: According to B007, it can be proved that the designed controller and observer can effectively achieve control effect, observation effect and optimization effect.

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