Novel oscillation damping control method

Abstract

The invention discloses a novel oscillation damping control method, which comprises the following steps of: enabling a system to be controlled to be equivalent to a single-input single-output discrete time nonlinear system, and establishing a novel dynamic linearization data model with a pseudo order L based on a plurality of historical output data; calculating a pseudo partial derivative estimated value # imgabs0 # by using input and output data of a to-be-controlled system, and setting different constraint conditions and initial values for different items in a pseudo partial derivative vector; based on a common control input criterion function, a control algorithm fully utilizing historical output data of a system is provided; the first item and the last item of historical output data in the sliding time window are reserved, the other items are sequentially sampled according to a certain interval, and a sparse vector with the length being m (m is smaller than or equal to L-1) is formed again; the influence of historical input signals except the m sampling signals on the system is ignored, and a new pseudo-partial derivative estimation algorithm and control algorithm are obtained. Compared with the prior art, the oscillation suppression effect is better, and the calculation amount and the storage burden are greatly reduced on the premise that the control effect is guaranteed.

Description

Technical Field

[0001] The invention belongs to the field of electric power technology, relates to oscillation suppression technology, and in particular to a new method for oscillation damping control. Background Art

[0002] In modern control theory, the design of the controller is usually based on the mathematical model of the controlled system. This model-driven control method has unmodeled dynamics and other uncertainties, and even a small modeling error may lead to control instability. With the introduction of model-free adaptive control theory proposed by Professor Hou Zhongsheng, this data-driven control method that does not require system modeling has quickly verified its correctness and usability in various fields.

[0003] In the dynamic linearization process of model-free adaptive control, there are three methods: compact format dynamic linearization, partial format dynamic linearization, and full format dynamic linearization, depending on the length of the required historical input data. Among them, the model-free adaptive scheme based on compact format and partial format can ensure the monotonic convergence of the closed-loop system tracking error and the bounded input-output stability. The compact format dynamic linearization method only requires the historical data of the previous moment, and the method is the easiest to implement, but it is difficult to ensure the accuracy of the data model; the partial format dynamic linearization method is based on multiple historical input data and can better capture the complex dynamics existing in the original system; among the existing dynamic linearization methods, only the full format dynamic linearization method needs to use the historical output data of the system, but its stability is worse than that of the compact format and partial format methods. At the same time, as the amount of historical data used increases, the description of the system becomes more accurate, and the complexity of each component in the control process will also decrease, but it greatly increases the requirements for calculation and storage.

[0004] In the current common damping control design, the direct measurement signal of the system is usually selected and applied to the signal input of the controller. It can be considered that the conventional damping controller is more dependent on the output signal of the system and its historical value. However, there is no universal method for the design and algorithm implementation of the damping controller.

[0005] In the prior art, although some scholars have successfully explored oscillation suppression using model-free adaptive control methods, they have not analyzed and solved the above-mentioned problems.

[0006] Purpose of the Invention

[0007] The purpose of the present invention is to solve the deficiencies of the prior art. The present invention proposes a new method for oscillation damping control, which has a better control effect than the prior art and does not require high computing and storage capabilities. Summary of the invention

[0008] According to a first aspect of the present application, a novel dynamic linearization method is provided, which may include the following steps:

[0009] Step 1: Convert the system to be controlled into a single-input single-output discrete-time nonlinear system;

[0010] In general, a single-input single-output discrete-time nonlinear system is represented as follows:

[0011] y(k+1)=f(y(k),…,y(kn y ),u(k),…,u(kn u )) (1)

[0012] Among them, u(k)∈R and y(k)∈R represent the input and output of the system at time k, respectively, and n u 、n y is the dimension of the system input and output, f(…) is the unknown nonlinear function that characterizes the system;

[0013] Step 2: Select historical data related to system output;

[0014] It is considered that the output change of the system at the next moment is related to the control input change at the current moment and several outputs at the previous moment. Assuming that the order of the system data model is L, the L-order vector H in a sliding time window [k-L+2, k] L (k)=[Δu(k),y(k),y(k-1),…,y(k-L+2)] T , is all the historical input and output data related to the output change Δy(k+1) of the system at the next moment;

[0015] Step 3: Considering the constraints, a new dynamic linearized data model is established;

[0016] For constraints ①: f(…) with respect to the (n y +2) to (n y +L+1) variables have continuous partial derivatives, constraint ②: the system that satisfies the generalized Lipschitz condition (1) establishes a new dynamic linearization data model:

[0017] Δy(k+1)=φ L T (k)H L (k) (2)

[0018] And for any k, φ(k)=[φ1(k),…,φ L (k)] T is bounded.

[0019] It should be noted that during the partial format dynamic linearization process, 1≤L≤n u And L is a positive integer; reasonable selection of L can improve the flexibility of the dynamic linearization data model in equivalently describing the original nonlinear system;

[0020] According to a second aspect of the present application, a novel control algorithm design method including sparse sampling is provided, which may include the following steps:

[0021] Step 1: Obtain a general control algorithm based on the input criterion function;

[0022] (1) Consider that the output change Δy(k+1) of the system at time k+1 is equal to the input change Δu(k) at the current time and the output Y(k) in the sliding time window [k-L+2,k] = [y(k), y(k-1), …, y(k-L+2)] T sensitive;

[0023] (2) Generally, the input criterion function of model-free adaptive control is:

[0024] J(u(k))=|y * (k+1)-y(k+1)| 2 +λ|u(k)-u(k-1)| 2 (3)

[0025] Among them, λ>0 is a weight factor, y * (k+1) is the desired output signal; Substitute equation (2) into the criterion function (5), take the derivative of u(k) and set it to 0. The general control algorithm is:

[0026]

[0027] Step 2: Introduce step size factor for the control algorithm;

[0028] In order to make the control algorithm design more flexible, the step size factor ρ is introduced i ∈(0,1], i = 0,1,2,…,L, where the typical value of ρ0 is 1, a more flexible control algorithm is obtained:

[0029]

[0030] Step 3: Output the historical vector Y(k) = [y(k), y(k-1), ..., y(k-L+2)] T Perform sparse sampling;

[0031] (1) Vector Y(k)=[y(k),y(k-1),…,y(k-L+2)] T The length is L-1;

[0032] (2) To ensure the length of the occupied time window, the first item y(k) and the last item y(k-L+2) are retained in Y(k), and the remaining elements are sampled in equal or unequal intervals according to the time interval or label interval. The new vector formed after sampling is recorded as Y a (k), vector Y a (k) the length is m, m≤L-1, and m is a positive integer;

[0033] (3) Step 3: For the Y in the new vector a (k) Each element is re-labeled in turn, denoted by Y a (k)=[y1,y2,…,y m ] T =[y(k),y(k-a1),y(k-a2),…,y(ka m-2 ),y(k-L+2)] T ;

[0034] Step 4: Update the new control algorithm after sparseness;

[0035] Ignoring the influence of historical signals other than m sampling signals on the system, a new control algorithm is obtained:

[0036]

[0037] Among them, the pseudo partial derivative sparse set There are also separate pseudo-partial derivatives Step factor ρ i ∈(0,1], i=0,1,2,…,m+2;

[0038] According to a third aspect of the present application, a desirable adaptive algorithm for pseudo partial derivatives in a novel damping control method is provided, which may include the following steps:

[0039] Step 1: Let the vector containing the historical data to be used be H L (k)=[Δu(k),y(k),y(k-1),…,y(k-L+2)] T , then the H of the previous moment L (k-1)=[Δu(k-1),y(k-1),y(k),…,y(k-L+1)] T , the pseudo partial derivative estimation algorithm at time k is

[0040]

[0041] Among them, η∈(0,2], μ>0;

[0042] Step 2: When or Added reset algorithm:

[0043]

[0044] in, for The initial value of , ε is a small positive number;

[0045] Step 3: Consider Different constraints are set for different items in Execute steps 1 and 2 and take the initial value is positive; Only execute step 1 and take the initial value Both are 0.

[0046] Step 4: After the algorithm is sparsely sampled, the pseudo-partial derivative estimate is sparsely sampled accordingly, and the new vector Y obtained after the historical data vector Y(k) is sparsely sampled is a (k)=[y1,y2,…,y m ] T =[y(k),y(k-a1),y(k-a2),…,y(ka m-1 )] T , The algorithm can be rewritten as:

[0047] BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 A simulation system model for IEEE 39 nodes connected to a doubly-fed wind turbine generator system.

[0049] Figure 2 This is the oscillation of the grid-connected active power of the doubly-fed wind turbine without control. Divergent subsynchronous oscillations begin to occur after 1 second.

[0050] Figure 3 This is an implementation block diagram of the novel adaptive oscillation suppression method.

[0051] Figure 4 The present invention is specifically implemented in a damping controller of a doubly-fed wind turbine generator system.

[0052] Figure 5 It is the model-free adaptive oscillation suppression effect of the traditional partial format dynamic linearization, L=100.

[0053] Figure 6 This is the suppression effect of a new method for oscillation damping control in the present invention, L=100. DETAILED DESCRIPTION

[0054] The embodiments are described in detail below in conjunction with the accompanying drawings.

[0055] Taking the calculation example of IEEE 39 node access to double-fed wind turbine in PSCAD / EMTDC simulation platform as an example, Figure 1 The wiring diagram of the simulation system model is shown in Figure 2. The double-fed wind turbine generator set is connected to the grid via a series compensation line. The grid-connected active power of the double-fed wind turbine generator set is taken as the observation object. Without additional control, Figure 2 As shown, divergent subsynchronous oscillations begin to occur at 1 second.

[0056] Figure 3 The implementation block diagram of the new adaptive oscillation suppression method is given. Figure 4 is a specific implementation method on the damping controller of the doubly-fed wind turbine generator set, taking the difference Δu between the actual value and the reference value of the grid-connected voltage of the doubly-fed wind turbine generator set ds As the system output y(k), the output expected value y * (k) is 0; the system input u(k) obtained by the adaptive oscillation suppression method is = Δu dMFAC Attached to the d-axis inner ring of the inverter on the rotor side of the doubly-fed wind turbine. The damping controller is put into control at 1.1 seconds.

[0057] In this example, the parameters of the adaptive oscillation suppression controller are: λ = 0.5; μ = 8.665; η = 0.79; ρ0 = 0.5, ρ i =0.9, i=1,2,…,m+1; ε=0.001;

[0058] When the sliding time window length L is 100 and sparse sampling is not considered, Figure 5 The oscillation suppression effect under the traditional partial format dynamic linearization model-free adaptive oscillation suppression method is given. Figure 6 The suppression effect of a new method of oscillation damping control in the present invention is given. It can be seen that this new control algorithm achieves oscillation suppression more quickly, and the ripple after suppression is smaller.

[0059] The inventor of the present invention compared the control effects before and after sparse sampling in another patent under review, "A New Adaptive Oscillation Suppression Method", which will not be repeated in this specification. It has been found that after sparse sampling, the speed and effect of oscillation suppression will not be significantly affected, and the computational workload and storage burden can be greatly reduced.

[0060] In summary, the oscillation suppression effect of the method of the present invention is better than that of the traditional partial format model-free adaptive control method, and the calculation amount and storage burden brought by the traditional dynamic linearization model are reduced while ensuring the control effect.

[0061] The method of the present invention is not only applicable to the suppression of subsynchronous oscillations in the power system in the embodiment, but is also applicable to the control and prevention of oscillations in actual systems in other fields, including but not limited to electromagnetic stable operation, electromechanical stable operation, chemical process, linear motor engineering, injection molding process, automation engineering and all other control fields that need to eliminate or weaken oscillation interference.

[0062] Those skilled in the art should understand that the embodiments of the present invention are only to describe the preferred specific implementation methods of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by those skilled in the art within the technical scope disclosed in the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A new method for oscillation damping control, characterized in that: The following steps are involved: Step 1: Equivalently treat the system to be controlled as a single-input single-output discrete-time nonlinear system. Considering that the output change Δy(k+1) of the system at time k+1 is sensitive to some input changes and historical outputs in a fixed-length sliding time window before time k, a new linearized data model of the system to be controlled is established: Among them, Δy(k+1)=y(k+1)-y(k) is the difference between the system outputs at two adjacent moments, φ Ly,Lu (k)=[φ1(k),φ2(k),…,φ L (k)] T ∈R L is an unknown but bounded pseudo-partial derivative, H L (k)=[Δu(k),y(k),y(k-1),…,y(k-L+2)] T , L is the pseudo-order of the system; Step 2: Remember H L The new vector Y(k)=[y(k),y(k-1),…,y(k-L+2)] reconstructed from the historical output in (k) T , the control algorithm of the system input is: Among them, the step factor ρ i ∈(0,1], i=0,1,…,L; is the unknown pseudo partial derivative φ i (k) estimated value; Step 3: For Y(k) = [y(k), y(k-1), ..., y(k-L+2)] T Perform sparse sampling, sample m historical outputs at a certain interval, and obtain a sparse new vector: Y a (k)=[y1,y2,…,y m ]=[y(k),y(k-a1),…,y(ka m-1 )] (3) Ignoring the influence of historical signals other than m sampling signals on the system, a new control algorithm is obtained: Among them, the pseudo partial derivative sparse set There are also separate pseudo-partial derivatives Step factor ρ i ∈(0,1], i=0,1,2,…,m+2.

2. The method according to claim 1, characterized in that: According to the general single-input single-output discrete-time nonlinear system, a new linearized data model of the system to be controlled is established, including the following steps: Step 1: Generally, a single-input single-output discrete-time nonlinear system is represented as follows: y(k+1)=f(y(k),…,y(k-n y ),u(k),…,u(k-n u )) (5) Among them, u(k)∈R and y(k)∈R represent the input and output of the system at time k, respectively, and n u 、n y is the dimension of the system input and output, f(…) is the unknown nonlinear function that characterizes the system; Step 2: Assume that the output change of the system at the next moment is related to the control input change at the current moment and several outputs at the previous moment. Assuming that the order of the system data model is L, the L-order vector H in a sliding time window [k-L+2, k] is L (k)=[Δu(k),y(k),y(k-1),…,y(k-L+2)] T , is all the historical input and output data related to the output change Δy(k+1) of the system at the next moment; Step 3: For constraints (1) that are satisfied at the same time: f(…) with respect to the (nth y +2) to (n y +L+1) variables have continuous partial derivatives respectively, constraint (2): the system that satisfies the generalized Lipschitz condition (5) establishes a new dynamic linear data model as shown in formula (1): And for any k, φ(k)=[φ1(k),…,φ L (k)] T is bounded.

3. The method according to claim 1, characterized in that: For the unknown pseudo partial derivative φ i (k) Taking the estimated value, considering the adaptability of the control algorithm, a desirable estimation algorithm includes the following steps: Step 1: Let the vector containing the historical data to be used be H L (k)=[Δu(k),y(k),y(k-1),…,y(k-L+2)] T , then the H of the previous moment L (k-1)=[Δu(k-1),y(k-1),y(k),…,y(k-L+1)] T , the pseudo partial derivative estimation algorithm at time k is Among them, η∈(0,2], μ>0; Step 2: When or Added reset algorithm: in, for The initial value of , ε is a small positive number; Step 3: Consider Different constraints are set for different items in Execute steps 1 and 2 and take the initial value is positive; Only execute step 1 and take the initial value Both are 0. Step 4: After the algorithm is sparsely sampled, the pseudo-partial derivative estimate is sparsely sampled accordingly, and the new vector Y obtained after the historical data vector Y(k) is sparsely sampled is a (k)=[y1,y2,…,y m ] T =[y(k),y(k-a1),y(k-a2),…,y(ka m-1 )] T , The algorithm can be rewritten as:

4. The method according to claim 1, characterized in that: The partial format dynamic linearization model determined based on a sliding time window with a fixed length of L-1 includes: 1≤L≤n u And L is a positive integer; reasonable selection of L can improve the flexibility of the dynamic linearization data model in equivalently describing the original nonlinear system.

5. The method according to claim 1, characterized in that: The control algorithm u(k) at time k is calculated based on the historical information in the sliding time window [k-L+2,k] of length L, including: It is considered that the output change Δy(k+1) of the system at time k+1 is sensitive to the input change Δu(k) at the current time and the output Y(k) in the sliding time window [k-L+2,k]; Generally, the input criterion function of model-free adaptive control is: J(u(k))=|y * (k+1)-y(k+1)| 2 +λ|u(k)-u(k-1)| 2 (11) Substitute equation (1) into the criterion function (11), take the derivative of u(k) and set it to 0. The general control algorithm is: In order to make the control algorithm design more flexible, the step size factor ρ is introduced i ∈(0,1], i = 0, 1, 2, ..., L, where the typical value of ρ0 is 1, the control algorithm is as follows:

6. The method according to claim 1, characterized in that: Sparse sampling of the system output in the sliding time window includes the following steps: Step 1: In the sliding time window [k-L+2, k] with a length of L-1, the historical signal H required in the control algorithm of formula (2) L (k)=[Δu(k),y(k),y(k-1),…,y(k-L+2)] T , vector H L (k) is of length L, where the output part Y(k) = [y(k), y(k-1), ..., y(k-L+2)] T , the length of vector Y(k) is L-1; Step 2: To ensure the length of the occupied time window, retain the first item y(k) and the last item y(k-L+2) in Y(k), and sample the remaining elements in equal or unequal intervals according to the time interval or label interval. The new vector formed after sampling is recorded as Y a (k), vector Y a (k) the length is m, m≤L-1, and m is a positive integer; Step 3: For the Y in the new vector a (k) Each element is re-labeled in turn, denoted by Y a (k)=[y1,y2,…,y m ] T =[y(k),y(k-a1),y(k-a2),…,y(ka m-2 ),y(k-L+2)] T .

7. The method according to claim 1, characterized in that: The output y of the controlled system will gradually approach the expected output value y under control. * ,include: y * (k+1) is the given bounded expected output signal; y * (k+1) can be a constant to keep the system output constant; y * (k+1) can also take a bounded time-varying function that changes with time, so that the system output tracks the function; the output actual value y(k+1) and the expected value y * The tracking error between (k+1) will converge to zero asymptotically.

8. The method according to claim 1, characterized in that: All control parameters included in this adaptive oscillation suppression scheme include: λ>0;μ>0;η∈(0,2];ρ i ∈(0,1], i=0,1,2,…,m+1; ε is a small positive number; The values ​​of various parameters can be adjusted according to the control requirements, and the size constraints of the control parameters can also be appropriately changed, thereby improving the adjustable freedom of the control method and greater design flexibility.

9. The method according to claim 1, characterized in that: New damping control methods for oscillation suppression include: The small disturbance stability control and protection of the power system (such as suppression of low-frequency oscillation, sub-synchronous oscillation, wide-band oscillation, etc.) is also applicable to the control and prevention of oscillations in actual systems in other fields, including but not limited to electromagnetic stable operation, electromechanical stable operation, chemical processes, linear motor engineering, injection molding processes, automation engineering and all other control fields that require elimination or reduction of oscillation interference.

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