New energy power generation adaptive damping control method and device

Abstract

The invention relates to the technical field of new energy power system control, and discloses a new energy power generation adaptive damping control method and device. The method comprises the steps of judging a system state and a system working condition of a power system by using a power signal in a preset period; when it is detected that the system state is a steady state and the system working condition changes, the real-time electric power signal serves as a first measurement signal, input-output identification is conducted through a random subspace identification method, and an output identification model is obtained; selecting a target oscillation mode with the minimum damping ratio based on the output identification model and the real-time power signal; calculating geometric observability indexes of the plurality of second measurement signals to the target oscillation mode; determining the second measurement signal with the highest geometric observability index as an update signal; and inputting the update signal to the damping controller, and performing optimization control on the preset controlled object mathematical model. According to the method, active modulation and reactive modulation are considered, the structure and parameters of the damping controller are adaptively updated, and the damping control effect is enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of renewable energy power system control, and in particular to a renewable energy power generation adaptive damping control method and device. Background Art

[0002] As the proportion of renewable energy in the power grid continues to rise, renewable energy has an increasingly greater impact on the power system. The damping characteristics of renewable energy stations are crucial to the transient stability and small disturbance stability of the power system. Insufficient damping can easily lead to ineffective suppression of oscillations in the system. Under small disturbances or grid failures, these oscillations are excited, which can easily lead to line overload or equipment damage, and in severe cases, cause power system instability. Wind and solar renewable energy are mostly based on power electronic converters for grid connection, and the active power and reactive power control of renewable energy units / stations are decoupled and respond quickly. Taking advantage of this feature, existing literature has proposed a method of additional damping control, which adjusts the active power or reactive power output of renewable energy units / stations by adding a damping controller to the renewable energy active control loop or reactive control loop to achieve the purpose of damping oscillations.

[0003] However, the existing technology mainly considers single damping control based on active power modulation or reactive power modulation of new energy, that is, active damping control or reactive damping control. After changes in system topology, operating mode, etc., due to changes in the controllability and observability of the oscillation mode by the input and output of the damping controller, there is a possibility that the effects of active damping control and reactive damping control will increase while the other will decrease. Therefore, the single damping control based on active or reactive modulation has the risk of deterioration of damping effect or even failure after changes in operating conditions. In addition, the above-mentioned damping controllers are mostly fixed parameter damping controllers designed based on typical operating conditions, which are difficult to adapt to the complex and changeable operating conditions of actual power systems. Summary of the invention

[0004] The present invention provides a method and device for adaptive damping control of renewable energy power generation, which comprehensively considers active modulation and reactive modulation when performing damping control, thereby improving the damping control effect; and enhances the adaptability of the damping controller to complex working conditions by adaptively updating the damping controller structure and parameters.

[0005] In order to solve the above technical problems, the present invention provides a new energy power generation adaptive damping control method, comprising:

[0006] Determine the system state and system operating condition of the power system by using the power signal within a preset period;

[0007] When it is detected that the system state is steady and the system operating condition changes, the real-time power signal is used as the first measurement signal, and the random subspace identification method is used to perform input-output identification to obtain an output identification model;

[0008] Based on the output identification model and the real-time power signal, selecting a target oscillation mode with a minimum damping ratio;

[0009] Calculating geometric observability indicators of a plurality of second measurement signals for the target oscillation mode; wherein the plurality of second measurement signals are power signals of different categories collected in real time at different locations of the power system;

[0010] Determine the second measurement signal with the highest geometric observability index as the update signal;

[0011] The update signal is input into the damping controller to optimize the control of the preset mathematical model of the controlled object.

[0012] Further, the system state and system operating condition of the power system are determined by using the power signal within a preset period, specifically:

[0013] Calculating a first data variation amplitude of the electric power signal within a preset period;

[0014] When the variation range of the first data is less than the preset first variation range, determining that the system state of the power system is a steady state;

[0015] When it is determined that the system state of the power system is a steady state, obtaining a power signal of a previous cycle of the preset cycle;

[0016] Acquire a second data change amplitude between a power signal of a preset cycle and a power signal of a previous cycle;

[0017] When the second data change range is greater than a preset second change range, it is determined that the system operating condition of the power system has changed.

[0018] Furthermore, the output identification model is specifically:

[0019]

[0020] Where k is the discrete sampling time; x k+1 and x k are the states of the output identification model at different times; y k is the first measurement signal; w k and v k is white noise with a mean of 0; A d is the system matrix of the output identification model; C d is the output matrix of the output identification model.

[0021] Furthermore, based on the output identification model and the real-time power signal, the target oscillation mode with the minimum damping ratio is selected, specifically:

[0022] Calculating a system matrix in a continuous time domain based on the system matrix of the output identification model and a sampling time interval of the real-time power signal;

[0023] By calculating the eigenvalue of the system matrix in the continuous time domain, the damping ratio of each oscillation mode is obtained;

[0024] The oscillation mode with the smallest damping ratio is determined as the target oscillation mode.

[0025] Furthermore, the calculation of the geometric observability index of the target oscillation mode for the plurality of second measurement signals is specifically:

[0026] The formula for calculating the geometric observability index is:

[0027]

[0028] Among them, m oi (j) is the geometric observability index of the second measurement signal j to the target oscillation mode; c j is the jth row of the output matrix of the output identification model; φ i is the j-th column of the right eigenvector matrix of the system matrix in the continuous time domain; the operator |·| represents the calculation of the vector modulus; the operator ||·|| represents the calculation of the Euclidean norm.

[0029] Furthermore, the update signal is input into the damping controller to optimize the mathematical model of the preset controlled object, specifically:

[0030] When the update signal is input to the damping controller, the feedback signal of the damping controller is updated, and the first test signal is added to the preset new energy station power instruction;

[0031] Taking the preset new energy station power command as the input signal and the feedback signal as the output data, performing input-output identification based on the subspace identification algorithm to obtain a mathematical model of the controlled object;

[0032] Converting a discrete model of a deterministic part of a discrete controlled object in the mathematical model of the controlled object into a continuous model of the controlled object;

[0033] Updating the output identification model using the continuous model of the controlled object;

[0034] The optimal state feedback control quantity is updated for the updated output identification model.

[0035] Furthermore, the updating of the optimal state feedback control amount of the updated output identification model is specifically as follows:

[0036] Determine the optimal feedback gain matrix of the output identification model based on the damping ratio of the target oscillation mode;

[0037] Based on the state observation value obtained by observing the output identification model through the Kalman filter, the state of the output identification model is obtained;

[0038] Based on the optimal feedback gain matrix of the output identification model and the state of the output identification model, the optimal state feedback control quantity of the output identification model is updated. The specific formula is:

[0039] u=-Kx

[0040] Among them, u is the optimal state feedback control quantity of the output identification model; K is the optimal feedback gain matrix of the output identification model; x is the state of the output identification model.

[0041] Furthermore, the optimal feedback gain matrix of the output identification model is determined based on the damping ratio of the target oscillation mode, specifically:

[0042] comparing the damping ratio of the target oscillation mode with a preset damping ratio threshold;

[0043] When the damping ratio of the target oscillation mode is greater than a preset damping ratio threshold, determining the optimal feedback gain matrix of the output identification model as a preset optimal feedback gain matrix;

[0044] When the damping ratio of the target oscillation mode is less than a preset damping ratio threshold, updating the weights of a preset weight matrix;

[0045] Based on the updated weights of the preset weight matrix, the optimal feedback gain matrix of the output identification model is solved.

[0046] Furthermore, the state observation value obtained by observing the output identification model based on the Kalman filter is used to obtain the state of the output identification model, specifically:

[0047] The Kalman filter is used to observe the state of the output identification model and obtain the state observation value;

[0048] The state of the output identification model is calculated based on the state observation value, the system matrix of the output identification model, the input matrix and the output matrix.

[0049] The present invention provides a new energy power generation adaptive damping control method, which uses power signals within a preset period to judge the system state and system operating conditions of the power system; when it is detected that the system state is steady and the system operating conditions change, the real-time power signal is used as the first measurement signal, and the random subspace identification method is used to perform input-output identification to obtain an output identification model; based on the output identification model and the real-time power signal, the target oscillation mode with the smallest damping ratio is selected; the geometric observability index of several second measurement signals to the target oscillation mode is calculated; the second measurement signal with the highest geometric observability index is determined as the update signal; the update signal is input to the damping controller to optimize the preset mathematical model of the controlled object. The present invention makes full use of the damping advantages of active modulation and reactive modulation under different working conditions, so as to achieve good damping effects under multiple working conditions, avoid or reduce the risk of the damping effect of a single damping control based on active or reactive modulation becoming worse or even failing after the working conditions change; the present invention also enhances the adaptability of the controller to complex working conditions by online identification and updating of feedback input signals and controller parameters.

[0050] Accordingly, the present invention provides a new energy power generation adaptive damping control device, comprising: a system judgment module, a system identification module, an oscillation mode determination module, an index calculation module, a signal determination module and an optimization control module;

[0051] The system determination module is used to determine the system state and system operating condition of the power system using the power signal within a preset period;

[0052] The system identification module is used to use the real-time power signal as the first measurement signal and adopt the random subspace identification method to perform input-output identification to obtain an output identification model when it is detected that the system state is stable and the system operating condition changes;

[0053] The oscillation mode determination module is used to select a target oscillation mode with a minimum damping ratio based on the output identification model and the real-time power signal;

[0054] The index calculation module is used to calculate the geometric observability index of the target oscillation mode for a plurality of second measurement signals; wherein the plurality of second measurement signals are power signals of different categories collected in real time at different locations of the power system;

[0055] The signal determination module is used to determine the second measurement signal with the highest geometric observability index as the update signal;

[0056] The optimization control module is used to input the update signal into the damping controller to perform optimization control on a preset mathematical model of the controlled object.

[0057] The present invention provides a new energy power generation adaptive damping control device, which is based on the organic combination of modules and uses the power signal within a preset period to judge the system state and system working condition of the power system; when it is detected that the system state is steady and the system working condition changes, the real-time power signal is used as the first measurement signal, and the random subspace identification method is used to perform input-output identification to obtain the output identification model; based on the output identification model and the real-time power signal, the target oscillation mode with the smallest damping ratio is selected; the geometric observability index of several second measurement signals to the target oscillation mode is calculated; the second measurement signal with the highest geometric observability index is determined as the update signal; the update signal is input to the damping controller to optimize the preset mathematical model of the controlled object. The present invention makes full use of the damping advantages of active modulation and reactive modulation under different working conditions, so as to achieve good damping effect under multiple working conditions, avoid or reduce the risk of the damping effect of the single damping control based on active or reactive modulation deteriorating or even failing after the working condition changes; the present invention also enhances the adaptability of the controller to complex working conditions by online identification and updating of feedback input signals and controller parameters. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 A schematic diagram of a flow chart of an embodiment of a method for adaptive damping control of renewable energy power generation provided by the present invention;

[0059] Figure 2 A schematic flow chart of another embodiment of the adaptive damping control method for renewable energy power generation provided by the present invention;

[0060] Figure 3 A schematic diagram of a flow chart of an embodiment of the damping controller parameters provided by the present invention;

[0061] Figure 4 A schematic structural diagram of an embodiment of a new energy power generation adaptive damping control device provided by the present invention;

[0062] Figure 5 A schematic structural diagram of an embodiment of a renewable energy power generation adaptive damping control system provided by the present invention. DETAILED DESCRIPTION

[0063] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0064] The flowcharts shown in the accompanying drawings are only examples and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may also be decomposed, combined or partially merged, so the actual execution order may change according to actual conditions.

[0065] Some embodiments of the present invention are described in detail below in conjunction with the accompanying drawings. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other.

[0066] Example 1

[0067] See also Figure 1 , is a flow chart of an embodiment of a method for adaptive damping control of renewable energy power generation provided by the present invention, the method comprises steps 101 to 106, each of which is specifically as follows:

[0068] Step 101: Determine the system state and system operating condition of the power system using the power signal within a preset period.

[0069] Furthermore, in the first embodiment of the present invention, the system state and system operating condition of the power system are determined by using the power signal within a preset period, specifically:

[0070] Calculating a first data variation amplitude of the electric power signal within a preset period;

[0071] When the variation range of the first data is less than the preset first variation range, determining that the system state of the power system is a steady state;

[0072] When it is determined that the system state of the power system is a steady state, obtaining a power signal of a previous cycle of the preset cycle;

[0073] Acquire a second data change amplitude between a power signal of a preset cycle and a power signal of a previous cycle;

[0074] When the second data change range is greater than a preset second change range, it is determined that the system operating condition of the power system has changed.

[0075] In the first embodiment of the present invention, a time period interval is set, and a power signal is collected in real time in each period to determine whether the system is in a steady state. For example, the power signal is measured once every 10 minutes, and the power signal includes line flow, line power, bus frequency and bus voltage, etc. Assuming that the measured power signal contains 20s of data, it can be determined whether the system is in a steady state by comparing the change amplitude of the measured data in the 20s time window. For example, if the mean value of the data in the first 10s time window is not more than 5%, the system is considered to be in a steady state, and if it exceeds 5%, the system is considered to be not in a steady state. When it is determined that the system is in a steady state, it is further determined whether the current system operating condition has changed based on the comparison of the current cycle measurement information with the previous cycle measurement information. For example, the mean value of the measured data in the current cycle time window is compared with the mean value of the measured data in the corresponding time window of the previous cycle. If the mean value changes by no more than 10%, it is considered that the system operating condition has not changed, and if the change amplitude is greater than 10%, it is considered that the system operating condition has changed.

[0076] Step 102: When it is detected that the system state is steady and the system operating condition changes, the real-time power signal is used as the first measurement signal, and a random subspace identification method is used to perform input-output identification to obtain an output identification model.

[0077] Furthermore, in the first embodiment of the present invention, the output identification model is specifically:

[0078]

[0079] Where k is the discrete sampling time; x k+1 and x k are the states of the output identification model at different times; y k is the first measurement signal; w k and v k is white noise with a mean of 0; A d is the system matrix of the output identification model; C d is the output matrix of the output identification model.

[0080] In the first embodiment of the present invention, when it is determined that the system is in a steady state and the system operating conditions change, based on the latest measurement signal (for example, the latest measurement signal of the 120s time window), an output identification model containing the system dynamics is obtained based on random subspace identification. Wherein, k represents the discrete sampling time; x k ∈R m is the state of the output identification model; y k ∈R p is the first measurement signal; w k ∈R m and v k ∈R pis white noise with a mean of 0; A d is the system matrix of the output identification model; C d is the output matrix of the output identification model.

[0081] Step 103: Based on the output identification model and the real-time power signal, a target oscillation mode with a minimum damping ratio is selected.

[0082] Furthermore, in the first embodiment of the present invention, based on the output identification model and the real-time power signal, a target oscillation mode with the minimum damping ratio is selected, specifically:

[0083] Calculating a system matrix in a continuous time domain based on the system matrix of the output identification model and a sampling time interval of the real-time power signal;

[0084] By calculating the eigenvalue of the system matrix in the continuous time domain, the damping ratio of each oscillation mode is obtained;

[0085] The oscillation mode with the smallest damping ratio is determined as the target oscillation mode.

[0086] In the first embodiment of the present invention, based on the system matrix of the output identification model and the sampling time interval of the real-time power signal, according to the transformation formula A c =log(A d ) / T s , get the system matrix A in the continuous time domain c , by calculating the matrix A c The characteristic value of can be used to obtain the damping ratio and oscillation frequency of each oscillation mode, and then the target oscillation mode with the smallest damping ratio can be selected.

[0087] Step 104: Calculate geometric observability indicators of a plurality of second measurement signals for the target oscillation mode; wherein the plurality of second measurement signals are power signals of different categories collected in real time at different locations in the power system.

[0088] Furthermore, in the first embodiment of the present invention, the geometric observability indexes of the target oscillation mode of the plurality of second measurement signals are calculated, specifically:

[0089] The formula for calculating the geometric observability index is:

[0090]

[0091] Among them, m oi (j) is the geometric observability index of the second measurement signal j to the target oscillation mode; c j is the jth row of the output matrix of the output identification model; φ iis the j-th column of the right eigenvector matrix of the system matrix in the continuous time domain; the operator |·| represents the calculation of the vector modulus; the operator ||·|| represents the calculation of the Euclidean norm.

[0092] In the first embodiment of the present invention, after obtaining A c Then, calculate A c The corresponding left and right eigenvalue vector matrices are denoted as Ψ=[ψ 1 ,ψ 2 ,…ψ m ] and Φ=[φ 1 ,φ 2 ,…φ m ] and satisfy ΦΨ H =I m . The superscript H represents the conjugate transpose of the matrix, I m represents the m-order unit matrix. For the target oscillation mode λ i , using the above formula, the geometric observability index of multiple different measurement signals to the target oscillation mode can be calculated.

[0093] Step 105: Determine the second measurement signal with the highest geometric observability index as the update signal.

[0094] In the first embodiment of the present invention, after solving the geometric observability index of multiple measurement signals for the target oscillation mode, the measurement signals are sorted from high to low according to the index value, and the measurement signals with the highest sorting are prioritized as the input signals of the damping controller, so that the damping control can update and utilize the current optimal feedback signal.

[0095] In the first embodiment of the present invention, see Figure 2 , is a flow chart of another embodiment of the adaptive damping control method for renewable energy power generation provided by the present invention. The damping control system is used to execute the damping control method. Before executing the damping control method, the models and parameters of each link of the damping control system are initialized according to the results of the offline analysis, and the damping control system is put into operation. The output identification link in the damping control system uses a measurable signal to perform periodic judgment. If the judgment result is that the system is in a steady state, then S 1 =1; if the system is not in a steady state, then S 1 = 0. Then use the real-time collected measurable signal to determine whether the system operating conditions have changed. If the system operating conditions have changed, S 2 =1; if the system operating conditions do not change, then S 2 = 0. When judging S 1 =1 and S 2 =1, judge the feedback signal update enable S 3 Is it 1? 3When S is 1, the feedback signal is updated, and a small test signal is injected into the input-output identification link in the damping control system to perform input-output identification; when S 3 When it is 0, the feedback signal is kept unchanged, and a small test signal is injected into the input-output identification link in the damping control system to perform input-output identification. After completing the input-output identification, the parameters of the mathematical model of the controlled object in the damping controller and the parameters of the damping controller are updated. 3 Used to set whether the input signal of the oscillation damping controller is updated. When there are no restrictions such as fixed feedback signals, in order to achieve better damping effects, the state S is generally set. 3 is 1, so that the input signal of the damping controller can be updated to a signal that is more observable for the current oscillation mode, so as to more effectively damp the target oscillation mode.

[0096] Step 106: Input the update signal to the damping controller to optimize the control of the preset mathematical model of the controlled object.

[0097] Furthermore, in the first embodiment of the present invention, the update signal is input into the damping controller to optimize the mathematical model of the preset controlled object, specifically:

[0098] When the update signal is input to the damping controller, the feedback signal of the damping controller is updated, and the first test signal is added to the preset new energy station power instruction;

[0099] Taking the preset new energy station power command as the input signal and the feedback signal as the output data, performing input-output identification based on the subspace identification algorithm to obtain a mathematical model of the controlled object;

[0100] Converting a discrete model of a deterministic part of a discrete controlled object in the mathematical model of the controlled object into a continuous model of the controlled object;

[0101] Updating the output identification model using the continuous model of the controlled object;

[0102] The optimal state feedback control quantity is updated for the updated output identification model.

[0103] In the first embodiment of the present invention, after obtaining the update signal, the update signal is used as the input signal of the damping controller, that is, the optimal feedback signal is updated. The input-output identification link of the damping control system adds a small test signal to the power command of the new energy station, performs input-output identification based on the subspace identification algorithm, and obtains a mathematical model of the controlled object with the power command of the new energy station as input and the feedback measurement signal as output.

[0104] As an example of the first embodiment of the present invention, the input-output identification is performed to obtain the mathematical model of the controlled object, which is specifically:

[0105] The small-amplitude test signal is two different white noises that have been low-pass filtered and have a certain duration (for example, 120s). The amplitude does not exceed 5% of the capacity of the new energy station. They are superimposed on the active and reactive power command values ​​of the new energy station, and the optimal feedback signal is measured at the same time. The above test signal and measurement signal are used as input-output data for subspace identification, and the input-output identification model of the controlled object shown in the following formula is obtained:

[0106]

[0107] In the formula, A d , B d , C d , D d are the system matrix, input matrix, output matrix and input direct matrix of the identification model respectively; x k ∈R n is the system status; k ∈R l is the system output; u k ∈R m is the system input; w k ∈R m is the process noise; v k ∈R l To measure noise.

[0108] According to A c =log(A d ) / T s The transformation formula converts the discrete model of the deterministic part of the discrete controlled object in the mathematical model of the controlled object into the continuous model of the controlled object:

[0109]

[0110] In the formula, A d , B d , C d , D d are the system matrix, input matrix, output matrix and input through matrix of the identification model respectively; u is the system input; y is the system output; x is the system state; is the differential of the system state variable x.

[0111] Furthermore, in the first embodiment of the present invention, the updated output identification model is updated with the optimal state feedback control amount, specifically:

[0112] Determine the optimal feedback gain matrix of the output identification model based on the damping ratio of the target oscillation mode;

[0113] Based on the state observation value obtained by observing the output identification model through the Kalman filter, the state of the output identification model is obtained;

[0114] Based on the optimal feedback gain matrix of the output identification model and the state of the output identification model, the optimal state feedback control quantity of the output identification model is updated. The specific formula is:

[0115] u=-Kx

[0116] Among them, u is the optimal state feedback control quantity of the output identification model; K is the optimal feedback gain matrix of the output identification model; x is the state of the output identification model.

[0117] In the first embodiment of the present invention, after the output identification model is updated using the continuous model of the controlled object, the optimal state feedback control quantity is updated for the updated output identification model. For the output identification model, a linear quadratic optimal damping control can be designed, and an optimal state feedback control is adopted. Among them, the optimal state feedback control quantity is calculated based on the optimal feedback gain matrix of the output identification model and the state of the output identification model, specifically:

[0118] u=-Kx

[0119] Where u is the optimal state feedback control quantity of the output identification model; K is the optimal feedback gain matrix of the output identification model; and x is the state of the output identification model.

[0120] Furthermore, in the first embodiment of the present invention, the optimal feedback gain matrix of the output identification model is determined based on the damping ratio of the target oscillation mode, specifically:

[0121] comparing the damping ratio of the target oscillation mode with a preset damping ratio threshold;

[0122] When the damping ratio of the target oscillation mode is greater than a preset damping ratio threshold, determining the optimal feedback gain matrix of the output identification model as a preset optimal feedback gain matrix;

[0123] When the damping ratio of the target oscillation mode is less than a preset damping ratio threshold, updating the weights of a preset weight matrix;

[0124] Based on the updated weights of the preset weight matrix, the optimal feedback gain matrix of the output identification model is solved.

[0125] Further, in the first embodiment of the present invention, the state of the output identification model is obtained based on the state observation value obtained by observing the output identification model by the Kalman filter, specifically:

[0126] The Kalman filter is used to observe the state of the output identification model and obtain the state observation value;

[0127] The state of the output identification model is calculated based on the state observation value, the system matrix of the output identification model, the input matrix and the output matrix.

[0128] In the first embodiment of the present invention, in order to suppress oscillation and limit the control input, a weight matrix Q=Q T ≥0 and R=R T >0 is the quadratic objective function of the parameter:

[0129]

[0130] The E{·} in the formula is the mathematical expectation. According to the above formula, the calculation formula of the optimal feedback gain matrix K can be obtained: K=R - 1 B T X. Where X = X T ≥0 is the algebraic Riccat i equation A T X+XA-XBR -1 B T The only positive semidefinite solution of X+Q=0.

[0131] As an example of the first embodiment of the present invention, see Figure 3 , is a flow chart of an embodiment of the damping controller parameters provided by the present invention. The optimal state feedback control quantity u is calculated based on the optimal feedback gain matrix K of the output identification model and the state x of the output identification model, as follows:

[0132] Solution of the optimal feedback gain matrix K:

[0133] The minimum value of the preset target oscillation mode damping ratio ξ is α%. The initialization matrix K is a zero matrix with all elements being 0, and the weight matrix R = diag{1,1,…,1}. Assume that the weight matrix Q = diag{q 1 ,q 2 ,…,q n} and initialize q i =0(i=1,2…,n). According to the offline analysis before the damping control system is operated, the weight q is preset in combination with the controller output limit value. i The value range of (i=1,2…,n) is [0,q max ] and weight step length Δq, calculate the closed-loop system matrix A s = the eigenvalue of A-BK, thereby obtaining the actual damping ratio ξ of the target oscillation mode. If ξ≥α%, the matrix K remains unchanged. If ξ<α%, the system state x with a larger participation factor for the target oscillation mode is determined based on the output identification model. i , i = 1, 2, ..., or n (when there are multiple target oscillation modes, there may be multiple corresponding system states) will be consistent with the above system state xi The corresponding weight q i Updated to q i +Δq. Using the updated matrix Q, based on the algebraic Riccati equation A T X+XA-XBR -1 B T X+Q=0 and the calculation formula of the optimal feedback gain matrix K=R -1 B T X, and obtain the new optimal feedback gain matrix K. Obtain the closed-loop system matrix eigenvalues ​​again, and repeat the above steps until the updated K matrix satisfies the target oscillation mode damping ratio or the elements of the matrix Q reach the maximum value.

[0134] Solution for the state x of the output identification model:

[0135] The Kalman filter is used to observe the state x of the output identification model, and the observation value is satisfy Where L can be minimized Therefore, L can be solved by minimizing the following formula:

[0136]

[0137] The calculation formula of L is obtained: L=HC T V -1 , where H = H T ≥0 is the algebraic Riccat i equation HA T +AH-HC T V -1 The only positive semidefinite solution of CH+W=0.

[0138] The desired closed-loop response characteristics are obtained by adjusting the weight matrices W and V. By default, W and V are set to unit diagonal matrices. Based on the above formula, the observed value of the state x of the identification model can be obtained: And the observed value The optimal state feedback control quantity u of the output identification model is calculated by replacing x in the optimal state feedback control quantity calculation formula u=-Kx of the output identification model.

[0139] See also Figure 4 , is a schematic diagram of the structure of an embodiment of a new energy power generation adaptive damping control device provided by the present invention, the device includes a system judgment module 201, a system identification module 202, an oscillation mode determination module 203, an index calculation module 204, a signal determination module 205 and an optimization control module 206;

[0140] The system determination module 201 is used to determine the system state and system operating condition of the power system by using the power signal within a preset period;

[0141] The system identification module 202 is used to use the real-time power signal as the first measurement signal and adopt the random subspace identification method to perform input-output identification to obtain an output identification model when it is detected that the system state is stable and the system operating condition changes;

[0142] The oscillation mode determination module 203 is used to select a target oscillation mode with a minimum damping ratio based on the output identification model and the real-time power signal;

[0143] The index calculation module 204 is used to calculate the geometric observability index of the target oscillation mode for a plurality of second measurement signals; wherein the plurality of second measurement signals are power signals of different categories collected in real time at different locations of the power system;

[0144] The signal determination module 205 is used to determine the second measurement signal with the highest geometric observability index as the update signal;

[0145] The optimization control module 206 is used to input the update signal into the damping controller to optimize the mathematical model of the preset controlled object;

[0146] Furthermore, in the first embodiment of the present invention, the system state and system operating condition of the power system are determined by using the power signal within a preset period, specifically:

[0147] Calculating a first data variation amplitude of the electric power signal within a preset period;

[0148] When the variation range of the first data is less than the preset first variation range, determining that the system state of the power system is a steady state;

[0149] When it is determined that the system state of the power system is a steady state, obtaining a power signal of a previous cycle of the preset cycle;

[0150] Acquire a second data change amplitude between a power signal of a preset cycle and a power signal of a previous cycle;

[0151] When the second data change range is greater than a preset second change range, it is determined that the system operating condition of the power system has changed.

[0152] Furthermore, in the first embodiment of the present invention, the output identification model is specifically:

[0153]

[0154] Where k is the discrete sampling time; x k+1 and x k are the states of the output identification model at different times; y k is the first measurement signal; w k and v kis white noise with a mean of 0; A d is the system matrix of the output identification model; C d is the output matrix of the output identification model.

[0155] Furthermore, in the first embodiment of the present invention, based on the output identification model and the real-time power signal, a target oscillation mode with the minimum damping ratio is selected, specifically:

[0156] Calculating a system matrix in a continuous time domain based on the system matrix of the output identification model and a sampling time interval of the real-time power signal;

[0157] By calculating the eigenvalue of the system matrix in the continuous time domain, the damping ratio of each oscillation mode is obtained;

[0158] The oscillation mode with the smallest damping ratio is determined as the target oscillation mode.

[0159] Furthermore, in the first embodiment of the present invention, the geometric observability indexes of the target oscillation mode of the plurality of second measurement signals are calculated, specifically:

[0160] The formula for calculating the geometric observability index is:

[0161]

[0162] Among them, m oi (j) is the geometric observability index of the second measurement signal j to the target oscillation mode; c j is the jth row of the output matrix of the output identification model; φ i is the j-th column of the right eigenvector matrix of the system matrix in the continuous time domain; the operator |·| represents the calculation of the vector modulus; the operator ||·|| represents the calculation of the Euclidean norm.

[0163] Furthermore, in the first embodiment of the present invention, the update signal is input into the damping controller to optimize the mathematical model of the preset controlled object, specifically:

[0164] When the update signal is input to the damping controller, the feedback signal of the damping controller is updated, and the first test signal is added to the preset new energy station power instruction;

[0165] Taking the preset new energy station power command as the input signal and the feedback signal as the output data, performing input-output identification based on the subspace identification algorithm to obtain a mathematical model of the controlled object;

[0166] Converting a discrete model of a deterministic part of a discrete controlled object in the mathematical model of the controlled object into a continuous model of the controlled object;

[0167] Updating the output identification model using the continuous model of the controlled object;

[0168] The optimal state feedback control quantity is updated for the updated output identification model.

[0169] Furthermore, in the first embodiment of the present invention, the updated output identification model is updated with the optimal state feedback control amount, specifically:

[0170] Determine the optimal feedback gain matrix of the output identification model based on the damping ratio of the target oscillation mode;

[0171] Based on the state observation value obtained by observing the output identification model through the Kalman filter, the state of the output identification model is obtained;

[0172] Based on the optimal feedback gain matrix of the output identification model and the state of the output identification model, the optimal state feedback control quantity of the output identification model is updated. The specific formula is:

[0173] u=-Kx

[0174] Among them, u is the optimal state feedback control quantity of the output identification model; K is the optimal feedback gain matrix of the output identification model; x is the state of the output identification model.

[0175] Furthermore, in the first embodiment of the present invention, the optimal feedback gain matrix of the output identification model is determined based on the damping ratio of the target oscillation mode, specifically:

[0176] comparing the damping ratio of the target oscillation mode with a preset damping ratio threshold;

[0177] When the damping ratio of the target oscillation mode is greater than a preset damping ratio threshold, determining the optimal feedback gain matrix of the output identification model as a preset optimal feedback gain matrix;

[0178] When the damping ratio of the target oscillation mode is less than a preset damping ratio threshold, updating the weights of a preset weight matrix;

[0179] Based on the updated weights of the preset weight matrix, the optimal feedback gain matrix of the output identification model is solved.

[0180] Further, in the first embodiment of the present invention, the state of the output identification model is obtained based on the state observation value obtained by observing the output identification model by the Kalman filter, specifically:

[0181] The Kalman filter is used to observe the state of the output identification model and obtain the state observation value;

[0182] The state of the output identification model is calculated based on the state observation value, the system matrix of the output identification model, the input matrix and the output matrix.

[0183] In summary, the first embodiment of the present invention provides a method and device for adaptive damping control of renewable energy power generation, which uses power signals within a preset period to judge the system state and system operating conditions of the power system; when it is detected that the system state is steady and the system operating conditions change, the real-time power signal is used as the first measurement signal, and the random subspace identification method is used for input-output identification to obtain the output identification model; based on the output identification model and the real-time power signal, the target oscillation mode with the smallest damping ratio is selected; the geometric observability index of several second measurement signals to the target oscillation mode is calculated; the second measurement signal with the highest geometric observability index is determined as the update signal; the update signal is input into the damping controller to optimize the preset mathematical model of the controlled object. The present invention makes full use of the damping advantages of active modulation and reactive modulation under different working conditions, so as to achieve good damping effects under multiple working conditions, avoid or reduce the risk of the damping effect of a single damping control based on active or reactive modulation becoming worse or even failing after the working conditions change; the present invention also enhances the adaptability of the controller to complex working conditions by online identification and updating of feedback input signals and controller parameters.

[0184] Example 2

[0185] See also Figure 5 , is a schematic diagram of a structure of an embodiment of the adaptive damping control system for renewable energy power generation provided by the present invention. The adaptive damping control system for renewable energy power generation is used to execute the adaptive damping control method for renewable energy power generation, and the system is composed of an output identification and index analysis link, an input-output identification link, a damping controller, an adjustable dead zone, a feedback signal selection and a filtering link.

[0186] The output identification and index analysis stage utilizes local signals of new energy stations (grid connection point frequency, grid connection point voltage, station active power, reactive power, etc.) or wide-area measurement signals (line power, bus frequency, bus voltage, etc.) to monitor the evolution of oscillation modes online and switch the feedback signal path and filter parameters at appropriate times to provide real-time optimal feedback signals for the damping controller.

[0187] The input-output identification link injects a small test signal into the system and records the feedback measurement signal after the identification conditions are met. The controlled object model is obtained through input-output data identification with little impact on the system, and the latest controlled object model is provided to the damping controller following the changes in system working conditions.

[0188] The output of the damping controller is superimposed on the active power command and reactive power command of the new energy station, and the active power and reactive power are modulated at the same time. The input of the damping controller is the two current optimal feedback signals provided by the output identification and index analysis link. These two feedback signals mainly contain the oscillation mode information with the smallest damping ratio in the system. If more oscillation modes need to be damped, the number of feedback signals can be appropriately increased. Based on the controlled object model provided by the input-output identification link, the damping controller adjusts the active and reactive power of the new energy station according to a certain control law to achieve the goal of damping oscillation and improving damping characteristics.

[0189] The use of the adjustable dead zone link makes the damping controller respond only to larger disturbances, avoiding the damping controller output frequently changing the power command value of the new energy station under normal working conditions and small test signal injection. The dead zone can generally be set to 5% of the input amplitude. The existence of this link makes the damping controller equivalent to an open-loop state under steady-state conditions, so it does not affect the identification analysis of the controlled object model in the identification link.

[0190] When the output identification link and the input-output identification link are not put into operation, the damping control degenerates into fixed input-fixed parameter damping control. The introduction of the online identification link allows the damping controller parameters to follow the working conditions, improving the controller performance. In addition, the damping controller also has the option of feedback input signal on the basis of parameter update, so that the damping control can use the current optimal feedback signal and has a certain structural update function, further improving the controller performance.

[0191] Before the damping control system is put into operation, according to the results of offline analysis, Figure 1 The signal with the strongest observability for the target oscillation mode with relatively small damping is selected from the local signal or wide-area measurement signal of the new energy station, and the feedback signal in the feedback signal selection and filtering link is set to the above two signals, and the center frequency of the bandpass filter in the filtering link is set to the oscillation frequency of the two most observable signals in the above target oscillation mode.

[0192] With the selected feedback signal as output and the power setpoint of the new energy station as input, the controlled object model corresponding to the input-output is obtained through offline analysis or identification:

[0193]

[0194] Where A, B, C, and D are the system matrix, input matrix, output matrix, and input direct matrix respectively; x∈R n is the system state; y∈R l is the system output; u∈R r Input to the system.

[0195] After obtaining the controlled object model, the parameters of the controlled object model are updated and controlled. Specifically:

[0196] Based on the controlled object in the above controlled object model, a linear quadratic optimal damping control is designed, and the optimal state feedback control is adopted:

[0197] u=-Kx

[0198] In the formula, K is the optimal feedback gain matrix. In order to suppress oscillation and limit the control input, the weight matrix Q = Q T ≥0 and R=R T >0 is a quadratic objective function with parameters;

[0199]

[0200] In the formula, E{·} is the mathematical expectation. By minimizing the above quadratic objective function, the calculation formula of the optimal feedback gain matrix is ​​obtained:

[0201] K=R -1 B T X

[0202] Where X = X T ≥0 is the algebraic Riccat i equation A T X+XA-XBR -1 B T The only positive semidefinite solution of X+Q=0.

[0203] When the adaptive damping control system for renewable energy generation is put into operation, the output identification and index analysis link periodically uses the measurement signal to determine whether the system is in a steady state. If the system is not currently in a steady state, the state S is set. 1 =0 (no controller update); if the system is currently in steady state, set state S 1 =1. When the system is judged to be in a steady state, that is, state S 1 =1, further compare the current cycle measurement information with the previous cycle measurement information, and judge whether the current system condition has changed according to the change range of the two cycle data. For example, compare the mean value of the measurement data in the current cycle time window with the mean value of the measurement data in the corresponding time window of the previous cycle. If the change range of the mean value is not greater than the change range threshold, it is considered that the system condition has not changed. If the change range is greater than the change range threshold, it is considered that the system condition has changed. If the system condition has not changed, set the state S 2 =0 (no need to update the controller); if the system condition changes, set the state S 2 = 1. When the system is judged to be in a steady state and the system operating conditions change, that is, state S 1 With state S 2When both are 1, based on the latest measurement signal (e.g. the latest measurement signal of the 120s time window), the output identification and index analysis link obtains an output identification model including system dynamics based on random subspace identification:

[0204]

[0205] Where k represents the discrete sampling time; x k ∈R m is the state of the output identification model; y k ∈R p is the first measurement signal; w k ∈R m and v k ∈R p is white noise with a mean of 0; A d is the system matrix of the output identification model; C d is the output matrix of the output identification model.

[0206] By transforming A c =log(A d ) / T s , and obtain the system matrix A in the continuous time domain c , where T s is the sampling time interval of the measured signal. By calculating the matrix A c The characteristic value of can be used to obtain the damping ratio and oscillation frequency of each oscillation mode, and then the target oscillation mode with the weakest damping ratio can be selected, which provides a basis for the feedback signal selection and the update of the center frequency of the bandpass filter in the filtering link.

[0207] At the same time, calculate A c The corresponding left and right eigenvalue vector matrices are recorded as and Φ=[φ 1 ,φ 2 ,…φ m ] and satisfy ΦΨ H =I m , the superscript H represents the conjugate transpose of the matrix, I m represents the m-order unit matrix.

[0208] For the target oscillation mode λ i , calculate the geometric observability index m of multiple measurement signals oi :

[0209]

[0210] In the formula, m oi (j) is the geometric observability index of the second measurement signal j to the target oscillation mode; c j is the jth row of the output matrix of the output identification model; φi is the j-th column of the right eigenvector matrix of the system matrix in the continuous time domain; the operator |·| represents the calculation of the vector modulus; the operator ||·|| represents the calculation of the Euclidean norm.

[0211] After solving and obtaining the geometric observability indices of multiple measurement signals, the measurement signals are sorted from high to low according to the index value, and the measurement signals with the highest order are prioritized as the input signals of the damping controller, thereby providing a basis for the selection of feedback signals and the selection of feedback signals in the filtering link, so that the damping control can update and utilize the current optimal feedback signal.

[0212] When the feedback signal selection and filtering links are updated, the damping controller input signal, that is, the optimal feedback signal, is updated. In the input-output identification link, a small test signal is added to the power command of the new energy station, and input-output identification is performed based on the subspace identification algorithm to obtain a mathematical model of the controlled object with the power command of the new energy station as input and the feedback measurement signal as output, as follows:

[0213] The small-amplitude test signal is two different white noises that have been low-pass filtered and have a certain duration (e.g., 120s). The amplitude does not exceed the capacity threshold of the new energy station (e.g., 5%), and is superimposed on the active and reactive power command values ​​of the new energy station, respectively, and the optimal feedback signal is measured at the same time. The above test signal and measurement signal are used as input-output data for subspace identification, and the input-output identification model of the controlled object shown in the following formula is obtained:

[0214]

[0215] In the formula, A d , B d , C d , D d are the system matrix, input matrix, output matrix and input direct matrix of the identification model respectively; x k ∈R n is the system status; k ∈R l is the system output; u k ∈R m is the system input; w k ∈R m is the process noise; v k ∈R l To measure noise.

[0216] According to A c =log(A d ) / T s The transformation formula converts the discrete model of the deterministic part of the discrete controlled object in the mathematical model of the controlled object into the continuous model of the controlled object:

[0217]

[0218] In the formula, A d , B d , C d , D d are the system matrix, input matrix, output matrix and input through matrix of the identification model respectively; u is the system input; y is the system output; x is the system state; is the differential of the system state variable x.

[0219] The continuous model of the controlled object is updated to the controlled object model before the operation of the adaptive damping control system of the new energy power generation, and the optimal state feedback control is repeatedly performed on it according to the parameter update control method of the controlled object model.

[0220] In summary, the adaptive damping control system for renewable energy power generation provided in the second embodiment of the present invention includes an output identification and index analysis link, an input-output identification link, a damping controller, an adjustable dead zone, a feedback signal selection and filtering link, and is used to execute the adaptive damping control method for renewable energy power generation. Through the coordinated cooperation of various links, the damping advantages of active modulation and reactive modulation under different working conditions are fully utilized, thereby achieving good damping effects under multiple working conditions, avoiding or reducing the risk of deterioration or even failure of the damping effect of a single damping control based on active or reactive modulation after changes in the working conditions; the controller's adaptability to complex working conditions is enhanced by online identification and updating of feedback input signals and controller parameters.

[0221] The specific embodiments described above further illustrate the purpose, technical solutions and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. It is particularly pointed out that for those skilled in the art, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of protection of the present invention.

Claims

1. A new energy power generation adaptive damping control method, characterized in that: include: Determine the system state and system operating condition of the power system by using the power signal within a preset period; When it is detected that the system state is steady and the system operating condition changes, the real-time power signal is used as the first measurement signal, and the random subspace identification method is used to perform input-output identification to obtain an output identification model; Based on the output identification model and the real-time power signal, selecting a target oscillation mode with a minimum damping ratio; Calculating geometric observability indicators of a plurality of second measurement signals for the target oscillation mode; wherein the plurality of second measurement signals are power signals of different categories collected in real time at different locations of the power system; Determine the second measurement signal with the highest geometric observability index as the update signal; The update signal is input into the damping controller to optimize the control of the preset mathematical model of the controlled object.

2. The adaptive damping control method for renewable energy power generation according to claim 1, characterized in that: The method of using the power signal within a preset period to determine the system state and system operating condition of the power system is specifically as follows: Calculating a first data variation amplitude of the electric power signal within a preset period; When the variation range of the first data is less than the preset first variation range, determining that the system state of the power system is a steady state; When it is determined that the system state of the power system is a steady state, obtaining a power signal of a previous cycle of the preset cycle; Acquire a second data change amplitude between a power signal of a preset cycle and a power signal of a previous cycle; When the second data change range is greater than a preset second change range, it is determined that the system operating condition of the power system has changed.

3. The adaptive damping control method for renewable energy power generation according to claim 1, characterized in that: The output identification model is specifically: Where k is the discrete sampling time; x k+1 and x k are the states of the output identification model at different times; y k is the first measurement signal; w k and v k is white noise with a mean of 0; A d is the system matrix of the output identification model; C d is the output matrix of the output identification model.

4. The adaptive damping control method for renewable energy power generation according to claim 3 is characterized in that: The target oscillation mode with the minimum damping ratio is selected based on the output identification model and the real-time power signal, specifically: Calculating a system matrix in a continuous time domain based on the system matrix of the output identification model and a sampling time interval of the real-time power signal; By calculating the eigenvalue of the system matrix in the continuous time domain, the damping ratio of each oscillation mode is obtained; The oscillation mode with the smallest damping ratio is determined as the target oscillation mode.

5. The adaptive damping control method for renewable energy power generation according to claim 4, characterized in that: The step of calculating the geometric observability indexes of the target oscillation mode of the plurality of second measurement signals is specifically: The formula for calculating the geometric observability index is: Among them, m oi (j) is the geometric observability index of the second measurement signal j to the target oscillation mode; c j is the jth row of the output matrix of the output identification model; φ i is the j-th column of the right eigenvector matrix of the system matrix in the continuous time domain; the operator |·| represents the calculation of the vector modulus; the operator ||·|| represents the calculation of the Euclidean norm.

6. The adaptive damping control method for renewable energy power generation according to claim 1, characterized in that: The updating signal is input into the damping controller to optimize the mathematical model of the preset controlled object, specifically: When the update signal is input to the damping controller, the feedback signal of the damping controller is updated, and the first test signal is added to the preset new energy station power instruction; Taking the preset new energy station power command as the input signal and the feedback signal as the output data, performing input-output identification based on the subspace identification algorithm to obtain a mathematical model of the controlled object; Converting a discrete model of a deterministic part of a discrete controlled object in the mathematical model of the controlled object into a continuous model of the controlled object; Updating the output identification model using the continuous model of the controlled object; The optimal state feedback control quantity is updated for the updated output identification model.

7. The adaptive damping control method for renewable energy power generation according to claim 6, characterized in that: The updating of the optimal state feedback control amount of the updated output identification model is specifically as follows: Determine the optimal feedback gain matrix of the output identification model based on the damping ratio of the target oscillation mode; Based on the state observation value obtained by observing the output identification model through the Kalman filter, the state of the output identification model is obtained; Based on the optimal feedback gain matrix of the output identification model and the state of the output identification model, the optimal state feedback control quantity of the output identification model is updated. The specific formula is: u=-Kx Among them, u is the optimal state feedback control quantity of the output identification model; K is the optimal feedback gain matrix of the output identification model; x is the state of the output identification model.

8. The adaptive damping control method for renewable energy power generation according to claim 7, characterized in that: The optimal feedback gain matrix of the output identification model is determined based on the damping ratio of the target oscillation mode, specifically: comparing the damping ratio of the target oscillation mode with a preset damping ratio threshold; When the damping ratio of the target oscillation mode is greater than a preset damping ratio threshold, determining the optimal feedback gain matrix of the output identification model as a preset optimal feedback gain matrix; When the damping ratio of the target oscillation mode is less than a preset damping ratio threshold, updating the weights of a preset weight matrix; Based on the updated weights of the preset weight matrix, the optimal feedback gain matrix of the output identification model is solved.

9. The adaptive damping control method for renewable energy power generation according to claim 7, characterized in that: The state observation value obtained by observing the output identification model based on the Kalman filter is used to obtain the state of the output identification model, specifically: The Kalman filter is used to observe the state of the output identification model and obtain the state observation value; The state of the output identification model is calculated based on the state observation value, the system matrix of the output identification model, the input matrix and the output matrix.

10. A new energy power generation adaptive damping control device, characterized in that: include: System judgment module, system identification module, oscillation mode determination module, index calculation module, signal determination module and optimization control module; The system determination module is used to determine the system state and system operating condition of the power system using the power signal within a preset period; The system identification module is used to use the real-time power signal as the first measurement signal and adopt the random subspace identification method to perform input-output identification to obtain an output identification model when it is detected that the system state is stable and the system operating condition changes; The oscillation mode determination module is used to select a target oscillation mode with a minimum damping ratio based on the output identification model and the real-time power signal; The index calculation module is used to calculate the geometric observability index of the target oscillation mode for a plurality of second measurement signals; wherein the plurality of second measurement signals are power signals of different categories collected in real time at different locations of the power system; The signal determination module is used to determine the second measurement signal with the highest geometric observability index as the update signal; The optimization control module is used to input the update signal into the damping controller to perform optimization control on a preset mathematical model of the controlled object.

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