A method, system, and computing device for a cascaded hybrid direct current low frequency oscillation control

Abstract

The application discloses a kind of cascade type hybrid DC low-frequency oscillation control method, system and computing device, the method comprises: in time domain simulation, the current setting value of hybrid DC rectifier side constant current controller is increased from 1 p.u. To 1.02p.u. by pre-setting, the rotational speed oscillation data of AC system engine caused by the change of current setting value is measured;Based on rotational speed oscillation data, the low-frequency oscillation mode is identified using the low-frequency oscillation identification algorithm based on TLS-ESPRIT;According to the identification result, the state feedback gain matrix of semi-positive definite matrix Q , positive definite matrix R And optimal control vector K , according to the obtained matrix Q 、 R And K , configure DC additional controller;The completed DC additional controller is installed in hybrid DC system rectifier side constant current place.The application can inhibit AC system low-frequency oscillation, and ensure power system stability.

Description

Technical Field

[0001] This invention relates to a cascaded hybrid DC low-frequency oscillation control method, system, and computing device, belonging to the field of hybrid DC technology for high-voltage DC transmission. Background Technology

[0002] Cascaded hybrid DC systems combine the advantages of conventional and flexible DC transmission systems, representing a significant development direction for DC transmission technology. The receiving-end cascaded hybrid DC rectifier station consists of two sets of 12-pulse LCCs connected in series, while the inverter station consists of one set of 12-pulse commutated converters (LCCs) and voltage source converters (VSCs) connected in parallel and in series. The low-end VSCs are expanded into multiple VSCs connected in parallel and distributed across different regional power grids, forming a multi-terminal system and endowing multiple VSC inverter stations with power distribution capabilities.

[0003] Low-frequency oscillations are actually dynamic power oscillations occurring on interconnected power system tie lines, with frequencies ranging from approximately 0.1 to 2.5 Hz. The widespread use of fast excitation devices in power grids introduces negative damping into the system, offsetting the original positive damping and resulting in very low or even negative total system damping. Disturbances occurring under negative damping are gradually amplified, eventually manifesting as changes in generator power and power angle. In severe cases, this can lead to system instability or even disconnection.

[0004] Existing technologies that use VSC-HVDC system power modulation to suppress low-frequency grid oscillations do not fully utilize the rapid controllability of hybrid DC and are only applicable to AC grids. Existing technologies that suppress low-frequency grid oscillations by designing wide-area power system stabilizers have the disadvantage of not being applicable to cascaded hybrid DC systems. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the prior art and provide a cascaded hybrid DC low-frequency oscillation control method, system, and computing device, which can suppress low-frequency oscillations in AC systems and ensure power system stability. To achieve the above objective, this invention employs the following technical solution:

[0006] In a first aspect, the present invention provides a cascaded hybrid DC low-frequency oscillation control method, the method comprising:

[0007] In the time-domain simulation, the current setting value of the constant current controller on the hybrid DC rectifier side was pre-set to increase from 1 p.u. to 1.02 pu, and the speed oscillation data of the AC system engine caused by the change in the current setting value were measured.

[0008] Based on rotational speed oscillation data, the low-frequency oscillation mode is identified using a TLS-ESPRIT-based low-frequency oscillation identification algorithm, and the identification results are obtained.

[0009] Based on the identification results, the semi-positive definite matrix Q, the positive definite matrix R, and the state feedback gain matrix K of the optimal control vector are obtained based on the linear quadratic optimal control theory. Based on the obtained matrices Q, R, and K, a DC auxiliary controller is configured.

[0010] The configured DC auxiliary controller is installed at the constant current position on the rectifier side of the hybrid DC system to control the low-frequency oscillation of the AC system.

[0011] In conjunction with the first aspect, the time-domain simulation further employs a hybrid DC PSCAD simulation model.

[0012] In conjunction with the first aspect, furthermore, the speed oscillation data of the AC system generator before and after the disturbance are collected by the data measurement unit of the hybrid DC PSCAD simulation model.

[0013] In conjunction with the first aspect, the TLS-ESPRIT-based low-frequency oscillation identification algorithm further identifies low-frequency oscillation modes, including:

[0014] Write the rotational speed oscillation data in the form of frequency and damping coefficient;

[0015] The measured rotational speed oscillation data is written as the Hankel data matrix X. H ;

[0016] For data matrix X H Singular value decomposition and rearrangement yield the state space matrix of the control system;

[0017] Based on the rotational speed oscillation data, the oscillation mode c is obtained;

[0018] Based on the oscillation mode c, the parameters of the low-frequency oscillation are obtained, including amplitude, initial phase, angular frequency and attenuation factor.

[0019] In conjunction with the first aspect, preferably, the rotational speed oscillation data is expressed in the form of frequency and damping coefficient, as shown by the following formula:

[0020]

[0021] In equation (1), x(n) represents the rotational speed oscillation data, and T... s Where P is the sampling period, P is twice the number of sinusoidal components in the signal, and a i Φ i ω i σi and σi represent the amplitude, initial phase, angular frequency, and attenuation factor of the i-th attenuation component, respectively, j is the imaginary unit, and c p For the p-th oscillation mode, z p n Let be the signal pole, and w be white noise with a mean of 0.

[0022] In conjunction with the first aspect, preferably, the measured rotational speed oscillation data is written as a Hankel data matrix X. H It can be expressed by the following formula:

[0023]

[0024] In equation (2), M is the number of rows in the Hankel matrix, L is the number of rows in the Hankel matrix, and the matrix satisfies the relationship that L>P, M>P, and L+M-1=N.

[0025] In conjunction with the first aspect, preferably, for the data matrix X H Perform singular value decomposition and simplification, including:

[0026] For data matrix X H Perform singular value decomposition to obtain the signal subspace V s With noise subspace V n It can be expressed by the following formula:

[0027]

[0028] In equation (3), svd represents singular value decomposition, H represents conjugate transpose, U and V are orthogonal matrices, and Σ is the singular value matrix;

[0029] Removing the first row of Vs yields matrix V1, and removing the last row yields matrix V2. Ignoring noise and interference and considering the error E, there exists an invertible matrix Ψ that satisfies the following conditions:

[0030] V2+E2=(V1+E1)ψ (4)

[0031] The orthogonal matrix V is decomposed into 4 P×P matrices using singular values, as follows:

[0032]

[0033] ψ op =-V′ 12 V′ 21 -1 (6)

[0034] In equation (5), V' is the orthogonal matrix after decomposition;

[0035] In equation (6), Ψ op Given an invertible matrix after decomposition into orthogonal matrices, solve for Ψ. op eigenvalues ​​λ p (p = 1, 2, 3, ... P);

[0036] Using equations (5) and (6), we obtain the data matrix X that minimizes the overall errors E1 and E2.H The optimal solution.

[0037] In conjunction with the first aspect, preferably, it also includes:

[0038] In conjunction with the first aspect, preferably, the diagonal elements d of the singular value matrix Σ i Let be the i-th singular value of the Hankel matrix.

[0039] In conjunction with the first aspect, preferably, the oscillation mode c is obtained based on the rotational speed oscillation data, including:

[0040] Examining the measured rotational speed oscillation data, the low-frequency oscillation mode is calculated using the following formula:

[0041] c = [c1 c2 … c P ] T =(λ H λ) -1 λ H Y (7)

[0042] In equation (7), c is the low-frequency oscillation mode, p is the p-th oscillation mode, λ is the eigenvalue, and Y is the sampled signal;

[0043] The amplitude and initial phase angle information are obtained by using the least squares method. For the N-point sampled signal, we have...

[0044] Y=λc=[x(0) x(1) ... x(N-1)] T (8)

[0045] The parameters of low-frequency oscillations include amplitude, initial phase, angular frequency, and attenuation factor, which are calculated using the following formulas:

[0046] a i =2|c i |

[0047] θ i =argc i

[0048]

[0049] In equation (9), a i Let θ be the amplitude of the i-th component in the rotational speed oscillation data. i Let ω be the initial phase of the i-th component in the rotational speed oscillation data. k σ is the angular frequency. k This is the attenuation factor.

[0050] In conjunction with the first aspect, further, obtaining the state feedback gain matrix K of the optimal control vector includes:

[0051] Based on the state-space model and the low-frequency oscillation mode identification results, and using the MATLAB statement K1=lqr(A,B,Q1,R1) based on the linear quadratic optimal control theory, the state feedback gain matrix K is obtained.

[0052] In conjunction with the first aspect, further, obtaining the positive semi-definite matrix Q and the positive definite matrix R includes:

[0053] Based on the linear quadratic optimal control theory, and according to empirical values, the semi-positive definite matrix Q is set to a matrix with diagonal elements equal to 1 and other elements equal to 0, and the positive definite matrix R is set to 1.0.

[0054] In conjunction with the first aspect, the configured DC-DC auxiliary controller further includes:

[0055] Based on the state equation and state feedback gain matrix K of the controlled system, the state feedback gain matrix K required to stabilize the controlled system can be obtained using the Ackermann formula. e The state feedback gain matrix K e This is a DC-assisted controller.

[0056] In conjunction with the first aspect, preferably, the state feedback gain matrix K required to stabilize the controlled system is obtained. e Calculated using the following formula:

[0057]

[0058] In equation (10), K* is the state feedback gain matrix of the dual system of the controlled system; A is the state matrix of the controlled system; C is the control matrix of the controlled system; and φ(A) is the combination of state matrices, calculated by the following formula:

[0059] φ(A)=A n +α1A n-1 +…+α n-1 A+α n I (11)

[0060] In equation (11), α1,……,α n Let I be the correlation coefficient, and I be the identity matrix.

[0061] Secondly, the present invention provides a cascaded hybrid DC low-frequency oscillation control system, comprising:

[0062] Measurement module: Used in time-domain simulation to measure the engine speed oscillation data of the AC system caused by the change in the current setting value of the hybrid DC rectifier side constant current controller, which is pre-set from 1 p.u. to 1.02 pu.

[0063] Identification module: Based on rotational speed oscillation data, it uses a TLS-ESPRIT-based low-frequency oscillation identification algorithm to identify low-frequency oscillation modes, low-frequency oscillation parameters, and the state-space matrix of the control system.

[0064] Configuration module: Based on the identification results, it obtains the semi-positive definite matrix Q, the positive definite matrix R, and the state feedback gain matrix K of the optimal control vector according to the linear quadratic optimal control theory, and configures the DC auxiliary controller according to the obtained matrices Q, R, and K.

[0065] Control module: Used to install the configured DC auxiliary controller at the constant current of the rectifier side of the hybrid DC system to control the low-frequency oscillation of the AC system.

[0066] Thirdly, the present invention provides a computing device, characterized in that it includes a processor and a storage medium;

[0067] The storage medium is used to store instructions;

[0068] The processor is configured to operate according to the instructions to perform the steps of the method described in the first aspect.

[0069] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, characterized in that the program, when executed by a processor, implements the steps of the method described in the first aspect.

[0070] Compared with the prior art, the beneficial effects achieved by the cascaded hybrid DC low-frequency oscillation control method, system, and computing device provided in the embodiments of the present invention include:

[0071] In time-domain simulation, this invention pre-sets the current setpoint of the hybrid DC rectifier side constant current controller from 1 p.u. to 1.02 p.u., and measures the speed oscillation data of the AC system engine caused by the change in the current setpoint. Based on time-domain simulation, this invention fully utilizes the technical advantages of the rapid and controllable cascaded hybrid DC system and has the advantage of being easy to implement.

[0072] This invention, based on rotational speed oscillation data, utilizes a TLS-ESPRIT-based low-frequency oscillation identification algorithm to identify low-frequency oscillation modes, obtaining the low-frequency oscillation modes, low-frequency oscillation parameters, and the state-space matrix of the control system. Based on the identification results, and using linear quadratic optimal control theory, it obtains a semi-definite matrix Q, a positive definite matrix R, and a state feedback gain matrix K for the optimal control vector. A DC auxiliary controller is configured based on the obtained matrices Q, R, and K. The configured DC auxiliary controller is installed at the constant current on the rectifier side of the hybrid DC system to control the low-frequency oscillations of the AC system. This invention can be applied to cascaded hybrid DC systems, suppressing low-frequency oscillations in AC systems and ensuring power system stability, offering advantages such as wide applicability and broad applicability. Attached Figure Description

[0073] Figure 1 This is a flowchart of a cascaded hybrid DC low-frequency oscillation optimal control method provided in Embodiment 1 of the present invention;

[0074] Figure 2 This is an observation-state feedback control system based on linear quadratic optimal control, obtained by a cascaded hybrid DC low-frequency oscillation optimal control method provided in Embodiment 1 of the present invention.

[0075] Figure 3 This is a schematic diagram of the cascaded hybrid DC system provided in Embodiment 2 of the present invention;

[0076] Figure 4 This is the low-frequency oscillation simulation result provided in Embodiment 2 of the present invention. Detailed Implementation

[0077] The present invention will be further described below with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and should not be used to limit the scope of protection of the present invention.

[0078] Example 1:

[0079] like Figure 1 This invention provides a cascaded hybrid DC low-frequency oscillation control method, comprising:

[0080] In the time-domain simulation, the current setting value of the constant current controller on the hybrid DC rectifier side was pre-set to increase from 1 p.u. to 1.02 pu, and the speed oscillation data of the AC system engine caused by the change in the current setting value were measured.

[0081] Based on rotational speed oscillation data, the low-frequency oscillation mode is identified using a TLS-ESPRIT-based low-frequency oscillation identification algorithm, resulting in the low-frequency oscillation mode, low-frequency oscillation parameters, and the state-space matrix of the control system.

[0082] Based on the identification results, the semi-positive definite matrix Q, the positive definite matrix R, and the state feedback gain matrix K of the optimal control vector are obtained based on the linear quadratic optimal control theory. Based on the obtained matrices Q, R, and K, a DC auxiliary controller is configured.

[0083] The configured DC auxiliary controller is installed at the constant current position on the rectifier side of the hybrid DC system to control the low-frequency oscillation of the AC system.

[0084] It should be noted that the time-domain simulation uses a hybrid DC PSCAD simulation model.

[0085] The specific steps include:

[0086] Step 1: In the hybrid DC PSCAD simulation model, the current setting value of the hybrid DC rectifier side constant current controller is pre-set to increase from 1 p.u. to 1.02 pu, and the speed oscillation data of the AC system engine caused by the change in the current setting value is measured.

[0087] Specifically, the data measurement unit of the hybrid DC PSCAD simulation model collects the speed oscillation data of the AC system generator before and after the disturbance.

[0088] Step 2: Based on the rotational speed oscillation data, the low-frequency oscillation mode is identified using the TLS-ESPRIT-based low-frequency oscillation identification algorithm to obtain the low-frequency oscillation mode, the parameters of the low-frequency oscillation, and the state-space matrix of the control system.

[0089] Step 2.1: Express the rotational speed oscillation data in the form of frequency and damping coefficient, using the following formula:

[0090]

[0091] In equation (1), x(n) represents the rotational speed oscillation data, and T... s Where P is the sampling period, P is twice the number of sinusoidal components in the signal, and a i Φ i ω i σi and σi represent the amplitude, initial phase, angular frequency, and attenuation factor of the i-th attenuation component, respectively, j is the imaginary unit, and c p For the p-th oscillation mode, z p n Let be the signal pole, and w be white noise with a mean of 0.

[0092] Step 2.2: Write the measured rotational speed oscillation data into a Hankel data matrix X. H It can be expressed by the following formula:

[0093]

[0094] In equation (2), M is the number of rows in the Hankel matrix, L is the number of rows in the Hankel matrix, and the matrix satisfies the relationship that L>P, M>P, and L+M-1=N.

[0095] Step 2.3: For the data matrix X H Perform singular value decomposition and simplification, including:

[0096] For data matrix X H Perform singular value decomposition to obtain the signal subspace V s With noise subspace V n It can be expressed by the following formula:

[0097]

[0098] In equation (3), svd represents singular value decomposition, H represents conjugate transpose, U and V are orthogonal matrices, Σ is the singular value matrix, and the diagonal elements d of the singular value matrix Σ are... i Let be the i-th singular value of the Hankel matrix.

[0099] Removing the first row of Vs yields matrix V1, and removing the last row yields matrix V2. Ignoring noise and interference and considering the error E, there exists an invertible matrix Ψ that satisfies the following conditions:

[0100] V2+E2=(V1+E1)ψ (4)

[0101] The orthogonal matrix V is decomposed into 4 P×P matrices using singular values, as follows:

[0102]

[0103] ψ op =-V′ 12 V′ 21 -1 (6)

[0104] In equation (5), V' is the orthogonal matrix after decomposition;

[0105] In equation (6), Ψ op Given an orthogonal matrix after decomposition, find the invertible matrix Ψ. op eigenvalues ​​λ p (p = 1, 2, 3, ... P);

[0106] Using equations (5) and (6), we obtain the data matrix X that minimizes the overall errors E1 and E2. H The optimal solution.

[0107] Step 2.4: Based on the rotational speed oscillation data, obtain the oscillation mode c, which is expressed by the following formula:

[0108] c = [c1 c2 … c P T = (λ H λ) -1 λ H Y(7)

[0109] In Equation (7), c is the mode of low-frequency oscillation, p is the p-th oscillation mode, λ is the eigenvalue, and Y is the sampled signal;

[0110] The amplitude and initial phase angle information are obtained by the least squares method. For the N-point sampled signal, we have

[0111] Y = λc = [x(0) x(1)... x(N - 1)] T (8).

[0112] Step 2.5: Based on the oscillation mode c, obtain the parameters of low-frequency oscillation, including amplitude, initial phase, angular frequency, and attenuation factor, which are calculated respectively by the following formulas:

[0113] a i = 2|c i |

[0114] θ i = argc i

[0115]

[0116] In Equation (9), a i is the amplitude of the i-th component in the rotational speed oscillation data, θ i is the initial phase of the i-th component in the rotational speed oscillation data, ω k is the angular frequency, and σ k is the attenuation factor.

[0117] Step 3: According to the identification results, based on the linear quadratic optimal control theory, obtain the positive semi-definite matrix Q, the positive definite matrix R, and the state feedback gain matrix K of the optimal control vector. Configure the DC additional controller according to the obtained matrices Q, R, and K.

[0118] Step 3.1: According to the state space model and the low-frequency oscillation mode identification results, based on the linear quadratic optimal control theory, use the MATLAB statement K1 = lqr(A, B, Q1, R1) to obtain the state feedback gain matrix K.

[0119] Step 3.2: Based on the linear quadratic optimal control theory, according to the empirical values, set the positive semi-definite matrix Q as a matrix with diagonal elements equal to 1 and other elements equal to 0, and set the positive definite matrix R as 1.0 to obtain the positive semi-definite matrix Q and the positive definite matrix R.

[0120] ​Step 3.3: Configure the DC auxiliary controller, including:

[0121] Based on the state equation and state feedback gain matrix K of the controlled system, the state feedback gain matrix K required to stabilize the controlled system can be obtained using the Ackermann formula. e The state feedback gain matrix K e This is a DC-assisted controller.

[0122] Specifically, consider the controlled system having state equations

[0123]

[0124] In equation (10): x is the state vector, y is the output vector, u is the control vector, and A, B and C are the state matrix, output matrix and control matrix, respectively.

[0125] The required state feedback gain matrix K is designed. e When the problem is solved, it is solved by solving its dual problem, that is, solving the pole placement problem of the dual system.

[0126]

[0127] In equation (11): z is the state vector, n is the output vector, v is the control vector, and A*, B* and C* are the state matrix, output matrix and control matrix, respectively.

[0128] Assuming the control signal v = -Kz, if the dual system is completely controllable, determine the state feedback gain matrix K such that the matrix A*-C*K obtains a set of desired eigenvalues.

[0129] If u1, u2, ..., u n If the eigenvalues ​​are the matrix eigenvalues ​​of the desired state observer, then by taking the same u... i As the desired eigenvalues ​​of the state feedback gain matrix of the dual system, we can obtain

[0130] |sI-(A * -C * K)|=(s-u1)×(s-u2)×L×(su n (12)

[0131] Notice A * -C * K and AK * C has the same eigenvalues, so we can obtain

[0132] |sI-(A * -C * K)|=|sI-(AK * C)| (13)

[0133] Comparison of feature objects |sI-(AK) * C)| and the characteristic polynomial of the observer system|sI-(AK) e C)|, can find K e and K * The relationship is: K e =K * Therefore, the required state feedback gain matrix K of the original system e K e It can be obtained through the relation K e =K * If determined, then the required state feedback gain matrix K e It can be obtained through the Ackermann formula:

[0134]

[0135] In equation (14): K* is the state feedback gain matrix of the dual system of the controlled system; A is the state matrix of the controlled system; C is the control matrix of the controlled system; φ(A) is the combination of state matrices, calculated by the following formula:

[0136] φ(A)=A n +α1A n-1 +…+α n-1 A+α n I (15)

[0137] In equation (15), α1,……,α n Let I be the correlation coefficient, and I be the identity matrix.

[0138] Step 4: Install the configured DC auxiliary controller at the constant current position on the rectifier side of the hybrid DC system to control the low-frequency oscillation of the AC system.

[0139] The configured DC auxiliary controller is implemented in PSCAD using a transfer function and installed in the constant current control module of the hybrid DC rectifier side control system, forming an observation-state feedback control system based on linear quadratic optimal control, such as... Figure 2 As shown.

[0140] Example 2:

[0141] For example Figure 3 The cascaded hybrid DC system shown is used as an example for calculation and verification.

[0142] When a small disturbance of 0.02 pu is applied to the rectifier side of the hybrid DC system at t = 3s, the power oscillation results of the AC system before and after adding the designed DC auxiliary controller are as follows: Figure 4 As shown.

[0143] Depend on Figure 4 It was found that adding a DC auxiliary controller to the rectifier side of the hybrid DC system can effectively improve the damping level of the system, thereby suppressing low-frequency oscillations in the AC system, proving the effectiveness of the present invention.

[0144] Example 3:

[0145] This invention provides a cascaded hybrid DC low-frequency oscillation control system, comprising:

[0146] Measurement module: Used in time-domain simulation to measure the engine speed oscillation data of the AC system caused by the change in the current setting value of the hybrid DC rectifier side constant current controller, which is pre-set from 1 p.u. to 1.02 pu.

[0147] Identification module: Based on rotational speed oscillation data, it uses a TLS-ESPRIT-based low-frequency oscillation identification algorithm to identify low-frequency oscillation modes, low-frequency oscillation parameters, and the state-space matrix of the control system.

[0148] Configuration module: Based on the identification results, it obtains the semi-positive definite matrix Q, the positive definite matrix R, and the state feedback gain matrix K of the optimal control vector according to the linear quadratic optimal control theory, and configures the DC auxiliary controller according to the obtained matrices Q, R, and K.

[0149] Control module: Used to install the configured DC auxiliary controller at the constant current of the rectifier side of the hybrid DC system to control the low-frequency oscillation of the AC system.

[0150] Example 4:

[0151] This invention provides a computing device, including a processor and a storage medium;

[0152] The storage medium is used to store instructions;

[0153] The processor is configured to operate according to the instructions to execute the steps of the method described in Embodiment 1.

[0154] Example 5:

[0155] This invention also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the method described in Embodiment 1.

[0156] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0157] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0158] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0159] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0160] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A cascaded hybrid DC low-frequency oscillation control method, characterized in that, include: In the time-domain simulation, the current setting value of the constant current controller on the hybrid DC rectifier side was pre-set to increase from 1 pu to 1.02 pu, and the speed oscillation data of the AC system engine caused by the change in the current setting value were measured. Based on rotational speed oscillation data, a low-frequency oscillation mode is identified using a TLS-ESPRIT-based low-frequency oscillation identification algorithm to obtain identification results; wherein, the TLS-ESPRIT-based low-frequency oscillation identification algorithm for identifying low-frequency oscillation modes includes: Write the rotational speed oscillation data in the form of frequency and damping coefficient; The measured rotational speed oscillation data is written as the Hankel data matrix X. H ; For data matrix X H Singular value decomposition and rearrangement yield the state space matrix of the control system; Based on the rotational speed oscillation data, the oscillation mode c is obtained, including: Examining the measured rotational speed oscillation data, the low-frequency oscillation mode is calculated using the following formula: (7)， In equation (7), c This is a low-frequency oscillation mode. p For the first p One oscillation mode, λ For eigenvalues, Y The sampled signal; The amplitude and initial phase angle information are obtained by using the least squares method. For the N-point sampled signal, we have... (8)， The parameters of low-frequency oscillations include amplitude, initial phase, angular frequency, and attenuation factor, which are calculated using the following formulas: (9)， In equation (9), a i The first in the speed oscillation data i The amplitude of each component, θ i The first in the speed oscillation data i The initial phase of each component, ω k Angular frequency, σ k As the attenuation factor, T s The sampling period; Based on the identification results, the positive semi-definite matrix Q, the positive definite matrix R, and the state feedback gain matrix K of the optimal control vector are obtained based on the linear quadratic optimal control theory. A DC auxiliary controller is then configured based on the obtained matrices Q, R, and K. The process of obtaining the positive semi-definite matrix Q and the positive definite matrix R includes: based on the linear quadratic optimal control theory and empirical values, setting the positive semi-definite matrix Q to a matrix with diagonal elements equal to 1 and other elements equal to 0, and setting the positive definite matrix R to 1.

0. The configured DC auxiliary controller is installed at the constant current position on the rectifier side of the hybrid DC system to control the low-frequency oscillation of the AC system.

2. The cascaded hybrid DC low-frequency oscillation control method according to claim 1, characterized in that, The time-domain simulation uses a hybrid DC PSCAD simulation model.

3. The cascaded hybrid DC low-frequency oscillation control method according to claim 2, characterized in that, The data measurement unit of the hybrid DC PSCAD simulation model collects the speed oscillation data of the AC system generator before and after the disturbance.

4. The cascaded hybrid DC low-frequency oscillation control method according to claim 1, characterized in that, The state feedback gain matrix K for obtaining the optimal control vector includes: Based on the state equation of the controlled system and the identification results of the low-frequency oscillation mode, and using the MATLAB statement K1=lqr(A,B,Q1,R1) based on the linear quadratic optimal control theory, the state feedback gain matrix K is obtained.

5. The cascaded hybrid DC low-frequency oscillation control method according to claim 1, characterized in that, The configured DC-DC auxiliary controller includes: Based on the state equations and the state feedback gain matrix K of the state-space model, the state feedback gain matrix K required to stabilize the controlled system can be obtained using the Ackermann formula. e The state feedback gain matrix K e This is a DC-assisted controller.

6. A cascaded hybrid DC low-frequency oscillation control system, characterized in that, include: Measurement module: Used in time-domain simulation to measure the engine speed oscillation data of the AC system caused by the change in the current setting value of the hybrid DC rectifier side constant current controller, which is pre-set from 1 pu to 1.02 pu. Identification module: used to identify low-frequency oscillation modes based on rotational speed oscillation data using a TLS-ESPRIT-based low-frequency oscillation identification algorithm, obtaining the low-frequency oscillation mode, low-frequency oscillation parameters, and the state-space matrix of the control system; wherein, the TLS-ESPRIT-based low-frequency oscillation identification algorithm for identifying low-frequency oscillation modes includes: Write the rotational speed oscillation data in the form of frequency and damping coefficient; The measured rotational speed oscillation data is written as the Hankel data matrix X. H ; For data matrix X H Singular value decomposition and rearrangement yield the state space matrix of the control system; Based on the rotational speed oscillation data, the oscillation mode c is obtained, including: Examining the measured rotational speed oscillation data, the low-frequency oscillation mode is calculated using the following formula: (7)， In equation (7), c This is a low-frequency oscillation mode. p For the first p One oscillation mode, λ For eigenvalues, Y The sampled signal; The amplitude and initial phase angle information are obtained by using the least squares method. For the N-point sampled signal, we have... (8)， The parameters of low-frequency oscillations include amplitude, initial phase, angular frequency, and attenuation factor, which are calculated using the following formulas: (9)， In equation (9), a i The first in the speed oscillation data i The amplitude of each component, θ i The first in the speed oscillation data i The initial phase of each component, ω k Angular frequency, σ k As the attenuation factor, T s The sampling period; Based on the oscillation mode c, the parameters of the low-frequency oscillation are obtained, including amplitude, initial phase, angular frequency and attenuation factor. Configuration module: used to obtain the positive semi-definite matrix Q, the positive definite matrix R, and the state feedback gain matrix K of the optimal control vector based on the identification results and linear quadratic optimal control theory, and to configure the DC auxiliary controller based on the obtained matrices Q, R, and K; wherein, obtaining the positive semi-definite matrix Q and the positive definite matrix R includes: based on linear quadratic optimal control theory and empirical values, setting the positive semi-definite matrix Q to a matrix with diagonal elements equal to 1 and other elements equal to 0, and setting the positive definite matrix R to 1.0; Control module: Used to install the configured DC auxiliary controller at the constant current of the rectifier side of the hybrid DC system to control the low-frequency oscillation of the AC system.

7. A computing device, characterized in that, Including processor and storage media; The storage medium is used to store instructions; The processor is configured to operate according to the instructions to perform the steps of the method according to any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the program implements the steps of the method according to any one of claims 1 to 5.

Images