A distributed parameter identification method for phase modifier

Abstract

This invention discloses a method for identifying parameters of a distributed synchronous condenser, relating to the field of synchronous condenser parameter identification. This invention aims to improve the accuracy of synchronous condenser parameter identification. First, a mathematical model of the distributed synchronous condenser is established to determine the parameters to be identified; then, the parameters are identified using the Grey Wolf algorithm. This invention combines the Grey Wolf algorithm with the Particle Swarm Optimization (PSO) algorithm for accurate identification of distributed synchronous condenser parameters.

Description

Technical Field

[0001] This invention relates to the field of synchronous condenser parameter identification, and more particularly to a method for distributed synchronous condenser parameter identification. Background Technology

[0002] In recent years, with the adjustment of industrial and energy structures, the large-scale development and utilization of new energy sources and the construction of a clean, low-carbon, safe, and efficient energy system have become imperative. Qinghai Province possesses abundant wind and solar resources, with a large installed capacity and high proportion of new energy. However, the relatively small installed capacity of conventional power sources and weak voltage support capabilities constrain the large-scale development and utilization of new energy. Against this backdrop, installing distributed synchronous condensers (SCDCs) on the power supply side has become a good solution to promote the efficient development and utilization of energy resources. Compared to traditional reactive power compensation devices, SCDCs have advantages such as strong voltage support capabilities during faults and fast transient response speeds, and can serve as stabilizers on the power supply side of the power system. In the future, they will be an important component of high-proportion new energy power systems. However, in actual operation, due to complex operating conditions, especially during transient and sub-transient states, the parameters of SCDCs may deviate from design values. If the identified values ​​are still used for analysis of SCDCs under these conditions, the analysis results will deviate from the actual situation. Furthermore, if the identification accuracy is not ideal, it will also have a significant impact on power system analysis. Summary of the Invention

[0003] In view of the above-mentioned deficiencies of the prior art, the present invention provides a method for identifying distributed phase condenser parameters, which combines the Grey Wolf algorithm and the Particle Swarm Optimization algorithm to accurately identify distributed phase condenser parameters.

[0004] To achieve the above objectives, the present invention provides a method for identifying parameters of a distributed synchronous condenser, comprising:

[0005] S1. Establish a mathematical model for distributed synchronous condensers and determine the parameters to be identified;

[0006] S2. Parameter identification is performed based on the Grey Wolf algorithm.

[0007] The d-axis electrical model of the mathematical model of the distributed synchronous condenser is as follows:

[0008]

[0009] The electrical model for the Q-axis is:

[0010]

[0011] Rotor motion equations:

[0012]

[0013] Where: u d id These represent the voltage and current along the d-axis, respectively; u q i q These represent the voltage and current along the q-axis, respectively; T′ d0 、T′ q0 These are the transient open-circuit time constants for the d-axis and q-axis, respectively; T″ d0 、T″ q0 These are the d-axis subtransient open-circuit time constants and the q-axis subtransient open-circuit time constants, respectively; E f E' is the magnetomotive force of the excitation system. d 、E' q These are the d-axis transient potential and the q-axis transient potential, respectively; E″ d0 、E″ q0 These are the d-axis subtransient potential and the q-axis subtransient potential, respectively; X d 、X' d For d-axis synchronous reactance and transient reactance; X q 、X' q These are the q-axis synchronous reactance and transient reactance, respectively; X″ d 、X″ q These are the d-axis subtransient reactance and the q-axis subtransient reactance, respectively; T J T is the inertial time constant. m T is the mechanical torque. e ω is the electromagnetic torque; D is the damping coefficient; δ is the power angle; ω is the rotor angular velocity.

[0014] The method for generating the initial population in the wolf pack algorithm includes:

[0015] S21. Use the particle swarm optimization algorithm to pre-identify the parameters to be identified, and use them as the initial population for the wolf pack algorithm.

[0016] S22. Use the wolf pack algorithm to identify the output results.

[0017] The inertia weights ω1, ω2, and ω3 of the particle swarm optimization algorithm are as follows:

[0018]

[0019] Where a is the convergence factor; r 11 r 12 r 21 r 22 r 31 and r 32 It is a random number between [0,1].

[0020] The convergence factor is:

[0021]

[0022] Where e is the base of the natural logarithm function, t is the current iteration number, and t max This represents the maximum number of iterations.

[0023] The method for updating the position of the gray wolf in the gray wolf algorithm is as follows:

[0024]

[0025] X (t+1) The position t of the gray wolf after iteration represents the current iteration number; X1, X2, and X3 represent the current position of the gray wolf.

[0026] Compared with the prior art, the present invention has the following beneficial effects:

[0027] 1. This invention uses the particle swarm optimization algorithm to pre-identify the initial population, and then uses the gray wolf algorithm to identify the parameters in one step. Furthermore, the three inertial weights in the particle swarm optimization algorithm are combined into the position update process in the gray wolf algorithm to improve the identification accuracy.

[0028] 2. Based on the adaptive concept, this invention improves the convergence factor in the Grey Wolf algorithm by making the convergence factor nonlinear, thereby improving the identification accuracy.

[0029] 3. This invention uses a practical model applicable to distributed synchronous condensers to identify parameters based on measured data and an improved Grey Wolf algorithm, thereby improving the identification method and enhancing its accuracy. It has a wide range of applications and is applicable.

[0030] The following will further explain the concept, specific structure, and technical effects of the present invention in conjunction with the accompanying drawings, so as to fully understand the purpose, features, and effects of the present invention. Attached Figure Description

[0031] Figure 1 This is a schematic diagram of the overall process of parameter identification according to a specific embodiment of the present invention;

[0032] Figure 2 This is a schematic flowchart of a pre-identification method according to a specific embodiment of the present invention;

[0033] Figure 3 This is a specific embodiment of the present invention, which shows the current identification curve and the actual current curve obtained based on the parameter identification method. Detailed Implementation

[0034] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that, unless otherwise specified, the following embodiments and features described therein can be combined with each other.

[0035] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Therefore, the illustrations only show the components related to the present invention and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0036] Some exemplary embodiments of the invention have been described for illustrative purposes. It should be understood that the invention may be implemented in other ways not specifically shown in the accompanying drawings.

[0037] like Figure 1 As shown in the figure, a distributed camera condenser parameter identification method based on a combination of the gray wolf algorithm and the particle swarm optimization algorithm is provided in a specific embodiment. The method includes the following steps:

[0038] S1. Establish a mathematical model for the distributed synchronous condenser and determine the parameter to be identified as X. d 、X' d 、X″ d 、T′ d0 、T″ d0 .

[0039] This application reduces rotor-side variables to stator-side variables, ignores stator transient processes, considers the role of rotor damping windings, that is, takes into account the electromagnetic transients of f winding, D winding, Q winding and the electromechanical transients of rotor motion, and on this basis considers g winding of q axis to describe q axis transient processes.

[0040] In this embodiment, the mathematical model for the distributed synchronous condenser is as follows:

[0041] d-axis electrical model:

[0042]

[0043] q-axis electrical model:

[0044]

[0045] Rotor motion equations:

[0046]

[0047] Where: u d i d These represent the voltage and current along the d-axis, respectively; u q i q The voltage and current T′ on the q-axis are respectively. d0 、T′ q0 T′ represents the transient open-circuit time constants for the d-axis and q-axis, respectively. d0 、T′ q0 These are the d-axis subtransient open-circuit time constants and the q-axis subtransient open-circuit time constants, respectively; E f E′ is the magnetomotive force of the excitation system. d E′ q These are the d-axis transient potential and the q-axis transient potential, respectively; E″ d0 、E″ q0 These are the d-axis subtransient potential and the q-axis subtransient potential, respectively; X d 、X′ d For d-axis synchronous reactance and transient reactance; X q 、X′ q These are the q-axis synchronous reactance and transient reactance, respectively; X″ d 、X″ q These are the d-axis subtransient reactance and the q-axis subtransient reactance, respectively; T J T is the inertial time constant. m T is the mechanical torque. e δ is the electromagnetic torque; D is the damping coefficient; δ is the power angle; ω is the rotor angular velocity;

[0048] S2. Identify the parameters of the synchronous synchrotron using the wolf pack algorithm, specifically including:

[0049] The selection of the initial population has a significant impact on the global convergence speed and the quality of the optimal solution of the intelligent algorithm. Traditional gray wolf algorithms involve random population generation during initialization, which may cause the initial values ​​to deviate from the desired target values, greatly affecting the accuracy and speed of parameter identification. This invention uses a particle swarm optimization algorithm to determine the initial population before identification. Based on the input and output data of the camera condenser, a set of pre-identified parameters is obtained, and the results of this pre-identification are used as the initial population. This significantly improves the accuracy and speed of parameter identification.

[0050] S21. The particle swarm optimization algorithm is used to pre-identify the parameters to be identified, which serves as the initial population for the wolf pack algorithm. Figure 2 As shown, it includes:

[0051] S212. Using the parameters to be identified as the population particles, initialize the particle velocity and position;

[0052] S213. Calculate the fitness of all particles, and determine the extreme values ​​of each particle and the population.

[0053] The fitness of the particles is:

[0054]

[0055] Among them, i dj Let i′ be the measured current at the j-th sampling point. dj The current is fitted to the identification value of the j-th sampling point, where K is the number of sampling points.

[0056] S214. Update the particle's velocity and position according to the following formula;

[0057] v ij (t+1)=ω·v ij (t)+c1r1(t)+[p ij (t)-x ij (t)]+c2r2(t)[p gi (t)-x ij (t)];

[0058] x ij (t+1)=x ij (t)+v ij (t+1);

[0059] Where c1 and c2 are learning factors, r1 and r2 are uniformly random numbers in the range [0,1], and v ij It represents the particle velocity, ω represents the inertial weight, and p ij p represents the best individual extreme value of the particle to date. gi denoted as the best global extremum of the particle to date, and t is the number of iterations.

[0060] S215. Calculate the fitness of all particles;

[0061] S216. Determine whether the iteration termination condition has been met. If yes, output the identified parameters. If no, execute step S214.

[0062] The iteration termination condition is reaching the required number of population iterations; in this embodiment, the number of iterations is set to 800.

[0063] S22. Set the relevant parameters and maximum number of iterations for the wolf pack algorithm, initialize the convergence factor a, substitute the measured data, and calculate the fitness of each wolf in the wolf pack.

[0064] The fitness level of each gray wolf is:

[0065] F(x) = 100 * 10 -f(x) ,

[0066] Among them, i dl Let i′ be the measured current at the l-th sampling point. dl The current is fitted to the identification value of the l-th sampling point, and M is the number of sampling points.

[0067] S23. Preserve the top three wolves with the best fitness: α, β, and δ.

[0068] S24. Update the position of individual gray wolves in the wolf pack according to the following formula:

[0069]

[0070] Where: ω1, ω2, ω3 are inertia weights; a is the convergence factor; r1, r2 are random numbers between [0,1]; t is the current iteration number; X1, X2, X3 represent the positions of α wolf, β wolf, and δ wolf at time t; X (t+1) This indicates the position of the gray wolf after the iteration.

[0071] S25, Update parameters and overall gray wolf fitness;

[0072] S25. Iterate to the maximum number of algebras and output the recognition result;

[0073] The convergence factor 'a' in the parameter identification method varies as shown below:

[0074]

[0075] Where e is the base of the natural logarithm function, t is the current iteration number, and t max This represents the maximum number of iterations.

[0076] The Grey Wolf algorithm incorporates the inertia weighting concept from the Particle Swarm Optimization algorithm in its search method, as shown below:

[0077]

[0078] Among them, r 11 r 12 r 21 r 22 r 31 and r 32 All numbers are random numbers selected according to actual needs, and their range is [0,1].

[0079] In one specific embodiment, the current identification value obtained by substituting the above parameter identification values ​​into the mathematical model is compared with the actual current, and the comparison result is as follows: Figure 3 As shown in the figure, the solid line represents the measured current value, and the dashed line represents the current identification value, with an error of 3.8*10. -4As can be seen, the results obtained by the parameter identification method used in this embodiment are very similar to the measured values.

[0080] The above embodiments are merely illustrative of the principles and effects of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in the present invention should still be covered by the claims of the present invention.

Claims

1. A method for distributed parameter identification of a phase modifier, the method comprising: include: S1. Establish a mathematical model for distributed synchronous condensers and determine the parameters to be identified; S2. Parameter identification is performed based on the Grey Wolf algorithm; The initial population generation methods for the wolf pack algorithm include: S21. Use the particle swarm optimization algorithm to pre-identify the parameters to be identified, and use them as the initial population for the wolf pack algorithm. S22. Use the wolf pack algorithm to identify the output results; The inertia weight of the particle swarm algorithm , and are respectively: ； wherein is a convergence factor; , , , , and is a random number between [0,1] The convergence factor is: ； in, Let be the base of the natural logarithm function. This represents the current iteration number. This represents the maximum number of iterations. The method for updating the position of the gray wolf in the gray wolf algorithm is as follows: ； Indicates the position of the gray wolf after iteration. This represents the current iteration number; , , This indicates the current location of the gray wolf.

2. The method of claim 1, wherein, The d-axis electrical model of the mathematical model of the distributed synchronous condenser is as follows: ； The electrical model for the Q-axis is: ； Rotor motion equations: ； in: , These represent the voltage and current along the d-axis, respectively. , These represent the voltage and current along the q-axis, respectively. , These are the transient open-circuit time constants for the d-axis and q-axis, respectively; , These are the d-axis subtransient open-circuit time constant and the q-axis subtransient open-circuit time constant, respectively. The excitation potential of the excitation system; , These are the d-axis transient potential and the q-axis transient potential, respectively. , These are the d-axis subtransient potential and the q-axis subtransient potential, respectively. , For d-axis synchronous reactance and transient reactance; , These are the q-axis synchronous reactance and transient reactance, respectively. , These are the d-axis subtransient reactance and the q-axis subtransient reactance, respectively. The inertial time constant, For mechanical torque, Electromagnetic torque; The damping coefficient; For the angle of attack; ω is the rotor angular velocity.

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