Key parameter identification method of new energy flexible direct current transmission system based on trajectory sensitivity

Abstract

The present invention belongs to the technical field of parameter identification of a new energy grid-connected system, and discloses a method for identifying key parameters of a new energy flexible direct current transmission system based on trajectory sensitivity. The method determines that the parameters to be identified are operating parameters and flexible direct current control parameters based on a quantitative index of voltage support strength of a new energy flexible direct current grid-connected system; adopts a trajectory sensitivity method to quantitatively analyze the influence of active output, line impedance in the operating parameters and a droop control proportional coefficient in the flexible direct current control parameters on the short-circuit ratio of a new energy flexible direct current grid-connected point, and the parameter with the greatest identification sensitivity is the key parameter that affects the voltage support strength of the new energy flexible direct current grid-connected system; adopts a chaotic particle swarm algorithm to optimize and solve an objective function based on various parameters and output results, and outputs an identification result, so that the identification result is more accurate and the identification speed is faster, thereby improving the safety and stability of the system.

Description

Technical Field

[0001] The present invention belongs to the technical field of parameter identification of a new energy grid-connected system, and specifically relates to a method for identifying key parameters of a new energy flexible direct current transmission system based on trajectory sensitivity. Background Art

[0002] Since flexible direct current transmission technology has the ability to provide voltage support to passive power grids, and has the characteristics of modularity, scalability, and small footprint, it has become the main technical approach for long-distance renewable energy grid connection. However, as the voltage level and transmission capacity of renewable energy grid-connected flexible direct current systems continue to increase, the system's voltage support capability in response to AC grid faults faces severe challenges. Therefore, it is urgent to conduct a quantitative analysis of the voltage support strength of renewable energy grid-connected flexible direct current systems.

[0003] During the operation of the new energy flexible direct current transmission system, it is affected by many factors such as grid fluctuations and load changes, which increases the complexity of the system operation; at the same time, the new energy station and the AC power grid will be decoupled from each other. When the new energy itself is volatile, unreasonable control strategies will also affect the voltage and frequency stability of the AC system. Through parameter identification, the dynamic characteristics and operating parameters of the system can be accurately obtained, and an optimized control strategy can be provided for the system, which is crucial for optimizing the performance of the system and improving the operating efficiency. However, the existing optimization algorithms have poor adaptability, slow convergence speed, long iteration time, and low accuracy. Therefore, it is urgent to study the accurate identification method of the parameters of the new energy flexible direct current grid-connected system to obtain accurate parameters to provide support for the optimized operation and control of new energy. Summary of the invention

[0004] In order to solve the above technical problems, the present invention provides a key parameter identification method for a new energy flexible direct current transmission system based on trajectory sensitivity. The trajectory sensitivity method is used to quantitatively analyze the influence of operating parameters and flexible direct current control parameters on the short-circuit ratio of the new energy flexible direct current grid-connected point, and the identifiability of each parameter is determined. The chaotic particle swarm algorithm is used to quickly and accurately identify the key parameters. The method has a global search capability during the identification process, thereby improving the identification accuracy.

[0005] The key parameter identification method of the new energy flexible direct current transmission system based on trajectory sensitivity of the present invention comprises the following steps:

[0006] S1. Based on the quantitative index of voltage support strength of the flexible direct current grid-connected system of new energy, the parameters to be identified are determined as operating parameters and flexible direct current control parameters;

[0007] S2. Using the trajectory sensitivity method, quantitatively analyze the impact of active output, line impedance and droop control proportional coefficient in the operating parameters on the short-circuit ratio of the flexible direct current grid-connected point of renewable energy. The parameter with the greatest identification sensitivity is the key parameter that affects the voltage support strength of the flexible direct current grid-connected system of renewable energy.

[0008] S3, constructing the short-circuit ratio parameter identification objective function of the new energy flexible direct current grid connection point;

[0009] S4. Use the chaotic particle swarm algorithm to optimize the objective function based on various parameters and output results, and output the identification results.

[0010] Furthermore, S1 is specifically:

[0011] S11. Calculate the reactive support capacity of the receiving VSC converter station during droop control :

[0012] ,

[0013] In the formula, is the capacity of the converter station; is the active power reference value of the converter station; is the DC voltage reference value of the converter station; is the droop control proportional coefficient;

[0014] S12. Set the quantitative index as the short-circuit ratio of new energy after flexible direct current grid connection , the expression is:

[0015] ,

[0016] In the formula, is the short-circuit capacity of the grid-connected point of new energy i; The total injected power for each grid connection point; is the apparent power at the grid connection point i; is the apparent power at the grid connection point j; is the system rated voltage; is the system potential; is the self-impedance of the grid-connected point i; is the impedance between the ij and the grid points; , is the voltage of grid connection points i and j; n is the number of new energy stations;

[0017] S13. Based on the result of S12, the parameters to be identified are determined as the apparent power S, the line impedance Z in the operating parameters, and the droop control proportional coefficient in the flexible direct current control parameters. ,have , is the reactive power; to simplify the calculation, only the active power P, line impedance Z, and droop control proportional coefficient are considered is the parameter to be identified.

[0018] Furthermore, S2 is specifically:

[0019] S21. Extract the trajectory sensitivity curve of the short-circuit ratio parameters affecting the new energy through the flexible direct current grid connection point:

[0020] ,

[0021] In the formula, Inject active power into the grid connection point of the new energy station i, is the change value of the parameter change;

[0022] S22. Calculate the trajectory sensitivity of the short-circuit ratio parameters affecting the grid connection of new energy via flexible direct current:

[0023] ,

[0024] In the formula, The parameter to be identified The mean trajectory sensitivity within the sampled time window; is the total number of sampling points in the observation time window, is the parameter to be identified within the observation time window No. The trajectory sensitivity value of each point. The greater the trajectory sensitivity, the greater the impact of this parameter on the short-circuit ratio of the new energy flexible direct current grid-connected point.

[0025] S23. The parameter with the greatest identification sensitivity is the key parameter that affects the voltage support strength of the new energy flexible direct current grid-connected system:

[0026] According to the drawing results of step S21 and the calculation results of step S22, the parameters with greater trajectory sensitivity are selected as the key parameters affecting the voltage support strength of the flexible direct current grid-connected system of renewable energy; if P has the largest sensitivity, the key parameter is the active power P; if Z has the largest sensitivity, the key parameter is the line impedance Z; if The sensitivity is the largest, so the key parameter is the droop control proportional coefficient .

[0027] Furthermore, in S3, the objective function, that is, the fitness function, is constructed as:

[0028] ,

[0029] In the formula, is the parameter to be identified; is the total number of sampling points in the observation time window, and The short-circuit ratio obtained by calculation and the short-circuit ratio of actual simulation.

[0030] Furthermore, S4 is specifically:

[0031] S4-1. Combine the operating parameters P, Z and control parameters As the position parameter of the particle, and initialize the particle's speed and position;

[0032] S4-2, calculating the short circuit ratio output value VSCR through the parameters and input data of the i-th particle;

[0033] S4-3. Calculated short circuit ratio VSCR and actual simulated short circuit ratio The sum of the fitting errors between the particles, that is, the individual fitness value of each particle ;

[0034] S4-4. Calculate and update the local optimal value P of the particle best and the global optimal value g best ;

[0035] S4-5, update the position and velocity of the particles, calculate the population fitness variance, and determine whether the algorithm converges prematurely;

[0036] S4-6, using the Logistic mapping equation to transform the prematurely converged particles into chaotic particles;

[0037] S4-7, determine whether the maximum number of iterations has been reached, if so, proceed to the next step, otherwise, return to S4-5;

[0038] S4-8. Output the identification result.

[0039] Furthermore, the particle position and velocity of the particle swarm algorithm are updated as follows:

[0040] ,

[0041] ,

[0042] In the formula, and are the current speed and position of the particle respectively; and are the historical optimal values ​​of particles and populations respectively; is the inertia weight of the particle; and is the acceleration factor; is the number of iterations of the algorithm; and is a uniformly distributed random number, whose value is between [0,1];

[0043] The variance of the population fitness is used to evaluate whether the algorithm has fallen into local premature convergence:

[0044] ,

[0045] In the formula, is the fitness value corresponding to the i-th particle in the m-th generation population; is the average fitness value of the m-generation population; is the total number of particles in the particle group; is the population fitness variance of the mth generation. The smaller its value is, the easier it is for the population to converge.

[0046] When the algorithm converges prematurely, the particle position is updated as follows:

[0047] ,

[0048] In the formula, is the updated particle position; and are the maximum and minimum values ​​that appear during the particle iteration process;

[0049] Logistic mapping is introduced to generate chaotic variables with chaotic states, specifically:

[0050] ,

[0051] In the formula, is the current position of the chaotic particle; As parameters, is a random number between [0,1].

[0052] The beneficial effects of the present invention are as follows: the present invention proposes a short-circuit ratio, a quantitative indicator of voltage support strength of a new energy system connected to a flexible direct current grid, to evaluate the stability and voltage support strength of the new energy system connected to a flexible direct current grid; trajectory sensitivity is used to identify key parameters of the short-circuit ratio of the new energy system connected to a flexible direct current grid, the identifiability of various parameters affecting the short-circuit ratio is determined, the accuracy of short-circuit ratio evaluation is improved, the reactive power configuration of the flexible direct current converter station is reasonably optimized, and a CPSO algorithm is used to identify key parameters, the safety and stability of the system are improved, and a new energy transmission plan is reasonably made according to the parameter identification results. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 is a flow chart of the method of the present invention;

[0054] Figure 2 It is a schematic diagram of the sensitivity curve of operating parameters;

[0055] Figure 3 It is a schematic diagram of the sensitivity curve of the flexible direct current control parameters;

[0056] Figure 4 Schematic diagram of the structure of the flexible direct current grid-connected system for new energy stations;

[0057] Figure 5 Convergence curve comparison of the two identification algorithms. DETAILED DESCRIPTION

[0058] In order to make the contents of the present invention more clearly understood, the present invention is further described in detail below based on specific embodiments in conjunction with the accompanying drawings.

[0059] like Figure 1 As shown, the key parameter identification method of the new energy flexible direct current transmission system based on trajectory sensitivity of the present invention comprises the following steps:

[0060] S1. Based on the quantitative index of voltage support strength of the flexible direct current grid-connected system of new energy, the parameters to be identified are determined as operating parameters and flexible direct current control parameters, specifically:

[0061] S11. Calculate the reactive support capacity of the receiving VSC converter station during droop control :

[0062] ,

[0063] In the formula, is the capacity of the converter station; is the active power reference value of the converter station; is the DC voltage reference value of the converter station; is the droop control proportional coefficient;

[0064] S12. Set the quantitative index as the short-circuit ratio of new energy after flexible direct current grid connection , the expression is:

[0065] ,

[0066] In the formula, is the short-circuit capacity of the grid-connected point of new energy i; The total injected power for each grid connection point; is the apparent power at the grid connection point i; is the apparent power at the grid connection point j; is the system rated voltage; is the system potential; is the self-impedance of the grid-connected point i; is the impedance between the ij and the grid points; , is the voltage of the grid connection point i and j; n is the number of new energy stations;

[0067] S13. Determine the parameters to be identified: From the calculation in step S12, it can be known that the apparent power S, line impedance Z in the system operation parameters and the droop control proportional coefficient in the flexible direct current control parameters are the parameters to be identified, To simplify the calculation, only active output P is considered, that is, active power P, line impedance Z, and droop control proportional coefficient It is the parameter to be identified in the present invention.

[0068] S2. Based on the trajectory sensitivity method, the influence of active output, line impedance and droop control proportional coefficient in the operating parameters on the short-circuit ratio of the flexible direct current grid-connected point of new energy is quantitatively analyzed. The parameter with the greatest identification sensitivity is the key parameter affecting the voltage support strength of the flexible direct current grid-connected system of new energy, specifically:

[0069] S21. Extract the trajectory sensitivity curve of the short-circuit ratio parameters affecting the new energy through the flexible direct current grid connection point:

[0070] ,

[0071] In the formula, Inject active power into the grid connection point of the new energy station i, is the change value of the parameter change;

[0072] S22. Calculate the trajectory sensitivity of the short-circuit ratio parameters affecting the grid connection of new energy via flexible direct current:

[0073] ,

[0074] In the formula, The parameter to be identified The mean trajectory sensitivity within the sampled time window; is the total number of sampling points in the observation time window, is the parameter to be identified within the observation time window No. The trajectory sensitivity value of each point. The greater the trajectory sensitivity, the greater the impact of this parameter on the short-circuit ratio of the new energy flexible direct current grid-connected point.

[0075] S23. Based on the results obtained in S21 and S22, the parameter with the largest trajectory sensitivity is selected as the key parameter affecting the voltage support strength of the flexible direct current grid-connected system of new energy; if P has the largest sensitivity, the key parameter is the active power P; if Z has the largest sensitivity, the key parameter is the line impedance Z; if The sensitivity is the largest, so the key parameter is the droop control proportional coefficient .

[0076] S3. Construct the objective function of identifying the short-circuit ratio parameters of the new energy flexible direct current grid connection point, that is, the fitness function is:

[0077] ,

[0078] In the formula, is the parameter to be identified; is the total number of sampling points in the observation time window, and The short-circuit ratio obtained by calculation and the short-circuit ratio of actual simulation.

[0079] S4, using the chaotic particle swarm algorithm, based on the parameters and output results to optimize the objective function solution, output identification results, specifically:

[0080] S4-1. Combine the operating parameters P, Z and control parameters As the position parameter of the particle, and initialize the particle's speed and position;

[0081] S4-2, calculating the short circuit ratio output value VSCR through the parameters and input data of the i-th particle;

[0082] S4-3. Calculated short circuit ratio VSCR and actual simulated short circuit ratio The sum of the fitting errors between the particles, that is, the individual fitness value of each particle ;

[0083] S4-4. Calculate and update the local optimal value P of the particle best and the global optimal value g best ;

[0084] S4-5, update the position and velocity of the particles, calculate the population fitness variance, and determine whether the algorithm converges prematurely; specifically:

[0085] ,

[0086] ,

[0087] In the formula, and are the current speed and position of the particle respectively; and are the historical optimal values ​​of particles and populations respectively; is the inertia weight of the particle; and is the acceleration factor; is the number of iterations of the algorithm; and is a uniformly distributed random number, whose value is between [0,1];

[0088] The variance of the population fitness is used to evaluate whether the algorithm has fallen into local premature convergence:

[0089] ,

[0090] In the formula, is the fitness value corresponding to the i-th particle in the m-th generation population; is the average fitness value of the m-generation population; is the total number of particles in the particle group; is the population fitness variance of the mth generation. The smaller its value is, the easier it is for the population to converge.

[0091] When the algorithm converges prematurely, the particle position is updated as follows:

[0092] ,

[0093] In the formula, is the updated particle position; and are the maximum and minimum values ​​that appear during the particle iteration process;

[0094] S4-6. Use the Logistic mapping equation to perform chaotic processing on the prematurely converged particles. Specifically:

[0095] ,

[0096] In the formula, is the current position of the chaotic particle; As parameters, is a random number between [0,1];

[0097] S4-7, determine whether the maximum number of iterations has been reached, if so, proceed to the next step, otherwise, return to S4-5;

[0098] S4-8. Output the identification result.

[0099] In order to effectively verify the correctness of the proposed method for identifying key parameters of the new energy flexible direct current transmission system based on trajectory sensitivity, the present invention first plots step S21, and the results are as follows: Figure 2 and Figure 3 shown.

[0100] The calculation and analysis of step S22 are shown in Table 1.

[0101] Table 1 Trajectory sensitivity of each parameter under different time windows

[0102]

[0103] Depend on Figure 2 , Figure 3As shown in Table 1, the short circuit ratio of the renewable energy through the flexible direct current grid connection point is more sensitive to the active output P, ​​and the sensitivity is relatively large, which can be identified as the key parameter affecting the voltage support strength of the renewable energy through the flexible direct current grid connection system in the present invention; the line impedance Z and the proportional coefficient k p The trajectory sensitivity is small and the identifiability is low.

[0104] Step S3 adopts Figure 4 The new energy station shown is analyzed through a flexible direct current grid-connected system. The present invention mainly studies the situation where the new energy equipment operates under rated conditions and unity power factor.

[0105] The CPSO algorithm is used to identify the parameters to be identified, and the identified parameter values ​​are shown in Table 2.

[0106] Table 2 Parameter values ​​identified by different optimization algorithms

[0107]

[0108] The identification results of the CPSO algorithm and the PSO algorithm used in the present invention are compared. The particle swarm size u of the two algorithms is 100; 100 iterations are performed respectively and 50 independent runs are performed; the average value of the 50 identification results is taken as the final parameter identification result, and the convergence curve of the objective function is Figure 5 shown.

[0109] It can be seen from Table 2 that the identification value using the CPSO algorithm is closer to the true value, and the relative error of the active output P is smaller, which is easier to identify than the other two parameters, which is consistent with the results of trajectory sensitivity analysis. Figure 5 It can be seen that the method of the present invention adopts the CPSO algorithm to optimize the objective function, with fast convergence speed and high identification accuracy. Its convergence effect is better than that of PSO, and CPSO provides higher identification accuracy.

[0110] The above description is only a preferred embodiment of the present invention and is not intended to be a further limitation of the present invention. All equivalent changes made using the contents of the present specification and drawings are within the protection scope of the present invention.

Claims

1. A method for identifying key parameters of a new energy flexible direct current transmission system based on trajectory sensitivity, characterized in that: The following steps are involved: S1. Based on the quantitative index of voltage support strength of the flexible direct current grid-connected system of new energy, the parameters to be identified are determined as operating parameters and flexible direct current control parameters; specifically: S11. Calculate the reactive support capacity of the receiving VSC converter station during droop control : ， In the formula, is the capacity of the converter station; is the active power reference value of the converter station; is the DC voltage reference value of the converter station; is the droop control proportional coefficient; S12. Set the quantitative index as the short-circuit ratio of new energy after flexible direct current grid connection , the expression is: ， In the formula, is the short-circuit capacity of the grid-connected point of new energy i; The total injected power for each grid connection point; is the apparent power at the grid connection point i; is the apparent power at the grid connection point j; is the system rated voltage; is the system potential; is the self-impedance of the grid-connected point i; is the impedance between the ij and the grid points; , is the voltage of grid connection points i and j; n is the number of new energy stations; S13. Based on the result of S12, the parameters to be identified are determined as the apparent power S, the line impedance Z in the operating parameters, and the droop control proportional coefficient in the flexible direct current control parameters. ,have , is the reactive power; to simplify the calculation, only the active power P, line impedance Z, and droop control proportional coefficient are considered is the parameter to be identified; S2. Using the trajectory sensitivity method, quantitatively analyze the influence of active output, line impedance and droop control proportional coefficient in the operating parameters on the short-circuit ratio of the flexible direct current grid-connected point of new energy. The parameter with the greatest identification sensitivity is the key parameter affecting the voltage support strength of the flexible direct current grid-connected system of new energy. Specifically: S21. Extract the trajectory sensitivity curve of the short-circuit ratio parameters affecting the new energy through the flexible direct current grid connection point: ， In the formula, Inject active power into the grid connection point of the new energy station i, is the change value of the parameter change; S22. Calculate the trajectory sensitivity of the short-circuit ratio parameters affecting the grid connection of new energy via flexible direct current: ， In the formula, The parameter to be identified The mean trajectory sensitivity within the sampled time window; is the total number of sampling points in the observation time window, is the parameter to be identified within the observation time window No. The trajectory sensitivity value of each point. The greater the trajectory sensitivity, the greater the impact of this parameter on the short-circuit ratio of the new energy flexible direct current grid-connected point. S23. Based on the results obtained in S21 and S22, the parameters with the greatest trajectory sensitivity are compared and selected as the key parameters affecting the voltage support strength of the flexible direct current grid-connected new energy system; If P sensitivity is the largest, the key parameter is active power P; If Z sensitivity is the largest, the key parameter is the line impedance Z; if The sensitivity is the largest, so the key parameter is the droop control proportional coefficient ; S3, constructing the short-circuit ratio parameter identification objective function of the new energy flexible direct current grid connection point; S4. Use the chaotic particle swarm algorithm to optimize the objective function based on various parameters and output results, and output the identification results.

2. The key parameter identification method of the new energy flexible direct current transmission system based on trajectory sensitivity according to claim 1 is characterized in that: In S3, the objective function, that is, the fitness function, is constructed as: ， In the formula, is the parameter to be identified; is the total number of sampling points in the observation time window, and The short-circuit ratio obtained by calculation and the short-circuit ratio of actual simulation.

3. The key parameter identification method of the new energy flexible direct current transmission system based on trajectory sensitivity according to claim 2 is characterized in that: S4 is specifically: S4-1. Combine the operating parameters P, Z and control parameters As the position parameter of the particle, and initialize the particle's speed and position; S4-2, calculating the short circuit ratio output value VSCR through the parameters and input data of the i-th particle; S4-3. Calculated short circuit ratio VSCR and actual simulated short circuit ratio The sum of the fitting errors between the particles, that is, the individual fitness value of each particle ; S4-4. Calculate and update the local optimal value P of the particle best and the global optimal value g best ; S4-5, update the position and velocity of the particles, calculate the population fitness variance, and determine whether the algorithm converges prematurely; S4-6, using the Logistic mapping equation to transform the prematurely converged particles into chaotic particles; S4-7, determine whether the maximum number of iterations has been reached, if so, proceed to the next step, otherwise, return to S4-5; S4-8. Output the identification result.

4. The key parameter identification method of the new energy flexible direct current transmission system based on trajectory sensitivity according to claim 3 is characterized in that: The particle position and velocity update of the particle swarm algorithm are as follows: ， ， In the formula, and are the current speed and position of the particle respectively; and are the historical optimal values ​​of particles and populations respectively; is the inertia weight of the particle; and is the acceleration factor; is the number of iterations of the algorithm; and is a uniformly distributed random number, whose value is between [0,1]; The variance of the population fitness is used to evaluate whether the algorithm has fallen into local premature convergence: ， In the formula, is the fitness value corresponding to the i-th particle in the m-th generation population; is the average fitness value of the m-generation population; is the total number of particles in the particle group; is the population fitness variance of the mth generation. The smaller its value is, the easier it is for the population to converge. When the algorithm converges prematurely, the particle position is updated as follows: ， In the formula, is the updated particle position; and are the maximum and minimum values ​​that appear during the particle iteration process; Logistic mapping is introduced to generate chaotic variables with chaotic states, specifically: ， In the formula, is the current position of the chaotic particle; As parameters, is a random number between [0,1].

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