A converter active support power flow optimization control method based on MOGWO

Abstract

This invention discloses a converter-actively supported power flow optimization control method based on MOGWO. The method first determines the expected fault categories of the wind farm and sets corresponding reference power changes for the converter stations based on the different fault categories. A sensitivity matrix is ​​then established, along with optimization targets and a MOGWO algorithm model. Finally, the established model is used to adjust system parameters under different fault types in the offshore wind farm to achieve the optimal optimization goal. This invention proposes a converter-actively supported flexible DC system line power flow control method. Based on system-level control of the DC grid, this method utilizes adaptive voltage droop control to provide additional degrees of freedom for DC power flow control, significantly reducing the investment cost and operating losses of power flow control.

Description

Technical Field

[0001] The present invention belongs to the technical field of flexible direct current transmission, and mainly relates to a converter active support type power flow optimization control method based on MOGWO. Background Art

[0002] Offshore wind power, a mainstream renewable energy technology, is rapidly expanding in many countries across Europe, North America, and Asia. There are two viable technologies for integrating offshore wind farms into existing AC systems: high-voltage AC grids and high-voltage DC (VSC-HVDC) grids based on voltage source converters. However, the advantages of independent control of active and reactive power, submarine connections, and power flow redirection capabilities make HVDC grids, also known as multi-terminal HVDC (MTDC) systems, the preferred solution for integrating offshore wind farms.

[0003] According to current research, achieving DC power flow control requires the introduction of a DC power flow controller based on power electronics to increase the degrees of control freedom. Currently, the focus of DC power flow control is primarily on improving the topology of the DC power flow controller. However, the high investment and operating costs of DC power flow controllers remain one of the main technical challenges limiting the development of DC system control. Therefore, if we can improve the degrees of control of DC power flow control from the perspective of DC grid system-level control, it will significantly reduce investment costs and operating losses, thereby bringing huge economic benefits. Such system-level improvements will become an important research direction in the field of DC system operation control in the future, and are expected to address current technical challenges and promote the more efficient and sustainable development of DC power systems. Summary of the Invention

[0004] The present invention addresses the existing problem of DC line current exceeding limits due to severe faults in wind farms. It provides a method for converter-actively supported current optimization control based on MOGWO. The method first determines the anticipated fault category of the wind farm and sets the corresponding reference power variation of the converter station according to the different fault categories. A sensitivity matrix is ​​then established, along with optimization targets and a MOGWO algorithm model. Finally, the established model is used to adjust system parameters under different types of faults in offshore wind farms to achieve the optimal optimization goal. The present invention proposes a converter-actively supported flexible DC system line current control method. Based on system-level control of the DC grid, the method utilizes adaptive voltage droop control to provide additional degrees of freedom for DC current control, significantly reducing the investment cost and operating losses of current control.

[0005] In order to achieve the above object, the technical solution adopted by the present invention is: a converter active support type power flow optimization control method based on MOGWO, comprising the following steps:

[0006] S1: Determine the expected fault categories of the wind farm and set the corresponding reference power variation of the converter station according to different fault categories; the fault categories include large power fluctuation faults caused by renewable energy, converter station faults under constant active power control, converter station fault interruption under droop control, and DC system disconnection faults;

[0007] S2: Establish a sensitivity matrix, the sensitivity matrix S is expressed as:

[0008] S=ΔP l / P l_rated

[0009]

[0010] Among them, P l_rated is the DC line rated power matrix; ΔP l is the line power flow change, Y L is the node admittance matrix; V is the DC node voltage; ΔV is the DC node voltage change vector; LN is the distance matrix; A is the correlation matrix;

[0011] S3: Setting an optimization target, wherein the optimization target is to minimize the DC line power flow score, the overall DC voltage deviation, and the power generation cost of conventional units after the accident;

[0012] S4: Establish the MOGWO algorithm model and determine the search area size in the initial search phase based on the constraint boundary; randomly generate a group of gray wolf individuals, each representing a potential system optimization parameter, and determine the fitness value of each individual. Set the algorithm parameters, maximum number of iterations, and group size, and iterate until the termination condition is met;

[0013] S5: Using the model established in step S4, the system parameters of the offshore wind farm under different types of faults are adjusted to achieve the optimal optimization goal.

[0014] As an improvement of the present invention, the large power fluctuation caused by the renewable energy is set to a reference power fluctuation amount ΔP dc,i * The change in the converter reference active power before and after the emergency event;

[0015] The converter station of the constant active power control fails and the reference power fluctuation ΔP is set. dc,i * The change in the converter reference active power before and after the emergency event;

[0016] The fault interruption of the converter station in droop control converts the faulty droop control converter into an active power control converter, sets its droop control coefficient to 0 and sets the reference power fluctuation ΔP dc,i* The value of is the negative of the original reference power value;

[0017] When the DC system is disconnected, two virtual DC power supplies are installed at both ends of the disconnected line in the DC system. The value of the virtual DC power supply is determined by the power of the line before the disconnection.

[0018] As another improvement of the present invention, the DC node voltage change vector ΔV in step S2 is specifically:

[0019]

[0020] Where J is the Jacobian matrix; R is the droop coefficient matrix at the converter station; and ΔV * are the changes in reference power and reference voltage of the converter station respectively.

[0021] As another improvement of the present invention, the optimization goal of step S3 is:

[0022]

[0023] Where g1 is the set of constraint equations for the AC / DC connection points; g2 represents the set of DC node equations for constant active power control and the set of DC node equations that are independent of the AC system; g3 represents the AC node equations that are independent of the DC system nodes; h is the inequality constraint, which includes AC system constraints, converter operation constraints, DC voltage constraints, and DC line capacity constraints; θ s and V s are column vectors representing the phase and voltage amplitude of the AC node respectively; V dc is the DC node voltage; P s (V dc ) is the DC power flow equation; P s (θ s ,V s ) is the AC power flow equation; f pl 、f V and f cost They represent the DC line power flow score after the accident, the overall DC voltage deviation, and the optimization target of power generation cost of conventional units.

[0024] As another improvement of the present invention, the DC line power flow fraction f pl Specifically:

[0025]

[0026] Where n is the total number of DC lines; P l,i is the actual active power flow of the ith DC line after the emergency; P l_rated,i is the rated power of the i-th DC line.

[0027] As another improvement of the present invention, in the iteration of step S4, the following steps are performed in each generation:

[0028] Select a leader gray wolf from the current population through non-dominated sorting, and update the position of each follower gray wolf according to the position of the leader gray wolf and the objective function value, specifically:

[0029]

[0030] Among them, The new location of the Gray Wolf i, is the current position, r is a random coefficient, and d is the adjustment vector of the follower gray wolf based on the position of the leader gray wolf;

[0031] According to the distance and fitness value between the gray wolves, they cooperate and compete to update the solutions in the population; they use non-dominated sorting and crowding distance techniques to select and maintain a set of non-dominated solutions;

[0032] Check whether the termination conditions are met, return the non-dominated solution set, and select the best compromise solution based on the TOPSIS method and set it as the converter station parameters after the emergency.

[0033] As another improvement of the present invention, the parameter setting in step S5 is specifically as follows: based on the calculated line power flow, if the power flow exceeds the limit, first calculate the reference voltage change of the converter station related to the line where the power flow exceeds the limit to control the over-limit power flow, and then use the established MOGWO model to optimize the parameters of the remaining converter stations; otherwise, use the established MOGWO model to optimize the controlled parameters of all converter stations under adaptive droop control.

[0034] Compared with the existing technology, the present invention has the following beneficial effects: the present invention proposes a line flow control method for a flexible DC system with active converter support, which is based on the system-level control of the DC power grid and uses adaptive voltage droop control to provide additional degrees of freedom for DC flow control, greatly reducing the investment cost and operating loss of flow control; the present invention establishes a DC flow control method based on multi-objective optimal flow, and for various types of faults that may occur in the flexible DC system, the operating parameters of the flexible DC system VSC converter under adaptive droop control are configured through optimal flow, eliminating the problem of the operating point exceeding the limit of the AC / DC hybrid system after the fault. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 This is a flowchart of the steps of the converter active support type power flow optimization control method based on MOGWO. DETAILED DESCRIPTION

[0036] The present invention will be further described below with reference to the accompanying drawings and specific embodiments. It should be understood that the following specific embodiments are only used to illustrate the present invention and are not used to limit the scope of the present invention.

[0037] Example 1

[0038] A converter active support type power flow optimization control method based on MOGWO, such as Figure 1 As shown, the following steps are included:

[0039] Step S1: Setting the expected fault categories of the wind farm and the reference power change types of the converter stations under different categories; the expected fault categories of the wind farm include:

[0040] The first category is the large power fluctuation caused by renewable energy, and the reference power fluctuation amount ΔP is set. dc,i * The change in the converter reference active power before and after the emergency event;

[0041] The second type is the converter station fault with constant active power control, setting the reference power fluctuation ΔP dc,i * The change in the converter reference active power before and after the emergency event;

[0042] The third type is a fault interruption in the droop control converter station. The faulty droop control converter is converted into an active power control converter, and its droop control coefficient is set to 0 and the reference power fluctuation amount ΔP is set. dc,i * The value of is the negative of the original reference power value;

[0043] The fourth type is a DC system disconnection fault. Two virtual DC power supplies are installed at both ends of the DC system disconnection. The value of the virtual DC power supply is determined by the power of the line before the disconnection.

[0044] Step S2: Calculate the changes in the power flow before and after the emergency and establish a sensitivity matrix; the calculation of the sensitivity matrix specifically includes the following steps:

[0045] Step S21: The generalized DC voltage droop control equation is expressed as:

[0046] P i -P i * +R i (V i -V i * )=0

[0047] Among them, P i and P i *are the actual value and set reference value of the active power injected by the i-th converter station, respectively, where i∈{1,…,n}, n represents the number of DC nodes; V i With V i * are the actual voltage and the voltage setting reference value respectively; R i is the droop coefficient of the converter station, in MW / kV, which can be calculated by the following formula

[0048]

[0049] Where k i is the unit droop constant; P i r With V r is the rated power and rated voltage. For the droop control converter station, its R i is non-zero, for the converter station with constant active power control, R i In addition, if the DC node is not connected to the converter station, R i With P i * are all set to zero;

[0050] Step S22: Establish an association matrix A. If there are x routes and n nodes in the MTDC system, the matrix A is called an association matrix. A contains three types of elements: -1, 0, or 1. -1 and 1 indicate that the current of the pth line enters and exits the node q; 0 indicates that the pth line has nothing to do with the node q.

[0051] Step S23: establishing an off-point matrix LN, where LN contains two elements: 0 or 1, where 1 indicates that the current of the p-th line enters or exits the node q; and 0 indicates that the p-th line has nothing to do with the node q.

[0052] Step S24: Establish the sensitivity matrix S. First, calculate the line power flow change caused by the line emergency, as follows:

[0053]

[0054] Where ΔP l is the line power flow change, Y L is the node admittance matrix; V is the DC node voltage; ΔV is the DC node voltage change vector; given by the following formula:

[0055]

[0056] J is the Jacobian matrix; R is the droop coefficient matrix of the converter station; and ΔV * are the changes of reference power and reference voltage of converter station respectively. The sensitivity matrix S can be expressed as:

[0057] S=ΔPl / P l_rated

[0058] Among them, P l_rated is the DC line rated power matrix.

[0059] Step S3: Setting an optimization goal, which can be expressed as

[0060]

[0061] Where g1 is the set of constraint equations for the AC / DC connection points; g2 represents the set of DC node equations for constant active power control and the set of DC node equations that are independent of the AC system; g3 represents the AC node equations that are independent of the DC system nodes; h is the inequality constraint, which includes AC system constraints, converter operation constraints, DC voltage constraints, and DC line capacity constraints; θ s and V s are column vectors representing the phase and voltage amplitude of the AC node respectively; V dc is the DC node voltage; P s (V dc ) is the DC power flow equation; P s (θ s ,V s ) is the AC power flow equation; f pl 、f V and f cos t represents the DC line power flow score after the accident, the overall DC voltage deviation, and the optimization target of the power generation cost of conventional units. The DC line power flow score can be calculated according to the following equation:

[0062]

[0063] Where n is the total number of DC lines; P l,i is the actual active power flow of the ith DC line after the emergency; P l_rated,i is the rated power of the i-th DC line.

[0064] Step S4: Establishing a MOGWO algorithm model. The MOGWO optimization establishment step includes:

[0065] Determine the size of the search area in the initial search phase based on the constraint boundary;

[0066] Randomly generate a group of gray wolves, each representing a potential system optimization parameter, and determine the fitness value of each individual. Set the algorithm parameters, including the maximum number of iterations and the group size.

[0067] In each generation the following steps are performed:

[0068] A leader gray wolf is selected from the current population through non-dominated sorting, and the position of each follower gray wolf is updated according to the position of the leader gray wolf and the objective function value.

[0069] This can be done with the following formula:

[0070]

[0071] Among them, The new location of the Gray Wolf i, is the current position, r is a random coefficient, and d is the adjustment vector of the follower gray wolf based on the position of the leader gray wolf.

[0072] The gray wolves collaborate and compete based on their distance and fitness. Based on the nature of the multi-objective optimization problem, the solutions in the population are updated. Techniques such as non-dominated sorting and crowding distance are used to select and maintain a set of non-dominated solutions.

[0073] The termination conditions are checked and a set of non-dominated solutions is returned, representing a set of potential optimal solutions to the multi-objective optimization problem. Based on the TOPSIS method, the best compromise solution is selected and set as the converter station parameters after the emergency.

[0074] Step S5: using the established model to adjust the system parameters under different types of faults in the offshore wind farm to achieve the optimal optimization goal;

[0075] The parameter setting process includes: based on the calculated line power flow, if the power flow exceeds the limit, first calculating the reference voltage change of the converter station related to the line with the power flow exceeding the limit to control the over-limit power flow, and then using the established MOGWO model to optimize the parameters of the remaining converter stations; otherwise, using the established MOGWO model to optimize the controlled parameters of all converter stations under adaptive droop control.

[0076] In summary, this invention proposes a method for controlling line flow in a flexible DC system using active converter support. Based on system-level control of the DC grid, this method utilizes adaptive voltage droop control to provide additional degrees of freedom for DC flow control, significantly reducing the investment cost and operating losses of flow control. A DC flow control method based on multi-objective optimal power flow is established. This method, based on optimal power flow configuration, configures the operating parameters of the VSC converter under adaptive droop control for various types of faults that may occur in the flexible DC system, eliminating the problem of the AC / DC hybrid system's operating point exceeding the limit after a fault.

[0077] It should be noted that the above content merely illustrates the technical idea of ​​the present invention and cannot be used to limit the scope of protection of the present invention. For ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications all fall within the scope of protection of the claims of the present invention.

Claims

1. A converter active support type power flow optimization control method based on MOGWO, characterized in that , including the following steps: S1: Determine the expected fault categories of the wind farm and set the corresponding reference power variation of the converter station according to different fault categories; the fault categories include large power fluctuation faults caused by renewable energy, converter station faults under constant active power control, converter station fault interruption under droop control, and DC system disconnection faults; S2: Establish a sensitivity matrix, the sensitivity matrix S is expressed as: S=ΔP l / P l_rated Among them, P l_rated is the DC line rated power matrix; ΔP l is the line power flow change, Y L is the node admittance matrix; V is the DC node voltage; ΔV is the DC node voltage change vector; LN is the distance matrix; A is the correlation matrix; S3: Setting an optimization target, wherein the optimization target is to minimize the DC line power flow score, the overall DC voltage deviation and the power generation cost of the conventional unit after the accident; the DC line power flow score f pl Specifically: Where n is the total number of DC lines; P l,i is the actual active power flow of the ith DC line after the emergency; P l_rated,i is the rated power of the i-th DC line; S4: Establish the MOGWO algorithm model and determine the search area size in the initial search phase based on the constraint boundary; randomly generate a group of gray wolf individuals, each representing a potential system optimization parameter, and determine the fitness value of each individual. Set the algorithm parameters, maximum number of iterations, and group size, and iterate until the termination condition is met; S5: Using the model established in step S4, the system parameters of the offshore wind farm under different types of faults are adjusted to achieve the optimal optimization goal.

2. The method for converter active support type power flow optimization control based on MOGWO according to claim 1, characterized in that: The large power fluctuation caused by the renewable energy is set to the reference power fluctuation amount ΔP dc,i * The change in the converter reference active power before and after the emergency event; The converter station of the constant active power control fails and the reference power fluctuation ΔP is set. dc,i * The change in the converter reference active power before and after the emergency event; The fault interruption of the converter station in droop control converts the faulty droop control converter into an active power control converter, sets its droop control coefficient to 0 and sets the reference power fluctuation ΔP dc,i * The value of is the negative of the original reference power value; When the DC system is disconnected, two virtual DC power supplies are installed at both ends of the disconnected line in the DC system. The value of the virtual DC power supply is determined by the line power before the disconnection.

3. The method for converter active support type power flow optimization control based on MOGWO according to claim 2, characterized in that: The DC node voltage change vector ΔV in step S2 is specifically: Where J is the Jacobian matrix; R is the droop coefficient matrix of the converter station; and ΔV * are the changes in reference power and reference voltage of the converter station respectively.

4. The method for optimizing converter active support power flow control based on MOGWO according to claim 3, characterized in that: The optimization goal of step S3 is: Where g1 is the set of constraint equations for the AC / DC connection points; g2 represents the set of DC node equations for constant active power control and the set of DC node equations that are independent of the AC system; g3 represents the AC node equations that are independent of the DC system nodes; h is the inequality constraint, which includes AC system constraints, converter operation constraints, DC voltage constraints, and DC line capacity constraints; θ s and V s are column vectors representing the phase and voltage amplitude of the AC node respectively; V dc is the DC node voltage; P s (V dc ) is the DC power flow equation; P s (θ s ,V s ) is the AC power flow equation; f pl 、f V and f cost They represent the DC line power flow score after the accident, the overall DC voltage deviation, and the optimization target of power generation cost of conventional units.

5. The method for converter active support type power flow optimization control based on MOGWO according to claim 1, characterized in that: In the iteration of step S4, the following steps are performed in each generation: Select a leader gray wolf from the current population through non-dominated sorting, and update the position of each follower gray wolf according to the position of the leader gray wolf and the objective function value, specifically: Among them, The new location of the Gray Wolf i, is the current position, r is a random coefficient, and d is the adjustment vector of the follower gray wolf based on the position of the leader gray wolf; According to the distance and fitness value between the gray wolves, they cooperate and compete to update the solutions in the population; they use non-dominated sorting and crowding distance techniques to select and maintain a set of non-dominated solutions; Check whether the termination conditions are met, return the non-dominated solution set, and select the best compromise solution based on the TOPSIS method and set it as the converter station parameters after the emergency.

6. The method for optimizing converter active support power flow control based on MOGWO according to claim 5, characterized in that: The parameter setting in step S5 is specifically as follows: based on the calculated line power flow, if the power flow exceeds the limit, first calculate the reference voltage change of the converter station related to the line with the power flow exceeding the limit to control the over-limit power flow, and then use the established MOGWO model to optimize the parameters of the remaining converter stations; otherwise, use the established MOGWO model to optimize the controlled parameters of all converter stations under adaptive droop control.

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