Control method for stabilizing broadband oscillation based on operation mode adjustment

Abstract

The invention discloses a control method for stabilizing broadband oscillation based on adjustment of an operation mode. The method comprises the following steps: screening key variables influencing the operation mode of a new energy station system; constructing a new energy station system oscillation stability margin analysis function according to a logarithmic derivative method; constructing an optimization problem of oscillation stability margin maximization based on the key variable and an oscillation stability margin analysis function; solving the optimization problem by adopting a particle swarm algorithm based on a dynamic inertia weight and a learning factor to obtain an operation mode with optimal oscillation stability; and the new energy station system is controlled to operate in the optimal operation mode, and the broadband oscillation stabilizing capability of the new energy station system is analyzed. According to the method, new energy consumption and oscillation risks can be considered at the same time, and the adverse effect of oscillation in a broadband range on a unit and a power grid is effectively reduced while it is ensured that the total output of the new energy unit meets the requirement.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system stabilization and control, and particularly relates to a control method for smoothing broadband oscillations based on adjusting an operating mode. Background Art

[0002] With the widespread adoption of renewable energy in power systems, the number of renewable energy sites, such as wind farms and photovoltaic power stations, connected to the power system continues to expand. However, the integration of renewable energy sites presents new stability challenges for the power system, with broadband oscillation being a particular concern. Broadband oscillation can cause significant fluctuations in power system parameters such as voltage and frequency, impacting the safe and stable operation of the power system and potentially even causing power outages.

[0003] Numerous wind turbine-related oscillation incidents have been documented in multi-terminal flexible direct current (HVDC) transmission projects. These incidents occurred when the output of the connected doubly-fed (DFIG) wind farm increased, triggering current oscillations in the DC line and ultimately causing short-term outages. Furthermore, lightning strikes on transmission lines caused single-phase grounding faults, triggering the reactive power control system of offshore wind farms. This resulted in abnormal reactive power oscillations at the connection point, subsequently disconnecting the wind farm and adjacent gas-fired power plants from the grid. This ultimately led to prolonged and widespread power outages for customers and impacted load demand. Subsynchronous oscillations at converter stations, triggered by the interaction between the DFIG wind farm and the flexible direct current (FDC), caused frequent wind turbine disconnections and equipment damage. Multiple high-frequency oscillations with frequencies between 650 and 1550 Hz occurred at the converter station, impacting the safe and stable operation of the system. The interaction between the DFIG wind farm and the FDC triggered high-frequency oscillations at 750 Hz. To ensure the safe grid connection of 7 GW of wind power within the UHVDC project, a comprehensive investigation of oscillation risks at each wind farm revealed broadband oscillations similar to "bridge resonance." These oscillations ranged from a few hertz to hundreds of hertz, potentially posing a risk of wind turbine damage and thermal power unit tripping. Therefore, effectively suppressing broadband oscillations has become a critical issue in the power system sector.

[0004] Traditional control methods have certain limitations when dealing with broadband oscillations, such as insufficient adaptability to the complex operating characteristics and changing operating conditions of new energy stations. Summary of the Invention

[0005] In order to address the deficiencies in the prior art, the present invention provides a control method for smoothing broadband oscillations based on adjusting the operating mode, which improves the power system's ability to suppress broadband oscillations and its stability when facing broadband oscillation problems by optimizing the power system's operating mode.

[0006] The present invention adopts the following technical solutions.

[0007] A first aspect of the present invention provides a control method for smoothing broadband oscillations based on adjusting an operating mode, comprising:

[0008] Screening of key variables that affect the operation of new energy station systems;

[0009] The oscillation stability margin analysis function of the new energy station system is constructed based on the logarithmic derivative method;

[0010] Constructing an optimization problem for maximizing the oscillation stability margin based on the key variables and the oscillation stability margin analysis function;

[0011] A particle swarm algorithm based on dynamic inertia weight and learning factor is used to solve the optimization problem and obtain the optimal operating mode of oscillation stability;

[0012] Control the new energy station system to operate in an optimal manner and analyze its ability to smooth out broadband oscillations.

[0013] Preferably, the key variables include the active power and reactive power of each new energy generator set and the total number of transmission lines put into operation in the system.

[0014] Preferably, the constructing of the new energy station system oscillation stability margin analysis function according to the logarithmic derivative method includes:

[0015] Construct a frequency impedance model of the new energy unit; based on the frequency impedance model of the new energy unit, construct an input-output relationship model between the disturbance voltage and the disturbance current of each node of the new energy station system; based on the input-output relationship model, calculate the logarithmic derivative of the determinant of the node admittance matrix; construct an oscillation stability margin analysis function of the new energy station system based on the logarithmic derivative, and obtain the oscillation stability margin M according to the function.

[0016] Preferably, the oscillation stability margin analysis function is specifically as follows:

[0017]

[0018] The slope of the real part curve Slope of the imaginary part curve ω0 is the dominant oscillation frequency, D L (D) is the logarithmic derivative, ω is the oscillation frequency, Re represents the real part of the complex number, and Im represents the imaginary part of the complex number.

[0019] Preferably, the optimization problem of maximizing the oscillation stability margin constructed based on the key variables and the oscillation stability margin analysis function is:

[0020] maxM=f(a)

[0021] s.ta∈S1

[0022] Where f(a) is the objective function, max represents the maximum value, a is the vector of key variables, M is the oscillation stability margin, and S1 is the value range of a.

[0023] Preferably, the dynamic inertia weight is:

[0024]

[0025] Among them, s(k) is the dynamic inertia weight at the kth iteration; s base is the basic inertia weight; T max is the maximum number of iterations; α is the decay coefficient; β is the diversity adjustment coefficient; σ(k) is the population diversity index at the kth iteration; σ max is the maximum value of population diversity; γ is the historical search information adjustment coefficient; D(k) is the particle historical search information index; D max It is the maximum value of the particle history search information.

[0026] Preferably, the population diversity index σ(k) and the particle history search information index D(k) at the kth iteration are as follows:

[0027]

[0028] Where n is the number of particles; x o,k is the position of the oth particle at the kth iteration, is the average position of the population at the kth iteration; p o,k is the optimal position of particle o after k iterations, p g,k is the optimal position of the particle swarm after k iterations.

[0029] Preferably, the dynamic learning factor is:

[0030]

[0031] Where c1(k) and c2(k) are dynamic learning factors used to adjust the maximum step length of flying towards the global best particle and the individual best particle respectively; 1,initial and c 2,initial are the initial learning factors corresponding to c1(k) and c2(k); c 1,final and c 2,final are the final learning factors corresponding to c1(k) and c2(k), respectively; k is the current iteration number; T max is the maximum number of iterations.

[0032] Preferably, the controlling of the new energy station system to operate in an optimal operating mode and analyzing its ability to smooth broadband oscillations includes:

[0033] By issuing commands from the dispatching master station, the new energy units are adjusted to the optimal operating mode, and the broadband oscillation suppression effect of the new energy station system under the optimal operating mode is quantitatively evaluated and qualitatively analyzed;

[0034] When the smoothing effect meets the expected requirements, the new energy station system will continue to operate in the current mode. If the new energy station system still has broadband oscillations exceeding the threshold, the key variables will be re-optimized to further suppress the broadband oscillations.

[0035] A second aspect of the present invention provides a terminal, comprising a processor and a storage medium; the storage medium is used to store instructions; and the processor is used to operate according to the instructions to execute the steps of the method.

[0036] A third aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of the method when executed by a processor.

[0037] Compared with the prior art, the beneficial effects of the present invention include at least:

[0038] The present invention screens the key variables that affect the operation mode of the system, constructs a stability margin analysis function and an objective function, uses a particle swarm optimization algorithm to solve the optimal operation mode, and then adjusts the operation mode of the new energy unit through the dispatching master station to achieve the effect of suppressing broadband oscillations. Compared with the method of directly cutting off the oscillation line in the existing station, by adjusting the operation mode of the new energy unit, the stability margin of the unit is increased, thereby smoothing the broadband oscillation, which can take into account both the new energy consumption and the oscillation risk at the same time, and effectively reduce the adverse effects of broadband oscillations on the unit and the power grid while ensuring that the total output of the new energy unit meets the requirements, which has very important practical significance.

[0039] This paper uses a particle swarm optimization algorithm based on dynamic inertia weights and learning factors to solve for the optimal operating mode. It can dynamically adjust the inertia weights based on the number of iterations, population diversity, and particle historical search information, balancing global and local search. It can adapt to the characteristics of different optimization problems, avoid premature convergence or falling into local optimality, and has a controllable computational load, making it suitable for practical applications. The dynamic learning factor used facilitates emphasizing individual experience in the early stages and group experience in the later stages, improving the performance of the particle swarm algorithm. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 This is a flow chart of the optimal operation mode search algorithm based on the particle swarm algorithm of the present invention;

[0041] Figure 2 This is a flow chart of the control method for smoothing broadband oscillation based on adjusting the operating mode of the present invention;

[0042] Figure 3Schematic diagram of a large-scale wind power access system in an embodiment of the present invention. DETAILED DESCRIPTION

[0043] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. The embodiments described in this application are only part of the embodiments of the present invention, not all of them. Based on the spirit of the present invention, other embodiments obtained by ordinary technicians in this field without making creative efforts are all within the scope of protection of the present invention.

[0044] like Figure 1-2 As shown, embodiment 1 of the present invention provides a control method for smoothing broadband oscillation based on adjusting the operating mode. Figure 3 The method is applied to the large-scale wind power access system shown in FIG. 1 , and includes the following steps:

[0045] Step 1: Screen key variables that affect the operation of new energy station systems;

[0046] Further preferably, key variables affecting the operation mode are selected according to the new energy station system under study. The key variables affecting the operation mode are targeted selections based on the specific characteristics and operation requirements of the new energy station system under study, including but not limited to the output active power and reactive power of the new energy station, the terminal voltage of the new energy unit, and the number of lines put into operation at the new energy station;

[0047] According to the key variables, an oscillation stability analysis function can be constructed to calculate the minimum damping value of each oscillation mode of the power system; according to the calculated minimum damping value of each oscillation mode of the power system, a particle swarm algorithm can be used to solve the oscillation stability analysis function.

[0048] The vector form of the optimization variable is defined as a=[a1,a2,…,a n ], where a1, a2, …, a n Indicates the key variables selected that affect the operating mode.

[0049] In this embodiment, key optimization variables that affect the operation mode are selected. Specifically, the optimization variables are: 1) the active power P and reactive power Q of each new energy generator; 2) the total number of 500kV transmission lines in the system.

[0050] Among them, the value range of variable P is 0~1p.u., the value range of variable Q is 0~1p.u., and the capacity constraint P is satisfied. 2 +Q 2 ≤1; the total number of 500kV transmission lines in the system is 1 to 4.

[0051] Broadband oscillations as shown in Table 1 below are detected on the wind power access lines L1 to L4, which are used to compare with the broadband oscillation data when the system operates in the optimal operating mode in the subsequent step 5, and then determine whether the control method based on adjusting the operating mode proposed in the present invention can effectively suppress the broadband oscillations.

[0052] Table 1 Statistics of superimposed broadband oscillation characteristics of wind power access lines L1 to L4

[0053]

[0054]

[0055] Step 2: Construct the oscillation stability margin analysis function of the new energy station system according to the logarithmic derivative method to analyze the oscillation stability margin;

[0056] Further preferably, the oscillation stability margin analysis function constructed according to the logarithmic derivative method can accurately reflect the relationship between the oscillation stability characteristics of the system and related variables; the construction process is as follows:

[0057] Step 2.1: First, obtain the frequency impedance model of the new energy unit:

[0058] I i =Y i (u i ,i i )U i

[0059] Among them I i is a pair of disturbance currents with coupling frequency at the port of branch i where the new energy unit is located; U i is the port disturbance voltage of branch i; u i ,i i are the power frequency voltage and current of the branch i port respectively; Y i (u i ,i i ) is the frequency-coupled admittance model function of the power frequency voltage and current at port i of branch;

[0060] Step 2.2: Then, based on the impedance network modeling theory and using circuit theory, construct the input-output relationship between the disturbance voltage and disturbance current at each node of the system:

[0061] I=Y(u,i)U

[0062] Where I and U are the vectors of disturbance voltage and disturbance current of each renewable energy unit in the system, u and i are the vectors of power frequency current and voltage, and Y(u,i) is the admittance model between the disturbance current and disturbance voltage of the system. Through power flow calculation, we can know that:

[0063] Y(u,i)=EYD (u,i)E T

[0064] where Y D (u,i) is the diagonal form of the frequency impedance model of each node of the new energy unit, which is specifically defined as Y D (u,i)=diag(Y1,Y2,…Y i …,Y l ), diag(.) means Y1, Y2, ... Y i …,Y l A diagonal matrix constructed for the diagonal elements, Y i (i=1,2,…,l) is the frequency coupling admittance model Y corresponding to branch i i (u i ,i i ), l is the total number of branches. E is the branch correlation matrix, which can be obtained according to the topological structure of the system. T represents the transpose. The dimension of E is l, where the element E yz The specific calculation method is:

[0065]

[0066] Step 2.3: Calculate the logarithmic derivative of the node admittance matrix determinant

[0067]

[0068] Where D(ω) = detY(u,i) is the determinant of Y(u,i), j is the imaginary unit, ω is the frequency, and Δω represents the interval frequency. Based on the logarithmic derivative criterion, the dominant oscillation modes in the system are identified by selecting the extreme points of the real part curve within the target frequency band, where the slope of the corresponding imaginary part curve is less than 0.

[0069] Step 2.4: Finally, the oscillation stability margin M is calculated as the minimum damping in each dominant oscillation mode. Specifically:

[0070] Based on the logarithmic derivative D L (D) Construct an oscillation stability margin analysis function for the new energy station system. This function uses the logarithmic derivative criterion to identify the dominant oscillation modes in the new energy station system by selecting the extreme points of the real part curve in the target frequency band, and the slope of the corresponding imaginary part curve is less than 0. The oscillation stability margin M is obtained based on the minimum damping in each dominant oscillation mode. The details are as follows:

[0071]

[0072] in ω0 is the dominant oscillation frequency, ω is the oscillation frequency, Re represents the real part of the complex number, and Im represents the imaginary part of the complex number.

[0073] Step 3: Constructing an optimization problem for maximizing the oscillation stability margin based on the key variables and the oscillation stability margin analysis function;

[0074] Further preferably, the optimization problem is constructed based on the constructed oscillation stability margin analysis function, as follows:

[0075] The search for the optimal operating mode for oscillation stability is transformed into an optimization problem as follows:

[0076] maxM=f(a)

[0077] s.ta∈S1

[0078] Where f(a) is the optimization objective function, its input is the optimization variable a, its output is the oscillation stability margin M, and S1 is the value range of a.

[0079] In this embodiment, a is a vector consisting of the active power P and reactive power Q of each new energy generator, and the total number of 500kV transmission lines in the system. A total of 3000 sets of disturbance current and voltage data of the new energy generator ports are used to optimize the key variable a. The value range of variable P is 0-1 p.u., the value range of variable Q is 0-1 p.u., and the capacity constraint P is satisfied. 2 +Q 2 ≤1; the total number of 500kV transmission lines in the system is 1 to 4.

[0080] It is understandable that the above f(a) represents the relationship between a and the stability margin M. Due to the complexity of the process of solving the oscillation stability margin, it is impossible to give an analytical expression for the optimization objective function f(a). The corresponding objective function f(a) can only be obtained based on the discrete independent variable a. Therefore, the optimization problem is reduced to a black box optimization problem. The characteristic of the black box optimization problem is that the analytical expression and gradient of its objective function or constraint function cannot be obtained. The traditional gradient-based optimization method is no longer applicable to the direct solution of the black box optimization problem. The solution of the black box optimization problem usually adopts a search method. Commonly used search methods are grid search method, random search method, etc. The particle swarm algorithm is used in the present invention.

[0081] Step 4: Using a particle swarm algorithm based on dynamic inertia weight and learning factor to solve the optimization problem, the optimal operating mode of oscillation stability is obtained;

[0082] Further preferably, a particle swarm algorithm is used to optimize the key variables that affect the operation mode, so that the system can operate in the optimal way. The idea is to select m particles from the optimization variable value space, and the objective function value of each particle position is the stability margin. Each particle determines the next move based on its historical optimal position and the optimal position of the entire group, with some random disturbances. Ultimately, the particle swarm moves towards the optimal point of the objective function as a whole. Each particle has two variables, its position and velocity, and its initial value is randomly selected. One step of iteration is performed according to the following formula until the difference between the stability margin of the optimal solution after the previous iteration and the stability margin of the optimal solution after this iteration is less than a given threshold or the maximum number of iterations is reached. The definitions of position and velocity variables are as follows:

[0083] v o,k+1 =s(k)v o,k +c1(k)r1(p o,k -x o,k )+c2(k)r2(p g,k -x o,k )

[0084] x o,k+1 =x o,k +v o,k+1

[0085] Among them, v o,k+1 and x o,k+1 is the velocity and position of particle o after k+1 iterations, p o,k is the optimal position of particle o after k iterations, p g,k is the optimal position of the particle swarm after k iterations;

[0086] r1 and r2 are random numbers between 0 and 1, and s(k) is the inertia weight, which determines the degree of influence of the particle's previous velocity on its current velocity, and is generally between 0.9 and 1.2. This value can be dynamically adjusted according to the number of iterations, as follows:

[0087]

[0088] Among them, s(k) is the inertia weight at the kth iteration; s base is the basic inertia weight, which is 0.9; k is the current number of iterations, T max is the maximum number of iterations; α is the decay coefficient, which controls the decay rate of the inertia weight with the number of iterations, and its value is 1.5; β is the diversity adjustment coefficient, which controls the impact of population diversity on the inertia weight, and its value is 2.0; σ(k) is the population diversity index of the current iteration, which is defined as: Where n is the number of particles, x o,k is the position of the oth particle at the kth iteration, is the average position of the population at k iterations; σ max is the maximum value of population diversity, which is taken as σ(0) at the initial iteration; γ is the historical search information adjustment coefficient, which controls the influence of particle historical search information on inertia weight, and is taken as 0.5; D(k) is the particle historical search information index, which is defined as the distance between the particle's historical optimal position and the current global optimal position: D max is the maximum value of the particle's historical search information, which is D(0) at the initial iteration;

[0089] c1(k) and c2(k) are learning factors that adjust the maximum step length of flying towards the global best particle and the individual best particle, respectively. Specifically:

[0090]

[0091] where c 1,initial and c 2,initial is the initial learning factor, c 1,final and c 2,final is the final learning factor. c1(k) gradually decreases and c2(k) gradually increases, so as to emphasize individual experience in the early stage and group experience in the later stage, thus improving the performance of the particle swarm algorithm. 1,final =c 2,initial =1.5, c 1,initial =c 2,final =2.5.

[0092] The optimal operation mode search algorithm based on particle swarm optimization is as follows Figure 1 The specific steps are as follows:

[0093] Step 1: Initialization, by randomly selecting points, determine the position and velocity of the particles within a certain range, and obtain the velocity and position of n particles (v o ,x o ), o is 1-n;

[0094] Step 2: Calculate the applicable value of particles: According to the speed and position of n particles (v o ,x o ), using the objective function, the oscillation stability margin of n particles is obtained as its applicable value;

[0095] Step 3: Update the optimal position: Compare the particle fitness value with its individual optimal value p o,k , if the particle suitability value is better than p o,k , then set the current particle position x to the individual optimal position. Compare the particle's applicable value with the group's optimal value p g,k , if the current value is better than p g,k , then the current particle position x is set as the optimal position of the group.

[0096] Step 4: Based on v o,k+1 and x o,k+1 The expression updates the particle's velocity v and position x;

[0097] Step 5: Stop condition: If the difference between the oscillation stability margin of the optimal solution after this iteration and the oscillation stability margin of the optimal solution after the previous iteration is less than the threshold or the maximum number of iterations is reached, the optimal solution after this iteration is output to form the optimal operation mode. Otherwise, the loop returns to Step 2 until the termination condition is met or the maximum number of iterations is reached.

[0098] In this embodiment, according to Figure 1 The steps shown use the particle swarm algorithm to optimize the designed vector a, and the results show that when the active power output of each renewable energy unit in the system reaches 0.901pu, the reactive power Q=0p.u., and the number of external transmission lines is 4, the stability margin reaches a maximum value of 2.290.

[0099] Step 5: Control the new energy station system to operate in an optimal manner and analyze its ability to smooth broadband oscillations.

[0100] Further preferably, based on the optimal operating mode achieved by the system, its ability to smooth out broadband oscillation is deeply analyzed, as follows:

[0101] Adjust the new energy units to the optimal operating mode, and conduct quantitative evaluation and qualitative analysis of the system's suppression effect on broadband oscillations under the optimal operating mode.

[0102] When the suppression effect meets the expected requirements, the system maintains the current operating mode and continues to operate. If the system still has broadband oscillations exceeding the threshold, it returns to step 3 and re-optimizes the key variables based on the current operating mode to further suppress broadband oscillations.

[0103] In this embodiment, the dispatching master station issues commands to ensure that key variables influencing the operation mode of the large-scale wind power access system, such as the output of renewable energy units and the transmission lines, operate according to the optimal operating mode obtained in step 4. Simulation analysis shows that the superimposed oscillations of lines L1 through L4 converge to zero after a period of time, with neither persistence nor divergence. No broadband oscillations are detected on the four lines again, and the oscillations have subsided. The proposed control method based on adjusting the operating mode can effectively suppress broadband oscillations.

[0104] Embodiment 2 of the present invention provides a terminal, including a processor and a storage medium; the storage medium is used to store instructions; the processor is used to operate according to the instructions to execute the steps of the method.

[0105] Embodiment 3 of the present invention provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of the method when executed by a processor.

[0106] Compared with the prior art, the beneficial effects of the present invention include at least:

[0107] The present invention screens the key variables that affect the operation mode of the system, constructs a stability margin analysis function and an objective function, uses a particle swarm optimization algorithm to solve the optimal operation mode, and then adjusts the operation mode of the new energy unit through the dispatching master station to achieve the effect of suppressing broadband oscillations. Compared with the method of directly cutting off the oscillation line in the existing station, by adjusting the operation mode of the new energy unit, the stability margin of the unit is increased, thereby smoothing the broadband oscillation, which can take into account both the new energy consumption and the oscillation risk at the same time, and effectively reduce the adverse effects of broadband oscillations on the unit and the power grid while ensuring that the total output of the new energy unit meets the requirements, which has very important practical significance.

[0108] This paper uses a particle swarm optimization algorithm based on dynamic inertia weights and learning factors to solve for the optimal operating mode. It can dynamically adjust the inertia weights based on the number of iterations, population diversity, and particle historical search information, balancing global and local search. It can adapt to the characteristics of different optimization problems, avoid premature convergence or falling into local optimality, and has a controllable computational load, making it suitable for practical applications. The dynamic learning factor used facilitates emphasizing individual experience in the early stages and group experience in the later stages, improving the performance of the particle swarm algorithm.

[0109] The present disclosure may be a system, method and / or computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present disclosure.

[0110] A computer-readable storage medium can be a tangible device that can hold and store instructions for use by an instruction execution device. A computer-readable storage medium can be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punch card or a raised structure in a groove on which instructions are stored, and any suitable combination thereof. As used herein, a computer-readable storage medium is not to be construed as a transient signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a light pulse through a fiber optic cable), or an electrical signal transmitted through an electrical wire.

[0111] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions to be stored in the computer-readable storage medium in each computing / processing device.

[0112] The computer program instructions for performing the operations of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages such as Smalltalk, C++, and conventional procedural programming languages such as "C" language or similar programming languages. Computer-readable program instructions may be executed entirely on a user's computer, partially on a user's computer, as an independent software package, partially on a user's computer, partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., utilizing an Internet service provider to connect via the Internet). In some embodiments, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), may be personalized by utilizing the state information of the computer-readable program instructions. The electronic circuit may execute the computer-readable program instructions, thereby realizing various aspects of the present disclosure.

[0113] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered by the scope of protection of the claims of the present invention.

Claims

1. A control method for smoothing broadband oscillation based on adjusting the operating mode, characterized in that: include: Screening of key variables that affect the operation of new energy station systems; The oscillation stability margin analysis function of the new energy station system is constructed based on the logarithmic derivative method; Constructing an optimization problem for maximizing the oscillation stability margin based on the key variables and the oscillation stability margin analysis function; A particle swarm algorithm based on dynamic inertia weight and learning factor is used to solve the optimization problem and obtain the optimal operating mode of oscillation stability; Control the new energy station system to operate in an optimal manner and analyze its ability to smooth out broadband oscillations.

2. The control method for smoothing broadband oscillation based on adjusting the operating mode according to claim 1, characterized in that: The key variables include the active power and reactive power of each new energy unit and the total number of system transmission lines.

3. The control method for smoothing broadband oscillation by adjusting the operating mode according to claim 1, characterized in that: The method of constructing the oscillation stability margin analysis function of the new energy station system according to the logarithmic derivative method includes: Construct a frequency impedance model of the new energy unit; based on the frequency impedance model of the new energy unit, construct an input-output relationship model between the disturbance voltage and the disturbance current of each node of the new energy station system; based on the input-output relationship model, calculate the logarithmic derivative of the determinant of the node admittance matrix; construct an oscillation stability margin analysis function of the new energy station system based on the logarithmic derivative, and obtain the oscillation stability margin M according to the function.

4. The control method for smoothing broadband oscillation by adjusting the operating mode according to claim 3, characterized in that: The oscillation stability margin analysis function is as follows: The slope of the real part curve Slope of the imaginary part curve ω0 is the dominant oscillation frequency, D L (D) is the logarithmic derivative, ω is the oscillation frequency, Re represents the real part of the complex number, and Im represents the imaginary part of the complex number.

5. The control method for smoothing broadband oscillation by adjusting the operating mode according to claim 1, characterized in that: The optimization problem of maximizing the oscillation stability margin based on the key variables and the oscillation stability margin analysis function is: maxM=f(a) s.ta∈S1 Where f(a) is the objective function, max represents the maximum value, a is the vector of key variables, M is the oscillation stability margin, and S1 is the value range of a.

6. The control method for smoothing broadband oscillation by adjusting the operating mode according to claim 1, characterized in that: The dynamic inertia weight is: Among them, s(k) is the dynamic inertia weight at the kth iteration; s base is the basic inertia weight; T max is the maximum number of iterations; α is the decay coefficient; β is the diversity adjustment coefficient; σ(k) is the population diversity index at the kth iteration; σ max is the maximum value of population diversity; γ is the historical search information adjustment coefficient; D(k) is the particle historical search information index; D max It is the maximum value of the particle history search information.

7. The control method for smoothing broadband oscillation based on adjusting the operating mode according to claim 6, characterized in that: The population diversity index σ(k) and particle history search information index D(k) at the kth iteration are as follows: Where n is the number of particles; x o,k is the position of the oth particle at the kth iteration, is the average position of the population at the kth iteration; p o,k is the optimal position of particle o after k iterations, p g,k is the optimal position of the particle swarm after k iterations.

8. The control method for smoothing broadband oscillation by adjusting the operating mode according to claim 1, characterized in that: The dynamic learning factor is: Where c1(k) and c2(k) are dynamic learning factors used to adjust the maximum step length of flying towards the global best particle and the individual best particle respectively; 1,initial and c 2,initial are the initial learning factors corresponding to c1(k) and c2(k); c 1,final and c 2,final are the final learning factors corresponding to c1(k) and c2(k), respectively; k is the current iteration number; T max is the maximum number of iterations.

9. The control method for smoothing broadband oscillation by adjusting the operating mode according to claim 1, characterized in that: The ability to control the new energy station system to operate in an optimal manner and analyze its ability to smooth broadband oscillations includes: By issuing commands from the dispatching master station, the new energy units are adjusted to the optimal operating mode, and the broadband oscillation suppression effect of the new energy station system under the optimal operating mode is quantitatively evaluated and qualitatively analyzed; When the smoothing effect meets the expected requirements, the new energy station system will continue to operate in the current mode. If the new energy station system still has broadband oscillations exceeding the threshold, the key variables will be re-optimized to further suppress the broadband oscillations.

10. A terminal comprising a processor and a storage medium; characterized in that: The storage medium is used to store instructions; The processor is configured to operate according to the instructions to execute the steps of the method according to any one of claims 1 to 9.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method according to any one of claims 1 to 9 are implemented.

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