New energy parameter optimization method and system for improving system power angle stability

Abstract

The invention provides a new energy parameter optimization method and system for improving the power angle stability of a system, and the method comprises the steps: calculating the voltage information and current information of a node in a power system through an alternating iteration method based on a new energy quasi-steady-state model; calculating electromagnetic power information of each generator in the power system based on the voltage information and the current information; a one-dimensional linear manifold is obtained through simulated power angle information; based on the electromagnetic power information and the one-dimensional linear manifold, the overall relative unbalanced power of the power system is calculated; based on parameters needing to be optimized in the new energy quasi-steady-state model, the gradient of the relative unbalanced power to the parameters needing to be optimized is calculated, and the optimal parameters most beneficial to the power angle stability are calculated through a gradient descent method. By optimizing the parameters of the new energy generator, the power angle stability of the power system can be remarkably improved, power system faults caused by power angle instability are reduced, and the reliability and effectiveness of the whole power system can be enhanced.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system optimization control, and specifically relates to a new energy parameter optimization method and system for improving system power angle stability. Background Art

[0002] At present, the installed capacity of new energy is growing rapidly, and the penetration rate is increasing, making it an important part of the power system. During the transient process, various new energy sources show different fault ride-through and recovery characteristics, and their active and reactive output fluctuations are significantly different from those of conventional power sources. After experiencing large disturbances such as power grid failures and voltage sags, the power angle stability of the power system is prone to large-scale changes. Therefore, the power angle stability problem of the power system will also be greatly affected by the continuous addition of new energy.

[0003] In the power system, the parameters of renewable energy, especially the fault ride-through control parameters, often play a decisive role in the fault ride-through characteristics of renewable energy, and the fault ride-through characteristics of renewable energy have an important impact on the power angle stability of the power system during transient conditions. Although some parameter optimization methods are currently used in the prior art to improve the transient power angle stability of the power system, the effectiveness of the current power system parameter optimization methods in improving the transient power angle stability of the power system still needs to be further improved, and cannot be applied to power systems under different working conditions. Summary of the invention

[0004] The present invention provides a new energy parameter optimization method and system for improving the power angle stability of the system, aiming to solve the problem that the current parameter optimization method of the power system needs to be further improved in terms of effectiveness in improving the transient power angle stability of the power system and cannot be applied to power systems under different working conditions.

[0005] In order to achieve the above object, the present invention adopts the following technical scheme:

[0006] The present invention provides a new energy parameter optimization method for improving system power angle stability, comprising the following steps:

[0007] S1. Based on the new energy quasi-steady-state model, the voltage and current information of the nodes in the power system are calculated using the alternating iteration method;

[0008] S2. Based on the voltage information and the current information, the electromagnetic power information of each generator in the power system is calculated;

[0009] S3, obtaining a one-dimensional linear manifold through the simulated power angle information;

[0010] S4. Based on the electromagnetic power information of each generator and the one-dimensional linear manifold, the relative unbalanced power of the entire power system is calculated;

[0011] S5. Based on the parameters that need to be optimized in the quasi-steady-state model of new energy, the gradient of the relative unbalanced power to the parameters that need to be optimized is calculated, and the optimal parameters that are most conducive to power angle stability are calculated using the gradient descent method.

[0012] In some implementations, in S1, the quasi-steady-state model of the new energy is constructed using a doubly-fed wind turbine model; in the quasi-steady-state model of the new energy, the normal operating mode of the new energy includes optimal speed control, active power control and reactive power control.

[0013] Furthermore, in S1, under quasi-steady state, the optimal speed control and active power control are expressed by the following algebraic equation (1):

[0014]

[0015] Among them, ω gen and ω ref are the measured value and reference value of the generator rotor speed, P WTT and P ref are the measured value and reference value of active power control in normal mode, Q WTT and Q ref are the measured value and reference value of reactive power control in normal mode respectively;

[0016] The current output by the new energy motor in the final normal operation mode is expressed by the algebraic equation of formula (2):

[0017]

[0018] Under fault conditions, the current output by the new energy generator is expressed by the following algebraic equation (3):

[0019]

[0020] Where: K P_LV is the active power calculation coefficient, P0 is the initial active power, P set_LV is the active power setting value, N is the number of units in the new energy plant, S base is the system base capacity, K 1_IQ_LV is the reactive current calculation coefficient 1, V Lin To enter the low voltage ride-through threshold, K 2_IQ_LV is the reactive current calculation coefficient 2, I Q0 is the initial reactive current, I Qset_LV is the reactive current setting value, S m_base It is the capacity of new energy stations;

[0021] Based on the above, the final algebraic equation of the new energy generator is obtained as follows:

[0022]

[0023] Among them, LF is the flag bit.

[0024] In some embodiments, in S1, the alternating iteration method is the following equation (5):

[0025]

[0026] Where Y is the power system node admittance matrix; [V], e jθ are the amplitude and phase angle of all nodes in the power system respectively; I d (V,p)-jI q (V,p) is the current injected into the power grid by the power electronic equipment, I g (δ,x) is the current injected by the synchronous generator into the grid, and δ is the power angle at the current moment.

[0027] In some embodiments, in S2, the expression of the electromagnetic power of each generator in the power system is as follows (6):

[0028] P ei =Re([V]e jθ I g (δ i ,x)) (6);

[0029] Here, Re(·) is the real part of the complex number.

[0030] In some implementations, in S3, the one-dimensional invariant manifold of the motor power angle information is obtained by first restricting the trajectory of the system to an invariant manifold, that is, formula (7):

[0031] δ=h(τ)=δ e +τη (7);

[0032] The approximate representation of the function h(τ) is obtained through historical simulation data, and a data-driven reduced-order model is obtained; assuming that historical data has been obtained for the same system and the same fault Note that under the invariant manifold assumption, the matrix Each column vector of is located in the one-dimensional subspace spanned by the vector η;

[0033] Select η to keep consistent with the direction of each column of the matrix Δ; consider the following formula optimization problem:

[0034]

[0035] It is equivalent to the following formula (9);

[0036]

[0037] The optimal solution to the above optimization problem is the symmetric matrix The maximum eigenvalue λ m The corresponding eigenvector η m ; h(τ) = δ e +τη m After obtaining the expression of h(τ), based on Estimate each sample The corresponding parameter τ is

[0038] In some embodiments, in S4, the expression of the relative unbalanced power of the entire power system is as follows (12):

[0039]

[0040] Among them, ΔP(τ,p) is the relative unbalanced power of the power system as a whole, P m and P e is the mechanical power and electromagnetic power of the generator, τ is the power angle information of the generator, and p is the parameter that needs to be optimized for new energy.

[0041] In some implementations, in S5, the gradient of the relative unbalanced power to the parameter to be optimized is calculated using the following formula (13):

[0042]

[0043] in, is the network characteristic expression of the power system, Characteristics of components in the power system.

[0044] Furthermore, in S5, the network characteristics of the power system are further expressed by the following formula:

[0045]

[0046]

[0047] The present invention also provides a new energy parameter optimization system for improving the power angle stability of the system. The new energy parameter optimization system includes a new energy quasi-steady-state module, a relatively unbalanced power module, and a parameter optimization module; wherein:

[0048] The new energy quasi-steady-state module is used to calculate the electromagnetic power information of each generator in the power system;

[0049] The relative unbalanced power module is used to: calculate the relative unbalanced power of the entire power system based on the electromagnetic power information of each generator and the one-dimensional linear manifold;

[0050] The parameter optimization module is used to calculate the gradient of the relative unbalanced power to the parameters that need to be optimized based on the parameters that need to be optimized in the quasi-steady-state model of new energy, and use the gradient descent method to calculate the optimal parameters that are most conducive to power angle stability.

[0051] Compared with the prior art, the new energy parameter optimization method and system for improving the power angle stability of the system of the present invention have the following beneficial effects:

[0052] The present invention provides a new energy parameter optimization method for improving the power angle stability of the system, comprising the following steps: S1, based on the new energy quasi-steady-state model, using the alternating iteration method to calculate the voltage information and current information of the nodes in the power system; S2, based on the voltage information and current information, calculating the electromagnetic power information of each generator in the power system; S3, obtaining a one-dimensional linear manifold through the simulated power angle information; S4, based on the electromagnetic power information and the one-dimensional linear manifold of each generator, calculating the relative unbalanced power of the entire power system; S5, based on the parameters to be optimized in the new energy quasi-steady-state model, calculating the gradient of the relative unbalanced power to the parameters to be optimized, and using the gradient descent method to calculate the optimal parameters that are most conducive to the power angle stability. Based on the above, the present invention calculates the voltage and current information by the alternating iteration method based on the new energy quasi-steady-state model, and then calculates the electromagnetic power, obtains the one-dimensional linear manifold of the power angle information by simulation, and calculates the relative unbalanced power of the system based on this information, and finally optimizes the parameters in the new energy model by the gradient descent method to achieve the best power angle stability. The present invention can significantly improve the power angle stability of the power system and reduce system failures caused by power angle instability by optimizing the parameters of the new energy generator. Moreover, the optimized new energy parameters can more effectively utilize renewable energy, improve the overall energy utilization efficiency, and introduce more stable new energy power generation into the power system, which helps to enhance the reliability and stability of the entire power system and has great practical significance. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] The drawings in the specification are used to provide further understanding of the present invention and constitute a part of the present invention. The schematic embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0054] Figure 1 A schematic diagram of a new energy parameter optimization method for improving the power angle stability of a system and a new energy structure in a quasi-steady state in the system according to the present invention;

[0055] Figure 2 A schematic diagram of a new energy parameter optimization method for improving system power angle stability and a CSEE-RAS power angle stability standard calculation example in the system according to the present invention;

[0056] Figure 3A new energy parameter optimization method for improving system power angle stability and a schematic diagram of a power angle curve after a three-machine system failure in the system according to the present invention;

[0057] Figure 4 A logic diagram of a new energy parameter optimization method for improving system power angle stability and a gradient descent method for optimizing parameters in the system according to the present invention;

[0058] Figure 5 It is a schematic diagram of a new energy parameter optimization method for improving the power angle stability of the system and a power angle time domain simulation diagram of the system before and after the new energy parameter optimization in the system. DETAILED DESCRIPTION

[0059] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0060] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0061] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0062] In the description of the embodiments of the present invention, it should be noted that if the terms "upper", "lower", "horizontal", "inner", etc. indicate an orientation or positional relationship based on the orientation or positional relationship shown in the drawings, or the orientation or positional relationship in which the product of the invention is usually placed when in use, it is only for the convenience of describing the present invention and simplifying the description, and does not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention. In addition, the terms "first", "second", etc. are only used to distinguish the description, and cannot be understood as indicating or implying relative importance.

[0063] In addition, if the term "horizontal" appears, it does not mean that the component must be absolutely horizontal, but can be slightly tilted. For example, "horizontal" only means that its direction is more horizontal than "vertical", which does not mean that the structure must be completely horizontal, but can be slightly tilted.

[0064] In the description of the embodiments of the present invention, it is also necessary to explain that, unless otherwise clearly specified and limited, the terms "set", "install", "connect", and "connect" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal connection of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0065] How to improve the stability of the power system power angle under the full situation of continuous addition of new energy.

[0066] The present invention provides a new energy parameter optimization method for improving system power angle stability, comprising the following steps:

[0067] S1. Based on the new energy quasi-steady-state model, the voltage and current information of the nodes in the power system are calculated using the alternating iteration method;

[0068] S2. Based on the voltage information and the current information, the electromagnetic power information of each generator in the power system is calculated;

[0069] S3, obtaining a one-dimensional linear manifold through the simulated power angle information;

[0070] S4. Based on the electromagnetic power information of each generator and the one-dimensional linear manifold, the relative unbalanced power of the entire power system is calculated;

[0071] S5. Based on the parameters that need to be optimized in the quasi-steady-state model of new energy, the gradient of the relative unbalanced power to the parameters that need to be optimized is calculated, and the optimal parameters that are most conducive to power angle stability are calculated using the gradient descent method.

[0072] The present invention is based on a new energy parameter optimization method for improving the power angle stability of the system, which can improve the transient power angle stability of the power system and improve the stability of the overall power system. The method of the present invention has certain applicability and can be applied to more working conditions. The present invention is further described in detail below.

[0073] like Figure 1 As shown, a quasi-steady-state model of new energy is established. The new energy model adopts a doubly fed wind turbine model, and its control strategy can switch between normal operating mode and voltage ride-through mode. The control strategy depends on the voltage of the connection point. According to the normal operating or fault ride-through control strategy, the new energy is interfaced with the grid through the VSC converter and behaves as a voltage-controlled current source. In some embodiments, the normal operating mode of the new energy includes optimal speed control, active power control, and reactive power control. In the quasi-steady state, the optimal speed control and active power control can be expressed by algebraic equations as:

[0074]

[0075] Among them, ω gen ,ω ref are the measured value and reference value of the generator rotor speed respectively; P WTT , P ref are respectively the measured value and reference value of active power control in normal mode; Q WTT , Q ref are the measured value and reference value of reactive power control in normal mode respectively. Finally, the current output by the new energy motor in normal operation mode is expressed by the algebraic equation:

[0076]

[0077] In the fault state, the new energy generator will generate less active power and more reactive power. At this time, its output current is expressed by the algebraic equation:

[0078] Under fault conditions, the current output by the new energy generator is expressed by the following algebraic equation (3):

[0079]

[0080] Where: K P_LV is the active power calculation coefficient; P0 is the initial active power; P set_LV is the active power setting value; N is the number of units in the new energy plant; S base is the system base capacity. K 1_IQ_LV is the reactive current calculation coefficient 1; V Lin To enter the low voltage crossing threshold; K 2_IQ_LV is the reactive current calculation coefficient 2; I Q0 is the initial reactive current; I Qset_LV is the reactive current setting value; S m_base It is the capacity of new energy stations.

[0081] In summary, the renewable energy generator can be regarded as a current source with switching state controlled by the access point voltage, and the final algebraic equation is:

[0082]

[0083] Among them, LF is a flag bit. When the new energy is in normal working state, LF=0; when the new energy is in voltage ride-through mode, LF=1.

[0084] The present invention describes the control mode of the new energy generator through accurate algebraic equations, which can improve its control accuracy and thus improve the overall stability of the system. The double-fed wind turbine model can adapt to different grid conditions and optimize the performance of the generator by adjusting the control parameters, thereby enhancing the adaptability and flexibility of the system. Through accurate current control equations, effective control of the current of the new energy generator can be achieved, thereby improving the stability and power generation efficiency of the system. In the case of a fault, by adjusting the control coefficient, it is possible to quickly respond to grid changes, maintain the stable operation of the generator, and reduce the impact of the fault on the system.

[0085] In some embodiments, the equation of the alternating iteration method is expressed as:

[0086]

[0087] Where Y is the power system node admittance matrix; [V], e jθ are the amplitude and phase angle of all nodes in the power system respectively; I d (V,p)-jI q (V,p) is the current injected by the power electronic equipment into the grid, indicating that it is affected by the grid connection point voltage V and the current control state p; I g (δ,x) is the current injected by the synchronous generator into the power grid, indicating that it is affected by the current power angle δ and the transient electromotive force of the state variable; [e jθ ]The i-th element is expressed by Euler's formula expansion as follows: In the formula, θ i is the phase angle of the ith node.

[0088] The alternating iteration method of the present invention can more accurately calculate the voltage and phase angle of the power system nodes, providing a reliable data basis for subsequent electromagnetic power calculation and stability analysis. Through iterative calculation, the real solution of the system can be gradually approached, thereby improving calculation efficiency.

[0089] The electromagnetic power information of each generator is calculated, and the expression of the electromagnetic power of each generator is:

[0090] P ei =Re([V]e jθ I g (δ i ,x)) (6);

[0091] In the formula, Re(·) represents the real part of the complex number.

[0092] In some embodiments, in S3, the new energy parameter optimization method for improving the power angle stability of the system according to claim 1 is characterized in that the one-dimensional invariant manifold of the motor power angle information is obtained in S3. First, the trajectory of the system is restricted to an invariant manifold, that is, formula (7):

[0093] δ=h(τ)=δ e +τη (7);

[0094] The approximate representation of the function h(τ) can be obtained through historical simulation data, thereby obtaining a data-driven reduced-order model. Assume that historical data has been obtained for the same system and the same fault. Note that under the invariant manifold assumption, the matrix Each column vector of is located in the one-dimensional subspace spanned by the vector η. Therefore, in order to make the invariant manifold assumption as valid as possible, it is necessary to select η so that it is as consistent as possible with the direction of each column of the matrix Δ. To this end, consider the following optimization problem:

[0095]

[0096] It is equivalent to formula (9),

[0097]

[0098] The optimal solution to the above optimization problem is the symmetric matrix The maximum eigenvalue λ m The corresponding eigenvector η m Therefore, h(τ) = δ e +τη m After obtaining the expression of h(τ), we can estimate the τη m Estimate each sample The corresponding parameter τ is

[0099] like Figure 2 and Figure 3 As shown, it is estimated that each sample The corresponding parameter τ is The test is mainly based on the CSEE-RAS power angle stability standard case. When a 0.1s ground short circuit fault is set at node B11, Figure 3 The element distribution of η values ​​obtained by this method for a three-machine system is given. The η value can roughly represent the swing of the power angle of each generator.

[0100] The present invention calculates the electromagnetic power of the generator through a precise expression, which can accurately reflect the actual output of the generator in the power grid. The unbalanced power of the system can be quantified through the expression, providing a clear goal for subsequent parameter optimization. The calculation result of the unbalanced power can guide the optimization direction of new energy parameters and ensure that the optimized system has better stability.

[0101] Construct the relative unbalanced power of the entire system. It can be seen that the power angle information of all generators in the system is represented by τ. Further, the speed information of all generators can be expressed by Indicates that the mechanical power and electromagnetic power of all generators are given by P m and P e The specific transformation relationship is expressed by the following algebraic equation system:

[0102]

[0103] Finally, the differential equations of the multi-machine system are successfully reduced from the above one-dimensional invariant manifold to a second-order differential equation set. The generator differential equation set after the reduction is:

[0104]

[0105] The expression of the overall relative unbalanced power of the system is:

[0106]

[0107] Where p is the parameter that needs to be optimized for new energy.

[0108] Calculate the gradient of the relative unbalanced power to the parameters to be optimized, and use the gradient descent method to find the optimal parameters that are most conducive to the stability of the power angle. By considering the complex characteristics of the power system, the present invention can accurately calculate the gradient of the unbalanced power to the parameters, provide reliable direction information for the gradient descent method, and accurate gradient information can accelerate the convergence speed of the gradient descent method and improve the efficiency of parameter optimization.

[0109] The gradient expression of the relative unbalanced power to the parameter to be optimized is:

[0110]

[0111] In the formula, is the network characteristic expression of the system, Specifically, the main optimized parameter in this patent is the active power calculation coefficient K in the new energy low voltage ride-through parameter. P_LV And reactive power calculation coefficient K 1_IQ_LV . Then the network characteristics of the system can be further expressed as:

[0112]

[0113] The present invention can further improve the mathematical model of the power system and enhance the accuracy of simulation and calculation through detailed network characteristic expressions. The gradient calculation provides necessary input data and ensures the correctness and effectiveness of the gradient calculation.

[0114] like Figure 4The following table shows the iterative logic of the final gradient descent method. The parameters of the system before and after the new energy optimization are shown in Table 1.

[0115] Table 1 Parameters of the system before and after new energy optimization

[0116]

[0117] like Figure 5 As shown, Figure 5 This is the power angle time domain simulation diagram of the system before and after optimizing the parameters of new energy. Figure 5 It can be seen that the power angle stability is improved after parameter optimization.

[0118] In summary, the present invention provides a new energy parameter optimization method and system for improving the power angle stability of the system. Through the new energy quasi-steady-state model, the alternating iteration method is used to calculate the voltage and current information, and then the electromagnetic power of the generator is obtained. By simulating the power angle information to construct a one-dimensional linear manifold, the relative unbalanced power of the system is calculated, and the new energy parameters are optimized based on the gradient descent method, which ultimately improves the stability of the system power angle to a large extent and has better practicality.

[0119] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Any ordinary technician in the industry can smoothly implement the present invention as shown in the specification and described above. However, any equivalent changes, modifications and evolutions made by technicians familiar with the profession without departing from the scope of the technical solution of the present invention using the technical content disclosed above are all equivalent embodiments of the present invention. At the same time, any equivalent changes, modifications and evolutions made to the above embodiments based on the essential technology of the present invention are still within the protection scope of the technical solution of the present invention.

Claims

1. A new energy parameter optimization method for improving system power angle stability, characterized in that: The steps include: S1. Based on the new energy quasi-steady-state model, the voltage and current information of the nodes in the power system are calculated using the alternating iteration method; S2. Based on the voltage information and the current information, the electromagnetic power information of each generator in the power system is calculated; S3, obtaining a one-dimensional linear manifold through the simulated power angle information; S4. Based on the electromagnetic power information of each generator and the one-dimensional linear manifold, the relative unbalanced power of the entire power system is calculated; S5. Based on the parameters that need to be optimized in the quasi-steady-state model of new energy, the gradient of the relative unbalanced power to the parameters that need to be optimized is calculated, and the optimal parameters that are most conducive to the stability of the power angle are calculated using the gradient descent method.

2. The new energy parameter optimization method for improving system power angle stability according to claim 1 is characterized in that: In S1, the quasi-steady-state model of new energy is constructed by adopting the doubly-fed wind turbine model; in the quasi-steady-state model of new energy, the normal operation mode of new energy includes optimal speed control, active power control and reactive power control.

3. The new energy parameter optimization method for improving system power angle stability according to claim 2 is characterized in that: In the S1, under quasi-steady state, the optimal speed control and active power control are expressed by the algebraic equation of the following formula (1): Among them, ω gen and ω ref are the measured value and reference value of the generator rotor speed, P WTT and P ref are the measured value and reference value of active power control in normal mode, Q WTT and Q ref are the measured value and reference value of reactive power control in normal mode respectively; The current output by the new energy motor in the final normal operation mode is expressed by the algebraic equation of formula (2): Under fault conditions, the current output by the new energy generator is expressed by the following algebraic equation (3): Where: K P_LV is the active power calculation coefficient, P0 is the initial active power, P set_LV is the active power setting value, N is the number of units in the new energy plant, S base is the system base capacity, K 1_IQ_LV is the reactive current calculation coefficient 1, V Lin To enter the low voltage ride-through threshold, K 2_IQ_LV is the reactive current calculation coefficient 2, I Q0 is the initial reactive current, I Qset_LV is the reactive current setting value, S m_base It is the capacity of new energy stations; Based on the above, the final algebraic equation of the new energy generator is obtained as follows: Among them, LF is the flag bit.

4. The new energy parameter optimization method for improving system power angle stability according to claim 1 is characterized in that: In S1, the alternating iteration method is the following equation (5): Where Y is the power system node admittance matrix; [V], e jθ are the amplitude and phase angle of all nodes in the power system respectively; I d (V,p)-jI q (V,p) is the current injected into the power grid by the power electronic equipment, I g (δ,x) is the current injected by the synchronous generator into the grid, and δ is the power angle at the current moment.

5. The new energy parameter optimization method for improving system power angle stability according to claim 1 is characterized in that: In S2, the expression of the electromagnetic power of each generator in the power system is as follows (6): P.S ei JRe([V]e jθ I g (δ i ,x)) (6) Here, Re(·) is the real part of the complex number.

6. The new energy parameter optimization method for improving system power angle stability according to claim 1 is characterized in that: In S3, the one-dimensional invariant manifold of the motor power angle information is obtained as follows: first, the trajectory of the system is restricted to an invariant manifold, that is, formula (7): δ=h(τ)=δ e +the (7); The approximate representation of the function h(τ) is obtained through historical simulation data, and a data-driven reduced-order model is obtained; assuming that historical data has been obtained for the same system and the same fault Note that under the invariant manifold assumption, the matrix Each column vector of is located in the one-dimensional subspace spanned by the vector η; Select η to keep consistent with the direction of each column of the matrix Δ; consider the following formula optimization problem: It is equivalent to the following formula (9): The optimal solution to the above optimization problem is the symmetric matrix The maximum eigenvalue λ m The corresponding eigenvector η m ; h(τ) = δ e +τη m After obtaining the expression of h(τ), based on Estimate each sample The corresponding parameter τ is 7. The new energy parameter optimization method for improving system power angle stability according to claim 1 is characterized in that: In S4, the expression of the relative unbalanced power of the entire power system is as follows (12): Among them, ΔP(τ,p) is the relative unbalanced power of the power system as a whole, P m and P e is the mechanical power and electromagnetic power of the generator, τ is the power angle information of the generator, and p is the parameter that needs to be optimized for new energy.

8. The new energy parameter optimization method for improving system power angle stability according to claim 1 is characterized in that: In S5, the gradient of the relative unbalanced power to the parameter to be optimized is calculated using the following formula (13): in, is the network characteristic expression of the power system, Characteristics of components in the power system.

9. The new energy parameter optimization method for improving system power angle stability according to claim 8, characterized in that: In S5, the network characteristics of the power system are further expressed by the following formula:

10. A system based on the new energy parameter optimization method for improving system power angle stability as described in any one of claims 1 to 9, characterized in that: The new energy parameter optimization system includes a new energy quasi-steady-state module, a relatively unbalanced power module, and a parameter optimization module; wherein: The new energy quasi-steady-state module is used to calculate the electromagnetic power information of each generator in the power system; The relative unbalanced power module is used to calculate the relative unbalanced power of the entire power system based on the electromagnetic power information of each generator and the one-dimensional linear manifold; The parameter optimization module is used to calculate the gradient of the relative unbalanced power to the parameters to be optimized based on the parameters to be optimized in the new energy quasi-steady-state model, and calculate the optimal parameters that are most conducive to power angle stability using the gradient descent method.

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