Affine Quantization Evaluation Method for Transient Angle Stability of Wind Power Integrated System Considering Uncertainty

Abstract

The present invention provides a transient power angle stability affine quantization evaluation method for a wind power integrated grid system considering uncertainties, including the following steps: constructing an affine coupling model between the wind power and the synchronous machine output to generate an affine expression containing noise elements; establishing a dynamic equivalent admittance model based on the affine expression and the low voltage ride-through parameters of the wind turbine, and equivalent the power external characteristics of the wind turbine to a dynamic shunt grounding admittance; using the Ward equivalent technology to simplify the network of non-generator nodes in the wind power integrated grid system; constructing an affine differential-algebraic equation set with uncertainties based on the simplified admittance matrix and the dynamic equivalent admittance, and alternately solving by the implicit trapezoidal method and affine arithmetic; decomposing the affine state variables for the noise elements, analyzing the contribution degrees of different uncertainty sources to the power angle deviation, and establishing a quantitative mapping relationship between multi-source disturbances and power angle dynamics; converting the affine state variables into interval envelopes, and evaluating the transient power angle stability margin according to the maximum power angle difference deviation amount.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system stability analysis and new energy grid connection, and particularly relates to an affine quantization evaluation method for transient power angle stability of a wind power grid-connected system considering uncertainty. Background Art

[0002] With the rapid increase in the penetration rate of new energy, the inertia characteristics of the power system have been significantly reduced, and the transient power angle stability problem caused by wind power grid connection has become increasingly prominent. In traditional power system stability analysis, time-domain numerical simulation provides technical support for the evaluation of synchronous generator power angle stability by constructing a differential-algebraic equation model and numerically solving the dynamic trajectory of system state variables. However, the inherent randomness and volatility of wind power output lead to an enhanced uncertainty characteristic of the system, and the existing deterministic simulation methods have the following technical bottlenecks:

[0003] Insufficient model adaptability: The fixed-parameter model cannot accurately represent the dynamic coupling effect of multi-source uncertainty in the new energy penetration scenario, and it is difficult to depict the influence of the interaction of multiple factors such as wind speed fluctuation and synchronous machine response on power angle stability.

[0004] Contradiction between calculation efficiency and accuracy: The Monte Carlo method requires large-scale repeated simulations, and the calculation cost is too high; although surrogate models such as the stochastic response surface method and polynomial chaos expansion improve efficiency, they face the "curse of dimensionality" in high-dimensional scenarios, and the sample generation mechanism is complex, making it difficult to balance real-time performance and accuracy.

[0005] Lack of physical authenticity: The interval analysis method ignores the dynamic correlation of variables, resulting in conservative results, and the stochastic differential equation model weakens the authenticity of dynamic response due to the simplified wind turbine control strategy. In addition, the existing stochastic analysis methods generally strongly correlate the wind power fluctuation frequency with the numerical integration step size, which is mismatched with the slow time-varying characteristics of the actual output.

[0006] Limitations in uncertainty characterization: Existing technologies mostly rely on single probability distributions or interval assumptions, lacking a unified quantization framework for the synergistic effect of multi-source uncertainty (such as wind speed randomness and grid parameter fluctuations), resulting in insufficient refined analysis ability of the transient stability boundary.

[0007] The above defects seriously restrict the engineering applicability of transient stability assessment for new energy-dominated power grids, and there is an urgent need to develop new uncertainty quantization methods to achieve efficient and accurate assessment of the transient power angle stability of wind power grid-connected systems. Summary of the Invention

[0008] Aiming at the defects of the existing technology, such as insufficient model adaptability, contradiction between calculation efficiency and accuracy, lack of physical authenticity, and limitations in multi-source uncertainty characterization, the present invention provides an affine quantization evaluation method for transient power angle stability of a wind power grid-connected system considering uncertainty, and achieves a technical breakthrough through the following innovative designs:

[0009] Multi-source uncertainty affine coupling mechanism: Establish a dynamic correlation model between the output of the wind turbine and the mechanical power of the synchronous machine, quantify multi-source uncertainties such as wind speed fluctuations and synchronous machine responses as affine expressions, and characterize their coupling strength to the system dynamics through noise element coefficients;

[0010] Full-time dynamic equivalent modeling: Based on the low-voltage ride-through parameters, construct a dynamic admittance model of the wind turbine, accurately depict the physical characteristics of active / reactive power control during faults, and combine the Ward equivalent technique to achieve refined simplification of the network topology;

[0011] Affine-numerical hybrid solution strategy: Combine the numerical stability of the implicit trapezoidal method and the uncertainty propagation ability of affine arithmetic, alternately solve the affine differential-algebraic equations, and achieve efficient tracking of the uncertainty dynamic trajectory in the time domain;

[0012] Perturbation source tracing and stability margin analysis: Quantify the contribution of multi-source uncertainties to the power angle deviation through noise element decomposition, convert the affine state variables into interval envelopes, and construct an engineering-oriented stability margin evaluation index based on the maximum power angle difference boundary.

[0013] The present invention breaks through the conservative limitations of traditional methods, significantly improves the accuracy and efficiency of transient stability assessment of new energy high-penetration power grids, and provides high-confidence decision-making support for the safe operation of power systems.

[0014] The technical solution specifically adopted by the present invention to solve its technical problems is as follows:

[0015] A transient power angle stability affine quantization assessment method for a wind power integrated system considering uncertainties, comprising the following steps:

[0016] Construct an affine coupling model between the wind power and the output of the synchronous machine, quantify the uncertainty influence of wind speed fluctuations on the mechanical power of the synchronous machine based on affine power flow, and generate an affine expression containing noise elements;

[0017] Based on the affine expression and the low-voltage ride-through parameters of the wind turbine, establish a dynamic equivalent admittance model, and equivalent the power external characteristics of the wind turbine to a dynamic shunt grounding admittance;

[0018] Use the Ward equivalent technique to simplify the network of non-generator nodes in the wind power integrated system to obtain a simplified nodal admittance matrix;

[0019] Based on the simplified nodal admittance matrix and the dynamic equivalent admittance, construct an affine differential-algebraic equation set containing uncertainties, and alternately solve it through the implicit trapezoidal method and affine arithmetic to obtain the affine state variables of the synchronous generator power angle;

[0020] Perform noise element decomposition on the affine state variables, analyze the contribution degrees of different uncertainty sources to the power angle deviation, and establish a quantitative mapping relationship between multi-source disturbances and power angle dynamics;

[0021] Convert the affine state variables into an interval envelope, and evaluate the transient power angle stability margin according to the maximum power angle difference deviation.

[0022] Furthermore, the construction method of the affine coupling model is as follows: Define the wind turbine output as the affine center value during the steady state period, and superimpose the linear combination of multiple noise elements representing the random fluctuations of the wind speed and their corresponding coefficients. The coefficients can reflect the dynamic coupling strength of each noise element on the mechanical power of the synchronous machine after being quantified by the affine tidal flow.

[0023] Furthermore, the derivation method of the dynamic equivalent admittance model is as follows: According to the complex power control characteristics during the low voltage ride-through of the wind turbine, establish a dynamic equivalent admittance model, and equivalent the wind turbine to a dynamic shunt grounding admittance. The value of the dynamic shunt grounding admittance is the ratio of the conjugate value of the wind turbine complex power at time t to the square of the voltage of the node where the wind turbine is connected.

[0024] Furthermore, the Ward equivalent technology specifically includes: Model the load node as a constant admittance model, and generate an equivalent network equation that only retains the synchronous generator nodes by combining the admittance parameters of all non-generator nodes except the synchronous generator nodes.

[0025] Furthermore, the numerical integration process of the implicit trapezoidal method is as follows: The affine state variable at time step n + 1 is equal to the state variable at time step n, plus the product of half of the integration step length and the sum of the right-hand sides of the differential equations at time step n and time step n + 1. The right-hand side represents the derivative term of the system dynamic equation, and the value of the derivative term is calculated from the affine state variable at the corresponding time step.

[0026] Furthermore, the noise element decomposition is realized by the following method: Extract the set of noise elements representing multi-source uncertainties from the affine expression of the synchronous generator power angle. The power angle deviation is equal to the sum of the linear combinations of each noise element and its corresponding influence coefficient, where the coefficient quantitatively reflects the contribution degree of different uncertainty sources to the power angle dynamics.

[0027] Furthermore, the conversion method of the interval envelope is as follows: Convert the power angle in affine form into an interval form. The lower bound of the interval is the affine center value minus the sum of the absolute values of all noise element coefficients, and the upper bound is the center value plus the sum of the absolute values of all noise element coefficients. Extract the maximum power angle difference interval at the simulation termination time, and calculate the transient power angle stability margin index according to the upper bound value of the interval. The calculation formula is the percentage of (180° minus the upper bound value) to 180°.

[0028] Furthermore, when alternately solving the affine differential-algebraic equations, the Chebyshev approximation method is used to linearize the affine trigonometric functions in the equations, which specifically includes the following steps:

[0029] Expand the trigonometric function into a linear combination of affine variables, and determine the coefficients by minimizing the approximation error;

[0030] Substitute the linearized expression into the differential-algebraic equation to reduce the conservativeness of the non-linear operation.

[0031] Furthermore, the alternate solution process includes:

[0032] (a) Set the initial simulation duration T = 0 and the integration step size Δt;

[0033] (b) Calculate the current-step affine state variables by the implicit trapezoidal method;

[0034] (c) Update the simulation duration T = T + Δt;

[0035] (d) Repeat steps (b)-(c) until T reaches the preset simulation duration.

[0036] In addition, an electronic device includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, the steps of the above method are implemented.

[0037] A non-transitory computer-readable storage medium stores a computer program thereon. When the computer program is executed by a processor, the steps of the above method are implemented.

[0038] Compared with the prior art, the present invention and its preferred solutions at least include the following beneficial effects:

[0039] Improve the accuracy of multi-source uncertainty dynamic characterization: By constructing an affine coupling mechanism for the output of the wind turbine-synchronous machine, overcome the simplified assumptions of the traditional model for the dynamic coupling relationship of multi-source uncertainty, and achieve a refined characterization of the influence of multiple factors such as wind speed fluctuations and grid parameter changes;

[0040] Enhance the physical authenticity of the dynamic response: Based on the low-voltage ride-through parameters, establish a full-time dynamic equivalent admittance model, avoid the over-simplification of the wind turbine control strategy by conventional methods, and accurately depict the dynamic regulation characteristics of active / reactive power during faults;

[0041] Optimize the balance between calculation efficiency and accuracy: Adopt a hybrid solution strategy of the implicit trapezoidal method and affine arithmetic. On the premise of ensuring numerical stability, significantly reduce the computational complexity of high-dimensional uncertainty propagation, and break through the "curse of dimensionality" limitation of traditional surrogate models;

[0042] Achieve quantitative analysis of stability margin: Through noise element decomposition and interval envelope conversion techniques, quantify the contribution of different uncertainty sources to power angle stability, construct an engineering evaluation index based on the maximum power angle difference boundary, and solve the problems of conservative results or fuzzy boundaries in existing methods;

[0043] Reduce the conservative error of non-linear operations: Introduce the Chebyshev approximation method to linearize affine trigonometric functions, effectively reduce the over-conservatism of interval analysis, and improve the credibility of transient stability assessment results;

[0044] Enhance the robustness of the simulation process: Through the loop logic design of iterative solution and step size adaptation, ensure the numerical convergence and result reliability of time-domain simulation under complex scenarios.

[0045] The present invention comprehensively solves the core problems such as model distortion, low efficiency, and fuzzy boundaries in the transient stability assessment of new energy high-penetration power grids, and provides technical support with both theoretical rigor and engineering applicability for the safe operation of power systems. Brief Description of the Drawings

[0046] The following further describes the present invention in detail in conjunction with the drawings and specific embodiments:

[0047] Figure 1 It is the flow chart of the transient power angle stability affine quantization assessment of the wind power integration system considering uncertainties in the embodiment of the present invention. Specific Embodiments

[0048] To make the features and advantages of the present invention more obvious and understandable, specific embodiments are given below and described in detail as follows:

[0049] It should be noted that the following detailed description is illustrative and is intended to provide further explanation of the present application. Unless otherwise specified, all technical and scientific terms used in this specification have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present application belongs.

[0050] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present application. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0051] To achieve the transient power angle stability affine quantization assessment of a wind power integrated grid system considering uncertainties, the embodiments of the present invention first construct an affine coupling mechanism for the uncertainties of the wind turbine-synchronous machine output, mathematically model the uncertainties of the output of traditional units caused by the fluctuations of wind power output, and construct a full-time dynamic model of the voltage sag process in combination with the low voltage ride-through parameters; secondly, establish an affine differential-algebraic equation set to completely characterize the dynamic behavior of the system with uncertainty factors; furthermore, realize the numerical analysis of the affine differential-algebraic equation through the trapezoidal integration method and the alternating solution strategy to accurately track the propagation law of uncertainty in the time domain; further, trace the source of the perturbation of the affine quantity of the power angle of the synchronous generator through the noise coefficient to quantitatively analyze the dynamic influence degree of wind power fluctuations on the power angle deviation; finally, convert the affine form of the power angle into an interval envelope, and construct an index for engineering evaluation of the transient stability margin through the upper and lower bounds of the trajectory. The method proposed by the present invention shows better calculation accuracy and efficiency compared with the traditional method on the premise of ensuring numerical stability, and at the same time significantly reduces the conservatism of interval analysis.

[0052] The solution of the embodiment of the present invention is roughly divided into the following steps:

[0053] (1) Construct an affine coupling mechanism for the uncertainties of the wind power-synchronous machine output to achieve a refined characterization of the mechanical power of the synchronous machine by multi-source uncertainty factors;

[0054] (2) Construct an affine model of wind power considering dynamic characteristics according to the low voltage ride-through parameters, and equivalently convert its power external characteristics into a dynamic shunt grounding admittance;

[0055] (3) Use the Ward equivalent technology to shrink the remaining nodes except the synchronous generator node, and construct a mathematical model to characterize the dynamic behavior of the wind power integrated grid system;

[0056] (4) Combine the implicit trapezoidal method and affine arithmetic, and obtain the affine state quantity of the wind power integrated grid system by alternately solving the affine differential and algebraic equations;

[0057] (5) Trace the source of the perturbation of the affine quantity of the synchronous generator power angle, quantitatively analyze the dynamic influence degree of different uncertainty factors on the power angle based on each noise coefficient, and establish a quantitative mapping relationship between multi-source perturbation and power angle deviation;

[0058] (6) Convert the affine quantity of the power angle into an interval form, obtain the power angle trajectory envelope, and evaluate the transient power angle stability margin.

[0059] The flow of the transient power angle stability affine quantization assessment method for the wind power integrated grid system considering uncertainties proposed by the embodiment of the present invention is as Figure 1 shown:

[0060] Input parameters

[0061] Step 1: Input the grid structure parameters, synchronous generator set parameters, and wind turbine dynamic characteristic parameters

[0062] First, it is necessary to input the parameters related to the grid structure, the parameters of the synchronous generator set, and the wind turbine dynamic characteristic parameters.

[0063] Model construction

[0064] Step 2: Construct the output affine correlation model between the wind turbine and the synchronous generator and the wind turbine dynamic admittance model

[0065] Construct the output affine correlation model between the wind turbine and the synchronous generator and the wind turbine dynamic admittance model according to the input parameters.

[0066] Step 3: Construct the affine differential-algebraic equations integrating the low voltage ride-through of the generator and the wind turbine

[0067] On the basis of the above models, further construct the affine differential-algebraic equations including the low voltage ride-through characteristics of the generator and the wind turbine.

[0068] Initialization and simulation calculation

[0069] Step 4: Set the fault type, location, and clearing time, and set the simulation duration T = 0

[0070] Set the fault type, occurrence location, and clearing time, and initialize the simulation duration to 0.

[0071] Step 5: Solve the affine differential equation using the implicit trapezoidal integration method

[0072] Solve the constructed affine differential equation using the implicit trapezoidal integration method.

[0073] Step 6: Substitute the solution of the differential equation into the affine algebraic equation to solve the system state variables

[0074] Substitute the solution of the obtained differential equation into the affine algebraic equation to solve the system state variables.

[0075] Loop judgment

[0076] Step 7: Judge whether the simulation duration is reached

[0077] Judge whether the current simulation reaches the preset simulation duration.

[0078] If not (N): Update the simulation duration T = T + Δt, and return to Step 5 to continue the simulation calculation.

[0079] If yes (Y): Proceed to the next step.

[0080] Result analysis

[0081] Step 8: Quantitatively analyze the influence degree of each uncertainty factor on the power angle of the synchronous machine

[0082] Conduct a quantitative analysis of various uncertainty factors to determine their influence degree on the power angle of the synchronous machine.

[0083] Step 9: Obtain the power angle trajectory envelope of the synchronous generator set and evaluate the transient power angle stability margin

[0084] Obtain the power angle trajectory envelope of the synchronous generator set according to the analysis results and evaluate its transient power angle stability margin.

[0085] The following is a specific introduction to the solution of the embodiment of the present invention:

[0086] 1 Fan-synchronous machine output affine correlation model

[0087] (1) Affine modeling of fan output during steady state

[0088] Quantify the uncertainty of fan output into an affine form:

[0089] (1)

[0090] In the formula: is the affine center value of fan output during steady state; ε i Noise element characterizing wind speed fluctuation; is the corresponding noise element coefficient.

[0091] (2) Affine coupling mechanism of fan-synchronous machine output uncertainty

[0092] Establish a nodal injection power equation under the affine power flow framework:

[0093] (2)

[0094] In the formula: 、 are the affine outputs of the synchronous machine during steady state; P Li 、 Q Li are the load powers; Y ij is the element in the i row and j column of the nodal admittance matrix; is the nodal affine voltage during steady state.

[0095] Iteratively solve the above equation by the affine Newton-Raphson method to obtain the affine expressions of the outputs of each synchronous machine:

[0096] (3)

[0097] In the formula: is the fan j influence degree of output uncertainty on the synchronous machine i output; is the newly added noise element and its coefficient generated by the affine operation.

[0098] The time-domain simulation uses the steady-state power flow state variables as the initial values for the transient stability calculation. Among them, the mechanical power of the synchronous machine , and remains unchanged during the transient power angle stability calculation (the generator adopts a second-order model).

[0099] 2 Fan control strategy and simulation model

[0100] (1) Low voltage ride-through characteristics of the fan

[0101] When a short-circuit fault occurs in the wind power grid-connected system, if the terminal voltage of the fan is lower than the threshold U in , the fan will enter the low voltage ride-through control mode; when the fault is removed and the terminal voltage returns to the threshold U out , the fan will switch to the output recovery state. During the low voltage ride-through period, in order to prevent the inverter from overcurrent, the fan will control the active current to decrease rapidly. In order to prevent the voltage from being too low, the fan will also control the reactive current to increase in a certain proportion according to the degree of voltage drop. After the fan completes a low voltage ride-through, its output will return to the operating state before the fault. The low voltage ride-through mathematical model of the fan is as follows:

[0102] (4)

[0103] In the formula: S w (t) represents t the complex power of the fan at time t; t 0, t 1, t 2 represent the end time of the steady-state operation stage, the end time of the low voltage ride-through stage, and the end time of the low voltage ride-through recovery stage respectively; , respectively represent t 0 and t 1 during the active power setting value of the fan; , respectively represent t 1 and t 2 during the reactive power setting value of the fan; is t 2 during the reactive power decay time constant; k p is tActive power recovery speed during the 2nd period.

[0104] (2) Wind turbine dynamic grounding admittance model

[0105] Assume the injected power of the wind turbine is S w , and the unity power factor control method is adopted during the steady state period. The power external characteristic of the wind power is equivalent to a shunt grounding admittance model. According to the low voltage ride-through characteristic of the wind turbine in Equation (4), its dynamic equivalent admittance can be deduced as:

[0106] (5)

[0107] In the formula: "*" is the conjugate operator; , respectively represent t the equivalent admittance matrix of the wind turbine at time

[0108] and the voltage of the node where the wind turbine is connected.

[0109] 3 Time-domain simulation affine model of the wind power grid-connected system

[0110] (1) Ward equivalent

[0111] In the transient stability analysis, a load model with static impedance characteristics is adopted, and the voltage-power coupling relationship of each node load is simplified to a linear admittance parameter model, that is, the electrical equivalence of the load is realized through a constant grounding admittance matrix. The mathematical model of the load is as follows:

[0112] (6)

[0113] In the formula: P L , Q L are the active / reactive power of the load respectively; is the voltage of the load node during the steady state period; is the load equivalent grounding admittance.

[0114] The network equation of the wind power grid-connected system can be described as:

[0115] (7)

[0116] In the formula: is the self-admittance of the synchronous generator; is the mutual admittance between the synchronous generator node and the network node, is the mutual admittance between the network node and the synchronous generator node, and there is ; is the nodal admittance matrix after incorporating the equivalent admittance of the load and the dynamic admittance of the wind turbine (only the self-admittance of the corresponding node needs to be corrected); is the terminal voltage of the synchronous generator, whose amplitude remains constant during the transient period, and the phase angle is the affine power angle of the generator; is the affine voltage of the network node; is the affine injection current of the synchronous generator.

[0117] Perform network simplification on Equation (7), and perform Ward equivalent processing on the remaining nodes (network nodes) except the synchronous generator, and we can get:

[0118] (8)

[0119] (9)

[0120] (2) Construct the affine differential-algebraic equation system

[0121] For a system with n synchronous generators, if we define ; , then Equation (8) can be rewritten as:

[0122] (10)

[0123] In the formula: is the affine injection current of the i-th synchronous generator; is the element in the i-th row and j-th column of the nodal admittance matrix, representing the mutual admittance between node i and node j; is the terminal potential of the j-th synchronous generator, whose amplitude remains constant during the transient period, and the phase angle is the affine power angle.

[0124] The i th synchronous generator's electromagnetic power can be expressed by the following formula:

[0125] (11)

[0126] In the formula: ; ; is the affine power angle of the synchronous generator.

[0127] The differential equation for the transient stability of the wind power integrated system can be expressed as:

[0128] (12)

[0129] In the formula: and are the inertia time constant, rotor angular velocity, damping coefficient, and mechanical power of the i th synchronous generator, respectively. Among them, the affine power flow calculation can be performed during the steady state to obtain the affine quantity of the mechanical power of the synchronous generator.

[0130] Equations (11) and (12) constitute an affine differential-algebraic equation set that describes the dynamic characteristics of the wind power integration system.

[0131] 4 Solution of Affine Differential-Algebraic Equations

[0132] Combining the implicit trapezoid and affine arithmetic, Equations (11) and (12) can be rewritten as:

[0133] (13)

[0134] (14)

[0135] (15)

[0136] Among them, for the real affine , according to the affine number is converted into an interval a , b , and its affine trigonometric function can be linearized through the Chebyshev approximation function to obtain:

[0137] (16)

[0138] (17)

[0139] In the formula: n is the n th numerical integration; Δ t is the time step of numerical integration; alternately solving the affine differential equation-algebraic equation composed of Equations (13)-(15) can obtain the affine state quantity of the time-domain simulation of the wind power integration system with uncertainty.

[0140] As Figure 1 shown, in the simulation calculation of this embodiment, the fault type is set as a three-phase short-circuit fault, the fault location is the bus where the wind turbine is connected, and the cut-off time is 0.1 second; the total simulation duration is preset to 1 second, and the integration step size Δt = 0.01 second. The affine differential-algebraic equation is iteratively solved by the implicit trapezoid method. After each step size calculation is completed, the simulation duration T = T + 0.01 second is updated until the simulation is terminated when T ≥ 1 second, and the result analysis stage is entered.

[0141] 5 Tracing and Quantification of Synchronous Machine Power Angle Disturbance

[0142] (1)Decomposition of power angle affine noise elements

[0143] Extract the set of noise elements in the power angle affine expression of the synchronous machine:

[0144] (18)

[0145] In the formula: are uncertainty sources such as wind power output fluctuations and different synchronous machine output fluctuations, is the noise element The corresponding coefficient; is the power angle affine center value of the i-th synchronous generator at time t, representing the deterministic power angle trajectory without wind power fluctuations.

[0146] (2)Quantify the impact of uncertainty factors on the power angle

[0147] The impact of each noise element on the power angle of the synchronous generator is given by the following formula:

[0148] (19).

[0149] 6 Transient power angle stability margin assessment

[0150] (1)Affine-interval conversion

[0151] Convert the power angle affine quantity at time t to the interval form:

[0152] (20)

[0153] Similarly, convert the affine power angle difference to an interval number to obtain the power angle envelope trajectory:

[0154] (21)

[0155] In the formula: i and j are the synchronous machine numbers. In the present invention, the power angle of the balancing machine is used as a reference to calculate the power angle difference of the remaining synchronous machines relative to the balancing machine.

[0156] (2)Transient power angle stability margin assessment

[0157] Convert the power angle affine quantity of each synchronous machine at time t to the interval form and calculate the relative power angle difference interval between two synchronous machine units:

[0158] (22)

[0159] In the formula: , are the lower bound and upper bound of the power angle interval of the unit i respectively; represents the power angle difference interval between the unit i and j .

[0160] At the simulation termination time t end , extract the deviation of the maximum power angle difference interval:

[0161] (23)

[0162] In the formula: , are t end the upper and lower bounds of the power angle difference interval at time

[0163] Define the transient power angle stability margin index:

[0164] (24)

[0165] If : It is determined that the system has a high stability margin; : It is determined that the system is in a critically stable state; if It is determined that the instability risk of the system is significant.

[0166] Based on the same inventive concept, the present invention also provides a computer device, which includes: one or more processors, and a memory for storing one or more computer programs; the program includes program instructions, and the processor is used to execute the program instructions stored in the memory. The processor may be a central processing unit (CPU), or may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, and is used to implement one or more instructions, specifically used to load and execute one or more instructions in the computer storage medium to implement the above method.

[0167] It should be further noted that, based on the same inventive concept, the present invention also provides a computer storage medium, on which a computer program is stored. When the computer program is run by a processor, it executes the above-mentioned method. The storage medium can adopt any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electrical, magnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples (non-exhaustive list) of the computer-readable storage medium include: an electrical connection with one or more wires, 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), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present invention, the computer-readable storage medium can be any tangible medium that contains or stores a program, and this program can be used by or combined with an instruction execution system, apparatus, or device.

[0168] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present invention should have the ordinary meanings understood by those with ordinary skills in the field to which the present invention belongs. The "first", "second", and similar terms used in the present invention do not indicate any order, quantity, or importance, but are only used to distinguish different components. Words such as "including" or "comprising" mean that the elements or objects appearing before this word cover the elements or objects listed after this word and their equivalents, without excluding other elements or objects. Words such as "connected" or "coupled" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. "Up", "down", "left", "right", etc. are only used to represent relative position relationships. When the absolute position of the object being described changes, the relative position relationship may also change accordingly.

[0169] As described above, these are only the preferred embodiments of the present invention, and the present invention is not limited to other forms. Any person skilled in the art may use the technical content disclosed above to make changes or modifications into equivalent embodiments with equivalent changes. However, as long as it does not depart from the technical solution content of the present invention, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention still fall within the protection scope of the technical solution of the present invention.

[0170] The present invention is not limited to the above-mentioned optimal implementation manner. Anyone inspired by the present invention can obtain various other forms of transient power angle stability affine quantization assessment methods for wind power integrated grid systems considering uncertainties. All equivalent changes and modifications made according to the scope of the patent application of the present invention shall fall within the scope covered by the present invention.

Claims

1. A transient power angle stability affine quantization assessment method for a wind power integrated grid system considering uncertainties, characterized in that Including the following steps: Construct an affine coupling model between wind power and the output of the synchronous machine, quantify the uncertain impact of wind power output fluctuations on the mechanical power of the synchronous machine based on affine tidal flow, and generate an affine expression containing noise elements; Based on the affine expression and the low voltage ride-through parameters of the wind turbine, establish a dynamic equivalent admittance model, and equivalent the power external characteristics of the wind turbine to a dynamic shunt grounding admittance; Use Ward equivalent technology to simplify the network of non-generator nodes in the wind power grid-connected system to obtain a simplified nodal admittance matrix; Based on the simplified nodal admittance matrix and the dynamic equivalent admittance, construct an affine differential-algebraic equation set with uncertainty, and alternately solve it through the implicit trapezoidal method and affine arithmetic to obtain the affine state quantity of the synchronous generator power angle; Decompose the affine state quantity into noise elements, analyze the contribution degree of different uncertainty sources to the power angle deviation, and establish a quantitative mapping relationship between multi-source disturbances and power angle dynamics; Convert the affine state quantity into an interval envelope, and evaluate the transient power angle stability margin according to the maximum power angle difference deviation; The construction method of the affine coupling model is: define the wind turbine output as the affine central value during the steady state period, and superimpose the linear combination of multiple noise elements representing the random fluctuations of the wind speed and their corresponding coefficients, where the coefficients reflect the dynamic coupling strength of each noise element on the mechanical power of the synchronous machine after being quantified by affine tidal flow; The numerical integration process of the implicit trapezoidal method is: the affine state quantity at time step n + 1 is equal to the state quantity at time step n plus the product of half of the integration step length and the sum of the right-hand sides of the differential equations at time steps n and n + 1, where the right-hand side represents the derivative term of the system dynamic equation, and the value of the derivative term is calculated from the affine state quantity at the corresponding time step; The noise element decomposition is realized by the following method: extract the set of noise elements representing multi-source uncertainties from the affine expression of the synchronous generator power angle, and the power angle deviation is equal to the sum of the linear combinations of each noise element and its corresponding influence coefficient, where the influence coefficient quantitatively reflects the contribution degree of different uncertainty sources to the power angle dynamics; The conversion method of the interval envelope is: convert the power angle in affine form into interval form, the lower bound of the interval is the affine central value minus the sum of the absolute values of all noise element coefficients, and the upper bound is the central value plus the sum of the absolute values of all noise element coefficients; extract the maximum power angle difference interval at the end of the simulation, and calculate the transient power angle stability margin index according to the upper bound value of the interval, and the calculation formula is the percentage of (180° minus the upper bound value) to 180°; 2. The transient power angle stability affine quantization assessment method for a wind power integrated system considering uncertainties according to claim 1, characterized in that: The derivation method of the dynamic equivalent admittance model is: based on the complex power control characteristics during the low voltage ride-through of the wind turbine, establish a dynamic equivalent admittance model, and equivalent the wind turbine to a dynamic shunt grounding admittance, and the value of the dynamic shunt grounding admittance is the conjugate value of the wind turbine complex power at time t divided by the square of the voltage of the node where the wind turbine is connected; 3. The transient power angle stability affine quantization assessment method for a wind power integrated grid system considering uncertainties according to claim 1, characterized in that: The Ward equivalent technology specifically includes: modeling the load node as a constant admittance model, and generating an equivalent network equation that only retains the synchronous generator node by combining the admittance parameters of all non-generator nodes except the synchronous generator node; 4. The transient power angle stability affine quantization evaluation method for a wind power integrated system considering uncertainty according to claim 1, characterized in that: When alternately solving the affine differential-algebraic equation, the Chebyshev approximation method is used to linearize the affine trigonometric functions in the equation, specifically including the following steps: Expand the trigonometric function into a linear combination of affine variables, and determine the coefficients by minimizing the approximation error; Substitute the linearized expression into the differential-algebraic equation to reduce the conservativeness of the non-linear operation.

5. The transient power angle stability affine quantization evaluation method for a wind power integrated system considering uncertainty according to claim 1, characterized in that: The alternate solution process includes: (a) Set the initial simulation duration T = 0 and the integration step size Δt; (b) Calculate the current step affine state variables by the implicit trapezoidal method; (c) Update the simulation duration T = T + Δt; (d) Repeat steps (b)-(c) until T reaches the preset simulation duration.

6. An electronic device, including a memory, a processor, and a computer program stored on the memory and capable of running on the processor, wherein when the processor executes the program, the steps of the method according to any one of claims 1-5 are implemented.

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