A control method, system and device of a new energy flexible direct current transmission and delivery system

Abstract

The application discloses a new energy flexible direct transmission system control method, system and device, the method is: based on the historical operation data obtained, a plurality of typical operation scenes are generated by using a clustering algorithm and system equivalent reactance values corresponding to each typical operation scene; for each typical operation scene, a transformation relationship is established based on the system equivalent reactance value to convert the sample distribution into impedance uncertainty input samples subject to the original distribution; the impedance uncertainty input samples are substituted into the pre-established small signal mathematical model, and the corresponding system stability index value under each typical operation scene is calculated; the probability distribution characteristics are determined according to the system stability index value, the control parameter optimization model is constructed based on the probability distribution characteristics and solved, and the optimal control parameter is obtained to realize the stable control of the new energy flexible direct transmission system, so that the stable control of the new energy flexible direct transmission system can be realized.

Description

Technical Field

[0001] This invention relates to the field of new energy control, and in particular to a control method, system and equipment for a new energy flexible direct transmission system. Background Technology

[0002] With the deepening of the global energy structure transformation, the installed capacity of new energy power generation, represented by photovoltaic and wind power, continues to climb. Large-scale transmission of new energy via flexible DC transmission systems has become an important way to consume electricity. However, new energy power generation is characterized by significant randomness, intermittency, and volatility. Its output is affected by factors such as weather conditions and day-night cycles, exhibiting strong uncertainty. When new energy is transmitted to the grid via a flexible DC system, short-term drastic fluctuations in power generation can cause disturbances in the voltage and frequency of the transmitting AC system, which are then transmitted to the DC side through the converter station, thus affecting the power balance and voltage stability of the multi-terminal flexible DC system. Without effective control measures, the system may face problems such as weakly damped oscillations, excessive dynamic response overshoot, and excessively long adjustment times. In severe cases, it may even induce subsynchronous oscillations or transient instability, threatening the safe operation of the power grid. Therefore, it is essential to scientifically tune and optimize the control parameters of the new energy flexible DC transmission system to improve its dynamic response and disturbance resistance capabilities under various operating scenarios, ensuring its adaptability to complex and ever-changing operating environments.

[0003] Currently, control parameters for new energy flexible direct transmission systems are typically tuned using trial-and-error methods based on typical operating conditions or deterministic small-signal analysis. Trial-and-error methods rely on engineering experience, repeatedly adjusting parameters through simulation and debugging until the system stabilizes. This method is inefficient and struggles to guarantee global optimality. Deterministic small-signal analysis, on the other hand, is based on a linearized model of the system at a specific equilibrium point, guiding parameter design through eigenvalue analysis. However, this method ignores the impact of uncertainties such as new energy output fluctuations and system impedance changes on the system's dynamic characteristics. Therefore, the shortcomings of existing technologies in uncertainty quantification lead to conservative or poorly adaptable control parameters in practical applications, failing to guarantee the system's dynamic stability and control performance across the entire operating range. Summary of the Invention

[0004] This invention provides a control method, system, and equipment for a new energy flexible direct transmission system, which can achieve stable control of the new energy flexible direct transmission system.

[0005] The first aspect of this invention provides a control method for a new energy flexible direct transmission system, comprising: Historical operating data of new energy power generation is obtained. Based on the historical operating data, a clustering algorithm is used to generate several typical operating scenarios and the corresponding equivalent system reactance values ​​that characterize the system impedance uncertainty for each typical operating scenario. For each typical operating scenario, a transformation relationship between the uncertainty variable and the standard normal distribution variable is established based on the equivalent reactance value of the system. Using the transformation relationship, the sample points are converted into impedance uncertainty input samples that follow the original distribution. The sample points are determined based on the standard normal distribution determined by the random response surface method. Substitute the impedance uncertainty input sample into the pre-established small-signal mathematical model of the new energy flexible direct transmission system to calculate the corresponding system stability index values ​​under each typical operating scenario. The probability distribution characteristics are determined based on the system stability index value. A control parameter optimization model is constructed based on the probability distribution characteristics. The control parameter optimization model is solved to obtain the optimal control parameters. Stable control of the new energy flexible direct transmission system is achieved based on the optimal control parameters.

[0006] This invention, through acquiring historical operational data and employing clustering algorithms to generate typical operational scenarios and their corresponding equivalent system reactance values, can extract a few representative operational modes from massive and complex uncertain operational data. This effectively solves the problem of exhaustively enumerating and quantifying actual operational scenarios, laying a physical foundation for subsequent analysis considering uncertainties. Secondly, for each typical scenario, a transformation relationship between uncertain variables and standard normally distributed variables is established based on the equivalent system reactance values. Furthermore, the sample collocation point transformation determined by the random response surface method is used to generate impedance uncertainty input samples that follow the original distribution. This method can accurately characterize the random fluctuation characteristics of system impedance with minimal computational cost, overcoming the limitations of traditional Monte Carlo methods. This invention overcomes the computational burden of traditional methods by efficiently quantifying uncertainties. Next, the input samples are substituted into a small-signal mathematical model to calculate system stability indices for various scenarios. This allows for prediction of the system's dynamic response behavior under different uncertainties during the planning and design phase, transforming abstract stability requirements into quantifiable performance indicators. Finally, a control parameter optimization model is constructed and solved based on the complete probability distribution characteristics of these indices. This model comprehensively optimizes the system's dynamic response performance (such as overshoot and settling time) under the worst operating conditions, ensuring stable system operation (i.e., satisfying eigenvalue constraints). Ultimately, a set of optimal control parameters that considers all typical scenarios and exhibits strong robustness to uncertainties is obtained. In summary, this invention effectively achieves precise and stable control of new energy flexible direct transmission systems in complex and variable operating environments.

[0007] Furthermore, the step of substituting the impedance uncertainty input sample into a pre-established small-signal mathematical model of the new energy flexible direct transmission system to calculate the corresponding system stability index values ​​under various typical operating scenarios includes: For each typical operating scenario, the equivalent reactance value of the system at each port corresponding to the impedance uncertainty input sample is substituted into the small-signal mathematical model to obtain the steady-state solution of the system at the stable operating point under the corresponding typical operating scenario. Based on the steady-state solution, the system stability index value corresponding to each impedance uncertainty input sample is calculated through small-signal stability analysis. The system stability index value includes the overshoot, adjustment time, AC voltage fluctuation of the sending-end converter station and receiving-end converter station after being disturbed, as well as the maximum value of the real part of the characteristic root and the damping ratio, which reflect the system stability.

[0008] By substituting the input samples into the small-signal mathematical model to calculate the system stability index values ​​under various scenarios, the dynamic response behavior of the system under different uncertainties can be predicted during the planning and design stage, thereby transforming the abstract stability requirements into quantifiable performance indicators.

[0009] Furthermore, based on the historical operating data, a clustering algorithm is used to generate several typical operating scenarios and corresponding equivalent system reactance values ​​representing the system impedance uncertainty for each typical operating scenario, including: The historical operating data was clustered using the K-means clustering algorithm to extract several typical operating scenarios; The power data under each typical operating scenario is substituted into the primary model of the system, and the equivalent reactance of the system at each port is calculated based on the steady-state power flow solution. Each typical operating scenario includes typical combinations of output power of photovoltaic power station, output power of hydropower station, and power transmitted from flexible DC receiving end.

[0010] By acquiring historical operating data and using clustering algorithms to generate typical operating scenarios and their corresponding equivalent system reactance values, massive and complex uncertain operating data can be extracted into a few representative operating modes. This effectively solves the problem of exhaustively listing and quantifying actual operating scenarios, laying a physical foundation for subsequent analysis that considers uncertainties.

[0011] Furthermore, the step of establishing a transformation relationship between the uncertainty variable and the standard normally distributed variable based on the equivalent reactance value of the system for each typical operating scenario includes: For each typical operating scenario, a multidimensional Gaussian distribution parameter is fitted based on the equivalent reactance value of the system at each port to obtain a multidimensional Gaussian distribution, wherein the multidimensional Gaussian distribution includes the expected value, standard deviation, and covariance matrix; Construct a correlation coefficient matrix for each dimension of data based on the covariance matrix; Based on the expected value, the standard deviation, and the correlation coefficient matrix, the Natav transformation is used to construct the transformation relationship between the uncertain variable and the standard normally distributed variable.

[0012] In this way, for each typical scenario, the transformation relationship between the uncertainty variable and the standard normal distribution variable is established based on the equivalent reactance value of the system, which facilitates the subsequent accurate characterization of the random fluctuation characteristics of the system impedance.

[0013] Furthermore, the transformation relationship includes an inverse transformation function, and the step of using the transformation relationship to convert the sample collocation points into impedance uncertainty input samples that follow the original distribution includes: Based on the random response surface method, using the characteristic root combination of Hermite orthogonal polynomials and the linear independence principle, several sets of arrays are selected from the standard normal distribution as sample points; For each typical operating scenario, the sample points are substituted into the inverse transformation function to obtain several sets of impedance uncertainty input samples that follow the original distribution under the corresponding typical operating scenario.

[0014] By using the sample collocation transformation determined by the random response surface method to generate impedance uncertainty input samples that follow the original distribution, the huge computational burden of the traditional Monte Carlo method can be overcome, and the uncertainty factors can be efficiently quantified.

[0015] Further, determining the probability distribution characteristics based on the system stability index value includes: For each typical operating scenario, a Hermitian matrix is ​​constructed based on the sample points, and the inverse of the Hermitian matrix is ​​solved. Based on the inverse matrix and the system stability index value, the coefficients of the Hermitian chaotic polynomial are calculated. Based on the coefficients, the probability distribution characteristics of the system stability index values ​​are determined, wherein the probability distribution characteristics include the expected value, standard deviation, and confidence interval.

[0016] Furthermore, the step of constructing a control parameter optimization model based on the probability distribution characteristics includes: The system control parameters are used as optimization variables, and the real part of the eigenvalues ​​of the small-signal mathematical model is less than zero as a stability constraint. The control parameter optimization model is constructed with the weighted sum and minimization of the upper bound of the confidence interval of the overshoot of the sending-end converter station response curve, the upper bound of the confidence interval of the adjustment time, the upper bound of the confidence interval of the overshoot of the receiving-end converter station response curve, the upper bound of the confidence interval of the adjustment time, and the upper bound of the confidence interval of the comprehensive value of the damping ratio as the optimization objective.

[0017] Furthermore, solving the control parameter optimization model to obtain the optimal control parameters includes: Initialize a number of particles, where the spatial position of each particle corresponds to a set of control parameters to be optimized; The fitness value of each particle is calculated based on the objective function in the optimization model using the control parameters. According to the update rules of the particle swarm optimization algorithm, the spatial position and velocity of each particle are iteratively updated, so that the particles gradually move towards a better solution region. The iteration is repeated until the preset termination condition is met, and the control parameters corresponding to the position of the best particle are taken as the optimal control parameters.

[0018] By constructing and solving a control parameter optimization model based on the complete probability distribution characteristics of these indicators, the dynamic response performance (such as overshoot and settling time) of the system under the worst operating conditions can be comprehensively optimized while ensuring stable system operation (i.e., satisfying the eigenvalue constraints). Ultimately, a set of optimal control parameters that takes into account all typical scenarios and has strong robustness to uncertain fluctuations is obtained. Another embodiment of the present invention provides a control system for a new energy flexible direct transmission system, comprising: The first module is used to acquire historical operating data of new energy power generation. Based on the historical operating data, a clustering algorithm is used to generate several typical operating scenarios and the corresponding equivalent reactance values ​​of the system to characterize the system impedance uncertainty of each typical operating scenario. The second module is used to establish a transformation relationship between uncertain variables and standard normal distribution variables based on the equivalent reactance value of the system for each typical operating scenario. Using the transformation relationship, the sample points are converted into impedance uncertainty input samples that follow the original distribution. The sample points are determined based on the standard normal distribution determined by the random response surface method. The third module is used to substitute the impedance uncertainty input sample into the pre-established small-signal mathematical model of the new energy flexible direct transmission system to calculate the corresponding system stability index values ​​under each typical operating scenario. The fourth module is used to determine the probability distribution characteristics based on the system stability index value, construct a control parameter optimization model based on the probability distribution characteristics, solve the control parameter optimization model to obtain the optimal control parameters, and realize stable control of the new energy flexible direct transmission system based on the optimal control parameters.

[0019] Another embodiment of the present invention provides a terminal device, including: a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the steps of the control method for the new energy flexible direct transmission system as described in the present invention.

[0020] Another embodiment of the present invention provides a computer-readable storage medium item, including: a stored computer program, which, when the computer program is running, controls the device where the computer-readable storage medium is located to perform the control method of the new energy flexible direct transmission system as described in the present invention. Attached Figure Description

[0021] To more clearly illustrate the technical solution of the present invention, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a flowchart illustrating an embodiment of the control method for the new energy flexible direct transmission system provided by the present invention. Figure 2 This is a flowchart illustrating an embodiment of steps S201 to S202 provided by the present invention; Figure 3 This is a schematic diagram of the system feature root location in one embodiment of the present invention; Figure 4 This is a schematic diagram of the participation factors of the dominant mode in one embodiment of the present invention; Figure 5 This is a schematic diagram of a pair of feasible domains of control parameters in one embodiment of the present invention; Figure 6 This is a schematic diagram of a three-terminal flexible DC transmission system in one embodiment of the present invention; Figure 7 This is a comparison chart of the cumulative distribution function curves of various performance indicators of SRSM and MC in one embodiment of the present invention; Figure 8 This is a comparison chart of the cumulative distribution function curves of the optimization method proposed in this patent in scenario 1 and scenario 2 before optimization, after optimization by the traditional optimization method, and in one embodiment of the present invention. Figure 9 This is a comparison chart of the cumulative distribution function curves of the optimization method proposed in this patent in scenarios 3 and 4 before optimization, after optimization using the traditional optimization method, and in one embodiment of the present invention. Figure 10 This is a comparison chart of the cumulative distribution function curves of the optimization method proposed in this patent in scenarios 5 and 6 before optimization, after optimization using the traditional optimization method, and in one embodiment of the present invention. Figure 11 This is a comparison curve of the PSCAD simulation response of scenario 1 before optimization, after optimization by the traditional optimization method, and the optimization method proposed in this patent in one embodiment of the present invention. Figure 12This is a comparison curve of the PSCAD simulation response of scenario 2 before optimization, after optimization by the traditional optimization method, and the optimization method proposed in this patent in one embodiment of the present invention. Figure 13 This is a comparison curve of the PSCAD simulation response of scenario 3 before optimization, after optimization by the traditional optimization method, and the optimization method proposed in this patent in one embodiment of the present invention. Figure 14 This is a comparison curve of the PSCAD simulation response of scenario 4 in one embodiment of the present invention before optimization, after optimization by the traditional optimization method, and the optimization method proposed in this patent. Figure 15 This is a comparison curve of the PSCAD simulation response of scenario 5 in one embodiment of the present invention, before optimization, after optimization by the traditional optimization method, and the optimization method proposed in this patent. Figure 16 This is a comparison curve of the PSCAD simulation response of scenario 6 in one embodiment of the present invention, before optimization, after optimization by the traditional optimization method, and the optimization method proposed in this patent. Figure 17 This is a schematic diagram of the structure of an embodiment of the control system of the new energy flexible direct transmission system provided by the present invention. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of this invention. Obviously, the described embodiments are only some embodiments of this invention, not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0024] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the invention; the terms “comprising” and “having”, and any variations thereof, in the specification, claims, and foregoing description of the invention, are intended to cover non-exclusive inclusion.

[0025] In the description of the embodiments of this invention, technical terms such as "first" and "second" are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this invention, "multiple" means two or more, unless otherwise explicitly defined.

[0026] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0027] In the description of the embodiments of this invention, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.

[0028] In the description of the embodiments of the present invention, the term "multiple" refers to two or more (including two), similarly, "multiple groups" refers to two or more (including two groups), and "multiple pieces" refers to two or more (including two pieces).

[0029] In the description of the embodiments of the present invention, unless otherwise explicitly specified and limited, the technical terms such as "installation," "connection," "joining," and "fixing" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in the embodiments of the present invention according to the specific circumstances.

[0030] See Figure 1 To achieve stable control of a new energy flexible direct transmission system, an embodiment of the present invention provides a control method for a new energy flexible direct transmission system, including steps S101 to S104: Step S101: Obtain historical operating data of new energy power generation; based on the historical operating data, use a clustering algorithm to generate several typical operating scenarios and the corresponding equivalent reactance values ​​of the system to characterize the uncertainty of system impedance for each typical operating scenario. In some embodiments, historical operation data of new energy power generation is obtained as follows: First, quarterly forecast curve data of major new energy power generation (such as photovoltaic power plants) and hydropower generation in the AC system of the flexible DC transmission end are collected from the power grid dispatching department, the new energy power plant monitoring system or related forecasting platform. At the same time, the power transmission curve data of the two receiving end systems of the flexible DC transmission are also collected. Then, the collected data are integrated in chronological order to form a time-series multidimensional sequence dataset. Each time segment in the dataset contains data values ​​of multiple dimensions, mainly including: output power of photovoltaic power plants, output power of hydropower plants, power transmission of flexible DC receiving end 1, power transmission of flexible DC receiving end 2, etc.

[0031] It should be noted that new energy power generation data, hydropower data, and power transmission curve data are usually in the form of time series, such as one sampling point every 15 minutes or hour, covering the entire quarterly time range, which can reflect the time series changes and uncertainties of new energy output and load demand.

[0032] In some embodiments, the step of generating several typical operating scenarios and corresponding system equivalent reactance values ​​representing the system impedance uncertainty based on the historical operating data using a clustering algorithm includes: performing cluster analysis on the historical operating data using a K-means clustering algorithm to extract several typical operating scenarios; substituting the power data under each typical operating scenario into the primary model of the system, and calculating the system equivalent reactance values ​​of each port based on the steady-state power flow solution, wherein each typical operating scenario includes typical combinations of output power of photovoltaic power plants, output power of hydropower plants, and power transmitted from the flexible DC receiving end.

[0033] In some embodiments, after collecting and integrating historical operating data, the acquired historical operating data is analyzed according to time segments. Each time segment constitutes a multidimensional data sample containing the output power of the photovoltaic power station, the output power of the hydropower station, the power transmitted from flexible DC receiving end 1, and the power transmitted from flexible DC receiving end 2. These samples are used as the input data set, and the K-means clustering algorithm is used for cluster analysis. During the clustering process, the algorithm divides the multidimensional samples into several clusters based on the Euclidean distance or similarity measure between the data. The center of each cluster represents a typical scenario with similar operating characteristics. By setting the number of clusters (e.g., clustering quarterly data into six categories), several typical operating scenarios are finally extracted. Then, the power data under each typical operating scenario is substituted into the system's primary model (i.e., the system's steady-state power flow calculation model) to solve the steady-state power flow equation, obtaining the steady-state operating point of the system under given power injection conditions. Based on the results of the steady-state power flow solution, the equivalent reactance value of each port can be further calculated.

[0034] It should be noted that the system equivalent reactance reflects the equivalent impedance of the AC system as seen from this port, and its value depends on the short-circuit capacity of this port, line parameters, and the operating mode of the system.

[0035] It should be noted that the primary model of the system is mainly used to solve the steady-state power flow distribution of the system. Its construction process encompasses the mathematical description of the system's main circuit topology, equipment parameters, and operating modes. First, based on the constructed electromagnetic transient simulation model, the main circuit structure of the system is extracted, including the electrical parameters and connection relationships of core modules such as photovoltaic power plants, hydropower plants, flexible DC converter stations, and AC / DC lines. These parameters constitute the basic data of the primary model. Subsequently, following the construction approach of the system state-space model, the primary model needs to reflect the power balance relationships between modules. For example, the output power of photovoltaic and hydropower plants, the injected or transmitted power of the flexible DC converter station, and the power losses on AC / DC lines all need to be described through power flow equations. In practical applications, the primary model of the system typically uses nodal power balance equations (i.e., power flow equations) for mathematical expression. By giving the injected power of each node (such as photovoltaic output, hydropower output, and load demand), the node voltage magnitude and phase angle are solved to obtain the steady-state operating point of the system. In simple terms, it can be understood as follows: with the core objective of solving the steady-state power flow distribution of the system, firstly, based on the established electromagnetic transient simulation model, the electrical parameters and topological connections of core modules such as photovoltaic power stations, hydropower stations, flexible DC converter stations, and AC / DC lines are extracted to form the basic data of the model; then, following the construction idea of ​​the system state-space model, the power balance relationship between each module is characterized by the power flow equation, covering all aspects such as power output, power interaction of converter stations, and power loss of lines; finally, the nodal power balance equation is used to complete the mathematical expression of the model, and the nodal voltage amplitude and phase angle are solved by giving the injected power of each node to obtain the steady-state operating point of the system.

[0036] In some embodiments, each typical operating scenario corresponds to a specific set of power value combinations, including typical values ​​of photovoltaic power plant output power, hydropower plant output power, and power transmitted from each flexible DC receiving end, as shown in Table 1. These values ​​can reflect the typical operating status of the system under different operating conditions within the quarter: Table 1 Representative Operating Scenarios Step S102: For each typical operating scenario, establish a transformation relationship between the uncertainty variable and the standard normal distribution variable based on the equivalent reactance value of the system. Using the transformation relationship, convert the sample points into impedance uncertainty input samples that follow the original distribution. The sample points are determined based on the standard normal distribution determined by the random response surface method. In some embodiments, establishing a transformation relationship between the uncertainty variable and the standard normally distributed variable based on the system equivalent reactance value for each typical operating scenario includes: for each typical operating scenario, fitting multidimensional Gaussian distribution parameters according to the system equivalent reactance value of each port to obtain a multidimensional Gaussian distribution, wherein the multidimensional Gaussian distribution includes expected value, standard deviation, and covariance matrix; constructing a correlation coefficient matrix for each dimension of data based on the covariance matrix; and constructing a transformation relationship between the uncertainty variable and the standard normally distributed variable using Natav transformation based on the expected value, the standard deviation, and the correlation coefficient matrix.

[0037] In some embodiments, firstly, the dimension of the uncertainty variable is determined based on the number of ports in the flexible DC system. For example, in a three-terminal flexible DC system, the dimension of the uncertainty variable is 3. Then, for each typical operating scenario, the equivalent reactance value of the system at each port in that scenario is regarded as an observed sample of a multidimensional random variable. A multidimensional Gaussian distribution is fitted to it to calculate the expected value (i.e., mean) and standard deviation of the data in each dimension, and the covariance between different dimensions is calculated to form a covariance matrix. Based on the above covariance matrix, the correlation coefficient matrix between the data in each dimension is derived after standardization, and the element values ​​range from [-1, 1]. After obtaining the expected value, standard deviation, and correlation coefficient matrix of the data in each dimension for each typical operating scenario, a mapping relationship from the original distributed variable to the independent standard normal distributed variable can be established based on the known marginal distribution (here, Gaussian distribution) and the correlation coefficient matrix, i.e., the Nataf transformation function; at the same time, its inverse mapping can also be established, i.e., the Nataf inverse transformation function, which is used to transform the sample points in the standard normal distribution space back to the impedance uncertainty input sample that follows the original distribution.

[0038] It should be noted that the covariance matrix not only reflects the fluctuation range of the equivalent reactance of each port system, but also characterizes the degree of correlation of impedance changes between different ports.

[0039] It should be noted that the Natav transformation is an effective method for handling correlated non-normal random variables. Its core idea is to first map the correlated random variables in the original distribution space to the standard normal distribution space through an equal probability transformation, perform correlation correction in the standard normal space, and then transform back to the original distribution space.

[0040] In some embodiments, the transformation relationship includes an inverse transformation function. The step of using the transformation relationship to convert the sample collocation points into impedance uncertainty input samples that follow the original distribution includes: selecting several sets of arrays as sample collocation points from the standard normal distribution using the random response surface method, the characteristic root combination of Hermitian orthogonal polynomials and the principle of linear independence; and substituting the sample collocation points into the inverse transformation function for each typical operating scenario to obtain several sets of impedance uncertainty input samples that follow the original distribution under the corresponding typical operating scenario.

[0041] In some embodiments, firstly, according to the theory of Hermite orthogonal polynomials, their third-order orthogonal polynomials have specific eigenvalues, typically taking values ​​of 0, ±3, ±3, etc. (the specific values ​​depend on the construction form of the orthogonal polynomial). These eigenvalues ​​are combined according to the principle of non-repetition to form several sets of N-dimensional arrays, i.e., standard sample collocations ξ, where N is the dimension of the uncertainty variable (e.g., N=3 in a three-terminal flexible straight-line system). Each set of ξ is substituted into the expression of the Hermite second-order chaotic polynomial to check whether it constitutes a linearly independent polynomial. The solution is obtained using Gaussian elimination, and the final result is retained. The array corresponding to the set of equations represents the valid standard sample collocation points. After obtaining the unified standard sample collocation points ξ, for each typical operating scenario, the same set of standard sample collocation points ξ is substituted into the Nataf inverse transform function for each typical operating scenario to obtain the corresponding standard sample collocation point for that scenario. (e.g., 10 groups) of impedance uncertainty input samples that follow the original multidimensional Gaussian distribution.

[0042] It should be noted that for a second-order Hermite chaotic polynomial with three-dimensional variables, the number of undetermined coefficients... The number is usually 10, so 10 sets of three-dimensional standard normal distribution sample points are finally selected.

[0043] It should be noted that for each typical operating scenario, since the expected value, standard deviation, and correlation coefficient matrix of the equivalent reactance of each port system are different, the parameters of the corresponding Nataf inverse transform function are also different.

[0044] In some embodiments, the expression for the Hermite second-order chaotic polynomial is: ; In the formula Representing the The, the A random standard sample of dimension Representing the The random response output corresponding to each random sample; It is a mapping stochastic process The undetermined coefficients of the chaotic polynomial, i.e., the distribution characteristic coefficients of the stochastic process model. The number of undetermined coefficients in the chaotic polynomial: It can be written in matrix form: .

[0045] Step S103: Substitute the impedance uncertainty input sample into the pre-established small-signal mathematical model of the new energy flexible direct transmission system to calculate the corresponding system stability index values ​​under each typical operating scenario. Please refer to Figure 2 In some embodiments, step S103 includes steps S201 to S202: Step S201: For each typical operating scenario, substitute the equivalent reactance of the system at each port corresponding to the impedance uncertainty input sample into the small-signal mathematical model to obtain the steady-state solution of the system at the stable point under the corresponding typical operating scenario. In some embodiments, for each typical operating scenario, the equivalent system reactance value corresponding to each set of samples in that scenario is first substituted into the pre-established small-signal mathematical model of the new energy flexible direct transmission system. By combining the equivalent system reactance value with known system main circuit parameters (such as transformer reactance, bridge arm reactance, submodule capacitance, etc.) and control parameters, the steady-state power flow equation of the system is solved to obtain the steady-state solution of the system under the operating conditions corresponding to the set of equivalent reactance values, that is, the values ​​of each state variable at the equilibrium point.

[0046] Step S202: Based on the steady-state solution, the system stability index value corresponding to each impedance uncertainty input sample is calculated through small-signal stability analysis. The system stability index value includes the overshoot, adjustment time, AC voltage fluctuation of the sending-end converter station and the receiving-end converter station after being disturbed, as well as the maximum value of the real part of the characteristic root and the damping ratio that reflect the system stability.

[0047] In some embodiments, the steady-state solution is used as the equilibrium point to linearize the nonlinear state-space model of the system, resulting in a small-signal model under the operating condition. Based on the state matrix A in the small-signal model, eigenvalue decomposition is performed to extract the real parts of all eigenvalues. The maximum value among them is taken as the maximum real part of the eigenvalue to determine the stability margin of the system. At the same time, the damping ratio of each pair of conjugate complex eigenvalues ​​is calculated, with particular attention paid to weakly damped modes with a damping ratio less than 0.1. The damping ratios of all weakly damped modes are summed to obtain a comprehensive weakly damped index to measure the oscillation suppression capability of the system. Furthermore, to evaluate the system's dynamic response performance after experiencing small disturbances, a typical disturbance is applied to the small-signal model: a 5% pu step signal is applied to the active power reference value at the sending-end converter station (MMC1), and simultaneously, a 5% pu step signal is applied to the DC voltage reference value at the receiving-end converter station (MMC2). By solving the time-domain response of the linear system, the response curves of the actual active power or DC voltage at the sending and receiving ends of the converter stations are obtained. From these curves, the maximum overshoot and settling time (the time required to enter and maintain the steady-state value within ±2% error band) are extracted, and the AC voltage fluctuation is obtained. For each set of impedance uncertainty input samples (i.e., each system equivalent reactance combination) under each typical operating scenario, a corresponding set of system stability index values ​​can be calculated according to the above steps, denoted as . Where the subscript i represents the i-th typical operating scenario, and the subscript j represents the j-th sample in that scenario, these index values ​​constitute the output response sample set required for subsequent random response surface methodology analysis, and can also be written as... .

[0048] In some embodiments, the construction process of the small-signal mathematical model is as follows: the overall system is decoupled into three core modules: a new energy power generation unit, a modular multilevel converter (MMC), and an AC / DC line. State-space models of their respective electrical and control systems are established, and the interaction of energy flow and information flow between different modules is achieved through dynamic interfaces. During modular modeling, for the MMC converter station, its internal dynamic characteristics must be fully considered, including the dynamic description of fluctuations in submodule capacitor voltage, AC current, DC current, and second-harmonic circulating current; internal control strategies such as conventional dual-loop vector control, circulating current suppression control, and phase-locked loop control are also considered. For photovoltaic power plants, a simplified modeling method is adopted, using a single-port MMC model to simulate the dynamic response of each new energy station in the collection station. The photovoltaic system is equivalent to a power supply model based on the inverter, focusing on the dynamic behavior characteristics of the inverter and the volt-ampere characteristics of the photovoltaic array. Maximum power point tracking (MPPT) control is adopted, and the grid-connected inverter uses a constant DC voltage control strategy, with its DC voltage reference value continuously tracking the voltage corresponding to the maximum power point of the photovoltaic array. For hydropower generation systems, an ideal voltage source is used as an equivalent substitution method to establish its state-space model. For AC / DC lines, the AC lines between photovoltaic power stations, between photovoltaic power stations and the flexible DC transmission terminal, and between the flexible DC transmission terminal and the receiving end are all modeled using π-type and T-type equivalent line models. Since the phase of each inverter is independently tracked by its own phase-locked loop during dynamic operation, the dq rotating coordinate systems of each inverter are not consistent. Since the power transmission between the photovoltaic power station and the flexible DC transmission terminal is constrained by the AC power flow equations, one of the MMCs at the flexible DC transmission terminal is selected as the common rotating coordinate system to establish a unified dq rotating coordinate system. Finally, the nonlinear state-space models of all modules, including the new energy power stations, flexible DC converter stations, AC / DC lines, and AC systems, are combined and linearized at the steady-state equilibrium point to establish a small-signal mathematical model of the new energy photovoltaic hydropower transmission system via the flexible DC transmission system. Its form is as follows: ; The state matrix A contains the dynamic parameters of each module of the system (such as converter station, line, and new energy power generation unit). These parameters include the equivalent reactance of the system given by the sample, as well as the control parameters to be evaluated (such as the proportional and integral coefficients of the phase-locked loop, the inner current loop, and the outer power loop). U, X, and Y are the input variables, state variables, and output variables of the specified system, respectively.

[0049] To clarify the key control parameters to be optimized and their reasonable adjustment range in the subsequent optimization model, based on the small-signal mathematical model established in step S2, the time-domain eigenvalue method is used to analyze the factors affecting system stability. First, the system stability is determined according to Lyapunov's first method, such as... Figure 3As shown, all eigenvalues ​​have negative real parts, indicating that the system possesses small perturbation stability. Secondly, key information such as the oscillation frequency and damping ratio of the dominant oscillation mode is extracted through modal identification. Simultaneously, participation factor analysis is used to quantify the degree of participation of each state variable in the dominant mode, obtaining the distribution of participation factors under each mode (e.g., ...). Figure 4 As shown, this identifies the dominant state variables affecting the system's dynamic response; furthermore, sensitivity analysis is used to quantify the impact of changes in system control parameters on eigenvalues; root locus analysis is used to visually demonstrate the movement trajectory of eigenvalues ​​as control parameters change, revealing the influence of parameter change direction on stability; finally, feasible region analysis is used to plot the feasible region boundaries of the main influencing parameters (e.g., ...). Figure 5 As shown in the figure, the reasonable adjustment limits of each key control parameter are clearly defined. Through the above analysis, the control parameters that have the most significant impact on system stability can be selected as optimization variables, and their optimization range can be determined, laying the foundation for constructing the control parameter optimization model in step S104.

[0050] Step S104: Determine the probability distribution characteristics based on the system stability index value, construct a control parameter optimization model based on the probability distribution characteristics, solve the control parameter optimization model to obtain the optimal control parameters, and realize stable control of the new energy flexible direct transmission system based on the optimal control parameters.

[0051] In some embodiments, determining the probability distribution characteristics based on the system stability index value includes: constructing a Hermitian matrix based on the sample points for each typical operating scenario, and solving for the inverse of the Hermitian matrix; calculating the coefficients of the Hermitian chaotic polynomial based on the inverse matrix and the system stability index value; and determining the probability distribution characteristics of the system stability index value based on the coefficients, wherein the probability distribution characteristics include the expected value, standard deviation, and confidence interval. Specifically, firstly, for each typical operating scenario, the previously selected standard sample points are used... Constructing the Hermite matrix And solve for its inverse matrix. Then, the system stability index values ​​corresponding to each impedance uncertainty input sample under this typical operating scenario are retrieved from the storage file to form a vector. (where i is the scene number), according to the formula The coefficient array of the Hermite chaotic polynomial was calculated. The expected value of the dynamic stability index for the i-th representative operating scenario is A1, and the standard deviation is... The overshoot Mp1 and settling time Ts1 of the sending-end MMC1 response curve, and the overshoot Mp2 and settling time Ts2 of the receiving-end MMC2 response curve, along with the combined damping ratio, are also included. Using these indicators as random response outputs, we obtain the Hermite chaotic polynomial array of undetermined coefficients and the upper and lower boundary values ​​of the confidence interval for each indicator's random response output: In the formula, z is the value of the confidence level corresponding to the Z-value in the normal Z-value table. and These are the upper and lower bounds of the confidence interval for Y. Common Z values ​​and probabilities are shown in Table 2.

[0052] Table 2 Common Z-values ​​and probabilities In some embodiments, constructing a control parameter optimization model based on the probability distribution characteristics includes: using system control parameters as optimization variables, and using the real part of the eigenvalues ​​of the small-signal mathematical model being less than zero as a stability constraint; and using the weighted sum minimization of the upper bound of the confidence intervals of the overshoot, the upper bound of the adjustment time, the upper bound of the confidence intervals of the overshoot, the upper bound of the adjustment time, and the upper bound of the confidence interval of the damping ratio of the response curve of the sending-end converter station as the optimization objective, thus constructing the control parameter optimization model. Specifically, in this model, strict constraints are set, requiring that the real part of the system eigenvalues ​​must be less than 0, thereby ensuring that the system has sufficient stability and avoiding abnormal operating states such as oscillation or instability. Simultaneously, the optimization objective function is set as the overshoot Mp1 and adjustment time Ts1 of the sending-end MMC1 response curve, the overshoot Mp2 and adjustment time Ts2 of the receiving-end MMC2 response curve, and the damping ratio. Weighted minimization of the upper bound of the confidence interval of the indicators: ,in, , , , , These are the weighting coefficients for the corresponding indicators, which can be set according to the importance requirements of each performance indicator in the actual project, and must meet the following requirements: + + + + =1; By setting this objective function, the aim is to comprehensively optimize the dynamic response performance of the system, reduce overshoot, shorten adjustment time, and ensure that the system has appropriate damping characteristics, thereby improving the overall operating quality and stability of the new energy direct transmission system.

[0053] In some embodiments, solving the control parameter optimization model to obtain the optimal control parameters includes: initializing a number of particles, wherein the spatial position of each particle corresponds to a set of control parameters to be optimized; calculating the fitness value of each particle based on the objective function in the control parameter optimization model; iteratively updating the spatial position and velocity of each particle according to the update rules of the particle swarm optimization algorithm, so that the particles gradually move towards a better solution region, repeating the iteration until a preset termination condition is met, and taking the control parameters corresponding to the position of the finally obtained optimal particle as the optimal control parameters. Specifically, firstly, the particle swarm optimization algorithm is started, the number of particles is set to Ns, and the dimension of the optimization variable is consistent with the number of control parameters to be optimized in the system. The spatial position and velocity of each particle are initialized, wherein the spatial position of each particle corresponds to a specific set of control parameters. Based on the preset objective function, the fitness value corresponding to each particle is calculated. This fitness value can intuitively reflect the quality of the combination of control parameters represented by the particle. According to the adjustment formula of velocity and spatial position in the particle swarm optimization algorithm, the spatial position of each particle is updated iteratively, thereby guiding the particles to gradually move towards a better solution region. After each iteration, the system checks whether a preset iteration termination condition is met. If it is, the iteration process terminates; otherwise, it returns to continue the iteration calculation. At the end of the iteration process, the system outputs a set of optimal control parameters corresponding to the position of the optimal solution particle found in the particle swarm. This set of parameters represents the combination of control parameters that achieves relatively optimal system performance under given optimization objectives and constraints.

[0054] In some embodiments, the velocity and space adjustment formulas of the particle swarm optimization algorithm are as follows: ; in, and A random number between [0,1] Inertial weights are used to balance the global and local search capabilities of the algorithm. and The learning factor is used to adjust the step size of the particle's flight towards the individual optimal position and the group optimal position, respectively. and These are random numbers uniformly distributed within the interval [0,1], used to increase the randomness of the search; This represents the velocity of the i-th particle in the d-th dimension; This represents the position of the i-th particle in the d-th dimension, corresponding to a set of control parameters to be optimized. This is the optimal position for the i-th particle, which is the best position that the particle has found so far. It is the global optimal position of the swarm, that is, the best position found so far by the entire particle swarm.

[0055] Compared with the prior art, the present invention has the following advantages and beneficial effects: It can accurately quantify risk and achieve efficient probability assessment. The stochastic response surface methodology (SFM) cleverly constructs a probability mapping relationship between input and output, enabling the quantification of risk indicators with a very small number of standardized samples. The chaotic multinomial substitution model built based on this method requires only a small number of highly representative standard samples to accurately simulate the intricate nonlinear relationships between input and output variables. It greatly improves the efficiency of probability assessment and demonstrates significant advantages in the utilization of computational resources. The SFM provides a powerful tool for system stability assessment and risk quantification in a concise and efficient manner, enabling relatively accurate results even with limited resources.

[0056] Breaking through traditional limitations, this method balances accuracy and efficiency. Traditional optimization methods often suffer from conservatism, tending to overly conservatively set parameters to ensure system safety when dealing with complex problems. The control parameter optimization method based on the stochastic response surface methodology proposed in this patent successfully overcomes this deficiency. While maintaining model accuracy, it significantly reduces computational complexity, not only improving the speed of optimization solutions and enabling results to be obtained in a shorter time, but also allowing power systems to operate more efficiently and accurately when optimizing control parameters. This provides solid theoretical support and technical assurance for the intelligent operation of large-scale power systems.

[0057] This embodiment uses Figure 6 The three-terminal flexible DC transmission system shown is presented as a case study for detailed explanation. Specifically, the design scheme of the sending-end converter station involves combining two converter stations to increase transmission capacity. In the configuration of system control parameters, a trial-and-error method is used to set the parameters.

[0058] Example: The main circuit parameters and control parameters of the system are listed in Table 3. The control parameters are consistent for converter stations with the same control method. MMC2 adopts a constant UdcQ control strategy, while MMC1, MMC3, and MMC4 adopt a constant PQ control strategy.

[0059] Table 3. Main circuit parameters and control parameters of the system To verify the accuracy of the dynamic performance evaluation of the new energy system via flexible direct transmission based on SRSM and considering system impedance uncertainty proposed in this patent, and the effectiveness of the optimization results, the cumulative distribution function curves of various performance indicators were plotted based on the chaotic polynomial coefficients to verify the accuracy of the dynamic performance evaluation of the new energy system via flexible direct transmission based on SRSM and considering system impedance uncertainty proposed in this patent.

[0060] Taking any given running scenario, the cumulative distribution function curves of various performance indicators are obtained based on the chaotic polynomial coefficients, as shown below. Figure 7 As shown by the purple curve, 1000 sets of standard normally distributed variable sample values ​​were extracted using the Monte Carlo method. The response results were then processed by a small perturbation model, and the cumulative distribution function curves of various performance indicators were obtained statistically. Figure 11-16 As shown by the blue curve, the curve indicates that the two methods have a high degree of consistency and no significant difference, which verifies the accuracy of the dynamic performance evaluation of the new energy transmission system based on SRSM that takes into account the system impedance uncertainty proposed in this paper.

[0061] Meanwhile, the outputs of the two methods were compared. The average and standard deviation of each performance index are listed in Table 4, and the upper and lower boundaries of the 95% confidence interval are listed in Table 5. The expected values, standard deviations, and upper and lower boundary values ​​of the confidence intervals for each performance index are not significantly different, ensuring the accuracy of the results obtained by the stochastic response surface methodology (SRSM). Table 6 shows the simulation running time of the Monte Carlo method and the stochastic response surface methodology. The Monte Carlo method took 54372.9 seconds for 1000 simulations, approximately 15 hours, while the SRSM method took only 586.3 seconds, approximately 10 minutes. The SRSM method significantly saves time while maintaining accuracy, indicating that the stochastic response surface methodology is more efficient than the Monte Carlo method.

[0062] Table 4 Comparison of SRSM and MC methods for assessing the standard deviation of expected dynamic response Table 5 Comparison of Upper and Lower Boundary Assessments of Dynamic Response using SRSM and MC Methods Table 6 Comparison of simulation times between SRSM and MC methods S7.2 The optimization results of the traditional method of ordinary particle swarm optimization under the adverse conditions of random selection before optimization are compared with those of the method to verify the effectiveness of the optimization method proposed in this patent.

[0063] To ensure that the optimized parameters exhibit superior performance across various typical scenarios, the summation of six typical scenarios was used as the final optimization objective. To verify the effectiveness of the SRSM-based particle swarm optimization method considering uncertainties, the optimization results of the traditional method using ordinary particle swarm optimization under randomly selected adverse scenarios were compared with the results before optimization. The upper boundaries of each performance index within the 95% confidence interval are shown in Tables 7, 8, and 9. Compared to the results before optimization, the traditional method optimized scenario one, resulting in improved performance in scenario one. However, in the other five scenarios, due to the smaller weighting of the weak damping index, overshoot and settling time were prioritized for optimization, leading to poor optimization of the weak damping index. The method proposed in this paper significantly improved both overshoot and settling time in all six scenarios. Although both algorithms can optimize the target object, the particle swarm optimization method based on SRSM, considering the uncertainty, has a smaller upper boundary in the 95% confidence interval than the traditional method. This indicates that the method is more adaptable to random disturbance scenarios and enables the system to have stronger dynamic response characteristics and better disturbance resistance in the worst scenarios.

[0064] Table 7 Upper Boundaries of Confidence Intervals for Each Indicator Before Optimization Table 8 Upper bounds of confidence intervals for each indicator after optimization using Method 1. Table 9 Upper bounds of confidence intervals for each indicator after optimization using Method 2. To verify the effectiveness of the optimization results, the proposed control parameter optimization method was compared with traditional methods. The control parameters before optimization, after optimization using the proposed method, and after optimization using the traditional method were substituted into the small-signal model of the example. Monte Carlo simulations were performed on six scenarios. Small-signal model calculations yielded small-disturbance analysis results. The sender-end response settling time, receiver-end response overshoot, and receiver-end response settling time were used as random response outputs and plotted as CDF curves, as shown below. Figure 8 (Comparison of cumulative distribution function curves for Scenario 1 and Scenario 2) Figure 9 (Comparison of cumulative distribution function curves for scenarios 3 and 4) Figure 10 (A comparison of the cumulative distribution function curves for scenarios 5 and 6 is shown.) As can be seen from the figure, the proposed method significantly improves the expected value and deviation of each indicator in various scenarios compared to traditional methods, demonstrating a clear optimization effect.

[0065] To further verify the effectiveness of the proposed method, the control parameters before optimization, after optimization using the proposed method, and after optimization using the traditional method were substituted into the PSCAD model. Six operating scenarios were then tested, and extreme probability scenarios were selected for modeling, with an error range of ±2%. Figure 11 (PSCAD simulation comparison diagram for Scenario 1) Figure 12 (PSCAD simulation comparison diagram for Scenario 2) Figure 13 (PSCAD simulation comparison diagram for Scenario 3) Figure 14 (PSCAD simulation comparison diagram for Scenario 4) Figure 15 (PSCAD simulation comparison diagram for Scenario 5) Figure 16 (PSCAD simulation comparison diagram of scenario 6) It can be seen that the control parameters obtained by the method proposed in this paper can enable the flexible direct current system to have better dynamic response characteristics and exhibit better control performance in various scenarios.

[0066] The comparative experiments above verify that the uncertainty optimization method for control parameters of flexible DC systems with uncertain system impedance mentioned in this paper can effectively reduce the overshoot of the system dynamic response, shorten the adjustment time, improve the dynamic response characteristics of the system, enable the system to adapt to more operating scenarios, and have better anti-disturbance capabilities.

[0067] like Figure 3 As shown, based on the above method embodiments, corresponding apparatus embodiments are provided; An embodiment of the present invention provides a control system for a new energy flexible direct transmission system, comprising: The first module 100 is used to acquire historical operating data of new energy power generation, and based on the historical operating data, to generate several typical operating scenarios and the corresponding system equivalent reactance values ​​representing the system impedance uncertainty of each typical operating scenario using a clustering algorithm. The second module 200 is used to establish a transformation relationship between uncertain variables and standard normal distribution variables based on the equivalent reactance value of the system for each typical operating scenario. Using the transformation relationship, the sample points are converted into impedance uncertainty input samples that follow the original distribution. The sample points are determined based on the standard normal distribution determined by the random response surface method. The third module 300 is used to substitute the impedance uncertainty input sample into the pre-established small-signal mathematical model of the new energy flexible direct transmission system to calculate the corresponding system stability index values ​​under each typical operating scenario. The fourth module 400 is used to determine the probability distribution characteristics based on the system stability index value, construct a control parameter optimization model based on the probability distribution characteristics, solve the control parameter optimization model to obtain the optimal control parameters, and realize stable control of the new energy flexible direct transmission system based on the optimal control parameters.

[0068] It is understood that the above-described device embodiments correspond to the method embodiments of the present invention, and can implement the control method of the new energy flexible direct transmission system provided by any of the above-described method embodiments of the present invention.

[0069] It should be noted that the device embodiments described above are merely illustrative, and some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can specifically be implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0070] Based on the above-described embodiments of the control method for the new energy flexible direct transmission system, another embodiment of the present invention provides a terminal device, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the control method for the new energy flexible direct transmission system of any embodiment of the present invention.

[0071] For example, in this embodiment, the computer program can be divided into one or more modules, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the terminal device.

[0072] The terminal device may be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0073] The processor can be a Central Processing Unit (CPU), or 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. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the terminal device, connecting all parts of the terminal device via various interfaces and lines.

[0074] Based on the above-described method embodiments, another embodiment of the present invention provides a computer-readable storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to execute the control method of the new energy flexible direct transmission system described in any of the above-described method embodiments of the present invention.

[0075] The modules / units integrated in the device / terminal equipment, if implemented as software functional units and sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0076] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A control method for a new energy flexible direct transmission system, characterized in that, include: Historical operating data of new energy power generation is obtained. Based on the historical operating data, a clustering algorithm is used to generate several typical operating scenarios and the corresponding equivalent system reactance values ​​that characterize the system impedance uncertainty for each typical operating scenario. For each typical operating scenario, a transformation relationship between the uncertainty variable and the standard normal distribution variable is established based on the equivalent reactance value of the system. Using the transformation relationship, the sample points are converted into impedance uncertainty input samples that follow the original distribution. The sample points are determined based on the standard normal distribution determined by the random response surface method. Substitute the impedance uncertainty input sample into the pre-established small-signal mathematical model of the new energy flexible direct transmission system to calculate the corresponding system stability index values ​​under each typical operating scenario. The probability distribution characteristics are determined based on the system stability index value. A control parameter optimization model is constructed based on the probability distribution characteristics. The control parameter optimization model is solved to obtain the optimal control parameters. Stable control of the new energy flexible direct transmission system is achieved based on the optimal control parameters.

2. The control method for the new energy flexible direct transmission system according to claim 1, characterized in that, The process involves substituting the impedance uncertainty input sample into a pre-established small-signal mathematical model of the new energy flexible direct transmission system to calculate the corresponding system stability index values ​​under various typical operating scenarios, including: For each typical operating scenario, the equivalent reactance value of the system at each port corresponding to the impedance uncertainty input sample is substituted into the small-signal mathematical model to obtain the steady-state solution of the system at the stable operating point under the corresponding typical operating scenario. Based on the steady-state solution, the system stability index value corresponding to each impedance uncertainty input sample is calculated through small-signal stability analysis. The system stability index value includes the overshoot, adjustment time, AC voltage fluctuation of the sending-end converter station and receiving-end converter station after being disturbed, as well as the maximum value of the real part of the characteristic root and the damping ratio, which reflect the system stability.

3. The control method for the new energy flexible direct transmission system according to claim 1, characterized in that, Based on the historical operating data, a clustering algorithm is used to generate several typical operating scenarios and corresponding equivalent system reactance values ​​representing the system impedance uncertainty for each typical operating scenario, including: The historical operating data was clustered using the K-means clustering algorithm to extract several typical operating scenarios; The power data under each typical operating scenario is substituted into the primary model of the system, and the equivalent reactance of the system at each port is calculated based on the steady-state power flow solution. Each typical operating scenario includes typical combinations of output power of photovoltaic power station, output power of hydropower station, and power transmitted from flexible DC receiving end.

4. The control method for the new energy flexible direct transmission system according to claim 1, characterized in that, For each typical operating scenario, the transformation relationship between the uncertainty variable and the standard normally distributed variable is established based on the equivalent reactance value of the system, including: For each typical operating scenario, a multidimensional Gaussian distribution parameter is fitted based on the equivalent reactance value of the system at each port to obtain a multidimensional Gaussian distribution, wherein the multidimensional Gaussian distribution includes the expected value, standard deviation, and covariance matrix; Construct a correlation coefficient matrix for each dimension of data based on the covariance matrix; Based on the expected value, the standard deviation, and the correlation coefficient matrix, the Natav transformation is used to construct the transformation relationship between the uncertain variable and the standard normally distributed variable.

5. The control method for the new energy flexible direct transmission system according to claim 3, characterized in that, The transformation relationship includes an inverse transformation function. The process of using the transformation relationship to convert sample collocations into impedance uncertainty input samples that follow the original distribution includes: Based on the random response surface method, using the eigenvalue combination of Hermite orthogonal polynomials and the principle of linear independence, several sets of arrays are selected from the standard normal distribution as sample points; For each typical operating scenario, the sample points are substituted into the inverse transformation function to obtain several sets of impedance uncertainty input samples that follow the original distribution under the corresponding typical operating scenario.

6. The control method for the new energy flexible direct transmission system according to claim 1, characterized in that, Determining the probability distribution characteristics based on the system stability index value includes: For each typical operating scenario, a Hermitian matrix is ​​constructed based on the sample points, and the inverse of the Hermitian matrix is ​​solved. Based on the inverse matrix and the system stability index value, the coefficients of the Hermitian chaotic polynomial are calculated. Based on the coefficients, the probability distribution characteristics of the system stability index values ​​are determined, wherein the probability distribution characteristics include the expected value, standard deviation, and confidence interval.

7. The control method for the new energy flexible direct transmission system according to claim 6, characterized in that, The construction of the control parameter optimization model based on the probability distribution characteristics includes: The system control parameters are used as optimization variables, and the real part of the eigenvalues ​​of the small-signal mathematical model is less than zero as a stability constraint. The control parameter optimization model is constructed with the weighted sum and minimization of the upper bound of the confidence interval of the overshoot of the sending-end converter station response curve, the upper bound of the confidence interval of the adjustment time, the upper bound of the confidence interval of the overshoot of the receiving-end converter station response curve, the upper bound of the confidence interval of the adjustment time, and the upper bound of the confidence interval of the comprehensive value of the damping ratio as the optimization objective.

8. The control method for the new energy flexible direct transmission system according to claim 1, characterized in that, Solving the control parameter optimization model to obtain the optimal control parameters includes: Initialize a number of particles, where the spatial position of each particle corresponds to a set of control parameters to be optimized; The fitness value of each particle is calculated based on the objective function in the optimization model using the control parameters. According to the update rules of the particle swarm optimization algorithm, the spatial position and velocity of each particle are iteratively updated, so that the particles gradually move towards a better solution region. The iteration is repeated until the preset termination condition is met, and the control parameters corresponding to the position of the best particle are taken as the optimal control parameters.

9. A control system for a new energy flexible direct transmission system, characterized in that, include: The first module is used to acquire historical operating data of new energy power generation. Based on the historical operating data, a clustering algorithm is used to generate several typical operating scenarios and the corresponding equivalent reactance values ​​of the system to characterize the system impedance uncertainty of each typical operating scenario. The second module is used to establish a transformation relationship between uncertain variables and standard normal distribution variables based on the equivalent reactance value of the system for each typical operating scenario. Using the transformation relationship, the sample points are converted into impedance uncertainty input samples that follow the original distribution. The sample points are determined based on the standard normal distribution determined by the random response surface method. The third module is used to substitute the impedance uncertainty input sample into the pre-established small-signal mathematical model of the new energy flexible direct transmission system to calculate the corresponding system stability index values ​​under each typical operating scenario. The fourth module is used to determine the probability distribution characteristics based on the system stability index value, construct a control parameter optimization model based on the probability distribution characteristics, solve the control parameter optimization model to obtain the optimal control parameters, and realize stable control of the new energy flexible direct transmission system based on the optimal control parameters.

10. A terminal device, characterized in that, include: One or more processors; A memory, coupled to the processor, for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the steps of the control method for the new energy flexible direct transmission system as described in any one of claims 1-7.

Images