Synchronous generator inertia identification method, system, equipment and medium

Abstract

The invention belongs to the technical field of dynamic safety and stability analysis of a power system, and discloses a synchronous generator inertia identification method, system and device and a medium, and the method comprises the steps: analyzing electromechanical characteristics through employing a rotor motion equation of a synchronous generator, and constructing a synchronous generator inertia identification function in combination with Laplace transformation; forming a synchronous generator inertia identification algorithm based on an adaptive forgetting factor recursive auxiliary variable by combining the adaptive forgetting factor based on a recursive least square method and an auxiliary variable method; and discretizing a synchronous generator inertia identification function by using a bilinear transformation method, and extracting dynamic change data of electromagnetic power at the machine end of the synchronous generator along with mechanical power after disturbance based on a synchronous generator inertia identification algorithm of an adaptive forgetting factor recursive auxiliary variable so as to realize inertia identification. According to the method, rapid and accurate online identification of the inertia of the synchronous generator in the power system can be realized, and accurate data support is provided for comprehensive evaluation of the inertia level.

Description

Technical Field

[0001] This invention relates to the field of dynamic safety and stability analysis technology for power systems, and in particular to a method, system, equipment, and medium for identifying the inertia of a synchronous generator. Background Technology

[0002] With the increasing global demand for low-carbon and clean energy and the continuous development of "high-energy-consuming and high-polluting" power systems, the proportion of renewable energy (such as wind and solar power) in the global energy structure is gradually increasing. However, although renewable energy plays an important role in mitigating climate change and reducing greenhouse gas emissions, its intermittency, volatility, and randomness pose unprecedented challenges to the stability of power systems. Specifically, due to the integration of power electronic converters, new energy generation cannot provide inertia support for the system, leading to a decrease in the overall inertia level of the system and further reducing the stability of the power system.

[0003] With the continuous development of new power systems, traditional synchronous generators are gradually being replaced, and the overall inertia of the power system has shown a significant downward trend, even exhibiting "zero inertia" characteristics. As the main generator in the power system, the identification and monitoring of the rotational inertia of synchronous generators is of great significance, facilitating grid dispatchers to comprehensively assess the inertia level of the new power system.

[0004] Previous methods for identifying the inertia of synchronous generators in power grids have insufficient accuracy when applied to large-scale practical systems, and require significant system disturbances to achieve identification. Existing methods generally rely on PMU data for offline identification, which demands high data accuracy. Therefore, providing a method, system, equipment, and medium for identifying the inertia of synchronous generators is an urgent problem to be solved. Summary of the Invention

[0005] This invention provides a method, system, device, and medium for identifying the inertia of a synchronous generator, in order to solve the problems mentioned above in the prior art.

[0006] To provide a basic understanding of some aspects of the disclosed embodiments, a brief summary is given below. This summary is not intended as a general commentary, nor is it intended to identify key / important components or to describe the scope of protection of these embodiments. Its sole purpose is to present some concepts in a simple form as a prelude to the detailed description that follows.

[0007] According to a first aspect of the present invention, a method for identifying the inertia of a synchronous generator is provided.

[0008] In one embodiment, the synchronous generator inertia identification method includes:

[0009] The electromechanical characteristics are analyzed using the rotor motion equations of a synchronous generator, and the inertia identification function of the synchronous generator is constructed by combining the Laplace transform.

[0010] Based on the recursive least squares method and the auxiliary variable method, combined with the adaptive forgetting factor, a synchronous generator inertia identification algorithm based on the adaptive forgetting factor and recursive auxiliary variables is formed.

[0011] The inertia identification function of the synchronous generator is discretized using the bilinear transform method. Based on the adaptive forgetting factor and the recursive auxiliary variable, the inertia identification algorithm of the synchronous generator extracts the dynamic change data of electromagnetic power at the generator terminal after disturbance with mechanical power, thereby realizing inertia identification.

[0012] In one embodiment, the step of analyzing electromechanical characteristics using the rotor motion equations of a synchronous generator and constructing a synchronous generator inertia identification function using Laplace transform includes:

[0013] Based on the rotor motion equation of the synchronous generator, the data flow at the port during grid-connected operation is analyzed, and the electromagnetic power data at the unit port is used to replace the data inside the unit to obtain the replaced rotor motion equation.

[0014] The Laplace transform method is used to transform the replaced rotor motion equation to obtain the synchronous generator inertia identification function of electromagnetic power change versus mechanical power change.

[0015] In one embodiment, the expression for the closed-loop transfer function is:

[0016]

[0017] In the formula, ΔP e (s) represents the frequency domain electromagnetic power change, ΔP m (s) represents the frequency domain mechanical power change, s is the Laplace operator, and μ and γ are the two system characteristic coefficients, respectively.

[0018] In one embodiment, the synchronous generator inertia identification algorithm based on recursive least squares method and auxiliary variable method, combined with adaptive forgetting factor, to form a recursive auxiliary variable based on adaptive forgetting factor includes:

[0019] Determine the system characteristic parameters and number of equations in the synchronous generator inertia identification function, and analyze the identifiability based on the solution of the linearized rotor motion equation;

[0020] Based on the identifiability analysis results, the number of observations is determined in conjunction with the number of system characteristic parameters, and the least squares iterative format is determined according to minimizing the loss function;

[0021] The consistency between the identified value and the true value of the synchronous generator inertia is evaluated using the evaluation equation, and the impact of noise on the identification accuracy is analyzed in combination with the number of observations.

[0022] Construct an auxiliary model and replace the actual output with the output of the auxiliary model to avoid the influence of environmental noise. Use the iterative format of the auxiliary variable method to deal with the noise interference problem and analyze its feasibility.

[0023] An iterative format for updating auxiliary variables using an adaptive variable forgetting factor is employed to reduce the impact of historical data on the current iteration step, thus forming a synchronous generator inertia identification algorithm based on adaptive forgetting factor recursive auxiliary variables.

[0024] In one embodiment, the expression for the least squares iterative format is:

[0025]

[0026] In the formula, Here is the estimated value, and N is the number of observations. Let y(k) be the system input-output matrix, y(k) be the system output, k be the kth element, and T be the transpose sign.

[0027] In one embodiment, the evaluation equation is expressed as follows:

[0028]

[0029] In the formula, θ is the true value, E(·) is the expected function, and v(k) is the noise.

[0030] In one embodiment, the iterative expression for the updated auxiliary variable method is:

[0031]

[0032] In the formula, K N+1 P is the intermediate variable for the (N+1)th iteration of the recursion. N P N+1 z are intermediate variables for the Nth and (N+1)th iterations, respectively. N+1 For the auxiliary variable matrix of the (N+1)th iteration, Let λ(k) and λ(k+1) be the system input-output matrix for the (N+1)th iteration, and let λ(k) and λ(k+1) be the k-th and (k+1)-th adaptive variable forgetting factors, respectively. These are the estimated system characteristic parameters for the Nth and (N+1)th iterations, respectively, y N+1 This represents the system output during the (N+1)th iteration.

[0033] In one embodiment, the discretization of the synchronous generator inertia identification function using the bilinear transform method, and the synchronous generator inertia identification algorithm based on the adaptive forgetting factor recursive auxiliary variable, extracting the dynamic change data of electromagnetic power at the synchronous generator terminal after disturbance with mechanical power, to achieve inertia identification includes:

[0034] Based on the bilinear transform method, the inertia identification function of the synchronous generator is discretized to facilitate recursive calculation. The moving average filtering method is used to filter the collected active power change and frequency change data to reduce noise interference.

[0035] By utilizing the iterative steps in the synchronous generator inertia identification algorithm based on adaptive forgetting factor recursive auxiliary variables, the electromagnetic power at the synchronous generator terminal after disturbance is extracted as the mechanical power changes dynamically, thus realizing inertia identification and obtaining estimated values ​​of system characteristic parameters and identification results.

[0036] According to a second aspect of the present invention, a synchronous generator inertia identification system is provided.

[0037] In one embodiment, the synchronous generator inertia identification system includes:

[0038] The inertia identification function construction module is used to analyze the electromechanical characteristics using the rotor motion equations of a synchronous generator and to construct the inertia identification function of the synchronous generator by combining the Laplace transform.

[0039] An improved inertia identification algorithm module is used to form a synchronous generator inertia identification algorithm based on adaptive forgetting factor and adaptive forgetting factor, which is based on recursive least squares method and auxiliary variable method and combined with adaptive forgetting factor.

[0040] The synchronous generator inertia identification module is used to discretize the synchronous generator inertia identification function using the bilinear transform method. Based on the synchronous generator inertia identification algorithm with adaptive forgetting factor recursive auxiliary variables, it extracts the dynamic change data of electromagnetic power at the synchronous generator terminal after disturbance with mechanical power, and realizes inertia identification.

[0041] In one embodiment, when the inertia identification function construction module analyzes the electromechanical characteristics using the rotor motion equation of the synchronous generator and constructs the synchronous generator inertia identification function by combining the Laplace transform, it analyzes the data flow at the port during grid-connected operation based on the rotor motion equation of the synchronous generator, and replaces the data inside the unit with the electromagnetic power data at the unit port to obtain the replaced rotor motion equation; it then uses the Laplace transform method to transform the replaced rotor motion equation to obtain the synchronous generator inertia identification function of electromagnetic power change versus mechanical power change.

[0042] In one embodiment, the expression for the closed-loop transfer function is:

[0043]

[0044] In the formula, ΔP e (s) represents the frequency domain electromagnetic power change, ΔP m (s) represents the frequency domain mechanical power change, s is the Laplace operator, and μ and γ are the two system characteristic coefficients, respectively.

[0045] In one embodiment, when the inertia identification algorithm improvement module forms a synchronous generator inertia identification algorithm based on adaptive forgetting factor recursive auxiliary variables, based on recursive least squares method and auxiliary variable method, and combined with adaptive forgetting factor, it determines the system characteristic parameters and the number of equations in the synchronous generator inertia identification function, and analyzes the identifiability based on the solution of the linearized rotor motion equation; based on the identifiability analysis results, it determines the number of observations based on the number of system characteristic parameters, and determines the least squares method iteration format based on minimizing the loss function; it uses the evaluation equation to evaluate the consistency between the synchronous generator inertia identification value and the true value, and analyzes the impact of noise on identification accuracy based on the number of observations; it constructs an auxiliary model, and replaces the true output with the output of the auxiliary model to avoid the influence of environmental noise, uses the iteration format of auxiliary variable method to handle noise interference problems and analyzes feasibility; it uses adaptive variable forgetting factor to update the iteration format of auxiliary variable method to reduce the influence of historical data on the current iteration step, thus forming a synchronous generator inertia identification algorithm based on adaptive forgetting factor recursive auxiliary variables.

[0046] In one embodiment, the expression for the least squares iterative format is:

[0047]

[0048] The expression for the evaluation equation is:

[0049]

[0050] The updated iterative expression for the auxiliary variable method is:

[0051]

[0052] In the formula, Here is the estimated value, and N is the number of observations. Let y(k) be the system input-output matrix, y(k) be the system output, k be the k-th element, T be the transpose sign, θ be the true value, E(·) be the expected function, v(k) be the noise, and K be the system input-output matrix. N+1 P is the intermediate variable for the (N+1)th iteration of the recursion. N P N+1z are intermediate variables for the Nth and (N+1)th iterations, respectively. N+1 For the auxiliary variable matrix of the (N+1)th iteration, Let λ(k) and λ(k+1) be the system input-output matrix for the (N+1)th iteration, and let λ(k) and λ(k+1) be the k-th and (k+1)-th adaptive variable forgetting factors, respectively. These are the estimated system characteristic parameters for the Nth and (N+1)th iterations, respectively, y N+1 This represents the system output during the (N+1)th iteration.

[0053] In one embodiment, the synchronous generator inertia identification module discretizes the synchronous generator inertia identification function using the bilinear transform method. Based on the synchronous generator inertia identification algorithm with adaptive forgetting factor recursive auxiliary variables, it extracts the dynamic change data of electromagnetic power at the synchronous generator terminals after disturbance as a function of mechanical power. To achieve inertia identification, the module discretizes the synchronous generator inertia identification function using the bilinear transform method for recursive calculation. It also uses a moving average filtering method to filter the collected active power change and frequency change data to reduce noise interference. Furthermore, it utilizes the iterative steps in the synchronous generator inertia identification algorithm with adaptive forgetting factor recursive auxiliary variables to extract the dynamic change data of electromagnetic power at the synchronous generator terminals after disturbance as a function of mechanical power, thereby achieving inertia identification and obtaining estimated system characteristic parameters and identification results.

[0054] According to a third aspect of the present invention, a computer device is provided.

[0055] In some embodiments, the computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps of the method described above.

[0056] According to a fourth aspect of the present invention, a computer-readable storage medium is provided.

[0057] In one embodiment, a computer program is stored on the computer-readable storage medium, which, when executed by a processor, implements the steps of the above method.

[0058] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects:

[0059] 1) Higher identification accuracy: By introducing an adaptive forgetting factor, this method can more effectively handle dynamically changing parameters and reduce the influence of historical data on the current estimate. Simulation and real-world power grid test results show that its identification error is far lower than traditional methods, with relative errors all within 1%, or even lower. This enables the method to provide more accurate inertia identification results in complex and ever-changing power grid environments.

[0060] 2) Longer identification time window: Compared with traditional methods, this method can select a longer identification time window. This not only avoids the loss of key inertial response information, but also allows for consideration of the dynamic characteristics of the inertial response over a longer time scale, thus reflecting the dynamic behavior of the system more comprehensively. For example, in the WSCC 3-machine 9-node system, the width of the identification time window is significantly increased, making the identification results more stable and reliable.

[0061] 3) Enhanced adaptability: This method combines the advantages of recursive least squares and auxiliary variable methods, enabling real-time and high-precision inertia identification while considering system dynamics. In real-world large power grids, facing frequently changing grid dynamics and complex environmental factors, this method still provides stable and accurate identification results, demonstrating good applicability.

[0062] 4) Fast parameter convergence: In simulations and actual power grid tests, the identified parameters of this method converge to stable values ​​in a short time. This indicates that the method has high computational efficiency and can quickly respond to dynamic changes in the system in practical applications, providing timely technical support for real-time monitoring and control of power systems.

[0063] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit the invention. Attached Figure Description

[0064] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0065] Figure 1 This is a flowchart illustrating a synchronous generator inertia identification method according to an exemplary embodiment;

[0066] Figure 2 This is a structural block diagram of a synchronous generator inertia identification system according to an exemplary embodiment;

[0067] Figure 3 This is a schematic diagram illustrating the principle of a synchronous generator inertia identification method according to an exemplary embodiment;

[0068] Figure 4 This is an equivalent circuit diagram of a synchronous generator operating in grid connection according to an exemplary embodiment;

[0069] Figure 5 This is a schematic diagram illustrating the principle of the auxiliary variable method according to an exemplary embodiment;

[0070] Figure 6 This is a schematic diagram of the structure of a computer device according to an exemplary embodiment. Detailed Implementation

[0071] The following description and accompanying drawings fully illustrate specific embodiments described herein to enable those skilled in the art to practice them. Some embodiments may include or substitute parts and features of other embodiments. The scope of the embodiments herein encompasses the entire scope of the claims and all available equivalents thereof. Throughout this document, the terms “first,” “second,” etc., are used only to distinguish one element from another without requiring or implying any actual relationship or order between the elements. Indeed, a first element can also be referred to as a second element, and vice versa. Furthermore, the terms “comprising,” “including,” or any other variations thereof are intended to cover non-exclusive inclusion, such that a structure, apparatus, or device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a structure, apparatus, or device. Without further limitation, an element defined by the phrase “comprising one…” does not exclude the presence of other identical elements in the structure, apparatus, or device that includes said element. The various embodiments described herein are presented in a progressive manner, with each embodiment focusing on its differences from other embodiments; similar or identical parts between embodiments can be referred to interchangeably.

[0072] The terms "longitudinal," "lateral," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer" used in this document to indicate orientations or positional relationships are based on the orientations or positional relationships shown in the accompanying drawings. They are used solely for the convenience of describing the document and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. In the description herein, unless otherwise specified and limited, the terms "installed," "connected," and "linked" should be interpreted broadly. For example, they can refer to mechanical or electrical connections, or internal connections between two elements; they can be direct connections or indirect connections through an intermediate medium. Those skilled in the art can understand the specific meaning of the above terms according to the specific circumstances.

[0073] In this document, unless otherwise stated, the term "multiple" means two or more.

[0074] In this article, the character " / " indicates that the objects before and after it are in an "or" relationship. For example, A / B means: A or B.

[0075] In this article, the term "and / or" describes an association between objects, indicating that three relationships can exist. For example, A and / or B means: A or B, or A and B.

[0076] It should be understood that although the steps in the flowchart are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order constraint on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the diagram may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0077] The modules in the apparatus or system of this application can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0078] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0079] Figure 1 An embodiment of a synchronous generator inertia identification method of the present invention is shown.

[0080] In this optional embodiment, the synchronous generator inertia identification method includes:

[0081] Step S101: Analyze the electromechanical characteristics using the rotor motion equation of the synchronous generator, and construct the synchronous generator inertia identification function by combining the Laplace transform.

[0082] Step S102: Based on the recursive least squares method and the auxiliary variable method, combined with the adaptive forgetting factor, a synchronous generator inertia identification algorithm based on the adaptive forgetting factor and recursive auxiliary variables is formed.

[0083] Step S103: Discretize the synchronous generator inertia identification function using the bilinear transform method. Based on the synchronous generator inertia identification algorithm with adaptive forgetting factor recursive auxiliary variable, extract the dynamic change data of electromagnetic power at the synchronous generator terminal after disturbance with mechanical power to achieve inertia identification.

[0084] In this optional embodiment, the step of analyzing the electromechanical characteristics using the rotor motion equations of the synchronous generator and constructing the synchronous generator inertia identification function by combining the Laplace transform includes:

[0085] Based on the rotor motion equation of the synchronous generator, the data flow at the port during grid-connected operation is analyzed, and the electromagnetic power data at the unit port is used to approximate the data inside the unit to obtain the replaced rotor motion equation.

[0086] The Laplace transform method is used to transform the replaced rotor motion equation to obtain the synchronous generator inertia identification function of electromagnetic power change versus mechanical power change.

[0087] In this optional embodiment, the expression for the closed-loop transfer function is:

[0088]

[0089] In the formula, ΔP e (s) represents the frequency domain electromagnetic power change, ΔP m (s) represents the frequency domain mechanical power change, s is the Laplace operator, and μ and γ are the two system characteristic coefficients, respectively.

[0090] In this optional embodiment, the synchronous generator inertia identification algorithm based on recursive least squares method and auxiliary variable method, combined with adaptive forgetting factor, to form a synchronous generator inertia identification algorithm based on adaptive forgetting factor recursive auxiliary variable includes:

[0091] The system characteristic parameters and number of equations in the synchronous generator inertia identification function are determined, and the identifiability is analyzed based on the solution of the linearized rotor motion equation. Specifically, for the synchronous generator identification function, there are 4 system characteristic parameters and 2 equations. Analyzing that its damping coefficient and rated speed are known quantities and do not change dynamically, they are regarded as constants. Then the number of system characteristic parameters is reduced by 2, and the linear equation system has a solution, that is, identifiability exists.

[0092] Based on the identifiability analysis results, the number of observations is determined in conjunction with the number of system characteristic parameters, and the least squares iterative format is determined according to minimizing the loss function; specifically, the number of observations determined based on the number of system characteristic parameters should be greater than the order of the system characteristic parameter matrix;

[0093] The consistency between the identified value and the true value of the synchronous generator inertia is evaluated using the evaluation equation, and the impact of noise on the identification accuracy is analyzed in conjunction with the number of observations. Specifically, the least squares consistency criterion for the estimated values ​​of the auxiliary variables has the same form as the least squares consistency criterion, and it can be seen that the estimated values ​​are consistent with the true values.

[0094] An auxiliary model is constructed, and its output is used to approximate the true output to avoid the influence of environmental noise. The iterative format of the auxiliary variable method is used to handle noise interference and its feasibility is analyzed. Specifically, the auxiliary model is used, and its output is used to approximate the true output, avoiding the influence of environmental noise. A recursive method is used... Alternative The output of the auxiliary model is obtained, and the observation data of the (N+1)th time is added to construct the recursive relationship;

[0095] By utilizing the iterative format of the adaptive variable forgetting factor update auxiliary variable method to reduce the influence of historical data on the current iteration step, dynamic parameters can be identified more accurately, thus forming a synchronous generator inertia identification algorithm based on adaptive forgetting factor recursive auxiliary variables.

[0096] In this optional embodiment, the expression for the least squares iterative format is:

[0097]

[0098] In the formula, Here is the estimated value, and N is the number of observations. Let y(k) be the system input-output matrix, y(k) be the system output, k be the kth element, and T be the transpose sign.

[0099] In this optional embodiment, the expression for the evaluation equation is:

[0100]

[0101] In the formula, θ is the true value, E(·) is the expected function, and v(k) is the noise.

[0102] In this optional embodiment, an auxiliary model is constructed, and the iterative format of the auxiliary variable method is as follows:

[0103]

[0104] The updated iterative expression for the auxiliary variable method is:

[0105]

[0106] In the formula, K N+1 P is the intermediate variable for the (N+1)th iteration of the recursion. N P N+1 z are intermediate variables for the Nth and (N+1)th iterations, respectively. N+1 For the auxiliary variable matrix of the (N+1)th iteration, Let λ(k) and λ(k+1) be the system input-output matrix for the (N+1)th iteration, and let λ(k) and λ(k+1) be the k-th and (k+1)-th adaptive variable forgetting factors, respectively. These are the estimated system characteristic parameters for the Nth and (N+1)th iterations, respectively, y N+1 This represents the system output during the (N+1)th iteration.

[0107] In this optional embodiment, the discretization of the synchronous generator inertia identification function using the bilinear transform method, and the synchronous generator inertia identification algorithm based on the adaptive forgetting factor recursive auxiliary variable, extracting the dynamic change data of electromagnetic power at the synchronous generator terminal after disturbance with mechanical power, to achieve inertia identification includes:

[0108] Based on the bilinear transform method, the inertia identification function of the synchronous generator is discretized to facilitate recursive calculation. The moving average filtering method is used to filter the collected active power change and frequency change data to reduce noise interference.

[0109] The iterative steps in the synchronous generator inertia identification algorithm based on adaptive forgetting factor recursive auxiliary variables are applied to the synchronous generator inertia identification. The electromagnetic power at the synchronous generator terminal after disturbance is extracted as the mechanical power changes dynamically, thus realizing inertia identification and obtaining the estimated values ​​of system characteristic parameters and identification results.

[0110] The estimated values ​​of the system characteristic parameters are expressed as follows:

[0111]

[0112] In the formula, These are the estimated values ​​of the intermediate identification parameters. T is the estimated value of the intermediate coefficient μ. s The sampling interval is... This is the estimated value of the intermediate coefficient γ.

[0113] Figure 2 An embodiment of a synchronous generator inertia identification system according to the present invention is shown.

[0114] In this optional embodiment, the synchronous generator inertia identification system includes:

[0115] The inertia identification function construction module 201 is used to analyze the electromechanical characteristics using the rotor motion equation of the synchronous generator and construct the inertia identification function of the synchronous generator by combining the Laplace transform.

[0116] The inertia identification algorithm improvement module 202 is used to form a synchronous generator inertia identification algorithm based on adaptive forgetting factor and auxiliary variable method, combined with adaptive forgetting factor.

[0117] The synchronous generator inertia identification module 203 is used to discretize the synchronous generator inertia identification function using the bilinear transformation method. Based on the synchronous generator inertia identification algorithm with adaptive forgetting factor recursive auxiliary variable, it extracts the dynamic change data of electromagnetic power at the synchronous generator terminal after disturbance with mechanical power, and realizes inertia identification.

[0118] To facilitate understanding of the above technical solutions of the present invention, the following further describes the above technical solutions of the present invention from the perspectives of architecture and principle, as follows:

[0119] Given the shortcomings of existing synchronous generator inertia identification methods, such as high requirements for disturbance handling and high data acquisition accuracy, and insufficient accuracy in practical large power grid environments, the present invention aims to provide a synchronous generator inertia identification method based on adaptive forgetting factors and recursive auxiliary variables. This method combines the advantages of recursive least squares and auxiliary variable methods, considering dynamically changing system factors and performing inertia identification in real time and accurately. By introducing an adaptive forgetting factor, historical data is effectively weighted, improving the identification accuracy of dynamically changing parameters during the identification process. Furthermore, it transcends the extremely short identification time window of traditional methods, seeking a longer-term identification window and overcoming the limitations of traditional methods.

[0120] This invention starts with the rotor motion equations of a synchronous generator, rewriting them in incremental form and simplifying them into a closed-loop transfer function. By analyzing the equivalent circuit of the synchronous generator, the relationship between active power and relevant parameters is derived, and an identification function is further obtained. This process provides a theoretical foundation for subsequent parameter identification. An adaptive forgetting factor is introduced based on the recursive auxiliary variable method. The forgetting factor reduces the influence of historical data on the current identification process, enabling the algorithm to adapt to parameter changes more quickly and improving the accuracy of parameter identification in dynamic environments. The continuous identification model is discretized using a bilinear transform method to facilitate recursive calculations on a computer. Simultaneously, the collected data is filtered and preprocessed, using a simple moving average filter to smooth the signal, reduce noise interference, and further improve identification accuracy.

[0121] like Figure 3 As shown, the synchronous generator inertia identification method based on adaptive forgetting factor recursive auxiliary variables specifically includes:

[0122] 1) Based on the rotor motion equation of the synchronous generator, analyze the electromechanical characteristics and construct the identification function;

[0123] 2) Analyze the system characteristic parameters and the number of equations in the identification function, and analyze the identifiability based on the solvability of the linear equation system;

[0124] 3) Determine the number of observations based on the number of system characteristic parameters, and propose a least squares iterative scheme based on minimizing the loss function;

[0125] 4) Evaluate the consistency between the identified value and the true value, and analyze the impact of noise on the identification accuracy in conjunction with the number of observations;

[0126] 5) Construct an auxiliary model, use the auxiliary variable method iterative format to solve the noise interference problem, and analyze its feasibility;

[0127] 6) Introduce an adaptive variable forgetting factor to update the iteration format and reduce the impact of historical data on the current iteration step, so as to more accurately identify dynamic parameters;

[0128] 7) The synchronous generator inertia identification function is discretized based on the bilinear transform method. The iterative steps of the identification method are applied to the synchronous generator inertia identification. The electromagnetic power at the synchronous generator terminal after disturbance is dynamically changed with the mechanical power to achieve inertia identification.

[0129] To better understand the technical solution of the present invention, the following describes in more detail the implementation process and / or effects of certain embodiments of the present invention in conjunction with some preferred or optional examples of the present invention.

[0130] I. Construction of the identification function:

[0131] Combined with appendix Figure 4 In step 1) above, assume that the grid reactance is much greater than the resistance, and the synchronous generator reactance is much greater than the resistance, i.e., R and R g Neglecting other factors, write down the apparent power at the synchronous generator port:

[0132]

[0133] In the formula, For apparent power, P e Let be the active power, j be the imaginary part, Q be the reactive power, E be the amplitude of the synchronous generator electromotive force, ∠ be the phase, δ be the power angle, X be the reactance, and U be the reactive power. g X represents the grid voltage amplitude. g For the generator's internal reactance, [·] * To obtain the conjugate of complex numbers;

[0134] Considering the relatively small changes in the grid voltage amplitude and the synchronous generator electromotive force amplitude, write the equation for the electromagnetic power change:

[0135]

[0136] In the formula, ΔP e S is the change in electromagnetic power. E Δδ represents the synchronous generator's step power factor when the electromotive force E is constant, and Δδ is the change in power angle.

[0137] By performing a Laplace transform, we can obtain ΔP. e For ΔP m The closed-loop transfer function is:

[0138]

[0139] γ=D / (2H)

[0140] μ=ω n S E / (2H)

[0141] In the formula, ΔP e (s) represents the frequency domain electromagnetic power change, ΔP m (s) represents the frequency domain mechanical power change, s is the Laplace operator, and ω n Let H be the system angular frequency, H be the unit inertia, D be the damping coefficient, and μ and γ be the two system characteristic coefficients, respectively.

[0142] II. Problem Analysis:

[0143] In step 2) above, ΔP is reflected. e For ΔP m The dynamic response is related to γ ​​and μ, and ΔP can be observed. e and ΔP m To determine the characteristic parameters of the two systems. The purpose of constructing the response relationship is to identify the H of the synchronous generator, since γ and μ are related to D, H, and ω. n and S E This relates to the fact that there are two equations with four unknowns, resulting in an infinite number of solutions. For a synchronous generator, D is usually a known parameter, and ω... n The rated speed is a fixed value, so H can be identified by γ and μ.

[0144] During the dynamic process of a synchronous generator, S E Since γ and μ are dynamically changing, the constructed linear model assumes that μ is constant. To reduce the impact of the linearized model on the identification results and improve the consistency of parameter identification, this invention adopts a method based on adaptive forgetting factor recursive auxiliary variables to identify γ and μ. Essentially, it introduces a forgetting factor into the recursive auxiliary variable method. The auxiliary variable method is an improvement on the least squares method due to noise interference.

[0145] III. Least Squares Iteration Scheme:

[0146] In step 3) above, assuming the identified system is a dynamic system with input u(k) and output y(k), the mathematical model can be expressed as:

[0147]

[0148] In the formula, z -1 For the unit delay operator, z -1 u(k)=u(k-1), a and b are system characteristic parameters, B(z) -1 ) is the numerator z-polynomial, A(z) -1) is a polynomial with denominator z, n a n b Let a and b represent the orders of the system characteristic parameters a and b, respectively. Substituting them, we get:

[0149]

[0150] In the formula, ε(k) is the error between the model estimate and the actual value.

[0151]

[0152] The least squares format is:

[0153]

[0154] Based on the input and output data from N observations, write the vector form of the least squares format (i.e., the simplified representation):

[0155] Y = Φθ

[0156]

[0157] In the formula, Y is the system output y vector, Φ is the system input-output matrix, y(N) is the Nth element of the system output y vector, and T is the transpose sign. Transpose the Nth row vector of Φ;

[0158] The number of observations depends on the number of system characteristic parameters that need to be identified, i.e., the order n of θ. a +n b +1, n a n b Let Φ represent the orders of the system characteristic parameters a and b, respectively. The constructed Φ is a square matrix. Given that Φ is invertible, θ can be solved to complete parameter identification. Generally, during the identification process, to make the identification results more accurate, the number of observations N is often increased. In this case, minimizing the estimation error is considered as a solution.

[0159] IV. Consistency Analysis:

[0160] In step 4) above, the least squares estimate is evaluated. The method for determining accuracy is to analyze its consistency with the true value, assuming the data satisfies:

[0161]

[0162] In the formula, θ0 is the true value of the system characteristic parameter, and v(k) is noise, which is independent of other variables;

[0163] The estimation error is the difference between the estimated value and the true value. As the number of observations approaches infinity:

[0164]

[0165] In the formula, θ is the true value, and E(·) is the expected function;

[0166] when Reversible, and Only when the least squares estimation is consistent can the results be achieved. However, due to... It is related to the output variable y(k), and therefore also to the past observation noise v(k). This is difficult to achieve unless v(k) is white noise, which is hard to satisfy in actual identification processes. Environmental noise is inevitable and will affect the observation results. Therefore, least squares consistency is difficult to achieve, and the estimated value will be biased. Due to this shortcoming of the least squares method, the auxiliary variable method is introduced to improve it.

[0167] V. Construction of Auxiliary Models and Application of Auxiliary Variable Method:

[0168] Combined with appendix Figure 5 In step 5) above, after constructing the auxiliary variables, the system excitation, system output, and the relationship between the system, auxiliary model, and their output are obtained. The auxiliary variable matrix is ​​of the same order as the original identified parameter matrix, and the auxiliary variable matrix satisfies the least squares estimation consistency condition. The auxiliary model is used to determine the auxiliary variable matrix. Based on the elements in Φ... The representation of z(k) is constructed from the elements z(k) in Z under the actual output y0 of the system:

[0169] z T (k)=[-y0(k-1),...,-y0(kn a ),u(k),...,u(kn b )]

[0170] In the formula, u(k) is the system input and y0(k) is the system output.

[0171] To approximate the system characteristic parameters based on the estimated parameters of the auxiliary model, the true output y0(k) is needed. However, in actual identification, y0(k) is unknown due to the influence of noise v(k). Therefore, the output of the auxiliary model is considered as an approximation. The least squares format of the auxiliary model is as follows:

[0172]

[0173] In the formula, This is an estimate of the system output.

[0174] If it can be obtained recursively Then you can get This approximates the solution for y0(k) to obtain the estimated values ​​of the auxiliary model parameters. Using a recursive approach, the observation data from the (N+1)th iteration is incorporated to construct the recursive relationship:

[0175]

[0176]

[0177] The final recursive formula obtained by the auxiliary variable method is:

[0178]

[0179] In the formula, K N+1 P is the intermediate variable for the (N+1)th iteration of the recursion. N P N+1 z are intermediate variables for the Nth and (N+1)th iterations, respectively. N+1 For the auxiliary variable matrix of the (N+1)th iteration, Let λ(k) and λ(k+1) be the system input-output matrix for the (N+1)th iteration, and let λ(k) and λ(k+1) be the k-th and (k+1)-th adaptive variable forgetting factors, respectively. These are the estimated system characteristic parameters for the Nth and (N+1)th iterations, respectively, y N+1 Let y(N+1) be the system output for the (N+1)th iteration, and let y(N+1) be the (N+1)th element of the output y. Y is the Nth element of the output quantity y estimate. N The output quantity is represented in vector form. Therefore, the result can be obtained recursively using the method of recursive auxiliary variables. Thus obtain The system characteristic parameters are identified. However, in order for the recursion to proceed, initial values ​​of the estimates are needed first. Consider first using some input and output data, and then determine the result based on the least squares method.

[0180] VI. Adaptive Variable Forgetting Factor:

[0181] In step 6) above, μ in the identification function has a dynamic characteristic, and the auxiliary variable method should be able to achieve real-time parameter identification. Since the changing characteristics of the current parameters are based on the current system input-output response data, historical observation data is not conducive to accurate identification of the current parameters. Therefore, we consider introducing an adaptive variable forgetting factor λ(k) into the auxiliary variable method to weaken the influence of historical data on the current identification process. The resulting synchronous generator inertia identification algorithm based on the adaptive forgetting factor and recursive auxiliary variables is as follows:

[0182]

[0183] VII. Discretization of Synchronous Generator Identification Model and Application of Identification Algorithm:

[0184] In step 7) above, the synchronous generator identification model is discretized using the bilinear transform method, resulting in:

[0185]

[0186] In the formula, B(z) -1 ) is the numerator z-polynomial, A(z) -1 ) is the denominator z polynomial, b0 is the 0th order system characteristic coefficient of the numerator polynomial, b1 is the 1st order system characteristic coefficient of the numerator polynomial, z is the unit delay operator, b2 is the 2nd order system characteristic coefficient of the numerator polynomial, a1 is the 1st order system characteristic coefficient of the denominator polynomial, and a2 is the 2nd order system characteristic coefficient of the denominator polynomial.

[0187] To further improve the identification accuracy, the collected data is preprocessed by filtering before being fed into the algorithm steps for parameter identification. A simple moving average filtering method is used.

[0188] The final estimated values ​​and identification results of the system characteristic parameters are expressed as follows:

[0189]

[0190] In the formula, These are the estimated characteristic parameters of the first-order and second-order a-systems, and the estimated characteristic parameters of the zero-order b-system, respectively. T is the estimated value of the system characteristic coefficient μ. s The sampling interval is... This is an estimate of the system characteristic coefficient γ. This is an estimate of the inertial time constant. This is an estimated value for the damping coefficient.

[0191] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 6 As shown, the computer device includes a processor, memory, and a network interface connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database stores static and dynamic information data. The network interface communicates with external terminals via a network connection. When the computer program is executed by the processor, it implements the steps in the above method embodiments.

[0192] Those skilled in the art will understand that Figure 6 The structure shown is merely a block diagram of a portion of the structure related to the present invention and does not constitute a limitation on the computer device to which the present invention is applied. A specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0193] In addition, the present invention also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above method embodiments.

[0194] In addition, the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the above method embodiments.

[0195] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the methods described above. Any references to memory, storage, databases, or other media used in the embodiments provided by this invention can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical storage, etc. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0196] This invention is not limited to the structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this invention is limited only by the appended claims.

Claims

1. A method for identifying the inertia of a synchronous generator, characterized in that, include: The electromechanical characteristics are analyzed using the rotor motion equations of a synchronous generator, and the inertia identification function of the synchronous generator is constructed by combining the Laplace transform. Based on the recursive least squares method and the auxiliary variable method, combined with the adaptive forgetting factor, a synchronous generator inertia identification algorithm based on the adaptive forgetting factor and recursive auxiliary variables is formed. The inertia identification function of the synchronous generator is discretized using the bilinear transform method. Based on the adaptive forgetting factor and the recursive auxiliary variable, the inertia identification algorithm of the synchronous generator extracts the dynamic change data of electromagnetic power at the generator terminal after disturbance with mechanical power, thereby realizing inertia identification.

2. The method for identifying the inertia of a synchronous generator according to claim 1, characterized in that, The method of analyzing electromechanical characteristics using the rotor motion equations of a synchronous generator and constructing the synchronous generator inertia identification function using Laplace transform includes: Based on the rotor motion equation of the synchronous generator, the data flow at the port during grid-connected operation is analyzed, and the electromagnetic power data at the unit port is used to replace the data inside the unit to obtain the replaced rotor motion equation. The Laplace transform method is used to transform the replaced rotor motion equation to obtain the synchronous generator inertia identification function of electromagnetic power change versus mechanical power change.

3. The method for identifying the inertia of a synchronous generator according to claim 2, characterized in that, The expression for the closed-loop transfer function is: In the formula, ΔP e (s) represents the frequency domain electromagnetic power change, ΔP m (s) represents the frequency domain mechanical power change, s is the Laplace operator, and μ and γ are the two system characteristic coefficients, respectively.

4. The method for identifying the inertia of a synchronous generator according to claim 1, characterized in that, The synchronous generator inertia identification algorithm based on recursive least squares method and auxiliary variable method, combined with adaptive forgetting factor, includes: Determine the system characteristic parameters and number of equations in the synchronous generator inertia identification function, and analyze the identifiability based on the solution of the linearized rotor motion equation; Based on the identifiability analysis results, the number of observations is determined in conjunction with the number of system characteristic parameters, and the least squares iterative format is determined according to minimizing the loss function; The consistency between the identified value and the true value of the synchronous generator inertia is evaluated using the evaluation equation, and the impact of noise on the identification accuracy is analyzed in combination with the number of observations. Construct an auxiliary model and replace the actual output with the output of the auxiliary model to avoid the influence of environmental noise. Use the iterative format of the auxiliary variable method to deal with the noise interference problem and analyze its feasibility. An iterative format for updating auxiliary variables using an adaptive variable forgetting factor is employed to reduce the impact of historical data on the current iteration step, thus forming a synchronous generator inertia identification algorithm based on adaptive forgetting factor recursive auxiliary variables.

5. The method for identifying the inertia of a synchronous generator according to claim 4, characterized in that, The expression for the least squares iterative scheme is: In the formula, Here is the estimated value, and N is the number of observations. Let y(k) be the system input-output matrix, y(k) be the system output, k be the kth element, and T be the transpose sign.

6. The method for identifying the inertia of a synchronous generator according to claim 5, characterized in that, The expression for the evaluation equation is: In the formula, θ is the true value, E(·) is the expected function, and v(k) is the noise.

7. The method for identifying the inertia of a synchronous generator according to claim 5, characterized in that, The updated iterative expression for the auxiliary variable method is: In the formula, K N+1 P is the intermediate variable for the (N+1)th iteration of the recursion. N P N+1 z are intermediate variables for the Nth and (N+1)th iterations, respectively. N+1 For the auxiliary variable matrix of the (N+1)th iteration, Let λ(k) and λ(k+1) be the system input-output matrix for the (N+1)th iteration, and let λ(k) and λ(k+1) be the k-th and (k+1)-th adaptive variable forgetting factors, respectively. These are the estimated system characteristic parameters for the Nth and (N+1)th iterations, respectively, y N+1 This represents the system output during the (N+1)th iteration.

8. The method for identifying the inertia of a synchronous generator according to claim 1, characterized in that, The method of discretizing the synchronous generator inertia identification function using the bilinear transform method, and the synchronous generator inertia identification algorithm based on the adaptive forgetting factor and recursive auxiliary variables, extracts the dynamic change data of electromagnetic power at the synchronous generator terminal after disturbance with mechanical power, thereby realizing inertia identification, including: Based on the bilinear transform method, the synchronous generator inertia identification function is discretized to facilitate recursive calculation. The moving average filtering method is used to filter the collected active power change and frequency change data to reduce noise interference. By utilizing the iterative steps in the synchronous generator inertia identification algorithm based on adaptive forgetting factor recursive auxiliary variables, the electromagnetic power at the synchronous generator terminal after disturbance is extracted as the mechanical power changes dynamically, thus realizing inertia identification and obtaining estimated values ​​of system characteristic parameters and identification results.

9. A synchronous generator inertia identification system, characterized in that, include: The inertia identification function construction module is used to analyze the electromechanical characteristics using the rotor motion equations of a synchronous generator and to construct the inertia identification function of the synchronous generator by combining the Laplace transform. An improved inertia identification algorithm module is used to form a synchronous generator inertia identification algorithm based on adaptive forgetting factor and adaptive forgetting factor, which is based on recursive least squares method and auxiliary variable method and combined with adaptive forgetting factor. The synchronous generator inertia identification module is used to discretize the synchronous generator inertia identification function using the bilinear transform method. Based on the synchronous generator inertia identification algorithm with adaptive forgetting factor recursive auxiliary variables, it extracts the dynamic change data of electromagnetic power at the synchronous generator terminal after disturbance with mechanical power, and realizes inertia identification.

10. The synchronous generator inertia identification system according to claim 9, characterized in that, The inertia identification function construction module analyzes the electromechanical characteristics using the rotor motion equation of the synchronous generator and constructs the synchronous generator inertia identification function by combining the Laplace transform. Based on the rotor motion equation of the synchronous generator, it analyzes the data flow at the port during grid-connected operation and replaces the data inside the unit with the electromagnetic power data at the unit port to obtain the replaced rotor motion equation. The Laplace transform method is then used to transform the replaced rotor motion equation to obtain the synchronous generator inertia identification function of electromagnetic power change versus mechanical power change.

11. The synchronous generator inertia identification system according to claim 10, characterized in that, The expression for the closed-loop transfer function is: In the formula, ΔP e (s) represents the frequency domain electromagnetic power change, ΔP m (s) represents the frequency domain mechanical power change, s is the Laplace operator, and μ and γ are the two system characteristic coefficients, respectively.

12. The synchronous generator inertia identification system according to claim 9, characterized in that, When the improved inertia identification algorithm module forms a synchronous generator inertia identification algorithm based on adaptive forgetting factor and adaptive forgetting factor recursive auxiliary variables, it determines the system characteristic parameters and number of equations in the synchronous generator inertia identification function, and analyzes the identifiability based on the solution of the linearized rotor motion equation. Based on the identifiability analysis results, the number of observations is determined by combining the number of system characteristic parameters, and the least squares iterative format is determined by minimizing the loss function; the consistency between the identified value and the true value of the synchronous generator inertia is evaluated using the evaluation equation, and the impact of noise on the identification accuracy is analyzed by combining the number of observations; an auxiliary model is constructed, and the output of the auxiliary model is used to replace the true output to avoid the influence of environmental noise; the iterative format of the auxiliary variable method is used to deal with the noise interference problem and the feasibility is analyzed. An iterative format for updating auxiliary variables using an adaptive variable forgetting factor is employed to reduce the impact of historical data on the current iteration step, thus forming a synchronous generator inertia identification algorithm based on adaptive forgetting factor recursive auxiliary variables.

13. The synchronous generator inertia identification system according to claim 12, characterized in that, The expression for the least squares iterative scheme is: The expression for the evaluation equation is: The updated iterative expression for the auxiliary variable method is: In the formula, Here is the estimated value, and N is the number of observations. Let y(k) be the system input-output matrix, y(k) be the system output, k be the k-th element, T be the transpose sign, θ be the true value, E(·) be the expected function, v(k) be the noise, and K be the system input-output matrix. N+1 P is the intermediate variable for the (N+1)th iteration of the recursion. N P N+1 z are intermediate variables for the Nth and (N+1)th iterations, respectively. N+1 For the auxiliary variable matrix of the (N+1)th iteration, Let λ(k) and λ(k+1) be the system input-output matrix for the (N+1)th iteration, and let λ(k) and λ(k+1) be the k-th and (k+1)-th adaptive variable forgetting factors, respectively. These are the estimated system characteristic parameters for the Nth and (N+1)th iterations, respectively, y N+1 This represents the system output during the (N+1)th iteration.

14. The synchronous generator inertia identification system according to claim 9, characterized in that, The synchronous generator inertia identification module discretizes the synchronous generator inertia identification function using the bilinear transform method. Based on the synchronous generator inertia identification algorithm with adaptive forgetting factor recursive auxiliary variables, it extracts the dynamic change data of electromagnetic power at the synchronous generator terminal after disturbance with mechanical power. When realizing inertia identification, the synchronous generator inertia identification function is discretized based on the bilinear transform method to facilitate recursive calculation. The moving average filtering method is used to filter the collected active power change and frequency change data to reduce noise interference. By utilizing the iterative steps in the synchronous generator inertia identification algorithm based on adaptive forgetting factor recursive auxiliary variables, the electromagnetic power at the synchronous generator terminal after disturbance is extracted as the mechanical power changes dynamically, thus realizing inertia identification and obtaining estimated values ​​of system characteristic parameters and identification results.

15. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 8.

16. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 8.

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