Power plant parameter identification method and device and computer readable storage medium

Abstract

The application discloses a power plant parameter identification method and device and a computer readable storage medium. The method comprises the following steps: obtaining voltage measurement values, current measurement values and power angle measurement values of a target power grid under a plurality of preset noise disturbance scenes based on a power management unit (PMU); solving an error function between model prediction values and measurement values of voltage, current and power angle of the target power grid constructed according to a least square method, and obtaining target voltage prediction values, target current prediction values and target power angle prediction values corresponding to minimum error values; and identifying power plant parameters according to the target voltage prediction values, the target current prediction values and the target power angle prediction values, and a pre-constructed third-order generator model, a speed regulation system model and an excitation system model. The application solves the technical problem that power plant parameters cannot be accurately identified in the related art.

Description

Technical Field

[0001] This invention relates to the field of power control, and more specifically, to a method, apparatus, and computer-readable storage medium for identifying power plant parameters. Background Technology

[0002] Identifying key parameters of power plants is of great significance for grid loop operation and economic operation.

[0003] In related technologies, techniques such as Fourier transform and Laplace transform are used to convert power grid-related physical quantities into frequency domain quantities for analysis. Power plant parameters are then identified based on this conversion; however, this method can only perform offline identification. A problem exists in these technologies: they cannot accurately identify power plant parameters.

[0004] There is currently no effective solution to the above problems. Summary of the Invention

[0005] This invention provides a method, apparatus, and computer-readable storage medium for identifying power plant parameters, thereby at least addressing the technical problem in the related art that the parameters of power plants cannot be accurately identified.

[0006] According to one aspect of the present invention, a power plant parameter identification method is provided, comprising: acquiring voltage, current, and power angle measurements of a target power grid under multiple preset noise disturbance scenarios based on a power management unit (PMU); solving an error function based on the multiple voltage, current, and power angle measurements of the target power grid under the multiple preset noise disturbance scenarios to obtain voltage, current, and power angle prediction values ​​corresponding to the minimum error value, wherein the error function is constructed using the least squares method, and the error function is used to characterize the error between the measured values ​​of multiple power parameters of the power grid and the corresponding predicted values ​​of the power parameters, the power parameters including voltage, current, and power angle; identifying power plant parameters corresponding to the generator third-order model, speed regulation system model, and excitation system model respectively based on the voltage, current, and power angle prediction values ​​corresponding to the minimum error value, and pre-constructed generator third-order model, speed regulation system model, and excitation system model; wherein the power plant parameters are parameters in the generator third-order model, speed regulation system model, and excitation system model.

[0007] Optionally, it further includes: constructing a third-order model of the generator based on the d-axis synchronous reactance, d-axis transient reactance, inertial constant, and d-axis transient open-circuit time constant of the generator in the target power grid, as well as the generator's power angle, acceleration, q-axis transient reactance, real and imaginary parts of current, active power and magnetomotive force of the generator, and quadrature-axis potential, active power, direct-axis current and quadrature-axis current of the node generator under noise disturbance scenarios; wherein the power plant parameters corresponding to the third-order model of the generator include at least one of the following: the generator's d-axis synchronous reactance, d-axis transient reactance, inertial constant, and d-axis transient open-circuit time constant.

[0008] Optionally, it further includes: constructing a speed regulation system model based on the acceleration time constant of the speed regulation system in the target power grid, the inertial time constant and buffer time constant of the generator units in the speed regulation system, the reference angular frequency and operating angular frequency of the speed regulation system, the rotor circuit resistance and mechanical torque of the speed regulation system, and the initial value of the mechanical torque of the speed regulation system; wherein, the power plant parameters corresponding to the speed regulation system model include at least one of the following: the inertial time constant of the generator units in the speed regulation system, the buffer time constant of the generator units in the speed regulation system, and the rotor circuit resistance of the speed regulation system.

[0009] Optionally, the method further includes: constructing the excitation system model based on the real voltage, imaginary voltage, time constant, and gain factor of the voltage regulator in the target power grid, the output voltage of the exciter, voltage regulator, and stabilizer, the gain factor and delay time constant of the exciter, the delay time constant of the excitation system, and the gain, decay time constant, and delay time constant of the voltage stabilizer; the power plant parameters corresponding to the excitation system model include at least one of the following: the time constant of the voltage regulator, the gain factor of the voltage regulator, the gain factor of the exciter of the voltage regulator, the delay time constant of the exciter, the delay time constant of the excitation system, the gain of the voltage stabilizer, the decay time constant of the voltage stabilizer, and the delay time constant of the voltage stabilizer.

[0010] Optionally, the step of solving the error function based on multiple voltage measurements, multiple current measurements, and multiple power angle measurements of the target power grid under multiple preset noise disturbance scenarios to obtain the voltage prediction value, current prediction value, and power angle prediction value corresponding to the minimum error value includes: determining target constraints; solving the error function based on the target constraints to obtain the voltage prediction value, current prediction value, and power angle prediction value corresponding to the minimum error value.

[0011] Optionally, determining the target constraints includes: determining the active power, reactive power, and electromotive force variation constraints of the generator based on the real and imaginary parts of the voltage, the real and imaginary parts of the current, the predetermined initial values ​​of the electromotive force, power angle, and q-axis transient electromotive force, and the q-axis synchronous reactance and d-axis transient reactance of the generator; constructing constraints on the real and imaginary parts of the injected current at each node in the target power grid based on network balance constraints; and constructing upper and lower limit constraints on the power plant parameters based on predetermined upper and lower limit values ​​of the power plant parameters.

[0012] Optionally, the plurality of noise disturbance scenarios include at least two of the following: a first predetermined disturbance applied to a plurality of voltage controller reference values ​​in the target power grid, a second predetermined disturbance applied to the spacing of power lines at the outlets of a plurality of power plants in the target power grid, and a third predetermined disturbance applied to the loads at the outlets of a plurality of power plants.

[0013] According to another aspect of the present invention, a power plant parameter identification device is also provided, characterized in that it includes: a first acquisition module, configured to acquire voltage measurement values, current measurement values, and power angle measurement values ​​of a target power grid under preset multiple noise disturbance scenarios based on a power management unit (PMU); a second acquisition module, configured to solve an error function based on the multiple voltage measurement values, multiple current measurement values, and multiple power angle measurement values ​​of the target power grid under preset multiple noise disturbance scenarios, to obtain voltage prediction values, current prediction values, and power angle prediction values ​​corresponding to the minimum error value; wherein, the error function is based on the minimum error value. The error function, constructed using the least squares method, is used to characterize the error between the measured values ​​and the corresponding predicted values ​​of multiple power parameters of the power grid. The power parameters include voltage, current, and power angle. The identification module is used to identify the power plant parameters corresponding to the generator third-order module, speed control system module, and excitation system module, respectively, based on the predicted voltage, current, and power angle values ​​corresponding to the minimum error value, as well as the pre-constructed generator third-order module, speed control system module, and excitation system module. The power plant parameters are the parameters in the generator third-order module, speed control system module, and excitation system module.

[0014] According to another aspect of the present invention, a computer-readable storage medium is also provided, the storage medium including a stored program, wherein, when the program is executed, it controls the device where the storage medium is located to execute the power plant parameter identification method described in any one of the above embodiments.

[0015] According to another aspect of the present invention, a computer device is also provided, comprising: a memory and a processor, the memory storing a computer program; the processor being configured to execute the computer program stored in the memory, wherein the computer program, when executed, causes the processor to perform the power plant parameter identification method described in any of the preceding embodiments.

[0016] In this embodiment of the invention, based on the power management unit (PMU), the power parameters of the target power grid under multiple preset noise disturbance scenarios are acquired. The power parameters include voltage measurements, current measurements, and power angle measurements. An error function is calculated based on the multiple voltage measurements, current measurements, and power angle measurements of the target power grid under the preset noise disturbance scenarios to obtain the voltage prediction, current prediction, and power angle prediction corresponding to the minimum error value. The error function is constructed using the least squares method and is used to characterize the error between the measured values ​​of multiple power parameters of the power grid and the corresponding predicted values. Based on the voltage prediction, current prediction, and power angle prediction corresponding to the minimum error value, and pre-constructed generator third-order model, speed control system model, and excitation system model, power plant parameters corresponding to the generator third-order model, speed control system model, and excitation system model are identified. The power plant parameters are the parameters in the generator third-order model, speed control system model, and excitation system model. By introducing multiple types of noise disturbances and identifying power plant parameters based on these disturbances, the efficiency and accuracy of power plant parameter identification can be improved, thereby solving the technical problem in related technologies that cannot accurately identify power plant parameters. Attached Figure Description

[0017] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0018] Figure 1 This is a flowchart of a power plant parameter identification method according to an embodiment of the present invention;

[0019] Figure 2 This is a schematic diagram of the structure of an IEEE 9-node system according to an embodiment of the present invention;

[0020] Figure 3 This is a framework diagram of an optional power plant parameter identification device according to an embodiment of the present invention. Detailed Implementation

[0021] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0022] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0023] Example 1

[0024] According to an embodiment of the present invention, an embodiment of a power plant parameter identification method is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0025] Figure 1 This is a power plant parameter identification method according to an embodiment of the present invention, such as... Figure 1 As shown, the method includes the following steps:

[0026] Step S102: Based on the power management unit (PMU), acquire the voltage measurement value, current measurement value, and power angle measurement value of the target power grid under multiple preset noise disturbance scenarios.

[0027] It is important to understand that a PMU (Power Management Unit) is a measurement unit that can be used to measure electrical parameters.

[0028] Step S104: Solve the error function based on multiple voltage measurements, multiple current measurements, and multiple power angle measurements of the target power grid under multiple preset noise disturbance scenarios to obtain the voltage prediction value, current prediction value, and power angle prediction value corresponding to the minimum error value. The error function is constructed based on the least squares method and is used to characterize the error between the measured values ​​of multiple power parameters of the power grid and the corresponding predicted values ​​of power parameters. The power parameters include voltage, current, and power angle.

[0029] Step S106: Based on the voltage prediction value, current prediction value, and power angle prediction value corresponding to the minimum error value, as well as the pre-constructed third-order generator model, speed regulation system model, and excitation system model, identify the power plant parameters corresponding to the third-order generator model, speed regulation system model, and excitation system model, respectively; wherein, the power plant parameters are the parameters in the third-order generator model, speed regulation system model, and excitation system model.

[0030] In this optional embodiment, based on the power management unit (PMU), the power parameters of the target power grid under multiple preset noise disturbance scenarios are acquired. These power parameters include voltage measurements, current measurements, and power angle measurements. An error function is calculated based on these measurements under the preset noise disturbance scenarios to obtain the predicted voltage, current, and power angle values ​​corresponding to the minimum error value. The error function is constructed using the least squares method and characterizes the error between the measured power parameters and their corresponding predicted values. Based on the predicted voltage, current, and power angle values ​​corresponding to the minimum error value, and pre-constructed generator third-order model, speed control system model, and excitation system model, the power plant parameters corresponding to these models are identified. These power plant parameters are parameters within the generator third-order model, speed control system model, and excitation system model. By introducing multiple different types of noise and applying different types of perturbations, and identifying power plant parameters based on these perturbations, the efficiency and accuracy of power plant parameter identification can be improved. This solves the problem in related technologies where power plant parameters cannot be accurately identified.

[0031] In some optional real-time examples, a third-order generator model is constructed based on the generator's d-axis synchronous reactance, d-axis transient reactance, inertial constant, and d-axis transient open-circuit time constant in the target power grid, as well as the generator's power angle, acceleration, q-axis transient reactance, real and imaginary parts of current, generator active power, and magnetomotive force, and the quadrature-axis potential, active power, direct-axis current, and quadrature-axis current of the node generator under noise disturbance scenarios. The power plant parameters corresponding to the third-order generator model include at least one of the following: the generator's d-axis synchronous reactance, d-axis transient reactance, inertial constant, and d-axis transient open-circuit time constant. Thus, a third-order generator model reflecting multiple power parameters in the target power grid can be obtained.

[0032] In some optional real-time examples, a speed control system model is constructed based on the acceleration time constant, inertial time constant and buffer time constant of the units in the speed control system, reference angular frequency and operating angular frequency of the speed control system, rotor circuit resistance and mechanical torque of the speed control system, and the initial value of the mechanical torque of the speed control system in the target power grid. The power plant parameters corresponding to the speed control system model include at least one of the following: the inertial time constant of the units in the speed control system, the buffer time constant of the units in the speed control system, and the rotor circuit resistance of the speed control system. Thus, a speed control system model reflecting multiple power parameters in the target power grid can be obtained.

[0033] In some optional real-time examples, an excitation system model is constructed based on the real voltage, imaginary voltage, time constant, and gain factor of the voltage regulator in the target power grid, as well as the output voltages of the exciter, voltage regulator, and stabilizer, the gain factor and delay time constant of the exciter, the delay time constant of the excitation system, and the gain, decay time constant, and delay time constant of the voltage stabilizer. The power plant parameters corresponding to the excitation system model include at least one of the following: the time constant of the voltage regulator, the gain factor of the voltage regulator, the gain factor of the exciter of the voltage regulator, the delay time constant of the exciter, the delay time constant of the excitation system, the gain of the voltage stabilizer, the decay time constant of the voltage stabilizer, and the delay time constant of the voltage stabilizer. Thus, an excitation system model reflecting multiple power parameters in the target power grid can be obtained.

[0034] In some optional real-time examples, the error function is solved based on multiple voltage measurements, multiple current measurements, and multiple power angle measurements of the target power grid under multiple preset noise disturbance scenarios to obtain the voltage prediction value, current prediction value, and power angle prediction value corresponding to the minimum error value. This includes: determining the target constraints; solving the error function based on the target constraints to obtain the voltage prediction value, current prediction value, and power angle prediction value corresponding to the minimum error value.

[0035] In some optional real-time examples, target constraints are determined, including: determining constraints on the changes in active power, reactive power, and electromotive force of the generator based on the real and imaginary parts of the voltage, the real and imaginary parts of the current, the predetermined initial values ​​of the generator's electromotive force, power angle, and q-axis transient electromotive force, as well as the generator's q-axis synchronous reactance and d-axis transient reactance; constructing constraints on the changes in the real and imaginary parts of the injected current at each node in the target power grid based on network balance constraints; and constructing upper and lower limit constraints on the power plant parameters based on predetermined upper and lower limits of the power plant parameters. Thus, target constraints related to multiple power parameters in the target power grid can be obtained. Based on these target constraints, highly accurate and applicable voltage, current, and power angle predictions can be obtained.

[0036] The multiple noise disturbance scenarios include at least two of the following: a first predetermined disturbance applied to multiple voltage controller reference values ​​in the target power grid; a second predetermined disturbance applied to the spacing of power lines at multiple power plant outlets in the target power grid; and a third predetermined disturbance applied to the loads at multiple power plant outlets. By applying different disturbances, power plant parameters can be accurately obtained based on the scenarios in which different disturbances are applied.

[0037] Based on the above embodiments and optional embodiments, an optional implementation method is provided, which is described in detail below.

[0038] Power plant parameter identification is crucial for grid loop operation and economical operation. Among related technologies, physical quantities can be converted into frequency domain variables for analysis based on techniques such as Fourier transform and Laplace transform. While this method can achieve power plant parameter identification, it can only be performed offline. Another related technology is to perform time-domain analysis on physical quantities to achieve online parameter identification. Although these methods can identify power plant parameters, they often rely on the occurrence of fault disturbances, which inherently have an adverse impact on the stable operation of the power system. Furthermore, the identification results are somewhat random and may lead to significant errors.

[0039] In view of this, this optional implementation introduces noise-like technology to apply perturbations from different angles and perform power plant parameter identification under multiple perturbation scenarios, thereby improving the accuracy of power plant parameter identification results. At the same time, taking advantage of the low degree of freedom and low coupling degree of multiple scenarios in power plant parameter identification, the simplified spatial point method and scenario decomposition are used to obtain power plant parameter identification results, thereby significantly improving parameter identification efficiency and achieving the goal of accurate and rapid online identification of power plant parameters.

[0040] The power plant parameter identification method provided in this disclosure includes the following steps:

[0041] Step S1: Acquire power data under multiple different noise disturbance scenarios using the PMU device. The power data includes grid voltage, current, and power angle data. Specifically, this includes the following steps:

[0042] S11: Set different types of noise perturbations.

[0043] It is important to understand that noise-like signals refer to the output of a system under normal operating conditions. For power systems, the bandwidth of noise-like signals coincides with the electromechanical dynamic bandwidth, which is typically 0.2 to 2.0 Hz.

[0044] In this optional embodiment, power plant parameters can be identified by setting the following three types of noise disturbances: applying a preset disturbance to the reference values ​​of different automatic voltage controllers; applying a preset disturbance to the spacing of power lines at different power plant outlets; and changing the load variable at the power plant outlet within a preset small range.

[0045] S12: Acquire power plant outlet voltage, current, and power angle data based on PMU device.

[0046] Step S2: Construct a power plant model. This model includes a generator model, a speed control system model, and an excitation system model to determine the parameters to be identified. Based on the constructed electric field model, determine the key parameters of the power plant to be identified. Specifically, this includes the following steps:

[0047] S21: The constructed generator model is a third-order model. The third-order generator model is shown below:

[0048]

[0049] in:

[0050]

[0051] In the formula: X dk 、X′ dk M k 、T′ d0k X qk The generator's d-axis synchronous reactance, d-axis transient reactance, inertial constant, d-axis transient open-circuit time constant, and generator q-axis synchronous reactance are respectively calculated. These represent the generator's power angle, angular velocity, q-axis transient reactance, real part of current, and imaginary part of current, respectively; P Gk E fk These are the generator's active power and magnetomotive force, respectively. ω represents the quadrature-axis potential of the generator at node k in scenario j; s The synchronous angular velocity has a nominal value; Let represent the active power of the generator at node k at time t under scenario j. This represents the direct-axis current of the generator at node k in scenario j at time t. Let n represent the quadrature-axis current of the generator at node k in scenario j at time t. G N represents the number of nodes. S This indicates the number of fault scenarios. The subscript k represents the generator node number; the superscript j represents the different types of noise disturbance scenario numbers.

[0052] Among them, X dk 、X′ dk M k 、T′ d0k These are the generator parameters to be identified.

[0053] S22: Ignoring the saturation component in the speed control system model, construct a speed control system model to determine the parameters to be identified in the speed control system. The speed control system model is shown below:

[0054]

[0055] In the formula, t g ω is the acceleration time constant of the speed control system; T1 and T2 are the unit inertia time constant and buffer time constant, respectively; ref ω and ω are the reference angular frequency and the operating angular frequency, respectively; R is the rotor circuit resistance; T mech T represents the mechanical torque. mech0 These are the values ​​of the mechanical torque before speed regulation. Where R, T1, and T2 are the speed regulation system parameters to be identified.

[0056] S23: Ignoring the saturation component in the excitation system model, construct a fourth-order excitation system model to determine the parameters to be identified in the excitation system. The fourth-order excitation system model is shown below:

[0057]

[0058] In the formula, V m For measuring voltage; V x V y T a K and V represent the real voltage, imaginary voltage, time constant, and gain factor of the voltage regulator, respectively; r1 V r2 V f These are the output voltages of the exciter, voltage regulator, and stabilizer, respectively; V ref Reference voltage; K f T f These represent the gain factor and delay time constant of the exciter, respectively; T r A is the time delay constant of the entire excitation system; e B e ,T eThese are the gain, decay time constant, and delay time constant of the voltage stabilizer, respectively.

[0059] Among them, K a ,T a ,K f ,T f ,T r ,T e A e B e These are the parameters of the excitation system to be identified.

[0060] Step S3: Establish a power plant parameter identification model with the goal of minimizing the errors between the model values ​​and measured values ​​of voltage, current, and power angle. The model values ​​are the corresponding predicted values ​​obtained from the generator model, speed control system model, and excitation system model described above; the measured values ​​are those collected by the PMU device. Specifically, this includes the following steps:

[0061] Based on ARIMA (Auto Regression and Moving Average) model, the measured voltage, current, and power angle are fitted, and the least squares error between the fitted curves of the three operating variables and the model prediction curve is minimized. A dynamic identification model for key parameters of multiple power plants under various noise scenarios is established (equivalent to the error function in the aforementioned example).

[0062]

[0063] In the formula, nT represents the number of time periods; n genl N is the number of generator nodes; Ns is the total number of fault scenarios; These are the predicted and measured values ​​of the generator power angle at node k under scenario j, respectively. These are the predicted and measured values ​​of the real part of the current, and the predicted and measured values ​​of the imaginary part of the current, respectively. These are the predicted and measured values ​​of the real part of the voltage, and the predicted and measured values ​​of the imaginary part of the voltage, respectively.

[0064] Since the generator voltage, current, and power angle have different dimensions and the data have different orders of magnitude, relative values ​​are used to characterize the error, and the above objective function is modified as follows:

[0065]

[0066] Step S4: Determine the constraints of the identification model. This includes the following steps:

[0067] Step S41: Assume that the initial state is the same under all noise disturbance scenarios, and the initial value constraints are as follows:

[0068]

[0069] Where: ΔP k，0 ΔQ k，0 , ΔE′ qk，0 These represent the changes in active power, reactive power, and electromotive force at node k, respectively; e k f k I ek I fk These represent the real and imaginary parts of the generator's initial operating voltage, and the real and imaginary parts of the current; E Qk δ k,0 E′ qk,0 The initial values ​​of the generator's virtual electromotive force, power angle, and q-axis transient electromotive force are: X qk , X′ dk These are the generator's q-axis synchronous reactance and d-axis transient reactance, respectively.

[0070] Step S42: The network equilibrium constraints are satisfied under all noise disturbance scenarios, as follows:

[0071]

[0072] in:

[0073]

[0074] In the formula: This represents the change in the real part and the change in the imaginary part of the node injected current; These are the real and imaginary parts of the network admittance matrix, respectively. These are the real and imaginary parts of the voltage, and the real and imaginary parts of the injected current, respectively. Let be the real and imaginary parts of the voltage of the generator at time t, respectively. X qk These represent the generator's q-axis transient electromotive force, d-axis transient reactance, power angle, and q-axis reactance at time t, respectively.

[0075] Step S43: All parameters to be identified satisfy the upper and lower limit constraints:

[0076] Upper and lower limit constraints on generator identification parameters:

[0077]

[0078] The upper and lower limit constraints of the parameters to be identified in the speed control system are:

[0079]

[0080] The upper and lower limits of the parameters to be identified in the excitation system are:

[0081]

[0082] In the formula, the subscript min represents the lower limit of the corresponding parameter, and the subscript max represents the upper limit of the corresponding parameter.

[0083] Step S5: Solve the above power plant parameter identification model based on the simplified spatial point method and scenario decomposition method to obtain the final key parameter values ​​of the power plant.

[0084] In this optional implementation, data such as grid voltage, current, and power angle under different noise disturbance scenarios are acquired using a PMU device; a power plant model, including a generator model, a speed control system model, and an excitation system model, is established to determine the key parameters of the power plant to be identified; a power plant parameter identification model is established with the goal of minimizing the error between the model values ​​and the measured values ​​of voltage, current, and power angle; constraints on the identification model are determined; and the power plant parameter identification model is solved using the simplified in-space point method and scenario decomposition method to obtain the final key parameter values ​​of the power plant. This approach allows for the application of disturbance types from different angles and the identification of multiple disturbance scenarios, improving the accuracy of the identification. Furthermore, by utilizing the low degree of freedom in parameter identification and the low coupling between multiple scenarios, a simplified in-space point method and scenario decomposition solution strategy are proposed, which can significantly improve the efficiency of parameter identification and achieve accurate and rapid online identification.

[0085] The following explanation uses the IEEE 9-node system as an example. Figure 2 This is a schematic diagram of the IEEE 9-node system. Figure 2 As shown, the generator is connected to nodes 1, 2, and 3, and the load is connected to nodes 5, 6, and 8.

[0086] The actual parameter values ​​of the generators in the IEEE 9-node system are shown in Table 1.

[0087] Table 1

[0088]

[0089] Figure 2 This is a schematic diagram of the IEEE 9-node system. Figure 2 As shown in Table 2, the actual parameter values ​​of the excitation system in the IEEE 9-bus system are as follows.

[0090] Table 2

[0091]

[0092] The actual parameter values ​​of the speed control system in the IEEE 9-node system are shown in Table 3.

[0093] Table 3

[0094]

[0095] The preset noise disturbance scenarios include: applying a disturbance of 0.1 to the reference value of the automatic voltage controller; applying a certain disturbance to the line spacing of lines 4-6, 9-8, and 7-5 respectively, i.e., disturbing the line impedance and changing it by 0.1; applying a small load disturbance of 2MW to load nodes 5, 6, and 8 respectively; a total of 7 disturbance scenarios. The test environment is VS2008, the platform is an i5 CPU with a CPU frequency of 3.19GHz, and 4GB of memory.

[0096] In this optional real-time mode, the problem size optimization is shown in Table 4.

[0097] Table 4

[0098]

[0099] In Table 4, Ns represents the number of scenarios, n and m are the number of state variables and equality constraints of the primal and dual systems, respectively, DIM and NNZ are the dimension and number of non-zero elements of the primal and dual systems, respectively, and DOF is the degree of freedom. As shown in Table 4, the optimization scale DIM of each example is very large, but its degree of freedom DOF is very low, making it very suitable for solving in a reduced space.

[0100] The generator parameter results identified based on this optional implementation are shown in Table 5.

[0101] Table 5

[0102]

[0103] The excitation parameter results identified based on this optional implementation method are shown in Table 6:

[0104] Table 6

[0105]

[0106] The speed regulation parameters identified based on this optional implementation method are shown in Table 7:

[0107] Table 7

[0108]

[0109] Based on the identification results of the above power plant parameters, it can be seen that in the traditional single disturbance scenario, the identification results are poor due to the poor ability to resist measurement errors. When the number of disturbance scenarios is increased to 7, the overall identification accuracy of the identification algorithm is improved. The errors of a very small number of generator parameters are large, but it can basically reflect the dynamic response process of the system.

[0110] The identification process of key parameters in power plants is generally an optimization process aimed at minimizing the error between measured and model values ​​of specific physical quantities. Currently, it mainly includes two categories: frequency domain identification methods and time domain identification methods. The former primarily uses techniques such as Fourier transform and Laplace transform to convert physical quantities into frequency domain variables for analysis, offering advantages such as simple and convenient computation, but it can only be used for offline identification. The latter directly performs time-domain analysis on physical quantities, enabling online parameter identification, such as using the least squares algorithm for online identification of third-order synchronous generators. However, these identification methods often rely on the occurrence of fault disturbances, which inherently have an adverse impact on the stable operation of the power system. Furthermore, parameter identification under a single disturbance has a certain degree of randomness, potentially leading to significant errors. In this optional embodiment, by introducing multiple types of noise and applying disturbance types from different angles, and performing power plant parameter identification in multiple disturbance scenarios, the accuracy of power plant parameter identification can be improved. At the same time, taking advantage of the low degree of freedom in parameter identification and the low coupling degree of multiple scenarios, a simplified spatial point method and scenario decomposition solution strategy are proposed, which can significantly improve the efficiency of parameter identification and achieve accurate and fast online identification.

[0111] Example 2

[0112] According to an embodiment of the present invention, a structural block diagram of a power plant parameter identification device is also provided. (Refer to...) Figure 3 As shown, the device includes a first acquisition module 302, a second acquisition module 304, and an identification module 306. These will be described in detail below.

[0113] The first acquisition module 302 is used to acquire voltage, current, and power angle measurements of the target power grid under multiple preset noise disturbance scenarios based on the power management unit (PMU). The second acquisition module 304, connected to the first acquisition module 302, is used to solve an error function based on the multiple voltage, current, and power angle measurements of the target power grid under the multiple preset noise disturbance scenarios to obtain the predicted voltage, current, and power angle values ​​corresponding to the minimum error value. The error function is constructed using the least squares method. The power parameters are used to characterize the error between the measured values ​​and the corresponding predicted values ​​of multiple power parameters of the power grid, including voltage, current and power angle; the identification module 306, connected to the second acquisition module 304, is used to identify the power plant parameters corresponding to the generator third-order model, speed regulation system model and excitation system model respectively, based on the voltage prediction value, current prediction value and power angle prediction value corresponding to the minimum error value, as well as the pre-constructed generator third-order model, speed regulation system model and excitation system model; wherein, the power plant parameters are the parameters in the generator third-order model, speed regulation system model and excitation system model.

[0114] It should be noted that the first acquisition module 302, the second acquisition module 304, and the identification module 306 mentioned above correspond to steps S102 to S106 in Embodiment 1. The three modules and the corresponding steps implement the same instances and application scenarios, but are not limited to the content disclosed in Embodiment 1.

[0115] According to another aspect of the present invention, a computer-readable storage medium is provided, the storage medium including a stored program, wherein, when the program is executed, the device where the storage medium is located executes the power plant parameter identification method described above.

[0116] According to another aspect of the present invention, a computer device is also provided, comprising: a memory and a processor, the memory storing a computer program; and a processor for executing the computer program stored in the memory, wherein the computer program, when running, causes the processor to execute the power plant parameter identification method described above.

[0117] The present invention provides an image processing solution. This achieves the objective and solves the technical problems in related technologies.

[0118] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing the hardware related to the terminal device. The program can be stored in a computer-readable storage medium, which may include: flash drive, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.

[0119] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0120] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0121] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, indirect coupling or communication connection between units or models, and may be electrical or other forms.

[0122] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0123] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0124] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0125] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for identifying power plant parameters, characterized in that, include: Based on the power management unit (PMU), the voltage, current, and power angle measurements of the target power grid under multiple preset noise disturbance scenarios are obtained respectively. Based on multiple voltage measurements, multiple current measurements, and multiple power angle measurements of the target power grid under multiple preset noise disturbance scenarios, an error function is calculated to obtain the voltage prediction value, current prediction value, and power angle prediction value corresponding to the minimum error value. The error function is constructed using the least squares method and is used to characterize the error between the measured values ​​of multiple power parameters of the power grid and the corresponding predicted values ​​of power parameters. The power parameters include voltage, current, and power angle. Based on the voltage prediction, current prediction, and power angle prediction values ​​corresponding to the minimum error value, and the pre-constructed third-order generator model, speed control system model, and excitation system model, identify the power plant parameters corresponding to the third-order generator model, speed control system model, and excitation system model, respectively; wherein, the power plant parameters are the parameters in the third-order generator model, speed control system model, and excitation system model; The step of solving the error function based on multiple voltage measurements, multiple current measurements, and multiple power angle measurements of the target power grid under multiple preset noise disturbance scenarios to obtain the voltage prediction value, current prediction value, and power angle prediction value corresponding to the minimum error value includes: determining target constraints; solving the error function based on the target constraints to obtain the voltage prediction value, current prediction value, and power angle prediction value corresponding to the minimum error value; The determination of target constraints includes: determining the active power, reactive power, and electromotive force variation constraints of the generator based on the real and imaginary parts of the voltage, the real and imaginary parts of the current, the predetermined initial values ​​of the generator's electromotive force, power angle, and q-axis transient electromotive force, as well as the generator's q-axis synchronous reactance and d-axis transient reactance; constructing constraints on the real and imaginary parts of the injected current at each node in the target power grid based on network balance constraints; and constructing upper and lower limit constraints on the power plant parameters based on predetermined upper and lower limit values ​​of the power plant parameters. The error function is: ， in, Number of time periods; N represents the number of generator nodes; Ns represents the number of noise disturbance scenarios. , These are the predicted and measured values ​​of the generator power angle at node k under scenario j, respectively. , , , These are the predicted and measured values ​​of the real part of the current, and the predicted and measured values ​​of the imaginary part of the current, respectively. , , , These are the predicted and measured values ​​of the real part of the voltage, and the predicted and measured values ​​of the imaginary part of the voltage, respectively.

2. The method according to claim 1, characterized in that, Also includes: Based on the d-axis synchronous reactance, d-axis transient reactance, inertial constant, and d-axis transient open-circuit time constant of the generator in the target power grid, as well as the generator's power angle, acceleration, q-axis transient reactance, real and imaginary parts of current, active power, and magnetomotive force, and the quadrature-axis potential, active power, direct-axis current, and quadrature-axis current of the node generator under noise disturbance scenarios, a third-order model of the generator is constructed; wherein, the power plant parameters corresponding to the third-order model of the generator include at least one of the following: the generator's d-axis synchronous reactance, d-axis transient reactance, inertial constant, and d-axis transient open-circuit time constant.

3. The method according to claim 1, characterized in that, Also includes: The speed regulation system model is constructed based on the acceleration time constant of the speed regulation system in the target power grid, the inertial time constant and buffer time constant of the generator units in the speed regulation system, the reference angular frequency and operating angular frequency of the speed regulation system, the rotor circuit resistance and mechanical torque of the speed regulation system, and the initial value of the mechanical torque of the speed regulation system. The power plant parameters corresponding to the speed regulation system model include at least one of the following: the inertial time constant of the generator units in the speed regulation system, the buffer time constant of the generator units in the speed regulation system, and the rotor circuit resistance of the speed regulation system.

4. The method according to claim 1, characterized in that, Also includes: Based on the real voltage, imaginary voltage, time constant, and gain factor of the voltage regulator in the target power grid, as well as the output voltages of the exciter, voltage regulator, and stabilizer, the gain factor and delay time constant of the exciter, the delay time constant of the excitation system, and the gain, decay time constant, and delay time constant of the voltage stabilizer, the excitation system model is constructed. The power plant parameters corresponding to the excitation system model include at least one of the following: the time constant of the voltage regulator, the gain factor of the voltage regulator, the gain factor of the exciter of the voltage regulator, the delay time constant of the exciter, the delay time constant of the excitation system, the gain of the voltage stabilizer, the decay time constant of the voltage stabilizer, and the delay time constant of the voltage stabilizer.

5. The method according to claim 1, characterized in that, The multiple noise disturbance scenarios include at least two of the following: A first predetermined disturbance is applied to multiple voltage controller reference values ​​in the target power grid, a second predetermined disturbance is applied to the spacing of power lines at multiple power plant outlets in the target power grid, and a third predetermined disturbance is applied to the loads at multiple power plant outlets.

6. A power plant parameter identification device, characterized in that, include: The first acquisition module is used to acquire, based on the power management unit (PMU), the voltage measurement value, current measurement value, and power angle measurement value of the target power grid under multiple preset noise disturbance scenarios. The second acquisition module is used to solve an error function based on multiple voltage measurements, multiple current measurements, and multiple power angle measurements of the target power grid under multiple preset noise disturbance scenarios, to obtain the voltage prediction value, current prediction value, and power angle prediction value corresponding to the minimum error value; wherein, the error function is constructed based on the least squares method, and the error function is used to characterize the error between the measured values ​​of multiple power parameters of the power grid and the corresponding predicted values ​​of power parameters, the power parameters including voltage, current, and power angle; The identification module is used to identify the power plant parameters corresponding to the generator third-order model, speed regulation system model, and excitation system model, respectively, based on the voltage prediction value, current prediction value, and power angle prediction value corresponding to the minimum error value, as well as the pre-constructed generator third-order model, speed regulation system model, and excitation system model; wherein, the power plant parameters are the parameters in the generator third-order model, speed regulation system model, and excitation system model; The second acquisition module is further configured to determine the target constraint conditions; solve the error function based on the target constraint conditions, and obtain the voltage prediction value, current prediction value, and power angle prediction value corresponding to the minimum error value; The second acquisition module is further configured to determine the active power, reactive power, and electromotive force variation constraints of the generator based on the real and imaginary parts of the voltage of the generator operating in a predetermined initial state, the real and imaginary parts of the current of the generator operating in the predetermined initial state, the predetermined initial values ​​of the electromotive force, power angle, and q-axis transient electromotive force of the generator, and the q-axis synchronous reactance and d-axis transient reactance of the generator; construct constraint conditions for the real and imaginary parts of the injected current at each node in the target power grid based on network balance constraints; and construct upper and lower limit constraint conditions for the power plant parameters based on predetermined upper and lower limit values ​​of the power plant parameters. The error function is: ， in, Number of time periods; N represents the number of generator nodes; Ns represents the number of noise disturbance scenarios. , These are the predicted and measured values ​​of the generator power angle at node k under scenario j, respectively. , , , These are the predicted and measured values ​​of the real part of the current, and the predicted and measured values ​​of the imaginary part of the current, respectively. , , , These are the predicted and measured values ​​of the real part of the voltage, and the predicted and measured values ​​of the imaginary part of the voltage, respectively.

7. A computer-readable storage medium, characterized in that, The storage medium includes a stored program, wherein, when the program is executed, it controls the device containing the storage medium to perform the power plant parameter identification method according to any one of claims 1 to 5.

8. A computer device, characterized in that, include: Memory and processor The memory stores computer programs; The processor is configured to execute a computer program stored in the memory, wherein when the computer program is executed, the processor performs the power plant parameter identification method according to any one of claims 1 to 5.

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