CN115117879A - Power plant parameter identification method and device and computer readable storage medium - Google Patents

Abstract

The invention discloses a power plant parameter identification method, a device and a computer-readable storage medium. Wherein, the method includes: acquiring, 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-like disturbance scenarios; The error function between the measured value of voltage, current and power angle and the predicted value of the model, obtain the predicted value of target voltage, predicted value of target current and predicted value of target power angle corresponding to the minimum error value; Current predicted value and target power angle predicted value, as well as pre-built third-order generator model, speed control system model, excitation system model, identify power plant parameters. The invention solves the technical problem that the parameters of the power plant cannot be accurately identified in the related art.

Description

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

technical field

The present invention relates to the field of power control, and in particular, to a method and device for identifying parameters of a power plant, and a computer-readable storage medium.

Background technique

The identification of the key parameters of the power plant is of great significance to the loop closing operation and economic operation of the power grid.

In the related art, the physical quantities related to the power grid are converted into frequency domain quantities for analysis by techniques such as Fourier transformation and Laplace transformation, and power plant parameters are identified based on this transformation, but this method can only be used for offline identification. In the related art, there is a problem that the parameters of the power plant cannot be accurately identified.

For the above problems, no effective solution has been proposed yet.

SUMMARY OF THE INVENTION

Embodiments of the present invention provide a power plant parameter identification method, device, and computer-readable storage medium, so as to at least solve the technical problem that the power plant parameters cannot be accurately identified in the related art.

According to an aspect of the embodiments of the present invention, a method for identifying parameters of a power plant is provided, including: based on a power management unit PMU, respectively acquiring a voltage measurement value, a current measurement value, and a power measurement value of a target power grid under multiple preset noise-like disturbance scenarios. angle measurement value; according to multiple voltage measurement values, multiple current measurement values and multiple power angle measurement values of the target power grid under multiple preset noise-like disturbance scenarios, the error function is solved to obtain the difference between the minimum error value and the The corresponding voltage predicted value, current predicted value and power angle predicted value, wherein the error function is constructed according to the least squares method, and the error function is used to characterize the measured values of multiple power parameters of the power grid and the corresponding power parameters Error between predicted values, the power parameters include voltage, current and power angle; according to the voltage predicted value, current predicted value and power angle predicted value corresponding to the minimum error value, and the pre-built generator third-order model, speed regulation system model, and excitation system model, and identify the power plant parameters corresponding to the generator third-order model, speed regulation system model, and excitation system model respectively; wherein, the power plant parameters are the generator third-order model, Parameters in the speed control system model and the excitation system model.

Optionally, it also includes: according to the d-axis synchronous reactance, d-axis transient reactance, inertia constant, d-axis transient open-circuit time constant of the generator in the target power grid, and the power angle, acceleration, q Shaft transient reactance, current real part and imaginary part, as well as the generator active power, excitation potential, and quadrature axis potential, active power, direct axis current and quadrature axis current of the node generator under the noise disturbance scenario, construct the The generator third-order model; wherein, the power plant parameters corresponding to the generator third-order model include at least one of the following: d-axis synchronous reactance, d-axis transient reactance, inertia constant, d-axis transient reactance of the generator Open circuit time constant.

Optionally, it also includes: according to the acceleration time constant of the speed regulation system in the target power grid, the inertia time constant and buffer time constant of the unit in the speed regulation system, the reference angular frequency and the operating angular frequency of the speed regulation system , the rotor loop resistance and mechanical torque of the speed control system, and the initial value of the mechanical torque of the speed control system, to construct the speed control system model; wherein, the power plant parameters corresponding to the speed control system model It includes at least one of the following: the inertia time constant of the unit in the speed control system, the buffer time constant of the unit in the speed control system, and the rotor loop resistance of the speed control system.

Optionally, it also includes: according to the real part voltage, imaginary part voltage, time constant, gain multiple of the voltage regulator in the target power grid, and the output voltage of the exciter, the voltage regulator and the stabilizer, and the exciter The gain multiple and delay time constant of the excitation system, as well as the delay time constant of the excitation system, as well as the gain, decay time constant and delay time constant of the voltage stabilizer, construct the excitation system model; 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 multiple of the voltage regulator, the gain multiple of the exciter of the voltage regulator, the delay time constant of the exciter, the delay of the excitation system time constant, gain of the voltage stabilizer, decay time constant of the voltage stabilizer, delay time constant of the voltage stabilizer.

Optionally, the error function is solved according to multiple voltage measurement values, multiple current measurement values and multiple power angle measurement values of the target power grid under multiple preset noise-like disturbance scenarios, to obtain a The corresponding voltage prediction value, current prediction value and power angle prediction value include: determining target constraints; solving the error function based on the target constraints, and obtaining the voltage prediction value and the current prediction value corresponding to the minimum error value and power angle predictions.

Optionally, the determining the target constraint condition includes: according to the real voltage part and the imaginary voltage part of the generator running in a predetermined initial state, the current real part and the current imaginary part of the generator running in the predetermined initial state. part, and the initial value of the predetermined potential, the initial value of the power angle and the initial value of the q-axis transient potential of the generator, as well as the q-axis synchronous reactance and the d-axis transient reactance of the generator, to determine the active power of the generator Constraints on power, reactive power and electromotive force variation; based on network balance constraints, construct constraints on the real and imaginary changes of the injected current at each node in the target grid; based on the predetermined upper limit of power plant parameters and The predetermined lower limit value of the power plant parameter is used to construct the upper and lower limit constraints of the power plant parameter.

Optionally, the multiple noise-like 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 the lines, and a third predetermined disturbance applied to the loads at the plurality of power plant outlets.

According to another aspect of the embodiments of the present invention, an apparatus for identifying parameters of a power plant is further provided, which is characterized by comprising: a first obtaining module, configured to obtain, based on the power management unit PMU, the target power grid in a plurality of preset categories respectively. voltage measurement value, current measurement value, and power angle measurement value under the noise disturbance scenario; the second acquisition module is configured to obtain multiple voltage measurement values, multiple The error function is solved for the current measurement value and the multiple power angle measurement values to obtain the voltage prediction value, the current prediction value and the power angle prediction value corresponding to the minimum error value; wherein, the error function is constructed and obtained according to the least square method, The error function is used to characterize the error between the measured values of a plurality of power parameters of the power grid and the corresponding predicted values of the power parameters, and the power parameters include voltage, current and power angle; the identification module is used for according to the minimum error value. The corresponding voltage prediction value, current prediction value and power angle prediction value, as well as the pre-built generator third-order module, speed regulation system module, excitation system module, are identified with the generator third-order module, speed regulation system module, Power plant parameters corresponding to the excitation system module; wherein, the power plant parameters are parameters in the generator third-order module, the speed regulation system module, and the excitation system module.

According to another aspect of the embodiments of the present invention, a computer-readable storage medium is further provided, where the storage medium includes a stored program, wherein when the program is executed, a device where the storage medium is located is controlled to execute any of the foregoing items The power plant parameter identification method.

According to another aspect of the embodiments of the present invention, a computer device is also provided, including: a memory and a processor, where the memory stores a computer program; the processor is configured to execute the computer program stored in the memory, When the computer program runs, the processor executes the power plant parameter identification method described in any one of the above.

In the embodiment of the present invention, based on the power management unit PMU, the power parameters of the target power grid under multiple preset noise-like disturbance scenarios are respectively obtained, and the power parameters include a voltage measurement value, a current measurement value, and a power angle measurement value; according to the target The error function is solved for multiple voltage measurements, multiple current measurements, and multiple power angle measurements of the power grid under multiple preset noise-like disturbance scenarios, and the voltage predicted value and current predicted value corresponding to the minimum error value are obtained. and the predicted value of power angle, in which the error function is constructed according to the least square 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 corresponding voltage prediction value, current prediction value and power angle prediction value, as well as the pre-built generator third-order model, speed control system model, excitation system model, identify the generator third-order model, speed control system model, excitation system model respectively. The parameters of the power plant corresponding to the model; wherein, the parameters of the power plant are the parameters in the third-order model of the generator, the model of the speed control system, and the model of the excitation system. By introducing multiple noise-like disturbances and identifying power plant parameters based on multiple noise-like disturbances, the efficiency and accuracy of power plant parameter identification can be improved, thereby solving the technical problem of inability to accurately identify power plant parameters in related technologies.

Description of drawings

The accompanying drawings described herein are used to provide a further understanding of the present invention and constitute a part of the present application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the attached image:

1 is a flowchart of a method for identifying parameters of a power plant according to an embodiment of the present invention;

2 is a schematic structural diagram of an IEEE9 node system according to an embodiment of the present invention;

FIG. 3 is a frame diagram of an optional power plant parameter identification device according to an embodiment of the present invention.

Detailed ways

In order to make those skilled in the art better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only Embodiments are part of the present invention, but not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

It should be noted that the terms "first", "second" and the like in the description and claims of the present invention and the above drawings are used to distinguish similar objects, and are not necessarily used to describe a specific sequence or sequence. It is to be understood that the data so used may be interchanged under appropriate circumstances such that the embodiments of the invention described herein can be practiced in sequences other than those illustrated or described herein. Furthermore, the terms "comprising" and "having" and any variations thereof, are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those expressly listed Rather, those steps or units may include other steps or units not expressly listed or inherent to these processes, methods, products or devices.

Example 1

According to an embodiment of the present invention, an embodiment of a method for identifying parameters of a power plant is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings may be executed in a computer system such as a set of computer-executable instructions, and, Although a logical order is shown in the flowcharts, in some cases steps shown or described may be performed in an order different from that herein.

Fig. 1 is a power plant parameter identification method according to an embodiment of the present invention. As shown in Fig. 1 , the method includes the following steps:

Step S102 , based on the power management unit PMU, respectively acquire voltage measurement values, current measurement values and power angle measurement values of the target power grid under multiple preset noise-like disturbance scenarios.

It should be understood that a PMU (Power Management Unit, power management unit) is a measurement unit that can be used to measure power parameters.

Step S104, solving an error function according to multiple voltage measurements, multiple current measurements, and multiple power angle measurements of the target power grid under multiple preset noise-like disturbance scenarios, to obtain a voltage prediction corresponding to the minimum error value value, predicted value of current and predicted value of power angle, wherein, the error function is constructed according to the least square 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, and the power Parameters include voltage, current, and power angle.

Step S106, according to the voltage prediction value, current prediction value and power angle prediction value corresponding to the minimum error value, as well as the pre-built generator third-order model, speed regulation system model, and excitation system model, identify the third-order generators respectively. The power plant parameters corresponding to the model, the speed control system model, and the excitation system model; wherein, the power plant parameters are the parameters in the generator third-order model, the speed control system model, and the excitation system model.

In this optional embodiment, based on the power management unit PMU, the power parameters of the target power grid under multiple preset noise-like disturbance scenarios are respectively obtained, and the power parameters include a voltage measurement value, a current measurement value, and a power angle measurement value; according to The error function is solved for multiple voltage measurements, multiple current measurements, and multiple power angle measurements of the target power grid under multiple preset noise-like disturbance scenarios, and the voltage prediction value and current prediction value corresponding to the minimum error value are obtained. value and predicted value of power angle, among which, the error function is constructed according to the least square method, and the error function is used to characterize the error between the measured value of multiple power parameters of the power grid and the corresponding predicted value of the power parameter; according to the minimum error value The corresponding voltage prediction value, current prediction value and power angle prediction value, as well as the pre-built generator third-order model, speed control system model and excitation system model, are identified with the generator third-order model, speed control system model, excitation system model respectively. Power plant parameters corresponding to the system model; wherein, the power plant parameters are parameters in the generator third-order model, the speed regulation system model, and the excitation system model. By introducing multiple different types of noise, applying different types of disturbances, and identifying power plant parameters based on different types of disturbances, the efficiency and accuracy of power plant parameter identification can be improved, thereby solving the problem of inability to accurately identify power plant parameters in related technologies. Identify technical issues.

In some optional real-time examples, according to the d-axis synchronous reactance, d-axis transient reactance, inertia constant, d-axis transient open-circuit time constant of the generator in the target grid, as well as the generator's power angle, acceleration, q-axis transient Reactance, real and imaginary parts of current, as well as generator active power, excitation potential, and quadrature axis potential, active power, direct axis current and quadrature axis current of node generators in the noise disturbance scenario to build a third-order generator model; The power plant parameters corresponding to the third-order generator model include at least one of the following: d-axis synchronous reactance, d-axis transient reactance, inertia constant, and d-axis transient open-circuit time constant of the generator. Thus, a third-order generator model that can reflect multiple power parameters in the target grid can be obtained.

In some optional real-time examples, according to the acceleration time constant of the speed control system in the target power grid, the inertia time constant and buffer time constant of the unit in the speed control system, the reference angular frequency and the operating angular frequency of the speed control system, the speed control system The rotor loop resistance, mechanical torque, and the initial value of the mechanical torque of the speed control system are used to construct a speed control system model; wherein, the power plant parameters corresponding to the speed control system model include at least one of the following: the inertia of the unit in the speed control system Time constant, buffer time constant of the unit in the speed control system, rotor loop resistance of the speed control system. Thus, a speed regulation system model that can reflect multiple power parameters in the target power grid can be obtained.

In some optional real-time examples, according to the real part voltage, imaginary part voltage, time constant, gain multiplier of the voltage regulator in the target grid, and the output voltage of the exciter, voltage regulator and stabilizer, and the gain multiplier of the exciter and delay time constant, as well as the delay time constant of the excitation system, as well as the gain, decay time constant and delay time constant of the voltage stabilizer, to construct the excitation system model; the power plant parameters corresponding to the excitation system model include at least one of the following: voltage regulation The time constant of the voltage regulator, the gain multiple of the voltage regulator, the gain multiple 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, Delay time constant for the voltage stabilizer. Thus, an excitation system model that can reflect multiple power parameters in the target grid can be obtained.

In some optional real-time examples, an error function is solved according to multiple voltage measurements, multiple current measurements, and multiple power angle measurements of the target power grid under multiple preset noise-like disturbance scenarios, and the minimum error value is obtained. The corresponding voltage prediction value, current prediction value and power angle prediction value include: determining the target constraint condition; solving the error function based on the target constraint condition, and obtaining the voltage prediction value, current prediction value and power angle prediction corresponding to the minimum error value value.

In some optional real-time examples, the target constraint conditions are determined, including: according to the voltage real part and the voltage imaginary part of the generator running in the predetermined initial state, the current real part and the current imaginary part of the generator running in the predetermined initial state, As well as the initial value of the predetermined potential, the initial value of the power angle and the initial value of the q-axis transient potential of the generator, as well as the q-axis synchronous reactance and the d-axis transient reactance of the generator, determine the active power, reactive power and electromotive force changes of the generator based on the network balance constraints, construct the constraints of the real and imaginary changes of the injected current at each node in the target power grid; The upper and lower bound constraints of the parameter. Thus, target constraints related to multiple power parameters in the target grid can be obtained, and based on the target constraints, voltage prediction values, current prediction values and power angle prediction values with high accuracy and applicability can be obtained.

The multiple noise-like 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 distance between the power lines at the outlets of the multiple power plants in the target power grid, A third predetermined disturbance applied to the loads at the plurality of power plant outlets. By applying different disturbances, the power plant parameters can be accurately obtained based on the scenarios in which different disturbances are applied.

Based on the foregoing embodiment and optional embodiments, an optional implementation manner is provided, which will be described in detail below.

The power plant parameter identification is of great significance to the power grid loop closing operation and economic operation. In the related art, physical quantities can be converted into frequency domain variables for analysis based on Fourier transform, Laplace transform and other technologies. Although this method can realize the identification of power plant parameters, it can only be identified offline. In the related art, online parameter identification can also be realized by performing time domain analysis on physical quantities. Although these methods can realize the identification of power plant parameters, these identification methods often rely on the occurrence of fault disturbances, which have adverse effects on the stable operation of the power system, and the identification results are contingent, which may lead to large errors in the identification results.

In view of this, this optional embodiment introduces noise-like technology, applies disturbances from different angles, and performs power plant parameter identification under multiple disturbance scenarios, thereby improving the accuracy of power plant parameter identification results; Due to the low degree of coupling of multiple scenarios, the simple space interior point method and scenario decomposition are used to obtain the identification results of power plant parameters, thereby significantly improving the efficiency of parameter identification and realizing accurate and rapid online identification of power plant parameters.

The power plant parameter identification method provided by the embodiment of the present disclosure includes the following steps:

Step S1 : based on the PMU device, obtain power data under multiple different types of noise disturbance scenarios, and the power data includes data such as grid voltage, current, and power angle. Specifically include the following steps:

S11: Set different noise-like disturbances.

It should be understood that a noise-like signal refers to the output of the system under normal operating conditions. For power systems, the bandwidth of the noise-like signal coincides with the electromechanical dynamic bandwidth, which is typically 0.2 to 2.0 Hz.

In this optional embodiment, the power plant parameter identification can be performed by setting the following three types of noise disturbances: respectively applying preset disturbances to different automatic voltage controller reference values; Set the size of the disturbance; change the load variable at the outlet of the power plant within a preset small range.

S12: Based on the PMU device, obtain the data of the output voltage, current and power angle of the power plant.

Step S2: constructing a power plant model. The power plant model includes a power plant model including a generator model, a speed regulation system model, and an excitation system model for determining the parameters of the generator to be identified, and the key parameters of the power plant to be identified are determined according to the constructed electric field model. Specifically include the following steps:

S21: The constructed generator model is a third-order model. The third-order model of the generator looks like this:

in:

分别为发电机功角、角速度、q轴暂态电抗、电流实部和虚部；P_Gk、E_fk分别为发电机有功功率和励磁电势；

表示情景j下k节点发电机的交轴电势；ω_s为同步角速度有名值；

表示情景j下k节点发电机在t时刻的有功功率，

表示情景j下k节点发电机在t时刻的直轴电流，

表示情景j下k节点发电机在t时刻的交轴电流，n_G表示节点个数，N_S表示故障场景个数。下标k表征发电机节点编号；上标j为不同的类噪声扰动场景编号。In the formula: X _dk , X′ _dk , M _k , T′ _d0k , X _qk are respectively the generator d-axis synchronous reactance, d-axis transient reactance, inertia constant, d-axis transient open-circuit time constant, and generator q-axis synchronous reactance ,

are generator power angle, angular velocity, q-axis transient reactance, real part and imaginary part of current; P _Gk and E _fk are generator active power and excitation potential, respectively;

represents the quadrature axis potential of the k-node generator under scenario j; ω _s is the named value of the synchronous angular velocity;

represents the active power of the k-node generator at time t under scenario j,

represents the direct-axis current of the k-node generator at time t under scenario j,

represents the quadrature axis current of the k-node generator at time t under scenario j, n _G represents the number of nodes, and N _S represents the number of fault scenarios. The subscript k represents the generator node number; the superscript j is the number of different noise-like disturbance scenarios.

Among them, X _dk , X' _dk , M _k , T' _d0k are generator parameters to be identified.

S22: Without taking into account the saturated part of the speed control system model, construct a speed control system model for determining the parameters to be identified for the speed control system. The model of the speed control system is as follows:

In the formula, t _g is the acceleration time constant of the speed control system; T ₁ and T ₂ are the inertia time constant and buffer time constant of the unit respectively; ω _ref , ω are the reference angular frequency and the operating angular frequency respectively; R is the rotor loop resistance; T _mech is the mechanical torque. T _mech0 is the value before mechanical torque speed regulation. Among them, R, T ₁ , T ₂ are the parameters of the speed control system to be identified.

S23: Without taking into account the saturation part in the excitation system model, construct a fourth-order excitation system model that determines the parameters to be identified for the excitation system. The fourth-order excitation system model is shown below:

In the formula, V _m is the measurement voltage; V _x , V _y , _Ta , K. V _r1 , V _r2 , V _f are the output voltages of the exciter, voltage regulator and stabilizer, respectively; V _ref is the reference voltage; K _f , T _f are the gain multiple and delay time constant of the exciter, respectively; _Tr is the delay time constant of the entire excitation system; A _e , _Be , T _e are the gain, decay time constant and delay time constant of the voltage stabilizer, respectively.

Among them, K _a , T _a , K _f , T _f , _Tr , T _e , A _e , B _e are the excitation system parameters to be identified.

Step S3: Establish a power plant parameter identification model with the goal of minimizing errors between the voltage, current and power angle model values and the measured values. Wherein, the model value is the corresponding predicted value obtained according to the above generator model, speed regulation system model and excitation system model; the measured value is the value collected according to the PMU device. Specifically include the following steps:

Based on ARIMA (Auto Regression and Moving Average, autoregressive moving average model), the measured voltage, current and power angle are fitted, and the goal is to minimize the least squares error between the fitting curve of the three operating variables and the model prediction curve , establish a dynamic identification model of key parameters of multiple power plants under multiple types of noise scenarios (equivalent to the error function in the foregoing embodiment):

分别为情景j下k节点发电机功角的预测值和测量值；

分别为电流实部的预测值和测量值、电流虚部的预测值和测量值；

分别为电压实部的预测值和测量值、电压虚部的预测值和测量值。In the formula, nT is the number of time periods; n _genl is the number of generator nodes; Ns is the total number of fault scenarios;

are the predicted value and the measured value of the generator power angle of node k under scenario j, respectively;

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;

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.

Since the dimensions of the generator voltage, current and power angle are different, and the magnitude of the data is different, the relative value is used to characterize the error, and the above objective function is modified as:

Step S4: Determine the constraints of the identification model. Specifically include the following steps:

Step S41: Assuming that the initial states under various noise disturbance scenarios are the same, the initial value constraints are:

In the formula: ΔP _k,0 , ΔQ _k,0 , ΔE′ _qk,0 are the changes of active power, reactive power and electromotive force of node k respectively; _ek , f _k , I _ek , I _fk are the initial state operation of the generator, respectively Real and imaginary parts of voltage, real and imaginary parts of current; E _Qk , δ _k,0 , E′ _qk,0 are the initial value of generator virtual potential, initial value of power angle and initial value of q-axis transient potential; X _qk , X′ _dk are the generator q-axis synchronous reactance and d-axis transient reactance, respectively.

Step S42: The network balance constraint is satisfied under various noise disturbance scenarios, as follows:

in:

表示节点注入电流实部变化量和电流虚部变化量；

分别为网络导纳矩阵实部和虚部；

分别为电压实部和虚部，注入电流实部和虚部；

分别为发电机在t时刻的电压实部和虚部。

X_qk分别表示发电机在t时刻q轴暂态电势值、d轴暂态电抗、功角值和q轴电抗。where:

Represents the change of the real part of the injected current and the change of the imaginary part of the current;

are the real and imaginary parts of the network admittance matrix, respectively;

are the real and imaginary parts of the voltage, and the real and imaginary parts of the injected current, respectively;

are the real and imaginary parts of the generator voltage at time t, respectively.

X _qk respectively represent the q-axis transient potential value, d-axis transient reactance, power angle value and q-axis reactance of the generator at time t.

Step S43: the parameters to be identified all satisfy the upper and lower limit constraints:

Upper and lower limit constraints of generator parameters to be identified:

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

The upper and lower limit constraints of the parameters to be identified in the excitation system are:

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.

Step S5: Solve the above-mentioned power plant parameter identification model based on the reduced space interior point method and the scenario decomposition method, and obtain the final key parameter values of the power plant.

In this optional embodiment, data such as grid voltage, current and power angle under different types of noise disturbance scenarios are obtained based on the PMU device; power plant models including generator models, speed regulation system models, and excitation system models are established to Determine the key parameters of the power plant to be identified; establish the power plant parameter identification model with the goal of minimizing the errors of the voltage, current and power angle model values and measurement values; determine the constraints of the identification model; The parameter identification model is solved to obtain the final key parameter values of the power plant. It can apply disturbance types from different angles and perform multi-disturbance scene identification, which can improve the accuracy of identification. At the same time, taking advantage of the characteristics of low degree of freedom in parameter identification and low degree of multi-scene coupling, a simplified space interior point method and a scenario decomposition solution strategy are proposed. , which can significantly improve the efficiency of parameter identification and achieve accurate and fast online identification.

The IEEE9 node system is taken as an example for further description below. Figure 2 is a schematic diagram of an IEEE9 node system. As shown in Figure 2, the generators are connected at nodes 1, 2, and 3, and the loads are connected at nodes 5, 6, and 8.

Among them, the actual parameter values of the generator in the IEEE9 node system are shown in Table 1.

Table 1

Figure 2 is a schematic diagram of an IEEE9 node system. As shown in Figure 2, the actual parameter values of the excitation system in the IEEE9 node system are shown in Table 2.

Table 2

Among them, the actual parameter values of the speed control system in the IEEE9 node system are shown in Table 3.

table 3

The preset various types of 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, that is, applying line impedance. Disturbance, change 0.1; apply 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 running platform is CPUi5, the CPU frequency is 3.19GHz, and the memory is 4GB.

In this optional real-time approach, the optimization problem size is shown in Table 4.

Table 4

In Table 4, Ns represents the number of scenarios, n and m are the number of state variables and equality constraints of the primal-dual system, respectively, DIM and NNZ are the dimension of the primal-dual system and the number of non-zero elements, and DOF is the degree of freedom. It can be seen from Table 4 that the optimization scale DIM of each calculation example is large, but its DOF is very low, which is very suitable for solving in a simple space.

The results of the generator parameters identified based on this optional embodiment are shown in Table 5.

table 5

The results of the excitation parameters identified based on this optional embodiment are shown in Table 6:

Table 6

The results of the speed regulation parameters identified based on this optional implementation are shown in Table 7:

Table 7

According to the identification results of the above power plant parameters, in the traditional single disturbance scenario, due to the poor ability to resist measurement errors, the identification results are poor. When the number of disturbance scenarios increases to 7, the overall identification algorithm identification accuracy is improved. Very few generator parameters have large errors, which can basically reflect the dynamic response process of the system.

The identification process of key parameters of a power plant is generally an optimization process with the goal of minimizing the error between the measured value of a specific physical quantity and the model value. The former is mainly based on Fourier transformation, Laplace transformation and other technologies to convert physical quantities into frequency domain variables for analysis, which has the advantages of simple and convenient calculation, but can only be identified offline; the latter directly analyzes physical quantities in time domain , can realize online parameter identification, such as using the least squares algorithm to identify the third-order synchronous generator online. However, the above identification methods often rely on the occurrence of fault disturbance, which itself has an adverse effect on the stable operation of the power system, and the parameter identification under a single disturbance has certain contingency, which may lead to large errors. In this optional embodiment, by introducing multiple types of noise, 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. Due to its characteristics and the low degree of multi-scenario coupling, a simple space interior point method and a scenario decomposition solution strategy are proposed, which can significantly improve the efficiency of parameter identification and achieve accurate and fast online identification.

Example 2

According to an embodiment of the present invention, a structural block diagram of a power plant parameter identification device is also provided. Referring to FIG. 3 , the apparatus includes a first acquisition module 302 , a second acquisition module 304 , and an identification module 306 . The specific description is given below.

The first acquisition module 302 is configured 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-like disturbance scenarios respectively; the second acquisition module 304 is connected to In the above-mentioned first acquisition module 302, it is used to solve the error function according to the multiple voltage measurement values, multiple current measurement values and multiple power angle measurement values of the target power grid under multiple preset noise-like disturbance scenarios, to obtain the minimum value. The voltage predicted value, current predicted value and power angle predicted value corresponding to the error value, wherein, the error function is constructed according to the least square method, and the error function is used to characterize the measured values of multiple power parameters of the power grid and the corresponding predicted power parameters The error between the values, the power parameters include voltage, current and power angle; the identification module 306, connected to the above-mentioned second acquisition module 304, is used for predicting the voltage, current and power angle corresponding to the minimum error value. value, and the pre-built generator third-order model, speed regulation system model, and excitation system model, and identify the power plant parameters corresponding to the generator third-order model, speed regulation system model, and excitation system model; among them, the power plant parameter is the generator Parameters in the third-order model, the speed control system model, and the excitation system model.

It should be noted here that the above-mentioned first acquisition module 302, second acquisition module 304, and identification module 306 correspond to steps S102 to S106 in Embodiment 1, and examples and application scenarios implemented by the three modules and corresponding steps The same, but not limited to the content disclosed in Example 1 above.

According to another aspect of the embodiments of the present invention, a computer-readable storage medium is provided, the storage medium includes a stored program, wherein when the program is run, a device where the storage medium is located is controlled to execute any one of the above power plant parameter identification methods.

According to another aspect of the embodiments of the present invention, a computer device is also provided, including: a memory and a processor, where the memory stores a computer program; the processor is configured to execute the computer program stored in the memory, and when the computer program runs, the processing The controller executes any of the power plant parameter identification methods described above.

With the embodiments of the present invention, an image processing solution is provided. Through, the purpose is achieved, and the technical problem in the related art is solved.

Those of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing the hardware related to the terminal device through a program, and the program can be stored in a computer-readable storage medium, and the storage medium can Including: flash disk, read-only memory (Read-Only Memory, ROM), random access device (RandomAccess Memory, RAM), magnetic disk or optical disk, etc.

The above-mentioned serial numbers of the embodiments of the present invention are only for description, and do not represent the advantages or disadvantages of the embodiments.

In the above-mentioned embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.

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 only illustrative, for example, the division of units may be a logical function division, and there may be other division methods in actual implementation, for example, multiple units or components may be combined or integrated into Another system, or some features can be ignored, or not implemented. On the other hand, the shown or discussed mutual coupling or direct coupling or communication connection may be indirect coupling or communication connection through some interfaces, units or models, and may be in electrical or other forms.

The units described as separate components may or may not be physically separated, and components shown as units may or may not be physical units, that is, may be located in one place, or may be distributed to multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution in this embodiment.

In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically alone, or two or more units may be integrated into one unit. The above-mentioned integrated units may be implemented in the form of hardware, or may be implemented in the form of software functional units.

The integrated unit, if implemented in the form of a software functional unit and sold or used as an independent product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention is essentially 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, and the computer software product is stored in a storage medium , including several instructions for causing a computer device (which may be a personal computer, a server, or a 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: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk and other media that can store program codes .

The above are only the preferred embodiments of the present invention. It should be pointed out that for those skilled in the art, without departing from the principles of the present invention, several improvements and modifications can be made. It should be regarded as the protection scope of the present invention.

Claims (10)

1. a power plant parameter identification method, is characterized in that, comprises: Based on the power management unit PMU, obtain the voltage measurement value, current measurement value and power angle measurement value of the target power grid under multiple preset noise-like disturbance scenarios respectively; According to the multiple voltage measurement values, multiple current measurement values and multiple power angle measurement values of the target power grid under multiple preset noise-like disturbance scenarios, the error function is solved, and the voltage prediction value corresponding to the minimum error value is obtained. , the predicted value of current and the predicted value of power angle, wherein the error function is constructed according to the least squares method, and the error function is used to characterize the difference between the measured values of multiple power parameters of the power grid and the corresponding predicted values of power parameters. error, the power parameters include voltage, current and power angle; According to the voltage prediction value, current prediction value and power angle prediction value corresponding to the minimum error value, as well as the pre-built generator third-order model, speed regulation system model, and excitation system model, identify the The power plant parameters corresponding to the order model, the speed regulation system model, and the excitation system model; wherein, the power plant parameters are the parameters in the third-order model of the generator, the speed regulation system model, and the excitation system model. 2. The method of claim 1, further comprising: According to the d-axis synchronous reactance, d-axis transient reactance, inertia constant, d-axis transient open-circuit time constant of the generator in the target power grid, as well as the power angle, acceleration, q-axis transient reactance, and actual current of the generator part and imaginary part, as well as the active power and excitation potential of the generator, and the quadrature axis potential, active power, direct axis current and quadrature axis current of the node generator under the noise disturbance scenario, to construct a third-order model of the generator; The power plant parameters corresponding to the third-order model of the generator include at least one of the following: d-axis synchronous reactance, d-axis transient reactance, inertia constant, and d-axis transient open-circuit time constant of the generator. 3. The method of claim 1, further comprising: According to the acceleration time constant of the speed control system in the target power grid, the inertia time constant and buffer time constant of the units in the speed control system, the reference angular frequency and the operating angular frequency of the speed control system, the speed control system The rotor loop resistance, the mechanical torque, and the initial value of the mechanical torque of the speed control system are used to construct the speed control system model; wherein, the power plant parameters corresponding to the speed control system model include at least one of the following: The inertia time constant of the unit in the speed control system, the buffer time constant of the unit in the speed control system, and the rotor loop resistance of the speed control system. 4. The method of claim 1, further comprising: According to the real part voltage, imaginary part voltage, time constant and gain multiplier of the voltage regulator in the target grid, and the output voltage of the exciter, voltage regulator and stabilizer, and the gain multiplier and delay time constant of the exciter , and the delay time constant of the excitation system, as well as the gain, decay time constant and delay time constant of the voltage stabilizer, to construct the excitation system model; the power plant parameters corresponding to the excitation system model include at least one of the following: the The time constant of the voltage regulator, the gain multiplier of the voltage regulator, the gain multiplier of the exciter of the voltage regulator, the delay time constant of the exciter, the delay time constant of the excitation system, the voltage stabilization 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 , wherein, according to the multiple voltage measurement values, multiple current measurement values, and multiple power angles of the target power grid under multiple preset noise-like disturbance scenarios. 6 . The measured value solves the error function, and obtains the predicted value of voltage, predicted current and predicted value of power angle corresponding to the minimum error value, including: Determine target constraints; The error function is solved based on the target constraint condition, and the voltage prediction value, the current prediction value and the power angle prediction value corresponding to the minimum error value are obtained. 6. The method according to claim 5, wherein the determining the target constraint comprises: According to the voltage real part and the voltage imaginary part of the generator running in the predetermined initial state, the current real part and the current imaginary part of the generator running in the predetermined initial state, and the predetermined potential initial value of the generator, The initial value of the power angle and the initial value of the q-axis transient potential, as well as the q-axis synchronous reactance and the d-axis transient reactance of the generator, determine the active power, reactive power and electromotive force variation constraints of the generator; Based on the network balance constraint, construct the constraint conditions of the variation of the real part of the injected current and the variation of the imaginary part of the current of each node in the target power grid; Based on the predetermined upper limit value of the power plant parameter and the predetermined lower limit value of the power plant parameter, the upper and lower limit constraints of the power plant parameter are constructed. 7. The method according to claim 1, wherein the multiple noise-like disturbance scenarios comprise at least two of the following: a first predetermined disturbance applied to a plurality of voltage controller reference values in the target grid, a second predetermined disturbance applied to the spacing of power lines at a plurality of power plant outlets in the target grid, a load at a plurality of power plant outlets The third predetermined perturbation applied. 8. A power plant parameter identification device, characterized in that, comprising: a first obtaining module, configured to obtain, based on the power management unit PMU, respectively the voltage measurement value, the current measurement value and the power angle measurement value of the target power grid under multiple preset noise-like disturbance scenarios; The second obtaining module is configured to solve an error function according to multiple voltage measurements, multiple current measurements, and multiple power angle measurements of the target power grid under multiple preset noise-like disturbance scenarios, and obtain the minimum error The voltage predicted value, current predicted value and power angle predicted value corresponding to the value; wherein, the error function is constructed according to the least squares method, and the error function is used to characterize the measured values of multiple power parameters of the power grid and the corresponding Errors between predicted values of power parameters including voltage, current and power angle; The identification module is used for identifying the voltage prediction value, current prediction value and power angle prediction value corresponding to the minimum error value, as well as the pre-built generator third-order module, speed regulation system module, and excitation system module, respectively. Power plant parameters corresponding to the generator third-order module, speed regulation system module, and excitation system module; wherein, the power plant parameters are parameters in the generator third-order module, speed regulation system module, and excitation system module. 9 . A computer-readable storage medium, characterized in that the storage medium comprises a stored program, wherein when the program is executed, a device where the storage medium is located is controlled to execute any one of claims 1 to 7 The power plant parameter identification method. 10. A computer device, comprising: a memory and a processor, the memory stores a computer program; The processor is configured to execute the computer program stored in the memory, and when the computer program runs, the processor executes the power plant parameter identification method according to any one of claims 1 to 7.

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