CN111931360A - Excitation system parameter online identification method and device - Google Patents
Excitation system parameter online identification method and device Download PDFInfo
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Abstract
The invention discloses an excitation system parameter online identification method and device, which comprises the steps of selecting a standard model corresponding to recorded wave data from a pre-established generator excitation system standard model according to the collected recorded wave data of a speed regulating system and an excitation regulator, identifying the selected standard model parameter by adopting a selected identification algorithm, and substituting each parameter identification result into the selected standard model to obtain an excitation system approximate calculation model; and performing control performance evaluation on the excitation system approximate calculation model according to the excitation system control performance evaluation index and the setting parameter. The invention adopts an artificial intelligence identification algorithm to realize the parameter identification of the excitation system of the unit, and solidifies the parameter identification into a hardware system to obtain good identification effect.
Description
Technical Field
The invention belongs to the technical field of excitation system parameter identification in an electric power system, and particularly relates to an excitation system parameter online identification method and device.
Background
With the rapid development of smart grid construction, the intellectualization level of grid equipment is continuously improved. The online monitoring device is used for reflecting the health state of the power grid equipment, can effectively prevent the equipment from breaking down and protect the operation safety of the power grid, and therefore people are more and more concerned. The generator excitation system provides excitation power for the generator set, has the functions of adjusting the voltage of the generator end and controlling the reactive power distribution of the generator, accurately masters the parameters of each grid-connected unit excitation system in an actual system, and is important for researching the stability of a power grid and formulating a reasonable operation mode.
In an actual generator excitation system, factors such as an amplitude limiting link generally exist, the model is not a simple linear model any more, and some links in the excitation system can enter a nonlinear region if the disturbance is slightly large, however, the traditional identification method such as a time domain identification method and a frequency domain identification method cannot well solve the problem of identification of parameters of the nonlinear generator excitation system.
At present, most researches are concentrated on an excitation system parameter online measurement and identification technology, the problem of parameter identification of a nonlinear part of an excitation system is solved, and researches on an online monitoring device of power grid equipment are lacked and are not used for practice. The existing excitation system identification device still uses the traditional identification method to realize the identification of the excitation system, and can not realize the good identification of the nonlinear part of the excitation system.
Disclosure of Invention
The invention provides an excitation system parameter online identification method and device for solving the technical problems and realizing good identification of a nonlinear part of an excitation system.
The invention adopts the following technical scheme.
In one aspect, the invention provides an excitation system parameter online identification method, which comprises the following steps:
selecting a standard model corresponding to wave recording data from a pre-established standard model of the generator excitation system according to the acquired wave recording data of the speed regulating system and the excitation regulator, identifying the parameters of the selected standard model by adopting a selected identification algorithm, and bringing the identification results of all the parameters into the selected standard model to obtain an approximate calculation model of the excitation system; and performing control performance evaluation on the excitation system approximate calculation model according to the excitation system control performance evaluation index and the setting parameter.
In a second aspect, the present invention provides an excitation system parameter online identification device, including: a main control algorithm unit and an evaluation unit; the main control algorithm comprises a standard model determining module, a parameter identification module and a standard model approximate calculation module; the standard model determining module is used for pre-establishing a generator excitation system standard model; the parameter identification module is used for identifying the selected standard model parameters by adopting a selected identification algorithm;
the standard model approximate calculation module is used for selecting a standard model corresponding to wave recording data from a pre-established standard model of the generator excitation system according to the acquired wave recording data of the speed regulating system and the excitation regulator, and bringing each parameter identification result determined by the parameter identification module into the selected standard model to obtain an excitation system approximate calculation model;
and the evaluation unit is used for carrying out control performance evaluation on the excitation system approximate calculation model according to the excitation system control performance evaluation index and the setting parameter.
The invention has the following beneficial technical effects:
the invention adopts an artificial intelligence identification algorithm to realize the parameter identification of the excitation system of the unit, and solidifies the parameter identification into a hardware system to obtain good identification effect.
Drawings
FIG. 1 is a schematic diagram illustrating a principle of parameter identification according to an embodiment of the present invention;
FIG. 2 is a standard model of the FV-type of BPA in an embodiment of the invention;
fig. 3 is a schematic structural diagram of an excitation system parameter online identification device according to an embodiment of the present invention.
Detailed Description
The preferred embodiments will be described in detail below with reference to the accompanying drawings. It should be emphasized that the following description is merely exemplary in nature and is not intended to limit the scope of the invention or its application.
The embodiment I provides an excitation system parameter online identification method, which comprises the following steps:
selecting a standard model corresponding to wave recording data from a pre-established standard model of the generator excitation system according to the acquired wave recording data of the speed regulating system and the excitation regulator, identifying the parameters of the selected standard model by adopting a selected identification algorithm, and bringing the identification results of all the parameters into the selected standard model to obtain an approximate calculation model of the excitation system; and performing control performance evaluation on the excitation system approximate calculation model according to the excitation system control performance evaluation index and the setting parameter.
In the embodiment, the input wave recording data are system operation data including an excitation regulator, a speed regulation system, a power management unit PMU and the like, and optional detailed information can be seen in an attached table 1;
table 1 input recorded broadcast data
In the specific embodiment, the generator excitation system standard model is constructed by the prior art, which is not described in detail in the invention,
the setting parameters of the device and the corresponding control performance evaluation indexes of the excitation system are from external input. The setting parameters of the device are mainly the structures and parameters of the rest components of the unit control system except the excitation system, and are detailed in attached table 2 and attached table 3 (including table 3.1, table 3.2 and table 3.3). The excitation system control performance evaluation index is mainly used for evaluating the control performance of an excitation system approximate calculation model. And storing the approximate check sum diagnosis result into a database, and generating an excitation system parameter correction scheme and alarm information for reference of operators.
TABLE 2 Generator tuning parameters
Attached table 3
TABLE 3.1 setting parameters of turbine speed regulation control system
Monitoring the set value of the upper computer
TABLE 3.2 turbine speed governing control system setting parameters
TABLE 3.3 setting parameters of speed control system of pumped storage power station
The identification of the parameters of the excitation system comprises the following steps: according to a certain specified criterion, selecting an approximate calculation model with best data fitting, and keeping the input and output characteristics of the model and the actual model consistent or within an error allowable range, wherein the essence is a parameter optimization process.
Fig. 1 illustrates the principle of parameter identification of the excitation system. The basic process of parameter identification is as follows: firstly, selecting an excitation system model which is closest to an actually measured model structure as an identification model; running data x of terminal voltage of unit in recording data1(t) and the excitation regulator voltage reference x2(t), and taking the sum of the two as an input signal x (t), as shown in formula (1). The excitation system regulator outputs excitation voltage operation data as y1(t), the output of the established standard model under the action of x (t) is y2(t)，
x(t)＝x2(t)-x1(t) (1)
According to the output data y1(t)、y2(t) and calculating an error E (t) and an error function E; if the error function E exceeds the deviation allowable range, continuously correcting the estimated model parameters by calling an identification algorithm; otherwise, the identification model is considered to meet the requirement of certain precision. And obtaining an optimal approximate calculation model close to the actual model through repeated iteration, wherein the error E (t) and the error function E are expressed as follows:
e(t)＝y1(t)-y2(t)
E＝∫e(t)dt。
therefore, when the excitation system parameter identification is performed, the structure of the identification model is firstly determined, i.e. a standard model is established. When parameter identification of the self-shunt excitation static excitation system is performed, the standard model structure is FV type in BPA (Bonneville Power addition) simulation environment, as shown in the structure diagram 2. In FIG. 2, Vt is the gate voltage, Vref is the gate voltage given reference value; vpss is the power system stabilizer output; OEL is output of an over-excitation link; UEL is the output of an underexcitation link; k is the regulator gain; kv is a proportional integral or pure integral adjustment selection factor; tc1, Tb1, Tc2, Tb2 are voltage regulator time constants; ka is the voltage regulator gain; ta is the time constant of the voltage regulator amplifier; kf is the gain of the stable loop of the voltage regulator; tf is the voltage regulator stabilizing loop time constant; VRmax is the maximum output of the voltage regulator; VRmin is the minimum output of the voltage regulator, and Kc is the rectifier load factor of the commutation reactance; if is exciting current; vf is the excitation voltage.
Establishing an approximate calculation model: the parameter identification confirms the parameter value of the established standard model, so that the standard model is basically consistent with the response of an actual system under the same input signal. And determining the numerical value of each parameter in the established standard model by calling an equipment built-in identification algorithm, and bringing the identification result of each parameter into the standard model to obtain the excitation system approximate calculation model.
In the embodiment, the parameters of the generator and the speed regulation control system are self-setting parameters, and the excitation control system adopts an approximate calculation model. And carrying out no-load one-time step response simulation calculation on the system to obtain the rise time, peak time and overshoot of the step response, and using the rise time, the peak time and the overshoot as the evaluation of the control performance of the generator excitation system. And comparing and analyzing the actually measured step response rise time, the peak time and the overshoot according to an evaluation standard input from the outside, such as the actually measured step response rise time, the actually measured peak time and the actually measured overshoot of the generator set during a factory test.
Optionally, the method further comprises outputting excitation system performance evaluation information: and if the rising time, the peak time, the overshoot and the like of the step response exceed the set allowable deviation range, outputting alarm information, and otherwise, outputting normal information. Outputting an information excitation system parameter identification result, an excitation system performance evaluation result or alarm information; if the correction method is adopted to correct the parameters, optionally, the method further comprises a correction mode of the parameters of the output excitation system.
In this embodiment, the selected standard model parameter is identified by using the selected identification algorithm, and the parameter to be identified may be solved by using a conventional optimization solving algorithm, such as a genetic algorithm or a particle swarm algorithm, which is not described in detail in this embodiment.
In the second embodiment, on the basis of the first embodiment, the differential evolution algorithm is adopted to perform optimization solution on the determined parameter to be identified until the error meets the accuracy requirement, so as to obtain the identification result. The differential evolution algorithm comprises the following steps:
a) initializing a population:
by pi(t) is the ith individual in the population of the t generation, pij(t) is the jth dimension component of the ith individual in the tth generation population, then the population is initialized as formula (2);
wherein p isij(0) Is the j dimension component of the ith individual in the 0 th generation population; rand () is [0,1 ]]A random number over the interval;
the upper and lower boundaries of the individual, respectively; NP is the size of the population, dim is the dimension of each individual;
b) mutation operation: randomly selecting two different individuals in the population, scaling the vector difference, and then carrying out vector synthesis with the individual to be mutated, wherein for each individual vector in the population of the t generation, p isr1(t)，pr2(t)，pr3(t), r1, r2, r3 epsilon {1, 2.., NP } are different integers, and mutation operation is carried out according to the formula (3);
vi(t+1)＝pr3(t)+F×(pr2(t)-pr1(t)) (3)
wherein F is a variation scaling factor of [0,1 ]]Random number, pr2(t)-pr1(t) is a difference vector;
c) and (4) performing a crossover operation. Vector p for each individual in the t-th generation populationi(t) combining it with a variant vi(t +1) performing crossover operation to generate new individual ui(t+1):
Wherein CR is a cross probability factor and is a fixed value; rand (j) is an element [0,1 ]]Is the corresponding random of the j dimension; k is the coefficient corresponding to the ith individual, and is typically from the sequence [1, 2, …, dim]Is randomly selected to ensure uiAt least one-dimensional component of (t +1) is derived from the variant vector individual vi；
d) Selecting operation: new individuals u generated after mutation and cross operationi(t +1) and target vector individual p in the t-th generation populationi(t +1) competing, and selecting the one with better fitness value to enter the next generation.
Optionally, on the basis of the second embodiment, in order to improve the search performance, the DE algorithm is further improved, and in a specific embodiment, an adaptive variation rate λ is introduced in the variation operation, and the expression is as follows:
wherein T is the maximum number of iterations; t is the current iteration number, F0Is a variation parameter.
Optionally, on the basis of the second embodiment, in a specific embodiment, a method for dynamically and nonlinearly increasing CR is adopted in the interleaving operation, and the expression is as follows:
count is the current number of iterations, gen _ max is the maximum number of iterations, and the correlation of CR is set to CRmax、CRminAnd k is a setting intermediate parameter.
Fourth embodiment, corresponding to the excitation system parameter online identification method provided in the foregoing embodiment, the present embodiment provides an excitation system parameter online identification device, including: a main control algorithm unit and an evaluation unit; the main control algorithm unit comprises a standard model determining module, a parameter identification module and a standard model approximate calculation module; the standard model determining module is used for pre-establishing a generator excitation system standard model; the parameter identification module is used for identifying the selected standard model parameters by adopting a selected identification algorithm;
the standard model approximate calculation module is used for selecting a standard model corresponding to wave recording data from a pre-established standard model of the generator excitation system according to the acquired wave recording data of the speed regulating system and the excitation regulator, and bringing each parameter identification result determined by the parameter identification module into the selected standard model to obtain an excitation system approximate calculation model;
and the evaluation unit is used for carrying out control performance evaluation on the excitation system approximate calculation model according to the excitation system control performance evaluation index and the setting parameter.
The device for online identification of parameters of the excitation system provided by the embodiment transmits the actually measured sampling data of the excitation regulator and the speed regulating system to the master station server, and realizes evaluation of the health condition of the excitation system by using a parameter identification algorithm and a unit performance state evaluation index embedded in the device.
The embedded software of the device is designed by adopting a Keil Vision5 integrated development environment, an ARM special version development tool MDK is carried, a software program is solidified in the online monitoring device, online monitoring of a network source can be implemented, and the structure of the device is shown in figure 1. The collected data is transmitted to the device through a communication protocol, and the running state of the generator excitation system in the actual system is diagnosed on line through an excitation system parameter identification fusion model constructed in software, so that whether the running state of the generator equipment is normal or not is judged quickly and accurately, and the on-line identified actual running parameters of the excitation system are provided for a scheduling side.
The main control unit internal algorithm selects a generator excitation system standard model according to different generator excitation systems to be identified, for example, the standard model of a self-shunt excitation static excitation system is an FV type, the modeling in figure 1 represents and establishes a standard model structure, and the device main control algorithm unit comprises a standard model determining module and an F type model under the condition that an internal modeling algorithm is included in a BPA environment. And then, the parameter identification module calls a built-in identification algorithm according to the recorded and broadcast data to identify the model parameters of the standard model. And finally, the standard model approximate calculation module brings the identification results of all the parameters into the standard model to obtain the excitation system approximate calculation model.
Optionally, the excitation system parameter online identification device provided in this embodiment is arranged in the master station server; the measured sampling data of the excitation regulator and the speed regulating system are transmitted to an excitation system parameter online identification device of a master station server, and a parameter identification module containing a parameter identification algorithm and an evaluation unit embedded in the device are used for evaluating the unit performance state evaluation index.
Optionally, the system further comprises a parameter correction and alarm module for correcting and alarming parameters, so as to evaluate the health condition of the excitation system and correct the parameters.
The specific implementation method of each module in this embodiment corresponds to the method provided in the above embodiment, and is not described herein again.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While the present invention has been described with reference to the embodiments shown in the drawings, the present invention is not limited to the embodiments, which are illustrative and not restrictive, and it will be apparent to those skilled in the art that various changes and modifications can be made therein without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (10)
1. An excitation system parameter online identification method is characterized by comprising the following steps:
selecting a standard model corresponding to wave recording data from a pre-established standard model of the generator excitation system according to the acquired wave recording data of the speed regulating system and the excitation regulator, identifying the parameters of the selected standard model by adopting a selected identification algorithm, and bringing the identification results of all the parameters into the selected standard model to obtain an approximate calculation model of the excitation system; and performing control performance evaluation on the excitation system approximate calculation model according to the excitation system control performance evaluation index and the setting parameter.
2. The method for online identification of the parameters of the excitation system according to claim 1, wherein the collected wave recording data of the speed regulating system and the excitation regulator comprises generator set end voltage operation data, an excitation regulator voltage reference value and an excitation system regulator output excitation voltage; specifically, the selected standard model parameters are identified by the following method:
running data x of terminal voltage of unit in recording data1(t) and the excitation regulator voltage reference x2(t), and taking the sum of the two as an input signal x (t), as shown in equation (1):
x(t)＝x2(t)-x1(t) (1)
outputting excitation voltage operation data as y according to excitation system regulator1(t), the output of the established standard model under the action of x (t) is y2And (t) carrying out optimization solution on the determined parameters to be identified by adopting an optimization algorithm until the error meets the precision requirement, and obtaining an identification result.
3. The method for identifying the parameters of the excitation system on line as claimed in claim 2, wherein the parameters to be identified are optimized and solved by adopting a differential evolution algorithm until errors meet the accuracy requirement, and identification results are obtained.
4. The excitation system parameter online identification method according to claim 3, wherein the differential evolution algorithm comprises the following steps:
a) initializing a population:
by pi(t) is the ith individual in the population of the t generation, pij(t) is the jth dimension component of the ith individual in the tth generation population, then the population is initialized as formula (2);
wherein p isij(0) Is the j dimension component of the ith individual in the 0 th generation population; rand () is [0,1 ]]A random number over the interval;
the upper and lower boundaries of the individual, respectively; NP is the size of the population, dim is the dimension of each individual;
b) mutation operation: randomly selecting two different individuals in the population, scaling the vector difference, and then carrying out vector synthesis with the individual to be mutated, wherein for each individual vector in the population of the t generation, p isr1(t)，pr2(t)，pr3(t), r1, r2, r3 epsilon {1, 2.., NP } are different integers, and mutation operation is carried out according to the formula (3);
vi(t+1)＝pr3(t)+F×(pr2(t)-pr1(t)) (3)
wherein F is a variation scaling factor of [0,1 ]]Random number, pr2(t)-pr1(t) is a difference vector;
c) and (3) cross operation: vector p for each individual in the t-th generation populationi(t) combining it with a variant vi(t +1) performing crossover operation to generate new individual ui(t+1):
Wherein C isR is a cross probability factor and is a fixed value; rand (j) is an element [0,1 ]]Is the corresponding random of the j dimension; k is the coefficient corresponding to the ith individual, and is typically from the sequence [1, 2, …, dim]Is randomly selected to ensure uiAt least one-dimensional component of (t +1) is derived from the variant vector individual vi；
d) Selecting operation: new individuals u generated after mutation and cross operationi(t +1) and target vector individual p in the t-th generation populationi(t +1) competing, and selecting the one with better fitness value to enter the next generation.
5. The method for online identification of parameters of an excitation system according to claim 4, wherein an adaptive variation rate λ is introduced in the variation operation, and the expression is as follows:
wherein T is the maximum number of iterations; t is the current iteration number, F0Is a variation parameter.
6. The method for online identification of excitation system parameters according to claim 4, wherein a method of dynamic nonlinear increase of CR is adopted in the interleaving operation, and the expression is as follows:
count is the current number of iterations, gen _ max is the maximum number of iterations, and the correlation of CR is set to CRmax、CRminAnd k is a setting intermediate parameter.
7. The method for on-line identification of the parameters of the excitation system according to claim 1, wherein the system is subjected to no-load one-time step response simulation calculation to obtain the rise time, peak time and overshoot of the step response, and the rise time, peak time and overshoot are used as evaluation indexes of the control performance of the excitation system of the generator.
8. An excitation system parameter online identification device is characterized by comprising: a main control algorithm unit and an evaluation unit; the main control algorithm comprises a standard model determining module, a parameter identification module and a standard model approximate calculation module; the standard model determining module is used for pre-establishing a generator excitation system standard model; the parameter identification module is used for identifying the selected standard model parameters by adopting a selected identification algorithm;
the standard model approximate calculation module is used for selecting a standard model corresponding to wave recording data from a pre-established standard model of the generator excitation system according to the acquired wave recording data of the speed regulating system and the excitation regulator, and bringing each parameter identification result determined by the parameter identification module into the selected standard model to obtain an excitation system approximate calculation model;
and the evaluation unit is used for carrying out control performance evaluation on the excitation system approximate calculation model according to the excitation system control performance evaluation index and the setting parameter.
9. The device for online identification of excitation system parameters according to claim 8, further comprising: and the parameter correcting and alarming module is used for correcting and alarming the parameters.
10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 7.
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