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

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

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Publication number
CN115117879A
CN115117879A CN202210764472.9A CN202210764472A CN115117879A CN 115117879 A CN115117879 A CN 115117879A CN 202210764472 A CN202210764472 A CN 202210764472A CN 115117879 A CN115117879 A CN 115117879A
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power
voltage
generator
predicted value
value
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Inventor
白晶
王方雨
于希娟
周运斌
陈茜
王卫
王海云
张再驰
董楠
杨莉萍
张雨璇
汪伟
姚艺迪
徐鹏
王腾飞
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State Grid Corp of China SGCC
State Grid Beijing Electric Power Co Ltd
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State Grid Corp of China SGCC
State Grid Beijing Electric Power Co Ltd
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Priority to CN202210764472.9A priority Critical patent/CN115117879A/en
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/003—Load forecast, e.g. methods or systems for forecasting future load demand
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/007—Arrangements for selectively connecting the load or loads to one or several among a plurality of power lines or power sources
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/007—Arrangements for selectively connecting the load or loads to one or several among a plurality of power lines or power sources
    • H02J3/0073—Arrangements for selectively connecting the load or loads to one or several among a plurality of power lines or power sources for providing alternative feeding paths between load and source when the main path fails, e.g. transformers, busbars
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/007—Arrangements for selectively connecting the load or loads to one or several among a plurality of power lines or power sources
    • H02J3/0075—Arrangements for selectively connecting the load or loads to one or several among a plurality of power lines or power sources for providing alternative feeding paths between load and source according to economic or energy efficiency considerations, e.g. economic dispatch
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
    • H02P21/00—Arrangements or methods for the control of electric machines by vector control, e.g. by control of field orientation
    • H02P21/14—Estimation or adaptation of machine parameters, e.g. flux, current or voltage
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
    • H02P21/00—Arrangements or methods for the control of electric machines by vector control, e.g. by control of field orientation
    • H02P21/14—Estimation or adaptation of machine parameters, e.g. flux, current or voltage
    • H02P21/18—Estimation of position or speed
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
    • H02P21/00—Arrangements or methods for the control of electric machines by vector control, e.g. by control of field orientation
    • H02P21/22—Current control, e.g. using a current control loop
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S40/00—Systems for electrical power generation, transmission, distribution or end-user application management characterised by the use of communication or information technologies, or communication or information technology specific aspects supporting them
    • Y04S40/20—Information technology specific aspects, e.g. CAD, simulation, modelling, system security

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  • Engineering & Computer Science (AREA)
  • Power Engineering (AREA)
  • Supply And Distribution Of Alternating Current (AREA)

Abstract

The invention discloses a power plant parameter identification method, a power plant parameter identification device and a computer readable storage medium. Wherein, the method comprises the following steps: obtaining a voltage measurement value, a current measurement value and a power angle measurement value of a target power grid under a plurality of preset noise-like disturbance scenes based on a Power Management Unit (PMU); solving an error function between the measured values of the voltage, the current and the power angle of the target power grid constructed according to the least square method and the model predicted value, and obtaining a target voltage predicted value, a target current predicted value and a target power angle predicted value which correspond to the minimum error value; and identifying parameters of the power plant according to the target voltage predicted value, the target current predicted value and the target power angle predicted value, as well as a pre-constructed three-order model of the generator, a speed regulating system model and an excitation system model. The method solves the technical problem that the parameters of the power plant cannot be accurately identified in the related technology.

Description

Power plant parameter identification method and device and computer readable storage medium
Technical Field
The invention relates to the field of power control, in particular to a power plant parameter identification method and device and a computer readable storage medium.
Background
The identification of the key parameters of the power plant has important significance on power grid closed loop operation, economic operation and the like.
In the related technology, the physical quantity related to the power grid is converted into frequency domain quantity through technologies such as Fourier change and Laplace change for analysis, and the power plant parameters are identified based on the conversion, but the method can only be used for offline identification. In the related art, the problem that the parameters of the power plant cannot be accurately identified exists.
In view of the above problems, no effective solution has been proposed.
Disclosure of Invention
The embodiment of the invention provides a power plant parameter identification method, a device and a computer readable storage medium, which at least solve the technical problem that the power plant parameters cannot be accurately identified in the related technology.
According to an aspect of an embodiment of the present invention, there is provided a power plant parameter identification method, including: respectively acquiring a voltage measurement value, a current measurement value and a power angle measurement value of a target power grid under a plurality of preset noise-like disturbance scenes based on a Power Management Unit (PMU); solving an error function according to a plurality of voltage measurement values, a plurality of current measurement values and a plurality of power angle measurement values of the target power grid under a plurality of preset noise-like disturbance scenes to obtain a voltage prediction value, a current prediction value and a power angle prediction value corresponding to a minimum error value, wherein the error function is constructed according to a least square method and is used for representing errors between a plurality of power parameter actual measurement values of the power grid and corresponding power parameter prediction values, and the power parameters comprise voltage, current and power angles; according to the voltage predicted value, the current predicted value and the power angle predicted value which correspond to the minimum error value, and a pre-constructed generator three-order model, a speed regulating system model and an excitation system model, power plant parameters which respectively correspond to the generator three-order model, the speed regulating system model and the excitation system model are identified; and the power plant parameters are parameters in the generator three-order model, the speed regulating system model and the excitation system model.
Optionally, the method further comprises: constructing a third-order model of the generator according to a d-axis synchronous reactance, a d-axis transient reactance, an inertia constant, a d-axis transient open-circuit time constant of the generator in the target power grid, a power angle, an acceleration, a q-axis transient reactance, a current real part and an imaginary part of the generator, active power and excitation potential of the generator, and quadrature-axis potential, active power, direct-axis current and quadrature-axis current of a node generator in a noise disturbance scene; wherein the power plant parameters corresponding to the third order model of the generator include at least one of: and the d-axis synchronous reactance, the d-axis transient reactance, the inertia constant and the d-axis transient open-circuit time constant of the generator are obtained.
Optionally, the method further comprises: constructing a speed regulating system model according to an acceleration time constant of a speed regulating system in the target power grid, an inertia time constant and a buffering time constant of a unit in the speed regulating system, a reference angular frequency and an operation angular frequency of the speed regulating system, a rotor loop resistance and a mechanical torque of the speed regulating system and a mechanical torque initial value of the speed regulating system; wherein, the power plant parameters corresponding to the speed regulation system model comprise at least one of the following parameters: the system comprises a speed regulating system, a motor control unit and a motor control unit, wherein the motor control unit is connected with the motor control unit through a motor control unit, the motor control unit is connected with the motor control unit through a motor control unit, the motor control unit, and the motor control unit is connected with the motor control unit, and the motor control unit, the motor control unit is connected with the motor control unit, the motor control unit is connected with the motor control unit, the inertia time constant of the motor control unit in the speed control unit, the inertia time constant, the speed control unit, the inertia time constant of the speed control unit, the inertia time constant of the speed control unit, the speed control unit and the inertia time constant of the speed control unit and the speed control unit, the inertia time constant of the speed control unit, the inertia time constant of the speed control unit and the speed control unit, the inertia time constant of the speed control unit, the speed control unit and the inertia time constant of the speed control unit, the inertia time constant, the speed control unit.
Optionally, the method further comprises: constructing an excitation system model according to the real part voltage, the imaginary part voltage, the time constant and the gain multiple of the voltage regulator in the target power grid, the output voltages of an exciter, the voltage regulator and a stabilizer, the gain multiple and the delay time constant of the exciter, the delay time constant of the excitation system and the gain, the attenuation time constant and the delay time constant of the voltage stabilizer; the power plant parameters corresponding to the excitation system model comprise at least one of the following: a time constant of the voltage regulator, a gain multiple of an exciter of the voltage regulator, a delay time constant of the exciter, a delay time constant of the excitation system, a gain of the voltage stabilizer, a decay time constant of the voltage stabilizer, a delay time constant of the voltage stabilizer.
Optionally, the solving an error function according to a plurality of voltage measurement values, a plurality of current measurement values, and a plurality of power angle measurement values of the target power grid in a plurality of preset noise-like disturbance scenes to obtain a voltage predicted value, a current predicted value, and a power angle predicted value corresponding to a minimum error value includes: determining a target constraint condition; and solving the error function based on the target constraint condition to obtain a voltage predicted value, a current predicted value and a power angle predicted value corresponding to the minimum error value.
Optionally, the determining the target constraint condition includes: determining active power, reactive power and electromotive force variation constraint conditions of a generator according to a voltage real part and a voltage imaginary part of the generator in a preset initial state, a current real part and a current imaginary part of the generator in the preset initial state, a preset initial potential value, a power angle initial value and a q-axis transient potential initial value of the generator, and a q-axis synchronous reactance and a d-axis transient reactance of the generator; based on network balance constraint, constructing constraint conditions of real part variable quantity and imaginary part variable quantity of injection current of each node in the target power grid; and constructing the upper and lower limit constraint conditions of the power plant parameters based on the preset upper limit value of the power plant parameters and the preset lower limit value of the power plant parameters.
Optionally, the plurality of noise-like disturbance scenarios includes at least two of: the method includes the steps of applying a first predetermined disturbance to a plurality of voltage controller reference values in the target power grid, applying a second predetermined disturbance to spacing of power lines at a plurality of plant outlets in the target power grid, and applying a third predetermined disturbance to loads at the plurality of plant outlets.
According to another aspect of the embodiment of the present invention, there is also provided a power plant parameter identification apparatus, including: the first obtaining module is used for respectively obtaining a voltage measurement value, a current measurement value and a power angle measurement value of a target power grid under a plurality of preset noise-like disturbance scenes based on a Power Management Unit (PMU); the second obtaining module is used for solving an error function according to a plurality of voltage measurement values, a plurality of current measurement values and a plurality of power angle measurement values of the target power grid under a plurality of preset noise-like disturbance scenes to obtain a voltage predicted value, a current predicted value and a power angle predicted value corresponding to a minimum error value; the error function is constructed according to a least square method, and is used for representing errors between a plurality of power parameter measured values of a power grid and corresponding power parameter predicted values, wherein the power parameters comprise voltage, current and power angles; the identification module is used for identifying power plant parameters corresponding to the generator three-order module, the speed regulating system module and the excitation system module respectively according to the voltage predicted value, the current predicted value and the power angle predicted value corresponding to the minimum error value, and the pre-constructed generator three-order module, the speed regulating system module and the excitation system module; the power plant parameters are parameters in the generator three-order module, the speed regulating system module and the excitation system module.
According to another aspect of the embodiment of the present invention, there is also provided a computer-readable storage medium, the storage medium includes a stored program, wherein when the program runs, the apparatus on which the storage medium is located is controlled to execute the power plant parameter identification method according to any one of the above items.
According to another aspect of the embodiments of the present invention, there is also provided a computer device, including: a memory and a processor, the memory storing a computer program; the processor is configured to execute a computer program stored in the memory, and the computer program is configured to, when executed, cause the processor to perform any one of the above-mentioned power plant parameter identification methods.
In the embodiment of the invention, based on a power management unit PMU, electric parameters of a target power grid under a plurality of preset noise-like disturbance scenes are respectively obtained, wherein the electric parameters comprise a voltage measurement value, a current measurement value and a power angle measurement value; solving an error function according to a plurality of voltage measurement values, a plurality of current measurement values and a plurality of power angle measurement values of a target power grid under a plurality of preset noise-like disturbance scenes to obtain a voltage predicted value, a current predicted value and a power angle predicted value corresponding to a minimum error value, wherein the error function is constructed according to a least square method and is used for representing errors between a plurality of power parameter measured values of the power grid and corresponding power parameter predicted values; identifying power plant parameters corresponding to the generator three-order model, the speed regulating system model and the excitation system model respectively according to a voltage predicted value, a current predicted value and a power angle predicted value corresponding to the minimum error value, and the pre-constructed generator three-order model, the speed regulating system model and the excitation system model; the power plant parameters are parameters in a generator three-order model, a speed regulating system model and an excitation system model. Through introducing a plurality of noise-like disturbances, the parameters of the power plant are identified based on the noise-like disturbances, so that the efficiency and accuracy of identifying the parameters of the power plant can be improved, and the technical problem that the parameters of the power plant cannot be accurately identified in the related technology is solved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the invention without limiting the invention. In the drawings:
FIG. 1 is a flow chart of a power plant parameter identification method according to an embodiment of the invention;
fig. 2 is a schematic structural diagram of an IEEE9 node system according to an embodiment of the present invention;
FIG. 3 is a block diagram of an alternative plant parameter identification device in accordance with an embodiment of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, 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 in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Moreover, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Example 1
In accordance with an embodiment of the present invention, there is provided an embodiment of a method for identifying plant parameters, it being noted that the steps illustrated in the flowchart of the drawings may be performed in a computer system, such as a set of computer executable instructions, and that while a logical order is illustrated in the flowchart, in some cases the steps illustrated or described may be performed in an order different than presented 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:
and step S102, respectively obtaining a voltage measurement value, a current measurement value and a power angle measurement value of a target power grid under a plurality of preset noise-like disturbance scenes based on a Power Management Unit (PMU).
It should be understood that a PMU (Power Management Unit) is a measurement Unit that can be used to measure Power parameters.
Step S104, solving an error function according to a plurality of voltage measurement values, a plurality of current measurement values and a plurality of power angle measurement values of the target power grid under a plurality of preset noise-like disturbance scenes to obtain a voltage predicted value, a current predicted value and a power angle predicted value corresponding to a minimum error value, wherein the error function is obtained by constructing according to a least square method, the error function is used for representing errors between a plurality of power parameter measured values of the power grid and corresponding power parameter predicted values, and the power parameters comprise voltage, current and power angles.
Step S106, power plant parameters corresponding to the generator three-order model, the speed regulating system model and the excitation system model respectively are identified according to a voltage predicted value, a current predicted value and a power angle predicted value corresponding to the minimum error value, and the pre-constructed generator three-order model, the speed regulating system model and the excitation system model; the power plant parameters are parameters in a generator three-order model, a speed regulating system model and an excitation system model.
In the optional embodiment, based on a power management unit PMU, electric power parameters of a target power grid under a plurality of preset noise-like disturbance scenes are respectively obtained, where the electric power parameters include a voltage measurement value, a current measurement value, and a power angle measurement value; solving an error function according to a plurality of voltage measurement values, a plurality of current measurement values and a plurality of power angle measurement values of a target power grid under a plurality of preset noise-like disturbance scenes to obtain a voltage predicted value, a current predicted value and a power angle predicted value corresponding to a minimum error value, wherein the error function is constructed according to a least square method and is used for representing errors between a plurality of power parameter measured values of the power grid and corresponding power parameter predicted values; identifying power plant parameters corresponding to the generator three-order model, the speed regulating system model and the excitation system model respectively according to a voltage predicted value, a current predicted value and a power angle predicted value corresponding to the minimum error value and the pre-constructed generator three-order model, the speed regulating system model and the excitation system model; the power plant parameters are parameters in a generator three-order model, a speed regulating system model and an excitation system model. Through introducing a plurality of different noise types, applying different types of disturbances, identifying the parameters of the power plant based on the different types of disturbances can improve the efficiency and the accuracy of identifying the parameters of the power plant, and further solve the technical problem that the parameters of the power plant cannot be accurately identified in the related art.
In some optional real-time examples, a third-order model of the generator is constructed according to a d-axis synchronous reactance, a d-axis transient reactance, an inertia constant, a d-axis transient open-circuit time constant of the generator in a target power grid, a power angle, an acceleration, a q-axis transient reactance, a current real part and an imaginary part of the generator, active power and excitation potential of the generator, and quadrature-axis potential, active power, direct-axis current and quadrature-axis current of a node generator in a noise disturbance scene; wherein the power plant parameters corresponding to the third order model of the generator include at least one of: d-axis synchronous reactance, d-axis transient reactance, inertia constant and d-axis transient open-circuit time constant of the generator. Thus, a generator third order model that reflects multiple power parameters in the target grid may be obtained.
In some optional real-time instances, a speed regulating system model is constructed according to an acceleration time constant of a speed regulating system in a target power grid, an inertia time constant and a buffer time constant of a unit in the speed regulating system, a reference angular frequency and an operation angular frequency of the speed regulating system, a rotor loop resistance and a mechanical torque of the speed regulating system and a mechanical torque initial value of the speed regulating system; wherein, the power plant parameter corresponding to the speed regulation system model comprises at least one of the following parameters: the inertia time constant of the unit in the speed regulating system, the buffer time constant of the unit in the speed regulating system and the rotor loop resistance of the speed regulating system. Therefore, the speed regulating system model capable of reflecting a plurality of power parameters in the target power grid can be obtained.
In some optional real-time examples, an excitation system model is constructed according to the real part voltage, the imaginary part voltage, the time constant and the gain multiple of the voltage regulator in the target power grid, the output voltages of the exciter, the voltage regulator and the stabilizer, the gain multiple and the delay time constant of the exciter, the delay time constant of the voltage stabilizer and the gain, the attenuation time constant and the delay time constant of the voltage stabilizer; the power plant parameters corresponding to the excitation system model comprise at least one of the following: a time constant of the voltage regulator, a gain multiple of an exciter of the voltage regulator, a delay time constant of the exciter, a delay time constant of the excitation system, a gain of the voltage stabilizer, a decay time constant of the voltage stabilizer, a delay time constant of the voltage stabilizer. In this way, an excitation system model that can reflect a plurality of power parameters in the target power grid can be obtained.
In some optional real-time examples, solving an error function according to a plurality of voltage measurement values, a plurality of current measurement values and a plurality of power angle measurement values of a target power grid under a preset plurality of noise-like disturbance scenes to obtain a voltage predicted value, a current predicted value and a power angle predicted value corresponding to a minimum error value, including: determining a target constraint condition; and solving an error function based on the target constraint condition to obtain a voltage predicted value, a current predicted value and a power angle predicted value corresponding to the minimum error value.
In some alternative embodiments, determining the target constraint includes: determining constraint conditions of active power, reactive power and electromotive force variation of the generator according to a voltage real part and a voltage imaginary part of the generator in a preset initial state, a current real part and a current imaginary part of the generator in the preset initial state, a preset potential initial value, a power angle initial value and a q-axis transient potential initial value of the generator, and a q-axis synchronous reactance and a d-axis transient reactance of the generator; based on network balance constraint, constructing constraint conditions of real part variable quantity and imaginary part variable quantity of injection current of each node in a target power grid; and constructing upper and lower limit constraint conditions of the power plant parameters based on the preset upper limit value of the power plant parameters and the preset lower limit value of the power plant parameters. Therefore, target constraint conditions related to a plurality of power parameters in a target power grid can be obtained, and a voltage predicted value, a current predicted value and a power angle predicted value which are high in accuracy and applicability can be obtained based on the target constraint conditions.
The plurality of noise-like perturbation scenarios includes at least two of: the method includes applying a first predetermined disturbance to a plurality of voltage controller reference values in a target power grid, applying a second predetermined disturbance to a spacing of power lines at a plurality of plant outlets in the target power grid, and applying a third predetermined disturbance to loads at the plurality of plant outlets. By applying different disturbances, the parameters of the power plant can be accurately acquired based on the scene of applying different disturbances.
Based on the above embodiments and alternative embodiments, an alternative implementation is provided, which is described in detail below.
The power plant parameter identification has important significance on power grid loop closing operation, economic operation and the like. In the related art, the physical quantity can be converted into a frequency domain variable for analysis based on technologies such as fourier transform and laplace transform, and although the method can realize power plant parameter identification, the method can only carry out off-line identification. In the related art, the online parameter identification can be realized by performing time domain analysis on the physical quantity. Although the methods can realize the identification of the parameters of the power plant, the identification methods usually depend on the occurrence of fault disturbance, the fault disturbance has adverse effect on the stable operation of the power system, and the identification result has certain contingency, so that the error of the identification result is possibly larger.
In view of this, the optional embodiment introduces a noise-like technology, applies disturbance from different angles, and performs power plant parameter identification in a multi-disturbance scene, thereby improving the accuracy of the power plant parameter identification result; meanwhile, the characteristics of low degree of freedom and low multi-scene coupling degree of parameter identification of the power plant are utilized, and a power plant parameter identification result is obtained by using a simplified space interior point method and scene decomposition, so that the purposes of obviously improving the parameter identification efficiency and accurately and quickly identifying the power plant parameters on line are achieved.
The power plant parameter identification method provided by the embodiment of the disclosure comprises the following steps:
step S1: electric power data under a plurality of different noise disturbance scenes are obtained based on the PMU device, and the electric power data comprise power grid voltage, current, power angle and other data. The method specifically comprises the following steps:
s11: different noise-like perturbations are set.
It should be understood that the 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 alternative embodiment, plant parameter identification may be performed by setting three types of noise disturbances: applying preset disturbance to different reference values of the automatic voltage controllers respectively; applying disturbance with preset size to the intervals of power lines at the outlets of different power plants respectively; and changing the load variable at the outlet of the power plant within a preset small range.
S12: and acquiring outlet voltage, current and power angle data of the power plant based on the PMU device.
Step S2: and constructing a power plant model, wherein the power plant model comprises a power plant model including a generator model for determining parameters to be identified of a generator, a speed regulating system model and an excitation system model, and determining key parameters to be identified of the power plant according to the constructed electric field model. The method specifically comprises the following steps:
s21: the constructed generator model is a three-order model. The third order model of the generator is as follows:
Figure BDA0003724951950000071
wherein:
Figure BDA0003724951950000081
in the formula: x dk 、X′ dk 、M k 、T′ d0k 、X qk Respectively a d-axis synchronous reactance of the generator, a d-axis transient reactance, an inertia constant, a d-axis transient open-circuit time constant and a q-axis synchronous reactance of the generator,
Figure BDA0003724951950000082
the power angle, the angular speed, the q-axis transient reactance, the real part and the imaginary part of the current of the generator are respectively; p Gk 、E fk The active power and the excitation potential of the generator are respectively;
Figure BDA0003724951950000083
representing the quadrature axis potential of the k-node generator under the scenario j; omega s There is a nominal value for the synchronous angular velocity;
Figure BDA0003724951950000084
representing the active power of the k-node generator at time t under scenario j,
Figure BDA0003724951950000085
representing the direct axis current of the k-node generator at time t under scenario j,
Figure BDA0003724951950000086
represents under scene jQuadrature axis current, n, of k-node generator at time t G Indicates the number of nodes, N S Indicating the number of fault scenarios. Subscript k represents the generator node number; the superscript j is the number of the different noise-like disturbance scenes.
Wherein, X dk 、X′ dk 、M k 、T′ d0k Is the generator parameter to be identified.
S22: and constructing a speed regulating system model for determining the to-be-identified parameters of the speed regulating system without considering the saturated part in the speed regulating system model. The model of the speed regulating system is as follows:
Figure BDA0003724951950000087
in the formula, t g Is an acceleration time constant of the speed regulating system; t is 1 And T 2 Respectively a unit inertia time constant and a buffering time constant; omega ref And omega are respectively a reference angular frequency and an operating angular frequency; r is a rotor loop resistance; t is mech Is a mechanical torque. T is mech0 The value before the speed regulation of the mechanical torque is obtained. Wherein, R, T 1 、T 2 Is the speed regulating system parameter to be identified.
S23: and constructing a fourth-order excitation system model for determining parameters to be identified of the excitation system without considering the saturated part in the excitation system model. The fourth order excitation system model is as follows:
Figure BDA0003724951950000091
in the formula, V m To measure the voltage; v x 、V y 、T a And K. Respectively a real part voltage, an imaginary part voltage, a time constant and a gain multiple of the voltage regulator; v r1 、V r2 、V f The output voltages of the exciter, the voltage regulator and the stabilizer are respectively; v ref Is a reference voltage; k f 、T f Gain multiple and delay time constant of the exciter respectively; t is r Delay time for the whole excitation systemA constant; a. the e ,B e ,T e Respectively, the gain, decay time constant and delay time constant of the voltage stabilizer.
Wherein, K a ,T a ,K f ,T f ,T r ,T e ,A e ,B e Is the excitation system parameter to be identified.
Step S3: and establishing a power plant parameter identification model by taking the minimum errors of the voltage, current and power angle model values and the measured values as targets. The model value is a corresponding predicted value obtained according to the generator model, the speed regulation system model and the excitation system model; the measured values are values collected according to the PMU device. The method specifically comprises the following steps:
based on ARIMA (Auto Regression and Moving Average, Auto Regression Moving Average model), fitting the measured voltage, current and power angle, and establishing a multi-power plant key parameter dynamic identification model (equivalent to an error function in the foregoing embodiment) under a multi-noise scene by taking the least square error of the fitted curve of the three operating variables and the model prediction curve as a target:
Figure BDA0003724951950000092
in the formula, nT is the time interval number; n is genl Number of generator nodes; ns is the total number of fault scenes;
Figure BDA0003724951950000093
respectively obtaining a predicted value and a measured value of the power angle of the k-node generator under the situation j;
Figure BDA0003724951950000094
respectively a predicted value and a measured value of the real part of the current and a predicted value and a measured value of the imaginary part of the current;
Figure BDA0003724951950000101
Figure BDA0003724951950000102
are respectively asThe predicted value and the measured value of the voltage real part and the predicted value and the measured value of the voltage imaginary part.
Because the voltage, current and power angle dimensions of the generator are different and the magnitude of data is different, the error is represented in a relative value mode, and the objective function is modified into:
Figure BDA0003724951950000103
step S4: and determining the constraint conditions of the recognition model. The method specifically comprises the following steps:
step S41: assuming that the initial states under various noise disturbance situations are the same, the initial value constraint is as follows:
Figure BDA0003724951950000104
in the formula: delta P k，0 、ΔQ k，0 、ΔE′ qk，0 Respectively are the active, reactive and electromotive force variable quantities of the node k; e.g. of a cylinder k ，f k ，I ek ，I fk The real part and the imaginary part of the initial running voltage of the generator and the real part and the imaginary part of the current are respectively; e Qk ，δ k,0 ，E′ qk,0 The method comprises the following steps of (1) obtaining an initial value of a virtual potential of a generator, an initial value of a power angle and an initial value of a q-axis transient potential; x qk ，X′ dk The q-axis synchronous reactance and the d-axis transient reactance of the generator are respectively.
Step S42: network balance constraints are satisfied under various noise disturbance situations, as follows:
Figure BDA0003724951950000105
wherein:
Figure BDA0003724951950000111
in the formula:
Figure BDA0003724951950000112
representing the variable quantity of the real part of the current injected into the node and the variable quantity of the imaginary part of the current;
Figure BDA0003724951950000113
respectively a real part and an imaginary part of a network admittance matrix;
Figure BDA0003724951950000114
respectively a voltage real part and an imaginary part, and an injection current real part and an injection current imaginary part;
Figure BDA0003724951950000115
the real part and the imaginary part of the voltage of the generator at the moment t are respectively.
Figure BDA0003724951950000116
X qk Respectively representing a q-axis transient potential value, a d-axis transient reactance, a power angle value and a q-axis reactance of the generator at the time t.
Step S43: the parameters to be identified all meet the upper and lower limit constraints:
the generator is subjected to upper and lower limit constraint of the parameter to be identified:
Figure BDA0003724951950000117
the upper and lower limits of the parameter to be identified of the speed regulating system are constrained as follows:
Figure BDA0003724951950000118
the upper and lower limits of the parameters to be identified of the excitation system are constrained as follows:
Figure BDA0003724951950000121
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: and solving the power plant parameter identification model based on a simplified space interior point method and a scenario decomposition method to obtain final power plant key parameter values.
In the optional embodiment, data such as power grid voltage, current, power angle and the like under different noise disturbance scenes are obtained based on the PMU device; establishing a power plant model comprising a generator model, a speed regulating system model and an excitation system model so as to determine key parameters of the power plant to be identified; establishing a power plant parameter identification model by taking the minimum errors of the voltage, current and power angle model values and the measured values as targets; determining an identification model constraint condition; and solving the power plant parameter identification model based on a simplified space interior point method and a scenario decomposition method to obtain final power plant key parameter values. Disturbance types can be applied from different angles, multi-disturbance scene identification can be carried out, and the identification accuracy can be improved; meanwhile, by utilizing the characteristic of low parameter identification freedom degree and low multi-scene coupling degree, a simplified space interior point method and a scene decomposition solving strategy are provided, the parameter identification efficiency can be obviously improved, and accurate and rapid online identification is realized.
The following further describes the IEEE9 node system as an example. Fig. 2 is a schematic diagram of an IEEE9 node system. As shown in fig. 2, the generator is connected at nodes 1, 2, 3 and the load is connected at nodes 5, 6, 8.
The actual parameter values of the generators in the IEEE9 node system are shown in table 1.
TABLE 1
Figure BDA0003724951950000122
Fig. 2 is a schematic diagram of an IEEE9 node system. Fig. 2 shows the actual parameter values of the excitation system in the IEEE9 node system for power generation, as shown in table 2.
TABLE 2
Figure BDA0003724951950000131
Table 3 shows actual parameter values of the governor system in the IEEE9 node system.
TABLE 3
Figure BDA0003724951950000132
The preset various noise disturbance scenes comprise: applying a disturbance of 0.1 to the automatic voltage controller reference; certain disturbance is applied to the line intervals of the lines 4-6, the lines 9-8 and the lines 7-5 respectively, namely the line impedance is disturbed, and the line impedance is changed by 0.1; respectively applying small load disturbance 2MW to the load nodes 5, 6 and 8; a total of 7 perturbation scenarios. The test environment is VS2008, the running platform is CPUi5, the CPU main frequency is 3.19GHz, and the memory is 4 GB.
In this alternative real-time approach, the optimization problem size is shown in table 4.
TABLE 4
Figure BDA0003724951950000133
In table 4, Ns denotes the number of scenes, n and m are the number of primal-dual system state variables and the number of equality constraints, respectively, DIM and NNZ are the primal-dual system dimension and the number of non-zero elements, respectively, and DOF is the degree of freedom. As can be seen from Table 4, the optimization scale DIM of each example is large, but the DOF is low, so that the method is very suitable for solving in a reduced space.
The generator parameter results identified based on this alternative embodiment are shown in table 5.
TABLE 5
Figure BDA0003724951950000141
The excitation parameter results identified based on this alternative embodiment are shown in table 6:
TABLE 6
Figure BDA0003724951950000142
The results of the speed governing parameters identified based on this alternative embodiment are shown in table 7:
TABLE 7
Figure BDA0003724951950000143
According to the identification result of the power plant parameters, under the traditional single disturbance situation, due to the fact that the capacity of resisting measurement errors is poor, the identification result is poor, when the number of disturbance scenes is increased to 7, the identification precision of the overall identification algorithm is improved, the parameter error of an extremely individual generator is large, and the dynamic response process of the system can be basically reflected.
The identification process of the key parameters of the power plant generally takes an optimization process with the minimum error between a specific physical quantity measurement value and a model value as a target, and at present, the identification process mainly comprises a frequency domain identification method and a time domain identification method. The former mainly converts physical quantity into frequency domain variable for analysis based on technologies such as Fourier change, Laplace change and the like, has the advantages of simple and convenient calculation, but can only carry out off-line identification; the latter directly carries out time domain analysis on the physical quantity, and can realize online parameter identification, such as online identification of a third-order synchronous generator by using a least square algorithm. However, the above identification method often depends on occurrence of fault disturbance, which itself has adverse effect on stable operation of the power system, and parameter identification under single disturbance has certain contingency, which may result in large error. In the optional implementation mode, a plurality of noise types are introduced, disturbance types are applied from different angles, power plant parameters of multiple disturbance scenes are identified, the accuracy of power plant parameter identification can be improved, meanwhile, the characteristic of low parameter identification freedom degree and the low multi-scene coupling degree are utilized, a simplified space interior point method and a scenario decomposition solving strategy are provided, the parameter identification efficiency can be obviously improved, and accurate and rapid online identification is realized.
Example 2
According to the embodiment of the invention, the invention further provides a structural block diagram of the power plant parameter identification device. Referring to fig. 3, the apparatus includes a first obtaining module 302, a second obtaining module 304, and an identifying module 306. The following is a detailed description.
A first obtaining module 302, configured to obtain, based on a power management unit PMU, a voltage measurement value, a current measurement value, and a power angle measurement value of a target power grid in a preset multiple noise-like disturbance scenes respectively; a second obtaining module 304, connected to the first obtaining module 302, configured to solve an error function according to a plurality of voltage measurement values, a plurality of current measurement values, and a plurality of power angle measurement values of a target power grid in a preset plurality of noise-like disturbance scenes, to obtain a voltage predicted value, a current predicted value, and a power angle predicted value corresponding to a minimum error value, where the error function is constructed according to a least square method, the error function is used to represent an error between a plurality of power parameter actual measurement values of the power grid and corresponding power parameter predicted values, and the power parameters include voltage, current, and power angle; an identification module 306, connected to the second obtaining module 304, configured to identify power plant parameters corresponding to the generator three-order model, the speed regulating system model, and the excitation system model according to a voltage predicted value, a current predicted value, and a power angle predicted value corresponding to the minimum error value, and the pre-constructed generator three-order model, the speed regulating system model, and the excitation system model; the power plant parameters are parameters in a generator three-order model, a speed regulating system model and an excitation system model.
It should be noted here that the first obtaining module 302, the second obtaining module 304, and the identifying module 306 correspond to steps S102 to S106 in embodiment 1, and the three modules are the same as the corresponding steps in the implementation example and the application scenario, but are not limited to the disclosure in embodiment 1.
According to another aspect of the embodiment of the invention, a computer-readable storage medium is provided, and the storage medium comprises a stored program, wherein when the program runs, the device on which the storage medium is located is controlled to execute the power plant parameter identification method.
According to another aspect of the embodiments of the present invention, there is also provided a computer device, including: a memory and a processor, the memory storing a computer program; a processor for executing a computer program stored in the memory, the computer program when executed causing the processor to perform any of the plant parameter identification methods described above.
The embodiment of the invention provides an image processing scheme. Through the adoption of the technical scheme, the purpose is achieved, and the technical problem in the related art is solved.
Those skilled in the art will appreciate that all or part of the steps in the methods of the above embodiments may be implemented by a program instructing hardware associated with the terminal device, where the program may be stored in a computer-readable storage medium, and the storage medium may include: flash disks, Read-Only memories (ROMs), Random Access Memories (RAMs), magnetic or optical disks, and the like.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
In the above embodiments of the present invention, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the embodiments provided in the present application, it should be understood that the disclosed technology can be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, a division of a unit may be a division of a logic function, and an actual implementation may have another division, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or may not be executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, units or models, and may be in an electrical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.

Claims (10)

1. A power plant parameter identification method is characterized by comprising the following steps:
respectively acquiring a voltage measurement value, a current measurement value and a power angle measurement value of a target power grid under a plurality of preset noise-like disturbance scenes based on a Power Management Unit (PMU);
solving an error function according to a plurality of voltage measurement values, a plurality of current measurement values and a plurality of power angle measurement values of the target power grid under a plurality of preset noise-like disturbance scenes to obtain a voltage predicted value, a current predicted value and a power angle predicted value corresponding to a minimum error value, wherein the error function is constructed according to a least square method and is used for representing errors between a plurality of power parameter actual measurement values of the power grid and corresponding power parameter predicted values, and the power parameters comprise voltage, current and power angles;
identifying power plant parameters respectively corresponding to the generator three-order model, the speed regulating system model and the excitation system model according to the voltage predicted value, the current predicted value and the power angle predicted value corresponding to the minimum error value, and the pre-constructed generator three-order model, the speed regulating system model and the excitation system model; and the power plant parameters are parameters in the generator three-order model, the speed regulating system model and the excitation system model.
2. The method of claim 1, further comprising:
constructing a third-order model of the generator according to a d-axis synchronous reactance, a d-axis transient reactance, an inertia constant, a d-axis transient open-circuit time constant of the generator in the target power grid, a power angle, an acceleration, a q-axis transient reactance, a current real part and an imaginary part of the generator, active power and excitation potential of the generator, and quadrature-axis potential, active power, direct-axis current and quadrature-axis current of a node generator in a noise disturbance scene; wherein the power plant parameters corresponding to the third order model of the generator include at least one of: the generator comprises a d-axis synchronous reactance, a d-axis transient reactance, an inertia constant and a d-axis transient open-circuit time constant.
3. The method of claim 1, further comprising:
constructing a speed regulating system model according to an acceleration time constant of a speed regulating system in the target power grid, an inertia time constant and a buffering time constant of a unit in the speed regulating system, a reference angular frequency and an operation angular frequency of the speed regulating system, a rotor loop resistance and a mechanical torque of the speed regulating system and a mechanical torque initial value of the speed regulating system; wherein, the power plant parameters corresponding to the speed regulation system model comprise at least one of the following parameters: the system comprises an inertia time constant of a unit in the speed regulating system, a buffering time constant of the unit in the speed regulating system and a rotor loop resistor of the speed regulating system.
4. The method of claim 1, further comprising:
constructing an excitation system model according to the real part voltage, the imaginary part voltage, the time constant and the gain multiple of the voltage regulator in the target power grid, the output voltages of an exciter, the voltage regulator and a stabilizer, the gain multiple and the delay time constant of the exciter, the delay time constant of the excitation system and the gain, the attenuation time constant and the delay time constant of the voltage stabilizer; the power plant parameters corresponding to the excitation system model comprise at least one of the following: a time constant of the voltage regulator, a gain multiple of an exciter of the voltage regulator, a delay time constant of the exciter, a delay time constant of the excitation system, a gain of the voltage stabilizer, a decay time constant of the voltage stabilizer, a delay time constant of the voltage stabilizer.
5. The method according to claim 1, wherein solving an error function according to a plurality of voltage measurement values, a plurality of current measurement values, and a plurality of power angle measurement values of the target power grid under a preset plurality of noise-like disturbance scenes to obtain a voltage predicted value, a current predicted value, and a power angle predicted value corresponding to a minimum error value comprises:
determining a target constraint condition;
and solving the error function based on the target constraint condition to obtain a voltage predicted value, a current predicted value and a power angle predicted value corresponding to the minimum error value.
6. The method of claim 5, wherein determining the target constraint comprises:
determining active power, reactive power and electromotive force variation constraint conditions of a generator according to a voltage real part and a voltage imaginary part of the generator in a preset initial state, a current real part and a current imaginary part of the generator in the preset initial state, a preset initial potential value, a power angle initial value and a q-axis transient potential initial value of the generator, and a q-axis synchronous reactance and a d-axis transient reactance of the generator;
based on network balance constraint, constructing constraint conditions of real part variable quantity and imaginary part variable quantity of injection current of each node in the target power grid;
and constructing the upper and lower limit constraint conditions of the power plant parameters based on the preset upper limit value of the power plant parameters and the preset lower limit value of the power plant parameters.
7. The method of claim 1, wherein the plurality of noise-like perturbation scenarios comprises at least two of:
the method includes the steps of applying a first predetermined disturbance to a plurality of voltage controller reference values in the target power grid, applying a second predetermined disturbance to spacing of power lines at a plurality of plant outlets in the target power grid, and applying a third predetermined disturbance to loads at the plurality of plant outlets.
8. A power plant parameter identification device, comprising:
the first obtaining module is used for respectively obtaining a voltage measurement value, a current measurement value and a power angle measurement value of a target power grid under a plurality of preset noise-like disturbance scenes based on a Power Management Unit (PMU);
the second obtaining module is used for solving an error function according to a plurality of voltage measurement values, a plurality of current measurement values and a plurality of power angle measurement values of the target power grid under a plurality of preset noise-like disturbance scenes to obtain a voltage predicted value, a current predicted value and a power angle predicted value corresponding to a minimum error value; the error function is constructed according to a least square method, and is used for representing errors between a plurality of power parameter measured values of a power grid and corresponding power parameter predicted values, wherein the power parameters comprise voltage, current and power angles;
the identification module is used for identifying power plant parameters respectively corresponding to the generator third-order module, the speed regulating system module and the excitation system module according to the voltage predicted value, the current predicted value and the power angle predicted value corresponding to the minimum error value, and the pre-constructed generator third-order module, the speed regulating system module and the excitation system module; the power plant parameters are parameters in the generator three-order module, the speed regulating system module and the excitation system module.
9. A computer-readable storage medium, characterized in that the storage medium comprises a stored program, wherein the program, when executed, controls a device on which the storage medium is located to perform the power plant parameter identification method of any one of claims 1 to 7.
10. A computer device, comprising: a memory and a processor, wherein the processor is capable of,
the memory stores a computer program;
the processor configured to execute a computer program stored in the memory, the computer program when executed causing the processor to perform the plant parameter identification method of any of claims 1 to 7.
CN202210764472.9A 2022-06-30 2022-06-30 Power plant parameter identification method and device and computer readable storage medium Pending CN115117879A (en)

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