CN111641208A - WAMS-based power grid parameter online identification method - Google Patents

WAMS-based power grid parameter online identification method Download PDF

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Publication number
CN111641208A
CN111641208A CN202010506212.2A CN202010506212A CN111641208A CN 111641208 A CN111641208 A CN 111641208A CN 202010506212 A CN202010506212 A CN 202010506212A CN 111641208 A CN111641208 A CN 111641208A
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wams
power grid
input
data
model
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摆世彬
蒙金有
徐伟
田志浩
陈颖杰
孙小湘
项丽
李桐
钟海亮
刘刚
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NARI Nanjing Control System Co Ltd
State Grid Ningxia Electric Power Co Ltd
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State Grid Ningxia Electric Power Co Ltd
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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
    • 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
    • H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
    • H02J13/00001—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by the display of information or by user interaction, e.g. supervisory control and data acquisition systems [SCADA] or graphical user interfaces [GUI]
    • 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
    • H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
    • H02J13/00002—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by monitoring
    • 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]

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

Abstract

The invention discloses a WAMS-based power grid parameter online identification method, which comprises the following steps: s1: establishing a state space model of the electric power system element to be identified, and determining a parameter to be identified and an input/output variable; s2: acquiring input and output data of the state space model from the WAMS, and preprocessing the input and output data; s3: analyzing the identifiability of the state space model; s4: and performing online identification and solution on model parameters by using the preprocessed input and output data by adopting a data conversion method. The invention can identify the power grid parameters in an online rolling manner by utilizing the WAMS real-time measurement data, realizes the real-time monitoring of the power grid running state and the online checking of the power grid parameters, improves the accuracy and effectiveness of the stable calculation of the power system, and effectively improves the utilization rate of resources.

Description

WAMS-based power grid parameter online identification method
Technical Field
The invention relates to the technical field of power system parameter identification and modeling, in particular to a WAMS-based power grid parameter online identification method.
Background
With the large capacity of new energy incorporated into the power grid and the development and application of power electronic devices, the operation scale of the power system is increasing, and the analysis and control of the stability of the power system become more and more complex. The advent of Wide Area Measurement Systems (WAMS) has provided new approaches and methods for modern power system stability computational analysis.
WAMS is a real-time dynamic measurement and control system based on a synchronous Phasor Measurement Unit (PMU) and combined with the modern communication technology for the purpose of measuring, analyzing and controlling the running state of a power system in real time. The main functions of WAMS are at present: the method comprises the steps of state estimation based on PMU data, stability monitoring and prediction, control strategy and protection scheme formulation, parameter estimation, model verification, fault detection and positioning and the like.
The PMU data can be used for carrying out online identification and monitoring on the power grid parameters. The parameter identification experiment of the power system element usually adopts a system response signal or a manual excitation signal, the system response signal generally comes from the voltage large-amplitude fluctuation caused by the grid fault or the action of a transformer tap, and the manual excitation signal is usually obtained by a pseudo-random disturbance signal artificially applied to a reference voltage point. Both signals need to be obtained under specific circumstances or experiments, are expensive to acquire, and do not support online roll recognition.
WAMS measurement data are daily operation data of a power grid, signals generated by small disturbance excitation such as random fluctuation of load of the power grid and the like contain rich dynamic operation information of the power grid, and parameters of the power grid can be identified and monitored on line by utilizing the signals. Different from the identification based on a large disturbance signal, the online identification of the power grid parameters under the small disturbance signal is closed-loop identification, and if the closed-loop identification problem is solved by using an open-loop identification method, biased estimation can be obtained.
Therefore, how to provide a WAMS-based online identification method for power grid parameters is a problem that needs to be solved urgently by those skilled in the art.
Disclosure of Invention
In view of this, the invention provides a WAMS-based online identification method for power grid parameters, which solves the problem that the online identification method for power grid parameters cannot adopt open-loop identification.
In order to achieve the purpose, the invention adopts the following technical scheme:
a WAMS-based power grid parameter online identification method comprises the following steps:
s1: establishing a state space model of the electric power system element to be identified, and determining a parameter to be identified and an input/output variable;
s2: acquiring input and output data of the state space model from the WAMS to form a data set, and preprocessing the input and output data;
s3: analyzing the identifiability of the state space model;
s4: and performing online identification and solution on model parameters by using the preprocessed input and output data by adopting a data conversion method.
Preferably, in S1, the state space model is:
Figure BDA0002526627400000021
Δy＝C(θ)Δx+D(θ)Δu
wherein, Δ x is the deviation of the state variable, Δ u is the deviation of the input variable, Δ y is the deviation of the output variable, θ is the parameter to be identified, and a (θ), B (θ), C (θ) and D (θ) are respectively the system state matrix, the input matrix, the output matrix and the transition matrix in sequence.
Preferably, the pretreatment comprises: per unit, detrending, dereferencing, and pre-filtering.
It should be noted that:
assume that the acquired data is
u(k)＝[u1(k),u2(k),...,un(k)]
y(k)＝[y1(k),y2(k),...,ym(k)]，k＝1,2,3,...,N，
Per unit: the obtained data is converted into u per unit*(k)＝[u1*(k),u2*(k),...,un*(k)]，y*(k)＝[y1*(k),y2*(k),...,ym*(k)]，k＝1,2,3,…,N；
Removing the wild value: outliers, i.e., erroneous data points, which may exist in the WAMS data, may adversely affect the recognition result, and therefore, outliers are removed;
trend removing: for an electric power system, it is necessary to remove a trend reflecting a long-term operation change in the electric power system, such as a slow frequency fluctuation caused by a load change, a power crosstalk of a tie line between large-area power grids, a direct current component of a signal, etc., from an original signal, and obtain Δ u (k) ([ Δ u ]) after the trend is removed1(k),Δu2(k),...,Δun(k)]，Δy(k)＝[Δy1(k),Δy2(k),...,Δym(k)]。
Pre-filtering: the data collected by the WAMS contains high frequency noise, and a low pass filter can be used to limit the frequency band of the data within a required range.
Preferably, the specific contents of S3 include: analyzing the information sufficiency of the data set and analyzing the continuous excitation, wherein the information sufficiency is judged by positive determination of the signal spectrum density of the data set, and the continuous excitation is judged by an advice function in Matlab.
It should be noted that:
s31, data set information sufficiency analysis: if a quasi-stationary data set Z∞For any two models W of a model set1(q) and W2(q) is as follows
Figure BDA0002526627400000031
Wherein z (t) ═ u (t) y (t)]TThen called Z∞Information is rich about such models. This also means that for all frequencies ω, there must be W1(ejω)≡W2(ejω)。
If for all frequencies ω, when z (t) ═ u (t) y (t)]TOf the frequency spectrum matrix phiz(ω) are all strictly positive timing, then the quasi-stationary data set Z is called∞Is informative. Namely, it is
Figure BDA0002526627400000032
Is positive for all ω. Wherein phiu(ω)，Φy(ω) self spectra of u (t), y (t), respectively, Φuy(ω)，ΦyuAnd (omega) is a cross-spectrum of u (t) and y (t).
S32, continuous excitation analysis of a data set: the sequence u (k), k being 0,1,2, is an N-th order continuous excitation signal if and only if there is a positive integer N, such that the matrix is a constant matrix
Figure BDA0002526627400000033
Full rank of order n.
When the sequence u (k) is ergodic or the sequence is deterministic, the above definition is equivalent to the autocorrelation matrix RuIs a non-singular matrix:
Figure BDA0002526627400000041
the persistent excitation property of the dataset may be given by the advice function in the matlab program.
Preferably, the specific contents of S4 include:
s41, the state space model is represented by a Box-Jenkins model as follows:
Figure BDA0002526627400000042
wherein y (t) is output data, u (t) is input data, t is an error, a ° (q), B ° (q), C ° (q), D ° (q) are polynomials, and the order of a ° (q), B ° (q), C ° (q), D ° (q) is determined by an L curve;
the single step prediction error of the Box-Jenkins model is as follows:
Figure BDA0002526627400000043
s42, calculating a group of parameters to minimize the following objective function:
Figure BDA0002526627400000044
wherein, VBJIn order to be the objective function, the target function,
Figure BDA0002526627400000045
is a polynomial estimation value, N is a data length;
after the Box-Jenkins model parameters are obtained through identification, the estimation value of the continuous transfer function is obtained through model conversion
Figure BDA0002526627400000046
Obtaining a parameter estimation value of the state space model to be identified by solving the following equation set:
Figure BDA0002526627400000047
wherein S is Laplace operator, and I is unit diagonal matrix.
It should be noted that:
a ° (q), B ° (q), C ° (q), D ° (q) are respectively:
Figure BDA0002526627400000048
Figure BDA0002526627400000049
Figure BDA00025266274000000410
Figure BDA00025266274000000411
wherein q is a forward operator;
the specific model transformation process comprises the following steps:
1) converting the BJ model into a discrete transfer function model;
2) converting the discrete transfer function model into a continuous transfer function model;
3) and converting the high-order continuous transfer function model into a low-order continuous transfer function model.
According to the technical scheme, compared with the prior art, the power grid parameter online identification method based on the WAMS breaks through the limitation that specific experimental data or large interference data are needed in the traditional identification, the power grid parameters can be identified in an online rolling mode by using the WAMS real-time measurement data by adopting a closed-loop identification method, the real-time monitoring of the power grid operation state and the online checking of the power grid parameters are realized, the accuracy and the effectiveness of the stable calculation of the power system are improved, and the utilization rate of resources is improved.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
Fig. 1 is a schematic flow chart of a WAMS-based online identification method for power grid parameters provided by the present invention;
FIG. 2 is a ratio of a minimum feature root to a maximum feature root of a signal spectral density matrix;
FIG. 3 is an L-curve for active power order selection;
FIG. 4 is an L-curve for reactive power order selection;
fig. 5 is a graph comparing simulated responses.
Detailed Description
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.
The embodiment of the invention discloses a WAMS-based power grid parameter online identification method, which comprises the following steps as shown in FIG. 1:
s1: establishing a state space model of the electric power system element to be identified, and determining a parameter to be identified and an input/output variable;
s2: acquiring input and output data of the state space model from the WAMS to form a data set, and preprocessing the input and output data;
s3: analyzing the identifiability of the model;
s4: and performing online identification and solution on model parameters by using the preprocessed input and output data by adopting a data conversion method.
In this embodiment, taking a simplified calculation model of a doubly-fed wind turbine generator in the PSD-BPA stability manual of the chinese institute of electrical and power science as an example, the following is shown after linearization:
Figure BDA0002526627400000061
Figure BDA0002526627400000062
where Δ x is the deviation of the state variable, Δ Pg、ΔQgDeviation of active and reactive power, DeltaU, delivered for doubly-fed wind generatorssThe voltage deviation value is the voltage deviation value of the grid-connected point of the doubly-fed wind generator. The model has five orders and comprises 9 parameters to be identified, which are respectively as follows:
θ＝[Hg,xs,xm,Kpp,Kpi,Kqp,Kqi,Td,Tq]
wherein HgIs the time constant of inertia, xs、xmRespectively stator reactance, excitation reactance, KppFor active control of the proportional link coefficient, KpiFor active control of integral element coefficient, KqpFor reactive control of the proportional link coefficient, KqiFor reactive control of integral link coefficient, Td、TqIs a time constant.
And S2, acquiring input and output signals of the doubly-fed wind generator model from the WAMS, and preprocessing the data. The total sample time is 60 seconds, the sampling frequency is 50Hz, 3000 data points are totally obtained, the data set is divided into 6 groups, each group comprises 500 data points, 0-10 seconds, 20-30 seconds and 40-50 seconds are identification data sets, and 10-20 seconds, 30-40 seconds and 50-60 seconds are verification data sets.
The identification data set is used to estimate model parameters and the verification data set is used to verify the reliability of the results. Preprocessing the acquired input signal UsAnd an output signal Pg、QgThe signal U (k) equal to delta U for identification is obtained after preprocessing such as per unit, value elimination, trend elimination, pre-filtering and the likes(k)、y(k)＝[ΔPg(k),ΔQg(k)]，k＝1,2,...,3000。
S3, analyzing the identifiability of the doubly-fed wind generator model, wherein the specific method comprises the following steps:
the information sufficiency of the data set is judged by the signal spectrum density positive determination, as shown in fig. 2, the ratio of the minimum characteristic root to the maximum characteristic root of the signal spectrum density matrix is greater than zero in the frequency band of 0-10H, which indicates that the signal spectrum density matrix is positive determination, and the data set meets the requirement of the information sufficiency.
The continuous excitation of the data set can be given by an advice function in Matlab, and the data set is a 24-order continuous excitation signal, can be used for identifying models below 24 orders and meets the requirement of identifiability.
And S4, performing online identification and solution on the model parameters by using the preprocessed input and output signals by adopting a data conversion method.
Assuming that the dynamic process of the doubly-fed wind generator can be represented by a Box-Jenkins model:
Figure BDA0002526627400000071
where y (t) is output data, u (t) is input data, t is error, a ° (q), B ° (q), C ° (q), and D ° (q) are polynomials, and the order of a ° (q), B ° (q), C ° (q), and D ° (q) is determined by an L curve, as shown in fig. 3 and 4.
A ° (q), B ° (q), C ° (q), D ° (q) are respectively:
Figure BDA0002526627400000072
Figure BDA0002526627400000073
Figure BDA0002526627400000074
Figure BDA0002526627400000075
wherein q is a forward operator.
The single step prediction error of the Box-Jenkins model is as follows:
Figure BDA0002526627400000076
estimating a set of parameters such that an objective function is minimized
Figure BDA0002526627400000077
Wherein, VBJIn order to be the objective function, the target function,
Figure BDA0002526627400000078
is a polynomial estimation value, N is a data length;
after the Box-Jenkins model parameters are obtained through identification, the estimation value of the continuous transfer function is obtained through model conversion
Figure BDA0002526627400000081
Obtaining a parameter estimation value of the model to be identified by solving the following equation:
Figure BDA0002526627400000082
wherein S is Laplace operator, and I is unit diagonal matrix.
The degree of fit of the verification dataset is shown in FIG. 5, and the identification results are shown in Table 1
TABLE 1 parameter identification results
Figure BDA0002526627400000083
In summary, the following steps: the WAMS-based power grid parameter online identification method breaks through the limitation that specific experimental data or large interference data are needed in the traditional identification, can identify the power grid parameters in an online rolling manner by using WAMS real-time measurement data, realizes the real-time monitoring of the power grid operation state and the online checking of the power grid parameters, improves the accuracy and effectiveness of the stable calculation of the power system, and improves the utilization rate of resources.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. The device disclosed by the embodiment corresponds to the method disclosed by the embodiment, so that the description is simple, and the relevant points can be referred to the method part for description.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims (5)

1. A WAMS-based power grid parameter online identification method is characterized by comprising the following steps:
s1: establishing a state space model of the electric power system element to be identified, and determining a parameter to be identified and an input/output variable;
s2: acquiring input and output data of the state space model from the WAMS to form a data set, and preprocessing the input and output data;
s3: analyzing the identifiability of the state space model;
s4: and performing online identification and solution on model parameters by using the preprocessed input and output data by adopting a data conversion method.
2. The WAMS-based online power grid parameter identification method according to claim 1, wherein in S1, the state space model is:
Figure FDA0002526627390000011
Δy＝C(θ)Δx+D(θ)Δu
wherein, Δ x is the deviation of the state variable, Δ u is the deviation of the input variable, Δ y is the deviation of the output variable, θ is the parameter to be identified, and a (θ), B (θ), C (θ) and D (θ) are respectively the system state matrix, the input matrix, the output matrix and the transition matrix in sequence.
3. The WAMS-based online power grid parameter identification method according to claim 1, wherein the preprocessing comprises: per unit, detrending, dereferencing, and pre-filtering.
4. The WAMS-based online identification method for the power grid parameters according to claim 1, wherein the specific content of S3 includes: analyzing the information sufficiency of the data set and analyzing the continuous excitation, wherein the information sufficiency is judged by positive determination of the signal spectrum density of the data set, and the continuous excitation is judged by an advice function in Matlab.
5. The WAMS-based online identification method for the power grid parameters according to claim 1, wherein the specific content of S4 includes:
s41, the state space model is represented by a Box-Jenkins model as follows:
Figure FDA0002526627390000012
wherein y (t) is output data, u (t) is input data, t) is error, A (q), B (q), C (q), D (q) are polynomials, and the order of A (q), B (q), C (q), D (q) is determined by L curve;
the single step prediction error of the Box-Jenkins model is as follows:
Figure FDA0002526627390000021
s42, calculating a group of parameters to minimize the following objective function:
Figure FDA0002526627390000022
wherein, VBJIn order to be the objective function, the target function,
Figure FDA0002526627390000023
is a polynomial estimation value, N is a data length;
after the Box-Jenkins model parameters are obtained through identification, the estimation value of the continuous transfer function is obtained through model conversion
Figure FDA0002526627390000024
Obtaining a parameter estimation value of the state space model to be identified by solving the following equation set:
Figure FDA0002526627390000025
wherein S is Laplace operator, and I is unit diagonal matrix.
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Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103414245A (en) * 2013-06-04 2013-11-27 浙江工业大学 Quantization-based wide-area power system output feedback control method

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103414245A (en) * 2013-06-04 2013-11-27 浙江工业大学 Quantization-based wide-area power system output feedback control method

Non-Patent Citations (2)

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Title
方若水 等: ""基于PMU实测小扰动数据的负荷建模方法研究"", 《中国优秀硕士学位论文全文数据库 工程科技Ⅱ辑》 *
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