CN110796385A - Power system state estimation method, device, equipment and storage medium - Google Patents

Power system state estimation method, device, equipment and storage medium Download PDF

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CN110796385A
CN110796385A CN201911067169.8A CN201911067169A CN110796385A CN 110796385 A CN110796385 A CN 110796385A CN 201911067169 A CN201911067169 A CN 201911067169A CN 110796385 A CN110796385 A CN 110796385A
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夏明超
陈奇芳
王玉彬
陈防渐
李锦艺
杨晓楠
郞燕生
韩锋
闫丽芬
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Beijing Jiaotong University
China Electric Power Research Institute Co Ltd CEPRI
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China Electric Power Research Institute Co Ltd CEPRI
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Abstract

The application discloses a method, a device, equipment and a storage medium for estimating the state of a power system, and belongs to the technical field of power systems. The method comprises the following steps: calling a measurement prediction model to process the power data of the first node to obtain first measurement data; acquiring second measurement data of a second node; and calculating a power system state estimation value through a line state estimation algorithm according to the first measurement data and the second measurement data. The power data of the first node are processed by calling the measurement prediction model to obtain first measurement data, and the first measurement data are vector data used for indicating the voltage of the first node, so that the state estimation value of the power system can be obtained by calculating through a linear state estimation algorithm based on the first measurement data and the collected second measurement data, and the nonlinear equation does not need to be solved through multiple iterations, so that the calculation speed is increased.

Description

Power system state estimation method, device, equipment and storage medium
Technical Field
The present application relates to the field of power system technologies, and in particular, to a method, an apparatus, a device, and a storage medium for estimating a state of a power system.
Background
State Estimation (SE) is a basic application of a power grid dispatching automation system and is used for obtaining an optimal estimated value of system State quantity through mathematical operation according to various real-time measurement data containing errors obtained in a power system. As a core of an Energy Management System (EMS) of the power System, the state estimation functions to provide basic data for various other high-level applications in the EMS.
In the related art, the state estimation of the power system is obtained by iteratively solving a nonlinear measurement equation through a weighted least square method based on real-time Data acquired by a computer based on a Supervisory Control And Data Acquisition (SCADA) system.
Because the computer needs to solve the nonlinear equation through multiple iterations, the calculation speed is low, the calculation efficiency is low, and the problem of non-convergence of the calculation result also occurs in some cases.
Disclosure of Invention
The embodiment of the application provides a method, a device, equipment and a storage medium for estimating the state of a power system, which can overcome the defects that the calculation efficiency is low and the calculation result is not converged under certain conditions in the related technology:
in one aspect, an embodiment of the present application provides a method for estimating a state of an electric power system, where the method includes:
calling a measurement prediction model to process power data of a first node to obtain first measurement data, wherein the power data are acquired by a data acquisition and monitoring control System (SCADA) of the first node, the first measurement data are vector data used for indicating the voltage of the first node, and the measurement prediction model is used for indicating an objective rule of the measurement data obtained based on historical power data training;
acquiring second measurement data of a second node, wherein the second measurement data is acquired by a PMU (phasor measurement Unit) of the second node;
and calculating a power system state estimation value through a line state estimation algorithm according to the first measurement data and the second measurement data.
In an optional embodiment, the calculating, according to the first measurement data and the second measurement data, a power system state estimation value by a line state estimation algorithm includes:
according to the first measurement data, a first error is obtained through calculation of a first error model, and the first error model is a calculation model for converting the first measurement data into the first error;
according to the second measurement data, calculating through a second error model to obtain a second error, wherein the second error model is a calculation model for converting the second measurement data into the second error;
and solving a linear solution equation by combining the first measurement data and the second measurement data according to the first error and the second error to obtain the state estimation value of the power system, wherein the linear solution equation is obtained by calculating a linear state equation for solving the state estimation value of the power system through a weighted least square method.
In an alternative embodiment, the metrology prediction model comprises a reactive power prediction model, the power data of the first node comprises reactive power data, the reactive power data being data of electrical power required to establish the alternating magnetic field and the induced magnetic flux in the power data of the first node;
the measurement prediction model further comprises an active prediction model, the power data of the first node comprise active power data, and the active power data are data of electric power required for keeping normal operation of electric equipment in the power data of the first node;
the first measurement data comprises first voltage amplitude data and first voltage phase data;
the calling a measurement prediction model to process the power data of the first node to obtain first measurement data, including:
calling the reactive power prediction model to process the reactive power data to obtain first voltage amplitude data;
calling the active prediction model to process the active power data to obtain the first voltage phase data;
the reactive power prediction model is obtained by training according to at least one group of reactive power data group, and each group of reactive power data group comprises: the active prediction model is obtained by training at least one active power data set, and each active power data set comprises: historical active power data and historical first voltage phase data of the power system.
In an optional embodiment, the calculating a first error through a first error model according to the first measurement data includes:
calculating to obtain first real part data and first imaginary part data according to the first voltage amplitude data and the first voltage phase data;
obtaining the first real part variance through the first error model according to the first real part data;
obtaining the first imaginary variance through the first error model according to the first imaginary data;
and the first error model is obtained by calculation according to the historical real part data of the power system and the real part data output by the measurement prediction model, and the historical imaginary part data of the power system and the imaginary part data output by the measurement prediction model.
In an alternative embodiment, the second metrology data comprises second voltage amplitude data and second voltage phase data;
the calculating a second error through a second error model according to the second measurement data includes:
and calculating to obtain the second real part variance and the second imaginary part variance through the second error model according to the second voltage amplitude data and the second voltage phase data.
In an alternative embodiment, the second voltage amplitude data is voltage amplitude data in a polar coordinate system, and the second voltage phase data is voltage phase data in a polar coordinate system;
the calculating the second real part variance and the second imaginary part variance through the second error model according to the second voltage amplitude data and the second voltage phase data includes:
and performing coordinate conversion on the voltage amplitude variance and the voltage phase variance under a polar coordinate system according to the second voltage amplitude data and the second voltage phase data through the second error model to obtain the second real part variance and the second imaginary part variance.
In an alternative embodiment, the linear solution equation is an equation based on a measurement error variance matrix and a measurement vector;
the solving a linear solution equation according to the first error and the second error and in combination with the first measurement data and the second measurement data to obtain the state estimation value of the power system includes:
constructing the metric error variance matrix from the first real-part variance, the first imaginary-part variance, the second real-part variance, and the second imaginary-part variance;
calculating to obtain second real part data and second imaginary part data according to the second voltage amplitude data and the second voltage phase data;
calculating to obtain the measurement vector according to the first real part data, the first imaginary part data, the second real part data and the second imaginary part data;
and solving the linear solution equation according to the measurement error variance matrix and the measurement vector to obtain the state estimation value of the power system.
In one aspect, an embodiment of the present application provides an apparatus for estimating a state of a power system, where the apparatus includes:
the system comprises a learning module, a measurement prediction model and a monitoring control System (SCADA), wherein the learning module is used for calling the measurement prediction model to process power data of a first node to obtain first measurement data, the power data is acquired by a SCADA of the first node, the first measurement data is vector data used for indicating voltage of the first node, and the measurement prediction model is used for indicating objective rules of the measurement data obtained based on historical power data training;
the acquisition module is used for acquiring second measurement data of a second node, wherein the second measurement data is acquired by a PMU (phasor measurement Unit) of the second node;
and the calculation module is used for calculating a power system state estimation value through a linear state estimation algorithm according to the first measurement data and the second measurement data.
In one aspect, embodiments of the present application provide a computer device, which includes a processor and a memory, where the memory stores at least one instruction, and the instruction is loaded and executed by the processor to implement the power system state estimation method described above.
In one aspect, embodiments of the present application provide a computer-readable storage medium, where at least one instruction is stored, and the instruction is loaded and executed by a processor to implement the power system state estimation method described above.
The technical scheme provided by the embodiment of the application at least comprises the following beneficial effects:
the power system state estimation method based on the SCADA comprises the steps that a measurement prediction model is called, power data of a first node collected by the SCADA are processed, first measurement data are obtained, second measurement data collected by the PMU are obtained, and the first measurement data are vector data used for indicating the voltage of the first node, so that a power system state estimation value can be obtained through calculation of a linear state estimation algorithm based on the first measurement data and the second measurement data, multiple iterations are not needed for solving a nonlinear equation, and therefore calculation efficiency is improved.
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In order to more clearly illustrate the detailed description of the present application or the technical solutions in the prior art, the drawings needed to be used in the detailed description of the present application or the prior art description will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a schematic illustration of a power system provided in an exemplary embodiment of the present application;
FIG. 2 is a flow chart of a power system state estimation method provided by an exemplary embodiment of the present application;
FIG. 3 is a block diagram of a metrology prediction model provided in an exemplary embodiment of the present application;
FIG. 4 is a flow chart illustrating the training of a metrology prediction model provided in an exemplary embodiment of the present application;
FIG. 5 is a flow chart of a power system state estimation method provided by an exemplary embodiment of the present application;
FIG. 6 is a flow chart of power system state estimation provided by an exemplary embodiment of the present application;
FIG. 7 is a block diagram of a power system state estimation device provided in an exemplary embodiment of the present application;
FIG. 8 is a block diagram of a computer device provided in an exemplary embodiment of the present application.
Detailed Description
The technical solutions of the present application will be described clearly and completely with reference to the accompanying drawings, and it should be understood that the described embodiments are only some embodiments of the present application, but not all 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 application.
In the description of the present application, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience of description and simplicity of description, and do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be construed as limiting the present application. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
In the description of the present application, it is to be noted that, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; can be mechanically or electrically connected; the two elements may be directly connected or indirectly connected through an intermediate medium, or may be communicated with each other inside the two elements, or may be wirelessly connected or wired connected. The specific meaning of the above terms in the present application can be understood in a specific case by those of ordinary skill in the art.
In addition, the technical features mentioned in the different embodiments of the present application described below may be combined with each other as long as they do not conflict with each other.
Fig. 1 shows a schematic diagram of a power system provided by an exemplary embodiment of the present application. As shown in fig. 1, the power system 100 includes at least one first node 110, at least one second node 120, and a computer device 130.
Optionally, the computer device 130 is an EMS, or the computer device 130 is a device in the EMS that performs a computing function.
Wherein the first node 110 is a power system node provided with SCADA; the second node 120 is a power system node provided with a Phasor Measurement Unit (PMU).
The first node 110 is configured to send the power data of the first node 110 collected by the SCADA to the computer device 130 through a wired or wireless network.
The second node 120 is configured to send the second measurement data of the second node 120 collected by the PMU to the computer device 130 through a wired or wireless network.
The computer device 130 is configured to invoke a measurement prediction model to process power data of a first node, so as to obtain first measurement data; and receiving second measurement data, and calculating by a linear state estimation algorithm according to the first measurement data and the second measurement data to obtain a power system state estimation value. The measurement prediction model is used for indicating objective rules of measurement data obtained based on historical power data training.
Optionally, a historical database and a metrology prediction model are stored in the computer device 130. The historical database stores historical data of the power system 100, wherein the historical data comprises historical power data, and historical voltage phase data and historical voltage amplitude data corresponding to the historical power data; the computer equipment can train the initial measurement prediction model through historical power data, corresponding historical voltage phase data and historical voltage amplitude data in the historical database to obtain the measurement prediction model.
Fig. 2 is a flowchart illustrating a method for estimating a state of a power system according to an exemplary embodiment of the present application. The method may be performed by the computer device 130 in the embodiment of fig. 1, and the method includes:
step 201, a measurement prediction model is called to process the power data of the first node, so as to obtain first measurement data.
Illustratively, the metrology prediction model is a machine learning model and the first metrology data is vector data indicative of a voltage of the first node.
Optionally, the first measurement data includes first voltage amplitude data and first voltage phase data of the first node. As described in the embodiment of fig. 1, the computer device may train the initial measurement prediction model through the historical power data, the corresponding historical voltage phase data, and the historical voltage amplitude data to obtain the measurement prediction model.
The method comprises the steps that power data of a first node collected by the SCADA are sent to computer equipment through a wired or wireless network, and after the computer equipment receives the power data of the first node, a measurement prediction model is called to process the power data of the first node to obtain first measurement data.
Step 202, obtain second measurement data of the second node.
Illustratively, the computer device receives second measurement data sent by the second node. The second measurement data comprises second voltage amplitude data and second voltage phase data of the second node.
Step 203, calculating to obtain a power system state estimation value through a line state estimation algorithm according to the first measurement data and the second measurement data.
Since the first measurement data includes first voltage amplitude data and first voltage amplitude data, and the second measurement data includes second voltage amplitude data and second voltage amplitude data, the computer device can calculate the power system state estimation value through a preset linear state estimation algorithm based on the first voltage amplitude data, the second voltage amplitude data and the second voltage amplitude data.
Illustratively, the computer device calculates to obtain first real part data and first imaginary part data according to the first voltage amplitude data and the first voltage phase data, calculates to obtain second real part data and second imaginary part data according to the second voltage amplitude data and the second voltage phase data, calculates to obtain a measurement error variance matrix based on the first real part data, the first imaginary part data, the second voltage amplitude data and the second voltage phase data, calculates to obtain a measurement vector based on the first real part data, the first imaginary part data, the second real part data and the second imaginary part data, and brings the measurement error variance and the measurement vector into a linear solution equation to obtain the power system state estimation value. The linear solution equation is obtained by calculating a linear state equation for solving the state estimation value of the power system through a weighted least square method.
Generally, because the voltage phase data of the node where the SCADA is located cannot be acquired, in each node of the power system, part of the nodes acquire data through the SCADA, and part of the nodes acquire data through the PMU, the data of each node received by the computer device is the voltage phase data lacking part of the nodes, and when the state estimation is performed, the nonlinear equation needs to be solved through multiple iterations, so that the calculation efficiency is low, and the problem of non-convergence of the calculation result also occurs in some cases.
In the embodiment of the application, the measurement prediction model is called to process the power data of the first node collected by the SCADA to obtain the first measurement data and obtain the second measurement data collected by the PMU, and the first measurement data are vector data used for indicating the voltage of the first node, so that the state estimation value of the power system can be calculated by a linear state estimation algorithm based on the first measurement data and the second measurement data, and the nonlinear equation is not required to be solved for multiple iterations, so that the calculation efficiency is improved.
Fig. 3 is a schematic diagram illustrating a framework of a metrology prediction model according to an exemplary embodiment of the present application. As shown in fig. 3, the metric prediction model 300 includes input layer neurons 310, hidden layer neurons 320, output layer neurons 330, and historical voltage vector data 340 (which includes historical voltage magnitude data and historical voltage magnitude data).
Illustratively, the metric prediction model 300 is an Extreme Learning Machine (ELM) model, and neurons in each layer of the metric prediction model 300 are connected by weights. All parameters are not required to be adjusted in the learning process, the input weight and the threshold are randomly generated, then the least square solution is directly solved for the weights of the hidden layer and the output layer through the generalized inverse matrix, and therefore the feedforward network structure is obtained in the form of the analytic solution, local optimization is not prone to occurring, and the generalization capability is strong.
As shown in fig. 4, the computer device inputs the power data into the measurement prediction model 410, the measurement prediction model 410 outputs voltage amplitude data and voltage phase data, the computer device detects whether the training of the measurement prediction model 410 is finished, calculates voltage real part data and voltage imaginary part data according to the output voltage amplitude data and voltage phase data when the training of the measurement prediction model 410 is finished, and inputs the voltage real part data and the voltage imaginary part data into the first error model 420; when the metrology prediction model 410 is not trained, the computer device adjusts relevant parameters of the metrology prediction model 410 based on the output magnitude data and phase data.
Since there is an error between the real part data and the imaginary part data output by the measurement prediction model 410 and the real part data and the imaginary part data of the real voltage of the node, the error may be fitted by normal distribution to obtain a mean and a variance, and specifically, the error may be applied to the normal distribution model:
Figure BDA0002259742420000111
where μ denotes the mean, σ2Represents variance, x represents input value, can be real part data or imaginary part data, when x is real part data, mu represents real part mean value, sigma2Representing the variance of the real part; when x is imaginary data, μ represents the imaginary mean, σ2Representing the imaginary variance.
Fig. 5 is a flowchart illustrating a method for estimating a state of a power system according to an exemplary embodiment of the present application. The method may be performed by the computer device 130 in the embodiment of fig. 1, and the method includes:
step 501, calling a measurement prediction model to process power data of a first node to obtain first measurement data.
The first node power data includes reactive power data and active power data. Wherein the reactive power data is data of electric power required for establishing an alternating magnetic field and an induced magnetic flux in the power data of the first node; the active power data is data of electric power required for keeping the electric equipment normally operating in the power data of the first node.
Optionally, the measurement prediction model includes a reactive power prediction model, and the computer device calls the reactive power prediction model to process the reactive power data to obtain the first voltage amplitude data.
The reactive power prediction model is obtained by training according to at least one group of reactive power data set, and each group of reactive power data set comprises: historical reactive power data and first voltage amplitude data for the power system.
Optionally, the measurement prediction model further includes an active prediction model, and the computer device calls the active prediction model to process the active power data to obtain the first voltage phase data.
The active prediction model is obtained by training according to at least one group of active power data sets, and each group of active power data sets comprises: historical active power data and historical first voltage phase data of the power system.
Step 502, obtain a second measurement data of the second node.
The method for acquiring the second measurement data of the second node by the computer device is as described in step 202 in the embodiment of fig. 2, and is not described herein again.
Step 503, calculating a first error through a first error model according to the first measurement data.
The first error model is a calculation model for converting first measurement data into a first error, the first measurement data comprises first voltage amplitude data and first voltage phase data, and the first error comprises a first real part variance and a first imaginary part variance.
And the computer equipment calculates to obtain first real part data and first imaginary part data according to the first voltage amplitude data and the first voltage phase data, wherein the first real part data is real part data of the first node voltage, and the first imaginary part data is imaginary part data of the first node voltage.
Optionally, the computer device converts the first real part data through a first error model to obtain a first real part variance; and converting the first imaginary part data through a first error model to obtain a first imaginary part variance. For example, the method for acquiring the first real part variance and the first imaginary part variance by the computer device through the first error model may refer to the embodiment in fig. 4, which is not described herein again.
Step 504, a second error is calculated by a second error model according to the second measurement data.
The second error model is a calculation model for converting second measured data into a second error, the second measured data includes second voltage amplitude data and second voltage phase data of the second node, and the second error includes a second real part variance and a second imaginary part variance.
Optionally, the computer device calculates a second real part variance and a second imaginary part variance according to the second voltage amplitude data and the second voltage phase data.
For example, the data collected by the PMU is data in a polar coordinate system. The computer device may convert the second voltage amplitude data and the second voltage phase data in a polar coordinate system into second real part data and second imaginary part data in a rectangular coordinate system.
Optionally, the second measurement data further includes second node current amplitude data and current phase data. The computer device may convert the current magnitude data and the current phase data in polar coordinates to real current data and imaginary current data in rectangular coordinates.
Illustratively, the computer device may perform the conversion of data in polar format to data in rectangular format by the following formula:
V＝V*cosθV+jV*sinθV
I＝I*cosθI+jI*sinθI
wherein V represents a second voltage vector in a rectangular coordinate system, j represents an imaginary part, and V*Representing second voltage amplitude data, thetaVTo representA second voltage phase data, I represents a second current vector in a rectangular coordinate system, I*Representing current magnitude data, thetaIRepresenting current phase data.
Illustratively, the computer device may calculate the corresponding variance by the second error model based on the following error transfer equation:
Figure BDA0002259742420000131
Figure BDA0002259742420000132
Figure BDA0002259742420000141
Figure BDA0002259742420000142
wherein the content of the first and second substances,
Figure BDA0002259742420000143
is the variance of the real part of the current,
Figure BDA0002259742420000144
for the variance of the imaginary part of the current,
Figure BDA0002259742420000145
is the second real-part variance and is,
Figure BDA0002259742420000146
is the second imaginary variance, σI 2Is the variance of the measurement error of the current amplitude,
Figure BDA0002259742420000147
is the variance of the measurement error of the current phase,
Figure BDA0002259742420000148
is the variance of the measurement error of the voltage amplitude,is the variance of the measurement error of the voltage phase, I represents the current amplitude data, thetaIRepresenting current phase data, V representing second voltage amplitude data, thetaVRepresenting the second voltage phase data.
And 505, according to the first error and the second error, combining the first measurement data and the second measurement data to obtain a state estimation value of the power system by solving a linear solution equation.
The linear solution equation is obtained by solving a linear state equation of the state estimation value of the power system through a weighted least square method.
Illustratively, the computer device brings the first error and the second error into a linear solution equation
Figure BDA00022597424200001410
And obtaining an estimated value of the state of the power system. Wherein the linear solution equation is a weighted least squares solution of the linear measurement equation z-Hx + v, wherein,
Figure BDA00022597424200001411
is a least squares solution of x.
Wherein the content of the first and second substances,
Figure BDA00022597424200001412
the estimated value of the power system state, z is a measurement vector, H is a measurement coefficient matrix, and R is a measurement error variance matrix.
The measurement vector includes first real data, first imaginary data, second real data, and second imaginary data.
Illustratively, the measurement vector z is:
Figure BDA00022597424200001413
wherein the content of the first and second substances,
Figure BDA00022597424200001414
is firstThe real part data is transmitted to the real part,is first imaginary data; e.g. of the typeiIs the second real part data, fiIs second imaginary data; IRiInjecting real part of current, IM, into the nodeiInjecting current imaginary part data for the node; IRijFor data of real part of current in partial branch, IMijIs the imaginary part data of the partial branch current.
The measurement system matrix can be obtained by solving a linear measurement equation. For example, the specific expansion of the linear metrology equation may be:
Figure BDA0002259742420000151
wherein the formula is as follows: gijAnd BijThe real part and the imaginary part of the ith row and jth column element of the node admittance matrix (of the first node or the second node) respectively; g, b and ycRespectively the conductance, susceptance and admittance to ground of the corresponding branch. They are network parameters and are therefore constants.
According to the above-mentioned linear measurement equation, for the state quantity (i.e. the real part e of the voltage)iAnd imaginary part fi) The partial derivation can obtain a measurement coefficient matrix H for measuring the quantity related to the state quantity, and H is a constant matrix as known from a linear measurement equation.
The metrology error variance matrix includes a first real variance, a first imaginary variance, a second real variance, and a second imaginary variance.
Illustratively, the measurement error variance matrix R is:
wherein the content of the first and second substances,
Figure BDA0002259742420000153
respectively referring to a first real part error and a first imaginary part error;
Figure BDA0002259742420000154
respectively referring to a second real part error and a second imaginary part error;
Figure BDA0002259742420000161
the error of the real part of the node injection current, the error of the imaginary part of the node injection current, the error of the real part of the partial branch current and the error of the imaginary part of the partial branch current are respectively referred.
For example, the current amplitude data and the current phase data collected by the second node include node injection current amplitude data, node injection current phase data, partial branch current amplitude data, and partial branch current phase data. The data is data in a polar coordinate system.
The computer equipment can convert the node injection current amplitude data and the node injection current phase data under the polar coordinate system to obtain node injection current real part data and node injection current imaginary part data; and converting the amplitude data of part of branch current and the phase data of part of branch current under the polar coordinate system to obtain real part data and imaginary part data of the branch current.
In summary, in the embodiment of the application, the measurement prediction model is called to process the power data of the first node collected by the SCADA, so as to obtain the first measurement data including the voltage amplitude data and the voltage phase angle data of the first node, and therefore, the state estimation value of the power system can be obtained through calculation of the linear state estimation algorithm based on the first measurement data and the second measurement data, and the nonlinear equation does not need to be solved through multiple iterations, so that the calculation efficiency is improved.
Fig. 6 shows a flow chart of power system state estimation provided by an exemplary embodiment of the present application. The process may be performed by the computer device 130 of fig. 1, and includes:
step 1, computer equipment obtains power data of a first node at time t.
Step 2, the computer device inputs the power data of the first node into the ELM model 610 to obtain first voltage amplitude data and first voltage phase data, obtains first real part data and first imaginary part data according to the first voltage amplitude data and the first voltage phase data, and inputs the first real part data and the first imaginary part data into the linear state estimator 650; prior to actual measurement, the computer device inputs historical data 630 into the ELM model 610 to train the ELM model 610.
Step 3, the computer device inputs the first real part data and the first imaginary part data into the first error model 640 for normal fitting to obtain a first real part variance and a first imaginary part variance, and inputs the first real part variance and the first imaginary part variance into the linear state estimator 650.
Step 4, the computer device converts the second measurement data 620 collected by the PMU from a polar coordinate to a direct coordinate system, and inputs the obtained data to the linear state estimator 650.
Step 5, the computer device inputs the second measured data 620 into the second error model 660 to obtain a second real part variance and a second imaginary part variance, and inputs the second real part variance and the second imaginary part variance into the linear state estimator 650.
And 6, calculating by the linear state estimator 650 according to the input data, and outputting the estimated value of the state of the power system at the time t.
Fig. 7 is a block diagram illustrating a power system state estimation apparatus provided in an exemplary embodiment of the present application, which may be implemented as a computer device in the embodiment of fig. 1 through software, hardware, or a combination of the two, and includes a learning module 710, an obtaining module 720, and a calculating module 730.
The learning module 710 is configured to invoke a measurement prediction model to process power data of the first node to obtain first measurement data, where the power data is acquired by SCADA of the first node, the first measurement data is vector data used to indicate a voltage of the first node, and the measurement prediction model is used to indicate an objective rule of the measurement data obtained based on historical power data training.
The obtaining module 720 is configured to obtain second measurement data of the second node, where the second measurement data is data acquired by a PMU of the second node.
The calculating module 730 is configured to calculate, according to the first measurement data and the second measurement data, a power system state estimation value by a line state estimation algorithm.
In an alternative embodiment, the calculating module 730 is further configured to calculate a first error according to the first measured data through a first error model, where the first error model is a calculation model for converting the first measured data into the first error; according to the second measurement data, a second error is obtained through calculation of a second error model, and the second error model is a calculation model for converting the second measurement data into the second error; and solving a linear solution equation by combining the first measurement data and the second measurement data according to the first error and the second error to obtain the state estimation value of the power system, wherein the linear solution equation is obtained by calculating the linear state equation for solving the state estimation value of the power system by a weighted least square method.
In an alternative embodiment, the metrology prediction model comprises a reactive power prediction model, the power data of the first node comprises reactive power data, the reactive power data being data of electrical power required to establish the alternating magnetic field and the induced magnetic flux in the power data of the first node;
the measurement prediction model further comprises an active prediction model, the power data of the first node comprise active power data, and the active power data are data of electric power required for keeping the normal operation of the electric equipment in the power data of the first node;
the first measurement data comprises first voltage amplitude data and first voltage phase data;
the learning module 710 is further configured to invoke a reactive power prediction model to process the reactive power data to obtain first voltage amplitude data; and calling an active prediction model to process the active power data to obtain first voltage phase data.
The reactive power prediction model is obtained by training according to at least one group of reactive power data set, and each group of reactive power data set comprises: the active power prediction model is obtained by training at least one group of active power data sets, and each group of active power data sets comprises: historical active power data and historical first voltage phase data of the power system.
In an optional embodiment, the learning module 710 is further configured to calculate a first real part data and a first imaginary part data according to the first voltage amplitude data and the first voltage phase data; obtaining a first real part variance through a first error model according to the first real part data; and obtaining a first imaginary variance through a first error model according to the first imaginary data.
The first error model is obtained by calculation according to historical real part data of the power system, real part data output by the measurement prediction model, historical imaginary part data of the power system and imaginary part data output by the measurement prediction model.
In an alternative embodiment, the second measurement data includes second voltage amplitude data and second voltage phase data;
the learning module 710 is further configured to calculate a second real part variance and a second imaginary part variance through a second error model according to the second voltage amplitude data and the second voltage phase data.
In an alternative embodiment, the second voltage amplitude data is voltage amplitude data in a polar coordinate system, and the second voltage phase data is voltage phase data in the polar coordinate system;
the learning module 710 is further configured to perform coordinate conversion on the voltage amplitude variance and the voltage phase variance in the polar coordinate system according to the second voltage amplitude data and the second voltage phase data through a second error model to obtain a second real part variance and a second imaginary part variance.
In an alternative embodiment, the linear solution equation is an equation based on a measurement error variance matrix and a measurement vector;
the learning module 710 is further configured to construct a metric error variance matrix according to the first real part variance, the first imaginary part variance, the second real part variance, and the second imaginary part variance; calculating to obtain second real part data and second imaginary part data according to the second voltage amplitude data and the second voltage phase data; calculating to obtain a measurement vector according to the first real part data, the first imaginary part data, the second real part data and the second imaginary part data; and solving a linear solution equation according to the measurement error variance matrix and the measurement vector to obtain the state estimation value of the power system.
Fig. 8 is a block diagram illustrating a computer device according to an exemplary embodiment of the present application. The computer device includes: a processor 810 and a memory 820.
The processor 810 may be a Central Processing Unit (CPU), a Network Processor (NP), or a combination of a CPU and an NP. The processor 810 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a Programmable Logic Device (PLD), or a combination thereof. The PLD may be a Complex Programmable Logic Device (CPLD), a field-programmable gate array (FPGA), a General Array Logic (GAL), or any combination thereof.
The memory 820 is connected to the processor 810 through a bus or other means, and at least one instruction, at least one program, a code set, or a set of instructions is stored in the memory 820, and is loaded and executed by the processor 810 to implement the power system state estimation method in the above-described embodiment. Optionally, the memory 820 stores a measurement prediction model 821, a first error model 822 and a second error model 823, and the measurement prediction model 821 includes a reactive prediction model 8211 and an active prediction model 8212.
The memory 820 may be a volatile memory (or a nonvolatile memory), a non-volatile memory (or a combination thereof). The volatile memory may be a random-access memory (RAM), such as a Static Random Access Memory (SRAM) or a Dynamic Random Access Memory (DRAM). The nonvolatile memory may be a Read Only Memory (ROM), such as a Programmable Read Only Memory (PROM), an Erasable Programmable Read Only Memory (EPROM), and an Electrically Erasable Programmable Read Only Memory (EEPROM). The non-volatile memory may also be a flash memory, a magnetic memory, such as a magnetic tape, a floppy disk, or a hard disk. The non-volatile memory may also be an optical disc.
The present embodiments also provide a computer-readable storage medium, in which at least one instruction, at least one program, a code set, or a set of instructions is stored, and the at least one instruction, the at least one program, the code set, or the set of instructions is loaded and executed by the processor to implement the power system state estimation method according to any of the above embodiments.
The present application also provides a computer program product, which when run on a computer, causes the computer to execute the power system state estimation method provided by the above method embodiments.
It should be understood that the above examples are only for clarity of illustration and are not intended to limit the embodiments. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. And obvious variations or modifications of this invention are intended to be covered by the scope of the invention as expressed herein.

Claims (10)

1. A method of estimating a state of a power system, the method comprising:
calling a measurement prediction model to process power data of a first node to obtain first measurement data, wherein the power data are acquired by a data acquisition and monitoring control System (SCADA) of the first node, the first measurement data are vector data used for indicating the voltage of the first node, and the measurement prediction model is used for indicating an objective rule of the measurement data obtained based on historical power data training;
acquiring second measurement data of a second node, wherein the second measurement data is acquired by a PMU (phasor measurement Unit) of the second node;
and calculating a power system state estimation value through a line state estimation algorithm according to the first measurement data and the second measurement data.
2. The method of claim 1, wherein calculating the power system state estimation value according to the first measurement data and the second measurement data by a line state estimation algorithm comprises:
according to the first measurement data, a first error is obtained through calculation of a first error model, and the first error model is a calculation model for converting the first measurement data into the first error;
according to the second measurement data, calculating through a second error model to obtain a second error, wherein the second error model is a calculation model for converting the second measurement data into the second error;
and solving a linear solution equation by combining the first measurement data and the second measurement data according to the first error and the second error to obtain the state estimation value of the power system, wherein the linear solution equation is obtained by calculating a linear state equation for solving the state estimation value of the power system through a weighted least square method.
3. The method of claim 2, wherein the metrology prediction model comprises a reactive prediction model, the power data of the first node comprises reactive power data, the reactive power data being data of electrical power required to establish the alternating magnetic field and the induced magnetic flux in the power data of the first node;
the measurement prediction model further comprises an active prediction model, the power data of the first node comprise active power data, and the active power data are data of electric power required for keeping normal operation of electric equipment in the power data of the first node;
the first measurement data comprises first voltage amplitude data and first voltage phase data;
the calling a measurement prediction model to process the power data of the first node to obtain first measurement data, including:
calling the reactive power prediction model to process the reactive power data to obtain first voltage amplitude data;
calling the active prediction model to process the active power data to obtain the first voltage phase data;
the reactive power prediction model is obtained by training according to at least one group of reactive power data group, and each group of reactive power data group comprises: the active prediction model is obtained by training at least one active power data set, and each active power data set comprises: historical active power data and historical first voltage phase data of the power system.
4. The method of claim 3, wherein calculating a first error from the first metrology data via a first error model comprises:
calculating to obtain first real part data and first imaginary part data according to the first voltage amplitude data and the first voltage phase data;
obtaining the first real part variance through the first error model according to the first real part data;
obtaining the first imaginary variance through the first error model according to the first imaginary data;
and the first error model is obtained by calculation according to the historical real part data of the power system and the real part data output by the measurement prediction model, and the historical imaginary part data of the power system and the imaginary part data output by the measurement prediction model.
5. The method of claim 4, wherein the second metrology data comprises second voltage amplitude data and second voltage phase data;
the calculating a second error through a second error model according to the second measurement data includes:
and calculating to obtain the second real part variance and the second imaginary part variance through the second error model according to the second voltage amplitude data and the second voltage phase data.
6. The method of claim 5, wherein the second voltage amplitude data is voltage amplitude data in a polar coordinate system, the second voltage amplitude data being voltage amplitude data in a polar coordinate system;
the calculating the second real part variance and the second imaginary part variance through the second error model according to the second voltage amplitude data and the second voltage phase data includes:
and performing coordinate conversion on the voltage amplitude variance and the voltage phase variance under a polar coordinate system according to the second voltage amplitude data and the second voltage phase data through the second error model to obtain the second real part variance and the second imaginary part variance.
7. The method of claim 6, wherein the linear solution equation is an equation based on a measurement error variance matrix and a measurement vector;
the solving a linear solution equation according to the first error and the second error and in combination with the first measurement data and the second measurement data to obtain the state estimation value of the power system includes:
constructing the metric error variance matrix from the first real-part variance, the first imaginary-part variance, the second real-part variance, and the second imaginary-part variance;
calculating to obtain second real part data and second imaginary part data according to the second voltage amplitude data and the second voltage phase data;
calculating to obtain the measurement vector according to the first real part data, the first imaginary part data, the second real part data and the second imaginary part data;
and solving the linear solution equation according to the measurement error variance matrix and the measurement vector to obtain the state estimation value of the power system.
8. An apparatus for estimating a state of a power system, the apparatus comprising:
the system comprises a learning module, a measurement prediction model and a monitoring control System (SCADA), wherein the learning module is used for calling the measurement prediction model to process power data of a first node to obtain first measurement data, the power data is acquired by a SCADA of the first node, the first measurement data is vector data used for indicating voltage of the first node, and the measurement prediction model is used for indicating objective rules of the measurement data obtained based on historical power data training;
the acquisition module is used for acquiring second measurement data of a second node, wherein the second measurement data is acquired by a PMU (phasor measurement Unit) of the second node;
and the calculation module is used for calculating a power system state estimation value through a linear state estimation algorithm according to the first measurement data and the second measurement data.
9. A computer device, characterized in that the device comprises a processor and a memory, in which at least one instruction is stored, which is loaded and executed by the processor to implement the power system state estimation method according to any one of claims 1 to 7.
10. A computer readable storage medium having stored therein at least one instruction, which is loaded and executed by a processor to implement the power system state estimation method of any of claims 1 to 7.
CN201911067169.8A 2019-11-04 2019-11-04 Power system state estimation method, device, equipment and storage medium Pending CN110796385A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US11581733B2 (en) * 2019-11-12 2023-02-14 Alliance For Sustainable Energy, Llc System state estimation with asynchronous measurements

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102609790A (en) * 2012-03-14 2012-07-25 华北电力大学 Online state estimation method for electric power system by means of hybrid measurement
CN103606113A (en) * 2013-12-02 2014-02-26 东北石油大学 Static state estimation method for electrical power system based on PMU device
US20170220521A1 (en) * 2016-01-30 2017-08-03 Tsinghua University Method and device for estimating state of power system
CN107482778A (en) * 2017-09-13 2017-12-15 国网辽宁省电力有限公司 A kind of method and system of improved power system health status monitoring
CN110414787A (en) * 2019-06-24 2019-11-05 北京交通大学 Power system state estimation method, device, equipment and storage medium

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102609790A (en) * 2012-03-14 2012-07-25 华北电力大学 Online state estimation method for electric power system by means of hybrid measurement
CN103606113A (en) * 2013-12-02 2014-02-26 东北石油大学 Static state estimation method for electrical power system based on PMU device
US20170220521A1 (en) * 2016-01-30 2017-08-03 Tsinghua University Method and device for estimating state of power system
CN107482778A (en) * 2017-09-13 2017-12-15 国网辽宁省电力有限公司 A kind of method and system of improved power system health status monitoring
CN110414787A (en) * 2019-06-24 2019-11-05 北京交通大学 Power system state estimation method, device, equipment and storage medium

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
贾东莉 等: "基于超短期负荷预测的智能配电网状态估计" *

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US11581733B2 (en) * 2019-11-12 2023-02-14 Alliance For Sustainable Energy, Llc System state estimation with asynchronous measurements

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