CN115409245A - Prediction auxiliary state estimation method, device, equipment and medium for power system - Google Patents
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Abstract
The invention relates to the field of data processing, and provides a prediction auxiliary state estimation method, a prediction auxiliary state estimation device and a prediction auxiliary state estimation medium for a power system, wherein the method comprises the following steps: acquiring an initial state prediction value and an initial state transition matrix of the power system; inputting the initial state prediction value and the initial state transition matrix into a pre-constructed state prediction model to obtain a state prediction value at a target moment; the state prediction model is constructed on the basis of a vector autoregressive model; and acquiring a state measured value of the target time, and acquiring a state estimation result of the target time based on the state predicted value of the target time and the state measured value of the target time. The state prediction model can comprehensively consider the time correlation and the space correlation of the power system data, so that the obtained state prediction value is more accurate, the accuracy and the reliability of the state estimation result can be improved, the application range is wider, and the problems of small application range, low accuracy and low reliability of the conventional prediction auxiliary state estimation method are solved.
Description
Technical Field
The present invention relates to the field of data processing technologies, and in particular, to a method, an apparatus, a device, and a medium for estimating a prediction aided state of an electrical power system.
Background
The state estimation of the power system is the most main way to acquire the real-time state of the power grid and is also the basis of load flow calculation and safety and stability analysis. From analysis of profile measurement data used for state estimation, state estimation of a power system can be classified into two broad categories, static state estimation and dynamic state estimation. The static state estimation is based on the measured data of a certain time section in the power system, and the state of the power system at the time of the section is estimated. The dynamic state estimation is based on the measured data of a plurality of time sections in the power system, predicts the state of the section of the power system at the next moment, and estimates the state of the power system by combining the measured values.
The dynamic State Estimation only considering the quasi-steady State condition of the power system is also called as predictive-assisted State Estimation (FASE), the conventional predictive-assisted State Estimation method of the power system is usually realized based on a kalman filtering algorithm, however, the predictive-assisted State Estimation method based on the kalman filtering is only suitable for the power system with simple working conditions, and for the power system with complex working conditions, the State Estimation result obtained by the method has low accuracy and reliability, and is difficult to meet the actual requirement.
Therefore, the existing prediction auxiliary state estimation method based on the Kalman filtering has the problems of small application range and low accuracy and reliability.
Disclosure of Invention
The invention provides a prediction auxiliary state estimation method, a prediction auxiliary state estimation device, prediction auxiliary state estimation equipment and a prediction auxiliary state estimation medium of a power system, which are used for overcoming the defects of small application range, low accuracy and low reliability of a prediction auxiliary state estimation method based on Kalman filtering in the prior art and realizing accurate and reliable estimation of the state of the power system.
In a first aspect, the present invention provides a method for estimating a predictive assistance state of a power system, the method including:
acquiring an initial state prediction value and an initial state transition matrix of the power system;
inputting the initial state predicted value and the initial state transition matrix into a pre-constructed state prediction model to obtain a state predicted value at a target moment; the state prediction model is constructed on the basis of a vector autoregressive model;
and acquiring the state measured value of the target time, and obtaining a state estimation result of the target time based on the state predicted value of the target time and the state measured value of the target time.
According to the prediction auxiliary state estimation method of the power system provided by the invention, the state prediction model is specifically used for:
determining a state predicted value at the kth moment through the vector autoregressive model based on the initial state predicted value and the initial state transition matrix, wherein k is more than or equal to 1;
determining a state transition matrix at the k +1 th moment based on the state prediction value at the k th moment and a preset number of state prediction values before the k th moment;
determining the state prediction value at the k +1 th moment through the vector autoregressive model based on the state prediction value at the k +1 th moment and the state transition matrix at the k +1 th moment;
and obtaining a state predicted value of the target moment until the k +1 th moment is the target moment.
According to the prediction assistance state estimation method of the power system provided by the present invention, the determining the state transition matrix at the k +1 th time based on the state prediction value at the k th time and a preset number of state prediction values before the k th time includes:
respectively calculating to obtain a first covariance matrix and a second covariance matrix at the k +1 th moment based on the state prediction value at the k th moment and a preset number of state prediction values before the k th moment;
determining a state transition matrix at the k +1 th time based on the first covariance matrix and the second covariance matrix at the k +1 th time.
According to the prediction auxiliary state estimation method of the power system provided by the invention, the vector autoregressive model is used for representing the functional relationship between the state predicted value at the k +1 th moment and the state predicted value at the k +1 th moment, the state transition matrix at the k +1 th moment and the preset noise value at the k +1 th moment.
According to the prediction support state estimation method of the power system according to the present invention, the obtaining of the state estimation result at the target time based on the state prediction value at the target time and the state actual measurement value at the target time includes:
determining a first covariance matrix of the target moment based on the state prediction value of the target moment and a preset number of state prediction values before the target moment;
determining a filter gain matrix of the target moment based on the first covariance matrix of the target moment, a preset measurement matrix of the target moment and a preset weight matrix of the target moment;
and determining a state estimation result of the target time based on the state prediction value of the target time, the state measured value of the target time, the measurement matrix of the target time and the filter gain matrix of the target time.
According to the prediction auxiliary state estimation method of the power system provided by the present invention, the determining the filter gain matrix of the target time based on the first covariance matrix of the target time, the preset measurement matrix of the target time and the preset weight matrix of the target time comprises:
determining a third covariance matrix of the target moment based on the first covariance matrix of the target moment, the measurement matrix of the target moment and the weight matrix of the target moment;
determining a filter gain matrix for the target time based on the third covariance matrix for the target time, the measurement matrix for the target time, and the weight matrix for the target time.
According to a prediction support state estimation method of a power system according to the present invention, the method for determining a state estimation result at the target time based on a state prediction value at the target time, a state actual measurement value at the target time, a measurement matrix at the target time, and a filter gain at the target time includes:
multiplying the measurement matrix at the target moment by the state prediction value at the target moment to obtain a first calculation value;
the state measured value of the target moment is differed from the first calculated value to obtain a second calculated value;
multiplying the filter gain of the target moment by the second calculated value to obtain a third calculated value;
and adding the state predicted value at the target moment and the third calculated value to obtain a state estimation result at the target moment.
In a second aspect, the present invention provides a prediction assist state estimation device for an electric power system, the device including:
the acquisition module is used for acquiring an initial state prediction value and an initial state transition matrix of the power system;
the first processing module is used for inputting the initial state prediction value and the initial state transition matrix into a pre-constructed state prediction model to obtain a state prediction value at a target moment; the state prediction model is constructed on the basis of a vector autoregressive model;
and the second processing module is used for acquiring the state measured value of the target time and obtaining a state estimation result of the target time based on the state predicted value of the target time and the state measured value of the target time.
In a third aspect, the present invention further provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method for estimating a predictive assistance state of a power system according to any of the above methods.
In a fourth aspect, the present invention also provides a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements a predictive assistance state estimation method for a power system as described in any one of the above.
According to the prediction auxiliary state estimation method, device, equipment and medium of the power system, the initial state prediction value and the initial state transition matrix of the power system are input into the state prediction model constructed on the basis of the vector autoregressive model, the state prediction value of the target moment is obtained, and the state estimation result of the target moment is obtained according to the state prediction value of the target moment and the state measured value of the target moment.
Drawings
In order to more clearly illustrate the present invention or the technical solutions in the prior art, the drawings used in the embodiments or the description of the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a schematic flow diagram of a method for estimating a predictive assistance status of a power system according to the present invention;
FIG. 2 is a second schematic flow chart illustrating a method for estimating a predicted auxiliary state of a power system according to the present invention;
FIG. 3 is a schematic diagram illustrating the comparison of the timing prediction results of the voltage amplitude and phase angle of node 4 in the IEEE 14 node case with the corresponding real values in accordance with an embodiment of the present invention;
fig. 4 is a schematic diagram of a comparison curve between the estimation result of the voltage amplitude corresponding to the bus 3 and the actually measured value of the voltage amplitude and the actual value of the voltage amplitude in the power system;
FIG. 5 is a graph illustrating the comparison between the estimated voltage phase angle of the bus 3 and the measured voltage phase angle and the actual voltage phase angle in the power system;
FIG. 6 is a graph illustrating the comparison between the estimated voltage amplitude corresponding to the bus 4 and the measured voltage amplitude and the actual voltage amplitude in the power system;
FIG. 7 is a graph illustrating the comparison between the estimated voltage phase angle of the bus 4 and the measured voltage phase angle and the actual voltage phase angle in the power system;
fig. 8 is a schematic structural diagram of a prediction auxiliary state estimation device of a power system provided by the present invention;
fig. 9 is a schematic structural diagram of an electronic device provided by the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is obvious that the described embodiments are some, but not all embodiments of the present invention. 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 following describes a prediction aided state estimation method, device, apparatus and medium of a power system according to an embodiment of the present invention with reference to fig. 1 to 9.
Fig. 1 illustrates a prediction assist state estimation method for a power system according to an embodiment of the present invention, where the method includes:
step 101: acquiring an initial state prediction value and an initial state transition matrix of the power system;
step 102: inputting the initial state prediction value and the initial state transition matrix into a pre-constructed state prediction model to obtain a state prediction value at a target moment; the state prediction model is constructed on the basis of a vector autoregressive model;
step 103: and acquiring a state measured value of the target time, and acquiring a state estimation result of the target time based on the state predicted value of the target time and the state measured value of the target time.
In this embodiment, the initial state prediction value and the initial state transition matrix may be obtained based on pre-obtained historical state data of the power system, the initial state prediction value may be preset according to the historical state data of the power system, and the initial state transition matrix may be obtained by solving the historical state data of the power system using a wacker equation.
The historical state data of the power system may be measured by a Phasor Measurement Unit (PMU) deployed in the power system, and in consideration of that the historical state data measured by the Phasor Measurement Unit has time correlation of a dynamic system and space correlation based on physical constraints, in order to fully utilize the time and space correlation of the historical state data of the power system, the embodiment employs a Vector Autoregressive (VAR) model in a signal processing field to realize state prediction of the power system.
Specifically, the present embodiment establishes a state prediction model for power system state prediction, which is essentially a mathematical model including a plurality of mathematical formulas, based on a vector autoregressive model. And inputting the initial state prediction value and the initial state transition matrix into the state prediction model to obtain the state prediction value of the target moment.
After the predicted value of the state at the target time is obtained, the estimated value of the state at the target time can be further calculated based on the measured value of the state at the target time obtained in real time, so that the measured value of the state can be further optimized.
In an exemplary embodiment, the state prediction model may be specifically used to:
determining a state prediction value at the kth moment through a vector autoregressive model based on the initial state prediction value and the initial state transition matrix, wherein k is more than or equal to 1;
determining a state transition matrix at the k +1 th moment based on the state prediction value at the k th moment and a preset number of state prediction values before the k th moment;
determining the state predicted value at the k +1 moment through a vector autoregressive model based on the state predicted value at the k moment and the state transition matrix at the k +1 moment;
and obtaining the state predicted value of the target moment until the k +1 th moment is the target moment.
In the process of obtaining the state predicted value of the target moment, the state measured value of each moment can be read in real time, and the state estimated value of each moment can be calculated. Fig. 2 shows a flow of acquiring a state estimation value at a target time in the present embodiment, which specifically includes the following steps:
step 201: first, each parameter is initialized, and in this embodiment, an initial time is set
k＝0，
And A 0 The initial state prediction value is
The initial state transition matrix is A 0 Due to the initial state transition matrix A 0 Can be calculated by means of a covariance matrix
The initial covariance matrix can be set in this embodiment by calculation
Step 202: enabling k = k +1, and entering the next moment;
step 203: determining a state prediction value at a current time (i.e., a next time)
Step 204: reading the state measured value z at the current time (i.e. the next time) k ；
Step 205: predicting value according to current state
And measured value z of the state k And calculating to obtain the state estimation value of the current moment
Step 206: for the initial covariance matrix
And an initial state transition matrix A 0 Updating is carried out;
step 207: judging whether k is more than k max If yes, repeating the steps 202 to 206; if k is not satisfied < k max When the target time is reached, the target time can be obtainedThe state estimate of (2).
In this embodiment, the vector autoregressive model may be used to characterize a functional relationship between the state prediction value at the k +1 th time and the state prediction value at the k +1 th time, the state transition matrix at the k +1 th time, and a preset noise value at the k +1 th time.
The main function expression of the vector autoregressive model in this embodiment is:
in the formula (I), the compound is shown in the specification,
indicates the predicted value of the state at the k +1 th time, A 1 A state transition matrix representing the current time of day,
indicates the predicted value of the state at the k-th time, epsilon k+1 Representing the noise value at time k + 1.
Further, determining the state transition matrix at the k +1 th time based on the state prediction value at the k th time and a preset number of state prediction values before the k th time may specifically include:
respectively calculating a first covariance matrix and a second covariance matrix at the k +1 th moment based on the state predicted value at the k th moment and the state predicted values of the preset number before the k th moment;
and determining the state transition matrix at the k +1 th moment based on the first covariance matrix and the second covariance matrix at the k +1 th moment.
State transition matrix A in the present embodiment 1 The first covariance matrix and the second covariance matrix at corresponding moments can be obtained through calculation, the first covariance matrix and the second covariance matrix can be obtained through a Gaussian maximum likelihood estimation mode, and the specific calculation formula is as follows:
in the formula (I), the compound is shown in the specification,
represents a covariance matrix, where h ≦ 0 ≦ N-1, h equals 0 or 1 in this embodiment, and when h equals 1,
is a first covariance matrix, when h is equal to 0,
a second covariance matrix, N being the number of historical state predictors considered within a predetermined interval,
indicating the predicted value of the state at the k-h,
indicates the predicted value of the state at the k-th time,
representing the mean of the historical state predictors.
A first covariance matrix is obtained by calculation according to the formula
And a second covariance matrix
Then, the state transition matrix a can be further calculated by the following formula 1 Namely:
in the formula, A 1 A state transition matrix is represented that represents the state transition,
a first covariance matrix is represented by a first covariance matrix,
representing a second covariance matrix.
Calculating according to the above formula to obtain state transition matrix A 1 Then, the state is transferred to the matrix A 1 The predicted value of the state at the k +1 th time can be obtained by substituting the predicted value into the formula (1).
The state prediction model in this embodiment can be understood as being mainly realized by the above-described formula (1), formula (2), and formula (3).
In this embodiment, the state transition matrix may be obtained by solving historical state data in the power system by using a wacker equation, and since diagonal elements of the state transition matrix in the vector autoregressive model may represent temporal correlations of the historical state data and off-diagonal elements may represent spatial correlations of the historical state data, the state transition matrix may represent temporal and spatial correlations of the historical state data in the power system. When the power system is in a steady state, the first-order vector autoregressive model is used for state prediction and prediction-aided state estimation, and a good effect can be obtained.
In an exemplary embodiment, obtaining the state estimation result at the target time based on the predicted state value at the target time and the measured state value at the target time may specifically include:
determining a first covariance matrix at the target time based on the state predicted value at the target time and the state predicted values of a preset number before the target time, where the first covariance matrix at the target time in this embodiment may be obtained by calculating, by using the above formula (2), that the value h is 1;
determining a filter gain matrix at the target moment based on a first covariance matrix at the target moment, a preset measurement matrix at the target moment and a weight matrix at the target moment;
and determining a state estimation result of the target time based on the state predicted value of the target time, the state measured value of the target time, the measurement matrix of the target time and the filter gain matrix of the target time.
The embodiment is based on the basic idea of least squares, and provides the following objective function on the basis of the static state estimation method:
in the formula, J (x) represents an objective function and is a residual value in nature; z represents a state measured value;
representing a state estimation value, namely a state estimation result;
representing a state prediction value; h represents a measurement matrix, which is satisfied in the present embodiment
Where ω represents an error value; r represents a weight matrix; m represents a state prediction value
The first covariance matrix, i.e. in equation (3)
When the objective function J (x) is minimal, the following relationship exists:
in the formula, H represents a measurement matrix, and R represents a weight matrix; m represents a state prediction value
Of the first covariance matrix of (a) is,
the result of the state estimation is represented,
indicating the state prediction value.
Since the mathematical model for state estimation based on synchrophasor measurement is a linear model, let
Substituting the solution of the above equation (5) to obtain a calculation formula of the state estimation result, which is specifically as follows:
in the formula (I), the compound is shown in the specification,
the result of the state estimation is represented,
representing a state prediction value, H representing a measurement matrix, and R representing a weight matrix; m represents a state prediction value
A first covariance matrix; z represents a state measured value.
Taking the target time as the k +1 th time as an example, the calculation formula for obtaining the state estimation result of the target time after performing recursive sorting on the formula (6) is as follows:
in the formula (I), the compound is shown in the specification,
indicating the state estimation result at the k +1 th time,
indicates the predicted value of the state at the K +1 th time, K k+1 A filter gain matrix representing the k +1 th time instant, z k+1 Represents the state measured value at the k +1 th time, H k+1 Representing the measurement matrix at time k + 1.
Filter gain matrix K at time K + 1 k+1 The calculation formula of (2) is as follows:
in the formula, K k+1 A filter gain matrix representing the k +1 th time instant; sigma k+1 A covariance matrix representing a state estimation result, i.e., a third covariance matrix; h k+1 A measurement matrix representing the k +1 th time; r k+1 Representing the weight matrix at time k + 1.
Third covariance matrix ∑ k+1 The calculation formula of (2) is as follows:
in the formula, sigma k+1 Representing a third covariance matrix; h k+1 A measurement matrix representing the k +1 th time; r k+1 A weight matrix representing the k +1 th time; m k+1 Representing the first covariance matrix at time k + 1, i.e. in equation (3)
Can be calculated by the above formula (2).
Further, determining a filter gain matrix at the target time based on the first covariance matrix at the target time, the preset measurement matrix at the target time, and the weight matrix at the target time may specifically include:
determining a third covariance matrix at the target moment based on the first covariance matrix at the target moment, the measurement matrix at the target moment and the weight matrix at the target moment;
and determining a filter gain matrix at the target moment based on the third covariance matrix at the target moment, the measurement matrix at the target moment and the weight matrix at the target moment.
The third covariance matrix at the target time in this embodiment refers to a covariance matrix of a state estimation result at the target time, the calculation formula is formula (9) above, and a specific manner of determining the filter gain matrix at the target time in this embodiment may be referred to as formula (8) above.
Further, determining a state estimation result at the target time based on the predicted state value at the target time, the measured state value at the target time, the measurement matrix at the target time, and the filter gain at the target time may specifically include:
multiplying the measurement matrix at the target moment by the state prediction value at the target moment to obtain a first calculation value;
the state measured value of the target moment is differed from the first calculated value to obtain a second calculated value;
multiplying the gain of the filter at the target moment by the second calculated value to obtain a third calculated value;
and adding the state predicted value at the target moment and the third calculated value to obtain a state estimation result at the target moment.
After the state prediction value at the target time is obtained, the optimal state estimation can be realized by using the state prediction value and the state measured value at the target time, so as to obtain a state estimation result at the target time, and a calculation process of the state estimation result at the target time can be specifically referred to the formula (7).
It should be noted that, in this embodiment, the matrices used in the state estimation process, such as the first covariance matrix, the second covariance matrix, the third covariance matrix, and the state transition matrix, may be updated in advance according to a number of state prediction values at a previous time and a number of state prediction values before the state prediction value and the state estimation result are calculated at a next time, and the updated correlation matrix may be used in the state prediction and state estimation process at the next time until the state estimation result at the target time is calculated.
The accuracy and reliability of the prediction auxiliary state estimation method of the power system provided by the invention are verified through a specific simulation example, and the specific process is as follows:
in this embodiment, the actual state value is simulated by the power flow calculated value, and specifically, a certain noise may be introduced as the actual state value on the amplitude and the phase angle of the actual state value, the noise added in the simulation test in this embodiment is gaussian noise with an average error value of zero, specifically, according to the related standard specification and the error condition of the measurement data of the synchrophasor measurement unit, the standard deviation setting result of the noise introduced in this embodiment is shown in table 1 below:
TABLE 1 standard deviation setting results for noise
In this embodiment, MAE (Mean Absolute Error) is used to evaluate the accuracy of the predicted state value, that is, the degree of proximity between the predicted state value and the true state value, specifically as follows:
in the formula, MAE represents a state prediction value
And the true value x of the state t Absolute average error between, x t，i The true value of the ith state is represented,
the ith state prediction value is represented, and n represents the data amount of the state prediction value.
In order to measure and evaluate the state estimation result of the power system, the present embodiment further defines a Total Vector Error index TVE (Total Vector Error), and the calculation formula of the index is as follows:
in the formula, TVE represents the total error of the vector, and the index can be understood as an average value after error normalization, and is used for characterizing the state estimation result
And measured value x of state m And the true value x of the state t The ratio of the distances between the two,
denotes the jth state estimation result, x m，j Indicates the j-th state measured value, x t，j And the j-th state true value is represented, and L represents the data volume of the state estimation result.
In this embodiment, the measured object of the total vector error index TVE may be a state quantity, that is, a voltage value, or a branch current measurement value. The total vector error index TVE can be understood as an index that can reflect the improvement of the accuracy of the measured state value after the state estimation. When the TVE is larger than 1, the state estimation basically has no effect, and when the TVE is smaller than 1, the state estimation plays a role, and the closer the TVE is to 0, the better the effect of the state estimation is.
In order to simulate an electric power system under load fluctuation, load data measured by a synchrophasor measurement device in a southern power grid is used in a simulation system, the measurement interval of the load data is 20ms, and the measured load data is normalized and then added to each load node of an existing IEEE 14 node case.
In this embodiment, in a verification test link, a state prediction process and a state estimation process are respectively tested by the following two embodiments, i.e., embodiment 1 and embodiment 2.
Example 1
In the present embodiment, first, a simulation verification test of the state prediction process (i.e., the state prediction model) is performed alone, so that the state prediction simulation is performed using the state measured value with a small error level (i.e., 10% of the standard deviation of the data error shown in table 1).
In this embodiment, the Voltage amplitude (Voltage/p.u.) and the Time-series prediction result of the phase Angle (Angle/°) of the node 4 in the IEEE 14 node case are compared with corresponding Real values, the comparison results are shown in fig. 3 (a) and (b), fig. 3 shows the prediction result obtained by the existing state prediction method based on the Holt model (Holt data shown by a short solid line in fig. 3), and shows the prediction result obtained by the state prediction method based on the VAR model provided by this embodiment (VAR data shown by a long solid line in fig. 3), and also shows the Real values (Real data shown by a solid line in fig. 3) corresponding to each Time (Time steps).
For further observation and analysis, the present embodiment intercepts and amplifies partial segments in fig. 3 (a) and (b), respectively, and the amplified time series data of the voltage amplitude and the phase angle can be referred to fig. 3 (c) and (d), and as can be seen from fig. 3 (c) and (d), both the existing Holt model-based state prediction method and the VAR model-based state prediction method provided by the present embodiment have certain accuracy, and both can keep up with the real-time changes of the power system. However, the error of the prediction result obtained by the existing state prediction method based on the Holt model is significantly higher than that of the state prediction method based on the VAR model provided by the embodiment, that is, the state prediction method based on the VAR model provided by the embodiment has higher accuracy.
Example 2
In order to verify the effectiveness of the prediction aided state estimation process, the present embodiment first performs simulation verification on the existing IEEE 14 node case, and uses the load data actually measured by the south electric network as the load data used by the test case. The state-prediction aided state estimation method (i.e., VAR-FASE) provided in this embodiment is compared with the prediction aided state estimation method based on the Holt model (i.e., holt-FASE), and compared with the static state estimation method using Complex number field Weighted Least Square (CWLS). Using TVE and MAE as evaluation indexes, the statistical results of the simulation are shown in table 2 below:
table 2 state estimation results comparison data
| State estimation method | TVE | MAE |
| VAR-FASE | 0.153112 | 0.000631 |
| HOLT-FASE | 0.185396 | 0.000750 |
| CWLS | 0.205950 | 0.000853 |
As can be seen from table 2, the error of the state estimation result obtained by the state prediction aided state estimation method (i.e., VAR-FASE) provided in this embodiment is significantly reduced compared with the state estimation result of the static state estimation method. Compared with a CWLS static state estimation method based on actually measured data of a synchrophasor measurement device, the two prediction aided state estimation methods of VAR-FASE and HOLT-FASE have higher accuracy of the obtained state estimation result.
As can be seen from the analysis of the results obtained by the state prediction in the above embodiment 1, the higher the accuracy of the state prediction is, the better the result of the prediction aided state estimation is, and it can be illustrated that the prediction aided state estimation method provided by this embodiment can further correct the state measured value by effectively using the state predicted value obtained by the historical state data.
Fig. 4, fig. 5, fig. 6 and fig. 7 respectively show curves of the estimation results of the Voltage amplitude (Voltage/p.u.) and the phase Angle (Angle/Degree) corresponding to the bus bar 3 and the bus bar 4 obtained by the prediction aided state estimation method provided by the present invention, and are compared with the actually measured curves of the Voltage amplitude and the phase Angle in the power system and the actual values of the Voltage amplitude and the phase Angle, specifically, fig. 4 shows a comparison curve between the estimation result of the Voltage amplitude (data shown by a long dashed line in fig. 4) corresponding to the bus bar 3 obtained by the prediction aided state estimation method provided by the present invention and the actually measured value of the Voltage amplitude (data shown by a short dashed line in fig. 4) and the actual value of the Voltage amplitude (data shown by a solid line in fig. 4) in the power system, fig. 5 shows a comparison curve between the estimation result of the Voltage phase Angle corresponding to the bus bar 3 (data shown by a long dashed line in fig. 5) and the actually measured value of the Voltage phase Angle (data shown by a short dashed line in fig. 5) and the actually measured value of the Voltage phase Angle (data shown by a solid line in fig. 5) in the power system obtained by the prediction aided state estimation method provided by the present invention, fig. 6 shows a comparison curve between the estimation result of the Voltage amplitude corresponding to the bus bar 4 (data shown by a long dashed line in fig. 6) and the actually measured value of the Voltage amplitude (data shown by a short dashed line in fig. 6) and the actually measured value of the Voltage amplitude (data shown by a solid line in fig. 6) in the power system obtained by the prediction aided state estimation method provided by the present invention, and fig. 7 shows the estimation result of the Voltage phase Angle corresponding to the bus bar 4 (data shown by a long dashed line in fig. 7) and the actually measured value of the Voltage amplitude (data shown by a long dashed line in fig. 6) in the power system obtained by the prediction aided state estimation method provided by the present invention Comparison between the measured value of the medium voltage phase angle (data shown by the short dashed line in fig. 7) and the actual value of the voltage phase angle (data shown by the solid line in fig. 7).
Compared with the prior art, the estimation results of the voltage amplitude and the phase angle of the bus 3 and the bus 4 obtained by the prediction auxiliary state estimation method provided by the invention are close to the actual values of the voltage amplitude and the phase angle in the power system, so that the accuracy of the state estimation result obtained by the prediction auxiliary state estimation method provided by the invention is further improved obviously.
In summary, the prediction-aided state estimation method for the power system provided in the embodiment of the present invention can fully utilize the time correlation of the dynamic system and the spatial correlation based on the physical constraint of the mass historical state data of the power system based on the vector autoregressive model, accurately predict the latest state of the power system when the power system is in a relatively stable state, and obtain the state prediction value of the target time.
The following describes the prediction assist state estimation device of the power system according to the present invention, and the prediction assist state estimation device of the power system described below and the prediction assist state estimation method of the power system described above may be referred to in correspondence with each other.
Fig. 8 shows a prediction assist state estimation apparatus for an electric power system according to an embodiment of the present invention, the apparatus including:
an obtaining module 801, configured to obtain an initial state prediction value and an initial state transition matrix of an electric power system;
a first processing module 802, configured to input the initial state prediction value and the initial state transition matrix into a pre-constructed state prediction model to obtain a state prediction value at a target time; the state prediction model is constructed on the basis of a vector autoregressive model;
the second processing module 803 is configured to obtain a state actual measurement value at a target time, and obtain a state estimation result at the target time based on the state predicted value at the target time and the state actual measurement value at the target time.
In an exemplary embodiment, the state prediction model may be specifically used to:
determining a state prediction value at the kth moment through a vector autoregressive model based on the initial state prediction value and the initial state transition matrix, wherein k is more than or equal to 1;
determining a state transition matrix at the k +1 th moment based on the state prediction value at the k th moment and a preset number of state prediction values before the k th moment;
determining the state prediction value at the k +1 th moment through a vector autoregressive model based on the state prediction value at the k +1 th moment and the state transition matrix at the k +1 th moment;
and obtaining the state predicted value of the target moment until the k +1 th moment is the target moment.
Further, the state prediction model may specifically determine the state transition matrix at the k +1 th time based on the state prediction value at the k th time and a preset number of state prediction values before the k th time in the following manner:
respectively calculating a first covariance matrix and a second covariance matrix at the k +1 th moment based on the state predicted value at the k th moment and the state predicted values of the preset number before the k th moment;
and determining the state transition matrix at the k +1 th moment based on the first covariance matrix and the second covariance matrix at the k +1 th moment.
In an exemplary embodiment, the vector autoregressive model is used for characterizing the functional relationship between the state prediction value at the k +1 th time and the state prediction value at the k +1 th time, the state transition matrix at the k +1 th time and the preset noise value at the k +1 th time.
In an exemplary embodiment, the second processing module 803 may be specifically configured to:
determining a first covariance matrix at a target moment based on the state predicted value at the target moment and a preset number of state predicted values before the target moment;
determining a filter gain matrix at the target moment based on a first covariance matrix at the target moment, a preset measurement matrix at the target moment and a weight matrix at the target moment;
and determining a state estimation result of the target time based on the state predicted value of the target time, the state measured value of the target time, the measurement matrix of the target time and the filter gain matrix of the target time.
Further, the second processing module 803 may specifically determine the filter gain matrix at the target time based on the first covariance matrix at the target time, the preset measurement matrix at the target time, and the preset weight matrix at the target time, as follows:
determining a third covariance matrix at the target moment based on the first covariance matrix at the target moment, the measurement matrix at the target moment and the weight matrix at the target moment;
and determining a filter gain matrix at the target moment based on the third covariance matrix at the target moment, the measurement matrix at the target moment and the weight matrix at the target moment.
Further, the second processing module 803 may specifically determine the state estimation result at the target time based on the predicted state value at the target time, the actual state measurement value at the target time, the measurement matrix at the target time, and the filter gain at the target time as follows:
multiplying the measurement matrix at the target moment by the state prediction value at the target moment to obtain a first calculation value;
the state measured value of the target moment is differed from the first calculated value to obtain a second calculated value;
multiplying the gain of the filter at the target moment by the second calculated value to obtain a third calculated value;
and adding the state predicted value at the target moment and the third calculated value to obtain a state estimation result at the target moment.
In summary, according to the prediction auxiliary state estimation apparatus for an electric power system provided in the embodiments of the present invention, the first processing module inputs the initial state prediction value and the initial state transition matrix of the electric power system into the state prediction model constructed based on the vector autoregressive model to obtain the state prediction value at the target time, and the second processing module obtains the state estimation result at the target time according to the state prediction value at the target time and the state measured value at the target time.
Fig. 9 illustrates a physical structure diagram of an electronic device, and as shown in fig. 9, the electronic device may include: a processor (processor) 901, a communication Interface (Communications Interface) 902, a memory (memory) 903 and a communication bus 904, wherein the processor 901, the communication Interface 902 and the memory 903 are communicated with each other through the communication bus 904. Processor 901 may invoke logic instructions in memory 903 to perform a predictive aided state estimation method of a power system, the method comprising: acquiring an initial state prediction value and an initial state transition matrix of the power system; inputting the initial state prediction value and the initial state transition matrix into a pre-constructed state prediction model to obtain a state prediction value at a target moment; the state prediction model is constructed on the basis of a vector autoregressive model; and acquiring a state measured value of the target time, and acquiring a state estimation result of the target time based on the state predicted value of the target time and the state measured value of the target time.
In addition, the logic instructions in the memory 903 may be implemented in a software functional unit and stored in a computer readable storage medium when the logic instructions are sold or used as a separate product. 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 removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
In another aspect, the present invention also provides a computer program product comprising a computer program stored on a non-transitory computer readable storage medium, the computer program comprising program instructions, which when executed by a computer, the computer is capable of executing the prediction aided state estimation method of the power system provided by the above embodiments, the method comprising: acquiring an initial state prediction value and an initial state transition matrix of the power system; inputting the initial state prediction value and the initial state transition matrix into a pre-constructed state prediction model to obtain a state prediction value at a target moment; the state prediction model is constructed on the basis of a vector autoregressive model; and acquiring a state measured value of the target time, and acquiring a state estimation result of the target time based on the state predicted value of the target time and the state measured value of the target time.
In yet another aspect, the present invention further provides a non-transitory computer-readable storage medium, on which a computer program is stored, the computer program being executed by a processor to implement the method for estimating the predictive assistance state of the power system provided in the above embodiments, the method comprising: acquiring an initial state prediction value and an initial state transition matrix of the power system; inputting the initial state prediction value and the initial state transition matrix into a pre-constructed state prediction model to obtain a state prediction value at a target moment; the state prediction model is constructed on the basis of a vector autoregressive model; and acquiring a state measured value of the target time, and acquiring a state estimation result of the target time based on the state predicted value of the target time and the state measured value of the target time.
The above-described embodiments of the apparatus are merely illustrative, and 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 network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, and not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A predictive assist state estimation method for an electric power system, comprising:
acquiring an initial state prediction value and an initial state transition matrix of the power system;
inputting the initial state prediction value and the initial state transition matrix into a pre-constructed state prediction model to obtain a state prediction value at a target moment; the state prediction model is constructed on the basis of a vector autoregressive model;
and acquiring the state measured value of the target time, and obtaining a state estimation result of the target time based on the state predicted value of the target time and the state measured value of the target time.
2. The method for estimating the predictive assistance state of the electric power system according to claim 1, wherein the state prediction model is specifically configured to:
determining a state prediction value at the kth moment through the vector autoregressive model based on the initial state prediction value and the initial state transition matrix, wherein k is more than or equal to 1;
determining a state transition matrix at the k +1 th moment based on the state prediction value at the k th moment and a preset number of state prediction values before the k th moment;
determining the state prediction value at the k +1 th moment through the vector autoregressive model based on the state prediction value at the k +1 th moment and the state transition matrix at the k +1 th moment;
and obtaining a state predicted value of the target moment until the k +1 th moment is the target moment.
3. The prediction assist state estimation method according to claim 2, wherein the determining the state transition matrix at the k +1 th time based on the state prediction value at the k th time and a preset number of state prediction values before the k th time comprises:
respectively calculating to obtain a first covariance matrix and a second covariance matrix at the k +1 th moment based on the state prediction value at the k th moment and a preset number of state prediction values before the k th moment;
determining a state transition matrix at the k +1 th time based on the first covariance matrix and the second covariance matrix at the k +1 th time.
4. The prediction assist state estimation method of the power system according to claim 2 or 3, wherein the vector autoregressive model is configured to characterize a functional relationship between the state prediction value at the k +1 th time and the state prediction value at the k +1 th time, the state transition matrix at the k +1 th time, and a preset noise value at the k +1 th time.
5. The prediction assist state estimation method of an electric power system according to claim 1, wherein obtaining the state estimation result at the target time based on the predicted state value at the target time and the actual state measurement value at the target time includes:
determining a first covariance matrix of the target moment based on the state prediction value of the target moment and a preset number of state prediction values before the target moment;
determining a filter gain matrix of the target moment based on the first covariance matrix of the target moment, a preset measurement matrix of the target moment and a preset weight matrix of the target moment;
and determining a state estimation result of the target time based on the state prediction value of the target time, the state measured value of the target time, the measurement matrix of the target time and the filter gain matrix of the target time.
6. The method according to claim 5, wherein determining the filter gain matrix for the target time based on the first covariance matrix for the target time and a preset measurement matrix for the target time and a preset weight matrix for the target time comprises:
determining a third covariance matrix of the target moment based on the first covariance matrix of the target moment, the measurement matrix of the target moment and the weight matrix of the target moment;
determining a filter gain matrix for the target time based on the third covariance matrix for the target time, the measurement matrix for the target time, and the weight matrix for the target time.
7. The prediction assist state estimation method of an electric power system according to claim 5, wherein the determining the state estimation result at the target time based on the predicted state value at the target time, the actual state measurement value at the target time, the measurement matrix at the target time, and the filter gain at the target time includes:
multiplying the measurement matrix at the target moment by the state prediction value at the target moment to obtain a first calculation value;
the state measured value of the target moment is differed from the first calculated value to obtain a second calculated value;
multiplying the filter gain of the target moment by the second calculated value to obtain a third calculated value;
and adding the state predicted value at the target moment and the third calculated value to obtain a state estimation result at the target moment.
8. A prediction assist state estimation device of an electric power system, characterized by comprising:
the acquisition module is used for acquiring an initial state prediction value and an initial state transition matrix of the power system;
the first processing module is used for inputting the initial state predicted value and the initial state transition matrix into a pre-constructed state prediction model to obtain a state predicted value at a target moment; the state prediction model is constructed on the basis of a vector autoregressive model;
and the second processing module is used for acquiring the state measured value of the target time, and obtaining a state estimation result of the target time based on the state predicted value of the target time and the state measured value of the target time.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program implements a method for predictive aided state estimation of an electrical power system according to any of claims 1 to 7.
10. A non-transitory computer readable storage medium having stored thereon a computer program, wherein the computer program, when executed by a processor, implements a predictive aided state estimation method of a power system according to any of claims 1 to 7.
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