CN110707693A - Ensemble Kalman filtering dynamic state estimation method based on AMI full-scale measuring point partition - Google Patents
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Abstract
The invention discloses an ensemble Kalman filtering dynamic state estimation method based on AMI full-scale measurement point partitioning, which comprises the following steps: determining a power distribution network partition rule according to the AMI full measurement node; obtaining completely decoupled distribution network subregions according to distribution network partition rules; determining a dynamic state estimation framework of the power distribution network, which is used in cooperation with static state estimation, according to the completely decoupled sub-region of the power distribution network; determining the adopted dynamic and static state estimation methods according to a power distribution network dynamic state estimation framework matched with the static state estimation; and determining an ensemble Kalman filtering dynamic state estimation method based on the AMI full-scale measuring point partition according to the completely decoupled power distribution network sub-area, the power distribution network dynamic state estimation framework matched with the static state estimation and the adopted dynamic and static state estimation methods.
Description
Technical Field
The invention relates to the technical field of power distribution networks, in particular to an ensemble Kalman filtering dynamic state estimation method based on AMI full-scale measurement point partitioning.
Background
The power system state estimation is an important component of an energy management system, and the accuracy and speed of the estimation method have important influence on operations such as electric energy distribution, scheduling and safety analysis of a power distribution network. Due to the characteristics of a large number of nodes and a small number of measuring devices of the power distribution network, the application of the state estimation method and the state estimation technology of the power distribution network is immature compared with the application of a power transmission network.
In recent years, synchronous Phasor Measurement Units (PMUs) are gradually applied to power distribution networks and advanced measurement Architectures (AMI) are popularized, but the problems of huge number of nodes of the power distribution network, fusion of data in different sampling periods and the like need to be solved. The dynamic state estimation of the subareas can solve the problems of low calculation speed and non-convergence caused by the large number of nodes, and can predict the change of the system state in the future to make a corresponding control strategy for the operation of the power distribution network so as to ensure the safe and stable operation of the power distribution network.
At present, for a power distribution network partition state estimation method, common partition methods include a partition based on a geographical position, a partition based on a multi-agent system, a partition based on a network topology, and the like, and a lap joint type and an overlap type are generally adopted for boundary nodes, and when the state estimation is performed, data needs to be transmitted to an adjacent region, so that communication pressure exists; common dynamic state estimation methods include Extended Kalman Filtering (EKF), Unscented Kalman Filtering (UKF), and the like. The EKF method adopts a Taylor formula to obtain an approximate linear state equation, and has larger error for a strong nonlinear system such as a power distribution network; the UKF is a method for approximating the mean value and the covariance when nonlinear transformation is carried out by carrying out unscented transformation on random variables, a large number of parameters need to be selected, the flexibility of parameter selection is poor, and the application has limitation.
In summary, it is necessary to invent a power distribution network dynamic state estimation method under a complete decoupling point partition to solve the problems of communication pressure of a reasonable partition, fusion between data of different sampling periods, application limitations and the like in the traditional partition dynamic state estimation, improve the frequency and accuracy of partition dynamic state estimation, and promote the application of the partition dynamic state estimation method in the actual power distribution network.
Disclosure of Invention
According to the problems in the prior art, the invention discloses an ensemble Kalman filtering dynamic state estimation method based on AMI full-scale measuring point partitioning, which adopts a dynamic state estimation method and a static state estimation method which are mixed for use, can perfectly fuse measured data obtained in different periods, and can improve the frequency of dynamic state estimation. The partition boundary node adopting the partition method needs to be provided with AMI measurement, so that all areas can be completely decoupled after reasonable partition is carried out, communication between adjacent sub-areas is not needed, and the problem of communication pressure limitation is solved, and the specific scheme of the method is as follows:
determining a power distribution network partition rule according to the AMI full measurement node;
obtaining completely decoupled distribution network subregions according to distribution network partition rules;
determining a dynamic state estimation framework of the power distribution network, which is used in cooperation with static state estimation, according to the completely decoupled sub-region of the power distribution network;
determining the adopted dynamic and static state estimation methods according to a power distribution network dynamic state estimation framework matched with the static state estimation;
and determining an ensemble Kalman filtering dynamic state estimation method based on the AMI full-scale measuring point partition according to the completely decoupled power distribution network sub-area, the power distribution network dynamic state estimation framework matched with the static state estimation and the adopted dynamic and static state estimation methods.
And when the partition rule of the power distribution network is determined, taking the node configured with the AMI full measurement device in the power distribution network as a partition boundary, carrying out non-overlapped partition, and changing a partition boundary point into two virtual nodes: a load node and a zero-power injection node, wherein the zero-power injection node provides a voltage value as a root node. Since various metrology data in the system boundary conditions may be provided by AMI metrology, fully decoupled sub-areas of the distribution network are formed.
Obtaining high-frequency AMI measurement data required by dynamic state estimation according to the estimation of the AMI measurement data in a non-AMI measurement period, and specifically comprising the following steps: at the sampling moment of AMI measurement data, carrying out dynamic state estimation of hybrid measurement through a remote terminal unit, a synchronous phasor measurement device and a high-level measurement system; at the non-AMI sampling time, performing linear static state estimation based on the measurement values of the dynamic state estimation result, the system state of ultra-short-term load prediction, the remote terminal unit and the synchronous phasor measurement device, and taking the data part corresponding to the AMI in the linear static state estimation result as the virtual measurement of the non-AMI sampling time; and when the AMI measurement time is reached again, returning to the dynamic state estimation process. Wherein the remote terminal unit RTU: remote Terminal Unit, synchrophasor measurement Unit PMU: phasor Measurement Unit, advanced Measurement architecture AMI: an Advanced Metering Infrastructure.
Further, a method for obtaining dynamic state estimation by using an improved ensemble Kalman filtering method specifically comprises the following steps:
performing ultra-short-term load prediction according to a linear extrapolation method, performing spline interpolation on the obtained load data to obtain load data with a shorter period, specifically, selecting loads of k similar days in a prediction time period, performing preprocessing, and calculating an average value of k days at the same time as shown in the following formula, wherein P (n, t) is0)、P(n,t1) And P (n, t)2) On day k, t0、t1、t2The value of the load at the moment of time,
according to
Three points were least squares fit as shown below, where t2Is the time to be predicted;
obtaining the load value P (t) at the predicted time2)＝P(t1)+ΔP＝P(t1) + k Δ t, and then carrying out interpolation;
the prediction steps of the ensemble Kalman filtering are as follows: predicting state X of system at K +1 moment according to load flow calculationk+1Applying disturbance to the state quantity to obtain state set momentMatrix of
Calculating to obtain a measurement prediction set, a measurement prediction error covariance, a measurement error covariance matrix and a cross covariance matrix, wherein the covariance matrix of the set is calculated as follows:
wherein, a, B set matrix, cov (a, a) is abbreviated as cov (a);
in the filtering step, a Kalman gain matrix is calculated by adopting the following formula, and the background field is updated;
in the formula ,Yk+1Measuring an ensemble matrix for the mixed quantities, K being an ensemble Kalman gain, h (-) being a non-linear measurement equation,an estimated background field set for the time k + 1;
and when the filtering step updates the state variable set, the average value of the set is the state estimation value at the moment.
Further, the static state estimation adopts linear state estimation based on a measurement transformation technology, and is based on the following measurement transformation formula:
wherein ,
the node voltage, the branch current, the node voltage phase angle and the branch current phase measured by PMUCorner, Ui,r、Ui,i、Iij,r、Iij,i、Ii,r、Ii,i、
The real part and the imaginary part of the node voltage, the real part and the imaginary part of the branch current, the real part and the imaginary part of the node injection current, and the real part and the imaginary part of the node voltage and the real part and the imaginary part of the branch current measured by the PMU are measured by the RTU after transformation;
the method comprises the steps of equivalently converting various data measured by the RTU and the PMU into real part and imaginary part measurement of voltage and current, obtaining the error of the converted measurement data by adopting an error transfer formula, and performing least square method state estimation by using a linear measurement function after equivalent conversion by using the real part and the imaginary part of the voltage as state variables.
Compared with the prior art, the power distribution network partition dynamic state estimation method provided by the invention has the following application advantages:
1. the invention provides a partitioned power distribution network dynamic state estimation method, which solves the problem that the traditional power distribution network state estimation serial calculation method is difficult to meet the real-time monitoring and safety control of a power distribution network due to the fact that the number of power distribution network branches is increased day by day, divides a system with a large scale into a plurality of regions to perform parallel calculation, reduces the calculation amount and accelerates the calculation speed.
2. According to the invention, after independent partitioning is carried out by adopting AMI full-measurement point partitions, boundary information transmission is not required for each partition, the problem of communication pressure generated by power distribution network partitions is solved, independent parallel operation is carried out, and the state estimation operation efficiency is improved.
3. The method adopts a state estimation algorithm based on improved ensemble Kalman filtering, adopts a linear extrapolation method to carry out ultra-short-term load prediction and carry out cubic spline interpolation in a state prediction step to obtain the system state of the prediction step.
4. According to the invention, the AMI data with long measurement period is subjected to static state estimation to obtain the measurement data required by the dynamic state estimation moment, the measurement data with different time scales can be fused, various measurement devices in the power distribution network are applied as far as possible, the system redundancy is improved, and the dynamic state estimation period is shortened.
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In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments described in the present application, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a schematic diagram of a conventional state estimation partitioning rule;
FIG. 2 is a schematic diagram of AMI-based full measurement node partitioning rules employed in the present invention;
FIG. 3 is a diagram of a dynamic state estimation architecture for a power distribution network in accordance with the present invention, wherein the dynamic state estimation architecture is used in conjunction with static state estimation;
FIG. 4 is a block flow diagram of a state estimation algorithm based on ensemble Kalman filtering as employed in the present invention;
FIG. 5 is a block flow diagram of a linear static state estimation algorithm based on metrology transformation as employed in the present invention.
Detailed Description
In order to make the technical solutions and advantages of the present invention clearer, the following describes the technical solutions in the embodiments of the present invention clearly and completely with reference to the drawings in the embodiments of the present invention:
as shown in fig. 1, an ensemble kalman filter dynamic state estimation method based on AMI full-scale measurement point partitioning specifically includes the following steps:
1) obtaining the initial state of the system by prior knowledge, and further generating a set member;
2) performing hybrid measurement dynamic state estimation based on ensemble Kalman filtering at the moment of AMI measurement data;
3) taking the result of the dynamic state estimation filtering step as pseudo measurement required by static state estimation, and performing linear static state estimation based on a measurement transformation technology;
4) ultra-short-term load prediction is carried out to obtain a load value in a short time in the future, and interpolation is carried out;
5) load flow calculation is carried out on the load values after interpolation to the prediction step state quantity of dynamic state estimation, corresponding AMI measurement is calculated according to the result of static state estimation, and dynamic state estimation at the moment of non-AMI measurement is carried out;
6) judging whether the measurement time is AMI measurement time, if yes, returning to the step 2 for carrying out; otherwise, returning to the step 3 for processing.
As shown in fig. 1, the main disadvantage of the boundary region non-overlapping partition rule adopted in the conventional state estimation is that the boundary node information of adjacent sub-partitions needs to be exchanged, which causes problems such as communication limitation; the adopted traditional boundary region overlapping type partition rule has the main defects that the boundary overlapping region is estimated twice, the estimation needs to be processed twice, the estimation precision is greatly influenced, and if the fusion algorithm is adopted, the algorithm complexity can be increased, and the calculation time is prolonged.
As shown in fig. 2, the partitioning rule using AMI full measurement nodes according to the present invention is an improvement process of the conventional partitioning rule in combination with the specific measurement devices of the distribution network. Compared with the traditional non-overlapping partition, the partition rule adopted by the invention utilizes the node configured with the AMI full-measurement device as the boundary node, and is improved on the traditional non-overlapping partition of the boundary region, so that mutual information transmission of sub-regions is not needed, independent operation is completely decoupled, and communication cost is not needed. Compared with the traditional overlapped partition, the method does not need to carry out secondary processing on the overlapped nodes subjected to repeated estimation, reduces the operation complexity and improves the calculation efficiency.
Fig. 3 is a diagram illustrating an architecture for estimating a dynamic state of a power distribution network according to the present invention, wherein the architecture is used with a static state estimation. In the figure, T represents a sampling period of AMI, and N represents a certain time of a non-AMI sampling period. And performing dynamic state estimation based on RTU (real time Unit), PMU (phasor measurement Unit) and AMI (adaptive measurement unit) mixed measurement at the sampling time of AMI (advanced metering infrastructure) data, performing linear static state estimation based on the system state predicted by the ultra-short-term load, the prediction result of the dynamic state estimation and RTU and PMU measurement at the X time of non-AMI sampling time, and performing dynamic state estimation at the time by taking the part corresponding to AMI in the linear static state estimation result as virtual measurement of AMI at the non-AMI sampling time. And when the moment of AMI measurement is reached again, the dynamic state estimation based on RTU, PMU and AMI mixed measurement is adopted again. The purpose of the linear static state estimation is to utilize the SCADA and PMU measurement to track the change of system node injection measurement in real time at the moment of non-AMI measurement so as to obtain the virtual measurement of AMI at the moment to keep the running of the partition dynamic state estimation.
Fig. 4 is a block flow diagram of the state estimation algorithm based on ensemble kalman filtering in step 2). The initial state of the power distribution network is obtained through priori knowledge, an initial set with set membership L is obtained through a Monte Carlo method and disturbance, the set mean value is calculated, and the accuracy and the speed of an algorithm are influenced by the size of L and the initial set is obtained through repeated tests. Performing ultra-short-term load prediction by a linear extrapolation method in the step 4), performing spline interpolation on the ultra-short-term load prediction to obtain load data with a shorter period, specifically selecting loads of k similar days in a prediction time period, preprocessing the loads to eliminate adverse effects caused by load mutation, ensuring that the load variation trend in the time period to be solved is constant, and then calculating the average value of k days at the same moment as shown in a formula (1), wherein P (n, t and t are shown in the specification, and the average value of P (n, t) in the k days at the same moment is calculated0)、P(n,t1) And P (n, t)2) On day k, t0、t1、t2The load value at the moment.
According to
Three points are least squares fit as in equation (2), where t2Is the time to be predicted.
Obtaining the load value P (t) at the predicted time2)＝P(t1)+ΔP＝P(t1) And + K delta t, then carrying out interpolation, predicting the state of each set member of the system at the moment K +1 through load flow calculation, and calculating to obtain a measurement prediction set, a measurement prediction error covariance, a measurement error covariance matrix and a cross covariance matrix, wherein the covariance matrix of the set is calculated as:
where A, B set matrix, cov (A, A) is abbreviated as cov (A).
The dynamic state estimation algorithm based on the improved ensemble Kalman filtering is as follows:
a prediction step: load values are obtained by ultra-short-term load prediction based on linear extrapolation as described in formulas (1) and (2), predicted system state quantities are obtained by load flow calculation through forward-backward substitution after interpolation, and a state set matrix is obtained after disturbance is applied to the state quantities
And (3) filtering:
in the formula ,YnMeasuring an ensemble matrix for the mixed quantities, K being an ensemble Kalman gain, h (-) being a non-linear measurement equation,
the set of estimated background fields for time n, where the covariance is calculated as above equation (3).
After the filtering step updates the state variable (background field) set, the mean value of the set is the state estimation value at the moment. The state equation is not linearized, so that the nonlinear characteristic of the power distribution network is better met, and the precision is higher.
Fig. 5 is a block flow diagram of the linear static state estimation algorithm adopted in the invention in step 3). The method comprises the steps of equivalently converting various data measured by the RTU and the PMU into real part and imaginary part measurement of voltage and current by adopting a measurement conversion technology, obtaining the error of the converted measurement data by adopting an error transfer formula, and performing least square method state estimation by taking the real part and the imaginary part of the voltage as state variables and adopting a linear measurement function after equivalent conversion. Because the measurement function is linear, the calculated Jacobian matrix is a constant matrix and is kept unchanged in the iteration process, the calculation amount is greatly reduced, and the calculation time is further reduced.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be considered to be within the technical scope of the present invention, and the technical solutions and the inventive concepts thereof according to the present invention should be equivalent or changed within the scope of the present invention.
Claims (5)
1. An ensemble Kalman filtering dynamic state estimation method based on AMI full-scale measurement point partitioning is characterized by comprising the following steps:
determining a power distribution network partition rule according to the AMI full measurement node;
obtaining completely decoupled distribution network subregions according to distribution network partition rules;
determining a dynamic state estimation framework of the power distribution network, which is used in cooperation with static state estimation, according to the completely decoupled sub-region of the power distribution network;
determining the adopted dynamic and static state estimation methods according to a power distribution network dynamic state estimation framework matched with the static state estimation;
and determining an ensemble Kalman filtering dynamic state estimation method based on the AMI full-scale measuring point partition according to the completely decoupled power distribution network sub-area, the power distribution network dynamic state estimation framework matched with the static state estimation and the adopted dynamic and static state estimation methods.
2. The method of claim 1, further characterized by: and when the partition rule of the power distribution network is determined, taking the node configured with the AMI full measurement device in the power distribution network as a partition boundary, carrying out non-overlapped partition, and changing a partition boundary point into two virtual nodes: a load node and a zero-power injection node, wherein the zero-power injection node provides a voltage value as a root node.
3. The method of claim 1, further characterized by: obtaining high-frequency AMI measurement data required by dynamic state estimation according to the estimation of the AMI measurement data in a non-AMI measurement period, and specifically comprising the following steps: at the sampling moment of AMI measurement data, carrying out dynamic state estimation of hybrid measurement through measurement data of a remote terminal unit, a synchronous phasor measurement device and an advanced measurement system; at the non-AMI sampling time, performing linear static state estimation based on the measurement values of the dynamic state estimation result, the system state of ultra-short-term load prediction, the remote terminal unit and the synchronous phasor measurement device, and taking the data part corresponding to the AMI in the linear static state estimation result as the virtual measurement of the non-AMI sampling time; and when the AMI measurement time is reached again, returning to the dynamic state estimation process.
4. The method of claim 1, further characterized by: the method for obtaining the dynamic state estimation by adopting the improved ensemble Kalman filtering method specifically comprises the following steps:
performing ultra-short-term load prediction according to a linear extrapolation method, performing spline interpolation on the obtained load data to obtain load data with a shorter period, specifically, selecting loads of k similar days in a prediction time period, performing preprocessing, and calculating an average value of k days at the same time as shown in the following formula, wherein P (n, t) is0)、P(n,t1) And P (n, t)2) On day k, t0、t1、t2The value of the load at the moment of time,
according toThree points were least squares fit as shown below, where t2Is the time to be predicted;
obtaining the load value P (t) at the predicted time2)＝P(t1)+ΔP＝P(t1) + k Δ t, and then carrying out interpolation;
the prediction steps of the ensemble Kalman filtering are as follows: predicting state X of system at K +1 moment according to load flow calculationk+1Applying disturbance to the state quantity to obtain a state set matrix
Calculating to obtain a measurement prediction set, a measurement prediction error covariance, a measurement error covariance matrix and a cross covariance matrix, wherein the covariance matrix of the set is calculated as follows:
wherein, a, B set matrix, cov (a, a) is abbreviated as cov (a);
in the filtering step, a Kalman gain matrix is calculated by adopting the following formula, and the background field is updated;
in the formula ,Yk+1Measuring the ensemble matrix for mixed quantities, K being the ensemble Kalman gainH (-) is a nonlinear measurement equation,
an estimated background field set for the time k + 1;
and when the filtering step updates the state variable set, the average value of the set is the state estimation value at the moment.
5. The method of claim 4, further characterized by: the static state estimation adopts linear state estimation based on measurement transformation technology, and is based on the following measurement transformation formula
wherein ,
is node voltage, branch current, node voltage phase angle and branch current phase angle, U measured by PMUi,r、Ui,i、Iij,r、Iij,i、Ii,r、Ii,i、
The real part and the imaginary part of the node voltage, the real part and the imaginary part of the branch current, the real part and the imaginary part of the node injection current, and the real part and the imaginary part of the node voltage and the real part and the imaginary part of the branch current measured by the PMU are measured by the RTU after transformation;
the method comprises the steps of equivalently converting various data measured by the RTU and the PMU into real part and imaginary part measurement of voltage and current, obtaining the error of the converted measurement data by adopting an error transfer formula, and performing least square method state estimation by using a linear measurement function after equivalent conversion by using the real part and the imaginary part of the voltage as state variables.
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| CN111245099A (en) * | 2020-03-19 | 2020-06-05 | 山东科技大学 | Power distribution network state estimation method based on event trigger transmission mechanism and hybrid measurement |
| CN111245099B (en) * | 2020-03-19 | 2021-03-12 | 山东科技大学 | Power distribution network state estimation method based on event trigger transmission mechanism and hybrid measurement |
| CN112990557A (en) * | 2021-02-25 | 2021-06-18 | 国网河北省电力有限公司保定供电分公司 | Power load prediction method and device based on data-driven ensemble Kalman filtering |
| CN115616333A (en) * | 2022-12-20 | 2023-01-17 | 国网江西省电力有限公司电力科学研究院 | Power distribution network line loss prediction method and system |
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