CN107069710B - Power system state estimation method considering new energy space-time correlation - Google Patents
Power system state estimation method considering new energy space-time correlation Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
Abstract
The invention relates to the technical field of power system operation and control, in particular to a power system state estimation method considering new energy space-time correlation, which comprises the following steps of: the first step is as follows: reading power grid information data; the second step is that: system measurement and configuration; the third step: modeling according to the time-space correlation of the new energy system state; the fourth step: using M sets of historical state data pairs phi1And SkCarrying out estimation; the fifth step: based on the estimated valueAndpredicting the state at the moment k; and a sixth step: performing a seventh step of prediction aided state estimation: sending the state estimation result at the moment k to a power grid control center; the method overcomes the defect that static state estimation cannot meet the randomness and the volatility of new energy grid connection, carries out real-time, effective and accurate state estimation on the new energy grid connection, improves the precision of short-term state prediction, improves the precision of final state estimation, fully reflects the dynamic characteristics of a power grid, and provides data support for related advanced applications of a power system control center.
Description
Technical Field
The invention relates to the technical field of power system operation and control, in particular to a power system state estimation method considering new energy space-time correlation.
Background
The smart grid is one of important features of large-scale grid-connected power generation of various new energy sources (renewable energy sources such as wind energy and solar energy). Taking a Xinjiang power grid as an example, in recent years, the Xinjiang power grid enters a spanning type large development period, the installed capacity keeps an increase rate of more than 20% every year, and the installed capacity of the whole power grid is expected to break through 10000 ten thousand kilowatts in 2017, wherein the installed capacity of new energy reaches 30%, and the first power grid is ranked in the northwest. The new energy power generation has the characteristics of randomness, volatility, intermittency and the like, and brings great uncertainty to the operation of a power grid. Meanwhile, the new energy grid connection changes the power generation, transmission and distribution unidirectional power supply mode of the traditional power grid; the uncertainty of the generation, supply and demand of electric energy and the multi-scale property of time and space are more obvious; the complexity and scale of the power grid is constantly increasing; these all present a great challenge to the safety and stability of the power grid and to the operation of dispatching.
The traditional power system state estimation mainly filters real-time information provided by a data acquisition and monitoring System (SCADA) to improve data accuracy and eliminate interference of error information, so that a power system real-time state database is obtained, and data support is provided for an energy management center to perform various important controls, such as real-time modeling of a power grid, load flow optimization and detection and identification of bad data. The main methods of state estimation include static methods such as weighted least square estimation and robust estimation, and dynamic estimation methods such as extended Kalman filtering. The research results still have many defects and shortcomings, for example, the existing state estimation algorithm has insufficient consideration on new energy grid connection, and no state estimation model capable of effectively reflecting the characteristics of new energy volatility, intermittence and uncertainty exists; although the static estimation algorithm is mature and estimation of the static estimation algorithm depends on the SACDA measurement with a slow sampling rate, an actually-operated power system is a dynamically-changed system, and with large-scale grid connection of new energy, the fluctuation, intermittence and load change uncertainty aggravate the change frequency of the system state, so that the static state estimation result cannot reflect the dynamic characteristics of a power grid, and the operation requirement of real-time monitoring of the smart power grid cannot be met.
Disclosure of Invention
The invention provides a power system state estimation method considering new energy space-time correlation, overcomes the defects of the prior art, and can effectively solve the problems that the existing power system static estimation method cannot carry out real-time, effective and accurate state estimation on a power grid system accessed with new energy and cannot reflect the dynamic characteristics of the power grid.
The technical scheme of the invention is realized by the following measures: a power system state estimation method considering new energy space-time correlation comprises the following steps:
the first step is as follows: reading power grid information data, obtaining a node admittance matrix and a branch-node association matrix according to the read power grid information data, wherein the power grid information data comprise historical state estimation data, current network parameters of a power system, a topological structure and line impedance, and then entering a second step;
the second step is that: the measurement and configuration of the power grid system are realized by establishing measurement functions of voltage amplitude measurement, power injection measurement and tidal volume measurement according to the node admittance matrix and the branch-node association matrix, and calculating measurement z according to the measurement functionskBased on the measurement zkConfiguring the system state, the measurement z of the grid systemkMeasuring node voltage amplitude, power injection and tidal current, and then entering a third step;
the third step: modeling according to the space-time correlation of new energy in the power grid system, specifically as follows:
establishing a vector self-recursive model shown as the following formula,
xk＝Φ1xk-1+...+Φpxk-p+εk (1)
wherein k represents a measurement sampling time; { xk-1,...,xk-pRepresents the system history status; x is the number ofkIs the predicted value of the state at the current moment; { phi1,...,ΦpIs the model parameter matrix; p is the order of the model；εkIs the model error, SkIs a covariance matrix, i.e., a gaussian random variable; { phi1,...,ΦpAnd SkThe diagonal elements of (a) represent the time correlation of node voltage and phase angle, while the off-diagonal elements thereof characterize the spatial correlation;
and (II) retaining the first-order vector, simplifying a vector self-recursion model, and completing the establishment of the model, wherein the simplified model is shown as the following formula:
xk＝Φ1xk-1+εk (2)
then entering the fourth step;
the fourth step: using M sets of historical state data pairs phi1And SkPerforming estimation to estimate the valueAndas shown in the following formula:
wherein pi (0) and pi (1) are sampling covariance matrixes, μ is the mean of the samples of the historical data,then entering the fifth step;
the fifth step: based on the estimated valueAndpredicting the state at k time, and predicting the state at k time xk|k-1And its prediction error covariance matrix sigmak|k-1As shown in the following formula:
then entering a sixth step;
and a sixth step: performing prediction aided state estimation, specifically as follows:
(I) measuring z according to the k time measured in the second stepkObtaining the state x of the system at the k sampling timek,xkIs expressed by the following formula:
zk＝h(xk)+vk (7)
wherein h (-) represents a m-dimensional nonlinear measurement function vector; v. ofkIs random white noise following a normal distribution, i.e. vk～N(0,Rk)，RkIs a measurement error covariance matrix;
(II) using extended Kalman filter to pair statesUpdating in a recursion manner to finish the estimation of the auxiliary prediction state by the system, which specifically comprises the following steps:
(1) an objective function is established as shown below:
(2) optimizing the objective function to obtain the stateRecursively updating the results, statesThe result of the recursive update is shown in the following equation:
wherein, KkIn order to be a matrix of gains, the gain matrix,Hkthe matrix of the Jacobian is obtained,Σkas an error covariance matrix, sigmak＝(I-KkHk)Σk|k-1(ii) a I is an identity matrix; then entering a seventh step;
the seventh step: and sending the state estimation result at the moment k to a power grid control center, and performing state estimation at the moment k +1 in the second step.
The following is further optimization or/and improvement of the technical scheme of the invention:
in the second step, each power transmission line in the power grid system is equivalent to a typical pi equivalent circuit for system measurement, and the measurement function is as follows:
the active and reactive power injection measurement function, the active and reactive power flow injection measurement function and the current amplitude measurement function of a node when the typical pi equivalent circuit does not contain a non-transformer branch are as follows:
the active injection measurement function of the node i is as follows:
the reactive injection measurement function of node i is:
the injected active power measurement function of nodes i to j is:
Pij＝Vi 2(gsi+gij)-ViVj(gijcosθij+bijsinθij) (12)
the injected reactive tidal flow measurement function for nodes i to j is:
Qij＝-Vi 2(bsi+bij)-ViVj(gijsinθij-bijcosθij) (13)
the line current magnitude measurement function for nodes i to j is:
and (II) when the typical pi equivalent circuit comprises a transformer branch circuit, the active and reactive power injection measurement function, the active and reactive power flow injection measurement function and the current amplitude measurement function of a node are as follows:
the active injection measurement function of the node i is as follows:
the reactive injection measurement function of node i is:
the injected active power measurement function of nodes i to j is:
the injected reactive tidal flow measurement function for nodes i to j is:
the injected active power measurement function of nodes j to i is:
the injected reactive tidal flow measurement function for nodes j to i is:
the line current magnitude measurement function for nodes i to j is:
wherein, ViAnd VjThe voltage amplitudes of nodes i and j, respectively; phase angle difference theta between nodes i and jij＝θi-θj，θiAnd thetajPhase angles of nodes i and j, respectively; n is a radical ofiIs the number of nodes connected to node i; gij+jBijIs the ith row and the jth column element of the admittance matrix; gij+jbijIs the order admittance between nodes i and j; gsi+jbsiIs the parallel admittance between nodes i and j; k is the nonstandard transformation ratio of the transformer; bTIs the susceptance of the standard side of the transformer.
The method can fully take the characteristics of randomness, intermittence, volatility and the like of the new energy into consideration, can effectively model the time and space correlation of system nodes by adopting a vector self-recursion model, and quickly track and predict the running state of each node of the power grid in real time, thereby overcoming the defect that the static state estimation cannot meet the randomness and volatility of new energy grid connection, carrying out real-time, effective and accurate state estimation on the new energy grid connection, improving the precision of short-term state prediction, improving the precision of final state estimation, fully reflecting the dynamic characteristics of the power grid, providing data support for economic dispatching, safety evaluation and other related advanced applications of a power system control center, and meeting the development requirement of the power grid.
Drawings
FIG. 1 is a flow chart of the present invention.
FIG. 2 is a diagram of the calculation of the pi-type equivalent circuit without the transformer branch according to the present invention.
FIG. 3 is a pi-type equivalent circuit measurement calculation chart of the transformer branch according to the present invention.
Fig. 4 is a test chart of the IEEE30 system according to embodiment 2 of the present invention.
Fig. 5 shows the result of estimating the voltage amplitude of each node by the conventional method in embodiment 2 of the present invention.
FIG. 6 shows the estimation result of the phase angle of each node voltage by the conventional method in embodiment 2 of the present invention.
FIG. 7 shows the result of voltage amplitude estimation of each node according to the present invention in example 2 of the present invention.
FIG. 8 shows the estimation result of the phase angle of the voltage at each node according to the present invention in embodiment 2 of the present invention.
Detailed Description
The present invention is not limited by the following examples, and specific embodiments may be determined according to the technical solutions and practical situations of the present invention.
The invention is further described with reference to the following examples and figures:
example 1: as shown in fig. 1, 2 and 3, the method for estimating the state of the power system taking into account the new energy space-time correlation includes the following steps:
the first step is as follows: reading power grid information data, obtaining a node admittance matrix and a branch-node association matrix according to the read power grid information data, wherein the power grid information data comprise historical state estimation data, current network parameters of a power system, a topological structure and line impedance, and then entering a second step;
the second step is that: the measurement and configuration of the power grid system are realized by establishing measurement functions of voltage amplitude measurement, power injection measurement and tidal volume measurement according to the node admittance matrix and the branch-node association matrix, and calculating measurement z according to the measurement functionskBased on the measurement zkConfiguring the system state, the measurement z of the grid systemkMeasuring node voltage amplitude, power injection and tidal current, and then entering a third step;
the third step: modeling according to the space-time correlation of new energy in the power grid system, specifically as follows:
establishing a vector self-recursive model shown as the following formula,
xk＝Φ1xk-1+...+Φpxk-p+εk (1)
wherein k represents a measurement sampling time; { xk-1,...,xk-pRepresents the system history status; x is the number ofkIs the predicted value of the state at the current moment; { phi1,...,ΦpIs the model parameter matrix; p is the model order; epsilonkIs the model error, SkIs a covariance matrix, i.e., a gaussian random variable; { phi1,...,ΦpAnd SkThe diagonal elements of (a) represent the time correlation of node voltage and phase angle, while the off-diagonal elements thereof characterize the spatial correlation;
and (II) retaining the first-order vector, simplifying a vector self-recursion model, and completing the establishment of the model, wherein the simplified model is shown as the following formula:
xk＝Φ1xk-1+εk (2)
then entering the fourth step;
the fourth step: using M sets of historical state data pairs phi1And SkPerforming estimation to estimate the valueAndas shown in the following formula:
wherein pi (0) and pi (1) are sampling covariance matrixes, μ is the mean of the samples of the historical data,then entering the fifth step;
the fifth step: based on the estimated valueAndpredicting the state at k time, and predicting the state at k time xk|k-1And its prediction error covariance matrix sigmak|k-1As shown in the following formula:
then entering a sixth step;
and a sixth step: performing prediction aided state estimation, specifically as follows:
(I) measuring z according to the k time measured in the second stepkObtaining the state x of the system at the k sampling timek,xkIs expressed by the following formula:
zk＝h(xk)+vk (7)
wherein h (-) represents a m-dimensional nonlinear measurement function vector; v. ofkIs random white noise following a normal distribution, i.e. vk～N(0,Rk)，RkIs a measurement error covariance matrix;
(II) using extended Kalman filter to pair statesUpdating in a recursion manner to finish the estimation of the auxiliary prediction state by the system, which specifically comprises the following steps:
(1) an objective function is established as shown below:
(2) optimizing the objective function to obtain the stateRecursively updating the results, statesThe result of the recursive update is shown in the following equation:
wherein, KkIn order to be a matrix of gains, the gain matrix,Hkthe matrix of the Jacobian is obtained,Σkas an error covariance matrix, sigmak＝(I-KkHk)Σk|k-1(ii) a I is an identity matrix; then entering a seventh step;
the seventh step: and sending the state estimation result at the moment k to a power grid control center, and performing state estimation at the moment k +1 in the second step.
Here, because the intermittency and volatility of the new energy increases the probability of sudden load change in a short time (in the order of minutes or seconds), the operation of the whole system is no longer quasi-steady, and the two factors of the change of the grid power injection mode and the intermittency of the new energy change the time and space correlation between the voltage and the phase angle of each node of the system, namely the new energy including a wind driven generator, a photovoltaic generator and the like are obviously shownAnd the load driving the system to make a state change also shows a temporal and spatial correlation, so that the states of the system also show a corresponding temporal and spatial correlation. Therefore, in the third step, the time and space correlation between the voltage and the phase angle of each node of the power grid is modeled according to the space-time correlation of new energy in the power grid system, and in the third step, the model error epsilonkTypically take the average of 0, and { Φ }1,...,ΦpAnd SkThe diagonal elements of the model represent the time correlation of the node voltage and the phase angle, while the off-diagonal elements represent the space correlation, and the model is simplified into a first-order vector self-recursion model in the third step because only the state in a super short term is usually predicted in the actual power system; r in the sixth stepkIs a known prior art measurement error covariance matrix.
The method can fully take the characteristics of randomness, intermittence, volatility and the like of the new energy into consideration, can effectively model the time and space correlation of system nodes by adopting a vector self-recursion model, and quickly track and predict the running state (voltage amplitude and phase angle) of each node of the power grid in real time, thereby overcoming the problem that the static state estimation cannot meet the randomness and volatility of new energy grid connection, carrying out real-time, effective and accurate state estimation on the new energy grid connection, improving the precision of short-term state prediction, improving the precision of final state estimation, fully reflecting the dynamic characteristics of the power grid, providing data support for economic dispatching, safety evaluation and other related advanced applications of a power system control center, and meeting the development requirement of the power grid.
The following is further optimization or/and improvement of the technical scheme of the invention:
as shown in fig. 2 and 3, in the second step, each transmission line in the power grid system is equivalent to a typical pi equivalent circuit to perform system measurement, and the measurement function is as follows:
the active and reactive power injection measurement function, the active and reactive power flow injection measurement function and the current amplitude measurement function of a node when the typical pi equivalent circuit does not contain a non-transformer branch are as follows:
the active injection measurement function of the node i is as follows:
the reactive injection measurement function of node i is:
the injected active power measurement function of nodes i to j is:
Pij＝Vi 2(gsi+gij)-ViVj(gijcosθij+bijsinθij) (12)
the injected reactive tidal flow measurement function for nodes i to j is:
Qij＝-Vi 2(bsi+bij)-ViVj(gijsinθij-bijcosθij) (13)
the line current magnitude measurement function for nodes i to j is:
and (II) when the typical pi equivalent circuit comprises a transformer branch circuit, the active and reactive power injection measurement function, the active and reactive power flow injection measurement function and the current amplitude measurement function of a node are as follows:
the active injection measurement function of the node i is as follows:
the reactive injection measurement function of node i is:
the injected active power measurement function of nodes i to j is:
the injected reactive tidal flow measurement function for nodes i to j is:
the injected active power measurement function of nodes j to i is:
the injected reactive tidal flow measurement function for nodes j to i is:
the line current magnitude measurement function for nodes i to j is:
wherein, ViAnd VjThe voltage amplitudes of nodes i and j, respectively; phase angle difference theta between nodes i and jij＝θi-θj，θiAnd thetajPhase angles of nodes i and j, respectively; n is a radical ofiIs the number of nodes connected to node i; gij+jBijIs the ith row and the jth column element of the admittance matrix; gij+jbijIs the order admittance between nodes i and j; gsi+jbsiIs the parallel admittance between nodes i and j; k is the nonstandard transformation ratio of the transformer; bTIs the susceptance of the standard side of the transformer.
Example 2:
as shown in FIGS. 4, 5, 6, 7, and 8, the invention was tested on the IEEE30 node system of FIG. 4, wherein the measurement errors are assumed to follow a mean of 0 and a variance of 10-6Is a Gaussian distribution, and is simulated by testingThe final test results of the voltage amplitude and phase angle of each node after being true are shown in fig. 7 and 8; then, the traditional static estimation method is used for testing on the IEEE30 node system in the figure 4, and the final test results of the voltage amplitude and the phase angle of each node after test simulation are shown in figures 5 and 6; as can be seen from fig. 5, 6, 7, and 8, compared with the conventional state estimation method, the method of the present invention has higher estimation accuracy, and can track the operation state of the system in real time.
The technical characteristics form an embodiment of the invention, which has strong adaptability and implementation effect, and unnecessary technical characteristics can be increased or decreased according to actual needs to meet the requirements of different situations.
Claims (2)
1. A power system state estimation method considering new energy space-time correlation is characterized by comprising the following steps: the method comprises the following steps:
the first step is as follows: reading power grid information data, obtaining a node admittance matrix and a branch-node association matrix according to the read power grid information data, wherein the power grid information data comprise historical state estimation data, current network parameters of a power system, a topological structure and line impedance, and then entering a second step;
the second step is that: the measurement and configuration of the power grid system are realized by establishing measurement functions of voltage amplitude measurement, power injection measurement and tidal volume measurement according to the node admittance matrix and the branch-node association matrix, and calculating measurement z according to the measurement functionskBased on the measurement zkConfiguring the system state, the measurement z of the grid systemkMeasuring node voltage amplitude, power injection and tidal current, and then entering a third step;
the third step: modeling according to the space-time correlation of new energy in the power grid system, specifically as follows:
establishing a vector self-recursive model shown as the following formula,
xk＝Φ1xk-1+...+Φpxk-p+εk (1)
wherein k represents a measurement sampling time; { xk-1,...,xk-pRepresents the system history status; x is the number ofkIs the predicted value of the state at the current moment; { phi1,...,ΦpIs the model parameter matrix; p is the model order; epsilonkIs the model error, SkIs a covariance matrix, i.e., a gaussian random variable; { phi1,...,ΦpAnd SkThe diagonal elements of (a) represent the time correlation of node voltage and phase angle, while the off-diagonal elements thereof characterize the spatial correlation;
and (II) retaining the first-order vector, simplifying a vector self-recursion model, and completing the establishment of the model, wherein the simplified model is shown as the following formula:
xk＝Φ1xk-1+εk (2)
then entering the fourth step;
the fourth step: using M sets of historical state data pairs phi1And SkPerforming estimation to estimate the valueAndas shown in the following formula:
wherein pi (0) and pi (1) are sampling covariance matrixes,μ is the mean of the samples of the historical data,then entering the fifth step;
the fifth step: based on the estimated valueAndpredicting the state at k time, and predicting the state at k time xk|k-1And its prediction error covariance matrix sigmak|k-1As shown in the following formula:
then entering a sixth step;
and a sixth step: performing prediction aided state estimation, specifically as follows:
(I) measuring z according to the k time measured in the second stepkObtaining the state x of the system at the k sampling timek,xkIs expressed by the following formula:
zk＝h(xk)+vk (7)
wherein h (-) represents a m-dimensional nonlinear measurement function vector; v. ofkIs random white noise following a normal distribution, i.e. vk～N(0,Rk)，RkIs a measurement error covariance matrix;
(II) using extended Kalman filter to pair statesUpdating in a recursion manner to finish the estimation of the auxiliary prediction state by the system, which specifically comprises the following steps:
(1) an objective function is established as shown below:
(2) to an objective functionOptimizing to obtain the stateRecursively updating the results, statesThe result of the recursive update is shown in the following equation:
wherein, KkIn order to be a matrix of gains, the gain matrix,Hkthe matrix of the Jacobian is obtained,Σkas an error covariance matrix, sigmak＝(I-KkHk)Σk|k-1(ii) a I is an identity matrix; then entering a seventh step;
the seventh step: and sending the state estimation result at the moment k to a power grid control center, and performing state estimation at the moment k +1 in the second step.
2. The method of claim 1, wherein the method comprises: and secondly, carrying out system measurement on each transmission line in the power grid system equivalent to a typical pi equivalent circuit, wherein the measurement function is as follows:
the active and reactive power injection measurement function, the active and reactive power flow injection measurement function and the current amplitude measurement function of a node when the typical pi equivalent circuit does not contain a non-transformer branch are as follows:
the active injection measurement function of the node i is as follows:
the reactive injection measurement function of node i is:
the injected active power measurement function of nodes i to j is:
Pij＝Vi 2(gsi+gij)-ViVj(gijcosθij+bijsinθij) (12)
the injected reactive tidal flow measurement function for nodes i to j is:
Qij＝-Vi 2(bsi+bij)-ViVj(gijsinθij-bijcosθij) (13)
the line current magnitude measurement function for nodes i to j is:
and (II) when the typical pi equivalent circuit comprises a transformer branch circuit, the active and reactive power injection measurement function, the active and reactive power flow injection measurement function and the current amplitude measurement function of a node are as follows:
the active injection measurement function of the node i is as follows:
the reactive injection measurement function of node i is:
the injected active power measurement function of nodes i to j is:
the injected reactive tidal flow measurement function for nodes i to j is:
the injected active power measurement function of nodes j to i is:
the injected reactive tidal flow measurement function for nodes j to i is:
the line current magnitude measurement function for nodes i to j is:
wherein, ViAnd VjThe voltage amplitudes of nodes i and j, respectively; phase angle difference theta between nodes i and jij＝θi-θj，θiAnd thetajPhase angles of nodes i and j, respectively; n is a radical ofiIs the number of nodes connected to node i;
Gij+jBijis the ith row and the jth column element of the admittance matrix; gij+jbijIs the order admittance between nodes i and j; gsi+jbsiIs the parallel admittance between nodes i and j; k is the nonstandard transformation ratio of the transformer; bTIs the susceptance of the standard side of the transformer.
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| CN103972884A (en) * | 2014-04-24 | 2014-08-06 | 西南交通大学 | Electric system state estimation method |
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