CN115249971A - Weighted average fast forward-push back substitution robust state estimation method for radiation network - Google Patents
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Abstract
The invention discloses a weighted average fast forward-backward substitution robust state estimation method of a radiation network, which replaces the traditional least square state estimation method by adopting a weighted average method, adopts a forward-backward substitution idea from local to global, firstly carries out node power estimation on tail end nodes of branches, then carries out weighted average state estimation on the branches, and obtains a corrected value of a system state quantity estimated value from a top layer to a bottom layer after finishing the estimation of all nodes and branches from the bottom layer to the top layer, thereby realizing the weighted average fast forward-backward substitution robust state estimation of the radiation network.
Description
Technical Field
The invention relates to the technical field of power system state estimation, in particular to a weighted average fast forward-push substitution robust state estimation method for a radiation network.
Background
The state estimation of the power system is one of the important functions of a modern power dispatching system, and the state estimation of the power system can estimate the state quantity of the current power system by using various types of measurement data provided by the power system. The existing global weighted least square method has good state estimation tolerance performance, but for a large-scale power system, the memory occupation is high, and the calculation speed is low. Due to interaction among a plurality of bad data, phenomena of residual inundation and pollution are easy to occur, and once a situation of unconvergence occurs, the method is insufficient in capacity of identifying and processing the bad data. According to the existing robust state estimation method by adopting a forward-backward substitution method, the state of each branch is estimated through a localization thought, the estimation precision is high, the bad data is well identified, but the calculation speed is greatly reduced due to the fact that the state of each branch needs to be estimated, and therefore the method for estimating the robust state by adopting the weighted average rapid forward-backward substitution of the radiation network is provided.
Disclosure of Invention
The present invention is directed to provide a method for estimating a weighted average fast forward-backward robust state of a radiation network, so as to solve the problems mentioned in the background art.
In order to achieve the purpose, the invention provides the following technical scheme: a method for estimating a weighted average fast forward-push surrogate robust state of a radiation network comprises the following steps:
1. obtaining basic data of a power grid, wherein the basic data of the power grid comprises a topological structure of the power grid and measurement information of a power system, and the basic data comprises injection active power, injection reactive power of a node, a voltage amplitude of the node, and branch active power and branch reactive power of the head end and the tail end of each branch;
2. preprocessing basic data of a power grid, and replacing obviously wrong data, wherein the purpose of preprocessing bad data is to firstly identify part of obviously wrong bad data and replace the bad data with a correct estimation value before state estimation;
3. taking the system branch at the bottom layer as a starting point and the system branch at the top layer as an end point, and performing weighted average state estimation on each branch at each layer to obtain an initial state estimation value of the power grid;
4. taking the system branch at the top layer as a starting point and the system branch at the bottom layer as a terminal point, carrying out tidal current regeneration according to the voltage distribution of the root node and the power distribution of the system, and correcting the voltage of the node to obtain a correction result of the state quantity of the power grid;
5. correcting the weight of the measured data through an exponential weight function according to the correction result of the state quantity of the power grid;
6. and (4) judging whether the difference value between the current power grid state quantity correction result and the last power grid state quantity correction result is smaller than a set convergence criterion, if so, finishing the state estimation, otherwise, returning to the step (3).
Preferably, the specific steps in step 2 are: A. according to
And carrying out power balance judgment on the tail end node of the branch, if the tail end node of the branch is unbalanced, indicating that bad data exists in the tail end node of the branch, not carrying out node power estimation on the node, and jumping to D, wherein k is h E j represents a node directly connected to node j, but does not include node j itself; p j ，Q j Respectively representing the injected active power and the injected reactive power of the node j; epsilon 1 Representing a balance judgment upper limit threshold determined according to the measurement type and the reference value thereof; B. and carrying out node power estimation on the node, and obtaining a head end estimation value derived from a tail end measurement value by a load flow calculation method, wherein the node power estimation process comprises the following steps: b1, establishing a node power balance equation
Wherein: k = k 1 ,k 2 ,…k l I; k ∈ j denotes all nodes adjacent to the node j, but does not include the node j itself; w is a pjk 、w pj 、w qjk 、w qj Respectively representing weights corresponding to four types of measurement data of branch active power, branch active power injection, branch reactive power injection and branch reactive power injection; p jk 、P j 、Q jk 、Q j Representing the estimated value after estimation; p is mjk 、P mj 、Q mjk 、Q mj Representing a measurement value corresponding to the estimated value; b2, solving the node power balance equation
In the formula:
is an estimated value after estimation; C. according to the formula
And
judging whether the power of the head end and the tail end is consistent with the voltage amplitude, if not, indicating that bad data exists in the head end measurement, replacing the bad data with an estimated value obtained by the tail end, and jumping to a single-branch state for estimation; if the two are consistent, the head end and the tail end have no bad data, and the single branch state estimation is directly carried out, wherein:
and P ij Respectively obtaining the active power of the head end of the branch circuit and the active power measurement value of the head end of the branch circuit from the tail end of the branch circuit;
and Q ij Respectively obtaining the reactive power of the head end of the branch and the reactive power measurement value of the head end of the branch by the tail end of the branch; epsilon 2 The method for determining the upper limit of the threshold value represents the balance judgment upper limit threshold value determined according to the power measurement type and the reference value thereof, and comprises the following steps:
in the formula: epsilon is the upper limit threshold of different measurement types; e is the error rate of estimated value allowed by different measuring types, wherein the requirements of active power not higher than 2%, reactive power not higher than 3%, voltage not higher than 0.5% of 220kV and above, voltage not higher than 2% below 220kV, and Z b Is a system reference value for voltage or power,Z base an estimate error rate reference value determined from a voltage or power system reference value; D. obtaining a head end estimation value obtained by deducting a tail end measurement value through a load flow calculation method; E. according to the formula
And
and judging whether the power of the head end and the tail end is consistent, if so, and if the lower layer branch of the tail end node has no bad data or has been replaced, indicating that the injection power of the tail end node has errors, and carrying out single-branch state estimation after replacing the injection power.
Preferably, the weighted average state estimation in step 3 includes the following steps: A. according to the measured data of the branch end, the trend method is adopted
Calculating to obtain a calculated value of the head end of the branch, wherein
As a measure or last estimate of the complex power at the end of the branch,
as a measure or last estimate of the branch end voltage, Z ij The impedance of the branch is taken as the branch impedance,
respectively calculating the complex voltage and the complex power of the head end of the branch circuit; B. according to the formula of the head end calculated value and the head end measured value obtained by the tail end calculation
Carrying out weighted average to obtain the estimation value of the head end of the branch, wherein h f As an estimate of this point, h mf Is a measured value of the head end of the measurement,
calculated value of head end for tail end, w f Weight of head end measurement, w t The weights of the end measurements.
Preferably, step 4 is according to formula
And performing tidal current back generation.
Preferably, step 5 includes a, establishing an exponential weight function model, where the exponential weight function model is as follows:
in the formula
In order to be the weight after the update,
is an initial fixed weight, r N Is a normalized residual; B. measuring, classifying and standardizing, namely dividing the residual dz into three types, namely a voltage type measuring residual dz _ V, an active power type measuring residual dz _ P and a reactive power type measuring residual dz _ Q according to the measuring types, dividing the three types of residual by corresponding types of standard deviations to obtain a standardized residual r on the assumption that the three types of measuring residual meet normal distribution N And then from the normalized residual r N Using exponential weight functions
A correction weight is obtained for each measurement.
Compared with the prior art, the invention has the following beneficial effects:
the method adopts a weighted averaging method to replace the traditional least square state estimation method, adopts a forward-backward substitution idea from local to global, firstly carries out node power estimation on the tail end node of the branch, then carries out weighted averaging state estimation on the branch, and carries out forward-backward substitution on the branch from the bottom layer to the top layer, and after finishing the estimation of all nodes and branches, obtains the corrected value of the system state quantity estimated value from the top layer to the bottom layer, thereby finishing the weighted average rapid forward-backward substitution robust state estimation of the radiation network.
Drawings
FIG. 1 is an IEEE33 node system node;
FIG. 2 is a schematic diagram of node power estimation;
FIG. 3 is a diagram illustrating weighted averaging state estimation;
FIG. 4 is a flow chart of weighted-average push-back surrogate robust state estimation.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention.
In order to verify the correctness of the invention, simulation analysis is carried out through an IEEE33 node system, and the steps are as follows:
1. establishing a simulation system, wherein an IEEE33 node system is shown in FIG. 1, the reference voltage of the system is 12.66kV, the reference capacity of the system is 100MVA, 10-20 times of threshold is superposed on a true value of power flow calculation to generate bad data, when the bad data at a special position is set, the bad data at the special position is superposed with 10 threshold on the true value of the power flow calculation, the rest of measured data adopts the true value of power flow, according to the above mentioned threshold calculation method, because there is no voltage and power reference value corresponding to 12.66kV, the line active or reactive power reference value 37MVA and the bus voltage reference value 42kV corresponding to 35kV are adopted, and the power reference value of the 12.66kV system is calculated to be 12.66/35 × 37MVA and 12.66/35 × 42kV and 15kV according to a linearization ratio, so that the threshold value can be obtained:
(1)
(2)
(3)
2. carrying out topology analysis on a system to obtain a network level matrix LL and a branch head and tail end matrix MM, wherein the network level matrix LL is used for describing the level structure of a network, the line number represents the level structure of the matrix, and the element of each line is used for representing the branch number belonging to the layer; the branch head and tail end matrix MM is used for describing the head and tail end node number of each branch, and if the number of branches is n, the MM is a matrix with 2 rows and n columns, wherein elements in the first row of the ith column represent the head end node number with the branch number being i; the elements in the second row represent the end node number of the branch with the branch number i, and after topology analysis is performed on IEEE33 nodes, a network level matrix LL can be obtained, and the branch head-end matrix MM is:
3. preprocessing bad data on system measurement data, starting from the tail end nodes of 16, 20, 23 and 31 branches at the bottom layer according to a network hierarchical structure matrix LL and a branch head and tail end node matrix MM, performing node power estimation and bad data identification and replacement on each branch until the branch 0 at the top layer, wherein the system state quantity subjected to bad data preprocessing is shown in Table 1, and the specific steps of node power estimation are as follows: from kirchhoff's law, electric powerThe injected power and the outflow power of any node in the system should be balanced, as shown in fig. 2, should be
Wherein
The power of the injection node j is represented, k epsilon j represents all nodes adjacent to the node j, but the node j is not included, and because a measured value and an estimated value after branch state estimation have certain errors, the measured value or the estimated value of the node power can be subjected to state estimation according to the principle of WLS, so that the measured value or the estimated value meets the kirchhoff law, and the state estimation calculation of a first-stage branch is referred to.
(4)
(5)
Wherein: k = k 1 ,k 2 ,…k l I; k ∈ j denotes all nodes adjacent to the node j, but does not include the node j itself; w is a pjk 、w pj 、w qjk 、w qj Respectively representing weights corresponding to four types of measurement data of branch active power, branch active power injection, branch reactive power injection and branch reactive power injection; p jk 、P j 、Q jk 、Q j Representing the estimated value after estimation; p mjk 、P mj 、Q mjk 、Q mj The measurement value corresponding to the estimated value is expressed, and the above formula is solved to obtain:
(6)
(7)
in the formula:
is an estimated value after estimation.
4. According to the LL and MM matrixes, weighted averaging state estimation is performed on each branch from the end node of the 16, 20, 23, 31 branches at the bottom layer to the branch 0 branch at the top layer, so as to obtain an initial value of the state quantity of the system, and at this time, the state quantity of the system is shown in table 1, and the specific steps of weighted averaging state estimation (fig. 3) are as follows: for a simple branch, assume a complete measurement system, i.e. there is a measurement: v im ,V jm ,P im ,P jm ,Q im ,Q jm ,P ijm ,Q ijm ,P jim ,Q jim From the end of the branch, measuring V as a measure of the end jm ，P jim ,Q jim Based on the calculation mode of load flow forward pushing, the calculation value V of the branch head end can be calculated according to the following formula ic ，P ijc ,Q ijc 。
(8)
In the formula
As a measure or last estimate of the complex power at the end of the branch,
as a measure or last estimate of the branch terminal voltage, Z ij Is the impedance of the branch circuit and is,
respectively the complex voltage and the complex power of the head end of the branch circuit obtained by calculation.
And taking weighted average of the obtained calculated value and the initial branch head end measurement value according to the formula (9) every time to obtain a new estimated value to participate in the calculation of the next-stage branch.
(9)
In the formula h f As an estimate of this point, h mf The measured value of the head end is the measured value,
estimate of head end, w, for tail end f Weight of head end measurement, w t The weights of the end measurements.
5. According to the LL and MM matrices, starting from the head end node 0 of the top layer branch No. 0, the voltage amplitudes of the respective nodes are corrected by the voltage at the head end and the power distribution of the whole system, and the state quantity correction value of the system is obtained, and the specific result is shown in table 1.
6. According to the corrected value of the system state quantity, the system state quantity is classified and standardized according to three types of voltage amplitude, active power and reactive power, and the weight of each type is corrected through an exponential type weight function.
7. Comparing the system state quantity correction value obtained this time with the system state quantity correction value obtained last time to judge whether the system state quantity correction value is converged, if so, exiting the circulation and calculating the index; and if not, jumping to the step 4, and carrying out next forward back substitution state estimation until the algorithm converges.
TABLE 1 simulation results
8. Effect of the Algorithm
8.1 index definition
When the robust function is not considered, in order to evaluate the calculation accuracy of the method, a whole-network target function mean value, a whole-network measurement error statistic value and a whole-network estimation error statistic value are defined.
(10)
In the formula:
is the average value of the target function of the whole network;
and
respectively obtaining a whole network measurement error statistic value and a whole network estimation error statistic value; t is the calculation order; t is the total number of times of calculation;
and S i,t Estimated and true values for the ith quantity measurement of the t-th calculation, respectively.
When considering the robust function, in order to evaluate the robust performance of the method, the mean error of the system and the maximum error of the system are defined,
(11)
(12)
in the formula: s 1 Is the system average error; s 2 Is the maximum error of the system;
and x i Respectively an estimated value and a true value of the ith state quantity.
8.2 simulation scheme
When the accuracy of the calculation result is compared, the complete forward-backward substitution state estimation method and the incomplete forward-backward substitution state estimation method are compared with the method without the robust function through the traditional WLS state estimation method; when the accuracy of the calculation result is verified, no bad data is set, and only the measurement error is superposed according to the formula (13).
(13)z＝PF_T(1+N(0,std_error))
In the formula, PF _ T is a power flow calculation value; std _ error is the standard deviation of the measurement error, and the standard deviation of the injection power and the branch power is respectively set to be 0.004,0.01 and 0.008 according to different measurement types, voltage amplitudes and the standard deviation of the branch power.
When the robust performance of the calculation result is compared, the robust state estimation is completely pushed back to replace the robust state estimation and the method containing the robust function is used for comparison through the traditional WLS robust state estimation of the exponential weight function, when special bad data are set, the bad data are thresholds which are 10-20 times of the sum of the true load flow values, the rest of measured data are true load flow values, when bad data with different proportions are set, the bad data are thresholds which are 10-20 times of the sum of the true load flow values, and the rest of measured data are load flow true value superposition measurement errors.
TABLE 2 various simulation scenarios
8.3 simulation results and analysis
The simulation results in the aspect of the accuracy of the calculation results are as follows:
TABLE 3 accuracy index of various methods
From the accuracy of the calculation results:
the measurement data of the four methods are superposed with the same measurement error on the basis of the true value of the power flow calculation, so the measurement errors of the whole network are the same, compared with the measurement errors of the whole network, the estimation errors of the whole network of the four methods are reduced, the four methods have certain filtering effect, the estimation error of the whole network of the method is higher than that of the traditional WLS method, but the estimation error of the whole network of the method is still lower than that of the method adopting the least square method in the forward substitution process, and the incomplete forward-backward substitution method M3 of the power flow backward substitution process is adopted in the backward substitution process.
In summary, the global WLS is equivalent to the estimation accuracy of the complete pushback state estimation method in normal measurement, and the estimation accuracy of the method of the present invention is slightly worse than the former two methods, but still higher than the incomplete pushback method.
TABLE 4 calculation time and Convergence accuracy of various methods
From the viewpoint of calculating the convergence of speed and power flow:
the power flow convergence precision of the least square method and the method of the invention reaches 10 -15 The tidal current precision of complete forward-backward substitution and incomplete forward-backward substitution is 10 -5 The reason is that the least square method has the constraint of an injection power equation, and the algorithm of the invention has the constraint of node power balance; although node power balance exists in several methods of forward-backward substitution, when branch state estimation is carried out, the accuracy of power flow convergence is reduced due to the fact that constraints of an injection equation do not exist.
From the calculation speed, the algorithm of the invention has the same speed as the global least square method and is obviously faster than other algorithms because the algorithm of the invention adopts load flow calculation, compared with local state estimation, the calculation times of each branch are greatly reduced, and compared with the complete forward-backward substitution method, the incomplete forward-backward substitution method only adopts branch state estimation during forward-backward, so the calculation time is also faster than the complete forward-backward substitution method, along with the continuous complication of the system scale, the calculation time required by the global least square method can obviously increase, and the advantage of the method of the invention on the calculation efficiency can be more obvious.
The simulation results in terms of poor resistance performance are as follows:
when special bad data at different positions are set, the method has excellent tolerance capability when single bad data is set or a plurality of quantity errors of the same node are set, because weighted averaging can effectively reduce the influence of bad data on only one side of a branch, and the obvious bad data is identified and replaced in a preprocessing link of the bad data.
TABLE 5 simulation results of bad data at special locations
After setting bad data with different proportions, the method has poorer anti-difference performance in the aspect of anti-difference performance compared with the traditional WLS anti-difference state estimation method and the complete forward-backward substitution anti-difference method. From the aspect of computational efficiency, because an iterative process of a least square method is not needed, the computational efficiency of the method is far faster than that of a complete forward-backward substitution robust method, and along with the continuous increase of the system scale, the computational efficiency of the method has more obvious advantages compared with the traditional WLS robust state estimation.
TABLE 6 simulation results of bad data in different proportions
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (5)
1. A method for estimating a weighted average fast forward-push surrogate robust state of a radiation network is characterized by comprising the following steps: the method comprises the following steps:
(1) Obtaining basic data of a power grid, wherein the basic data of the power grid comprises a topological structure of the power grid and measurement information of a power system, and the basic data comprises injection active power, injection reactive power of a node, a voltage amplitude of the node, and branch active power and branch reactive power of the head end and the tail end of each branch;
(2) Preprocessing basic data of a power grid, and replacing obviously wrong data, wherein the purpose of preprocessing bad data is to firstly identify part of obviously wrong bad data and replace the bad data with a correct estimation value before state estimation;
(3) Taking the system branch at the bottom layer as a starting point and the system branch at the top layer as an end point, and performing weighted average state estimation on each branch at each layer to obtain a state estimation initial value of the power grid;
(4) Taking the system branch at the top layer as a starting point and the system branch at the bottom layer as a terminal point, carrying out tidal current regeneration according to the voltage distribution of the root node and the power distribution of the system, and correcting the voltage of the node to obtain a correction result of the state quantity of the power grid;
(5) According to the correction result of the power grid state quantity, correcting the weight of the measured data through an exponential weight function;
(6) And (3) judging whether the difference value between the current power grid state quantity correction result and the last power grid state quantity correction result is smaller than a set convergence criterion, if so, ending the state estimation, and otherwise, returning to the step (3).
2. The method of claim 1, wherein the method comprises: the specific steps in the step 2 are as follows: A. according to
And carrying out power balance judgment on the tail end node of the branch, if the tail end node of the branch is unbalanced, indicating that bad data exists in the tail end node of the branch, not carrying out node power estimation on the node, and jumping to D, wherein k is h E j represents a node directly connected to node j, but does not include node j itself; p j ，Q j Respectively representing the injection active power and the injection reactive power of the node j; epsilon 1 Representing a balance judgment upper limit threshold value determined according to the measurement type and the reference value thereof; B. go to the nodeEstimating the node power, and obtaining a head end estimation value derived from a tail end measurement value through a load flow calculation method, wherein the node power estimation process comprises the following steps: b1, establishing a node power balance equation
Wherein: k = k 1 ,k 2 ,…k l I; k belongs to j and represents all nodes adjacent to the node j, but does not comprise the node j per se; w is a pjk ，w pj ，w qjk ，w qj Respectively representing weights corresponding to four types of measurement data of branch active power, branch active power injection, branch reactive power injection and branch reactive power injection; p jk 、P j 、Q jk 、Q j Representing the estimated value after estimation; p mjk 、P mj 、Q mjk 、Q mj Representing a measurement value corresponding to the estimated value; b2, solving the node power balance equation
In the formula:
is an estimated value after estimation; C. according to the formula
And
judging whether the power of the head end and the tail end is consistent with the voltage amplitude, if not, indicating that bad data exists in the head end measurement, replacing the bad data with an estimated value obtained by the tail end, and jumping to a single-branch state for estimation; if the two branch state estimation values are consistent, the head end and the tail end have no bad data, and the single branch state estimation is directly carried out, wherein:
and P ij Respectively obtaining the active power of the head end of the branch circuit and the active power measurement value of the head end of the branch circuit from the tail end of the branch circuit;
and Q ij Respectively obtaining the reactive power of the head end of the branch circuit and the reactive power measurement value of the head end of the branch circuit by the tail end of the branch circuit; epsilon 2 The method represents a balance judgment upper limit threshold value determined according to the power measurement type and a reference value thereof, and the threshold value upper limit determination method comprises the following steps:
in the formula: epsilon is the upper threshold of different measurement types; e is the estimated error rate allowed by different measurement types, wherein the requirements of active power not higher than 2%, reactive power not higher than 3%, voltage not higher than 0.5% at 220kV and above, voltage not higher than 2% below 220kV, and Z b Being a system reference value of voltage or power, Z base An estimate error rate reference value determined from a voltage or power system reference value; D. obtaining a head end estimation value obtained by pushing a tail end measurement value through a load flow calculation method; E. according to the formula
And
and judging whether the power of the head end and the tail end is consistent, if so, and if the lower layer branch of the tail end node has no bad data or has been replaced, indicating that the injection power of the tail end node is wrong, and carrying out single-branch state estimation after replacing the lower layer branch.
3. The method of claim 1, wherein the method comprises: the weighted averaging state estimation in the step 3 specifically comprises the following steps: A. according to the measured data of branch end, making use of tide method
Calculating to obtain a calculated value of the head end of the branch, wherein
As a measure or last estimate of the complex power at the end of the branch,
as a measure or last estimate of the branch end voltage, Z ij The impedance of the branch is taken as the branch impedance,
respectively calculating the complex voltage and the complex power of the head end of the branch; B. head end calculation value and head end measurement value obtained by tail end calculation according to formula
Carrying out weighted average to obtain an estimated value of the head end of the branch, wherein h f As an estimate of this point, h mf The measured value of the head end is the measured value,
calculated values for the head end, w, derived for the tail end f Weight of head end measurement, w t The weights of the end measurements.
4. The method of claim 1, wherein the method comprises: in the step 4 according to formula
And performing tidal current back generation.
5. The method of claim 1, wherein the estimation method comprises weighted averaging fast forward-forward substitution robust state estimationIs characterized in that: the step 5 comprises A, establishing an exponential weight function model, wherein the exponential weight function model is as follows:
in the formula
In order to be the weight after the update,
is an initial fixed weight, r N Is a normalized residual; B. measuring, classifying and standardizing, namely dividing the residual dz into three types, namely a voltage type measuring residual dz _ V, an active power type measuring residual dz _ P and a reactive power type measuring residual dz _ Q according to the measuring types, dividing the three types of residual by corresponding types of standard deviations to obtain a standardized residual r on the assumption that the three types of measuring residual meet normal distribution N And then from the normalized residual r N Using exponential weight functions
A correction weight for each measurement value is obtained.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2002051464A (en) * | 2000-08-01 | 2002-02-15 | Kansai Electric Power Co Inc:The | State estimation method in distribution system |
| WO2016078477A1 (en) * | 2014-11-18 | 2016-05-26 | 国电南瑞科技股份有限公司 | Transformer substation three-phase linear generalized state estimation method |
| CN112803402A (en) * | 2021-02-23 | 2021-05-14 | 重庆大学 | Radiation network forward-push back substitution robust state estimation method containing bad data preprocessing |
| CN113595072A (en) * | 2021-07-30 | 2021-11-02 | 广东电网有限责任公司广州供电局 | Distribution network robust state estimation method based on GPU parallel acceleration conjugate gradient algorithm |
-
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Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2002051464A (en) * | 2000-08-01 | 2002-02-15 | Kansai Electric Power Co Inc:The | State estimation method in distribution system |
| WO2016078477A1 (en) * | 2014-11-18 | 2016-05-26 | 国电南瑞科技股份有限公司 | Transformer substation three-phase linear generalized state estimation method |
| CN112803402A (en) * | 2021-02-23 | 2021-05-14 | 重庆大学 | Radiation network forward-push back substitution robust state estimation method containing bad data preprocessing |
| CN113595072A (en) * | 2021-07-30 | 2021-11-02 | 广东电网有限责任公司广州供电局 | Distribution network robust state estimation method based on GPU parallel acceleration conjugate gradient algorithm |
Non-Patent Citations (1)
| Title |
|---|
| 杨成龙: "基于支路带节点功率估计的配网状态估计实用算法", 《电网技术》, 31 December 2003 (2003-12-31) * |
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