CN110633489A - Line parameter identification method based on parameter comprehensive suspicion degree - Google Patents
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Abstract
The invention relates to a line parameter identification method based on parameter comprehensive suspicion degree, which comprises the following steps: 1) constructing a state estimation model based on a Lagrange multiplier method; 2) establishing and constructing a suspicious parameter set based on parameter comprehensive suspicious degree indexes of the suspicious measurement judging function and the suspicious parameter judging function; 3) and for the parameters in the suspicious parameter set, a method of gradually correcting the variable step length is provided for closed-loop correction. Compared with the prior art, the method has the advantages of higher calculation precision, good convergence and the like.
Description
Technical Field
The invention relates to the technical field of power system state estimation, in particular to a line parameter identification method based on parameter comprehensive suspicion degree.
Background
The electric power system has the characteristics of distribution, massive parameters and complex models, a set of hierarchical system of hierarchical management, hierarchical control and distributed processing is naturally formed in China for years for electric power production management, namely, the electric network is divided into a plurality of sub-networks according to regional characteristics of distribution of the electric network, topological structure of the network, electric characteristics of the electric network and the like, the scheduling and operation of each sub-network are responsible for one scheduling center, each scheduling center establishes and maintains a model of the administered electric network, stores topological relation and electric parameters of each device of the electric power system in the administration range, receives and processes real-time data reflecting the operation state of the electric power system in the administration range in real time, and provides various automatic control, analysis and simulation software.
With the continuous expansion of the scale of the power grid, periodic power grid maintenance and frequent power grid transformation put high requirements on maintenance work of equipment parameters, and due to the reasons of management, personnel quality and the like, artificial errors of the equipment parameters are easy to occur, so that the results including state estimation, load flow analysis and other various analysis and calculation are inaccurate or cannot be converged; moreover, the number of various devices in the power grid is large, the operation time nodes and the characteristics of the devices are different, and when the devices are close to the service life cycle of the devices, the devices are prone to drift and mutation of parameters triggered by accidental factors, so that various analysis and calculation are inaccurate. The method and the device realize timely screening, elimination and correction of equipment parameter errors caused by various factors, comprehensively improve the quality of basic data of the power grid, correspondingly improve the reliability of various advanced application calculations of the power grid, and have important significance.
Model-based power system analysis and calculation has been widely used in power system planning design, operation and control. The mathematical model of the element is an equivalent model established for an actual system, physical characteristics are approximately described in a mathematical expression form, the process is analyzed through digital simulation calculation, and the accuracy of parameter values has great influence on the reliability of simulation analysis. In recent years, a series of power failure accidents occurred internationally, such as "8.14" in the year of maka 2003 and "11.4" in the year of european union 2006, have been reported as accidents, and it has been pointed out that the models adopted are not accurate enough to reproduce the accidents easily. The current method for acquiring the parameters of the power transmission line mainly includes four aspects of a theoretical calculation method, a parameter real-time Measurement method, line parameter estimation based on a Supervisory Control and Data Acquisition (SCADA) system, and line parameter identification based on a Phasor Measurement Unit (PMU), wherein the most active method is to study how to measure and improve the accuracy of parameter identification. With the popularization and application of the integrated model management system, a parameter identification method meeting the requirements of the integrated model management system is considered in future parameter identification.
Disclosure of Invention
The purpose of the invention can be realized by the following technical scheme:
the invention provides a line parameter identification method based on parameter comprehensive suspicion degree, which comprises the following steps:
1) constructing a state estimation model based on a Lagrange multiplier method;
2) establishing and constructing a suspicious parameter set based on parameter comprehensive suspicious degree indexes of the suspicious measurement judging function and the suspicious parameter judging function;
3) and for the parameters in the suspicious parameter set, a method of gradually correcting the variable step length is provided for closed-loop correction.
The construction of the state estimation model based on the Lagrange multiplier method in the step 1) mainly comprises the following steps:
wherein x is an n-dimensional state variable, peFor p-dimensional network parameter error vector, r is z-h (x, p)e) Is m-dimensional measurement residual vector, W is measurement weight diagonal matrix, c (x, p)e) 0 is the zero injection equality constraint.
And calculating a Lagrange multiplier corresponding to the line parameter according to the state estimation result:
wherein HpAnd CpJacobi matrices with respect to line parameters are constrained for the measurement vector and the zero injection equation, respectively.
After the Lagrange multiplier and the measurement residual corresponding to the network parameters are obtained, the Lagrange multiplier and the measurement residual are further standardized to obtain a standardized Lagrange multiplier vector lambdaNAnd a normalized measurement residual vector rN。
Step 2) comprises parameter comprehensive suspicion degree index establishment and suspicion parameter set establishment based on a suspicion measurement judgment function and a suspicion parameter judgment function, and the specific contents comprise:
firstly, defining a suspicious measurement judging function:
wherein if the absolute value of the normalized residual of the ith measurement | rN,i| is less than threshold value crSuspicious measurement decision function SMi(rN,i) 0, indicating that the measurement is normal; otherwise, SMi(rN,i) 1, this measurement value is suspicious.
Next, a suspicious parameter decision function is defined:
wherein, if the normalized Lagrange multiplier corresponding to the jth line parameter is less than the threshold value cλDetermination of function value K for suspicious parametersj(λN,j) 0, indicating that the parameter is normal; otherwise, Kj(λN,j) 1, this parameter is indicated as suspect. Wherein, crAnd cλAre all constants.
Further defining line parameter comprehensive suspicion degree index CSj：
Wherein d represents the topological level distance between the line parameter j and each measurement, and the specific calculation method comprises the following steps: for the suspicious measurement on the branch where a certain error parameter j is located, d is 1, the suspicious measurement on the adjacent branch of the branch is located, d is 2, the suspicious measurement on the alternate branch of the branch is located, and d is 3; by analogy, the topological level distance of the measurement error caused by the parameter error of each line can be obtained. When a parameter on a certain branch has an error, the influence on the measurement residual error size on the branch with the topological level distance d > 3 is relatively insignificant, so that the suspicious measurement has a certain local aggregation characteristic around the branch with the parameter error, and generally only the parameter error is considered for the branch with the level distance d < ndThe influence of the measurements on the branches.
SMi(rN,i) Suspicious measurement decision function value, SP, representing the ith measurementj(λN,j) Determining a function value, ω, for a suspect parameter corresponding to parameter jd,jIs the weight value of the measured suspicious degree with the distance d from the branch topology level where the parameter j is located.
Step 3) for the parameters in the suspicious parameter set, a method for gradually correcting the parameters in the suspicious parameter set by a variable step size is provided, and the closed-loop correction is specifically as follows:
according to the suspicious line parameter set constructed in the step 2), starting from the line parameter with the maximum comprehensive suspicious degree, carrying out successive iterative correction on the line parameter by a step length changing strategy of 'starting size and ending size', and finally obtaining correct parameters. Accordingly, the k-th parameter correction amount is:
wherein, Δ p(k)Represents the correction step size of the k-th time, p0Representing the initial parameter value, p(k-1)Represents the line parameter value after the k-1 time of correction,
a fixed value set artificially.
The identified suspicious parameters are divided into two categories: one is the correct line parameters that are determined to be suspect because of the presence of bad data, which we call the misidentified line parameters; and the other part is the error parameter that we need to correct.
And recalculating the corresponding comprehensive suspicion degree after each correction of the suspicious line parameters, wherein if the suspicious line parameters simultaneously meet the following correction criteria, the suspicious line parameters are wrong line parameters, and are subjected to correction:
1) after parameter correction (increasing or decreasing parameter values), the comprehensive suspicion degree (or Lagrange multiplier) corresponding to the line is obviously and generally reduced compared with the original value.
2) After parameter correction (increasing or decreasing parameter values), the maximum comprehensive suspicion degree (or the maximum Lagrange multiplier) corresponding to all lines is obviously reduced compared with the original value.
For the suspicious branches which do not meet the correction criterion, the suspicious branches are regarded as the suspicious parameters of the false identification, the parameters are recovered to the original values, and the suspicious parameters are collected from the suspicious line parameter set PSAnd (5) removing.
And further provides a parameter correction completion criterion: for a certain line parameter in the suspicious branch set, after iterative correction, the corresponding Lagrange multiplier is smaller than a threshold value, and then the parameter is corrected; and switching to the next suspicious parameter for correction until the Lagrangians corresponding to all the lines in the suspicious branch set are smaller than the threshold value.
Based on the parameter correction strategy and criterion, the line parameter error identification and correction based on the comprehensive suspicion degree are realized by the following steps:
1) according to the state estimation result based on the Lagrange multiplier method, the measurement residual r of each measurement is calculated, the Lagrange multiplier lambda corresponding to each line parameter is calculated and respectively standardized, and the standardized measurement residual r is obtainedNAnd lagrange multiplier λN。
2) Based on rN，λNAnd a given threshold value cr，cλCalculating the comprehensive suspicion degree CS corresponding to each parameter, adding the line parameter with the comprehensive suspicion degree larger than 1 into the suspicion line parameter set PS。
3) For a set of suspicious line parameters PSAnd in the suspicious parameters, the variable step length iterative correction of the parameters is carried out one by one according to the sequence of the comprehensive suspicious degree from large to small, the correction criterion and the correction completion criterion are judged in the correction process, and the corrected error parameters and the suspicious parameters which do not meet the correction criterion are continuously removed.
4) When the parameter set of the suspicious line is set
And then, finishing the whole correction process to obtain the corrected parameter value.
Compared with the prior art, the invention has the following advantages:
first, compared with the conventional identification method, the parameter identification method of the invention can have higher identification accuracy under the condition of measurement errors and even poor data.
And secondly, the parameter correction precision is higher, the numerical stability is good, and compared with the conventional parameter estimation method for the augmented state estimation, the calculation method provided by the invention does not reduce the redundancy of the state estimation and has good stability.
Thirdly, the application prospect is wide: at present, the continuous and rapid expansion of the scale of the power grid in China, the periodic power grid maintenance and the frequent power grid transformation put forward high requirements on the maintenance work of the power grid parameters.
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FIG. 1 is a flow chart of the method of the present invention
Detailed Description
The present invention will be described in detail with reference to specific examples.
The invention relates to the technical field of power system state estimation, and provides a line parameter identification method based on parameter comprehensive suspicion degree. Comprising the following steps performed in sequence:
step 1) establishing a state estimation model based on a Lagrange multiplier method:
wherein x is an n-dimensional state variable, peFor p-dimensional network parameter error vector, r is z-h (x, p)e) Is m-dimensional measurement residual vector, W is measurement weight diagonal matrix, c (x, p)e) 0 is the zero injection equality constraint.
And calculating a Lagrange multiplier corresponding to the line parameter according to the state estimation result:
wherein HpAnd CpJacobi matrices with respect to line parameters are constrained for the measurement vector and the zero injection equation, respectively.
After the Lagrange multiplier and the measurement residual corresponding to the network parameters are obtained, the Lagrange multiplier and the measurement residual are further standardized to obtain a standardized Lagrange multiplier vector lambdaNAnd a normalized measurement residual vector rN。
The specific method for establishing the suspicious line parameter set in the step 2) comprises the following steps:
the suspicious measurement decision function and the suspicious parameter decision function are as follows:
respectively calculating suspicious measurement determination function values SM of each measurementi(rN,i) And each parameter suspicious parameter decision function value SPj(λN,j). Wherein, if the normalized Lagrange multiplier corresponding to the jth line parameter is less than the threshold value cλDetermination of function value K for suspicious parametersj(λN,j) 0, indicating that the parameter is normal; otherwise, Kj(λN,j) 1, this parameter is indicated as suspect. Wherein, crAnd cλAre all constants.
Further calculating line parameter comprehensive suspicion degree index CSj：
Wherein d represents the topological level distance between the line parameter j and each measurement, SMi(rN,i) Suspicious measurement decision function value, SP, representing the ith measurementj(λN,j) Determining a function value, ω, for a suspect parameter corresponding to parameter jd,jIs the weight value of the measured suspicious degree with the distance d from the branch topology level where the parameter j is located.
All line parameters with the suspicious degree larger than 1 are constructed to form a suspicious line parameter set PS. And according to the sequence of the comprehensive suspicion degree from large to small, the parameter set P of the suspicious line is processedSThe suspicious line parameters in (1) are sorted.
Step 3) carrying out step-length-variable successive iterative correction on the parameters in the suspicious line parameter set:
the k-th parameter correction quantity is as follows:
wherein, Δ p(k)Represents the correction step size of the k-th time, p0Representing the initial parameter value, p(k-1)Represents the line parameter value after the k-1 time of correction,
a fixed value set artificially.
And judging the correction criterion and the correction completion criterion in the correction process, and continuously eliminating the corrected parameters and the mistakenly identified suspicious parameters until the suspicious line parameter set
And then, finishing the whole correction process to obtain the corrected parameter value.
Examples of embodiment
The method is characterized in that complete measurement configuration is adopted in an IEEE-39 power transmission network system, measurement comprises root node three-phase voltage amplitude and each node three-phase injection power measurement, random errors are added on the basis of three-phase load flow calculation results, the standard deviation of the random errors is 1% of the measured value, and the state estimation convergence precision is 0.001. Get crAnd cλAll are 3, when d is 1, take ωd,j1 is ═ 1; when d is 2, take ωd,j0.3; when d is 3, take ωd,j0.1; when d is>When 4, take omegad,j＝0。
The calculated line parameters with the comprehensive suspicion degree greater than 1 in the system are shown in table 1:
TABLE 1. initial comprehensive suspicion degree ranking and Lagrange multiplier for each parameter
As can be seen from table 1, there are 8 suspicious parameters, and the suspicious line parameter set is:
PS＝{x7-8,x6-11,x21-26,r28-26,r6-7,r19-16,x10-11,x21-22are in accordance withAnd (4) sorting the parameter comprehensive suspicion degree, and correcting the suspicious line parameters one by one.
First to x7-8The correction is performed by considering the value of the reduction parameter, which is obtained from equation (7), and the step size is corrected
Then
Again, equality constrained state estimation and synthetic suspect computation were performed, with the results shown in table 2.
TABLE 2 Pair x7-8Comprehensive suspicion degree and Lagrange multiplier of each parameter after primary parameter correction
As can be seen from Table 2, for x7-8The comprehensive suspicion degree of each suspicion parameter after one-time correction is generally reduced, the correction criterion is met, and x is shown7-8Is a wrong parameter due to its lagrange multiplier λNIs still greater than cλIf the correction completion criterion is not satisfied, the correction is continued until the correction is completedThe comprehensive suspicion degree of each parameter and the Lagrange multiplier lambdaNAs shown in table 3.
TABLE 3 Pair x7-8Comprehensive suspicion degree and Lagrange multiplier of each parameter after 5 times of parameter correction
At this point, it can be seen that x7-8Corresponding lambdaNIs less than cλTherefore, criterion for completion of correctionSatisfy, accomplish pair x7-8Correction of (2) x7-8And removing the parameters from the suspicious line parameter set.
According to the method, all parameters in the suspicious parameter set are corrected, and parameters which meet the correction completion criterion and do not meet the correction criterion are continuously eliminated, so that all error parameters and correction values thereof are obtained, as shown in table 4.
TABLE 4 Branch parameters for all errors and their correction values and Lagrange multiplier
As can be seen from Table 4, x7-8，x21-16，r28-26，r19-16The line parameters are wrong, and the correction process has higher correction precision.
As can be seen from the above embodiments, the line parameter identification method based on parameter comprehensive suspicion degree provided by the present invention has the advantages of good convergence, high calculation accuracy, and the like.
The above-mentioned embodiments are merely illustrative of the implementation of the present invention, and are not intended to limit the present invention. Any insubstantial modifications, alterations and improvements, which come within the spirit and framework of the proposed method, are intended to be covered by the scope of the invention.
Claims (4)
1. A line parameter identification method based on parameter comprehensive suspicion degree is characterized by comprising the following steps:
1) constructing a state estimation model based on a Lagrange multiplier method;
2) establishing and constructing a suspicious parameter set based on parameter comprehensive suspicious degree indexes of the suspicious measurement judging function and the suspicious parameter judging function;
3) and for the parameters in the suspicious parameter set, a method of gradually correcting the variable step length is provided for closed-loop correction.
2. The method according to claim 1, wherein the lagrangian multiplier based state estimation model is constructed by:
wherein x is an n-dimensional state variable, peFor p-dimensional network parameter error vector, r is z-h (x, p)e) Is m-dimensional measurement residual vector, W is measurement weight diagonal matrix, c (x, p)e) 0 is the zero injection equality constraint.
And calculating a Lagrange multiplier corresponding to the line parameter according to the state estimation result:
wherein HpAnd CpJacobi matrices with respect to line parameters are constrained for the measurement vector and the zero injection equation, respectively.
After the Lagrange multiplier and the measurement residual corresponding to the network parameters are obtained, the Lagrange multiplier and the measurement residual are further standardized to obtain a standardized Lagrange multiplier vector lambdaNAnd a normalized measurement residual vector rN。
3. The method according to claim 1, wherein the method comprises the following steps:
firstly, defining a suspicious measurement judging function:
wherein, ifAbsolute value of normalized residual | r of ith measurementN,i| is less than threshold value crSuspicious measurement decision function SMi(rN,i) 0, indicating that the measurement is normal; otherwise, SMi(rN,i) 1, this measurement value is suspicious.
Next, a suspicious parameter decision function is defined:
wherein, if the normalized Lagrange multiplier corresponding to the jth line parameter is less than the threshold value cλDetermination of function value K for suspicious parametersj(λN,j) 0, indicating that the parameter is normal; otherwise, Kj(λN,j) 1, this parameter is indicated as suspect. Wherein, crAnd cλAre all constants.
Further defining line parameter comprehensive suspicion degree index CSj：
Wherein d represents the topological level distance between the line parameter j and each measurement, and the specific calculation method comprises the following steps: for the suspicious measurement on the branch where a certain error parameter j is located, d is 1, the suspicious measurement on the adjacent branch of the branch is located, d is 2, the suspicious measurement on the alternate branch of the branch is located, and d is 3; by analogy, the topological level distance of the measurement error caused by the parameter error of each line can be obtained. When a parameter on a certain branch has an error, the influence on the measurement residual error size on the branch with the topological level distance d > 3 is relatively insignificant, so that the suspicious measurement has a certain local aggregation characteristic around the branch with the parameter error, and generally only the parameter error is considered for the branch with the level distance d < ndThe influence of the measurements on the branches. Omegad,jIs the weight value of the measured suspicious degree with the distance d from the branch topology level where the parameter j is located.
4. The method according to claim 2, for performing closed-loop correction by using a method of gradually correcting parameters in a suspicious parameter set by a variable step size, wherein the method comprises:
according to the suspicious line parameter set constructed in the step 2), starting from the line parameter with the maximum comprehensive suspicious degree, carrying out successive iterative correction on the line parameter by a step length changing strategy of 'starting size and ending size', and finally obtaining correct parameters. Accordingly, the k-th parameter correction amount is:
wherein, Δ p(k)Represents the correction step size of the k-th time, p0Representing the initial parameter value, p(k-1)Represents the line parameter value after the k-1 time of correction,
a fixed value set artificially.
The identified suspicious parameters are divided into two categories: one is the correct line parameters that are determined to be suspect because of the presence of bad data, which we call the misidentified line parameters; and the other part is the error parameter that we need to correct.
And recalculating the corresponding comprehensive suspicion degree after each correction of the suspicious line parameters, wherein if the suspicious line parameters simultaneously meet the following correction criteria, the suspicious line parameters are wrong line parameters, and are subjected to correction:
1) after parameter correction (increasing or decreasing parameter values), the comprehensive suspicion degree (or Lagrange multiplier) corresponding to the line is obviously and generally reduced compared with the original value.
2) After parameter correction (increasing or decreasing parameter values), the maximum comprehensive suspicion degree (or the maximum Lagrange multiplier) corresponding to all lines is obviously reduced compared with the original value.
For the suspicious branches which do not meet the correction criterion, the suspicious branches are regarded as the suspicious parameters of the false identification, the parameters are recovered to the original values, and the suspicious parameters are collected from the suspicious line parameter set PSAnd (5) removing.
And further provides a parameter correction completion criterion: for a certain line parameter in the suspicious branch set, after iterative correction, the corresponding Lagrange multiplier is smaller than a threshold value, and then the parameter is corrected; and switching to the next suspicious parameter for correction until the Lagrangians corresponding to all the lines in the suspicious branch set are smaller than the threshold value.
Based on the parameter correction strategy and criterion, the line parameter error identification and correction based on the comprehensive suspicion degree are realized by the following steps:
1) according to the state estimation result based on the Lagrange multiplier method, the measurement residual r of each measurement is calculated, the Lagrange multiplier lambda corresponding to each line parameter is calculated and respectively standardized, and the standardized measurement residual r is obtainedNAnd lagrange multiplier λN。
2) Based on rN，λNAnd a given threshold value cr，cλCalculating the comprehensive suspicion degree CS corresponding to each parameter, adding the line parameter with the comprehensive suspicion degree larger than 1 into the suspicion line parameter set PS。
3) For a set of suspicious line parameters PSAnd in the suspicious parameters, the variable step length iterative correction of the parameters is carried out one by one according to the sequence of the comprehensive suspicious degree from large to small, the correction criterion and the correction completion criterion are judged in the correction process, and the corrected error parameters and the suspicious parameters which do not meet the correction criterion are continuously removed.
4) When the parameter set of the suspicious line is set
And then, finishing the whole correction process to obtain the corrected parameter value.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116935234A (en) * | 2023-09-18 | 2023-10-24 | 众芯汉创(江苏)科技有限公司 | Automatic classification and tree obstacle early warning system and method for power transmission line corridor point cloud data |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104836223A (en) * | 2014-11-14 | 2015-08-12 | 浙江大学 | Power grid parameter error and bad data coordinated identification and estimation method |
| CN109829246A (en) * | 2018-12-31 | 2019-05-31 | 国网辽宁省电力有限公司 | A kind of line parameter circuit value discrimination method based on the suspicious degree of parametric synthesis |
-
2019
- 2019-07-19 CN CN201910653656.6A patent/CN110633489A/en not_active Withdrawn
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104836223A (en) * | 2014-11-14 | 2015-08-12 | 浙江大学 | Power grid parameter error and bad data coordinated identification and estimation method |
| CN109829246A (en) * | 2018-12-31 | 2019-05-31 | 国网辽宁省电力有限公司 | A kind of line parameter circuit value discrimination method based on the suspicious degree of parametric synthesis |
Non-Patent Citations (2)
| Title |
|---|
| 曾兵等: "基于Karush-Kuhn-Tucker最优条件的电网可疑参数辨识与估计", 《电网技术》 * |
| 翟晶晶等: "基于可疑度的电网参数错误辨识与估计方法", 《电工电能新技术》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116935234A (en) * | 2023-09-18 | 2023-10-24 | 众芯汉创(江苏)科技有限公司 | Automatic classification and tree obstacle early warning system and method for power transmission line corridor point cloud data |
| CN116935234B (en) * | 2023-09-18 | 2023-12-26 | 众芯汉创(江苏)科技有限公司 | Automatic classification and tree obstacle early warning system and method for power transmission line corridor point cloud data |
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