CN112419093B - Load model characteristic parameter extraction method and device based on clustering algorithm - Google Patents

Load model characteristic parameter extraction method and device based on clustering algorithm Download PDF

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CN112419093B
CN112419093B CN202011350230.2A CN202011350230A CN112419093B CN 112419093 B CN112419093 B CN 112419093B CN 202011350230 A CN202011350230 A CN 202011350230A CN 112419093 B CN112419093 B CN 112419093B
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王颖
王卫
陈茜
赵瑞
陆超
吴沛萱
王海云
周运斌
张再驰
张绍峰
杨莉萍
张雨璇
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State Grid Corp of China SGCC
State Grid Beijing Electric Power Co Ltd
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Abstract

The invention discloses a load model characteristic parameter extraction method and device based on a clustering algorithm. The method comprises the following steps: measuring data of a plurality of load nodes of the power system are obtained; obtaining a model identification parameter corresponding to the measurement data according to the measurement data and a preset load model structure; acquiring a sample set according to the measurement data and the model identification parameters; calculating the fitting degree between any two samples in the sample set; obtaining the local density of each sample in the sample set according to the fitting degree; obtaining the distance offset of each sample in the sample set according to the fitting degree and the local density; and determining the clustering quantity and the central point sample according to the local density and the distance offset. The method can take the measured data and the model identification parameters as clustering characteristics, take the fitting degree as distance measurement, and determine the clustering quantity and the central point sample, thereby realizing the extraction of the characteristic parameters of the load model and meeting the requirement on the simplicity of the load model parameters in system simulation.

Description

Load model characteristic parameter extraction method and device based on clustering algorithm
Technical Field
The invention relates to the technical field of electric power system loads, in particular to a load model characteristic parameter extraction method and device based on a clustering algorithm, electronic equipment and a storage medium.
Background
In order to maintain stable operation of the power system, load analysis of the power system is crucial. At present, load analysis of a power system comprises tracking of time-varying property and distribution of a load of the power system, in order to overcome the defects that a large amount of statistical work needs to be completed by a statistical synthesis method and the dependence of a traditional general measurement and identification method on fault disturbance data in the related technology, noise-like data can be adopted for load analysis, and a large amount of noise-like data identification parameters cannot be effectively extracted, so that the requirement for simplicity of load model parameters in system simulation cannot be met.
Disclosure of Invention
The present invention is directed to solving, at least to some extent, one of the technical problems in the art described above. Therefore, an object of the present invention is to provide a load model feature parameter extraction method based on a clustering algorithm, which can obtain measurement data of a plurality of load nodes of an electrical power system, obtain a model identification parameter corresponding to the measurement data according to the measurement data and a preset load model structure, obtain a sample set according to the measurement data and the model identification parameter, calculate a degree of fitting between any two samples in the sample set, obtain a local density of each sample in the sample set according to the degree of fitting, obtain a distance offset of each sample in the sample set according to the degree of fitting and the local density, and determine a clustering number and a center point sample according to the local density and the distance offset. Therefore, the measured data and the model identification parameters can be used as clustering characteristics, the fitting degree is used as distance measurement, and the clustering quantity and the central point sample are determined, so that the extraction of the characteristic parameters of the load model can be realized, and the requirement on the simplicity and convenience of the load model parameters in system simulation can be met.
The second purpose of the invention is to provide a load model characteristic parameter extraction device based on a clustering algorithm.
A third object of the invention is to propose an electronic device.
A fourth object of the invention is to propose a computer-readable storage medium.
In order to achieve the above object, an embodiment of a first aspect of the present invention provides a load model feature parameter extraction method based on a clustering algorithm, including: measuring data of a plurality of load nodes of the power system are obtained; obtaining a model identification parameter corresponding to the measured data according to the measured data and a preset load model structure; obtaining a sample set according to the measured data and the model identification parameters; calculating the fitting degree between any two samples in the sample set; according to the fitting degree, obtaining the local density of each sample in the sample set; obtaining the distance offset of each sample in the sample set according to the fitting degree and the local density; and determining the clustering quantity and the central point sample according to the local density and the distance deviation.
According to the load model characteristic parameter extraction method based on the clustering algorithm, the measurement data of a plurality of load nodes of the power system can be obtained, the model identification parameters corresponding to the measurement data are obtained according to the measurement data and the preset load model structure, the sample set is obtained according to the measurement data and the model identification parameters, the fitting degree between any two samples in the sample set is calculated, the local density of each sample in the sample set is obtained according to the fitting degree, the distance deviation of each sample in the sample set is obtained according to the fitting degree and the local density, and the clustering number and the central point sample are determined according to the local density and the distance deviation. Therefore, the measured data and the model identification parameters can be used as clustering characteristics, the fitting degree is used as distance measurement, the clustering quantity and the central point sample are determined, the extraction of the characteristic parameters of the load model can be realized, and the requirement on the simplicity of the load model parameters in system simulation can be met.
In addition, the load model feature parameter extraction method based on the clustering algorithm provided by the above embodiment of the present invention may further have the following additional technical features:
in an embodiment of the present invention, the obtaining a model identification parameter corresponding to the metrology data according to the metrology data and a preset load model structure includes: calculating the deviation between the measured data and the preset load model structure output; and acquiring the minimum value of the deviation by adopting a differential evolution algorithm, and taking the model parameter of the preset load model structure corresponding to the minimum value of the deviation as the model identification parameter.
In an embodiment of the present invention, when the measurement data includes a voltage phasor, active power and reactive power, the calculating a fitting degree between any two samples in the sample set includes: the fit between any two samples in the set is calculated using the following formula:
Figure BDA0002801042110000021
wherein d is ij Is the degree of fit, P, between sample i and sample j i For the active power, Q, corresponding to sample i i For the reactive power, U, corresponding to sample i i For the voltage phasor corresponding to sample i, para j Is the model identification parameter, P, corresponding to sample j p (U i ，para j ) For the voltage phasor U corresponding to the sample i i Then, the model identification parameter para corresponding to the sample j is adopted j Calculated active power, Q p (U i ，para j ) For the voltage phasor U corresponding to the sample i i Then, the model identification parameter para corresponding to the sample j is adopted j And (3) calculating the reactive power, wherein mean (mean) is a vector mean function, and | | is a vector two-norm function.
In an embodiment of the present invention, the obtaining the distance offset of each sample in the sample set according to the fitting degree and the local density includes: sequencing each sample in the sample set from high to low according to the corresponding local density to obtain a sequenced sample sequence; respectively obtaining the fitting degrees between the ith sample and the 1 st sample in the sample sequence and between the 2 nd sample and the (i-1) th sample, wherein i is more than or equal to 2 and is less than or equal to N, and N is the number of the samples in the sample set; taking the maximum value of the fitting degree corresponding to the ith sample in the sample sequence as the distance offset corresponding to the ith sample; traversing the sample sequence to obtain N-1 distance offsets corresponding to the 2 nd sample to the Nth sample in the sample sequence; and taking the minimum value of the N-1 distance offsets as the distance offset corresponding to the 1 st sample in the sample sequence.
In an embodiment of the present invention, after determining the cluster number and the center point sample, the method further includes: and according to the fitting degree, classifying the non-central point samples in the sample set into the cluster where the central point sample closest to the non-central point sample is located.
In one embodiment of the invention, the measurement data comprises at least one of voltage amplitude, voltage phase angle, active power, and reactive power.
In one embodiment of the invention, the model identification parameter comprises at least one of a steady state reactance, a transient reactance, a d-axis transient time constant, and a static resistance.
In order to achieve the above object, an embodiment of a second aspect of the present invention provides a load model feature parameter extraction device based on a clustering algorithm, including: the system comprises a first acquisition module, a second acquisition module and a control module, wherein the first acquisition module is used for acquiring measurement data of a plurality of load nodes of the power system; the second acquisition module is used for acquiring model identification parameters corresponding to the measured data according to the measured data and a preset load model structure; a third obtaining module, configured to obtain a sample set according to the metrology data and the model identification parameter; the calculation module is used for calculating the fitting degree between any two samples in the sample set; the fourth obtaining module is used for obtaining the local density of each sample in the sample set according to the fitting degree; a fifth obtaining module, configured to obtain a distance offset of each sample in the sample set according to the fitting degree and the local density; and the determining module is used for determining the clustering quantity and the central point sample according to the local density and the distance deviation.
The load model characteristic parameter extraction device based on the clustering algorithm can acquire measurement data of a plurality of load nodes of an electric power system, acquire model identification parameters corresponding to the measurement data according to the measurement data and a preset load model structure, acquire a sample set according to the measurement data and the model identification parameters, calculate the fitting degree between any two samples in the sample set, acquire the local density of each sample in the sample set according to the fitting degree, acquire the distance deviation of each sample in the sample set according to the fitting degree and the local density, and determine the clustering number and the central point samples according to the local density and the distance deviation. Therefore, the measured data and the model identification parameters can be used as clustering characteristics, the fitting degree is used as distance measurement, the clustering quantity and the central point sample are determined, the extraction of the characteristic parameters of the load model can be realized, and the requirement on the simplicity of the load model parameters in system simulation can be met.
In addition, the load model characteristic parameter extraction device based on the clustering algorithm provided by the above embodiment of the present invention may also have the following additional technical features:
in an embodiment of the present invention, the second obtaining module is specifically configured to: calculating the deviation between the measured data and the preset load model structure output; and acquiring the minimum value of the deviation by adopting a differential evolution algorithm, and taking the model parameter of the preset load model structure corresponding to the minimum value of the deviation as the model identification parameter.
In an embodiment of the present invention, when the measurement data includes a voltage phasor, active power, and reactive power, the calculation module is specifically configured to: the fit between any two samples in the set is calculated using the following formula:
Figure BDA0002801042110000041
wherein d is ij Is the degree of fit, P, between sample i and sample j i For the active power, Q, corresponding to sample i i For the reactive power, U, corresponding to sample i i For the voltage phasor corresponding to sample i, para j For the model identification parameter, P, corresponding to sample j p (U i ，para j ) For the voltage phasor U corresponding to the sample i i Then, the model identification parameter para corresponding to the sample j is adopted j Calculated active power, Q p (U i ，para j ) For the voltage phasor U corresponding to the sample i i Then, the model identification parameter para corresponding to the sample j is adopted j And (3) calculating the reactive power, wherein mean (mean) is a vector mean function, and | | is a vector two-norm function.
In an embodiment of the present invention, the fifth obtaining module is specifically configured to: sequencing each sample in the sample set from high to low according to the corresponding local density to obtain a sequenced sample sequence; respectively obtaining the fitting degrees between the ith sample and the 1 st sample in the sample sequence and between the 2 nd sample and the (i-1) th sample, wherein i is more than or equal to 2 and is less than or equal to N, and N is the number of the samples in the sample set; taking the maximum value of the fitting degree corresponding to the ith sample in the sample sequence as the distance offset corresponding to the ith sample; traversing the sample sequence to obtain N-1 distance offsets corresponding to the 2 nd sample to the Nth sample in the sample sequence; and taking the minimum value in the N-1 distance offsets as the distance offset corresponding to the 1 st sample in the sample sequence.
In an embodiment of the present invention, the determining module is further configured to: after the cluster number and the central point samples are determined, classifying the non-central point samples in the sample set into the cluster where the central point sample closest to the non-central point sample is located according to the fitting degree.
In one embodiment of the present invention, the measured data includes at least one of voltage amplitude, voltage phase angle, active power and reactive power.
In one embodiment of the invention, the model identification parameter comprises at least one of a steady state reactance, a transient reactance, a d-axis transient time constant, and a static resistance.
In order to achieve the above object, a third embodiment of the present invention provides an electronic device, which includes a memory, a processor; the processor runs a program corresponding to the executable program code by reading the executable program code stored in the memory, so as to implement the load model feature parameter extraction method based on the clustering algorithm described in the embodiment of the first aspect of the present invention.
According to the electronic equipment provided by the embodiment of the invention, the processor executes the computer program stored on the memory, so that the measurement data of a plurality of load nodes of the power system can be obtained, the model identification parameters corresponding to the measurement data are obtained according to the measurement data and the preset load model structure, the sample set is obtained according to the measurement data and the model identification parameters, the fitting degree between any two samples in the sample set is calculated, the local density of each sample in the sample set is obtained according to the fitting degree, the distance offset of each sample in the sample set is obtained according to the fitting degree and the local density, and the clustering number and the center point sample are determined according to the local density and the distance offset. Therefore, the measured data and the model identification parameters can be used as clustering characteristics, the fitting degree is used as distance measurement, the clustering quantity and the central point sample are determined, the extraction of the characteristic parameters of the load model can be realized, and the requirement on the simplicity of the load model parameters in system simulation can be met.
To achieve the above object, a fourth aspect of the present invention provides a computer-readable storage medium, where a computer program is stored, and when the computer program is executed by a processor, the computer program implements the method for extracting load model feature parameters based on a clustering algorithm according to the first aspect of the present invention.
The computer-readable storage medium of the embodiment of the invention can acquire measurement data of a plurality of load nodes of a power system by storing a computer program and executing the measurement data by a processor, acquire a model identification parameter corresponding to the measurement data according to the measurement data and a preset load model structure, acquire a sample set according to the measurement data and the model identification parameter, calculate the fitting degree between any two samples in the sample set, acquire the local density of each sample in the sample set according to the fitting degree, acquire the distance offset of each sample in the sample set according to the fitting degree and the local density, and determine the clustering number and the center point sample according to the local density and the distance offset. Therefore, the measured data and the model identification parameters can be used as clustering characteristics, the fitting degree is used as distance measurement, the clustering quantity and the central point sample are determined, the extraction of the characteristic parameters of the load model can be realized, and the requirement on the simplicity of the load model parameters in system simulation can be met.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
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The foregoing and/or additional aspects and advantages of the present invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
FIG. 1 is a flowchart of a load model feature parameter extraction method based on a clustering algorithm according to an embodiment of the present invention;
FIG. 2 is a decision diagram of clustering in a load model feature parameter extraction method based on a clustering algorithm according to an embodiment of the present invention;
FIG. 3 is a flowchart of obtaining a distance offset of each sample in a load model feature parameter extraction method based on a clustering algorithm according to an embodiment of the present invention;
FIG. 4 is a block diagram of a load model feature parameter extraction device based on a clustering algorithm according to an embodiment of the present invention; and
fig. 5 is a block schematic diagram of an electronic device according to an embodiment of the invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are illustrative and intended to be illustrative of the invention and are not to be construed as limiting the invention.
The following describes a load model feature parameter extraction method based on a clustering algorithm, an apparatus, an electronic device and a computer-readable storage medium according to an embodiment of the present invention with reference to the drawings.
Fig. 1 is a flowchart of a load model feature parameter extraction method based on a clustering algorithm according to an embodiment of the present invention.
As shown in fig. 1, the method for extracting characteristic parameters of a load model based on a clustering algorithm in an embodiment of the present invention includes:
s101, measurement data of a plurality of load nodes of the power system are obtained.
In an embodiment of the present invention, the measurement data may be one or more types, and the measurement data of each load node in different time periods may be obtained, for example, the measurement data of one load node in 8:00, 12:00, 16:00, and 20:00 may be obtained, and each time period may obtain one set of measurement data, so that each load node may correspond to multiple sets of measurement data. Optionally, the measurement data includes at least one of a voltage amplitude U', a voltage phase angle θ, an active power P, and a reactive power Q.
Optionally, the measurement data of the plurality of load nodes of the power system may be obtained by a phasor measurement unit of the power system.
And S102, obtaining model identification parameters corresponding to the measured data according to the measured data and a preset load model structure.
In the embodiment of the present invention, the preset load model structure may be set according to actual conditions, for example, the preset load model structure may be a load model structure of a constant impedance parallel induction motor.
In an embodiment of the invention, the model identification parameter may be one or more types, and each set of measurement data may correspond to a set of model identification parameters. Optionally, the model identification parameter includes a steady-state reactance X, a transient reactance X ', and a d-axis transient time constant T' d0 And in static resistance RAt least one.
Optionally, the obtaining of the model identification parameter corresponding to the measured data according to the measured data and the preset load model structure may include calculating a deviation between the measured data and an output of the preset load model structure, obtaining a minimum value of the deviation by using a Differential Evolution Algorithm (DE), and using the model parameter of the preset load model structure corresponding to the minimum value of the deviation as the model identification parameter.
S103, acquiring a sample set according to the measured data and the model identification parameters.
In the embodiment of the invention, each load node can correspond to a plurality of groups of measurement data, each group of measurement data can correspond to a group of model identification parameters, so that each group of measurement data and the corresponding model identification parameters thereof can be used as a sample, a plurality of samples corresponding to each load node can be obtained, and the plurality of samples form a sample set. It is to be appreciated that multiple load nodes may correspond to multiple sample sets.
For example, if the number of load nodes is 1, the measurement data corresponding to the time period m includes the voltage amplitude U m ', phase angle of voltage theta m Active power P m Reactive power Q m The model identification parameter corresponding to the measured data is para m Then the sample corresponding to the time period m is x m ＝{U m ’，θ m ，P m ，Q m ，para m In analogy, N samples x corresponding to the load node in N time periods may be obtained 1 、x 2 、x 3 To x N Sample set S ═ x 1 ，x 2 ，x 3 ……x N }。
And S104, calculating the fitting degree between any two samples in the sample set.
In embodiments of the present invention, a degree of fit between any two samples in a sample set may be calculated. That is, a degree of fit may be calculated between each sample in the sample set and any other sample in the sample set. For example, if the sample set S includes N samples 1, 2, 3 through N, a degree of fit between sample i and the other N-1 samples in the sample set S may be calculated.
Optionally, when the measurement data includes voltage phasor U, active power P, and reactive power Q, calculating a fitting degree between any two samples in the sample set may include:
the fit between any two samples in the sample set is calculated using the following formula:
Figure BDA0002801042110000071
wherein d is ij Is the degree of fit, P, between sample i and sample j i For the active power, Q, corresponding to sample i i For the reactive power, U, corresponding to sample i i For the voltage phasor corresponding to sample i, para j Is the model identification parameter, P, corresponding to sample j p (U i ，para j ) For the voltage phasor U corresponding to the sample i i Then, the model identification parameter para corresponding to the sample j is adopted j Calculated active power, Q p (U i ，para j ) For the voltage phasor U corresponding to the sample i i Then, the model identification parameter para corresponding to the sample j is adopted j And (3) calculating the reactive power, wherein mean (mean) is a vector mean function, and | | is a vector two-norm function.
From the above formula, the degree of fitting d ij Has a value range of [ - ∞, 1 [ ]]When d is 1, the output of the preset load model structure and the measured data can be completely fitted.
And S105, acquiring the local density of each sample in the sample set according to the fitting degree.
In the embodiment of the invention, the fitting degree between each sample in the sample set and any other sample in the sample set can be calculated, and the sum of all the fitting degrees corresponding to each sample is used as the local density of each sample. For example, if the sample set S includes N samples 1, 2, 3 through N, d ij Is the degree of fit between sample i and sample j, then the local density ρ of sample j j ＝∑d ij ，1≤i≤N，1≤j≤N，i≠j。
And S106, acquiring the distance deviation of each sample in the sample set according to the fitting degree and the local density.
In the embodiment of the invention, the distance offset of each sample in the sample set can be obtained according to the fitting degree and the local density, and the influence of the fitting degree and the local density on the distance offset of the samples can be considered.
And S107, determining the clustering number and the central point sample according to the local density and the distance offset.
In the embodiment of the invention, the clustering quantity and the central point sample can be determined according to the local density and the distance deviation, and the model identification parameter corresponding to the central point sample can be used as the characteristic parameter of the load model, so that the extraction of the characteristic parameter of the load model is realized.
Optionally, determining the clustering number and the central point sample according to the local density and the distance offset may include obtaining a clustering decision graph by using the local density as an abscissa and using the distance offset as an ordinate, selecting a sample from the clustering decision graph, where the local density is greater than a preset density threshold and the distance offset is less than a preset distance offset threshold, as the central point sample, and determining the clustering number according to the number of the central point samples. The preset density threshold and the preset distance offset threshold can be set according to actual conditions, and are not limited too much.
For example, the local density ρ is used as the abscissa and the distance offset δ is used as the ordinate to obtain the decision graph of the cluster shown in fig. 2, samples having local densities greater than the preset density threshold and distance offsets smaller than the preset distance offset threshold may be selected from the decision graph of the cluster shown in fig. 2 as the central point samples C, as shown in fig. 2, the number of the central point samples C selected in this embodiment is 2, and the central point samples C are respectively C 1 、C 2 If the cluster number k is 2, each central point sample can be used as a cluster category, and the central point sample C can be used 1 、C 2 Respectively as a cluster category.
For example, cluster category C 1 、C 2 The corresponding model identification parameters may be as shown in table 1.
TABLE 1 clustering Categories C 1 、C 2 Corresponding modelIdentifying parameters
Figure BDA0002801042110000081
As shown in table 1, cluster category C 1 Corresponding recognition time of 08:00, cluster type C 2 Corresponding recognition time of 20:00, clustering class C 1 、C 2 The corresponding model identification parameters have differences, which can reflect the differences of the identification parameters of the working time model and the non-working time model.
In summary, according to the load model characteristic parameter extraction method based on the clustering algorithm in the embodiment of the present invention, measurement data of a plurality of load nodes of an electric power system can be obtained, a model identification parameter corresponding to the measurement data is obtained according to the measurement data and a preset load model structure, a sample set is obtained according to the measurement data and the model identification parameter, a degree of fitting between any two samples in the sample set is calculated, a local density of each sample in the sample set is obtained according to the degree of fitting, a distance offset of each sample in the sample set is obtained according to the degree of fitting and the local density, and a clustering number and a center point sample are determined according to the local density and the distance offset. Therefore, the measured data and the model identification parameters can be used as clustering characteristics, the fitting degree is used as distance measurement, the clustering quantity and the central point sample are determined, the extraction of the characteristic parameters of the load model can be realized, and the requirement on the simplicity of the load model parameters in system simulation can be met.
Based on any of the above embodiments, as shown in fig. 3, the obtaining the distance offset of each sample in the sample set in step S106 according to the fitting degree and the local density may include:
s201, sequencing each sample in the sample set from high to low according to the corresponding local density, and obtaining a sequenced sample sequence.
In the embodiment of the present invention, each sample in the sample set may correspond to one local density, and then each sample in the sample set may be sorted from high to low according to the corresponding local density, so as to obtain a sorted sample sequence. For example, assume that the ordered sample sequence Q ═ x 1 ，x 2 ，x 3 ……x N H, N samples x 1 、x 2 、x 3 To x N The corresponding local densities decrease in turn.
S202, fitting degrees between the ith sample and the 1 st sample and between the 2 nd sample and the (i-1) th sample in the sample sequence are respectively obtained, wherein i is more than or equal to 2 and is less than or equal to N, and N is the number of samples in the sample set.
In the embodiment of the invention, the fitting degrees between the ith sample and the 1 st sample in the sample sequence and between the 2 nd sample and the (i-1) th sample can be respectively obtained, wherein i is more than or equal to 2 and less than or equal to N, and N is the number of samples in a sample set. That is, the degree of fitting between the ith sample and the i-1 sample located before it in the sample sequence is obtained, respectively.
For example, if the sample sequence Q ═ { x ═ x 1 ，x 2 ，x 3 ，x 4 Get sample x 2 And sample x 1 Fitting degree between them, sample x can be obtained 3 Respectively with sample x 1 Sample x 2 Fitting degree between them, sample x can be obtained 4 Respectively with sample x 1 Sample x 2 Sample x 3 The degree of fit between.
S203, the maximum value of the fitting degree corresponding to the ith sample in the sample sequence is used as the distance offset corresponding to the ith sample.
In the embodiment of the present invention, the fitting degrees between the ith sample and the i-1 samples located before the ith sample in the sample sequence can be respectively obtained, so that the ith sample can correspond to the i-1 fitting degrees, and the maximum value of the i-1 fitting degrees can be used as the distance offset corresponding to the ith sample.
For example, if the sample sequence Q ═ { x ═ x 1 ，x 2 ，x 3 ，x 4 Get sample x 2 And sample x 1 The degree of fit between, sample x can be determined 2 And sample x 1 The degree of fit between them as sample x 2 Corresponding distance offsets. Samples x may also be taken 3 Respectively with sample x 1 Sample x 2 Degree of fit between, if sample x 3 And sample x 1 Greater than sample x 3 Sample x 2 The degree of fit between, then sample x can be determined 3 And sample x 1 The degree of fit between as sample x 3 Corresponding distance offsets.
S204, traversing the sample sequence, and acquiring N-1 distance offsets corresponding to the 2 nd sample to the Nth sample in the sample sequence.
It can be understood that, if the sample sequence includes N samples, the sample sequence may be traversed, and the steps S202 and S203 are respectively performed on the 2 nd sample to the nth sample in the sample sequence, so that N-1 distance offsets corresponding to the 2 nd sample to the nth sample may be obtained.
S205, taking the minimum value of the N-1 distance offsets as the distance offset corresponding to the 1 st sample in the sample sequence.
Therefore, the method can be used for obtaining the distance offset of each sample in the sample set by sequencing each sample in the sample set from high to low according to the corresponding local density to obtain a sequenced sample sequence, then obtaining the fitting degree between the ith sample and the i-1 sample before the ith sample in the sample sequence, taking the maximum value of the fitting degree corresponding to the ith sample in the sample sequence as the distance offset corresponding to the ith sample, and taking the minimum value of the N-1 distance offsets as the distance offset corresponding to the 1 st sample in the sample sequence.
Optionally, after determining the number of clusters and the center point samples in step S107, the non-center point samples in the sample set may be classified into the cluster where the center point sample closest to the non-center point sample is located according to the degree of fitting, so as to complete the clustering process. That is, the fit is used as a distance measure to classify the non-centroid samples, e.g., if there is a centroid sample C 1 、C 2 Obtaining a non-center sample A and a center sample C in the sample set 1 Degree of fit d between 1 And non-center sample A and center sample C 2 Degree of fit d between 2 Degree of fitting d 1 Greater than or equal to the degree of fit d 2 This indicates that the non-center sample A is at a distance from the center sample C 1 Closer, non-central spots can be spottedThis A is classified into the center point sample C 1 The cluster in which it is located.
It can be understood that the clusters where the plurality of center point samples are located can be obtained according to the clustering result, and different clusters can correspond to different load model characteristic parameters.
Fig. 4 is a schematic block diagram of a load model feature parameter extraction device based on a clustering algorithm according to an embodiment of the present invention.
As shown in fig. 4, the load model feature parameter extraction apparatus 100 based on a clustering algorithm according to an embodiment of the present invention includes: the device comprises a first obtaining module 11, a second obtaining module 12, a third obtaining module 13, a calculating module 14, a fourth obtaining module 15, a fifth obtaining module 16 and a determining module 17.
The first obtaining module 11 is configured to obtain measurement data of a plurality of load nodes of the power system;
the second obtaining module 12 is configured to obtain a model identification parameter corresponding to the measured data according to the measured data and a preset load model structure;
the third obtaining module 13 is configured to obtain a sample set according to the metrology data and the model identification parameter;
the calculation module 14 is used for calculating the fitting degree between any two samples in the sample set;
the fourth obtaining module 15 is configured to obtain a local density of each sample in the sample set according to the fitting degree;
the fifth obtaining module 16 is configured to obtain a distance offset of each sample in the sample set according to the fitting degree and the local density;
the determining module 17 is configured to determine a cluster number and a center point sample according to the local density and the distance offset.
In an embodiment of the present invention, the second obtaining module 12 is specifically configured to: calculating the deviation between the measured data and the preset load model structure output; and acquiring the minimum value of the deviation by adopting a differential evolution algorithm, and taking the model parameter of the preset load model structure corresponding to the minimum value of the deviation as the model identification parameter.
In an embodiment of the present invention, when the measurement data includes a voltage phasor, an active power, and a reactive power, the calculating module 14 is specifically configured to: the fit between any two samples in the set is calculated using the following formula:
Figure BDA0002801042110000111
wherein d is ij Is the degree of fit, P, between sample i and sample j i Active power, Q, for sample i i For the reactive power, U, corresponding to sample i i For the voltage phasor corresponding to sample i, para j Is the model identification parameter, P, corresponding to sample j p (U i ，para j ) For the voltage phasor U corresponding to the sample i i Then, the model identification parameter para corresponding to the sample j is adopted j Calculated active power, Q p (U i ，para j ) For the voltage phasor U corresponding to the sample i i Then, the model identification parameter para corresponding to the sample j is adopted j And (3) calculating the reactive power, wherein mean (mean) is a vector mean function, and | | is a vector two-norm function.
In an embodiment of the present invention, the fifth obtaining module 16 is specifically configured to: sequencing each sample in the sample set from high to low according to the corresponding local density to obtain a sequenced sample sequence; respectively obtaining the fitting degrees between the ith sample and the 1 st sample in the sample sequence and between the 2 nd sample and the (i-1) th sample, wherein i is more than or equal to 2 and is less than or equal to N, and N is the number of the samples in the sample set; taking the maximum value of the fitting degree corresponding to the ith sample in the sample sequence as the distance offset corresponding to the ith sample; traversing the sample sequence to obtain N-1 distance offsets corresponding to the 2 nd sample to the Nth sample in the sample sequence; and taking the minimum value of the N-1 distance offsets as the distance offset corresponding to the 1 st sample in the sample sequence.
In an embodiment of the present invention, the determining module 17 is further configured to: after the cluster number and the central point samples are determined, classifying the non-central point samples in the sample set into the cluster where the central point sample closest to the non-central point sample is located according to the fitting degree.
In one embodiment of the invention, the measurement data comprises at least one of voltage amplitude, voltage phase angle, active power, and reactive power.
In one embodiment of the invention, the model identification parameter comprises at least one of a steady state reactance, a transient reactance, a d-axis transient time constant, and a static resistance.
It should be noted that, in the load model feature parameter extraction device based on the clustering algorithm according to the embodiment of the present invention, please refer to the details disclosed in the load model feature parameter extraction method based on the clustering algorithm according to the above embodiment of the present invention, which are not described herein again.
In summary, the load model characteristic parameter extraction device based on the clustering algorithm according to the embodiment of the present invention can obtain measurement data of a plurality of load nodes of an electric power system, obtain a model identification parameter corresponding to the measurement data according to the measurement data and a preset load model structure, obtain a sample set according to the measurement data and the model identification parameter, calculate a degree of fitting between any two samples in the sample set, obtain a local density of each sample in the sample set according to the degree of fitting, obtain a distance offset of each sample in the sample set according to the degree of fitting and the local density, and determine a clustering number and a center point sample according to the local density and the distance offset. Therefore, the measured data and the model identification parameters can be used as clustering characteristics, the fitting degree is used as distance measurement, the clustering quantity and the central point sample are determined, the extraction of the characteristic parameters of the load model can be realized, and the requirement on the simplicity of the load model parameters in system simulation can be met.
In order to implement the above embodiment, the present invention further proposes an electronic device 200, as shown in fig. 5, the electronic device 200 includes a memory 21 and a processor 22. Wherein, the processor 22 runs a program corresponding to the executable program code by reading the executable program code stored in the memory 21, so as to implement the above-mentioned load model characteristic parameter extraction method based on the clustering algorithm.
The electronic device of the embodiment of the invention can acquire the measurement data of a plurality of load nodes of the power system by executing the computer program stored in the memory through the processor, acquire the model identification parameters corresponding to the measurement data according to the measurement data and the preset load model structure, acquire the sample set according to the measurement data and the model identification parameters, calculate the fitting degree between any two samples in the sample set, acquire the local density of each sample in the sample set according to the fitting degree, acquire the distance deviation of each sample in the sample set according to the fitting degree and the local density, and determine the clustering number and the central point sample according to the local density and the distance deviation. Therefore, the measured data and the model identification parameters can be used as clustering characteristics, the fitting degree is used as distance measurement, and the clustering quantity and the central point sample are determined, so that the extraction of the characteristic parameters of the load model can be realized, and the requirement on the simplicity and convenience of the load model parameters in system simulation can be met.
In order to implement the above embodiments, the present invention further provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the computer program implements the above load model feature parameter extraction method based on a clustering algorithm.
The computer-readable storage medium of the embodiment of the invention can acquire measurement data of a plurality of load nodes of a power system by storing a computer program and executing the measurement data by a processor, acquire a model identification parameter corresponding to the measurement data according to the measurement data and a preset load model structure, acquire a sample set according to the measurement data and the model identification parameter, calculate the fitting degree between any two samples in the sample set, acquire the local density of each sample in the sample set according to the fitting degree, acquire the distance offset of each sample in the sample set according to the fitting degree and the local density, and determine the clustering number and the center point sample according to the local density and the distance offset. Therefore, the measured data and the model identification parameters can be used as clustering characteristics, the fitting degree is used as distance measurement, and the clustering quantity and the central point sample are determined, so that the extraction of the characteristic parameters of the load model can be realized, and the requirement on the simplicity and convenience of the load model parameters in system simulation can be met.
In the description of the present invention, it is to be understood that the terms "central," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," "counterclockwise," "axial," "radial," "circumferential," and the like are used in the orientations and positional relationships indicated in the drawings for convenience in describing the invention and to simplify the description, and are not intended to indicate or imply that the referenced device or element must have a particular orientation, be constructed and operated in a particular orientation, and are not to be considered limiting of the invention.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the present invention, "a plurality" means two or more unless specifically defined otherwise.
In the present invention, unless otherwise expressly stated or limited, the terms "mounted," "connected," "secured," and the like are to be construed broadly and can, for example, be fixedly connected, detachably connected, or integrally formed; can be mechanically or electrically connected; they may be directly connected or indirectly connected through intervening media, or may be connected through the use of two elements or the interaction of two elements. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations.
In the present invention, unless otherwise expressly stated or limited, the first feature "on" or "under" the second feature may be directly contacting the first and second features or indirectly contacting the first and second features through an intermediate. Also, a first feature "on," "over," and "above" a second feature may be directly or diagonally above the second feature, or may simply indicate that the first feature is at a higher level than the second feature. A first feature being "under," "below," and "beneath" a second feature may be directly under or obliquely under the first feature, or may simply mean that the first feature is at a lesser elevation than the second feature.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made to the above embodiments by those of ordinary skill in the art within the scope of the present invention.

Claims (9)

1. A load model characteristic parameter extraction method based on a clustering algorithm is characterized by comprising the following steps:
measuring data of a plurality of load nodes of the power system are obtained;
obtaining a model identification parameter corresponding to the measured data according to the measured data and a preset load model structure;
obtaining a sample set according to the measured data and the model identification parameters;
calculating the fitting degree between any two samples in the sample set;
according to the fitting degree, obtaining the local density of each sample in the sample set;
obtaining the distance offset of each sample in the sample set according to the fitting degree and the local density;
determining the clustering number and the central point sample according to the local density and the distance deviation;
wherein the obtaining of the distance offset of each sample in the sample set according to the fitting degree and the local density comprises:
sequencing each sample in the sample set from high to low according to the corresponding local density to obtain a sequenced sample sequence;
respectively obtaining the fitting degrees between the ith sample and the 1 st sample in the sample sequence and between the 2 nd sample and the (i-1) th sample, wherein i is more than or equal to 2 and is less than or equal to N, and N is the number of the samples in the sample set;
taking the maximum value of the fitting degree corresponding to the ith sample in the sample sequence as the distance offset corresponding to the ith sample;
traversing the sample sequence to obtain N-1 distance offsets corresponding to the 2 nd sample to the Nth sample in the sample sequence;
and taking the minimum value of the N-1 distance offsets as the distance offset corresponding to the 1 st sample in the sample sequence.
2. The extraction method as claimed in claim 1, wherein the obtaining of the model identification parameter corresponding to the metrology data according to the metrology data and a predetermined load model structure comprises:
calculating the deviation between the measured data and the preset load model structure output;
and acquiring the minimum value of the deviation by adopting a differential evolution algorithm, and taking the model parameter of the preset load model structure corresponding to the minimum value of the deviation as the model identification parameter.
3. The extraction method according to claim 1, wherein the calculating a degree of fit between any two samples in the set of samples when the measured data includes voltage phasor, active power, and reactive power comprises:
the fit between any two samples in the sample set is calculated using the following formula:
Figure FDA0003758213110000021
wherein, d ij Is the degree of fit, P, between sample i and sample j i Active power, Q, for sample i i For the reactive power, U, corresponding to sample i i For the voltage phasor corresponding to sample i, para j Is the model identification parameter, P, corresponding to sample j p (U i ，para j ) For the voltage phasor U corresponding to the sample i i Then, the model identification parameter para corresponding to the sample j is adopted j Calculated active power, Q p (U i ，para j ) For the voltage phasor U corresponding to the sample i i Then, the model identification parameter para corresponding to the sample j is adopted j And (3) calculating the reactive power, wherein mean (mean) is a vector mean function, and | | is a vector two-norm function.
4. The extraction method according to claim 1, wherein after determining the cluster number and the center point sample, the method further comprises:
and classifying the non-central point samples in the sample set into the cluster where the central point sample closest to the non-central point sample is located according to the fitting degree.
5. The extraction method according to any one of claims 1 to 4, wherein the measurement data comprises at least one of voltage amplitude, voltage phase angle, active power, and reactive power.
6. The extraction method according to any one of claims 1 to 4, wherein the model identification parameter includes at least one of a steady-state reactance, a transient reactance, a d-axis transient time constant, and a static resistance.
7. A load model characteristic parameter extraction device based on clustering algorithm is characterized by comprising:
the system comprises a first acquisition module, a second acquisition module and a control module, wherein the first acquisition module is used for acquiring measurement data of a plurality of load nodes of the power system;
the second acquisition module is used for acquiring model identification parameters corresponding to the measured data according to the measured data and a preset load model structure;
a third obtaining module, configured to obtain a sample set according to the metrology data and the model identification parameter;
the calculation module is used for calculating the fitting degree between any two samples in the sample set;
the fourth obtaining module is used for obtaining the local density of each sample in the sample set according to the fitting degree;
a fifth obtaining module, configured to obtain a distance offset of each sample in the sample set according to the fitting degree and the local density;
the determining module is used for determining the clustering quantity and the central point sample according to the local density and the distance deviation;
wherein the fifth obtaining module is further configured to:
sequencing each sample in the sample set from high to low according to the corresponding local density to obtain a sequenced sample sequence;
respectively obtaining the fitting degrees between the ith sample and the 1 st sample in the sample sequence and between the 2 nd sample and the (i-1) th sample, wherein i is more than or equal to 2 and is less than or equal to N, and N is the number of the samples in the sample set;
taking the maximum value of the fitting degree corresponding to the ith sample in the sample sequence as the distance offset corresponding to the ith sample;
traversing the sample sequence to obtain N-1 distance offsets corresponding to the 2 nd sample to the Nth sample in the sample sequence;
and taking the minimum value in the N-1 distance offsets as the distance offset corresponding to the 1 st sample in the sample sequence.
8. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor executes the program to implement the load model feature parameter extraction method based on clustering algorithm according to any one of claims 1 to 6.
9. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out a load model feature parameter extraction method based on a clustering algorithm according to any one of claims 1 to 6.
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