CN117057227A - Equivalent modeling method and related device for photovoltaic power station - Google Patents
Equivalent modeling method and related device for photovoltaic power station Download PDFInfo
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Abstract
The invention belongs to the technical field of equivalent modeling methods, and provides a photovoltaic power station equivalent modeling method and a related device, which aim to solve the technical problem of insufficient parameter setting accuracy in the existing photovoltaic power station equivalent method. Compared with the existing modeling method, the method has the advantages that the number of equivalent units is not required to be input, the problem that the accuracy of setting the number of equivalent units in parameter setting is insufficient is avoided, the probability of deviation in simulation analysis is greatly reduced, and the accuracy of a modeling result is effectively improved.
Description
Technical Field
The invention belongs to the technical field of equivalent modeling methods, and relates to a photovoltaic power station equivalent modeling method and a related device.
Background
In recent years, under the introduction of a two-carbon target, the duty ratio of renewable energy sources typified by photovoltaic in an electric power system is gradually increasing. The photovoltaic power generation is used as an important renewable energy source, has an important influence on the stable operation of the power system, and brings a series of challenges in the aspect of safety and stability for a novel power system taking new energy as a main body. At present, the capacity of the photovoltaic inverter is 1MW-3MW, more than hundred inverters are usually arranged in only one station, if the detailed electromechanical model of each inverter is considered during power grid operation mode simulation and fault simulation, the calculated amount is too large, calculation resources are wasted, the simulation result lacks timeliness, the safety and stability analysis of a power system is affected, and therefore an equivalent model of the photovoltaic power station is required to be built on the basis of ensuring the simulation accuracy, so that the whole-grid simulation calculation complexity is reduced.
In the existing photovoltaic power station equivalent method, most of the photovoltaic power station equivalent methods need to be preferentially determined and clustered, and parameter identification is not considered for the photovoltaic inverter low voltage ride through control parameters, so that insufficient parameter setting accuracy can be caused.
Disclosure of Invention
The invention provides a photovoltaic power station equivalent modeling method and a related device, which aim to solve the technical problem that the existing photovoltaic power station equivalent method has insufficient parameter setting accuracy.
In order to achieve the above purpose, the invention is realized by adopting the following technical scheme:
in a first aspect, the invention provides a photovoltaic power station equivalent modeling method, which comprises the following steps:
performing cluster analysis on inverters in the photovoltaic power station to obtain cluster centers of all the clusters;
calculating equivalent machine parameters of each cluster, and carrying out equivalent treatment on the box transformer and the current collecting circuit;
and obtaining an equivalent model of the photovoltaic power station according to the cluster center of each cluster, the equivalent machine parameters and the equivalent processing result of the box transformer and the current collecting line.
Preferably, the cluster analysis specifically includes:
step 1, dividing a characteristic vector used for representing an inverter in a photovoltaic power station into k clustering clusters, and selecting k clustering clusters from all the characteristic vectors as initial cluster centers;
step 2, taking each feature vector as one sample, calculating the distance from each sample to the centers of k initial clusters according to the value of the clustering index corresponding to each sample, and dividing each sample into cluster clusters corresponding to the centers of the sub-clusters with the minimum distance;
step 3, calculating the number of edge samples of each cluster according to the percentage of the edge samples to obtain the edge samples of each cluster;
And 4, considering the edge samples of each cluster, carrying out iterative optimization on the initial cluster centers of all clusters until the difference value of the sum of the distances from all samples to the initial cluster centers of the clusters where the samples are located meets the preset requirement between two adjacent iterations, and obtaining the cluster centers of all clusters.
Preferably, in step 2, the cluster index includes a steady-state voltage average Us during normal operation of the photovoltaic inverter, an active power average Pe output by the inverter during a fault, and a reactive power average Qe output by the inverter during the fault.
Preferably, in step 2, the distance from each sample to the center of k initial clusters is calculated, specifically by the following formula:
wherein d ij (x i ,c j ) For sample x i To the initial cluster center c j J=1, 2, …, k, u is a variable, x iu For sample x i The value of the (u) th cluster index, c ju For the initial cluster center c j A value of a u-th cluster index of (a);
in step 4, the iterative optimization specifically includes:
1) The new initial cluster center c 'is recalculated by' j ：
Wherein g is the current sample number of the jth cluster, X m The method comprises the steps that a sample set is formed by all samples of a photovoltaic power station;
M aη representing the number of edge samples of clusters other than cluster a; m is m bη For m in the b-th cluster b The number of edge samples of the individual samples;
Y aη representing edge samples of clusters other than cluster a;
2) Returning to the step 2, performing iterative optimization, and after each iteration, calculating the sum P of the distances from all samples to the initial cluster centers of the clusters where the samples are located until the difference P between two adjacent iterative calculations meets the preset requirement, taking the initial cluster center of each cluster at present as the new cluster center after optimization, and calculating the P by the following formula:
preferably, the equivalent machine parameters comprise equivalent machine power, an equivalent machine low voltage ride through period control parameter and an equivalent machine low voltage ride through active current recovery control parameter;
the method comprises the steps of calculating the equivalent machine parameters of each cluster, and carrying out equivalent treatment on the box transformer and the current collecting line, wherein the specific steps are as follows:
1) Defining all inverters in each cluster as an engine group, and respectively adding the steady-state active power and the steady-state reactive power of all the inverters in each engine group to obtain the active power of the equivalent machine and the reactive power of the equivalent machine, namely the equivalent machine power;
2) Adopting a gradient descent algorithm to carry out multiple linear regression on the low voltage ride-through active current and reactive current of all inverters in each machine group respectively to obtain a low voltage ride-through active current set value Ip set_LV Active current control parameter K for low voltage ride through 1-Ip-LV And K 2-Ip-LV Low voltage ride through reactive current setpoint Iq set_LV And a low voltage ride through reactive current control parameter K 1-Iq-LV And K 2-Iq-LV The inverter low voltage ride through active current Ip is obtained by LVRT And inverter low voltage ride through reactive current Iq LVRT As equivalent valueControl parameters during low voltage ride through:
Ip LVRT ＝K 1-Ip-LV *Vt+K 2-Ip-LV *Ip 0 +Ip set_LV
Iq LVRT ＝K 1-Iq-LV *(0.9-Vt)+K 2-Iq-LV *Iq 0 +Iq set_LV
wherein Vt is the terminal voltage during failure, ip 0 To enter the active current before low voltage ride through Iq 0 Reactive current before low voltage ride through;
3) The active current recovery rate parameter of the equivalent machine is obtained by the following stepsAs the recovery control parameter of the equivalent machine low voltage ride through active current:
where m is the total number of inverters in the group,active current recovery rate parameter for ith inverter +.>
Wherein I is n For the rated current of the inverter,the slope of a line segment between two data points of a steady-state starting time t1 and a steady-state ending time t2 on an active current curve after the fault recovery of the ith inverter is as follows:
wherein,outputting active current for the inverter at t1 time after the fault recovery of the ith inverter, +.>Outputting active current for the inverter at the time t2 after the fault recovery of the ith inverter;
4) The total power consumption delta P of the transformer in the equivalent rear box transformer is obtained by Teq Equivalent impedance Z Teq And equivalent volume S Teq As a box variant equivalent processing result:
ΔP Teq ＝(NI T ) 2 Z Teq
Z Teq ＝Z T /N
S Teq ＝NS T
wherein N is the total number of the box transformer substation, I T For each box change current, Z T Is a box-type impedance S T The capacity of the box is changed for a single box;
5) The equivalent power loss delta P of the current collection line is obtained by eq And total impedance Z eq As the equivalent treatment result of the current collecting circuit:
ΔP eq ＝(FI) 2 Z eq
wherein I is output current of the photovoltaic array, Z e The impedance of the e-th collecting line, and F is the total number of the collecting lines.
In a second aspect, the present invention provides a photovoltaic power station equivalent modeling system, including:
the cluster analysis module is used for carrying out cluster analysis on the inverter in the photovoltaic power station to obtain cluster centers of all the clusters;
the equivalence module is used for calculating the equivalent machine parameters of each cluster and carrying out equivalent treatment on the box transformer and the current collecting line;
the modeling module is used for obtaining an equivalent model of the photovoltaic power station according to the cluster center of each cluster, the equivalent machine parameters and the equivalent processing results of the box transformer and the current collecting line.
Preferably, the cluster analysis module includes:
the cluster center sub-module is used for dividing the characteristic vector for representing the inverter in the photovoltaic power station into k cluster clusters, and selecting k clusters from all the characteristic vectors as initial cluster centers;
The classifying sub-module is used for taking each feature vector as one sample, calculating the distance from each sample to the centers of k initial clusters according to the value of the clustering index corresponding to each sample, and then dividing each sample into cluster clusters corresponding to the cluster centers with the minimum distance;
the edge sample submodule is used for calculating the number of edge samples of each cluster according to the percentage of the edge samples to obtain the edge samples of each cluster;
and the iteration optimization sub-module is used for taking the edge samples of each cluster into consideration, carrying out iteration optimization on the initial cluster centers of all clusters until the difference value of the sum of the distances from all the samples to the initial cluster centers of the clusters where the samples are located between two adjacent iterations meets the preset requirement, and obtaining the cluster centers of all the clusters.
Preferably, the equivalent machine parameters comprise equivalent machine power, an equivalent machine low voltage ride through period control parameter and an equivalent machine low voltage ride through active current recovery control parameter;
the equivalence module comprises:
the equivalent machine power sub-module is used for respectively adding the steady-state active power and the steady-state reactive power of all the inverters in each machine group to obtain the active power of the equivalent machine and the reactive power of the equivalent machine, namely the equivalent machine power; defining all inverters in each cluster as one machine group;
A low voltage pass-through control sub-module for respectively controlling the groups of machines by adopting gradient descent algorithmMultiple linear regression is carried out on the low voltage ride through active current and reactive current with the inverter, and a set value Ip of the low voltage ride through active current is obtained set_LV Active current control parameter K for low voltage ride through 1-Ip-LV And K 2-Ip-LV Low voltage ride through reactive current setpoint Iq set_LV And a low voltage ride through reactive current control parameter K 1-Iq-LV And K 2-Iq-LV The inverter low voltage ride through active current Ip is obtained by LVRT And inverter low voltage ride through reactive current Iq LVRT As a control parameter during the low voltage ride through of the equivalent machine:
Ip LVRT ＝K 1-Ip-LV *Vt+K 2-Ip-LV *Ip 0 +Ip set_LV
Iq LVRT ＝K 1-Iq-LV *(0.9-Vt)+K 2-Iq-LV *Iq 0 +Iq set_LV
wherein Vt is the terminal voltage during failure, ip 0 To enter the active current before low voltage ride through Iq 0 Reactive current before low voltage ride through;
obtaining the active current recovery rate parameter of the equivalent machine through the following stepsAs the recovery control parameter of the equivalent machine low voltage ride through active current:
where m is the total number of inverters in the group,active current recovery rate parameter for ith inverter +.>
Wherein I is n For the rated current of the inverter,after the fault of the ith inverter is recovered, the slope of a line segment between two data points of a steady-state starting time t1 and a steady-state ending time t2 on the active current curve is as follows:
Wherein,outputting active current for the inverter at t1 time after the fault recovery of the ith inverter, +.>Outputting active current for the inverter at the time t2 after the fault recovery of the ith inverter;
the box transformer sub-module is used for obtaining the total power consumption delta P of the transformer in the equivalent box transformer through the following steps Teq Equivalent impedance Z Teq And equivalent volume S Teq As a box variant equivalent processing result:
ΔP Teq ＝(NI T ) 2 Z Teq
Z Teq ＝Z T /N
S Teq ＝NS T
wherein N is the total number of the box transformer substation, I T For each box change current, Z T Is a box-type impedance S T The capacity of the box is changed for a single box;
the power collecting line sub-module is used for obtaining the equivalent power loss delta P of the power collecting line through the following steps eq And total impedance Z eq As the equivalent treatment result of the current collecting circuit:
ΔP eq ＝(FI) 2 Z eq
wherein I is output current of the photovoltaic array, Z e The impedance of the e-th collecting line, and F is the total number of the collecting lines.
In a third aspect, the invention proposes a computer device comprising a memory, a processor and a computer program stored in said memory and executable on said processor, said processor implementing the steps of the above method when said computer program is executed.
In a fourth aspect, the present invention proposes a computer readable storage medium storing a computer program which, when executed by a processor, implements the steps of the above method.
Compared with the prior art, the invention has the following beneficial effects:
the invention provides a photovoltaic power station equivalent modeling method, which is based on cluster analysis of feature vectors used for representing an inverter, obtains cluster centers of all cluster clusters, calculates equivalent machine parameters of all cluster clusters, and performs equivalent processing on box transformer and current collecting lines. Through practical verification, the equivalent model obtained by the modeling method of the invention has extremely high fitness when compared with a detailed model, and the effectiveness and accuracy of the modeling method of the invention are fully illustrated.
Further, when cluster analysis is carried out in the invention, edge samples are considered in a K-means clustering algorithm, the cluster center of each cluster is confirmed through iterative optimization, and the cluster center obtained after optimization is verified to have extremely high conformity with the actual situation, so that the existing cluster analysis method is fully optimized.
Further, when cluster analysis is carried out in the invention, the selected cluster indexes comprise a steady-state voltage average value during normal operation of the photovoltaic inverter, an active power average value output by the inverter during a fault period and a reactive power average value output by the inverter during the fault period, and the cluster indexes are closer to the actual operation condition of the photovoltaic power station, and the indexes possibly influenced by the actual working condition of the photovoltaic power station are fully considered, so that the modeling method of the invention is more consistent with the actual working condition.
Further, in the invention, when the equivalent computer parameters are calculated, the control parameters are identified in the low voltage crossing period of clustered data by adopting multiple linear regression based on a gradient descent algorithm, so that the equivalent model can reflect the characteristics of the detailed model in detail.
Furthermore, the invention also provides a photovoltaic power station equivalent modeling system, the equivalent modeling method is realized through the cluster analysis module, the equivalent module and the modeling module, and an equivalent model with extremely high accuracy of the photovoltaic power station can be obtained through a modularized structural form.
Drawings
For a clearer description of the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present invention and should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of a first embodiment of the present invention;
FIG. 2 is a flow chart of a second embodiment of the present invention;
FIG. 3 is a schematic flow chart of a third embodiment of the present invention;
FIG. 4 is a hierarchical clustering number diagram drawn in a third embodiment of the present invention;
fig. 5 is a schematic distribution diagram of three clustering indexes of a photovoltaic power station after cluster center optimization in the third embodiment of the present invention;
FIG. 6 is a reactive power comparison chart of an equivalent model and a detailed model obtained in the third embodiment of the present invention;
FIG. 7 is a graph showing the active power comparison between the equivalent model and the detailed model obtained in the third embodiment of the present invention;
FIG. 8 is a schematic diagram of a connection according to a fourth embodiment of the present invention;
FIG. 9 is a schematic connection diagram of a cluster analysis module in an equivalent modeling embodiment of a photovoltaic power station according to the present invention;
fig. 10 is a schematic diagram of connection of medium value modules in an equivalent modeling embodiment of a photovoltaic power station according to the present invention.
The system comprises a 401-cluster analysis module, a 501-equivalence module, a 601-modeling module, a 701-cluster center sub-module, a 801-classification sub-module, a 901-edge sample sub-module, a 1001-iteration optimization sub-module, a 1101-equivalence machine power sub-module, a 1201-low voltage through-control sub-module, a 1301-box transformer sub-module and a 1401-collection line sub-module.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. The components of the embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the invention, as presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further definition or explanation thereof is necessary in the following figures.
In the description of the embodiments of the present invention, it should be noted that, if the terms "upper," "lower," "horizontal," "inner," and the like indicate an azimuth or a positional relationship based on the azimuth or the positional relationship shown in the drawings, or the azimuth or the positional relationship in which the inventive product is conventionally put in use, it is merely for convenience of describing the present invention and simplifying the description, and does not indicate or imply that the apparatus or element to be referred to must have a specific azimuth, be configured and operated in a specific azimuth, and thus should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and the like, are used merely to distinguish between descriptions and should not be construed as indicating or implying relative importance.
Furthermore, the term "horizontal" if present does not mean that the component is required to be absolutely horizontal, but may be slightly inclined. As "horizontal" merely means that its direction is more horizontal than "vertical", and does not mean that the structure must be perfectly horizontal, but may be slightly inclined.
In the description of the embodiments of the present invention, it should also be noted that, unless explicitly specified and limited otherwise, the terms "disposed," "mounted," "connected," and "connected" should be construed broadly, and may be, for example, fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the above terms in the present invention can be understood by those of ordinary skill in the art according to the specific circumstances.
The invention is described in further detail below with reference to the attached drawing figures:
aiming at the problems existing in the prior art, the invention provides a multi-cluster index photovoltaic power station equivalent method considering low voltage ride through, and the scheme of the invention is described in detail through a plurality of embodiments of the invention.
Example 1
Referring to fig. 1, a basic embodiment of the multi-cluster index photovoltaic power station equivalence method of the invention specifically comprises the following steps:
s101, carrying out cluster analysis on the characteristic vector for representing the inverter in the photovoltaic power station, and determining the cluster center of each cluster. And selecting a clustering index. Typically including steady state voltage indicators, active indicators during faults, and reactive indicators during faults.
S102, calculating equivalent machine parameters of each cluster, carrying out equivalent treatment on the box transformer and the current collecting circuit, and obtaining an equivalent model of the photovoltaic power station according to the cluster center, the equivalent machine parameters, the box transformer and the current collecting circuit equivalent treatment result of each cluster.
S103, obtaining an equivalent model of the photovoltaic power station according to the cluster center of each cluster, the equivalent machine parameters and the equivalent processing results of the box transformer and the current collecting line.
Example two
As shown in fig. 2, the method for modeling equivalent of a photovoltaic power station according to the present invention comprises the following specific steps:
s201, selecting a plurality of clustering indexes. Typically, the selected cluster indicators include steady-state voltage indicators, active indicators during faults, and reactive indicators during faults. However, in practice, the method can be selected as a clustering index, and other index items can be also used, and the three indexes in the first embodiment can enable the equivalent method of the method to be closer to the actual working condition.
The steady-state voltage index is obtained, and the machine end voltage distribution of the machine set during normal operation can be represented. The active power index in the fault period refers to the active power output by each inverter in the fault period, and can represent the active output characteristic in the low-voltage ride-through period of the unit. The reactive power index in the fault period refers to reactive power output by each inverter in the fault period, and can represent reactive power output characteristics in the low-voltage ride-through period of the unit.
And performing cluster analysis on the inverter in the photovoltaic power station to obtain cluster centers of all the clusters.
S202, setting the clustering number of the photovoltaic power station as k, and correspondingly obtaining k clustering clusters.
S203, characterizing all inverters in the photovoltaic power station by using feature vectors, wherein each feature vector is used as one sample, k samples are selected from all samples as initial cluster centers, and k initial cluster centers c=c are obtained 1 ,c 2 ,…c k 。
S204, according to the value of each cluster index corresponding to each sample, calculating the distance from each sample to the centers of k initial clusters, and dividing each sample into cluster clusters corresponding to the centers of the initial clusters with the minimum distance.
S205, calculating the number of edge samples of each cluster respectively, and further obtaining edge samples.
S206, combining the edge samples of each cluster, carrying out iterative optimization on the initial cluster centers of all clusters, calculating the sum P of the distances from all samples to the initial cluster center of the cluster where the samples are located after each iterative calculation until the P difference between two adjacent iterative calculations meets the preset requirement, and obtaining a new cluster center after each cluster is optimized.
S207, calculating equivalent machine parameters of each cluster of the photovoltaic power station: (1) equivalent machine power; (2) controlling parameters during low voltage ride through of the equivalent machine; (3) The equivalent machine low voltage ride through active current recovers the control parameters. And respectively carrying out equivalent treatment on the box transformer and the current collecting circuit.
S208, obtaining an equivalent model of the photovoltaic power station from the optimized cluster center, equivalent machine parameters of each cluster, and equivalent processing results of the box transformer and the current collection circuit.
Example III
As shown in fig. 3, a more specific embodiment of the equivalent modeling method for a photovoltaic power station of the present invention is shown in fig. 3, where a photovoltaic power station including 30 inverters is taken as an example, and the specific steps are as follows:
s301, selecting a steady-state voltage average value Us of the photovoltaic inverter during normal operation, an active power average value Pe output by each inverter during a fault period and a reactive power average value Qe output by each inverter during the fault period as clustering indexes.
S302, drawing a hierarchical clustering tree as shown in FIG. 4, wherein in FIG. 4, the ordinate represents Euclidean distance, and the abscissa represents serial number of an inverter in a photovoltaic power station. And taking 0.01 as a clustering boundary line, determining the clustering number of the photovoltaic power station as k=2 according to the clustering boundary line.
In step S302, the clustering boundary may be adjusted according to the actual working condition, and in addition, other methods than hierarchical clustering tree may be used for determining the number of clusters, and even if a hierarchical clustering tree method is used, a specific clustering method may be adjusted.
S303, taking all eigenvectors representing inverters in a photovoltaic power station as samples, randomly selecting k samples from all the samples as initial cluster centers, and obtaining k initial cluster centers c=c 1 ,c 2 ,…c k 。
S304, let the number of all samples be n, and the n samples be denoted as x i I=1, 2, … n. Calculate sample x by i The euclidean distance to the center of the k initial clusters, i.e. the straight line distance. Since three cluster indexes are selected in step S301, sample x i Dimension w=3, euclidean distance d ij (x i ,c j ) The calculation formula of (2) is as follows:
wherein d ij (x i ,c j ) For sample x i To the initial cluster center c j J=1, 2, …, k, u is a variable, x iu For sample x i The value of the (u) th cluster index, c ju For the initial cluster center c j Is the value of the u-th cluster index of (c).
S305, calculating m in the a-th cluster by the following formula a Edge sample number of samples m aη ：
m aη ＝ηm a
In the third embodiment, since the number of clusters is k=2, a is 1 or 2, η is an edge sample percentage, and the setting can be performed.
M being the greatest distance from the center of the initial cluster in each cluster aη The samples are taken as edge samples, denoted X jη 。
S306, re-calculating a new initial cluster center c by j ′：
Wherein g is the current sample number of the jth cluster, X m A sample set consisting of all samples of the photovoltaic power station.
M aη Representing the number of edge samples of clusters other than cluster a; m is m bη For m in the b-th cluster b Edge sample number of individual samples.
Y aη Representing edge samples of clusters other than cluster a.
And (3) repeatedly executing the steps S304 to S306, performing iterative optimization, and calculating the sum P of the distances from all samples to the initial cluster centers of the clusters where the samples are located after each repeated execution until the P difference between two adjacent iterative calculations meets the preset requirement, wherein the current initial cluster center of each cluster is used as the new cluster center after optimization. P is calculated by the formula:
when iterative optimization is performed, if the difference value of P between two iterations is found to be too large, the adjustment of the edge sample percentage eta can be tried, the edge sample percentage eta is reduced, and then iterative optimization is performed.
As shown in fig. 5, which is a distribution diagram of three indexes of a photovoltaic power station unit, it can be seen that the effect accords with the expectation by adopting the K-means algorithm considering the edge samples to perform cluster analysis on the feature vector used for representing the inverter in the photovoltaic power station.
S307, calculating equivalent machine parameters of each cluster of the photovoltaic power station: (1) equivalent machine power; (2) controlling parameters during low voltage ride through of the equivalent machine; (3) The equivalent machine low voltage ride through active current recovers the control parameters. And taking the inverter in each cluster as one group, and obtaining k groups after cluster analysis is completed. Parameters (2) and (3) are obtained by identifying control parameters of the photovoltaic inverter in the machine group, and the specific calculation method of each parameter is as follows:
1) Equivalent machine power calculation
And respectively adding the steady-state active power and the steady-state reactive power of all the inverters in each machine group to obtain the active power of the equivalent machine and the reactive power of the equivalent machine, and obtaining the equivalent machine power of each machine group.
2) Control parameter calculation during low voltage ride through of equivalent machine
2.1 Control parameter identification during low voltage ride through of an inverter
And adopting a gradient descent algorithm to carry out multiple linear regression on the low voltage ride through active current and reactive current of all inverters in each machine group respectively.
(a) Acquiring to-be-identified data of all inverters in each machine group, including active current Ip during low voltage ride through LVRT Active current Ip before entering low voltage ride through 0 (as initial active current), the machine side voltage Vt during failure, and form matrix X:
Wherein m represents the number of inverters in the group;
let y= [ Ip ] LVRT (1) ,Ip LVRT (2) ,…,Ip LVRT (m) ] T ，
Wherein K is 1-Ip-LV And K 2-Ip-LV For two low voltage ride through active current control parameters, ip set_LV Is a low voltage ride through active current set point.
(b) Calculating a loss functionAnd loss function gradient->
(c) Setting initial parametersThe iteration step length psi and the iteration precision epsilon are subjected to iterative calculation according to the following formula:
up toAnd (3) completing iterative calculation to obtain a corresponding control parameter identification result in the low voltage ride through period: ip (internet protocol) set_LV 、K 1-Ip-LV And K 2-Ip-LV 。
2.2 Obtaining the control parameter identification result during the low voltage ride through of another part by adopting the same method as the step 2.1): two low voltage ride through reactive current control parameters K 1-Iq-LV And K 2-Iq-LV And a low voltage ride through reactive current setpoint Iq set_LV 。
2.3 Calculating the inverter low voltage ride through active current Ip by LVRT ：
Ip LVRT ＝K 1-Ip-LV *Vt+K 2-Ip-LV *Ip 0 +Ip set_LV
Inverter low power calculation byVoltage ride through reactive current Iq LVRT ：
Iq LVRT ＝K 1-Iq-LV *(0.9-Vt)+K 2-Iq-LV *Iq 0 +Iq set_LV
Wherein Iq 0 To enter the reactive current before low voltage ride through.
Active current Ip LVRT And reactive current Iq LVRT The control parameter is the control parameter of the equivalent machine in the low voltage crossing period.
3) Low voltage ride through active current recovery control parameter calculation of equivalent machine
3.1 For the active current recovery phase, the slope of the active current curve after the recovery of each inverter fault is obtained by:
Wherein,the slope of a line segment between two data points of a steady-state starting time t1 and a steady-state ending time t2 on an active current curve after the fault recovery of the ith inverter is as follows:Outputting active current for the inverter at t2 time after the fault recovery of the ith inverter, +.>And outputting active current for the inverter at the time t1 after the fault recovery of the ith inverter.
3.2 Calculating an i-th inverter active current recovery rate parameter by
Wherein I is n Is the rated current of the inverter.
3.3 For all the inverters in each machine group, the active current is output according to the inverter at the time t1 after the fault recovery of the ith inverterActive current recovery rate parameter for ith inverter>Weighted average is carried out, specifically weighted average is carried out by the following formula, and the equivalent machine active current recovery rate parameter +.>As the recovery control parameter of the equivalent machine low voltage ride through active current: />
S308, carrying out equivalent treatment on the box transformer and the current collecting line by the following method
1) Equivalent processing of box transformer
For unit transformers, the total power loss ΔP before equivalence T The method comprises the following steps:
wherein N is the total number of the box transformer substation, I T For each box change current, Z T Is a tank variable impedance.
Total loss Δp of transformer after equivalence Teq The method comprises the following steps:
ΔP Teq ＝(NI T ) 2 Z Teq
obtaining the equivalent impedance Z Teq The method comprises the following steps:
Z Teq ＝Z T /N
equivalent volume S Teq The method comprises the following steps:
S Teq ＝NS T
wherein S is T The capacity of the box is changed for a single box.
And obtaining the equivalent impedance and the equivalent capacity of the box transformer after the box transformer equivalent treatment.
2) Equivalent treatment of collector lines
According to the principle that the total loss is unchanged before and after the equivalence, for radial topology, namely, all power generation units are connected in parallel at grid-connected points, considering all photovoltaic power generation units, the line power loss delta P before the equivalence is as follows:
wherein F is the total number of current collecting circuits, I e Collecting line current for the e-th strip, Z e The impedance of the e-th current collecting line is I, and the output current of the photovoltaic array is I. Under the condition of the same illumination condition and temperature, the output current of all the photovoltaic arrays is I, and the equivalent power loss delta P of the current collecting circuit eq The method comprises the following steps:
ΔP eq ＝(FI) 2 Z eq
wherein Z is eq Is the total impedance of the equivalent post-collector circuit.
Ensure the same loss of the power transmission line and the total impedance Z of the current collection line after equivalent eq The method comprises the following steps:
wherein, the equivalent post-current-collecting circuit power loss delta P eq And the total impedance Z of the equal-value current collection circuit eq The result is obtained after the equivalent treatment of the current collecting circuit.
S309, obtaining an equivalent model of the photovoltaic power station from the optimized cluster center, equivalent machine parameters of each cluster, and equivalent processing results of the box transformer and the current collection circuit.
For the photovoltaic power station comprising 30 inverters in the third embodiment, the clustering number is 2, the photovoltaic power station enters low voltage ride through due to the power grid fault, the equivalent result obtained by the equivalent method in the third embodiment is taken as an equivalent model to be compared with a detailed model, fig. 6 is a reactive power comparison chart, fig. 7 is an active power comparison chart, and as can be seen from the comparison results of fig. 6 and 7, the equivalent model obtained by the method has extremely high consistency with the detailed model.
The invention provides a photovoltaic power station equivalent modeling method, which is characterized in that a K-means algorithm based on considering edge samples is used for clustering a plurality of clustering indexes of a photovoltaic power station, multiple linear regression based on a gradient descent algorithm is used for identifying control parameters in a low voltage crossing period of clustered data, and equivalent machine control parameters are determined, so that an electromechanical transient equivalent model of the photovoltaic power station is established.
Example IV
As shown in FIG. 8, the invention is a basic embodiment of a photovoltaic power station equivalent modeling system, which comprises a cluster analysis module 401, an equivalent module 501 and a modeling module 601 which are sequentially connected.
The cluster analysis module 401 is configured to perform cluster analysis on feature vectors for characterizing an inverter in a photovoltaic power station, so as to obtain cluster centers of each cluster;
The equivalence module 501 is configured to calculate an equivalence machine parameter of each cluster, and perform equivalence processing on the box transformer and the current collecting line;
the modeling module 601 is configured to obtain an equivalent model of the photovoltaic power station according to the cluster center of each cluster, the equivalent machine parameters, and the equivalent processing result of the tank transformer and the current collecting line.
The cluster analysis module 401, the equivalence module 501 and the modeling module 601 are connected through data, so that the modules can process direct transmission.
In other embodiments of the equivalent modeling system for a photovoltaic power station according to the present invention, the cluster analysis module 401 and the equivalent module 601 may be further divided into a plurality of sub-modules, for respectively implementing the preferred steps of the equivalent modeling methods in the second embodiment and the third embodiment.
As a preferred embodiment of the fourth embodiment, as shown in fig. 9, the cluster analysis module includes a cluster center sub-module 701, a classification sub-module 801, an edge sample sub-module 901, and an iterative optimization sub-module 1001, which are sequentially connected.
The cluster center submodule 701 is configured to divide a feature vector for representing an inverter in a photovoltaic power station into k clusters, and select k clusters from all feature vectors as cluster centers;
The classifying sub-module 801 is configured to calculate distances from each sample to the centers of k initial clusters according to the value of the clustering index corresponding to each sample by using each feature vector as one sample, and then divide each sample into clusters corresponding to the cluster centers with the minimum distance;
the edge sample sub-module 901 is configured to calculate the number of edge samples of each cluster according to the percentage of edge samples, so as to obtain an edge sample of each cluster;
the iterative optimization submodule 1001 is configured to consider edge samples of each cluster, perform iterative optimization on initial cluster centers of all clusters until a difference value between a sum of distances from all samples to the initial cluster center of the cluster where the sample is located between two adjacent iterations meets a preset requirement, and obtain cluster centers of all clusters.
As shown in fig. 10, the equivalence modules include an equivalence machine power sub-module 1101, a low voltage ride through control sub-module 1201, a tank transformer sub-module 1301, and a power line sub-module 1401.
The equivalent machine power submodule 1101 is configured to add the steady-state active power and the steady-state reactive power of all the inverters in each machine group respectively to obtain the active power of the equivalent machine and the reactive power of the equivalent machine, i.e. the equivalent machine power; defining all inverters in each cluster as one machine group;
The low voltage ride through control submodule 1201 is configured to perform multiple linear regression on the low voltage ride through active current and the reactive current of all the inverters in each machine group by using a gradient descent algorithm, so as to obtain a low voltage ride through active current set value Ip set_LV Active current control parameter K for low voltage ride through 1-Ip-LV And K 2-Ip-LV Low voltage ride through reactive current setpoint Iq set_LV And a low voltage ride through reactive current control parameter K 1-Iq-LV And K 2-Iq-LV The inverter low voltage ride through active current Ip is obtained by LVRT And inverter low voltage ride through reactive current Iq LVRT As a control parameter during the low voltage ride through of the equivalent machine:
Ip LVRT ＝K 1-Ip-LV *Vt+K 2-Ip-LV *Ip 0 +Ip set_LV
Iq LVRT ＝K 1-Iq-LV *(0.9-Vt)+K 2-Iq-LV *Iq 0 +Iq set_LV
wherein Vt is the terminal voltage during failure, ip 0 To enter the active current before low voltage ride through Iq 0 Reactive current before low voltage ride through;
obtaining the active current recovery rate parameter of the equivalent machine through the following stepsAs the recovery control parameter of the equivalent machine low voltage ride through active current:
where m is the total number of inverters in the group,active current recovery rate parameter for ith inverter +.>
I n For the rated current of the inverter,recovery for ith inverter faultSlope of line segment between two data points of steady state starting time t1 and steady state ending time t2 on the active current curve:
Outputting active current for the inverter at t1 time after the fault recovery of the ith inverter, +.>Outputting active current for the inverter at the time t2 after the fault recovery of the ith inverter;
the box transformer submodule 1301 is configured to obtain the total power consumption Δp of the transformer in the equivalent box transformer after the equivalent box transformer Teq Equivalent impedance Z Teq And equivalent volume S Teq As a box variant equivalent processing result:
ΔP Teq ＝(NI T ) 2 Z Teq
Z Teq ＝Z T /N
S Teq ＝NS T
wherein N is the total number of the box transformer substation, I T For each box change current, Z T Is a box-type impedance S T The capacity of the box is changed for a single box;
the collecting line submodule 1401 is configured to obtain the equivalent power loss Δp of the collecting line through the following steps eq And total impedance Z eq As the equivalent treatment result of the current collecting circuit:
ΔP eq ＝(FI) 2 Z eq
wherein I is output current of the photovoltaic array, Z e The impedance of the e-th collecting line, and F is the total number of the collecting lines.
Still another embodiment of the present invention provides a computer device. The computer device of this embodiment includes: a processor, a memory, and a computer program stored in the memory and executable on the processor. The steps of the various method embodiments described above are implemented when the processor executes the computer program. Alternatively, the processor may implement the functions of the modules/units in the above-described device embodiments when executing the computer program.
The computer program may be divided into one or more modules/units, which are stored in the memory and executed by the processor to accomplish the present invention.
The computer equipment can be a desktop computer, a notebook computer, a palm computer, a cloud server and other computing equipment. The computer device may include, but is not limited to, a processor, a memory.
The processor may be a central processing unit (Central Processing Unit, CPU), but may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), off-the-shelf programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like.
The memory may be used to store the computer program and/or modules, and the processor may implement various functions of the computer device by running or executing the computer program and/or modules stored in the memory, and invoking data stored in the memory.
The modules/units integrated with the computer device may be stored in a computer readable storage medium if implemented in the form of software functional units and sold or used as stand alone products. Based on such understanding, the present invention may implement all or part of the flow of the method of the above embodiment, or may be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, and when the computer program is executed by a processor, the computer program may implement the steps of each of the method embodiments described above. Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer memory, a Read-only memory (ROM), a random access memory (RAM, randomAccessMemory), an electrical carrier signal, a telecommunication signal, a software distribution medium, and so forth. It should be noted that the computer readable medium contains content that can be appropriately scaled according to the requirements of jurisdictions in which such content is subject to legislation and patent practice, such as in certain jurisdictions in which such content is subject to legislation and patent practice, the computer readable medium does not include electrical carrier signals and telecommunication signals.
The above is only a preferred embodiment of the present invention, and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. The equivalent modeling method of the photovoltaic power station is characterized by comprising the following steps of:
performing cluster analysis on the feature vector for representing the inverter in the photovoltaic power station to obtain cluster centers of all the clusters;
calculating equivalent machine parameters of each cluster, and carrying out equivalent treatment on the box transformer and the current collecting circuit;
and obtaining an equivalent model of the photovoltaic power station according to the cluster center of each cluster, the equivalent machine parameters and the equivalent processing result of the box transformer and the current collecting line.
2. The photovoltaic power station equivalent modeling method according to claim 1, wherein the clustering analysis specifically comprises:
step 1, dividing a characteristic vector used for representing an inverter in a photovoltaic power station into k clustering clusters, and selecting k clustering clusters from all the characteristic vectors as initial cluster centers;
step 2, taking each feature vector as one sample, calculating the distance from each sample to the centers of k initial clusters according to the value of the clustering index corresponding to each sample, and dividing each sample into cluster clusters corresponding to the centers of the sub-clusters with the minimum distance;
Step 3, calculating the number of edge samples of each cluster according to the percentage of the edge samples to obtain the edge samples of each cluster;
and 4, considering the edge samples of each cluster, carrying out iterative optimization on the initial cluster centers of all clusters until the difference value of the sum of the distances from all samples to the initial cluster centers of the clusters where the samples are located meets the preset requirement between two adjacent iterations, and obtaining the cluster centers of all clusters.
3. The photovoltaic power station equivalent modeling method according to claim 2, characterized by: in step 2, the clustering index includes a steady-state voltage average Us during normal operation of the photovoltaic inverter, an active power average Pe output by the inverter during a fault, and a reactive power average Qe output by the inverter during the fault.
4. The photovoltaic power station equivalent modeling method according to claim 2, characterized by: in step 2, the distance from each sample to the center of k initial clusters is calculated specifically by the following formula:
wherein d ij (x i ,c j ) For sample x i To the initial cluster center c j J=1, 2, …, k, u is a variable, x iu For sample x i The value of the (u) th cluster index, c ju Is the center of the initial cluster c j A value of a u-th cluster index of (a);
in step 4, the iterative optimization specifically includes:
1) The new initial cluster center c 'is recalculated by' j ：
Wherein g is the current sample number of the jth cluster, X m The method comprises the steps that a sample set is formed by all samples of a photovoltaic power station;
M aη representing the number of edge samples of clusters other than cluster a; m is m bη For m in the b-th cluster b The number of edge samples of the individual samples;
Y aη representing edge samples of clusters other than cluster a;
2) Returning to the step 2, performing iterative optimization, and after each iteration, calculating the sum P of the distances from all samples to the initial cluster centers of the clusters where the samples are located until the difference P between two adjacent iterative calculations meets the preset requirement, taking the initial cluster center of each cluster at present as the new cluster center after optimization, and calculating the P by the following formula:
5. the photovoltaic power station equivalent modeling method according to claim 2, characterized by:
the equivalent machine parameters comprise equivalent machine power, an equivalent machine low voltage ride through period control parameter and an equivalent machine low voltage ride through active current recovery control parameter;
the method comprises the steps of calculating the equivalent machine parameters of each cluster, and carrying out equivalent treatment on the box transformer and the current collecting line, wherein the specific steps are as follows:
1) Defining all inverters in each cluster as an engine group, and respectively adding the steady-state active power and the steady-state reactive power of all the inverters in each engine group to obtain the active power of the equivalent machine and the reactive power of the equivalent machine, namely the equivalent machine power;
2) Adopting a gradient descent algorithm to carry out multiple linear regression on the low voltage ride-through active current and reactive current of all inverters in each machine group respectively to obtain a low voltage ride-through active current set value Ip set_LV Active current control parameter K for low voltage ride through 1_Ip_LV And K 2_Ip_LV Low voltage ride through reactive current setpoint Iq set_LV And a low voltage ride through reactive current control parameter K 1_Iq_LV And K 2_Iq_LV The inverter low voltage ride through active current Ip is obtained by LVRT And inverter low voltage ride through reactive current Iq LVRT As a control parameter during the low voltage ride through of the equivalent machine:
Ip LVRT ＝K 1_Ip_LV *Vt+K 2_Ip_LV *Ip 0 +Ip set_LV
Iq LVRT ＝K 1_Iq_LV *(0.9-Vt)+K 2_Iq_LV *Iq 0 +Iq set_LV
wherein Vt is the terminal voltage during failure, ip 0 To enter the active current before low voltage ride through Iq 0 Reactive current before low voltage ride through;
3) The active current recovery rate parameter of the equivalent machine is obtained by the following stepsAs the recovery control parameter of the equivalent machine low voltage ride through active current:
where m is the total number of inverters in the group, Active current recovery rate parameter for ith inverter +.>
Wherein I is n For the rated current of the inverter,the slope of a line segment between two data points of a steady-state starting time t1 and a steady-state ending time t2 on an active current curve after the fault recovery of the ith inverter is as follows:
wherein,outputting active current for the inverter at t1 time after the fault recovery of the ith inverter, +.>Outputting active current for the inverter at the time t2 after the fault recovery of the ith inverter;
4) The total power consumption delta P of the transformer in the equivalent rear box transformer is obtained by Teq Equivalent impedance Z Teq And equivalent volume S Teq As a box variant equivalent processing result:
ΔP Teq ＝(NI T ) 2 Z Teq
Z Teq ＝Z T /N
S Teq ＝NS T
wherein N is the total number of the box transformer substation, I T For each box change current, Z T Is a box-type impedance S T The capacity of the box is changed for a single box;
5) The equivalent power loss delta P of the current collection line is obtained by eq And total impedance Z eq As the equivalent treatment result of the current collecting circuit:
ΔP eq ＝(FI) 2 Z eq
wherein I is output current of the photovoltaic array, Z e The impedance of the e-th collecting line, and F is the total number of the collecting lines.
6. A photovoltaic power plant equivalent modeling system, comprising:
the cluster analysis module is used for carrying out cluster analysis on the feature vector for representing the inverter in the photovoltaic power station to obtain cluster centers of all the clusters;
The equivalence module is used for calculating the equivalent machine parameters of each cluster and carrying out equivalent treatment on the box transformer and the current collecting line;
the modeling module is used for obtaining an equivalent model of the photovoltaic power station according to the cluster center of each cluster, the equivalent machine parameters and the equivalent processing results of the box transformer and the current collecting line.
7. The photovoltaic power plant equivalent modeling system of claim 6, wherein said cluster analysis module comprises:
the cluster center sub-module is used for dividing the characteristic vector for representing the inverter in the photovoltaic power station into k cluster clusters, and selecting k clusters from all the characteristic vectors as initial cluster centers;
the classifying sub-module is used for taking each feature vector as one sample, calculating the distance from each sample to the centers of k initial clusters according to the value of the clustering index corresponding to each sample, and then dividing each sample into cluster clusters corresponding to the cluster centers with the minimum distance;
the edge sample submodule is used for calculating the number of edge samples of each cluster according to the percentage of the edge samples to obtain the edge samples of each cluster;
and the iteration optimization sub-module is used for taking the edge samples of each cluster into consideration, carrying out iteration optimization on the initial cluster centers of all clusters until the difference value of the sum of the distances from all the samples to the initial cluster centers of the clusters where the samples are located between two adjacent iterations meets the preset requirement, and obtaining the cluster centers of all the clusters.
8. The photovoltaic power plant equivalent modeling system of claim 7, wherein: the equivalent machine parameters comprise equivalent machine power, an equivalent machine low voltage ride through period control parameter and an equivalent machine low voltage ride through active current recovery control parameter;
the equivalence module comprises:
the equivalent machine power sub-module is used for respectively adding the steady-state active power and the steady-state reactive power of all the inverters in each machine group to obtain the active power of the equivalent machine and the reactive power of the equivalent machine, namely the equivalent machine power; defining all inverters in each cluster as one machine group;
the low-voltage pass-through control submodule is used for respectively carrying out multiple linear regression on the low-voltage pass-through active current and the reactive current of all the inverters in each machine group by adopting a gradient descent algorithm to obtain a low-voltage pass-through active current set value Ip set_LV Active current control parameter K for low voltage ride through 1_Ip_LV And K 2_Ip_LV Low voltage ride through reactive current setpoint Iq set_LV And a low voltage ride through reactive current control parameter K 1_Iq_LV And K 2_Iq_LV The inverter low voltage ride through active current Ip is obtained by LVRT And inverter low voltage passMore reactive current Iq LVRT As a control parameter during the low voltage ride through of the equivalent machine:
Ip LVRT ＝K 1_Ip_LV *Vt+K 2_Ip_LV *Ip 0 +Ip set_LV
Iq LVRT ＝K 1_Iq_LV *(0.9-Vt)+K 2_Iq_LV *Iq 0 +Iq set_LV
Wherein Vt is the terminal voltage during failure, ip 0 To enter the active current before low voltage ride through Iq 0 Reactive current before low voltage ride through;
obtaining the active current recovery rate parameter of the equivalent machine through the following stepsAs the recovery control parameter of the equivalent machine low voltage ride through active current:
where m is the total number of inverters in the group,active current recovery rate parameter for ith inverter +.>
Wherein I is n For the rated current of the inverter,after the ith inverter is recovered from faults, the slope of a line segment between two data points of a steady-state starting time t1 and a steady-state ending time t2 on an active current curve：
Wherein,outputting active current for the inverter at t1 time after the fault recovery of the ith inverter, +.>Outputting active current for the inverter at the time t2 after the fault recovery of the ith inverter;
the box transformer sub-module is used for obtaining the total power consumption delta P of the transformer in the equivalent box transformer through the following steps Teq Equivalent impedance Z Teq And equivalent volume S Teq As a box variant equivalent processing result:
ΔP Teq ＝(NI T ) 2 Z Teq
Z Teq ＝Z T /N
S Teq ＝NS T
wherein N is the total number of the box transformer substation, I T For each box change current, Z T Is a box-type impedance S T The capacity of the box is changed for a single box;
the power collecting line sub-module is used for obtaining the equivalent power loss delta P of the power collecting line through the following steps eq And total impedance Z eq As the equivalent treatment result of the current collecting circuit:
ΔP eq ＝(FI) 2 Z eq
wherein I is output current of the photovoltaic array, Z e The impedance of the e-th collecting line, and F is the total number of the collecting lines.
9. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of the method according to any of claims 1-5 when the computer program is executed.
10. A computer readable storage medium storing a computer program, characterized in that the computer program when executed by a processor implements the steps of the method according to any one of claims 1-5.
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