CN115102190A - Parameter optimization method for in-station/station network oscillation suppression of photovoltaic power station grid-connected system - Google Patents
Parameter optimization method for in-station/station network oscillation suppression of photovoltaic power station grid-connected system Download PDFInfo
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Abstract
The invention discloses a parameter optimization method for in-station/station network oscillation suppression of a photovoltaic power station grid-connected system, which comprises the following steps: 1. obtaining a coherent photovoltaic power generation unit suitable for in-station/station network oscillation analysis; 2. establishing an intra-station/station network decoupling reduced-order system of a photovoltaic power station grid-connected system; 3. and performing parameter optimization on the in-station/station network decoupling reduced-order system. The method can establish a reduced order model suitable for in-station/station network oscillation analysis of the photovoltaic power station grid-connected system, decouple the in-station oscillation and the station network oscillation, optimize parameters under the condition of keeping a certain stability margin of the system, and further provide an effective parameter optimization model and method for in-station/station network oscillation suppression of the photovoltaic power station grid-connected system.
Description
Technical Field
The invention belongs to the field of stability analysis and control of an electric power system, and particularly relates to a parameter optimization method for in-station/station network oscillation suppression of a grid-connected system of a photovoltaic power station.
Background
With the introduction of new power systems, which are mainly new energy, photovoltaic power generation capacity has increased year by year. However, the photovoltaic power plant grid-connected system has an oscillation problem. Li Chun indicates that a broadband oscillation phenomenon from tens of hertz to thousands of hertz occurs in an actual photovoltaic power station grid-connected system in the 'Unstable operation of photovoltaic inverter from field experiences', but does not relate to a parameter optimization method for oscillation suppression; the Zhao book strongly indicates that the photovoltaic access weak alternating current network system has subsynchronous oscillation problem in the photovoltaic incorporation weak alternating current network subsynchronous oscillation mechanism and characteristic analysis. The oscillation of the actual grid-connected project of the photovoltaic power station easily causes photovoltaic off-grid, equipment damage and system outage. Therefore, the parameter optimization method for suppressing the oscillation of the grid-connected system of the photovoltaic power station is provided, and has important significance for analyzing and planning the safe and stable operation of the power system.
At present, parameter optimization related to oscillation suppression of a photovoltaic power station grid-connected system is mainly based on a detailed system model, the ground is only a single station network oscillation mode, and the selection of constraint conditions in the optimization process is not comprehensive enough. However, parameter optimization based on a detailed model takes longer time, and the problem of dimension disaster is faced, so that the existing numerical calculation method may have the situation that the result is not converged; meanwhile, the damping of the oscillation mode in the photovoltaic power station grid-connected system station may present negative damping or weak damping, has a large influence on the system stability, and needs to be considered in the parameter optimization process; in addition, the constraint conditions of the existing parameter optimization method only consider the damping ratio of a non-dominant oscillation mode, and do not consider the dynamic performance of a control system and the maximum transmission power of the system, which is not comprehensive enough. Therefore, the traditional parameter optimization method is used for inhibiting the problems of long time consumption, poor accuracy and poor applicability of the oscillation of the grid-connected system of the photovoltaic power station.
Disclosure of Invention
The invention aims to solve the defects in the prior art, and provides a parameter optimization method for in-station/station network oscillation suppression of a photovoltaic power station grid-connected system, so that the calculation efficiency of parameter optimization can be improved through a reduced order decoupling model of in-station/station network oscillation analysis; meanwhile, in the parameter optimization process, the in-station oscillation and the station network oscillation can be simultaneously inhibited, so that the accuracy and the applicability of parameter optimization can be improved.
In order to achieve the purpose, the invention adopts the following technical scheme:
the invention relates to a parameter optimization method for in-station/station network oscillation suppression of a photovoltaic power station grid-connected system, which comprises the following steps: the system comprises n photovoltaic power generation units and an alternating current power grid, and the number of any one photovoltaic power generation unit is marked as i, i is 1, …, n; the method is characterized by comprising the following steps:
step S1: obtaining a coherent photovoltaic power generation unit suitable for in-station/station network oscillation analysis;
step S1.1: acquiring a master station in/station network oscillation mode of a photovoltaic power station grid-connected system:
step S1.1.1: obtaining an initial operating point x of a photovoltaic power station grid-connected system through load flow calculation 0 And obtaining the initial operation point x of the photovoltaic power station grid-connected system by using the formula (1) 0 A linearized state space model of (a);
in the formula (1), d represents a differential, t is a time, x pg For state variables of the grid-connected system of photovoltaic power stations, A pg State matrix for grid-connected system of photovoltaic power station, B pg Input matrix u for grid-connected system of photovoltaic power station pg The method comprises the following steps of (1) inputting variables of a photovoltaic power station grid-connected system; Δ x pg Denotes x pg Increment of (a), u pg Represents u pg An increment of (d);
step S1.1.2: solve equation (2) and obtain the state matrix A pg Characteristic value of (1 [ [ lambda ]) e 1, | e ═ 1,2, …, l }; wherein l is A pg Order of (a) ("lambda") e Is represented by A pg The e-th eigenvalue of (c);
|λ e E pg -A pg |＝0 (2)
in the formula (2), E pg Is and state matrix A pg Identity matrix of the same order;
step S1.1.3: screening out { lambda e Z oscillation modes { s } with real part and imaginary part both different from 0 in | e ═ 1,2, …, l | o 1,2, …, z, and { s } is obtained by equation (3) o Left eigenvector { U } corresponding to 1,2, …, z | o ═ 1,2, … go 1,2, …, z and right eigenvector { V | go 1,2, …, z }; wherein s is o For the o-th oscillatory mode, U, in which both the real and imaginary parts are non-0 go Is s is o Corresponding left eigenvector, V go Is s is o A corresponding right eigenvector;
in the formula (3), the reaction mixture is,
represents V g Transposing;
step S1.1.4: calculating a state variable x pg Middle l state variables { f k I k 1,2, …, l participates in the oscillation mode s o Participation factor { P } of 1,2, …, z | o ═ o | k,go ＝U k,go V k,go 1, | k ═ 1,2, …, l; o ═ 1,2, …, z }; wherein f is k Is a state variable x pg Of (1) a k-th state variable, U k,go Is a right eigenvector U go The kth element of (1), V k,go Is a left eigenvector V go The kth element of (1), P k,go Is the k-th state variable f k Participating in the o-th oscillation mode s o The participation factor of (a);
step S1.1.5: screening of l × z participating factors { P k,go ＝U k,go V k,go 1, | k ═ 1,2, …, l; in-station oscillation mode s corresponding to 1,2, …, z in And station network oscillation mode s out (ii) a The in-station oscillation mode refers to an oscillation mode in which the participation degree of the state variable of the photovoltaic power station is greater than a set threshold value; the station network oscillation mode refers to an oscillation mode in which the participation degrees of the photovoltaic power station state variable and the alternating current power network state variable are both greater than a set threshold value;
step S1.1.6: oscillating pattern s in slave station in The oscillation mode lambda closest to the virtual axis is screened out pc And is used as a main station internal oscillation mode of a photovoltaic power station grid-connected system;
slave station network oscillation mode s out The oscillation mode lambda closest to the virtual axis is screened out gc And is used as a leading station network oscillation mode of a photovoltaic power station grid-connected system;
step S1.2: acquiring a lambda of oscillation mode participating in the master station pc And master station network oscillation mode lambda gc The dominant state variable of (2);
step S1.2.1: solving oscillation mode lambda in the main guide station by using formula (4) pc Corresponding left eigenvector U pc And right eigenvector V pc (ii) a Solving the oscillation mode lambda of the master station network by using the formula (5) gc Corresponding left eigenvector U gc And right eigenvector V gc ；
In the formula (4), the reaction mixture is,
represents V pc Transposing;
in the formula (5), the reaction mixture is,
represents V gc Transposing;
step S1.2.2: calculating l state variables { f k Participating in an oscillation mode λ | -1, 2, …, l | -within the master station pc Participation factor of { P } k,pc 1, | k ═ 1,2, …, l }; wherein the kth state variable participates in the oscillation mode lambda in the master station pc Of (2) is involved in factor P k,pc ＝U k,pc V k,pc ，U k,pc Is a right eigenvector U pc Item k of (1), V k,pc Is a left eigenvector V pc The kth term of (1);
calculating l state variables { f k Participating in the master station network oscillation mode λ is | -1, 2, …, l | gc Participation factor of { P } k,gc 1, | k ═ 1,2, …, l }; wherein the kth state variable participates in the oscillation mode lambda of the master station network gc Of (2) is involved in factor P k,gc ＝U k,gc V k,gc ，U k,gc Is a right eigenvector U gc Item k of (1), V k,gc Is a left eigenvector V gc The kth term of (1);
step S1.2.3: for { P k,pc Sorting the | k | -1, 2, …, l } in descending order according to the number m of dominant state variables of the oscillation mode in the dominant station to be selected pc Selecting the top m after descending order pc Participation state variable { x corresponding to each participation factor pc,j |j＝1,2,…,m pc The state variable is used as a leading state variable of a vibration mode in a leading station; wherein x is pc,j Is { P k,pc The participation state variables corresponding to j participation factors after descending sorting of | k ═ 1,2, … and l };
for { P k,gc 1,2, …, l, sorting in descending order according to the number m of leading state variables of leading station network oscillation mode to be selected gc Selecting the top m after descending order gc Participation state variable { x corresponding to each participation factor gc,q |q＝1,2,…,m gc The state variable is used as the leading state variable of the leading station network oscillation mode; wherein x is gc,q Is { P k,gc Participation state variables corresponding to the q-th participation factor after descending sorting of | k ═ 1,2, …, l };
step S1.3: obtaining a coherent photovoltaic power generation unit of a photovoltaic power station:
step S1.3.1: dominant state variable { x) of oscillation mode from within the dominant station pc,j |j＝1,2,…,m pc Screening out state variables { x) of n photovoltaic power generation units pcp,i 1,2, …, n and a state variable x of the ac power supply system pcg (ii) a Wherein x is pcp,i Represents a number from { x pc,j |j＝1,2,…,m pc Shape of ith photovoltaic power generation unit screened out inA state variable;
leading state variable { x) from leading station network oscillation mode gc,q |q＝1,2,…,m gc Screening out state variables { x) of n photovoltaic power generation units gcp,i 1,2, …, n and a state variable x of the ac power supply system gcg (ii) a Wherein x is gcp,i Represents a number from { x } gc,q |q＝1,2,…,m gc The state variable of the ith photovoltaic power generation unit screened out in the step (b);
step S1.3.2: obtaining state variable { x through load flow calculation pcp,i Initial value { x } of 1,2, …, n | i pcp,i,0 1,2, …, n and a state variable { x | gcp,i Initial value { x } of 1,2, …, n | i gcp,i,0 1,2, …, n }; wherein x is pcp,i,0 Denotes x pcp,i Initial value of (1), x gcp,i,0 Denotes x gcp,i An initial value of (1);
step S1.3.3: x is to be pcp,i,0 、x gcp,i,0 The illumination intensity and the geographic position of the photovoltaic power generation unit are used as the homomorphic indexes, and the photovoltaic power station is divided into w homomorphic photovoltaic power generation groups PV by adopting a k-means clustering algorithm 1 ,PV 2 ,…,PV w (ii) a Wherein PV w Representing the w-th coherent photovoltaic power generation group;
step S2: establishing an intra-station/station network decoupling reduced order system of a photovoltaic power station grid-connected system;
step S2.1: test w coherent photovoltaic power generation group PV respectively 1 ,PV 2 ,…,PV w Damping of the photovoltaic power generation unit;
step S2.2: selecting the parameter of the photovoltaic power generation unit with the worst damping in the coherent photovoltaic power generation group as the parameter of all the photovoltaic power generation units in the coherent photovoltaic power generation group, thereby obtaining w coherent photovoltaic power generation groups PV subjected to parameter setting 1|r ,PV 2|r ,…,PV w|r (ii) a Wherein PV w|r Representing the w-th coherent photovoltaic power generation group after parameter setting;
step S2.3: will PV 1|r ,PV 2|r ,…,PV w|r The decoupling is respectively reduced to two decoupling photovoltaic power generation units, wherein the first decoupling photovoltaic power generation unit and the coherent photovoltaic power generation unit areThe voltage amplification times and the current amplification times of the second decoupling photovoltaic power generation units are equal to the number of the photovoltaic power generation units in the coherent photovoltaic power generation group;
step S2.4: connecting a first decoupling photovoltaic power generation unit with an infinite bus, and connecting a second decoupling photovoltaic power generation unit with an alternating current power grid, so as to correspondingly obtain an intra-station/station-network decoupling reduced-order system of a photovoltaic power station grid-connected system;
step S3: performing parameter optimization on the in-station/station network decoupling reduced-order system;
step S3.1: screening leading state variables { x ] of leading station internal oscillation mode in outbound internal/station network decoupling reduced order system pc,j |j＝1,2,…,m pc And leading state variable { x) of leading station network oscillation mode gc,q |q＝1,2,…,m gc The corresponding primary parameters and control parameters are used as optimization variables;
step S3.2: with oscillating pattern lambda in the main station to be improved pc And master station network oscillation mode lambda gc Establishing an objective function F in a parameter optimization model of the in-station/station network decoupling reduced order system by using the formula (6) as a target mode;
F＝max(η pc ζ pc +η gc ζ gc ) (6)
in the formula (6), eta pc Is a vibration mode lambda in a main station pc Weighting coefficient of damping ratio, ζ pc Is a vibration mode lambda in a main station pc Damping ratio of gc As the oscillation mode lambda of the master station network gc Weighting coefficient of damping ratio, ζ gc As the oscillation mode lambda of the master station network gc The damping ratio of (d);
step S3.3: in the parameter optimization process, the following constraint conditions are set:
1) oscillation mode lambda in the master station pc And master station network oscillation mode lambda gc Is greater than a set threshold value epsilon 0 ；
2) Damping ratio of non-dominant oscillation mode not lower than set threshold epsilon 1 (ii) a Wherein the non-dominant oscillation mode represents an oscillation mode{s o Division of λ in 1,2, …, z | pc And λ gc An external oscillation mode;
3) the overshoot and the adjustment time of the photovoltaic power station control system meet the actual requirements of a photovoltaic power station grid-connected system;
4) the maximum transmission power of the grid-connected system of the photovoltaic power station is not lower than the set threshold epsilon 2 ；
Step S3.4: and solving the parameter optimization model by using an intelligent optimization algorithm so as to obtain optimized primary parameters and control parameters.
Compared with the prior art, the invention has the beneficial effects that:
1. the method is different from the traditional homodyne equivalence method which reduces the order of a homodyne photovoltaic power generation group into a single photovoltaic power generation unit, decouples the order of the homodyne photovoltaic power generation group into two photovoltaic power generation units, can simultaneously reflect the problem of in-station/station network oscillation, overcomes the defect that the traditional homodyne equivalence method is difficult to reflect the in-station oscillation, and improves the applicability of a parameter optimization model;
2. the parameter optimization is carried out based on the reduced decoupling system, so that the parameter optimization time is obviously shortened, and the parameter optimization efficiency is improved;
3. compared with the traditional optimization method which only considers the problem of single station network oscillation, the method disclosed by the invention considers the problem of in-station oscillation and the problem of station network oscillation simultaneously in the selection of the target function in the parameter optimization process, and the selection of the constraint condition is more comprehensive, so that the accuracy and the applicability of the parameter optimization method can be improved.
Drawings
FIG. 1 is a block diagram of a photovoltaic power plant grid-connected system of the present invention;
FIG. 2 is a flow chart of the present invention;
FIG. 3 is a flow chart of in-station/station-network decoupling order reduction of a photovoltaic power station grid-connected system of the invention;
FIG. 4 is a flow chart of parameter optimization according to the present invention.
Detailed Description
The technical scheme of the invention is specifically explained below with reference to the accompanying drawings.
In this embodiment, as shown in fig. 1, the photovoltaic power station grid-connected system includes: n photovoltaic power generation units and an alternating current power grid, and the number of any one photovoltaic power generation unit is marked as i, i is 1, …, n;
as shown in fig. 2, a parameter optimization method for in-station/station-network oscillation suppression of a photovoltaic power station grid-connected system is performed according to the following steps:
step S1: obtaining a coherent photovoltaic power generation unit suitable for in-station/station network oscillation analysis;
step S1.1: acquiring a master station in/station network oscillation mode of a photovoltaic power station grid-connected system:
step S1.1.1: obtaining an initial operating point x of a photovoltaic power station grid-connected system through load flow calculation 0 And obtaining the initial operation point x of the photovoltaic power station grid-connected system by using the formula (1) 0 A linearized state space model of (a);
in the formula (1), d represents a differential, t is a time, x pg For state variables of the grid-connected system of photovoltaic power stations, A pg For a state matrix of a grid-connected system of photovoltaic power stations, B pg Input matrix u for grid-connected system of photovoltaic power station pg The method comprises the following steps of (1) inputting variables of a photovoltaic power station grid-connected system; Δ x pg Denotes x pg Increment of (a), u pg Represents u pg The increment of (d);
step S1.1.2: solve equation (2) and obtain the state matrix A pg Characteristic value of (1 [ [ lambda ]) e 1, | e ═ 1,2, …, l }; wherein l is A pg Order of (a) ("lambda") e Is represented by A pg The e-th feature value of (1);
|λ e E pg -A pg |＝0 (2)
in the formula (2), E pg Is and state matrix A pg Identity matrix of the same order;
step S1.1.3: since the characteristic values with the real part of 0 or the imaginary part of 0 are all non-oscillation modes, the { lambda is screened out e Z oscillations with real and imaginary parts both different from 0 in | e ═ 1,2, …, l |Oscillation mode { s } o 1,2, …, z, and { s } is obtained by equation (3) o Left eigenvector { U } corresponding to | o ═ 1,2, …, z | go 1,2, …, z and right eigenvector { V | go 1,2, …, z }; wherein s is o For the o-th oscillatory mode, U, in which both the real and imaginary parts are non-0 go Is s is o Corresponding left eigenvector, V go Is as s o A corresponding right eigenvector;
in the formula (3), the reaction mixture is,
represents V g Transposing;
step S1.1.4: calculating a state variable x pg Middle l state variables { f k I k 1,2, …, l participates in the oscillation mode s o Participation factor { P } of | o ═ 1,2, …, z k,go ＝U k,go V k,go 1, | k ═ 1,2, …, l; o ═ 1,2, …, z }; wherein f is k Is a state variable x pg Of (1) a k-th state variable, U k,go Is a right eigenvector U go The kth element of (1), V k,go Is a left eigenvector V go Element of (k), P k,go Is the kth state variable f k Participating in the o-th oscillation mode s o The participation factor of (a);
step S1.1.5: screening of l × z participating factors { P k,go ＝U k,go V k,go 1, | k ═ 1,2, …, l; o ═ 1,2, …, z } corresponding in-station oscillation mode s in And station network oscillation mode s out (ii) a The in-station oscillation mode refers to an oscillation mode in which the participation degree of the state variable of the photovoltaic power station is greater than a set threshold value and is 0.9; the station network oscillation mode refers to an oscillation mode in which the participation degree of the photovoltaic power station state variable and the alternating current power network state variable is greater than a set threshold value and is 0.1;
step S1.1.6: since the dominant oscillation mode has a large influence on the system stability, the oscillation mode s in the slave station in Middle screening out the nearest to the virtual axisOf the oscillation mode λ pc And is used as a main station internal oscillation mode of a photovoltaic power station grid-connected system;
slave station network oscillation mode s out The oscillation mode lambda closest to the virtual axis is screened out gc And is used as a leading station network oscillation mode of a photovoltaic power station grid-connected system;
step S1.2: obtaining oscillation mode lambda in participating main guide station pc And master station network oscillation mode lambda gc The dominant state variable of (2);
step S1.2.1: solving oscillation mode lambda in the main guide station by using formula (4) pc Corresponding left eigenvector U pc And right eigenvector V pc (ii) a Solving oscillation mode lambda of main guide station network by using formula (5) gc Corresponding left eigenvector U gc And right eigenvector V gc ；
In the formula (4), the reaction mixture is,
denotes V pc Transposing;
in the formula (5), the reaction mixture is,
denotes V gc Transposing;
step S1.2.2: calculating l state variables { f k I k is 1,2, …, l participates in the oscillation mode lambda in the main station pc Participation factor of { P } k,pc 1, | k ═ 1,2, …, l }; wherein the kth state variable participates in the oscillation mode lambda in the master station pc Of (2) is involved in factor P k,pc ＝U k,pc V k,pc ，U k,pc Is a right eigenvector U pc Item k of (1), V k,pc Is a left eigenvector V pc The kth term of (1);
calculating l state variables { f k 1,2, …, l participating in the master station network oscillation mode lambda gc Participation factor of { P } k,gc 1, | k ═ 1,2, …, l }; wherein the kth state variable participates in the oscillation mode lambda of the master station network gc Of (2) is involved in factor P k,gc ＝U k,gc V k,gc ，U k,gc Is a right eigenvector U gc Item k of (1), V k,gc Is a left eigenvector V gc The kth term of (1);
step S1.2.3: for { P k,pc 1,2, …, l, sorting in descending order according to the number m of dominant state variables of the oscillation mode in the dominant station to be selected pc Selecting the top m after descending sorting pc Participation state variable { x corresponding to each participation factor pc,j |j＝1,2,…,m pc The state variable is used as a leading state variable of a vibration mode in a leading station; wherein x is pc,j Is { P k,pc The j < th > participation factor is sorted in descending order, and the j < th > participation factor corresponds to the I < k > -1, 2, …, l;
to { P k,gc Sorting the I k-1, 2, …, l in descending order according to the number m of leading state variables of leading station network oscillation mode to be selected gc Selecting the top m after descending order gc Participation state variable { x) corresponding to each participation factor gc,q |q＝1,2,…,m gc The state variable is used as the leading state variable of the leading station network oscillation mode; wherein x is gc,q Is { P k,gc Participation state variables corresponding to the q-th participation factor after descending sorting of | k ═ 1,2, …, l };
wherein, { x pc,j |j＝1,2,…,m pc And { x } gc,q |q＝1,2,…,m gc There may be repeated leading state variables, and one of the repeated leading state variables may be selected in the actual leading state variable selection process;
step S1.3: obtaining a coherent photovoltaic power generation unit of a photovoltaic power station:
step S1.3.1: dominant state variables { x ] from the oscillation mode within the dominant station pc,j |j＝1,2,…,m pc Screening out state variables { x) of n photovoltaic power generation units pcp,i 1,2, …, n and state variables of the ac power supply systemx pcg (ii) a Wherein x is pcp,i Represents a number from { x pc,j |j＝1,2,…,m pc The state variable of the ith photovoltaic power generation unit screened out in the step (b);
leading state variable { x) of oscillation mode of leading station network gc,q |q＝1,2,…,m gc Screening out state variables { x) of n photovoltaic power generation units gcp,i 1,2, …, n and a state variable x of the ac power supply system gcg (ii) a Wherein x is gcp,i Represents a number from { x gc,q |q＝1,2,…,m gc The state variable of the ith photovoltaic power generation unit screened out in the step (b);
step S1.3.2: obtaining state variable { x through load flow calculation pcp,i Initial value { x } of 1,2, …, n | i pcp,i,0 1,2, …, n and a state variable { x | gcp,i Initial value { x } of 1,2, …, n | i gcp,i,0 |i＝1,2,…,n}；x pcp,i,0 Represents x pcp,i Initial value of (1), x gcp,i,0 Denotes x gcp,i An initial value of (1);
step S1.3.3: x is to be pcp,i,0 、x gcp,i,0 The illumination intensity and the geographic position of the photovoltaic power generation unit are used as coherent indexes, and a k-means clustering algorithm is adopted to divide the photovoltaic power station into w coherent photovoltaic power generation groups PV 1 ,PV 2 ,…,PV w (ii) a Wherein PV w Representing the w-th coherent photovoltaic power generation group; the method for acquiring the coherent photovoltaic power generation group based on the k-means algorithm comprises the following steps:
1) collecting { x of photovoltaic power generation unit in photovoltaic power station pcp,i,0 |i＝1,2,…,n}、{x gcp,i,0 1,2, …, n }, illumination intensity and geographic position, and standardizing the parameters as sample data, i.e. subtracting the average value of the sample data and dividing by the standard deviation of the sample data;
2) calculating Euclidean distances among samples, and setting a weighted average value of the distances among the photovoltaic power generation groups as a clustering index;
3) screening the object with the largest distance, and selecting y initial clustering center points from the rest samples;
4) respectively allocating the samples to the groups closest to the samples according to the similarity between the samples and the clustering centers, and performing iterative computation;
5) repeating steps 3) and 4) until all samples cannot be distributed;
step S2: as shown in fig. 3, the in-station/station network decoupling reduced-order system modeling step of the photovoltaic power station grid-connected system is as follows:
step S2.1: respectively testing w coherent photovoltaic power generation groups PV 1 ,PV 2 ,…,PV w Damping of the photovoltaic power generation unit; in actual engineering, equipment manufacturers generally test the damping of the photovoltaic power generation unit in advance, so that the damping of the photovoltaic power generation unit can be obtained by means of data acquisition from the equipment manufacturers or field test;
step S2.2: selecting the parameter of the photovoltaic power generation unit with the worst damping in the coherent photovoltaic power generation group as the parameter of all the photovoltaic power generation units in the coherent photovoltaic power generation group, thereby obtaining w coherent photovoltaic power generation groups PV subjected to parameter setting 1|r ,PV 2|r ,…,PV w|r (ii) a Wherein PV w|r Representing the w-th coherent photovoltaic power generation group after parameter setting; in the step 2.2, although the parameters of the photovoltaic power generation units in the coherent photovoltaic power generation group are selected, the parameter optimization process has certain conservatism, the order reduction of a parameter optimization model is facilitated, and the system has sufficient stability margin after the parameters are optimized, so that the convenience and sufficient stability margin of parameter optimization are obtained at the cost of certain conservatism;
step S2.3: will PV 1|r ,PV 2|r ,…,PV w|r Respectively reducing and decoupling into two decoupling photovoltaic power generation units, wherein the first decoupling photovoltaic power generation unit is the same as the photovoltaic power generation units in the coherent photovoltaic power generation group, and the voltage amplification times and the current amplification times of the second decoupling photovoltaic power generation unit are equal to the number of the photovoltaic power generation units in the coherent photovoltaic power generation group;
step S2.4: connecting a first decoupling photovoltaic power generation unit with an infinite bus, and connecting a second decoupling photovoltaic power generation unit with an alternating current power grid, so as to correspondingly obtain an intra-station/station-network decoupling reduced-order system of a photovoltaic power station grid-connected system; for a photovoltaic power plant containing w coherent photovoltaic power generation groups,if z photovoltaic power generation units exist in each coherent photovoltaic power generation group, the order of each photovoltaic power generation unit is h p Order of the AC network is h g The order of the photovoltaic power station grid-connected system can be h after in-station/station network decoupling and order reduction p wz+h g Step down to 2h p w+h g Step (2); the larger the z is, the more obvious the order reduction effect is;
step S3: as shown in fig. 4, the parameter optimization steps of the intra-site/site-network decoupling reduced-order system are as follows:
step S3.1: screening leading state variables { x ] of leading station internal oscillation mode in outbound internal/station network decoupling reduced order system pc,j |j＝1,2,…,m pc And dominant state variable { x) of dominant station network oscillation mode gc,q |q＝1,2,…,m gc Primary parameters and control parameters corresponding to the parameters are used as optimization variables;
step S3.2: with oscillating pattern lambda in the main station to be improved pc And master station network oscillation mode lambda gc Establishing an objective function F in a parameter optimization model of the in-station/station network decoupling reduced order system by using the formula (6) as a target mode; the target function shown in the formula (6) simultaneously considers two risks of intra-station oscillation and station network oscillation, and the applicability of parameter optimization can be improved;
F＝max(η pc ζ pc +η gc ζ gc ) (6)
in the formula (6), eta pc Is a vibration mode lambda in a main station pc Weighting coefficient of damping ratio, ζ pc Is a main oscillation mode lambda in the station pc Damping ratio of [, ] gc As the oscillation mode lambda of the master station network gc Weighting coefficient of damping ratio, ζ gc As the oscillation mode lambda of the master station network gc The damping ratio of (d);
step S3.3: in the parameter optimization process, the following constraint conditions are set:
1) oscillation mode lambda in the master station pc And master station network oscillation mode lambda gc Is greater than a set threshold value epsilon 0 ；
2) Damping ratio of non-dominant oscillation mode not lower than set threshold epsilon 1 (ii) a Wherein is not the mainThe guided oscillation mode represents the oscillation mode s o Division of λ in 1,2, …, z | pc And λ gc An external oscillation mode;
3) the overshoot and the adjustment time of the photovoltaic power station control system meet the actual requirements of a photovoltaic power station grid-connected system;
4) the maximum transmission power of the grid-connected system of the photovoltaic power station is not lower than the set threshold epsilon 2 ；
The constraint conditions are selected to ensure that the rest performances of the system are not influenced under the condition that the photovoltaic power station grid-connected system has certain in-station/station network oscillation damping;
step S3.4: solving the parameter optimization model by using an intelligent optimization algorithm so as to obtain optimized primary parameters and control parameters; in specific implementation, the solution of the parameter optimization model can adopt intelligent optimization algorithms such as a genetic algorithm, a particle swarm algorithm, an ant colony algorithm and the like.
Claims (1)
1. A parameter optimization method for in-station/station-network oscillation suppression of a photovoltaic power station grid-connected system comprises the following steps: n photovoltaic power generation units and an alternating current power grid, and the number of any one photovoltaic power generation unit is marked as i, i is 1, …, n; the method is characterized by comprising the following steps:
step S1: obtaining a coherent photovoltaic power generation unit suitable for in-station/station network oscillation analysis;
step S1.1: acquiring a master station in/station network oscillation mode of a photovoltaic power station grid-connected system:
step S1.1.1: obtaining an initial operating point x of a photovoltaic power station grid-connected system through load flow calculation 0 And obtaining the initial operation point x of the photovoltaic power station grid-connected system by using the formula (1) 0 A linearized state space model of (a);
in the formula (1), d represents a differential, t is a time, x pg For the state change of a grid-connected system of a photovoltaic power stationAmount, A pg For a state matrix of a grid-connected system of photovoltaic power stations, B pg Input matrix u for grid-connected system of photovoltaic power station pg The method comprises the following steps of (1) inputting variables of a photovoltaic power station grid-connected system; Δ x pg Denotes x pg Increment of (a), u pg Represents u pg An increment of (d);
step S1.1.2: solve equation (2) and obtain the state matrix A pg Characteristic value of (1 [ [ lambda ]) e 1, | e ═ 1,2, …, l }; wherein l is A pg Order of (a) e Is represented by A pg The e-th feature value of (1);
λ e E pg -A pg ＝0(2)
in the formula (2), E pg Is and state matrix A pg Identity matrix of the same order;
step S1.1.3: screening out { lambda e Z oscillation modes { s } with real part and imaginary part both different from 0 in | e ═ 1,2, …, l | o 1,2, …, z, and { s } is obtained by equation (3) o Left eigenvector { U } corresponding to 1,2, …, z | o ═ 1,2, … go 1,2, …, z and right eigenvector { V | go 1,2, …, z }; wherein s is o For the o-th oscillatory mode, U, with both real and imaginary parts being non-0 go Is s is o Corresponding left eigenvector, V go Is s is o A corresponding right eigenvector;
in the formula (3), the reaction mixture is,
represents V g Transposing;
step S1.1.4: calculating a state variable x pg Middle l state variables { f k 1,2, …, l participates in the oscillation mode s o Participation factor { P } of | o ═ 1,2, …, z k,go ＝U k,go V k,go 1, | k ═ 1,2, …, l; o ═ 1,2, …, z }; wherein, f k Is a state variable x pg Of (1) a k-th state variable, U k,go Is a right eigenvector U go The kth element of (1), V k,go Is a left eigenvector V go The kth element of (1), P k,go Is the kth state variable f k Participating in the o-th oscillation mode s o The participation factor of (c);
step S1.1.5: screening of l × z participating factors { P k,go ＝U k,go V k,go 1,2, …, l; o ═ 1,2, …, z } corresponding in-station oscillation mode s in And station network oscillation mode s out (ii) a The in-station oscillation mode refers to an oscillation mode in which the participation degree of the state variable of the photovoltaic power station is greater than a set threshold value; the station network oscillation mode refers to an oscillation mode in which the participation degrees of the state variable of the photovoltaic power station and the state variable of the alternating current power network are both larger than a set threshold value;
step S1.1.6: oscillating pattern s in slave station in The oscillation mode lambda closest to the virtual axis is screened out pc And is used as a main station internal oscillation mode of a photovoltaic power station grid-connected system;
slave station network oscillation mode s out The oscillation mode lambda closest to the virtual axis is screened out gc And is used as a leading station network oscillation mode of a photovoltaic power station grid-connected system;
step S1.2: acquiring a vibration mode lambda participating in the main control station pc And master station network oscillation mode lambda gc The dominant state variable of (2);
step S1.2.1: solving oscillation mode lambda in the main control station by using formula (4) pc Corresponding left eigenvector U pc And right eigenvector V pc (ii) a Solving the oscillation mode lambda of the master station network by using the formula (5) gc Corresponding left eigenvector U gc And right eigenvector V gc ；
In the formula (4), the reaction mixture is,
represents V pc Transposing;
in the formula (5), the reaction mixture is,
represents V gc Transposing;
step S1.2.2: calculating l state variables { f k Participating in an oscillation mode λ | -1, 2, …, l | -within the master station pc Participation factor of { P } k,pc 1, | k ═ 1,2, …, l }; wherein the kth state variable participates in the oscillation mode lambda in the master station pc Of (2) is involved in factor P k,pc ＝U k,pc V k,pc ，U k,pc Is a right eigenvector U pc Item k of (1), V k,pc Is a left eigenvector V pc The kth term of (1);
calculate l state variables { f k Participating in the master station network oscillation mode λ is | -1, 2, …, l | gc Participation factor of { P } k,gc 1, | k ═ 1,2, …, l }; wherein the kth state variable participates in the oscillation mode lambda of the master station network gc Of (2) is involved in factor P k,gc ＝U k,gc V k,gc ，U k,gc Is a right eigenvector U gc Item k of (1), V k,gc Is a left eigenvector V gc The kth term of (1);
step S1.2.3: to { P k,pc Sorting the | k | -1, 2, …, l } in descending order according to the number m of dominant state variables of the oscillation mode in the dominant station to be selected pc Selecting the top m after descending order pc Participation state variable { x corresponding to each participation factor pc,j |j＝1,2,…,m pc The state variable is used as a leading state variable of a vibration mode in a leading station; wherein x is pc,j Is { P k,pc The participation state variables corresponding to j participation factors after descending sorting of | k ═ 1,2, … and l };
for { P k,gc Sorting the I k-1, 2, …, l in descending order according to the number m of leading state variables of leading station network oscillation mode to be selected gc Selecting the top m after descending order gc Participation state variable { x) corresponding to each participation factor gc,q |q＝1,2,…,m gc The state variable is used as the leading state variable of the leading station network oscillation mode; wherein x is gc,q Is { P k,gc The participation state variable corresponding to the q-th participation factor after descending sorting of 1,2, … and l is obtained;
step S1.3: obtaining a coherent photovoltaic power generation unit of a photovoltaic power station:
step S1.3.1: dominant state variables { x ] from the oscillation mode within the dominant station pc,j |j＝1,2,…,m pc Screening state variables (x) of n photovoltaic power generation units pcp,i 1,2, …, n and a state variable x of the ac power supply system pcg (ii) a Wherein x is pcp,i Represents a number from { x pc,j |j＝1,2,…,m pc The state variable of the ith photovoltaic power generation unit screened out in the step (b);
leading state variable { x) of oscillation mode of leading station network gc,q |q＝1,2,…,m gc Screening state variables (x) of n photovoltaic power generation units gcp,i 1,2, …, n and a state variable x of the ac power supply system gcg (ii) a Wherein x is gcp,i Represents a number from { x gc,q |q＝1,2,…,m gc The state variable of the ith photovoltaic power generation unit screened out in the step (b);
step S1.3.2: obtaining state variable { x through load flow calculation pcp,i Initial value { x } of 1,2, …, n | i pcp,i,0 1,2, …, n and a state variable { x | gcp,i Initial value { x } of 1,2, …, n | i gcp,i,0 1,2, …, n }; wherein x is pcp,i,0 Denotes x pcp,i Initial value of (1), x gcp,i,0 Denotes x gcp,i An initial value of (1);
step S1.3.3: x is to be pcp,i,0 、x gcp,i,0 The illumination intensity and the geographic position of the photovoltaic power generation unit are used as coherent indexes, and a k-means clustering algorithm is adopted to divide the photovoltaic power station into w coherent photovoltaic power generation groups PV 1 ,PV 2 ,…,PV w (ii) a Wherein PV w Representing the w-th coherent photovoltaic power generation group;
step S2: establishing an intra-station/station network decoupling reduced order system of a photovoltaic power station grid-connected system;
step S2.1: respectively testing w coherent photovoltaic power generation groupsPV 1 ,PV 2 ,…,PV w Damping of the photovoltaic power generation unit;
step S2.2: selecting the parameter of the photovoltaic power generation unit with the worst damping in the coherent photovoltaic power generation group as the parameter of all the photovoltaic power generation units in the coherent photovoltaic power generation group, thereby obtaining w coherent photovoltaic power generation groups PV subjected to parameter setting 1|r ,PV 2|r ,…,PV w|r (ii) a Wherein PV w|r Representing the w-th coherent photovoltaic power generation group after parameter setting;
step S2.3: will PV 1|r ,PV 2|r ,…,PV w|r Respectively reducing and decoupling into two decoupling photovoltaic power generation units, wherein the first decoupling photovoltaic power generation unit is the same as the photovoltaic power generation units in the coherent photovoltaic power generation group, and the voltage amplification times and the current amplification times of the second decoupling photovoltaic power generation unit are equal to the number of the photovoltaic power generation units in the coherent photovoltaic power generation group;
step S2.4: connecting the first decoupling photovoltaic power generation unit with an infinite bus, and connecting the second decoupling photovoltaic power generation unit with an alternating current power grid, so as to correspondingly obtain an intra-station/station-network decoupling reduced-order system of a photovoltaic power station grid-connected system;
step S3: performing parameter optimization on the in-station/station network decoupling reduced-order system;
step S3.1: screening leading state variables { x ] of leading station internal oscillation mode in outbound internal/station network decoupling reduced order system pc,j |j＝1,2,…,m pc And leading state variable { x) of leading station network oscillation mode gc,q |q＝1,2,…,m gc The corresponding primary parameters and control parameters are used as optimization variables;
step S3.2: with oscillating pattern lambda in the master station to be improved pc And master station network oscillation mode lambda gc Establishing an objective function F in a parameter optimization model of the in-station/station network decoupling reduced order system by using the formula (6) as a target mode;
F＝max(η pc ζ pc +η gc ζ gc )(6)
in the formula (6), eta pc Is a vibration mode lambda in a main station pc Weighted system of damping ratioNumber, ζ pc Is a vibration mode lambda in a main station pc Damping ratio of gc As the main station network oscillation mode lambda gc Weighting coefficient of damping ratio, ζ gc As the oscillation mode lambda of the master station network gc The damping ratio of (d);
step S3.3: in the parameter optimization process, the following constraint conditions are set:
1) oscillation mode lambda in the master station pc And master station network oscillation mode lambda gc Is greater than a set threshold value epsilon 0 ；
2) Damping ratio of non-dominant oscillation mode not lower than set threshold epsilon 1 (ii) a Wherein the non-dominant oscillation mode represents the oscillation mode s o Division of λ in 1,2, …, z |) pc And λ gc An external oscillation mode;
3) the overshoot and the adjustment time of the photovoltaic power station control system meet the actual requirements of a photovoltaic power station grid-connected system;
4) the maximum transmission power of the grid-connected system of the photovoltaic power station is not lower than the set threshold epsilon 2 ；
Step S3.4: and solving the parameter optimization model by using an intelligent optimization algorithm so as to obtain optimized primary parameters and control parameters.
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2014154027A1 (en) * | 2013-03-29 | 2014-10-02 | 甘肃省电力公司风电技术中心 | Method for improving system small disturbance stability after access of double-fed unit |
| WO2017067120A1 (en) * | 2015-10-19 | 2017-04-27 | 国家电网公司 | Method for acquiring low-voltage ride-through data of photovoltaic power station |
| CN109217289A (en) * | 2018-08-24 | 2019-01-15 | 南京理工大学 | A kind of photovoltaic additional damping controller parameter optimization method based on grey wolf algorithm |
| CN110233503A (en) * | 2019-07-10 | 2019-09-13 | 华北电力大学（保定） | Grid-connected photovoltaic inverter Optimization about control parameter method |
| CN110611325A (en) * | 2018-06-15 | 2019-12-24 | 南京理工大学 | Wind power plant subsynchronous oscillation suppression method based on particle swarm optimization |
| CN111404196A (en) * | 2020-05-20 | 2020-07-10 | 上海电力大学 | Grid-connected resonance analysis method and system based on photovoltaic virtual synchronous generator |
| CN112448410A (en) * | 2019-08-31 | 2021-03-05 | 南京理工大学 | Modal comprehensive analysis method applied to low-frequency oscillation of power system containing photovoltaic power station |
-
2022
- 2022-07-05 CN CN202210791747.8A patent/CN115102190B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2014154027A1 (en) * | 2013-03-29 | 2014-10-02 | 甘肃省电力公司风电技术中心 | Method for improving system small disturbance stability after access of double-fed unit |
| WO2017067120A1 (en) * | 2015-10-19 | 2017-04-27 | 国家电网公司 | Method for acquiring low-voltage ride-through data of photovoltaic power station |
| CN110611325A (en) * | 2018-06-15 | 2019-12-24 | 南京理工大学 | Wind power plant subsynchronous oscillation suppression method based on particle swarm optimization |
| CN109217289A (en) * | 2018-08-24 | 2019-01-15 | 南京理工大学 | A kind of photovoltaic additional damping controller parameter optimization method based on grey wolf algorithm |
| CN110233503A (en) * | 2019-07-10 | 2019-09-13 | 华北电力大学（保定） | Grid-connected photovoltaic inverter Optimization about control parameter method |
| CN112448410A (en) * | 2019-08-31 | 2021-03-05 | 南京理工大学 | Modal comprehensive analysis method applied to low-frequency oscillation of power system containing photovoltaic power station |
| CN111404196A (en) * | 2020-05-20 | 2020-07-10 | 上海电力大学 | Grid-connected resonance analysis method and system based on photovoltaic virtual synchronous generator |
Non-Patent Citations (2)
| Title |
|---|
| 杨亚兰;巨国娇;黄睿;唐榆东;毕悦;: "光伏电站抑制次同步振荡措施研究", 电气应用, no. 07, 15 July 2020 (2020-07-15) * |
| 高本锋;姚磊;李忍;赵书强;杨琳;: "大规模光伏电站并网的振荡模式分析", 电力自动化设备, no. 08, 10 August 2017 (2017-08-10) * |
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