CN114784784A - Direct-current micro-grid stability optimization control method based on longitudinal and transverse intersection algorithm - Google Patents

Direct-current micro-grid stability optimization control method based on longitudinal and transverse intersection algorithm Download PDF

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CN114784784A
CN114784784A CN202210344826.4A CN202210344826A CN114784784A CN 114784784 A CN114784784 A CN 114784784A CN 202210344826 A CN202210344826 A CN 202210344826A CN 114784784 A CN114784784 A CN 114784784A
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黄泽杭
杨苓
陈璟华
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Abstract

The invention discloses a method for controlling the stability trend of a direct-current micro-grid based on a longitudinal and transverse intersection algorithm, which comprises the following steps: s1: solving a small signal model of the multi-source multi-load direct current micro-grid system; s2: the dominant mode of the system is solved through a characteristic root analysis method, and the influence degree of the state variable on the dominant mode is decoupled through a participation factor method; s3: applying a criss-cross algorithm to stability analysis of the direct-current microgrid, obtaining optimization-seeking parameters to achieve the optimal stability of the system, and enabling the direct-current microgrid to have larger stability margin, thereby realizing stability optimization-seeking control; the invention can realize global parallel search and has high convergence speed by utilizing the advantages of a crisscross algorithm, can quickly obtain the optimization solution of the parameters, effectively avoids the influence of the local solution of the parameters on stability analysis, enables the direct current micro-grid to have stability margin as large as possible, and realizes stability optimization control.

Description

Direct-current micro-grid stability optimization control method based on longitudinal and transverse intersection algorithm
Technical Field
The invention relates to the field of new energy distributed power generation, in particular to a method for controlling the stability trend of a direct current micro-grid based on a crisscross algorithm.
Background
With the trend of high penetration of new energy, the water rises to the ship height, so that the direct-current micro-grid is widely concerned. Most loads are connected to the direct-current micro-grid through the power electronic interface circuit and are always represented as constant-power loads, the negative impedance characteristic of the loads easily causes system instability and reduces the quality of system electric energy, and therefore improvement of system stability becomes the key point of direct-current micro-grid operation research. The system parameter optimization is a mode capable of effectively improving the system stability, most of research system parameter optimization is to analyze the movement track of the characteristic root under the parameter change through a discrete form, and although the influence rule of the parameter adjustment trend on the system stability can be qualitatively analyzed, the quantitative result of the multi-parameter optimization control of the system cannot be obtained. Therefore, the invention provides a parameter Optimization control scheme based on a cross-bar Algorithm (CSO).
In order to achieve the purpose, the technical scheme provided by the invention is as follows:
a DC micro-grid stability optimization control method based on a criss-cross algorithm comprises the following steps:
s1: solving a small signal model of the multi-source multi-load direct current micro-grid system;
s2: the dominant mode of the system is solved through a characteristic root analysis method, and the influence degree of the state variable on the dominant mode is decoupled through a participation factor method;
s3: applying a criss-cross algorithm to stability analysis of the direct-current microgrid, obtaining optimization-seeking parameters to achieve the optimal stability of the system, and enabling the direct-current microgrid to have larger stability margin, thereby realizing stability optimization-seeking control;
further, in step S1, the specific steps of obtaining the small-signal model of the multi-source multi-load dc microgrid system are as follows:
s1-1: small signal model of power module:
the power module utilizes the two-way DC/DC converter to carry out voltage conversion, adopts droop control and virtual inertia control method, wherein droop control guarantees the power equipartition between many powers, and virtual inertia control is the theory of operation of similar electric capacity, and the purpose is to improve direct current bus voltage inertia and improve the dynamic behavior when voltage variation, and power module state space model is:
Figure BDA0003575995830000021
in the formula (1), d is the duty ratio of the bidirectional DC/DC converter in the power module, ub、uoNAnd uoFor the pre-conversion voltage, the post-conversion rated voltage and the post-conversion outlet voltage, i, of the bidirectional DC/DC converter in the power supply modulebAnd ibrefFor a pre-conversion current and its reference value, i, of a bidirectional DC/DC converter in a power supply moduleoFor the outlet current, R, after conversion by a bidirectional DC/DC converter in a power supply moduleb、LbAnd CsParasitic resistance, filter inductance and support capacitance of the power supply module, SuRated voltage u converted by bidirectional DC/DC converter in power moduleoNAnd an outlet voltage uoThe squared difference of (a) is passed through a first-order inertia element and then a variable, k, is outputpAnd kiProportional and integral coefficients, S, for the power module PI controlleriPre-conversion current reference i for a bidirectional DC/DC converter in a power supply modulebrefAnd current ibThe difference is output as a variable k after an integration stepdroopIs the sag factor, CvirbThe method is characterized in that a small signal model of a power supply module is obtained by linearizing a formula (1) in the vicinity of a steady state, wherein the model is a virtual inertia coefficient, T is a time constant, and s is a Laplace transform complex variable operator:
Figure BDA0003575995830000022
in the formula (2), Δ xbIs the state vector of the power supply module, Δ xb＝[Δib,ΔSu,ΔSi,Δuo]T，ΔibPre-conversion current i for a bidirectional DC/DC converter in a power supply modulebState variable of, Δ SuRated voltage u after conversion of bidirectional DC/DC converter in power moduleoNAnd an outlet voltage uoThe squared difference of the first-order inertia element outputs a variable SuState variable of (a), Δ SiPre-conversion current reference i for a bidirectional DC/DC converter in a power supply modulebrefAnd current ibThe difference is output as variable S after integraliState variable of (a), Δ uoFor the outlet voltage u after conversion of a bidirectional DC/DC converter in a power supply moduleoA state variable ofbAs a coefficient matrix of the power supply module, d0Is a steady-state quantity, u, of the duty cycle d of a bidirectional DC/DC converter in a power supply moduleo0For the outlet voltage u after conversion of a bidirectional DC/DC converter in a power supply moduleoSteady state quantity of ib0Pre-conversion current i for a bidirectional DC/DC converter in a power supply modulebA steady state quantity of (c);
s1-2: small signal model of constant power load module:
the constant power load module utilizes a Buck converter to carry out voltage conversion, a voltage-current double-loop control strategy is adopted, and a state space model of the constant power load module is as follows:
Figure BDA0003575995830000031
in the formula (3), g is the duty ratio of the Buck converter in the constant-power load module, and u isF、uLNAnd uLFor the conversion of forward port voltage, constant power load rated voltage and constant power load actual voltage, idcConversion of forward port current, i, for Buck converters in constant power load modulesLAnd iLrefIs a constant power load current and its reference value, PLFor constant power load power, RL、LLAnd CLParasitic resistance, filter inductance and filter capacitance of constant power load module, CFSupporting capacitor, k, for constant power load modulepL_uAnd kiL_uProportional and integral coefficients, k, of an outer loop PI controller for constant power load module voltagepL_iAnd kiL_iProportional and integral coefficients of a constant power load module current inner loop PI controller, SuLAnd SiLIs constantThe integral links of the voltage outer loop PI controller and the current inner loop PI controller of the power load module output variables, and a small signal model of the constant power load module is as follows:
Figure BDA0003575995830000032
in the formula (4), Δ xLIs the state vector of the constant-power load module, Δ xL＝[ΔuF,ΔiL,ΔuL,ΔSuL,ΔSiL]T，ΔuFConversion of forward port voltage u for Buck converter in constant power load moduleFState variable of, Δ iLIs a constant power load current iLState variable of (1), Δ uLIs a constant power load actual voltage uLState variable of (a), Δ SuLOutputting variable S for integral link of constant power load module voltage outer loop PI controlleruLState variable of, Δ SiLOutput variable S for integral link of constant power load module current inner loop PI controlleriLA state variable ofLIs a coefficient matrix of the constant power load module, g0Is the steady-state quantity u of the duty ratio g of the Buck converter in the constant power load moduleF0Conversion of forward port voltage u for Buck converter in constant power load moduleFSteady state quantity of (ii), iL0Is a constant power load current iLA steady state quantity of (c);
s1-3: small signal model of the direct current transmission line:
a direct current transmission line is arranged between the power module and the constant power load module, and the state space model of the direct current transmission line is as follows:
Figure BDA0003575995830000041
in the formula (5), uDCAnd CDCFor collecting bus voltage and supporting capacitance, RsAnd LsResistance and inductance of the source side transmission line, RFAnd LFResistance and inductance of load side transmission line, and DC transmission lineThe small signal model of (c) is:
Figure BDA0003575995830000042
in the formula (6), Δ xdIs the state vector, Δ x, of the DC transmission lined＝[Δuo,Δio,ΔuDC,Δidc,ΔuF]T，ΔioFor the outlet current i after conversion of a bidirectional DC/DC converter in a power supply moduleoState variable of (a), Δ uDCFor converging bus voltage uDCState variable of, Δ idcConversion of forward port current i for Buck converter in constant power load moduledcState variable of (D)dA coefficient matrix of the direct current transmission line;
s1-4: a small signal model of the direct-current micro-grid system:
the direct-current micro-grid system is composed of three power modules, three direct-current transmission lines and three constant-power load modules, the structure is in a conventional radiation type, the total length of the transmission lines on the source side and the load side is set to be a certain value and is marked as 1, and the lengths of the transmission lines from the power modules 1, 2 and 3 to a convergence bus are respectively lb1、lb2、lb3The lengths from the convergent bus to the constant power load modules 1, 2 and 3 are respectively lL1、lL2、lL3The small signal model of the system is:
Figure BDA0003575995830000051
in the formula (7), Δ xsysIs a state vector of the DC micro-grid system, Δ xsys＝[Δxbn,Δxd123,ΔxLn]T，ΔxbnIs the state vector of three power supply modules, n is 1, 2, 3, Δ xLnIs the state vector of three constant power load modules, n is 1, 2, 3, delta xd123State vectors, Δ x, for three direct current transmission linesd123＝[Δuon,Δion,ΔuDC,Δidcn,ΔuFn]T，ΔuonAnd Δ ionThe outlet voltage u after conversion of the bidirectional DC/DC converter in the three power supply modulesonAnd an outlet current ionN is 1, 2, 3, Δ idcnAnd Δ uFnConversion of forward port current i for Buck converter in three constant power load modulesdcnAnd inlet voltage uFnN is 1, 2, 3, asysIs a coefficient matrix of the DC micro-grid system, AbnIs a coefficient matrix of three power supply modules, n is 1, 2, 3, Dd123Coefficient matrices for three direct current transmission lines, ALnA coefficient matrix of three constant power load modules, wherein n is 1, 2 and 3;
further, the specific steps of step S2 are as follows:
s2-1: and (3) solving the dominant mode of the system through a characteristic root analysis method:
according to the coefficient matrix A of the direct current micro-grid systemsysSolving the characteristic value to obtain the oscillation mode of the system, wherein the dominant mode is the mode closest to the virtual axis in the oscillation mode and plays a leading role in system response, and the dominant mode solving expression is as follows:
Figure BDA0003575995830000052
in the formula (8), n is the nth oscillation mode with real part not being zero, DsysCoefficient matrix A of DC micro-grid systemsysCharacteristic root matrix of (c), n1Root matrix D of features for dominant modesysPosition of (5), R (n)1) And I (n)1) Being the real and imaginary parts of the dominant mode, DMCoordinates that are dominant modalities;
s2-2: carrying out coupling analysis of a participation factor method on the dominant mode:
in order to research the relation between the dominant mode and the state variable, the influence degree of the state variable on the dominant mode is decoupled by a participation factor method, and a mechanism influencing the stable operation characteristic of a system is obtained, wherein the expression is as follows:
Figure BDA0003575995830000053
in formula (9), pa (k, n)1) To what extent the state variable k affects the dominant modality,
Figure BDA0003575995830000054
is the k-th position in the left eigenvector of the system state matrix, phi (n)1K) is the kth position in the right eigenvector of the system state matrix;
introducing a state variable participation degree evaluation index eta on the basis of participation factor analysiskAfter the participation degree of the state variable k on the dominant mode is comprehensively considered for all the state variables, the expression is as follows:
Figure BDA0003575995830000061
further, the step of step S3 is as follows:
s3-1: determining parameters of the direct current micro-grid system needing to be optimized as population particles, and specifying upper and lower limits of the particles;
s3-2: setting maximum iteration times, population scale and penalty function; because of constraint relation among system parameters, the parameters can not be randomly selected, so a penalty function is defined to solve the problem of constraint conditions, and the objective function and the penalty function are as follows:
Figure BDA0003575995830000062
in formula (11), f1(X) target function fitness before penalty, f2(X) is the fitness of the objective function after punishment, P is the punishment coefficient, P isXTo constrain real-time values of variables, PrefIs a parameter constraint condition;
s3-3: performing vertical interleaving to generate a filial population, wherein the vertical interleaving process is an arithmetic operation for interleaving particles with different dimensions:
Xzc(i,d1)＝r·X(i,d1)+(1-r)·X(i,d2)+c·(X(i,d1)-X(i,d2)) (12)
in the formula (12), r is a random number between 0 and 1, c is a random number between 0 and 1, and X (i, d)1) And X (i, d)2) Being a parent of different dimensions, Xzc(i,d1) Offspring generated for longitudinal intersection of parents of different dimensions;
s3-4: substituting the population particles into a coefficient matrix A of the direct-current micro-grid systemsysSolving the objective function value, checking whether constraint conditions are met, and if so, carrying out the next step; otherwise, adding a penalty function to the particle for calculation, and performing step S3-5;
s3-5: executing a competition operator, storing the population particles with the best current fitness, wherein the competition operator is a mechanism for carrying out parent and offspring comparison elimination, comparing the fitness of the parent and the offspring, and keeping the particles with stronger fitness to participate in the next iteration, so that the whole population finally moves towards the best direction;
s3-6: performing transverse crossing to generate a filial population, wherein the transverse crossing process is an arithmetic operation for crossing all particles with different solutions, and performing steps S3-4 and S3-5:
Figure BDA0003575995830000063
in the formula (13), r1And r2Is a random number between 0 and 1, c1And c2Is a random number between-1 and 1, X (i, d) and X (j, d) are two different parent solutions, Xhc(i, d) and Xhc(j, d) child solutions generated by transversely crossing parent solutions;
s3-7: checking whether the maximum iteration times are reached, if so, selecting the population particles with the best fitness as a system trend optimal solution, and ending the iteration; otherwise, go to step S3-3.
Compared with the prior art, the principle and the advantages of the scheme are as follows:
according to the scheme, a small signal model of the multi-source multi-load direct-current micro-grid system is established, a dominant mode of the system is solved through a characteristic root analysis method, the influence degree of a state variable on the dominant mode is decoupled through a participation factor method, a vertical and horizontal cross algorithm is applied to stability analysis of the direct-current micro-grid, the optimization-oriented parameters are obtained to achieve the optimal stability of the system, the direct-current micro-grid has larger stability margin, and stability optimization-oriented control is achieved.
According to the scheme, the advantage that the vertical and horizontal cross algorithm can realize global parallel search and high convergence speed is utilized, the optimization approach solution of the parameters can be obtained quickly, the influence of the local solution of the parameters on stability analysis is effectively avoided, the direct-current micro-grid has the stability margin as large as possible, and stability optimization approach control is realized.
Drawings
Fig. 1 is a flow chart of the optimization trend control of the stability of the direct current microgrid in the embodiment of the invention;
FIG. 2 is a topological structure and a control block diagram of a multi-source multi-load direct-current micro-grid system in the embodiment of the invention;
FIG. 3 is a characteristic root analysis diagram of the DC micro-grid system in an embodiment of the invention;
FIG. 4 is a graph of a droop coefficient trend control curve according to an embodiment of the present invention;
FIG. 5 is a graph of source side line parameter optimization-seeking control curves in an embodiment of the present invention;
FIG. 6 is a graph showing the change of the voltage waveform of the DC bus before and after the droop coefficient control in the embodiment of the present invention;
fig. 7 is a diagram illustrating voltage waveform changes of the dc bus before and after the control of the source side line parameters according to the embodiment of the present invention.
Detailed Description
The invention will be further illustrated with reference to specific examples:
fig. 1 shows a stability optimization control flow diagram of a dc microgrid, and fig. 2 shows a topology structure and a control block diagram of a multi-source multi-load dc microgrid system, and the stability optimization control method for a dc microgrid based on a crossbar algorithm in this embodiment includes the following steps:
s1: solving a small signal model of the multi-source multi-load direct current micro-grid system; the specific process is as follows:
s1-1: small signal model of power module:
the power module utilizes a bidirectional DC/DC converter to perform voltage conversion, adopts a droop control method and a virtual inertia control method, wherein the droop control method ensures power equalization among multiple power supplies, the virtual inertia control method is a working principle similar to a capacitor, and aims to improve the voltage inertia of a direct-current bus and improve the dynamic performance when the voltage changes, and the state space model of the power module is as follows:
Figure BDA0003575995830000081
in the formula (14), d is the duty ratio of the bidirectional DC/DC converter in the power module, ub、uoNAnd uoFor the pre-conversion voltage, the post-conversion rated voltage and the post-conversion outlet voltage, i, of a bidirectional DC/DC converter in a power supply modulebAnd ibrefFor converting the pre-current and its reference value, i, for a bidirectional DC/DC converter in a power supply moduleoFor the outlet current, R, after conversion by a bidirectional DC/DC converter in a power supply moduleb、LbAnd CsParasitic resistance, filter inductance and support capacitance of the power supply module, SuRated voltage u converted by bidirectional DC/DC converter in power moduleoNAnd an outlet voltage uoThe squared difference of (a) is passed through a first-order inertia element and then a variable, k, is outputpAnd kiProportional and integral coefficients, S, for power module PI controllersiPre-conversion current reference i for a bidirectional DC/DC converter in a power supply modulebrefAnd current ibThe difference is output as a variable k after an integration stepdroopIs the sag factor, CvirbThe method is characterized in that a small signal model of a power supply module is obtained by linearizing a formula (1) in the vicinity of a steady state, wherein the model is a virtual inertia coefficient, T is a time constant, and s is a Laplace transform complex variable operator:
Figure BDA0003575995830000082
formula (15)) In, Δ xbBeing the state vector of the power supply module, Δ xb＝[Δib,ΔSu,ΔSi,Δuo]T，ΔibCurrent i before conversion for bidirectional DC/DC converter in power modulebState variable of (a), Δ SuRated voltage u after conversion of bidirectional DC/DC converter in power moduleoNAnd an outlet voltage uoThe squared difference of the first order inertia element outputs a variable SuState variable of (a), Δ SiCurrent reference value i before conversion for bidirectional DC/DC converter in power modulebrefAnd current ibThe difference is output as variable S after integraliState variable of (a), Δ uoFor the outlet voltage u after conversion of a bidirectional DC/DC converter in a power supply moduleoA state variable ofbAs a coefficient matrix of the power supply module, d0Is a steady-state quantity, u, of the duty cycle d of a bidirectional DC/DC converter in a power supply moduleo0For the outlet voltage u after conversion of the bidirectional DC/DC converter in the power supply moduleoSteady state quantity of ib0Current i before conversion for bidirectional DC/DC converter in power modulebA steady state quantity of;
s1-2: small signal model of constant power load module:
the constant power load module utilizes a Buck converter to carry out voltage conversion, adopts a voltage and current double-loop control strategy, and has a state space model as follows:
Figure BDA0003575995830000091
in the formula (16), g is the duty ratio of the Buck converter in the constant power load module, and u isF、uLNAnd uLConverting forward port voltage, constant power load rated voltage and constant power load actual voltage, i, for Buck converter in constant power load moduledcConversion of forward port current, i, for Buck converters in constant-power load modulesLAnd iLrefIs a constant power load current and its reference value, PLFor constant power load power, RL、LLAnd CLIs a constant power load moduleParasitic resistance, filter inductance and filter capacitance of CFSupporting capacitance, k, for constant power load modulepL_uAnd kiL_uProportional and integral coefficients, k, of an outer loop PI controller for constant power load module voltagepL_iAnd kiL_iProportional and integral coefficients of a constant power load module current inner loop PI controller, SuLAnd SiLOutputting variables for an integral link of a constant power load module voltage outer loop PI controller and a current inner loop PI controller, wherein a small signal model of the constant power load module is as follows:
Figure BDA0003575995830000092
in the formula (17), Δ xLIs the state vector of the constant-power load module, Δ xL＝[ΔuF,ΔiL,ΔuL,ΔSuL,ΔSiL]T，ΔuFConversion of forward port voltage u for Buck converter in constant power load moduleFState variable of, Δ iLIs a constant power load current iLState variable of (1), Δ uLIs a constant power load actual voltage uLState variable of, Δ SuLOutputting variable S for integral link of constant power load module voltage outer loop PI controlleruLState variable of, Δ SiLOutputting variable S for integral link of constant power load module current inner loop PI controlleriLA state variable ofLIs a coefficient matrix of the constant power load module, g0Is the steady-state quantity u of the duty ratio g of the Buck converter in the constant power load moduleF0Converting forward port voltage u for Buck converter in constant power load moduleFSteady state quantity of iL0Is a constant power load current iLA steady state quantity of;
s1-3: small signal model of the direct current transmission line:
a direct current transmission line is arranged between the power module and the constant power load module, and the state space model of the direct current transmission line is as follows:
Figure BDA0003575995830000101
in the formula (18), uDCAnd CDCFor collecting bus voltage and supporting capacitance, RsAnd LsResistance and inductance of the source side transmission line, RFAnd LFThe small signal model of the direct current transmission line is as follows:
Figure BDA0003575995830000102
in the formula (19), Δ xdIs the state vector, Δ x, of the DC transmission lined＝[Δuo,Δio,ΔuDC,Δidc,ΔuF]T，ΔioFor the outlet current i after conversion of a bidirectional DC/DC converter in a power supply moduleoState variable of (1), Δ uDCFor converging bus voltage uDCState variable of (a), Δ idcConverting forward port current i for Buck converter in constant power load moduledcState variable of (D)dA coefficient matrix of the direct current transmission line is obtained;
s1-4: the small signal model of the direct current micro-grid system is as follows:
the direct-current microgrid system is composed of three power modules, three direct-current transmission lines and three constant-power load modules, the structure is of a conventional radiation type, the total length of the transmission lines on the source side and the load side is set to be a certain value and is marked as 1, and the lengths of the transmission lines from the power modules 1, 2 and 3 to a convergence bus are respectively marked as lb1、lb2、lb3The lengths from the convergent bus to the constant power load modules 1, 2 and 3 are respectively lL1、lL2、lL3The small signal model of the system is:
Figure BDA0003575995830000111
in the formula (20), Δ xsysIs a state vector, deltax, of the DC microgrid systemsys＝[Δxbn,Δxd123,ΔxLn]T，ΔxbnIs the state vector of three power modules, n is 1, 2, 3, Δ xLnIs the state vector of three constant power load modules, n is 1, 2, 3, delta xd123State vectors, Δ x, for three direct current transmission linesd123＝[Δuon,Δion,ΔuDC,Δidcn,ΔuFn]T，ΔuonAnd Δ ionThe outlet voltage u after conversion of the bidirectional DC/DC converter in the three power supply modulesonAnd an outlet current ionN is 1, 2, 3, Δ idcnAnd Δ uFnConverting forward port current i for Buck converter in three constant power load modulesdcnAnd inlet voltage uFnN is 1, 2, 3, asysIs a coefficient matrix of the DC microgrid system, AbnIs a coefficient matrix of three power supply modules, n is 1, 2, 3, Dd123Coefficient matrices for three DC transmission lines, ALnA coefficient matrix of three constant power load modules, wherein n is 1, 2 and 3;
step S2 is to calculate the dominant mode of the system by the feature root analysis method, and decouple the influence degree of the state variable on the dominant mode by the participation factor method, and the specific process is as follows:
s2-1: and (3) solving the dominant mode of the system through a characteristic root analysis method:
according to the coefficient matrix A of the direct current micro-grid systemsysSolving the characteristic value to obtain the oscillation mode of the system, wherein the dominant mode is the mode closest to the virtual axis in the oscillation mode and plays a leading role in system response, and the dominant mode solving expression is as follows:
Figure BDA0003575995830000112
in the formula (21), n is the nth oscillation mode with real part not being zero, DsysCoefficient matrix A of DC micro-grid systemsysCharacteristic root matrix of (c), n1Root matrix D of features for dominant modesysPosition of (5), R(n1) And I (n)1) Being the real and imaginary parts of the dominant mode, DMCoordinates that are dominant modalities;
s2-2: carrying out coupling analysis of a participation factor method on the dominant mode:
in order to research the relation between the dominant mode and the state variable, the influence degree of the state variable on the dominant mode is decoupled by a participation factor method, and a mechanism influencing the stable operation characteristic of a system is obtained, wherein the expression is as follows:
Figure BDA0003575995830000121
in the formula (22), pa (k, n)1) To the extent that the state variable k affects the dominant modality,
Figure BDA0003575995830000122
is the k-th position in the left eigenvector of the system state matrix, phi (n)1K) is the kth position in the right eigenvector of the system state matrix;
introducing a state variable participation degree evaluation index eta on the basis of participation factor analysiskAfter the participation degree of the state variable k on the dominant mode is comprehensively considered for all the state variables, the expression is as follows:
Figure BDA0003575995830000123
step S3 is to apply the crossbar algorithm to the stability analysis of the dc microgrid, and the specific process is as follows:
s3-1: determining parameters of the direct current micro-grid system needing to be optimized as population particles, and specifying upper and lower limits of the particles;
s3-2: setting maximum iteration times, population scale and penalty function; because of the constraint relationship among the system parameters, the parameters can not be randomly selected, so a penalty function is defined to solve the problem of constraint conditions, and the target function and the penalty function are as follows:
Figure BDA0003575995830000124
in the formula (24), f1(X) target function fitness before penalty, f2(X) is the fitness of the objective function after punishment, P is a punishment coefficient, and P isXTo constrain real-time values of variables, PrefIs a parameter constraint condition;
s3-3: performing vertical interleaving to generate a filial population, wherein the vertical interleaving process is an arithmetic operation for interleaving particles with different dimensions:
Xzc(i,d1)＝r·X(i,d1)+(1-r)·X(i,d2)+c·(X(i,d1)-X(i,d2)) (25)
in the formula (25), r is a random number between 0 and 1, c is a random number between 0 and 1, and X (i, d)1) And X (i, d)2) Being a parent of different dimensions, Xzc(i,d1) Child generation for longitudinal cross generation of parent generation with different dimensionality;
s3-4: substituting the population particles into a coefficient matrix A of the direct-current micro-grid systemsysSolving the objective function value, checking whether constraint conditions are met, and if so, carrying out the next step; otherwise, adding a penalty function to the particle for calculation, and performing step S3-5;
s3-5: executing a competition operator, storing the population particles with the best current fitness, wherein the competition operator is a mechanism for performing the relative elimination of parent generation and offspring generation, comparing the fitness of the parent generation and the offspring generation, and keeping the particles with stronger fitness to participate in the next iteration, so that the aim of making the whole population move towards the best direction is finally achieved;
s3-6: performing transverse crossing to generate a filial population, wherein the transverse crossing process is an arithmetic operation for crossing all particles with different solutions, and performing steps S3-4 and S3-5:
Figure BDA0003575995830000125
in the formula (26), r1And r2Is a random number between 0 and 1, c1And c2Is random between-1 and 1Number, X (i, d) and X (j, d) are two different parent solutions, Xhc(i, d) and Xhc(j, d) child solutions generated by transversely crossing parent solutions;
s3-7: checking whether the maximum iteration times are reached, if so, selecting the population particles with the best fitness as a system trend optimal solution, and ending the iteration; otherwise, go to step S3-3.
In order to verify the effectiveness of the optimization-seeking control method, a direct-current micro-grid system model shown in the figure 2 is established on an RT-LAB experimental platform. The root-of-feature analysis of the dc microgrid system is depicted in fig. 3. Setting algorithm parameters: the iteration number is 200, the initial solution population size is 30, and the penalty coefficient is 0.5. Taking the droop coefficient as an optimization-driving control object, taking the voltage deviation of the direct current bus as a constraint condition, obtaining the optimization-driving solutions of the droop coefficient by an algorithm, wherein the solutions are 0.3903, 0.2077 and 0.3897 respectively, drawing a droop coefficient optimization-driving control curve in a graph 4, and drawing a voltage waveform change of the direct current bus before and after control in a graph 6; the source side line parameters are taken as optimization-trending control objects, the total length of the three source side lines is equal to 1 and taken as constraint conditions, the optimization-trending solutions of the source side line parameters are respectively 0.5058, 0.4924 and 0.0018 through an algorithm, source side line parameter optimization-trending control curves are plotted in a graph 5, and voltage waveform changes of direct current buses before and after control are plotted in a graph 7.
As can be seen from fig. 3 to 7, in the implementation example, the real part of the dominant mode under the initial parameters of the system is-34.95, the real part of the dominant mode of the system is-49.92 after the droop coefficient is controlled to be optimal, the stability margin of the system is improved by 42.83% compared with the initial value, the optimal effect is achieved after iteration for 44 times, the oscillation degree of the dc bus voltage before control is larger, and the control is more stable; the real part of the dominant mode of the system is-42.62 after the source side line parameter optimization control, the stability margin of the system is improved by 21.95% compared with the initial value, the optimal effect is achieved after 13 iterations, the direct current bus voltage oscillation degree is larger before the control, and the control is more stable. The method shows that in the process of improving the stability of the direct-current micro-grid, the convergence speed of the criss-cross algorithm is high, the optimization approach solution of the parameters can be quickly obtained, the direct-current micro-grid has the stability margin as large as possible, and the stability optimization approach control is realized.
The above-mentioned embodiments are merely preferred embodiments of the present invention, and the scope of the present invention is not limited thereto, so that variations based on the shape and principle of the present invention should be covered within the scope of the present invention.

Claims (3)

1. A direct current micro-grid stability optimization control method based on a criss-cross algorithm is characterized by comprising the following steps:
s1: solving a small signal model of the multi-source multi-load direct current micro-grid system;
s2: the dominant mode of the system is solved through a characteristic root analysis method, and the influence degree of the state variable on the dominant mode is decoupled through a participation factor method;
s3: applying a criss-cross algorithm to stability analysis of the direct-current micro-grid to obtain optimization parameters to achieve the optimal stability of the system, so that the direct-current micro-grid has larger stability margin, and stability optimization control is realized;
in step S1, the specific steps of obtaining the small signal model of the multi-source multi-load dc micro-grid system are as follows:
s1-1: small signal model of power module:
the power module utilizes the two-way DC/DC converter to carry out voltage conversion, adopts droop control and virtual inertia control method, wherein droop control guarantees the power equipartition between many powers, and virtual inertia control is the theory of operation of similar electric capacity, and the purpose is to improve direct current bus voltage inertia and improve the dynamic behavior when voltage variation, and power module state space model is:
Figure FDA0003575995820000011
in the formula (1), d is the duty ratio of the bidirectional DC/DC converter in the power module, ub、uoNAnd uoFor the pre-conversion voltage, the post-conversion rated voltage and the post-conversion outlet voltage, i, of a bidirectional DC/DC converter in a power supply modulebAnd ibrefFor a pre-conversion current and its reference value, i, of a bidirectional DC/DC converter in a power supply moduleoFor bidirectional DC/DC conversion in power supply modulesOutlet current after converter conversion, Rb、LbAnd CsParasitic resistance, filter inductance and support capacitance of the power supply module, SuRated voltage u after conversion of bidirectional DC/DC converter in power moduleoNAnd an outlet voltage uoThe squared difference of (a) is passed through a first-order inertia element and then a variable, k, is outputpAnd kiProportional and integral coefficients, S, for power module PI controllersiCurrent reference value i before conversion for bidirectional DC/DC converter in power modulebrefAnd current ibThe difference is output as a variable k after an integration stepdroopAs sag factor, CvirbThe method is characterized in that a small signal model of a power supply module is obtained by linearizing a formula (1) in the vicinity of a steady state, wherein the model is a virtual inertia coefficient, T is a time constant, and s is a Laplace transform complex variable operator:
Figure FDA0003575995820000021
in the formula (2), Δ xbBeing the state vector of the power supply module, Δ xb＝[Δib,ΔSu,ΔSi,Δuo]T，ΔibCurrent i before conversion for bidirectional DC/DC converter in power modulebState variable of, Δ SuRated voltage u after conversion of bidirectional DC/DC converter in power moduleoNAnd an outlet voltage uoThe squared difference of the first-order inertia element outputs a variable SuState variable of, Δ SiCurrent reference value i before conversion for bidirectional DC/DC converter in power modulebrefAnd current ibThe difference is output as variable S after integraliState variable of (1), Δ uoFor the outlet voltage u after conversion of a bidirectional DC/DC converter in a power supply moduleoA state variable ofbAs a coefficient matrix of the power supply module, d0Is a steady-state quantity u of the duty cycle d of a bidirectional DC/DC converter in the power supply moduleo0For the outlet voltage u after conversion of a bidirectional DC/DC converter in a power supply moduleoSteady state quantity of (ii), ib0Pre-conversion current i for a bidirectional DC/DC converter in a power supply modulebA steady state quantity of (c);
s1-2: small signal model of constant power load module:
the constant power load module utilizes a Buck converter to carry out voltage conversion, a voltage-current double-loop control strategy is adopted, and a state space model of the constant power load module is as follows:
Figure FDA0003575995820000022
in the formula (3), g is the duty ratio of the Buck converter in the constant-power load module, and u isF、uLNAnd uLConverting forward port voltage, constant power load rated voltage and constant power load actual voltage, i, for Buck converter in constant power load moduledcConversion of forward port current, i, for Buck converters in constant-power load modulesLAnd iLrefIs a constant power load current and its reference value, PLFor constant power load power, RL、LLAnd CLParasitic resistance, filter inductance and filter capacitance of constant power load module, CFSupporting capacitor, k, for constant power load modulepL_uAnd kiL_uProportional and integral coefficients, k, of an outer loop PI controller for constant power load module voltagepL_iAnd kiL_iProportional and integral coefficients, S, of a constant-power load module current inner loop PI controlleruLAnd SiLOutputting variables for an integral link of a constant power load module voltage outer loop PI controller and a current inner loop PI controller, wherein a small signal model of the constant power load module is as follows:
Figure FDA0003575995820000031
in the formula (4), Δ xLIs the state vector of the constant-power load module, Δ xL＝[ΔuF,ΔiL,ΔuL,ΔSuL,ΔSiL]T，ΔuFConverting forward port voltage u for Buck converter in constant power load moduleFState variable of，ΔiLIs a constant power load current iLState variable of (1), Δ uLIs a constant power load actual voltage uLState variable of, Δ SuLOutputting variable S for integral link of constant power load module voltage outer loop PI controlleruLState variable of, Δ SiLOutputting variable S for integral link of constant power load module current inner loop PI controlleriLA state variable ofLIs a coefficient matrix of the constant power load module, g0Is the steady-state quantity u of the duty ratio g of the Buck converter in the constant-power load moduleF0Conversion of forward port voltage u for Buck converter in constant power load moduleFSteady state quantity of iL0Is a constant power load current iLA steady state quantity of (c);
s1-3: small signal model of the direct current transmission line:
a direct current transmission line is arranged between the power module and the constant power load module, and the state space model of the direct current transmission line is as follows:
Figure FDA0003575995820000032
in the formula (5), uDCAnd CDCFor collecting bus voltage and supporting capacitance, RsAnd LsResistance and inductance of the source side transmission line, RFAnd LFThe small signal model of the direct current transmission line is as follows:
Figure FDA0003575995820000041
in the formula (6), Δ xdIs the state vector of the DC transmission line, Δ xd＝[Δuo,Δio,ΔuDC,Δidc,ΔuF]T，ΔioFor the outlet current i after conversion of a bidirectional DC/DC converter in a power supply moduleoState variable of (a), Δ uDCFor converging bus voltage uDCState variable of (a), Δ idcIs constant workBuck converter conversion forward port current i in rate load moduledcState variable of (D)dA coefficient matrix of the direct current transmission line is obtained;
s1-4: the small signal model of the direct current micro-grid system is as follows:
the direct-current micro-grid system is composed of three power modules, three direct-current transmission lines and three constant-power load modules, the structure is in a conventional radiation type, the total length of the transmission lines on the source side and the load side is set to be a certain value and is marked as 1, and the lengths of the transmission lines from the power modules 1, 2 and 3 to a convergence bus are respectively lb1、lb2、lb3The lengths from the convergent bus to the constant power load modules 1, 2 and 3 are respectively lL1、lL2、lL3The small signal model of the system is:
Figure FDA0003575995820000042
in the formula (7), Δ xsysIs a state vector of the DC micro-grid system, Δ xsys＝[Δxbn,Δxd123,ΔxLn]T，ΔxbnIs the state vector of three power supply modules, n is 1, 2, 3, Δ xLnIs the state vector of three constant power load modules, n is 1, 2, 3, delta xd123State vectors, Δ x, for three direct current transmission linesd123＝[Δuon,Δion,ΔuDC,Δidcn,ΔuFn]T，ΔuonAnd Δ ionThe outlet voltage u after conversion of the bidirectional DC/DC converter in the three power supply modulesonAnd outlet current ionN is 1, 2, 3, Δ idcnAnd Δ uFnConversion of forward port current i for Buck converter in three constant power load modulesdcnAnd inlet voltage uFnN is 1, 2, 3, asysIs a coefficient matrix of the DC micro-grid system, AbnIs a coefficient matrix of three power supply modules, n is 1, 2, 3, Dd123Coefficient matrices for three direct current transmission lines, ALnOf three constant-power load modulesAnd (3) a coefficient matrix, wherein n is 1, 2 and 3.
2. The method for controlling the trend of the stability of the direct-current microgrid based on the crossbar algorithm of claim 1, wherein in the step S2, a dominant mode of a system is solved through a characteristic root analysis method, and the degree of influence of a state variable on the dominant mode is decoupled through a participation factor method; the specific steps of step S2 are as follows:
s2-1: and (3) solving the dominant mode of the system through a characteristic root analysis method:
according to the coefficient matrix A of the direct current micro-grid systemsysSolving the characteristic value to obtain the oscillation mode of the system, wherein the dominant mode is the mode closest to the virtual axis in the oscillation mode and plays a leading role in system response, and the dominant mode solving expression is as follows:
Figure FDA0003575995820000051
in the formula (8), n is the nth oscillation mode with real part not being zero, DsysCoefficient matrix A of DC micro-grid systemsysCharacteristic root matrix of (n)1Root matrix D of features for dominant modesysPosition of (5), R (n)1) And I (n)1) Being the real and imaginary parts of the dominant mode, DMCoordinates that are dominant modalities;
s2-2: carrying out coupling analysis of a participation factor method on the dominant mode:
in order to research the relation between the dominant mode and the state variable, the influence degree of the state variable on the dominant mode is decoupled by a participation factor method, and a mechanism influencing the stable operation characteristic of a system is obtained, wherein the expression is as follows:
Figure FDA0003575995820000052
in formula (9), pa (k, n)1) To what extent the state variable k affects the dominant modality,
Figure FDA0003575995820000053
is the k-th position in the left eigenvector of the system state matrix, phi (n)1K) is the kth position in the right eigenvector of the system state matrix;
introducing a state variable participation degree evaluation index eta on the basis of participation factor analysiskThe participation degree of the state variable k to the dominant mode after the comprehensive consideration of all the state variables is expressed as follows:
Figure FDA0003575995820000054
3. the method for controlling the trend of the stability of the direct-current microgrid based on the crossbar algorithm of claim 1, wherein the step S3 is implemented by applying the crossbar algorithm to the stability analysis of the direct-current microgrid, and comprises the following specific steps:
s3-1: determining parameters of the direct current micro-grid system needing to be optimized as population particles, and specifying upper and lower limits of the particles;
s3-2: setting maximum iteration times, population scale and penalty function; because of constraint relation among system parameters, the parameters can not be randomly selected, so a penalty function is defined to solve the problem of constraint conditions, and the objective function and the penalty function are as follows:
Figure FDA0003575995820000061
in the formula (11), f1(X) target function fitness before penalty, f2(X) is the fitness of the objective function after punishment, P is the punishment coefficient, P isXTo constrain real-time values of variables, PrefIs a parameter constraint condition;
s3-3: performing vertical interleaving to generate a child population, wherein the vertical interleaving process is an arithmetic operation for interleaving particles with different dimensions:
Xzc(i,d1)＝r·X(i,d1)+(1-r)·X(i,d2)+c·(X(i,d1)-X(i,d2)) (12)
in the formula (12), r is a random number between 0 and 1, c is a random number between 0 and 1, and X (i, d)1) And X (i, d)2) Being a parent of different dimensions, Xzc(i,d1) Child generation for longitudinal cross generation of parent generation with different dimensionality;
s3-4: substituting the population particles into a coefficient matrix A of the direct-current micro-grid systemsysSolving the objective function value, checking whether constraint conditions are met, and if so, carrying out the next step; otherwise, adding a penalty function to the particle for calculation, and performing step S3-5;
s3-5: executing a competition operator, storing the population particles with the best current fitness, wherein the competition operator is a mechanism for carrying out parent and offspring comparison elimination, comparing the fitness of the parent and the offspring, and keeping the particles with stronger fitness to participate in the next iteration, so that the whole population finally moves towards the best direction;
s3-6: performing a horizontal crossing to generate a child population, wherein the horizontal crossing is an arithmetic operation for crossing all particles with different solutions, and performing steps S3-4 and S3-5:
Figure FDA0003575995820000062
in the formula (13), r1And r2Is a random number between 0 and 1, c1And c2Is a random number between-1 and 1, X (i, d) and X (j, d) are two different parent solutions, Xhc(i, d) and Xhc(j, d) child solutions generated by transversely crossing parent solutions;
s3-7: checking whether the maximum iteration times are reached, if so, selecting the population particles with the best fitness as the system trend optimal solution, and ending the iteration; otherwise, go to step S3-3.
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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116093917A (en) * 2023-03-22 2023-05-09 广东工业大学 Multi-control parameter optimization design method for energy storage power station
CN116169661A (en) * 2023-03-14 2023-05-26 广东工业大学 Comprehensive control method for busbar voltage of direct-current micro-grid

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109861199A (en) * 2019-03-20 2019-06-07 湖南大学 A kind of fault recovery method in DC distribution net
CN110071496A (en) * 2019-03-29 2019-07-30 广东工业大学 A kind of configuration of direct-current grid power optimization and operation method based on wave-activated power generation
CN110348605A (en) * 2019-06-12 2019-10-18 广东工业大学 Micro-capacitance sensor economic operation optimization method based on improved adaptive GA-IAGA
CN111859796A (en) * 2020-07-14 2020-10-30 温州大学 Harris eagle photovoltaic model parameter optimization method based on longitudinal and transverse intersection and NM type
CN112100907A (en) * 2020-08-24 2020-12-18 广东工业大学 Power grid optimal power flow problem solution method based on distributed criss-cross algorithm
CN114169252A (en) * 2021-12-27 2022-03-11 广东工业大学 Short-term region wind power prediction method for dynamically selecting representative wind power plant

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109861199A (en) * 2019-03-20 2019-06-07 湖南大学 A kind of fault recovery method in DC distribution net
CN110071496A (en) * 2019-03-29 2019-07-30 广东工业大学 A kind of configuration of direct-current grid power optimization and operation method based on wave-activated power generation
CN110348605A (en) * 2019-06-12 2019-10-18 广东工业大学 Micro-capacitance sensor economic operation optimization method based on improved adaptive GA-IAGA
CN111859796A (en) * 2020-07-14 2020-10-30 温州大学 Harris eagle photovoltaic model parameter optimization method based on longitudinal and transverse intersection and NM type
CN112100907A (en) * 2020-08-24 2020-12-18 广东工业大学 Power grid optimal power flow problem solution method based on distributed criss-cross algorithm
CN114169252A (en) * 2021-12-27 2022-03-11 广东工业大学 Short-term region wind power prediction method for dynamically selecting representative wind power plant

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116169661A (en) * 2023-03-14 2023-05-26 广东工业大学 Comprehensive control method for busbar voltage of direct-current micro-grid
CN116169661B (en) * 2023-03-14 2023-07-28 广东工业大学 Comprehensive control method for busbar voltage of direct-current micro-grid
CN116093917A (en) * 2023-03-22 2023-05-09 广东工业大学 Multi-control parameter optimization design method for energy storage power station
CN116093917B (en) * 2023-03-22 2023-08-04 广东工业大学 Multi-control parameter optimization design method for energy storage power station

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