LU505155B1 - Design method of damping controller based on power system, controller and power system - Google Patents
Design method of damping controller based on power system, controller and power system Download PDFInfo
- Publication number
- LU505155B1 LU505155B1 LU505155A LU505155A LU505155B1 LU 505155 B1 LU505155 B1 LU 505155B1 LU 505155 A LU505155 A LU 505155A LU 505155 A LU505155 A LU 505155A LU 505155 B1 LU505155 B1 LU 505155B1
- Authority
- LU
- Luxembourg
- Prior art keywords
- identification
- power system
- model
- transfer function
- damping controller
- Prior art date
Links
- 238000013016 damping Methods 0.000 title claims abstract description 76
- 238000000034 method Methods 0.000 title claims abstract description 71
- 238000013461 design Methods 0.000 title claims abstract description 46
- 238000012546 transfer Methods 0.000 claims abstract description 83
- 230000010355 oscillation Effects 0.000 claims abstract description 10
- 230000003044 adaptive effect Effects 0.000 claims description 31
- 230000035772 mutation Effects 0.000 claims description 19
- 239000011159 matrix material Substances 0.000 claims description 6
- 238000005457 optimization Methods 0.000 claims description 3
- 238000012360 testing method Methods 0.000 claims description 3
- 238000004364 calculation method Methods 0.000 claims description 2
- 238000004422 calculation algorithm Methods 0.000 abstract description 32
- 230000001629 suppression Effects 0.000 abstract description 6
- 230000008569 process Effects 0.000 description 9
- 238000010586 diagram Methods 0.000 description 7
- 230000000694 effects Effects 0.000 description 6
- 238000013473 artificial intelligence Methods 0.000 description 4
- 230000004044 response Effects 0.000 description 4
- 230000001934 delay Effects 0.000 description 3
- 230000005540 biological transmission Effects 0.000 description 2
- 230000008859 change Effects 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 239000000543 intermediate Substances 0.000 description 2
- 238000004088 simulation Methods 0.000 description 2
- 108010023321 Factor VII Proteins 0.000 description 1
- 241001580017 Jana Species 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 210000000349 chromosome Anatomy 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 230000000052 comparative effect Effects 0.000 description 1
- 238000007796 conventional method Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000012067 mathematical method Methods 0.000 description 1
- 238000013178 mathematical model Methods 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000012216 screening Methods 0.000 description 1
- 230000035945 sensitivity Effects 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 230000006641 stabilisation Effects 0.000 description 1
- 238000011105 stabilization Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/24—Arrangements for preventing or reducing oscillations of power in networks
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
Landscapes
- Engineering & Computer Science (AREA)
- Power Engineering (AREA)
- Health & Medical Sciences (AREA)
- Artificial Intelligence (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Evolutionary Computation (AREA)
- Medical Informatics (AREA)
- Software Systems (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Feedback Control In General (AREA)
Abstract
A design method of the damping controller based on a power system, a controller and the power system provided by the invention solve the initial controller model according to a preset power system model; taking the preset power system model as a controlled object transfer function of a damping controller, and introducing a time delay factor into the controlled object transfer function; performing an on-line identification on the parameters to be identified and the time-delay factor in the transfer function of the controlled object by using the optimized multi-parameter differenlial evolution identification model to obtain an identification result; designing the damping controller according to the identification result, and the parameters to be identified and the time delay factor in the transfer function of the controlled object arc identified online by using the optimized multi-parameter differential evolution identification model, which can accelerate the convergence speed of the algorithm and avoid falling into the local optimal solution, and the identification accuracy is higher, the corresponding damping controller design is more reasonable, and the low-frequency oscillation suppression effect of the power system with wind power under multi-interference conditions is better.
Description
DESIGN METHOD OF DAMPING CONTROLLER BASED ON
POWER SYSTEM, CONTROLLER AND POWER SYSTEM
The invention relates to the technical field of power systems, and in particular to a design method of a damping controller based on the power system, a controller and the power system.
The data acquisition of power system uses data network transmission technology, and the average data transmission delay is close to 100ms, but the fixed value of time delay cannot be determined. The existence of time delay makes the stability analysis and control of power system more complicated. In recent years, with the large-scale wind power entering the power system, the problem of small signal stability has become increasingly prominent, which has aggravated the influence of time delay factors on the wide-area damping control of the power system. In the related technology, the influence of time delay is eliminated by compensating the predictive parameters output by Smith predictive control controller. The application of Smith predictor in dealing with time-delay problems can be roughly divided into three categories. The first is to change the structure of Smith predictor to optimize it, and the second is to optimize the parameter performance index by using conventional mathematical methods, such as robust performance index, Lyapunov theory, linear inequality theory and so on. The third is to combine artificial intelligence algorithm with conventional Smith predictor to improve its performance index. Although the combination of artificial intelligence algorithm and conventional Smith predictor has a good fitting effect on nonlinear parameters, there is a common problem that it is easy to fall into local optimal solution, and it is impossible to determine the real global optimal solution, which leads to the deviation of model identification, then leads to the failure of the Smith predictor to fully compensate for time delay, and further may lead to a system instability.
The invention provides a design method of a damping controller based on the power system, a controller and the power system, which are used for solving the defect that in the conventional design method of a damping controller based on a power system, the combination method of an artificial intelligence algorithm and a conventional Smith predictor is easy to fall into a local optimal solution, and the real global optimal solution cannot be determined, which leads to model identification deviation, and may further lead to instability of the power system.
The invention provides a design method of a damping controller based on a power system, which comprises the following steps: solving an initial controller model according to a preset power system model; taking the preset power system model as a controlled object transfer function of a damping controller, and introducing a time delay factor into the controlled object transfer function; performing an on-line identification on the parameters to be identified and the time-delay factor in the transfer function of the controlled object by using the optimized multi-parameter differential evolution identification model to obtain an identification result, where the identification result includes an identification result of the transfer function parameters of the controlled object and an identification result of the time-delay factor; designing the damping controller according to the identification result.
According to the design method of the damping controller based on the power system provided by the invention, the optimization method of the multi-parameter differential evolution identification model includes following steps: inputting the parameters to be identified and the time delay factor in the transfer function of the controlled object into the multi-parameter differential evolution identification model to obtain an identification transfer function containing the time delay factor;
calculating the Vinncombe distance between the transfer function of the 17905155 controlled object and the identification transfer function; if the Vinncombe distance does not meet the preset identification accuracy requirement, optimizing the parameters in the multi-parameter differential evolution identification model; re-identifying by using the optimized multi-parameter differential evolution identification model until the Vinncombe distance reaches the preset identification accuracy requirement, and outputting the optimized multi-parameter differential evolution identification model.
According to the design method of the damping controller based on the power system provided by the invention, the calculation of the Vinncombe distance between the transfer function of the controlled object and the identification transfer function includes the following steps: obtaining Vinncombe distance constraint formula | (1 + G}G,)(e/®) + Vw w(l + G1G2) + n(G,) — 7(G1) =0 when the constraint formula is satisfied, the Vinncombe distance between the transfer function of the controlled object and the identification transfer function is expressed as: maxi (ele) when the constraint formula is not satisfied, the Vinncombe distance between the transfer function of the controlled object and the identification transfer function is expressed as 1; in the formula, G}(e®) = G,(e®), n(Gz) is the number of poles in the open right half-plane of G,, n(G1) is the number of poles in the closed right half-plane of
Gq, w(x) is the number of turns of the transfer function Nyquist curve around the origin counterclockwise, K(G,(e/®),G,(el®)) are the chord distance obtained by projecting G, and G, to the unit Riemannian sphere.
According to the design method of the damping controller based on the power system provided by the invention, the method for judging whether the Vinncombe 17905155 distance reaches the preset identification accuracy requirement includes the following steps: judging whether the Vinncombe distance is within a preset error threshold under the premise of stable output of the system; if so, it is judged that the Vinncombe distance reaches the preset identification accuracy requirement.
According to the design method of the damping controller based on the power system provided by the invention, the design of the damping controller according to the identification result includes: obtaining a parameter matrix according to a preset power system model, an initial controller model and interference signals; estimating the system frequency stability margin on the error boundary according to the parameter matrix; when the system frequency stability margin is greater than the maximum value of the Vinncombe distance, using the initial controller model as a damping controller.
According to the design method of the damping controller based on the power system provided by the invention, the design of the damping controller according to the identification result further includes: when the frequency stability margin of the system is not greater than the maximum value of the Vinncombe distance, if |P| < p,p(|P|&, + &,) < 1, where P isa single-input single-output transfer function, then the damping controller is:
Kien = EC SAP where S; = (1 + GK) LSI = (1 +GK)™", & is the difference between the stability margin in the update frequency domain and the previous stability margin, €, is the previous frequency domain stability margin, p is the maximum distance between the identification model and the target model transfer function, and K; is the initial controller model.
According to the design method of the damping controller based on the power system provided by the invention, the multi-parameter differential evolution 17905155 identification model includes an adaptive crossover factor and an adaptive mutation factor; the adaptive crossover factor is: 5 CR = CRrin + A nar = Zin) Rin) where CRmin is the minimum adaptive crossover factor, CRmax is the maximum adaptive crossover factor, Gm is the maximum number of iterations, and G is the evolutionary algebra; the adaptive mutation factor is: 1— Gm
F = Frax — (Fax — Fmin)e CmTE+1 where Fmin is the minimum adaptive mutation factor, Fmax is the maximum adaptive mutation factor, Gn is the maximum number of iterations, and G is the evolutionary algebra.
According to the design method of the damping controller based on the power system provided by the invention, solving the initial controller model according to the preset power system model comprises the following steps: estimating preset power system model parameter according to an on-line identification method of low-frequency oscillation mode noise signals of a power system; testing the estimated results according to the model order criterion, and when the estimated results meet the requirements of the model order criterion, obtaining reduced-order power system model; solving the initial controller model according to the closed-loop stability conditions corresponding to the reduced-order power system model.
The invention also provides a damping controller, which is designed based on any one of the above-mentioned damping controller design methods based on the power system.
The invention also provides a power system, which includes the damping controller.
The design method of the damping controller based on the power system, the 17905155 controller and the power system provided by the invention solve the initial controller model according to a preset power system model; taking the preset power system model as a controlled object transfer function of a damping controller, and introducing a time delay factor into the controlled object transfer function; performing an on-line identification on the parameters to be identified and the time-delay factor in the transfer function of the controlled object by using the optimized multi-parameter differential evolution identification model to obtain an identification result, where the identification result includes an identification result of the transfer function parameters of the controlled object and an identification result of the time-delay factor; designing the damping controller according to the identification result, and the parameters to be identified and the time delay factor in the transfer function of the controlled object are identified online by using the optimized multi-parameter differential evolution identification model, which can accelerate the convergence speed of the algorithm and avoid falling into the local optimal solution, and the identification accuracy is higher, the corresponding damping controller design is more reasonable, and the low-frequency oscillation suppression effect of the power system with wind power under multi-interference conditions is better.
In order to explain the technical scheme of the present invention or the prior art more clearly, the drawings needed in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained according to these drawings without creative work for ordinary people in the field.
FIG. 1 is one of the flow charts of the design method of the damping controller based on the power system provided by the present invention;
FIG. 2 is the second of the flow charts of the design method of the damping controller based on the power system provided by the present invention;
FIG. 3 is the third of the flow charts of the design method of the damping 17905155 controller based on the power system;
FIG. 4 is a power system identification model diagram provided by the present invention;
FIG. 5 is a schematic diagram of Smith predictive control structure provided by the present invention;
FIG. 6 is the identification result and convergence graph of the first-order system provided by the present invention;
FIG. 7 is a step response curve of Smith predictor under different conditions provided by the present invention;
FIG. 8 is a schematic diagram of a four-machine two-zone system provided by the present invention;
FIG. 9 is a first parameter identification result diagram of the adaptive multi-parameter differential evolution algorithm provided by the present invention;
FIG. 10 is a second parameter identification result diagram of the adaptive multi-parameter differential evolution algorithm provided by the present invention;
FIG. 11 is a diagram of the dynamic change relationship between the frequency domain stability margin and the Vinnicombe distance provided by the present invention;
FIG. 12 is a graph of rotor angle under different time delays provided by the present invention;
FIG. 13 is a comparison diagram between the least square iterative identification method provided by the present invention and the method provided by the present invention.
FIG. 14 is a graph of rotor angle when the time delay is 100ms provided by the invention;
FIG. 15 is a graph of rotor angle when the time delay is 150ms provided by the present invention.
In order to make the purpose, technical scheme and advantages of the present 17905155 invention more clear, the technical scheme in the present invention will be described clearly and completely with reference to the attached drawings. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of them. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in the field without creative work belong to the scope of protection of the present invention.
FIG. 1 is a flowchart of a design method of the damping controller based on the power system provided by an embodiment of the present invention. As shown in FIG. 1, the design method of a damping controller based on a power system provided by an embodiment of the present invention includes:
Step 101, solving an initial controller model according to a preset power system model; in the embodiment of the invention, solving the initial controller model according to the preset power system model includes the following steps:
Step 1011, estimating preset power system model parameter according to an on-line identification method of low-frequency oscillation mode noise signals of a power system;
Step 1012: testing the estimated results according to the model order criterion, and when the estimated results meet the requirements of the model order criterion, obtaining reduced-order power system model;
Step 1013: solving the initial controller model according to the closed-loop stability conditions corresponding to the reduced-order power system model.
Step 102, taking the preset power system model as a controlled object transfer function of a damping controller, and introducing a time delay factor into the controlled object transfer function;
Step 103, performing an on-line identification on the parameters to be identified and the time-delay factor in the transfer function of the controlled object by using the optimized multi-parameter differential evolution identification model to obtain an identification result, where the identification result includes an identification result of the transfer function parameters of the controlled object and an identification result of 17905155 the time-delay factor;
In the embodiment of the invention, the multi-parameter differential evolution identification model includes an adaptive crossover factor and an adaptive mutation factor; the adaptive crossover factor is:
CR = CRin + mes — Emi) CRımin) m where CRmin is the minimum adaptive crossover factor, CRmax is the maximum adaptive crossover factor, Gm is the maximum number of iterations, and G is the evolutionary algebra; in the evolution process of adaptive differential evolution algorithm, because the crossover factor changes dynamically with the increase of iteration times, the larger crossover factor ensures the global variation in the initial stage, and the smaller crossover rate pays more attention to the local convergence in the later stage.
The adaptive mutation factor is:
F = Fnax = (Fax = Fmin)e 7°" where Fmin is the minimum adaptive mutation factor, Fmax is the maximum adaptive mutation factor, Gm is the maximum number of iterations, and G is the evolutionary algebra.
In differential evolution algorithm, the setting of control parameters has a direct impact on the convergence speed and reliability of the algorithm. Differential evolution algorithm controls fewer parameters, including population size NP, scaling factor (mutation parameter) F and crossover rate (crossover probability parameter) CR.
The selection of control parameters has great influence on the performance of differential evolution algorithm. Scaling factor (mutation parameter) F controls the size attached to the base vector. The smaller F is, the smaller the compensation of mutation is, which can improve the development ability of the algorithm and easily find the optimal solution with higher quality, but it will lead to the convergence time of the algorithm and easily lead the algorithm to fall into the local optimal region. The 17905155 larger F is, the larger the difference vector will be, which will help to maintain the diversity of the population and improve the search ability of the algorithm. The disadvantage is that the algorithm tends to random search, which will make it difficult to find the optimal solution.
Therefore, designing the adaptive crossover factor and adaptive mutation factor mentioned above can speed up the convergence of the model and avoid falling into the local optimal solution.
Step 104: designing the damping controller according to the identification result.
The conventional nonlinear parameter fitting method combining artificial intelligence algorithm with Smith predictor generally has the problem of easily falling into local optimal solution, and can't determine the truly global optimal solution, which leads to the deviation of model identification, which leads to the failure of the
Smith predictor to fully compensate the time delay, which may lead to the instability of the system.
The design method of the damping controller based on the power system provided by the embodiment of the invention solves an initial controller model according to a preset power system model; taking the preset power system model as a controlled object transfer function of a damping controller, and introducing a time delay factor into the controlled object transfer function; performing an on-line identification on the parameters to be identified and the time-delay factor in the transfer function of the controlled object by using the optimized multi-parameter differential evolution identification model to obtain an identification result, where the identification result includes an identification result of the transfer function parameters of the controlled object and an identification result of the time-delay factor; designing the damping controller according to the identification result, and the parameters to be identified and the time delay factor in the transfer function of the controlled object are identified online by using the optimized multi-parameter differential evolution identification model, which can accelerate the convergence speed of the algorithm and avoid falling into the local optimal solution, and the identification accuracy is higher, the corresponding damping controller design is more 17905155 reasonable, and the low-frequency oscillation suppression effect of the power system with wind power under multi-interference conditions is better.
Based on any of the above embodiments, as shown in FIG. 2, the optimization method of the multi-parameter differential evolution identification model includes following steps:
Step 201: inputting the parameters to be identified and the time delay factor in the transfer function of the controlled object into the multi-parameter differential evolution identification model to obtain an identification transfer function containing the time delay factor;
Step 202: calculating the Vinncombe distance between the transfer function of the controlled object and the identification transfer function;
In the embodiment of the present invention, the Vinnicombe distance refers to the distance between two frequency responses and represents a measurement of the distance between two transfer functions.
In the embodiment of the invention, calculating the Vinncombe distance between the transfer function of the controlled object and the identification transfer function includes: obtaining Vinncombe distance constraint formula (1 + G1G,)(e/®) # OV oc + G1G,) +1(G,) — 7(G,) = 0 when the constraint formula is satisfied, the Vinncombe distance between the transfer function of the controlled object and the identification transfer function is expressed as: maxat0,; Ce") Ge le when the constraint formula is not satisfied, the Vinncombe distance between the transfer function of the controlled object and the identification transfer function is expressed as 1;
in the formula, G}(e®) = G,(e”*), n(Gz) is the number of poles in the open 17005155 right half-plane of G,, 1(Gy) is the number of poles in the closed right half-plane of
G,, w(x) is the number of turns of the transfer function Nyquist curve around the origin counterclockwise, x(G,(el®), Gz(e/®)) are the chord distance obtained by projecting G, and G, to the unit Riemannian sphere.
Step 203: if the Vinncombe distance does not meet the preset identification accuracy requirement, optimizing the parameters in the multi-parameter differential evolution identification model;
In the embodiment of the invention, the method for judging whether the
Vinncombe distance meets the preset identification accuracy requirement includes the following steps: judging whether the Vinncombe distance is within a preset error threshold under the premise of stable output of the system; if so, it is determined that the Vinncombe distance meets the preset identification accuracy requirements.
When the Vinncombe distance reaches the preset identification accuracy, the controller can keep the identified model set stable, which shows that the controller has robust stability, so it can be directly used as a wide-area time-delay damping controller.
Otherwise, it is judged that the Vinncombe distance does not meet the preset identification accuracy requirements.
If the Vinncombe distance is not within the preset error threshold in the process of power system closed-loop identification, the damping controller of the system needs to be improved, and the improvement method of the damping controller needs to be given again.
In the embodiment of the present invention, the preset error threshold is, for example, 0.05. If the error range is met, it means that the result closest to the real identification model can be output under the condition of system stability, which not only ensures that the system can meet the frequency domain stability, but also makes the identification result as accurate as possible. If the error range is not met, it means 17905155 that the system identification effect has a large error, and it is necessary to conduct new identification or adjust relevant parameters to achieve a better identification effect.
Step 204: re-identifying by using the optimized multi-parameter differential evolution identification model until the Vinncombe distance reaches the preset identification accuracy requirement, and outputting the optimized multi-parameter differential evolution identification model.
Based on any of the above embodiments, as shown in FIG. 3, the design of the damping controller according to the identification result includes:
Step 301: obtaining a parameter matrix according to a preset power system model, an initial controller model and interference signals;
Step 302: estimating the system frequency stability margin on the error boundary according to the parameter matrix;
Step 303: when the system frequency stability margin is greater than the maximum value of the Vinncombe distance, using the initial controller model as a damping controller.
Step 304: when the frequency stability margin of the system is not greater than the maximum value of the Vinncombe distance, if |P| <p, p(|P|¢; + &) < 1, where P is a single-input single-output transfer function, then the damping controller is 1
K,. |= a6 +5})"1p 1 — 1] where S; = (1 + G;K)7},53 = (1 +GK)™, & is the difference between the stability margin in the update frequency domain and the previous stability margin, €, is the previous frequency domain stability margin, p is the maximum distance between the identification model and the target model transfer function.
In the embodiment of the present invention, the power system identification model is shown in FIG. 4, where u and y respectively represent the input and output signals of the controlled system, € represents a random interference signal with a mean value of zero and a variance of À, r represents an external reference signal independent of the signal e, G(q,0) represents a forward channel controlled system 17905155 model, H(q,0) represents a filter model of the forward channel interference signal e, and K(q,0) represents the feedback channel controller model, 0 represents the model parameters to be identified, the closed-loop system expression can be written as: , I® =Sq.0)6(q Ort) + 5a 0)H@e® u(t) = S(q,0)r(t) — S(q,9)K(q, 0)e (49 H(g)e(t) it can be further written as:
PI _ Ë ee 6) S(q,0)H @ ” | [] (q, 9) —S(q,9)K(q,9)e "47" H(q)] le where S(q,6) = (1 + G(q,9)K(g,9)e *@%)7? is the sensitivity function.
The transfer function of the time-delay system model in continuous time is shown in the following formula, which is expressed as the product of a conventional transfer function and a time-delay term e7*P*,
G(s) = es p where k, is the proportional coefficient of the transfer function, T, is a time constant, and t, is a time delay term.
From the above formula, it can be seen that when a closed-loop system is formed, the denominator of the closed-loop transfer function H(s) of the following formula will contain time-delay terms, which will lead to H(s) having infinite poles, so the system may be unstable and it is difficult to achieve effective control. pe) GS) Ke 1+G,(s) T,s+1+k,e "6°
Smith predictor is a kind of advanced predictive controller, and the time-delay term can be completely offset by using Smith predictor. Its control structure is shown in FIG. 5 below, assuming that the model transfer function is
G(s) = g(s)e”">°
The transfer function of Smith controller controlled object is
G,(s) = 9, (se "»°
From the above formula, it can be seen that its closed-loop transfer function is
Y(s) G.(s)G,(s) LU505155
MOT RS) 14 6G) + GGG
Gc(s)Go(s) ors 1+ G.(s)Gm.(s) + G.(s)[G,(5)e™™ — Gp (s)e~Tm™’] the characteristic equation is 1+ K(s)G(s)e™*™ = 0 in order to eliminate the lag term in the characteristic equation, the denominator cannot contain the lag term, and its closed-loop transfer function is required. 1 + K(s)G,(s) = 0
If the model is completely matched with the controlled object, that is, G(s) = G,(s), and there is no external interference, a good control effect can be obtained by designing a reasonable controller. However, due to the complexity of the controlled object in industrial field or actual environment and the difficulty in establishing a relatively accurate mathematical model, and accompanied by external interference, the conventional Smith control structure will be difficult to adapt to the real situation.
Differential evolution algorithm is a kind of evolutionary algorithm. In the initialization process, the population randomly generates NP individuals in
D-dimensional direction. In the process of population evolution, individuals are operated by mutation, crossover and selection in turn. Population initialization assumes that G = 0, 1, 2, ..., Gmax represents the evolutionary algebra, then the i-th representation in the population under the current algebra is: {x:(0) 1x;; < %;,(0) < xjpt = 1,2, ,NP;j = 1,2, D} where D is the dimension of the individual population. During initialization, the population to be initialized is required to cover all the search spaces. x;1(0) = x} + rand (0,1) x (x; — xj)
In the formula, xi(0) — the i-th chromosome of the O-th generation in the population; rand(0,1) —— random numbers evenly distributed in the (0,1) interval.
The difference strategy is adopted to realize individual difference, and two different individuals in the population are randomly selected, and their vector differences are scaled and then combined with the individuals to be mutated to produce 17905155 intermediates: v;(g + 1) = x,,(g) + F + [x,2(9) — x,3(9)] à # 7, # T2 # T3
In the formula, F —— mutation factor; xi(g) —— i-th individual in the g-generation population.
Performing a crossover operation, a binomial crossover and an exponential crossover; carrying out the crossover operation between individuals on the g generation population xi(g) and its mutated intermediate vi(g+1): v;;(g + 1), ifrand j[0,1] < CR or j = jana wig+l)= | x;;(g), otherwise where CR is the cross factor;
Selection operation: after the differential algorithm generates offspring groups through mutation operation and crossover operation, the individual is compared with the corresponding parent by one-to-one greedy screening operator, and the better one is saved to the next generation. The selection operation is as follows: 1 alg + 1) = {40+ DFG + 1) < FO constructing multi-parameter differential evolution identification model according to differential algorithm can improve the accuracy of identification results.
In order to solve the problem of time delay compensation and control of power system under the condition that wind power is connected with small interference, the embodiment of the invention improves the adaptive differential evolution algorithm by using the theory of Vinncombe metric and frequency domain stability margin, and applies it to power system model identification and online Smith predictor control, deduces the system stability judgment condition, and is used to improve the problem that the adaptive multi-parameter differential evolution algorithm is easy to fall into local optimal solution in the identification process. The reduced-order model of wind power connected to power system with small interference is identified, and a wide-area damping controller with time delay is designed. Combined with the identification results, the Smith predictive controller is improved, and the conditions of interference signal, model mismatch with parameters and time delay factor 17905155 mismatch are improved.
The Smith control structure of the improved multi-parameter identification differential evolution algorithm is shown in FIG. 4, and its design steps are as follows:
Step 1: giving the power system model Go to be initially reduced, and obtaining the controller model according to the above.
Step 2: taking the above model Go as the controlled object transfer function of smith predictor, and introducing the related lag factor e-”°. Using the controller model
K as the controller of Smith predictor to construct the smith predictive control structure.
Step 3: using the improved multi-parameter differential evolution algorithm to identify the time-delay item e-”* and the item to be identified in the feedback link online.
Step 4, calculating the distance &,(Go, Gp) between the transfer function Gp of the controlled object and the transfer function identify by the differential evolution algorithm according to the related theory of Vinncombe.
Step 5: according to the theory of frequency stability margin, judging whether the identification model reaches the identification accuracy, and if it cannot meet the preset accuracy requirements, returning to the above step 3 for the model identification.
Step 6: substituting the identification models obtained in step 3, step 4 and step 5 into the smith predictive controller established in step 2, and making an online real-time compensation for the time delay link.
In order to verify the design effect of the damping controller, in the embodiment of the invention, the designed damping controller is simulated and verified by taking a first-order system as an example, assuming that the system transfer function is
G(s) = — e7505 the time-delay factors do not match, the target time-delay factor t = 50 and the time-delay compensation factor t = 10 are preset, the model parameters to be identified in the time-delay compensation link are initialized to random parameters according to the differential evolution algorithm, and the input signal is a step signal. The parameter identification result of the improved multi-objective differential evolution algorithm is shown in FIG. 6, assuming that the parameters to be identified are {a,, a,, a3, T}, the scattered points in FIG. 6 represent the parameter changes in the identification process, and the identification result output by the model is
G(s) — 4.05 e 49.995 51s + 0.99 the identification error index is,
N
1 5.12
J= > 70-95) —2 i=l where N is the number of iterative identification; y, is the output of the i-th sample. The identification error of the identification result is 6.1221e-8. This identification process can directly identify the time delay factor, without using the conventional method of reducing the order of time delay, which reduces the conservatism of the system identification stage, and the identification accuracy is close to the ideal situation. It can be seen that the output result of the damping controller design method provided in this embodiment is very close to the actual system.
As shown in FIG. 7, the comparative simulation is conducted in three situations, respectively, when the parameters of the controlled object do not match, when the time delay factor does not match, and when the controlled object and the time delay factor do not match. It can be seen from FIG. 7 that the step response of the system will obviously oscillate when the time delay factors are not matched, when the parameters of the controlled object are not matched, and when both the controlled object and the time delay factors are not matched, and the method provided in this embodiment is very close to the step response in the ideal state of the system.
Compared with the conventional smith predictor, it has a better compensation effect for time delay, and can solve the problems of parameter mismatch and time delay factor mismatch of the controlled object to a certain extent.
In the embodiment of the present invention, a four-machine two-zone power system is used for simulation. As shown in FIG. 8, the transfer function of the reduced-order model of the power system can be expressed as c z°+3z+2 0 23+522+5252+5 according to the above transfer function, the initial damping controller of the system 1s:
K = —0.2797z% + 0.13362 — 0.0606 z3 + 0.543022 — 0.5078z — 0.0098 assuming that the identification parameters of the target transfer function of the . . a a; az T . reduced-order power system to be identified are {4 B. BB } the transfer function 1° 27 37 4 after the identification of the solid system can be expressed as 2 c= a,z° + AZ + a; gos
B,z3 + B,2° + fz + B, the time delay factor 7 = 0.8 of the target transfer function is preset, and the first power system identification result is a, = 1, a, = 2.995, a; = 2, as shown in FIG. 9, and the second power system identification result is f, = 0.998, B, = 4.998, ß, = 5.24, B, = 4.998, as shown in FIG. 10. Similar to the first-order system, the identified power system model is z% +2.995z +2 i —{— e- 085 0.998z* + 4.99822 + 5.240z + 4.998
The dynamic relationship between frequency domain stability margin and
Vinnicombe distance is shown in FIG. 11. It can be seen that the frequency domain stability margin of the system is smaller than Vinnicombe distance before the fifth identification, which will not satisfy the above stabilization conditions. It can be known that the controller is not enough to stabilize the system. With the increase of iterations, after the fifth identification, the frequency domain stability margin is larger than Vinnicombe distance, which shows that the controller can stabilize the system and the frequency domain stability margin gradually increases, so the optimal controller can be obtained. 17005155
As shown in FIG. 12, the rotor angle curves with different time delays are simulated in the following four situations. At 50ms, 100ms and 150ms, and without time delay link and additional control link, it can be seen from the comparison that the method provided by the embodiment of the invention has obvious suppression effect on low-frequency oscillation under the influence of different time delays.
As shown in FIG. 13, both the least squares iterative identification method and the method provided by the embodiment of the present invention can achieve the distance close to the real model in the identification process, and it can be seen from the convergence curve that the method provided by the embodiment of the present invention has a faster approximation speed and is closer to the real model in the identification process. FIG. 14 and FIG. 15 compare and simulate the two algorithms when the time delay is 100ms and 150ms, respectively. It can be seen that the method provided by the embodiment of the invention has faster convergence speed and relatively smaller rotor angle oscillation amplitude, and this method has better suppression effect on low-frequency oscillation of power system with time delay than the iterative identification method.
According to the design method of damping controller based on the power system provided by the embodiment of the invention, for the influence of model identification error and communication delay existing when wind power is connected to the power system on system damping control, an improved design method of Smith time delay compensator and optimal controller based on multi-parameter identification differential evolution algorithm is proposed, and compared with the method based on recursive least square iterative identification, and simulated and verified in a four-machine two-zone system. The results show that compared with the iterative identification method of recursive least squares method, the method provided by the embodiment of the invention has faster convergence speed and higher identification accuracy, the corresponding damping controller design is more reasonable, and the low-frequency oscillation suppression effect of the power system with wind power is better under multi-interference conditions.
On the other hand, the invention also provides a damping controller, which is 17905155 designed based on any one of the above-mentioned damping controller design methods based on the power system.
On the other hand, the invention also provides a power system, which comprises the damping controller.
In the embodiment of the invention, the power system can be a low-order system or a high-order system.
The device embodiments described above are only schematic, in which the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of this embodiment. Ordinary technicians in this field can understand and implement it without creative labor.
From the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be realized by means of software and necessary general hardware platform, and of course it can also be realized by hardware. Based on this understanding, the essence of the above technical scheme or the part that has contributed to the prior art can be embodied in the form of a software product, which can be stored in a computer-readable storage medium, such as
ROM/RAM, magnetic disk, optical disk, etc., and includes several instructions for making a computer device (which can be a personal computer, a server, or a network device, etc.) execute the methods described in various embodiments or some parts of the embodiments.
Finally, it should be explained that the above embodiments are only used to illustrate the technical scheme of the present invention, but not to limit it; Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that it is still possible to modify the technical solutions described in the foregoing embodiments, or to replace some technical features with equivalents; However, these modifications or substitutions do not make the essence of the corresponding technical solutions deviate 17005155 from the spirit and scope of the technical solutions of various embodiments of the present invention.
Claims (10)
1. A design method of the damping controller based on the power system, characterized by comprising: solving an initial controller model according to a preset power system model; taking the preset power system model as a controlled object transfer function of a damping controller, and introducing a time delay factor into the controlled object transfer function; performing an on-line identification on the parameters to be identified and the time-delay factor in the transfer function of the controlled object by using the optimized multi-parameter differential evolution identification model to obtain an identification result, where the identification result includes an identification result of the transfer function parameters of the controlled object and an identification result of the time-delay factor; designing the damping controller according to the identification result.
2. The design method of the damping controller based on the power system according to claim 1, characterized in that the optimization method of the multi-parameter differential evolution identification model comprises: inputting the parameters to be identified and the time delay factor in the transfer function of the controlled object into the multi-parameter differential evolution identification model to obtain an identification transfer function containing the time delay factor; calculating the Vinncombe distance between the transfer function of the controlled object and the identification transfer function; if the Vinncombe distance does not meet the preset identification accuracy requirement, optimizing the parameters in the multi-parameter differential evolution identification model; re-identifying by using the optimized multi-parameter differential evolution identification model until the Vinncombe distance reaches the preset identification accuracy requirement, and outputting the optimized multi-parameter differential evolution identification model.
3. The design method of the damping controller based on the power system 17905155 according to claim 2, characterized in that the calculation of the Vinncombe distance between the transfer function of the controlled object and the identification transfer function comprises: obtaining Vinncombe distance constraint formula (1 + G}G2)(e/®) + OVw w(1 + G7G2) + n(G2) =n (Gy) = 0 when the constraint formula is satisfied, the Vinncombe distance between the transfer function of the controlled object and the identification transfer function is expressed as: maxel Gy le”) ler when the constraint formula is not satisfied, the Vinncombe distance between the transfer function of the controlled object and the identification transfer function is expressed as 1; in the formula, G}(e/®) = G,(e-®), n(Gz) is the number of poles in the open right half-plane of G,, n(G1) is the number of poles in the closed right half-plane of Gy, w(x) is the number of turns of the transfer function Nyquist curve around the origin counterclockwise, x(G,(el®), Gz(e/®)) are the chord distance obtained by projecting G, and G, to the unit Riemannian sphere.
4. The design method of the damping controller based on the power system according to claim 2, characterized in that the method for judging whether the Vinncombe distance reaches the preset identification accuracy requirement comprises: judging whether the Vinncombe distance is within a preset error threshold under the premise of stable output of the system; if so, it is judged that the Vinncombe distance reaches the preset identification accuracy requirement.
5. The design method of the damping controller based on the power system according to claim 2, characterized in that the design of the damping controller according to the identification result comprises:
obtaining a parameter matrix according to a preset power system model, an 17905155 initial controller model and interference signals; estimating the system frequency stability margin on the error boundary according to the parameter matrix; when the system frequency stability margin is greater than the maximum value of the Vinncombe distance, using the initial controller model as a damping controller.
6. The design method of the damping controller based on the power system according to claim 5, characterized in that the design of the damping controller according to the identification result further comprises: when the frequency stability margin of the system is not greater than the maximum value of the Vinncombe distance, if |P| < p,p(|P|&, + &,) < 1, where P isa single-input single-output transfer function, then the damping controller is: Kies = [G+ SAP where S; = (1 + GK) LS = (1 +GK)™", & is the difference between the stability margin in the update frequency domain and the previous stability margin, &, is the previous frequency domain stability margin, p is the maximum distance between the identification model and the target model transfer function, and K; is the initial controller model.
7. The design method of the damping controller based on the power system according to claim 1, characterized in that the multi-parameter differential evolution identification model includes an adaptive crossover factor and an adaptive mutation factor; the adaptive crossover factor is: CR = CRmin + Tan men) where CRmin is the minimum adaptive crossover factor, CRmax is the maximum adaptive crossover factor, Gm is the maximum number of iterations, and G is the evolutionary algebra; the adaptive mutation factor is: 1-_ Gm F = Fnax — (Fax — Fmin)e Gm=6+1 where Fmin is the minimum adaptive mutation factor, Fmax is the maximum 17905155 adaptive mutation factor, Gm is the maximum number of iterations, and G is the evolutionary algebra.
8. The design method of the damping controller based on the power system according to claim 1, characterized in that solving the initial controller model according to the preset power system model comprises: estimating preset power system model parameter according to an on-line identification method of low-frequency oscillation mode noise signals of a power system, testing the estimated results according to the model order criterion, and when the estimated results meet the requirements of the model order criterion, obtaining reduced-order power system model; solving the initial controller model according to the closed-loop stability conditions corresponding to the reduced-order power system model.
9. A damping controller designed based on the design method of the damping controller based on the power system according to claim 1.
10. A power system comprising the damping controller according to claim 9.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| LU505155A LU505155B1 (en) | 2023-09-22 | 2023-09-22 | Design method of damping controller based on power system, controller and power system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| LU505155A LU505155B1 (en) | 2023-09-22 | 2023-09-22 | Design method of damping controller based on power system, controller and power system |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| LU505155B1 true LU505155B1 (en) | 2024-03-22 |
Family
ID=90526783
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| LU505155A LU505155B1 (en) | 2023-09-22 | 2023-09-22 | Design method of damping controller based on power system, controller and power system |
Country Status (1)
| Country | Link |
|---|---|
| LU (1) | LU505155B1 (en) |
-
2023
- 2023-09-22 LU LU505155A patent/LU505155B1/en active IP Right Grant
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US20080172349A1 (en) | Neural network controller with fixed long-term and adaptive short-term memory | |
| CN108520325B (en) | Integrated life prediction method based on accelerated degradation data in variable environment | |
| US20140330402A1 (en) | Computer Apparatus And Method using Model Structure Information of Model Predictive Control | |
| CN111273544B (en) | Radar pitching motion control method based on prediction RBF feedforward compensation type fuzzy PID | |
| CN113641722A (en) | Long-term time series data prediction method based on variant LSTM | |
| CN111142404A (en) | Micro-positioning platform based on piezoelectric ceramic drive and modeling and control method thereof | |
| CN112462611A (en) | Sliding friction modeling method for precise electromechanical system | |
| CN110802589A (en) | Hysteresis compensation method for single-joint servo control of industrial robot | |
| CN109062040A (en) | Predictive PID method based on the optimization of system nesting | |
| CN115773569A (en) | Ocean platform ventilation system air volume control method based on active disturbance rejection decoupling | |
| CN113741184A (en) | Wind turbine load prediction control method based on sliding-mode observer | |
| CN110851911B (en) | Terminal state calculation model training method, control sequence searching method and device | |
| LU505155B1 (en) | Design method of damping controller based on power system, controller and power system | |
| CN111240201B (en) | Disturbance suppression control method | |
| Huzmezan et al. | Reconfiguration and scheduling in flight using quasi-LPV high-fidelity models and MBPC control | |
| CN115933647B (en) | OMR track tracking control method based on composite control algorithm and storage medium | |
| CN114670856B (en) | Parameter self-tuning longitudinal control method and system based on BP neural network | |
| Yang et al. | Research on power system small signal stability analysis and correction based on LightGBM algorithm | |
| CN112346342B (en) | Single-network self-adaptive evaluation design method of non-affine dynamic system | |
| CN112748657B (en) | Parameter setting method for rotary inertial navigation active disturbance rejection controller based on adaptive genetic algorithm | |
| CN113219840B (en) | Self-adaptive sliding mode cross-coupling contour control method for three-axis motion platform | |
| CN114139442A (en) | Method for optimizing steam turbine valve flow characteristic function based on K-means-LSTM | |
| CN112379601A (en) | MFA control system design method based on industrial process | |
| CN110308655A (en) | Servo system compensation method based on A3C algorithm | |
| CN117096895A (en) | Damping controller design method based on power system, controller and power system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| FG | Patent granted |
Effective date: 20240322 |