CN115774959A - SM-AFA optimization algorithm-based optimal design method for parameters of multi-machine power system stabilizer - Google Patents
SM-AFA optimization algorithm-based optimal design method for parameters of multi-machine power system stabilizer Download PDFInfo
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Abstract
The invention relates to an optimal design method for parameters of a multi-machine power system stabilizer based on an SM-AFA optimization algorithm. Firstly, searching by adopting an AFA algorithm, when the fitness reaches a certain value, indicating that the AFA algorithm converges to a globally optimal area, switching to an SM algorithm, and carrying out local optimization on the SM algorithm on the basis of the AFA algorithm; and a new discrimination mode is introduced for algorithm switching. The PSS controller set by the SM-AFA algorithm has better adjustability on the whole, and the set PSS parameters have better adaptability, so that the stable operation level of the system can be improved under large disturbance.
Description
Technical Field
The invention relates to the field of low-frequency oscillation suppression of power systems, in particular to an optimal design method for parameters of a multi-machine power system stabilizer based on an SM-AFA optimization algorithm.
Background
With the rapid growth of global economy and the rapid increase of population, the usage amount of electric energy in the world is continuously increased, so that the load of a power grid is continuously increased; meanwhile, modern power systems have entered the period of large power grids interconnected across regions, and long-distance and large-capacity power transmission lines widely exist; these will induce low frequency oscillations in the power system. If the low-frequency oscillation cannot be well inhibited, a series of system faults are caused, so that large-scale power failure is caused, and huge economic loss is caused. In recent years, low-frequency oscillation of a power system has become one of the key problems affecting whether a power grid can operate reliably and safely. Therefore, it is important to quickly, effectively and real-timely identify the parameters related to the low frequency oscillation and adjust the damper to suppress the low frequency oscillation.
Because the Power System Stabilizer (PSS) is additionally arranged, the low-frequency oscillation can be inhibited, and the advantages of simpler control and good performance are also achieved; and the PSS has clear physical concept, when the PSS is used for damping control, the calculation and debugging of the PSS have direct mathematical models and can be used, and meanwhile, the input debugging and the exit can be carried out according to different operation conditions, so that the PSS is in inhibition. Therefore, adding PSS is the first control measure to suppress low frequency oscillations.
The key problem of PSS control is parameter setting of PSS; currently, a phase compensation method, a pole allocation method and the like are mainly adopted in a PSS setting method. The phase compensation method has the advantages of clear physical significance, convenience in debugging and the like; the pole allocation method is to allocate the main pole of the system to a new position of the S plane, so as to improve the stability of the system, but because the pole allocation method is not directly related to the parameters of the excitation system, the pole allocation method is limited in the application of practical engineering. Recently, a great number of optimization algorithms, such as neural networks, fuzzy control, artificial intelligence algorithms, etc., have appeared in the relevant adjustment of PSS parameters. However, the neural network needs a large number of learning samples and has a slow convergence rate, the genetic algorithm has a too long search time and is easy to converge to a sub-global optimal solution too early, but the fuzzy control may have the consequences of poor adaptive capacity, difficult rule setting and the like. The application of the artificial intelligence algorithm in the control of the power system still has great potential in the aspects of accelerating the optimization speed, avoiding falling into local extreme values and the like, and further intensive research is needed.
Disclosure of Invention
The invention aims to provide a multi-machine power system stabilizer parameter optimal design method based on an SM-AFA optimization algorithm, so as to overcome the defects in the prior art.
In order to realize the purpose, the technical scheme of the invention is as follows: a multi-machine power system stabilizer parameter optimal design method based on an SM-AFA optimization algorithm comprises the following steps:
s1, initializing SM and AFA algorithm parameters;
s2, enabling the iteration times t =1, and performing iterative operation;
s3, operating the power system simulation model, and acquiring signals between each generator and a connecting line in the power system simulation model;
s4, acquiring a low-frequency oscillation signal of the power system from the acquired signal;
s5, identifying and analyzing the low-frequency oscillation signals of the power system to obtain corresponding oscillation modes and characteristic values lambda i ＝σ i ±jw i Wherein σ i Representing the real part of the eigenvalue, w i An imaginary part representing a characteristic value;
s6, determining a damping ratio according to the magnitude of the characteristic value;
s7, taking the damping ratio as a PSS parameter optimization target;
s8, optimizing PSS parameters through the constraint conditions of the PSS;
s9, carrying out optimization calculation on the objective function by adopting an SM-AFA algorithm;
step S10, obtaining the individual optimal position P i (t) and a global extremum P g (t)；
S11, judging whether the iteration time t reaches the maximum iteration number MaxGenerator or not when the iteration time t = t +1, and if the iteration time t does not reach the maximum iteration number MaxGenerator, turning to the step S2 to continue iteration;
step S12, if the iteration number t reaches the maximum iteration number MaxGeneration, the optimal position P of the individual is determined i (t) global extremum is P g (t) outputting; individual optimum position P i (t) is the PSS parameter obtained by optimization, the global extreme value P g (t) is the optimal damping ratio of the power system;
and step S13, finishing all calculations and ending.
In one embodiment of the present invention, in step S1, in the initialization process, the number of fireflies n =20, the number of iterations MaxGeneration =50, the random number coefficient α =0.25, the light absorption coefficient γ =1, and the maximum attraction β 0 =1, reflection coefficient δ =1, expansion coefficient Φ =2, contraction coefficient and compression coefficient
And randomly generates initialization parameters.
In an embodiment of the present invention, in step S6, the damping ratio is:
in an embodiment of the present invention, in step S7, the objective function is:
J＝min{ξ i,j ,i∈S,j＝1,...,k}
in the formula, k is the number of operation modes adopted in the optimization process; s is a set of oscillations; xi i,j And the damping ratio of the ith electromechanical oscillation mode in the jth interference mode is shown.
In an embodiment of the present invention, in step S8, the expression of parameter optimization is as follows:
wherein the gain K i In the range of [0.1,50]，T 1i 、T 3i Is in the range of [0.01,1]PSS time constant T 5i 、T 2i 、T 4i Is a preset value.
In an embodiment of the invention, the PSS time constant T 5i 、T 2i 、T 4i The preset values are respectively: 10. 0.25 and 0.25.
In an embodiment of the present invention, the step S9 includes the following steps:
s91, rapidly converging the fish school to the domain of the global optimal solution by adopting an AFA algorithm;
step S92, judging whether the AFA algorithm converges to a global optimal area, wherein the adopted switching discrimination mode adopts the following mode:
in the formula: y is k 、Y k+1 、Y n+1 Corresponding fitness values obtained by iteration of the k, k +1 and n +1 times of the AFSA algorithm are respectively obtained; alpha, beta and r are preset values;
and S93, when the formula is met, the AFA algorithm is shown to be converged to the globally optimal area, the convergence begins to slow, and at the moment, the SM algorithm is switched to.
In an embodiment of the present invention, in step S93, when the optimal fitness value of the AFA algorithm reaches the threshold r and the results of n +1 times of continuous operations are the same, the AFA algorithm is skipped and the SM algorithm is switched.
In an embodiment of the present invention, the preset values α, β, and r are 0.2, 0.02, and 0.15, respectively.
Compared with the prior art, the invention has the following beneficial effects:
1. the invention combines the AFA algorithm and the SM to form the SM-AFA hybrid algorithm, and fully utilizes the global searching capability of the AFA algorithm and the local searching capability of the SM algorithm.
2. The first method of the hybrid algorithm provided by the invention adopts an AFA algorithm, and when the fitness reaches a certain value, the hybrid algorithm is switched to an SM algorithm; and introducing a new discrimination mode for the switching point of the algorithm. The switching mode between the two algorithms is applied, and the speed of algorithm convergence can be improved under the condition of ensuring the convergence precision in the parameter optimization process.
3. The method provided by the invention is applied to PSS setting, has better regulation performance and better adaptability on the whole, and can still improve the stable operation level of the system under large disturbance.
Drawings
FIG. 1 is a flow chart of the optimal design of the parameters of the multi-machine power system stabilizer based on the SM-AFA optimization algorithm in the invention.
FIG. 2 shows an oscillation signal and a suppression signal after adding a PSS according to an embodiment of the present invention; in the figure, (a) a generator 2; (b) a generator 4.
Detailed Description
The technical scheme of the invention is specifically explained below with reference to the accompanying drawings.
The invention provides an optimal design of parameters of a multi-machine power system stabilizer based on an SM-AFA optimization algorithm, which comprises the following steps as shown in figure 1:
s1, initializing AFA and SM algorithm parameters; in the present embodiment, the number of fireflies n =20, the number of iterations MaxGeneration =50, the random number coefficient α =0.25, the light absorption coefficient γ =1, and the maximum attraction β 0 =1, reflection coefficient δ =1, expansion coefficient Φ =2, contraction coefficient and compression coefficient
And randomly generates initialization parameters.
And S2, performing iterative operation by making t = 1.
S3, operating a corresponding simulation model, and acquiring signals between each generator and a connecting line in the power system simulation model;
s4, acquiring a low-frequency oscillation signal of the power system from the acquired signal; as shown in fig. 2, the solid line is the acquired oscillation signal.
S5, correspondingly identifying and analyzing the low-frequency oscillation signals of the power system to obtain related oscillation modes and characteristic values lambda i ＝σ i ±jw i ，σ i Representing the real part of the eigenvalue, w i Representing the imaginary part of the characteristic value.
S6, according to the size of the characteristic value, defining the damping ratio as follows:
in the formula, λ i Real part of (a) i Determines the rate of decay of the system response. If σ is i If the value of (d) is large, the response decay rate is slow, and the system is unstable; otherwise, the system is more stable. Xi i Representing the dynamic performance of the power system, not only reflecting the position of the characteristic value on the complex plane, but also determining the maximum overshoot; the larger the damping ratio, the smaller the overshoot. In the context of an electrical power system, a power system,it is generally required that the damping ratio is not less than 0.05 to ensure good dynamic characteristics of the system.
S7, using the damping ratio as a target to serve as a target for PSS parameter optimization;
J＝min{ξ i,j ,i∈S,j＝1,...,k}
in the formula, k represents the number of operation modes considered in the optimization process; s is a set of oscillations; xi i,j And the damping ratio of the ith electromechanical oscillation mode in the jth interference mode is shown. The ultimate goal of the optimization is to maximize the minimum damping ratio under a variety of operating conditions;
further, the selection will select 2 more typical operating conditions, and the most severe of them will be used for the adaptability check of the PSS.
Step S8, considering some constraint conditions of the PSS, the PSS parameter optimization can be expressed as the following form:
wherein the gain K i In the range of [0.1,50]，T 1i 、T 3i Is in the range of [0.01,1]. PSS time constant T 5i 、T 2i 、T 4i Generally given before optimization; in this embodiment, the PSS time constant T 5i 、T 2i 、T 4i Given before optimization as: 10. 0.25 and 0.25.
And S9, performing optimization calculation on the objective function by adopting SM-AFA.
Further, in step S9, the method specifically includes the following steps:
and S91, rapidly converging the fish school to the domain of the global optimal solution by adopting an AFA algorithm.
Step S92, judging whether the AFA algorithm is converged to a global optimal area, wherein the switching judgment mode adopts the following formula:
in the formula: y is k 、Y k+1 、Y n+1 Corresponding fitness values obtained by iteration of the k, k +1 and n +1 times of the AFSA algorithm are respectively obtained; α, β and r are predetermined values.
And S93, when the formula is met, the AFA algorithm is shown to be converged to the globally optimal area, the convergence begins to slow, and at the moment, switching to the SM algorithm is considered.
In this embodiment, the values of α and β will affect the participation time of the SM algorithm, and if the AFA algorithm takes too large a value, the AFA algorithm may not find the optimal range well and then enter the SM algorithm, which results in a longer time for the whole algorithm. And if the value setting is too small, the AFA algorithm is over-calculated. The AFA algorithm is faster than the SM algorithm in calculation speed; in selecting the parameters, the values of α and β are set relatively large, which enables the SM algorithm to start searching for a range in a range close to the optimal solution.
Further, in order to prevent the algorithm from falling into the overall optimal solution of the AFA, the optimal fitness value of the AFA algorithm is set to reach the threshold r, and the AFA algorithm is skipped to be converted into the SM algorithm when the results of n +1 times of succession are the same.
Further, in the simulation, the parameters α, β, and γ were taken as 0.2, 0.02, and 0.15, respectively.
Step S10, obtaining the individual optimal position P i (t) and a global extremum P g (t)；
Step S11, judging whether the iteration time t reaches the maximum iteration number MaxGenerator or not when the iteration time t = t +1, and if the iteration time t does not reach the maximum iteration number MaxGenerator, turning to the step S2 to continue iteration;
step S12, if the iteration number t reaches the maximum iteration number MaxGeneration, the optimal position P of the individual is determined i (t) global extremum is P g (t) outputting; individual optimum position P i (t) is the PSS parameter obtained by optimization, the global extreme value P g (t) is the optimal damping ratio of the power system;
as shown in table 1 below, PSS parameter results were optimized for the algorithm, while AFA calculation results were compared; as can be seen from FIG. 2, the parameter set by the SM-AFA algorithm has the best effect of suppressing the low-frequency oscillation.
TABLE 1
And step S13, finishing all calculations and ending.
In conclusion, the invention combines the AFA algorithm and the SM, and fully utilizes the global search capability of the AFA algorithm and the local search capability of the SM algorithm; the designed switching mode can improve the convergence speed of the algorithm under the condition of ensuring the convergence precision in the parameter optimization process; the PSS set by the algorithm has better adjustability and adaptability on the whole, and can still improve the stable operation level of the system under large disturbance.
The above are preferred embodiments of the present invention, and all changes made according to the technical scheme of the present invention that produce functional effects do not exceed the scope of the technical scheme of the present invention belong to the protection scope of the present invention.
Claims (9)
1. A multi-machine power system stabilizer parameter optimal design method based on an SM-AFA optimization algorithm is characterized by comprising the following steps:
s1, initializing SM and AFA algorithm parameters;
s2, enabling the iteration times t =1, and performing iterative operation;
s3, operating the power system simulation model, and acquiring signals between each generator and a connecting line in the power system simulation model;
s4, acquiring a low-frequency oscillation signal of the power system from the acquired signal;
s5, identifying and analyzing the low-frequency oscillation signals of the power system to obtain corresponding oscillation modes and characteristic values lambda i ＝σ i ±jw i Wherein σ i Representing the real part of the eigenvalue, w i An imaginary part representing a characteristic value;
s6, determining a damping ratio according to the magnitude of the characteristic value;
s7, taking the damping ratio as a PSS parameter optimization target;
s8, optimizing PSS parameters through the constraint conditions of the PSS;
s9, carrying out optimization calculation on the objective function by adopting an SM-AFA algorithm;
step S10, obtaining the individual optimal position P i (t) and a global extremum P g (t)；
S11, judging whether the iteration time t reaches the maximum iteration number MaxGenerator or not when the iteration time t = t +1, and if the iteration time t does not reach the maximum iteration number MaxGenerator, turning to the step S2 to continue iteration;
step S12, if the iteration number t reaches the maximum iteration number MaxGeneration, the optimal position P of the individual is determined i (t) global extremum is P g (t) outputting; individual optimum position P i (t) is the PSS parameter obtained by optimization, the global extreme value P g (t) is the optimal damping ratio of the power system;
and step S13, finishing all calculations and ending.
2. The method for optimally designing the parameters of the multi-machine power system stabilizer based on the SM-AFA optimization algorithm as claimed in claim 1, wherein in the step S1, in the initialization process, the number of fireflies n =20, the iteration number MaxGeneration =50, the random number coefficient α =0.25, the light absorption coefficient γ =1, and the maximum attraction β 0 =1, reflection coefficient δ =1, expansion coefficient Φ =2, contraction coefficient and compression coefficient
And randomly generates initialization parameters.
3. The method for optimally designing the parameters of the multi-machine power system stabilizer based on the SM-AFA optimization algorithm according to the claim 1, wherein in the step S6, the damping ratio is as follows:
4. the method for optimally designing the parameters of the multi-machine power system stabilizer based on the SM-AFA optimization algorithm according to the claim 1, wherein in the step S7, the objective function is:
J＝min{ξ i,j ,i∈S,j＝1,...,k}
in the formula, k is the number of the operation modes adopted in the optimization process; s is a set of oscillations; xi i,j And the damping ratio of the ith electromechanical oscillation mode in the jth interference mode is shown.
5. The SM-AFA optimization algorithm based multi-machine power system stabilizer parameter optimal design method according to claim 1, wherein in step S8, the expression of parameter optimization is as follows:
wherein the gain K i Has a range of [0.1,50]，T 1i 、T 3i Is in the range of [0.01,1]PSS time constant T 5i 、T 2i 、T 4i Is a preset value.
6. The SM-AFA optimization algorithm-based multi-machine power system stabilizer parameter optimal design method as claimed in claim 1, wherein the PSS time constant T 5i 、T 2i 、T 4i The preset values are respectively: 10. 0.25 and 0.25.
7. The method for optimally designing the parameters of the multi-machine power system stabilizer based on the SM-AFA optimization algorithm according to the claim 1, wherein the step S9 comprises the following steps:
s91, rapidly converging the fish school to the domain of the global optimal solution by adopting an AFA algorithm;
step S92, judging whether the AFA algorithm converges to a global optimal area, wherein the adopted switching discrimination mode adopts the following mode:
in the formula: y is k 、Y k+1 、Y n+1 Corresponding fitness values obtained by iteration of the k, k +1 and n +1 times of the AFSA algorithm are respectively obtained; alpha, beta and r are preset values;
and S93, when the formula is met, the AFA algorithm is shown to be converged to the globally optimal area, the convergence begins to slow, and at the moment, the SM algorithm is switched to.
8. The method for optimally designing the parameters of the multi-machine power system stabilizer based on the SM-AFA optimization algorithm as claimed in claim 7, wherein in step S93, when the optimal fitness value of the AFA algorithm reaches the threshold r and the results of n +1 times of continuous times are the same, the AFA algorithm is skipped and the SM algorithm is switched to.
9. The method for optimally designing the parameters of the multi-machine power system stabilizer based on the SM-AFA optimization algorithm according to the claim 7 or 8, wherein the preset values α, β and r are respectively 0.2, 0.02 and 0.15.
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