CN112290534B - Power system stabilizer parameter optimization method and system - Google Patents
Power system stabilizer parameter optimization method and system Download PDFInfo
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- H—ELECTRICITY
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- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
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- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
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- H02J3/466—Scheduling the operation of the generators, e.g. connecting or disconnecting generators to meet a given demand
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- H—ELECTRICITY
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- 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
- H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
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Abstract
The invention discloses a power system stabilizer parameter optimization method, which comprises the following steps: determining an oscillation operation mode of the power system, and determining the operation mode of the power system which causes oscillation according to the oscillation frequency of the operation mode of the power system oscillation; determining an oscillating unit, and further determining the oscillating unit in the power system on the basis of determining the running mode of the power system causing oscillation; and optimizing excitation control of the oscillating unit.
Description
Technical Field
The invention belongs to the technical field of power systems, and particularly relates to a power system stabilizer parameter optimization method and system.
Background
In recent years, the economic and social development of China is rapid, the power consumption of industry and commerce is increased, the configuration of a large-scale generator set is increased, the optimization of a power grid structure is relatively delayed, the distance of part of inter-section connecting lines is too long, the load is too heavy, and a serious low-frequency oscillation phenomenon exists. Particularly, the response speed of the quick excitation system of the unit is accelerated, so that the use of high amplification factor of the automatic voltage regulator can seriously increase the negative damping torque of the electromechanical oscillation mode, generate low-frequency oscillation and influence the dynamic stability of the system.
Aiming at low-frequency oscillation of an electric power system, the main research hot spot is in the aspects of oscillation mechanism analysis, inhibition measures and the like. In terms of mechanism analysis, F.P. deMello and C.Concordia start with Heffron-Phillips model, and damping torque analysis is applied to obtain: the overlong line distance, heavier line load and negative damping torque caused by high amplification factor of a unit automatic voltage regulator (Automatic Voltage Regulation, AVR) are important factors for the low-frequency oscillation of a power grid; in addition, adding a controller to the AVR to counteract the negative damping torque provided by the AVR is an effective measure of damping. In recent years, a mode analysis method based on a state matrix has been popularized in power simulation. The method can quantitatively analyze the electromechanical oscillation mode, and is favorable for analyzing the dominant oscillation mode and designing the stabilizer. In the aspect of low-frequency oscillation inhibition measures, the power system stabilizer (Power system stabilizer, PSS) is used as a damping optimization link of an excitation system, and is widely applied to practical application. The PSS takes certain electric quantity (such as frequency, electric power and the like) in the unit as a signal, and a damping torque output is added to the AVR, so that the dynamic stability of the system is improved.
The PSS parameter setting and optimization method is mainly divided into a traditional modern control theory (such as a phase compensation method, a root locus method and the like) and a swarm intelligent algorithm. The traditional control method has the advantages that the state equation is difficult to build in a large system and is easy to locally optimize, and the group intelligent algorithm is well applied by the global optimizing capability. The existing algorithm applied to PSS parameter optimization mainly comprises a particle swarm algorithm, but the convergence speed is slower; the strategy algorithm is evolved, and the solution precision is not high; bat algorithm but poor robustness, etc., and the algorithm application environment is mostly a single infinite system or a typical system structure with simplicity and fewer nodes at home and abroad, and is applied to fewer actual systems.
Disclosure of Invention
The invention aims to provide a power system stabilizer parameter optimization method which can optimize PSS parameters of a related unit generating a low-frequency oscillation problem of an actual large power grid so as to optimize excitation.
In order to achieve the above purpose, the present invention provides the following technical solutions:
In a first aspect, a method for optimizing parameters of a stabilizer of an electric power system is provided, including:
determining an oscillation operation mode of the power system, and determining the operation mode of the power system with the most serious oscillation according to the oscillation frequency of the operation mode of the power system oscillation;
determining an oscillating unit, and further determining the oscillating unit in the power system on the basis of determining the running mode of the power system causing oscillation;
And optimizing parameters of a power system stabilizer in an excitation control system of the oscillating unit.
With reference to the first aspect, further, the operation mode of the power system for determining the oscillation is specifically:
obtaining the damping ratio of the power system according to the formula (1)
Where α i is the damping factor of the i-th oscillation mode, f i represents the oscillation frequency of the i-th oscillation mode;
And taking the power system oscillation operation mode with the smallest damping ratio as the power system operation mode with the most serious oscillation.
With reference to the first aspect, further, the determining a unit that causes oscillation in the power system specifically includes:
A group of units which cause oscillation in an oscillation mode corresponding to the minimum damping ratio is found out through small interference stability new analysis, and then the oscillation unit which finally needs excitation optimization is found out through the following characteristic conditions:
4) The participation factor is greater than 0.1;
5) The absolute value of the difference between the right characteristic vector angle and 0 or 180 DEG is within 30 DEG;
the unit is provided with a power system stabilizer.
With reference to the first aspect, further, the optimizing parameters of the power system stabilizer in the excitation control system of the oscillating unit specifically includes:
The objective function of establishing the power system stabilizer parameters of the unit to be optimized is as follows:
max g＝max{minξij}(i＝1,2,…,p；j＝1,2,…,q) (2)
The constraints are as follows:
Wherein p is the number of electromechanical oscillation modes, q is the number of operation modes, and ζ ij is the damping ratio of the ith oscillation mode in the jth operation mode; g represents the minimum damping ratio under each operation mode and mode; the optimizing direction is the smallest damping ratio is the largest; kp i、Kpimin and Kp imax are the gain of the ith oscillation mode, the lower gain limit and the upper gain limit, and T 1i、T13i and T 3i are time constants; t 1imin、T1imax is the upper and lower limits of T 1i, respectively; t 13imin、T13imax is the upper and lower limits of T 13i, respectively; t 3imin、T3imax is the upper and lower limits of T 3i, respectively;
and optimizing the objective function by adopting a butterfly algorithm.
With reference to the first aspect, further, the optimizing the objective function by using a butterfly algorithm specifically includes:
the optimization procedure is expressed as an optimal solution of the following triples:
BOA＝(I,P,T) (4)
i is the objective function between the candidate butterfly set and the fitness set, i.e. equation (2)
B is a candidate butterfly set and an OB bit adaptation set;
P is an updating function of butterfly position;
P:Bt→Bt+1 (6)
b t is the position of the butterfly at the t-th iteration;
T is a judgment matrix function, the load ending condition is true, otherwise, the load ending condition is false:
T:B→{true,false} (7)
And calculating the system damping ratio under each PSS parameter through the I function, and updating the butterfly set position according to the P function until the iteration termination condition is met.
With reference to the first aspect, further, the butterfly position update function P may be expressed as a function of a butterfly position and a nectar source position, as shown in the following formula:
Wherein, The position of the ith butterfly in the t iteration is represented by g which is the nectar source position, and S which is a position transfer function, wherein the position transfer function is represented by the following formula:
Wherein r1, r2 and r3 are random numbers between [0,1] and Pswitch is a selection probability, global searching is carried out when r3 is less than or equal to Pswitch, and local searching is carried out when r3 is more than Pswitch; f=c a is the fragrance of the honey source of the flowers sought by the butterflies, wherein I is the stimulus intensity and is related to the optimizing fitness; a is a power exponent and c is a sensory factor.
With reference to the first aspect, further, a monotonically decreasing inertial weight is introduced to update the self-cognition portion of the butterfly, as shown in the following formula:
Wherein ω max is the initial inertial weight, ω min is the inertial weight at the end of the iteration, t is the current iteration number, and t max is the maximum iteration number.
In combination with the first aspect, further, in the global search process of the butterfly algorithm, setting a constant Pcapture epsilon [0,1] as the capturing probability of the three-black-hole system, generating a random number r 4 r4 epsilon [0,1] for each butterfly b i each time, if r 4 r4 is less than or equal to Pcapture, capturing b i by the three-black-hole system, otherwise updating according to the conventional global search mode of the butterfly algorithm;
If b i is captured by the three-black-hole system, g, (g+b max)/2 and (g+b min)/2 are taken as centers, r is the radius of the black hole, three black-hole areas are formed, a random number r 5 E [0,1] is generated, and if r 5＞p1, b i is captured by the black hole 1 in the system; if r 5 e [ p2, p1], b i is captured by black hole 2; if r 5＜p2, b i is captured by the black hole 3, and the positions of the captured particles are as follows:
Where b max/bmin bmax/bmin is the upper/lower limit of the butterfly position search region, p 2 is a constant threshold, p 1 e [0,1], and p 1＞p2,r6 is a random number of [ -1,1 ].
In a second aspect, a power system stabilizer parameter optimization system includes:
the operation mode determining module: the method comprises the steps of determining an oscillation operation mode of the power system, and determining the operation mode of the power system which causes oscillation according to the oscillation frequency of the operation mode of the power system oscillation;
And the vibration unit determining module is used for: the system comprises a power system, a power system control unit and a control unit, wherein the power system control unit is used for controlling the power system control unit to control the power system according to the power system control unit;
parameter optimization module: and optimizing excitation control of the oscillating unit.
The beneficial technical effects are as follows: the method is based on a BPA stabilization program and a small interference program, optimizes PSS parameters of the low-frequency oscillation main participation unit by using a butterfly optimization algorithm, improves the dynamic stability of the system, simplifies the model building process, improves the system stability judging time by time domain analysis of the stabilization program, facilitates the extraction of the low-frequency oscillation main participation unit by using the small interference program, and is suitable for large-scale power grid analysis and calculation.
The influences of different running modes and different oscillation modes are comprehensively considered to perform parameter optimization, so that the robustness of the PSS device damping system low-frequency oscillation is improved; the butterfly optimization algorithm is used for carrying out parameter searching, so that the global optimizing capability of the system is improved; the use of parallel programs speeds up the program computation speed.
Drawings
FIG. 1 is a diagram of a SG-type PSS model structure in the invention;
FIG. 2 is a diagram of the structure of the SI and SI+ type PSS model in the present invention;
FIG. 3 is a flow chart of a butterfly optimization algorithm of the present invention;
FIG. 4 is a flowchart of PSS parameter optimization in the present invention;
FIG. 5 is a geographical wiring diagram of a south ARQ send mode in accordance with an embodiment of the present invention;
FIG. 6 is a graph of fitness curves in the present invention;
FIG. 7 is a graph of the high power angle of the new chisel before and after the PSS parameter optimization in the invention;
FIG. 8 is a graph showing the voltage of the new urban and north bus before and after the PSS parameter optimization in the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
As shown in fig. 1-8:
Embodiment 1 provides a power system stabilizer parameter optimization method, which comprises the following steps:
Step one, determining an oscillation operation mode of the power system, and determining the operation mode of the power system which causes oscillation according to the oscillation frequency of the operation mode of the power system oscillation;
And taking a limiting operation mode which possibly causes low-frequency oscillation of the system as a typical operation mode set, carrying out stable program simulation on each mode, and obtaining the oscillation frequency and damping ratio of the dominant oscillation mode of the system by applying Prony analysis carried by BPA. The Prony algorithm is a method for sampling data at equal intervals by using a linear combination of a series of exponential functions, so that the oscillation frequency and damping ratio of the system can be calculated according to the related exponential functions, and whether the system has a dynamic stability problem can be judged. The algorithm is a time domain analysis method, is based on an actual measurement curve, has a simple and clear calculation process, and is suitable for confirming whether a system has a low-frequency oscillation problem and quick calculation of oscillation characteristics.
The oscillation frequency and the damping ratio of the system under a certain operation mode can be obtained through Prony analysis, and a damping ratio calculation formula is shown in a formula 1.
Where α i is the damping factor of the i-th oscillation mode, f i represents the oscillation frequency of the i-th oscillation mode; and taking the power system oscillation operation mode with the smallest damping ratio as the power system operation mode causing oscillation.
To ensure accuracy of the calculation result, the Prony analysis start time should avoid the near zone of the fault moment and the calculation duration cannot be less than two low-frequency oscillation periods, for example: the oscillation frequencies in the two operation modes are 0.3445HZ and 1.0HZ respectively, and the oscillation periods are 2.821s and 1s respectively. The failure 1.1s disappears and the damping ratio can be calculated from 2 to 10 s.
Assuming that m operation modes exist in total and n modes exist in the ith operation mode, the oscillation frequency and damping ratio of the jth mode are f ij、ξij (i=1, 2, …, m; j=1, 2, …, n), and the operation mode where the minimum damping ratio is located is selected as a basic operation method, and the corresponding oscillation mode is used as a dominant oscillation mode to serve as a basis for optimizing subsequent PSS parameters.
Step two, determining an oscillating unit, and further determining the oscillating unit in the power system on the basis of determining the running mode of the power system causing oscillation;
The Prony analysis using time domain analysis is computationally fast and straightforward, but cannot be implemented for global analysis of the grid. The small interference analysis can apply the power grid structural parameters and the augmented state matrix of the power flow distribution construction system to carry out the global analysis of the low-frequency oscillation based on the eigenvalue analysis method. The method can be used for equally dividing the characteristic value right characteristic vector calculated by the system augmentation state matrix into electromechanical loop correlation ratios, participation factors, characteristic value sensitivity and the like of all modes, and is favorable for extracting a dominant oscillation mode of the system, distinguishing a main unit influencing low-frequency oscillation of the system, indicating the sensitivity of all control links and the like.
When the small interference analysis is applied, the characteristics of the large power grid can be converted into an equation set shown in the following formula for analysis:
Where f i is the nonlinear derivative part of the system and g i is the nonlinear algebraic part. The matrix form after linearization and Taylor series expansion around the equilibrium point x 0 is as follows:
Wherein Δx= [ X 1,x2,…,xm]T ] is a state variable; Δy= [ x m+1,xm+2,…,xn]T is a non-state vector; j A、JB、JC is a factor related to system parameters and taylor series coefficients. After elimination of the non-state variables:
Wherein, the matrix A is an n multiplied by n state matrix; j A、JB、JC is a factor related to system parameters and taylor series coefficients. And calculating the eigenvalue and corresponding eigenvector of the matrix A to calculate the oscillation frequency and damping ratio of the system, wherein the oscillation frequency and damping ratio are shown in the following formula.
Wherein lambda i is a characteristic value; i is an identity matrix; phi i is the right eigenvector; phi i is the left eigenvector; σ i is the real part of the eigenvalue; omega i is the imaginary part of the eigenvalue; f i is the oscillation frequency; ζ i is the damping ratio.
Eigenvalues typically occur in pairs and different oscillation frequencies represent different oscillation modes, the degree of association of a mode with a state variable can be determined by a participation factor, which is the product of the right eigenvector and the left eigenvector of the state matrix a:
pki＝φkiψik (6)
p ki represents the magnitude of the association of the kth state variable with the ith mode; phi i is the right eigenvector; and phi i is the left eigenvector. The angle of the right eigenvector characterizes the coherent grouping characteristic of the different generators for a certain mode. The phase angles are identical and coherent, and the opposite (the difference is 180 DEG) is the low-frequency oscillation between the units.
After the system is subjected to small interference analysis, a corresponding oscillation mode and characteristic vector table are selected according to the oscillation frequency and damping ratio of the dominant oscillation mode analyzed by Prony. In general, the low-frequency oscillation problem existing in a large power grid is mainly a local oscillation mode and a section oscillation mode. The oscillation frequency of the local oscillation mode is generally 0.7-2.0 Hz, and the local oscillation mode is an oscillation mode of a unit or a relatively large system of a power plant, and the mode can be weakened by adjusting the on-off condition; the interval oscillation mode is mainly divided into two machine group oscillation modes of 0.1-0.4 Hz and a multi-machine group oscillation mode of 0.4-0.7 Hz, and the influence range of the modes is wider, so that the mode needs to be analyzed in detail. The participating units in the interval oscillation mode are characterized in that: the right characteristic vector angles differ by about 180 °.
In order to ensure the calculation accuracy and improve the calculation speed, the main participating units are selected through the following characteristics:
1) The participation factor is greater than 0.1;
2) The absolute value of the difference between the right characteristic vector angle and 0 or 180 DEG is within 30 DEG;
3) The unit is provided with a PSS device.
Step three, optimizing parameters of a Power System Stabilizer (PSS) in an excitation control system of an excitation control of an oscillating unit;
The optimization is realized by optimizing PSS parameters.
In practical engineering applications, the PSS structures adopted are PSS1A, PSS A and PSS2B models in IEEE Std 421.5-2016. The PSS1A corresponds to SG type PSS in the BPA, the PSS2B corresponds to SI and SI+ type PSS, the PSS2A is less than the PSS2B by only one phase compensation link, the compensation phase angle of a certain phase compensation link can be set to be zero to be equivalent, the SG type PSS model structure is shown in figure 1, and the SI and SI+ type PSS model structure is shown in figure 2.
Taking the SI, SI + type rotational speed deviation input as an example, wherein,A signal converter; /(I)/>Is a blocking differential link; /(I)And/>The two links are wave traps for blocking corresponding torsional frequencies, namely torsional filters; For three-stage lead-lag link, the method is used for phase frequency characteristic adjustment, and the T 1>T2 lead link (/ >) Generally 0.2-0.05), or else, the hysteresis is a lead phase angle/>The electromagnetic power bias input is similar to the frequency difference, and the SG type is a simplified version of SI and SI < + >. The transfer function of PSS can thus be expressed as:
Wherein R is PSS input signals (one or two of rotation speed difference and electromagnetic power difference); c is PSS output signal; k P is PSS gain; g (S) is a set of blocking links, torsional vibration filtering links and the like; t 1、T2、T3、T4、T13、T14 is the time constant of the lead-lag link. In engineering practice, T 2、T14、T4 of the blocking link, the torsional vibration filtering link and the lead-lag link of the PSS is a fixed value, and the parameter for optimization is K P、T1、T13、T3.
The setting rule of the PSS gain is as follows: first, K P is increased from zero, a feature root is calculated, and the PSS gain when the oscillation mode of the PSS control loop is reduced to 0 is a critical gain. For PSS with power bias as input signal, the gain is generally criticalFor PSS with rotational speed deviation as input signal, the gain is generally the/> of the critical gainIn order to prevent the PSS gain from being too high, the PSS gain adjustment range is set to be 1-2 times of the original parameter.
When PSS phase compensation is considered, robustness of the PSS must be considered, but damping influence of one operation mode and one oscillation mode cannot be considered, and local oscillation frequency and system oscillation frequency should be considered comprehensively. For the local oscillation frequency, the delay angle of the rapid excitation system is generally 50-60 degrees, and the lead phase angle provided by the lead-lag link is balanced with the delay angle; in order to achieve the effects of the system oscillation frequency and noise reduction, the adjustment range of T 1、T13、T3 is set to be 0.5-2.5 times of the original parameters.
According to the stability requirement of the power system, in order to make the system have better dynamic characteristics, the damping ratio should be greater than 0.03, so the PSS parameter optimization problem can be described as:
max g＝max{minξij}(i＝1,2,…,p；j＝1,2,…,q)(8)
The constraints are as follows:
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
Wherein p is the number of electromechanical oscillation modes, q is the number of operation modes, and xi ij is the damping ratio of the ith oscillation mode in the jth operation mode; g represents the minimum damping ratio under each operation mode and mode; the optimizing direction is the smallest damping ratio is the largest; the gain Kp range is 1-2 times of the original parameter; the time constant T 1、T13、T3 is in the range of 0.5 to 2.5 times the original parameters. The search termination condition is that the minimum system damping ratio is greater than 0.03 or the number of iterations is reached.
The PSS parameter optimization method based on the butterfly optimization algorithm.
The butterfly algorithm (butterfly optimization algorithm) proposed by Arora et al is a brand-new meta-heuristic intelligent optimization algorithm, and the inspiration of the algorithm is derived from butterfly foraging and coupling behaviors. The butterfly captures and analyzes odors in the air to determine the likely direction of the target. In BOA, there are several assumptions: 1) All butterflies emit a fragrance so that the butterflies can attract each other. 2) Each butterfly moves randomly or along with the fragrance towards the optimal butterfly, and the fitness of the butterfly changes correspondingly in the moving process. 3) The stimulus intensity of the butterfly is influenced by the objective function value. 4)
The global search and the local search are controlled by using the switching probability P, and the influence of factors such as wind, rain, thunder and the like in the nature is simulated, so that the method has important significance.
The butterfly optimization algorithm is now applied to PSS parameter optimization. Taking PSS parameters to be optimized as butterfly individuals, and assuming that m units needing to be optimized in a certain mode are altogether, namely [ C 1,C2,…,Cm ], wherein the i-th unit needing to be optimized is [ K Pi,T1i,T13i,T3i ], and the dimension of the butterfly individuals is 4m. The matrix M is all butterfly individuals in the current iteration, and the system damping ratio corresponding to each parameter candidate solution is an fitness set OB; and sequencing the candidate parameter sets according to the fitness sets to obtain a butterfly individual set F, wherein the corresponding fitness set is OF. Each butterfly moves in the direction of the honey source according to the position transfer function.
The butterfly algorithm optimization process may be expressed as an optimal solution of the following triples:
BOA＝(I,P,T) (10)
I is an objective function between the candidate butterfly set and the fitness set:
p is the butterfly position updating mode, and the updated butterfly set is brought into a matrix M:
P:Bt→Bt+1 (12)
T is a judgment matrix function, the load ending condition is true, otherwise, the load ending condition is false:
T:B→{true,false} (13)
and calculating the system damping ratio under each PSS parameter through the I function, and updating the butterfly set position according to the P function until the iteration termination condition is met. The butterfly's position update function P is related to the butterfly and nectar source positions as shown in the following equation:
Wherein, The position of the ith butterfly in the t iteration is g, the position is the nectar source position, the S is a position transfer function, and the specific calculation formula is as follows, wherein the position is related to the global/local search selection probability:
Where r 1、r2、r3 is a random number between [0,1], P switch is a selection probability, and when r 3≤Pswitch, a global search (conventional global search) is performed, and when r 3>Pswitch, a local search is performed. f=c a is the flavour of the honey source of the butterfly, where I (the objective function in equation 11) is expressed herein as the stimulus intensity, related to the degree of optimization fitness; a is a power exponent, and is usually 0.1; c is a sensory factor and is usually 0.01.
To improve subjective motility of the butterfly, monotonically decreasing inertial weights are introduced to update the self-cognition part of the butterfly:
Wherein ω max is the initial inertial weight, ω min is the inertial weight at the end of the iteration, t is the current iteration number, and t max is the maximum iteration number. Initial inertial weight ω max =0.9, where the individual movement range is small, and the individual movement range is not aggregated very fast; and at the end, the inertia weight is omega min =0.4, and the algorithm has strong search development capability.
To improve the global searching capability of the algorithm, a three black hole system is introduced to capture the particle mechanism. In the global search process, a constant P capture epsilon [0,1] is set as the capturing probability of the three-black-hole system, a random number r 4 epsilon [0,1] is generated for each butterfly b i each time, if r 4≤Pcapture, b i is captured by the three-black-hole system, otherwise, the three-black-hole system is updated according to the conventional global search mode of a butterfly algorithm.
If b i is captured by the three black hole system, the three black hole areas are formed by taking g, (g+b max)/2 and (g+b min)/2 as centers and r as the black hole radius. A random number r 5 E [0,1] is generated. If r 5>p1, b i is captured by black hole 1 in the system; if r 5∈[p2,p1 ], b i is captured by black hole 2; if r 5<p2, b i is captured by the black hole 3, and the positions of the captured particles are as follows:
Where b max/bmin is the upper/lower limit of the butterfly position search region, the constant threshold p 2,p1 e [0,1], and p 1>p2,r6 is a random number of [ -1,1 ].
And the optimized PSS parameters are used for realizing the optimized control of excitation of the electric power unit.
In addition, aiming at the BPA program characteristics and the PSS control parameter setting principle, the following improvement measures for improving the calculation accuracy and the calculation speed are provided:
1) According to the habit of filling in the BPA card, the corresponding digits of partial parameters can be defaulted, for example 0.4358 can be written in only;
2) PSS parameters of the unit with participation factors above 0.1 are modified, and rationality and high efficiency of the optimization process are ensured;
3) Setting different optimization processes according to the PSS type and the number of the lead-lag links;
4) And (3) adopting multithread parallel processing, and calling a respective stable program for each butterfly to calculate an fitness value, namely performing time domain analysis of a damping ratio.
In the case of example 2,
There is provided a power system stabilizer parameter optimization system comprising:
the operation mode determining module: the method comprises the steps of determining an oscillation operation mode of the power system, and determining the operation mode of the power system which causes oscillation according to the oscillation frequency of the operation mode of the power system oscillation;
the oscillating unit determining module: the system comprises a power system, a power system control unit and a control unit, wherein the power system control unit is used for controlling the power system control unit to control the power system according to the power system control unit;
Parameter optimization module: parameters of a Power System Stabilizer (PSS) in an excitation control system of an oscillating group are optimized.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The effectiveness of the invention is checked by taking low-frequency oscillation under a mode of a south ARUM power grid as an example.
(1) Determining an oscillating mode of operation
The system reference capacity was 100MVA. The south ARUM power grid delivery section has a Chu-Chu Sha-Chu Ga-golden section, an Chu-Ping deer section, a Ku-Kuz section, and a Sha-Pi She Duanmian section, chu Ka-Chu Sha-Chu Ga-golden section comprises Bachu-Keshen single-pass, bachu-Sha car single-pass, bachu-Galileo single-pass, jinlu-Ji single-pass, and Alchu-Ping deer section comprises Bachu-Ackersu double-pass, kong-Ping double-pass, ku-Kuz section comprises Akesu-Ku car double-pass, ku Che Touzdouble-pass, and Sha-Pi She Duanmian comprises Bachu double-pass and Tiansha car double-pass, yecheng-Pishan double-pass. The following method is selected as a typical method, and PSS parameter optimization is carried out on the system: the south ARUM power grid Chu Ka-Chu Sha-Chu Ga-golden section transmits 924.3MW, the Chu-Ping deer section transmits 1596.4MW, the Ku-Kuz section transmits 2294.3MW, and the Sha-Pi She Duanmian section transmits 541.5MW, the transmission power of the rest sections is unchanged, and the geographic wiring diagram is shown in FIG. 5. The method is obtained after stable calculation and prony time domain analysis: the constraint fault is that the main variable Sanheng N-1 of the Sha car, the system has the dynamic stability problem, the oscillation frequency is 0.5228Hz, and the damping ratio is-0.006;
According to the oscillation frequency in the mode, 2-10 s is set as the prony calculation time length in order to ensure the calculation time length of more than 3 oscillation periods.
(2) Oscillating unit determination
And carrying out linearization treatment on the system grid to form a state matrix A, and carrying out small interference program analysis. And according to the analysis result, a weak damping low-frequency oscillation mode exists between areas of the south ARUM and the northwest power grid, and the south ARUM unit is basically opposite to the northwest unit.
Under the oscillation mode with the oscillation frequency of 0.603Hz, a generator with the participation factor of more than 0.1 and the right characteristic vector angle within 30 degrees with the absolute value of the difference of 0 degrees or 180 degrees is selected, wherein 10 south ARUM units, 7 Gansu units and 7 Qinghai units are selected. However, the total of 2 # machines of Gansu blue aluminum 3# machine set and Jinsheng 1# machine set is not provided with PSS devices, and PSS parameter optimization is required to be carried out on 15 machine sets, as shown in table 1.
TABLE 1 Low frequency oscillation Main participant Unit
(3) PSS parameter optimization based on butterfly optimization algorithm
Searching relevant PSS parameter positions of main participating units in the stable file, reading original parameter values, and setting the range of updated parameters as follows:
Where K p、T1、T13、T3 is the original parameter value, K 'p、T1'、T'13、T3' is the updated parameter range, and if the updated value exceeds the range, the value returns to the adjacent boundary value.
The results of performing PSS parameter optimization on 15 generators with K p、T1、T13、T3 as butterfly positions are shown in table 2. The final optimization result is: the system oscillation frequency is 0.2382Hz and the damping ratio is 0.0382.
Table 2 PSS parameter optimization results Table
The adaptability change curve of the iterative process is shown in fig. 6, the system damping ratio is larger than 0.03 after the iteration is performed for 64 times, the PSS parameter optimization process is completed, the damping ratio is stable, the overall convergence speed is high, and the capacity of jumping out of local optimum to a certain extent is achieved.
(4) Optimizing result analysis
And writing the optimized PSS parameters, wherein the oscillation frequency of the system is 0.2382Hz, and the damping ratio is increased from-0.006 to 0.0382. The oscillation attenuation is accelerated, the oscillation amplitude is recovered to 10% of the steady-state level after about 11 oscillation periods, the dynamic stability characteristic is good, and the safety and stability requirements of the power system are met.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
The foregoing is merely a preferred embodiment of the present invention, and it should be noted that modifications and variations could be made by those skilled in the art without departing from the technical principles of the present invention, and such modifications and variations should also be regarded as being within the scope of the invention.
Claims (5)
1. A method for optimizing parameters of a power system stabilizer, comprising:
determining an oscillation operation mode of the power system, and determining the operation mode of the power system with the most serious oscillation according to the oscillation frequency of the operation mode of the power system oscillation;
determining an oscillating unit, and further determining the oscillating unit in the power system on the basis of determining the running mode of the power system causing oscillation;
optimizing parameters of a power system stabilizer in an excitation control system of the oscillating unit;
The unit for determining the oscillation in the power system comprises the following components:
finding out a group of units which cause oscillation in an oscillation mode corresponding to the minimum damping ratio through small interference analysis, and then finding out an oscillation unit which finally needs excitation optimization through the following characteristic conditions:
1) The participation factor is greater than 0.1;
2) The absolute value of the difference between the right characteristic vector angle and 0 or 180 DEG is within 30 DEG;
3) The unit is provided with a power system stabilizer;
The optimization of parameters of the power system stabilizer in the excitation control system of the oscillating unit is specifically as follows:
The objective function of establishing the power system stabilizer parameters of the unit to be optimized is as follows:
maxg＝max{minξij}(i＝1,2,…,p；j＝1,2,…,q) (2)
The constraints are as follows:
Wherein p is the number of electromechanical oscillation modes, q is the number of operation modes, and ζ ij is the damping ratio of the ith oscillation mode in the jth operation mode; g represents the minimum damping ratio under each operation mode and mode; the optimizing direction is the smallest damping ratio is the largest; kp i、Kpimin and Kp imax are the gain of the ith oscillation mode, the lower gain limit and the upper gain limit, and T 1i、T13i and T 3i are time constants; t 1imin、T1imax is the upper and lower limits of T 1i, respectively; t 13imin、T13imax is the upper and lower limits of T 13i, respectively; t 3imin、T3imax is the upper and lower limits of T 3i, respectively;
optimizing an objective function by adopting a butterfly algorithm;
The optimization of the objective function by adopting the butterfly algorithm is specifically as follows:
the optimization procedure is expressed as an optimal solution of the following triples:
BOA＝(I,P,T) (4)
i is the objective function between the candidate butterfly set and the fitness set, i.e. equation (2)
B is a candidate butterfly set and an OB bit adaptation set;
P is an updating function of butterfly position;
P:Bt→Bt+1 (6)
b t is the position of the butterfly at the t-th iteration;
T is a judgment matrix function, the load ending condition is true, otherwise, the load ending condition is false:
T:B→{true,false} (7)
Calculating a system damping ratio under each PSS parameter through an I function, and updating the butterfly set position according to a P function until an iteration termination condition is met;
In the global search process of the butterfly algorithm, setting a constant Pcapture epsilon [0,1] as the capturing probability of the three-black-hole system, generating a random number r 4 r4 epsilon [0,1] for each butterfly b i each time, if r 4 r4 is less than or equal to Pcapture, capturing b i by the three-black-hole system, otherwise updating according to the conventional global search mode of the butterfly algorithm;
If b i is captured by the three-black-hole system, g, (g+b max)/2 and (g+b min)/2 are taken as centers, r is the radius of the black hole, three black-hole areas are formed, a random number r 5 E [0,1] is generated, and if r 5＞p1, b i is captured by the black hole 1 in the system; if r 5 e [ p2, p1], b i is captured by black hole 2; if r 5＜p2, b i is captured by the black hole 3, and the positions of the captured particles are as follows:
Where b max/bmin bmax/bmin is the upper/lower limit of the butterfly position search region, p 2 is a constant threshold, p 1 e [0,1], and p 1＞p2,r6 is a random number of [ -1,1 ].
2. The method for optimizing parameters of a power system stabilizer according to claim 1, wherein the power system operation mode for determining the most serious oscillation is specifically:
obtaining the damping ratio of the power system according to the formula (1)
Where α i is the damping factor of the i-th oscillation mode, f i represents the oscillation frequency of the i-th oscillation mode;
And taking the power system oscillation operation mode with the smallest damping ratio as the power system operation mode causing oscillation.
3. The power system stabilizer parameter optimization method according to claim 1, characterized in that: the butterfly position update function P may be expressed as a function of butterfly position and nectar source position as shown in the formula:
Wherein, The position of the ith butterfly in the t iteration is represented by g which is the nectar source position, and S which is a position transfer function, wherein the position transfer function is represented by the following formula:
Wherein r1, r2 and r3 are random numbers between [0,1] and Pswitch is a selection probability, global searching is carried out when r3 is less than or equal to Pswitch, and local searching is carried out when r3 is more than Pswitch; f=c a is the fragrance of the honey source of the flowers sought by the butterflies, wherein I is the stimulus intensity and is related to the optimizing fitness; a is a power exponent and c is a sensory factor.
4. A power system stabilizer parameter optimization method according to claim 3, characterized in that: a monotonically decreasing inertial weight is introduced to update the self-cognition portion of the butterfly as shown in the following equation:
Wherein ω max is the initial inertial weight, ω min is the inertial weight at the end of the iteration, t is the current iteration number, and t max is the maximum iteration number.
5. An electric power system stabilizer parameter optimization system based on the method according to any one of claims 1-4, characterized by comprising:
the operation mode determining module: the method comprises the steps of determining an oscillation operation mode of the power system, and determining the operation mode of the power system with the most serious oscillation according to the oscillation frequency of the operation mode of the power system oscillation;
the oscillating unit determining module: the system comprises a power system, a power system control unit and a control unit, wherein the power system control unit is used for controlling the power system control unit to control the power system according to the power system control unit;
parameter optimization module: and optimizing parameters of a power system stabilizer in an excitation control system of the oscillating unit.
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