CN113050425A - Control parameter optimization method for generator excitation system - Google Patents

Control parameter optimization method for generator excitation system Download PDF

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Publication number
CN113050425A
CN113050425A CN202110300502.6A CN202110300502A CN113050425A CN 113050425 A CN113050425 A CN 113050425A CN 202110300502 A CN202110300502 A CN 202110300502A CN 113050425 A CN113050425 A CN 113050425A
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particle
excitation system
transfer function
generator
above formula
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赵力航
王新
童小忠
陈新琪
张浙波
刘刚
杨敏
翁建明
鲍丽娟
马聪
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Zhejiang Energy Group Research Institute Co Ltd
Zhejiang Zheneng Jiahua Power Generation Co Ltd
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Zhejiang Energy Group Research Institute Co Ltd
Zhejiang Zheneng Jiahua Power Generation Co Ltd
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    • 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
    • G05B13/042—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 in which a parameter or coefficient is automatically adjusted to optimise the performance

Abstract

The invention relates to a control parameter optimization method for a generator excitation system, which comprises the following steps: step 1, representing a simplified transient model of a synchronous generator in a frequency domain by using a transfer function; expressing a self-shunt excitation system model by using a transfer function; step 2, representing the obtained initial PI controller by using a transfer function according to a pole allocation method; and 3, establishing an initial particle swarm by using a particle swarm optimization algorithm, searching the optimal historical position of the particle and the global optimal historical position gbest, and then endowing the initial value to a lookout particle pwatcher. The invention has the beneficial effects that: the problems that the existing excitation system controller parameter setting method depends on manual experience and test time consumption are solved, the debugging time of a power generation system is shortened, the cost is reduced and the risk is reduced; the system can be ensured to stably operate within a certain parameter deviation range by stably judging Harytornov and optimizing by combining the Harytornov theorem.

Description

Control parameter optimization method for generator excitation system
Technical Field
The invention belongs to the field of motor control, and particularly relates to a control parameter optimization method for a synchronous generator excitation system.
Background
The performance of the excitation system directly influences the reliability and stability of the synchronous generator, and is also an important guarantee for the stable operation of the power system. The excitation controller is the core of the excitation system, and controls the operation of the excitation power rectifier cabinet according to the comparison between the collected real-time electrical quantity and a given value, and adjusts the excitation current, the terminal voltage and the reactive power of the synchronous generator. The excitation system is responsible for maintaining generator terminal voltage and system voltage, maintains the stable running state of the generator by adjusting excitation current when the power system is disturbed, provides the limiting protection function of the excitation loop, voltage and frequency for the generator, and can realize the distribution of reactive power among the parallel generators of the enlarged unit wiring.
At present, parameters of an excitation system controller are generally set by carrying out a terminal voltage no-load small step response test on site, namely a set of PI controller initial parameters are given according to empirical values, a step test of 5% step amount is carried out at a lower terminal voltage, generator terminal voltage, excitation voltage and excitation current are recorded, step response quality is observed and analyzed, accordingly, the PI controller parameters are adjusted until the step response quality is basically proper, the terminal voltage is increased to a rated value, and the PI controller parameters are finely adjusted. The setting method needs repeated no-load step test, takes longer time and causes loss to the excitation system and the de-excitation system of the generator.
Disclosure of Invention
The invention aims to overcome the defects in the prior art and provides a method for optimizing control parameters of a generator excitation system.
The method for optimizing the control parameters of the generator excitation system comprises the following steps:
step 1, representing a simplified transient model of a synchronous generator in a frequency domain by using a transfer function; expressing a self-shunt excitation system model by using a transfer function;
step 2, representing the obtained initial PI controller by using a transfer function according to a pole allocation method;
step 3, establishing an initial particle swarm by using a particle swarm optimization algorithm, searching an optimal historical position of the particle and a global optimal historical position gbest, and then endowing the initial value to a lookout particle pwatcher;
step 3.1, initializing a deployed particle swarm, and setting a set frequency, wherein the set frequency is greater than 0; setting the current iteration number to 0; randomly deploying all the particles in a small range area near the looker particle pwatcher, wherein in the two-dimensional case, the area is a rectangle, and the area is expressed as:
Figure BDA0002986061700000021
in the above formula, p [, ]]A list representing objects storing the particle class; i represents the ith particle; rand is a random variable, p [ i ], uniformly distributed between-10% and + 10%]Is an object of the particle class; x and y each represent a parameter KpAnd Ki；
Step 3.2, if the current iteration times are less than the set times, calculating a fitness function for the ith particle:
α(X)＝w1σ+w2Tr+w3Ts
in the above formula, α (X) is a fitness function,the method is used for judging the advantages and disadvantages of dynamic characteristic parameters such as overshoot and steady-state error; sigma represents the terminal voltage overshoot, TrDenotes the rise time, TsTo adjust the time (the difference between the voltage value and the steady state value reaches and no longer exceeds a certain allowable error range); w is a1、w2And w3Respectively is sigma and TrAnd TsThe weight coefficient of (a);
step 3.3, judging the stability of the ith particle based on Haritornov theorem:
Figure BDA0002986061700000022
in the above formula, β (X) is a stability decision function;
step 3.4, calculate the evaluation value of each particle according to the following formula:
F(X)＝α(X)β(X)
in the above formula, f (X) represents an objective function, X is an optimization target, and X ═ Kp，KiIn which K ispIs the proportional gain, KiIs the integral gain; alpha (X) is a fitness function, and beta (X) is a stability judgment function;
updating the optimal location pbest of the locations traversed by the ith particleiThe search equation for a particle is described as:
Figure BDA0002986061700000023
in the above formula, the first and second carbon atoms are,
Figure BDA0002986061700000024
representing the displacement of the particle moving relative to the current position by the time of the next iteration; w is the inertial weight;
Figure BDA0002986061700000025
representing the displacement of the particle moving relative to the previous position by the time the current iteration is performed; c. C1And c2Is a positive constant, representing an acceleration factor; rand1And rand2Is at [0,1 ]]Random numbers within the interval; k and k +1 represent the number of iterations; pbestiThe optimal position is the optimal position in the positions where the ith particle passes through, the optimal position is determined by the evaluation value, and the smaller the value of the optimal position is, the better the value is;
Figure BDA0002986061700000026
and
Figure BDA0002986061700000027
position vectors of the ith particle at the (k + 1) th iteration and at the kth iteration respectively; gbest is the position where the evaluation value is minimized in the entire particle swarm;
step 3.5, updating the position gbest which enables the evaluation value to be minimum in the whole particle swarm;
step 4, judging whether to update the position of the looker particle pwatcher: if the evaluation value of the gbest is equal to the evaluation value of the looker particle pwatcher, updating the position of the looker particle pwatcher, otherwise, randomly deploying the particle swarm again in the range near the looker particle pwatcher; the position where the fitness function is minimum is the gbest, and only when pwatcher is not the gbest, pwatcher updates the position to the position of the gbest and takes its evaluation value as its own evaluation value;
and 5, adding 1 to the current iteration times, returning to execute the steps 3 to 4 until the current iteration times is more than or equal to the set times, completing the optimization process after a certain number of iterations, and taking the PI parameter contained in the globally optimal particle corresponding to the globally optimal historical position gbest as the parameter setting reference value of the final generator excitation system voltage automatic controller.
Preferably, the simplified transient model of the synchronous generator in the frequency domain represented by the transfer function in step 1 is specifically:
Figure BDA0002986061700000031
in the above formula, Gg(s) is the transfer function of the simplified transient model of the synchronous generator in the frequency domain, s is the frequency domain operator, KGIs the synchronous generator magnification, T'd0Is the d-axis transient time constant of the generator.
Preferably, the self-shunt excitation system model expressed by the transfer function in step 1 is specifically as follows:
Figure BDA0002986061700000032
in the above formula, GA(s) is the transfer function of the self-shunt excitation system, s is the frequency domain operator, KAIs the amplification factor, T, of the self-shunt excitation systemAIs the self shunt excitation system time constant.
Preferably, the initial PI controller obtained in step 2 is represented as:
Figure BDA0002986061700000033
in the above formula, Gc(s) is the transfer function of the initial PI controller, KiIs the integral gain, s is the frequency domain operator, KpIs the proportional gain.
Preferably, step 3.3 specifically comprises the steps of:
step 3.3.1, the transfer function of the excitation closed loop system of the synchronous generator is as follows:
Figure BDA0002986061700000034
in the above formula, Gb(s) is the excitation system closed loop transfer function of the synchronous generator, Gc(s) is the transfer function of the initial PI controller, Gg(s) is the transfer function of a simplified transient model of the synchronous generator in the frequency domain, GA(s) is the transfer function of the self-shunt excitation system;
step 3.3.2, the characteristic polynomial of the excitation closed-loop system of the synchronous generator is as follows:
D(s)＝KGKAKi+(1+KGKAKp)s+(T'd0+TA)s2+T'd0TAs3
in the above formula, D(s) is a characteristic polynomial of an excitation closed-loop system of the synchronous generator, KGIs the amplification factor, K, of the synchronous generatorAIs the amplification factor of a self-shunt excitation system, KiIs the integral gain; kpIs proportional gain, T'd0Is d-axis transient time constant of the generator, s is frequency domain operator, TAIs the self shunt excitation system time constant;
writing the above formula as D(s) ═ d0+d1s+d2s2+d3s2Of the form (d)0＝KGKAKi，d1＝1+KGKAKp，d2＝T'd0+TA，d3＝T'd0TA；
Step 3.3.3, obtaining four Harlituofu polynomials according to Harlituofu theorem:
Figure BDA0002986061700000041
Figure BDA0002986061700000042
Figure BDA0002986061700000043
Figure BDA0002986061700000044
in the above formula, the first and second carbon atoms are,
Figure BDA0002986061700000045
and
Figure BDA0002986061700000046
represents a parameter d0The upper and lower boundaries of (a) are,
Figure BDA0002986061700000047
Figure BDA0002986061700000048
and
Figure BDA0002986061700000049
represents a parameter d1The upper and lower boundaries of (a) are,
Figure BDA00029860617000000410
Figure BDA00029860617000000411
and
Figure BDA00029860617000000412
represents a parameter d2The upper and lower boundaries of (a) are,
Figure BDA00029860617000000413
Figure BDA00029860617000000414
and
Figure BDA00029860617000000415
represents a parameter d3The upper and lower boundaries of (a) are,
Figure BDA00029860617000000416
and 3.3.4, judging whether the four Harlitooff polynomials in the step 3.3.3 meet the Laus-Helvelz stability criterion or not according to the Harlitooff theorem, if all the Harlitooff polynomials meet the Laus-Helvelz stability criterion, all the Harlitooff polynomials are stable, and the corresponding PI controller can stabilize all objects in the whole interval function family and meet the robustness requirement.
Preferably, in the step 3.2, the fitness function alpha (X) analyzes dynamic characteristics according to a step response result of 5% step quantity of the no-load rated voltage of the generator; the dynamics include a characteristic overshoot σ, a rise time TrAnd adjusting the time Ts。
The invention has the beneficial effects that: the invention overcomes the problems that the existing excitation system controller parameter setting method depends on manual experience and test time consumption, and provides a generator excitation system control parameter optimization method based on Harytornov stable judgment and particle swarm optimization, the method realizes the optimized self-setting of the excitation system controller parameters based on a generator excitation system model widely accepted in the industry, obtains more accurate parameters through simulation, does not need to repeatedly perform step tests on actual equipment, shortens the debugging time of a power generation system, reduces the cost and reduces the risk; and optimization is carried out by Harytornov stable judgment and by combining Harytornov theorem, so that even if parameters in a generator model and an excitation system model deviate in a small range, stable operation of the system in a certain parameter deviation range can be ensured.
Drawings
FIG. 1 is a schematic diagram of a synchronous generator set;
FIG. 2 is a model schematic diagram of a self-shunt excitation static excitation system in GB/T7409.2-2020;
FIG. 3 is a simplified transfer function logic diagram of a synchronous generator and its excitation system;
fig. 4 is a flow chart of the particle swarm optimization procedure adopted by the present invention.
Detailed Description
The present invention will be further described with reference to the following examples. The following examples are set forth merely to aid in the understanding of the invention. It should be noted that, for a person skilled in the art, several modifications can be made to the invention without departing from the principle of the invention, and these modifications and modifications also fall within the protection scope of the claims of the present invention.
The invention provides a method for optimizing control parameters of a generator excitation system. Firstly, establishing a simplified transfer function model of a generator and an excitation system thereof; and secondly, optimizing by adopting an improved particle swarm optimization method, wherein the evaluation value of each particle is obtained by multiplying a fitness function considering the dynamic characteristic of the step response by a stable judgment function of Harringov's theorem. When the control parameter setting value meets Harlituofu theorem, the setting parameter can also ensure the stability of the system even if the parameters in the generator model and the excitation system model deviate in a small range. The invention optimizes the control parameters without repeated step tests, effectively shortens the debugging time of the power generation system, reduces the cost and lowers the risk.
As an embodiment, a method for optimizing control parameters of a generator excitation system includes the following steps:
step 1, a schematic diagram of the synchronous generator is shown in fig. 1, and a simplified transient model of the synchronous generator in a frequency domain is represented by a transfer function:
Figure BDA0002986061700000051
in the above formula, Gg(s) is the transfer function of the simplified transient model of the synchronous generator in the frequency domain, s is the frequency domain operator, KGIs the synchronous generator magnification, T'd0Is the d-axis transient time constant of the generator;
the self-shunt excitation static excitation system structure schematic diagram in GB/T7409.2 is shown in FIG. 2, and the self-shunt excitation system model is expressed by a transfer function:
Figure BDA0002986061700000061
in the above formula, GA(s) is the transfer function of the self-shunt excitation system, s is the frequency domain operator, KAIs the amplification factor, T, of the self-shunt excitation systemAIs the self shunt excitation system time constant; a typical set of parameters provided by the GB/T7409.2-2020 standard is shown in Table 1 below:
TABLE 1 GB/T7409.2-2020 Standard parameters Table
Figure BDA0002986061700000062
The above parameters make the voltage regulator stable loop and the supplement correction link not work, and can be ignoredBut not shown. Proportional integral selection factor KvZero, so the PID controller can be simplified as a PI controller. The simplified transfer function block diagram of fig. 3 is obtained by considering the simple model of the excitation winding of the synchronous generator together. Wherein
Figure BDA0002986061700000063
Figure BDA0002986061700000064
Since the above typical parameters already contain KA、TC1And TB1Therefore, the set of parameters can be directly used as the initial parameters. For a system which cannot determine initial parameters by means of typical parameters, obtaining initial PI controller parameters by adopting a pole configuration method;
step 2, according to a pole allocation method, expressing the obtained initial PI controller by using a transfer function:
Figure BDA0002986061700000065
in the above formula, Gc(s) is the transfer function of the initial PI controller, KiIs the integral gain, s is the frequency domain operator, KpIs the proportional gain;
step 3, constructing a transfer function (U in FIG. 3) shown in FIG. 3 in the mathematical simulation softwareGrefIs given voltage at the outlet of the generator, UfIs generator excitation voltage, UGIs the generator outlet actual voltage), the dynamic characteristics and the stable characteristics are analyzed, and the optimal solution is found by using a particle swarm optimization method containing the lookout particle pwatcher shown in fig. 4: establishing an initial particle swarm by using a particle swarm optimization algorithm, searching an optimal historical position of the particle and a global optimal historical position gbest, and then endowing the initial value to a looker particle pwatcher;
step 3.1, initializing a deployed particle swarm, and setting a set frequency, wherein the set frequency is greater than 0; setting the current iteration number to 0; randomly deploying all the particles in a small range area near the looker particle pwatcher, wherein in the two-dimensional case, the area is a rectangle, and the area is expressed as:
Figure BDA0002986061700000071
in the above formula, p [, ]]A list representing objects storing the particle class; i represents the ith particle; rand is a random variable, p [ i ], uniformly distributed between-10% and + 10%]Is an object of the particle class; x and y each represent a parameter KpAnd Ki；
Step 3.2, if the current iteration times are less than the set times, calculating a fitness function for the ith particle:
α(X)＝w1σ+w2Tr+w3Ts
in the above formula, α (X) is a fitness function for determining the quality of dynamic characteristic parameters such as overshoot and steady-state error; sigma represents the terminal voltage overshoot, TrDenotes the rise time (time required to rise from 10% of the steady-state value to 90% of the steady-state value), TsTo adjust the time (the difference between the voltage value and the steady state value reaches and no longer exceeds a certain allowable error range); w is a1、w2And w3Respectively is sigma and TrAnd TsThe weight coefficient of (a); the fitness function alpha (X) analyzes dynamic characteristics according to a step response result of 5% step quantity of the no-load rated voltage of the generator; the dynamics include a characteristic overshoot σ, a rise time TrAnd adjusting the time Ts；
Step 3.3, judging the stability of the ith particle based on Haritornov theorem:
Figure BDA0002986061700000072
in the above formula, β (X) is a stability decision function;
step 3.3.1, the transfer function of the excitation closed loop system of the synchronous generator is as follows:
Figure BDA0002986061700000073
in the above formula, Gb(s) is the excitation system closed loop transfer function of the synchronous generator, Gc(s) is the transfer function of the initial PI controller, Gg(s) is the transfer function of a simplified transient model of the synchronous generator in the frequency domain, GA(s) is the transfer function of the self-shunt excitation system;
step 3.3.2, the characteristic polynomial of the excitation closed-loop system of the synchronous generator is as follows:
D(s)＝KGKAKi+(1+KGKAKp)s+(T'd0+TA)s2+T'd0TAs3
in the above formula, D(s) is a characteristic polynomial of an excitation closed-loop system of the synchronous generator, KGIs the amplification factor, K, of the synchronous generatorAIs the amplification factor of a self-shunt excitation system, KiIs the integral gain; kpIs proportional gain, T'd0Is d-axis transient time constant of the generator, s is frequency domain operator, TAIs the self shunt excitation system time constant;
writing the above formula as D(s) ═ d0+d1s+d2s2+d3s2Of the form (d)0＝KGKAKi，d1＝1+KGKAKp，d2＝T'd0+TA，d3＝T'd0TA；
Step 3.3.3, obtaining four Harlituofu polynomials according to Harlituofu theorem:
Figure BDA0002986061700000081
Figure BDA0002986061700000082
Figure BDA0002986061700000083
Figure BDA0002986061700000084
in the above formula, the first and second carbon atoms are,
Figure BDA0002986061700000085
and
Figure BDA0002986061700000086
represents a parameter d0The upper and lower boundaries of (a) are,
Figure BDA0002986061700000087
Figure BDA0002986061700000088
and
Figure BDA0002986061700000089
represents a parameter d1The upper and lower boundaries of (a) are,
Figure BDA00029860617000000810
Figure BDA00029860617000000811
and
Figure BDA00029860617000000812
represents a parameter d2The upper and lower boundaries of (a) are,
Figure BDA00029860617000000813
Figure BDA00029860617000000814
and
Figure BDA00029860617000000815
represents a parameter d3Upper and lower boundaries of，
Figure BDA00029860617000000816
Step 3.3.4, according to the Harlituov theorem, judging whether the four Harlituov polynomials in the step 3.3.3 meet the Laus-Helvelz stability criterion or not, if all the Harlituov polynomials meet the Laus-Helvelz stability criterion, all the Harlituov polynomials are stable, and the corresponding PI controller can stabilize all objects in the whole interval function family and meet the robustness requirement;
step 3.4, calculate the evaluation value of each particle according to the following formula:
F(X)＝α(X)β(X)
in the above formula, f (X) represents an objective function, X is an optimization target, and X ═ Kp，KiIn which K ispIs the proportional gain, KiIs the integral gain; alpha (X) is a fitness function, and beta (X) is a stability judgment function;
updating the optimal location pbest of the locations traversed by the ith particleiThe search equation for a particle is described as:
Figure BDA0002986061700000091
in the above formula, the first and second carbon atoms are,
Figure BDA0002986061700000092
representing the displacement of the particle moving relative to the current position by the time of the next iteration; w is the inertial weight, and the value is generally 1;
Figure BDA0002986061700000093
representing the displacement of the particle moving relative to the previous position by the time the current iteration is performed; c. C1And c2Is a normal number which represents an acceleration factor, and the value is 2, so that a random variable with the average value of 1 is generated; rand1And rand2Is at [0,1 ]]Random numbers within the interval; (ii) a k and k +1 represent the number of iterations; pbestiIs the bit traveled by the ith particleCentering an optimal position, wherein the optimal position is determined by an evaluation value, and the smaller the value of the optimal position is, the better the value is;
Figure BDA0002986061700000094
and
Figure BDA0002986061700000095
position vectors of the ith particle at the (k + 1) th iteration and at the kth iteration respectively; gbest is the position where the evaluation value is minimized in the entire particle swarm;
step 3.5, updating the position gbest which enables the evaluation value to be minimum in the whole particle swarm;
step 4, judging whether to update the position of the looker particle pwatcher: if the evaluation value of the gbest is equal to the evaluation value of the looker particle pwatcher, updating the position of the looker particle pwatcher, otherwise, randomly deploying the particle swarm again in the range near the looker particle pwatcher; the position where the fitness function is minimum is the gbest, and only when pwatcher is not the gbest, pwatcher updates the position to the position of the gbest and takes its evaluation value as its own evaluation value;
and 5, adding one to the current iteration times, returning to execute the steps 3 to 4 until the current iteration times is more than or equal to the set times, completing the optimization process after a certain number of iterations, and taking the PI parameter contained in the globally optimal particle corresponding to the globally optimal historical position gbest as the parameter setting reference value of the final generator excitation system voltage automatic controller.

Claims (6)

1. A control parameter optimization method for a generator excitation system is characterized by comprising the following steps:
step 1, representing a simplified transient model of a synchronous generator in a frequency domain by using a transfer function; expressing a self-shunt excitation system model by using a transfer function;
step 2, representing the obtained initial PI controller by using a transfer function according to a pole allocation method;
step 3, establishing an initial particle swarm by using a particle swarm optimization algorithm, searching an optimal historical position of the particle and a global optimal historical position gbest, and then endowing the initial value to a lookout particle pwatcher;
step 3.1, initializing a deployed particle swarm, and setting a set frequency, wherein the set frequency is greater than 0; setting the current iteration number to 0; randomly deploying all the particles in the region near the looker particle pwatcher is expressed as:
Figure FDA0002986061690000011
in the above formula, p [, ]]A list representing objects storing the particle class; i represents the ith particle; rand is a random variable, p [ i ], uniformly distributed between-10% and + 10%]Is an object of the particle class; x and y each represent a parameter KpAnd Ki；
Step 3.2, if the current iteration times are less than the set times, calculating a fitness function for the ith particle:
α(X)＝w1σ+w2Tr+w3Ts
in the above formula, α (X) is a fitness function; sigma represents the terminal voltage overshoot, TrDenotes the rise time, TsTo adjust the time; w is a1、w2And w3Respectively is sigma and TrAnd TsThe weight coefficient of (a);
step 3.3, judging the stability of the ith particle based on Haritornov theorem:
Figure FDA0002986061690000012
in the above formula, β (X) is a stability decision function;
step 3.4, calculate the evaluation value of each particle according to the following formula:
F(X)＝α(X)β(X)
in the above formula, f (X) represents an objective function, X is an optimization target, and X ═ Kp，KiIn which K ispIs the proportional gain, KiIs the integral gain; alpha (X) is a fitness function, and beta (X) is a stability judgment function;
updating the optimal location pbest of the locations traversed by the ith particleiThe search equation for a particle is described as:
Figure FDA0002986061690000013
in the above formula, the first and second carbon atoms are,
Figure FDA0002986061690000021
representing the displacement of the particle moving relative to the current position by the time of the next iteration; w is the inertial weight;
Figure FDA0002986061690000022
representing the displacement of the particle moving relative to the previous position by the time the current iteration is performed; c. C1And c2Is a positive constant, representing an acceleration factor; rand1And rand2Is at [0,1 ]]Random numbers within the interval; k and k +1 represent the number of iterations; pbestiIs the optimal position among the positions experienced by the ith particle;
Figure FDA0002986061690000023
and
Figure FDA0002986061690000024
position vectors of the ith particle at the (k + 1) th iteration and at the kth iteration respectively; gbest is the position where the evaluation value is minimized in the entire particle swarm;
step 3.5, updating the position gbest which enables the evaluation value to be minimum in the whole particle swarm;
step 4, judging whether to update the position of the looker particle pwatcher: if the evaluation value of the gbest is equal to the evaluation value of the looker particle pwatcher, updating the position of the looker particle pwatcher, otherwise, randomly deploying the particle swarm again in the range near the looker particle pwatcher;
and 5, adding 1 to the current iteration number, returning to execute the steps 3 to 4 until the current iteration number is larger than or equal to the set number, and taking the PI parameter contained in the globally optimal particle corresponding to the globally optimal historical position gbest as a parameter setting reference value of the final generator excitation system voltage automatic controller.
2. The generator excitation system control parameter optimization method according to claim 1, wherein the step 1 of representing the simplified transient model of the synchronous generator in the frequency domain by using the transfer function specifically comprises:
Figure FDA0002986061690000025
in the above formula, Gg(s) is the transfer function of the simplified transient model of the synchronous generator in the frequency domain, s is the frequency domain operator, KGIs the synchronous generator magnification, T'd0Is the d-axis transient time constant of the generator.
3. The generator excitation system control parameter optimization method according to claim 1, wherein the step 1 of representing the self-shunt excitation system model by the transfer function specifically comprises:
Figure FDA0002986061690000026
in the above formula, GA(s) is the transfer function of the self-shunt excitation system, s is the frequency domain operator, KAIs the amplification factor, T, of the self-shunt excitation systemAIs the self shunt excitation system time constant.
4. The generator excitation system control parameter optimization method according to claim 1, wherein the initial PI controller obtained in step 2 is represented as:
Figure FDA0002986061690000031
the upper typeIn (G)c(s) is the transfer function of the initial PI controller, KiIs the integral gain, s is the frequency domain operator, KpIs the proportional gain.
5. The generator excitation system control parameter optimization method according to claim 1, wherein step 3.3 specifically comprises the steps of:
step 3.3.1, the transfer function of the excitation closed loop system of the synchronous generator is as follows:
Figure FDA0002986061690000032
in the above formula, Gb(s) is the excitation system closed loop transfer function of the synchronous generator, Gc(s) is the transfer function of the initial PI controller, Gg(s) is the transfer function of a simplified transient model of the synchronous generator in the frequency domain, GA(s) is the transfer function of the self-shunt excitation system;
step 3.3.2, the characteristic polynomial of the excitation closed-loop system of the synchronous generator is as follows:
D(s)＝KGKAKi+(1+KGKAKp)s+(T’d0+TA)s2+T’d0TAs3
in the above formula, D(s) is a characteristic polynomial of an excitation closed-loop system of the synchronous generator, KGIs the amplification factor, K, of the synchronous generatorAIs the amplification factor of a self-shunt excitation system, KiIs the integral gain; kpIs proportional gain, T'd0Is d-axis transient time constant of the generator, s is frequency domain operator, TAIs the self shunt excitation system time constant;
writing the above formula as D(s) ═ d0+d1s+d2s2+d3s2Of the form (d)0＝KGKAKi，d1＝1+KGKAKp，d2＝T’d0+TA，d3＝T’d0TA；
Step 3.3.3, obtaining four Harlituofu polynomials according to Harlituofu theorem:
Figure FDA0002986061690000033
Figure FDA0002986061690000034
Figure FDA0002986061690000035
Figure FDA0002986061690000036
in the above formula, the first and second carbon atoms are,
Figure FDA0002986061690000037
and
Figure FDA0002986061690000038
represents a parameter d0The upper and lower boundaries of (a) are,
Figure FDA0002986061690000039
Figure FDA00029860616900000310
and
Figure FDA00029860616900000311
represents a parameter d1The upper and lower boundaries of (a) are,
Figure FDA0002986061690000041
Figure FDA0002986061690000042
and
Figure FDA0002986061690000043
represents a parameter d2The upper and lower boundaries of (a) are,
Figure FDA0002986061690000044
Figure FDA0002986061690000045
and
Figure FDA0002986061690000046
represents a parameter d3The upper and lower boundaries of (a) are,
Figure FDA0002986061690000047
and 3.3.4, judging whether the four Harlitooff polynomials in the step 3.3.3 meet the Laus-Helvelz stability criterion or not according to the Harlitooff theorem, and if all the Harlitooff polynomials meet the Laus-Helvelz stability criterion, all the Harlitooff polynomials are stable.
6. The generator excitation system control parameter optimization method of claim 1, wherein: 3.2, analyzing the dynamic characteristics of the fitness function alpha (X) according to the step response result of the 5% step quantity of the no-load rated voltage of the generator; the dynamics include a characteristic overshoot σ, a rise time TrAnd adjusting the time Ts。
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