CN113721460B - Power system stability control method based on probability robust random algorithm - Google Patents
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Abstract
The invention discloses a power system stability control method based on a probability robust random algorithm, which relates to the technical field of power system stability control, and aims to solve the problems that the calculation amount is greatly reduced, the robust stability margin is increased, the solution is not NP-hard any more, and the transient stability and the robust stability of a power system can be effectively improved by using the probability robust excitation control law.
Description
Technical Field
The invention belongs to the technical field of power system stability control, and particularly relates to a power system stability control method based on a probability robust random algorithm.
Background
The problem of robust control design of systems has long been a hot spot in the field of system control. Tools for solving robust control problems are well known as H ∞ Theory, structural singular values, and the like. However, these theories have heretofore been applicable only to systems with a specific uncertainty structure, and suffer from computational complexity, NP-hard as the solution, conservation and discontinuity of the robust stability margin, and the like. The power system is a typical uncertainty system, which contains numerous uncertainty factors,it is therefore necessary to design a power system robust nonlinear controller taking into account disturbances.
A probability robust random algorithm is introduced, and the algorithm converts the classical robust control problem into a probability robust control problem, so that the calculated amount is greatly reduced, the robust stability margin is increased, and the solution is no longer the problem of NP-hard. The probability robust control random algorithm is used for robust control of the power system, and a robust controller which is used for stably running the power system and meets certain performance indexes is designed, so that the power system has good dynamic performance and good static performance.
Disclosure of Invention
The invention aims to provide a power system stability control method based on a probability robust random algorithm, so that the defect of unstable control of the existing power system is overcome.
In order to achieve the above purpose, the invention provides a power system stability control method based on a probability robust random algorithm, which comprises the following steps:
establishing a single machine infinite system model;
linearizing the single-machine infinite system model, and replacing a system state variable of the single-machine infinite system model with a system state variable which is easy to measure;
giving a robust stable performance index to the processed single machine infinite system model;
a scene probability stochastic algorithm is adopted, probability levels are designated, and uncertainty parameter samples in the single machine infinite system model are extracted to calculate, so that a control matrix is obtained according to a set setting degree;
and obtaining an excitation control rule of the single machine infinite system model according to the control matrix, so that the system of the single machine infinite system model is gradually stable and meets the robustness index.
Further, the stand-alone infinite system model is as follows:
wherein ,
in the formula (1), delta, omega, E' q Is a system state variable, delta is the power angle of the generator, omega is the angular speed of the generator, E' q Is the reactance after transient potential, omega 0 For synchronous rotation speed of generator rotor, T J Representing the rotor inertia time constant of the generator, P m To input mechanical power, P e The electromagnetic power of the generator is D is the damping coefficient of the generator, p 1 ,p 2 ,p 3 Is an uncertain parameter of the system, T' d0 Indicating the time constant of the exciting winding when the stator of the generator is open, x de ＝x d +x e ，x d and xq Respectively represents the direct axis reactance and the quadrature axis reactance of the generator,x T and xL Representing the reactance on the transformer and the line, x, respectively qe ＝x q +x e ，x′ de ＝x′ d +x e ，x′ d Is a direct axis transient reactance; u (U) f Representing the generator terminal voltage, U fq and Ufd Respectively represent U f Components on the q and d axes; u (U) s Is the voltage on an infinite bus, E qe Is a control law of the system.
Further, the single machine infinite system model is subjected to linearization, and a system equation obtained by replacing a system state variable of the single machine infinite system model with a system state variable easy to measure is as follows:
in the formula (2) [ ΔP ] e ,Δω,ΔU f ]ΔP as an easily measured system state variable e As the active power deviation value, deltaomega is the angular velocity deviation value of the generator, deltaU f As the voltage deviation value of the machine end, delta P m U as a mechanical power deviation value f0 For initial operation of the machine terminal voltage, U fd0 Is U (U) fq0 and Uf0 The components of the initial operating machine voltage on the d-axis and the q-axis, K 1 ，K 2 ，K 3 ，K 4 ，K 5 ，K 6 The expression of β is:
β＝K 1 K 6 -K 2 K 5 。
further, the robust stability performance index is:
in the formula (4), delta is an uncertainty set of the system, belongs to an uncertainty factor set T (delta) allowed by the system, z is an output vector of the system, and x is a system state vector; d (D) zu As a constant matrix corresponding to the control vector dimension,for D zu Is used to determine the transposed vector of (c),is reversible and ∈>u is a control vector, u T Transposed vector of u, C z Is a constant matrix corresponding to the system state vector dimension, < >>Is C z Is a transposed matrix of (a);
for any gamma >0, the control target is to design matrix variables P >0 and control rules u= -Kx, K as control vectors, so that a closed loop system formed by a single machine infinite system model is stable, and the robust performance index is established for all delta epsilon T (delta), and the final robust performance index J (delta) is:
J(Δ)≤γ -1 x T P -1 x (5)
in the formula (5), γ is an arbitrary number greater than zero, and P is a matrix variable.
Further, a scene probability stochastic algorithm is adopted, probability levels are designated, and uncertainty parameter samples in the single machine infinite system model are extracted to calculate, and a control matrix is obtained according to a set setting degree, wherein the control matrix comprises the following steps:
given a probability level ρ * Xi epsilon (0, 1), the step number N of the random algorithm is selected as follows:
in formula (7), n is the dimension of the system;
the target equation for the stochastic algorithm is-trace (P), where P is the control matrix,
the constraint conditions of the random algorithm are:
the control matrix P is solved with a confidence level of at least 1- ζ according to the set parameters.
6. The power system stability control method based on the probability robust random algorithm according to claim 1, wherein the excitation control law is:
in the formula (9), u is an excitation control vector, P -1 Is the inverse of the control matrix; and the matrix P is controlled such that the matrix inequality holds for all ΔεT (Δ), the matrix inequality is shown as:
further, the random algorithm tool box RACT is used for calculating the random scene algorithm of the scene probability.
Further, a robust controller is constructed according to the excitation control law, and the single machine infinite system model is controlled through the robust controller
Compared with the prior art, the invention has the following beneficial effects:
according to the power system stability control method based on the probability robust random algorithm, the single-machine infinite system model is built, linear processing is carried out on the single-machine infinite system model, then a robust stability performance index is given to the single-machine infinite system model, a scene probability random algorithm is adopted, a probability level is designated, a control matrix of the single-machine infinite system model is obtained, finally an excitation control rule of the single-machine infinite system model is obtained according to the control matrix, the single-machine infinite system model is controlled according to the excitation control rule, the classical robust control problem is converted into the probability robust control problem, so that the calculated amount is greatly reduced, the robust stability margin is increased, the problem that the solution is not NP-hard any longer is solved, the probability robust excitation control rule can effectively improve the transient stability and the robust stability of the power system, meanwhile, the probability robust excitation control rule can effectively inhibit adverse effects of disturbance on system output relative to the linear optimal excitation control rule, and the robustness of the system is improved.
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In order to more clearly illustrate the technical solutions of the present invention, the drawings that are needed in the description of the embodiments will be briefly described below, it being obvious that the drawing in the description below is only one embodiment of the present invention, and that other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a representation of the invention when p 1 ＝-4；p 2 ＝-0.85；p 3 At = -0.87, P e Response curves of (2);
FIG. 2 is a view of the simulation of the present invention when p 1 ＝-4；p 2 ＝-0.85；p 3 -0.87, the response curve of ω;
FIG. 3 is the simulation of the present invention when p 1 ＝-4；p 2 ＝-0.85；p 3 When= -0.87, U f Response curves of (2);
FIG. 4 shows the simulation of the present invention when p 1 ＝0；p 2 ＝0.85；p 3 When=0.848, P e Response curves of (2);
FIG. 5 is the simulation of the present invention when p 1 ＝0；p 2 ＝0.85；p 3 Response curve of ω =0.848;
FIG. 6 is a simulation of the present invention when p 1 ＝0；p 2 ＝0.85；p 3 When=0.848, U f Response curve U of (2) f ；
FIG. 7 is a simulation of the present invention when p 1 ＝0；p 2 ＝0.85；p 3 When=0.848, P m Under disturbance P e Response curves of (2);
FIG. 8 is a view of the simulation of the present invention when p 1 ＝0；p 2 ＝0.85；p 3 When=0.848, P m A response curve of ω under disturbance;
FIG. 9 is a view of p when simulated according to the invention 1 ＝0；p 2 ＝0.85；p 3 When=0.848, P m U under disturbance f Response curves of (2);
FIG. 10 is a view of the simulation of the present invention when p 1 ＝0；p 2 ＝0.85；p 3 When=0.848, three phasesP under short-circuit disturbance e Response curves of (2);
FIG. 11 is a view of p when simulated according to the invention 1 ＝0；p 2 ＝0.85；p 3 When=0.848, the response curve of ω under three-phase short circuit disturbance;
FIG. 12 is a view of p when simulated according to the invention 1 ＝0；p 2 ＝0.85；p 3 When=0.848, U is under three-phase short circuit disturbance f Response curves of (2);
fig. 13 is a flowchart of a power system stability control method based on a probability robust random algorithm of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made more apparent and fully by reference to the accompanying drawings, in which it is shown, however, only some, but not all embodiments of the invention. 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.
Fig. 13 shows that the power system stability control method based on the probability robust random algorithm provided by the invention comprises the following steps:
s1, establishing a single machine infinite system model;
specifically, the stand-alone infinite system model is:
in the formula (1), the components are as follows,
in the formula (1), delta, omega, E' q Is a system state variable, deltaFor the power angle of the generator, ω is the angular velocity of the generator, E' q Is the reactance after transient potential, omega 0 For synchronous rotation speed of generator rotor, T J Representing the rotor inertia time constant of the generator, P m To input mechanical power, P e The electromagnetic power of the generator is D is the damping coefficient of the generator, p 1 ,p 2 ,p 3 Is an uncertain parameter of the system, T' d0 Indicating the time constant of the exciting winding when the stator of the generator is open, x de ＝x d +x e ，x d and xq Respectively represents the direct axis reactance and the quadrature axis reactance of the generator, x' de ＝x′ d +x e ，x′ d Is a direct-axis transient reactance, U s Is the voltage on an infinite bus, E qe Is a control law of the system;x T and xL Representing the reactance on the transformer and the line, x, respectively qe ＝x q +x e ；U f Representing the generator terminal voltage, U fq and Ufd Respectively represent U f A component on the q d axis. X is x e 、x qe 、x′ de 、x de The expression of (2) is:
x′ de ＝x′ d +x e ；x qe ＝x q +x e ；x de ＝x d +x e 。
s2, the control target is to make U f ,P e And ω is stable in the presence of uncertain parameters for a single machine infinite system model at equilibrium point [ delta ] 0 ,ω 0 ,E′ q0 ]Linearizing, and replacing the system state variable of the single machine infinite system model with the system state variable which is easy to measure;
specifically, the system state variables [ delta, omega, E 'of a single machine infinite system model are calculated' q ]Replaced by easy-to-measureSystem state variable [ delta P e ,Δω,ΔU f ]The resulting system equation is:
in the formula (2) [ ΔP ] e ,Δω,ΔU f ]ΔP as an easily measured system state variable e As the active power deviation value, deltaomega is the angular velocity deviation value of the generator, deltaU f As the voltage deviation value of the machine end, delta P m U as a mechanical power deviation value f0 For initial operation of the machine terminal voltage, U fd0 Is U (U) fq0 and Uf0 The components of the initial operating machine voltage on the d-axis and the q-axis, K 1 ，K 2 ，K 3 ，K 4 ，K 5 ，K 6 The specific expression of beta is:
β＝K 1 K 6 -K 2 K 5 ；
the formula (2) is abbreviated as follows:
in the formula (3), x is a system state vector, A is a state space matrix, A T Is the transposed matrix of A, B u For the output state matrix, u is the control vector, u T Transposed vector of u, D zu As a constant matrix corresponding to the control vector dimension,for D zu Is the transposed vector of>Is reversible and has->C z Is a constant matrix corresponding to the system state vector dimension, < >>Is C z Delta is the system uncertainty parameter set [ p ] 1 ,p 2 ,p 3 ]Z is the output vector of the system.
S3, giving a robust stable performance index to the single machine infinite system model obtained in the step S2;
the robust stability performance index is:
in the formula (4), Δ is an uncertainty set of the system, belongs to an uncertainty factor set T (Δ) allowed by the system, and z is an output vector of the system.
For any gamma >0, the control target is to design matrix variables P >0 and control rules u= -Kx, K as control vectors, so that a closed loop system formed by a single machine infinite system model is stable, and the robust performance index is established for all delta epsilon T (delta), and the final robust performance index J (delta) is:
J(Δ)≤γ -1 x T P -1 x (5)
in the formula (5), γ is an arbitrary number greater than zero, and P is a control parameter matrix.
S4, a scene probability random algorithm is adopted, probability levels are designated, uncertainty parameter samples in a single machine infinite system model are extracted for calculation, a control matrix is obtained at a certain setting degree, and the control rule enables the closed-loop system to have given performance indexes.
The scene probability random algorithm is as follows: let delta epsilon D satisfy random distribution, delta is an uncertain parameter set of the system, and delta is approximateThe rate density function is f Δ Let ρ be (delta) * E (0, 1) and xi E (0, 1) are specified probability levels; give the performance function L (delta, P N ) D x Λ - & gtR and performance level ζ, the robust performance synthesis stochastic algorithm solves for a control vector P with a confidence level of at least 1- ζ N E Λ, such that
Probability{L(Δ,P N )≤ζ}≥ρ * (6)
In the formula (6), L (delta, P) N ) Is a performance function of the system, and ζ is a performance index of the system; p (P) N Is obtained by calculating N finite samples belonging to Δ.
Specifically, the calculation is performed through a random algorithm tool box RACT, and the scene probability random algorithm comprises the following steps:
s41, giving a probability level ρ * Xi epsilon (0, 1), the step number N of the random algorithm is selected as follows:
in formula (7), n is the dimension of the system;
the objective equation for the scene probability stochastic algorithm is-trace (P), where P is the control matrix,
constraint conditions of the scene probability random algorithm are as follows:
s42, solving a control matrix P according to the parameters of the step S41 with a confidence degree of at least 1-zeta.
S5, obtaining an excitation control rule of the single-machine infinite system model according to the control matrix P, so that the system of the single-machine infinite system model is gradually stable and meets the robustness index, and the single-machine infinite system model can be controlled according to the excitation control rule.
The excitation control law is:
in the formula (9), u is an excitation control vector, P -1 Is the inverse of the control matrix; and the matrix P is controlled such that the matrix inequality holds for all ΔεT (Δ), the matrix inequality is shown as:
according to the power system stability control method based on the probability robust random algorithm, the single-machine infinite system model is built, linear processing is carried out on the single-machine infinite system model, then a robust stability performance index is given to the single-machine infinite system model, a scene probability random algorithm is adopted, a probability level is designated, a control matrix of the single-machine infinite system model is obtained, finally an excitation control rule of the single-machine infinite system model is obtained according to the control matrix P, the single-machine infinite system model is controlled according to the excitation control rule, the classical robust control problem is converted into the probability robust control problem, so that the calculated amount is greatly reduced, the robust stability margin is increased, the solution is not NP-hard, the transient stability and the robust stability of the power system can be effectively improved by using the probability robust excitation control rule, meanwhile, adverse effects of disturbance on system output can be effectively restrained relative to the linear optimal excitation control rule, and the robustness of the system is enhanced.
In one embodiment, a robust controller is constructed according to the excitation control law obtained in the steps S1-S5, and the single machine infinite system model is controlled through the robust controller. The probability robust control random algorithm is used for robust control of the power system, and a robust controller which is used for stably running the power system and meets certain performance indexes is designed, so that the power system has good dynamic performance and good static performance.
The simulation of the power system stability control method based on the probability robust random algorithm is described in detail so that the person skilled in the art can better understand the method:
in step S1, simulation is performed in a stand-alone infinite system model, and parameters of the system are as follows:
x d ＝2.12p.u.；x q ＝2.12p.u.；x′ d ＝0.26p.u.；x e ＝0.24p.u.；D＝2p.u.；T J ＝4.06s；T′ d0 ＝5.8s.
the initial operating point of the system is: p (P) m0 ＝P e0 ＝0.6p.u.；δ＝50°；U f0 ＝1.0293p.u..
Uncertainty parameter p 1 ,p 2 and p3 The range of the value of the number is respectively [ -4,0]，[-0.85,0.85]And [ -0.87,0.848]. The uncertainty vector of the system can be expressed as Δ= [ p ] 1 p 2 p 3 ] T 。
In the probabilistic random scene algorithm in step S4, ζ=e is selected -5 ρ * =0.99 such that the probabilistic random scene algorithm sums ρ with a probability greater than 1- ζ=0.99999 * Confidence level=0.99 solves for control matrix P. The number of random samples involved in the calculation is n=5488, and then let
γ＝1.5，C Z ＝[50,0,0；0,50,0；0,0,50；0,0,0]，D zu ＝[0；0；0；1]Then the excitation control rule of the single machine infinite system model can be calculated:
u＝-64.3ΔP e +99.2Δω-977.12ΔU f
to illustrate the superiority of the excitation robust control Law (LEPRC) of the proposed system, the excitation robust control Law (LEPRC) is compared with a linear optimal excitation control Law (LOEC). The parameters of the LOEC control law can be obtained by solving the licarpi equation.
The simulation is as follows:
simulation 1: external interference
To verify the robustness of the control law, an uncertainty parameter p will be applied to the system when t=1s 1 ,p 2 P 3 Take different values. From fig. 1-6, it can be seen that when the generator is subject to external disturbances, the generator terminal voltage and workThe angle produces a smaller static shift under the action of the LEPRC than under the action of the LOEC, since the LEPRC has a suppressing effect on external disturbances. LEPRC makes the generator when suffering external interference, can not only subside the mechanical oscillation of unit in transient state process more fast than LOEC and can better restrain the overshoot of system to make the generator have good dynamic performance and robustness.
Simulation 2: input mechanical power P m Increase the disturbance by 20%
FIGS. 7-9 show that when the mechanical power P m Increase by 20% at t=1s and presence of uncertainty parameter p 1 ＝0,p 2 ＝0.85,p 3 When the voltage of the generator terminal is=0.848, the static offset generated by the voltage of the generator terminal under the action of the LEPRC is smaller than the static offset generated by the voltage of the generator terminal under the action of the LOEC, and the LEPRC enables the generator to have good dynamic performance and robustness compared with the LOEC when the generator is subjected to input mechanical power disturbance.
Simulation 3: three-phase short circuit disturbance
At t=1s, the system experiences a three-phase short circuit disturbance, after 0.2s the short circuit clears and reclosing is successful, and there is an uncertain parameter disturbance to the system. As can be seen from fig. 10-12, when a large disturbance occurs in the system, the LEPRC recovers the terminal voltage to the initial level faster than the LOEC, and the swing of the active power is subsided earlier, although both the terminal voltage and the active power of the generator are recovered under the action of 2 control laws. LEPRC can better calm system frequency oscillation than LOEC, and more quickly makes the system return to the initial operating point, more effectively suppresses overshoot of the system, and makes the generator have good dynamic and static performance.
According to the simulation experiment, the power system stability control method based on the probability robust random algorithm can enable a single infinite power system to be stable under the condition of disturbance and uncertain parameters. Simulation results show that the control law can effectively improve the robustness of the system in static and transient processes.
The foregoing disclosure is merely illustrative of specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art will readily recognize that changes and modifications are possible within the scope of the present invention.
Claims (6)
1. The power system stability control method based on the probability robust random algorithm is characterized by comprising the following steps of:
establishing a single machine infinite system model;
linearizing the single-machine infinite system model, and replacing a system state variable of the single-machine infinite system model with a system state variable which is easy to measure;
giving a robust stable performance index to the processed single machine infinite system model;
a scene probability stochastic algorithm is adopted, probability levels are designated, and uncertainty parameter samples in the single machine infinite system model are extracted to calculate, so that a control matrix is obtained according to a set setting degree;
according to the control matrix, an excitation control rule of the single machine infinite system model is obtained, so that a system of the single machine infinite system model is gradually stable and meets a robustness index;
the single machine infinite system model is as follows:
wherein ,
in the formula (1), delta, omega, E' q Is a system state variable, delta is the power angle of the generator, omega is the angular speed of the generator, E' q Is the reactance after transient potential, omega 0 For synchronous rotation speed of generator rotor, T J Representing hairRotor inertia time constant, P of motor m To input mechanical power, P e The electromagnetic power of the generator is D is the damping coefficient of the generator, p 1 ,p 2 ,p 3 Is an uncertain parameter of the system, T' d0 Indicating the time constant of the exciting winding when the stator of the generator is open, x de ＝x d +x e ，x d and xq Respectively represents the direct axis reactance and the quadrature axis reactance of the generator,x T and xL Representing the reactance on the transformer and the line, x, respectively qe ＝x q +x e ，x′ de ＝x′ d +x e ，x′ d Is a direct axis transient reactance; u (U) f Representing the generator terminal voltage, U fq and Ufd Respectively represent U f Components on the q and d axes; u (U) s Is the voltage on an infinite bus, E qe Is a control law of the system;
linearizing the single machine infinite system model, and replacing the system state variable of the single machine infinite system model with the system state variable easy to measure to obtain a system equation:
in the formula (2) [ ΔP ] e ,Δω,ΔU f ]ΔP as an easily measured system state variable e As the active power deviation value, deltaomega is the angular velocity deviation value of the generator, deltaU f As the voltage deviation value of the machine end, delta P m U as a mechanical power deviation value f0 For initial operation of the machine terminal voltage, U fd0 and Ufq0 The components of the initial operating machine voltage on the d-axis and the q-axis, K 1 ，K 2 ，K 3 ，K 4 ，K 5 ，K 6 The expression of β is:
β＝K 1 K 6 -K 2 K 5 。
2. the power system stability control method based on the probabilistic robust random algorithm according to claim 1, wherein the robust stability performance index is:
in the formula (4), delta is an uncertainty set of the system, belongs to an uncertainty factor set T (delta) allowed by the system, z is an output vector of the system, and x is a system state vector; d (D) zu As a constant matrix corresponding to the control vector dimension,for D zu Is used to determine the transposed vector of (c),is reversible and ∈>u is a control vector, u T Transposed vector of u, C z Is a constant matrix corresponding to the system state vector dimension, < >>Is C z Is a transposed matrix of (a);
for any gamma >0, the control target is to design matrix variable P >0 and control rule u= -Kx, K as control vector, so that the closed loop system formed by the single machine infinite system model is stable, and the robust performance index is established for all delta epsilon T (delta), and the final robust performance index J (delta) is:
J(Δ)≤γ -1 x T P -1 x (5)
in the formula (5), γ is an arbitrary number greater than zero, and P is a matrix variable.
3. The method for controlling the stability of the electric power system based on the probability robust stochastic algorithm according to claim 1, wherein the step of adopting the scene probability stochastic algorithm to designate probability levels and calculating by extracting uncertainty parameter samples in the single machine infinite system model to obtain a control matrix according to the set placement degree comprises the following steps:
given a probability level ρ * Xi epsilon (0, 1), the step number N of the random algorithm is selected as follows:
in formula (7), n is the dimension of the system;
the target equation for the stochastic algorithm is-trace (P), where P is the control matrix,
the constraint conditions of the random algorithm are:
the control matrix P is solved with a confidence level of at least 1- ζ according to the set parameters.
4. The power system stability control method based on the probabilistic robust random algorithm according to claim 1, wherein the excitation control law is:
in the formula (9), u is an excitation control vector, P -1 Is the inverse of the control matrix; and the matrix P is controlled such that the matrix inequality holds for all ΔεT (Δ), the matrix inequalityThe formula is shown as follows:
5. the power system stability control method based on the probability robust random algorithm according to claim 1, wherein the scene probability random scene algorithm calculation is performed through a random algorithm tool box rack.
6. The power system stability control method based on the probabilistic robust random algorithm according to claim 1, wherein a robust controller is constructed according to the excitation control law, and the stand-alone infinite system model is controlled by the robust controller.
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| US5991525A (en) * | 1997-08-22 | 1999-11-23 | Voyan Technology | Method for real-time nonlinear system state estimation and control |
| WO2009011615A1 (en) * | 2007-07-19 | 2009-01-22 | Yury Vladimirovich Sharov | Method for improving the dynamic stability of electric-power systems and for damping oscillations thereof and a device for carrying out said method |
| CN110417003A (en) * | 2019-07-09 | 2019-11-05 | 西安工程大学 | Based on two-parameter adaptive STATCOM and excitation Robust Coordinated method |
| CN111884215A (en) * | 2020-07-30 | 2020-11-03 | 海南电网有限责任公司海口供电局 | Uncertainty-containing single-machine infinite power system optimization control method |
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Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5991525A (en) * | 1997-08-22 | 1999-11-23 | Voyan Technology | Method for real-time nonlinear system state estimation and control |
| WO2009011615A1 (en) * | 2007-07-19 | 2009-01-22 | Yury Vladimirovich Sharov | Method for improving the dynamic stability of electric-power systems and for damping oscillations thereof and a device for carrying out said method |
| CN110417003A (en) * | 2019-07-09 | 2019-11-05 | 西安工程大学 | Based on two-parameter adaptive STATCOM and excitation Robust Coordinated method |
| CN111884215A (en) * | 2020-07-30 | 2020-11-03 | 海南电网有限责任公司海口供电局 | Uncertainty-containing single-machine infinite power system optimization control method |
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