CN113721460A - 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 comprises the steps of establishing a single machine infinite system model, 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 assigned, 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 model of the single-machine infinite system is controlled according to the excitation control rule, the classical robust control problem is converted into the probabilistic robust control problem, thereby greatly reducing the calculated amount, increasing the robust stability margin, the solution is no longer the problem of NP-hard, and the transient stability and robust stability of the power system can be effectively improved by applying 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 robust control design problem of the system has been a hot issue in the field of system control for a long time. Tools for solving robust control problems are known as H∞Theory, structural singular values, etc. However, these theories can only be applied to a system with a certain uncertain structure, and the calculation is complicated, the solution is NP-hard, and the problem that the robust stability margin is conservative and discontinuous exists. The power system is a typical uncertainty system which contains a lot of uncertainty factors, so it is necessary to design a robust nonlinear controller of the power system in consideration of interference.
A probabilistic robust random algorithm is introduced, and converts a classical robust control problem into a probabilistic robust control problem, so that the calculation amount is greatly reduced, the robust stability margin is increased, and the solution of the probabilistic robust random algorithm is not the NP-hard problem any more. The probabilistic robust control random algorithm is used for robust control of the power system, and a robust controller which stably operates the power system and meets certain performance indexes is designed, so that the power system not only has good dynamic performance, but also has good static performance.
Disclosure of Invention
The invention aims to provide a power system stability control method based on a probability robust random algorithm, thereby solving the defect of unstable control of the existing power system.
In order to achieve the 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;
carrying out linearization processing on the single-machine infinite system model, and replacing the system state variable of the single-machine infinite system model with a system state variable which is easy to measure;
giving a robust stability performance index to the processed single-machine infinite system model;
a scene probability random algorithm is adopted, a probability level is designated, uncertainty parameter samples in the single-machine infinite system model are extracted for calculation, and a control matrix is obtained according to a set degree;
and obtaining an excitation control rule of the single infinite system model according to the control matrix, so that the system of the single infinite system model is gradually stable and meets the robustness index.
Further, the model of the single-machine infinite system is as follows:
wherein ,
in the formula (1), delta, omega, E'qIs a system state variable, delta is the power angle of the generator, omega is the angular speed of the generator, E'qPost-transient-potential reactance, ω0For synchronous speed of the generator rotor, TJRepresenting the rotor inertia time constant, P, of the generatormFor input of mechanical power, PeFor the electromagnetic power of the generator, D is the damping coefficient of the generator, p1,p2,p3Is an uncertain parameter of the system, T'd0Representing the time constant, x, of the field winding during an open circuit of the stator of the generatorde＝xd+xe，xd and xqRespectively representing the direct-axis reactance and quadrature-axis reactance of the generator,
xT and xLRepresenting the reactance, x, on the transformer and line, respectivelyqe＝xq+xe，x′de＝x′d+xe，x′dIs a direct axis transient reactance; u shapefRepresenting generator terminal voltage, Ufq and UfdRespectively represent UfComponents on the q and d axes; u shapesIs the voltage on the infinite bus, EqeIs the control law of the system.
Further, the single-machine infinite system model is subjected to linearization processing, and a system state variable of the single-machine infinite system model is replaced by a system state variable which is easy to measure, so that a system equation is obtained:
in the formula (2), [ Delta P ]e,Δω,ΔUf]For easily measurable system state variables, Δ PeIs the active power deviation value, and delta omega is the angular velocity deviation value of the generator, delta UfAs terminal voltage offset value, Δ PmAs a deviation value of mechanical power, Uf0For initial operation terminal voltage, Ufd0Is Ufq0 and Uf0The components of the voltage of the initial running machine end in the d-axis and q-axis, K1，K2，K3，K4，K5，K6And β is expressed as:
further, the robust stability performance index is as follows:
in the formula (4), Δ is an uncertainty set of the system, and belongs to an uncertainty factor set T (Δ) allowed by the system, z is an output vector of the system, and x is a system state vector; dzuIs a matrix of constants corresponding to the dimensions of the control vector,
is DzuThe transposed vector of (a) is,
is reversible, and
u is a control vector, uTTransposed vector of u, CzA constant matrix corresponding to the dimension of the system state vector,
is CzThe transposed matrix of (2);
for any gamma >0, the control target is that a design matrix variable P >0 and a control rule u ═ Kx, K is a control vector, 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 e T (delta), the obtained final robust performance index J (delta) is as follows:
J(Δ)≤γ-1xTP-1x (5)
in the formula (5), γ is an arbitrary number larger than zero, and P is a matrix variable.
Further, a scene probability random algorithm is adopted, a probability level is designated, uncertainty parameter samples in the single-machine infinite system model are extracted for calculation, and a control matrix is obtained according to a set degree, and the method comprises the following steps:
given a probability level of p*ξ ∈ (0,1), the number of steps N of the random algorithm is chosen as:
in formula (7), n is the system dimension;
the objective equation of the stochastic algorithm is-trace (P), where P is the control matrix,
the constraints of the stochastic algorithm are:
the control matrix P is solved with a confidence level of at least 1- ξ as a function of the set parameters.
6. The power system stability control method based on the probabilistic robust random algorithm of claim 1, wherein the excitation control law is as follows:
in the formula (9), u is an excitation control vector, P-1Is the inverse of the control matrix; and the matrix P is controlled such that a matrix inequality is true for all Δ ∈ T (Δ), the matrix inequality being as follows:
further, a random scene algorithm of scene probability is calculated through a random algorithm tool box RACT.
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:
the method for controlling the stability of the electric power system based on the probability robust random algorithm comprises the steps of establishing a single-machine infinite system model, carrying out linear processing on the single-machine infinite system model, giving a robust stability performance index to the single-machine infinite system model, adopting a scene probability random algorithm to assign a probability level to obtain a control matrix of the single-machine infinite system model, finally obtaining an excitation control rule of the single-machine infinite system model according to the control matrix, controlling the single-machine infinite system model according to the excitation control rule, converting the classical robust control problem into the probability robust control problem, greatly reducing the calculated amount, increasing the robust stability margin, solving the problem of NP-hard, and effectively improving the transient stability and the robust stability of the electric power system by using the probability robust excitation control rule, meanwhile, compared with the linear optimal excitation control law, the probability robust excitation control law can effectively inhibit the adverse effect of disturbance on system output, and the robustness of the system is enhanced.
Drawings
In order to more clearly illustrate the technical solution of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only one embodiment of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on the drawings without creative efforts.
FIG. 1 is a graph of p when the present invention is simulated1＝-4；p2＝-0.85；p3When is-0.87, PeThe response curve of (a);
FIG. 2 is a graph of p when the present invention is simulated1＝-4；p2＝-0.85；p3Response curve for ω at-0.87;
FIG. 3 is a graph of p when the present invention is simulated1＝-4；p2＝-0.85；p3When the value is-0.87, UfThe response curve of (a);
FIG. 4 is a graph of p when the present invention is simulated1＝0；p2＝0.85；p3When equal to 0.848, PeThe response curve of (a);
FIG. 5 is a graph of p when the invention is simulated1＝0；p2＝0.85；p3Response curve for ω at 0.848;
FIG. 6 is a graph of p when the invention is simulated1＝0；p2＝0.85；p3When equal to 0.848, UfResponse curve U off；
FIG. 7 is a graph of p when the invention is simulated1＝0；p2＝0.85；p3When equal to 0.848, PmP under disturbanceeThe response curve of (a);
FIG. 8 is a graph of p when the invention is simulated1＝0；p2＝0.85；p3When equal to 0.848, PmResponse curve of ω under disturbance;
FIG. 9 is a graph of p when the invention is simulated1＝0；p2＝0.85；p3When equal to 0.848, PmU under disturbancefThe response curve of (a);
FIG. 10 is a graph of p when the invention is simulated1＝0；p2＝0.85；p30.848 hour, P under three-phase short circuit disturbanceeThe response curve of (a);
FIG. 11 is a graph of p when the invention is simulated1＝0；p2＝0.85；p3When the response is equal to 0.848, the response curve of omega under three-phase short circuit disturbance;
FIG. 12 is a graph of p when the invention is simulated1＝0；p2＝0.85；p30.848 hour, U under three-phase short circuit disturbancefThe response curve of (a);
FIG. 13 is a flow chart of a power system stability control method based on a probabilistic robust random algorithm according to the present invention.
Detailed Description
The technical solutions in the present invention are clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Fig. 13 shows that the power system stability control method based on the probabilistic robust random algorithm provided by the invention comprises the following steps:
s1, establishing a single-machine infinite system model;
specifically, the model of the single-machine infinite system is as follows:
in the formula (1), the reaction mixture is,
in the formula (1), delta, omega, E'qIs a system state variable, delta is the power angle of the generator, omega is the angular speed of the generator, E'qPost-transient-potential reactance, ω0For synchronous speed of the generator rotor, TJRepresenting the rotor inertia time constant, P, of the generatormFor input of mechanical power, PeFor the electromagnetic power of the generator, D is the damping coefficient of the generator, p1,p2,p3Is an uncertain parameter of the system, T'd0Representing the time constant, x, of the field winding during an open circuit of the stator of the generatorde＝xd+xe，xd and xqRespectively representing the direct-axis reactance and the quadrature-axis reactance, x 'of the generator'de＝x′d+xe，x′dIs a direct axis transient reactance, UsIs the voltage on the infinite bus, EqeIs the control law of the system;
xT and xLRepresenting the reactance, x, on the transformer and line, respectivelyqe＝xq+xe；UfRepresenting generator terminal voltage, Ufq and UfdRespectively represent UfOn q d axesAmount of the compound (A). x is the number ofe、xqe、x′de、xdeThe expression of (a) is:
s2, controlling the aim to ensure that U isf,PeAnd omega is stable when uncertain parameters exist in the system, and is stable at a balance point [ delta ] of a single-machine infinite system model0,ω0,E′q0]Carrying out linearization processing, 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 the single-machine infinite system model'q]Replacement by a readily measurable system state variable [ Δ P ]e,Δω,ΔUf]The resulting system equation is:
in the formula (2), [ Delta P ]e,Δω,ΔUf]For easily measurable system state variables, Δ PeIs the active power deviation value, and delta omega is the angular velocity deviation value of the generator, delta UfAs terminal voltage offset value, Δ PmAs a deviation value of mechanical power, Uf0For initial operation terminal voltage, Ufd0Is Ufq0 and Uf0The components of the voltage of the initial running machine end in the d-axis and q-axis, K1，K2，K3，K4，K5，K6And β is a specific expression:
the formula (2) is abbreviated as follows:
in formula (3), x is the system state vector, A is the state space matrix, ATIs a transposed matrix of A, BuIs an output state matrix, u is a control vector, uTTransposed vector of u, DzuIs a matrix of constants corresponding to the dimensions of the control vector,
is DzuThe transposed vector of (a) is,
is reversible and has the characteristics that,
Cza constant matrix corresponding to the dimension of the system state vector,
is CzWith Δ being the system uncertainty parameter set [ p ]1,p2,p3]And z is the output vector of the system.
S3, giving a robust stability performance index to the single-machine infinite system model obtained in the step S2;
the robust stability performance indexes are as follows:
in equation (4), Δ is the uncertainty set of the system, and belongs to the uncertainty factor set T (Δ) allowed by the system, and z is the output vector of the system.
For any gamma >0, the control target is that a design matrix variable P >0 and a control rule u ═ Kx, K is a control vector, 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 e T (delta), the obtained final robust performance index J (delta) is as follows:
J(Δ)≤γ-1xTP-1x (5)
in the formula (5), gamma is a number arbitrarily larger than zero, and P is a control parameter matrix.
And S4, assigning a probability level by adopting a scene probability random algorithm, calculating by extracting uncertainty parameter samples in a single-machine infinite system model, and obtaining a control matrix with a certain degree, wherein the closed-loop system has a set performance index by the control rule.
The scene probability random algorithm is as follows: let Delta belong to D and satisfy random distribution, Delta is the uncertain parameter set of the system, and the probability density function of Delta is fΔ(Δ), let ρ*E (0,1) and xi e (0,1) are the designated probability levels; giving the performance function L (Δ, P)N) DxLambda → R and performance level ζ, the robust performance synthetic stochastic algorithm solves a control vector P with a confidence level of at least 1- ξNE is left to Λ so that
Probability{L(Δ,PN)≤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; pNIs obtained by calculating N finite samples belonging to Δ.
Specifically, the calculation is performed through a random algorithm tool kit RACT, and the scene probability random algorithm comprises the following steps:
s41, the given probability level is rho*ξ ∈ (0,1), the number of steps N of the random algorithm is chosen as:
in formula (7), n is the system dimension;
the target equation of the scene probability stochastic algorithm is-trace (P), wherein P is a control matrix,
the constraint conditions of the scene probability stochastic algorithm are as follows:
and S42, solving the control matrix P according to the parameters of the step S41 with a confidence degree of at least 1- ξ.
S5, obtaining an excitation control rule of the single-machine infinite system model according to the control matrix P, enabling the system of the single-machine infinite system model to be gradually stable and meet robustness indexes, and accordingly controlling the single-machine infinite system model according to the excitation control rule.
The excitation control law is as follows:
in the formula (9), u is an excitation control vector, P-1Is the inverse of the control matrix; and the matrix P is controlled such that a matrix inequality is true for all Δ ∈ T (Δ), the matrix inequality being as follows:
the power system stability control method based on the probability robust random algorithm comprises the steps of establishing a single machine infinite system model, carrying out linear processing on the single machine infinite system model, giving a robust stability performance index to the single machine infinite system model, adopting a scene probability random algorithm to assign a probability level to obtain a control matrix of the single machine infinite system model, finally obtaining an excitation control rule of the single machine infinite system model according to the control matrix P, controlling the single machine infinite system model according to the excitation control rule, converting a classical robust control problem into a probability robust control problem, greatly reducing the calculated amount, increasing the robust stability margin, solving the problem that the robust control margin is not NP-hard any more, and effectively improving the transient stability and the robust stability of the power system by using the probability excitation control rule, meanwhile, compared with the linear optimal excitation control law, the probability robust excitation control law can effectively inhibit the adverse effect of disturbance on system output, 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 probabilistic robust control random algorithm is used for robust control of the power system, and a robust controller which stably operates the power system and meets certain performance indexes is designed, so that the power system not only has good dynamic performance, but also has good static performance.
The simulation of the power system stability control method based on the probability robust random algorithm is explained in detail, so that the technical personnel in the field can understand the invention more:
in step S1, the simulation is performed in a model of a single-machine infinite system, and the parameters of the system are as follows:
xd＝2.12p.u.；xq＝2.12p.u.；x′d＝0.26p.u.；xe＝0.24p.u.；D＝2p.u.；TJ＝4.06s；T′d0＝5.8s.
the initial operating points of the system are: pm0＝Pe0＝0.6p.u.；δ＝50°；Uf0＝1.0293p.u..
Uncertainty parameter p1,p2 and p3The value range interval of (A) is [ -4,0 respectively]，[-0.85,0.85]And [ -0.87,0.848 ]]. The uncertainty vector of the system can be expressed as Δ ═ p1 p2 p3]T。
In the probabilistic random scene algorithm in step S4, ξ ═ e is selected-5And rho*0.99, so that the probabilistic stochastic scene algorithm sums ρ with a probability greater than 1- ξ 0.99999*A confidence of 0.99 solves the control matrix P. Taking the random sample number N5488, and then order
γ＝1.5，CZ＝[50,0,0；0,50,0；0,0,50；0,0,0]，Dzu＝[0；0；0；1]Then, the excitation control law of the single machine infinite system model can be solved as follows:
u＝-64.3ΔPe+99.2Δω-977.12ΔUf
to illustrate the superiority of the proposed excitation robust control Law (LEPRC) of the system, the excitation robust control Law (LEPRC) is compared with the linear optimal excitation control Law (LOEC). The parameters of the LOEC control law can be obtained by solving the licarbatt equation.
The simulation is as follows:
simulation 1: external interference
To verify the robustness of the control law, when t is 1s, an uncertain parameter p acting on the system1,p2And p3Take different values. As can be seen from fig. 1-6, when the generator is subjected to external interference, the static offset generated by the generator terminal voltage and power angle under the action of LEPRC is smaller than that under the action of LOEC, because LEPRC has a restraining effect on the external interference. The LEPRC enables the generator to be capable of rapidly settling the mechanical oscillation of the unit in the transient process and better inhibiting the overshoot of the system compared with the LOEC when the generator is subjected to external interference, so that the generator has good dynamic performance and robustness.
Simulation 2: input mechanical power PmIncrease 20% of the perturbation
FIGS. 7-9 show that when the mechanical power P is appliedmIncrease by 20% when t is 1s and there is an uncertainty parameter p1＝0,p2＝0.85,p3At 0.848, the static offset of the generator terminal voltage under the action of LEPRC is smaller than that under the action of LOEC, and LEPRC enables the generator to have good dynamic performance and robustness compared with LOEC when the generator is subjected to input mechanical power disturbance.
Simulation 3: three-phase short circuit disturbance
When t is 1s, the system generates three-phase short circuit disturbance, after 0.2s, the short circuit is cleared and the reclosing is successful, and the system has uncertain parameter disturbance. As can be seen from fig. 10-12, even though the generator terminal voltage and active power are recovered under the action of 2 control laws when the system is in large disturbance, LEPRC recovers the terminal voltage to the initial level faster than LOEC, and the swing of active power is subsided earlier. LEPRC can better than LOEC smooth system frequency oscillation, makes the system return to initial operating point more fast, has restrained the overshoot of system more effectively for the generator has good dynamic, static performance.
The simulation experiments show that the power system stability control method based on the probability robust random algorithm can enable a single-machine infinite power system to be stable under the conditions of disturbance and uncertain parameters. Simulation results show that the control law can effectively improve the robust performance of the system in the static and transient processes.
The above disclosure is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of changes or modifications within the technical scope of the present invention, and shall be covered by the scope of the present invention.
Claims (8)
1. A power system stability control method based on a probability robust random algorithm is characterized by comprising the following steps:
establishing a single-machine infinite system model;
carrying out linearization processing on the single-machine infinite system model, and replacing the system state variable of the single-machine infinite system model with a system state variable which is easy to measure;
giving a robust stability performance index to the processed single-machine infinite system model;
a scene probability random algorithm is adopted, a probability level is designated, uncertainty parameter samples in the single-machine infinite system model are extracted for calculation, and a control matrix is obtained according to a set degree;
and obtaining an excitation control rule of the single infinite system model according to the control matrix, so that the system of the single infinite system model is gradually stable and meets the robustness index.
2. The power system stability control method based on the probabilistic robust random algorithm of claim 1, wherein the single infinite system model is:
wherein ,
in the formula (1), delta, omega, E'qIs a system state variable, delta is the power angle of the generator, omega is the angular speed of the generator, E'qPost-transient-potential reactance, ω0For synchronous speed of the generator rotor, TJRepresenting the rotor inertia time constant, P, of the generatormFor input of mechanical power, PeFor the electromagnetic power of the generator, D is the damping coefficient of the generator, p1,p2,p3Is an uncertain parameter of the system, T'd0Representing the time constant, x, of the field winding during an open circuit of the stator of the generatorde＝xd+xe，xd and xqRespectively representing the direct-axis reactance and quadrature-axis reactance of the generator,
xT and xLRepresenting the reactance, x, on the transformer and line, respectivelyqe＝xq+xe，x′de＝x′d+xe，x′dIs a direct axis transient reactance; u shapefRepresenting generator terminal voltage, Ufq and UfdRespectively represent UfComponents on the q and d axes; u shapesIs on infinite busVoltage, EqeIs the control law of the system.
3. The power system stability control method based on the probabilistic robust random algorithm of claim 1, wherein the single infinite system model is linearized, and a system equation obtained by replacing the system state variables of the single infinite system model with system state variables easy to measure is:
in the formula (2), [ Delta P ]e,Δω,ΔUf]For easily measurable system state variables, Δ PeIs the active power deviation value, and delta omega is the angular velocity deviation value of the generator, delta UfAs terminal voltage offset value, Δ PmAs a deviation value of mechanical power, Uf0For initial operation terminal voltage, Ufd0Is Ufq0 and Uf0The components of the voltage of the initial running machine end in the d-axis and q-axis, K1，K2，K3，K4，K5，K6And β is expressed as:
4. the power system stability control method based on the probabilistic robust random algorithm of claim 1, wherein the robust stability performance index is:
in the formula (4), Δ is an uncertainty set of the system, and belongs to an uncertainty factor set T (Δ) allowed by the system, z is an output vector of the system, and x is a system state vector; dzuIs a matrix of constants corresponding to the dimensions of the control vector,
is DzuThe transposed vector of (a) is,
is reversible, and
u is a control vector, uTTransposed vector of u, CzA constant matrix corresponding to the dimension of the system state vector,
is CzThe transposed matrix of (2);
for any gamma >0, the control target is that a design matrix variable P >0 and a control rule u ═ Kx, K is a control vector, 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 e T (delta), the obtained final robust performance index J (delta) is as follows:
J(Δ)≤γ-1xTP-1x (5)
in the formula (5), γ is an arbitrary number larger than zero, and P is a matrix variable.
5. The power system stability control method based on the probabilistic robust random algorithm of claim 1, wherein the method comprises the following steps of adopting a scene probabilistic random algorithm, assigning a probability level, calculating by extracting uncertainty parameter samples in the single machine infinite system model, and obtaining a control matrix according to a set degree:
given a probability level of p*Xi e (0,1), random algorithmThe step number N is selected as follows:
in formula (7), n is the system dimension;
the objective equation of the stochastic algorithm is-trace (P), where P is the control matrix,
the constraints of the stochastic algorithm are:
the control matrix P is solved with a confidence level of at least 1- ξ as a function of the set parameters.
6. The power system stability control method based on the probabilistic robust random algorithm of claim 1, wherein the excitation control law is as follows:
in the formula (9), u is an excitation control vector, P-1Is the inverse of the control matrix; and the matrix P is controlled such that a matrix inequality is true for all Δ ∈ T (Δ), the matrix inequality being as follows:
7. the power system stability control method based on the probabilistic robust stochastic algorithm of claim 1, wherein the scene probabilistic stochastic scene algorithm calculation is performed by a stochastic algorithm tool box, RACT.
8. The power system stability control method based on the probabilistic robust random algorithm of claim 1, wherein a robust controller is constructed according to the excitation control law, and the single infinite system model is controlled by the robust controller.
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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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| 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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