CN113642143A - Power system control method and device, computer equipment and storage medium - Google Patents
Power system control method and device, computer equipment and storage medium Download PDFInfo
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Abstract
The application relates to a power system control method, a device, computer equipment and a storage medium, wherein the power system control method comprises the steps of monitoring an operation numerical value of a power parameter to be processed in a power system, if the monitored operation numerical value is larger than a preset threshold value, obtaining a linearized fuzzy state model corresponding to the power parameter, obtaining a global observer fuzzy model according to a variable of the fuzzy state model, obtaining a target Lyapunov function corresponding to the power system by using the fuzzy state model and the global observer fuzzy model, obtaining a judgment matrix corresponding to the power parameter according to the target Lyapunov function, obtaining a matrix solution of the judgment matrix, adjusting the operation numerical value based on the matrix solution, enabling the judgment matrix to be in a preset range, and controlling the power system to recover normally. Compared with the traditional power system stability control technology, the scheme controls the power parameters by utilizing the matrix based on the Lyapunov function, so that the stability of the power system is improved.
Description
Technical Field
The present application relates to the field of power technologies, and in particular, to a method and an apparatus for controlling a power system, a computer device, and a storage medium.
Background
Today, as the new energy industry rapidly develops, more and more renewable energy sources and distributed micro-grids are incorporated into the main grid framework, and the randomness, volatility and uncertainty of the renewable energy sources and distributed micro-grids are extremely disadvantageous to the stability of the main grid. Therefore, how to control the stability of the main network and solve the problem of influencing the randomness and the volatility of the distributed micro-grid is very important for maintaining the normal operation of the power system. However, with respect to the increasingly complex power system structure, the conventional power system stability control technology has been difficult to meet the current demands.
Therefore, the existing control method for the power system has the defect of insufficient stability.
Disclosure of Invention
In view of the above, it is necessary to provide a power system control method, apparatus, computer device, and storage medium capable of improving the stability of a power system in view of the above technical problems.
A power system control method, the method comprising:
monitoring an operation numerical value of a power parameter to be processed in a power system, and if the operation numerical value is larger than a preset threshold value, acquiring a linearized fuzzy state model corresponding to the power parameter; acquiring a corresponding global observer fuzzy model according to the variable of the fuzzy state model;
acquiring a target Lyapunov function corresponding to the power system according to the fuzzy state model and the global observer fuzzy model;
acquiring a judgment matrix corresponding to the power parameter according to the target Lyapunov function;
and acquiring a matrix solution corresponding to the judgment matrix, and adjusting the operation value according to the matrix solution so as to enable the judgment matrix to be in a preset value range and control the power system to recover to be normal.
In one embodiment, the monitoring the operation value of the power parameter to be processed in the power system includes:
monitoring a rotor operating angle of a generator in the power system, a relative rotational speed of the generator, and a transient potential of the generator.
In one embodiment, the obtaining the linearized fuzzy state model corresponding to the power parameter includes:
acquiring a first mathematical model corresponding to the rotor operating angle according to the relative rotating speed;
acquiring a second mathematical model corresponding to the relative rotation speed according to the initial angular speed of the generator, the rotational inertia of the generator rotor, the mechanical power output by a motor in the power system, the transient potential, the sum of transient reactances of the generator, the bus voltage of the power system, the rotor operating angle, the relative rotation speed and the damping coefficient of the generator;
obtaining a third mathematical model corresponding to the transient electric potential according to a first time constant of an excitation winding when a stator winding in the power system is in a closed circuit, an axial transient reactance of the generator, the transient electric potential, the sum of the transient electric potentials, a second time constant of the excitation winding when the excitation winding is in the closed circuit, the bus voltage, the rotor operating angle and the excitation winding voltage of the excitation winding;
and carrying out linearization processing on the first mathematical model, the second mathematical model and the third mathematical model to obtain the fuzzy state model.
In one embodiment, the obtaining a corresponding global observer fuzzy model according to the variables of the fuzzy state model includes:
obtaining the global observer fuzzy model according to a first constant matrix in the fuzzy state model, a second constant matrix in the fuzzy state model, an output value in the fuzzy state model and an observer gain matrix; the first constant matrix is derived based on the damping coefficient, the moment of inertia, the initial angular velocity, the excitation winding voltage, the sum of the transient reactances, the transient reactance, the first time constant, the second time constant, and the bus voltage; the second constant matrix is derived based on the second time constant.
In one embodiment, the difference between a first input value of the fuzzy state model and a second input value of the global observer fuzzy model is used as the error of the global observer fuzzy model; taking the difference between the input threshold corresponding to the global observer fuzzy model and the second input value as a trigger threshold corresponding to the power system;
the obtaining of the target Lyapunov function corresponding to the power system according to the fuzzy state model and the global observer fuzzy model includes:
and respectively inputting the fuzzy state model and the global observer fuzzy model into a preset Lyapunov function according to the first input value of the fuzzy state model, the second input value of the global observer fuzzy model, the error and the trigger threshold value to obtain the target Lyapunov function.
In one embodiment, the obtaining, according to the target lyapunov function, a determination matrix corresponding to the power parameter includes:
and acquiring a judgment matrix corresponding to the power parameter according to the input items in the target Lyapunov function and the unit matrices with preset quantity.
In one embodiment, the determination matrix includes: a preset number of symmetric positive definite matrices;
the obtaining of the matrix solution corresponding to the judgment matrix and the adjusting of the operation value according to the matrix solution include:
obtaining the solution of the symmetrical positive definite matrixes with the preset number when the judgment matrix is smaller than zero;
and adjusting the running numerical value according to the demodulation of the preset number of the symmetrical positive definite matrixes.
A power system control apparatus, the apparatus comprising:
the monitoring module is used for monitoring the operation numerical value of the power parameter to be processed in the power system, and if the operation numerical value is larger than a preset threshold value, acquiring a linearized fuzzy state model corresponding to the power parameter; acquiring a corresponding global observer fuzzy model according to the variable of the fuzzy state model;
the first obtaining module is used for obtaining a target Lyapunov function corresponding to the electric power system according to the fuzzy state model and the global observer fuzzy model;
the second obtaining module is used for obtaining a judgment matrix corresponding to the power parameter according to the target Lyapunov function;
and the control module is used for acquiring a matrix solution corresponding to the judgment matrix and adjusting the operation value according to the matrix solution so as to enable the judgment matrix to be in a preset value range and control the power system to recover to be normal.
A computer device comprising a memory storing a computer program and a processor implementing the steps of the method described above when executing the computer program.
A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the above-mentioned method.
According to the power system control method, the power system control device, the computer equipment and the storage medium, the operation value of the power parameter to be processed in the power system is monitored, if the monitored operation value is larger than the preset threshold value, the linearized fuzzy state model corresponding to the power parameter is obtained, the global observer fuzzy model is obtained according to the variable of the fuzzy state model, the target Lyapunov function corresponding to the power system is obtained by using the fuzzy state model and the global observer fuzzy model, the judgment matrix corresponding to the power parameter is obtained according to the target Lyapunov function, the matrix solution of the judgment matrix is obtained, and therefore the operation value is adjusted based on the matrix solution, the judgment matrix is in the preset range, and the power system is controlled to be recovered to be normal. Compared with the traditional power system stability control technology, the scheme controls the power parameters by utilizing the matrix based on the Lyapunov function when the operation numerical value of the power parameters to be processed exceeds a specific threshold value, so that the stability of the power system is improved.
Drawings
FIG. 1 is a diagram of an exemplary power system control method;
FIG. 2 is a flow diagram illustrating a method for controlling a power system according to one embodiment;
FIG. 3 is a flow chart illustrating a method for controlling a power system according to another embodiment;
FIG. 4 is a diagram illustrating a state curve of a predetermined parameter according to an embodiment;
FIG. 5 is a schematic diagram of a power system control input in one embodiment;
FIG. 6 is a schematic diagram of an embodiment of a power system error response;
FIG. 7 is a schematic of an observer response of an electrical system in one embodiment;
FIG. 8 is a schematic diagram of an event trigger signal for a power system in one embodiment;
FIG. 9 is a block diagram showing the structure of a control device of the power system in one embodiment;
FIG. 10 is a diagram showing an internal structure of a computer device according to an embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
The power system control method provided by the application can be applied to the application environment shown in fig. 1. The terminal 102 may monitor an operation value of a power parameter to be processed in the power system, acquire a fuzzy state model corresponding to the power parameter when the operation value is greater than a preset threshold, acquire a global observer fuzzy model by using the fuzzy state model, acquire a target lyapunov function based on the fuzzy state model and the global observer fuzzy model, and acquire a judgment matrix corresponding to the power parameter by using the target lyapunov function, so that the operation value of the power parameter is adjusted based on a solution of the matrix, and the power system is recovered to be normal. Additionally, in some embodiments, a server 104 may also be included, with the terminal 102 communicating with the server 104 over a network. The terminal 102 may upload the adjusted operation value to the server 104 for storage, and may upload the operation value change process of the power parameter to be processed to the server 104 for storage. The terminal 102 may be, but is not limited to, various personal computers, notebook computers, smart phones, and tablet computers, and the server 104 may be implemented by an independent server or a server cluster formed by a plurality of servers.
In one embodiment, as shown in fig. 2, a power system control method is provided, which is described by taking the method as an example applied to the terminal in fig. 1, and includes the following steps:
step S202, monitoring an operation numerical value of an electric power parameter to be processed in the electric power system, and if the operation numerical value is larger than a preset threshold value, acquiring a linearized fuzzy state model corresponding to the electric power parameter; and acquiring a corresponding global observer fuzzy model according to the variable of the fuzzy state model.
The power system may be a system in which the terminal 102 is located, and the power system includes various power devices, such as a generator, a prime mover, and the like. Each electrical device in the electrical power system may have corresponding electrical parameters, e.g. a generator having corresponding electrical parameters for a generator and a prime mover having corresponding electrical parameters for a prime mover. The terminal 102 may monitor the operation values of the power parameters to be processed of each power device in the power system in which the terminal is located. For example, in some embodiments, the power parameter to be processed may include a rotor operating angle of the generator, a relative rotation speed of the generator, and a transient potential of the generator, and the terminal 102 monitors an operating value of the power parameter to be processed in the power system, including: rotor operating angles of the generators, relative rotational speeds of the generators, and transient potentials of the generators in the power system are monitored. In this embodiment, the terminal 102 may detect a rotor operating angle of a generator in the power system, a relative rotation speed of the generator, and a transient electric potential of the generator, where the rotor operating angle may be δ, the relative rotation speed may be ω, the transient electric potential may be a q-axis transient electric potential of the generator, and the q-axis transient electric potential may be E'q。
The terminal 102 may monitor the operation values of the power parameters, and if the terminal 102 detects that the operation values are greater than a preset threshold, for example, the power equipment receives interference, and the operation values of the corresponding power parameters fluctuate and are greater than the preset threshold, the terminal 102 may obtain a fuzzy state model corresponding to the power parameters, where the fuzzy state model may be a linearized model, for example, the terminal 102 may first establish a mathematical model of a power system according to the power parameters and perform linearization by using the mathematical model, so as to obtain a fuzzy state model corresponding to the power parameters. The mathematical model and the fuzzy state model may each include a plurality of functions. In addition, the terminal 102 may further obtain a global observer fuzzy model corresponding to the power parameter by using the fuzzy state model, for example, the terminal 102 may obtain the global observer fuzzy model by using an output value of the fuzzy state model. The global observer fuzzy model can be used for observing output values of various power parameters in the power system.
And step S204, acquiring a target Lyapunov function corresponding to the power system according to the fuzzy state model and the global observer fuzzy model.
The fuzzy state model and the global observer fuzzy model can be models corresponding to the electric power parameters, the fuzzy state model and the global observer fuzzy model can comprise input values and output values, and the fuzzy state model and the global observer fuzzy model can further comprise a plurality of constant value matrixes and functions of a fuzzy controller. Wherein the fuzzy controller may be operable to limit the operational values of the respective power parameters in the power system. The terminal 102 may obtain a target lyapunov function corresponding to the power system by using each function in the fuzzy state model and the global observer fuzzy model. For example, the terminal 102 may first acquire the observer error and event-triggered form of the power system described above using the input values in the fuzzy state model and the global observer fuzzy model. The observer error may be an error between an operating value of the power parameter obtained by the terminal 102 using each model and an operating value of the actual power parameter; the event trigger form may be a function for triggering the terminal 102 to perform the power system control method described above. The terminal 102 may obtain the target lyapunov function through a lyapunov standard formal function by using the fuzzy state model, the global observer fuzzy model, the observer error, and the event trigger form. For example, the terminal 102 may substitute each parameter in the fuzzy state model, the global observer fuzzy model, the observer error, and the event trigger form into each corresponding parameter in the standard form function of lyapunov, thereby obtaining the target lyapunov function.
And S206, acquiring a judgment matrix corresponding to the power parameter according to the target Lyapunov function.
The lyapunov function may be a function describing the stability of lyapunov, and may be used to describe the stability of a powertrain system. If the trajectory of any initial condition of the power system near the equilibrium state can be maintained near the equilibrium state, the power system can be called at-lyapunov stable, and the stability of the system can be proved without knowing the actual energy of the system by using the lyapunov analysis mode. The target lyapunov function can be obtained based on various parameters in the fuzzy state model and the global observer fuzzy model, namely various variables in the model. The terminal 102 may obtain the determination matrix corresponding to the power parameter based on the target lyapunov function. For example, the terminal 102 may use the combination of the variables in the target lyapunov function to form a corresponding matrix, and make the matrix within a preset value range, so as to obtain the above-mentioned determination matrix. The determination matrix may be a matrix for adjusting each to-be-processed power parameter in the power system.
And S208, acquiring a matrix solution corresponding to the judgment matrix, and demodulating an integral operation value according to the matrix so as to enable the judgment matrix to be in a preset value range and control the power system to recover to be normal.
The determination matrix may be a matrix obtained based on the target lyapunov function, and the terminal 102 may adjust the operation value of the power parameter by using the determination matrix, so that the operation value is restored to a normal range, and the power system is restored to a normal state. For example, the terminal 102 may solve the determination matrix to obtain a corresponding matrix solution, and adjust the operation value corresponding to the power parameter based on the matrix solution. The matrix solution may be a matrix, for example, a positive definite matrix. In linear algebra, the property of a positive definite matrix is similar to the positive real number in a complex number. The positive definite matrix definition may be: let M be an n-th order square matrix, if for any non-zero vector z, there is zTMz > 0, wherein zTTranspose representing z, so-calledM is a positive definite matrix.
In the power system control method, the operation value of the power parameter to be processed in the power system is monitored, if the monitored operation value is larger than a preset threshold value, a linearized fuzzy state model corresponding to the power parameter is obtained, a global observer fuzzy model is obtained according to variables of the fuzzy state model, a target Lyapunov function corresponding to the power system is obtained by using the fuzzy state model and the global observer fuzzy model, a judgment matrix corresponding to the power parameter is obtained according to the target Lyapunov function, and a matrix solution of the judgment matrix is obtained, so that the operation value is adjusted based on the matrix solution, the judgment matrix is in a preset range, and the power system is controlled to be recovered to be normal. Compared with the traditional power system stability control technology, the scheme controls the power parameters by utilizing the matrix based on the Lyapunov function when the operation numerical value of the power parameters to be processed exceeds a specific threshold value, so that the stability of the power system is improved.
In one embodiment, obtaining a linearized fuzzy state model corresponding to the power parameter includes: acquiring a first mathematical model corresponding to the rotor operating angle according to the relative rotating speed; acquiring a second mathematical model corresponding to the relative rotating speed according to the initial angular speed of the generator, the rotational inertia of a generator rotor, the mechanical power output by a motor in the power system, the transient potential, the sum of transient reactances of the generator, the bus voltage of the power system, the rotor operating angle, the relative rotating speed and the damping coefficient of the generator; acquiring a third mathematical model corresponding to the transient potential according to a first time constant of an excitation winding when a stator winding is closed in the power system, a shaft transient reactance of a generator, the transient potential, the sum of the transient reactances, a second time constant of the excitation winding when the excitation winding is closed, a bus voltage, a rotor operating angle and the excitation winding voltage of the excitation winding; and carrying out linearization processing on the first mathematical model, the second mathematical model and the third mathematical model to obtain a fuzzy state model.
In this embodiment, the power parameter may be a power parameter to be processed in the power system. The terminal 102 may use the power parameter to obtain the correspondenceThe fuzzy state model of (1). For example, the terminal 102 may first obtain a mathematical model corresponding to the power parameter, where the mathematical model may include a plurality of functions. The power parameters to be processed can comprise a rotor operation angle delta of the generator, a relative rotation speed omega of the generator and a q-axis transient potential E 'of the generator'q. The terminal 102 may obtain a first mathematical model corresponding to the rotor operating angle by using the relative rotation speed; the terminal 102 may further obtain a second mathematical model corresponding to the relative rotation speed by using the initial angular velocity of the generator, the rotational inertia of the generator rotor, the mechanical power output by the motor in the power system, the above transient potential, the sum of the transient reactances of the generator, the bus voltage in the power system, the rotor operating angle, the relative rotation speed, and the damping coefficient of the generator; the terminal 102 may further obtain a third mathematical model corresponding to the transient electric potential by using a first time constant of the excitation winding when the stator winding is in a closed circuit in the power system, a transient reactance of the shaft of the generator, the transient electric potential, a sum of the transient electric potentials, a second time constant of the excitation winding when the excitation winding is in a closed circuit, a bus voltage, the rotor operating angle, and an excitation winding voltage of the excitation winding. The stator winding refers to a winding mounted on the stator, namely a copper wire wound on the stator; the field winding is a coil winding that can generate a magnetic field. Generally, in motors and generators, there are series excitation and shunt excitation. The excitation winding used in the generator can replace a permanent magnet, can generate strong magnetic flux density which can not be generated by the permanent magnet, and can be conveniently adjusted, thereby realizing high-power generation. The terminal 102 may obtain a mathematical model corresponding to the power parameter to be processed in the power system based on the first mathematical model, the second mathematical model, and the third mathematical model.
Specifically, the power system may be a stand-alone power system, and the mathematical model of the stand-alone power system may be as follows:
wherein the content of the first and second substances,
may be the first mathematical model described above;
may be the second mathematical model described above;
may be the third mathematical model described above; delta is the running angle of the generator rotor; omega is the relative rotating speed of the generator; e'qIs a generator q-axis transient potential; omega0Is the initial angular velocity of the generator; h is the rotational inertia of the generator rotor; pmMechanical power output by the prime mover; vsInfinite bus voltage; x'dzIs the sum of transient reactances of the generators; d is a damping coefficient of the generator; t'doThe time constant of the excitation winding when the stator winding is closed; x'dIs a generator shaft transient reactance; t isdoIs the time constant of the exciting winding when the exciting winding is closed; vfFor the field winding voltage, a control variable is defined.
After obtaining the mathematical model, the terminal 102 may perform linearization on the mathematical model to obtain a fuzzy state model. For example, the terminal 102 may linearize the mathematical model of the single-machine power system to obtain a global fuzzy state model, which may be represented by the following equation:
wherein the content of the first and second substances,
E1＝[8 0.1 0.9]；E2＝[5 0.5 0.6]，1＝1，2；h＝1，2；Ghrepresenting a fuzzy control gain matrix. Wherein, alpha is-D/H, beta is-omega0Vs/Hx′dz，ζ＝(xd-x′d)Vs/Tdox′dz，η＝-1/T′do，λ＝1/Tdo。a1＝-0.01，a2＝-0.02，
And the number of the first and second electrodes,
x(t)＝[x1(t) x2(t) x3(t)]Tis a state variable; x is the number of1(t) ═ δ (t), a variable corresponding to the rotor running angle; x is the number of2(t) ═ ω (t), a variable corresponding to the above-described relative rotational speed; x is the number of3(t)＝E′q(t) a variable corresponding to the transient potential, A1、B1May be a constant matrix of appropriate dimensions, u (t) VfMay be a suitable fuzzy controller, and the power system related parameters are controlled and changed.
Through the embodiment, the terminal 102 can utilize the mathematical model of the single-machine power system to carry out linearization to obtain the corresponding fuzzy state model, so that the stability of the power system is controlled based on the fuzzy state model, and the stability of the power system is improved.
In one embodiment, further comprising: obtaining a corresponding global observer fuzzy model according to variables of the fuzzy state model, wherein the steps comprise: obtaining a global observer fuzzy model according to a first constant matrix in the fuzzy state model, a second constant matrix in the fuzzy state model, an output value in the fuzzy state model and an observer gain matrix; the first constant matrix is obtained based on a damping coefficient, a rotational inertia, an initial angular velocity, an excitation winding voltage, a sum of transient reactances, the transient reactances, a first time constant, a second time constant and a bus voltage; the second constant matrix is derived based on the second time constant.
In this embodiment, the fuzzy state model may beA model based on mathematical models of various power parameters in the power system. The terminal 102 may derive a global observer fuzzy model based on various variables in the fuzzy state model described above. For example, the terminal 102 may obtain the global observer fuzzy model based on the first constant matrix in the fuzzy state model, the second constant matrix in the fuzzy model, the output value of the fuzzy state model, and the observer gain matrix. The first constant matrix may be a1, as can be seen from the a1 matrix, and is obtained based on the damping coefficient, the moment of inertia, the initial angular velocity, the voltage of the excitation winding, the sum of the transient reactances, the first time constant, the second time constant, and the bus voltage; the second constant matrix may be B1From the above B1As can be seen, the second constant matrix is obtained based on the second time constant.
Specifically, the global observer fuzzy model may be as follows:
wherein L islIs an observer gain matrix;
representing the input of a fuzzy observer, y (t) and
respectively representing the output of the fuzzy system and the output of the fuzzy observer. The global observer fuzzy model may be used to observe the output value of the stand-alone power system.
Where u (t) may be an input value in a controller for the power system, a specific expression of the controller may be as follows:
wherein, KhThe static gain feedback matrix is controlled for 1 x 3 dimensions corresponding to the fuzzy rule.
Through the embodiment, the terminal 102 can obtain the global observer fuzzy model by using each variable in the fuzzy state model, so that the terminal 102 can perform stability control on the electric power system by using the global observer fuzzy model, and the stability of the electric power system is improved.
In one embodiment, acquiring a target lyapunov function corresponding to an electric power system according to a fuzzy state model and a global observer fuzzy model includes: and respectively inputting the fuzzy state model and the global observer fuzzy model into a preset Lyapunov function according to the first input value of the fuzzy state model, the second input value of the global observer fuzzy model, the error and the trigger threshold value to obtain a target Lyapunov function.
In this embodiment, the terminal 102 may obtain the target lyapunov function corresponding to the power system by using the fuzzy state model and the global observer fuzzy model, for example, the terminal 102 may input the lyapunov function in a standard form by using variables in the fuzzy state model and the global observer fuzzy model, such as a first input value in the fuzzy state model, a second input value in the global observer fuzzy model, and the like, so as to obtain the target lyapunov function. Wherein, in one embodiment, further comprising: taking the difference between the first input value of the fuzzy state model and the second input value of the global observer fuzzy model as the error of the global observer fuzzy model; and taking the difference between the input threshold corresponding to the global observer fuzzy model and the second input value as a trigger threshold corresponding to the power system. In this embodiment, the terminal 102 may obtain an error of the global observer fuzzy model by using a difference between a first input value of the fuzzy state model and a second input value of the global observer fuzzy model, and a specific formula thereof may be as follows:
the terminal 102 may further obtain a trigger threshold corresponding to the power system by using a difference between an input threshold corresponding to the global observer fuzzy model and the second input value, where a specific formula of the trigger threshold may be as follows:
wherein k is a positive integer in the event-triggered sampling pattern, and represents the number of event triggers. tk is a discrete time transition form of the event trigger, i.e., the time at the kth event trigger. In addition, the terminal 102 may also define trigger conditions for the power system
Where ρ is a dimensionless number set by the user to adjust the trigger condition. The terminal 102 may be at the power parameter x described aboveiAnd (t) when the expression of the trigger condition is met, starting to execute the control flow of the power system.
The terminal 102 may use the lyapunov theory to obtain the target lyapunov function of the above single-machine power system stability criterion. For example, the expression of the preset lyapunov function may be as follows:
the expression may be a standard form of expression for the lyapunov function. The terminal 102 may substitute each parameter of the fuzzy state model, the global observer fuzzy model, the error, and the trigger threshold into the preset lyapunov function to obtain a target lyapunov function, where the target lyapunov function may be as follows:
wherein, Px、PeAre all 3 x 3 dimensional symmetric positive definite matrixes. The symmetric positive definite matrix can be, let A ∈ Rn×nIf A is ═ ATFor any 0 ≠ X ∈ RnAll have XTAX > 0, then A is called a symmetric positive definite matrix.
Through the embodiment, the terminal 102 can obtain the target lyapunov function by using the variables in the fuzzy state model and the global observer model and the transformation relation between the error and the trigger threshold, so that the terminal 102 can perform stability adjustment on the power system by using the target lyapunov function, and the stability of the power system is improved.
In one embodiment, obtaining a judgment matrix corresponding to a power parameter according to a target lyapunov function includes: and acquiring a judgment matrix corresponding to the power parameters according to the input items in the target Lyapunov function and the unit matrices with preset quantity.
In this embodiment, the terminal 102 may obtain a determination matrix for adjusting parameters of the power system by using the target lyapunov function. For example, the terminal 102 may obtain the determination matrix corresponding to the power parameter by using the input items in the target lyapunov function, that is, each variable in the target lyapunov function and a preset number of unit matrices. For example, the terminal 102 may add or subtract corresponding variables in the target lyapunov function as one item in the determination matrix, and use the identity matrix as the remaining item in the determination matrix, thereby forming the determination matrix.
The terminal 102 may further solve the determination matrix, and in one embodiment, obtaining a matrix solution corresponding to the determination matrix, and adjusting the operation value according to the matrix solution includes: obtaining solutions of a preset number of symmetric positive definite matrixes when the judgment matrix is smaller than zero; and adjusting the running numerical value according to the demodulation of the symmetrical positive definite matrixes with the preset number. In this embodiment, the determination matrix may include a symmetric positive definite matrix, and the terminal 102 solves the determination matrix, that is, solves the symmetric positive definite matrix. For example, the terminal 102 may use the schuler theorem to find the linear matrix inequality of the stability squat function in the stand-alone power system, i.e., the above-mentioned decision matrix, which may be expressed as follows:
wherein the content of the first and second substances,
Hh＝KhQx，l＝1，2，h＝1，2，K1、K2for a 1-by-3-dimensional control gain matrix, P, corresponding to the respective fuzzy rulex、PeAre all 3 x 3 dimensional symmetric positive definite matrixes.
The terminal 102 may solve the matrix, for example, when the determination matrix is smaller than zero, the terminal 102 may obtain a solution of a symmetric positive definite matrix in the determination matrix, and adjust an operation value corresponding to the power parameter based on the demodulation of the symmetric positive definite matrix, thereby controlling the power system to return to normal. Wherein, there may be two symmetric positive definite matrixes.
Through the embodiment, the terminal 102 can adjust the operation value of the power system by using the solution of the judgment matrix, so that the stability of the power system is improved.
In one embodiment, as shown in fig. 3, fig. 3 is a schematic flow chart of a power system control method in another embodiment. The method comprises the following steps: the power system may be a stand-alone power system, and the terminal 102 may first establish a mathematical model of the stand-alone power system, then linearize the mathematical model of the stand-alone power system to obtain a global fuzzy state model, and design a fuzzy model of a global observer of a stand-alone power system stability control method; designing a controller of a single-machine electric power system stability control method; defining observer error for stand-alone electrical power systems
And time triggered form
And obtaining a Lyapunov function expression corresponding to the stand-alone power system and used for stability judgment, namely the target Lyapunov function, according to the Lyapunov theory. The terminal 102 may also use the Schur (Schur) theorem to obtain a stability criterion function of the stand-alone power system, that is, a linear matrix inequality corresponding to the target lyapunov function, that is, the determination matrix. Therefore, the terminal 102 may solve based on the determination matrix, adjust the power parameter of the power system, and control the power system to return to normal after receiving the interference. The schur theorem is derived from one of the theorems of numbers, which was published in 1916 by schur (i.schur), and it can be known that there is a minimum integer Sn, so that {1, 2, …, Sn } is arbitrarily divided into n subsets S1, S2, …, Sn, and there is a Si containing x, y, z, satisfying x + y ═ z, and this minimum is called the schur number.
The following experiments were carried out with a single-machine power system, and the main technical performance indicators and equipment parameters were selected as follows: d0.15, H12.9, Vs 1, Td0＝6.45，T′d0＝1.2，xd＝0.83，x′d＝0.105，x′d∑＝0.16，ω0314.154. Then, the first constant matrix can be obtained as follows:
the second constant matrix is:
the 1 x 3 dimensional control static gain feedback matrix corresponding to the fuzzy rule in the fuzzy time trigger controller is as follows:
K1＝[-9.58 -23.29 -11.93]
K2＝[-9.77 -23.67 -12.24]；
therefore, the terminal 102 can obtain a symmetric positive definite matrix in the judgment matrix according to the parameters and the data as follows:
the observer matrix is thus:
the terminal 102 may set the initial condition of the power system to x0＝[0.1 0.31 0.41]T，
As shown in fig. 4, fig. 4 is a schematic diagram of a state curve of a preset parameter in an embodiment. Various parameters in the system, e.g. X as mentioned above1(t)402、X2(t)404、X3(t)406 after being disturbed, the stability is gradually stabilized after being controlled by the method, the original state is basically recovered within 6s after being disturbed, and the stability is completely recovered within about 10 s. Referring again to fig. 5, fig. 5 is a schematic diagram of the control inputs of the power system in one embodiment. The control input of the power system is basically restored to the original state within 2s, and is completely restored to the original state within about 4 s. As shown in fig. 6, fig. 6 is a schematic diagram of an error response of a power system in one embodiment. It can be seen that the error of the power system is basically eliminated in about 2s, which proves that the power system has stronger stability. As shown in fig. 7, fig. 7 is a schematic diagram of an observer response of an electrical power system in one embodiment. Comparing fig. 4, it can be seen that the behavior of the corresponding curve of the state is similar, which proves the effectiveness of the observer. Referring again to fig. 8, fig. 8 is a schematic diagram of an event trigger signal of the power system in one embodiment. The terminal 102 may also define trigger conditions for the power system
It can be seen that after the power system triggers the start-up control flow, 2And the control is basically finished within s, and the state is completely restored to the original state about 6 s.
With the above-described embodiment, the terminal 102 controls the power parameter by using the matrix based on the lyapunov function, thereby improving the stability of the power system.
It should be understood that although the various steps in the flowcharts of fig. 2-3 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least some of the steps in fig. 2-3 may include multiple steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, which are not necessarily performed in sequence, but may be performed in turn or alternately with other steps or at least some of the other steps or stages.
In one embodiment, as shown in fig. 9, there is provided a power system control device including: a monitoring module 500, a first acquisition module 502, a second acquisition module 504, and a control module 506, wherein:
the monitoring module 500 is configured to monitor an operation value of a power parameter to be processed in the power system, and if the operation value is greater than a preset threshold, obtain a linearized fuzzy state model corresponding to the power parameter; and acquiring a corresponding global observer fuzzy model according to the variable of the fuzzy state model.
The first obtaining module 502 is configured to obtain a target lyapunov function corresponding to the power system according to the fuzzy state model and the global observer fuzzy model.
The second obtaining module 504 is configured to obtain a determination matrix corresponding to the power parameter according to the target lyapunov function.
The control module 506 is configured to obtain a matrix solution corresponding to the determination matrix, and demodulate an integral operation value according to the matrix solution, so that the determination matrix is within a preset value range, and the power system is controlled to return to normal.
In one embodiment, the monitoring module 500 is specifically configured to monitor a rotor operating angle of a generator, a relative rotational speed of the generator, and a transient potential of the generator in a power system.
In an embodiment, the monitoring module 500 is specifically configured to obtain a first mathematical model corresponding to a rotor operating angle according to the relative rotation speed; acquiring a second mathematical model corresponding to the relative rotating speed according to the initial angular speed of the generator, the rotational inertia of a generator rotor, the mechanical power output by a motor in the power system, the transient potential, the sum of transient reactances of the generator, the bus voltage of the power system, the rotor operating angle, the relative rotating speed and the damping coefficient of the generator; acquiring a third mathematical model corresponding to the transient potential according to a first time constant of an excitation winding when a stator winding is closed in the power system, a shaft transient reactance of a generator, the transient potential, the sum of the transient reactances, a second time constant of the excitation winding when the excitation winding is closed, a bus voltage, a rotor operating angle and the excitation winding voltage of the excitation winding; and carrying out linearization processing on the first mathematical model, the second mathematical model and the third mathematical model to obtain a fuzzy state model.
In an embodiment, the monitoring module 500 is specifically configured to obtain a global observer fuzzy model according to a first constant matrix in the fuzzy state model, a second constant matrix in the fuzzy state model, an output value in the fuzzy state model, and an observer gain matrix; the first constant matrix is obtained based on a damping coefficient, a rotational inertia, an initial angular velocity, an excitation winding voltage, a sum of transient reactances, the transient reactances, a first time constant, a second time constant and a bus voltage; the second constant matrix is derived based on the second time constant.
In an embodiment, the first obtaining module 502 is specifically configured to input the fuzzy state model and the global observer fuzzy model into preset lyapunov functions respectively according to a first input value of the fuzzy state model, a second input value of the global observer fuzzy model, an error, and a trigger threshold, so as to obtain the target lyapunov function.
In an embodiment, the second obtaining module 504 is specifically configured to obtain the determination matrix corresponding to the power parameter according to the input items in the target lyapunov function and a preset number of identity matrices.
In an embodiment, the control module 506 is specifically configured to obtain solutions of a preset number of symmetric positive definite matrices when the determination matrix is smaller than zero; and adjusting the running numerical value according to the demodulation of the symmetrical positive definite matrixes with the preset number.
For specific limitations of the power system control device, reference may be made to the above limitations of the power system control method, which are not described herein again. The respective modules in the above power system control apparatus may be entirely or partially implemented by software, hardware, and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as shown in fig. 10. The computer device includes a processor, a memory, a communication interface, a display screen, and an input device connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The communication interface of the computer device is used for carrying out wired or wireless communication with an external terminal, and the wireless communication can be realized through WIFI, an operator network, NFC (near field communication) or other technologies. The computer program is executed by a processor to implement a power system control method. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, a key, a track ball or a touch pad arranged on the shell of the computer equipment, an external keyboard, a touch pad or a mouse and the like.
Those skilled in the art will appreciate that the architecture shown in fig. 10 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a computer device is provided, comprising a memory having a computer program stored therein and a processor that implements the power system control method described above when the processor executes the computer program.
In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements the power system control method described above.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database or other medium used in the embodiments provided herein can include at least one of non-volatile and volatile memory. Non-volatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical storage, or the like. Volatile Memory can include Random Access Memory (RAM) or external cache Memory. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM), among others.
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. A power system control method, the method comprising:
monitoring an operation numerical value of a power parameter to be processed in a power system, and if the operation numerical value is larger than a preset threshold value, acquiring a linearized fuzzy state model corresponding to the power parameter; acquiring a corresponding global observer fuzzy model according to the variable of the fuzzy state model;
acquiring a target Lyapunov function corresponding to the power system according to the fuzzy state model and the global observer fuzzy model;
acquiring a judgment matrix corresponding to the power parameter according to the target Lyapunov function;
and acquiring a matrix solution corresponding to the judgment matrix, and adjusting the operation value according to the matrix solution so as to enable the judgment matrix to be in a preset value range and control the power system to recover to be normal.
2. The method of claim 1, wherein monitoring the operational value of the power parameter to be processed in the power system comprises:
monitoring a rotor operating angle of a generator in the power system, a relative rotational speed of the generator, and a transient potential of the generator.
3. The method of claim 2, wherein the obtaining the linearized fuzzy state model corresponding to the power parameter comprises:
acquiring a first mathematical model corresponding to the rotor operating angle according to the relative rotating speed;
acquiring a second mathematical model corresponding to the relative rotation speed according to the initial angular speed of the generator, the rotational inertia of the generator rotor, the mechanical power output by a motor in the power system, the transient potential, the sum of transient reactances of the generator, the bus voltage of the power system, the rotor operating angle, the relative rotation speed and the damping coefficient of the generator;
obtaining a third mathematical model corresponding to the transient electric potential according to a first time constant of an excitation winding when a stator winding in the power system is in a closed circuit, an axial transient reactance of the generator, the transient electric potential, the sum of the transient electric potentials, a second time constant of the excitation winding when the excitation winding is in the closed circuit, the bus voltage, the rotor operating angle and the excitation winding voltage of the excitation winding;
and carrying out linearization processing on the first mathematical model, the second mathematical model and the third mathematical model to obtain the fuzzy state model.
4. The method of claim 3, wherein obtaining a corresponding global observer fuzzy model based on the variables of the fuzzy state model comprises:
obtaining the global observer fuzzy model according to a first constant matrix in the fuzzy state model, a second constant matrix in the fuzzy state model, an output value in the fuzzy state model and an observer gain matrix; the first constant matrix is derived based on the damping coefficient, the moment of inertia, the initial angular velocity, the excitation winding voltage, the sum of the transient reactances, the transient reactance, the first time constant, the second time constant, and the bus voltage; the second constant matrix is derived based on the second time constant.
5. The method of claim 1, further comprising: taking the difference between a first input value of the fuzzy state model and a second input value of the global observer fuzzy model as the error of the global observer fuzzy model; taking the difference between the input threshold corresponding to the global observer fuzzy model and the second input value as a trigger threshold corresponding to the power system;
the obtaining of the target Lyapunov function corresponding to the power system according to the fuzzy state model and the global observer fuzzy model includes:
and respectively inputting the fuzzy state model and the global observer fuzzy model into a preset Lyapunov function according to the first input value of the fuzzy state model, the second input value of the global observer fuzzy model, the error and the trigger threshold value to obtain the target Lyapunov function.
6. The method according to claim 1, wherein the obtaining a determination matrix corresponding to the power parameter according to the target lyapunov function includes:
and acquiring a judgment matrix corresponding to the power parameter according to the input items in the target Lyapunov function and the unit matrices with preset quantity.
7. The method of claim 1, wherein the determining matrix comprises: a preset number of symmetric positive definite matrices;
the obtaining of the matrix solution corresponding to the judgment matrix and the adjusting of the operation value according to the matrix solution include:
obtaining the solution of the symmetrical positive definite matrixes with the preset number when the judgment matrix is smaller than zero;
and adjusting the running numerical value according to the demodulation of the preset number of the symmetrical positive definite matrixes.
8. An electric power system control apparatus, characterized in that the apparatus comprises:
the monitoring module is used for monitoring the operation numerical value of the power parameter to be processed in the power system, and if the operation numerical value is larger than a preset threshold value, acquiring a linearized fuzzy state model corresponding to the power parameter; acquiring a corresponding global observer fuzzy model according to the variable of the fuzzy state model;
the first obtaining module is used for obtaining a target Lyapunov function corresponding to the electric power system according to the fuzzy state model and the global observer fuzzy model;
the second obtaining module is used for obtaining a judgment matrix corresponding to the power parameter according to the target Lyapunov function;
and the control module is used for acquiring a matrix solution corresponding to the judgment matrix and adjusting the operation value according to the matrix solution so as to enable the judgment matrix to be in a preset value range and control the power system to recover to be normal.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor, when executing the computer program, implements the steps of the method of any of claims 1 to 7.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 7.
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