CN117200260B - Method and system for inhibiting low-frequency oscillation of power system - Google Patents
Method and system for inhibiting low-frequency oscillation of power system Download PDFInfo
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Abstract
The invention discloses a method and a system for inhibiting low-frequency oscillation of a power system, wherein the method comprises the following steps: constructing a grid-connected model of the wind storage system; respectively carrying out low-frequency oscillation adjustment and frequency stability adjustment on the grid-connected model according to the power oscillation damper and the proportional integral controller to obtain gain parameters and proportional parameters; performing compensation control on the synchronous generator based on a preset virtual inertia compensation control strategy to obtain virtual inertia corresponding to the grid-connected sub-model of the energy storage device; constructing a Pareto optimal solution set, and performing multi-objective optimization on the Pareto optimal solution set based on an improved particle swarm algorithm to obtain an optimal gain parameter, an optimal proportion parameter and an optimal virtual inertia; and inputting the optimal gain parameter, the optimal proportion parameter and the optimal virtual inertia into the power system, so that the power system is subjected to low-frequency oscillation suppression. The power oscillation damper is controlled to reduce oscillation amplitude better, and has stronger capability of quickly recovering a power angle, so that a good low-frequency oscillation suppression effect is achieved.
Description
Technical Field
The invention belongs to the technical field of wind-storage combined system control, and particularly relates to a method and a system for inhibiting low-frequency oscillation of a power system.
Background
Low frequency oscillation suppression for an electrical power system is an important task to ensure stable operation of the electrical power system. On the basis of the existing researches, the low-frequency oscillation can be suppressed by a Power System Stabilizer (PSS), a Static Var Compensator (SVC), and the like. However, how to consider the synergy of various control strategies and devices for a specific power system is an important point and difficulty of research. On the basis of wind power integration, the traditional stabilizer can not effectively inhibit oscillation caused by wind power integration. On the premise of effective inhibition, the response speed must also be considered, and the control strategy should be fast adapted to the change and disturbance of the system.
The existing literature is still more general and fuzzy in discussion of modeling of wind turbine generator, vibration influencing factors, classification of damping controllers and advantages and disadvantages of control strategies when low-frequency vibration of a new energy grid-connected system is researched.
The traditional power oscillation damper is slower in energy storage response speed, and is difficult to ensure the frequency stability of the system under different low-frequency oscillation conditions. The existing research utilizes damping torque to analyze how dynamic interaction between DFIGs affects system low-frequency oscillation, and a designed phase-locked loop of a wind turbine generator has an influence on grid stability.
The virtual inertia calculation problem is studied in the prior art, but the researches only define the virtual inertia of the wind storage system, and no further analytic solution is performed. In addition, the characteristics and influencing factors of the wind-storage virtual inertia are not discussed. More importantly, these studies fail to establish a correlation between the virtual inertia of the stored energy and its operating conditions and control parameters. This would not be as effective as would be expected to assist the stored energy in stabilizing the low frequency oscillations.
In the algorithm level, although the mathematical foundation of the traditional optimization algorithm is perfect, the programming difficulty is high, the setting of a plurality of parameters is not proper enough, and the global optimal solution is difficult to find. The search efficiency for the high-dimensional problem is insufficient. And the method is easy to sink into local optimum, and is not easy to prove the optimality.
Disclosure of Invention
The invention provides a method and a system for inhibiting low-frequency oscillation of a power system, which are used for solving the technical problem that energy storage participates in the low-frequency oscillation and is poor in stability in the prior art.
In a first aspect, the present invention provides a method of suppressing low frequency oscillations of an electrical power system connected in parallel with a wind energy storage system and a synchronous generator, respectively, the method comprising:
constructing a grid-connected model of a wind power storage system, wherein the grid-connected model comprises a wind power plant grid-connected sub-model and an energy storage device grid-connected sub-model;
respectively carrying out low-frequency oscillation adjustment on a wind power plant grid-connected sub-model and frequency stability adjustment on an energy storage device grid-connected sub-model according to a power oscillation damper and a proportional integral controller to obtain gain parameters and proportional parameters of the wind storage system;
performing compensation control on the synchronous generator based on a preset virtual inertia compensation control strategy to obtain virtual inertia corresponding to the energy storage device grid-connected sub-model;
constructing a Pareto optimal solution set according to the gain parameter, the proportion parameter and the virtual inertia, and performing multi-objective optimization on the Pareto optimal solution set based on an improved particle swarm algorithm to obtain an optimal gain parameter, an optimal proportion parameter and an optimal virtual inertia;
and inputting the optimal gain parameter, the optimal proportion parameter and the optimal virtual inertia into a power system, so that the power system is subjected to low-frequency oscillation suppression.
In a second aspect, the present invention provides a system for suppressing low frequency oscillations of an electric power system, the electric power system being connected in parallel with a wind energy storage system and a synchronous generator, respectively, the system comprising:
the system comprises a building module, a storage module and a control module, wherein the building module is configured to build a grid-connected model of a wind power plant grid-connected sub-model and an energy storage device grid-connected sub-model;
the adjusting module is configured to respectively carry out low-frequency oscillation adjustment on the wind power plant grid-connected sub-model and frequency stability adjustment on the energy storage device grid-connected sub-model according to the power oscillation damper and the proportional integral controller to obtain gain parameters and proportional parameters of the wind storage system;
the compensation control module is configured to carry out compensation control on the synchronous generator based on a preset virtual inertia compensation control strategy to obtain virtual inertia corresponding to the energy storage device grid-connected sub-model;
the optimization module is configured to construct a Pareto optimal solution set according to the gain parameter, the proportion parameter and the virtual inertia, and carry out multi-objective optimization on the Pareto optimal solution set based on an improved particle swarm algorithm to obtain an optimal gain parameter, an optimal proportion parameter and an optimal virtual inertia;
and the input module is configured to input the optimal gain parameter, the optimal proportion parameter and the optimal virtual inertia into a power system, so that the power system is subjected to low-frequency oscillation suppression.
In a third aspect, there is provided an electronic device, comprising: the system comprises at least one processor and a memory communicatively connected with the at least one processor, wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the steps of the method of suppressing low frequency oscillations of the power system of any of the embodiments of the invention.
In a fourth aspect, the present invention also provides a computer readable storage medium having stored thereon a computer program, which when executed by a processor, causes the processor to perform the steps of the method of suppressing low frequency oscillations of a power system according to any embodiment of the present invention.
According to the method and the system for suppressing the low-frequency oscillation of the power system, the power oscillation damper is arranged on the energy storage side frequency converter, the frequency is adjusted according to the frequency track planning method, the energy storage virtual inertia control parameter is determined by the inertia compensation target, the strategy of tracking the inertia compensation target through dynamic adjustment of the control parameter is provided, the energy storage device can accurately track the compensation target, and the optimal gain parameter, the optimal proportion parameter and the optimal virtual inertia are obtained through the synergistic effect of the three control methods. In the traditional particle swarm algorithm, the wind accumulation output has larger variation amplitude, obvious power fluctuation and slower flat power fluctuation rate. After the particle swarm algorithm is improved, the globally optimal parameter combination is quickly and efficiently searched, so that the change amplitude of the wind storage output is smaller, the smooth power fluctuation speed is faster, the power oscillation damper is controlled to better reduce the oscillation amplitude, the capability of quickly recovering the power angle is stronger, and a good low-frequency oscillation suppression effect is achieved.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flowchart of a method for suppressing low frequency oscillations of a power system according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of an energy storage device according to an embodiment of the present invention;
fig. 3 is a control structure diagram of a POD according to an embodiment of the present invention;
FIG. 4 is a schematic diagram showing a comparison between a calculated virtual inertia value and a simulated value of an energy storage device according to an embodiment of the present invention;
FIG. 5 is a schematic diagram showing a state of charge change rate of an energy storage device according to an embodiment of the present invention;
FIG. 6 is a schematic diagram showing a frequency response curve of the virtual inertia compensation control effect of the energy storage device according to an embodiment of the present invention;
FIG. 7 is a block diagram of a system for suppressing low frequency oscillations of a power system according to an embodiment of the present invention;
fig. 8 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present 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.
Referring to fig. 1, a flowchart of a method for suppressing low frequency oscillations of a power system is shown.
As shown in fig. 1, the method for suppressing the low-frequency oscillation of the power system specifically includes the following steps:
step S101, a grid-connected model of a wind power plant storage system is built, wherein the grid-connected model comprises a wind power plant grid-connected sub-model and an energy storage device grid-connected sub-model.
In the step, a state equation and an algebraic equation of the wind storage system at a stable working point are established, wherein the expression of the state equation is as follows:
，
in the method, in the process of the invention,for differentiating operator +.>For the state variable of the synchronous generator, +.>For the state equation of synchronous generator->Algebraic variable of synchronous generator->Is algebraic variable of system->Is the state equation of the doubly-fed wind turbine,is a state variable of the doubly-fed fan, +.>Is algebraic variable of doubly-fed fan, +.>For the state equation of the battery energy storage system, +.>For the state variable of the battery energy storage system, +.>Algebraic variables of the battery energy storage system;
the expression of the algebraic equation is:
，
in the method, in the process of the invention,algebraic equation for synchronous generator->Is algebraic equation of doubly-fed fan, +.>Algebraic equation for battery energy storage system, +.>Algebraic equations for the power system;
linearizing the state equation and the algebraic equation to obtain a grid-connected model of the wind storage system, wherein the expression of the grid-connected model is as follows:
，
in the method, in the process of the invention,for the increment of the synchronous generator state variable, +.>Is the increment of the state variable of the doubly-fed fan,delta for battery energy storage system state variable, +.>Matrix coefficients for the first row and first column, < >>Matrix coefficients for the second column of the first row, +.>Matrix coefficients for the first column of the second row, < >>And is the matrix coefficient of the second row and the second column.
Step S102, respectively carrying out low-frequency oscillation adjustment on the wind power plant grid-connected sub-model and frequency stability adjustment on the energy storage device grid-connected sub-model according to the power oscillation damper and the proportional integral controller to obtain gain parameters and proportional parameters of the wind power storage system.
In this step, the DFIG output is controlled to increase damping based on wind reservoir system modeling. The structural design on which the Power Oscillation Damper (POD) is mounted can be divided into: the power oscillation amplifier comprises a power oscillation amplifier gain unit, an isolation link, a lead module and a hysteresis module. The feedback signal suppressing the oscillation mode in the POD mainly includes two kinds: rotor speed (equivalent to grid frequency) and power angle of the generator.
The research combines two types of feedback signals, and the synchronous electric angular velocity is obtained at the port of the wind storage system through a filter by utilizing the phase angle change information of the bus voltage of the coupling nodeIn a variation, a substitute for a conventional synchronous generator is used as an input to the POD. The synchronous problem of the system receiving speed signal is solved, the response time is shortened, the time for modulating and outputting active power is shortened, and POD feedback signals of the wind turbine generator and the stored energy are unified. The POD control structure is shown in fig. 2. Wherein (1)>Algebraic form of a power oscillation damper +.>For gain parameter +.>Is a filter time constant, +.>For Laplace operator>Control parameters for Module one, +.>Control parameter for Module three, +.>For angular velocity increment, ++>For the sensor time constant, < >>Control parameters for Module two, +.>Control parameter for Module four, +.>Is the transfer function of the POD.
It should be noted that the mathematical expression of the power oscillation damper is:
，
furthermore, the frequency deviation of the feedback wind power system is taken as an input signal, and the active output of the energy storage device is controlled by the proportional-integral controller to serve as a solution, so that the implementation principle is shown in figure 3. In the figureIndicating that the energy storage device is injecting an active current command into the power system,/->Active instruction for energy storage device, < >>Integrating the amplification factor for the energy storage controller, +.>Proportional amplification of the energy storage controller, +.>Input voltage for phase-locked loop, < >>For the actual phase of the input voltage>For angular velocity deviation, the PLL is a phase locked loop, +.>Maximum input current for energy storage device, +.>Minimum input current for energy storage device, +.>For the energy storage device voltage->For the rated capacity of the energy storage device, < >>Is an active instruction of the energy storage device. And the normal operation of the energy storage device is effectively ensured by limiting the output current and the power in the controller. And neglecting the influence of a nonlinear link, the linearization state equation of the grid-connected sub-model of the energy storage device is as follows:
，
in the method, in the process of the invention,for the phase increment per unit value of the phase locked loop, < >>For the impedance angle increment per unit value, +.>For the per unit value of the active current increment, < >>Is the proportional amplification of the phase-locked loop, +.>Integration amplification for phase-locked loop, +.>For voltage variation, ">Integrating the amplification factor for the energy storage controller, +.>Proportional amplification of the energy storage controller, +.>For phase-locked loop phase increment, +.>Is the impedance angle increment, +>For active current increment, +.>Is voltage phase increment, ">Is the per unit value of the voltage phase increment.
And step S103, performing compensation control on the synchronous generator based on a preset virtual inertia compensation control strategy to obtain virtual inertia corresponding to the energy storage device grid-connected sub-model.
In the step, the virtual inertia compensation control strategy controls the energy exchange between the wind energy storage system and the conventional synchronous generator in real time by detecting a system frequency signal at an outlet bus of the wind power plant. The inertia of the wind power plant is dynamically compensated, the frequency change of the power system is effectively prevented, the frequency characteristic of the power system is improved, all fans can be controlled in a maximum power mode, and the problem that the wind power generation set participates in the frequency control process of the power system is avoided.
When an unbalanced disturbance occurs in the electric power system, the synchronous generator in grid-connected operation can spontaneously perform inertia response, release kinetic energy stored in the rotor to resist rotational kinetic energy stored in a mechanical part of the rotor with system frequency fluctuation, and can be expressed as:
，
in the method, in the process of the invention,stored rotational kinetic energy for mechanical parts of the rotor, +.>For the pole pair number of synchronous generators>For moment of inertia>Is the rotor angular velocity. The inertia of a synchronous generator is defined as the ratio of the kinetic energy of the generator rotor to its rated capacity at synchronous angular velocity:
，
in the method, in the process of the invention,for synchronizing generator inertia->Is the rated capacity of the synchronous generator.
The state of charge of the energy storage device is the most important operating parameter, and the expression for calculating the state of charge of the energy storage device is as follows:
，
in the method, in the process of the invention,as the remaining power of the energy storage device,
，
in the method, in the process of the invention,for the discharge current at time t of the energy storage device, +.>Is the state of charge.
In summary, the expression of the virtual inertia corresponding to the energy storage device grid-connected sub-model is:
，
in the method, in the process of the invention,for the virtual inertia of the energy storage device, +.>For the energy change of the energy storage device, < >>For the rated capacity of the energy storage device, < >>For the pole pair number of synchronous generators>For the virtual moment of inertia of the energy storage device, +.>Rated angular velocity for synchronous generator, < >>For the nominal voltage of the energy storage device, < >>For the rated power of the energy storage device in the full charge state, < >>For the state of charge change rate +.>The rated angular velocity increment of the synchronous generator is set;
the calculated virtual inertia value and the simulation value of the energy storage device are compared as shown in fig. 4, and the change rate curve of the charge state of the energy storage device is shown in fig. 5. The frequency response curve of the virtual inertia compensation control effect of the energy storage device is shown in fig. 6.
The inertia compensation control wind power station can have inertia response capability similar to that of an isovolumetric synchronous generator, and is beneficial to improving the frequency stability of the system. When the wind power plant adopts the proposed control strategy, the energy storage device can effectively compensate inertia of the wind power plant no matter under the condition that wind speed is changed upwards or downwards, and can inhibit fluctuation of active output of the wind power plant from the system level, so that unbalanced active power which needs to be stabilized by the synchronous generator in the system is reduced, and frequency operation characteristics of the system are improved.
And step S104, constructing a Pareto optimal solution set according to the gain parameter, the proportion parameter and the virtual inertia, and performing multi-objective optimization on the Pareto optimal solution set based on an improved particle swarm algorithm to obtain an optimal gain parameter, an optimal proportion parameter and an optimal virtual inertia.
In this step, the fill attribute is initialized: the position of each particle comprises n dimensions, and the n dimensions correspond to gain parameters, the ratio parameters and virtual inertia in a Pareto optimal solution set, wherein the state attribute of the particle i is as follows:
current position of particle: xi= (xi 1, xi2,..xin), where xi1 is the position of particle i in one-dimensional coordinates, xi2 is the position of particle i in two-dimensional coordinates, and xin is the position of particle i in n-dimensional coordinates;
flow velocity of particles: vi= (vi 1, vi2,., vin), vi1 is the velocity of particle i in one-dimensional coordinates, vi2 is the velocity of particle i in two-dimensional coordinates, vin is the velocity of particle i in n-dimensional coordinates;
calculating a fitness function, updating an optimal solution and a global optimal solution, wherein after initialization, the position of each particle is used as a parameter value, and F functions are used for simulation to obtain the fitness value, and the expression of the F functions is as follows:
，
in the method, in the process of the invention,for the time range of oscillation, +.>For outputting error +.>Is an independent variable;
the speed and position of each particle in the next state are updated by adopting a dynamic inertia weight and a dynamic learning factor, wherein the speed and position of each particle in the next state are determined according to a single extremum, a global extremum and the speed and position of the previous state, and the expression for calculating the dynamic inertia weight is as follows:
，
in the method, in the process of the invention,for dynamic inertia weight, +.>For maximum number of iterations +.>For the current iteration number>For the initial value of the inertial weight, +.>Is the final value of the inertial weight;
the expression for calculating the dynamic learning factor is:
，
in the method, in the process of the invention,for dynamic learning factors, < >>For the final value of the learning factor, +.>Is the initial value of the learning factor;
and when the maximum iteration times are reached or the iteration convergence is met, finishing the optimization process to obtain the optimal gain parameter, the optimal proportion parameter and the optimal virtual inertia.
Step S105, inputting the optimal gain parameter, the optimal ratio parameter and the optimal virtual inertia into a power system, so as to perform low-frequency oscillation suppression on the power system.
In conclusion, the virtual inertia compensation control strategy is adopted, so that the energy storage accurately tracks the target quantity to execute inertia compensation control, the frequency safety of the system is guaranteed, wind power provides proper power support in design and acts on the generator, the electromechanical oscillation process can obtain additional damping effect, the electromechanical oscillation process is finally weakened or disappeared rapidly, the system is restored to be stable again, on the basis, the energy storage technology is utilized to improve the rapid frequency response characteristic of the wind power plant, the risk of out-of-step operation of new energy sources after faults is reduced, the system actively damps and regulates and improves the damping coefficient of the system, and the occurrence probability of low-frequency oscillation is reduced.
Referring to fig. 7, a block diagram of a system for suppressing low frequency oscillations of a power system is shown.
As shown in fig. 7, a system 200 for suppressing low frequency oscillations of a power system includes a build module 210, a regulation module 220, a compensation control module 230, an optimization module 240, and an input module 250.
Wherein, the construction module 210 is configured to construct a grid-connected model of the wind power plant grid-connected sub-model and an energy storage device grid-connected sub-model; the adjusting module 220 is configured to perform low-frequency oscillation adjustment on the wind power plant grid-connected sub-model and perform frequency stable adjustment on the energy storage device grid-connected sub-model according to the power oscillation damper and the proportional integral controller respectively, so as to obtain gain parameters and proportion parameters of the wind power storage system; the compensation control module 230 is configured to perform compensation control on the synchronous generator based on a preset virtual inertia compensation control strategy to obtain virtual inertia corresponding to the energy storage device grid-connected sub-model; the optimization module 240 is configured to construct a Pareto optimal solution set according to the gain parameter, the proportion parameter and the virtual inertia, and perform multi-objective optimization on the Pareto optimal solution set based on an improved particle swarm algorithm to obtain an optimal gain parameter, an optimal proportion parameter and an optimal virtual inertia; the input module 250 is configured to input the optimal gain parameter, the optimal proportion parameter and the optimal virtual inertia into a power system, so as to inhibit low-frequency oscillation of the power system.
It should be understood that the modules depicted in fig. 7 correspond to the various steps in the method described with reference to fig. 1. Thus, the operations and features described above for the method and the corresponding technical effects are equally applicable to the modules in fig. 7, and are not described here again.
In other embodiments, embodiments of the present invention further provide a computer readable storage medium having stored thereon a computer program, the program instructions, when executed by a processor, cause the processor to perform the method for suppressing low frequency oscillations of a power system in any of the method embodiments described above;
as one embodiment, the computer-readable storage medium of the present invention stores computer-executable instructions configured to:
constructing a grid-connected model of a wind power storage system, wherein the grid-connected model comprises a wind power plant grid-connected sub-model and an energy storage device grid-connected sub-model;
respectively carrying out low-frequency oscillation adjustment on a wind power plant grid-connected sub-model and frequency stability adjustment on an energy storage device grid-connected sub-model according to a power oscillation damper and a proportional integral controller to obtain gain parameters and proportional parameters of the wind storage system;
performing compensation control on the synchronous generator based on a preset virtual inertia compensation control strategy to obtain virtual inertia corresponding to the energy storage device grid-connected sub-model;
constructing a Pareto optimal solution set according to the gain parameter, the proportion parameter and the virtual inertia, and performing multi-objective optimization on the Pareto optimal solution set based on an improved particle swarm algorithm to obtain an optimal gain parameter, an optimal proportion parameter and an optimal virtual inertia;
and inputting the optimal gain parameter, the optimal proportion parameter and the optimal virtual inertia into a power system, so that the power system is subjected to low-frequency oscillation suppression.
The computer readable storage medium may include a storage program area and a storage data area, wherein the storage program area may store an operating system, at least one application program required for a function; the storage data area may store data created from the use of a system that suppresses low frequency oscillations of the power system, and the like. In addition, the computer-readable storage medium may include high-speed random access memory, and may also include memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid-state storage device. In some embodiments, the computer readable storage medium optionally includes memory remotely located with respect to the processor, the remote memory being connectable via a network to a system for suppressing low frequency oscillations of the power system. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
Fig. 8 is a schematic structural diagram of an electronic device according to an embodiment of the present invention, as shown in fig. 8, where the device includes: a processor 310 and a memory 320. The electronic device may further include: an input device 330 and an output device 340. The processor 310, memory 320, input device 330, and output device 340 may be connected by a bus or other means, for example in fig. 8. Memory 320 is the computer-readable storage medium described above. The processor 310 executes various functional applications of the server and data processing by running nonvolatile software programs, instructions and modules stored in the memory 320, i.e., implements the method of suppressing low frequency oscillations of the power system of the above-described method embodiment. The input device 330 may receive input numeric or character information and generate key signal inputs related to user settings and function control of the system that suppress low frequency oscillations of the power system. The output device 340 may include a display device such as a display screen.
The electronic equipment can execute the method provided by the embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method. Technical details not described in detail in this embodiment may be found in the methods provided in the embodiments of the present invention.
As an embodiment, the electronic device is applied to a system for suppressing low-frequency oscillation of a power system, and is used for a client, and includes: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executable by the at least one processor to enable the at least one processor to:
constructing a grid-connected model of a wind power storage system, wherein the grid-connected model comprises a wind power plant grid-connected sub-model and an energy storage device grid-connected sub-model;
respectively carrying out low-frequency oscillation adjustment on a wind power plant grid-connected sub-model and frequency stability adjustment on an energy storage device grid-connected sub-model according to a power oscillation damper and a proportional integral controller to obtain gain parameters and proportional parameters of the wind storage system;
performing compensation control on the synchronous generator based on a preset virtual inertia compensation control strategy to obtain virtual inertia corresponding to the energy storage device grid-connected sub-model;
constructing a Pareto optimal solution set according to the gain parameter, the proportion parameter and the virtual inertia, and performing multi-objective optimization on the Pareto optimal solution set based on an improved particle swarm algorithm to obtain an optimal gain parameter, an optimal proportion parameter and an optimal virtual inertia;
and inputting the optimal gain parameter, the optimal proportion parameter and the optimal virtual inertia into a power system, so that the power system is subjected to low-frequency oscillation suppression.
From the above description of the embodiments, it will be apparent to those skilled in the art that the embodiments may be implemented by means of software plus necessary general hardware platforms, or of course may be implemented by means of hardware. Based on such understanding, the foregoing technical solutions may be embodied essentially or in part in the form of a software product, which may be stored in a computer-readable storage medium, such as a ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to perform the various embodiments or methods of some parts of the embodiments.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (8)
1. A method of suppressing low frequency oscillations of an electrical power system connected in parallel with a wind storage system and a synchronous generator, respectively, the method comprising:
constructing a grid-connected model of a wind power storage system, wherein the grid-connected model comprises a wind power plant grid-connected sub-model and an energy storage device grid-connected sub-model;
respectively carrying out low-frequency oscillation adjustment on a wind power plant grid-connected sub-model and frequency stability adjustment on an energy storage device grid-connected sub-model according to a power oscillation damper and a proportional integral controller to obtain gain parameters and proportional parameters of the wind storage system;
performing compensation control on the synchronous generator based on a preset virtual inertia compensation control strategy to obtain virtual inertia corresponding to the energy storage device grid-connected sub-model, wherein the expression for calculating the virtual inertia corresponding to the energy storage device grid-connected sub-model is as follows:
，
in the method, in the process of the invention,for the virtual inertia of the energy storage device, +.>For the energy change of the energy storage device, < >>For the rated capacity of the energy storage device, < >>For the pole pair number of synchronous generators>For the virtual moment of inertia of the energy storage device, +.>Rated angular velocity for synchronous generator, < >>For the nominal voltage of the energy storage device, < >>For the rated power of the energy storage device in the full charge state, < >>For the state of charge change rate +.>The rated angular velocity increment of the synchronous generator is set;
，
in the middle of，For the discharge current at time t of the energy storage device, +.>Is in a state of charge;
，
in the method, in the process of the invention,the residual electric quantity of the energy storage device;
constructing a Pareto optimal solution set according to the gain parameter, the proportion parameter and the virtual inertia, and performing multi-objective optimization on the Pareto optimal solution set based on an improved particle swarm algorithm to obtain an optimal gain parameter, an optimal proportion parameter and an optimal virtual inertia, wherein the performing multi-objective optimization on the Pareto optimal solution set based on the improved particle swarm algorithm to obtain the optimal gain parameter, the optimal proportion parameter and the optimal virtual inertia comprises the following steps:
initializing a filling attribute: the position of each particle comprises n dimensions, and the n dimensions correspond to gain parameters, the ratio parameters and virtual inertia in a Pareto optimal solution set, wherein the state attribute of the particle i is as follows:
current position of particle: xi= (xi 1, xi2,..xin), where xi1 is the position of particle i in one-dimensional coordinates, xi2 is the position of particle i in two-dimensional coordinates, and xin is the position of particle i in n-dimensional coordinates;
flow velocity of particles: vi= (vi 1, vi2,., vin), vi1 is the velocity of particle i in one-dimensional coordinates, vi2 is the velocity of particle i in two-dimensional coordinates, vin is the velocity of particle i in n-dimensional coordinates;
calculating a fitness function, updating an optimal solution and a global optimal solution, wherein after initialization, the position of each particle is used as a parameter value, and F functions are used for simulation to obtain the fitness value, and the expression of the F functions is as follows:
，
in the method, in the process of the invention,for the time range of oscillation, +.>For outputting error +.>Is an independent variable;
the speed and position of each particle in the next state are updated by adopting a dynamic inertia weight and a dynamic learning factor, wherein the speed and position of each particle in the next state are determined according to a single extremum, a global extremum and the speed and position of the previous state, and the expression for calculating the dynamic inertia weight is as follows:
，
in the method, in the process of the invention,for dynamic inertia weight, +.>For maximum number of iterations +.>For the current iteration number>For the initial value of the inertial weight, +.>Is the final value of the inertial weight;
the expression for calculating the dynamic learning factor is:
，
in the method, in the process of the invention,for dynamic learning factors, < >>For the final value of the learning factor, +.>Is the initial value of the learning factor;
when the maximum iteration times are reached or iteration convergence is met, completing an optimization process to obtain an optimal gain parameter, an optimal proportion parameter and optimal virtual inertia;
and inputting the optimal gain parameter, the optimal proportion parameter and the optimal virtual inertia into a power system, so that the power system is subjected to low-frequency oscillation suppression.
2. The method for suppressing low-frequency oscillations of an electrical power system according to claim 1, wherein said constructing a grid-tie model of a wind storage system comprises:
establishing a state equation and an algebraic equation of the wind storage system at a stable working point, wherein the expression of the state equation is as follows:
，
in the method, in the process of the invention,for differentiating operator +.>For the state variable of the synchronous generator, +.>For the state equation of synchronous generator->Algebraic variable of synchronous generator->Is algebraic variable of system->Is the state equation of the doubly-fed fan, +.>Is a state variable of the doubly-fed fan, +.>Is algebraic variable of doubly-fed fan, +.>As an equation of state for a battery energy storage system,for the state variable of the battery energy storage system, +.>Algebraic variables of the battery energy storage system;
the expression of the algebraic equation is:
，
in the method, in the process of the invention,algebraic equation for synchronous generator->Is algebraic equation of doubly-fed fan, +.>Algebraic equation for battery energy storage system, +.>Algebraic equations for the power system;
linearizing the state equation and the algebraic equation to obtain a grid-connected model of the wind storage system, wherein the expression of the grid-connected model is as follows:
，
in the method, in the process of the invention,for the increment of the synchronous generator state variable, +.>Is the increment of the state variable of the doubly-fed fan,delta for battery energy storage system state variable, +.>Matrix coefficients for the first row and first column, < >>Matrix coefficients for the second column of the first row, +.>Matrix coefficients for the first column of the second row, < >>Matrix coefficients for the second row and the second column。
3. The method of claim 1, wherein the power oscillation damper comprises a power oscillation amplifier gain unit, an isolation link, a lead module and a hysteresis module connected in sequence.
4. The method of suppressing low frequency oscillations of an electrical power system according to claim 1, wherein said power oscillation damper has a mathematical expression:
，
in the method, in the process of the invention,algebraic form of a power oscillation damper +.>For gain parameter +.>Is a filter time constant, +.>For Laplace operator>Control parameters for Module one, +.>Control parameter for Module three, +.>For angular velocity increment, ++>For the sensor time constant, < >>Control parameters for Module two, +.>Control parameter for Module four, +.>Is the transfer function of the POD.
5. The method of claim 1, wherein the linearized state equation of the energy storage device grid-tie sub-model is:
，
in the method, in the process of the invention,for the phase increment per unit value of the phase locked loop, < >>For the impedance angle increment per unit value, +.>For the per unit value of the active current increment, < >>Is the proportional amplification of the phase-locked loop, +.>Integration amplification for phase-locked loop, +.>For voltage variation, ">Integrating the amplification factor for the energy storage controller, +.>Proportional amplification of the energy storage controller, +.>For phase-locked loop phase increment, +.>Is the impedance angle increment, +>For active current increment, +.>Is voltage phase increment, ">Is the per unit value of the voltage phase increment.
6. A system for suppressing low frequency oscillations of an electrical power system, the electrical power system being connected in parallel with a wind storage system and a synchronous generator, respectively, the system comprising:
the system comprises a building module, a storage module and a control module, wherein the building module is configured to build a grid-connected model of a wind power plant grid-connected sub-model and an energy storage device grid-connected sub-model;
the adjusting module is configured to respectively carry out low-frequency oscillation adjustment on the wind power plant grid-connected sub-model and frequency stability adjustment on the energy storage device grid-connected sub-model according to the power oscillation damper and the proportional integral controller to obtain gain parameters and proportional parameters of the wind storage system;
the compensation control module is configured to perform compensation control on the synchronous generator based on a preset virtual inertia compensation control strategy to obtain virtual inertia corresponding to the energy storage device grid-connected sub-model, wherein the expression for calculating the virtual inertia corresponding to the energy storage device grid-connected sub-model is as follows:
，
in the method, in the process of the invention,for the virtual inertia of the energy storage device, +.>For the energy change of the energy storage device, < >>For the rated capacity of the energy storage device, < >>For the pole pair number of synchronous generators>For the virtual moment of inertia of the energy storage device, +.>Rated angular velocity for synchronous generator, < >>For the nominal voltage of the energy storage device, < >>For the rated power of the energy storage device in the full charge state, < >>For the state of charge change rate +.>The rated angular velocity increment of the synchronous generator is set;
，
in the method, in the process of the invention,for the discharge current at time t of the energy storage device, +.>Is in a state of charge;
，
in the method, in the process of the invention,the residual electric quantity of the energy storage device;
the optimization module is configured to construct a Pareto optimal solution set according to the gain parameter, the proportion parameter and the virtual inertia, and perform multi-objective optimization on the Pareto optimal solution set based on an improved particle swarm algorithm to obtain an optimal gain parameter, an optimal proportion parameter and an optimal virtual inertia, wherein the performing multi-objective optimization on the Pareto optimal solution set based on the improved particle swarm algorithm to obtain the optimal gain parameter, the optimal proportion parameter and the optimal virtual inertia comprises:
initializing a filling attribute: the position of each particle comprises n dimensions, and the n dimensions correspond to gain parameters, the ratio parameters and virtual inertia in a Pareto optimal solution set, wherein the state attribute of the particle i is as follows:
current position of particle: xi= (xi 1, xi2,..xin), where xi1 is the position of particle i in one-dimensional coordinates, xi2 is the position of particle i in two-dimensional coordinates, and xin is the position of particle i in n-dimensional coordinates;
flow velocity of particles: vi= (vi 1, vi2,., vin), vi1 is the velocity of particle i in one-dimensional coordinates, vi2 is the velocity of particle i in two-dimensional coordinates, vin is the velocity of particle i in n-dimensional coordinates;
calculating a fitness function, updating an optimal solution and a global optimal solution, wherein after initialization, the position of each particle is used as a parameter value, and F functions are used for simulation to obtain the fitness value, and the expression of the F functions is as follows:
，
in the method, in the process of the invention,for the time range of oscillation, +.>For outputting error +.>Is an independent variable;
the speed and position of each particle in the next state are updated by adopting a dynamic inertia weight and a dynamic learning factor, wherein the speed and position of each particle in the next state are determined according to a single extremum, a global extremum and the speed and position of the previous state, and the expression for calculating the dynamic inertia weight is as follows:
，
in the method, in the process of the invention,for dynamic inertia weight, +.>For maximum number of iterations +.>For the current iteration number>For the initial value of the inertial weight, +.>Is the final value of the inertial weight;
the expression for calculating the dynamic learning factor is:
，
in the method, in the process of the invention,for dynamic learning factors, < >>For the final value of the learning factor, +.>Is the initial value of the learning factor;
when the maximum iteration times are reached or iteration convergence is met, completing an optimization process to obtain an optimal gain parameter, an optimal proportion parameter and optimal virtual inertia;
and the input module is configured to input the optimal gain parameter, the optimal proportion parameter and the optimal virtual inertia into a power system, so that the power system is subjected to low-frequency oscillation suppression.
7. An electronic device, comprising: at least one processor, and a memory communicatively coupled to the at least one processor, wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1 to 5.
8. A computer readable storage medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the method of any one of claims 1 to 5.
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Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109193752A (en) * | 2018-08-22 | 2019-01-11 | 华北电力大学 | Containing virtual inertia double-fed blower grid-connected system low-frequency oscillation Optimization about control parameter method |
| CN110970933A (en) * | 2019-11-23 | 2020-04-07 | 国网辽宁省电力有限公司电力科学研究院 | Virtual inertia compensation method of light-storage combined power generation system based on active support control |
| WO2020192006A1 (en) * | 2019-03-25 | 2020-10-01 | 山东理工大学 | Virtual inertia-based charge/discharge control method for electric vehicle |
| CN114759618A (en) * | 2021-12-31 | 2022-07-15 | 华北电力大学 | Method and system for determining stability of double-fed wind power grid-connected system based on dynamic energy |
| CN115395576A (en) * | 2022-04-28 | 2022-11-25 | 国网湖南省电力有限公司 | Energy storage adaptive damping-inertia control method facing wind power maximum power tracking |
| CN116260157A (en) * | 2023-02-15 | 2023-06-13 | 广西电网有限责任公司电力科学研究院 | Active output control method of doubly-fed fan based on virtual inertia and virtual damping |
| CN116316671A (en) * | 2022-09-07 | 2023-06-23 | 三峡大学 | Wind power plant virtual inertia optimization distribution method based on improved particle swarm optimization |
| CN116937546A (en) * | 2023-07-10 | 2023-10-24 | 国网山东省电力公司青岛供电公司 | Wind storage grid connection considered power grid low-frequency oscillation suppression method and system |
-
2023
- 2023-11-07 CN CN202311465044.7A patent/CN117200260B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109193752A (en) * | 2018-08-22 | 2019-01-11 | 华北电力大学 | Containing virtual inertia double-fed blower grid-connected system low-frequency oscillation Optimization about control parameter method |
| WO2020192006A1 (en) * | 2019-03-25 | 2020-10-01 | 山东理工大学 | Virtual inertia-based charge/discharge control method for electric vehicle |
| CN110970933A (en) * | 2019-11-23 | 2020-04-07 | 国网辽宁省电力有限公司电力科学研究院 | Virtual inertia compensation method of light-storage combined power generation system based on active support control |
| CN114759618A (en) * | 2021-12-31 | 2022-07-15 | 华北电力大学 | Method and system for determining stability of double-fed wind power grid-connected system based on dynamic energy |
| CN115395576A (en) * | 2022-04-28 | 2022-11-25 | 国网湖南省电力有限公司 | Energy storage adaptive damping-inertia control method facing wind power maximum power tracking |
| CN116316671A (en) * | 2022-09-07 | 2023-06-23 | 三峡大学 | Wind power plant virtual inertia optimization distribution method based on improved particle swarm optimization |
| CN116260157A (en) * | 2023-02-15 | 2023-06-13 | 广西电网有限责任公司电力科学研究院 | Active output control method of doubly-fed fan based on virtual inertia and virtual damping |
| CN116937546A (en) * | 2023-07-10 | 2023-10-24 | 国网山东省电力公司青岛供电公司 | Wind storage grid connection considered power grid low-frequency oscillation suppression method and system |
Non-Patent Citations (1)
| Title |
|---|
| 基于状态观测器的风电机组单机储能***虚拟惯量控制;王晓东;李凯凯;刘颖明;程红辉;;电工技术学报(第06期);第1257-1264页 * |
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