Parameter optimization method and device for improving dynamic reactive power characteristic of static synchronous phase modifier

Abstract

The invention discloses a parameter optimization method and device for improving the dynamic reactive power characteristic of a static synchronous phase modifier, and the method comprises the steps: constructing a dynamic reactive power model of the static synchronous phase modifier and a simulation excitation system, and determining full parameters; an evaluation index for representing the reactive power regulation performance of the static synchronous phase modifier is proposed, and a direction is provided for parameter optimization of the static synchronous phase modifier; establishing a key parameter identification model of the static synchronous phase modifier under different time scales, determining parameters influencing reactive power characteristics of the static synchronous phase modifier under different application scenes, and reducing parameter optimization time; determining an optimization interval of the key parameters based on the system stability boundary; parameters are optimized by adopting an artificial intelligence algorithm, so that the matching degree of the static synchronous phase modifier to the current power grid is highest. According to the method, the key parameters influencing the dynamic reactive power characteristics of the phase modifier can be identified and optimized, and the difficulty of parameter optimization is reduced.

Description

Technical Field The present invention belongs to the field of electric power technology, and in particular relates to a parameter optimization method and device for improving dynamic reactive power characteristics of a static synchronous phase condenser. Background Art With the continuous advancement of the "dual carbon" goal and the in-depth construction of new power systems, the scale of new energy installed capacity has expanded rapidly and gradually become one of the main power sources in some regions. However, for large-scale new energy transmission systems, they often present the characteristics of "strong DC and weak AC", the local power grid structure is weak and the power generation space of synchronous units is severely compressed. The voltage stability problem caused by weak reactive power support capacity during power grid failures has gradually become prominent. The new phase regulator has a strong dynamic reactive power support capability and can provide necessary voltage support during system failures. However, traditional synchronous phase regulators are rotating equipment with large operating losses, high maintenance costs, and slower response speeds than static reactive power compensation devices. Therefore, some scholars have proposed a static synchronous phase regulator based on the SVG structure. On the traditional SVG structure, supercapacitors are added on the DC side, which is equivalent to large-capacity energy storage, and then grid-type control is adopted to make its external characteristics similar to those of synchronous phase regulators, but its cost is low and its components are all static components, with low losses and convenient operation and maintenance. However, the dynamic reactive characteristics of static synchronous condensers are closely related to their simulated electromagnetic parameters and simulated excitation parameters. The factory model parameters are not fully compatible with the actual grid operation. It is urgent to develop static synchronous condensers parameter optimization methods to comprehensively improve their active support capabilities for the grid. Reference [1] identifies the key parameters affecting the reactive characteristics of condensers based on a simplified condenser model, and optimizes the parameters based on experimental analysis; References [2-4] analyze the effects of different parameters of condensers such as d-axis subtransient reactance, open-circuit and short-circuit time constants, excitation multiples, excitation reactance, excitation resistance and damping resistance on the dynamic reactive characteristics. However, most of the above studies are conducted on traditional condensers, and the coupling effects between many parameters are not considered. Related literature information:

[0001] Pan Xueping, Xu Yi, Zhao Tianqi, et al. Joint optimization of synchronous condenser excitation and boost transformer parameters considering system stability boundary[J]. Power System Protection and Control, 2024, 52(08): 45-54. DOI: 10.19783 / j.cnki.pspc.231472.

[0002] Yuan Bin, Li Hui, Xiang Weixue, et al. Optimization strategy of phased excitation control considering saturation parameters of phase condenser[J]. Power System Protection and Control, 2023, 51(15): 42-54. DOI: 10.19783 / j.cnki.pspc.221732.

[0003] Sun Shuangkui, Shi Junjun, Zhang Cunchao, et al. Adaptive optimization of dynamic performance parameters of new distributed phase condenser[J]. Power System and Clean Energy, 2023, 39(04): 91-98.

[0004] Fu Min, Cui Cancan, Wang Luyao, et al. Research on parameter optimization model of distributed phase-converter excitation system[J]. Journal of Electric Machines and Control, 2022, 26(02): 102-110. DOI: 10.15938 / j.emc.2022.02.011. Summary of the invention The purpose of the present invention is to provide a parameter optimization method and device for improving the dynamic reactive power characteristics of a static synchronous phase condenser, which can identify and optimize the key parameters affecting the dynamic reactive power characteristics of the phase condenser, thereby reducing the difficulty of parameter optimization. In order to achieve the above object, the solution of the present invention is: A parameter optimization method for improving the dynamic reactive power characteristics of a static synchronous condenser comprises the following steps: Step 1, construct a dynamic reactive power model of the static synchronous condenser and the simulated excitation system, and determine the full parameters; Step 2, propose an evaluation index to characterize the reactive power regulation performance of the static synchronous condenser, and provide a direction for the optimization of static synchronous condenser parameters; Step 3: Establish a key parameter identification model of the static synchronous condenser under different time scales, determine the parameters that affect the reactive power characteristics of the static synchronous condenser under different application scenarios, and reduce the time for parameter optimization; Step 4, determining the optimization range of key parameters based on the system stability boundary; Step 5: Use artificial intelligence algorithms to optimize parameters so that the static synchronous condenser has the best matching degree with the current power grid. In step 1 above, the following equation is used to characterize the transient mathematical model of the electromagnetic part of the static synchronous phase condenser: Among them, X′ d and X′ d ′ are the d-axis simulated transient reactance and d-axis simulated subtransient reactance of the synchronous condenser respectively; T d ' 0 and T d ″ 0 are the synchronous condenser d-axis simulated transient time constant and d-axis simulated sub-transient time constant respectively; E′ q and E′ q ′ are the q-axis simulated transient potential and q-axis simulated sub-transient potential of the synchronous condenser, respectively; T q ″ 0 is the sub-transient time constant of the synchronous condenser q axis simulation; E′ d′ is the simulated sub-transient potential of the synchronous condenser d axis; i d and i q They are the d-axis current and q-axis current of the synchronous condenser respectively; The analog excitation system in the static synchronous condenser consists of three key links: analog voltage regulator, analog excitation winding transfer function and analog negative feedback link, and their models are as follows: The analog voltage regulator model is, Among them, T a is the time constant of the analog voltage regulator; K a is the amplification constant of the analog voltage regulator; U ref , U PSS , U R The guidance voltage, additional control voltage and system output voltage of a given system; The simulated excitation winding transfer function model is: Among them, E f To simulate the excitation electromotive force; T e K is the time constant of the simulated excitation winding link; e To simulate the magnification of the excitation winding link; The simulated negative feedback loop model is: Among them, T f is the time constant of the simulated negative feedback loop; K f is the amplification factor of the simulated negative feedback link; U F is the simulated negative feedback voltage. In step 2 above, the dynamic reactive current gain coefficient K is proposed iQ Characterize the reactive power support capability of static synchronous condenser, its expression is: Among them, Δi d , ΔU R are the current variation and voltage variation of the static synchronous condenser respectively; According to Δi d and ΔU R The functional relationship between Δi d =f(d-axis parameter and q-axis parameter; T a ,T e ,T f ,K a ,K e ,K f |ΔU R ) Among them, T a , T e , T f , K a , K e , K f To simulate the parameters of the excitation system to be identified; Thus, K iQ The larger it is, the stronger the dynamic reactive power support capability of the static synchronous condenser is. In the above step 3, the expression of the key parameter identification model of the static synchronous phase condenser is: in, is the dynamic reactive current gain coefficient sensitivity, K iQ is the dynamic reactive current gain coefficient; T a , T e , T f , K a , K e , K f To simulate the parameters of the excitation system to be identified; If the parameter θ satisfies the following formula, it means that the parameter is a key parameter, in, It is the critical value of the sensitivity of the dynamic reactive current gain coefficient. In the above step 4, according to the following formula, i d =A·U R Among them, i d is the d-axis current of the synchronous condenser; A is the key matrix containing the d-axis parameters, q-axis parameters and the parameters to be identified of the simulated excitation system, U R is the system output voltage; Assume the real part of the eigenvalue of A is λ, then, λ=Re(lamda(A)) Adjust a parameter in the key matrix A so that λ is exactly 0, and the corresponding parameter value is its upper limit. In step 5 above, the objective function of parameter optimization is, Among them, K iQ is the dynamic reactive current gain coefficient; θ 关键参数 is the key parameter of the static synchronous condenser, ΔU R is the voltage variation of the static synchronous condenser; Artificial intelligence algorithm is used to solve the problem and obtain the final optimization parameters. A computer device comprises a memory, a processor, and a computer program stored in the memory and executable on the processor; when the processor executes the computer program, the steps of the parameter optimization method for improving the dynamic reactive power characteristics of a static synchronous phase condenser as described above are implemented. A computer-readable storage medium stores a computer program; when the computer program is executed by a processor, the steps of the parameter optimization method for improving the dynamic reactive power characteristics of a static synchronous phase condenser as described above are implemented. After adopting the above scheme, the present invention first constructs a dynamic reactive model of a static synchronous condenser and an excitation system (simulation) to determine the total parameters; then proposes an evaluation index to characterize the reactive regulation performance of a static synchronous condenser, providing a direction for the optimization of condenser parameters; then establishes a key parameter identification model of a static synchronous condenser under different time scales, determines the parameters that affect the reactive characteristics of the condenser under different application scenarios, and reduces the time for parameter optimization; then determines the optimization interval of key parameters based on the system stability boundary; finally, uses an artificial intelligence algorithm to optimize the parameters, so that the static synchronous condenser has the highest matching degree with the current power grid. This method identifies and optimizes the key parameters that affect the dynamic reactive characteristics of the condenser, reducing the difficulty of parameter optimization. BRIEF DESCRIPTION OF THE DRAWINGS Figure 1 It is a flow chart of the present invention. DETAILED DESCRIPTION The technical solutions and beneficial effects of the present invention will be described in detail below with reference to the accompanying drawings. like Figure 1 As shown, the present invention provides a parameter optimization method for improving the dynamic reactive power characteristics of a static synchronous condenser, comprising the following steps: S1. Construct a dynamic reactive power model of the static synchronous condenser and excitation system (simulation) and determine the full parameters; S101, the transient mathematical model of the electromagnetic part of the static synchronous phase condenser can be represented by the following differential equation. Where: X′ d and X′ d ′ are the d-axis simulated transient reactance and d-axis simulated subtransient reactance of the synchronous condenser respectively; T d ' 0 and T d ″ 0 are the synchronous condenser d-axis simulated transient time constant and d-axis simulated sub-transient time constant respectively; E′ q and E′ q′ are the q-axis simulated transient potential and q-axis simulated sub-transient potential of the synchronous condenser, respectively; T q ″ 0 is the sub-transient time constant of the synchronous condenser q axis simulation; E′ d ′ is the simulated sub-transient potential of the synchronous condenser d axis; i d and i q They are the d-axis current and q-axis current of the synchronous condenser respectively. It can be seen from formula (1) that the parameters of the static synchronous condenser mainly include the d-axis parameters and the q-axis parameters. S102, the static synchronous condenser also includes an analog excitation system, which includes three key links: analog voltage regulator, analog excitation winding transfer function and analog negative feedback link, and their models are as follows: Analog Voltage Regulator Model Where: T a is the time constant of the analog voltage regulator; K a is the amplification constant of the analog voltage regulator; U ref , U PSS , U R For a given system's guidance voltage, additional control voltage and system output voltage. Simulating the field winding transfer function model Where: E f To simulate the excitation electromotive force; T e K is the time constant of the simulated excitation winding link; e It is the magnification factor of the simulated excitation winding link. Simulating negative feedback loop model Where: T f is the time constant of the simulated negative feedback loop; K f is the amplification factor of the simulated negative feedback link; U F is the simulated negative feedback voltage. The parameters that need to be identified are the time constants and magnifications of the three key links: T a , T e , T f , K a , K e , K f . S2. Propose evaluation indicators to characterize the reactive power regulation performance of synchronous condensers, and provide direction for the optimization of condenser parameters; The dynamic reactive current gain coefficient K is proposed iQCharacterizing the reactive power support capability of static synchronous condenser, its expression can be written as: In the formula, Δi d , ΔU R They are the current and voltage changes of the static phase regulator respectively. Combining equations (1) to (5), we know that Δi d and ΔU R There is a functional relationship between them, and the parameters of the synchronous condenser mainly include the d-axis parameters and the q-axis parameters and the parameters to be identified of the simulated excitation system T a , T e , T f , K a , K e , K f , Δi d =f(d-axis parameter and q-axis parameter; T a ,T e ,T f ,K a ,K e ,K f |ΔU R )(6) Substituting into equation (5), the dynamic reactive current gain coefficient is: K iQ The larger it is, the stronger the dynamic reactive power support capability of the static synchronous condenser is. S3. Establish key parameter identification models of static synchronous condensers under different time scales, determine the parameters that affect the reactive power characteristics of condensers under different application scenarios, and reduce the time for parameter optimization; The parameters in the static synchronous phase condenser include the d-axis parameters, q-axis parameters and the amplification factor and time constant of the three key links of the analog excitation system. However, not all parameters play a key role in the reactive characteristics of the phase condenser. When optimizing parameters, only the key parameters need to be optimized and the remaining parameters can take standard values. Since equation (7) represents the reactive power support capability of the static synchronous condenser, if the parameter to be optimized changes, it will lead to the dynamic reactive current gain coefficient K shown in equation (7): iQ The huge change indicates that this parameter is a key parameter. Therefore, a key parameter identification index based on the sensitivity of the dynamic reactive current gain coefficient is proposed: When a parameter θ satisfies formula (9), it means that the parameter is a key parameter. In the formula, It is the critical value of the sensitivity of the dynamic reactive current gain coefficient, which can be comprehensively determined according to the specific power network and actual identification accuracy. S4. Determine the optimization range of key parameters based on the system stability boundary; In order to further improve the efficiency of parameter optimization, the optimization interval of the parameters is first determined. This embodiment proposes a method for determining the upper limit of key parameters based on the system stability boundary. According to stability theory, when the real part of the system characteristic equation is greater than 0, the system loses stability. Combining equations (1) to (5), we can get i d =A·U R (10) A is the d-axis parameter, q-axis parameter and excitation system parameter to be identified T a , T e , T f , K a , K e , K f Key Matrix. Let's assume that the real part of the eigenvalue of A is λ, then: λ=Re(lamda(A)) (11) Adjust the parameter θ so that λ is exactly 0, then the corresponding value of θ is its upper limit. S5. Use artificial intelligence algorithms to optimize parameters so that the static synchronous phase condenser can best match the current power grid. Since equation (7) represents the reactive power support capability of the static synchronous condenser, K iQ The larger the value is, the stronger the dynamic reactive power support capability of the phase regulator is. The objective function of parameter optimization is: Artificial intelligence algorithm is used to solve the above model and obtain the final optimization parameters. An embodiment of the present invention further provides a computer device, comprising a processor and a memory configured to store a computer program that can be run on the processor; wherein the processor is configured to execute the method steps in the aforementioned embodiment when running the computer program. In practical applications, the processor includes a field programmable gate array (FPGA), and the processor may be a central processing unit (CPU) or a digital signal processor (DSP). It is understandable that for different devices, the electronic device used to implement the function of the processor may be other, and the embodiment of the present invention does not specifically limit it. The above-mentioned memory can be a volatile memory (volatile memory), such as a random access memory (RAM); or a non-volatile memory (non-volatile memory), such as a read-only memory (ROM), a flash memory, a hard disk (HDD) or a solid-state drive (SSD); or a combination of the above-mentioned types of memory, and provide instructions and data to the processor. In an exemplary embodiment, an embodiment of the present invention further provides a computer-readable storage medium for storing a computer program. Optionally, the computer-readable storage medium can be applied to any one of the methods in the embodiments of the present invention, and the computer program enables the computer to execute the corresponding processes implemented by the processor in each method in the embodiments of the present invention. For the sake of brevity, they are not described here. In the several embodiments provided by the present invention, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms. It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes. The schemes in the embodiments of the present invention may be implemented in various computer languages, for example, object-oriented programming language Java and literal scripting language JavaScript, etc. The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks. These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes. These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes. Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention. Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A parameter optimization method for improving the dynamic reactive power characteristics of a static synchronous condenser, characterized in that The steps include: Step 1, construct a dynamic reactive power model of the static synchronous condenser and the simulated excitation system, and determine the full parameters; Step 2, propose an evaluation index to characterize the reactive power regulation performance of the static synchronous condenser, and provide a direction for the optimization of static synchronous condenser parameters; Step 3: Establish a key parameter identification model of the static synchronous condenser under different time scales, determine the parameters that affect the reactive power characteristics of the static synchronous condenser under different application scenarios, and reduce the time for parameter optimization; Step 4, determining the optimization range of key parameters based on the system stability boundary; Step 5: Use artificial intelligence algorithms to optimize parameters so that the static synchronous condenser has the best matching degree with the current power grid.

2. The method according to claim 1, characterized in that: In step 1, the following equation is used to characterize the transient mathematical model of the electromagnetic part of the static synchronous phase condenser: Among them, X′ d and X′ d ′ are the d-axis simulated transient reactance and d-axis simulated subtransient reactance of the synchronous condenser respectively; T d ′0 and T d ″0 are the synchronous condenser d-axis simulated transient time constant and d-axis simulated sub-transient time constant respectively; E′ q and E′ q ′ are the q-axis simulated transient potential and q-axis simulated sub-transient potential of the synchronous condenser, respectively; T q ″0 is the q-axis simulated sub-transient time constant of the synchronous condenser; E′ d ′ is the simulated sub-transient potential of the synchronous condenser d axis; i d and i q They are the d-axis current and q-axis current of the synchronous condenser respectively; The analog excitation system in the static synchronous condenser consists of three key links: analog voltage regulator, analog excitation winding transfer function and analog negative feedback link, and their models are as follows: The analog voltage regulator model is, Among them, T a is the time constant of the analog voltage regulator; K a is the amplification constant of the analog voltage regulator; U ref , U PSS , U R The guidance voltage, additional control voltage and system output voltage of a given system; The simulated excitation winding transfer function model is: Among them, E f To simulate the excitation electromotive force; T e K is the time constant of the simulated excitation winding link; e To simulate the magnification of the excitation winding link; The simulated negative feedback loop model is: Among them, T f is the time constant of the simulated negative feedback loop; K f is the amplification factor of the simulated negative feedback link; U F is the simulated negative feedback voltage.

3. The method according to claim 1, characterized in that: In step 2, the dynamic reactive current gain coefficient K is proposed iQ Characterize the reactive power support capability of static synchronous condenser, its expression is: Among them, Δi d , ΔU R are the current variation and voltage variation of the static synchronous condenser respectively; According to Δi d and ΔU R The functional relationship between Δi d =f(d-axis parameter and q-axis parameter; T a ,T e ,T f ,K a ,K e ,K f |ΔU R ) Among them, T a , T e , T f , K a , K e , K f To simulate the parameters of the excitation system to be identified; Thus, K iQ The larger it is, the stronger the dynamic reactive power support capability of the static synchronous condenser is.

4. The method according to claim 1, characterized in that: In step 3, the expression of the key parameter identification model of the static synchronous phase condenser is: in, is the dynamic reactive current gain coefficient sensitivity, K iQ is the dynamic reactive current gain coefficient; T a , T e , T f , K a , K e , K f To simulate the parameters of the excitation system to be identified; If the parameter θ satisfies the following formula, it means that the parameter is a key parameter, in, It is the critical value of the sensitivity of the dynamic reactive current gain coefficient.

5. The method according to claim 1, characterized in that: In step 4, according to the following formula, i d =A·U R Among them, i d is the d-axis current of the synchronous condenser; A is the key matrix containing the d-axis parameters, q-axis parameters and the parameters to be identified of the simulated excitation system, U R is the system output voltage; Assume the real part of the eigenvalue of A is λ, then, λ=Re(lamda(A)) Adjust a parameter in the key matrix A so that λ is exactly 0, and the corresponding parameter value is its upper limit.

6. The method according to claim 1, characterized in that: In step 5, the objective function of parameter optimization is, Among them, K iQ is the dynamic reactive current gain coefficient; θ 关键参数 is the key parameter of the static synchronous condenser, ΔU R is the voltage variation of the static synchronous condenser; Artificial intelligence algorithm is used to solve the problem and obtain the final optimization parameters.

7. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor; characterized in that: When the processor executes the computer program, the steps of the parameter optimization method for improving the dynamic reactive power characteristics of a static synchronous condenser as described in any one of claims 1 to 6 are implemented.

8. A computer-readable storage medium storing a computer program; characterized in that: When the computer program is executed by a processor, the steps of the parameter optimization method for improving the dynamic reactive power characteristics of a static synchronous condenser as claimed in any one of claims 1 to 6 are implemented.

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