Double-shaft excitation phase modifier virtual inertia self-adaptive control method, system and equipment based on rotor rotating speed and medium

Abstract

The invention belongs to the technical field of double-shaft excitation phase modifier control, and discloses a double-shaft excitation phase modifier virtual inertia self-adaptive control method, system and device based on rotor rotating speed and a medium. The excessive energy release at low rotating speed is easy to cause equipment off-network, and the supporting potential cannot be fully exerted at high rotating speed. The method comprises the following steps: acquiring real-time data of rotor speed and power grid frequency; calculating a rotating speed margin according to a preset rotating speed operation interval, taking a power grid frequency change rate as input of a fuzzy logic controller, and calculating a virtual inertia gain coefficient through fuzzy logic reasoning; multiplying the reference inertia by the gain coefficient to obtain a final virtual inertia coefficient; and generating a control instruction based on the coefficient, changing the electromagnetic torque, and realizing virtual inertia self-adaptive control.

Description

Technical Field

[0001] This invention belongs to the field of dual-axis excitation phase shifter control technology, specifically relating to a method, system, device, and medium for adaptive control of virtual inertia of a dual-axis excitation phase shifter based on rotor speed. Background Technology

[0002] Synchronous condensers, as rotating electric motors without prime movers and specifically designed to provide reactive power support and improve system short-circuit capacity, have regained attention in industry. Traditional synchronous condensers primarily operate at synchronous speeds. Although their rotors can provide a certain amount of physical rotational inertia, this inertia is inherent and uncontrollable. More importantly, traditional synchronous condensers cannot adjust their speed to handle active power, resulting in extremely limited support for grid frequency fluctuations. When facing frequency fluctuations caused by large-scale renewable energy fluctuations, traditional synchronous condensers cannot provide the flexible active power buffering that energy storage systems offer.

[0003] To overcome the aforementioned shortcomings, a novel synchronous condenser system combining flywheel energy storage technology and dual-axis AC excitation technology has been introduced into existing technologies. By coaxially coupling a large-inertia flywheel onto the rotor shaft of the synchronous condenser and using a dual-axis AC excitation converter to control the rotor, this system can achieve variable speed operation over a wide range. This variable-speed synchronous condenser not only retains the advantages of traditional synchronous condensers in providing reactive power support and short-circuit capacity, but also participates in grid frequency regulation like a virtual synchronous machine by releasing or absorbing the kinetic energy of the flywheel.

[0004] However, existing dual-axis excitation synchronous condensers typically use a fixed virtual inertia coefficient when employing a virtual synchronous machine control strategy. This static control logic has significant drawbacks under complex operating conditions: when the system operates in the low-speed range, if a severe disturbance occurs in the power grid, the fixed high inertia coefficient will force the flywheel to continue releasing energy at high speed, causing the rotor speed to drop rapidly to the lower operating limit, or even triggering the protection mechanism to disconnect the equipment from the grid, thereby leading to the risk of secondary frequency drops; conversely, when the flywheel operates in the high-speed range, the fixed inertia coefficient limits its support capacity, failing to provide maximum support when the system needs it most, resulting in a waste of the flywheel's energy storage potential.

[0005] Therefore, how to dynamically and intelligently adjust the virtual inertia output of the flywheel energy storage synchronous condenser according to the flywheel speed state while ensuring its own safe operation, and achieve adaptive frequency support of "distribution on demand and operation within capacity", has become a key technical problem that urgently needs to be solved in the field of power system stability control. Summary of the Invention

[0006] Based on the aforementioned shortcomings and deficiencies in the prior art, one of the objectives of this invention is to at least solve one or more of the aforementioned problems in the prior art. In other words, one of the objectives of this invention is to provide a method, system, device, and medium for adaptive virtual inertia control of a dual-axis excitation phase shifter based on rotor speed that meets one or more of the aforementioned requirements, so as to achieve the purpose of dynamically adjusting the virtual inertia output according to the rotational speed state while ensuring the safe operation of the equipment, thereby achieving adaptive frequency support.

[0007] To achieve the above-mentioned objectives, the present invention adopts the following technical solution: In a first aspect, the present invention provides a method for adaptive virtual inertia control of a dual-axis excitation phase-shifting camera based on rotor speed, comprising the following steps: S1. Obtain real-time data on the rotor speed and grid frequency of the dual-axis excitation synchronous condenser; S2. Calculate the virtual inertia coefficient based on the real-time data, including: S21. Calculate the current rotor energy reserve status as the speed margin based on the preset speed operating range; S22. Calculate the grid frequency change rate based on the grid frequency in the real-time data, use the speed margin and the grid frequency change rate as inputs to the fuzzy logic controller, and use fuzzy logic reasoning to calculate the current virtual inertia gain coefficient. S23. Obtain the reference inertia, multiply the reference inertia by the current virtual inertia gain coefficient, and obtain the final virtual inertia coefficient. S3. Based on the virtual inertia coefficient, generate corresponding control commands to control the dual-axis excitation synchronous condenser to change the electromagnetic torque, thereby realizing adaptive control of the virtual inertia.

[0008] As a preferred embodiment, the formula for calculating the speed margin in step S21 is: , In the formula, The current rotor speed, The lower limit of the preset operating speed range, This is the upper limit of the preset operating speed range.

[0009] As a preferred embodiment, in the fuzzy logic reasoning: The fuzzy states of speed margin include energy depletion state, energy normal state, and energy sufficient state; The fuzzy states of the power grid frequency change rate include slight disturbance state, moderate disturbance state, and severe disturbance state; The fuzzy states of the virtual inertia gain coefficient include zero support state, small support state, medium support state, and large support state.

[0010] As a preferred embodiment, in the fuzzy logic reasoning, a membership function combining trapezoidal and triangular membership methods is used to quantify the fuzzy state, including: Regarding speed margin, a left trapezoidal membership function is used for the energy depletion state, a triangular membership function is used for the energy normal state, and a right trapezoidal membership function is used for the energy sufficient state. For the power grid frequency variation rate, a left trapezoidal membership function is used for slight disturbances, a triangular membership function is used for moderate disturbances, and a right trapezoidal membership function is used for severe disturbances.

[0011] As a preferred embodiment, the fuzzy logic reasoning divides the control interval into three functional regions based on different fuzzy states of the speed margin, including: In the safety defense zone, when the speed margin is in a state of energy depletion, priority is given to ensuring the safe operation of the equipment, and the virtual inertia gain coefficient is determined to be in a zero support state based on the power grid frequency change rate. In the balance adjustment zone, when the speed margin is in the normal energy state, the virtual inertia gain coefficient is linearly adjusted according to the grid frequency change rate. In the strong support region, when the speed margin is in a state of sufficient energy and the grid frequency change rate is in a state of severe disturbance, the virtual inertia gain coefficient is in a state of large support.

[0012] As a preferred embodiment, the fuzzy logic reasoning, as a control strategy for implementing the functional area division, employs the following fuzzy rules: When the speed margin is in an energy depletion state, if the grid frequency change rate is in a slight or moderate disturbance state, the virtual inertia gain coefficient is in a zero support state; if the grid frequency change rate is in a severe disturbance state, the virtual inertia gain coefficient is in a small support state. When the speed margin is in a normal energy state, if the grid frequency change rate is in a slight disturbance state, the virtual inertia gain coefficient is in a small support state; if the grid frequency change rate is in a moderate or severe disturbance state, the virtual inertia gain coefficient is in a medium support state. When the speed margin is in a state of sufficient energy, if the grid frequency change rate is in a state of slight disturbance, the virtual inertia gain coefficient is in a state of medium support; if the grid frequency change rate is in a state of moderate or severe disturbance, the virtual inertia gain coefficient is in a state of large support.

[0013] As a preferred option, step S3 specifically involves: The reference value of the q-axis current is calculated based on the final virtual inertia coefficient, and the excitation current is adjusted through closed-loop control to change the electromagnetic torque.

[0014] In a second aspect, the present invention provides a dual-axis excitation camera adaptive virtual inertia control system based on rotor speed, used to implement the dual-axis excitation camera virtual inertia adaptive control method as described in the first aspect, comprising: The data acquisition module is used to acquire real-time data on the rotor speed and grid frequency of the dual-axis excitation synchronous condenser. The speed margin calculation module is used to calculate the current rotor's energy reserve status as the speed margin based on the preset speed operating range; A fuzzy logic controller is used to calculate the grid frequency change rate based on the grid frequency in the real-time data, take the speed margin and the grid frequency change rate as input, use fuzzy logic reasoning to calculate the current virtual inertia gain coefficient, obtain the reference inertia, and multiply the reference inertia with the current virtual inertia gain coefficient to obtain the final virtual inertia coefficient. The excitation control module is used to generate corresponding control commands based on the final virtual inertia coefficient, and to control the dual-axis excitation synchronous condenser to change the electromagnetic torque based on the control commands.

[0015] Thirdly, the present invention provides an electronic device, the computer device including a memory, a processor and a computer program, wherein when the computer program is executed by the processor, it implements the dual-axis excitation camera virtual inertia adaptive control method as described in the first aspect.

[0016] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the dual-axis excitation camera virtual inertia adaptive control method as described in the first aspect.

[0017] Compared with the prior art, the present invention has the following beneficial effects: 1. This invention divides the control zone into a safety defense zone, a balance adjustment zone, and a strong support zone by real-time monitoring of rotor speed and calculation of speed margin. In the safety defense zone, when the synchronous condenser's energy is insufficient, it actively withdraws support or provides only minimal support, effectively avoiding the risk of rotor stall and shutdown due to excessive energy release, and ensuring the safe operation of the equipment. In the strong support zone, when the synchronous condenser has sufficient energy and the power grid experiences severe disturbances, it can provide maximum inertia support, fully utilizing the support potential of flywheel energy storage.

[0018] 2. This invention uses the rotational speed margin and the grid frequency variation rate as inputs to a fuzzy logic controller. It dynamically calculates the virtual inertia gain coefficient using fuzzy logic inference, enabling the final virtual inertia coefficient to be adjusted in real time according to the rotor speed and the degree of grid disturbance. Compared to the fixed virtual inertia coefficient in existing technologies, this invention allows the synchronous condenser to provide the most suitable support strength based on its own energy reserve, achieving adaptive frequency support that is "allocated on demand and operates within its capacity."

[0019] 3. This invention employs a membership function combining trapezoidal and triangular membership functions. In the boundary region with extremely low energy, a trapezoidal membership function is used to achieve forced shutdown, ensuring a clear safety boundary. In the intermediate energy region, a triangular membership function is used to achieve a smooth transition of the control quantity. This design effectively avoids the impact on the dual-axis excitation system caused by sudden changes in control commands, ensuring the stability of the control process.

[0020] 4. When the synchronous condenser has sufficient energy, this invention can provide maximum inertia support for large disturbances in the power grid, significantly enhancing the transient frequency immunity of power systems with low inertia. Simultaneously, the control strategy of providing moderate support when energy is adequate and actively disengaging when energy is scarce avoids the risk of secondary frequency drops caused by equipment disconnection from the grid, further improving the frequency stability of the power grid.

[0021] Further or more detailed beneficial effects will be described in conjunction with specific embodiments in the detailed implementation. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 This is a flowchart illustrating the virtual inertia adaptive control method for dual-axis excitation camera as described in Embodiment 1 of the present invention.

[0024] Figure 2 This is a schematic diagram of the membership function of the input and output variables as described in Embodiment 1 of the present invention.

[0025] Figure 3 It is the fuzzy control surface of the fuzzy inference system described in Embodiment 1 of the present invention.

[0026] Figure 4 This is a structural diagram of the electronic device provided in the embodiment of the present invention.

[0027] Icon labels: 400. Electronic devices; 401. Processor; 402. Communication bus; 403. User interface; 404. Network interface; 405. Memory. Detailed Implementation

[0028] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0029] In the following description, several embodiments of the present invention are provided. Different embodiments can be substituted or combined. Therefore, the present invention can also be considered to include all possible combinations of the same and / or different embodiments described. Thus, if one embodiment includes features A, B, and C, and another embodiment includes features B and D, then the present invention should also be considered to include embodiments containing one or more other possible combinations of A, B, C, and D, even if such embodiments are not explicitly described in the following text.

[0030] The following description provides examples and does not limit the scope, applicability, or examples set forth in the claims. Changes may be made to the function and arrangement of the described elements without departing from the scope of the invention. Various processes or components may be appropriately omitted, substituted, or added to the various examples. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Furthermore, features described with respect to some examples may be combined into other examples.

[0031] To facilitate a better understanding of the embodiments of the present invention, its application scenarios will be explained before providing a detailed explanation of the specific implementation methods.

[0032] The dual-axis excitation synchronous condenser virtual inertia adaptive control method described in the embodiments of this specification is applied to power system frequency stability control scenarios. In these scenarios, the application of the dual-axis excitation synchronous condenser virtual inertia adaptive control method aims to dynamically adjust the virtual inertia output according to the rotor speed state of the synchronous condenser. Under the premise of ensuring the safe operation of the equipment, it realizes adaptive frequency support that is allocated on demand and according to capacity, effectively improving the transient frequency stability of the weak inertia power system.

[0033] The following is a brief explanation of the dual-axis excitation synchronous condenser, flywheel energy storage, virtual inertia, virtual inertia coefficient, virtual inertia gain coefficient, speed margin, grid frequency change rate, fuzzy logic controller, fuzzy state, membership function, and per-unit value involved in several embodiments of this specification: Dual-axis excitation synchronous condenser: A synchronous condenser that adopts dual-axis AC excitation technology. Its rotor has two sets of excitation windings. By independently controlling the excitation current, it can achieve decoupled control of active power and reactive power, and can operate at variable speeds over a wide speed range.

[0034] Flywheel energy storage: A mechanical energy storage method that stores kinetic energy through a rotating flywheel. In this invention, a high-inertia flywheel is coaxially coupled to the rotor of a synchronous condenser. When the grid frequency fluctuates, the flywheel's kinetic energy can be released or absorbed to participate in frequency regulation.

[0035] Virtual inertia: refers to the ability of a synchronous condenser to simulate the inertial response of a traditional synchronous generator through control algorithms. The inertia of a traditional synchronous generator originates from the physical rotating mass of its rotor, while virtual inertia is achieved by adjusting the electromagnetic torque to enable the synchronous condenser to actively transmit and receive active power when the grid frequency changes, simulating a frequency support effect similar to physical inertia.

[0036] Virtual inertia coefficient: A quantitative indicator that measures the magnitude of virtual inertia, measured in seconds (s). The larger the virtual inertia coefficient, the stronger the active power support capability that the synchronous condenser can provide when the frequency fluctuates.

[0037] Virtual inertia gain coefficient: An adjustment coefficient output by the fuzzy logic controller, used to scale the reference virtual inertia. This coefficient ranges from 0 to 1 and determines the final virtual inertia output strength.

[0038] Speed ​​margin: an indicator of the current energy reserve status of the synchronous condenser. The closer the speed margin is to 0, the more depleted the energy is, and the closer it is to 1, the more sufficient the energy is.

[0039] Grid frequency variation rate: The derivative of grid frequency with respect to time. This indicator reflects the severity of grid disturbances; the larger the value, the more severe the disturbance.

[0040] Fuzzy logic controller: A controller based on fuzzy set theory that achieves intelligent control of nonlinear systems by fuzzifying input variables, performing inference based on fuzzy rules, and then defuzzifying the inference results.

[0041] Fuzzy state: In fuzzy logic, a classification used to describe the qualitative state of a variable, such as "high," "medium," and "low." Each fuzzy state corresponds to a membership function, used to calculate the degree to which an input value belongs to that state.

[0042] Membership function: A mathematical function used to quantify the degree to which an input variable belongs to a certain fuzzy state. This invention uses a membership function combining trapezoidal and triangular methods. Trapezoidal methods are used in the boundary region to achieve forced shutdown, while triangular methods are used in the intermediate region to achieve smooth transition.

[0043] Per-unit value: A relative unit system, a dimensionless numerical value obtained by dividing the actual physical quantity by a reference value. In this invention, the power grid frequency change rate is normalized using 0.5Hz / s as the reference value, unifying the domain to the range [0,1], which facilitates fuzzy logic processing.

[0044] Example 1: This embodiment provides a virtual inertia adaptive control method for dual-axis excitation synchronous condensers based on rotor speed. This method can dynamically adjust the virtual inertia output according to the rotor speed state, and achieve on-demand support for the power grid frequency while ensuring the safe operation of the equipment.

[0045] This embodiment uses a unit with a rated speed of 1500 r / min (corresponding to a frequency of 50 Hz) and a number of pole pairs. The following explanation uses a dual-axis excitation synchronous condenser as an example. This condenser has a large-inertia flywheel coaxially coupled to its rotor shaft. The rotor is controlled by a dual-axis AC excitation converter, enabling variable-speed operation within a wide speed range.

[0046] like Figure 1 As shown, the dual-axis excitation camera virtual inertia adaptive control method based on rotor speed provided in this embodiment includes the following steps: S1. Obtain real-time data on the rotor speed and grid frequency of the dual-axis excitation synchronous condenser.

[0047] Specifically, the current rotor speed of the dual-axis excitation synchronous condenser is acquired in real time via a speed sensor. And obtain real-time data of the grid frequency f from the grid connection point.

[0048] S2. Calculate the virtual inertia coefficient based on the real-time data. This step further includes S21 to S23: S21. Calculate the current rotor energy reserve status as the speed margin based on the preset speed operating range.

[0049] Based on the mechanical strength and operating characteristics of the synchronous condenser, the preset safe operating range for the rotational speed is as follows: Set the lower limit of the speed. Set the maximum speed. Speed ​​margin The calculation formula is as follows: , when When it approaches 0, it indicates energy depletion. A larger value indicates sufficient energy reserves.

[0050] S22. Calculate the grid frequency change rate based on the grid frequency in the real-time data, use the speed margin and the grid frequency change rate as inputs to the fuzzy logic controller, and use fuzzy logic reasoning to calculate the current virtual inertia gain coefficient.

[0051] First, calculate the rate of change of the power grid frequency based on the power grid frequency f. Speed ​​margin With the rate of change of grid frequency As an input variable to the fuzzy logic controller, the virtual inertia gain coefficient is output through fuzzy logic inference. .

[0052] In fuzzy logic reasoning, the fuzzy states of input and output variables are defined as follows: For speed margin Define the fuzzy set as: {L (low / energy depletion), M (medium / moderate energy), H (high / sufficient energy)}, and define the domain as: ; Rate of change of power grid frequency Define the fuzzy set as: {S (small / slight perturbation), M (medium / moderate perturbation), B (large / severe perturbation)}, and define the domain as follows: ; Regarding the inertia gain coefficient Define the fuzzy set as: {Z (zero / stopping support), S (small), M (medium), B (large / full power support)}, and define the domain as: .

[0053] The membership functions for each fuzzy state adopt a combination of trapezoidal and triangular forms, such as... Figure 2 As shown.

[0054] For speed margin : The energy depletion state (L) adopts a left trapezoidal membership function, when At this time, the membership degree is 1, the forced output gain is 0, and it enters a safe defense state. The interval linearly decreases to 0; The general energy state (M) uses a triangular membership function, with the vertex set at... The bottom interval is ; The energy-sufficient state (H) adopts a right trapezoidal membership function. The interval is linearly increasing, in The membership degree of the interval is 1.

[0055] For the rate of change of power grid frequency Due to the physical boundary constraints of power grid operation, its maximum value is taken as... In actual calculations, it can be... by Standardize to limit the domain to : The slightly perturbed state (S) uses a left trapezoidal membership function. The membership degree is 1 within the range. The interval linearly decreases to 0 The moderately disturbed state (M) uses a triangular membership function, with the vertex set at... The bottom interval is ; The severely disturbed state (B) adopts a right trapezoidal membership function. The interval is linearly increasing, in The membership degree of the interval is 1.

[0056] Regarding the inertia gain coefficient : The zero-support state (Z) employs an extremely narrow triangular function, with the vertex set at 0. The interval linearly decreases to 0; The small support state (S) adopts a triangle membership function, with the vertex set at... The bottom interval is ; The middle support state (M) adopts the triangular membership function, with the vertex set at... The bottom interval is ; The large support state (B) adopts a right trapezoidal membership function, in The interval is linearly increasing, in The membership degree of the interval is 1.

[0057] Fuzzy logic reasoning divides the control interval into three functional regions based on different fuzzy states of the speed margin, including: Safety defense zone: When the speed margin is in the state of energy depletion, priority is given to ensuring the safe operation of the equipment. The virtual inertia gain coefficient is determined to be in the state of zero support or small support based on the power grid frequency change rate. Balanced adjustment zone: When the speed margin is in a normal energy state, the virtual inertia gain coefficient is linearly adjusted according to the grid frequency change rate. Strong support zone: When the speed margin is sufficient and the grid frequency change rate is under severe disturbance, the virtual inertia gain coefficient is in a state of strong support.

[0058] To implement the control strategy for the above functional area division, fuzzy logic reasoning adopts the fuzzy rules shown in Table 1.

[0059] Table 1:

[0060] Under the above rules and membership functions, we can obtain the following: Figure 3 The fuzzy control surface shown.

[0061] S23. Obtain the reference inertia, multiply the reference inertia by the current virtual inertia gain coefficient, and obtain the final virtual inertia coefficient.

[0062] The baseline virtual inertia coefficient is set to The final virtual inertia coefficient is calculated using the following formula: , S3. Based on the virtual inertia coefficient, generate corresponding control commands to control the dual-axis excitation synchronous condenser to change the electromagnetic torque, thereby realizing adaptive control of the virtual inertia.

[0063] Calculate the q-axis current reference value based on the final virtual inertia coefficient. :

[0064] Where p is the number of pole pairs of the motor. This represents the amplitude of the stator terminal voltage.

[0065] When the dual-axis excitation converter receives Then, closed-loop control is used to quickly adjust the q-axis excitation current, change the electromagnetic torque, thereby changing the rotational kinetic energy of the synchronous condenser rotor and releasing or absorbing active power to the power grid.

[0066] The technical effects of this embodiment will be explained below through specific working conditions.

[0067] When the power grid experiences severe disturbances and the frequency drops rapidly, if... .

[0068] like The output of the fuzzy inference system is: It can be seen that when the speed margin When sufficient, the virtual inertia can reach its maximum value, fully supporting frequency modulation; like The output of the fuzzy inference system is: It can be seen that when the speed margin At medium speeds, the virtual inertia can be 0.55 times the base value, providing some support for frequency modulation. like The output of the fuzzy inference system is: It can be seen that, although the speed margin Although insufficient, since the power grid was already in an emergency state, it still provided active power support to the power grid at 0.25 times the baseline value.

[0069] When the power grid frequency fluctuation is not significant, such as taking .

[0070] like The output of the fuzzy inference system is: It can be seen that, due to the small system disturbance, when the speed margin is... When sufficient, in order to avoid unnecessary power waste or system oscillation, the control logic only requires moderate support in the output. like The output of the fuzzy inference system is: It can be seen that when the speed margin is at a medium level, the fuzzy logic chooses to output a medium-strength inertia gain coefficient in order to balance the frequency support effect and energy conservation. like The output of the fuzzy inference system is: It can be seen that when the rotational speed is severely insufficient, protecting the camera from shutting down due to excessive power output is the top priority. Therefore, the fuzzy system forcibly cuts off the inertia support, and the output is 0.

[0071] Through the above control strategy, this embodiment realizes the adaptive adjustment of virtual inertia. Under the premise of ensuring the safe operation of the synchronous condenser, the virtual inertia output is dynamically adjusted according to the rotor speed state, realizing adaptive frequency support of "allocation on demand and within capacity", which effectively improves the frequency stability of the power grid.

[0072] Example 2: This embodiment provides a dual-axis excitation camera adaptive virtual inertia control system based on rotor speed, used to implement the dual-axis excitation camera virtual inertia adaptive control method as described in Embodiment 1, including: The data acquisition module is used to acquire real-time data on the rotor speed and grid frequency of the dual-axis excitation synchronous condenser. The speed margin calculation module is used to calculate the current rotor's energy reserve status as the speed margin based on the preset speed operating range; A fuzzy logic controller is used to calculate the grid frequency change rate based on the grid frequency in the real-time data, take the speed margin and the grid frequency change rate as input, use fuzzy logic reasoning to calculate the current virtual inertia gain coefficient, obtain the reference inertia, and multiply the reference inertia with the current virtual inertia gain coefficient to obtain the final virtual inertia coefficient. The excitation control module is used to generate corresponding control commands based on the final virtual inertia coefficient, and to control the dual-axis excitation synchronous condenser to change the electromagnetic torque based on the control commands.

[0073] Example 3: like Figure 4 As shown, this embodiment provides an electronic device, which may include: at least one processor, at least one network interface, a user interface, a memory, and at least one communication bus.

[0074] The communication bus can be used to enable communication between the various components mentioned above.

[0075] The user interface may include buttons, and optional user interfaces may also include standard wired interfaces and wireless interfaces.

[0076] The network interface may include, but is not limited to, Bluetooth modules, NFC modules, Wi-Fi modules, etc.

[0077] The processor may include one or more processing cores. It connects various parts of the electronic device via various interfaces and lines, executing instructions, programs, code sets, or instruction sets stored in memory, and accessing data stored in memory to perform various functions and process data. Optionally, the processor can be implemented using at least one hardware form of DSP, FPGA, or PLA. The processor may integrate one or more of the following: CPU, GPU, and modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content required for display; and the modem handles wireless communication. It is understood that the modem may also be implemented as a separate chip without being integrated into the processor.

[0078] The memory may include RAM or ROM. Optionally, the memory may include a non-transitory computer-readable medium. The memory can be used to store instructions, programs, code, code sets, or instruction sets. The memory may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-described method embodiments, etc.; the data storage area may store data involved in the above-described method embodiments, etc. Optionally, the memory may also be at least one storage device located remotely from the aforementioned processor. The memory, as a computer storage medium, may include an operating system, a network communication module, a user interface module, and a control application program. The processor can be used to call the control application program stored in the memory and execute the steps of the dual-axis excitation-adjusting camera virtual inertia adaptive control method mentioned in the foregoing embodiments.

[0079] Example 4: This embodiment provides a computer-readable storage medium storing instructions that, when executed on a computer or processor, cause the computer or processor to perform the above-described instructions. Figure 1 One or more steps in the illustrated embodiment. If the constituent modules of the above-described electronic device are implemented as software functional units and sold or used as independent products, they can be stored in the computer-readable storage medium.

[0080] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this specification are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in or transmitted through a computer-readable storage medium. The computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium accessible to a computer or a data storage device such as a server or data center that integrates one or more available media. The available media may be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., Digital Versatile Discs (DVDs)), or semiconductor media (e.g., Solid State Disks (SSDs)).

[0081] Those skilled in the art will understand that all or part of the processes in the method of Embodiment 1 described above can be implemented by a computer program instructing related hardware. This program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the methods described above. The aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks. Unless otherwise specified, the technical features of this embodiment and the implementation scheme can be combined arbitrarily.

[0082] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that the present invention is not limited to the described order of actions, because according to the present invention, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to the present invention.

[0083] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0084] The above description is merely an exemplary embodiment of the present invention and should not be construed as limiting the scope of the invention. Any equivalent changes and modifications made in accordance with the teachings of this invention are still within the scope of this invention. Those skilled in the art will readily conceive of embodiments of the invention upon considering the specification and practicing the disclosure herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not described herein. The specification and embodiments are to be considered exemplary only, and the scope and spirit of the invention are defined by the claims.

Claims

1. A method for adaptive virtual inertia control of a dual-axis excitation phase-shifting camera based on rotor speed, characterized in that, Including the following steps: S1. Obtain real-time data on the rotor speed and grid frequency of the dual-axis excitation synchronous condenser; S2. Calculate the virtual inertia coefficient based on the real-time data, including: S21. Calculate the current rotor energy reserve status as the speed margin based on the preset speed operating range; S22. Calculate the grid frequency change rate based on the grid frequency in the real-time data, use the speed margin and the grid frequency change rate as inputs to the fuzzy logic controller, and use fuzzy logic reasoning to calculate the current virtual inertia gain coefficient. S23. Obtain the reference inertia, multiply the reference inertia by the current virtual inertia gain coefficient, and obtain the final virtual inertia coefficient. S3. Based on the virtual inertia coefficient, generate corresponding control commands to control the dual-axis excitation synchronous condenser to change the electromagnetic torque, thereby realizing adaptive control of the virtual inertia.

2. The method for adaptive virtual inertia control of a dual-axis excitation camera based on rotor speed according to claim 1, characterized in that, The formula for calculating the speed margin in step S21 is as follows: ， In the formula, The current rotor speed, The lower limit of the preset operating speed range, This is the upper limit of the preset operating speed range.

3. The method for adaptive virtual inertia control of a dual-axis excitation camera based on rotor speed according to claim 1, characterized in that, In the aforementioned fuzzy logic reasoning: The fuzzy states of speed margin include energy depletion state, energy normal state, and energy sufficient state; The fuzzy states of the power grid frequency change rate include slight disturbance state, moderate disturbance state, and severe disturbance state; The fuzzy states of the virtual inertia gain coefficient include zero support state, small support state, medium support state, and large support state.

4. The method for adaptive virtual inertia control of a dual-axis excitation camera based on rotor speed according to claim 3, characterized in that, In the fuzzy logic reasoning, a membership function combining trapezoidal and triangular membership methods is used to quantify the fuzzy state, including: Regarding speed margin, a left trapezoidal membership function is used for the energy depletion state, a triangular membership function is used for the energy normal state, and a right trapezoidal membership function is used for the energy sufficient state. For the power grid frequency variation rate, a left trapezoidal membership function is used for slight disturbances, a triangular membership function is used for moderate disturbances, and a right trapezoidal membership function is used for severe disturbances.

5. The method for adaptive virtual inertia control of a dual-axis excitation camera based on rotor speed according to claim 3, characterized in that, The fuzzy logic reasoning divides the control interval into three functional areas based on different fuzzy states of the speed margin, including: In the safety defense zone, when the speed margin is in a state of energy depletion, priority is given to ensuring the safe operation of the equipment, and the virtual inertia gain coefficient is determined to be in a zero support state based on the power grid frequency change rate. In the balance adjustment zone, when the speed margin is in the normal energy state, the virtual inertia gain coefficient is linearly adjusted according to the grid frequency change rate. In the strong support region, when the speed margin is in a state of sufficient energy and the grid frequency change rate is in a state of severe disturbance, the virtual inertia gain coefficient is in a state of large support.

6. The method for adaptive virtual inertia control of a dual-axis excitation camera based on rotor speed according to claim 5, characterized in that, The fuzzy logic reasoning, as a control strategy for implementing the functional area division, adopts the following fuzzy rules: When the speed margin is in an energy depletion state, if the grid frequency change rate is in a slight or moderate disturbance state, the virtual inertia gain coefficient is in a zero support state; if the grid frequency change rate is in a severe disturbance state, the virtual inertia gain coefficient is in a small support state. When the speed margin is in a normal energy state, if the grid frequency change rate is in a slight disturbance state, the virtual inertia gain coefficient is in a small support state; if the grid frequency change rate is in a moderate or severe disturbance state, the virtual inertia gain coefficient is in a medium support state. When the speed margin is in a state of sufficient energy, if the grid frequency change rate is in a state of slight disturbance, the virtual inertia gain coefficient is in a state of medium support; if the grid frequency change rate is in a state of moderate or severe disturbance, the virtual inertia gain coefficient is in a state of large support.

7. The method for adaptive virtual inertia control of a dual-axis excitation camera based on rotor speed according to claim 1, characterized in that, Step S3 is as follows: The reference value of the q-axis current is calculated based on the final virtual inertia coefficient, and the excitation current is adjusted through closed-loop control to change the electromagnetic torque.

8. A dual-axis excitation camera adaptive virtual inertia control system based on rotor speed, characterized in that, The method for implementing the dual-axis excitation camera virtual inertia adaptive control as described in any one of claims 1 to 7 includes: The data acquisition module is used to acquire real-time data on the rotor speed and grid frequency of the dual-axis excitation synchronous condenser. The speed margin calculation module is used to calculate the current rotor's energy reserve status as the speed margin based on the preset speed operating range; A fuzzy logic controller is used to calculate the grid frequency change rate based on the grid frequency in the real-time data, take the speed margin and the grid frequency change rate as input, use fuzzy logic reasoning to calculate the current virtual inertia gain coefficient, obtain the reference inertia, and multiply the reference inertia with the current virtual inertia gain coefficient to obtain the final virtual inertia coefficient. The excitation control module is used to generate corresponding control commands based on the final virtual inertia coefficient, and to control the dual-axis excitation synchronous condenser to change the electromagnetic torque based on the control commands.

9. A computer device, the computer device comprising a memory, a processor, and a computer program, characterized in that, When the computer program is executed by the processor, it implements the dual-axis excitation camera virtual inertia adaptive control method as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the dual-axis excitation camera virtual inertia adaptive control method as described in any one of claims 1 to 7.

Images