Low-frequency new energy gathering system stability control collaborative semi-physical simulation verification method

Abstract

The application discloses a kind of low-frequency new energy collection system stability control collaborative semi-physical simulation verification methods, it is related to new energy power system simulation technical field, including the semi-physical simulation platform of parallel control structure is constructed, and unified time synchronization and data acquisition mechanism are established;Low-frequency disturbance scene is constructed in semi-physical simulation platform and triggers stability control priority action;Around the moment of stability control action triggering, identify control right transfer transient and carry out coordination strategy comparison verification.The method is described in the application, by constructing transient analysis window, and from power change, energy change and frequency recovery trajectory multiple dimensions quantitative analysis is carried out to system dynamic behavior, realizes the identifiable and determinable of control mismatch risk;Combined with baseline strategy and coordination strategy comparison verification, the quantitative evaluation method of control strategy effect is established.

Description

Technical Field

[0001] This invention relates to the field of new energy power system simulation technology, specifically to a semi-physical simulation verification method for the stability control and coordination of a low-frequency new energy collection system. Background Technology

[0002] With the rapid development of large-scale grid connection and long-distance collection and transmission of new energy sources, new energy power sources, represented by wind power and photovoltaics, are gradually exhibiting low inertia and weak damping characteristics, making the dynamic stability problem of the power system under low-frequency disturbances increasingly prominent. To ensure the safe and stable operation of the system, a multi-level control system with a safety and stability control system as the core is usually constructed in the new energy collection and transmission system. This includes a continuous regulation control system (such as primary frequency regulation by energy storage) and a safety and stability control system (such as load shedding, power generation limiting, and grid disconnection measures), used to regulate and control the system frequency and power balance at different time scales. In recent years, with the increasing complexity of power grid operation, the role of the stability control system in new energy collection and transmission has become increasingly critical. It not only undertakes the function of rapid safety protection after faults, but also gradually participates in the dynamic regulation during the low-frequency stabilization process. At the same time, hardware-in-the-loop (HIL) simulation technology, because it can connect actual stability control devices and controllers to the simulation environment, has gradually become an important technical means to verify new energy stability control strategies and their synergistic control effects, especially under complex operating conditions and extreme disturbance scenarios, providing a test environment that is closer to the actual operating state.

[0003] However, existing technologies still have shortcomings in the simulation and verification of low-frequency disturbances and the synergistic effects of stability control systems in new energy sources. Traditional simulation methods are mostly based on a single control mode or assume sequential execution of control strategies, making it difficult to accurately reflect the dynamic interaction between the continuous regulation system and the stability control system when they operate in parallel. This is especially true during the priority triggering process of stability control, where the control system's override, weakening, or takeover of the continuous regulation system leads to a transfer of control power, lacking effective modeling and analysis methods to truly reflect the dominant role of the stability control system in the low-frequency stabilization process. Existing simulation methods typically employ offline or non-uniform time-base data processing, resulting in insufficient time synchronization accuracy between different devices. This leads to time mismatches between the stability control action trigger signal, the continuous regulation response, and system frequency changes, making it difficult to accurately characterize the dynamic process at the moment of stability control intervention. Existing technologies primarily focus on steady-state or quasi-steady-state performance evaluation, lacking systematic quantitative analysis methods for issues such as power surges, energy impacts, and frequency recovery path distortions that may occur during the priority triggering process of stability control. This makes it impossible to effectively assess the impact of different stability control strategies and their synergistic mechanisms on the system's dynamic behavior. Therefore, existing methods are insufficient to meet the high-precision verification requirements of transient behavior under the coordinated action of multiple control systems with the stability control system as the core in low-frequency gathering and transmission systems of new energy sources. In particular, they cannot effectively identify and evaluate the impact of the control transfer process under the dominance of stability control on the system frequency recovery path and energy constraints. Summary of the Invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problem solved by this invention is that existing simulation and stability control verification methods for low-frequency disturbances in new energy sources cannot accurately depict the dynamic interaction between the parallel operation of the continuous regulation system and the stability control system and the process of stability control priority takeover. The lack of a unified time reference leads to time mismatch in multi-source data, making it difficult to identify transient characteristics of stability control intervention. It is also impossible to quantitatively analyze and evaluate the power mutation and energy shock caused by the transfer of control power during the stability control triggering process. Furthermore, the invention addresses the problem of how to achieve transient identification of control power transfer under stability control dominance based on hardware-in-the-loop simulation and multi-strategy comparison verification.

[0006] To address the aforementioned technical problems, this invention provides the following technical solution: a hardware-in-the-loop simulation verification method for the coordinated stability control of a low-frequency renewable energy aggregation system, comprising constructing a hardware-in-the-loop simulation platform with parallel control structures and establishing a unified time synchronization and data acquisition mechanism; constructing a low-frequency disturbance scenario in the hardware-in-the-loop simulation platform and triggering stability control priority actions; identifying the transient state of control transfer around the triggering time of the stability control actions and performing comparative verification of coordination strategies.

[0007] As a preferred embodiment of the hardware-in-the-loop simulation verification method for the stable control of a low-frequency renewable energy aggregation system described in this invention, the hardware-in-the-loop simulation platform for constructing a parallel control structure includes: establishing a real-time digital simulation model of the renewable energy aggregation and transmission system to describe the dynamic operating relationship between renewable energy sources, aggregation networks, transmission channels, and receiving-end power grids; reflecting the system operation process by real-time calculation of power, frequency, and voltage status of grid nodes; connecting a continuously adjusting physical controller and a stable control device to the real-time simulation system through an input / output interface module, outputting system operating status information to the control device in real time, and simultaneously receiving control commands returned by the control device; during the simulation operation, the real-time simulation system continuously updates the system operating status according to a preset time step and sends the system status data at each moment to the control device; after receiving the system status data, the control device generates control commands according to its internal control logic and feeds them back to the simulation system, which then updates the system operating status based on the control commands.

[0008] As a preferred embodiment of the hardware-in-the-loop simulation verification method for the stable control and coordination of the low-frequency new energy aggregation system described in this invention, the establishment of a unified time synchronization and data acquisition mechanism includes: setting a unified time reference module in the hardware-in-the-loop simulation platform to send synchronization time signals to the real-time simulation system and each control device, so that each device operates under the same time reference; during the data acquisition process, adding time identifier information to each data record to mark the data sampling time; continuously acquiring system operation data during system operation; and in the data processing stage, uniformly caching and sorting the data uploaded by different devices, and when there is a data time deviation between different devices, aligning the data through a time matching method, finding the data record with the smallest time difference based on the time identifier information and considering it as data from the same sampling time.

[0009] As a preferred embodiment of the hardware-in-the-loop simulation verification method for the stability control coordination of the low-frequency renewable energy aggregation system described in this invention, the construction of the low-frequency disturbance scenario includes: setting a disturbance injection module in the real-time simulation system to simulate power imbalance or network structure change events that occur during power system operation; during the simulation operation phase, injecting power disturbances into the system at specified times to change the balance between the original power generation and load power, causing a system frequency shift; the real-time simulation system dynamically calculates the frequency change process according to the grid operation characteristics; when the system's power generation is less than the load demand, the system frequency gradually decreases, and when the power generation recovers or the load decreases, the system frequency gradually rises; by changing the disturbance location, disturbance duration, and disturbance amplitude, various low-frequency operation scenarios are constructed in the simulation system to bring the system into the trigger range of the stability control device.

[0010] As a preferred embodiment of the hardware-in-the-loop simulation verification method for the stabilization and coordination of a low-frequency renewable energy aggregation system described in this invention, the triggering of stabilization and control priority actions includes: setting a stabilization and control trigger judgment module in the real-time simulation system to continuously monitor the frequency change status during system operation and determine whether stabilization and control actions need to be executed according to preset trigger rules; when the system frequency drops below a preset frequency threshold and remains below it for a set period of time, it is determined that the system has entered a low-frequency abnormal operating state, triggering the stabilization and control device to execute corresponding control actions; the stabilization and control actions include cutting off part of the load, limiting the output of some renewable energy sources, or disconnecting some transmission channels; during the execution of the stabilization and control actions, the stabilization and control device simultaneously sends control constraint signals to the continuous regulation controller, so that the control output of the continuous regulation system is subject to priority restriction or partially weakened, so that the system control is gradually transferred from the continuous regulation system to the stabilization and control system.

[0011] As a preferred embodiment of the hardware-in-the-loop simulation verification method for the stable control and coordination of the low-frequency new energy aggregation system described in this invention, the identification of transient control transfer includes: establishing a transient analysis time interval centered on the trigger time of the stable control action; continuously recording the changes in system frequency, output power of the energy storage device, and changes in control commands within the transient analysis time interval; analyzing the collected data to identify the speed and direction of system power changes during the control transfer phase, and observing whether power abrupt changes or reverse power changes occur; simultaneously, based on the output power changes of the energy storage device within the transient analysis time interval, cumulatively analyzing the energy release or absorption of the energy storage system to determine whether transient energy impacts occur during the control transfer process; when the system experiences excessively rapid power changes, abnormal energy changes, or significant deviations in the frequency recovery trajectory during the control transfer phase, it is determined that the system has a risk of control mismatch during the control transfer phase.

[0012] As a preferred embodiment of the hardware-in-the-loop simulation verification method for the stabilization and coordination of a low-frequency renewable energy aggregation system described in this invention, the following steps are included: The comparative verification of the coordination strategy involves setting up a baseline control strategy without a control coordination mechanism and an improved control strategy including a control coordination mechanism under the same disturbance conditions and initial operating state for hardware-in-the-loop simulation verification. In the baseline control strategy, the continuous regulation system and the stabilization and control system operate independently according to their respective control logics. In the coordinated control strategy, the output change of the continuous regulation system is buffered during the control transfer phase, and the target power of the continuous regulation controller is gradually aligned with the stabilization and control target through a control command alignment mechanism. During the simulation operation, the impact of different control methods on the system's dynamic behavior is analyzed by comparing the system's frequency recovery process, power change process, and energy change under different control strategies, and the role of the control coordination mechanism in suppressing transient shocks is identified.

[0013] Another objective of this invention is to provide a hardware-in-the-loop simulation verification system for the stable control and coordination of a low-frequency new energy collection system.

[0014] As a preferred embodiment of the hardware-in-the-loop simulation verification system for the stability control coordination of the low-frequency new energy aggregation system described in this invention, the system includes: a simulation platform construction module, a low-frequency disturbance processing module, and a comparison verification module; the simulation platform construction module is used to construct a hardware-in-the-loop simulation platform with parallel control structures and establish a unified time synchronization and data acquisition mechanism; the low-frequency disturbance processing module is used to construct low-frequency disturbance scenarios in the hardware-in-the-loop simulation platform and trigger stability control priority actions; the comparison verification module is used to identify the transient state of control transfer around the trigger time of the stability control action and perform comparative verification of the coordination strategy.

[0015] A computer device includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement a semi-physical simulation verification method for the stable control and coordination of a low-frequency new energy collection system.

[0016] A computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the steps of a hardware-in-the-loop simulation verification method for the stable control and coordination of a low-frequency new energy collection system are implemented.

[0017] The beneficial effects of this invention are as follows: The hardware-in-the-loop simulation verification method for the stable control coordination of a low-frequency renewable energy aggregation system provided by this invention achieves synchronous modeling and data alignment of multiple control systems in the same physical process by constructing a parallel control structure and introducing a unified time reference, ensuring the temporal consistency and causal traceability of the transient process; by constructing a parameterizable low-frequency disturbance scenario and establishing a stable control triggering mechanism based on frequency and duration, the repeatable excitation and precise positioning of the control transfer process are realized; furthermore, by constructing a transient analysis window and quantitatively analyzing the dynamic behavior of the system from multiple dimensions such as power change, energy change, and frequency recovery trajectory, the identification and determination of control mismatch risk are realized; and by combining the comparative verification of baseline strategy and coordination strategy, a quantitative evaluation method for the effect of control strategy is established. Attached Figure Description

[0018] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. 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.

[0019] Figure 1 The flowchart below shows the overall process of a hardware-in-the-loop simulation verification method for the stable control of a low-frequency new energy collection system provided in Embodiment 1 of the present invention.

[0020] Figure 2 The transient identification flowchart is provided for a semi-physical simulation verification method for stable control and coordination of a low-frequency new energy collection system according to Embodiment 2 of the present invention. Detailed Implementation

[0021] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0022] Example 1, referring to Figures 1-2 As an embodiment of the present invention, a semi-physical simulation verification method for the stable control and coordination of a low-frequency new energy collection system is provided, comprising: S1: Construct a hardware-in-the-loop simulation platform with parallel control structures and establish a unified time synchronization and data acquisition mechanism.

[0023] Furthermore, the construction of a hardware-in-the-loop (HIL) simulation platform for a parallel control structure includes: establishing a real-time digital simulation model of the new energy collection and transmission system to describe the dynamic operating relationship between the new energy power source, the collection network, the transmission channel, and the receiving-end power grid; reflecting the system operation process by calculating the power, frequency, and voltage status of the grid nodes in real time; connecting the continuously regulating physical controller and the stabilization device to the real-time simulation system through input / output interface modules, outputting system operating status information to the control device in real time, and receiving control commands returned by the control device; during the simulation operation, the real-time simulation system continuously updates the system operating status according to a preset time step and sends the system status data at each moment to the control device; after receiving the system status data, the control device generates control commands according to its internal control logic and feeds them back to the simulation system, which then updates the system operating status based on the control commands.

[0024] It should also be noted that a preferred scheme for constructing a hardware-in-the-loop simulation platform with a parallel control structure specifically includes, firstly, setting a unified discrete-time reference. ,in Indicates that it is based on a unified time base The global discrete-time index. , This is the start time of the simulation. This serves as a unified calculation step size for the hardware-in-the-loop (HIL) simulation platform. A unified time base is applied simultaneously to the real-time digital simulation system, the continuously adjustable physical controller, and the stabilization device, ensuring that all state calculations, control command generation, and data recording occur on the same time axis. The reason for using this unified time base is that this invention will subsequently identify the control transfer transients within the millisecond to second range before and after the triggering of stabilization priority actions. If each device uses an independent local time, the sequence of system frequency changes, energy storage power changes, and stabilization action signals cannot be reliably correlated, making it impossible to quantitatively assess the risk of control mismatch. First, a real-time digital simulation model of the renewable energy aggregation and transmission system is established. The verification focus of this invention is on the active power balance, power dynamics of the transmission channel, continuous energy storage regulation response, and voltage changes at key nodes of the renewable energy aggregation and transmission system under low-frequency disturbances. Therefore, a unified time base is crucial. The system state vector is defined as: , in, Indicates time The system state vector, This represents the equivalent phase angle of the new energy collection and transmission section. Indicates the system frequency. Indicates the voltage amplitude of the key busbar. This represents the total power generation of the system. Indicates the total load power of the system. This indicates the actual active power of the transmitting channel. Indicates the actual output active power of the energy storage device, superscript This indicates the transpose. All state variables can be obtained by the real-time digital simulation system at each discrete time step through solving the network equations and updating the control input, where... , , and It will directly participate in the construction of low-frequency disturbance scenarios and the calculation of stability triggering criteria.

[0025] The state update process is specifically broken down into phase angle update, frequency update, power output channel update, energy storage power update, and node voltage update. First, the phase angle is discretely updated according to the frequency deviation: , in, Indicates the next moment The equal phase angle, Indicates the system's rated frequency. This represents the rate of change of phase angle. It is used to accumulate the frequency offset into the phase angle state required for calculating the output channel power, thus establishing a quantitative relationship between subsequent channel power changes and system frequency dynamics. Secondly, the output channel power is updated according to a first-order dynamic relationship: , in, This indicates the active power of the transmitting channel at the next moment. This represents the power response time constant of the output channel. This represents the equivalent conversion factor from phase angle difference to channel power. This represents the reference phase angle of the receiving-end power grid. The channel power will not instantaneously equal the target value determined by the phase angle difference, but will gradually approximate it according to the dynamic characteristics of the sending channel itself, thus reflecting the true response inertia of the sending system in the hardware-in-the-loop simulation. Furthermore, the actual output power of the energy storage device is updated according to the first-order execution model driven by the control commands: , in, This indicates the actual output active power of the energy storage device at the next moment. Indicates time The energy storage target power command is generated by combining a continuously adjustable physical controller with stability constraints. This represents the time constant for energy storage power execution. The controller output is not directly equal to the actual energy storage output, but rather it is tracked step by step through the energy storage power loop and the execution stage. Furthermore, the system frequency is updated based on the system power balance relationship. , in, Indicates the system frequency at the next moment. This represents the system's equivalent inertia parameter. This indicates the power generation capacity after adjustments for power stabilization and limiting. This indicates the load power after load reduction and stabilization correction. This indicates the input power of external disturbance. This represents the equivalent damping coefficient. The purpose of this update is twofold: firstly, to allow the impact of low-frequency disturbances on the system frequency to be directly reflected through power imbalance; and secondly, to ensure that stabilization actions, continuous regulation actions, and disturbance injection are all incorporated into the frequency dynamic equations in a unified manner. It should be noted that if disturbance injection has not yet been performed, then [the value can be omitted]. When constructing a low-frequency disturbance scenario, then... A corresponding perturbation value is assigned, and this variable has been reserved as a unified input interface for subsequent scenario construction. Finally, the voltage of the key node is updated using a first-order target tracking model: , in, Indicates the node voltage at the next moment. Indicates the target value for voltage control. This represents the voltage response time constant. This formula is used to reflect the dynamic processes such as reactive power support and node voltage recovery in the new energy aggregation system. Although this invention mainly focuses on the low-frequency stabilization process, changes in node voltage will affect the actual operating environment of the continuous regulation system and the stability control system. Therefore, this quantity is retained in the state update.

[0026] In state update relationships , and All of these stem from the closed-loop interaction between the real-time digital simulation system and the external control device; therefore, further explanation is needed regarding the operation of the continuously adjusting physical controller, the stabilizing device, and the input / output interface module. For this purpose, at any given time... The real-time digital simulation system starts from the current state vector Extract the measurement output vector: , in, This represents the measurement output vector sent to the external control device. The rate of change of frequency is expressed as: , in, This represents the system frequency at the previous discrete moment. Both the continuously adjusting physical controller and the stabilization device can obtain information on frequency, rate of change of frequency, voltage, output channel power, and actual energy storage output power under the same time base, thereby ensuring that different control devices in the parallel control structure make decisions based on the same system state. In this embodiment, the continuously adjusting physical controller uses proportional-integral control logic based on frequency deviation to generate the original energy storage power command. First, the frequency deviation is calculated: , in, Indicates time The frequency deviation. Then update the controller integral state: , in, Indicates the current integration state. This indicates the integral state at the previous moment. This represents the integral coefficient. To prevent excessive integral accumulation from causing controller saturation, an integral limit is set, represented as: , in, and These represent the upper and lower limits of the integral state, respectively. An unlimited, continuously adjustable output is then generated: , in, Indicates the continuous adjustment of the physical controller at time... The original control output, This represents the proportionality coefficient. Considering the power limit of energy storage devices, the limited target power command for energy storage is further obtained, expressed as: , in, This represents the target energy storage power after the output of the continuously variable controller is limited. This indicates the maximum allowable charge and discharge power of the energy storage device. At this point, the continuously adjusted physical controller has been adjusted based on the measurement vectors provided by the real-time simulation system. The frequency state is obtained and control inputs that can be directly used in the energy storage power execution model are generated.

[0027] Meanwhile, in this step, the stabilization control device is connected to the real-time simulation system in parallel with the continuously adjusting physical controller. Considering that the primary task is platform construction rather than stabilization triggering, the initial output of the stabilization control device is set as follows before entering the stabilization triggering range: , in, This indicates the amount of renewable energy generation limited by the stabilization and control device. This indicates the load reduction output by the load control device. This indicates the status variable for disconnection of the transmission channel. This indicates that the channel remains in a network-connected state. Time indicates channel uncoupling; This represents the retention coefficient of the steady-state control for the output of the continuous regulation controller. This indicates that all continuous regulation outputs are effective. This set of variables is explicitly initialized in this step to ensure that the system can operate in the conventional mode of "parallel control, not yet under stable control" during the platform setup phase. After the stable control priority action is triggered, the stable control device will change the values ​​of these variables, gradually transferring control from the continuous regulation system to the stable control system. Based on the outputs of the continuous regulation physical controller and the stable control device, the real-time digital simulation system further constructs a comprehensive control input acting on the simulated object. First, the effective output of continuous regulation is defined as: , in, This indicates the target power command for energy storage after the stabilization constraints have been applied. Secondly, stabilization corrections are made for power generation, load power, and the status of the transmission channels. , , , in, This represents the total power generation capacity after adjustments for power stabilization and limiting. This represents the total load power after load reduction and stabilization correction. This indicates the available power status of the output channel after the stabilization and de-splitting correction.

[0028] It should be noted that establishing a unified time synchronization and data acquisition mechanism includes: setting up a unified time reference module in the hardware-in-the-loop simulation platform to send synchronization time signals to the real-time simulation system and various control devices, so that all devices operate under the same time reference; during the data acquisition process, marking the data sampling time by adding time identifier information to each data record; continuously acquiring system operation data during system operation; and in the data processing stage, uniformly caching and sorting the data uploaded by different devices, and aligning the data by using a time matching method when there is a time deviation between different devices, finding the data record with the smallest time difference based on the time identifier information and considering it as data from the same sampling time.

[0029] It should also be noted that a preferred scheme for establishing a unified time synchronization and data acquisition mechanism specifically includes, after completing the definition of the platform dynamic model and control input structure, establishing a unified time synchronization and data acquisition mechanism to ensure that subsequent event localization and transient analysis are based on the same time reference. To this end, a unified time reference module is set up in the hardware-in-the-loop simulation platform, and local original record sequences are established for the real-time digital simulation system, the continuously adjustable physical controller, and the stabilization device, respectively. For the real-time digital simulation system, its first... A single original record is defined as: , For a continuously adjustable physical controller, its first... A single original record is defined as: , For the stability control device, its first A single original record is defined as: , in, , and These represent one raw data record uploaded by the real-time digital simulation system, the continuously adjustable physical controller, and the stabilization device, respectively. , and These represent the local time identifiers of the corresponding records; , and These represent the data content uploaded by the corresponding devices. Here we use... , and Distinguish between the local record serial numbers of the three types of devices. Normally, At least include , , and ; At least include , and ; At least include , , and Although different devices share a unified time base module, their local time signatures may still have slight deviations due to interface caching, communication latency, and controller scan cycles. Therefore, this embodiment uses a unified time base during the data processing stage. Perform time matching on multi-source data. For a unified time point... The record with the smallest time difference is selected from the original records of the continuously adjusting physical controller and the stabilizing device, respectively: , , in, Indicates the moment of unification The record number closest to the local time of the continuously adjustable physical controller. Indicates the moment of unification The record number closest to the local time of the stability control device. This indicates the sequence number that minimizes the absolute time difference. To prevent data with excessively large time deviations from being mismatched, a maximum allowable time difference threshold is also set. When the following conditions are met: At that time, the corresponding records are received as a unified time. Valid data is used; if the above conditions are not met, valid data confirmed at the previous unified time point is used. This processing rule of "recent time matching + threshold verification + previous value preservation" ensures that a continuous and analyzable unified time series is formed even with slight differences in device sampling periods, while preventing mismatches due to abnormal delays. After completing time matching, the aligned integrated data record is constructed: , in, Indicates a unified time point The integrated alignment data record includes synchronous data from the real-time digital simulation system, the continuously adjustable physical controller, and the stabilization device.

[0030] It should also be noted that, in the scenario of low-frequency aggregation and transmission of new energy, this approach aims to unify the logically parallel, heterogeneously implemented, and temporally asynchronous continuous regulation and control systems into a closed-loop verification environment that reflects real dynamic interactions. Existing technologies can typically verify the independent functions of continuous regulation controllers or control devices separately, but they struggle to address the consistency of modeling when two types of control systems simultaneously perceive, make decisions, and influence each other during the same disturbance process. This is particularly problematic when dealing with timing misalignments and causal distortions caused by different sampling periods, communication delays, and the invisibility of internal control states. Without establishing a unified state representation, a unified time reference, and a unified data alignment mechanism at the platform level, the subsequently observed "control mismatch" is likely just a recording error or interface error, rather than a real physical phenomenon. By constructing a parallel control structure, unifying the discrete time axis, establishing state update equations, and implementing a multi-source data alignment mechanism, this approach essentially solves the deep-seated technical problem of synchronous observability and traceable modeling of multiple control systems in the same physical process. This allows subsequent identification of control transfer transients to be based on reliable data and reliable timing.

[0031] S2: Construct a low-frequency disturbance scenario in the hardware-in-the-loop simulation platform and trigger a stabilization priority action.

[0032] Furthermore, constructing low-frequency disturbance scenarios involves setting up a disturbance injection module in the real-time simulation system to simulate power imbalances or network structure changes that occur during power system operation. During the simulation operation phase, power disturbances are injected into the system at specified times to change the original balance between power generation and load power, causing a system frequency shift. The real-time simulation system dynamically calculates the frequency change process based on the grid operation characteristics. When the system's power generation is less than the load demand, the system frequency gradually decreases, while when power generation recovers or the load decreases, the system frequency gradually rises. By changing the disturbance location, duration, and amplitude, various low-frequency operating scenarios are constructed in the simulation system to bring the system into the trigger range of the stability control device.

[0033] It should also be noted that a preferred scheme for constructing low-frequency disturbance scenarios specifically includes setting up a disturbance injection module in the real-time digital simulation system and performing a unified time series. Select the start time of the disturbance A power disturbance is injected into the system after this moment. The disturbance is defined as the equivalent disturbance affecting the system's power balance, expressed as: , in, Indicates at time The disturbance power, Indicates the amplitude of the disturbance. This represents the time variation factor of the disturbance, and its value range is... This is used to describe the changing process of a disturbance. When Time represents a constant disturbance, when When it changes over time, it represents a gradual disturbance. This can be achieved by adjusting... and Different types of low-frequency disturbance scenarios can be constructed. Under the influence of the disturbance, the system power imbalance is defined as: , in, Indicates at time The system's net power imbalance. It should be noted that the term used here refers to... , , A stabilization correction interface has been reserved, so even if the stabilization action has not been triggered in this step, its expression form remains consistent with S1.

[0034] Under this power imbalance, the system frequency evolves according to the discrete update relationship given in S1: , From this update relationship, we can see that when At that time, the system frequency It will gradually decrease; when At that time, the system frequency will rise again. This can be achieved by selecting different disturbance amplitudes. Disturbance start time and the duration range of the disturbance It can control the rate and magnitude of frequency decrease, thereby allowing the system frequency to enter the stable trigger range. Indicates the time when the disturbance ends.

[0035] It should be noted that triggering the stability control priority action includes setting a stability control trigger judgment module in the real-time simulation system to continuously monitor the frequency change status during system operation and determine whether a stability control action needs to be executed according to preset trigger rules; when the system frequency drops below the preset frequency threshold and remains below it for a set period of time, it is determined that the system has entered a low-frequency abnormal operation state, triggering the stability control device to execute corresponding control actions; stability control actions include cutting off part of the load, limiting the output of some new energy sources, or disconnecting some transmission channels; during the execution of the stability control action, the stability control device simultaneously sends control constraint signals to the continuous regulation controller, so that the control output of the continuous regulation system is subject to priority restriction or partially weakened, so that the system control right is gradually transferred from the continuous regulation system to the stability control system.

[0036] It should also be noted that a preferred scheme for triggering stabilization priority actions specifically includes a stabilization priority action triggering mechanism that, after completing the disturbance injection and obtaining the frequency sequence... Subsequently, a stability trigger judgment module was set up in the real-time simulation system to continuously monitor the frequency change process. This module is based on a unified time series. Determine whether the frequency has entered the low-frequency abnormal range. First, define the frequency threshold. And define a low-frequency determination function: , in, Indicates at time Is the system in a low-frequency range? Further define the low-frequency duration variable: , in, This indicates the duration during which the frequency remains below a threshold. This represents the accumulated "low-frequency duration" of the system at the previous discrete time step. The duration is reset to zero when the system frequency rises above the threshold again. This is triggered when the following conditions are met: and When the system is deemed to have entered a low-frequency abnormal operating state, among which... This represents the low-frequency duration threshold. The minimum time interval that satisfies this condition is defined as the stabilization trigger moment. At that moment This will serve as the center point for the transient analysis window of control transfer. After triggering the stabilization action, the stabilization device begins to output control inputs and updates the control correction variables.

[0037] Furthermore, the stability control actions are executed according to a layered strategy of "prioritizing power generation limitation, compensating for load reduction, and protecting transmission channels," specifically including the following process: First, based on the degree of system frequency deviation, the demand for power generation limitation of new energy sources is analyzed, and the limitation amount is allocated to each new energy output unit according to a preset allocation coefficient, so that the output of new energy sources is reduced proportionally; when the system power imbalance is still not effectively suppressed after power generation limitation, a load shedding strategy is further triggered, and some loads are gradually shedding according to the load importance level or a preset shedding sequence to quickly restore power balance; at the same time, to prevent overload or instability of transmission channels, when the power of the transmission channel is detected to exceed the maximum power, a channel disconnection strategy is triggered, and the system structure is reconfigured by disconnecting the corresponding transmission channel, thereby reducing the risk of system operation. Various stability control actions can be triggered sequentially according to a preset priority order, or they can be combined and executed according to the real-time power imbalance degree, thus forming a multi-dimensional collaborative stability control response mechanism. The stability control device performs unified calculations on each control quantity, expressed as: , , , in, Indicates the power generation capacity of new energy sources under restriction. Indicates the load shedding power. and These represent the proportional coefficients for limiting power generation and reducing load, respectively. Indicates the channel unblocking status. This represents the maximum allowable power of the channel. Simultaneously, to reflect the constraint effect of stability control priority on the continuous regulation system, the continuous regulation retention coefficient is updated: , in, This indicates that the effective proportion of the continuously adjustable output is... Indicates the minimum retention ratio. Indicates the rate of attenuation. This represents the discrete time index corresponding to the trigger moment of the stabilization action. This formula shows that after stabilization is triggered, the effect of the continuous control controller gradually weakens, thereby transferring control from the continuous control system to the stabilization system. Subsequently, relevant quantities in the integrated control input are updated: , Thus, the system has completed the transition from a "state without stability control intervention" to a "state dominated by stability control" under a unified time benchmark.

[0038] It should also be noted that, in order to solve the problem of how to construct a repeatable low-frequency scenario in a semi-physical simulation environment that both conforms to the physical mechanism of a new energy low-frequency collection and transmission system and can stably trigger the control transfer process, and to transform the stabilization priority takeover process from engineering experience logic into a calculable, locatable, and reproducible experimental event, the existing technology often only uses disturbances to observe whether the frequency exceeds the limit, lacking a unified description of the complete chain of "disturbance - frequency drop - stabilization trigger - continuous adjustment limitation," and especially making it difficult to accurately anchor the stabilization takeover process to a specific discrete moment and further use it as the central event for subsequent transient analysis. The difficulty of this problem lies in the fact that if the disturbance construction is too idealized, it cannot truly stimulate the low-frequency vulnerability of the system; if the triggering logic relies only on a single threshold, it is easy to have false triggers, premature triggers, or triggering time drift, resulting in an inability to fairly compare different strategies. By establishing a parameterizable disturbance injection model, a unified power imbalance expression, and a dual-criteria triggering mechanism of "frequency threshold + duration", and further using the continuous adjustment retention coefficient to describe the stabilization priority coverage process, the problem of how to reliably reconstruct the starting point of control transfer and its causes and consequences in experiments is actually solved. Ultimately, the control transfer is no longer a discrete switching, but an analyzable dynamic evolution process.

[0039] S3: Identify the transient state of control transfer around the trigger moment of the stabilization action and compare and verify the coordination strategy.

[0040] Furthermore, identifying transient control transfers includes establishing a transient analysis time interval centered on the trigger moment of the stabilization action, continuously recording system frequency changes, energy storage device output power, and control command changes within this interval; analyzing the collected data to identify the speed and direction of system power changes during the control transfer phase, and observing whether power abrupt changes or reverse power changes occur; simultaneously, based on the output power changes of the energy storage device within the transient analysis time interval, cumulatively analyzing the energy release or absorption of the energy storage system to determine whether transient energy impacts occur during the control transfer process; when the system experiences excessively rapid power changes, abnormal energy changes, or significant deviations in the frequency recovery trajectory during the control transfer phase, it is determined that the system faces a control mismatch risk during the control transfer phase.

[0041] It should also be noted that the reference Figure 2 A preferred scheme for identifying control transfer transients specifically includes, in this embodiment, identifying control transfer transients by stabilizing the trigger time. Construct the transient analysis time interval around the center: , in, Indicates the time interval for transient analysis. and These represent the offset counts selected before and after the trigger. This time interval is used to fully cover the entire process of "no intervention before stability control—initial triggering of stability control—transfer of control—gradual system recovery." Within this interval, data is uniformly aligned. Extracting analysis variables: , in, This represents the dataset used for transient analysis. The change in energy storage power between adjacent time steps is defined as follows: , in, This represents the actual output power of the energy storage at the previous discrete moment, and further defines the rate of power change: , in, This represents the rate of change of energy storage power. When the following conditions are met... At that time, it is determined that at time... A power surge occurred, in which A preset rate of change threshold is set. Simultaneously, the change in power direction is determined by the power sign relationship, expressed as: , When this condition is met, it is determined that the energy storage power has changed in the opposite direction, meaning the system exhibits inconsistent response behavior during the control transfer process. Within the transient interval, the energy storage output power is cumulatively calculated: , in, This represents the energy change of the energy storage system during the transient phase. This represents the number of discrete time steps selected relative to the stabilization trigger moment. This represents the number of discrete time steps selected relative to the stabilization trigger moment. A reference energy trajectory is introduced to determine the presence of abnormal energy impacts. Its source is the calculation result of the baseline control strategy under the same disturbance conditions, then the energy deviation Defined as: , When satisfied At that time, it was determined that a transient energy impact existed, in which This is the energy deviation threshold. The frequency offset is defined within the transient range. , is represented as: , in, This represents the frequency recovery trajectory under the baseline strategy.

[0042] When satisfied At that time, it was determined that the system frequency recovery path had significantly deviated. Indicates the frequency offset threshold. Defines the control mismatch flag. , is represented as: , in, This indicates a risk of mismatch during the transfer of control.

[0043] It should be noted that the comparative verification of the coordination strategy includes, under the same disturbance conditions and the same initial operating state, setting up a baseline control strategy without a control coordination mechanism and an improved control strategy with a control coordination mechanism respectively for hardware-in-the-loop simulation verification; in the baseline control strategy, the continuous regulation system and the steady-state control system operate independently according to their respective control logics, while in the coordinated control strategy, the output change of the continuous regulation system is buffered during the control transfer phase, and the target power of the continuous regulation controller is gradually brought closer to the steady-state control target through the control command alignment mechanism; during the simulation operation, by comparing the system frequency recovery process, power change process and energy change under different control strategies, the impact of different control methods on the dynamic behavior of the system is analyzed, and the role of the control coordination mechanism in suppressing transient shocks is identified.

[0044] It should also be noted that a preferred approach for comparing and verifying coordination strategies specifically includes, after identifying transient characteristics, verifying the effectiveness of the control coordination strategy under the same disturbance input. Same initial state Two control strategies are constructed under the following conditions: Under the baseline strategy: continuously adjust the controller output. Independent operation; the stability control device intervenes directly according to the triggering rules; not... Perform buffering or alignment processing, that is: , In the coordination strategy, after the stabilization trigger, the continuously adjusted output is processed in two ways: Rate of change limit: , in, This indicates the maximum allowable power variation.

[0045] Instruction alignment mechanism: Defines the alignment process, represented as: , in: The goal is to continuously adjust the alignment. Indicates the target power for stabilization (by , (As a result of comprehensive analysis) For the transition coefficient, satisfying And increasing over time, eventually we get: , Calculate under the two strategies respectively: Maximum power change rate: , Energy deviation Frequency offset , constitute the evaluation vector: , By comparing baseline strategy and coordination strategy The effect of the coordination mechanism on the transient response of control transfer can be determined. When the maximum power change rate, energy deviation and frequency recovery offset under the coordinated control strategy are all less than the corresponding indicators of the baseline control strategy, it is determined that the coordination mechanism can effectively suppress the transient impact of control transfer and improve the dynamic response performance of the system.

[0046] It should also be noted that, to address the issue of how to identify and quantify the control transfer mismatch between the continuous regulation system and the control system at the moment of triggering the stabilization priority, and how this mismatch specifically manifests as power surges, power reversals, energy surges, and frequency recovery path distortions, existing technologies mostly focus on outcome-level judgments such as whether triggering was successful, whether the frequency recovered, and whether steady-state requirements were met. They lack an analytical framework for the dynamic details at the moment of triggering, and even more so, a method to unify the three different dimensions of "power-energy-frequency trajectory" into a single judgment system. Control mismatch often occurs only within a very short time window, and its manifestations may be scattered across different physical quantities. A single indicator cannot accurately reveal the essence of the problem, and without a unified analytical window and a unified evaluation system, it is impossible to explain what a particular coordination strategy has improved and to what extent. By constructing a transient analysis time interval around the moment of stabilization triggering, extracting characteristic quantities such as power change rate, energy deviation, and frequency trajectory offset, and further establishing improvement criteria through comparison and verification between the baseline strategy and the coordination strategy, the key problem of "how to identify, attribute, and verify the transient risk of control transfer" is essentially solved. This not only transforms transient risk assessment from experience-based judgment to quantitative determination, but also elevates the evaluation of coordination strategies from subjective description to structured verification.

[0047] Example 2, an embodiment of the present invention, provides a hardware-in-the-loop simulation verification system for the stable control and coordination of a low-frequency new energy collection system, including a simulation platform construction module, a low-frequency disturbance processing module, and a comparison verification module.

[0048] The simulation platform construction module is used to build a hardware-in-the-loop simulation platform with parallel control structures and establish a unified time synchronization and data acquisition mechanism; the low-frequency disturbance processing module is used to construct low-frequency disturbance scenarios in the hardware-in-the-loop simulation platform and trigger stabilization priority actions; the comparison and verification module is used to identify the transient state of control transfer around the trigger time of stabilization actions and to compare and verify the coordination strategy.

Claims

1. A semi-physical simulation verification method for the stable control and coordination of a low-frequency new energy collection system, characterized in that, include: Construct a hardware-in-the-loop simulation platform with parallel control structures and establish a unified time synchronization and data acquisition mechanism; Construct a low-frequency disturbance scenario in a hardware-in-the-loop simulation platform and trigger stabilization priority actions; Identify the transient state of control transfer around the trigger moment of the stabilization action and compare and verify the coordination strategy.

2. The hardware-in-the-loop simulation verification method for stable control and coordination of a low-frequency new energy collection system as described in claim 1, characterized in that: The hardware-in-the-loop simulation platform for constructing parallel control structures includes, A real-time digital simulation model of a new energy collection and transmission system is established to describe the dynamic operation relationship between new energy sources, collection networks, transmission channels and receiving-end power grids. The system operation process is reflected by real-time calculation of power, frequency and voltage status of power grid nodes. The continuous adjustment physical controller and the stabilization device are connected to the real-time simulation system through the input / output interface module, so as to output the system operation status information to the control device in real time and receive the control commands returned by the control device. During the simulation operation, the real-time simulation system continuously updates the system operating status according to the preset time step and sends the system status data at each moment to the control device. After receiving the system status data, the control device generates control commands based on its internal control logic and feeds them back to the simulation system, which then updates the system's operating status according to the control commands.

3. The hardware-in-the-loop simulation verification method for stable control and coordination of a low-frequency new energy collection system as described in claim 2, characterized in that: The establishment of a unified time synchronization and data acquisition mechanism includes: A unified time reference module is set up in the hardware-in-the-loop simulation platform to send synchronous time signals to the real-time simulation system and various control devices, so that all devices can operate under the same time reference. During the data acquisition process, the data sampling time is marked by attaching time identifier information to each data record; The system continuously collects system operation data during operation. During the data processing stage, data uploaded from different devices are uniformly cached and sorted. When there is a time difference between different devices, the data is aligned using a time matching method. The data record with the smallest time difference is found based on the time identifier information and regarded as data from the same sampling time.

4. The hardware-in-the-loop simulation verification method for stable control and coordination of a low-frequency new energy collection system as described in claim 3, characterized in that: The constructed low-frequency disturbance scenario includes: A disturbance injection module is set up in the real-time simulation system to simulate power imbalance or network structure change events that occur during the operation of the power system. During the simulation operation phase, power disturbances are injected into the system at specified times to change the original balance between the system's power generation and load power, causing a system frequency shift. The real-time simulation system dynamically calculates the frequency change process based on the power grid operation characteristics. When the power generation is less than the load demand, the system frequency gradually decreases, while when the power generation recovers or the load decreases, the system frequency gradually rises. By changing the location, duration, and amplitude of the disturbance, various low-frequency operating scenarios are constructed in the simulation system, causing the system to enter the trigger range of the stability control device.

5. The hardware-in-the-loop simulation verification method for stable control and coordination of a low-frequency new energy collection system as described in claim 4, characterized in that: The triggering of the stability control priority action includes, In the real-time simulation system, a stability control trigger judgment module is set up to continuously monitor the frequency change status during system operation and determine whether stability control actions need to be performed based on preset trigger rules. When the system frequency drops below the preset frequency threshold and remains below it for a set period of time, the system is determined to have entered a low-frequency abnormal operating state, triggering the stability control device to execute corresponding control actions. Stabilization measures include cutting off some loads, limiting the output of some new energy sources, or disconnecting some transmission channels. During the execution of the stabilization and control action, the stabilization and control device simultaneously sends control constraint signals to the continuous regulation controller, which limits or weakens the control output of the continuous regulation system, and gradually transfers the control of the system from the continuous regulation system to the stabilization and control system.

6. The hardware-in-the-loop simulation verification method for stable control and coordination of a low-frequency new energy collection system as described in claim 5, characterized in that: The identification of control transfer transients includes, A transient analysis time interval is established with the trigger moment of the stabilization action as the center. Within the transient analysis time interval, the changes in system frequency, energy storage device output power, and control command are continuously recorded. By analyzing the collected data, the speed and direction of system power changes during the control transfer phase can be identified, and it can be observed whether there are power abrupt changes or reverse power changes. Simultaneously, based on the changes in the output power of the energy storage device within the transient analysis time interval, the energy release or absorption of the energy storage system is cumulatively analyzed to determine whether a transient energy impact occurs during the transfer of control. If the system experiences excessively rapid power changes, abnormal energy changes, or significant deviations in the frequency recovery trajectory during the control transfer phase, it is determined that the system is at risk of control mismatch during the control transfer phase.

7. The hardware-in-the-loop simulation verification method for stable control and coordination of a low-frequency new energy collection system as described in claim 6, characterized in that: The comparison and verification of the coordination strategy includes... Under the same disturbance conditions and the same initial operating state, a baseline control strategy without a control coordination mechanism and an improved control strategy with a control coordination mechanism were set up for hardware-in-the-loop simulation verification. In the baseline control strategy, the continuous regulation system and the stability control system operate independently according to their respective control logics. In the coordinated control strategy, the output changes of the continuous regulation system are buffered during the control transfer phase, and the target power of the continuous regulation controller is gradually brought closer to the stability control target through the control command alignment mechanism. During the simulation, the effects of different control methods on the dynamic behavior of the system are analyzed by comparing the system frequency recovery process, power change process and energy change under different control strategies, and the role of control coordination mechanism in suppressing transient shocks is identified.

8. A hardware-in-the-loop simulation verification system for the stability control and coordination of a low-frequency renewable energy collection system, employing the hardware-in-the-loop simulation verification method for the stability control and coordination of a low-frequency renewable energy collection system as described in any one of claims 1 to 7, characterized in that: It includes a simulation platform construction module, a low-frequency disturbance processing module, and a comparison and verification module; The simulation platform construction module is used to build a hardware-in-the-loop simulation platform with parallel control structure and establish a unified time synchronization and data acquisition mechanism; The low-frequency disturbance processing module is used to construct a low-frequency disturbance scenario in the hardware-in-the-loop simulation platform and trigger stabilization priority actions. The comparison and verification module is used to identify the transient state of control transfer around the trigger moment of the stabilization action and to compare and verify the coordination strategy.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the hardware-in-the-loop simulation verification method for the stable control of a low-frequency new energy collection system 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 steps of the semi-physical simulation verification method for the stable control and coordination of the low-frequency new energy collection system as described in any one of claims 1 to 7.

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