Virtual inertia regulation method and system for grid-side energy storage facing network construction type control

Abstract

A power grid side energy storage virtual inertia regulation method and system for network-oriented control, the method first acquires the operation sensing data of the power grid side energy storage system and constructs the power grid dynamic state characteristics, and identifies the local electromagnetic transient disturbance event in combination with the new energy station operation state information to obtain the electromagnetic transient disturbance characteristics, then evaluates the frequency observation signal credibility value based on a frequency credibility evaluation model, and generates target frequency response information and target inertia control strategy according to the credibility value, and then controls the power grid side energy storage system to perform virtual inertia regulation according to the target inertia control strategy; the application is aimed at the power grid of network-oriented control, and the influence of local electromagnetic transient disturbance is identified, so as to inhibit the virtual inertia false triggering caused by the switching of new energy station equipment, improve the reliability of the frequency observation result in the network-oriented weak power grid scene and the virtual inertia regulation accuracy, and realize the adaptive inertia support of the power grid side energy storage system to the real frequency disturbance.

Description

Technical Field

[0001] This invention relates to virtual inertia control methods, belonging to the field of machine learning, and particularly to a method and system for virtual inertia control of grid-side energy storage for grid-type control. Background Technology

[0002] With the continuous increase in installed capacity of new energy power generation, new energy power plants, represented by wind farms and photovoltaic power stations, have gradually become an important part of the power system. Since most new energy power generation units are connected to the grid through power electronic converters, the frequency support capability of the system is gradually weakening due to the continuous decline in the proportion of traditional synchronous generators and the continuous reduction in the equivalent rotational inertia of the grid. Against this background, grid-side energy storage systems with rapid power regulation capabilities are widely used in frequency regulation and inertia support scenarios. By simulating the inertia response characteristics of synchronous generators through grid-based control technology, virtual inertia support is provided for the system, thereby enhancing the frequency stability capability in weak grid environments and improving the safe and stable operation level of the power system under conditions of high proportion of new energy access.

[0003] However, in weak grid operating environments, local electromagnetic transient disturbances such as the switching of renewable energy power plant equipment, the switching of reactive power compensation devices, the commissioning and decommissioning of capacitor banks, and transformer inrush currents can easily cause rapid fluctuations in grid-connected voltage, current, and phase angle, leading to transient distortions in frequency observation results. Existing grid-based energy storage virtual inertia control systems typically determine inertia support requirements directly based on the rate of frequency change or frequency deviation, lacking the ability to identify the impact of local electromagnetic transient disturbances. This can easily lead to misjudging frequency observation anomalies caused by equipment switching events as real frequency disturbances, triggering unnecessary virtual inertia responses. This not only results in additional consumption of energy storage system power resources but may also cause problems such as excessive frequency regulation, power oscillations, and decreased control stability. Therefore, there is an urgent need for a grid-side energy storage virtual inertia control method for grid-based control to address the aforementioned shortcomings of existing technologies. Summary of the Invention

[0004] The purpose of this invention is to overcome the above-mentioned defects and problems in the prior art and to provide a grid-side energy storage virtual inertia control method and system with high control accuracy for grid-type control.

[0005] To achieve the above objectives, the technical solution of this invention is: a method for regulating the virtual inertia of grid-side energy storage for grid-based control, comprising:

[0006] The system acquires operational sensing data of the grid-side energy storage system and constructs dynamic state characteristics of the power grid based on the operational sensing data. The operational sensing data includes three-phase voltage signal data, three-phase current signal data, frequency observation signal data, and operational status information of new energy power stations.

[0007] Based on the operation status information of new energy power stations, local electromagnetic transient disturbance events are identified, and disturbance propagation analysis is performed based on the dynamic state characteristics of the power grid to obtain electromagnetic transient disturbance characteristics.

[0008] A frequency reliability assessment model is constructed, and the characteristics of electromagnetic transient disturbances and the dynamic state characteristics of the power grid are input into the frequency reliability assessment model to evaluate the reliability value of the current frequency observation signal.

[0009] The frequency observation signal data is corrected based on the confidence level value to obtain the target frequency response information, and the target inertia control strategy corresponding to the grid-side energy storage system is constructed based on the target frequency response information.

[0010] Based on the target inertia control strategy, energy storage inertia support commands are generated, and the grid-side energy storage system is controlled to perform virtual inertia regulation based on the energy storage inertia support commands.

[0011] Optionally, the step of acquiring operational sensing data of the grid-side energy storage system and constructing grid dynamic state characteristics based on the operational sensing data specifically includes:

[0012] Obtain continuous time series corresponding to three-phase voltage signals, three-phase current signals, and frequency observation signals;

[0013] A voltage disturbance propagation trajectory is constructed based on three-phase voltage signals to characterize the propagation process of voltage anomalies between consecutive time points;

[0014] A current response propagation trajectory is constructed based on three-phase current signals to characterize the current change following the voltage disturbance process;

[0015] Construct a frequency evolution propagation trajectory based on frequency observation signals to characterize the continuous evolution process of frequency perturbation;

[0016] The voltage disturbance propagation trajectory, current response propagation trajectory, and frequency evolution propagation trajectory are time-aligned to establish a node disturbance propagation chain that characterizes the transmission relationship of disturbances between the voltage, current, and frequency domains. Based on the node disturbance propagation chain, the dynamic state characteristics of the power grid are constructed.

[0017] Optionally, the step of identifying local electromagnetic transient disturbance events based on the operating status information of new energy power plants and performing disturbance propagation analysis based on the dynamic state characteristics of the power grid to obtain electromagnetic transient disturbance characteristics specifically includes:

[0018] Based on the operating status information of the new energy power station, corresponding candidate disturbance events are determined; the operating status information of the new energy power station includes at least one of equipment operating status information, equipment switching status information, and power regulation status information.

[0019] The propagation behavior of candidate disturbance events is verified based on the node disturbance propagation chain, and the disturbance source is traced in reverse to determine the initial disturbance location corresponding to the candidate disturbance event.

[0020] Determine whether the initial disturbance location is within the voltage disturbance propagation trajectory and whether a corresponding current response propagation trajectory has not yet formed:

[0021] If the initial disturbance location is within the voltage disturbance propagation trajectory and no corresponding current response propagation trajectory is formed, the candidate disturbance event is identified as a local electromagnetic transient disturbance event; the local electromagnetic transient disturbance events include collector line switching events, reactive power compensation device switching events, capacitor bank switching events, wind turbine grid connection and disconnection events, and transformer excitation inrush current events;

[0022] Decoupling features between frequency disturbances and current responses are extracted based on local electromagnetic transient disturbance events; these decoupling features characterize the degree to which there is a lack of synchronous correlation between frequency changes and current responses.

[0023] Determine whether the initial disturbance location is within the voltage disturbance propagation trajectory and whether a corresponding current response propagation trajectory has not yet formed:

[0024] If the initial disturbance location is not located in the voltage disturbance propagation trajectory and no corresponding current response propagation trajectory is formed, then the power coupling relationship between the frequency evolution propagation trajectory and the current response propagation trajectory is determined.

[0025] Based on local electromagnetic transient disturbance events and decoupling characteristics, local electromagnetic transient disturbance characteristics are constructed; based on power coupling relationships, power imbalance disturbance characteristics are constructed.

[0026] Electromagnetic transient disturbance characteristics are constructed by analyzing the differences between local electromagnetic transient disturbance characteristics and power imbalance disturbance characteristics.

[0027] Optionally, the construction of the frequency reliability assessment model specifically includes:

[0028] Obtain the historical electromagnetic transient disturbance characteristics and historical power grid dynamic state characteristics corresponding to different historical local electromagnetic transient disturbance events;

[0029] Based on the characteristics of historical electromagnetic transient disturbances and the dynamic state characteristics of historical power grids, electromagnetic environment characterization parameters of weak power grids under different events are constructed, and a frequency reliability assessment model is built based on the electromagnetic environment characterization parameters of weak power grids.

[0030] Optionally, the construction of electromagnetic environment characterization parameters for weak power grids under different events based on historical electromagnetic transient disturbance characteristics and historical power grid dynamic state characteristics specifically includes:

[0031] The characteristics of historical electromagnetic transient disturbances are classified based on the types of historical local electromagnetic transient disturbance events.

[0032] Based on the classification results, the corresponding historical power grid dynamic state characteristics are obtained, and a disturbance propagation topology is constructed to characterize the propagation path and direction of local electromagnetic transient disturbances in the voltage domain, current domain, frequency domain, and power domain.

[0033] The first propagation intensity of a local electromagnetic transient disturbance event during its propagation from the voltage domain to the frequency domain is determined based on the disturbance propagation topology; the second propagation intensity of the local electromagnetic transient disturbance event during its propagation from the frequency domain to the power domain is also determined based on the disturbance propagation topology.

[0034] The deviation between the first propagation intensity and the second propagation intensity is calculated, a propagation deviation index is constructed, and electromagnetic environment characterization parameters of weak power grids are constructed based on the propagation deviation index.

[0035] Optionally, the step of correcting the frequency observation signal data based on the confidence level value to obtain the target frequency response information specifically includes:

[0036] Acquire the first frequency change sequence corresponding to the frequency observation signal data, and determine the abnormal frequency response interval in the first frequency change sequence affected by local electromagnetic transient disturbance events based on the confidence level value;

[0037] Disturbance components are identified within the abnormal frequency response range to determine the local electromagnetic transient-induced frequency change components.

[0038] The first frequency change sequence is compensated and corrected based on the local electromagnetic transient induced frequency change component, and the corrected target frequency response information is output.

[0039] Optionally, the step of constructing a target inertia control strategy for the grid-side energy storage system based on the target frequency response information specifically includes:

[0040] Obtain the second frequency change sequence corresponding to the target frequency response information, and determine the frequency shift degree and frequency evolution trend based on the second frequency change sequence;

[0041] Based on the frequency evolution trend, predict the frequency change state within a preset time range, and determine the frequency instability risk level based on the degree of frequency offset and the frequency change state.

[0042] Inertia support requirement levels are generated based on frequency instability risk levels, and target inertia control strategies are constructed based on these inertia support requirement levels.

[0043] Optionally, after the virtual inertia regulation of the grid-side energy storage system based on the energy storage inertia support command is performed, it further includes:

[0044] The system response results during the virtual inertia control process are obtained, and the frequency reliability assessment model and target inertia control strategy are iteratively optimized based on the system response results, as follows:

[0045] Obtain the frequency recovery results, power response results, and local electromagnetic transient disturbance suppression results during the virtual inertia control process;

[0046] Frequency reliability evaluation results are constructed based on frequency recovery results; disturbance identification evaluation results are constructed based on local electromagnetic transient disturbance suppression results.

[0047] Based on the frequency reliability evaluation results and the disturbance identification evaluation results, the reliability mapping relationship in the frequency reliability assessment model is corrected, and the frequency reliability assessment model is updated based on the corrected reliability mapping relationship.

[0048] The inertia support evaluation results are constructed based on the power response results, and the inertia response control parameters in the target inertia control strategy are adjusted based on the inertia support evaluation results.

[0049] The target inertia control strategy is updated based on the adjusted inertia response control parameters.

[0050] A grid-side energy storage virtual inertia regulation system for grid-based control, the system being applied to the aforementioned method, the system comprising:

[0051] The power grid dynamic state feature construction module is used to acquire the operation perception data of the power grid-side energy storage system and construct the power grid dynamic state features based on the operation perception data; the operation perception data includes three-phase voltage signal data, three-phase current signal data, frequency observation signal data, and operation status information of new energy power stations;

[0052] The electromagnetic transient disturbance feature acquisition module is used to identify local electromagnetic transient disturbance events based on the operating status information of new energy power plants, and to perform disturbance propagation analysis based on the dynamic status characteristics of the power grid to obtain electromagnetic transient disturbance features.

[0053] The credibility assessment module is used to construct a frequency credibility assessment model. It inputs electromagnetic transient disturbance characteristics and power grid dynamic state characteristics into the frequency credibility assessment model to assess the credibility value of the current frequency observation signal.

[0054] The target inertia control strategy acquisition module is used to correct the frequency observation signal data based on the confidence level value, obtain the target frequency response information, and construct the target inertia control strategy corresponding to the grid-side energy storage system based on the target frequency response information.

[0055] The virtual inertia control module is used to generate energy storage inertia support commands based on the target inertia control strategy, and to control the grid-side energy storage system to perform virtual inertia control based on the energy storage inertia support commands.

[0056] A grid-side energy storage virtual inertia regulation device for grid-based control, the device including a processor and a memory;

[0057] The memory is used to store computer program code and to transmit the computer program code to the processor;

[0058] The processor is used to execute the above-described grid-side energy storage virtual inertia regulation method for grid-oriented control according to the instructions in the computer program code.

[0059] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0060] 1. This invention discloses a method and system for virtual inertia regulation of grid-side energy storage for grid-based control. The method first acquires the operational sensing data of the grid-side energy storage system and constructs the dynamic state characteristics of the grid. It then identifies local electromagnetic transient disturbance events by combining the operational status information of new energy power plants, obtaining electromagnetic transient disturbance characteristics. Next, it evaluates the reliability of the frequency observation signal based on a frequency reliability assessment model, and generates target frequency response information and a target inertia control strategy based on the reliability value. Finally, it controls the grid-side energy storage system to perform virtual inertia regulation according to the target inertia control strategy. In application, this design, for grid-based control grids, identifies the impact of local electromagnetic transient disturbances, thereby suppressing the false triggering of virtual inertia caused by the switching of new energy power plant equipment. This improves the reliability of frequency observation results and the accuracy of virtual inertia regulation in weak grid scenarios, realizing adaptive inertia support of the grid-side energy storage system for real frequency disturbances.

[0061] 2. The present invention discloses a method and system for regulating virtual inertia of grid-side energy storage for grid-based control. First, it acquires historical electromagnetic transient disturbance characteristics and historical grid dynamic state characteristics corresponding to different historical local electromagnetic transient disturbance events. Then, based on these characteristics, it constructs weak grid electromagnetic environment characterization parameters for different events. Next, it constructs a frequency reliability assessment model based on these parameters. In this application, by acquiring and evaluating the characteristics of different local electromagnetic transient disturbance events, it achieves differentiated characterization of frequency observation distortion characteristics under different local electromagnetic transient disturbance scenarios. This improves the accuracy of the frequency reliability assessment model in identifying frequency observation anomalies caused by local electromagnetic transient disturbances, providing data support for suppressing virtual inertia false triggering. Attached Figure Description

[0062] Figure 1 This is a flowchart of the method of the present invention.

[0063] Figure 2 This is a system structure diagram of the present invention.

[0064] Figure 3 This is a structural diagram of the device of the present invention.

[0065] In the diagram: 1. Power grid dynamic state characteristic construction module; 2. Electromagnetic transient disturbance characteristic acquisition module; 3. Credibility value evaluation module; 4. Target inertia control strategy acquisition module; 5. Virtual inertia regulation module; 6. Processor; 7. Memory; 8. Computer program code. Detailed Implementation

[0066] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0067] Example 1:

[0068] In new power systems with a high proportion of new energy sources, grid-based energy storage is gradually taking on key functions such as voltage support, frequency stability, and inertia response. Unlike traditional synchronous generators that rely on mechanical rotational inertia to maintain system stability, grid-based converters actively construct voltage source characteristics through control algorithms and provide virtual inertia and damping support to the grid. When the grid is in a weak grid operation state, the output of renewable energy fluctuates rapidly, or the load changes significantly, the grid-based converter needs to continuously sense the dynamic characteristics of the grid and adjust the inertia output in real time.

[0069] However, existing technologies typically employ fixed inertia parameter configurations or adjust inertia based solely on single operating indicators such as frequency deviation and frequency change rate. This fails to fully reflect the unique dynamic electromagnetic characteristics of grid-type control power grids. In actual operation, due to control coupling, voltage source interaction, and virtual synchronization behavior among grid-type converters, the grid inertia demand exhibits significant time-varying characteristics under different operating conditions. Existing methods struggle to accurately characterize the real-time dynamic characteristics of grid-type power grids, which can easily lead to insufficient inertia support or excessive inertia injection, thereby causing problems such as frequency oscillations, voltage fluctuations, and a decrease in system stability margin.

[0070] Furthermore, to avoid confusion with the operating characteristics of grid-controlled power grids and traditional synchronous machine-dominated power grids, all data sources, signal processing objects, and feature parameter extraction samples involved in this embodiment are limited to power grid operating conditions dominated by grid-controlled converters.

[0071] The term "grid-type power grid" refers to a power grid environment in which grid-type energy storage converters participate in system operation as the main voltage establishment units, undertaking functions such as voltage formation, frequency support, and virtual inertia output. The collected data includes operational data such as output voltage, current, active power, reactive power, frequency deviation, frequency change rate, DC-side energy state, virtual synchronization control parameters, and coupling interaction information between grid-type converters. The processed dynamic characteristics of the power grid all originate from the electromagnetic response process formed by grid-type control behavior. The extracted feature parameters are all based on the frequency support characteristics, inertia response characteristics, voltage regulation characteristics, and multi-converter collaborative characteristics after the grid-type converters construct voltage sources. From the perspective of data sources, this clearly distinguishes the grid-type control power grid from the characteristic mechanism of relying on phase-locked loops to track the power grid operation and the traditional synchronous machine's operation mechanism of relying on mechanical rotational inertia to maintain stability. This ensures that subsequent parameter modeling, feature analysis, and inertia control processes are all carried out for the operating scenario of grid-type weak power grids.

[0072] See Figure 1 A method for regulating the virtual inertia of grid-side energy storage for grid-based control includes:

[0073] S101. Obtain the operation sensing data of the grid-side energy storage system, and construct the dynamic state characteristics of the power grid based on the operation sensing data; the operation sensing data includes three-phase voltage signal data, three-phase current signal data, frequency observation signal data, and operation status information of new energy power stations;

[0074] In this embodiment, the grid-side energy storage system is deployed in a grid-type controlled grid environment. The voltage establishment, frequency support, and virtual inertia response in the grid are mainly undertaken by the grid-type energy storage converter. To ensure that the subsequent analysis results can accurately reflect the actual operating status of the grid-type weak grid, all operation sensing data are collected from the grid-connected nodes of the grid-side energy storage system and the operation process of the new energy power stations electrically connected to it, so as to avoid introducing the operating characteristics of the grid-type controlled grid or the grid dominated by traditional synchronous machines.

[0075] The three-phase voltage signal data is used to characterize the voltage operation status at the grid-connected node of the grid-type energy storage system, reflecting the voltage amplitude change, voltage phase angle change and voltage fluctuation. When the new energy power station experiences equipment switching, reactive power compensation device switching or local electromagnetic transient disturbance, the three-phase voltage signal data can reflect the propagation process of the disturbance in the voltage domain.

[0076] The three-phase current signal data is used to characterize the power exchange status between the grid-type energy storage system and the power grid, reflecting changes in current amplitude, current direction, and current response process. When the system frequency changes or the operating status of the new energy power station changes, the three-phase current signal data can reflect the current response to voltage disturbances and power changes.

[0077] The frequency observation signal data is used to characterize the grid frequency operating status observed by the grid-type energy storage system, reflecting the frequency offset, frequency change rate, and frequency dynamic evolution process, providing a basis for subsequent judgment of the source of frequency disturbance and the construction of virtual inertia support requirements; since local electromagnetic transient disturbances may cause instantaneous distortion of frequency observation results, the frequency observation signal data contains both real frequency change information and frequency observation anomaly information caused by local electromagnetic transient disturbances.

[0078] The operational status information of the new energy power stations is used to characterize the current operation of new energy power stations that are electrically connected to the grid-side energy storage system. This includes equipment operational status information, equipment switching status information, and power regulation status information. Specifically, the equipment operational status information reflects the operation of equipment such as wind turbines, photovoltaic inverters, reactive power compensation devices, and transformers; the equipment switching status information reflects equipment commissioning, decommissioning, grid connection, off-grid operation, and switching operations; the power regulation status information reflects the active and reactive power regulation process of the new energy power stations; and the operational status information of the new energy power stations is used to assist in identifying local electromagnetic transient disturbance events and to provide a basis for subsequent judgment on whether frequency observation anomalies originate from equipment switching behavior.

[0079] Based on three-phase voltage signal data, three-phase current signal data, frequency observation signal data, and the operating status information of new energy power stations, a dynamic state characteristic of the power grid reflecting the dynamic operation process of the grid-type weak power grid is constructed, providing basic data support for subsequent identification of local electromagnetic transient disturbances, frequency reliability assessment, and virtual inertia control.

[0080] Furthermore, the acquisition of operational sensing data from the grid-side energy storage system, and the construction of grid dynamic state characteristics based on the operational sensing data, specifically includes:

[0081] Obtain continuous time series corresponding to three-phase voltage signals, three-phase current signals, and frequency observation signals;

[0082] A voltage disturbance propagation trajectory is constructed based on three-phase voltage signals to characterize the propagation process of voltage anomalies between consecutive time points;

[0083] A current response propagation trajectory is constructed based on three-phase current signals to characterize the current change following the voltage disturbance process;

[0084] Construct a frequency evolution propagation trajectory based on frequency observation signals to characterize the continuous evolution process of frequency perturbation;

[0085] The voltage disturbance propagation trajectory, current response propagation trajectory, and frequency evolution propagation trajectory are time-aligned to establish a node disturbance propagation chain that characterizes the transmission relationship of disturbances between the voltage, current, and frequency domains. Based on the node disturbance propagation chain, the dynamic state characteristics of the power grid are constructed.

[0086] Specifically, to accurately depict the dynamic evolution of local disturbances in a grid-type weak power grid from their generation to propagation and impact on frequency observation results, the three-phase voltage signals, three-phase current signals, and frequency observation signals collected at the corresponding grid-connected nodes of the grid-side energy storage system are first continuously sampled under a unified time reference to form a continuous time series. The continuous time series corresponding to the three-phase voltage signals records the voltage amplitude and phase angle changes at each sampling moment; the continuous time series corresponding to the three-phase current signals records the current amplitude and phase changes at each sampling moment; and the continuous time series corresponding to the frequency observation signals records the frequency change process at each sampling moment. Because all three types of data are sampled according to a unified time reference, the time correspondence between voltage, current, and frequency changes can be maintained, providing a foundation for subsequent disturbance propagation analysis.

[0087] When constructing a voltage disturbance propagation trajectory based on three-phase voltage signals, voltage anomalies are first identified from the continuous time series, including voltage surges, voltage drops, voltage oscillations, and phase transitions. Then, adjacent anomalies are connected in chronological order, and the voltage change amplitude, direction, and duration corresponding to each anomaly are recorded, thus forming the voltage disturbance propagation trajectory. This trajectory is represented as a set of voltage disturbance nodes arranged in chronological order and their connections, with each node corresponding to a voltage anomaly event. The connections between adjacent nodes represent the development process of the voltage disturbance over consecutive moments. The voltage disturbance propagation trajectory allows observation of the generation time, duration, and decay process of the voltage disturbance, thereby reflecting the propagation behavior of local electromagnetic transient disturbances in the voltage domain.

[0088] When constructing a current response propagation trajectory based on three-phase current signals, the current change events corresponding to voltage anomalies in a continuous time series are first detected, including current abrupt changes, current oscillations, and current recovery processes. Then, the correlation between each current change event is established in chronological order, and the current change amplitude, response direction, and response duration corresponding to each event are recorded, thus forming the current response propagation trajectory. The current response propagation trajectory is represented as a set of current response nodes arranged in chronological order and their connections. Each current response node corresponds to a current change event, and the connections between adjacent nodes represent the current's response to voltage disturbances. The current response propagation trajectory can characterize whether the current responds after a disturbance occurs, the response speed, and the response intensity.

[0089] When constructing a frequency evolution propagation trajectory based on frequency observation signals, frequency offset events and frequency change rate anomaly events are first identified. Then, the correlation between frequency change events is established in chronological order, and the frequency offset, frequency change rate, and duration corresponding to each event are recorded, thus forming the frequency evolution propagation trajectory. The frequency evolution propagation trajectory is represented by a set of frequency evolution nodes arranged in chronological order and their connection relationships. Each frequency evolution node corresponds to a frequency change event, and the connection relationships between adjacent nodes represent the continuous evolution process of the frequency disturbance. The frequency evolution propagation trajectory can reflect the generation, expansion, and recovery process of frequency disturbances.

[0090] After constructing the three types of propagation trajectories, the voltage disturbance propagation trajectory, current response propagation trajectory, and frequency evolution propagation trajectory are time-aligned: the occurrence time of event nodes in each trajectory is determined according to a unified time reference, and cross-trajectory correlations are established according to the order of event occurrence; when a voltage disturbance node occurs and a corresponding current response node appears within a preset time window, a propagation correlation from the voltage domain to the current domain is established; when a current response node occurs and a corresponding frequency evolution node appears within a preset time window, a propagation correlation from the current domain to the frequency domain is established, thus forming a node disturbance propagation chain.

[0091] The nodal disturbance propagation chain is represented as a time-series propagation network consisting of multiple propagation nodes and propagation edges between nodes. The propagation nodes include voltage disturbance nodes, current response nodes, and frequency evolution nodes. The propagation edges record the propagation direction, propagation delay, and propagation intensity between different nodes. Since each propagation edge corresponds to the transmission process of the actual disturbance between different physical quantities, the nodal disturbance propagation chain can reflect the transmission relationship of the disturbance between the voltage domain, current domain, and frequency domain. Specifically, when the propagation chain is "voltage disturbance node → current response node → frequency evolution node," it indicates that the disturbance first acts on the voltage domain, then causes a current response, and finally affects the frequency observation results. When the propagation chain lacks a current response node and directly shows a frequency evolution node, it indicates that the frequency observation results may be affected by local electromagnetic transient disturbances, resulting in abnormal changes.

[0092] When constructing dynamic state characteristics of a power grid based on node disturbance propagation chains, the following steps are taken: First, the starting position of the disturbance in the node disturbance propagation chain is extracted to characterize the physical domain where the disturbance first appears. Then, the propagation direction is extracted to characterize the transmission path of the disturbance between different physical domains. Next, the propagation path is extracted to characterize the propagation nodes and propagation edge set traversed by the disturbance. Then, the propagation timing relationship is extracted to characterize the sequential response relationship between each propagation node. Finally, the response correlation relationship is extracted to characterize the degree of response consistency between the voltage domain, current domain, and frequency domain. Subsequently, the above propagation information is fused and expressed to form the dynamic state characteristics of the power grid. The dynamic state characteristics of the power grid are represented as a set of state information describing the disturbance propagation behavior, used to comprehensively reflect the propagation law, propagation intensity, and response correlation degree of the disturbance between the voltage domain, current domain, and frequency domain, thereby providing a dynamic operating state basis for subsequent local electromagnetic transient disturbance identification, frequency reliability assessment, and virtual inertia control.

[0093] S102. Identify local electromagnetic transient disturbance events based on the operating status information of new energy power stations, and perform disturbance propagation analysis based on the dynamic state characteristics of the power grid to obtain electromagnetic transient disturbance characteristics.

[0094] In this embodiment, since the frequency observation results in the grid-type weak power grid are not only affected by the actual power imbalance, but are also easily affected by the switching of new energy power station equipment, the switching of reactive power compensation devices, and local electromagnetic transient processes, it is necessary to identify the source of frequency disturbance.

[0095] Specifically, the process begins by acquiring the operational status information of new energy power plants and identifying candidate disturbance events that may trigger local electromagnetic transient disturbances based on changes in equipment operating status, equipment switching status, and power regulation status. Then, combining the dynamic characteristics of the power grid, the propagation process of the disturbances in the voltage, current, and frequency domains is analyzed to determine if there are characteristics such as mismatch between frequency changes and power response, decoupling between frequency changes and current response, or abnormal disturbance propagation paths. Based on this, local electromagnetic transient disturbance events caused by equipment switching behavior are identified, and feature information reflecting the propagation law, propagation impact range, and propagation correlation of local electromagnetic transient disturbances is extracted, ultimately forming electromagnetic transient disturbance features. These electromagnetic transient disturbance features characterize the propagation behavior of local electromagnetic transient disturbances affecting frequency observation results, providing a basis for subsequent frequency reliability assessment and virtual inertia false triggering suppression.

[0096] Furthermore, the process of identifying local electromagnetic transient disturbance events based on the operating status information of new energy power plants and performing disturbance propagation analysis based on the dynamic state characteristics of the power grid to obtain electromagnetic transient disturbance characteristics specifically includes:

[0097] Based on the operating status information of the new energy power station, corresponding candidate disturbance events are determined; the operating status information of the new energy power station includes at least one of equipment operating status information, equipment switching status information, and power regulation status information.

[0098] The propagation behavior of candidate disturbance events is verified based on the node disturbance propagation chain, and the disturbance source is traced in reverse to determine the initial disturbance location corresponding to the candidate disturbance event.

[0099] Determine whether the initial disturbance location is within the voltage disturbance propagation trajectory and whether a corresponding current response propagation trajectory has not yet formed:

[0100] If the initial disturbance location is within the voltage disturbance propagation trajectory and no corresponding current response propagation trajectory is formed, the candidate disturbance event is identified as a local electromagnetic transient disturbance event; the local electromagnetic transient disturbance events include collector line switching events, reactive power compensation device switching events, capacitor bank switching events, wind turbine grid connection and disconnection events, and transformer excitation inrush current events;

[0101] Decoupling features between frequency disturbances and current responses are extracted based on local electromagnetic transient disturbance events; these decoupling features characterize the degree to which there is a lack of synchronous correlation between frequency changes and current responses.

[0102] Determine whether the initial disturbance location is within the voltage disturbance propagation trajectory and whether a corresponding current response propagation trajectory has not yet formed:

[0103] If the initial disturbance location is not located in the voltage disturbance propagation trajectory and no corresponding current response propagation trajectory is formed, then the power coupling relationship between the frequency evolution propagation trajectory and the current response propagation trajectory is determined.

[0104] Based on local electromagnetic transient disturbance events and decoupling characteristics, local electromagnetic transient disturbance characteristics are constructed; based on power coupling relationships, power imbalance disturbance characteristics are constructed.

[0105] Electromagnetic transient disturbance characteristics are constructed by analyzing the differences between local electromagnetic transient disturbance characteristics and power imbalance disturbance characteristics.

[0106] Specifically, in order to accurately distinguish between frequency observation anomalies caused by local electromagnetic transient disturbances such as the switching of renewable energy power plant equipment and actual frequency disturbances caused by system active power imbalance, candidate disturbance events that may cause local electromagnetic transient effects are first identified using the operating status information of renewable energy power plants. The operating status information of renewable energy power plants includes equipment operating status information, equipment switching status information, and power regulation status information. Among them, the equipment operating status information is used to reflect the commissioning and operation status of equipment such as wind turbines, reactive power compensation devices, capacitor banks, and transformers; the equipment switching status information is used to reflect the commissioning, decommissioning, grid connection, and off-grid operation records of equipment; and the power regulation status information is used to reflect the active and reactive power regulation behavior of renewable energy power plants.

[0107] When a sudden change in equipment state is detected, the corresponding equipment state change behavior is identified as a candidate disturbance event. For example, when the SVG in a renewable energy power station switches from an off-state to an on-state, the system records the change in the switching state of the reactive power compensation device, and the SVG switching behavior corresponding to this state change is identified as a candidate disturbance event. As another example, when the operating state of a wind turbine switches from grid-connected to off-grid, the wind turbine off-grid behavior corresponding to this state change is identified as a candidate disturbance event.

[0108] After obtaining candidate disturbance events, the propagation behavior of the candidate disturbance events is verified using the node disturbance propagation chain. Specifically, the occurrence time of the candidate disturbance event is used as a reference time, and the disturbance propagation path corresponding to that time is searched in the node disturbance propagation chain. Starting from the frequency change node corresponding to the frequency evolution propagation trajectory, a reverse tracing is performed to sequentially determine the current response node and voltage disturbance node associated with it, thereby determining the initial disturbance position in the disturbance propagation chain. If the propagation chain corresponding to the frequency change node eventually traces back to the voltage disturbance node, and no complete current response propagation process is formed between the voltage disturbance node and the frequency change node, it indicates that the current frequency change mainly originates from the electromagnetic transient propagation process rather than the actual power transfer process. In this case, the candidate disturbance event is considered to have passed the propagation behavior verification.

[0109] Next, it is determined whether the initial disturbance location is within the voltage disturbance propagation trajectory and whether a corresponding current response propagation trajectory has not been formed. If the above conditions are met, the candidate disturbance event is identified as a local electromagnetic transient disturbance event. The local electromagnetic transient disturbance events include collector line switching events, reactive power compensation device switching events, capacitor bank switching events, wind turbine grid connection / disconnection events, and transformer inrush current events.

[0110] Among them, the following events are considered: **Channel switching events:** These refer to network topology changes caused by the commissioning or decommissioning of collector lines at renewable energy power plants. These events typically manifest as sudden voltage phase angle changes and abnormal phase angle propagation. **Reactive power compensation device switching events:** These refer to local electromagnetic disturbances caused by the commissioning or decommissioning of dynamic reactive power compensation devices such as SVG or SVC. These events typically manifest as rapid changes in reactive current. **Capacitor bank switching events:** These refer to transient processes formed during the commissioning or decommissioning of parallel capacitor banks. These events typically manifest as voltage step changes and short-term voltage oscillations. **Wind turbine grid connection / disconnection events:** These refer to changes in the operating status of wind turbines when they are connected to or disconnected from the grid. These events typically manifest as short-term power fluctuations and voltage fluctuations. **Transformer inrush current events:** These refer to the inrush current process generated during the no-load closing of transformers. These events typically manifest as current surges and current distortions.

[0111] After identifying local electromagnetic transient disturbance events, the decoupling features between frequency disturbances and current responses are further extracted. Since different types of local electromagnetic transient disturbance events correspond to different propagation mechanisms, the decoupling features are not judged using a uniform threshold. Instead, a baseline correlation model is dynamically generated based on historical operational data. Specifically, the correlation distribution between frequency change responses and current responses corresponding to historical local electromagnetic transient disturbance events of the same type is statistically analyzed. Based on the statistical results, baseline correlation features for the corresponding events are generated. Subsequently, the baseline correlation features are dynamically corrected by combining the voltage disturbance intensity, current response intensity, frequency change intensity, and disturbance duration corresponding to the current event, resulting in a dynamic correlation threshold or dynamic correlation weight for the current event.

[0112] Furthermore, a correlation consistency evaluation model can be used to calculate the degree of correlation between frequency variation and current response, the expression of which is as follows:

[0113] ;

[0114] in: The degree of overall correlation between frequency change and current response; This represents the number of correlation analysis samples corresponding to the current disturbance event. For the first The frequency change corresponding to each sample; This represents the maximum absolute value of the frequency change within the current analysis window. For the first The change in current corresponding to each sample; This represents the maximum absolute value of the current change within the current analysis window. For the first The time when the frequency change occurred for each sample; For the first The time of current change corresponding to each sample; This represents the propagation delay in the corresponding sample. , , These are the frequency correlation weight, current correlation weight, and time series correlation weight, respectively, all ranging from 0.1 to 0.8, and satisfying the following conditions: ; For the first The event contribution weight corresponding to each sample ranges from 0.05 to 1. The propagation delay attenuation coefficient has a value range of 0.1 to 5.

[0115] To reflect the different characteristics of various local electromagnetic transient disturbance events, an adaptive weight matrix for the event scenario is constructed, the expression of which is as follows:

[0116] ;

[0117] in: The event type matching coefficient; This is the disturbance propagation intensity coefficient; The similarity coefficient for the running scenarios; Contribute weights to the normalized events; , , The values ​​of are all in the range of 0 to 1.

[0118] Among them, the event type matching coefficient corresponding to the switching event of the collector line is mainly determined by the phase angle transition amplitude, phase angle recovery time, and phase angle propagation range; the event type matching coefficient corresponding to the switching event of the reactive power compensation device is mainly determined by the reactive current change amplitude, current change rate, and reactive power regulation duration; the event type matching coefficient corresponding to the switching event of the capacitor bank is mainly determined by the voltage step amplitude, voltage oscillation amplitude, and oscillation duration; the event type matching coefficient corresponding to the transformer excitation inrush current event is mainly determined by the current impact amplitude, impact duration, and current distortion degree; and the event type matching coefficient corresponding to the grid connection and disconnection event of the wind turbine is mainly determined by the power fluctuation amplitude, power recovery time, and frequency response duration.

[0119] Furthermore, to avoid artificially setting fixed thresholds, a dynamic correlation benchmark value can be constructed using historical local electromagnetic transient disturbance events of the same type, and its expression is as follows:

[0120] ;

[0121] in: This is the dynamic correlation baseline value corresponding to the current event; This represents the total number of samples of similar historical events. For the first The degree of comprehensive correlation between each historical event; This represents the credibility weight of historical events, with a value ranging from 0.1 to 1.

[0122] Based on this, a dynamic decision threshold is constructed by combining the current perturbation characteristics, and its expression is as follows:

[0123] ;

[0124] in: For dynamic association thresholds; The voltage disturbance intensity corresponding to the current event; The current disturbance intensity corresponding to the current event; The frequency perturbation intensity corresponding to the current event; To indicate the duration of the disturbance; , , , These are the corresponding maximum values ​​in the historical samples; , , , The weights are dynamically adjusted, and their values ​​range from 0.05 to 0.5, and satisfy the following conditions: .

[0125] When the overall correlation degree Below the dynamic association threshold When the frequency change and current response lack synchronous correlation, it indicates that the current disturbance has obvious decoupling characteristics; when the overall correlation degree is considered... Approaching or exceeding the dynamic association threshold If the frequency change is strong, it is considered that there is a strong coupling relationship between the current response and the frequency change, and further power coupling analysis is needed.

[0126] In this way, the judgment criteria corresponding to different local electromagnetic transient disturbance events can avoid being preset by humans. Instead, they are determined based on the statistical patterns of historical events of the same type, the characteristics of the current operating scenario, and the real-time disturbance propagation characteristics, thereby realizing adaptive correlation analysis between local electromagnetic transient disturbance events and decoupling characteristics.

[0127] S103. Construct a frequency reliability assessment model, input the electromagnetic transient disturbance characteristics and the power grid dynamic state characteristics into the frequency reliability assessment model, and assess the reliability value of the current frequency observation signal.

[0128] In this embodiment, electromagnetic transient disturbance characteristics and power grid dynamic state characteristics are input into the frequency reliability assessment model. By analyzing the correlation between the current power grid operating state and local electromagnetic transient disturbances, the degree to which the frequency observation signal is affected by local electromagnetic transient disturbances is assessed, and the corresponding reliability value is output. The reliability value is used to characterize the reliability of the current frequency observation signal in reflecting the actual power grid frequency change state, and provides a basis for subsequent frequency observation signal data correction, inertia support requirement construction, and virtual inertia control.

[0129] Furthermore, the construction of the frequency reliability assessment model specifically includes:

[0130] Obtain the historical electromagnetic transient disturbance characteristics and historical power grid dynamic state characteristics corresponding to different historical local electromagnetic transient disturbance events;

[0131] Based on the characteristics of historical electromagnetic transient disturbances and the dynamic state characteristics of historical power grids, electromagnetic environment characterization parameters of weak power grids under different events are constructed, and a frequency reliability assessment model is built based on the electromagnetic environment characterization parameters of weak power grids.

[0132] Specifically, in order to ensure that the frequency reliability assessment model can accurately reflect the impact of different local electromagnetic transient disturbances on frequency observation results, the historical electromagnetic transient disturbance characteristics and historical power grid dynamic state characteristics corresponding to different historical local electromagnetic transient disturbances are first obtained. Among them, the historical electromagnetic transient disturbance characteristics are used to characterize the disturbance propagation behavior, disturbance influence range, frequency response characteristics, and power coupling characteristics corresponding to the historical disturbance events; the historical power grid dynamic state characteristics are used to characterize the dynamic propagation relationship between the voltage domain, current domain, and frequency domain within the grid-type weak power grid when the disturbance occurs.

[0133] Since different local electromagnetic transient disturbance events correspond to different propagation mechanisms, electromagnetic environment characterization parameters for weak power grids are constructed for different event scenarios. The local electromagnetic transient disturbance event scenarios include collector line switching scenarios, reactive power compensation device switching scenarios, capacitor bank switching scenarios, wind turbine grid connection and disconnection scenarios, and transformer excitation inrush current scenarios.

[0134] Among these scenarios, the switching scenarios for collector lines mainly involve abnormal phase angle propagation caused by changes in network topology; therefore, the focus is on analyzing the propagation path of phase angle transitions, voltage propagation levels, and frequency following relationships. The switching scenarios for reactive power compensation devices mainly involve voltage fluctuation propagation caused by rapid changes in reactive current; therefore, the focus is on analyzing the reactive current propagation path, voltage recovery process, and frequency response process. The switching scenarios for capacitor banks mainly involve voltage step changes and oscillation propagation; therefore, the focus is on analyzing the oscillation propagation range, oscillation attenuation characteristics, and frequency disturbance persistence characteristics. The on-grid and off-grid scenarios for wind turbines mainly involve the joint propagation of rapid power changes and voltage fluctuations; therefore, the focus is on analyzing the coupling relationship between the power propagation path and the frequency propagation path. The transformer inrush current scenario mainly involves the propagation of large-amplitude current surges; therefore, the focus is on analyzing the current surge propagation process and the abnormal frequency response process.

[0135] For each historical local electromagnetic transient disturbance event, the corresponding historical electromagnetic transient disturbance characteristics and historical power grid dynamic state characteristics are obtained, and a disturbance propagation topology is constructed. The disturbance propagation topology is used to characterize the propagation path, propagation direction, propagation level, and propagation intensity of the local electromagnetic transient disturbance in the voltage domain, current domain, frequency domain, and power domain.

[0136] Furthermore, the historical electromagnetic transient disturbance features are classified according to the types of historical local electromagnetic transient disturbance events: First, a set of historical electromagnetic transient disturbance features corresponding to the historical local electromagnetic transient disturbance events is obtained, and an event label set is established according to the event type of the historical local electromagnetic transient disturbance events. Among them, historical local electromagnetic transient disturbance events include collector line switching events, reactive power compensation device switching events, capacitor bank switching events, wind turbine grid connection and disconnection events, and transformer excitation inrush current events; since different local electromagnetic transient disturbance events correspond to different propagation mechanisms, disturbance source location features, disturbance duration features, disturbance propagation range features, frequency response features, current response features, and power coupling features are extracted from the historical electromagnetic transient disturbance features, and a historical disturbance behavior feature vector is constructed.

[0137] The historical disturbance behavior feature vector corresponding to the switching events of the collector lines mainly exhibits phase angle transition propagation characteristics; the historical disturbance behavior feature vector corresponding to the switching events of the reactive power compensation devices mainly exhibits reactive current mutation propagation characteristics; the historical disturbance behavior feature vector corresponding to the switching events of the capacitor banks mainly exhibits voltage step change and oscillation propagation characteristics; the historical disturbance behavior feature vector corresponding to the wind turbine grid connection and disconnection events mainly exhibits power fluctuation propagation characteristics; and the historical disturbance behavior feature vector corresponding to the transformer excitation inrush current events mainly exhibits current impulse propagation characteristics.

[0138] Subsequently, the historical electromagnetic transient disturbance features were classified and aggregated according to the event tags to form multiple historical feature subsets corresponding to different local electromagnetic transient disturbance events, in order to establish a sample library of propagation behavior corresponding to different local electromagnetic transient disturbance events.

[0139] Based on the classification results and the corresponding historical power grid dynamic state features, a disturbance propagation topology is constructed: voltage disturbance propagation trajectory, current response propagation trajectory, frequency evolution propagation trajectory, and power response propagation trajectory are extracted from the historical power grid dynamic state features associated with the corresponding historical feature subsets.

[0140] Subsequently, key propagation nodes in the disturbance propagation trajectory are used as topological nodes, and propagation connections between nodes are established based on the propagation timing relationships recorded in the node disturbance propagation chain. Specifically, nodes in the voltage disturbance propagation trajectory are used to construct voltage domain nodes; nodes in the current response propagation trajectory are used to construct current domain nodes; nodes in the frequency evolution propagation trajectory are used to construct frequency domain nodes; and nodes in the power response propagation trajectory are used to construct power domain nodes. When a disturbance propagation relationship exists between two nodes, a propagation edge is established between the corresponding nodes, and the propagation direction, propagation delay, and propagation correlation strength are recorded.

[0141] Furthermore, a directed propagation relationship is established based on the propagation direction, the propagation order is determined based on the propagation delay, and the propagation edge weights are determined based on the propagation correlation strength, thereby forming a disturbance propagation topology. This disturbance propagation topology characterizes the propagation path, propagation direction, propagation level, and propagation intensity relationships of local electromagnetic transient disturbances in the voltage, current, frequency, and power domains.

[0142] Furthermore, the propagation path between voltage domain nodes and frequency domain nodes in the disturbance propagation topology is used to characterize the influence process of voltage disturbance propagation to frequency response; the propagation path between frequency domain nodes and power domain nodes is used to characterize the influence process of frequency disturbance propagation to power response.

[0143] Therefore, the first propagation intensity calculated subsequently actually comes from the comprehensive propagation capability corresponding to the propagation path from the voltage domain to the frequency domain in the disturbance propagation topology; the second propagation intensity comes from the comprehensive propagation capability corresponding to the propagation path from the frequency domain to the power domain in the disturbance propagation topology; by comparing the differences between the two types of propagation capabilities, it can be determined whether the frequency change forms a real power transfer process, thus providing a basis for the subsequent construction of electromagnetic environment characterization parameters for weak power grids.

[0144] The first propagation intensity during the propagation process from the voltage domain to the frequency domain is determined based on the perturbation propagation topology, and its expression is as follows:

[0145] ;

[0146] in: The first propagation intensity; For the first The voltage disturbance amplitude corresponding to each voltage disturbance node; For the first The frequency change amplitude corresponding to each frequency response node; This is the propagation delay from the voltage domain to the frequency domain; To propagate association weights, their values ​​range from 0.05 to 1; The propagation attenuation coefficient has a value range of 0.01 to 10.

[0147] The second propagation intensity of a local electromagnetic transient disturbance event during its propagation from the frequency domain to the power domain is determined based on the disturbance propagation topology, and its expression is as follows:

[0148] ;

[0149] in: This represents the second propagation intensity; For the first The power fluctuation amplitude corresponding to each power node; This is the propagation correlation weight between the frequency domain and the power domain, and its value ranges from 0.05 to 1. The propagation attenuation coefficient has a value range of 0.01 to 10.

[0150] Calculate the deviation between the first propagation intensity and the second propagation intensity, and construct the propagation deviation index, the expression of which is as follows:

[0151] ;

[0152] in: The propagation deviation index; This is a correction factor, and its value range is... .

[0153] To fully reflect the differences in the impact of various local electromagnetic transient disturbance events on the electromagnetic environment, a scenario impact coefficient is further constructed, the expression of which is as follows:

[0154] ;

[0155] in: The scene influence coefficient; The first in the characteristics of historical electromagnetic transient disturbances The perturbation propagation characteristic quantity; The first in the dynamic state characteristics of historical power grids Item dynamic state characteristic quantity; The perturbation propagation characteristic weights range from 0.01 to 1. The dynamic state feature weights range from 0.01 to 1. To propagate deviation from the exponential weight, its value ranges from 0.1 to 5.

[0156] The electromagnetic environment characterization parameters for weak power grids are constructed by combining historical electromagnetic transient disturbance characteristics, historical power grid dynamic state characteristics, and scenario influence coefficients. Their expressions are as follows:

[0157] ;

[0158] in: Parameters characterizing the electromagnetic environment of weak power grids; The first in the characteristics of historical electromagnetic transient disturbances Item characteristic quantity; The first in the dynamic state characteristics of historical power grids Item characteristic quantity; The characteristic weight of historical electromagnetic transient disturbances ranges from 0.01 to 1. The weights for the dynamic state characteristics of the historical power grid range from 0.01 to 1.

[0159] Since historical data can only reflect the patterns of disturbances that have already occurred, it is difficult to cover sudden operating conditions such as extreme weather conditions, fault conditions, and concentrated access of nonlinear loads. Therefore, real-time online monitoring data is further introduced to dynamically correct the electromagnetic environment characterization parameters of weak power grids.

[0160] The real-time electromagnetic transient disturbance characteristics, real-time power grid dynamic state characteristics, and real-time operating condition characteristics corresponding to the current operating moment are obtained, and real-time correction coefficients are constructed, the expressions of which are as follows:

[0161] ;

[0162] in: For real-time correction coefficients; Characteristics of real-time electromagnetic transient disturbances; Real-time dynamic state characteristics of the power grid; This represents the characteristics of real-time operating conditions. , , These are the corresponding weights, and their values ​​range from 0.01 to 1.

[0163] Based on the real-time correction coefficient, the dynamically corrected electromagnetic environment characterization parameters of the weak power grid are obtained, and their expressions are as follows:

[0164] ;

[0165] in: These are the dynamically corrected electromagnetic environment characterization parameters for weak power grids.

[0166] Furthermore, the construction of the frequency reliability assessment model specifically includes:

[0167] Obtain the historical electromagnetic transient disturbance characteristics and historical power grid dynamic state characteristics corresponding to different historical local electromagnetic transient disturbance events;

[0168] Based on the characteristics of historical electromagnetic transient disturbances and the dynamic state characteristics of historical power grids, electromagnetic environment characterization parameters of weak power grids under different events are constructed, and a frequency reliability assessment model is built based on the electromagnetic environment characterization parameters of weak power grids.

[0169] Specifically, the electromagnetic environment characterization parameters of the weak power grid corresponding to historical local electromagnetic transient disturbance events are used as training samples, and the degree of deviation between the frequency observation results and the actual frequency results at the corresponding time is used as a supervision label to train the frequency reliability assessment model.

[0170] Furthermore, the dynamically corrected electromagnetic environment characterization parameters of the weak power grid and the real-time frequency observation characteristics are input into the frequency reliability assessment model, and the frequency reliability value is calculated, as shown in the following expression:

[0171] ;

[0172] in: This represents the reliability value of the frequency. These are the dynamically corrected electromagnetic environment characterization parameters for weak power grids. Features for real-time frequency observation; The electromagnetic environment characteristic weights range from 0.01 to 10. The frequency observation feature weights range from 0.01 to 10. This is the model bias term.

[0173] To further enhance adaptability under complex operating conditions, the frequency reliability assessment model continuously receives real-time online monitoring data during operation and incrementally updates the model parameters using the latest dynamically corrected electromagnetic environment characterization parameters of the weak power grid. This enables the model to continuously learn the propagation laws of newly emerging local electromagnetic transient disturbances and the operating characteristics of the weak power grid, thereby improving the accuracy and robustness of the frequency reliability assessment results under all operating conditions.

[0174] Furthermore, the construction of electromagnetic environment characterization parameters for weak power grids under different events, based on historical electromagnetic transient disturbance characteristics and historical power grid dynamic state characteristics, specifically includes:

[0175] The characteristics of historical electromagnetic transient disturbances are classified based on the types of historical local electromagnetic transient disturbance events.

[0176] Based on the classification results, the corresponding historical power grid dynamic state characteristics are obtained, and a disturbance propagation topology is constructed to characterize the propagation path and direction of local electromagnetic transient disturbances in the voltage domain, current domain, frequency domain, and power domain.

[0177] The first propagation intensity of a local electromagnetic transient disturbance event during its propagation from the voltage domain to the frequency domain is determined based on the disturbance propagation topology; the second propagation intensity of the local electromagnetic transient disturbance event during its propagation from the frequency domain to the power domain is also determined based on the disturbance propagation topology.

[0178] The deviation between the first propagation intensity and the second propagation intensity is calculated, a propagation deviation index is constructed, and electromagnetic environment characterization parameters of weak power grids are constructed based on the propagation deviation index.

[0179] Specifically, since different types of local electromagnetic transient disturbances have different propagation mechanisms, a clustering method based on disturbance propagation features is used for classification. Specifically, disturbance source location features, disturbance duration features, disturbance propagation path features, frequency response features, current response features, and power coupling features are extracted from historical electromagnetic transient disturbance features, and a historical disturbance feature vector is constructed, the expression of which is as follows:

[0180] ;

[0181] in: This represents the historical perturbation feature vector; This represents the corresponding historical disturbance characteristic.

[0182] The scene matching degree between the historical perturbation feature vector and the center of each historical event category is calculated as follows:

[0183] ;

[0184] in: For scene matching degree; This represents the current historical disturbance characteristic quantity; This refers to the central feature quantity of the corresponding event category; The feature weights range from 0.01 to 1. This is a correction factor, and its value range is... .

[0185] The event category with the highest scene matching degree is identified as the corresponding historical local electromagnetic transient disturbance event type, thus completing the classification; the classification results include collector line switching, capacitor bank switching, reactive power compensation device switching, wind turbine grid connection and disconnection, and transformer excitation inrush current.

[0186] After completing the classification, the historical dynamic state characteristics of the power grid under the corresponding classification results are obtained, and the disturbance propagation topology is constructed. The specific steps are as follows:

[0187] First, the corresponding voltage disturbance propagation trajectory, current response propagation trajectory, frequency evolution propagation trajectory, and power response propagation trajectory are extracted from the historical dynamic state characteristics of the power grid, and the disturbance nodes in the trajectory are used as topology nodes. Among them, the voltage disturbance node corresponds to the disturbance propagation process in the voltage domain; the current response node corresponds to the response propagation process in the current domain; the frequency evolution node corresponds to the frequency propagation process in the frequency domain; and the power response node corresponds to the power propagation process in the power domain.

[0188] Subsequently, topological edges are established based on the propagation time sequence relationship recorded in the node perturbation propagation chain; when there is a perturbation propagation relationship between two nodes, a corresponding propagation edge is established, and the propagation direction, propagation delay, and propagation association strength are recorded.

[0189] The perturbation propagation topology matrix is ​​further constructed, and its expression is as follows:

[0190] ;

[0191] in: The topology matrix for perturbation propagation; For nodes With nodes The propagation correlation weight between them; when there is no propagation relationship, ;

[0192] The topological edge weights are determined based on the propagation path length, propagation delay, and propagation correlation strength, and their expressions are as follows:

[0193] ;

[0194] in: The propagation correlation coefficient ranges from 0.01 to 1. For transmission delay; This is the time delay attenuation coefficient, and its value ranges from 0.01 to 10. This represents the propagation path length between nodes.

[0195] The resulting disturbance propagation topology can fully characterize the propagation path and direction of local electromagnetic transient disturbances in the voltage domain, current domain, frequency domain, and power domain.

[0196] Furthermore, the first propagation intensity of the local electromagnetic transient disturbance during its propagation from the voltage domain to the frequency domain is determined based on the disturbance propagation topology.

[0197] Since the disturbance propagation topology has recorded all propagation paths and corresponding edge weights between voltage nodes and frequency nodes, the first propagation strength essentially reflects the comprehensive influence capability of voltage disturbances propagating to frequency disturbances.

[0198] First, extract the set of all propagation paths in the disturbance propagation topology that start at voltage domain nodes and terminate at frequency domain nodes. Its expression is as follows:

[0199] ;

[0200] Then, the path propagation contribution value corresponding to each propagation path is calculated, and its expression is as follows:

[0201] ;

[0202] in: For the first The propagation contribution value corresponding to each propagation path; This is a product of the weights of all propagating edges in the path; For the first One transmission path;

[0203] The first propagation strength is further calculated, and its expression is as follows:

[0204] ;

[0205] in: The first propagation intensity; This represents the path weight, with a value ranging from 0.01 to 1.

[0206] Similarly, based on the perturbation propagation topology, extract the set of all propagation paths that start at frequency domain nodes and terminate at power domain nodes. Its expression is as follows:

[0207] ;

[0208] Then, the path propagation contribution value corresponding to each propagation path is calculated, and its expression is as follows:

[0209] ;

[0210] in: For the first The propagation contribution value corresponding to each propagation path; For nodes With nodes Propagation correlation weights between them; For the first One transmission path;

[0211] The second propagation intensity is further calculated, and its expression is as follows:

[0212] ;

[0213] in: This represents the second propagation intensity; This represents the path weight, with a value ranging from 0.01 to 1.

[0214] Since the first propagation intensity reflects the degree of influence of voltage domain disturbances propagating to the frequency domain, while the second propagation intensity reflects the degree of influence of frequency domain disturbances further propagating to the power domain, the difference between the two can reflect whether the frequency change originates from the actual power transfer process.

[0215] If the frequency change is mainly caused by local electromagnetic transient disturbances, the propagation capability from the voltage domain to the frequency domain is strong, while the propagation capability from the frequency domain to the power domain is weak, and the deviation between the two is relatively large. If the frequency change originates from the actual power imbalance process, there is a clear propagation correlation between the frequency domain and the power domain, and the deviation between the two is relatively small.

[0216] Further calculations of the degree of deviation are performed to construct the propagation deviation index, the expression of which is as follows:

[0217] ;

[0218] in: The propagation deviation index; This is a correction factor, and its value range is... .

[0219] To improve the ability to distinguish between complex scenarios, we further construct electromagnetic environment characterization parameters for weak power grids by combining scenario impact factors corresponding to different historical local electromagnetic transient disturbance events. The expression is as follows:

[0220] ;

[0221] in: Parameters characterizing the electromagnetic environment of weak power grids; As a scene-related influencing factor; These are characteristic quantities of historical electromagnetic transient disturbances; The corresponding feature weights range from 0.01 to 1.

[0222] Among them, the scenario impact factor is dynamically generated based on the historical event classification results. Different local electromagnetic transient disturbance events correspond to different scenario impact factors, so that the final weak power grid electromagnetic environment characterization parameters can simultaneously reflect the disturbance propagation law, the dynamic propagation characteristics of the power grid, and the differentiated impact characteristics under different local electromagnetic transient disturbance event scenarios.

[0223] A frequency reliability assessment model is constructed based on dynamically corrected electromagnetic environment characterization parameters of weak power grids.

[0224] It should be noted that the final electromagnetic environment characterization parameters of the weak power grid are... It should continue to be adjusted by real-time coefficients. The dynamic correction is obtained and based on Constructing a frequency reliability assessment model and calculating frequency reliability values This ensures that the frequency reliability assessment model can not only learn the propagation patterns and dynamic characteristics of historical local electromagnetic transient disturbances, but also reflect the changes in the electromagnetic environment under the current operating conditions in real time. This improves the model's adaptability to complex scenarios such as extreme weather conditions, abnormal equipment conditions, and nonlinear load fluctuations, and avoids the frequency reliability assessment results from over-reliance on historical sample data, which can lead to lag bias. In this way, the frequency reliability value can accurately represent the authenticity and reliability of the current frequency observation results, and improve the accuracy and robustness of subsequent virtual inertia control decisions.

[0225] S104. Based on the confidence level value, the frequency observation signal data is corrected to obtain the target frequency response information, and the target inertia control strategy corresponding to the grid-side energy storage system is constructed based on the target frequency response information.

[0226] In this embodiment, since frequency observation results under weak power grid conditions are easily affected by local electromagnetic transient disturbances, resulting in abnormal frequency changes inconsistent with the actual system power imbalance, it is necessary to perform reliability correction on the frequency observation signal data to improve the accuracy of subsequent inertia support requirement judgment. This step first obtains the frequency change sequence corresponding to the frequency observation signal data and analyzes the degree of influence of local electromagnetic transient disturbances on the current frequency change response based on the reliability value; then, it identifies the abnormal frequency change portion caused by local electromagnetic transient disturbances and the true frequency change portion caused by actual power imbalance in the frequency change sequence, and suppresses, compensates for, or corrects the abnormal frequency change portion; furthermore, it retains the true frequency change information that matches the actual operating state of the system, weakening the impact of local electromagnetic transient disturbances on the frequency observation results; finally, it forms the target frequency response information. Among them, the target frequency response information is used to characterize the real frequency dynamic response process after reliability correction. It can more accurately reflect the actual frequency change state and system inertia demand state in the current grid-type weak power grid, thus providing a reliable basis for the subsequent construction of target inertia control strategy and virtual inertia regulation, and avoiding the false triggering of virtual inertia caused by local electromagnetic transient disturbance events such as the switching of new energy power station equipment, the switching of reactive power compensation devices, the switching of capacitor banks, and transformer excitation inrush current.

[0227] Furthermore, the step of correcting the frequency observation signal data based on the confidence level value to obtain the target frequency response information specifically includes:

[0228] Acquire the first frequency change sequence corresponding to the frequency observation signal data, and determine the abnormal frequency response interval in the first frequency change sequence affected by local electromagnetic transient disturbance events based on the confidence level value;

[0229] Disturbance components are identified within the abnormal frequency response range to determine the local electromagnetic transient-induced frequency change components.

[0230] The first frequency change sequence is compensated and corrected based on the local electromagnetic transient induced frequency change component, and the corrected target frequency response information is output.

[0231] Specifically, since frequency observation results in a grid-type weak power grid may simultaneously include frequency variation components caused by the actual power imbalance of the system and frequency observation anomalies caused by local electromagnetic transient disturbances, it is necessary to perform reliability correction on the frequency observation signal data in order to preserve the actual frequency variation process as much as possible and eliminate the interference effects caused by local electromagnetic transient disturbances.

[0232] First, the first frequency change sequence corresponding to the frequency observation signal data is obtained. The first frequency change sequence is used to characterize the continuous dynamic change process of the frequency observation results over time, including information such as the amplitude of the frequency change, the direction of the frequency change, and the duration of the frequency change.

[0233] Subsequently, the abnormal frequency response intervals affected by local electromagnetic transient disturbances in the first frequency change sequence are determined based on the confidence level value. Specifically, the confidence level value is used as the basis for judging the reliability of frequency observations, and is combined with the frequency change characteristics in the first frequency change sequence for analysis; when the confidence level value is consistently lower than the corresponding confidence judgment condition, it indicates that the current frequency change process is likely to be affected by local electromagnetic transient disturbances.

[0234] By combining the frequency abrupt change position, frequency oscillation position, and frequency recovery position in the first frequency change sequence, the time segment in which the abnormal frequency change behavior occurs continuously is located, and this time segment is determined as the abnormal frequency response interval.

[0235] The determination of the abnormal frequency response interval considers not only the changes in the confidence level value, but also a comprehensive judgment based on the continuity and stability of the frequency change. When the frequency change process exhibits rapid abrupt changes, short-term oscillations, or abnormal recovery behavior, and the confidence level value decreases synchronously during the corresponding period, then this part of the frequency change is considered to be inconsistent with the frequency evolution law caused by the actual power imbalance, and thus the corresponding interval is classified as an abnormal frequency response interval. Conversely, when the frequency change process has continuous evolution characteristics and can be consistent with the actual power change process, it is retained as a normal frequency response interval.

[0236] After determining the abnormal frequency response range, the disturbance components of the abnormal frequency response range are identified in order to determine the local electromagnetic transient induced frequency change components.

[0237] Specifically, firstly, the frequency change characteristics, frequency change duration characteristics, and frequency change propagation characteristics corresponding to the abnormal frequency response interval are extracted; then, correlation analysis is performed in conjunction with electromagnetic transient disturbance characteristics to determine the degree of matching between the abnormal frequency change and the propagation behavior of the local electromagnetic transient disturbance. If the current frequency change process has a high degree of consistency with the propagation characteristics of the corresponding local electromagnetic transient disturbance event, then the corresponding frequency change part is identified as the local electromagnetic transient-induced frequency change component.

[0238] Furthermore, the propagation source of abnormal frequency changes is analyzed using the node disturbance propagation chain. If the abnormal frequency change can be traced back to the voltage disturbance propagation trajectory, and no complete current response propagation process or continuous power transfer process is formed during the propagation, then the frequency change is considered to originate from the local electromagnetic transient disturbance propagation process. If the frequency change is accompanied by both current response propagation and power response propagation, then the frequency change is related to the actual power imbalance process and does not belong to the local electromagnetic transient induced frequency change component.

[0239] After determining the local electromagnetic transient induced frequency change components, the first frequency change sequence is compensated and corrected based on the local electromagnetic transient induced frequency change components.

[0240] Specifically, firstly, the degree of influence of abnormal frequency changes on frequency observation results is determined based on the local electromagnetic transient-induced frequency change components; then, the corresponding abnormal frequency change position is located in the first frequency change sequence, and the local electromagnetic transient-induced frequency change components are stripped to eliminate the additional influence of local electromagnetic transient disturbances on frequency observation results.

[0241] Furthermore, to avoid disrupting the true frequency change process during correction, a continuity analysis is performed on the normal frequency response intervals before and after the abnormal frequency change location. The frequency change sequence after stripping is then smoothed and compensated based on the frequency evolution trend between adjacent normal frequency response intervals, ensuring that the corrected frequency change process maintains continuous evolution characteristics. Simultaneously, the consistency of the compensated frequency change results is verified by combining the power response and current response information at the corresponding moments, guaranteeing that the corrected frequency change process reflects the true frequency evolution law under actual power imbalance conditions.

[0242] The final output is the corrected target frequency response information; the target frequency response information is used to characterize the real frequency dynamic change process after eliminating the influence of local electromagnetic transient disturbances. It can more accurately reflect the actual frequency operation status and system inertia demand status of the current grid-type weak power grid, thereby providing a reliable basis for the subsequent construction of inertia support requirements and virtual inertia regulation, and reducing the risk of virtual inertia false triggering caused by local electromagnetic transient disturbances such as the switching of new energy power station equipment.

[0243] In this embodiment, after completing the reliability correction of the frequency observation signal data, the obtained target frequency response information can characterize the true frequency dynamic change process after eliminating the influence of local electromagnetic transient disturbances. Therefore, this step uses the target frequency response information to analyze the frequency stability state and inertia support demand state of the current grid-type weak power grid, and constructs the target inertia control strategy corresponding to the grid-side energy storage system accordingly.

[0244] Furthermore, the construction of the target inertia control strategy for the grid-side energy storage system based on the target frequency response information specifically includes:

[0245] Obtain the second frequency change sequence corresponding to the target frequency response information, and determine the frequency shift degree and frequency evolution trend based on the second frequency change sequence;

[0246] Based on the frequency evolution trend, predict the frequency change state within a preset time range, and determine the frequency instability risk level based on the degree of frequency offset and the frequency change state.

[0247] Inertia support requirement levels are generated based on frequency instability risk levels, and target inertia control strategies are constructed based on these inertia support requirement levels.

[0248] Specifically, a second frequency change sequence corresponding to the target frequency response information is obtained. This second frequency change sequence originates from the same frequency change process as the first frequency change sequence, but the corresponding frequency information differs. The first frequency change sequence corresponds to the raw frequency observation signal data without reliability correction, containing both true frequency change components and anomalous frequency change components caused by local electromagnetic transient disturbances. The second frequency change sequence corresponds to the target frequency response information after reliability correction, primarily reflecting the true dynamic frequency change process after eliminating the influence of local electromagnetic transient disturbances. Therefore, the first and second frequency change sequences share the same data source and time correspondence, but the corresponding frequency change content differs; the second frequency change sequence is the result of the first frequency change sequence after correction processing.

[0249] Subsequently, the frequency shift degree and frequency evolution trend are determined based on the second frequency change sequence. The frequency shift degree characterizes the extent to which the current frequency deviates from the rated frequency; the frequency evolution trend characterizes the tendency of the frequency change process to recover to a stable state or to develop towards an unstable state.

[0250] Frequency evolution trends can be determined by analyzing the direction, rate, and duration of frequency changes within a continuous time window. When the frequency deviation continues to widen and the rate of frequency change continues to increase, the frequency evolution trend is determined to be an unstable development trend; when the frequency deviation continues to decrease and the rate of frequency change gradually decreases, the frequency evolution trend is determined to be a recovery and stabilization trend; when the direction of frequency change frequently switches, the frequency evolution trend is determined to be an oscillation trend.

[0251] Furthermore, based on the frequency evolution trend, the frequency change state within a preset time range is predicted: the second frequency change sequence corresponding to multiple consecutive sampling periods before the current moment is used as the prediction sample, and the frequency evolution prediction sequence is constructed in combination with the current frequency evolution trend; in the prediction process, not only the current direction of frequency change is considered, but also the change in the rate of frequency change and the continuity of the frequency change trend are considered, so as to obtain the frequency change state within the preset time range in the future.

[0252] For example, if the current frequency continues to decline and the rate of decline continues to increase, it is predicted that the frequency will continue to develop in the direction of instability within the preset time range in the future; if the current frequency has begun to recover and the recovery rate continues to increase, it is predicted that the frequency will gradually recover to the stable operating range within the preset time range in the future; if the current frequency exhibits periodic fluctuations, it is predicted that the frequency oscillation state may continue to exist within the preset time range in the future.

[0253] The frequency instability risk level is determined based on the degree of frequency offset and the state of frequency change; the frequency instability risk level is used to characterize the degree of possibility that the system will experience frequency instability within a preset time range in the future.

[0254] The frequency instability risk level can be divided into five risk levels: Level 1 risk, corresponding to a small frequency deviation and a trend towards stabilization; Level 2 risk, corresponding to a small frequency deviation but a weak oscillation trend; Level 3 risk, corresponding to a moderate frequency deviation and a continuous oscillation trend; Level 4 risk, corresponding to a large frequency deviation and a continuous deviation from the stable operating range; and Level 5 risk, corresponding to a large frequency deviation and a rapid instability development trend.

[0255] The above classification is merely an example of a method for constructing frequency instability risk levels, intended to illustrate the influence of the correlation between the degree of frequency offset and the state of frequency change on the frequency instability risk level. In practical applications, the frequency instability risk level can be dynamically or adaptively classified based on factors such as grid scale, grid-based energy storage capacity, grid operation mode, frequency control requirements, and system stability evaluation standards. This application does not limit the number of frequency instability risk levels, the classification standards, or the corresponding judgment conditions for each level. Any technical solution that can reflect the current system frequency instability risk level based on the degree of frequency offset and the state of frequency change falls within the scope of protection of this application.

[0256] Subsequently, an inertia support demand level is generated based on the frequency instability risk level. Since the purpose of providing virtual inertia support for grid-type energy storage systems is to suppress the frequency instability process, there is a positive correlation between the frequency instability risk level and the inertia support demand level. When the frequency instability risk level increases, it indicates that the system frequency change rate increases and the frequency stability capability decreases, at which point it is necessary to increase the virtual inertia support capability. When the frequency instability risk level decreases, it indicates that the system itself has a strong frequency stability capability, so there is no need to provide a high level of inertia support.

[0257] There is a positive correlation between the frequency instability risk level and the inertia support requirement level; that is, the higher the frequency instability risk level, the higher the corresponding inertia support requirement level, and vice versa. This application does not limit the specific classification method, number of classifications, or mapping relationship of the frequency instability risk level and the inertia support requirement level. Those skilled in the art can set these according to actual application scenarios, as long as they can characterize the correlation between the system's frequency stability risk and inertia support requirement. Specifically, the higher the inertia support requirement level, the stronger the virtual inertia capacity that the subsequent grid-type energy storage system needs to release.

[0258] Finally, a target inertia control strategy is constructed based on the inertia support demand level; the corresponding inertia response priority, inertia release intensity, and inertia response duration demand are determined based on the inertia support demand level, and a target inertia control strategy is formed; the target inertia control strategy is used to characterize the virtual inertia support level required by the current grid-type weak power grid under real frequency disturbance conditions, thereby providing a basis for the subsequent generation of target inertia control strategies and the regulation of energy storage virtual inertia.

[0259] S105. Generate energy storage inertia support commands based on the target inertia control strategy, and control the grid-side energy storage system to perform virtual inertia regulation based on the energy storage inertia support commands.

[0260] In this embodiment, the target inertia control strategy corresponding to the current grid-side energy storage system is determined based on the target frequency response information. Therefore, this step generates a target inertia control strategy based on the target inertia control strategy and further drives the grid-side energy storage system to perform virtual inertia regulation in order to achieve frequency stability support for the grid-type weak grid.

[0261] First, the target inertia control strategy is obtained, and then constructed in conjunction with the current operating status of the grid-side energy storage system. The operating status of the grid-side energy storage system includes information such as the energy storage state of charge, available power margin, current output power status, and grid-based control operation status.

[0262] Furthermore, the target inertia response level is determined based on the target inertia control strategy, and the corresponding inertia response capability boundary is determined in conjunction with the operating status of the energy storage system.

[0263] For example, when the inertia support requirement level is low, only a small adjustment needs to be made to the inertia response capability in the grid-type control to maintain the basic inertia support state of the energy storage system; when the inertia support requirement level is high, the inertia response capability is increased to enable the energy storage system to have a faster frequency response capability and a stronger inertia support capability; when the inertia support requirement level reaches the highest level, the maximum inertia response capability is configured within the available operating boundary of the energy storage system to suppress the rapid frequency deterioration process.

[0264] Subsequently, energy storage inertia support commands are generated based on the target inertia control strategy. These commands describe the inertia support level that the energy storage system should provide at the current moment, as well as the corresponding grid control and adjustment requirements.

[0265] The energy storage inertia support command includes at least one or more of the following: target virtual inertia parameters, target damping parameters, inertia response level, and inertia response duration.

[0266] Since this application adopts a grid-based control method, the virtual inertia regulation does not directly control the power output, but changes the dynamic response capability of the energy storage system to frequency changes by adjusting the grid control parameters.

[0267] Furthermore, the grid-side energy storage system is controlled to perform virtual inertia regulation according to the energy storage inertia support command; after receiving the energy storage inertia support command, the grid-type controller adjusts the virtual inertia parameters in the grid control link so that the grid-type controller can re-establish inertia response characteristics that match the current frequency instability risk level.

[0268] For example, when the system frequency shows a downward trend and the risk level of frequency instability increases, the virtual inertia parameter is increased, so that the energy storage system releases more inertia support capacity in the early stage of frequency change, thereby reducing the rate of frequency change; when the system frequency gradually recovers to stability, the virtual inertia parameter is reduced, so that the energy storage system gradually exits the enhanced inertia support state and avoids over-response.

[0269] Furthermore, during the virtual inertia control process, the network controller continuously monitors frequency changes and dynamically adjusts the virtual inertia response process based on the frequency change trend.

[0270] Among them, the network-type controller constructs a dynamic mapping relationship between frequency deviation and inertia support capability, enabling the virtual inertia output capability to adaptively adjust with changes in system operating status, thereby avoiding the problems of insufficient or excessive response caused by using fixed inertia parameters.

[0271] Meanwhile, since the frequency observation results have already been analyzed for reliability using the frequency reliability assessment model, the virtual inertia control in this step is performed only based on the obtained target frequency response information.

[0272] In other words, for frequency observation anomalies caused by local electromagnetic transient disturbances such as power line switching events, reactive power compensation device switching events, capacitor bank switching events, wind turbine grid connection and disconnection events, and transformer excitation inrush current events, since their corresponding confidence values ​​are low, they will not form a high-level target inertia control strategy after correction, thereby avoiding the energy storage system from mistakenly believing that the system has experienced real frequency instability and releasing virtual inertia.

[0273] As for frequency disturbances caused by real power imbalance, since the corresponding frequency has a high degree of reliability, a target inertia control strategy that matches the frequency instability risk level can be formed, and the energy storage system can be further driven to release the corresponding virtual inertia support capability.

[0274] After the virtual inertia regulation of the grid-side energy storage system based on energy storage inertia support command control is performed, it also includes:

[0275] S106. Obtain the system response results during the virtual inertia control process, and iteratively optimize the frequency reliability assessment model and the target inertia control strategy based on the system response results;

[0276] In this embodiment, to improve the adaptability of the frequency reliability assessment model and the target inertia control strategy to the operating conditions of a grid-type weak power grid, this step obtains the system response results during the virtual inertia regulation process and optimizes and updates the frequency reliability assessment model and the target inertia control strategy based on the system response results. The system response results include frequency recovery results, power response results, and local electromagnetic transient disturbance suppression results. Furthermore, the frequency reliability mapping relationship in the frequency reliability assessment model is optimized based on the frequency recovery results and the local electromagnetic transient disturbance suppression results to improve the accuracy of frequency reliability assessment; the inertia response control parameters in the target inertia control strategy are optimized based on the power response results to improve the matching degree between inertia support and actual frequency support requirements. By continuously using the system response results for feedback correction, iterative optimization of the frequency reliability assessment model and the target inertia control strategy is achieved, thereby improving the virtual inertia regulation effect and the ability to suppress virtual inertia false triggering under local electromagnetic transient disturbance scenarios, as detailed below:

[0277] Obtain the frequency recovery results, power response results, and local electromagnetic transient disturbance suppression results during the virtual inertia control process;

[0278] Frequency reliability evaluation results are constructed based on frequency recovery results; disturbance identification evaluation results are constructed based on local electromagnetic transient disturbance suppression results.

[0279] Based on the frequency reliability evaluation results and the disturbance identification evaluation results, the reliability mapping relationship in the frequency reliability evaluation model is corrected, and the frequency reliability evaluation model is updated based on the corrected reliability mapping relationship.

[0280] The inertia support evaluation results are constructed based on the power response results, and the inertia response control parameters in the target inertia control strategy are adjusted based on the inertia support evaluation results.

[0281] The target inertia control strategy is updated based on the adjusted inertia response control parameters.

[0282] Specifically, the frequency recovery result after virtual inertia adjustment is obtained, and the frequency recovery time, maximum frequency deviation, frequency overshoot, and frequency stabilization time are extracted during the frequency recovery process; at the same time, the reliability value output by the frequency reliability assessment model at the corresponding time is obtained; and the frequency reliability evaluation result is constructed based on the consistency between the reliability value and the actual frequency recovery result.

[0283] Furthermore, the frequency reliability evaluation result is constructed, and its expression is as follows:

[0284] ;

[0285] in: The results are for frequency reliability evaluation. The prediction results are from the frequency reliability assessment model. This is the evaluation value corresponding to the actual frequency recovery result; The weights for the evaluation indicators range from 0.01 to 1. This is a correction factor, and its value range is... ;when The closer the result is to 1, the more consistent the model's evaluation results are with the actual recovery effect.

[0286] Based on the results of suppressing local electromagnetic transient disturbances, a disturbance identification and evaluation result is constructed: This involves statistically analyzing the cases where frequency observation anomalies are correctly identified, false triggers are suppressed, and true frequency disturbances are preserved after a local electromagnetic transient disturbance event occurs. The disturbance identification and evaluation result is then constructed, and its expression is as follows:

[0287] ;

[0288] in: The results of disturbance identification and evaluation; The number of correctly identified local electromagnetic transient disturbance events; To successfully suppress the number of falsely triggered events; To accurately preserve the true number of frequency disturbances; This represents the total number of events. , , All are evaluation weights, with values ​​ranging from 0.1 to 1, and satisfying the following conditions: ;

[0289] Furthermore, based on the frequency reliability evaluation results and disturbance identification evaluation results, the reliability mapping relationship in the frequency reliability assessment model is revised: The frequency reliability assessment model contains a mapping relationship between the electromagnetic environment characterization parameters of the weak power grid and the reliability value; if the frequency reliability evaluation result decreases, it indicates that the current reliability mapping relationship is insufficient in representing the actual frequency changes; if the disturbance identification evaluation result decreases, it indicates that the current reliability mapping relationship is insufficient in identifying local electromagnetic transient disturbances; therefore, a model correction coefficient is constructed, the expression of which is as follows:

[0290] ;

[0291] in: These are the model correction coefficients; , All are adjusted weights, with values ​​ranging from 0.1 to 1, and satisfying the following conditions: ;

[0292] Subsequently, the mapping parameters in the frequency reliability assessment model are updated using model correction coefficients: the frequency reliability assessment model is updated based on the corrected mapping parameters. The weak grid electromagnetic environment characterization parameters, reliability values, and system response results corresponding to the latest local electromagnetic transient disturbance event are added to the model sample library, and the model parameters are updated using incremental learning, enabling the model to continuously learn new local electromagnetic transient propagation laws. Inertia support evaluation results are constructed based on the power response results. These inertia support evaluation results are not the inertia support demand level, but rather used to evaluate the rationality of the current target inertia control strategy. The response speed, inertia release amplitude, power tracking accuracy, and frequency support effect are extracted from the power response results, and the inertia support evaluation results are constructed, with the following expression:

[0293] ;

[0294] in: The evaluation results are for inertia support. For the corresponding evaluation indicators; The weights are set to a range of 0.01 to 1.

[0295] Furthermore, the inertia response control parameters in the target inertia control strategy are adjusted based on the inertia support evaluation results. When the inertia support evaluation results are low, it indicates that there is insufficient inertia release, excessive inertia release, or response lag. In this case, the virtual inertia parameters, damping parameters, and inertia response gain parameters in the target inertia control strategy are adjusted.

[0296] Furthermore, based on the inertia support evaluation results, parameter adjustment coefficients are constructed, and their expressions are as follows:

[0297] ;

[0298] in: Adjustment coefficients for parameters; This is an adjustment factor, with a value range of 0.01 to 2;

[0299] The inertia response control parameters in the target inertia control strategy are corrected based on the parameter adjustment coefficients, and the target inertia control strategy is updated based on the corrected inertia response control parameters. The expression is as follows:

[0300] ;

[0301] in: The updated inertia response control parameters; These are the original inertia response control parameters.

[0302] Example 2:

[0303] See Figure 2 A grid-side energy storage virtual inertia regulation system for grid-based control, the system being applied to the method described in Example 1, the system comprising:

[0304] The power grid dynamic state feature construction module 1 is used to acquire the operation perception data of the power grid-side energy storage system and construct the power grid dynamic state features based on the operation perception data; the operation perception data includes three-phase voltage signal data, three-phase current signal data, frequency observation signal data, and operation status information of new energy power stations;

[0305] The electromagnetic transient disturbance feature acquisition module 2 is used to identify local electromagnetic transient disturbance events based on the operating status information of new energy power stations, and to perform disturbance propagation analysis based on the dynamic status characteristics of the power grid to obtain electromagnetic transient disturbance features.

[0306] The credibility assessment module 3 is used to construct a frequency credibility assessment model. It inputs electromagnetic transient disturbance characteristics and power grid dynamic state characteristics into the frequency credibility assessment model to assess the credibility value of the current frequency observation signal.

[0307] The target inertia control strategy acquisition module 4 is used to correct the frequency observation signal data based on the confidence level value, obtain the target frequency response information, and construct the target inertia control strategy corresponding to the grid-side energy storage system based on the target frequency response information.

[0308] The virtual inertia control module 5 is used to generate energy storage inertia support commands based on the target inertia control strategy, and to control the grid-side energy storage system to perform virtual inertia control based on the energy storage inertia support commands.

[0309] Furthermore, for the specific implementation steps of the above modules, please refer to the corresponding description in Example 1, which will not be repeated here.

[0310] Example 3:

[0311] See Figure 3 A grid-side energy storage virtual inertia regulation device for grid-based control, the device including a processor 6 and a memory 7;

[0312] The memory 7 is used to store computer program code 71 and to transmit the computer program code 71 to the processor 6;

[0313] The processor 6 is used to execute the grid-side energy storage virtual inertia control method described in Embodiment 1 according to the instructions in the computer program code 71.

[0314] This embodiment also includes a computer-readable storage medium storing computer-executable instructions. When the computer-executable instructions are executed on a computer, the grid-side energy storage virtual inertia control method for grid-type control described in Embodiment 1 is implemented.

[0315] Generally, computer instructions for implementing the method of the present invention can be carried on any combination of one or more computer-readable storage media. Non-transitory computer-readable storage media can include any computer-readable medium except for the signal itself which is temporarily propagating.

[0316] Computer-readable storage media can be, for example, but not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatuses, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0317] Computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof. These programming languages ​​include object-oriented programming languages—such as Java, Smalltalk, and C++—as well as conventional procedural programming languages—such as the "C" language or similar programming languages. In particular, Python, suitable for neural network computation, and platform frameworks based on TensorFlow, PyTorch, etc., can be used. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or connected to an external computer (e.g., via the Internet using an Internet service provider).

[0318] For details regarding the aforementioned equipment and non-transitory computer-readable storage media, please refer to the specific description of a grid-side energy storage virtual inertia regulation method for grid-based control and its beneficial effects, which will not be repeated here.

[0319] Although embodiments of the present invention have been shown and described above, it should be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A method for regulating the virtual inertia of grid-side energy storage for grid-based control, characterized in that, include: Acquire operational sensing data of grid-side energy storage systems and construct dynamic state characteristics of the power grid based on the operational sensing data; The operational sensing data includes three-phase voltage signal data, three-phase current signal data, frequency observation signal data, and operational status information of new energy power stations; Based on the operation status information of new energy power stations, local electromagnetic transient disturbance events are identified, and disturbance propagation analysis is performed based on the dynamic state characteristics of the power grid to obtain electromagnetic transient disturbance characteristics. A frequency reliability assessment model is constructed, and the characteristics of electromagnetic transient disturbances and the dynamic state characteristics of the power grid are input into the frequency reliability assessment model to evaluate the reliability value of the current frequency observation signal. The frequency observation signal data is corrected based on the confidence level value to obtain the target frequency response information, and the target inertia control strategy corresponding to the grid-side energy storage system is constructed based on the target frequency response information. Based on the target inertia control strategy, energy storage inertia support commands are generated, and the grid-side energy storage system is controlled to perform virtual inertia regulation based on the energy storage inertia support commands.

2. The grid-side energy storage virtual inertia regulation method for grid-type control according to claim 1, characterized in that: The acquisition of operational sensing data from the grid-side energy storage system, and the construction of grid dynamic state characteristics based on the operational sensing data, specifically includes: Obtain continuous time series corresponding to three-phase voltage signals, three-phase current signals, and frequency observation signals; A voltage disturbance propagation trajectory is constructed based on three-phase voltage signals to characterize the propagation process of voltage anomalies between consecutive time points; A current response propagation trajectory is constructed based on three-phase current signals to characterize the current change following the voltage disturbance process; Construct a frequency evolution propagation trajectory based on frequency observation signals to characterize the continuous evolution process of frequency perturbation; The voltage disturbance propagation trajectory, current response propagation trajectory, and frequency evolution propagation trajectory are time-aligned to establish a node disturbance propagation chain that characterizes the transmission relationship of disturbances in the voltage, current, and frequency domains. Based on the node disturbance propagation chain, the dynamic state characteristics of the power grid are constructed.

3. The grid-side energy storage virtual inertia regulation method for grid-type control according to claim 2, characterized in that: The method of identifying local electromagnetic transient disturbance events based on the operating status information of new energy power plants and performing disturbance propagation analysis based on the dynamic state characteristics of the power grid to obtain electromagnetic transient disturbance characteristics specifically includes: Based on the operating status information of the new energy power station, corresponding candidate disturbance events are determined; the operating status information of the new energy power station includes at least one of equipment operating status information, equipment switching status information, and power regulation status information. The propagation behavior of candidate disturbance events is verified based on the node disturbance propagation chain, and the disturbance source is traced in reverse to determine the initial disturbance location corresponding to the candidate disturbance event. Determine whether the initial disturbance location is within the voltage disturbance propagation trajectory and whether a corresponding current response propagation trajectory has not yet formed: If the initial disturbance location is within the voltage disturbance propagation trajectory and no corresponding current response propagation trajectory is formed, the candidate disturbance event is identified as a local electromagnetic transient disturbance event; the local electromagnetic transient disturbance events include collector line switching events, reactive power compensation device switching events, capacitor bank switching events, wind turbine grid connection and disconnection events, and transformer excitation inrush current events; Decoupling features between frequency disturbances and current responses are extracted based on local electromagnetic transient disturbance events; these decoupling features characterize the degree to which there is a lack of synchronous correlation between frequency changes and current responses. Determine whether the initial disturbance location is within the voltage disturbance propagation trajectory and whether a corresponding current response propagation trajectory has not yet formed: If the initial disturbance location is not located in the voltage disturbance propagation trajectory and no corresponding current response propagation trajectory is formed, then the power coupling relationship between the frequency evolution propagation trajectory and the current response propagation trajectory is determined. Based on local electromagnetic transient disturbance events and decoupling characteristics, local electromagnetic transient disturbance characteristics are constructed; based on power coupling relationships, power imbalance disturbance characteristics are constructed. Electromagnetic transient disturbance characteristics are constructed by analyzing the differences between local electromagnetic transient disturbance characteristics and power imbalance disturbance characteristics.

4. The grid-side energy storage virtual inertia regulation method for grid-type control according to claim 1, characterized in that: The construction of the frequency reliability assessment model specifically includes: Obtain the historical electromagnetic transient disturbance characteristics and historical power grid dynamic state characteristics corresponding to different historical local electromagnetic transient disturbance events; Based on the characteristics of historical electromagnetic transient disturbances and the dynamic state characteristics of historical power grids, electromagnetic environment characterization parameters of weak power grids under different events are constructed, and a frequency reliability assessment model is built based on the electromagnetic environment characterization parameters of weak power grids.

5. The grid-side energy storage virtual inertia regulation method for grid-type control according to claim 4, characterized in that: The method for constructing electromagnetic environment characterization parameters for weak power grids under different events, based on historical electromagnetic transient disturbance characteristics and historical power grid dynamic state characteristics, specifically includes: The characteristics of historical electromagnetic transient disturbances are classified based on the types of historical local electromagnetic transient disturbance events. Based on the classification results, the corresponding historical power grid dynamic state characteristics are obtained, and a disturbance propagation topology is constructed to characterize the propagation path and direction of local electromagnetic transient disturbances in the voltage domain, current domain, frequency domain, and power domain. The first propagation intensity of a local electromagnetic transient disturbance event during its propagation from the voltage domain to the frequency domain is determined based on the disturbance propagation topology; the second propagation intensity of the local electromagnetic transient disturbance event during its propagation from the frequency domain to the power domain is also determined based on the disturbance propagation topology. The deviation between the first propagation intensity and the second propagation intensity is calculated, a propagation deviation index is constructed, and electromagnetic environment characterization parameters of weak power grids are constructed based on the propagation deviation index.

6. The grid-side energy storage virtual inertia regulation method for grid-type control according to claim 1, characterized in that: The process of correcting the frequency observation signal data based on the confidence level value to obtain the target frequency response information specifically includes: Acquire the first frequency change sequence corresponding to the frequency observation signal data, and determine the abnormal frequency response interval in the first frequency change sequence affected by local electromagnetic transient disturbance events based on the confidence level value; Disturbance components are identified within the abnormal frequency response range to determine the local electromagnetic transient-induced frequency change components. The first frequency change sequence is compensated and corrected based on the local electromagnetic transient induced frequency change component, and the corrected target frequency response information is output.

7. The grid-side energy storage virtual inertia regulation method for grid-type control according to claim 1, characterized in that: The target inertia control strategy for the grid-side energy storage system, constructed based on target frequency response information, specifically includes: Obtain the second frequency change sequence corresponding to the target frequency response information, and determine the frequency shift degree and frequency evolution trend based on the second frequency change sequence; Based on the frequency evolution trend, predict the frequency change state within a preset time range, and determine the frequency instability risk level based on the degree of frequency offset and the frequency change state. Inertia support requirement levels are generated based on frequency instability risk levels, and target inertia control strategies are constructed based on these inertia support requirement levels.

8. The grid-side energy storage virtual inertia regulation method for grid-type control according to claim 1, characterized in that: After the virtual inertia regulation of the grid-side energy storage system based on energy storage inertia support command control is performed, it also includes: The system response results during the virtual inertia control process are obtained, and the frequency reliability assessment model and target inertia control strategy are iteratively optimized based on the system response results, as follows: Obtain the frequency recovery results, power response results, and local electromagnetic transient disturbance suppression results during the virtual inertia control process; Frequency reliability evaluation results are constructed based on frequency recovery results; disturbance identification evaluation results are constructed based on local electromagnetic transient disturbance suppression results. Based on the frequency reliability evaluation results and the disturbance identification evaluation results, the reliability mapping relationship in the frequency reliability evaluation model is corrected, and the frequency reliability evaluation model is updated based on the corrected reliability mapping relationship. The inertia support evaluation results are constructed based on the power response results, and the inertia response control parameters in the target inertia control strategy are adjusted based on the inertia support evaluation results. The target inertia control strategy is updated based on the adjusted inertia response control parameters.

9. A grid-side energy storage virtual inertia regulation system for grid-based control, characterized in that, The system is applied to the method according to any one of claims 1-8, the system comprising: The grid dynamic state feature construction module (1) is used to acquire the operation perception data of the grid-side energy storage system and construct the grid dynamic state features based on the operation perception data; the operation perception data includes three-phase voltage signal data, three-phase current signal data, frequency observation signal data and new energy power station operation status information; The electromagnetic transient disturbance feature acquisition module (2) is used to identify local electromagnetic transient disturbance events based on the operating status information of new energy power stations, and to perform disturbance propagation analysis based on the dynamic status characteristics of the power grid to obtain electromagnetic transient disturbance features. The credibility assessment module (3) is used to construct a frequency credibility assessment model. It inputs electromagnetic transient disturbance characteristics and power grid dynamic state characteristics into the frequency credibility assessment model to assess the credibility value of the current frequency observation signal. The target inertia control strategy acquisition module (4) is used to correct the frequency observation signal data based on the confidence level value, obtain the target frequency response information, and construct the target inertia control strategy corresponding to the grid-side energy storage system based on the target frequency response information. The virtual inertia control module (5) is used to generate energy storage inertia support commands based on the target inertia control strategy, and to control the grid-side energy storage system to perform virtual inertia control based on the energy storage inertia support commands.

10. A grid-side energy storage virtual inertia regulation device for grid-based control, characterized in that: The device includes a processor (6) and a memory (7); The memory (7) is used to store computer program code (71) and to transmit the computer program code (71) to the processor (6). The processor (6) is used to execute the grid-side energy storage virtual inertia control method according to any one of the claims 1-8 based on the instructions in the computer program code (71).

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