New energy field inter-station equivalent impedance updating method and device in electromagnetic transient simulation, electronic equipment and storage medium

Abstract

The invention discloses a method and device for updating equivalent impedance between new energy stations in electromagnetic transient simulation, electronic equipment and a storage medium, and belongs to the field of power systems, and the method comprises the steps: obtaining historical time sequence data of a plurality of key operation parameters, such as photovoltaic, wind power, voltage and frequency, through periodic sampling; calculating an influence factor of each historical time point based on the historical data, and predicting an operation parameter state of the next time period by using the influence factors; and substituting the predicted parameters into a preset demand model to generate a quantized updated demand index, and updating the equivalent impedance parameters in the simulation model only when the index exceeds a preset threshold value. By implementing the method, the problem that the reliability of simulation precision and stability evaluation is insufficient due to the fact that comprehensive consideration of complex interaction among a plurality of key operation parameters is generally lacked in the prior art can be solved.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to a method, device, electronic equipment and storage medium for updating equivalent impedances between new energy stations in electromagnetic transient simulation. Background Art

[0002] With the increasing penetration of renewable energy sources, such as photovoltaics and wind power, in power systems, their inherent intermittency and volatility, as well as their grid-connected nature through power electronic converters, present unprecedented challenges to the safe and stable operation of power grids. Electromagnetic transient simulation is a key technical approach for studying and analyzing the dynamic behavior of power systems containing large-scale renewable energy sources under faults or disturbances. The accuracy of its simulation results is directly related to the reliability of system safety and stability assessments, protection strategy formulation, and fault diagnosis. In these simulation models, the equivalent impedance between renewable energy stations is a core parameter that describes the dynamic interaction between stations and the system. Therefore, the ability to accurately update this equivalent impedance parameter during the simulation process has become a top priority for ensuring that electromagnetic transient simulation results are highly consistent with physical reality, and thus for guaranteeing the safe and reliable operation of modern power systems.

[0003] However, existing electromagnetic transient simulation methods still have obvious deficiencies in this field. At present, the vast majority of simulation models still use fixed equivalent impedance parameters. This static model cannot adapt to the rapid and dynamic power changes of new energy stations due to factors such as weather and scheduling, resulting in a large deviation between the simulation results and the actual operating conditions. Even if some technologies take parameter updates into account, their update mechanisms are often relatively extensive, such as relying only on fixed time periods for updates, or triggering based on simple thresholds of a single operating parameter. These methods generally lack comprehensive consideration of the complex interactions between multiple key operating parameters (such as active / reactive power, voltage, frequency, etc. between stations). This lack of comprehensive consideration of the update strategy makes the simulation accuracy and stability assessment unreliable. Summary of the Invention

[0004] Embodiments of the present invention provide a method, device, electronic device, and storage medium for updating equivalent impedances between renewable energy stations in electromagnetic transient simulation, which can solve the problem in the prior art of insufficient reliability of simulation accuracy and stability assessment due to the general lack of comprehensive consideration of the complex interactions between multiple key operating parameters.

[0005] An embodiment of the present invention provides a method for updating equivalent impedances between renewable energy stations in electromagnetic transient simulation, comprising:

[0006] Acquire first time series data characterizing the interaction between the new energy sites; wherein the first time series data is a set of time series data obtained by periodically sampling various operating parameters within a current time period; the operating parameters include the reactive power of the photovoltaic units, the reactive power of the wind turbine units, the active power of the photovoltaic units, the active power of the wind turbine units, the voltage, and the frequency between the new energy sites;

[0007] Calculating the impact factor of each sampling moment according to the first time series data;

[0008] Predicting second time series data representing the interaction between new energy stations based on the influencing factors at each sampling moment and the first time series data; the second time series data is a set of time series data generated by predicting each operating parameter at each predicted sampling moment;

[0009] According to the second time series data, updating the demand model based on a preset equivalent impedance to generate an index prediction value;

[0010] Determine whether the index prediction value is greater than a preset update threshold. If so, update the equivalent impedance parameter between the new energy stations in the electromagnetic transient simulation model to a predetermined multiple of the current equivalent impedance parameter. If not, do not update the equivalent impedance parameter.

[0011] Furthermore, before updating the demand model based on the preset equivalent impedance according to the second time series data and generating the index prediction value, the method further includes:

[0012] For each operating parameter in the first time series data, extract a maximum value and a minimum value of the operating parameter in the first time series data, and record the maximum value and the minimum value as a historical maximum value and a historical minimum value of the corresponding operating parameter, respectively;

[0013] The second time series data is normalized according to the historical maximum value and the historical minimum value of each operating parameter to generate updated second time series data.

[0014] Furthermore, the calculating of the influence factor of each sampling moment according to the first time series data includes:

[0015] The reactive power of the photovoltaic units between the new energy stations, the reactive power of the wind turbine units, the active power of the photovoltaic units, and the active power of the wind turbine units in the first time series data are used as target parameters;

[0016] For each sampling moment, the average change trend of each target parameter at the current sampling moment is calculated based on the historical data of the target parameter in the preset time window before the current sampling moment;

[0017] The parameter value of each target parameter at each sampling moment is added to the corresponding average change trend value to generate the short-term forecast value of each target parameter at each sampling moment;

[0018] The impact factor of each sampling moment is calculated according to the parameter value of the target parameter at the current sampling moment and the short-term prediction value of each target parameter at each sampling moment.

[0019] Furthermore, the second time series data is generated by the following formula:

[0020]

[0021] in, is the predicted value of any operating parameter in the second time series data in the next time period; m is the total number of sampling moments included in the first time series data; is the historical value of the operating parameter X at the zth sampling moment in the first time series data; X is any one of the operating parameters; is the impact factor corresponding to the z-th sampling moment.

[0022] Furthermore, the demand model is updated by the following equivalent impedance to generate index prediction values:

[0023]

[0024] Among them, I pred is the predicted value of the index; The time between new energy stations in the next time period T m+1 The predicted voltage value; The time between new energy stations in the next time period T m+1 The frequency prediction value of The photovoltaic units PV between new energy stations in the next time period T m+1 The predicted value of active power; The photovoltaic units PV between new energy stations in the next time period T m+1 The reactive power prediction value; The wind turbine WT between new energy stations in the next time period T m+1 The predicted value of active power; The wind turbine WT between new energy stations in the next time period T m+1 The reactive power prediction value; j is the imaginary unit; e is a natural constant.

[0025] Furthermore, the sampling time T is calculated by the following formula: z Impact Factor:

[0026]

[0027] in, is the sampling time T z Impact factor; is the sampling time T z The historical value of reactive power of photovoltaic units; is the sampling time T z Calculated short-term forecast value of reactive power of photovoltaic units; is the sampling time T z The historical value of reactive power of wind turbines; is the sampling time T z The calculated short-term forecast value of wind turbine reactive power; is the sampling time T z The historical value of active power of photovoltaic units; is the sampling time T z The calculated short-term forecast value of the active power of the photovoltaic unit; is the sampling time T z The historical value of active power of wind turbines; is the sampling time T z The calculated short-term forecast value of the wind turbine active power.

[0028] Based on the above method embodiments, the present invention provides corresponding device embodiments.

[0029] An embodiment of the present invention provides an updating device for equivalent impedance between new energy stations in electromagnetic transient simulation, comprising: a first time series data acquisition module, an impact factor calculation module, a second time series data prediction module, an index prediction value generation module, and an equivalent impedance parameter updating module;

[0030] The first time series data acquisition module is used to acquire first time series data representing the interaction between the new energy sites; wherein the first time series data is a set of time series data acquired by periodically sampling various operating parameters within a current time period; the operating parameters include the reactive power of the photovoltaic units, the reactive power of the wind turbine units, the active power of the photovoltaic units, the active power of the wind turbine units, the voltage, and the frequency between the new energy sites;

[0031] The impact factor calculation module is used to calculate the impact factor of each sampling moment according to the first time series data;

[0032] The second time series data prediction module is used to predict second time series data representing the interaction between new energy stations based on the influencing factors at each sampling time and the first time series data; the second time series data is a set of time series data generated by predicting each operating parameter at each predicted sampling time;

[0033] The index prediction value generating module is configured to generate an index prediction value based on the second time series data and a preset equivalent impedance updating demand model;

[0034] The equivalent impedance parameter updating module is used to determine whether the index prediction value is greater than a preset update threshold. If so, the equivalent impedance parameter between the new energy stations in the electromagnetic transient simulation model is updated to a predetermined multiple value of the current equivalent impedance parameter; if not, the equivalent impedance parameter is not updated.

[0035] Furthermore, the device for updating equivalent impedances between new energy stations in electromagnetic transient simulation further includes: a normalization processing module;

[0036] The normalization processing module is used to extract the maximum value and minimum value of each operating parameter in the first time series data, and record the maximum value and the minimum value as the historical maximum value and the historical minimum value of the corresponding operating parameter, respectively; according to the historical maximum value and the historical minimum value of each operating parameter, the second time series data is normalized to generate updated second time series data.

[0037] Based on the above method embodiment, the present invention provides a corresponding electronic device embodiment.

[0038] An embodiment of the present invention provides an electronic device, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the method for updating the equivalent impedance between new energy stations in electromagnetic transient simulation as described in any one of the above-mentioned method embodiments.

[0039] Based on the above method embodiment, the present invention provides a corresponding storage medium embodiment.

[0040] An embodiment of the present invention provides a storage medium having a computer program stored thereon, wherein, when the computer program is running, the device where the storage medium is located is controlled to execute the method for updating the equivalent impedance between new energy stations in electromagnetic transient simulation as described in any one of the above-mentioned method embodiments.

[0041] Compared with the prior art, the present invention has the following beneficial effects:

[0042] Embodiments of the present invention provide a method, device, electronic device, and storage medium for updating equivalent impedances between renewable energy stations in electromagnetic transient simulation. The method periodically samples historical time series data for multiple key operating parameters, such as photovoltaics, wind power, voltage, and frequency. The method then calculates the impact factor for each historical moment based on this historical data and uses this impact factor to predict the operating parameter state for the next time period. Finally, the predicted parameters are substituted into a preset demand model to generate a quantified "update demand index." The equivalent impedance parameters in the simulation model are updated only when this index exceeds a preset threshold, thereby implementing an on-demand update strategy.

[0043] This invention first constructs dynamic time series data by periodically sampling multiple interacting key operating parameters, such as photovoltaics, wind power, voltage, and frequency. This directly addresses the core flaws of existing technologies, which rely on static, fixed equivalent impedance models that are unable to adapt to the dynamic changes of renewable energy sites and lack comprehensive consideration of multiple parameters. Furthermore, this invention predicts the future state of the system by calculating influencing factors. Based on a quantitative update demand model and threshold judgment mechanism, this method enables precise decision-making on update timing, thus overcoming the fundamental problem of existing technologies that rely on fixed cycles or simple triggers. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 The present invention provides a flowchart of a method for updating equivalent impedances between renewable energy stations in electromagnetic transient simulation according to an embodiment of the present invention.

[0045] Figure 2 It is a structural schematic diagram of a device for updating equivalent impedance between new energy stations in electromagnetic transient simulation provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0046] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0047] like Figure 1 As shown, to address the problem in the prior art of insufficient simulation accuracy and reliability of stability assessment due to a general lack of comprehensive consideration of the complex interactions between multiple key operating parameters, an embodiment of the present invention provides a method for updating equivalent impedances between new energy stations in electromagnetic transient simulation, comprising at least the following steps:

[0048] Step S1: Acquire first time series data characterizing the interaction between the new energy sites; wherein the first time series data is a set of time series data acquired by periodically sampling various operating parameters within a current time period; the operating parameters include the reactive power of the photovoltaic units, the reactive power of the wind turbine units, the active power of the photovoltaic units, the active power of the wind turbine units, the voltage, and the frequency between the new energy sites;

[0049] This method first needs to establish an accurate and reliable data foundation for subsequent dynamic prediction and decision-making. The specific steps are to obtain a set of first time series data that characterize the interaction between renewable energy sites. The core of this step is to no longer use static, preset theoretical values, but to capture its dynamic behavior through real-time monitoring of the actual system. Specifically, these monitored operating parameters are carefully selected key indicators that best reflect the stability and dynamic characteristics of the system. They mainly include the active and reactive power of photovoltaic units and wind turbines between renewable energy sites, as well as the voltage and frequency measured at the common connection point. These parameters together constitute a complete, multi-dimensional description of the interaction status between the site and the power grid.

[0050] In a specific implementation, this set of first time series data is obtained by periodically sampling the above-mentioned operating parameters at fixed time intervals (for example, every minute or every second) within a preset, continuous period of time. These data can come from the existing SCADA system, phasor measurement unit (PMU) or other online monitoring devices in the power system. Each sampling will record the instantaneous values ​​of all operating parameters at the sampling moment, and eventually form a data set containing multiple time points, arranged in chronological order. Through this step, a real, quantitative input basis that can reflect the historical behavior of the system is provided for the subsequent calculation of influencing factors and state prediction, thereby fundamentally solving the problem of model distortion caused by the existing technology relying on static, fixed parameters.

[0051] Step S2: Calculate the impact factor of each sampling moment according to the first time series data;

[0052] In a preferred embodiment, calculating the impact factor of each sampling moment according to the first time series data includes:

[0053] The reactive power of the photovoltaic units between the new energy stations, the reactive power of the wind turbine units, the active power of the photovoltaic units, and the active power of the wind turbine units in the first time series data are used as target parameters;

[0054] For each sampling moment, the average change trend of each target parameter at the current sampling moment is calculated based on the historical data of the target parameter in the preset time window before the current sampling moment;

[0055] The parameter value of each target parameter at each sampling moment is added to the corresponding average change trend value to generate the short-term forecast value of each target parameter at each sampling moment;

[0056] The impact factor of each sampling moment is calculated according to the parameter value of the target parameter at the current sampling moment and the short-term prediction value of each target parameter at each sampling moment.

[0057] Specifically, after acquiring the first time series data, one of the core steps of this method is to calculate the impact factors at each sampling moment, thereby providing key input with in-depth information for subsequent system state prediction. This calculation process first selects the active and reactive power of photovoltaic units and wind turbines between renewable energy stations as the core "target parameters" from the multiple operating parameters contained in the first time series data. These power parameters are selected because they are the most direct and sensitive physical quantities that characterize the dynamic behavior of renewable energy power generation units, and their fluctuations are directly related to system stability.

[0058] Subsequently, this method performs an iterative calculation for each sampling moment in the historical data. In each iteration, in order to determine an average change trend that can reflect the recent "inertia" of the system, the method first calculates the average change trend at the current sampling moment T z The parameter change between adjacent sampling moments within a preset time window (window size is n) Among them, k is the sampling time index in the time window, and its value range is from zn to z-1; then the arithmetic average of all parameter changes in the window is calculated to obtain the average change trend Next, the current sampling time T z The actual parameter value Compared with the average change trend just calculated Add together to generate a corresponding short-term forecast value The calculation formula is: It should be noted here that for the sake of simplicity and universality, the calculation process of the average change trend and short-term forecast value is described above using the following terms: and Among them, the general symbol X is a placeholder, which can represent any "target parameter" selected in the previous steps. In specific implementation, X will be replaced by a specific target parameter symbol. For example, when the reactive power of a photovoltaic system is predicted in the short term, the general calculation formula is It will be specifically reflected in the calculation formula for photovoltaic reactive power: Similarly, the calculation process is also applied to the reactive power (Q WT ), the active power of the photovoltaic unit (PPV ) and the active power of the wind turbine (P WT ) on these three target parameters, thereby generating a corresponding short-term forecast value for each target parameter.

[0059] Finally, by comparing the sampling time T z The actual parameter value and the corresponding short-term prediction value are used to calculate the final impact factor of the sampling time. Specifically, the sampling time T is calculated by the following formula z Impact Factor:

[0060]

[0061] in, is the sampling time T z Impact factor; is the sampling time T z The historical value of reactive power of photovoltaic units; is the sampling time T z Calculated short-term forecast value of reactive power of photovoltaic units; is the sampling time T z The historical value of reactive power of wind turbines; is the sampling time T z The calculated short-term forecast value of wind turbine reactive power; is the sampling time T z The historical value of active power of photovoltaic units; is the sampling time T z The calculated short-term forecast value of the active power of the photovoltaic unit; is the sampling time T z The historical value of active power of wind turbines; is the sampling time T z The calculated short-term forecast value of the wind turbine active power.

[0062] As a quantitative indicator, the impact factor characterizes the degree of deviation, or "surprise," between the actual value and the trend forecast at the sampling moment. This step transforms the original time series data into an impact factor sequence that quantifies the degree of dynamic deviation at each historical moment, providing critical, forward-looking input for subsequent, more accurate weighted forecasts.

[0063] Step S3: predicting second time series data representing the interaction between new energy stations based on the influencing factors at each sampling moment and the first time series data; the second time series data is a set of time series data generated by predicting each operating parameter at each predicted sampling moment;

[0064] After calculating the impact factors that reflect the dynamic characteristics of each historical moment, this method enters the forward-looking prediction stage. The core task of this step is to jointly infer the system's "future" state based on the "history" and "historical weight" that have been mastered. Specifically, this step uses the impact factor sequence calculated in the previous step, which corresponds to each sampling moment, and the original first time series data containing all historical operating parameters, as two key inputs. Its internal logic is not a simple linear extrapolation, but rather uses the impact factor as a dynamic weight to perform a weighted combination of historical data, thereby achieving a comprehensive prediction of the future.

[0065] In a specific mathematical implementation, the first time series data can be constructed as a historical data matrix, and the influencing factors at each sampling moment can be constructed as an influencing factor vector. By multiplying these two matrices, the second time series data can be calculated and obtained. The essence of this second time series data is a set of predicted values ​​of all key operating parameters (including power, voltage and frequency) in the next time period at the corresponding predicted sampling moment. Through this step, the original data reflecting historical dynamics and the influencing factors that quantify their degree of deviation are combined to achieve an accurate and weighted prediction of the future state of the system, providing forward-looking data support for subsequent decisions on whether parameters need to be updated.

[0066] In a preferred embodiment, the second time series data is generated by the following formula:

[0067]

[0068] in, is the predicted value of any operating parameter in the second time series data in the next time period; m is the total number of sampling moments included in the first time series data; is the historical value of the operating parameter X at the zth sampling moment in the first time series data; X is any one of the operating parameters; is the impact factor corresponding to the z-th sampling moment.

[0069] Step S4: updating the demand model based on the preset equivalent impedance according to the second time series data to generate an index prediction value;

[0070] After obtaining the second time series data containing the predicted values ​​of each operating parameter for the next time period, this method needs to convert this multi-dimensional, complex prediction information into a single quantitative indicator that can be directly used for decision-making. To this end, this step comprehensively processes the second time series data based on a preset equivalent impedance update demand model to generate a final index prediction value. This demand model is essentially a pre-established mathematical function or algorithm designed to integrate the prediction information of multiple operating parameters and assess the urgency of updating the equivalent impedance parameters from the perspective of overall system stability.

[0071] In a specific implementation, the demand model receives the predicted values ​​of various parameters in the second time series data as input. In a preferred embodiment, the input model should be the predicted value after normalization to eliminate the influence of different physical dimensions and ensure that all parameters can be fairly considered in the model. The model performs a series of combined operations on the input power, voltage, and frequency predicted values ​​according to its internally defined operation rules, and finally outputs a single, scalar exponential prediction value. Through this step, the complex, multi-dimensional future state prediction is converted into a single, quantitative decision-making indicator, which provides a clear, reliable and intelligent basis for the subsequent judgment of whether to update the parameters. In another preferred embodiment, the input model can also be a predicted value that has not been normalized.

[0072] In a preferred embodiment, before updating the demand model based on the preset equivalent impedance according to the second time series data and generating the index prediction value, the method further includes:

[0073] For each operating parameter in the first time series data, extract a maximum value and a minimum value of the operating parameter in the first time series data, and record the maximum value and the minimum value as a historical maximum value and a historical minimum value of the corresponding operating parameter, respectively;

[0074] The second time series data is normalized according to the historical maximum value and the historical minimum value of each operating parameter to generate updated second time series data.

[0075] After completing the prediction of each operating parameter for the next time period and before using it for the final index calculation, this set of predicted data (i.e., the second time series data) needs to undergo a key preprocessing, namely, normalization. The purpose of this step is to solve a fundamental problem: different operating parameters have completely different physical units and numerical ranges. For example, the value of active power may reach the megawatt level, while the frequency variation range may only be in the thousandths of a Hertz. If these original predicted values ​​with huge differences in magnitude are directly substituted into the same mathematical model for calculation, the parameters with larger values ​​will disproportionately dominate the model output, thereby drowning out the influence of other important parameters, resulting in serious deviations in the final index prediction value.

[0076] Therefore, this step first traverses all the historical data for each type of operating parameter in the first time series data to extract the global maximum and global minimum values ​​of each parameter in the historical time period. These two values ​​establish the actual operating boundaries of the parameter in the past observation period. Subsequently, using this pair of unique "historical maximum" and "historical minimum" as a scale, the predicted value of the corresponding parameter in the second time series data is linearly scaled (i.e., standard "minimum-maximum normalization"). This process is performed independently for each operating parameter. Through the normalization process of this step, it is ensured that operating parameters of different physical units and magnitudes can be comprehensively evaluated fairly and unbiasedly in the subsequent demand model, laying the foundation for the accuracy and reliability of the final index prediction value.

[0077] In a preferred embodiment, the demand model is updated by the following equivalent impedance to generate the index prediction value:

[0078]

[0079] Among them, I pred is the predicted value of the index; The time between new energy stations in the next time period T m+1 The predicted voltage value; The time between new energy stations in the next time period T m+1 The frequency prediction value of The photovoltaic units PV between new energy stations in the next time period T m+1 The predicted value of active power; The photovoltaic units PV between new energy stations in the next time period T m+1 The reactive power prediction value; The wind turbine WT between new energy stations in the next time period T m+1 The predicted value of active power; The wind turbine WT between new energy stations in the next time period T m+1 The reactive power prediction value; j is the imaginary unit; e is a natural constant.

[0080] Step S5: determine whether the index prediction value is greater than a preset update threshold. If so, update the equivalent impedance parameter between the new energy stations in the electromagnetic transient simulation model to a predetermined multiple of the current equivalent impedance parameter. If not, do not update the equivalent impedance parameter.

[0081] After the aforementioned steps generate an index prediction value that can quantify future update needs, the method enters the final decision-making and execution phase. The core of this phase is a clear, threshold-based judgment mechanism. Specifically, the system will directly compare the index prediction value obtained in the previous step with a preset update threshold. This preset threshold is a key judgment benchmark. It can be predetermined before the simulation begins based on the typical operating characteristics of the power grid under study, expert experience, or a large number of offline simulation tests. Its role is to serve as a "dividing line" to distinguish whether the system state will undergo significant dynamic changes. In one embodiment, the preset threshold can be set at 0.663.

[0082] Based on the comparison results, the system will perform two completely different operations. If it is determined that the index prediction value is greater than the preset threshold, it means that the model predicts that the dynamic fluctuation of the system in the next time period is large, and the existing equivalent impedance parameters are no longer sufficient to accurately describe the system characteristics. At this time, an update action must be triggered. The update action is to update the equivalent impedance parameters used in the electromagnetic transient simulation model to characterize the new energy stations according to a predetermined multiple (for example, to [1+log 10 Conversely, if the exponential prediction value is not greater than the threshold, the system will remain relatively stable in the next time period, and no model parameter adjustments are required. In this case, no update operation is performed to maintain simulation stability and save unnecessary computational overhead. This step ultimately transforms complex prediction results into a clear, binary action of either updating or not updating, achieving intelligent, adaptive closed-loop control of simulation parameters and ensuring the necessity and timeliness of update operations.

[0083] After updating the equivalent impedance parameters based on the judgment results, the method further includes applying the updated parameters to subsequent electromagnetic transient simulation calculations to obtain an accurate assessment of the system's dynamic response under specific operating conditions. Specifically, the adjusted equivalent impedance parameters can be substituted back into the power grid model within the simulation tool, and preset fault scenarios such as short circuits and voltage sags can be configured according to analysis requirements. Based on this, the simulation tool uses the updated parameters to solve the electromagnetic transient equations of the power grid, thereby calculating and outputting a series of specific physical quantities that can characterize the system's dynamic characteristics, such as voltage recovery curves, frequency offset curves, or fault current waveforms at key nodes.

[0084] Based on the above method embodiments, the present invention provides corresponding device embodiments.

[0085] like Figure 2 As shown, an embodiment of the present invention provides an updating device for equivalent impedance between new energy stations in electromagnetic transient simulation, comprising: a first time series data acquisition module, an impact factor calculation module, a second time series data prediction module, an index prediction value generation module and an equivalent impedance parameter updating module;

[0086] The first time series data acquisition module is used to acquire first time series data representing the interaction between the new energy sites; wherein the first time series data is a set of time series data acquired by periodically sampling various operating parameters within a current time period; the operating parameters include the reactive power of the photovoltaic units, the reactive power of the wind turbine units, the active power of the photovoltaic units, the active power of the wind turbine units, the voltage, and the frequency between the new energy sites;

[0087] The impact factor calculation module is used to calculate the impact factor of each sampling moment according to the first time series data;

[0088] The second time series data prediction module is used to predict second time series data representing the interaction between new energy stations based on the influencing factors at each sampling time and the first time series data; the second time series data is a set of time series data generated by predicting each operating parameter at each predicted sampling time;

[0089] The index prediction value generating module is configured to generate an index prediction value based on the second time series data and a preset equivalent impedance updating demand model;

[0090] The equivalent impedance parameter updating module is used to determine whether the index prediction value is greater than a preset update threshold. If so, the equivalent impedance parameter between the new energy stations in the electromagnetic transient simulation model is updated to a predetermined multiple value of the current equivalent impedance parameter; if not, the equivalent impedance parameter is not updated.

[0091] In a preferred embodiment, the device for updating equivalent impedances between new energy stations in electromagnetic transient simulation further includes: a normalization processing module;

[0092] The normalization processing module is used to extract the maximum value and minimum value of each operating parameter in the first time series data, and record the maximum value and the minimum value as the historical maximum value and the historical minimum value of the corresponding operating parameter, respectively; according to the historical maximum value and the historical minimum value of each operating parameter, the second time series data is normalized to generate updated second time series data.

[0093] It should be noted that the embodiment of the device described above corresponds to the above-mentioned embodiment of the present invention, and it can implement the method for updating the equivalent impedance between new energy stations in the electromagnetic transient simulation described in any one of the above-mentioned embodiments of the present invention. In addition, the embodiment of the above-mentioned device is merely schematic, wherein the modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, in the drawings of the embodiment of the device provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which can be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art can understand and implement it without paying any creative work.

[0094] Based on the above method embodiment of the present invention, a corresponding electronic device embodiment is provided.

[0095] An embodiment of the present invention provides an electronic device, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, the method for updating the equivalent impedance between new energy stations in electromagnetic transient simulation described in any one of the present inventions is implemented, or when the processor executes the computer program, the functions of the modules in the above-mentioned device embodiments are implemented.

[0096] Exemplarily, the computer program may be divided into one or more modules, which are stored in the memory and executed by the processor to implement the present invention. The one or more modules may be a series of computer program instruction segments capable of performing specific functions, and the instruction segments are used to describe the execution process of the computer program in the terminal device.

[0097] The terminal device may be a computing device such as a desktop computer, a notebook computer, a PDA, a cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0098] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the terminal device, connecting various parts of the entire terminal device using various interfaces and lines.

[0099] The memory can be used to store the computer programs and / or modules, and the processor implements various functions of the terminal device by running or executing the computer programs and / or modules stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application required for a function, etc.; the data storage area can store data created based on the use of the mobile phone, etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage device.

[0100] Based on the above method embodiment, the present invention provides a corresponding storage medium embodiment;

[0101] Another embodiment of the present invention provides a storage medium, which includes a stored computer program. When the computer program is running, the device where the storage medium is located is controlled to execute the method for updating the equivalent impedance between new energy stations in any of the above-mentioned electromagnetic transient simulations of the present invention.

[0102] The storage medium is a computer-readable storage medium, and the computer program includes computer program code, which may be in source code form, object code form, executable file, or some intermediate form. The computer-readable medium may include any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunications signal, and a software distribution medium.

[0103] In the description of this specification, the reference terms "one embodiment," "some embodiments," "example," "specific example," or "some examples" mean that the specific features, structures, materials, or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. Moreover, the specific features, structures, materials, or characteristics described may be combined in any appropriate manner in any one or more embodiments or examples. In addition, those skilled in the art may combine and integrate different embodiments or examples described in this specification, as well as features of different embodiments or examples, unless they are mutually inconsistent.

[0104] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for updating equivalent impedance between new energy stations in electromagnetic transient simulation, characterized in that: include: Acquire first time series data characterizing the interaction between the new energy sites; wherein the first time series data is a set of time series data obtained by periodically sampling various operating parameters within a current time period; the operating parameters include the reactive power of the photovoltaic units, the reactive power of the wind turbine units, the active power of the photovoltaic units, the active power of the wind turbine units, the voltage, and the frequency between the new energy sites; Calculating the impact factor of each sampling moment according to the first time series data; Predicting second time series data representing the interaction between new energy stations based on the influencing factors at each sampling moment and the first time series data; the second time series data is a set of time series data generated by predicting each operating parameter at each predicted sampling moment; According to the second time series data, updating the demand model based on a preset equivalent impedance to generate an index prediction value; Determine whether the index prediction value is greater than a preset update threshold. If so, update the equivalent impedance parameter between the new energy stations in the electromagnetic transient simulation model to a predetermined multiple of the current equivalent impedance parameter. If not, do not update the equivalent impedance parameter.

2. The method for updating equivalent impedances between new energy stations in electromagnetic transient simulation according to claim 1, characterized in that: Before updating the demand model based on the preset equivalent impedance according to the second time series data and generating the index prediction value, the method further includes: For each operating parameter in the first time series data, extract a maximum value and a minimum value of the operating parameter in the first time series data, and record the maximum value and the minimum value as a historical maximum value and a historical minimum value of the corresponding operating parameter, respectively; The second time series data is normalized according to the historical maximum value and the historical minimum value of each operating parameter to generate updated second time series data.

3. The method for updating equivalent impedance between new energy stations in electromagnetic transient simulation according to claim 2, characterized in that: Calculating the impact factor of each sampling moment according to the first time series data includes: The reactive power of the photovoltaic units between the new energy stations, the reactive power of the wind turbine units, the active power of the photovoltaic units, and the active power of the wind turbine units in the first time series data are used as target parameters; For each sampling moment, the average change trend of each target parameter at the current sampling moment is calculated based on the historical data of the target parameter in the preset time window before the current sampling moment; The parameter value of each target parameter at each sampling moment is added to the corresponding average change trend value to generate the short-term forecast value of each target parameter at each sampling moment; The impact factor of each sampling moment is calculated according to the parameter value of the target parameter at the current sampling moment and the short-term prediction value of each target parameter at each sampling moment.

4. The method for updating equivalent impedance between new energy stations in electromagnetic transient simulation according to claim 3 is characterized in that: The second time series data is generated by the following formula: in, is the predicted value of any operating parameter in the second time series data in the next time period; m is the total number of sampling moments included in the first time series data; is the historical value of the operating parameter X at the zth sampling moment in the first time series data; X is any one of the operating parameters; is the impact factor corresponding to the z-th sampling moment.

5. The method for updating equivalent impedance between new energy stations in electromagnetic transient simulation according to claim 4 is characterized in that: The demand model is updated with the following equivalent impedances to generate exponential forecasts: Among them, I pred is the predicted value of the index; The time between new energy stations in the next time period T m+1 The predicted voltage value; The time between new energy stations in the next time period T m+1 The frequency prediction value of The photovoltaic units PV between new energy stations in the next time period T m+1 The predicted value of active power; The photovoltaic units PV between new energy stations in the next time period T m+1 The reactive power prediction value; The wind turbine WT between new energy stations in the next time period T m+1 The predicted value of active power; The wind turbine WT between new energy stations in the next time period T m+1 The reactive power prediction value; j is the imaginary unit; e is a natural constant.

6. The method for updating equivalent impedances between new energy stations in electromagnetic transient simulation according to claim 5, characterized in that: The sampling time T is calculated by the following formula z Impact Factor: in, is the sampling time T z Impact factor; is the sampling time T z The historical value of reactive power of photovoltaic units; is the sampling time T z Calculated short-term forecast value of reactive power of photovoltaic units; is the sampling time T z The historical value of reactive power of wind turbines; is the sampling time T z The calculated short-term forecast value of wind turbine reactive power; is the sampling time T z The historical value of active power of photovoltaic units; is the sampling time T z The calculated short-term forecast value of the active power of the photovoltaic unit; is the sampling time T z The historical value of active power of wind turbines; is the sampling time T z The calculated short-term forecast value of the wind turbine active power.

7. A device for updating equivalent impedance between new energy stations in electromagnetic transient simulation, characterized in that: include: A first time series data acquisition module, an impact factor calculation module, a second time series data prediction module, an index prediction value generation module, and an equivalent impedance parameter update module; The first time series data acquisition module is used to acquire first time series data representing the interaction between the new energy sites; wherein the first time series data is a set of time series data acquired by periodically sampling various operating parameters within a current time period; the operating parameters include the reactive power of the photovoltaic units, the reactive power of the wind turbine units, the active power of the photovoltaic units, the active power of the wind turbine units, the voltage, and the frequency between the new energy sites; The impact factor calculation module is used to calculate the impact factor of each sampling moment according to the first time series data; The second time series data prediction module is used to predict second time series data representing the interaction between new energy stations based on the influencing factors at each sampling time and the first time series data; the second time series data is a set of time series data generated by predicting each operating parameter at each predicted sampling time; The index prediction value generating module is configured to generate an index prediction value based on the second time series data and a preset equivalent impedance updating demand model; The equivalent impedance parameter updating module is used to determine whether the index prediction value is greater than a preset update threshold. If so, the equivalent impedance parameter between the new energy stations in the electromagnetic transient simulation model is updated to a predetermined multiple value of the current equivalent impedance parameter; if not, the equivalent impedance parameter is not updated.

8. The device for updating equivalent impedance between new energy stations in electromagnetic transient simulation according to claim 7, characterized in that: Also includes: Normalization processing module; The normalization processing module is configured to extract, for each operating parameter in the first time series data, a maximum value and a minimum value of the operating parameter in the first time series data, and record the maximum value and the minimum value as a historical maximum value and a historical minimum value of the corresponding operating parameter, respectively; The second time series data is normalized according to the historical maximum value and the historical minimum value of each operating parameter to generate updated second time series data.

9. An electronic device, characterized in that: The method comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, the method for updating the equivalent impedance between new energy stations in the electromagnetic transient simulation according to any one of claims 1 to 6 is implemented.

10. A storage medium, characterized in that: The storage medium includes a stored computer program, wherein, when the computer program is running, the device where the storage medium is located is controlled to execute the method for updating the equivalent impedance between new energy stations in electromagnetic transient simulation according to any one of claims 1 to 6.

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