Determination Method, Device and Storage Medium for Power System Frequency Security

Abstract

The present invention discloses a method, device and storage medium for determining the frequency security of a power system. The method includes: obtaining the multi-machine frequency response models of multiple generator sets of the power system to be detected; using each frequency response model to perform frequency simulation on the corresponding generator set to obtain a frequency time-domain curve, fitting the parameters of each frequency time-domain curve to obtain a frequency time-domain formula; performing Laplace transform on the frequency time-domain formula to obtain a single-machine frequency response model; using a generalized regression neural network to establish a non-linear relationship between the single-machine frequency response model and the multi-machine frequency response model, and based on the non-linear relationship, determining the single-machine frequency response parameters of the single-machine frequency response model, and based on the single-machine frequency response parameters, determining the aggregated power system frequency response model; using the aggregated power system frequency response model to simulate the frequency response process of the power system to be detected, and based on the simulation results, determining the frequency security of the power system to be detected.

Description

Technical Field

[0001] The present invention relates to the field of power system frequency, and in particular to a method, device and storage medium for determining power system frequency safety. Background Art

[0002] Frequency is an important operating indicator of the power system. With the construction and development of new power systems, frequency security issues have become prominent. Therefore, it is urgent to propose a method that can quickly and accurately estimate the frequency dynamic response of the power system and determine whether the frequency of the power system is safe, so as to respond to power system frequency issues in a timely manner.

[0003] At present, the frequency safety of each unit is usually predicted through the frequency response model corresponding to each unit. However, this frequency safety prediction method has a low efficiency due to the large number of units in the power system. At the same time, the frequency safety needs to be predicted as many times as there are generators. When the number of frequency safety predictions increases, the number of frequency safety prediction errors will increase accordingly. Summary of the invention

[0004] The present invention provides a method, device and storage medium for determining the frequency security of an electric power system, which are mainly capable of improving the prediction efficiency and prediction accuracy of the frequency security of an electric power system.

[0005] According to a first aspect of the present invention, there is provided a method for determining frequency security of a power system, comprising:

[0006] Obtaining a multi-machine frequency response model corresponding to multiple generator sets of the power system to be detected;

[0007] Using each of the frequency response models to perform frequency simulation on the corresponding generator sets in the power system to be detected, obtaining the frequency-time domain curves corresponding to each of the generator sets, and performing parameter fitting on each of the frequency-time domain curves to obtain the frequency-time domain formula of the power system to be detected;

[0008] Performing Laplace transformation on the frequency-time domain formula to obtain a single-machine frequency response model of the power system to be detected;

[0009] A nonlinear relationship between the single-machine frequency response model and the multi-machine frequency response model is established by using a preset generalized regression neural network, and based on the nonlinear relationship, a single-machine frequency response parameter of the single-machine frequency response model is determined, and based on the single-machine frequency response parameter, an aggregated power system frequency response model of the power system to be detected is determined;

[0010] Use the aggregated power system frequency response model to simulate the frequency response process of the power system to be detected in a predetermined disturbance scenario, obtain the simulation results, and determine the frequency security of the power system to be detected based on the simulation results.

[0011] Optionally, the multi-machine frequency response model includes a thermal power frequency response model, a nuclear power frequency response model, a hydropower frequency response model, a wind-solar-storage frequency response model, and a UHV frequency response model; the frequency time-domain curves include the thermal power frequency time-domain curve corresponding to the thermal power frequency response model, the nuclear power frequency time-domain curve corresponding to the nuclear power frequency response model, the hydropower frequency time-domain curve corresponding to the hydropower frequency response model, the wind frequency time-domain curve corresponding to the wind-solar-storage frequency response model, and the high-voltage frequency time-domain curve corresponding to the UHV frequency response model.

[0012] Optionally, the step of performing parameter fitting on each of the frequency time-domain curves to obtain the frequency time-domain formula of the power system to be detected includes:

[0013] Respectively determine the parameters to be fitted corresponding to the thermal power frequency time-domain curve, the nuclear power frequency time-domain curve, the hydropower frequency time-domain curve, the wind frequency time-domain curve, and the high-voltage frequency time-domain curve, as well as the range of parameter values of the parameters to be fitted;

[0014] Based on the parameters to be fitted and their corresponding parameter value ranges, use the nonlinear least squares method to perform parameter fitting on the thermal power frequency time-domain curve, the nuclear power frequency time-domain curve, the hydropower frequency time-domain curve, the wind frequency time-domain curve, and the high-voltage frequency time-domain curve to obtain the frequency time-domain formula of the power system to be detected.

[0015] Optionally, the step of performing Laplace transform on the frequency time-domain formula to obtain the single-machine frequency response model of the power system to be detected includes:

[0016] Determine the Laplace transform formula;

[0017] Substitute the frequency time-domain formula into the Laplace transform formula to obtain the substituted formula, and perform the integral of the substituted formula from time zero to positive infinity, and determine the single-machine frequency response model of the power system to be detected based on the integral result.

[0018] Optionally, the preset generalized regression neural network includes an input layer, a radial basis layer, and an output layer;

[0019] The step of using the preset generalized regression neural network to establish the nonlinear relationship between the single-machine frequency response model and the multi-machine frequency response model, and determining the single-machine frequency response parameters of the single-machine frequency response model based on the nonlinear relationship includes:

[0020] Determine the multi-machine frequency response parameter vector corresponding to the multi-machine frequency response model, the identity matrix composed of the model parameters of the single-machine frequency response model, the multi-dimensional matrix composed of the center points of the radial basis functions in the input layer, and the comprehensive parameter matrix composed of the model parameters of the single-machine frequency response model and the model parameters of the multi-machine frequency response model;

[0021] Based on the multi-dimensional matrix, calculate the Euclidean distance between the multi-machine frequency response parameter vector and the center points of each radial basis function to obtain the multi-machine frequency spacing, and use the input layer to perform eigenprocessing on the identity matrix to obtain the single-machine eigenvector, and use the input layer to perform eigenprocessing on the multi-machine frequency spacing to obtain the multi-machine eigenvector;

[0022] Perform Hadamard product processing on the single-machine eigenvector and the multi-machine eigenvector, and input the result of the Hadamard product processing into the radial basis layer, and perform activation processing on the result of the Hadamard product processing through the activation function in the radial basis layer to obtain the activation eigenvector;

[0023] Perform normalized dot product operation on the activation eigenvector and the comprehensive parameter matrix to obtain the dot product eigenvector;

[0024] Input the dot product eigenvector into the output layer, and perform linear weighting in the output layer to obtain the single-machine frequency response parameter of the single-machine frequency response model.

[0025] Optionally, the process of using the aggregated power system frequency response model to simulate the frequency response process of the power system to be detected in a predetermined disturbance scenario to obtain a simulation result includes:

[0026] Based on the actual operating environment, actual operating characteristics, and actual interference conditions of the power system to be detected, set a predetermined disturbance scenario;

[0027] Based on the inertia constant of the generator, the gain of the governor, and the frequency regulation effect of the load in the power system to be detected, set the model parameters of the aggregated power system frequency response model;

[0028] Use the preset simulation software to control the aggregated power system frequency response model with set parameters to simulate the frequency response process of the power system to be detected in the predetermined disturbance scenario to obtain a simulation result.

[0029] Optionally, the process of determining the frequency security of the power system to be detected based on the simulation result includes:

[0030] Based on the simulation result, determine the disturbance frequency value of the power system to be detected after being disturbed in the predetermined disturbance scenario;

[0031] If the disturbance frequency value is greater than a preset frequency threshold, it is determined that the frequency of the power system to be detected is unsafe; otherwise, it is determined that the frequency of the power system to be detected is safe.

[0032] According to a second aspect of the present invention, there is provided a device for determining the frequency safety of a power system, including:

[0033] An acquisition unit, configured to acquire multi-machine frequency response models corresponding to multiple generator sets of the power system to be detected;

[0034] A simulation unit, configured to perform frequency simulation on the corresponding generator sets in the power system to be detected by using each of the frequency response models, obtain frequency time-domain curves corresponding to each of the generator sets, and perform parameter fitting on each of the frequency time-domain curves to obtain a frequency time-domain formula of the power system to be detected;

[0035] A transformation unit, configured to perform Laplace transform on the frequency time-domain formula to obtain a single-machine frequency response model of the power system to be detected;

[0036] A determination unit, configured to establish a non-linear relationship between the single-machine frequency response model and the multi-machine frequency response model by using a preset generalized regression neural network, and based on the non-linear relationship, determine single-machine frequency response parameters of the single-machine frequency response model, and based on the single-machine frequency response parameters, determine an aggregated power system frequency response model of the power system to be detected;

[0037] A simulation unit, configured to simulate a frequency response process of the power system to be detected in a predetermined disturbance scenario by using the aggregated power system frequency response model, obtain a simulation result, and based on the simulation result, determine the frequency safety of the power system to be detected.

[0038] Optionally, the multi-machine frequency response model includes a thermal power frequency response model corresponding to a thermal power generator set, a nuclear power frequency response model corresponding to a nuclear power generator set, a hydropower frequency response model corresponding to a hydropower generator set, a wind-solar storage frequency response model corresponding to a wind-solar generator set, and a UHV frequency response model corresponding to a UHV generator set; the frequency time-domain curves include a thermal power frequency time-domain curve corresponding to the thermal power frequency response model, a nuclear power frequency time-domain curve corresponding to the nuclear power frequency response model, a hydropower frequency time-domain curve corresponding to the hydropower frequency response model, a wind frequency time-domain curve corresponding to the wind-solar storage frequency response model, and a high-voltage frequency time-domain curve corresponding to the UHV frequency response model.

[0039] Optionally, the simulation unit includes a first determination module and a fitting module;

[0040] The first determination module is configured to respectively determine the fitting parameters corresponding to the thermal power frequency time-domain curve, the nuclear power frequency time-domain curve, the hydropower frequency time-domain curve, the wind-solar frequency time-domain curve, the high-voltage frequency time-domain curve, and the parameter value range of the fitting parameters;

[0041] The fitting module is configured to perform parameter fitting on the thermal power frequency time-domain curve, the nuclear power frequency time-domain curve, the hydropower frequency time-domain curve, the wind-solar frequency time-domain curve, and the high-voltage frequency time-domain curve by using the nonlinear least squares method based on the fitting parameters and their corresponding parameter value ranges, so as to obtain the frequency time-domain formula of the power system to be detected.

[0042] Optionally, the transformation unit includes a second determination module and an integration module;

[0043] The second determination module is configured to determine the Laplace transform formula;

[0044] The integration module is configured to substitute the frequency time-domain formula into the Laplace transform formula to obtain the substituted formula, perform integration on the substituted formula from time zero to positive infinity, and determine the single-machine frequency response model of the power system to be detected based on the integration result.

[0045] Optionally, the preset generalized regression neural network includes an input layer, a radial basis layer, and an output layer; the determination unit includes a third determination module, a calculation module, a processing module, a dot product operation module, and a weighting module;

[0046] The third determination module is configured to determine the multi-machine frequency response parameter vector corresponding to the multi-machine frequency response model, the identity matrix composed of the model parameters of the single-machine frequency response model, the multi-dimensional matrix composed of the center points of the radial basis functions of the input layer, and the comprehensive parameter matrix composed of the model parameters of the single-machine frequency response model and the model parameters of the multi-machine frequency response model;

[0047] The calculation module is configured to calculate the Euclidean distance between the multi-machine frequency response parameter vector and the center points of the respective radial basis functions based on the multi-dimensional matrix to obtain the multi-machine frequency spacing, perform feature processing on the identity matrix by using the input layer to obtain a single-machine feature vector, and perform feature processing on the multi-machine frequency spacing by using the input layer to obtain a multi-machine feature vector;

[0048] The processing module is configured to perform Hadamard product processing on the single-machine feature vector and the multi-machine feature vector, and input the Hadamard product processing result into the radial basis layer, and perform activation processing on the Hadamard product processing result through the activation function in the radial basis layer to obtain an activation feature vector;

[0049] The dot product operation module is configured to perform a normalized dot product operation on the activation feature vector and the comprehensive parameter matrix to obtain a dot product feature vector;

[0050] The weighting module is configured to input the dot product feature vector into the output layer and perform linear weighting in the output layer to obtain the single-machine frequency response parameter of the single-machine frequency response model.

[0051] Optionally, the simulation unit includes a setting module and a simulation module;

[0052] The setting module is configured to set a predetermined disturbance scenario based on the actual operating environment, actual operating characteristics, and actual interference situations faced by the power system to be detected;

[0053] The setting module is configured to set the model parameters of the aggregated power system frequency response model based on the inertia constant of the generator, the gain of the speed governor, and the frequency regulation effect of the load in the power system to be detected;

[0054] The simulation module is configured to use a preset simulation software to control the aggregated power system frequency response model after setting parameters to simulate the frequency response process of the power system to be detected in the predetermined disturbance scenario, and obtain a simulation result.

[0055] Optionally, the simulation unit further includes a determination module;

[0056] The determination module is configured to determine the disturbance frequency value of the power system to be detected after being disturbed in the predetermined disturbance scenario based on the simulation result;

[0057] The determination module is configured to determine that the frequency of the power system to be detected is unsafe if the disturbance frequency value is greater than a preset frequency threshold, otherwise, determine that the frequency of the power system to be detected is safe.

[0058] According to a third aspect of the present invention, there is provided a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the above method for determining the frequency safety of a power system is implemented.

[0059] According to a fourth aspect of the present invention, there is provided a computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, and when the processor executes the program, the above method for determining the frequency safety of a power system is implemented.

[0060] A method, device, and storage medium for determining the frequency security of a power system according to the present invention, compared with the current method of predicting the frequency security of each unit through the frequency response models corresponding to each unit separately, the present invention performs frequency simulation on the multi-machine frequency response models of each generator set to obtain a frequency time-domain curve, performs parameter fitting on the frequency time-domain curve to obtain a frequency time-domain formula, then performs Laplace transform on the frequency time-domain formula to obtain a single-machine frequency response model of the power system, establishes a non-linear relationship between the single-machine frequency response model and the multi-machine frequency response model, determines the single-machine frequency response parameters of the single-machine frequency response model based on the non-linear relationship, then determines the aggregated power system frequency response model of the power system based on the single-machine frequency response parameters, and finally uses the single aggregated power system frequency response model to predict the frequency security of the entire power system, that is, only one aggregated power system frequency response model can be used to predict the frequency security of each generator set in the entire power system, avoiding the time and resources wasted by using multiple models for frequency security prediction, and also being able to avoid the risk of increased error degree due to the increase in the number of predictions. Therefore, the present invention can improve the prediction efficiency and prediction accuracy of the frequency security of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The illustrative embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0062] Figure 1 Shows a flowchart of a method for determining the frequency security of a power system provided by an embodiment of the present invention;

[0063] Figure 2 Shows a schematic diagram of a single-machine frequency response model provided by an embodiment of the present invention;

[0064] Figure 3 Shows a flowchart of another method for determining the frequency security of a power system provided by an embodiment of the present invention;

[0065] Figure 4 Shows a schematic structural diagram of a device for determining the frequency security of a power system provided by an embodiment of the present invention;

[0066] Figure 5 Shows a schematic physical structure diagram of a computer device provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0067] The present invention will be described in detail below with reference to the drawings and in conjunction with the embodiments. It should be noted that, without conflict, the embodiments in this application and the features in the embodiments can be combined with each other.

[0068] Currently, in the method of predicting the frequency security of each unit separately through the frequency response model corresponding to each unit, due to the large number of units in the power system, the efficiency of predicting frequency security is low. When the number of times of predicting frequency security increases, the number of prediction errors of frequency security will increase accordingly.

[0069] To solve the above problems, an embodiment of the present invention provides a method for determining the frequency security of a power system, as Figure 1 shown, the method includes:

[0070] 101. Obtain the multi-machine frequency response models corresponding to multiple generator sets of the power system to be detected.

[0071] Among them, the power system to be detected contains multiple generator sets, and each generator set corresponds to a frequency response model. The frequency response models corresponding to each generator set are collectively referred to as the multi-machine frequency response model. The frequency response model of each generator set has been pre-constructed. Specifically, the differential equation modeling method can be used, or modern modeling techniques such as neural networks can be used to pre-construct the frequency response models corresponding to each generator set.

[0072] 102. Use each frequency response model to perform frequency simulation on the corresponding generator set in the power system to be detected, obtain the frequency time-domain curves corresponding to each generator set, and perform parameter fitting on each frequency time-domain curve to obtain the frequency time-domain formula of the power system to be detected.

[0073] Among them, the frequency time-domain curve refers to the frequency characteristic curve of each generator set, that is, the relationship curve between the generator set frequency and the active power load under the condition of continuously changing load.

[0074] For the embodiment of the present invention, set the simulation parameters. The simulation parameters include parameters such as the initial state of each generator set, load change, fault type and its duration, as well as the output variables to be monitored, such as the system frequency, etc. Then, input the frequency response models and simulation parameters of each generator set into the simulation tool respectively, and run the simulation program to realize the frequency simulation of the corresponding generator set in the power system to be detected. During the simulation process, monitor the frequency response characteristics of the power system to be detected in real time, so as to obtain the frequency time-domain curves of each generator set. Then, use the nonlinear least squares method to perform parameter fitting on each frequency time-domain curve, so as to fit each frequency time-domain curve into a frequency time-domain formula. Specifically, first select the frequency time-domain fitting formula as shown below:

[0075]

[0076] where, ΔP dis a step disturbance, C0, C1, C2, T1, T2, ω2, and θ2 are all fitting parameters, t is time. According to the above frequency-time domain fitting formula, the nonlinear least squares method is used to fit each frequency-time domain curve, and the expression is as follows:

[0077]

[0078] Among them, Δf represents the Laplace transform of frequency, and ΔP d is a step disturbance, C0, C1, C2, T1, T2, ω2, and θ2 are all fitting parameters in each frequency-time domain curve, t is time, and solving the above nonlinear equations can obtain the frequency-time domain formula.

[0079] 103. Perform Laplace transform on the frequency-time domain formula to obtain the single-machine frequency response model of the power system to be detected.

[0080] For the embodiment of the present invention, perform Laplace transform on the frequency-time domain formula, as shown in the following formula:

[0081]

[0082] Among them, Δf(s) represents the complex frequency domain form of frequency, and ΔP d is a step disturbance, s represents a complex variable, and C0, C1, C2, T1, T2, ω2, and θ2 are all fitting parameters in each frequency-time domain curve.

[0083] The order of the denominator of the transfer function of the real system is greater than the order of the numerator, and the above formula can be transformed into the following formula:

[0084]

[0085] Among them, Δf represents the Laplace transform of frequency, G(s) represents a second-order transfer function, and B0, B1, A0, A1, and A2, A3 are all coefficients.

[0086] Then G m (s) is a second-order transfer function, and its standard form is as follows:

[0087]

[0088] Among them, b0, b1, a0, and a1 are all coefficients.

[0089] The transfer function of the single-machine frequency response model is as follows:

[0090]

[0091] Among them, T J represents inertia, and D Σ represents damping.

[0092] The values of a0, a1, and T can be determined successively through the following formulas J , D Σ , and b0.

[0093] a0 = B0

[0094] a1 = B1

[0095]

[0096] b0 = A2 - T J - D Σ a1

[0097] The value of b1 can be determined through the following formula.

[0098]

[0099] where C0 represents the fitting parameter, Δf +∞ represents the steady-state value of the frequency, and ΔP d+∞ represents the steady-state value of the disturbance. Thus, through the transfer function of the single-machine frequency response model above, the single-machine frequency response model can be determined. As Figure 2 shows a single-machine frequency response model, where ΔP d in the single-machine frequency response model is a step disturbance, ΔP a represents the input net power, Σ represents summation, s represents a complex variable, Δf represents the Laplace transform of the frequency, and ΔP m represents the frequency modulation voltage response power.

[0100] 104. Establish the nonlinear relationship between the single-machine frequency response model and the multi-machine frequency response model using a preset generalized regression neural network, and based on the nonlinear relationship, determine the single-machine frequency response parameters of the single-machine frequency response model. Based on the single-machine frequency response parameters, determine the aggregated power system frequency response model of the power system to be detected.

[0101] Among them, the single-machine frequency response parameters refer to the dynamic response characteristics of the power system when the frequency changes, including the inertia time constant, governor parameters, load frequency control parameters, etc.

[0102] For the embodiments of the present invention, after determining the single-machine frequency response model, a preset generalized regression neural network is used to establish a non-linear relationship between the single-machine frequency response model and the multi-machine frequency response model. According to the non-linear relationship, the single-machine frequency response parameters of the single-machine frequency response model are determined, and the single-machine frequency response model is updated using the single-machine frequency response parameters, so as to obtain the aggregated power system frequency response model of the power system to be detected. Thus, by using the generalized regression neural network to establish the aggregated power system frequency response model, the construction efficiency and construction accuracy of the aggregated power system frequency response model can be improved.

[0103] 105. Use the aggregated power system frequency response model to simulate the frequency response process of the power system to be detected in a predetermined disturbance scenario, obtain the simulation results, and determine the frequency security of the power system to be detected based on the simulation results.

[0104] Among them, the predetermined disturbance scenario includes scenarios such as load mutation, generator fault, and line fault of the power system to be detected. For each scenario, parameters such as the type, magnitude, occurrence time, and duration of the disturbance need to be determined.

[0105] For the embodiments of the present invention, first, simulation parameters are set. The simulation parameters include disturbance data. The disturbance data of the predetermined disturbance scenario is input into the aggregated power system frequency response model. The disturbance data includes the type, magnitude, occurrence time, and duration of the disturbance, etc. And simulation conditions are set: according to actual requirements, parameters such as the initial conditions, time step, and simulation duration of the simulation are set. Run the simulation and analyze the results: Run the simulation: Use the established aggregated power system frequency response model and the set simulation parameters and simulation conditions to run the simulation program. The simulation program will calculate the frequency response process of the power system to be detected in the predetermined disturbance scenario. Analyze the results: After the simulation is completed, analyze the simulation results, such as observing the curve of frequency changing with time, calculating the frequency deviation, so as to analyze the frequency security of the power system to be detected. Thus, by comprehensively analyzing the multi-machine frequency response models corresponding to multiple generator sets to construct a single aggregated power system frequency response model capable of performing frequency analysis on the entire power system, the frequency security of each generator set in the entire power system can be predicted using only one aggregated power system frequency response model, avoiding the time and resources wasted by using multiple models for frequency security prediction, and also avoiding the risk of increased error degree due to the increase in the number of predictions. Therefore, the present invention can improve the prediction efficiency and prediction accuracy of the frequency security of the power system.

[0106] According to a method for determining the frequency security of a power system provided by the present invention, compared with the current method of predicting the frequency security of each unit separately through the frequency response model corresponding to each unit, the present invention performs frequency simulation on the multi-machine frequency response model of each generator set to obtain a frequency time domain curve, and performs parameter fitting on the frequency time domain curve to obtain a frequency time domain formula, then performs Laplace transform on the frequency time domain formula to obtain a single-machine frequency response model of the power system, and establishes a nonlinear relationship between the single-machine frequency response model and the multi-machine frequency response model, determines the single-machine frequency response parameters of the single-machine frequency response model based on the nonlinear relationship, and then determines the aggregated power system frequency response model of the power system based on the single-machine frequency response parameters, and finally uses the single aggregated power system frequency response model to predict the frequency security of the entire power system, that is, only one aggregated power system frequency response model is used to predict the frequency security of each generator set in the entire power system, thereby avoiding the time and resources wasted by using multiple models to predict the frequency security, and also avoiding the risk of increased error due to an increase in the number of predictions, so that the present invention can improve the prediction efficiency and prediction accuracy of the frequency security of the power system.

[0107] Further, in order to better illustrate the above process of determining the frequency security of the power system, as a refinement and extension of the above embodiment, the embodiment of the present invention provides another method for determining the frequency security of the power system, such as Figure 3 As shown, the method includes:

[0108] 201. Obtain a multi-machine frequency response model corresponding to multiple generator sets of a power system to be detected.

[0109] Among them, multiple generator sets include: thermal power generator sets, nuclear power generator sets, hydropower generator sets, wind and solar generator sets, ultra-high voltage generator sets, etc.: the multi-machine frequency response model includes: the thermal power frequency response model corresponding to the thermal power generator set, the nuclear power frequency response model corresponding to the nuclear power generator set, the hydropower frequency response model corresponding to the hydropower generator set, the wind and solar storage frequency response model corresponding to the wind and solar generator set, and the ultra-high voltage frequency response model corresponding to the ultra-high voltage generator set; the multi-machine frequency response model is specifically a mathematical model of each generator set. It should be noted that each parameter in the multi-machine frequency response model is a fixed parameter. In order to improve the calculation accuracy, the power gain coefficient can also be obtained, and the power gain coefficient is determined by the unit capacity ratio to the total capacity.

[0110] 202. Use each frequency response model to perform frequency simulation on the corresponding generator set in the power system to be tested, obtain the frequency-time domain curve corresponding to each generator set, and perform parameter fitting on each frequency-time domain curve to obtain the frequency-time domain formula of the power system to be tested.

[0111] Among them, the frequency time-domain curve includes: the thermal power frequency time-domain curve corresponding to the thermal power frequency response model, the nuclear power frequency time-domain curve corresponding to the nuclear power frequency response model, the hydropower frequency time-domain curve corresponding to the hydropower frequency response model, the wind-solar-storage frequency time-domain curve corresponding to the wind-solar frequency response model, and the UHV frequency time-domain curve corresponding to the UHV frequency response model. The frequency time-domain formula refers to the relationship between the frequency of the power system to be detected and time.

[0112] For the embodiments of the present invention, relevant parameters and data of each generating unit are collected, including the type of generator, rated power, inertia time constant, governor parameters, etc. Based on the collected data, the mathematical model of the generating unit, that is, the frequency response model, is input into the simulation tool. This frequency response model can accurately reflect the dynamic response characteristics of the generating unit when the frequency changes. Then, a disturbance is defined: that is, according to the actual situation and test requirements of the power system to be detected, appropriate disturbance scenarios are defined, including scenarios such as load mutation, generator fault removal, and line fault. At the same time, simulation parameters are set, including simulation time, time step, output variables, etc., to ensure that the simulation parameters can accurately reflect the operating conditions of the actual power system. Finally, the frequency response models of each pre-established generating unit are run in the simulation tool, and the defined disturbance scenarios are applied to observe the frequency response process of the generating unit under the disturbance, so as to obtain the frequency time-domain curves corresponding to each generating unit.

[0113] Further, after determining the frequency time-domain curves corresponding to each generating unit, it is also necessary to perform parameter fitting on each frequency time-domain curve. Based on this, step 202 specifically includes: respectively determining the fitting parameters to be fitted corresponding to the thermal power frequency time-domain curve, the nuclear power frequency time-domain curve, the hydropower frequency time-domain curve, the wind-solar frequency time-domain curve, and the UHV frequency time-domain curve, as well as the value range of the fitting parameters; based on the fitting parameters to be fitted and their corresponding value ranges, the thermal power frequency time-domain curve, the nuclear power frequency time-domain curve, the hydropower frequency time-domain curve, the wind-solar frequency time-domain curve, and the UHV frequency time-domain curve are subjected to parameter fitting by using the nonlinear least squares method to obtain the frequency time-domain formula of the power system to be detected.

[0114] Specifically, first, the form of the objective function for fitting needs to be determined. In power system analysis, the frequency time-domain curve may exhibit complex non-linear characteristics. Therefore, it is crucial to select an appropriate non-linear function form for fitting. Common non-linear function forms include exponential functions, power functions, trigonometric functions, etc. The specific selection should be determined according to the actual situation and the characteristics of the data. During the fitting process, first collect the frequency time-domain data of each generator set, including time points and corresponding frequency values. Then define the error function: The core of the non-linear least squares method is to minimize the error function, that is, the sum of squared residuals. For the parameter fitting of the frequency time-domain curve, the error function can be defined as the sum of the squares of the differences between the actual frequency values and the values of the fitting function. Select the initial parameters: Select a set of initial parameter values for the fitting function based on actual requirements and use these initial parameter values as the starting point for iterative optimization. Construct the objective function: Use the error function as the objective function and minimize the objective function value by adjusting the parameters of the fitting function. Then select the solution algorithm of the non-linear least squares method to continuously adjust the parameters of the fitting function through iterative optimization until the objective function value reaches the minimum or meets the predetermined convergence condition, so as to obtain the frequency time-domain formula of the power system to be detected. The embodiment of the present invention performs parameter fitting through the non-linear least squares method, which can handle the non-linear relationship between each frequency time-domain curve, capture the non-linear characteristics in the data, and thus provide a more accurate fitting result.

[0115] 203. Determine the Laplace transform formula.

[0116] 204. Substitute the frequency time-domain formula into the Laplace transform formula to obtain the substituted formula, perform the integration of the substituted formula from time zero to positive infinity, and based on the integration result, determine the single-machine frequency response model of the power system to be detected.

[0117] For the embodiments of the present invention, first, a Laplace transform formula is selected, such as F(s) = ∫[0,∞]f(t)e∧(-st)dt, where f(t) is a time-domain function, F(s) is a complex frequency-domain function, s is a complex variable, t is a time variable, and e^(-st) is an exponential function. First, the time-domain function f(t) in the frequency time-domain formula is substituted into the Laplace transform formula for integral operation. Through the integral operation, the representation F(s) of the frequency in the complex frequency domain is obtained. This function describes the distribution characteristics of the frequency on the complex plane and is the basis of the single-machine frequency response model. Then, the obtained complex frequency-domain function F(s) is analyzed to understand its characteristics such as poles and zeros on the complex plane. These characteristics are closely related to the frequency response characteristics of the power system. Based on the analysis results of the complex frequency-domain function, a single-machine frequency response model of the power system to be detected is established. The single-machine frequency response model usually includes key elements such as the transfer function and frequency response curve of the system. Finally, the accuracy and reliability of the model can be verified through simulation experiments or actual data. If there are large differences between the model and the actual data, the model needs to be adjusted and optimized.

[0118] 205. Use a preset generalized regression neural network to establish a nonlinear relationship between the single-machine frequency response model and the multi-machine frequency response model, and based on the nonlinear relationship, determine the single-machine frequency response parameters of the single-machine frequency response model. Based on the single-machine frequency response parameters, determine the aggregated power system frequency response model of the power system to be detected.

[0119] Among them, the preset generalized regression neural network includes an input layer, a radial basis layer, and an output layer. The output layer is a special linear network layer; the nonlinear relationship between the single-machine frequency response model and the multi-machine frequency response model is a nonlinear mapping between the power gain coefficient and the fitting parameters of the multi-machine frequency response model.

[0120] For the embodiments of the present invention, in order to construct an aggregated power system frequency response model of the power system to be detected, it is first necessary to determine the single-machine frequency response parameters of the single-machine frequency response model. Based on this, step 205 specifically includes: determining a multi-machine frequency response parameter vector corresponding to the multi-machine frequency response model, an identity matrix composed of the model parameters of the single-machine frequency response model, a multi-dimensional matrix composed of the center points of the radial basis functions in the input layer, and a comprehensive parameter matrix composed of the model parameters of the single-machine frequency response model and the model parameters of the multi-machine frequency response model; based on the multi-dimensional matrix, calculating the Euclidean distance between the multi-machine frequency response parameter vector and the center points of each of the radial basis functions to obtain a multi-machine frequency spacing, and using the input layer to perform feature processing on the identity matrix to obtain a single-machine feature vector, and using the input layer to perform feature processing on the multi-machine frequency spacing to obtain a multi-machine feature vector; performing a Hadamard product operation on the single-machine feature vector and the multi-machine feature vector, and inputting the result of the Hadamard product operation into the radial basis layer, and performing activation processing on the result of the Hadamard product operation through an activation function in the radial basis layer to obtain an activation feature vector; performing a normalized dot product operation on the activation feature vector and the comprehensive parameter matrix to obtain a dot product feature vector; inputting the dot product feature vector into the output layer, and performing linear weighting in the output layer to obtain the single-machine frequency response parameters of the single-machine frequency response model.

[0121] Among them, the multi-machine frequency response parameters refer to the response characteristics of each generator set to different frequency signals, and specifically can be obtained by performing simulation operations on the frequency response models corresponding to each generator set.

[0122] Specifically, first determine the multi-machine frequency response parameter vector of the frequency response model corresponding to each generator set, and at the same time determine the identity matrix composed of the model parameters of the single-machine frequency response model, the multi-dimensional matrix composed of the center points of the radial basis functions in the input layer, such as a q×r-dimensional matrix, calculate the Euclidean distance between the multi-machine frequency response parameter vector and the center points of each radial basis function to obtain a multi-machine frequency spacing, and input the multi-machine frequency spacing into the input layer, output a multi-machine feature vector through the input layer, use the input layer to perform feature processing on the identity matrix to obtain a single-machine feature vector, perform a Hadamard product operation on the single-machine feature vector and the multi-machine feature vector, and input the result of the Hadamard product operation into the radial basis layer for activation processing to obtain an activation feature vector, and then perform a normalized dot product operation on the activation feature vector and the comprehensive parameter matrix composed of the model parameters of the single-machine frequency response model and the model parameters of the multi-machine frequency response model to obtain a dot product feature vector, and finally input the dot product feature vector into the output layer for linear weighting to obtain the single-machine frequency response parameters, and the weight parameters are all 1.

[0123] Further, aggregate the single-machine frequency response parameters based on the topological structure of the power system to be detected to obtain an aggregated power system frequency response model.

[0124] 206. Use the aggregated power system frequency response model to simulate the frequency response process of the power system to be detected in a predetermined disturbance scenario, obtain the simulation results, and determine the frequency security of the power system to be detected based on the simulation results.

[0125] For the embodiments of the present invention, after determining the aggregated power system frequency response model, in order to determine the frequency security of the power system to be detected, it is first necessary to simulate the frequency response process of the power system to be detected. Based on this, step 206 specifically includes: setting a predetermined disturbance scenario based on the actual operating environment, actual operating characteristics, and actual interference situations faced by the power system to be detected; setting the model parameters of the aggregated power system frequency response model based on the inertia constant of the generators, the gain of the governors, and the frequency regulation effect of the loads in the power system to be detected; using a preset simulation software to control the aggregated power system frequency response model with the set parameters to simulate the frequency response process of the power system to be detected in the predetermined disturbance scenario, and obtaining the simulation results.

[0126] Among them, the actual operating environment includes temperature, humidity, geographical location, etc.; the actual operating characteristics refer to the load characteristics, power generation characteristics, network structure characteristics, etc. of the power system to be detected; the actual interference situations faced refer to load disturbances, generator failures, line failures, external interferences, etc. of the power system to be detected.

[0127] Specifically, based on the analysis of the actual operating environment, actual operating characteristics, and actual interference situations faced by the power system to be detected, set a predetermined disturbance scenario. The set disturbance scenario should be able to reflect various situations that may be encountered in the actual operation of the power system, such as setting a scenario where the load suddenly increases or decreases in a short period of time. Determine the parameters of the aggregated power system frequency response model, including the inertia constant of the generators, governor parameters, load characteristics, etc. These parameters are set according to the actual situation of the power system, configure the aggregated power system frequency response model, such as setting the inertia constant, governor parameters, load characteristics, etc. of each generator in the power system to be detected, and configure the predetermined disturbance scenario. Run the configured aggregated power system frequency response model in the simulation tool to simulate the frequency response process of the power system under the predetermined disturbance scenario. During the simulation process, monitor the frequency response characteristics of the power system in real time, including key indicators such as frequency deviation and frequency change rate, so as to obtain the simulation results.

[0128] Further, after using the aggregated power system frequency response model to simulate the frequency response process of the power system to be detected and obtaining the simulation results, it is necessary to determine the frequency security of the power system to be detected based on the simulation results. Based on this, the method includes: determining the disturbance frequency value of the power system to be detected after being disturbed in the predetermined disturbance scenario based on the simulation results; if the disturbance frequency value is greater than the preset frequency threshold, it is determined that the frequency of the power system to be detected is insecure, otherwise, it is determined that the frequency of the power system to be detected is secure.

[0129] Among them, the preset frequency threshold is set according to actual needs. Specifically, according to the frequency response process simulation results, the disturbance frequency value of the power system to be detected after being disturbed in the predetermined disturbance scenario can be obtained. If this disturbance frequency value is greater than the preset frequency threshold, it is determined that the power system to be detected is unstable, that is, the frequency is insecure. If this disturbance frequency value is less than or equal to the preset frequency threshold, it is determined that the power system to be detected is stable, that is, the frequency is secure.

[0130] According to another method for determining the frequency security of a power system provided by the present invention, compared with the current method of predicting the frequency security of each unit separately through the frequency response models corresponding to each unit, the present invention performs frequency simulation on the multi-machine frequency response models of each generator set, obtains the frequency time-domain curve, performs parameter fitting on the frequency time-domain curve to obtain the frequency time-domain formula, then performs Laplace transform on the frequency time-domain formula to obtain the single-machine frequency response model of the power system, and establishes the non-linear relationship between the single-machine frequency response model and the multi-machine frequency response model. Based on the non-linear relationship, the single-machine frequency response parameters of the single-machine frequency response model are determined. Then, based on the single-machine frequency response parameters, the aggregated power system frequency response model of the power system is determined. Finally, the single aggregated power system frequency response model is used to predict the frequency security of the entire power system, that is, only one aggregated power system frequency response model can be used to predict the frequency security of each generator set in the entire power system, avoiding the time and resources wasted by using multiple models for frequency security prediction, and also being able to avoid the risk of increased error degree due to the increase in the number of predictions. Therefore, the present invention can improve the prediction efficiency and prediction accuracy of the frequency security of the power system.

[0131] Further, as Figure 1 a specific implementation of, the embodiment of the present invention provides a device for determining the frequency security of a power system, as Figure 4 shown, the device includes: an acquisition unit 31, a simulation unit 32, a transformation unit 33, a determination unit 34, and a simulation unit 35.

[0132] The acquisition unit 31 can be used to acquire the multi-machine frequency response models corresponding to multiple generator sets of the power system to be detected.

[0133] The simulation unit 32 can be used to perform frequency simulation on the corresponding generator sets in the power system to be detected by using each of the frequency response models, obtain the frequency time-domain curves corresponding to each of the generator sets, and perform parameter fitting on each of the frequency time-domain curves to obtain the frequency time-domain formula of the power system to be detected.

[0134] The transformation unit 33 can be used to perform Laplace transformation on the frequency time-domain formula to obtain the single-machine frequency response model of the power system to be detected.

[0135] The determination unit 34 can be used to establish a non-linear relationship between the single-machine frequency response model and the multi-machine frequency response model by using a preset generalized regression neural network, and based on the non-linear relationship, determine the single-machine frequency response parameters of the single-machine frequency response model, and based on the single-machine frequency response parameters, determine the aggregated power system frequency response model of the power system to be detected.

[0136] The simulation unit 35 can be used to simulate the frequency response process of the power system to be detected in a predetermined disturbance scenario by using the aggregated power system frequency response model, obtain a simulation result, and based on the simulation result, determine the frequency security of the power system to be detected.

[0137] In a specific application scenario, the multi-machine frequency response model includes a thermal power frequency response model, a nuclear power frequency response model, a hydropower frequency response model, a wind-solar-storage frequency response model, and a UHV frequency response model; the frequency time-domain curves include the thermal power frequency time-domain curve corresponding to the thermal power frequency response model, the nuclear power frequency time-domain curve corresponding to the nuclear power frequency response model, the hydropower frequency time-domain curve corresponding to the hydropower frequency response model, the wind frequency time-domain curve corresponding to the wind-solar-storage frequency response model, and the high-voltage frequency time-domain curve corresponding to the UHV frequency response model.

[0138] In a specific application scenario, in order to determine the frequency time-domain formula, the simulation unit 32 includes a first determination module 321 and a fitting module 322.

[0139] The first determination module 321 can be used to respectively determine the fitting parameters corresponding to the thermal power frequency time-domain curve, the nuclear power frequency time-domain curve, the hydropower frequency time-domain curve, the wind frequency time-domain curve, and the high-voltage frequency time-domain curve, and the value range of the fitting parameters.

[0140] The fitting module 322 can be used to perform parameter fitting on the thermal power frequency time-domain curve, the nuclear power frequency time-domain curve, the hydropower frequency time-domain curve, the wind-solar frequency time-domain curve, and the high-voltage frequency time-domain curve based on the parameters to be fitted and their corresponding parameter value ranges by using the non-linear least squares method, so as to obtain the frequency time-domain formula of the power system to be detected.

[0141] In a specific application scenario, in order to determine the single-machine frequency response model, the transformation unit 33 includes a second determination module 331 and an integration module 332.

[0142] The second determination module 331 can be used to determine the Laplace transform formula.

[0143] The integration module 332 can be used to substitute the frequency time-domain formula into the Laplace transform formula to obtain the substituted formula, perform the integration of the substituted formula from time zero to positive infinity, and determine the single-machine frequency response model of the power system to be detected based on the integration result.

[0144] In a specific application scenario, the preset generalized regression neural network includes an input layer, a radial basis layer, and an output layer. In order to determine the single-machine frequency response parameters of the single-machine frequency response model, the determination unit 34 includes a third determination module 341, a calculation module 342, a processing module 343, a dot product operation module 344, and a weighting module 345.

[0145] The third determination module 341 can be used to determine the multi-machine frequency response parameter vector corresponding to the multi-machine frequency response model, the identity matrix composed of the model parameters of the single-machine frequency response model, the multi-dimensional matrix composed of the center points of the radial basis functions of the input layer, and the comprehensive parameter matrix composed of the model parameters of the single-machine frequency response model and the model parameters of the multi-machine frequency response model.

[0146] The calculation module 342 can be used to calculate the Euclidean distance between the multi-machine frequency response parameter vector and the center points of each radial basis function based on the multi-dimensional matrix to obtain the multi-machine frequency spacing, perform eigenprocessing on the identity matrix by using the input layer to obtain the single-machine eigenvector, and perform eigenprocessing on the multi-machine frequency spacing by using the input layer to obtain the multi-machine eigenvector.

[0147] The processing module 343 can be used to perform Hadamard product processing on the single-machine eigenvector and the multi-machine eigenvector, input the Hadamard product processing result into the radial basis layer, and perform activation processing on the Hadamard product processing result through the activation function in the radial basis layer to obtain the activation eigenvector.

[0148] The dot product operation module 344 can be used to perform a normalized dot product operation on the activation feature vector and the comprehensive parameter matrix to obtain a dot product feature vector.

[0149] The weighting module 345 can be used to input the dot product feature vector into the output layer and perform linear weighting in the output layer to obtain the single-machine frequency response parameter of the single-machine frequency response model.

[0150] In a specific application scenario, in order to perform frequency response simulation on the power system to be detected, the simulation unit 35 includes a setting module 351 and a simulation module 352.

[0151] The setting module 351 can be used to set a predetermined disturbance scenario based on the actual operating environment, actual operating characteristics, and actual interference situations faced by the power system to be detected.

[0152] The setting module 351 can also be used to set the model parameters of the aggregated power system frequency response model based on the inertia constant of the generator, the gain of the governor, and the frequency regulation effect of the load in the power system to be detected.

[0153] The simulation module 352 can be used to use a preset simulation software to control the aggregated power system frequency response model after setting parameters to simulate the frequency response process of the power system to be detected in the predetermined disturbance scenario to obtain a simulation result.

[0154] In a specific application scenario, in order to determine the frequency security of the power system to be detected, the simulation unit 35 further includes a determination module 353.

[0155] The determination module 353 can specifically be used to determine the disturbance frequency value of the power system to be detected after being disturbed in the predetermined disturbance scenario based on the simulation result; if the disturbance frequency value is greater than a preset frequency threshold, it is determined that the frequency of the power system to be detected is unsafe, otherwise, it is determined that the frequency of the power system to be detected is safe.

[0156] It should be noted that for other corresponding descriptions of each functional module involved in the power system frequency security determination device provided in the embodiments of the present invention, reference can be made to Figure 1 the corresponding description of the method shown, which will not be elaborated here.

[0157] Based on the above as Figure 1The method described above, correspondingly, an embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, the following steps are implemented: obtaining multi-machine frequency response models corresponding to multiple generator sets of a power system to be detected; using each of the frequency response models to perform frequency simulation on the corresponding generator set in the power system to be detected, obtaining frequency time-domain curves corresponding to each of the generator sets, and performing parameter fitting on each of the frequency time-domain curves to obtain a frequency time-domain formula of the power system to be detected; performing Laplace transform on the frequency time-domain formula to obtain a single-machine frequency response model of the power system to be detected; using a preset generalized regression neural network to establish a non-linear relationship between the single-machine frequency response model and the multi-machine frequency response models, and based on the non-linear relationship, determining single-machine frequency response parameters of the single-machine frequency response model, and based on the single-machine frequency response parameters, determining an aggregated power system frequency response model of the power system to be detected; using the aggregated power system frequency response model to simulate a frequency response process of the power system to be detected in a predetermined disturbance scenario, obtaining a simulation result, and based on the simulation result, determining the frequency security of the power system to be detected.

[0158] Based on the above method as Figure 1 shown and the embodiment of the device as Figure 4 shown, an embodiment of the present invention further provides an entity structure diagram of a computer device, as Figure 5 shown. The computer device includes: a processor 41, a memory 42, and a computer program stored on the memory 42 and executable on the processor. The memory 42 and the processor 41 are both arranged on a bus 43. When the processor 41 executes the program, the following steps are implemented: obtaining multi-machine frequency response models corresponding to multiple generator sets of a power system to be detected; using each of the frequency response models to perform frequency simulation on the corresponding generator set in the power system to be detected, obtaining frequency time-domain curves corresponding to each of the generator sets, and performing parameter fitting on each of the frequency time-domain curves to obtain a frequency time-domain formula of the power system to be detected; performing Laplace transform on the frequency time-domain formula to obtain a single-machine frequency response model of the power system to be detected; using a preset generalized regression neural network to establish a non-linear relationship between the single-machine frequency response model and the multi-machine frequency response models, and based on the non-linear relationship, determining single-machine frequency response parameters of the single-machine frequency response model, and based on the single-machine frequency response parameters, determining an aggregated power system frequency response model of the power system to be detected; using the aggregated power system frequency response model to simulate a frequency response process of the power system to be detected in a predetermined disturbance scenario, obtaining a simulation result, and based on the simulation result, determining the frequency security of the power system to be detected.

[0159] Through the technical solution of the present invention, the present invention performs frequency simulation on the multi-machine frequency response models of each generating unit to obtain a frequency time-domain curve, and performs parameter fitting on the frequency time-domain curve to obtain a frequency time-domain formula. Then, the frequency time-domain formula is subjected to Laplace transform to obtain a single-machine frequency response model of the power system, and a non-linear relationship between the single-machine frequency response model and the multi-machine frequency response model is established. Based on the non-linear relationship, the single-machine frequency response parameters of the single-machine frequency response model are determined. Then, based on the single-machine frequency response parameters, an aggregated power system frequency response model of the power system is determined. Finally, the single aggregated power system frequency response model is used to predict the frequency security of the entire power system, that is, only one aggregated power system frequency response model can be used to predict the frequency security of each generating unit in the entire power system, avoiding the time and resources wasted by using multiple models for frequency security prediction, and also being able to avoid the risk of increasing error degrees due to the increase in the number of predictions. Therefore, the present invention can improve the prediction efficiency and prediction accuracy of the frequency security of the power system.

[0160] Obviously, those skilled in the art should understand that the above-mentioned modules or steps of the present invention can be implemented by a general-purpose computing device. They can be concentrated on a single computing device or distributed on a network composed of multiple computing devices. Optionally, they can be implemented by program codes executable by the computing device. Thus, they can be stored in a storage device and executed by the computing device. And in some cases, the steps shown or described can be executed in a different order than here, or they can be separately made into individual integrated circuit modules, or multiple modules or steps among them can be made into a single integrated circuit module to implement. In this way, the present invention is not limited to any specific combination of hardware and software.

[0161] The above are only the preferred embodiments of the present invention and are not used to limit the present invention. For those skilled in the art, the present invention can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for determining the frequency security of a power system, characterized in that, Including: Obtain multi-machine frequency response models corresponding to multiple generator sets of the power system to be detected; Use the multi-machine frequency response models to perform frequency simulation on the corresponding generator sets in the power system to be detected, obtain frequency time-domain curves corresponding to each of the generator sets, and perform parameter fitting on each of the frequency time-domain curves to obtain the frequency time-domain formula of the power system to be detected; Perform Laplace transform on the frequency time-domain formula to obtain the single-machine frequency response model of the power system to be detected; Use a preset generalized regression neural network to establish a non-linear relationship between the single-machine frequency response model and the multi-machine frequency response models, and based on the non-linear relationship, determine the single-machine frequency response parameters of the single-machine frequency response model, and based on the single-machine frequency response parameters, determine the aggregated power system frequency response model of the power system to be detected; Use the aggregated power system frequency response model to simulate the frequency response process of the power system to be detected in a predetermined disturbance scenario, obtain a simulation result, and based on the simulation result, determine the frequency security of the power system to be detected.

2. The method according to claim 1, wherein The multi-machine frequency response models include a thermal power frequency response model corresponding to a thermal power generator set, a nuclear power frequency response model corresponding to a nuclear power generator set, a hydropower frequency response model corresponding to a hydropower generator set, a wind-solar storage frequency response model corresponding to a wind-solar generator set, and a UHV frequency response model corresponding to a UHV generator set; the frequency time-domain curves include a thermal power frequency time-domain curve corresponding to the thermal power frequency response model, a nuclear power frequency time-domain curve corresponding to the nuclear power frequency response model, a hydropower frequency time-domain curve corresponding to the hydropower frequency response model, a wind frequency time-domain curve corresponding to the wind-solar storage frequency response model, and a high-voltage frequency time-domain curve corresponding to the UHV frequency response model.

3. The method according to claim 2, wherein The performing parameter fitting on each of the frequency time-domain curves to obtain the frequency time-domain formula of the power system to be detected includes: Respectively determine the parameters to be fitted corresponding to the thermal power frequency time-domain curve, the nuclear power frequency time-domain curve, the hydropower frequency time-domain curve, the wind frequency time-domain curve, the high-voltage frequency time-domain curve, and the value range of the parameters to be fitted; Based on the parameters to be fitted and their corresponding value ranges, use the non-linear least squares method to perform parameter fitting on the thermal power frequency time-domain curve, the nuclear power frequency time-domain curve, the hydropower frequency time-domain curve, the wind frequency time-domain curve, the high-voltage frequency time-domain curve to obtain the frequency time-domain formula of the power system to be detected.

4. The method according to claim 1, wherein The performing Laplace transform on the frequency time-domain formula to obtain the single-machine frequency response model of the power system to be detected includes: Determine the Laplace transform formula; Substitute the frequency time-domain formula into the Laplace transform formula to obtain the substituted formula, and perform integration on the substituted formula from time zero to positive infinity, and based on the integration result, determine the single-machine frequency response model of the power system to be detected.

5. The method according to claim 1, wherein The preset generalized regression neural network includes an input layer, a radial basis layer, and an output layer; Establishing the non - linear relationship between the single - machine frequency response model and the multi - machine frequency response model by using a preset generalized regression neural network, and determining the single - machine frequency response parameters of the single - machine frequency response model based on the non - linear relationship, includes: Determining the multi - machine frequency response parameter vector corresponding to the multi - machine frequency response model, the identity matrix composed of the model parameters of the single - machine frequency response model, the multi - dimensional matrix composed of the center points of the radial basis functions in the input layer, and the comprehensive parameter matrix composed of the model parameters of the single - machine frequency response model and the model parameters of the multi - machine frequency response model; Based on the multi - dimensional matrix, calculating the Euclidean distances between the multi - machine frequency response parameter vector and the center points of each radial basis function to obtain the multi - machine frequency spacing, using the input layer to perform feature processing on the identity matrix to obtain the single - machine feature vector, and using the input layer to perform feature processing on the multi - machine frequency spacing to obtain the multi - machine feature vector; Performing Hadamard product processing on the single - machine feature vector and the multi - machine feature vector, and inputting the result of the Hadamard product processing into the radial basis layer, and performing activation processing on the result of the Hadamard product processing through the activation function in the radial basis layer to obtain the activation feature vector; Performing normalized dot - product operation on the activation feature vector and the comprehensive parameter matrix to obtain the dot - product feature vector; Inputting the dot - product feature vector into the output layer, and performing linear weighting in the output layer to obtain the single - machine frequency response parameters of the single - machine frequency response model.

6. The method according to claim 1, wherein Simulating the frequency response process of the power system to be detected in a predetermined disturbance scenario by using the aggregated power system frequency response model, and obtaining the simulation result, includes: Setting a predetermined disturbance scenario based on the actual operating environment, actual operating characteristics, and actual interference situations faced by the power system to be detected; Setting the model parameters of the aggregated power system frequency response model based on the inertia constant of the generators, the gain of the governors, and the frequency regulation effect of the loads in the power system to be detected; Using a preset simulation software to control the aggregated power system frequency response model with set parameters to simulate the frequency response process of the power system to be detected in the predetermined disturbance scenario, and obtaining the simulation result.

7. The method according to claim 1, wherein Based on the simulation result, determining the frequency security of the power system to be detected, includes: Based on the simulation result, determining the disturbance frequency value of the power system to be detected after being disturbed in the predetermined disturbance scenario; If the disturbance frequency value is greater than the preset frequency threshold, it is determined that the frequency of the power system to be detected is insecure; otherwise, it is determined that the frequency of the power system to be detected is secure.

8. A determining device for the frequency security of a power system, characterized in that, Includes: An acquisition unit, configured to acquire the multi - machine frequency response model corresponding to multiple generator sets of the power system to be detected; A simulation unit, configured to use the multi - machine frequency response model to perform frequency simulation on the corresponding generator sets in the power system to be detected, obtain the frequency time - domain curves corresponding to each generator set, and perform parameter fitting on each frequency time - domain curve to obtain the frequency time - domain formula of the power system to be detected; A transformation unit for performing Laplace transform on the frequency-time domain formula to obtain a single-machine frequency response model of the power system to be detected; A determination unit for establishing a non-linear relationship between the single-machine frequency response model and the multi-machine frequency response model by using a preset generalized regression neural network, and determining the single-machine frequency response parameters of the single-machine frequency response model based on the non-linear relationship. Based on the single-machine frequency response parameters, an aggregated power system frequency response model of the power system to be detected is determined; A simulation unit for simulating the frequency response process of the power system to be detected in a predetermined disturbance scenario by using the aggregated power system frequency response model, obtaining a simulation result, and determining the frequency security of the power system to be detected based on the simulation result.

9. The determination device according to claim 8, characterized in that, The multi-machine frequency response model includes a thermal power frequency response model corresponding to a thermal power generating unit, a nuclear power frequency response model corresponding to a nuclear power generating unit, a hydropower frequency response model corresponding to a hydropower generating unit, a wind-solar storage frequency response model corresponding to a wind-solar generating unit, and a UHV frequency response model corresponding to a UHV generating unit; The frequency-time domain curve includes a thermal power frequency-time domain curve corresponding to the thermal power frequency response model, a nuclear power frequency-time domain curve corresponding to the nuclear power frequency response model, a hydropower frequency-time domain curve corresponding to the hydropower frequency response model, a wind frequency-time domain curve corresponding to the wind-solar storage frequency response model, and a high-voltage frequency-time domain curve corresponding to the UHV frequency response model.

10. The determination device according to claim 9, characterized in that The simulation unit includes a first determination module and a fitting module; The first determination module is used to respectively determine the fitting parameters corresponding to the thermal power frequency-time domain curve, the nuclear power frequency-time domain curve, the hydropower frequency-time domain curve, the wind frequency-time domain curve, and the high-voltage frequency-time domain curve, and the parameter value range of the fitting parameters; The fitting module is used to perform parameter fitting on the thermal power frequency-time domain curve, the nuclear power frequency-time domain curve, the hydropower frequency-time domain curve, the wind frequency-time domain curve, and the high-voltage frequency-time domain curve by using the non-linear least squares method based on the fitting parameters and their corresponding parameter value ranges, to obtain the frequency-time domain formula of the power system to be detected.

11. The determination device according to claim 8, characterized in that, The transformation unit includes a second determination module and an integration module; The second determination module is used to determine the Laplace transform formula; The integration module is used to substitute the frequency-time domain formula into the Laplace transform formula to obtain the substituted formula, and perform integration on the substituted formula from time zero to positive infinity, and determine the single-machine frequency response model of the power system to be detected based on the integration result.

12. The determination device according to claim 8, wherein The preset generalized regression neural network includes an input layer, a radial basis layer, and an output layer; The determination unit includes a third determination module, a calculation module, a processing module, a dot product operation module, and a weighting module; The third determination module is configured to determine a multi-machine frequency response parameter vector corresponding to the multi-machine frequency response model, an identity matrix composed of model parameters of the single-machine frequency response model, a multi-dimensional matrix composed of center points of radial basis functions in the input layer, and a comprehensive parameter matrix composed of model parameters of the single-machine frequency response model and model parameters of the multi-machine frequency response model; The calculation module is configured to calculate Euclidean distances between the multi-machine frequency response parameter vector and center points of the respective radial basis functions based on the multi-dimensional matrix to obtain multi-machine frequency spacings, perform eigenprocessing on the identity matrix using the input layer to obtain a single-machine eigenvector, and perform eigenprocessing on the multi-machine frequency spacings using the input layer to obtain a multi-machine eigenvector; The processing module is configured to perform Hadamard product processing on the single-machine eigenvector and the multi-machine eigenvector, and input the result of the Hadamard product processing into the radial basis layer, and perform activation processing on the result of the Hadamard product processing through an activation function in the radial basis layer to obtain an activation eigenvector; The dot product operation module is configured to perform a normalized dot product operation on the activation eigenvector and the comprehensive parameter matrix to obtain a dot product eigenvector; The weighting module is configured to input the dot product eigenvector into the output layer and perform linear weighting in the output layer to obtain the single-machine frequency response parameters of the single-machine frequency response model.

13. The determining device according to claim 8, wherein The simulation unit includes a setting module and a simulation module; The setting module is configured to set a predetermined disturbance scenario based on the actual operating environment, actual operating characteristics, and actual interference situations faced by the power system to be detected; The setting module is configured to set model parameters of the aggregated power system frequency response model based on the inertia constant of generators, the gain of governors, and the frequency regulation effect of loads in the power system to be detected; The simulation module is configured to use a preset simulation software to control the aggregated power system frequency response model with set parameters to simulate the frequency response process of the power system to be detected in the predetermined disturbance scenario to obtain a simulation result.

14. The determination device according to claim 8, characterized in that, The simulation unit further includes a determination module; The determination module is configured to determine a disturbance frequency value of the power system to be detected after being disturbed in the predetermined disturbance scenario based on the simulation result; The determination module is configured to determine that the frequency of the power system to be detected is unsafe if the disturbance frequency value is greater than a preset frequency threshold, otherwise, determine that the frequency of the power system to be detected is safe.

15. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

16. A computer device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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