Stability control strategy checking method, device and equipment and readable storage medium

Abstract

The invention discloses a stability control strategy checking method, apparatus and device, and a readable storage medium. The method comprises the steps of determining a plurality of fault scenes covered by a to-be-checked stability control strategy; for each fault scene, a first operation parameter of the fault scene is acquired in combination with a static load flow calculation mode; generating an adjustment measure based on the first operation parameter of the fault scene and the to-be-checked stability control strategy, and obtaining a second operation parameter after the adjustment measure is adopted in the fault scene in combination with a static load flow calculation mode; utilizing an index prediction model to predict a voltage steady state index for measuring the instability degree of the corresponding fault scene based on the first operation parameter, the second operation parameter and the adjustment measure corresponding to each fault scene; and selecting N fault scenes with high degree of instability to perform time domain simulation, and evaluating the stability control strategy to be checked. Visibly, the method can be combined with a static load flow calculation mode and an index prediction model, and the efficiency of stability control strategy checking is improved while the checking reliability is ensured.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power grid, more particularly, to a stability control strategy checking method, device, equipment and readable storage medium. BACKGROUND

[0002] In the operation of modern power grid system, when a specific fault occurs, the active power of the corresponding control object can be controlled according to the stability control strategy to reduce the scope of the fault on the power grid system and ensure the safe and stable operation of the power grid system.

[0003] To ensure the adaptability and control accuracy of the stability control strategy, the stability control strategy needs to be checked regularly, that is, to generate a predicted system operation mode online and to apply a fault and a strategy to the predicted system operation mode to obtain the system stability after the fault. The existing checking method is to use time-domain simulation software to simulate the stability of the power grid system after the fault in batches. However, with the continuous expansion of the power grid, the diversification of the operation mode brought by the high proportion of new energy and the complexity of the dynamic characteristics of AC-DC systems, the time-domain simulation data volume is large, and the time-domain simulation efficiency is further reduced, resulting in a reduction in the stability control strategy checking efficiency. Therefore, how to provide a method for improving the stability control strategy checking efficiency has become the focus of the technical personnel in the field. SUMMARY

[0004] Therefore, the present application provides a stability control strategy checking method, device, equipment and readable storage medium to solve the low efficiency of the existing stability control strategy checking technology.

[0005] In order to achieve the above purpose, the present scheme is as follows:

[0006] A stability control strategy checking method comprises:

[0007] determining a plurality of fault scenarios covered by a to-be-checked stability control strategy;

[0008] For each fault scenario, a first operation parameter of the fault scenario is obtained in combination with a static power flow calculation mode; based on the first operation parameter of the fault scenario, an adjustment measure is generated in reference to the to-be-checked stability control strategy, and a second operation parameter of the fault scenario after adopting the adjustment measure is obtained in combination with the static power flow calculation mode;

[0009] an index prediction model trained is obtained, and the index prediction model is used to predict a voltage steady-state index for measuring the instability degree of the corresponding fault scenario based on the first operation parameter, the second operation parameter and the adjustment measure of each fault scenario;

[0010] the corresponding voltage steady-state index is referred to for sorting the various fault scenarios, N fault scenarios with high instability degree are selected for time-domain simulation, and the to-be-checked stability control strategy is evaluated.

[0011] Optionally, the first operating parameter of the fault scenario is obtained by combining a static power flow calculation method, and the method comprises the following steps:

[0012] A static power flow model of the power grid applicable to the to-be-checked stability control strategy is constructed.

[0013] The fault scenario is simulated in the static power flow model of the power grid, and a first power flow distribution matched with the fault scenario is obtained by static power flow calculation.

[0014] Optionally, the second operating parameter of the fault scenario after the adjustment measure is obtained by combining a static power flow calculation method, and the method comprises the following steps:

[0015] The adjustment measure is adopted in the static power flow model of the power grid in which the fault scenario is simulated, and a second power flow distribution is obtained by static power flow calculation.

[0016] Optionally, the adjustment measure is generated based on the first operating parameter of the fault scenario and by referring to the to-be-checked stability control strategy, and the method comprises the following steps:

[0017] A safety and stability control device setting value matched with the first operating parameter of the fault scenario is selected from the to-be-checked stability control strategy.

[0018] The adjustment measure is generated based on the safety and stability control device setting value.

[0019] Optionally, the trained index prediction model is obtained by the following steps:

[0020] An initial prediction model is constructed.

[0021] A plurality of training samples are obtained, each training sample has a unique corresponding training fault, and each training sample comprises a training measure adopted for the corresponding training fault, power grid state parameters before and after the execution of the training measure, and a transient training index used for quantitatively evaluating the instability degree of the power grid after the execution of the training measure.

[0022] The initial prediction model is trained by using each training sample until a preset stop condition is reached, and the obtained initial prediction model is the trained index prediction model.

[0023] Optionally, the plurality of training samples are obtained by the following steps:

[0024] A simulation model is obtained by performing time domain simulation on the power grid system.

[0025] A plurality of training faults are determined.

[0026] For each training fault, determine the training measure matched with the training fault, and set the training fault in the simulation model to obtain the power grid state parameter before execution of the corresponding training measure; execute the training measure in the simulation model with the set training fault to obtain the power grid state parameter after execution of the corresponding training measure; and calculate the transient training index corresponding to the training fault based on the power grid state parameter after execution of the corresponding training measure.

[0027] Optionally, the calculating of the transient training index corresponding to the training fault based on the power grid state parameter after execution of the corresponding training measure comprises:

[0028] The calculating of the transient training index corresponding to the training fault based on the power grid state parameter after execution of the corresponding training measure comprises combining the multi-binary table method.

[0029] A device for checking a stability control strategy, comprising:

[0030] A determining module configured to determine a plurality of fault scenarios covered by a to-be-checked stability control strategy;

[0031] A generating module configured to, for each fault scenario, acquire a first operating parameter of the fault scenario by combining a static power flow calculation method; and generate an adjustment measure by referring to the to-be-checked stability control strategy based on the first operating parameter of the fault scenario, and acquire a second operating parameter of the fault scenario after the adjustment measure is adopted by combining the static power flow calculation method.

[0032] An acquiring module configured to acquire a trained index prediction model, and predict a voltage steady-state index for measuring an instability degree of a corresponding fault scenario by using the index prediction model based on the first operating parameter, the second operating parameter and the adjustment measure of each fault scenario.

[0033] An evaluating module configured to sort the fault scenarios according to the sizes of the corresponding voltage steady-state indexes, select N fault scenarios with high instability degrees for time-domain simulation, and evaluate the to-be-checked stability control strategy.

[0034] A device for checking a stability control strategy, comprising a memory and a processor;

[0035] The memory is configured to store a program.

[0036] The processor is configured to execute the program to implement each step of the above-described method for checking a stability control strategy.

[0037] A readable storage medium having a computer program stored thereon, the computer program being executed by a processor to implement each step of the above-described method for checking a stability control strategy.

[0038] As can be seen from the above technical solution, the stability control strategy verification method provided in this application can determine multiple fault scenarios covered by the stability control strategy to be verified; for each fault scenario, a first operating parameter of the fault scenario is obtained by combining a static power flow calculation method; based on the first operating parameter of the fault scenario, and referring to the stability control strategy to be verified, adjustment measures are generated, and a second operating parameter of the fault scenario after adopting the adjustment measures is obtained by combining a static power flow calculation method; based on this, this application, when verifying the stability control strategy, clearly defines all fault scenarios covered by the stability control strategy to be verified, avoiding [fault scenarios due to factors such as fault scenarios]. Omissions in scenarios can lead to potential vulnerabilities in the strategy. The first operating parameter can reflect the operation of the power grid system when a fault occurs. Based on this, adjustment measures are generated in combination with the stability control strategy to be verified to ensure the matching degree between the adjustment measures and the fault. The stability control strategy to be verified covering multiple scenarios is converted into measures that match specific scenarios. The second operating parameter is determined by comprehensively adjusting the adjustment measures and the first operating parameter. The intervention effect of the stability control strategy to be verified on the fault is intuitively reflected by the second operating parameter. Moreover, the first and second operating parameters are obtained by static power flow calculation, which eliminates the need for time-consuming time-domain simulation, reduces the amount of data processing, and simplifies the verification process. Based on this, this application can obtain a trained index prediction model, and use the index prediction model to predict the voltage steady-state index used to measure the instability degree of each fault scenario, based on the first operating parameter, the second operating parameter, and the adjustment measures corresponding to each fault scenario. Thus, this application can quantify the calculation process of the voltage steady-state index into a model prediction process, ensuring that the voltage steady-state index matched to each fault scenario is determined according to a unified standard, avoiding subjective bias from manual judgment. Subsequently, this application can rank each fault scenario by referring to the magnitude of the corresponding voltage steady-state index, and select N fault scenarios with high instability degrees for time-domain simulation. The stability control strategy to be verified is evaluated. Based on this, this application can use static power flow calculation combined with an index prediction model to select extreme scenarios with relatively high instability after adopting the stability control strategy from various fault scenarios matched with the stability control strategy. Priority time-domain simulations are then performed on these extreme scenarios to obtain the final evaluation result of the stability control strategy. This ensures that limited computing resources are concentrated on scenarios most likely to expose problems with the stability control strategy, avoiding wasting resources on low-risk scenarios. This application still uses time-domain simulation to verify extreme scenarios, retaining the evaluation accuracy of traditional methods while reducing the number of fault scenario simulations through pre-screening, significantly shortening the overall verification cycle. Therefore, this application can improve the efficiency of stability control strategy verification while ensuring verification reliability by combining static power flow calculation and an index prediction model. Attached Figure Description

[0039] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of this application. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0040] Figure 1 This is a flowchart of a stability control strategy verification method disclosed in an embodiment of this application;

[0041] Figure 2 A schematic diagram illustrating the correlation between transient training metrics and voltage stability, provided as an embodiment of this application;

[0042] Figure 3 This is a structural block diagram of a stability control strategy verification device disclosed in an embodiment of this application;

[0043] Figure 4 This is a hardware structure block diagram of a stability control strategy verification device disclosed in an embodiment of this application. Detailed Implementation

[0044] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0045] This application provides a method for verifying a stability control strategy. This method can be applied to various power grid detection systems or power grid management systems, as well as to various computer terminals or smart terminals. The executing entity can be the processor or server of the computer terminal or smart terminal.

[0046] Next, combine Figure 1 The method for verifying the stabilization strategy in this application is described in detail, including the following steps:

[0047] Step S1: Determine the multiple fault scenarios covered by the stability control strategy to be verified.

[0048] Specifically, the stability control strategy that needs to be verified can be used as the stability control strategy to be verified.

[0049] The stability control strategy to be checked can be a newly generated stability control strategy or a stability control strategy that requires setting adjustment.

[0050] It can analyze the various fault types covered by the stability control strategy to be verified and generate fault scenarios matching each fault type.

[0051] Different fault scenarios can correspond to the same fault type.

[0052] For example, there are two fault scenarios that both correspond to the N-2 fault. One fault scenario is the simultaneous tripping of the 220kV A-B line and A-C line; the other fault scenario is the simultaneous shutdown of power plant unit 1 and unit 2.

[0053] Different fault points correspond to different fault conditions, but different fault scenarios may contain some of the same fault points.

[0054] For example, one fault scenario is a single-phase ground fault near the 220kV A-B line; while another fault scenario could be a single-phase ground fault near the 220kV A-B line and a short-circuit fault near the A-C line.

[0055] Step S2: For each fault scenario, obtain the first operating parameters of the fault scenario by combining the static power flow calculation method; based on the first operating parameters of the fault scenario, generate adjustment measures by referring to the stability control strategy to be verified, and obtain the second operating parameters of the fault scenario after adopting the adjustment measures by combining the static power flow calculation method.

[0056] Specifically, the first and second operating parameters may include node status parameters, branch power flow parameters, network loss parameters, and node injection parameters, etc.

[0057] It should be noted that each first operating parameter is the power grid system parameter without stability control adjustment for the corresponding fault scenario; while each second operating parameter is the power grid system parameter after stability control adjustment for the corresponding fault scenario.

[0058] The first and second operating parameters that have a corresponding relationship can correspond to the same fault scenario.

[0059] Step S3: Obtain the trained index prediction model, and use the index prediction model to predict the voltage steady-state index for measuring the degree of instability of the corresponding fault scenario based on the first operating parameter, the second operating parameter and the adjustment measures corresponding to each fault scenario.

[0060] Specifically, one can obtain a pre-trained indicator prediction model or directly train a CNN model to obtain an indicator prediction model.

[0061] The first operating parameter, the second operating parameter, and the adjustment measures corresponding to the same fault scenario can be input into the index prediction model to obtain the voltage steady-state index output by the index prediction model.

[0062] The higher the value of the voltage steady-state index, the higher the degree of power grid instability in the corresponding fault scenario.

[0063] Step S4: Based on the magnitude of the corresponding voltage steady-state index, sort the various fault scenarios, select N fault scenarios with high instability for time-domain simulation, and evaluate the stability control strategy to be verified.

[0064] Specifically, the various fault scenarios can be sorted in descending order of voltage steady-state index to obtain the sorting results.

[0065] The top N fault scenarios can be selected from the sorting results as multiple target fault scenarios;

[0066] N can be set in advance according to the actual verification needs, or N fault scenarios that exceed the indicator threshold can be selected.

[0067] It is possible to build a time-domain simulation model of the power grid;

[0068] The triggering process of each target fault scenario and the stability control strategy to be verified is simulated sequentially in the power grid time-domain simulation model to obtain the dynamic parameter sequence of each target fault scenario;

[0069] By comparing the dynamic parameter sequence of each target fault scenario with the power grid stability standard, the effectiveness of the stability control strategy under the corresponding target fault scenario is evaluated.

[0070] As can be seen from the above technical solution, the stability control strategy verification method provided in this application can determine multiple fault scenarios covered by the stability control strategy to be verified; for each fault scenario, a first operating parameter of the fault scenario is obtained by combining a static power flow calculation method; based on the first operating parameter of the fault scenario, and referring to the stability control strategy to be verified, adjustment measures are generated, and a second operating parameter of the fault scenario after adopting the adjustment measures is obtained by combining a static power flow calculation method; based on this, this application, when verifying the stability control strategy, clearly defines all fault scenarios covered by the stability control strategy to be verified, avoiding [fault scenarios due to factors such as fault scenarios]. Omissions in scenarios can lead to potential vulnerabilities in the strategy. The first operating parameter can reflect the operation of the power grid system when a fault occurs. Based on this, adjustment measures are generated in combination with the stability control strategy to be verified to ensure the matching degree between the adjustment measures and the fault. The stability control strategy to be verified covering multiple scenarios is converted into measures that match specific scenarios. The second operating parameter is determined by comprehensively adjusting the adjustment measures and the first operating parameter. The intervention effect of the stability control strategy to be verified on the fault is intuitively reflected by the second operating parameter. Moreover, the first and second operating parameters are obtained by static power flow calculation, which eliminates the need for time-consuming time-domain simulation, reduces the amount of data processing, and simplifies the verification process. Based on this, this application can obtain a trained index prediction model, and use the index prediction model to predict the voltage steady-state index used to measure the instability degree of each fault scenario, based on the first operating parameter, the second operating parameter, and the adjustment measures corresponding to each fault scenario. Thus, this application can quantify the calculation process of the voltage steady-state index into a model prediction process, ensuring that the voltage steady-state index matched to each fault scenario is determined according to a unified standard, avoiding subjective bias from manual judgment. Subsequently, this application can rank each fault scenario by referring to the magnitude of the corresponding voltage steady-state index, and select N fault scenarios with high instability degrees for time-domain simulation. The stability control strategy to be verified is evaluated. Based on this, this application can use static power flow calculation combined with an index prediction model to select extreme scenarios with relatively high instability after adopting the stability control strategy from various fault scenarios matched with the stability control strategy. Priority time-domain simulations are then performed on these extreme scenarios to obtain the final evaluation result of the stability control strategy. This ensures that limited computing resources are concentrated on scenarios most likely to expose problems with the stability control strategy, avoiding wasting resources on low-risk scenarios. This application still uses time-domain simulation to verify extreme scenarios, retaining the evaluation accuracy of traditional methods while reducing the number of fault scenario simulations through pre-screening, significantly shortening the overall verification cycle. Therefore, this application can improve the efficiency of stability control strategy verification while ensuring verification reliability by combining static power flow calculation and an index prediction model.

[0071] In some embodiments of this application, the process of obtaining the first operating parameters of the fault scenario in step S2 by combining static power flow calculation is described in detail, and the steps are as follows:

[0072] S20. Construct a static power flow model of the power grid applicable to the stability control strategy to be verified.

[0073] Specifically, it can obtain basic power grid data of the power grid system, such as topology connections and component parameters.

[0074] Static power flow models of the power grid can be built based on topology connections and component parameters in static power flow software such as PSASP and BPA.

[0075] S21. Simulate the fault scenario in the static power flow model of the power grid, and calculate the first power flow distribution matched by the fault scenario using static power flow calculation.

[0076] Specifically, based on the fault conditions of the fault scenario, fault equivalence operations can be performed in the static power flow model of the power grid to modify the power grid topology to match the power grid structure after the fault.

[0077] Use static power flow calculation tools such as the Newton-Raphson method to solve the steady-state power flow after the fault.

[0078] Key data such as active power flow, reactive power flow, and node voltage of each line are extracted from the calculation results to form the first power flow distribution corresponding to the fault scenario.

[0079] As can be seen from the above technical solution, this embodiment provides an optional method for obtaining the first operating parameters of the fault scenario by combining static power flow calculation. This method can better achieve the acquisition of the first operating parameters and improve their accuracy.

[0080] In some embodiments of this application, the process of obtaining the second operating parameters of the fault scenario after applying the adjustment measures in step S2 by combining the static power flow calculation method is described in detail, and the steps are as follows:

[0081] S20. The adjustment measures are adopted in the static power flow model of the power grid simulating the fault scenario, and the second power flow distribution is obtained by static power flow calculation.

[0082] Specifically, based on the static power flow model of the power grid that has simulated fault scenarios, the adjustment measures are transformed into static equivalent parameter modifications;

[0083] Keeping the fault topology unchanged in the static power flow model of the power grid, updating the parameters related to the adjustment measures, calling the static power flow calculation tool, solving the steady-state power flow after the implementation of the adjustment measures, extracting data such as power flow of each line and node voltage, and forming the corresponding second power flow distribution under the fault scenario.

[0084] As can be seen from the above technical solution, this embodiment provides an optional method for obtaining the second operating parameters of the fault scenario after applying the adjustment measures, combined with static power flow calculation. Through this method, the second power flow distribution can be directly obtained through power flow calculation, simplifying the difficulty of power flow calculation.

[0085] In some embodiments of this application, the process of generating adjustment measures in step S2 based on the first operating parameters of the fault scenario and referring to the stability control strategy to be verified is described in detail, and the steps are as follows:

[0086] S20. Select a safety and stability control device setting value from the stability control strategies to be verified that matches the first operating parameter of the fault scenario.

[0087] Specifically, the stability control strategy to be verified may include various safety and stability control device settings.

[0088] The setpoints of the safety and stability control device can be used to characterize the actions taken by the corresponding power grid system when the corresponding power grid parameters reach the preset trigger range.

[0089] The first operating parameter of the fault scenario can be matched with the set values ​​of each safety and stability control device to determine the safety and stability control device that can be triggered by the first operating parameter.

[0090] S21. Based on the set value of the safety and stability control device, generate adjustment measures.

[0091] Specifically, the set values ​​of the matching safety and stability control devices can be used to generate targeted adjustment measures.

[0092] The adjustment measures may include the type of measure, the target of implementation, and the specific control quantity.

[0093] As can be seen from the above technical solution, this embodiment provides an optional method for generating adjustment measures based on the first operating parameters of the fault scenario and referring to the stability control strategy to be verified. This method allows for the generation of more targeted adjustment measures.

[0094] In some embodiments of this application, the process of obtaining the trained index prediction model in step S3 is described in detail, and the steps are as follows:

[0095] S30. Construct the initial prediction model.

[0096] Specifically, a convolutional neural network (CNN) can be built as the initial prediction model.

[0097] S31. Obtain multiple training samples. Each training sample has a unique corresponding training fault. Each training sample contains the training measures adopted for the corresponding training fault, the power grid state parameters before and after the implementation of the training measures, and the transient training index used to quantitatively evaluate the degree of power grid instability after the implementation of the training measures.

[0098] Specifically, the training samples corresponding to each training fault can be obtained.

[0099] Each training sample may contain the training measures adopted for the corresponding training fault, the power grid state parameters before and after the implementation of the training measures, and transient training indicators.

[0100] The training measures can be derived from different stability control strategies.

[0101] S32. Train the initial prediction model using each training sample until the preset stopping condition is met. The final initial prediction model is the trained index prediction model.

[0102] Specifically, the initial prediction model can be trained using various training samples, the prediction error of the initial prediction model can be calculated using cross-entropy, and the parameters of the initial prediction model can be adjusted based on the prediction error until the initial prediction model converges. The final initial prediction model is the trained index prediction model.

[0103] As can be seen from the above technical solution, this embodiment provides an optional method for training an indicator prediction model. Using this method, an indicator prediction model can be trained using multiple training samples, thus improving the robustness of the indicator prediction model.

[0104] Furthermore, experiments were conducted on the trained index prediction model. The experimental results show that the index prediction model has high accuracy, and the predicted values ​​are mostly lower than the actual values, making the results more conservative, which is beneficial for the safe and stable operation of the power grid system. To further quantify the model's performance, MSE, R², and NDCG were calculated, yielding MSE of 3.9328, RMSE of 1.9831, R² of 0.9537, and NDCG of 0.9934. This indicates that the index prediction model has high accuracy and high ranking quality. Therefore, using this application, the top n fault scenarios with the highest instability can be quickly selected, greatly improving the efficiency of short-term voltage stability assessment of the power grid system.

[0105] In some embodiments of this application, the process of obtaining multiple training samples in step S31 is described in detail, and the steps are as follows:

[0106] S310. Perform time-domain simulation of the power grid system to obtain the simulation model.

[0107] Specifically, time-domain simulations of the power grid system can be performed to build simulation models.

[0108] S311. Identify multiple training faults.

[0109] Specifically, a list of key components of the power grid system can be determined, and fault types can be screened by combining historical fault data and operational experience.

[0110] For each type of fault, different variables such as fault location and fault duration can be set to generate multiple training faults.

[0111] Each training fault can be addressed by a variety of stability control strategies.

[0112] S310. For each training fault, determine the training measures matched to the training fault, and set the training fault in the simulation model to obtain the power grid state parameters before the corresponding training measures are executed; execute the training measures in the simulation model with the training fault set to obtain the power grid state parameters after the corresponding training measures are executed; calculate the transient training index corresponding to the training fault based on the power grid state parameters after the corresponding training measures are executed.

[0113] Specifically, each training fault can be simulated in the simulation model, a system of high-dimensional nonlinear differential-algebraic equations can be solved to obtain the power grid state parameters before the training measures are implemented, the corresponding training measures can be implemented in the simulation model simulating each training fault, and the power grid state parameters after the training measures are implemented can be collected.

[0114] Transient training indices can be calculated based on the power grid state parameters after the implementation of training measures.

[0115] Training samples can be formed by combining power grid state parameters, training measures, and transient training indicators corresponding to the same training fault.

[0116] As can be seen from the above technical solution, this embodiment provides an optional method for obtaining training samples. This method allows for the use of time-domain simulation, improving the reliability of the obtained training samples.

[0117] In some embodiments of this application, the process of calculating the transient training index corresponding to the training fault based on the power grid state parameters after the corresponding training measures are implemented in step S310 is described in detail, and the steps are as follows:

[0118] S3100. Combining the multi-binary table method, based on the power grid state parameters after the corresponding training measures are implemented, calculate the transient training index corresponding to the training fault.

[0119] Specifically, a binary table containing multiple transient voltage instability criteria can be obtained;

[0120] For example, a binary table can record (Vcr, Tcr), which indicates that the maximum time that the transient voltage of a node can be below the critical value Vcr is Tcr.

[0121] A binary table can contain n (Vcr, Tcr) entries.

[0122] The transient voltage stability index can be calculated using the weighted area method, and the specific formula is as follows:

[0123]

[0124]

[0125] In the formula, S m V represents the transient training metric of node m; N V(t) is the voltage reference value; V(t) is the voltage value at node m at time t; t i ω represents the moment when the voltage drops below Vcr,i, and ti' represents the moment when the voltage rises back above Vcr,i; i represents the weights for different intervals; n represents the number of transient voltage instability criteria recorded in the binary table.

[0126] The principle behind the above formula is that when the power grid system is stable at the critical voltage, S=0; when the power grid system is stable and the voltage is stable, S=1.

[0127] Furthermore, the most commonly used values ​​in actual production, (0.80pu, 10s) and (0.75pu, 1s), can be selected to calculate the transient training index.

[0128] For example, based on the grid state parameters after the corresponding training measures are implemented, combined with (0.80pu, 10s) and (0.75pu, 1s), the S of all nodes in the grid system can be calculated. i It can be obtained from the same training fault S i The smallest one is selected as the transient training index.

[0129] To further illustrate the effectiveness of the transient training index, 767 training samples were randomly selected during the experiment of this application, and the transient training index was calculated according to the above formula. After sorting the training samples in ascending order of index size, the transient training index and voltage stability of each training sample were plotted on a graph, as shown below. Figure 2 As shown in the figure, the bar chart represents the transient training index of each training sample, and the line chart represents the transient voltage stability of each sample. Transient voltage stability is a 0 / 1 variable, where 0 indicates transient voltage stability and 1 indicates transient voltage instability.

[0130] from Figure 2As can be seen, when the power grid system's transient voltage is stable, the vast majority of transient training indices are greater than 0; when the power grid system's transient voltage is unstable, all transient training indices are less than 0. However, it can be noted that the transient voltage stability indices of a small number of samples with stable transient voltage are also less than zero. This is because in some scenarios, the voltage drop in the power grid system is significant after the N-2 fault occurs, and the voltage recovers to normal values ​​slowly after stabilization control measures are implemented. This results in a negative calculated index even though the power grid system is ultimately transiently stable. However, this phenomenon does not adversely affect the application of this application: firstly, even if the indices for a small number of transient voltage stable scenarios are negative, their absolute values ​​are small, while the absolute values ​​of the indices for transient voltage instability scenarios are generally large; secondly, judging such scenarios on the verge of instability as unstable based on the transient voltage stability index increases the conservatism of the results, which is beneficial to the accuracy of the short-term voltage stability assessment conclusions of the entire system.

[0131] As can be seen from the above technical solution, this embodiment provides a method for calculating the transient training index corresponding to the training fault based on the power grid state parameters after the corresponding training measures are implemented. This method allows for the calculation of transient training indices using a binary table method, further improving the effectiveness of this application.

[0132] Next, we will combine Figure 3 The stability control strategy verification device provided in this application is described in detail. The stability control strategy verification device described below can be compared with the stability control strategy verification method described above.

[0133] See Figure 3 It can be observed that the stability control strategy verification device may include:

[0134] Module 10 is used to determine multiple fault scenarios covered by the stability control strategy to be verified;

[0135] The generation module 20 is used to obtain the first operating parameters of each fault scenario by combining the static power flow calculation method; based on the first operating parameters of the fault scenario and referring to the stability control strategy to be verified, generate adjustment measures, and combine the static power flow calculation method to obtain the second operating parameters of the fault scenario after adopting the adjustment measures;

[0136] The acquisition module 30 is used to acquire the trained index prediction model and use the index prediction model to predict the voltage steady-state index for measuring the degree of instability of the corresponding fault scenario based on the first operating parameter, the second operating parameter and the adjustment measures corresponding to each fault scenario.

[0137] The evaluation module 40 is used to sort the various fault scenarios by referring to the magnitude of the corresponding voltage steady-state index, select N fault scenarios with high instability for time-domain simulation, and evaluate the stability control strategy to be verified.

[0138] Furthermore, the generation module 20 may include:

[0139] The power grid static power flow model construction unit is used to construct a power grid static power flow model applicable to the stability control strategy to be verified.

[0140] The first power flow distribution calculation unit is used to simulate the fault scenario in the static power flow model of the power grid, and to calculate the first power flow distribution matched by the fault scenario through static power flow calculation.

[0141] Furthermore, the generation module 20 may also include:

[0142] The second power flow distribution calculation unit is used to apply the adjustment measures in the static power flow model of the power grid simulating the fault scenario to calculate the second power flow distribution.

[0143] Furthermore, the generation module 20 may also include:

[0144] A safety and stability control device setting selection unit is used to select a safety and stability control device setting that matches the first operating parameter of the fault scenario from the stability control strategy to be verified.

[0145] The adjustment measure generation unit is used to generate adjustment measures based on the set values ​​of the safety and stability control device.

[0146] Furthermore, the acquisition module 30 may include:

[0147] Initial prediction model building unit, used to build the initial prediction model;

[0148] The training sample acquisition unit is used to acquire multiple training samples. Each training sample has a unique corresponding training fault. Each training sample contains the training measures adopted for the corresponding training fault, the power grid state parameters before and after the implementation of the training measures, and the transient training index used to quantitatively evaluate the degree of power grid instability after the implementation of the training measures.

[0149] The initial prediction model training unit is used to train the initial prediction model using various training samples until a preset stopping condition is met. The final initial prediction model is the trained indicator prediction model.

[0150] Furthermore, the training sample acquisition unit may include:

[0151] The simulation model is built into a sub-unit for time-domain simulation of the power grid system, thus obtaining the simulation model.

[0152] The training fault determination subunit is used to determine multiple training faults;

[0153] The transient training index calculation subunit is used to determine the training measures matched to each training fault, set the training fault in the simulation model, and obtain the power grid state parameters before the corresponding training measures are executed; execute the training measures in the simulation model with the training fault set, and obtain the power grid state parameters after the corresponding training measures are executed; and calculate the transient training index corresponding to the training fault based on the power grid state parameters after the corresponding training measures are executed.

[0154] Furthermore, the transient training metric calculation subunit may include:

[0155] The binary table method combined component is used to combine the multi-binary table method to calculate the transient training index corresponding to the training fault based on the power grid state parameters after the corresponding training measures are implemented.

[0156] The stability control strategy verification device provided in this application embodiment can be applied to stability control strategy verification equipment, such as PC terminals, cloud platforms, servers, and server clusters. Optionally, Figure 4 The hardware structure block diagram of the stability control strategy verification device is shown. (Refer to...) Figure 4 The hardware structure of the stability control strategy verification device may include: at least one processor 1, at least one communication interface 2, at least one memory 3 and at least one communication bus 4;

[0157] In this embodiment of the application, the number of processor 1, communication interface 2, memory 3, and communication bus 4 is at least one, and processor 1, communication interface 2, and memory 3 communicate with each other through communication bus 4;

[0158] Processor 1 may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present invention.

[0159] Memory 3 may include high-speed RAM, and may also include non-volatile memory, such as at least one disk storage device;

[0160] The memory stores a program, which the processor can call. The program is used for:

[0161] Identify multiple fault scenarios covered by the stability control strategy to be verified;

[0162] For each fault scenario, the first operating parameters of the fault scenario are obtained by combining the static power flow calculation method; based on the first operating parameters of the fault scenario, and referring to the stability control strategy to be verified, adjustment measures are generated, and the second operating parameters of the fault scenario after adopting the adjustment measures are obtained by combining the static power flow calculation method.

[0163] Obtain the trained index prediction model, and use the index prediction model to predict the voltage steady-state index for measuring the degree of instability of the corresponding fault scenario based on the first operating parameter, the second operating parameter and the adjustment measures corresponding to each fault scenario.

[0164] Based on the magnitude of the corresponding steady-state voltage index, the various fault scenarios are sorted, and N fault scenarios with high instability are selected for time-domain simulation to evaluate the stability control strategy to be verified.

[0165] Optionally, the refined and extended functions of the program can be referred to the above description.

[0166] This application embodiment also provides a readable storage medium that can store a program suitable for execution by a processor, the program being used for:

[0167] Identify multiple fault scenarios covered by the stability control strategy to be verified;

[0168] For each fault scenario, the first operating parameters of the fault scenario are obtained by combining the static power flow calculation method; based on the first operating parameters of the fault scenario, and referring to the stability control strategy to be verified, adjustment measures are generated, and the second operating parameters of the fault scenario after adopting the adjustment measures are obtained by combining the static power flow calculation method.

[0169] Obtain the trained index prediction model, and use the index prediction model to predict the voltage steady-state index for measuring the degree of instability of the corresponding fault scenario based on the first operating parameter, the second operating parameter and the adjustment measures corresponding to each fault scenario.

[0170] Based on the magnitude of the corresponding steady-state voltage index, the various fault scenarios are sorted, and N fault scenarios with high instability are selected for time-domain simulation to evaluate the stability control strategy to be verified.

[0171] Optionally, the refined and extended functions of the program can be referred to the above description.

[0172] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0173] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0174] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. The various embodiments of this application can be combined with each other. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for verifying a stability control strategy, characterized in that, include: Identify multiple fault scenarios covered by the stability control strategy to be verified; For each fault scenario, the first operating parameters of the fault scenario are obtained by combining the static power flow calculation method; Based on the first operating parameters of the fault scenario, and referring to the stability control strategy to be verified, adjustment measures are generated, and combined with the static power flow calculation method, the second operating parameters of the fault scenario after adopting the adjustment measures are obtained; Obtain the trained index prediction model, and use the index prediction model to predict the voltage steady-state index for measuring the degree of instability of the corresponding fault scenario based on the first operating parameter, the second operating parameter and the adjustment measures corresponding to each fault scenario. Based on the magnitude of the corresponding steady-state voltage index, the various fault scenarios are sorted, and N fault scenarios with high instability are selected for time-domain simulation to evaluate the stability control strategy to be verified.

2. The stability control strategy verification method according to claim 1, characterized in that, The method of combining static power flow calculation to obtain the first operating parameters of the fault scenario includes: Construct a static power flow model of the power grid applicable to the stability control strategy to be verified; The fault scenario is simulated in the static power flow model of the power grid, and the first power flow distribution matched by the fault scenario is obtained by static power flow calculation.

3. The method for verifying the stabilization strategy according to claim 2, characterized in that, The method of combining static power flow calculation to obtain the second operating parameters of the fault scenario after applying the adjustment measures includes: The adjustment measures are applied in the static power flow model of the power grid simulating the fault scenario, and the second power flow distribution is obtained by static power flow calculation.

4. The method for verifying the stability control strategy according to claim 1, characterized in that, The first operating parameters based on the fault scenario, with reference to the stability control strategy to be verified, generate adjustment measures, including: Select a safety and stability control device setting value that matches the first operating parameter of the fault scenario from the stability control strategies to be verified; Adjustment measures are generated based on the set values ​​of the aforementioned safety and stability control device.

5. The stability control strategy verification method according to claim 1, characterized in that, The process of obtaining the trained metric prediction model includes: Construct an initial prediction model; Multiple training samples are obtained, each training sample has a unique corresponding training fault, and each training sample contains the training measures adopted for the corresponding training fault, the power grid state parameters before and after the implementation of the training measures, and the transient training index used to quantitatively evaluate the degree of power grid instability after the implementation of the training measures. The initial prediction model is trained using various training samples until a preset stopping condition is met. The final initial prediction model is the trained index prediction model.

6. The stability control strategy verification method according to claim 5, characterized in that, The acquisition of multiple training samples includes: A time-domain simulation of the power grid system is performed to obtain a simulation model; Multiple training faults were identified; For each training fault, a matching training measure is determined, and the training fault is set in the simulation model to obtain the power grid state parameters before the corresponding training measure is executed; the training measure is executed in the simulation model with the training fault set to obtain the power grid state parameters after the corresponding training measure is executed; based on the power grid state parameters after the corresponding training measure is executed, the transient training index corresponding to the training fault is calculated.

7. The stability control strategy verification method according to claim 6, characterized in that, The calculation of the transient training index corresponding to the training fault based on the power grid state parameters after the corresponding training measures are implemented includes: By combining the multi-binary table method, the transient training index corresponding to the training fault is calculated based on the power grid state parameters after the corresponding training measures are implemented.

8. A stability control strategy verification device, characterized in that, include: The determination module is used to determine multiple fault scenarios covered by the stability control strategy to be verified; The generation module is used to obtain the first operating parameters of each fault scenario by combining the static power flow calculation method. Based on the first operating parameters of the fault scenario, and referring to the stability control strategy to be verified, adjustment measures are generated, and combined with the static power flow calculation method, the second operating parameters of the fault scenario after adopting the adjustment measures are obtained; The acquisition module is used to acquire the trained index prediction model and use the index prediction model to predict the voltage steady-state index for measuring the degree of instability of the corresponding fault scenario based on the first operating parameter, the second operating parameter and the adjustment measures corresponding to each fault scenario. The evaluation module is used to sort the various fault scenarios by referring to the magnitude of the corresponding voltage steady-state index, select N fault scenarios with high instability for time-domain simulation, and evaluate the stability control strategy to be verified.

9. A stability control strategy verification device, characterized in that, Including memory and processor; The memory is used to store programs; The processor is used to execute the program to implement each step of the stability control strategy verification method as described in any one of claims 1-7.

10. A readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements each step of the stability control strategy verification method as described in any one of claims 1-7.

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