Transient voltage stability control method based on data mechanism fusion and terminal

Abstract

The invention provides a transient voltage stability control method based on data mechanism fusion and a terminal. The method comprises the following steps: constructing a voltage threshold data set; constructing and training a gating cycle unit network as a threshold enhancement model; monitoring whether a power current criterion is triggered or not in real time; and transient voltage stability and emergency control are evaluated in real time. Therefore, transient voltage evaluation and control can be accurately, quickly and interpretably realized, so that safe and efficient operation of an actual power grid is guaranteed.

Description

Technical Field

[0001] The present invention belongs to the application field of large power grid stability analysis and control, and more specifically, relates to a transient voltage stability control method and terminal with data mechanism fusion. Background Art

[0002] In recent years, large-scale power outages caused by voltage collapse have occurred frequently around the world. On the one hand, the rapid growth in electricity demand has increased the density of load centers, causing the power transmission capacity of the power system to approach its limit. On the other hand, with the increase in the proportion of renewable energy and dynamic loads, the controllability of the power system continues to decrease, and it frequently operates in the high-risk critical voltage stability area. Therefore, the problem of transient voltage stability of the system after disturbance has become increasingly prominent.

[0003] At present, the more mature transient voltage stability assessment methods include time domain simulation method, energy function method and PV plane. The above methods can clearly explain the instability phenomenon and have a high dependence on the model parameters of the system. However, due to the complexity of modern power systems, it is difficult to establish a detailed physical model for them. This makes it difficult to evaluate the transient voltage stability of the system online, and it is urgent to establish an accurate, fast and explainable real-time assessment and control method.

[0004] With the widespread deployment of phasor measurement units, a large number of model-free methods have emerged, such as the Lyapunov index method, machine learning method, etc. Although the above methods have good accuracy, the timeliness and interpretability of the evaluation are poor, which makes it difficult to promote and apply related research in actual power grids.

[0005] Therefore, it is urgent to establish an accurate, fast and interpretable data mechanism fusion transient voltage stability control technology solution for transient voltage assessment and control to ensure the safe and efficient operation of the actual power grid. Summary of the invention

[0006] In view of this, the present invention proposes a transient voltage stability control method and terminal with data mechanism fusion, aiming to solve the technical problems of defects in accuracy, timeliness or interpretability in the prior art. This method can realize real-time evaluation of transient voltage stability with only measurement, which is logically rigorous, convenient and fast, and is helpful to carry out safety and stability characteristics analysis of transient voltage in AC / DC hybrid power grid, and ensure the safe and efficient operation of actual power grid.

[0007] In a first aspect, the present application provides a transient voltage stability control method with data mechanism fusion, comprising:

[0008] Construct voltage threshold data set;

[0009] Build and train a gated recurrent unit network as a threshold enhancement model;

[0010] Real-time monitoring of whether the power and current criteria are triggered;

[0011] Real-time assessment of transient voltage stability and emergency control.

[0012] Furthermore, the stable samples and unstable samples correspond to input features respectively;

[0013] Select the voltage amplitude and phase angle of the monitoring node as input features;

[0014] The voltage amplitude and phase angle of the monitoring node before the fault occurs, at the time of fault occurrence, at the time of fault removal, at the start and end time of the first criterion triggering or at the start and end time of more than one criterion triggering are selected as input features.

[0015] Furthermore, when training a gated recurrent unit network as a threshold enhancement model,

[0016] Gated recurrent unit networks use reset gates and update gates to control the flow of information;

[0017] According to the missed detection rate, false positive rate, accuracy rate and average response time of the test model, the hyperparameters are adjusted repeatedly until the evaluation performance reaches the optimal level.

[0018] Furthermore, the real-time monitoring of whether the power current criterion is triggered includes:

[0019] Once a fault is detected in real time and removed, the active power P, apparent power S and current I are monitored to see whether the following criteria 1 and 2 are met:

[0020] I k+1 -I k >ε&S k+1 -S k >ε

[0021] I k+1 -I k >ε&P k+1 -P k >ε

[0022] The variable ε ranges from 0.00001 to 0.0002; k is the current moment, and k+1 is the next moment;

[0023] When the voltage continuous reduction process determined by the above criterion 1 or criterion 2 is greater than or equal to 0.05s, the trigger power current criterion is determined.

[0024] Furthermore, the real-time evaluation of transient voltage stability and emergency control includes:

[0025] After the power current criterion is triggered for the first time, the measured data is preprocessed to form input features and input into the trained gated recurrent unit network as a threshold enhancement model;

[0026] The trained gated recurrent unit network as a threshold enhancement model quickly predicts the voltage threshold based on the learned mapping relationship;

[0027] Compare the operating voltage value after the first judgment is completed with the predicted voltage threshold;

[0028] If the operating voltage value is less than the predicted voltage threshold, it is determined that the system is unstable; otherwise, it is determined that the system is not unstable.

[0029] Furthermore, the real-time evaluation of transient voltage and emergency control includes:

[0030] After each power and current criterion is triggered, the measured data is preprocessed to form input features and input into the trained gated recurrent unit network as a threshold enhancement model;

[0031] The trained gated recurrent unit network as a threshold enhancement model quickly predicts the voltage threshold based on the learned mapping relationship;

[0032] Compare the operating voltage value after each judgment with the predicted voltage threshold;

[0033] If the operating voltage value is less than the predicted voltage threshold, it is determined that the system is unstable; otherwise, it is determined that the system is not unstable.

[0034] Furthermore, once a fault is detected in real time and removed, the power current criterion is continuously monitored in real time to see if it is triggered within the set maximum time of 10s.

[0035] At least once, it is determined by the above criterion 1 or criterion 2 that the voltage drop process lasts longer than or equal to 0.05 s and the trigger power current criterion is determined.

[0036] Furthermore, it also includes:

[0037] Once the system is identified as unstable, emergency control measures are immediately implemented to reduce the risk of instability;

[0038] The emergency control measures include: load shedding and DC power regulation.

[0039] In a second aspect, the present application provides a terminal, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method described in the first aspect.

[0040] In a third aspect, the present application provides a computer storage medium storing computer executable instructions, wherein the computer executable instructions are used to execute the method described in the first aspect.

[0041] Additional aspects and advantages of the present invention will be set forth in part in the following description and in part will be obvious from the following description, or may be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0043] Figure 1 A schematic flow chart of a transient voltage stability control method with data mechanism fusion according to an embodiment of the present application;

[0044] Figure 2 A schematic flow chart of a transient voltage stability control method with data mechanism fusion according to another embodiment of the present application;

[0045] Figure 3 Schematic diagram of the CSEE-VC-88 system topology used in the embodiments of the present application;

[0046] Figure 4 for Figure 3 The label distribution diagram of the CSEE-VC-88 example shown;

[0047] Figure 5 A schematic diagram of the composition of a terminal of a transient voltage stability control method using data mechanism fusion according to an embodiment of the present application. DETAILED DESCRIPTION

[0048] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art. It should be noted that the embodiments of the present invention and the features in the embodiments can be combined with each other without conflict.

[0049] The transient voltage index designed based on the voltage collapse phenomenon can determine the system stability by simply comparing the index value calculated in real time with the fixed threshold set by human experience. However, it lacks a reasonable starting mechanism, and its accuracy is highly dependent on the selection of the prediction threshold.

[0050] Unlike other indicators, the power current criterion uses the increase in current and the decrease in power at the busbar after a fault as the starting mechanism. Once the mechanism is triggered, it means that the system is in an unstable area, and the stability is determined by combining a fixed threshold. However, the choice of a fixed threshold still affects the accuracy of the criterion to a certain extent, making it have great potential for improvement.

[0051] The technical solution provided in this application takes power current criterion as the dominant method and utilizes deep learning technology to enhance the voltage threshold selection process, thereby establishing a complementary relationship between data-driven and knowledge-driven, and forming a transient voltage assessment step that is accurate, timely, and interpretable.

[0052] The technical solution provided in this application also discloses an intelligent enhancement method for improving the voltage support strength index.

[0053] like Figure 1 As shown, the transient voltage stability control method of the data mechanism fusion embodiment of the present application includes the following steps:

[0054] S100: constructing a voltage threshold data set;

[0055] S200: Build and train a gated recurrent unit network as a threshold enhancement model;

[0056] S300: Real-time monitoring of power and current criteria;

[0057] S400: Real-time assessment of transient voltage stability and emergency control.

[0058] In this way, transient voltage assessment and control can be achieved accurately, quickly and explainably to ensure the safe and efficient operation of the actual power grid.

[0059] In some embodiments, step S100 constructs a voltage threshold data set, that is, Figure 2 When the sample set is shown, the data set consists of input features and labels. The selection principle of input features is as follows 1A to 1B, and the calculation process of labels is as follows 2A to 2E.

[0060] In some embodiments, the principles for selecting input features are as follows 1A to 1B:

[0061] 1A. Considering the measurability of features, the training cost of the model and the configuration cost of the phase measurement unit, too high a feature dimension will lead to poor practicality of the transient voltage assessment step. Therefore, the dimension of the feature cannot be greater than the feature dimension used in the criterion. The voltage amplitude and phase angle of the monitoring node are selected as input features;

[0062] 1B. Considering the response speed of the power and current criteria, the model needs to provide a prediction threshold before the first criterion is triggered. Therefore, the input features consist of the node voltage amplitude and phase angle before the fault occurs, at the time of fault occurrence, at the time of fault removal, and at the start and end of the first criterion triggering. The above sampling points respectively characterize the steady-state power flow level, the degree of fault impact, the degree of voltage recovery after the fault, and the degree of voltage drop after entering the voltage instability area for the first time.

[0063] In some embodiments, the tag calculation process is as follows 2A to 2E:

[0064] 2A. After the fault is removed, the power and current change relationship of the monitoring point is started to slide window;

[0065] 2B. When a certain monitoring point triggers the judgment, the line number and the operating voltage value at the end of the judgment are recorded;

[0066] 2C. When the time domain simulation (TDS) ends, if the voltage recovers to above 0.80 pu within 10 s, the system transient voltage is considered stable and the classification label is set to 1. Otherwise, it is considered unstable and the classification label is set to 0.

[0067] 2D,At the same time, the dimension of the operating voltage set in the simulation process is changed to a 3D array, and the dimensions are the number of samples, the line number, and the number of times the power and current criterion is triggered.

[0068] 2E), the minimum and maximum values ​​of the array are taken as threshold labels for stable samples and unstable samples respectively.

[0069] In some embodiments, Figure 2 As shown, when step S200 constructs and trains a gated recurrent unit network as a threshold enhancement model,

[0070] The gated recurrent unit (GRU) uses two gated units to control the flow of information: the reset gate and the update gate. The reset gate determines the influence of the previous hidden state on the current hidden state, and the update gate determines the influence of the current hidden state on the next hidden state. At time t, the input of the GRU is x t , the hidden state of the previous input is h t-1 .

[0071] As shown in equations (1) to (3), first, GRU calculates the reset gate r t and update gate z t , then r t and h t-1 Calculate candidate hidden states Finally, according to z t 、ht-1 and Calculate the current hidden state h t , the calculation formulas are as follows:

[0072] z t =σ(W z [h t-1 ,x t ]+b z ) (1)

[0073] r t =σ(W r [h t-1 ,x t ]+b r ) (2)

[0074]

[0075] Where: W r , W z and W h They are reset gate, update gate and current hidden state respectively. The corresponding weight matrix; b r 、b z and b h They are reset gate, update gate and current hidden state respectively. The corresponding bias matrix; σ(·) is the sigmoid activation function. Finally, the hidden state h t To transfer information between units.

[0076] When training a gated recurrent unit network as a threshold enhancement model, set hyperparameters such as learning rate, batch size, dropout, penalty factor, and number of network layers.

[0077] According to the above calculation formulas (1) to (3), the GRU is trained offline on the training set to establish the mapping relationship between the electrical quantity and the threshold. In this way, the trained gated recurrent unit network is used as a threshold enhancement model to learn the mapping relationship between the electrical quantity and the threshold. The dynamic voltage threshold data set generated in step S100 is divided into a training set and a test set.

[0078] Finally, according to the missing alarm rate (MAR), false alarm rate (FAR), accuracy (ACC) and average response time (ART) of the test model, the hyperparameters are repeatedly adjusted until the evaluation performance reaches the optimal level.

[0079] In some embodiments, when step 300 monitors the power current criterion in real time,

[0080] like Figure 4 As shown in Figure 2, according to the observability of the power grid, the monitoring scope can be divided into full-grid measurement or local measurement.

[0081] In online applications, once a fault occurs and is cleared, the active power P, apparent power S and current I at the monitoring point are monitored to see whether the following criteria 1 and 2 are met:

[0082] I k+1 -I k >ε&S k+1 -S k >ε (5)

[0083] I k+1 -I k >ε&P k+1 -P k >ε (6)

[0084] In the formula, the variable ε ranges from 0.00001 to 0.0002; k is the current moment, and k+1 is the next moment;

[0085] In some embodiments, the variable ε is a very small positive number, such as 0.0001, and its function is to avoid misjudgment caused by calculation errors.

[0086] When the voltage continuous reduction process determined by the above criterion 1 or criterion 2 is greater than or equal to 0.05s, it is determined that the power current criterion is triggered and the system is in the voltage continuous reduction process.

[0087] In this way, once a fault is detected in real time and removed, within the set maximum time of 10s (10s can have a maximum of 200 0.05s), it may be detected multiple times that the power current criterion is triggered and the system is in a process of continuous voltage reduction.

[0088] In the above, the voltage continuous reduction process determined by criterion 1 or criterion 2 should be greater than or equal to 0.05s, which can filter out interference points that may be caused by switch action or data acquisition errors.

[0089] Once the voltage continues to decrease and triggers the power current criterion, the process proceeds to step S400.

[0090] In some embodiments, Figure 2 As shown, step S400 evaluates transient voltage stability and emergency control in real time, including:

[0091] 3A. After the first triggering criterion, the measured data is preprocessed to form input features and input into the model. The model quickly predicts the voltage threshold based on the learned mapping relationship.

[0092] 3B. Compare the operating voltage value after each judgment with the predicted threshold. ei Less than the prediction threshold U r , the system is deemed to be unstable; otherwise, the system is deemed to be not unstable and the monitoring is continued until the set maximum time of 10s.

[0093] Figure 2 In, t si and t ei Respectively represent the start time and end time of the i-th criterion triggering; U ei U is the operating voltage of the monitoring point at the end of the judgment; r is the voltage threshold predicted by GRU, where i is a natural number greater than or equal to 1 and less than 300.

[0094] 3C. Once instability occurs, immediately implement emergency control measures such as load shedding and DC power regulation to reduce the risk of instability.

[0095] As mentioned above, in the transient voltage stability control method of data mechanism fusion in the embodiment of the present application, a transient voltage intelligent enhancement criterion that integrates power current criterion and GRU is provided. Compared with the traditional machine learning method, the method is a white box model that integrates physical mechanism and has better interpretability; compared with the existing mechanism discrimination method, the proposed method only requires real-time measurement of key sections to achieve accurate discrimination of transient voltage stability, and has good engineering application prospects.

[0096] In some embodiments, the stable samples and the unstable samples correspond to input features respectively;

[0097] Select the voltage amplitude and phase angle of the monitoring node as input features;

[0098] The voltage amplitude and phase angle of the monitoring node before the fault occurs, at the time of fault occurrence, at the time of fault removal, at the start and end time of the first criterion triggering or at the start and end time of more than one criterion triggering are selected as input features.

[0099] In this way, the voltage amplitude and phase angle of any monitoring node before a fault occurs, at the time of fault occurrence, at the time of fault removal, at the start and end of the first criterion triggering, or at the start and end of more criterion triggering form a voltage amplitude array and a phase angle array.

[0100] In this way, the minimum value and / or maximum value of the voltage amplitude array of the stable sample and the unstable sample can be taken as the threshold label respectively.

[0101] Specifically, a dynamic voltage threshold sample set may be generated using a fault set through time domain simulation, which will not be described in detail here.

[0102] In some embodiments, the real-time monitoring of whether the power current criterion is triggered includes:

[0103] Once a fault is detected in real time and removed, the active power P, apparent power S and current I are monitored to see whether the following criteria 1 and 2 are met:

[0104] I k+1 -I k >ε&S k+1 -S k >ε

[0105] I k+1 -I k >ε&P k+1 -P k >ε

[0106] The variable ε ranges from 0.00001 to 0.0002; k is the current moment, and k+1 is the next moment;

[0107] When the voltage continuous reduction process determined by the above criterion 1 or criterion 2 is greater than or equal to 0.05s, the trigger power current criterion is determined.

[0108] In this way, once a fault is detected in real time and removed, within the set maximum time of 10s (10s can have a maximum of 200 0.05s), it may be detected multiple times that the power current criterion is triggered and the system is in a process of continuous voltage reduction.

[0109] In some embodiments, Figure 2 As shown, the real-time evaluation of transient voltage stability and emergency control includes:

[0110] After the power current criterion is triggered for the first time, the measured data is preprocessed to form input features and input into the trained gated recurrent unit network as a threshold enhancement model;

[0111] The trained gated recurrent unit network as a threshold enhancement model quickly predicts the voltage threshold based on the learned mapping relationship;

[0112] Compare the operating voltage value after the first judgment is completed with the predicted voltage threshold;

[0113] If the operating voltage value is less than the predicted voltage threshold, it is determined that the system is unstable; otherwise, it is determined that the system is not unstable.

[0114] In some embodiments, Figure 2 As shown, the real-time evaluation of transient voltage stability and emergency control includes:

[0115] Each time (such as more times as mentioned above) the power current criterion is triggered, the measured data is preprocessed to form input features and input into the trained gated recurrent unit network as a threshold enhancement model;

[0116] The trained gated recurrent unit network as a threshold enhancement model quickly predicts the voltage threshold based on the learned mapping relationship;

[0117] Compare the operating voltage value after each judgment with the predicted voltage threshold;

[0118] If the operating voltage value is less than the predicted voltage threshold, it is determined that the system is unstable; otherwise, it is determined that the system is not unstable.

[0119] In this way, the voltage amplitude and phase angle of the monitoring node before the fault occurs, at the time of fault occurrence, at the time of fault removal, at the start and end times of the first criterion triggering, or at the start and end times of more criterion triggering are used as input features, and the trained voltage threshold enhancement model based on the penalty loss function quickly predicts the voltage threshold according to the learned mapping relationship, thereby realizing the evaluation of transient voltage and emergency control.

[0120] In some embodiments, once a fault is detected in real time and removed, the power current criterion is continuously monitored in real time to see if it is triggered within a set maximum time of 10 seconds.

[0121] At least once, it is determined by the above criterion 1 or criterion 2 that the voltage drop process lasts longer than or equal to 0.05 s and the trigger power current criterion is determined.

[0122] In this way, real-time evaluation of transient voltage and emergency control are achieved.

[0123] In some embodiments, it also includes:

[0124] Once the system is identified as unstable, emergency control measures are immediately implemented to reduce the risk of instability;

[0125] The emergency control measures include: load shedding and DC power regulation.

[0126] In this way, accurate evaluation of transient voltage and emergency control are achieved.

[0127] In some embodiments, it also includes:

[0128] Once the system is deemed to be unstable, emergency control measures will be implemented after a preset delay (no more than the aforementioned 0.05s and less than the aforementioned 10s) to reduce the risk of instability;

[0129] The emergency control measures include: load shedding and DC power regulation.

[0130] In this way, accurate evaluation of transient voltage and emergency control are achieved.

[0131] The following is combined with Figure 3 and Figure 4 , taking the CSEE-VC-88 node system as the test system, the transient voltage stability control method of the data mechanism fusion embodiment of the present application is further explained.

[0132] Specifically, for Figure 3 The CSEE-VC-88 node system shown in the figure sets various fault conditions to generate a data set related to transient voltage stability, generating a total of 11,826 samples, of which the number of stable samples and unstable samples are 7,609 and 4,217 respectively. 80% of the data set is divided into a training set, and the remaining 20% ​​is a test set.

[0133] The monitoring scope is divided into full network measurement and local measurement. Full network measurement represents 60 525KV lines, and local measurement represents 10 key lines. Figure 4 shown.

[0134] In order to demonstrate the enhancement effect, the original criterion, the data-driven method and the transient voltage stability control method fused with the data mechanism of the embodiment of the application are compared. Figure 4 As shown in the figure, in the original criterion, 0.75 pu is used as the power current criterion for the voltage threshold. In the data-driven method, GRU is directly used to classify transients. The three methods are tested on the test set, and the results are shown in Table 1. The ART of the data-driven method is calculated according to the end time of the first criterion of the unstable sample.

[0135] Table 1 Comparison of intelligent enhancement effects

[0136]

[0137]

[0138] As can be seen from Table 1, the conservative 0.75pu makes the evaluation performance of the original criterion at a low level. When the monitoring range is global measurement, MAR is as high as 24.09% and ACC is as low as 84.38%. This shows that in a large number of stable scenarios, the operating voltage value at the end of the criterion is lower than 0.75pu and then returns to normal. When the monitoring range is narrowed to local measurement, FAR drops to 14.51%. This is because the reduction of the monitoring range increases the threshold distribution of stable samples as a whole, reducing the number of misjudgments, but the evaluation performance of the original criterion still cannot meet the needs.

[0139] Compared with the original criterion, the evaluation performance of the data-driven method is at a higher level, but the class imbalance causes GRU to overfit the stable samples (class imbalance means that the number of stable samples is much larger than the number of unstable samples), which leads to MAR being much higher than FAR. However, the misjudgment of stable samples will lead to the incorrect triggering of control actions, while the missed judgment of unstable samples will seriously affect the stable operation of the system. Therefore, the misjudgment costs of the two types of samples are different, and the high MAR makes the data-driven method unable to meet the evaluation requirements.

[0140] Compared with the data-driven method, the MAR of the enhanced criterion is below 0.6% for the following reasons: 1) In the proposed method, GRU only predicts the voltage threshold and is not affected by class imbalance; 2) The unstable sample has a high tolerance for the threshold, so its misjudgment is less likely to occur. Compared with the original criterion, the FAR of the enhanced criterion is greatly reduced, and the ACC is maintained above 99%. In addition, the enhanced criterion is less affected by the monitoring range, and its evaluation performance is still at a high level even when the input information is only a local measurement. The above test results show that the enhancement effect is significant, verifying the effectiveness of the proposed method.

[0141] like Figure 5 As shown, the embodiment of the present application also provides a terminal to execute the method described above. Figure 5 FIG. 8 is a schematic diagram of a terminal 8 provided in some embodiments of the present application. Figure 5 As shown, the terminal 8 includes: a processor 800, a memory 801, a bus 802 and a communication interface 803, and the processor 800, the communication interface 803 and the memory 801 are connected via the bus 802; the memory 801 stores a computer program that can be run on the processor 800, and the processor 800 executes the method described in any of the aforementioned embodiments of the present application when running the computer program.

[0142] The memory 801 may include a high-speed random access memory (RAM), and may also include a non-volatile memory, such as at least one disk storage. The communication connection between the device network element and at least one other network element is realized through at least one communication interface 803 (which may be wired or wireless), and the Internet, wide area network, local area network, metropolitan area network, etc. may be used.

[0143] The bus 802 may be an ISA bus, a PCI bus, or an EISA bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. The memory 801 is used to store programs, and the processor 800 executes the programs after receiving execution instructions. The method disclosed in any implementation of the above embodiments of the present invention may be applied to the processor 800, or implemented by the processor 800.

[0144] The processor 800 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit in the processor 800 or the instruction in the form of software. The above processor 800 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present invention can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor. The steps of the method disclosed in the embodiment of the present invention can be directly embodied as a hardware decoding processor to execute, or the hardware and software modules in the decoding processor can be executed. The software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory 801, and the processor 800 reads the information in the memory 801 and completes the steps of the above method in combination with its hardware.

[0145] The terminal provided by the embodiment of the present invention and the method provided by the embodiment of the present invention are based on the same inventive concept and have the same beneficial effects as the method adopted, run or implemented by them.

[0146] An embodiment of the present application also provides a computer-readable storage medium corresponding to the method described in the aforementioned embodiment. The computer-readable storage medium is a CD on which a computer program (i.e., a program product) is stored. When the computer program is run by a processor, it will execute the method described in any of the aforementioned embodiments.

[0147] It should be noted that examples of the computer-readable storage medium may also include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other optical or magnetic storage media, which are not listed here one by one.

[0148] The computer-readable storage medium provided by the above-mentioned embodiments of the present application is based on the same inventive concept as the method of the embodiments of the present invention, and has the same beneficial effects as the method adopted, run or implemented by the application program stored therein.

[0149] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A transient voltage stability control method based on data mechanism fusion, characterized in that: include: Construct voltage threshold data set; Build and train a gated recurrent unit network as a threshold enhancement model; Real-time monitoring of whether the power and current criteria are triggered; Real-time assessment of transient voltage stability and emergency control.

2. The transient voltage stability control method with data mechanism fusion as claimed in claim 1, characterized in that: The stable samples and the unstable samples correspond to input features respectively; Select the voltage amplitude and phase angle of the monitoring node as input features; The voltage amplitude and phase angle of the monitoring node before the fault occurs, at the time of fault occurrence, at the time of fault removal, at the start and end time of the first criterion triggering or at the start and end time of more than one criterion triggering are selected as input features.

3. The transient voltage stability control method with data mechanism fusion as claimed in claim 1, characterized in that: When training a gated recurrent unit network as a threshold-enhanced model, Gated recurrent unit networks use reset gates and update gates to control the flow of information; According to the missed detection rate, false positive rate, accuracy rate and average response time of the test model, the hyperparameters are adjusted repeatedly until the evaluation performance reaches the optimal level.

4. The transient voltage stability control method with data mechanism fusion as claimed in claim 1, characterized in that: The real-time monitoring of whether the power current criterion is triggered includes: Once a fault is detected in real time and removed, the active power P, apparent power S and current I are monitored to see whether the following criteria 1 and 2 are met: I k+1 -I k >e&S k+1 -S k >e I k+1 -I k >e&P k+1 -P k >e The variable ε ranges from 0.00001 to 0.0002; k is the current moment, and k+1 is the next moment; When the voltage continuous reduction process determined by the above criterion 1 or criterion 2 is greater than or equal to 0.05s, the trigger power current criterion is determined.

5. The transient voltage stability control method with data mechanism fusion as claimed in claim 4, characterized in that: The real-time assessment of transient voltage stability and emergency control includes: After the power current criterion is triggered for the first time, the measured data is preprocessed to form input features and input into the trained gated recurrent unit network as a threshold enhancement model; The trained gated recurrent unit network as a threshold enhancement model quickly predicts the voltage threshold based on the learned mapping relationship; Compare the operating voltage value after the first judgment is completed with the predicted voltage threshold; If the operating voltage value is less than the predicted voltage threshold, it is determined that the system is unstable; otherwise, it is determined that the system is not unstable.

6. The transient voltage stability control method with data mechanism fusion as claimed in claim 4, characterized in that: The real-time transient voltage assessment and emergency control includes: After each power and current criterion is triggered, the measured data is preprocessed to form input features and input into the trained gated recurrent unit network as a threshold enhancement model; The trained gated recurrent unit network as a threshold enhancement model quickly predicts the voltage threshold based on the learned mapping relationship; Compare the operating voltage value after each judgment with the predicted voltage threshold; If the operating voltage value is less than the predicted voltage threshold, it is determined that the system is unstable; otherwise, it is determined that the system is not unstable.

7. The transient voltage stability control method based on data mechanism fusion as claimed in claim 4, characterized in that: Once a fault is detected in real time and removed, the power current criterion is continuously monitored in real time to see if it is triggered within the set maximum time of 10s. At least once, it is determined by the above criterion 1 or criterion 2 that the voltage drop process lasts longer than or equal to 0.05 s and the trigger power current criterion is determined.

8. The transient voltage stability control method with data mechanism fusion as claimed in claim 5 or 6, characterized in that: Also includes: Once the system is identified as unstable, emergency control measures are immediately implemented to reduce the risk of instability; The emergency control measures include: load shedding and DC power regulation.

9. A terminal, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method according to any one of claims 1 to 8.

10. A computer storage medium, characterized in that: Computer executable instructions are stored, and the computer executable instructions are used to execute the method according to any one of claims 1 to 8.

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