Power-current enhancement control method for transient voltage stabilization and terminal

Abstract

The invention provides a transient voltage stable power-current enhancement control method and a terminal. The method comprises the following steps: generating a dynamic voltage threshold sample set by using a fault set; the sample set comprises stable samples and unstable samples; the stable sample and the unstable sample respectively correspond to labels; constructing and training a voltage threshold enhancement model based on a penalty loss function, wherein the enhancement model selects a long-short term memory network; monitoring whether a power current criterion is triggered or not and whether the system is in a continuous voltage reduction process or not in real time; when it is determined that a power current criterion is triggered and the system is in a continuous voltage reduction process, transient voltage is evaluated and emergency control is performed in a time-adaptive manner. 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 power-current enhancement control method and a terminal for transient voltage stability. Background Art

[0002] In recent years, due to the rapid growth of load, the increase of renewable energy access, fluctuations in power demand and other factors, voltage safety problems in power grids have frequently occurred, mainly including voltage fluctuations, voltage imbalances and voltage imbalances. These problems have led to the emergence of transient voltage stability issues in the system after the disturbance, and an accurate and fast real-time assessment method is urgently needed to ensure the safe operation of the power grid.

[0003] However, the more mature transient voltage stability assessment methods all rely on the detailed parameters of the system and are not responsive methods. Therefore, a large number of model-free methods that do not rely on system parameters have emerged, and typical methods include the Lyapunov index method and machine learning method. Both of these methods have high accuracy, but have serious defects in timeliness or interpretability, making it difficult to apply related research to actual power grids.

[0004] In addition, model-free methods also include transient voltage indicators designed based on voltage collapse phenomena. Such indicators can determine system stability by simply comparing the indicator value with a fixed threshold set by manual experience. However, there is a lack of a reasonable starting mechanism, and the accuracy is highly dependent on the selection of the threshold.

[0005] Therefore, it is urgent to establish an accurate, fast and explainable power-current enhancement control technology solution for transient voltage stability 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 power-current enhancement control method and terminal for transient voltage stabilization, aiming to solve the technical problems of defects in accuracy, timeliness or interpretability in the prior art.

[0007] In a first aspect, the present application provides a power-current enhancement control method for transient voltage stabilization, comprising:

[0008] Generate a dynamic voltage threshold sample set using the fault set; the sample set includes stable samples and unstable samples; the stable samples and unstable samples correspond to labels respectively;

[0009] Constructing and training a voltage threshold enhancement model based on a penalty loss function, wherein the enhancement model selects a long short-term memory network;

[0010] Real-time monitoring of whether the power and current criteria are triggered and whether the system is in the process of continuous voltage reduction;

[0011] When it is determined that the power current criterion is triggered and the system is in the process of continuous voltage reduction, the transient voltage and emergency control are evaluated in a time-adaptive manner.

[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 the voltage threshold enhancement model based on the penalty loss function,

[0016] If the predicted value of the stable sample Greater than the true value The calculation formula for the loss value corresponding to the stable samples in each batch is as follows:

[0017]

[0018] If the predicted value of the unstable sample Less than the true value The calculation formula for the loss value corresponding to the unstable samples in each batch is as follows:

[0019]

[0020] If the predicted value of the unstable sample is neither less than the true value nor the predicted value of the stable sample is greater than the true value, the calculation formula for the loss value corresponding to the stable sample or unstable sample in each batch is as follows:

[0021]

[0022] Among them, α s and α u are the penalty factors for stable samples and unstable samples, respectively, and their value range is (1, +∞); N 1 、N 2 and N 3 Respectively represent the number of samples that meet the corresponding conditions in each batch, i is not less than 1 and not greater than N 1 、N 2 or N 3 A natural number.

[0023] Furthermore, the real-time monitoring of whether the power current criterion is triggered and whether the system is in a process of continuous voltage reduction includes:

[0024] 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:

[0025] I k+1 -I k >ε&S k+1 -S k >ε

[0026] I k+1 -I k >ε&P k+1 -P k >ε

[0027] The variable ε ranges from 0.00001 to 0.0002; k is the current moment, and k+1 is the next moment;

[0028] 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.

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

[0030] After the power and current criterion is triggered for the first time, the measured data is preprocessed to form input features and then input into the trained voltage threshold enhancement model based on penalty loss function;

[0031] The trained voltage threshold enhancement model based on penalty loss function quickly predicts the voltage threshold according to the learned mapping relationship;

[0032] Compare the operating voltage value after the first judgment is completed 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, the time-adaptive evaluation of transient voltage and emergency control includes:

[0035] After each power and current criterion is triggered, the measured data is preprocessed to form input features and input into the trained voltage threshold enhancement model based on penalty loss function;

[0036] The trained voltage threshold enhancement model based on penalty loss function quickly predicts the voltage threshold according to the learned mapping relationship;

[0037] Compare the operating voltage value after each judgment with the predicted voltage threshold;

[0038] 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.

[0039] Furthermore, once a fault is detected in real time and removed, the power current criterion is continuously monitored in real time within the set maximum time of 10s to see whether it is triggered and whether the system is in a process of continuous voltage reduction.

[0040] 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.

[0041] Furthermore, it also includes:

[0042] Once the system is identified as unstable, emergency control measures are immediately implemented to reduce the risk of instability;

[0043] The emergency control measures include: load shedding and DC power regulation.

[0044] 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.

[0045] 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.

[0046] 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

[0047] 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:

[0048] Figure 1 A schematic flow chart of a power-current enhancement control method for transient voltage stabilization according to an embodiment of the present application;

[0049] Figure 2 A schematic diagram of a dynamic threshold calculation process according to an embodiment of the present application;

[0050] Figure 3 A grid structure diagram of an actual power grid according to an embodiment of the present application;

[0051] Figure 4 is the label distribution of the actual power grid in the embodiment of the present application;

[0052] Figure 5 This is the process of distinguishing unstable samples in the embodiment of the present application;

[0053] Figure 6 Application of the embodiments of this application Figure 1 A schematic diagram of the composition of the terminal of the method shown. DETAILED DESCRIPTION

[0054] 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.

[0055] The transient voltage index designed based on the voltage collapse phenomenon can determine the system stability by simply comparing the index value with a fixed threshold set by human experience. However, it lacks a reasonable starting mechanism, and its accuracy is highly dependent on the selection of the threshold.

[0056] 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.

[0057] The technical solution provided by this application enhances the power current criterion, deeply integrates the power current criterion with deep learning technology, establishes a complementary relationship between data-driven and knowledge-driven, and thus forms an intelligent enhanced transient voltage criterion with both accuracy, timeliness and interpretability. The technical solution provided by this application also discloses an intelligent enhanced transient voltage stability criterion.

[0058] like Figure 1 As shown, the power-current enhancement control method for transient voltage stabilization of an embodiment of the present application includes the following steps:

[0059] S100: Generate a dynamic voltage threshold sample set using a fault set; the sample set includes stable samples and unstable samples; the stable samples and unstable samples correspond to labels respectively;

[0060] S200: constructing and training a voltage threshold enhancement model based on a penalty loss function, wherein the enhancement model selects a long short-term memory network;

[0061] S300: Real-time monitoring of whether the power current criterion is triggered and whether the system is in a process of continuous voltage reduction;

[0062] S400: When it is determined that the power current criterion is triggered and the system is in a process of continuous voltage reduction, transient voltage and emergency control are evaluated in a time-adaptive manner.

[0063] 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.

[0064] In some embodiments, Figure 2 As shown, the stable samples and unstable samples correspond to input features respectively;

[0065] Select the voltage amplitude and phase angle of the monitoring node as input features;

[0066] 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.

[0067] 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.

[0068] 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.

[0069] For the above, the generation of the dynamic voltage threshold sample set by using the fault set can refer to Table 2 and Table 3 described later, which will not be repeated here.

[0070] In some embodiments, when training a voltage threshold enhancement model based on a penalty loss function,

[0071] If the predicted value of the stable sample Greater than the true value The calculation formula for the loss value corresponding to the stable samples in each batch is as follows:

[0072]

[0073] If the predicted value of the unstable sample Less than the true value The calculation formula for the loss value corresponding to the unstable samples in each batch is as follows:

[0074]

[0075] If the predicted value of the unstable sample is neither less than the true value nor the predicted value of the stable sample is greater than the true value, the calculation formula for the loss value corresponding to the stable sample or unstable sample in each batch is as follows:

[0076]

[0077] Among them, α s and α u are the penalty factors for stable samples and unstable samples, respectively, and their value range is (1, +∞); N 1 、N 2 and N 3 Respectively represent the number of samples that meet the corresponding conditions in each batch, i is not less than 1 and not greater than N 1 、N 2 or N 3 A natural number.

[0078] In some embodiments, the real-time monitoring of whether the power current criterion is triggered and whether the system is in a process of continuous voltage reduction includes:

[0079] 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:

[0080] I k+1 -I k >ε&S k+1 -S k >ε

[0081] I k+1 -I k >ε&P k+1 -P k >ε

[0082] The variable ε ranges from 0.00001 to 0.0002; k is the current moment, and k+1 is the next moment;

[0083] 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.

[0084] 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.

[0085] In some embodiments, Figure 2 As shown, the time adaptively evaluates transient voltage and emergency control, including:

[0086] After the power and current criterion is triggered for the first time, the measured data is preprocessed to form input features and then input into the trained voltage threshold enhancement model based on penalty loss function;

[0087] The trained voltage threshold enhancement model based on penalty loss function quickly predicts the voltage threshold according to the learned mapping relationship;

[0088] Compare the operating voltage value after the first judgment is completed with the predicted voltage threshold;

[0089] 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.

[0090] In some embodiments, Figure 2 As shown, the time adaptively evaluates transient voltage and emergency control, including:

[0091] 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 voltage threshold enhancement model based on the penalty loss function;

[0092] The trained voltage threshold enhancement model based on penalty loss function quickly predicts the voltage threshold according to the learned mapping relationship;

[0093] Compare the operating voltage value after each judgment with the predicted voltage threshold;

[0094] 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.

[0095] 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 time-adaptive evaluation of transient voltage and emergency control.

[0096] In some embodiments, once a fault is detected in real time and removed, the power current criterion is continuously monitored in real time within a set maximum time of 10s to see whether it is triggered and whether the system is in a process of continuous voltage reduction. Accordingly,

[0097] 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.

[0098] In this way, time-adaptive evaluation of transient voltage and emergency control are achieved.

[0099] In some embodiments, it also includes:

[0100] Once the system is identified as unstable, emergency control measures are immediately implemented to reduce the risk of instability;

[0101] The emergency control measures include: load shedding and DC power regulation.

[0102] In this way, time-adaptive evaluation of transient voltage and emergency control are achieved.

[0103] In some embodiments, it also includes:

[0104] 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;

[0105] The emergency control measures include: load shedding and DC power regulation.

[0106] In this way, time-adaptive evaluation of transient voltage and emergency control are achieved.

[0107] In some embodiments, in step S100, the dynamic voltage threshold sample set is composed of input features and classification labels or threshold labels. Each sample includes input features, classification labels and threshold labels, that is, true values.

[0108] The input feature selection strategy includes the following steps S111 to S112:

[0109] S111: Considering the training cost of the enhanced model and the practicality of the transient voltage assessment method, the feature dimension should be less than or equal to the feature dimension used for the power current criterion.

[0110] Specifically, the voltage amplitude and phase angle of the monitoring node are selected as input features. This step is the type of physical quantity of the input feature.

[0111] S112: Considering the response speed of the power and current criterion (usually no more than the aforementioned 0.05s and less than the aforementioned 10s), the input feature consists of the voltage amplitude and phase angle of the monitoring node before the fault occurs, at the time of the fault occurrence, at the time of fault removal, and at the start and end time of the first criterion triggering. This step is the span of the input feature in the time domain.

[0112] The above sampling points can respectively characterize the steady-state power flow level, fault impact degree, voltage recovery degree after fault, and voltage drop degree after entering the voltage unstable area for the first time. This is conducive to constructing knowledge-driven transient voltage intelligent enhancement criteria.

[0113] The calculation steps of the classification label or the threshold label include the following S121 to S125:

[0114] S121: After the fault is cleared, the power and current change relationship of the monitoring point is started to slide. If the current increases and the power decreases at the monitoring point, the next step S122 is started;

[0115] S122: Record the monitoring point information of the triggering criterion, including the line number and the operating voltage value at the end of the criterion; and record the number of times the power and current criterion is triggered;

[0116] S123: When the system ends, if the voltage recovers to above 0.80pu within 10s, the transient voltage of the system is considered stable, and the classification label is set to 1, which is a stable sample. Otherwise, it is considered unstable, and the classification label is set to 0, which is an unstable sample.

[0117] S124: At the same time, the dimension of the operating voltage set in the simulation process is changed into a three-dimensional array, and the dimensions are the number of samples, the line number, and the number of times the power current criterion is triggered.

[0118] S125: The minimum and maximum values ​​of the arrays are taken as threshold labels for stable samples and unstable samples respectively.

[0119] Threshold labels are different from classification labels. The threshold label is the voltage threshold that needs to be predicted by the intelligent model. Therefore, the threshold label is used to evaluate the accuracy of the threshold predicted by the model. The classification label represents the voltage stability or instability of the power system after the transient state, which is used to verify whether the proposed method correctly identifies the stability of the system.

[0120] The principle of this step is as follows Figure 2 shown. Figure 2 In the example, sample_1, sample_2, sample_3, sample_n are used to indicate n samples respectively, label_1, label_2, label_3, label_n are used to indicate the labels corresponding to n samples respectively; line_1, line_2, line_3, line_k are used to indicate k lines respectively; trigger_1, trigger_2, trigger_3, trigger_m are used to indicate the mth power and current criteria trigger respectively. S / P / I / U indicate apparent power / active power / effective current / voltage amplitude respectively; U e (n,1),U e (n,2),U e (n,3),U e (n,m) respectively indicate the bus voltage amplitude corresponding to the mth power and current criterion being triggered.

[0121] In some embodiments, in step S200, when constructing and training a voltage threshold enhancement model based on a penalty loss function, a long short-term memory network (LSTM) is selected as the enhancement model.

[0122] The LSTM network model includes a forget gate, an input gate, and an output gate. During the training process, the input X t With the state memory unit S t-1 、Intermediate output h t-1 Together they determine the forgotten part of the state memory unit. The output value is calculated through a sigmoid function, and the output value is between 0 and 1. If the output is close to 0, it means that the previous state is forgotten; if it is close to 1, it means that the previous state is retained. The input gate is responsible for updating the cell state. It calculates the current input and the previous hidden state, and then calculates the new cell state through the sigmoid function and the tanh function. In this way, LSTM can selectively update the cell state.

[0123] X in the input gate t After the sigmoid and tanh functions are applied, the vector retained in the state memory unit is determined. The output gate determines the output of the current time step. Based on the current input and hidden state calculation, the final output is generated through the sigmoid and tanh functions. The intermediate output h t After updating S t With output O t Together, the cell state update formula is as follows:

[0124] f t =σ(W fx x t +W th h t-1 +b f ) (1)

[0125] i t =σ(W ix x t +W ih h t-1 +b i ) (2)

[0126] g t =φ(W gx x t +W gh h t-1 +b g ) (3)

[0127] o t =σ(W ox x t +Woh h t-1 +b o ) (4)

[0128] S t =g t ⊙i t +S t-1 ⊙f t (5)

[0129] h t =φ(S t )⊙o t (6)

[0130] In the formula, f t ,i t , g t , O t ,h t and S t are the states of the forget gate, input gate, input node, output gate, intermediate output and state unit respectively; W fx , W th , W ix , W ih , W gx , W gh , W ox and W oh are the matrix weights of the corresponding gate multiplied by the input and intermediate output; b f , b i , b g , b o are the bias terms of the corresponding gates respectively; ⊙ represents the bitwise multiplication of the elements in the vector; σ represents the change of the sigmoid function; φ represents the change of the tanh function.

[0131] During the training of the threshold enhancement model, the loss function calculation logic is as follows:

[0132] 1) If the predicted value of the stable sample Greater than the true value The calculation formula for the loss value corresponding to the stable samples in each batch is as follows:

[0133]

[0134] 2) If the predicted value of the unstable sample Less than the true value The calculation formula for the loss value corresponding to the unstable samples in each batch is as follows:

[0135]

[0136] 3) If the above 1) or 2) situations or conditions are not met, the calculation formula for the loss value corresponding to the stable sample or unstable sample in each batch is as follows:

[0137]

[0138] In the formula, α s and α u are the penalty factors for stable samples and unstable samples, respectively, and their value range is (1, +∞); N 1 、N 2 and N 3 Respectively represent the number of samples in each batch that meet the above 1) or 2) or 3) situations or conditions, i is not less than 1 and not greater than N 1 、N 2 or N 3 A natural number.

[0139] The dynamic voltage threshold sample set generated in step S100 is divided into a training set and a test set.

[0140] Set hyperparameters such as learning rate, batch size, random dropout, etc. According to the above calculation formula, train the voltage threshold enhancement model based on the penalty loss function on the training set to learn the mapping relationship between each electrical quantity in the input feature and the voltage threshold.

[0141] 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.

[0142] Specifically, when the missing alarm rate (MAR), false alarm rate (FAR), accuracy (ACC) and average response time (ART) are used as evaluation indicators, the confusion matrix is ​​first given, as shown in Table 1.

[0143] Table 1 Confusion matrix

[0144]

[0145] Among them, MAR reflects the probability that an unstable sample is missed as a stable sample, and its formula is:

[0146]

[0147] Among them, FAR reflects the probability that a stable sample is misjudged as an unstable sample, and its formula is:

[0148]

[0149] Among them, ACC reflects the overall evaluation accuracy of the sample, and its formula is:

[0150]

[0151] Among them, ART reflects the recognition time of unstable samples after fault removal, and its formula is:

[0152]

[0153] Where: T i Indicates the time when the i-th unstable sample is correctly identified, N u Indicates the number of unstable samples.

[0154] In this way, the voltage threshold enhancement model based on the penalty loss function is trained by using deep learning technology, and the trained voltage threshold enhancement model based on the penalty loss function is used to construct a data-driven transient voltage intelligent enhancement criterion in the following step S400.

[0155] In some embodiments, in step S300, real-time monitoring of whether the power current criterion is triggered and whether the system is in a process of continuous voltage reduction includes:

[0156] S310: According to the observability of the power grid, the monitoring scope can be divided into full-network measurement or local measurement. In online applications, the voltage amplitude and phase angle, power and current measurement data of the monitoring point are transmitted to the server or Figure 6 Terminal 8 is shown.

[0157] S320: Once a fault occurs and is cleared, the active power P, apparent power S and current I of the selected monitoring point are monitored to see whether the following power and current criteria 1 and 2 are met:

[0158] I k+1 -I k >ε&S k+1 -S k >ε (10)

[0159] I k+1 -I k >ε&P k+1 -P k >ε (11)

[0160] Wherein, the variable ε is a very small positive number ranging from 0.00001 to 0.0002, such as 0.0001, which is used to avoid misjudgment caused by calculation errors; k is the current moment, and k+1 is the next moment.

[0161] The power current criterion is triggered and the voltage is in a continuous reduction process. 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 voltage is in a continuous reduction process.

[0162] Generally, the voltage continuous reduction process determined by the above power current criterion 1 (formula (10)) or criterion 2 (formula (11)) should be greater than or equal to 0.05s, which is conducive to filtering out interference points that may be caused by switch action or data acquisition errors. Once the voltage continuous reduction process is triggered, step S400 is entered.

[0163] Step S400: Time-adaptive evaluation of transient voltage and emergency control

[0164] S410: After the power current criterion is triggered for the first time or more times, the measured data is preprocessed to form input features, and the input features are input into the trained voltage threshold enhancement model based on the penalty loss function.

[0165] S420: The trained voltage threshold enhancement model based on the penalty loss function quickly predicts the voltage threshold according to the learned mapping relationship;

[0166] S430: Compare the operating voltage value of the selected monitoring point 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; and return to step S410 to continue monitoring until the set maximum time of 10s.

[0167] S440: Once the system is determined to be unstable, emergency control measures are immediately implemented for the unstable fault to reduce the risk of instability, and the step S400 is terminated or the step S410 is returned to. The emergency control measures may include load shedding, DC power regulation, etc.

[0168] In the above, by comparing the operating voltage value after each judgment with the predicted voltage threshold, time-adaptive transient voltage assessment can be achieved.

[0169] Above, the power-current enhanced control method for transient voltage stability of the embodiment of the present application takes the voltage threshold as the enhanced parameter, and proposes a data set or sample set construction process for the dynamic threshold. According to the principle of the criterion, a penalty loss function is designed, and the dynamic prediction of the voltage threshold is realized in combination with LSTM. The enhanced criterion based on the dynamic threshold has both accuracy and evaluation speed. At the same time, the method uses real-time quantity measurement (the aforementioned measurement data) as the input feature, has good timeliness, and meets the response requirements of large-scale power grids. In addition, the method only requires real-time quantity measurement, has good applicability, and has engineering application value.

[0170] The following combination Figure 3 , Figure 4 and Figure 5 , specifically illustrate the power-current enhancement control method for transient voltage stability of the embodiment of the present application. Take a certain actual power grid as the test system, the power grid consists of a sending end system and a receiving end system, the details of which are as follows: a total of 10,575 nodes; about 550 synchronous power supplies; 912 RES sites mainly based on wind power and photovoltaic power; the total installed capacity of power sources is 289,580MW, the installed capacity of conventional units is 178,960MW, and the installed capacity of new energy is 110,620MW; 6 DC loops, of which the sending end and the receiving end are connected by 3 DC loops, 1 DC loop at the sending end is sent to other power grids, and 2 DC loops at the receiving end are fed in by other power grids, and its grid structure is as follows Figure 3 The monitoring scope includes global measurement and local measurement, where global measurement refers to the 525KV line at the receiving end, and local measurement refers to the 24 525KV AC interconnection lines of the 4 zones (zones 1R, 2R, 3R and 4R) at the receiving end.

[0171] The power grid has 14 typical modes, including 6 peak loads, 4 low peak loads and 4 valley loads, details of which are shown in Table 2. The fault set includes deterministic conventional fault sets and unexpected fault sets, details of which are shown in Table 3.

[0172] Table 2 Operation mode of actual power grid

[0173]

[0174] Table 3 Fault Set

[0175]

[0176]

[0177] A total of 22,986 samples were generated by scanning the fault set in all modes, including 19,439 stable samples and 3,547 unstable samples. The label distribution of the statistical sample set is as follows: Figure 4 As shown. Figure 4It can be seen that compared with the empirical threshold (0.75pu), the dynamic threshold has a wider distribution range, which can assist the judgment to adapt to a variety of scenarios and avoid misjudgment. In addition, for different monitoring ranges, there are differences in the threshold distribution of the two types of samples.

[0178] In order to demonstrate the effect of the voltage threshold enhancement criterion (shown as the enhancement criterion in Table 4), the original criterion and the data-driven method are used as comparison methods. Figure 4 As shown in Figure 4, the original criterion is a power current criterion with 0.75 pu as the voltage threshold, which is also the empirical threshold. The data-driven method directly uses LSTM to classify transients. The three methods are tested on the test set, and the results are shown in Table 4. The ART of the data-driven method is calculated according to the end time of the first criterion of the unstable sample.

[0179] Table 4 Comparison of enhancement effects

[0180]

[0181] In Table 4, the evaluation performance of the original criterion is greatly affected by the monitoring range, while the evaluation performance of the data-driven method is always at a high level. However, the category imbalance phenomenon in the actual power grid is more serious (the number of stable samples is 5.5 times the number of unstable samples), resulting in the MAR of the data-driven method as high as 4.0% and 5.31%. Compared with other methods, the ACC of the enhanced criterion is always above 98%, especially the MAR is around 1%. Especially for a power grid with a scale of 10,000 nodes, the enhanced criterion only needs to monitor 24 line information to quickly and accurately determine the stability of the system.

[0182] To further analyze the working principle of the enhanced threshold, taking global measurement as an example, the number of criterion triggers corresponding to the correct classification of all unstable samples is counted, such as Figure 5 As shown in the figure, the horizontal axis represents the number of times the criterion is triggered, and the vertical axis represents the number of correctly classified unstable samples. Figure 5 It can be seen that the enhanced criterion can identify 3400 unstable samples when it is triggered for the first time, while the original criterion can only identify 1165 unstable samples. As the number of triggers increases, the voltage collapse phenomenon of the unstable samples becomes more obvious, and the original criterion can identify more unstable samples. Different from the original criterion, the enhanced criterion can identify a large number of unstable samples when the number of triggers is small, so the ART is improved. In addition, until the end of the monitoring time, the original criterion still cannot identify the number of unstable samples that is much higher than the enhanced criterion. This shows that the enhanced threshold effectively improves the criterion's perception of unstable samples.

[0183] like Figure 6 As shown, the embodiment of the present application also provides a terminal to execute the method described above. Figure 6 FIG. 8 is a schematic diagram of a terminal 8 provided in some embodiments of the present application. Figure 6 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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 power-current enhancement control method for transient voltage stabilization, characterized in that: include: Generate a dynamic voltage threshold sample set using the fault set; the sample set includes stable samples and unstable samples; The stable samples and unstable samples correspond to labels respectively; Constructing and training a voltage threshold enhancement model based on a penalty loss function, wherein the enhancement model selects a long short-term memory network; Real-time monitoring of whether the power and current criteria are triggered and whether the system is in the process of continuous voltage reduction; When it is determined that the power current criterion is triggered and the system is in the process of continuous voltage reduction, the transient voltage and emergency control are evaluated in a time-adaptive manner.

2. The power-current enhancement control method for transient voltage stabilization according to 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 power-current enhancement control method for transient voltage stabilization according to claim 1, characterized in that: When training the voltage threshold enhancement model based on the penalty loss function, If the predicted value of the stable sample Greater than the true value The calculation formula for the loss value corresponding to the stable samples in each batch is as follows: If the predicted value of the unstable sample Less than the true value The calculation formula for the loss value corresponding to the unstable samples in each batch is as follows: If the predicted value of the unstable sample is neither less than the true value nor the predicted value of the stable sample is greater than the true value, the calculation formula for the loss value corresponding to the stable sample or unstable sample in each batch is as follows: Among them, α s and α u are the penalty factors for stable samples and unstable samples, respectively, and their value range is (1, +∞); N1, N2 and N3 represent the number of samples that meet the corresponding conditions in each batch, respectively, and i is a natural number not less than 1 and not greater than N1, N2 or N3.

4. The power-current enhancement control method for transient voltage stabilization according to claim 1, characterized in that: The real-time monitoring of whether the power current criterion is triggered and whether the system is in a process of continuous voltage reduction 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 power-current enhancement control method for transient voltage stabilization according to claim 4, characterized in that: The time adaptively evaluates transient voltage and emergency control, including: After the power and current criterion is triggered for the first time, the measured data is preprocessed to form input features and then input into the trained voltage threshold enhancement model based on penalty loss function; The trained voltage threshold enhancement model based on penalty loss function quickly predicts the voltage threshold according to 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 power-current enhancement control method for transient voltage stabilization according to claim 4, characterized in that: The time adaptively evaluates transient voltage and emergency control, including: After each power and current criterion is triggered, the measured data is preprocessed to form input features and input into the trained voltage threshold enhancement model based on penalty loss function; The trained voltage threshold enhancement model based on penalty loss function quickly predicts the voltage threshold according to 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 power-current enhancement control method for transient voltage stabilization according to claim 4, characterized in that: Once a fault is detected in real time and removed, the system continuously monitors in real time whether the power current criterion is triggered and whether the system is in a process of continuous voltage reduction 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 power-current enhancement control method for transient voltage stabilization according to 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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