Voltage disturbance detection method and device, readable storage medium and electronic equipment

Abstract

The invention provides a voltage disturbance detection method and device, a readable storage medium and electronic equipment, and the method comprises the steps: obtaining a pre-adjustment strategy of a first time period, the first time period being a preorder time period of a real-time time period, the pre-adjustment strategy being a strategy for carrying out pre-adjustment operation on the preorder time period in the first time period, and being used for reducing the degree of voltage disturbance in the real-time time period; first voltage information of a second time period is obtained, and the second time period comprises a real-time time period which is a sequence connection time period of the first time period; determining a first identification result based on the first voltage information and a pre-trained disturbance identification model, wherein the first identification result represents a disturbance occurrence condition identified in the second time period; and determining a disturbance coping method based on the first identification result and the pre-adjustment strategy, the disturbance coping method being used for adjusting the pre-adjustment strategy so as to keep the voltage stable. Therefore, through combination of prediction and real-time monitoring and application of the disturbance identification model, the detection efficiency and accuracy can be improved, and the response speed can be increased.

Description

Technical Field

[0001] The present invention relates to the field of voltage detection, and in particular to a voltage disturbance detection method, device, readable storage medium and electronic equipment. Background Art

[0002] With the rapid development of precision manufacturing, the requirements for power quality are getting higher and higher. Power quality issues, especially voltage sags, have a significant impact on the production process and equipment operation of these industries. Voltage sag refers to the phenomenon that the power supply voltage suddenly drops in a short period of time and then recovers quickly. This voltage fluctuation may cause unstable operation or even damage of precision equipment, thus affecting production efficiency and product quality. However, there are still problems such as low detection efficiency, low accuracy, and untimely response of voltage disturbances. Summary of the invention

[0003] The embodiments of the present application provide a voltage disturbance detection method, device, readable storage medium and electronic device, which can improve detection efficiency and accuracy and accelerate response speed.

[0004] In a first aspect, an embodiment of the present application provides a voltage disturbance detection method, which is applied to an electronic device, and the method includes:

[0005] Acquire a pre-regulation strategy for a first time period, wherein the first time period is a preceding time period of a real-time time period, and the pre-regulation strategy is a strategy for performing a pre-regulation operation on a preceding time period in the first time period, so as to reduce the degree of voltage disturbance in the real-time time period;

[0006] Acquire first voltage information of a second time period, where the second time period includes the real-time time period and is the sequential time period of the first time period;

[0007] Determine a first recognition result based on the first voltage information and a pre-trained disturbance recognition model, wherein the first recognition result represents the occurrence of a disturbance recognized in the second time period;

[0008] A disturbance coping method is determined based on the first identification result and the pre-adjustment strategy, and the disturbance coping method is used to adjust the pre-adjustment strategy so that the voltage remains stable.

[0009] In a second aspect, an embodiment of the present application provides a voltage disturbance detection device, which is applied to an electronic device, and the device includes:

[0010] A first acquisition module is used to acquire a pre-regulation strategy for a first time period, wherein the first time period is a pre-sequence time period of a real-time time period, and the pre-regulation strategy is a strategy for performing a pre-regulation operation on a pre-sequence time period in the first time period, so as to reduce the degree of voltage disturbance in the real-time time period;

[0011] A second acquisition module is used to acquire the first voltage information of a second time period, where the second time period includes the real-time time period and is the sequential time period of the first time period;

[0012] a first determination module, configured to determine a first recognition result based on the first voltage information and a pre-trained disturbance recognition model, wherein the first recognition result represents the occurrence of a disturbance recognized in the second time period;

[0013] The second determination module is used to determine a disturbance response method based on the first identification result and the pre-adjustment strategy, and the disturbance response method is used to adjust the pre-adjustment strategy to keep the voltage stable.

[0014] In a third aspect, an embodiment of the present application provides a computer-readable storage medium on which a voltage disturbance detection program is stored. The voltage disturbance detection program includes program instructions. When the processor of the electronic device executes the above-mentioned execution instructions, the processor executes part or all of the steps described in the first aspect.

[0015] In a fourth aspect, an embodiment of the present application provides an electronic device, comprising a processor, a memory, a communication interface, and one or more programs. The memory stores the one or more programs and is configured to be executed by the processor. When the processor executes the one or more programs stored in the memory, the processor executes instructions of some or all of the steps described in the first aspect of the embodiment of the present application.

[0016] In a fifth aspect, an embodiment of the present application provides a computer program product, wherein the computer program product includes a computer-readable storage medium storing a voltage disturbance detection program, and the voltage disturbance detection program is operable to cause a computer to execute some or all of the steps described in the first aspect of the embodiment of the present application. The computer program product may be a software installation package.

[0017] By implementing the embodiment of the present application, a pre-regulation strategy for a first time period is obtained, the first time period is a pre-sequence time period of a real-time time period, and the pre-regulation strategy is a strategy for pre-regulating a sequence time period in the first time period, which is used to reduce the degree of voltage disturbance in the real-time time period; the first voltage information of a second time period is obtained, the second time period includes the real-time time period, and is the sequence time period of the first time period; a first recognition result is determined based on the first voltage information and a pre-trained disturbance recognition model, and the first recognition result characterizes the occurrence of disturbances recognized in the second time period; a disturbance response method is determined based on the first recognition result and the pre-regulation strategy, and the disturbance response method is used to adjust the pre-regulation strategy to keep the voltage stable. In this way, by combining prediction and real-time monitoring and using a disturbance recognition model, the detection efficiency and accuracy can be improved, and the response speed can be accelerated. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the background technology, the drawings required for use in the embodiments of the present invention or the background technology will be described below.

[0019] Figure 1 It is a schematic diagram of the architecture of a voltage disturbance detection system provided in an embodiment of the present application;

[0020] Figure 2 is a method flow chart of a voltage disturbance detection method provided by an embodiment of the present application;

[0021] Figure 3 is a model architecture diagram of a disturbance identification model provided in an embodiment of the present application;

[0022] Figure 4 It is a structural schematic diagram of a voltage disturbance detection device provided in an embodiment of the present application;

[0023] Figure 5 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0024] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0025] The terms "first", "second", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or electronic device that includes a series of steps or units is not limited to the listed steps or units, but in an optional example also includes steps or units that are not listed, or in an optional example also includes other steps or units inherent to these processes, methods, products, or electronic devices.

[0026] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0027] The voltage disturbance detection method, device, readable storage medium and electronic device provided in the embodiments of the present application can be applied to Figure 1 In the voltage disturbance detection system shown, refer to Figure 1 , Figure 1 1 is a schematic diagram of the architecture of a voltage disturbance detection system provided in an embodiment of the present application. The voltage disturbance detection system 100 includes an electronic device 110 and a data acquisition device 120 .

[0028] Among them, the data acquisition device 120 can communicate with the electronic device 110 through the network. The data acquisition device 120 refers to a device for acquiring voltage data, which can be a recording device, a sensor, etc. The electronic device 110 refers to a remote computer for processing a large number of computing tasks and storing data, which can be a processor or a server, etc., or an ordinary computer. In this solution, the disturbance identification model and the disturbance prediction model are deployed on the electronic device 110 to perform disturbance identification and disturbance prediction on the data acquired by the data acquisition device 120. The electronic device 110 can also be used to collect data during the use of the model, so as to facilitate the subsequent optimization of the disturbance identification model and the disturbance prediction model.

[0029] Based on this, the present application provides a voltage disturbance detection method, device, readable storage medium and electronic device, and the present application is described in detail below in conjunction with the accompanying drawings.

[0030] See also Figure 2 , Figure 2 is a method flow chart of a voltage disturbance detection method provided in an embodiment of the present application, the method is applied to electronic equipment, such as Figure 2 As shown, the method comprises the following steps:

[0031] S210, obtaining a pre-regulation strategy for a first time period, where the first time period is a preceding time period of a real-time time period, and the pre-regulation strategy is a strategy for performing a pre-regulation operation on a preceding time period in the first time period, so as to reduce the degree of voltage disturbance in the real-time time period.

[0032] Among them, the first time period is the preceding time period of the real-time time period, and a disturbance prediction is performed on the next sequential time period in the first time period to obtain a disturbance prediction result. When time flows from the first time period to the next sequential time period, that is, when it is in the next sequential time period, the time period is the time period. When it is in the real-time time period, the previous time period has performed a disturbance prediction on the current time period to obtain a disturbance prediction result, and the pre-adjustment strategy has been determined based on the disturbance prediction result.

[0033] The first period can be any length of time, which is understood as a detection period.

[0034] In a possible embodiment, before the step of obtaining the pre-regulation strategy for the first time period, the method further includes: obtaining a disturbance prediction result for the first time period, the disturbance prediction result representing disturbance occurrence information for a subsequent time period predicted during the first time period; determining the pre-regulation strategy based on the disturbance prediction result, and performing a pre-regulation operation based on the pre-regulation strategy.

[0035] Among them, the disturbance prediction result is the disturbance occurrence information of the second time period, i.e., the real-time time period, predicted during the first time period. The disturbance prediction result can be obtained according to the prediction model, and specifically, the prediction model can be a trained neural network model. Then, the pre-regulation strategy is determined according to the obtained disturbance prediction result. The pre-regulation strategy is used to perform pre-regulation operations so that the disturbance can be overcome and responded to in time when the disturbance occurs in the second time period. Specifically, the disturbance prediction result can include but is not limited to the predicted disturbance duration, the predicted disturbance degree, and the disturbance factors, etc. The disturbance factors are factors that may cause voltage disturbances, such as bad weather, equipment failure, load increase or decrease, etc.

[0036] It can be seen that in this embodiment, disturbance prediction is performed in the time period before each current real-time time period, and a pre-adjustment strategy is determined, so that a rapid response can be made according to the pre-adjustment strategy before a disturbance may occur, thereby reducing the possibility of disturbance occurrence.

[0037] In a possible embodiment, obtaining the disturbance prediction result of the first time period includes: obtaining second voltage information and influencing factor information of the first time period, wherein the influencing factor information is a possible factor affecting the voltage; determining a second recognition result based on the second voltage information and the pre-trained disturbance recognition model, wherein the second recognition result characterizes the occurrence of the disturbance recognized in the first time period; and determining the disturbance prediction result based on the second recognition result, the influencing factor information and the pre-trained disturbance prediction model.

[0038] The second voltage information is the voltage information obtained during the first period, and the second voltage information may be a voltage curve, specifically, a time-voltage curve, or a time-voltage curve diagram, etc., which is not limited here. The influencing factor information is the information that affects the voltage disturbance obtained when the second voltage information is obtained, and may include but is not limited to weather conditions, equipment failure conditions, and load increase or decrease conditions.

[0039] Among them, the pre-trained disturbance recognition model can be a recognition model based on a convolutional neural network, specifically a model built based on a VGG neural network, which is used to perform a recognition task based on voltage information to obtain a recognition result that characterizes whether a voltage disturbance occurs. In this embodiment, the second voltage information is recognized to obtain a second recognition result, and the second recognition result is the occurrence of a disturbance recognized in the first time period. The pre-trained disturbance prediction model can be a prediction model based on a neural network, a random forest model, and other models, and specifically can include but are not limited to the following types: convolutional neural network (CNN), recurrent neural network (RNN), long short-term memory network (LSTM), residual network (ResNet), etc.

[0040] Specifically, the training method of the pre-trained disturbance prediction model may include the following steps: obtaining multiple historical voltage information and historical influencing factor information with a time series relationship, each of the historical voltage information corresponds to one historical influencing factor information, the time series relationship includes time periods with a chronological relationship, and the historical influencing factor information includes historical weather information and / or historical equipment status information and / or historical load level information; determining multiple training identification results corresponding to each historical voltage information based on the multiple historical voltage information and the pre-trained disturbance identification model; performing horizontal analysis on the historical influencing factor information to determine multiple historical influencing factor levels, the multiple influencing factor levels including a weather level corresponding to the weather information, an equipment status level corresponding to the equipment status information, and a load level information corresponding to the load level information; inputting the disturbance prediction model to be trained based on the historical influencing factor levels and the multiple training identification results for multiple trainings to obtain multiple disturbance prediction results; and obtaining the pre-trained disturbance prediction model based on the disturbance prediction results.

[0041] Among them, the above-mentioned historical voltage information and historical influencing factor information are information included in the training set, and the multiple historical voltage information are in time sequence. The historical influencing factor information corresponds to the historical voltage information and is the historical influencing factor corresponding to the historical voltage information in each period. Each historical influencing factor information includes one or more of historical weather information, historical equipment status information, and historical load level information. The pre-trained disturbance recognition model is not repeated here, and the training recognition result is obtained based on the disturbance recognition model and the historical voltage information. The historical equipment status information can be the fault state of the electrical equipment or the equipment operation state of the electrical equipment, and the historical load level information can be the load information of the electrical load. The historical influencing factors are graded. Specifically, for example, the historical weather information is divided according to the weather factors that may affect the voltage in the specific weather, and is divided into multiple levels. The greater the possible impact, the higher the level. For example, the higher the level, the worse the weather factors that may affect the voltage are; similarly, the historical equipment status information and the historical load level information are also divided according to the possible impact on the voltage; then the above-mentioned levels are divided accordingly to obtain multiple historical influencing factor levels. Then the historical image factor level and multiple training recognition results are input into the disturbance prediction model with training for repeated iterative training. Each input obtains the disturbance prediction result, which includes the disturbance occurrence time and the disturbance degree. Then, based on the disturbance prediction result and the subsequent actual result, the model accuracy analysis is performed, that is, the subsequent historical voltage information and the prediction result are analyzed for accuracy, and the trained disturbance prediction model is finally obtained based on the accuracy analysis. The specific method of the above accuracy analysis can be a variety of methods such as mean square error, root mean square error, mean absolute error, determination coefficient, etc., which are not limited here.

[0042] For details, please refer to Figure 3 , Figure 3 This is a model architecture diagram of a disturbance recognition model provided in an embodiment of the present application. The pre-trained disturbance recognition model may be a RepVGG model. The architecture of the disturbance recognition model may include a convolutional layer, a pooling layer, a fully connected layer, and an input and output. Please refer to Figure 3, the specific process of determining the recognition result according to the pre-trained disturbance recognition model may include: inputting voltage information, specifically, the input voltage disturbance waveform; then inputting the voltage information into the convolution layer, and the convolution layer performs convolution calculation on the input voltage information, specifically, the convolution layer performs convolution calculation on the voltage information based on the convolution kernel, and then stacks the convolution layers, each convolution layer follows the activation function, wherein the number of convolution layers included in the convolution layer includes multiple layers, and the specific number of layers is not limited here; then the calculation result obtained by the convolution layer is input to the pooling layer, and the maximum pooling operation is performed; finally, the overfitting risk is reduced in the fully connected layer, and the Dropout function can be used to reduce the overfitting risk; finally, the disturbance recognition result is output, and the disturbance time identification result includes the disturbance degree and the disturbance time.

[0043] It can be seen that in this embodiment, disturbance prediction is performed based on the pre-trained disturbance prediction model and the time period before the next time period to obtain the possible disturbance in the next time period. It can be known that the disturbance may occur before the disturbance in the next stage occurs, so that the response to the disturbance in the next time period is more timely.

[0044] In a possible embodiment, the influencing factor information includes weather information and / or equipment status information and / or load level information, the disturbance prediction result includes predicted disturbance duration, predicted disturbance degree and a first disturbance factor, the first disturbance factor characterizes the actual influencing factor, the first disturbance factor is determined based on the influencing factor information, the disturbance degree characterizes the voltage variation amplitude, and the first disturbance factor includes weather factors and / or equipment status factors and / or load level factors; the pre-adjustment strategy determined based on the disturbance prediction result includes: determining the pre-adjustment time according to the predicted disturbance duration; and, determining the pre-adjustment voltage according to the predicted disturbance degree; determining a coordination scheme according to the first disturbance factor, the coordination scheme including a first coordination scheme for the weather factor and / or a second coordination scheme for the equipment status factor and / or a third coordination scheme for the load level factor; the pre-adjustment strategy is determined according to the pre-adjustment time, the pre-adjustment voltage, the first coordination scheme and / or the second coordination scheme for the equipment status factor and / or the third coordination scheme for the load level factor.

[0045] Among them, the influencing factor information is the information that affects the voltage disturbance obtained at the same time when the second voltage information is obtained, which may include but is not limited to weather conditions, equipment failure conditions, load increase or decrease conditions; specifically corresponding to weather information, equipment status information, and load level information. The first disturbance factor is the factor that actually affects the voltage disturbance obtained based on the disturbance prediction model and the influencing factor information, and can be one or more of the influencing factors corresponding to the above-mentioned influencing factor information. The predicted disturbance duration is the predicted duration and start and end time of the disturbance, and the predicted disturbance degree is the predicted voltage fluctuation degree, which can specifically be the voltage change amount.

[0046] Specifically, first analyze the predicted disturbance duration, and set a suitable pre-adjustment time window according to the disturbance duration to ensure that there is enough time for adjustment before the disturbance occurs. Evaluate the predicted disturbance degree, and calculate the required pre-adjustment voltage value according to the disturbance degree to offset or reduce the impact of the disturbance on the system. Analyze weather factors and formulate a first coordination plan for weather; analyze equipment status factors and formulate a second coordination plan for equipment status; analyze load level factors and formulate a third coordination plan for load level. Comprehensively consider the pre-adjustment time, pre-adjustment voltage and the above coordination plans. Formulate a comprehensive pre-adjustment strategy that should take into account all relevant factors to ensure that the system can operate stably when a disturbance occurs. The first coordination plan includes taking countermeasures and weather plans to improve the impact of the environment, and the specific plan is not limited here. The second coordination plan includes rapid repair or elimination of equipment to ensure that the line will not suffer a larger failure due to equipment failure or other situations, and the specific plan is not limited here. The third coordination plan includes coordinating the load or coordinating the power generation to ensure the stability of the power in the line, and the specific plan is not limited here. Through these specific coordination schemes, different weather conditions, equipment states and load levels can be better coped with, thereby improving the reliability and efficiency of the entire system. Then, the pre-regulation strategy is determined according to the pre-regulation time and the pre-regulation voltage, or the pre-regulation strategy is determined according to the pre-regulation time, the pre-regulation voltage and any of the first coordination scheme, the second coordination scheme and the third coordination scheme.

[0047] It can be seen that in this embodiment, it is proposed to determine the pre-regulation strategy according to different influencing factors and the predicted possible disturbance time and disturbance degree, so that the response to the voltage disturbance is more timely and effective.

[0048] In a possible embodiment, the training process of the pre-trained disturbance recognition model includes the following steps: obtaining multiple training voltage information; inputting the multiple training voltage information into the disturbance recognition model to be trained, the disturbance recognition model to be trained includes a multi-branch convolution layer, a first pooling layer and the first fully connected layer, the multi-branch convolution layer includes multiple branch convolution layers of different types; performing multi-branch convolution operations on the multiple different types of branch convolution layers to obtain multiple first convolution output results; performing residual fusion based on the multiple first convolution output results to obtain a second convolution output result; the first pooling layer performs a pooling operation based on the first output result to obtain a pooling output result; the first fully connected layer integrates based on the pooling result to obtain multiple training recognition results; performing loss analysis based on the training recognition result to obtain a loss result; performing repeated training multiple times based on the loss result and performing branch fusion to obtain the pre-trained disturbance recognition model, the pre-trained disturbance recognition model includes a reconstructed single-branch convolution layer, a second pooling layer and a second fully connected layer.

[0049] Among them, multiple training voltage information is used as training data for the training model. Specifically, the training voltage information can be a time-voltage curve of the voltage. The architecture of the disturbance recognition model to be trained includes a multi-branch convolution layer, a first pooling layer, and a first fully connected layer. The difference between the first pooling layer and the first fully connected layer and the second pooling layer and the second fully connected layer included in the trained disturbance recognition model is the difference before and after training, and there may be differences in specific parameters. The multi-branch convolution layer includes constructing convolution branches containing convolution kernels of different sizes, such as 1×1 branches, 3×3 branches, and identity mapping branches. In the training stage, these branches work independently, and each branch has its own convolution kernel and parameters. The above-mentioned first convolution output result includes the convolution result of at least one branch. Then, residual fusion is performed based on the first convolution output result to obtain a second convolution output result, and the second convolution output result is passed through the first pooling layer to reduce the feature size and reduce the computational complexity, and obtain the pooled output result. Then, the pooled training result passes through the fully connected layer to reduce the dimension and reduce the risk of overfitting, and obtain the final output training recognition result. Then, based on the training recognition results, repeated iterative training is performed to obtain a pre-trained perturbation recognition model. The pre-trained perturbation recognition model re-parameterizes and fuses the multi-branch convolutional layer to obtain a reconstructed single-branch convolutional layer.

[0050] Specifically, during the training process, the multi-branch convolution layer includes a 1×1 branch, a 3×3 branch, and an identity mapping branch, where a 1x1 convolution is equivalent to a special 3x3 convolution (with many 0s in the convolution kernel), and an identity mapping is a special 1x1 convolution (with the unit matrix as the convolution kernel). During the training process, the perturbation recognition model also has a residual network structure, so that the gradient vanishing and gradient explosion in the deep neural network can be effectively solved. Specifically, the residual network performs residual fusion on the features of the multi-branch convolution layer and the input convolution layer. The features of the input convolution layer can be the features of the first voltage information, the features of the first voltage information, etc. The calculation formula of the residual network is: output = Conv main (x)+Conv residual (x)+x, where Conv main (x) is the branch other than the identity mapping, Conv residual (x) is the identity mapping branch, and x is the feature of the input convolution layer. main (x) includes the convolution results of the 1×1 branch and the 3×3 branch, Conv residual (x) Convolution result including the identity mapping branch.

[0051] Among them, the convolution layer of the perturbation recognition model in the training process also includes a BN layer, and the BN layer is fused with each convolution layer. The convolution layer of each convolution kernel has a convolution weight. For example, the multi-branch convolution layer in this scheme has convolution branches of multiple convolution kernels, and each convolution branch has different weights. The convolution layer also includes an activation function, and each convolution layer follows an activation function. Specifically, the ReLU activation function can be used in this scheme. The ReLU activation function can specifically include ReLU(x)=max(0,x), where x is the value of the input activation function, where the ReLU activation function compares the input value x with 0, and if x is greater than 0, it outputs x; if x is less than or equal to 0, it outputs 0. This allows ReLU to truncate to 0 when processing negative values, thereby introducing sparsity and further improving computational efficiency.

[0052] Among them, the formation process of the reconstructed single-branch convolution layer is to fuse each branch of the multi-branch convolution layer into one of the convolution kernels. In this embodiment, the reconstructed single-branch convolution layer is a convolution of a 3×3 convolution kernel, and the fused reconstructed single-branch convolution layer also has a reconstructed convolution weight. The process of determining the reconstructed convolution weight includes: determining multiple branch weights of the multi-branch convolution layer; performing weight fusion based on multiple branch weights to obtain the reconstructed convolution weight of the reconstructed single-branch convolution layer. The fusion process of fusing multiple branches includes: converting the identity mapping branch into a 1x1 convolution, converting it into a 3×3 convolution, and converting the 1×1 convolution into a 3×3 convolution. The specific method of converting a 1x1 convolution to a 3x3 convolution equivalently includes filling with 0.

[0053] For example, if the multi-branch convolution layer includes a 1×1 branch, a 3×3 branch, and an identity mapping branch, these branches need to be merged into a reconstructed single-branch convolution layer with a 3x3 convolution kernel, and the reconstructed convolution weight of the merged 3x3 convolution kernel branch is determined. The process of determining the reconstructed convolution weight of the merged 3x3 convolution kernel branch includes: first determining the branch weight of each branch, specifically including: first fusing the original weight of the 3×3 branch with the BN layer to obtain the first weight, the first weight is specifically W'=γW+βB, where W is the original weight of the 3×3 branch, γ is the scaling factor of the BN layer, β is the offset of the BN layer, and B is the original bias term. Then obtain the second weight W of the 1×1 branch 1×1 and the third weight W of the identity mapping branch identity The reconstructed convolution weight is obtained by fusing the first weight, the second weight, and the third weight: W”=W'+W 1×1+ W identity .

[0054] For example, a voltage waveform with time (seconds) on the horizontal axis and voltage (volts) on the vertical axis is obtained, and it is transmitted as input to the convolution layer of the disturbance recognition model for analysis. The convolution layer uses the fused 3x3 convolution kernel with a stride of 1 and a padding of 1 to extract features from the input waveform, and introduces nonlinearity through the ReLU activation function to obtain the output feature map of the convolution layer. Next, the output feature map is input to the maximum pooling layer with a pooling window size of 2x2 and a stride of 2. After pooling, the feature map F2 is obtained, and its size is halved. Then, the feature map F2 is flattened into a one-dimensional vector and input to the fully connected layer. The Dropout technology is used to prevent overfitting, and the result R of voltage sag disturbance recognition is finally output, indicating whether there is a voltage disturbance.

[0055] Among them, the process of performing loss analysis based on training recognition results to obtain loss results can be to compare the training recognition results obtained in each round of training with the actual situation, obtain the training and actual loss values, perform trend analysis on the loss results, and determine that the loss value of the model is stable and within the preset threshold, indicating that the model training is completed, and the above actual situation is the actual disturbance occurrence.

[0056] Specifically, by monitoring the loss curve, you can intuitively understand the learning status of the model during the training process. If the loss value gradually decreases, it means that the model is gradually learning the characteristics of the data and the fitting effect is improving. The loss curve can help determine whether the model has overfitting or underfitting problems. Overfitting is manifested as the training loss continues to decrease, but the loss of the validation set (or test set) begins to rise, which means that the model performs well on the training data but poorly on unseen data. Underfitting is manifested as both the training loss and the validation loss are high, indicating that the model has not learned the characteristics of the data well. In addition, by analyzing the loss curve, it is also possible to guide the adjustment of hyperparameters, such as learning rate, batch size, regularization parameters, etc., to optimize the model training process.

[0057] It can be seen that in this embodiment, a disturbance identification model is proposed, which can accurately and quickly identify voltage disturbances, capture subtle features of voltage sags through a deep learning model, and achieve high-precision identification.

[0058] S220, obtaining first voltage information of a second time period, where the second time period includes the real-time time period and is the sequential time period of the first time period.

[0059] The second time period is the current real-time time period, that is, the time period to which the first time period sequentially flows, and the first voltage information is the voltage information obtained during the real-time time period.

[0060] S230: Determine a first recognition result based on the first voltage information and a pre-trained disturbance recognition model, where the first recognition result represents the occurrence of a disturbance recognized in the second time period.

[0061] The training process of the pre-trained disturbance identification model has been described in detail in step S210, which will not be repeated here. The disturbance identification model is the same method for the disturbance of the first voltage information and the second voltage information, which will not be repeated here. Through the disturbance identification model, the disturbance occurrence of the second time period can be identified. Specifically, the disturbance occurrence includes at least one of the disturbance occurrence time and the disturbance occurrence degree.

[0062] S240, determining a disturbance coping method based on the first identification result and the pre-adjustment strategy, wherein the disturbance coping method is used to adjust the pre-adjustment strategy so that the voltage remains stable.

[0063] The pre-adjustment strategy is described in detail in reference to the above step S210, which will not be described here in detail. The disturbance response method is a response method made in the current real-time period, that is, the second period, and is a correction method based on the pre-adjustment strategy.

[0064] In a possible embodiment, the disturbance response method includes a first response method and a second response method, and determining the disturbance response method based on the first recognition result and the pre-adjustment strategy includes: determining the presence of a disturbance according to the first recognition result, and the disturbance presence includes the presence of a disturbance and the absence of a disturbance; if the disturbance presence is the presence of a disturbance, determining that the disturbance response method is the first disturbance response method, and the first disturbance response method includes correcting the pre-adjustment strategy according to the first recognition result to obtain a real-time adjustment strategy; if the disturbance presence is the absence of a disturbance, determining that the disturbance response method is the second disturbance response method, and the second disturbance response method includes not correcting the pre-adjustment strategy.

[0065] Among them, parameters or operation strategies are adjusted in advance to prevent or mitigate the impact of predicted disturbances. By real-time monitoring of status and performance indicators, it is determined whether the pre-adjustment strategy is effective and whether there are unpredicted disturbances. If it is determined that there is a disturbance, the pre-adjustment strategy is dynamically adjusted according to the real-time data and disturbance situation to form a real-time adjustment strategy. The revised real-time adjustment strategy is implemented and measures are taken to eliminate or mitigate the current disturbance.

[0066] The presence or absence of disturbance can be determined comprehensively based on the degree and duration of disturbance, or can be determined based on one of the degree and duration of disturbance. Specifically, the disturbance degree level and disturbance duration level can be preset, and the level can be divided according to the existing disturbance and the disturbance duration, and the presence or absence of disturbance can be determined based on the preset threshold.

[0067] It can be seen that in this embodiment, by combining the real-time situation and the pre-adjustment strategy to obtain a more in-depth and detailed response strategy, the voltage disturbance can be effectively dealt with, the voltage stability can be ensured, and the speed of responding to the disturbance occurring in the current period can be improved.

[0068] It can be seen that in this embodiment, the pre-regulation strategy of the first time period is obtained, the first time period is the preceding time period of the real-time time period, and the pre-regulation strategy is a strategy for performing pre-regulation operations on the sequence time period in the first time period, which is used to reduce the degree of voltage disturbance in the real-time time period; the first voltage information of the second time period is obtained, and the second time period includes the real-time time period, which is the sequence time period of the first time period; the first recognition result is determined based on the first voltage information and the pre-trained disturbance recognition model, and the first recognition result characterizes the occurrence of disturbances recognized in the second time period; the disturbance response method is determined based on the first recognition result and the pre-regulation strategy, and the disturbance response method is used to adjust the pre-regulation strategy to keep the voltage stable. In this way, through the combination of prediction and real-time monitoring and the use of disturbance recognition models, the detection efficiency and accuracy can be improved, and the response speed can be accelerated.

[0069] See also Figure 4 , Figure 4 4 is a schematic diagram of a voltage disturbance detection device provided in an embodiment of the present application, and the device is applied to electronic equipment. The voltage disturbance detection device 400 includes: a first acquisition module 410, a second acquisition module 420, a first determination module 430 and a second determination module 440, wherein:

[0070] A first acquisition module 410 is used to acquire a pre-regulation strategy for a first period, where the first period is a preceding period of a real-time period, and the pre-regulation strategy is a strategy for performing a pre-regulation operation on a preceding period in the first period, so as to reduce the degree of voltage disturbance in the real-time period;

[0071] A second acquisition module 420 is used to acquire first voltage information of a second time period, where the second time period includes the real-time time period and is the sequential time period of the first time period;

[0072] A first determination module 430, configured to determine a first recognition result based on the first voltage information and a pre-trained disturbance recognition model, wherein the first recognition result represents the occurrence of a disturbance recognized in the second time period;

[0073] The second determination module 440 is used to determine a disturbance coping method based on the first identification result and the pre-adjustment strategy, and the disturbance coping method is used to adjust the pre-adjustment strategy to keep the voltage stable.

[0074] In a possible embodiment, before the step of obtaining the pre-regulation strategy for the first time period, the method further includes: obtaining a disturbance prediction result for the first time period, the disturbance prediction result representing disturbance occurrence information for a subsequent time period predicted during the first time period; determining the pre-regulation strategy based on the disturbance prediction result, and performing a pre-regulation operation based on the pre-regulation strategy.

[0075] In a possible embodiment, obtaining the disturbance prediction result of the first time period includes: obtaining second voltage information and influencing factor information of the first time period, wherein the influencing factor information is a possible factor affecting the voltage; determining a second recognition result based on the second voltage information and the pre-trained disturbance recognition model, wherein the second recognition result characterizes the occurrence of the disturbance recognized in the first time period; and determining the disturbance prediction result based on the second recognition result, the influencing factor information and the pre-trained disturbance prediction model.

[0076] In a possible embodiment, the training method of the pre-trained disturbance prediction model includes the following steps: obtaining multiple historical voltage information and historical influencing factor information with a time series relationship, each of the historical voltage information corresponds to one historical influencing factor information, the time series relationship includes a time period with a chronological relationship, and the historical influencing factor information includes historical weather information and / or historical equipment status information and / or historical load level information; determining multiple training identification results corresponding to each historical voltage information based on the multiple historical voltage information and the pre-trained disturbance identification model; performing horizontal analysis on the historical influencing factor information to determine multiple historical influencing factor levels, the multiple influencing factor levels including a weather level corresponding to the weather information, an equipment status level corresponding to the equipment status information, and a load level information corresponding to the load level information; inputting the disturbance prediction model to be trained based on the historical influencing factor level and the multiple training identification results for multiple trainings to obtain multiple disturbance prediction results; and obtaining the pre-trained disturbance prediction model based on the disturbance prediction results.

[0077] In a possible embodiment, the influencing factor information includes weather information and / or equipment status information and / or load level information, the disturbance prediction result includes predicted disturbance duration, predicted disturbance degree and a first disturbance factor, the first disturbance factor characterizes the actual influencing factor, the first disturbance factor is determined based on the influencing factor information, the disturbance degree characterizes the voltage variation amplitude, and the first disturbance factor includes weather factors and / or equipment status factors and / or load level factors; the pre-adjustment strategy determined based on the disturbance prediction result includes: determining the pre-adjustment time according to the predicted disturbance duration; and, determining the pre-adjustment voltage according to the predicted disturbance degree; determining a coordination scheme according to the first disturbance factor, the coordination scheme including a first coordination scheme for the weather factor and / or a second coordination scheme for the equipment status factor and / or a third coordination scheme for the load level factor; the pre-adjustment strategy is determined according to the pre-adjustment time, the pre-adjustment voltage, the first coordination scheme and / or the second coordination scheme for the equipment status factor and / or the third coordination scheme for the load level factor.

[0078] In a possible embodiment, the disturbance response method includes a first response method and a second response method. The second determination module 440 is specifically used to determine the disturbance response method based on the first recognition result and the pre-adjustment strategy, and is used to determine the presence of a disturbance according to the first recognition result, and the disturbance presence includes the presence of a disturbance and the absence of a disturbance; if the disturbance presence is the presence of a disturbance, then the disturbance response method is determined to be the first disturbance response method, and the first disturbance response method includes correcting the pre-adjustment strategy according to the first recognition result to obtain a real-time adjustment strategy; if the disturbance presence is the absence of a disturbance, then the disturbance response method is determined to be the second disturbance response method, and the second disturbance response method includes not correcting the pre-adjustment strategy.

[0079] In a possible embodiment, the training process of the pre-trained disturbance recognition model includes the following steps:

[0080] Acquire multiple training voltage information; input the multiple training voltage information into a disturbance recognition model to be trained, the disturbance recognition model to be trained includes a multi-branch convolution layer, a first pooling layer and the first fully connected layer, the multi-branch convolution layer includes multiple branch convolution layers of different types; perform multi-branch convolution operations on the multiple different types of branch convolution layers to obtain multiple first convolution output results; perform residual fusion based on the multiple first convolution output results to obtain a second convolution output result; the first pooling layer performs a pooling operation based on the first output result to obtain a pooling output result; the first fully connected layer integrates based on the pooling result to obtain multiple training recognition results; perform loss analysis based on the training recognition result to obtain a loss result; perform repeated training based on the loss result and perform branch fusion to obtain the pre-trained disturbance recognition model, the pre-trained disturbance recognition model includes a reconstructed single-branch convolution layer, a second pooling layer and a second fully connected layer.

[0081] It is worth noting that the specific functional implementation of the voltage disturbance detection device 400 is shown in the above Figure 2 The description of the voltage disturbance detection method shown in the figure, for example, the first acquisition module 410 is used to implement the relevant content of executing S210. Each unit or module in the voltage disturbance detection device 400 can be separately or completely combined into one or several other units or modules to constitute, or one (some) of the units or modules can also be divided into multiple smaller units or modules to constitute, which can achieve the same operation without affecting the realization of the technical effect of the embodiment of the present invention. The above-mentioned units or modules are divided based on logical functions. In practical applications, the function of one unit (or module) is realized by multiple units (or modules), or the function of multiple units (or modules) is realized by one unit (or module).

[0082] It can be seen that the voltage disturbance detection device described in the embodiment of the present application first obtains the pre-regulation strategy of the first time period, the first time period is the preceding time period of the real-time time period, and the pre-regulation strategy is a strategy for pre-regulating the sequence time period in the first time period, which is used to reduce the degree of voltage disturbance in the real-time time period; obtains the first voltage information of the second time period, the second time period includes the real-time time period, which is the sequence time period of the first time period; determines the first recognition result based on the first voltage information and the pre-trained disturbance recognition model, and the first recognition result characterizes the occurrence of the disturbance recognized in the second time period; determines the disturbance response method based on the first recognition result and the pre-regulation strategy, and the disturbance response method is used to adjust the pre-regulation strategy to keep the voltage stable. In this way, through the combination of prediction and real-time monitoring and the use of disturbance recognition models, the detection efficiency and accuracy can be improved and the response speed can be accelerated.

[0083] See also Figure 5 , Figure 5 5 is a schematic diagram of the structure of an electronic device provided by an embodiment of the present application. As shown in the figure, the electronic device 500 includes a processor 510, a memory 520, a communication interface 530, and one or more programs 521. The one or more programs 521 are stored in the memory 520 and are configured to be executed by the processor 510. The electronic device 500 corresponds to Figure 1 An electronic device 110 in an embodiment.

[0084] The processor 510, the memory 520, and the communication interface 530 are interconnected and perform communication work with each other;

[0085] The memory 520 may be a volatile memory such as a dynamic random access memory DRAM, or a non-volatile memory such as a mechanical hard disk. The memory 520 is used to store a set of executable program codes, and the processor 510 is used to call one or more programs 521 stored in the memory 520, and may execute part or all of the steps of any method described in the above method embodiment.

[0086] The electronic device 500 may include a server, a laptop, a mobile computer, a computer, etc. The above are only examples and not exhaustive, and include but are not limited to the above electronic devices.

[0087] It can be seen that the electronic device 500 described in the embodiment of the present application obtains a pre-regulation strategy for a first time period, the first time period is a pre-sequence time period of a real-time time period, and the pre-regulation strategy is a strategy for performing pre-regulation operations on a sequence time period in the first time period, which is used to reduce the degree of voltage disturbance in the real-time time period; obtains first voltage information for a second time period, the second time period includes the real-time time period, and is the sequence time period of the first time period; determines a first recognition result based on the first voltage information and a pre-trained disturbance recognition model, the first recognition result characterizes the occurrence of disturbances recognized in the second time period; determines a disturbance response method based on the first recognition result and the pre-regulation strategy, and the disturbance response method is used to adjust the pre-regulation strategy to keep the voltage stable. In this way, by combining prediction and real-time monitoring and using a disturbance recognition model, the detection efficiency and accuracy can be improved and the response speed can be accelerated.

[0088] An embodiment of the present application also provides a computer storage medium, wherein the computer storage medium stores a computer program for electronic data exchange, wherein the computer program enables a computer to execute part or all of the steps of any method described in the above method embodiments, and the above computer includes an electronic device.

[0089] The embodiment of the present application also provides a computer program product, the computer program product includes a non-transitory computer-readable storage medium storing a computer program, the computer program is operable to cause a computer to execute some or all of the steps of any method described in the method embodiment. The computer program product may be a software installation package, and the computer includes an electronic device.

[0090] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0091] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0092] In the several embodiments provided in the present application, it should be understood that the disclosed device can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of the above-mentioned units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0093] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0094] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0095] If the above-mentioned integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a memory, including a number of instructions to enable a computer electronic device (which can be a personal computer, electronic device or network electronic device, etc.) to perform all or part of the steps of the above-mentioned methods of each embodiment of the present application. The aforementioned memory includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, disk or optical disk and other media that can store program codes.

[0096] A person skilled in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable memory, and the memory can include: a flash drive, a read-only memory (English: Read-Only Memory, abbreviated as: ROM), a random access memory (English: Random Access Memory, abbreviated as: RAM), a magnetic disk or an optical disk, etc.

[0097] The embodiments of the present application are introduced in detail above. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method and core idea of ​​the present application. At the same time, for general technical personnel in this field, according to the idea of ​​the present application, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A voltage disturbance detection method, characterized in that: Applied to electronic equipment, the method comprises: Acquire a pre-regulation strategy for a first time period, wherein the first time period is a preceding time period of a real-time time period, and the pre-regulation strategy is a strategy for performing a pre-regulation operation on a preceding time period in the first time period, so as to reduce the degree of voltage disturbance in the real-time time period; Acquire first voltage information of a second time period, where the second time period includes the real-time time period and is the sequential time period of the first time period; Determine a first recognition result based on the first voltage information and a pre-trained disturbance recognition model, wherein the first recognition result represents the occurrence of a disturbance recognized in the second time period; A disturbance coping method is determined based on the first identification result and the pre-adjustment strategy, and the disturbance coping method is used to adjust the pre-adjustment strategy so that the voltage remains stable.

2. The method according to claim 1, characterized in that Before the step of obtaining the pre-adjustment strategy for the first time period, the method further includes: Acquire a disturbance prediction result of the first time period, wherein the disturbance prediction result represents disturbance occurrence information of a subsequent time period predicted during the first time period; The pre-adjustment strategy is determined based on the disturbance prediction result, and a pre-adjustment operation is performed based on the pre-adjustment strategy.

3. The method according to claim 2, characterized in that The obtaining of the disturbance prediction result for the first time period includes: Acquire second voltage information and influencing factor information in the first time period, where the influencing factor information is a possible factor that may affect the voltage; Determine a second recognition result based on the second voltage information and the pre-trained disturbance recognition model, wherein the second recognition result represents the occurrence of the disturbance recognized in the first time period; The disturbance prediction result is determined based on the second recognition result, the influencing factor information and a pre-trained disturbance prediction model.

4. The method according to claim 3, characterized in that: The training method of the pre-trained disturbance prediction model comprises the following steps: Acquire multiple historical voltage information and historical influencing factor information with a time series relationship, each of the historical voltage information corresponds to one of the historical influencing factor information, the time series relationship includes time periods with a chronological relationship, and the historical influencing factor information includes historical weather information and / or historical equipment status information and / or historical load level information; Determine a plurality of training recognition results corresponding to each piece of historical voltage information based on the plurality of historical voltage information and the pre-trained disturbance recognition model; Performing a level analysis on the historical influencing factor information to determine a plurality of historical influencing factor levels, the plurality of influencing factor levels comprising a weather level corresponding to the weather information, an equipment status level corresponding to the equipment status information, and a load level information corresponding to the load level information; Based on the historical influencing factor level and the multiple training identification results, the disturbance prediction model to be trained is input for multiple trainings to obtain multiple disturbance prediction results; The pre-trained disturbance prediction model is obtained based on the disturbance prediction result.

5. The method according to claim 3, characterized in that: The influencing factor information includes weather information and / or equipment status information and / or load level information, the disturbance prediction result includes predicted disturbance duration, predicted disturbance degree and a first disturbance factor, the first disturbance factor represents the actual influencing factor, the first disturbance factor is determined based on the influencing factor information, the disturbance degree represents the voltage variation amplitude, and the first disturbance factor includes weather factors and / or equipment status factors and / or load level factors; The determining the pre-adjustment strategy based on the disturbance prediction result comprises: Determining a pre-adjustment time according to the predicted disturbance duration; and determining a pre-adjustment voltage according to the predicted disturbance degree; Determining a coordination scheme according to the first disturbance factor, the coordination scheme comprising a first coordination scheme for the weather factor and / or a second coordination scheme for the equipment status factor and / or a third coordination scheme for the load level factor; The pre-regulation strategy is determined according to the pre-regulation time, the pre-regulation voltage, the first coordination scheme and / or the second coordination scheme for the device status factor and / or the third coordination scheme for the load level factor.

6. The method according to claim 5, characterized in that The disturbance coping method includes a first coping method and a second coping method, and the determining of the disturbance coping method based on the first identification result and the pre-adjustment strategy includes: Determining a disturbance presence condition according to the first recognition result, wherein the disturbance presence condition includes the presence of disturbance and the absence of disturbance; If the disturbance existence condition is the disturbance existence, determining that the disturbance coping method is the first disturbance coping method, the first disturbance coping method comprising modifying the pre-adjustment strategy according to the first identification result to obtain a real-time adjustment strategy; If the disturbance existence condition is the disturbance not existing, it is determined that the disturbance coping method is the second disturbance coping method, and the second disturbance coping method includes not modifying the pre-adjustment strategy.

7. The method according to claim 1 or 4, characterized in that: The training process of the pre-trained disturbance recognition model includes the following steps: Get multiple training voltage information; Inputting the plurality of training voltage information into a disturbance recognition model to be trained, wherein the disturbance recognition model to be trained comprises a multi-branch convolutional layer, a first pooling layer and the first fully connected layer, and the multi-branch convolutional layer comprises a plurality of branch convolutional layers of different types; The multiple different types of branch convolution layers respectively perform multi-branch convolution operations to obtain multiple first convolution output results; Perform residual fusion based on the multiple first convolution output results to obtain a second convolution output result; The first pooling layer performs a pooling operation based on the first output result to obtain a pooling output result; The first fully connected layer integrates the pooling results to obtain multiple training recognition results; Perform loss analysis based on the training recognition result to obtain a loss result; Based on the loss result, multiple repeated trainings and branch fusions are performed to obtain the pre-trained perturbation recognition model, wherein the pre-trained perturbation recognition model includes a reconstructed single-branch convolutional layer, a second pooling layer, and a second fully connected layer.

8. A voltage disturbance detection device, characterized in that: Applied to electronic equipment, the device comprises: A first acquisition module is used to acquire a pre-regulation strategy for a first time period, wherein the first time period is a pre-sequence time period of a real-time time period, and the pre-regulation strategy is a strategy for performing a pre-regulation operation on a pre-sequence time period in the first time period, so as to reduce the degree of voltage disturbance in the real-time time period; A second acquisition module is used to acquire the first voltage information of a second time period, where the second time period includes the real-time time period and is the sequential time period of the first time period; a first determination module, configured to determine a first recognition result based on the first voltage information and a pre-trained disturbance recognition model, wherein the first recognition result represents the occurrence of a disturbance recognized in the second time period; The second determination module is used to determine a disturbance response method based on the first identification result and the pre-adjustment strategy, and the disturbance response method is used to adjust the pre-adjustment strategy to keep the voltage stable.

9. A computer-readable storage medium, characterized in that: A voltage disturbance detection program is stored, including execution instructions. When a processor of the electronic device executes the execution instructions, the processor executes the method as claimed in any one of claims 1 to 7.

10. An electronic device, characterized in that: It comprises a processor and a memory storing execution instructions, wherein the memory stores one or more programs; when the processor executes the execution instructions stored in the memory, the processor executes the method according to any one of claims 1 to 7.

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