Power grid equipment-based start-up scheme generation and processing system, method, apparatus, device, medium, and program product

Abstract

The application relates to a power grid equipment-based start scheme generation and processing system, method, device, equipment, medium and program product, and relates to the technical field of power control. The system can improve the accuracy and reliability of the scheme. The system comprises a scheme generation unit configured to generate a power grid equipment-based start scheme based on an acquired start step model and a start condition model; an execution debugging unit configured to perform equipment debugging and on-site verification based on the start scheme, and acquire debugging and verification data; an automatic simulation unit configured to simulate a start process of the power grid equipment in a virtual environment based on the start scheme, and record current and voltage change data in the simulation process; and a comprehensive evaluation unit configured to receive the debugging and verification data and the current and voltage change data, perform similarity calculation on the debugging and verification data and the current and voltage change data, and determine that the start scheme evaluation is passed when the similarity meets a threshold condition.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power control, and in particular to a start-up scheme generation and processing system, method and device based on power grid equipment, computer equipment, computer readable storage medium and computer program product. BACKGROUND

[0002] A start-up scheme is an important guarantee for the safe operation of a power system, and especially before new equipment is put into use, a detailed start-up scheme must be prepared to verify whether the equipment can operate safely. With the rapid development of the economy and the rapid growth of power demand, a large number of infrastructure projects are put into production every year, which puts forward higher requirements for the efficiency and quality of start-up scheme preparation.

[0003] In the traditional technology, the preparation and review of the equipment start-up scheme can mostly only rely on manual operation, which not only has low efficiency, but also is prone to errors in the correctness and rationality of the scheme due to the mistakes of the staff. Although some start-up scheme preparation systems have appeared today, these systems usually only generate a start-up scheme, resulting in low accuracy of the output results and low flexibility of the system itself. SUMMARY

[0004] Therefore, it is necessary to provide a start-up scheme generation and processing system, method and device based on power grid equipment, computer equipment, computer readable storage medium and computer program product to solve the above technical problems.

[0005] In a first aspect, the present application provides a start-up scheme generation and processing system based on power grid equipment, which comprises a scheme generation unit, an execution debugging unit, an automated simulation unit and a comprehensive evaluation unit. The scheme generation unit is in communication connection with the execution debugging unit and the automated simulation unit, and the execution debugging unit and the automated simulation unit are in communication connection with the comprehensive evaluation unit.

[0006] The scheme generation unit is configured to obtain a start-up step model and a start-up condition model, and generate a start-up scheme based on the power grid equipment based on the start-up step model and the start-up condition model.

[0007] The execution debugging unit is configured to perform equipment debugging and on-site verification based on the start-up scheme, and obtain debugging and verification data.

[0008] The automated simulation unit is configured to simulate the start-up process of the power grid equipment in a virtual environment based on the start-up scheme, and record current and voltage change data during the simulation process.

[0009] The comprehensive evaluation unit is configured to receive the debugging and checking data and the current-voltage change data, perform similarity calculation on the debugging and checking data and the current-voltage change data, and determine that the start-up scheme evaluation is passed when the similarity meets a threshold condition.

[0010] In one of the embodiments, the scheme generation unit comprises a word segmentation module, a model separation module, a feature extraction module, a corpus generation module, and a model training module. The word segmentation module is configured to perform word segmentation on the historical start-up steps and condition texts of the power grid equipment by using natural language processing technology to form a start-up operation segmented text library. The model separation module is configured to separate a device name library and a power grid topology model from a power grid equipment model generated by an energy management system, and form a topology model within a start-up range. The feature extraction module is configured to extract topology features within the start-up range in the topology model by using a graph attention neural network. The corpus generation module is configured to form a start-up step and start-up condition text corpus according to the device name library and the start-up operation segmented text library. The model training module is configured to train start-up step statements by using a sequential deep learning model, and train start-up condition statements by using a non-sequential deep learning model, to obtain the start-up step model and the start-up condition model.

[0011] In one of the embodiments, the model training module is further configured to obtain standard operation steps in historical operation data of a power grid, convert the standard operation steps into sequence data containing command identifiers and command descriptions, take the command identifiers as inputs of the start-up step model, and take the command descriptions as outputs of the start-up step model, to train the start-up steps and obtain the start-up step model. The model training module is further configured to obtain standard condition statements in the historical operation data, train the condition statements based on the standard condition statements and a full connection layer, and obtain the start-up condition model.

[0012] In one of the embodiments, the automatic simulation unit is further configured to establish a digital twin model of a power grid at a description subsystem layer according to detailed parameters and historical operation data of the power grid, collect first data for adjusting power system flow behavior, and generate second data by analyzing the first data, establish a long short-term memory network model based on the second data, generate a new power grid operation flow sample according to a power grid operation state predicted by the long short-term memory network model, perform power grid simulation calculation according to the power grid operation flow sample, and record simulation data.

[0013] In one of the embodiments, the system further comprises a user interaction unit and an adjustment feedback unit; the user interaction unit is configured to provide an input window of instructions to a user and show the user the obtained target data; the target data comprises a single-line diagram of a power system, a device status indication, a real-time data chart, an operation log and an alarm signal; the adjustment feedback unit is configured to adjust the start-up scheme generation strategy of the scheme generation unit according to the difference between the debugging and checking results and the automatic simulation results.

[0014] In a second aspect, the application further provides a power grid device-based start-up scheme generation and processing method, which is applied to a power grid device-based start-up scheme generation and processing system, and comprises the following steps:

[0015] obtaining a start-up step model and a start-up condition model, and generating a power grid device-based start-up scheme based on the start-up step model and the start-up condition model;

[0016] performing device debugging and on-site checking based on the start-up scheme, and obtaining debugging and checking data;

[0017] simulating a start-up process of the power grid device in a virtual environment based on the start-up scheme, and recording current and voltage change data in the simulation process;

[0018] performing similarity calculation on the debugging and checking data and the current and voltage change data, and determining that the start-up scheme evaluation is passed when the similarity meets a threshold condition.

[0019] In a third aspect, the application further provides a power grid device-based start-up scheme generation and processing device, which comprises:

[0020] a scheme generation module configured to obtain a start-up step model and a start-up condition model, and generate a power grid device-based start-up scheme based on the start-up step model and the start-up condition model;

[0021] an execution debugging module configured to perform device debugging and on-site checking based on the start-up scheme, and obtain debugging and checking data;

[0022] an automatic simulation module configured to simulate a start-up process of the power grid device in a virtual environment based on the start-up scheme, and record current and voltage change data in the simulation process;

[0023] a comprehensive evaluation module configured to perform similarity calculation on the debugging and checking data and the current and voltage change data, and determine that the start-up scheme evaluation is passed when the similarity meets a threshold condition.

[0024] In a fourth aspect, the present application provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the following steps when executing the computer program:

[0025] obtaining a start step model and a start condition model, generating a start scheme based on the power grid equipment based on the start step model and the start condition model; based on the start scheme, performing device debugging and on-site verification, and obtaining debugging and verification data; based on the start scheme, simulating the start process of the power grid equipment in a virtual environment, and recording current and voltage change data in the simulation process; performing similarity calculation on the debugging and verification data and the current and voltage change data, and determining that the start scheme evaluation is passed when the similarity meets the threshold condition.

[0026] In a fifth aspect, the present application further provides a computer readable storage medium having a computer program stored thereon, wherein the computer program is executed by a processor to implement the following steps:

[0027] obtaining a start step model and a start condition model, generating a start scheme based on the power grid equipment based on the start step model and the start condition model; based on the start scheme, performing device debugging and on-site verification, and obtaining debugging and verification data; based on the start scheme, simulating the start process of the power grid equipment in a virtual environment, and recording current and voltage change data in the simulation process; performing similarity calculation on the debugging and verification data and the current and voltage change data, and determining that the start scheme evaluation is passed when the similarity meets the threshold condition.

[0028] In a sixth aspect, the present application further provides a computer program product comprising a computer program, wherein the computer program is executed by a processor to implement the following steps:

[0029] obtaining a start step model and a start condition model, generating a start scheme based on the power grid equipment based on the start step model and the start condition model; based on the start scheme, performing device debugging and on-site verification, and obtaining debugging and verification data; based on the start scheme, simulating the start process of the power grid equipment in a virtual environment, and recording current and voltage change data in the simulation process; performing similarity calculation on the debugging and verification data and the current and voltage change data, and determining that the start scheme evaluation is passed when the similarity meets the threshold condition.

[0030] The power grid equipment-based start-up scheme generation and processing system, method, device, computer device, computer readable storage medium and computer program product can realize automatic, standard and accurate compilation of the start-up scheme by establishing the power grid equipment-based start-up scheme generation and processing system, using the state machine principle, the expert system rule base and the man-machine interaction rule. The automatic scheme generation and processing method greatly improves the efficiency of scheme compilation, reduces human errors, and improves the accuracy and reliability of the scheme. Moreover, the application promotes the standardization of the start-up scheme content and terminology through the automatic start-up scheme generation and checking process, and forms a standardized process. This means that the scheme compilation no longer depends on personal experience, but follows a unified standard and process, which helps to improve the intelligent level of power grid dispatching, reduces the work pressure of the scheme personnel, and ensures the consistency and traceability of the scheme. In addition, the application adjusts the start-up scheme generation strategy based on the simulation results, further improves the accuracy of the start-up scheme generation, and is in a dynamically adjustable state with flexibility. BRIEF DESCRIPTION OF DRAWINGS

[0031] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related art, the drawings needed to be used in the description of the embodiments of the present application or the related art will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other related drawings can be obtained by those skilled in the art without creative labor.

[0032] Figure 1 The structural block diagram of the power grid equipment-based start-up scheme generation and processing system in an embodiment is shown in the figure.

[0033] Figure 2 The structural block diagram of the scheme generation unit in an embodiment is shown in the figure.

[0034] Figure 3 The structural block diagram of the power grid equipment-based start-up scheme generation and processing system in another embodiment is shown in the figure.

[0035] Figure 4 The flowchart of the power grid equipment-based start-up scheme generation and processing method in an embodiment is shown in the figure.

[0036] Figure 5 The structural block diagram of the power grid equipment-based start-up scheme generation and processing device in an embodiment is shown in the figure.

[0037] Figure 6 The internal structure diagram of the computer device in an embodiment is shown in the figure. DETAILED DESCRIPTION

[0038] In order to make the purposes, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and not to limit the present application.

[0039] In one embodiment, as shown in Figure 1 A power grid equipment-based startup scheme generation and processing system is provided, which includes a scheme generation unit, an execution debugging unit, an automated simulation unit and a comprehensive evaluation unit. The scheme generation unit is in communication connection with the execution debugging unit and the automated simulation unit, and the execution debugging unit and the automated simulation unit are in communication connection with the comprehensive evaluation unit.

[0040] The scheme generation unit is configured to obtain a startup step model and a startup condition model, and generate a power grid equipment-based startup scheme based on the startup step model and the startup condition model.

[0041] The execution debugging unit is configured to perform equipment debugging and on-site verification based on the startup scheme, and obtain debugging and verification data.

[0042] The automated simulation unit is configured to simulate a startup process of the power grid equipment in a virtual environment based on the startup scheme, and record data including current and voltage change data in the simulation process.

[0043] The comprehensive evaluation unit is configured to receive the debugging and verification data and the current and voltage change data, perform similarity calculation on the debugging and verification data and the current and voltage change data, and determine that the startup scheme evaluation is passed when the similarity meets a threshold condition.

[0044] Specifically, referring to Figure 1 The scheme generation unit is configured to obtain a startup step model and a startup condition model, and automatically generate a power grid equipment-based startup scheme based on the startup step model and the startup condition model. The execution debugging unit is configured to perform equipment debugging and other on-site verification operations based on the startup scheme, and obtain debugging and verification data including equipment startup time and current impact response. The automated simulation unit is configured to simulate a startup process of the power grid equipment in a virtual environment based on the startup scheme, and record data including current and voltage change in the simulation process. The comprehensive evaluation unit is configured to receive the debugging and verification data and the current and voltage change data, perform similarity calculation on the debugging and verification data and the current and voltage change data, and compare the similarity of the two output results according to the similarity calculation result. The verification is passed only when the threshold is exceeded, otherwise an alarm is given. The similarity calculation is as follows:

[0045]

[0046] In the above formula, A and B represent the actual result and the simulation result, respectively. This indicates a measure of the difference between the two.

[0047] In the aforementioned power grid equipment startup scheme generation and processing system, by establishing such a system and utilizing state machine principles, expert system rule bases, and human-computer interaction rules, the automatic, standardized, and accurate compilation of startup schemes is achieved. This automated scheme generation and processing method significantly improves the efficiency of scheme compilation, reduces human error, and thus enhances the accuracy and reliability of the schemes. Furthermore, this application promotes the standardization of startup scheme content and terminology through an automated startup scheme generation and verification process, forming a standardized process. This means that scheme compilation no longer relies on personal experience but follows unified standards and processes, which helps improve the intelligence level of power grid dispatching, reduces the workload of personnel compiling startup schemes, and ensures the consistency and traceability of the schemes. In addition, this application also adjusts the startup scheme generation strategy based on simulation results, further making the startup scheme generation more accurate and dynamically adjustable, possessing the flexibility to adjust itself.

[0048] In one embodiment, such as Figure 2 As shown, the scheme generation unit includes: a word segmentation module, a model separation module, a feature extraction module, a corpus generation module, and a model training module. The word segmentation module is used to segment the historical startup steps and conditional texts of power grid equipment using natural language processing technology to form a startup operation word segmentation text library. The model separation module is used to separate the equipment name library and the power grid topology model based on the power grid equipment model generated by the energy management system, and form a topology model within the startup range. The feature extraction module is used to extract topological features within the startup range from the topology model using a graph attention neural network. The corpus generation module is used to form a startup step and startup condition text corpus based on the equipment name library and the startup operation word segmentation text library. The model training module is used to train the startup step statements using a sequential deep learning model and the startup condition statements using an unsequential deep learning model to obtain the startup step model and the startup condition model.

[0049] Specifically, the word segmentation module utilizes natural language processing techniques to segment the historical start-up step and condition text of the power grid equipment, forming a start-up operation segmented text library; the model separation module separates the device name library and the power grid topology model according to the power grid equipment model generated by the energy management system, and forms a topology model within the operation range; the feature extraction module extracts the topology features within the start-up range using a graph attention neural network; the corpus generation module forms a start-up step and start-up condition text corpus according to the device name library and the start-up operation segmented text library; the model training module trains the start-up step statement using a sequential deep learning model and trains the start-up condition statement using a non-sequential deep learning model, obtaining a start-up step model and a start-up condition model.

[0050] In one embodiment, the model training module is further configured to obtain standard operation steps in historical operation data of the power grid, convert the standard operation steps into sequence data containing command identifiers and command descriptions; use the command identifiers as inputs of the start-up step model and the command descriptions as outputs of the start-up step model to train the start-up steps, obtaining a start-up step model; and obtain standard condition statements in the historical operation data, train the condition statements based on the standard condition statements and a fully connected layer, obtaining a start-up condition model.

[0051] Specifically, the training process of the start-up step model is as follows:

[0052] The historical start-up step text is segmented to form a segmented text library; the power grid equipment model is feature extracted to form a power grid topology model; an LSTM (Long Short-Term Memory) model structure is defined, including an input layer, an LSTM layer, and an output layer. Using the PyTorch framework, the LSTM network structure is defined, including the dimensions and number of hidden layers; the learning rate, batch size, and epoch number are set as hyperparameters; MSELoss is used as the loss function and Adam is used as the optimizer. Forward propagation is performed to calculate the output and loss. Backward propagation is performed to calculate the gradient and update the model parameters. At the end of each epoch, the loss value is recorded and printed to monitor the training process; the test set is used to evaluate the model to calculate the accuracy or other performance indicators of the model; after training is completed, the model parameters are saved for subsequent use or further training.

[0053] In addition, the start-up condition model training steps are as follows:

[0054] The historical start condition text is segmented to form a segmented text library, the power grid equipment model is feature extracted to form a power grid topology model; a multi-layer perception (MLP) model structure is defined, including an input layer, a hidden layer, and an output layer, a PyTorch framework is used to define the MLP network structure, including the number of neurons in each layer; learning rate, batch size, epoch number, and other hyperparameters are set; CrossEntropyLoss is used as the loss function, Adam is used as the optimizer, forward propagation is performed, the output and loss are calculated. Back propagation is performed, the gradient is calculated, and the model parameters are updated. At the end of each epoch, the loss value is recorded and printed to monitor the training process; the test set is used to evaluate the model, and the accuracy or other performance indicators of the model are calculated; after training, the model parameters are saved for subsequent use or further training.

[0055] In one embodiment, the automation simulation unit is also configured to establish a digital twin model of the power grid at the description subsystem level based on detailed parameters and historical operation data of the power grid; collect first data for adjusting the power system flow behavior and parse the first data to generate second data; based on the second data, establish a long short-term memory network model; generate new power grid operation flow samples based on the predicted power grid operation state of the long short-term memory network model; perform power grid simulation calculation based on the power grid operation flow samples and record simulation data.

[0056] Specifically, the automation simulation unit can perform the following operations:

[0057] The digital twin model of the power grid is established at the description subsystem level, which can reflect the state of the primary equipment, secondary equipment, and environment of the power grid. This can be achieved by collecting detailed parameters and historical operation data of the power grid, including the model, rated parameters, and connection relationship of the equipment. The first data for adjusting the power system flow behavior is collected and parsed by the data parsing unit to generate second data, including the names of the adjusted equipment, which are the first equipment names. Based on the second data, an LSTM model is established, which can learn the historical data of the power grid operation and predict the future power grid operation state. New power grid operation flow samples are generated based on the predicted power grid operation state of the LSTM model, which include the equipment operation state of the power grid after different operation events. Open-source power grid simulation software such as PythonPower Network (Pypownet) is used for power grid simulation calculation. Pypownet can model and simulate various system operation states of large power grids, and supports AC and DC flow solution. Simulation data is recorded. According to the pre-set simulation total time, simulation clock compression ratio, stored power grid model data snapshots, and power grid equipment state snapshots, the simulation clock is advanced to perform power grid control operation simulation time series flow simulation.

[0058] For example, the automated simulation unit performs device simulation debugging or other simulation verification operations based on the start-up scheme, and obtains simulation debugging or verification data. This module corresponds to the terminal execution module one-to-one, which is equivalent to obtaining actual results and simulation results. This unit simulates the start-up process of power grid equipment in a virtual environment, records the current, voltage changes, equipment response time, and system stability data during simulation, and the simulation data obtained are as follows: simulation device start-up time , in seconds; simulation current impact response , in amperes (A); simulation voltage stability , in volts (V); simulation protection device action , binary value, 1 indicates action, and 0 indicates no action.

[0059] For further illustration, the following are specific implementation steps for simulating the start-up process of power grid equipment in a virtual environment based on the start-up scheme, and how to record the current, voltage changes, equipment response time, and system stability data during simulation:

[0060] Step one, establish a digital twin model of the power grid at the description subsystem level. This model can reflect the state of primary and secondary equipment and the environment of the power grid. This can be achieved by collecting detailed parameters and historical operation data of the power grid, including equipment models, rated parameters, connection relationships, etc.

[0061] Step two, use the data analysis unit to collect and analyze the first data for adjusting the power system flow behavior to generate second data. These data include the names of the adjusted devices, which are the first device names.

[0062] Step three, based on the second data, establish an LSTM model using the LSTM algorithm. This model can learn from historical data of power grid operation and predict future power grid operation states.

[0063] Step four, generate new power grid operation flow samples based on the predicted power grid operation state of the LSTM model. These samples include the device operation state of the power grid after different operation events.

[0064] Step five, use open-source power grid simulation software such as Python Power Network (Pypownet) for power grid simulation calculation. Pypownet can model large power grids and simulate various system operation states, and supports AC and DC flow solution.

[0065] Step six, record simulation data, including simulation device start-up time , simulation current impact response , simulation voltage stability and simulation protection device action .

[0066] Step seven, according to the pre-set simulation total time, simulation clock compression ratio, call stored power grid model data snapshot and power grid equipment state snapshot, promote the simulation clock to carry out power grid control operation analog time sequence power flow simulation.

[0067] In one embodiment, the data obtained by the execution debugging unit includes device startup time , current impulse response , voltage stability and protection device action ; the data obtained by the automatic simulation terminal unit includes simulation device startup time , simulation current impulse response , simulation voltage stability , simulation protection device action .

[0068] The calculation formula of the similarity is as follows:

[0069]

[0070] In the above formula, w1, w2, w3, w4 are weight coefficients, used to adjust the importance of different parameters in the similarity calculation, and the value range of these weight coefficients is 0 to 1.

[0071] For example, the execution debugging unit performs device debugging or other field verification operation based on the startup scheme, obtains field debugging or verification data, and these data are used for the calculation of the comprehensive total score; the unit receives the startup scheme generated from the scheme generation unit, and performs field device debugging operation according to the startup scheme, and records the operation results. It is used to collect key data in the device debugging process, and the collected data includes: device startup time

[0072] Device startup time , unit: seconds; current impulse response , unit: ampere (A); voltage stability , unit: volt (V); protection device action , binary value, 1 indicates action, and 0 indicates no action.

[0073] In one embodiment, as Figure 3As shown, the power grid equipment-based startup scheme generation and processing system further comprises a user interaction unit and an adjustment feedback unit; the user interaction unit is configured to provide an input window for user instructions and to show the user the obtained target data; the target data includes a power system single-line diagram, device state indication, real-time data chart, operation log and alarm signal; the adjustment feedback unit is configured to adjust the startup scheme generation strategy of the scheme generation unit according to the difference between the debugging and verification results and the automatic simulation results.

[0074] Specifically, the user interaction unit is configured to input instructions for the user or to show the obtained data; the data shown by the visual user interaction unit includes a power system single-line diagram, device state indication, real-time data chart, operation log and alarm signal. The feedback adjustment unit is configured to adjust the strategy of the startup scheme automatic generation module according to the difference between the actual results and the simulation results; the specific adjustment method is as follows: optimizing the atomic operation sequence: if the difference exceeds a preset threshold, adjusting the operation sequence in the startup step model to reduce the difference in startup time; adjusting the risk assessment model: if or the difference exceeds a preset threshold, adjusting the risk assessment parameters in the startup condition model to improve the accuracy of the model; updating the expert system rule base: if the difference exceeds a preset threshold, updating the protection device action rules in the expert system rule base to improve the reliability of the model.

[0075] In one embodiment, as Figure 4 shown, a power grid equipment-based startup scheme generation and processing method is provided; the method is applied to the power grid equipment-based startup scheme generation and processing system as described above and will be described in detail below. Figure 1

[0076] Step S401: Obtain a startup step model and a startup condition model; based on the startup step model and the startup condition model, generate a power grid equipment-based startup scheme.

[0077] Step S402: Based on the startup scheme, perform device debugging and on-site verification, and obtain debugging and verification data.

[0078] Step S403: Based on the startup scheme, simulate the startup process of the power grid equipment in a virtual environment, and record the current and voltage change data during the simulation process.

[0079] Step S404: Calculate the similarity of the debugging and verification data and the current and voltage change data; if the similarity meets the threshold condition, determine that the startup scheme evaluation is passed.

[0080] ​Specifically, the power grid equipment-based startup scheme generation and processing system acquires a startup step model and a startup condition model based on the startup scheme generation, generates a power grid equipment-based startup scheme based on the startup step model and the startup condition model, performs device debugging and on-site verification based on the startup scheme, and acquires debugging and verification data, simulates the startup process of the power grid equipment in a virtual environment based on the startup scheme, and records current and voltage change data during the simulation process, and finally performs similarity calculation on the debugging and verification data and the current and voltage change data, and determines that the startup scheme evaluation is passed when the similarity meets the threshold condition.

[0081] For specific limitations of the above steps, please refer to the related embodiments of the power grid equipment-based startup scheme generation and processing system, which will not be repeated here.

[0082] Compared with the prior art, the application has the following advantages:

[0083] 1. The traditional startup scheme preparation relies on manual writing, which is time-consuming, prone to errors, and has low reliability. In contrast, the present scheme establishes a power grid startup scheme automatic preparation system, uses state machine principles, expert system rule bases, and human-computer interaction rules to realize automatic, standardized, and accurate preparation of the startup scheme. This automated method greatly improves the efficiency of scheme preparation, reduces human errors, and thus improves the quality and reliability of the scheme.

[0084] 2. The present application promotes the standardization of the content and terminology of the startup scheme through an automated startup scheme generation and verification process, forming a standardized process. This means that the preparation of the scheme is no longer dependent on individual experience, but follows a unified standard and process, which helps to improve the intelligent level of power grid dispatching, reduces the work pressure of personnel preparing the startup scheme, and at the same time ensures the consistency and traceability of the scheme.

[0085] 3. The application of terminal simulation technology gives the present application a significant advantage in the field of industrial automation. Through virtual terminals, users can access and operate industrial automation systems on different devices in different locations, without being limited by the location and restrictions of specific physical terminal devices. This flexibility and scalability make system deployment and maintenance more convenient, better adapting to the changing needs and scenarios in industrial production environments. In addition, terminal simulation helps to improve system security by isolating user operations from actual production equipment, reducing potential security risks.

[0086] It should be understood that although the steps in the flowcharts involved in the embodiments described above are shown in sequence according to the arrows, the steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, the execution of the steps is not strictly limited in sequence, and the steps can be executed in other orders. Moreover, at least some of the steps in the flowcharts involved in the embodiments described above can include multiple steps or multiple stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution order of the steps or stages is not necessarily sequential, but can be alternately or alternately executed with at least part of other steps or steps or stages in other steps.

[0087] Based on the same inventive concept, the embodiments of the present application also provide a power grid equipment-based startup scheme generation and processing device for implementing the power grid equipment-based startup scheme generation and processing method described above. The implementation solutions provided by the device for solving the problem are similar to those described in the above method, so the specific limitations in one or more power grid equipment-based startup scheme generation and processing device embodiments provided below can refer to the limitations of the power grid equipment-based startup scheme generation and processing method described above, which will not be repeated here.

[0088] In one exemplary embodiment, as shown in Figure 5 a power grid equipment-based startup scheme generation and processing device is provided, comprising:

[0089] The scheme generation module 501 is configured to obtain a startup step model and a startup condition model, and generate a power grid equipment-based startup scheme based on the startup step model and the startup condition model.

[0090] The execution debugging module 502 is configured to perform device debugging and on-site verification based on the startup scheme, and obtain debugging and verification data.

[0091] The automatic simulation module 503 is configured to simulate the startup process of the power grid equipment in a virtual environment based on the startup scheme, and record current and voltage change data during the simulation process.

[0092] The comprehensive evaluation module 504 is configured to perform similarity calculation on the debugging and verification data and the current and voltage change data, and determine that the startup scheme evaluation is passed when the similarity meets the threshold condition.

[0093] The specific limitations of the grid equipment-based start-up scheme generation and processing device can be referred to the limitations of the grid equipment-based start-up scheme generation and processing method, which will not be repeated here. Each module in the above-mentioned grid equipment-based start-up scheme generation and processing device can be realized by software, hardware and their combination in whole or in part. The above-mentioned modules can be embedded in the processor in the computer device in hardware form or independent of the processor in the computer device, or stored in the memory in the computer device in software form, so as to be called and executed by the processor to perform the operations corresponding to each module.

[0094] In an exemplary embodiment, a computer device, which can be a server, is provided, and an internal structure diagram of the computer device can be as shown in Figure 6 The computer device includes a processor, a memory, an input / output interface (I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operating system and the computer program in the non-volatile storage medium to run. The database of the computer device is configured to store debugging and verification data and current-voltage change data. The input / output interface of the computer device is configured to exchange information between the processor and external devices. The communication interface of the computer device is configured to communicate with external terminals through network connection. The computer program is executed by the processor to implement a grid equipment-based start-up scheme generation and processing method.

[0095] Those skilled in the art can understand that Figure 6 The structure shown in the above-mentioned figure is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied. The specific computer device can include more or fewer components than those shown in the figure, or combine certain components, or have a different component arrangement.

[0096] In an embodiment, a computer device is also provided, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps in the above-mentioned method embodiments.

[0097] In an embodiment, a computer readable storage medium is provided, which stores a computer program. The computer program is executed by a processor to implement the steps in the above-mentioned method embodiments.

[0098] In an embodiment, a computer program product is provided, including a computer program which, when executed by a processor, implements the steps of any of the above method embodiments.

[0099] It should be noted that the user information (including but not limited to user equipment information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present application are all information and data authorized by the user or authorized by all parties, and the collection, use and processing of related data need to comply with relevant regulations.

[0100] It can be understood by those skilled in the art that all or part of the processes in the above-mentioned embodiments can be completed by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer readable storage medium. When the computer program is executed, it can include the processes of the above-mentioned embodiments. Any reference to memory, database or other medium used in the embodiments provided by the present application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical storage, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration but not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The database involved in the embodiments provided by the present application can include at least one of a relational database and a non-relational database. The non-relational database can include a distributed database based on a block chain, etc., without being limited thereto. The processor involved in the embodiments provided by the present application can be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., without being limited thereto.

[0101] The technical features of the above embodiments can be combined in any manner. For the sake of brevity, not all possible combinations of the technical features described above are described, but it is understood that any combination of the technical features is within the scope of the present application as long as the combination does not result in a contradiction.

[0102] The above-described embodiments are merely illustrative of several embodiments of the present application, and the description is relatively specific and detailed, but should not be construed as limiting the scope of the patent. It should be noted that for those skilled in the art, some modifications and improvements can be made without departing from the concept of the present application, and these are within the scope of the present application. Therefore, the scope of the present application should be determined by the appended claims.

Claims

1. A system for generating and processing startup schemes based on power grid equipment, characterized in that, The system includes: a scheme generation unit, an execution and debugging unit, an automated simulation unit, and a comprehensive evaluation unit; the scheme generation unit is communicatively connected to the execution and debugging unit and the automated simulation unit, respectively, and the execution and debugging unit and the automated simulation unit are communicatively connected to the comprehensive evaluation unit, respectively. The scheme generation unit is used to obtain a startup step model and a startup condition model, and generate a startup scheme based on the startup step model and the startup condition model. The execution debugging unit is used to perform equipment debugging and on-site verification based on the startup scheme, and to acquire debugging and verification data; The automated simulation unit is used to simulate the startup process of the power grid equipment in a virtual environment based on the startup scheme, and record current and voltage change data during the simulation process. The comprehensive evaluation unit is used to receive the debugging and verification data and the current and voltage change data, calculate the similarity between the debugging and verification data and the current and voltage change data, and determine that the startup scheme passes the evaluation if the similarity meets a threshold condition; wherein, the formula for calculating the similarity is as follows: In the above formula, w1, w2, w3, and w4 are weighting coefficients. It is the device startup time. It is a current impulse response. This is under stable voltage conditions. It refers to the activation status of the protection device. It is the startup time of the simulation equipment, It simulates the current impulse response. It simulates voltage stability and This simulates the operation of the protection device.

2. The system according to claim 1, characterized in that, The scheme generation unit includes: a word segmentation module, a model separation module, a feature extraction module, a corpus generation module, and a model training module; The word segmentation module is used to segment the historical startup steps and conditional text of the power grid equipment using natural language processing technology to form a startup operation word segmentation text library. The model separation module is used to separate the equipment name library and the power grid topology model based on the power grid equipment model generated by the energy management system, and form a topology model within the start-up range; The feature extraction module is used to extract topological features within the activation range in the topological model using a graph attention neural network. The corpus generation module is used to form a text corpus of startup steps and startup conditions based on the device name library and the startup operation word segmentation text library. The model training module is used to train the startup step statement using a sequential deep learning model and the startup condition statement using a non-sequential deep learning model, so as to obtain the startup step model and the startup condition model.

3. The system according to claim 2, characterized in that, The model training module is also used to acquire standard operating steps from historical power grid operation data, convert the standard operating steps into sequence data containing command identifiers and command descriptions, use the command identifiers as input to the startup step model, and use the command descriptions as output to train the startup steps to obtain the startup step model. The model training module is also used to obtain standard condition statements from the historical running data, and to train the condition statements based on the standard condition statements and the fully connected layer to obtain the startup condition model.

4. The system according to claim 1, characterized in that, The automated simulation unit is also used to establish a digital twin model of the power grid at the description subsystem layer based on detailed parameters and historical operating data of the power grid; collect first data for adjusting the power flow behavior of the power system and parse the first data to generate second data; establish a long short-term memory network model based on the second data; generate new power grid operating flow samples based on the power grid operating state predicted by the long short-term memory network model; perform power grid simulation calculations based on the power grid operating flow samples, and record the simulation data.

5. The system according to claim 1, characterized in that, The system also includes: a user interaction unit and an adjustment feedback unit; The user interaction unit is used to provide the user with an input window for instructions and to display the acquired target data to the user; the target data includes: power system single-line diagram, equipment status indication, real-time data chart, operation log and alarm signal; The adjustment feedback unit is used to adjust the startup scheme generation strategy of the scheme generation unit based on the difference between the debugging and verification results and the automated simulation results.

6. A method for generating and processing startup schemes based on power grid equipment, characterized in that, A method for generating and processing startup schemes based on power grid equipment, comprising: Obtain the startup step model and startup condition model, and generate a startup scheme based on the startup step model and startup condition model. Based on the aforementioned startup scheme, equipment debugging and on-site verification are performed, and debugging and verification data are obtained; Based on the aforementioned startup scheme, the startup process of the power grid equipment is simulated in a virtual environment, and data including current and voltage changes during the simulation process are recorded. The similarity between the debugging and verification data and the current and voltage change data is calculated, and the startup scheme is deemed to have passed the evaluation if the similarity meets a threshold condition; wherein, the formula for calculating the similarity is as follows: In the above formula, w1, w2, w3, and w4 are weighting coefficients. It is the device startup time. It is a current impulse response. This is under stable voltage conditions. It refers to the activation status of the protection device. It is the startup time of the simulation equipment, It simulates the current impulse response. It simulates voltage stability and This simulates the operation of the protection device.

7. A device for generating and processing startup schemes based on power grid equipment, characterized in that, The device includes: The scheme generation module is used to obtain the startup step model and the startup condition model, and generate a startup scheme based on the startup step model and the startup condition model. The debugging module is used to perform equipment debugging and on-site verification based on the startup scheme, and to acquire debugging and verification data; An automated simulation module is used to simulate the startup process of the power grid equipment in a virtual environment based on the startup scheme, and record current and voltage change data during the simulation process. The comprehensive evaluation module is used to calculate the similarity between the debugging and verification data and the current and voltage change data, and to determine whether the startup scheme passes the evaluation if the similarity meets the threshold condition; wherein, the formula for calculating the similarity is as follows: In the above formula, w1, w2, w3, and w4 are weighting coefficients. It is the device startup time. It is a current impulse response. This is under stable voltage conditions. It refers to the activation status of the protection device. It is the startup time of the simulation equipment, It simulates the current impulse response. It simulates voltage stability and This simulates the operation of the protection device.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method of claim 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method of claim 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method of claim 6.

Images