CN117748733A - Power grid information control method based on digital twin model - Google Patents
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Abstract
The invention discloses a power grid information control method based on a digital twin model, which comprises the following steps: collecting various data of a power grid, preprocessing the collected original data, and establishing a digital twin model of the power grid by using the collected data; the method comprises the steps of calibrating and verifying a digital twin model, updating data acquired in real time and the digital twin model in real time, carrying out simulation and optimization analysis by using the digital twin model, predicting and optimizing the running state of a power grid, generating a strategy for controlling power grid information based on simulation results and optimization analysis of the digital twin model, applying the generated control strategy to an actual power grid system, and carrying out real-time monitoring and feedback. Through the steps, the power grid information control method based on the digital twin model can realize accurate prediction, optimization adjustment and real-time monitoring of the running state of the power grid, and improves the reliability, safety and efficiency of the power grid.
Description
Technical Field
The invention belongs to the field of power grids, and particularly relates to a power grid information control method based on a digital twin model.
Background
Digital twin refers to equivalent mapping of the information world to the physical world, and intelligent management of the physical entity and related sensing equipment is realized by establishing a digital model of the physical entity and utilizing advanced Internet of things key technologies such as sensors, cloud computing, edge computing, artificial intelligence and the like to monitor and make intelligent decisions on line in real time. The digital twin comprises two parts of a physical twin and a virtual unit, wherein the physical twin comprises a series of physical modules, and the virtual unit comprises various simulation modules, algorithm modules and the like. The digital twinning utilizes the interaction fusion of the physical entity and the virtual machine to realize the mapping of the real scene to the physical model of the virtual machine. In the prior art, the control of the power grid information is realized by means of sensor technology, data communication and network technology, data processing and analysis technology, control algorithm and optimization technology, an automatic control system, an intelligent monitoring and management system and the like, so that the reliability, the safety and the efficiency of the power grid are improved, but related research on the control of the power grid information is not realized by a digital twin model in the prior art.
In view of this, the present invention has been proposed.
Disclosure of Invention
In order to solve the problems in the background technology, the invention adopts the basic conception of the technical proposal:
the power grid information control method based on the digital twin model comprises the following steps:
s1, data acquisition: collecting various data of the power grid, including sensor data, measurement data and equipment state data;
s2, data preprocessing: preprocessing the collected original data, including data cleaning, denoising and anomaly detection;
s3, establishing a digital twin model: establishing a digital twin model of the power grid by using the acquired data;
s4, model calibration and verification: calibrating and verifying the digital twin model to ensure that the digital twin model is matched with an actual power grid system;
s5, updating real-time data: updating the data acquired in real time and the digital twin model in real time to reflect the running state of the current power grid;
s6, model simulation and optimization: carrying out simulation and optimization analysis by using a digital twin model, and predicting and optimizing the running state of the power grid;
s7, control strategy generation: generating a strategy for controlling power grid information based on simulation results and optimization analysis of the digital twin model;
s8, control execution and monitoring: the generated control strategy is applied to an actual power grid system, and real-time monitoring and feedback are carried out;
s9, effect evaluation and optimization: and evaluating the effect of the power grid information control, and carrying out optimization adjustment according to the evaluation result.
In step S3, a digital twin model is built, specifically, the power grid data is learned and analyzed through a machine learning algorithm, the power grid digital twin model is built, the running state of the power grid is monitored and predicted in real time, or a large amount of power grid data is processed through a deep learning algorithm, and the power grid digital twin model can be more accurately realized through learning and prediction through a neural network structure.
Further, in the step S4, the digital twin model is calibrated and verified, specifically: and (5) calibrating by using historical data or real-time monitoring data, and comparing and verifying with the actual running condition.
In step S5, the data collected in real time and the digital twin model are updated in real time through the sensor network and the communication system.
Further, in the step S6, the running state of the power grid is predicted and optimized, specifically, the performance, reliability and safety of the power grid are evaluated by simulating different scenes and operation strategies.
Further, in S7, the policies of the grid information control include device scheduling, load management, fault detection and diagnosis.
In S8, the control is performed using an automated control system or a remote control system.
Further, in S9, the control effect is evaluated using an index evaluation and simulation verification method.
Compared with the prior art, the invention has the following beneficial effects:
through the steps, the power grid information control method based on the digital twin model can realize accurate prediction, optimization adjustment and real-time monitoring of the running state of the power grid, and improves the reliability, safety and efficiency of the power grid.
According to the invention, the power grid can be monitored and predicted in real time by a digital twin technology, and the dynamic change of the power grid is managed and controlled, so that the running stability and reliability of the power grid are improved. The digital twin system simulates and optimizes the power grid, so that the intelligent power dispatching and energy management of the power grid can be facilitated, the fine management and control of the power grid are realized, and the efficiency and flexibility of the power grid are improved. Through simulation and analysis of the digital twin system on the power grid, safety evaluation and early warning on the power grid can be realized, potential safety hazards can be found and solved in time, and safe operation of the power grid is guaranteed.
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FIG. 1 is a block diagram of a power grid information control method based on a digital twin model;
Detailed Description
The technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the embodiments of the present invention, and it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The power grid information control method based on the digital twin model, as shown in fig. 1, comprises the following steps:
s1, data acquisition: collecting various data of the power grid, including sensor data, measurement data and equipment state data; the data such as sensor data, measurement data, equipment state data and the like can be obtained through the Internet of things equipment, sensors, monitoring systems and the like;
s2, data preprocessing: preprocessing the collected original data, including data cleaning, denoising and anomaly detection; ensuring data quality and consistency.
S3, establishing a digital twin model: using the acquired data to build a digital twin model of the grid: the digital twin model is a virtual simulation model of a real power grid system, can accurately reflect the running state and behavior of a power grid, specifically, learns and analyzes power grid data through a machine learning algorithm, establishes a power grid digital twin model, monitors and predicts the running state of the power grid in real time, or processes a large amount of power grid data through a deep learning algorithm, learns and predicts through a neural network structure, and can more accurately realize the power grid digital twin model.
S4, model calibration and verification: and calibrating and verifying the digital twin model to ensure that the digital twin model is matched with an actual power grid system. The system can be calibrated by using historical data or real-time monitoring data, and can be compared with actual running conditions for verification.
S5, updating real-time data: and updating the data acquired in real time and the digital twin model in real time to reflect the running state of the current power grid. This may be achieved by means of a sensor network, a communication system or the like.
S6, model simulation and optimization: and carrying out simulation and optimization analysis by using a digital twin model, and predicting and optimizing the running state of the power grid. The performance, reliability and safety of the power grid can be evaluated by simulating different scenes and operation strategies.
S7, control strategy generation: and generating a strategy for controlling the power grid information based on simulation results and optimization analysis of the digital twin model. These policies may include device scheduling, load management, fault detection and diagnostics, and the like.
S8, control execution and monitoring: and applying the generated control strategy to an actual power grid system, and carrying out real-time monitoring and feedback. The control execution may be performed using an automated control system, a telemetry and remote control system, or the like.
S9, effect evaluation and optimization: and evaluating the effect of the power grid information control, and carrying out optimization adjustment according to the evaluation result. The control effect may be evaluated using index evaluation, simulation verification, or the like.
Through the steps, the power grid information control method based on the digital twin model can realize accurate prediction, optimal adjustment and real-time monitoring of the running state of the power grid, and improves the reliability, safety and efficiency of the power grid.
According to the invention, the power grid can be monitored and predicted in real time by a digital twin technology, and the dynamic change of the power grid is managed and controlled, so that the running stability and reliability of the power grid are improved. The digital twin system simulates and optimizes the power grid, so that the intelligent power dispatching and energy management of the power grid can be facilitated, the fine management and control of the power grid are realized, and the efficiency and flexibility of the power grid are improved. Through simulation and analysis of the digital twin system on the power grid, safety evaluation and early warning on the power grid can be realized, potential safety hazards can be found and solved in time, and safe operation of the power grid is guaranteed.
The neural network algorithm is a calculation method for simulating a human brain neural network structure, and is formed by interconnecting a large number of neurons. Each neuron may receive an input signal and perform a nonlinear transformation by an activation function to produce an output signal. The structure of the neural network can be divided into an input layer, a hidden layer and an output layer, wherein the number of hidden layers and the number of neurons per layer can be adjusted according to specific problems. The learning process of the neural network algorithm is realized by adjusting the connection weight and bias among the neurons. In the training process, the neural network receives input data, calculates an output result, compares the output result with an expected result, and adjusts the connection weight and the bias according to an error back propagation algorithm so that the output result gradually approaches the expected result. The neural network algorithm has strong nonlinear mapping capability and self-organizing learning capability, and can handle complex, nonlinear and uncertain problems. Meanwhile, the neural network algorithm can be optimized in parallel computing, distributed storage and other modes, so that the computing efficiency and the performance are improved. However, neural network algorithms also have problems such as poor interpretability, slow model training, strong data dependence, etc. In addition, the problems of local optimal solution and the like can also occur in the training process of the neural network algorithm, and proper optimization and adjustment are required. In a word, the neural network algorithm is a powerful calculation method and has wide application prospects in the fields of artificial intelligence, machine learning, natural language processing and the like.
The machine learning algorithm to which the present invention relates is an algorithm for regression (prediction) or classification by mining a rule implicit therein from a large amount of history data. In machine learning, model training is training by using a dataset and learning therefrom how to classify or predict new data points. There are a wide variety of machine learning algorithms including regression algorithms, decision trees, naive bayes classification, k-nearest neighbor algorithms, and the like. The application range of the algorithms is wide, and the algorithms can be used for image and voice recognition, natural language processing, recommendation systems and the like. Regression algorithms are algorithms for predicting continuous values, such as linear regression and logistic regression. Linear regression is best matching all data points by fitting a straight line, and logistic regression is used to classify data points into one of two categories. The decision tree is a predictive model representing a mapping relationship between object properties and object values. It does this by splitting the data set into subsets and further splitting according to the characteristics of each subset until a stop condition is reached. Decision trees can be used for classification and regression tasks. The naive Bayes classification is a classification algorithm based on Bayes theorem and is suitable for the condition that features are mutually independent. It calculates the posterior probability of a given data point by calculating the prior probability for each category and the conditional probability for each feature under each category, and classifies accordingly. The k-nearest neighbor algorithm is an instance-based learning that finds the nearest k instances by comparing the distance of the new data point to each data point in the training dataset, and votes according to the categories of those instances, classifying the new data point into the category that occurs most frequently. Machine learning algorithms are widely used in various fields, such as finance, medicine, electronic commerce, etc. They may help people better understand data, predict future trends, optimize decisions, etc.
In a preferred embodiment of the invention a k-nearest neighbor algorithm is used in which a series of training samples are known, each sample containing an input feature and a corresponding output label. The algorithm does not establish a clear generalized description of the objective function, but simply stores the training samples. When a new query instance is encountered, the k-nearest neighbor algorithm analyzes the relationship of the new instance to the previously stored instance and assigns an objective function value to the new instance accordingly. Specifically, it finds out k nearest examples by calculating the distance between the new example and the stored training examples, and votes according to the labels of these examples, classifying the new example into the category with the largest occurrence number. Example-based learning approaches have their advantages in dealing with complex classification problems. For example, when an objective function is complex, but it can be described with a less complex local approximation, an instance-based approach can build a local approximation of the objective function and apply it to instances that are adjacent to the new query instance, without the need to perform a good approximation over the entire instance space. However, there are also some disadvantages to the example-based approach. For example, the overhead of classifying new instances may be significant because almost all of the computation occurs at the time of classification, not at the time of first encountering a training sample. Furthermore, how to efficiently index training samples to reduce the computation required at query time is also an important practical issue.
Embodiments of the present application that require protection include:
the power grid information control method based on the digital twin model, as shown in fig. 1, comprises the following steps:
s1, data acquisition: collecting various data of the power grid, including sensor data, measurement data and equipment state data;
s2, data preprocessing: preprocessing the collected original data, including data cleaning, denoising and anomaly detection;
s3, establishing a digital twin model: establishing a digital twin model of the power grid by using the acquired data;
s4, model calibration and verification: calibrating and verifying the digital twin model to ensure that the digital twin model is matched with an actual power grid system;
s5, updating real-time data: updating the data acquired in real time and the digital twin model in real time to reflect the running state of the current power grid;
s6, model simulation and optimization: carrying out simulation and optimization analysis by using a digital twin model, and predicting and optimizing the running state of the power grid;
s7, control strategy generation: generating a strategy for controlling power grid information based on simulation results and optimization analysis of the digital twin model;
s8, control execution and monitoring: the generated control strategy is applied to an actual power grid system, and real-time monitoring and feedback are carried out;
s9, effect evaluation and optimization: and evaluating the effect of the power grid information control, and carrying out optimization adjustment according to the evaluation result.
Preferably, in the step S3, the digital twin model is built specifically by learning and analyzing the power grid data through a machine learning algorithm, building the digital twin model of the power grid, and monitoring and predicting the running state of the power grid in real time, or processing a large amount of power grid data through a deep learning algorithm, and learning and predicting through a neural network structure, so that the digital twin model of the power grid can be more accurately realized.
Preferably, in S4, the digital twin model is calibrated and verified, specifically: and (5) calibrating by using historical data or real-time monitoring data, and comparing and verifying with the actual running condition.
Preferably, in the step S5, the data collected in real time and the digital twin model are updated in real time through a sensor network and a communication system.
Preferably, in the step S6, the running state of the power grid is predicted and optimized, and specifically, the performance, reliability and safety of the power grid are evaluated by simulating different scenes and operation strategies.
Preferably, in S7, the strategy for controlling the grid information includes device scheduling, load management, fault detection and diagnosis.
Preferably, in S8, the control is performed using an automated control system or a telemetry and remote control system.
Preferably, in S9, the control effect is evaluated using an index evaluation and simulation verification method.
The embodiment of the application also provides a computer device, which may include a terminal device or a server, and the method data computing program may be configured in the computer device. The computer device is described below.
If the computer device is a terminal device, the embodiment of the present application provides a terminal device, taking the terminal device as a mobile phone as an example:
the mobile phone comprises: radio Frequency (RF) circuitry, memory, input unit, display unit, sensors, audio circuitry, wireless fidelity (Wireless Fidelity, wiFi) module, processor, and power supply.
The RF circuit can be used for receiving and transmitting signals in the process of receiving and transmitting information or communication, particularly, after receiving downlink information of the base station, the downlink information is processed by the processor; in addition, the data of the design uplink is sent to the base station. Typically, RF circuitry includes, but is not limited to, antennas, at least one amplifier, transceivers, couplers, low noise amplifiers (Low NoiseAmplifier, LNA for short), diplexers, and the like. In addition, the RF circuitry may also communicate with networks and other devices via wireless communications. The wireless communication may use any communication standard or protocol, including but not limited to global system for mobile communications (Global System of Mobile communication, GSM for short), general packet radio service (GeneralPacket Radio Service, GPRS for short), code division multiple access (Code Division Multiple Access, CDMA for short), wideband code division multiple access (Wideband Code Division Multiple Access, WCDMA for short), long term evolution (Long Term Evolution, LTE for short), email, short message service (Short Messaging Service, SMS for short), and the like.
The memory may be used to store software programs and modules, and the processor executes the software programs and modules stored in the memory to perform various functional applications and data processing of the handset. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program (such as a sound playing function, an image playing function, etc.) required for at least one function, and the like; the storage data area may store data (such as audio data, phonebook, etc.) created according to the use of the handset, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid-state storage device.
The input unit may be used to receive input numeric or character information and to generate key signal inputs related to user settings and function control of the handset. In particular, the input unit may include a touch panel and other input devices. The touch panel, also referred to as a touch screen, may collect touch operations thereon or thereabout by a user (e.g., operations thereon or thereabout by a user using any suitable object or accessory such as a finger, stylus, etc.), and drive the corresponding connection device according to a predetermined program. Alternatively, the touch panel may include two parts, a touch detection device and a touch controller. The touch detection device detects the touch azimuth of a user, detects a signal brought by touch operation and transmits the signal to the touch controller; the touch controller receives touch information from the touch detection device, converts the touch information into touch point coordinates, sends the touch point coordinates to the processor, and can receive and execute commands sent by the processor. In addition, the touch panel may be implemented in various types such as resistive, capacitive, infrared, and surface acoustic wave. The input unit may include other input devices in addition to the touch panel. In particular, other input devices may include, but are not limited to, one or more of a physical keyboard, function keys (such as volume control keys, switch keys, etc.), a trackball, mouse, joystick, etc.
The display unit may be used to display information input by a user or information provided to the user and various menus of the mobile phone. The display unit may include a display panel, which may be optionally configured in the form of a liquid crystal display (LiquidCrystal Display, LCD) or an Organic Light-Emitting Diode (OLED) or the like. Further, the touch panel may overlay the display panel, and upon detection of a touch operation thereon or thereabout, the touch panel is transferred to the processor to determine the type of touch event, and the processor then provides a corresponding visual output on the display panel in accordance with the type of touch event. Although in the figures the touch panel and the display panel are shown as two separate components to implement the input and output functions of the cell phone, in some embodiments the touch panel and the display panel may be integrated to implement the input and output functions of the cell phone.
The handset may also include at least one sensor, such as a light sensor, a motion sensor, and other sensors. In particular, the light sensor may include an ambient light sensor that may configure the brightness of the display panel according to the brightness of ambient light, and a proximity sensor that may turn off the display panel and/or backlight when the phone is moved to the ear. As one of the motion sensors, the accelerometer sensor can detect the acceleration in all directions (generally three axes), and can detect the gravity and direction when stationary, and can be used for applications of recognizing the gesture of a mobile phone (such as horizontal and vertical screen switching, related games, magnetometer gesture calibration), vibration recognition related functions (such as pedometer and knocking), and the like; other sensors such as gyroscopes, barometers, hygrometers, thermometers, infrared sensors, etc. that may also be configured with the handset are not described in detail herein.
Audio circuitry, speakers, and microphone may provide an audio interface between the user and the handset. The audio circuit can transmit the received electric signal after the audio data conversion to a loudspeaker, and the loudspeaker converts the electric signal into a sound signal to be output; on the other hand, the microphone converts the collected sound signals into electrical signals, which are received by the audio circuit and converted into audio data, which are processed by the audio data output processor and sent via the RF circuit to, for example, another mobile phone, or which are output to a memory for further processing.
WiFi belongs to a short-distance wireless transmission technology, and a mobile phone can help a user to send and receive an email, browse a webpage, access streaming media and the like through a WiFi module, so that wireless broadband Internet access is provided for the user. Although a WiFi module is illustrated, it is understood that it does not belong to the necessary configuration of the handset, and can be omitted entirely as needed within the scope of not changing the essence of the invention.
The processor is a control center of the mobile phone, and is connected with various parts of the whole mobile phone by various interfaces and lines, and executes various functions and processes data of the mobile phone by running or executing software programs and/or modules stored in the memory and calling data stored in the memory, so that the mobile phone is monitored integrally. In the alternative, the processor may include one or more processing units; preferably, the processor may integrate an application processor and a modem processor, wherein the application processor primarily handles operating systems, user interfaces, applications, etc., and the modem processor primarily handles wireless communications. It will be appreciated that the modem processor described above may not be integrated into the processor.
The handset further includes a power source (e.g., a battery) for powering the various components, preferably in logical communication with the processor through a power management system, such that functions such as managing charge, discharge, and power consumption are performed by the power management system.
Although not shown, the mobile phone may further include a camera, a bluetooth module, etc., which will not be described herein.
In this embodiment, the processor included in the terminal device further has the following functions:
a data calculation program for performing a method of configuring an access point address of a local area network.
If the computer device is a server, the embodiments of the present application further provide a server, where the server may generate a relatively large difference due to different configurations or performances, and may include one or more central processing units (Central Processing Units, abbreviated as CPUs) (e.g., one or more processors) and a memory, one or more storage media (e.g., one or more mass storage devices) storing application programs or data. The memory and storage medium may be transitory or persistent. The program stored on the storage medium may include one or more modules, each of which may include a series of instruction operations on the server. Still further, the central processor may be configured to communicate with a storage medium and execute a series of instruction operations on the storage medium on a server.
The server may also include one or more power supplies, one or more wired or wireless network interfaces, one or more input/output interfaces, and/or one or more operating systems, such as Windows Server, mac OS XTM, unixTM, linuxTM, freeBSDTM, etc.
In addition, the embodiment of the application also provides a storage medium for storing a computer program for executing the method provided by the embodiment.
The present embodiments also provide a computer program product comprising instructions which, when run on a computer, cause the computer to perform the method provided by the above embodiments.
Those of ordinary skill in the art will appreciate that: all or part of the steps for implementing the above method embodiments may be implemented by hardware related to program instructions, where the above program may be stored in a computer readable storage medium, and when the program is executed, the program performs steps including the above method embodiments; and the aforementioned storage medium may be at least one of the following media: read-only Memory (ROM), RAM, magnetic disk or optical disk, etc.
It should be noted that, in the present specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment is mainly described in a different point from other embodiments. In particular, for the apparatus and system embodiments, since they are substantially similar to the method embodiments, the description is relatively simple, with reference to the description of the method embodiments in part. The apparatus and system embodiments described above are merely illustrative, in which elements illustrated as separate elements may or may not be physically separate, and elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
Claims (8)
1. The power grid information control method based on the digital twin model is characterized by comprising the following steps:
s1, data acquisition: collecting various data of the power grid, including sensor data, measurement data and equipment state data;
s2, data preprocessing: preprocessing the collected original data, including data cleaning, denoising and anomaly detection;
s3, establishing a digital twin model: establishing a digital twin model of the power grid by using the acquired data;
s4, model calibration and verification: calibrating and verifying the digital twin model to ensure that the digital twin model is matched with an actual power grid system;
s5, updating real-time data: updating the data acquired in real time and the digital twin model in real time to reflect the running state of the current power grid;
s6, model simulation and optimization: carrying out simulation and optimization analysis by using a digital twin model, and predicting and optimizing the running state of the power grid;
s7, control strategy generation: generating a strategy for controlling power grid information based on simulation results and optimization analysis of the digital twin model;
s8, control execution and monitoring: the generated control strategy is applied to an actual power grid system, and real-time monitoring and feedback are carried out;
s9, effect evaluation and optimization: and evaluating the effect of the power grid information control, and carrying out optimization adjustment according to the evaluation result.
2. The method for controlling power grid information based on the digital twin model according to claim 1, wherein in S3, the digital twin model is established, specifically, the power grid data is learned and analyzed by a machine learning algorithm, the digital twin model of the power grid is established, the running state of the power grid is monitored and predicted in real time, or a large amount of power grid data is processed by a deep learning algorithm, and the digital twin model of the power grid is learned and predicted by a neural network structure, so that the digital twin model of the power grid can be realized more accurately.
3. The method for controlling power grid information based on a digital twin model according to claim 1, wherein in S4, the digital twin model is calibrated and verified, specifically: and (5) calibrating by using historical data or real-time monitoring data, and comparing and verifying with the actual running condition.
4. The method for controlling power grid information based on a digital twin model according to claim 1, wherein in S5, the data collected in real time and the digital twin model are updated in real time through a sensor network and a communication system.
5. The method for controlling power grid information based on the digital twin model according to claim 1, wherein in S6, the running state of the power grid is predicted and optimized, specifically, the performance, reliability and safety of the power grid are evaluated by simulating different scenes and operation strategies.
6. The method for controlling power grid information based on the digital twin model according to claim 1, wherein in S7, the strategy for controlling power grid information includes equipment scheduling, load management, fault detection and diagnosis.
7. The method for controlling power grid information based on a digital twin model according to claim 1, wherein in S8, the control is performed using an automated control system or a telemetry and remote control system.
8. The method for controlling power grid information based on a digital twin model according to claim 1, wherein in S9, the control effect is evaluated using an index evaluation and simulation verification method.
Priority Applications (1)
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| CN112018758A (en) * | 2020-08-26 | 2020-12-01 | 国网重庆市电力公司电力科学研究院 | Modeling method of high-proportion new energy-containing alternating current-direct current hybrid system based on digital twinning |
| CN112417619A (en) * | 2020-11-23 | 2021-02-26 | 江苏大学 | Pump unit optimal operation adjusting system and method based on digital twinning |
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| CN115859700A (en) * | 2023-03-02 | 2023-03-28 | 国网湖北省电力有限公司电力科学研究院 | Power grid modeling method based on digital twinning technology |
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| CN112417619A (en) * | 2020-11-23 | 2021-02-26 | 江苏大学 | Pump unit optimal operation adjusting system and method based on digital twinning |
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