Synchronous phase modifier phase modulation method and system adopting digital twinborn model

Abstract

The invention relates to the technical field of phase adjustment, and discloses a synchronous phase modifier phase modulation method adopting a digital twinborn model, and the method comprises the steps: carrying out the modularization processing of a synchronous phase modifier, and obtaining a system module; identifying a power grid topological structure of the power system, and collecting load data, generator data and line parameters of the power system to construct a simulation model of the power system; integrating the system module and the simulation model to obtain an integrated model; performing impedance adjustment on the module integration system to obtain an impedance adjustment model; collecting system data of the synchronous phase modifier, and analyzing control characteristics of the synchronous phase modifier; and executing the phase adjustment reference of the impedance adjustment model by using the control characteristics, obtaining a digital twin model when the model precision meets the preset model precision, analyzing the real-time power data by using the digital twin model, and performing phase adjustment on the power system based on the analysis result. The accuracy of phase adjustment of the synchronous phase modifier can be improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of phase adjustment, and particularly relates to a synchronous phase modifier phase adjustment method using a digital twin model. BACKGROUND

[0002] A synchronous phase modifier is a device used in power systems to synchronize the voltage and phase between different parts of the system. It is commonly used to ensure that individual generators or power equipment maintain their voltage and frequency in sync when operating in a network, to avoid system failures. The synchronous phase modifier can help ensure that the phase difference between various components within the system is as small as possible to maintain the stability and reliability of the system. Therefore, the synchronous phase modifier can accurately detect various parameters of the power system, and it is of great significance to adjust the phase of the power system according to different situations.

[0003] At present, the model and parameters provided by the synchronous phase modifier manufacturer have some deviation from the actual parameters in use, and as the equipment ages, the parameters will also change, leading to difficulties in reactive voltage characteristic analysis and optimal control, and making the phase adjustment of the synchronous phase modifier to the power system not accurate enough. SUMMARY

[0004] To solve the above problems, the present application provides a synchronous phase modifier phase adjustment method using a digital twin model, which can improve the accuracy of the phase adjustment of the synchronous phase modifier.

[0005] In a first aspect, the present application provides a synchronous phase modifier phase adjustment method using a digital twin model, comprising:

[0006] The component system of the synchronous phase modifier is modularly processed to obtain a system module, wherein the synchronous phase modifier is deployed in a pre-constructed power system to be adjusted;

[0007] The power grid topology of the power system to be adjusted is identified, and generator data and line parameters of the power system to be adjusted are collected. Based on the power grid topology, the generator data and the line parameters, an initial simulation model of the power system to be adjusted is constructed. The power system to be adjusted is simulated by using the initial simulation model to obtain a target simulation model;

[0008] The system module and the target simulation model are integrated to obtain a module integrated system;

[0009] Based on the power grid topology and the line parameters, the component impedance of the power system to be adjusted is determined to obtain a first component impedance, and the component impedance of the synchronous phase modifier is extracted to obtain a second component impedance. The module integrated system is adjusted based on the first component impedance and the second component impedance to obtain an impedance adjustment model;

[0010] The system data of the synchronous condenser is collected, the system data is clustered to obtain clustered data, the data sensitivity of the clustered data is analyzed, and the control characteristics of the synchronous condenser are determined based on the data sensitivity.

[0011] After executing the phase adjustment reference of the impedance adjustment model using the control characteristics, an initial digital twin model is obtained. The model accuracy of the initial digital twin model is calculated. When the model accuracy meets the preset model accuracy, a digital twin model is obtained. The synchronous condenser is used to collect real-time power data of the power system to be adjusted, and the digital twin model is used to perform data analysis on the real-time power data to obtain data analysis results. Based on the data analysis results, the synchronous condenser is used to perform phase adjustment on the power system to be adjusted.

[0012] In one possible implementation of the first aspect, constructing the initial simulation model of the power system to be adjusted based on the power grid topology, the generator data, and the line parameters includes:

[0013] Construct a physical model of the power system to be adjusted;

[0014] After arranging the components of the physical model using the power grid topology, the operating parameters of the physical model are configured according to the generator data and the line parameters to obtain the initial simulation model.

[0015] In one possible implementation of the first aspect, the step of using the initial simulation model to perform a power simulation on the power system to be adjusted to obtain a target simulation model includes:

[0016] Define the operating conditions of the initial simulation model;

[0017] The initial simulation model is run based on the aforementioned operating conditions to obtain the running model;

[0018] The operating model was subjected to steady-state testing to obtain steady-state test results;

[0019] When the steady-state test results meet the preset results, a steady-state operation model is obtained;

[0020] Based on the test data from the steady-state test, the goodness of fit of the steady-state model is calculated using the following formula:

[0021]

[0022] Where α represents the goodness of fit, N represents the amount of test data, and B i This represents the predicted value of the i-th data point in the test data. This represents the true value of the i-th data point in the test data. This represents the mean of the true values ​​of the i-th data point in the test data.

[0023] When the fitting degree meets the preset fitting degree, the target simulation model is obtained.

[0024] In one possible implementation of the first aspect, performing a steady-state test on the operating model to obtain the steady-state test result includes:

[0025] Determine the phase angle and signal amplitude of the running nodes in the operating model;

[0026] Based on the phase angle and the signal amplitude, the voltage value of the operating node is calculated using the following formula:

[0027] V = |δ|cosθ + E|δ|sinθ

[0028] Where V represents the voltage value, θ represents the phase angle, E represents an imaginary unit, and |δ| represents the signal amplitude;

[0029] After comparing the voltage value with the input voltage value of the operating model, the steady-state test is completed.

[0030] In one possible implementation of the first aspect, the modular integration of the system module and the target simulation model to obtain a modular integrated system includes:

[0031] Identify the component connection points of the target simulation model;

[0032] The system module and the target simulation model are connected based on the component connection points to obtain a connection module;

[0033] Data transmission is performed on the connection module to obtain a data transmission module;

[0034] The data transmission module is calibrated to obtain the module integration system.

[0035] In one possible implementation of the first aspect, the impedance adjustment of the module integration system based on the impedance of the first component and the impedance of the second component to obtain an impedance adjustment model includes:

[0036] Calculate the impedance difference between the impedance of the first component and the impedance of the second component;

[0037] Construct the impedance network of the module integration system;

[0038] Using the impedance network and the impedance difference, the module integration system is subjected to a first impedance adjustment, and the output efficiency of the module integration system under the first impedance adjustment is calculated to obtain the first output efficiency.

[0039] The module integration system is subjected to a second impedance adjustment, and the output efficiency of the module integration system under the second impedance adjustment is calculated to obtain the second output efficiency;

[0040] Based on the first output efficiency and the second output efficiency, the impedance adjustment scheme of the module integration system is determined;

[0041] The impedance of the module integration system is adjusted based on the impedance adjustment scheme to obtain an impedance adjustment model.

[0042] In one possible implementation of the first aspect, the clustering process of the system data to obtain clustered data includes:

[0043] The system data is filtered to obtain filtered data;

[0044] The filtered data is then transformed to obtain vector data;

[0045] Construct the vector centroids of the vector data;

[0046] The vector distance between the vector data and the centroid of the vector is calculated using the following formula:

[0047]

[0048] Where D represents the vector distance, φ represents the spatial dimension of the vector data, and x i Let y represent the x-coordinate of the centroid of the i-th vector. i The ordinate represents the centroid of the i-th vector;

[0049] Based on the vector distance, the filtered data is clustered to obtain clustered data.

[0050] In one possible implementation of the first aspect, the data sensitivity of analyzing the clustered data includes:

[0051] Construct the DC axis and orthogonal axis of the clustered data;

[0052] DC parameter variation analysis was performed on the DC axis to obtain the DC variation analysis results;

[0053] The DC axis sensitivity of the clustering data is determined based on the DC variation analysis results.

[0054] Orthogonal parameter variation analysis was performed on the orthogonal axes to obtain orthogonal variation analysis results;

[0055] The orthogonal axis sensitivity of the clustering data is determined based on the orthogonal variation analysis results.

[0056] The data sensitivity is obtained by combining the DC axis sensitivity and the quadrature axis sensitivity.

[0057] In one possible implementation of the first aspect, calculating the model accuracy of the initial digital twin model includes:

[0058] Collect historical data of the power system to be adjusted;

[0059] The initial digital twin model is tested using the historical data to obtain a test digital twin model;

[0060] The root mean square error of the test digital twin model is calculated using the following formula:

[0061]

[0062] Where RS represents the root mean square error, n represents the amount of historical data, and A i This represents the predicted value of the i-th historical data point. This represents the true value of the i-th historical data point;

[0063] The model accuracy of the initial digital twin model is determined based on the root mean square error.

[0064] Secondly, the present invention provides a synchronous phase-shifting system employing a digital twin model, the system comprising:

[0065] A modular processing module is used to deploy synchronous condensers in a pre-built power system to be adjusted, and to perform modular processing on the component system of the synchronous condensers to obtain system modules;

[0066] The system simulation module is used to identify the power grid topology of the power system to be adjusted, and to collect the generator data and line parameters of the power system to be adjusted. Based on the power grid topology, the generator data and the line parameters, an initial simulation model of the power system to be adjusted is constructed. Using the initial simulation model, power simulation is performed on the power system to be adjusted to obtain the target simulation model.

[0067] A module integration module is used to integrate the system modules and the target simulation model to obtain a module integration system;

[0068] An impedance adjustment module is used to determine the component impedance of the power system to be adjusted based on the power grid topology and the line parameters, to obtain a first component impedance, and to extract the component impedance of the synchronous condenser to obtain a second component impedance. Based on the first component impedance and the second component impedance, the module integrated system is impedance adjusted to obtain an impedance adjustment model.

[0069] The control characteristic analysis module is used to collect system data of the synchronous condenser, perform clustering processing on the system data to obtain clustered data, analyze the data sensitivity of the clustered data, and determine the control characteristics of the synchronous condenser based on the data sensitivity.

[0070] A phase adjustment module is used to extract the power parameters of the power system to be adjusted, construct a phase control strategy for the power system to be adjusted using the control characteristics and the power parameters, and obtain an initial digital twin model after executing the phase adjustment reference of the impedance adjustment model using the phase control strategy. The module calculates the model accuracy of the initial digital twin model, and obtains a digital twin model when the model accuracy meets a preset model accuracy. The module uses a synchronous condenser to collect real-time power data of the power system to be adjusted, and performs data analysis on the real-time power data using the digital twin model to obtain data analysis results. Based on the data analysis results, the module uses the synchronous condenser to perform phase adjustment on the power system to be adjusted.

[0071] Compared with existing technologies, the technical principles and beneficial effects of this solution are as follows:

[0072] This invention, through the deployment of synchronous condensers in a pre-constructed power system to be adjusted, allows for power analysis of the pre-constructed power system. This facilitates timely phase adjustment when the power system is unstable, ensuring stable power output. It also allows for identification of the power grid topology of the power system to be adjusted, and the collection of generator data and line parameters to understand its structural composition. This enables analysis of the circuit principles of the power system and understanding parameters such as resistance, inductance, and capacitance to understand the transmission characteristics of the power transmission lines, including line impedance, losses, and their impact on system stability. Furthermore, this invention integrates the system modules and the target simulation model to obtain a modular integrated system. The synchronous condenser and the power system to be adjusted can be connected in a simulation system to facilitate the establishment and analysis of a digital twin model. Furthermore, this embodiment of the invention determines the component impedance of the power system to be adjusted based on the power grid topology and line parameters, obtaining a first component impedance, and extracts the component impedance of the synchronous condenser to obtain a second component impedance. This allows understanding the impedance of two different systems, facilitating adjustment to improve system compatibility or performance. The system data collected from the synchronous condenser can be used to monitor and control the operation of the power system, and help users understand the voltage and phase relationships between power equipment and the system stability. Based on the data analysis results, using the synchronous condenser to perform phase adjustment on the power system to be adjusted can obtain accurate phase adjustment results, thereby helping the power system to output power more stably and efficiently. This embodiment of the invention proposes a synchronous condenser phase adjustment method and system using a digital twin model, which can improve the accuracy of synchronous condenser phase adjustment. Attached Figure Description

[0073] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0074] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0075] Figure 1 This is a flowchart illustrating a synchronous phase adjustment method using a digital twin model, provided in an embodiment of the present invention.

[0076] Figure 2This is a schematic diagram of a synchronous phase-shifting system using a digital twin model, provided as an embodiment of the present invention. Detailed Implementation

[0077] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0078] This invention provides a synchronous phase-shifting method using a digital twin model. The executing entity of this method includes, but is not limited to, at least one of the following electronic devices configured to execute the method provided in this invention: a server, a terminal, etc. In other words, the synchronous phase-shifting method using a digital twin model can be executed by software or hardware installed on a terminal device or a server device. The software can be a blockchain platform. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cluster of cloud servers. The server can be an independent server or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDN), and big data and artificial intelligence platforms.

[0079] See Figure 1 The diagram shown is a flowchart illustrating a synchronous phase-shifting method using a digital twin model, according to an embodiment of the present invention. Figure 1 The synchronous phase-shifting method using a digital twin model, as described in the article, includes:

[0080] S1. Modularize the component system of the synchronous condenser to obtain system modules, wherein the synchronous condenser is deployed in a pre-constructed power system to be adjusted.

[0081] In this embodiment of the invention, the component system of the synchronous condenser is modularized, and the resulting system modules can divide the various components of the synchronous condenser to better understand the function of each component.

[0082] As an embodiment of the present invention, the modular processing of the component system of the synchronous condenser to obtain system modules includes: identifying the component functions of the component system, dividing the component system into modules based on the component functions to obtain partitioned modules, defining the module interfaces of the partitioned modules, and connecting the partitioned modules based on the module interfaces to obtain system modules.

[0083] The component functions refer to the roles of different components of the synchronous condenser, such as the power generation module and the current conversion module. The module interface that defines the division of the module refers to defining the connection positions of different components of the synchronous condenser. It should be noted that the representation of components in the computer system is different from that in reality. For example, a generator may be represented by a block.

[0084] It should be noted that the power system to be adjusted refers to a power system that requires phase analysis and adjustment, which is an integrated system composed of power plants, transmission lines, substations and distribution systems. The synchronous condenser refers to a device in a power system used to synchronize the voltage and phase between different parts of the power system.

[0085] S2. Identify the power grid topology of the power system to be adjusted, and collect the generator data and line parameters of the power system to be adjusted. Based on the power grid topology, the generator data and the line parameters, construct an initial simulation model of the power system to be adjusted. Use the initial simulation model to perform power simulation on the power system to be adjusted to obtain the target simulation model.

[0086] This invention, through identifying the power grid topology of the power system to be adjusted and collecting generator data and line parameters of the power system to be adjusted, can understand the structural composition of the power system to be adjusted, analyze the circuit principle of the power system to be adjusted, and understand the resistance, inductance, capacitance and other parameters of the power system to be adjusted in order to understand the transmission characteristics of the power transmission lines of the power system to be adjusted, including the impedance, loss and impact on system stability of the lines.

[0087] Furthermore, in this embodiment of the invention, by constructing an initial simulation model of the power system to be adjusted based on the power grid topology, the generator data, and the line parameters, different generator scheduling schemes, line configurations, and load allocation strategies can be tried to maximize system efficiency, reduce costs, or improve power supply quality.

[0088] As an embodiment of the present invention, constructing an initial simulation model of the power system to be adjusted based on the power grid topology, the generator data, and the line parameters includes: constructing a physical model of the power system to be adjusted; after arranging the components of the physical model using the power grid topology; and configuring the operating parameters of the physical model according to the generator data and the line parameters to obtain the initial simulation model. The physical model refers to a mathematical representation describing a physical system or phenomenon, which simulates the behavior and characteristics of the physical system through mathematical equations, graphs, or other mathematical tools.

[0089] Optionally, the physical model of the power system to be adjusted can be constructed using MATLAB tools.

[0090] The embodiments of the present invention utilize the initial simulation model to perform power simulation on the power system to be adjusted, and obtain the target simulation model, which can provide an important reference for power system operation and planning.

[0091] As an embodiment of the present invention, the step of using the initial simulation model to perform power simulation on the power system to be adjusted to obtain a target simulation model includes: defining the operating conditions of the initial simulation model; running the initial simulation model based on the operating conditions to obtain an operating model; performing a steady-state test on the operating model to obtain steady-state test results; obtaining a steady-state operating model when the steady-state test results meet preset results; and calculating the goodness of fit of the steady-state model based on the test data of the steady-state test using the following formula:

[0092]

[0093] Where α represents the goodness of fit, N represents the amount of test data, and B i This represents the predicted value of the i-th data point in the test data. This represents the true value of the i-th data point in the test data. This represents the mean of the true values ​​of the i-th data point in the test data.

[0094] When the fitting degree meets the preset fitting degree, the target simulation model is obtained.

[0095] Optionally, defining the operating conditions of the initial simulation model refers to determining the initial and boundary conditions of the simulation model, including conditions such as load level, generator output, and line status, which can be adaptively defined according to user needs.

[0096] Furthermore, in another optional embodiment of the present invention, the step of performing a steady-state test on the operating model to obtain the steady-state test result includes: determining the phase angle and signal amplitude of the operating node in the operating model, and calculating the voltage value of the operating node based on the phase angle and the signal amplitude using the following formula:

[0097] V = |δ|cosθ + E|δ|sinθ

[0098] Where V represents the voltage value, θ represents the phase angle, E represents an imaginary unit, and |δ| represents the signal amplitude;

[0099] After comparing the voltage value with the input voltage value of the operating model, the steady-state test is completed.

[0100] It should be noted that if the voltage comparison result shows small fluctuation, it indicates that the steady-state test result is stable, and the first test result is obtained. If the voltage comparison result shows large fluctuation, the small fluctuation means that the ratio of the voltage value to the input voltage value is between 98% and 102%. For example, if the voltage value is 98 and the input voltage value is 100, the ratio is 98%; if the voltage value is 102 and the input voltage value is 100, the ratio is 102%. The large fluctuation means that the ratio is less than 98% or greater than 102%.

[0101] S3. Integrate the system modules and the target simulation model to obtain a module-integrated system.

[0102] In this embodiment of the invention, the system modules and the target simulation model are integrated to obtain a module-integrated system that can connect the synchronous camera and the power system to be adjusted to a simulation system, facilitating the establishment and analysis of a digital twin model.

[0103] As an embodiment of the present invention, the step of integrating the system module and the target simulation model to obtain a module integration system includes: identifying the component connection points of the target simulation model; connecting the system module and the target simulation model based on the component connection points to obtain a connection module; transmitting data through the connection module to obtain a data transmission module; and calibrating the data transmission module to obtain the module integration system.

[0104] Optionally, the component connection points of the target simulation model can be obtained by identifying the power grid topology. The data calibration of the data transmission module to obtain the module integration model is obtained by calculating the electrical signal output of the data transmission module. If the calculated result of the electrical signal output is not much different from the predicted value (e.g., the difference is within 1%), it indicates that the data transmission of the data transmission module is a reliable value, and the module calibration is completed.

[0105] S4. Based on the power grid topology and the line parameters, determine the component impedance of the power system to be adjusted to obtain the first component impedance, and extract the component impedance of the synchronous condenser to obtain the second component impedance. Based on the first component impedance and the second component impedance, perform impedance adjustment on the module integration system to obtain the impedance adjustment model.

[0106] This invention, through the determination of the component impedance of the power system to be adjusted based on the power grid topology and the line parameters, obtains the first component impedance and extracts the component impedance of the synchronous condenser to obtain the second component impedance. This allows for understanding the impedance of two different systems, thereby facilitating adjustment and improving system compatibility or performance.

[0107] Wherein, the first component impedance refers to the degree to which each component (such as generator, transformer, transmission line, etc.) in the power system impedes the current at a given frequency, and the second component impedance refers to the impedance of each component (such as motor, transmission system, etc.) in the synchronous condenser equipment to the current at a given frequency.

[0108] Furthermore, in this embodiment of the invention, by adjusting the impedance of the module integration system based on the impedance of the first component and the impedance of the second component, an impedance adjustment model is obtained that can reduce impedance matching interference of the module integration system, thereby improving the energy transmission efficiency of the module integration system or reducing signal reflection problems.

[0109] As an embodiment of the present invention, the step of adjusting the impedance of the modular integrated system based on the impedance of the first component and the impedance of the second component to obtain an impedance adjustment model includes: calculating the impedance difference between the impedance of the first component and the impedance of the second component; constructing an impedance network of the modular integrated system; using the impedance network and the impedance difference to perform a first impedance adjustment on the modular integrated system and calculating the output efficiency of the modular integrated system under the first impedance adjustment to obtain a first output efficiency; performing a second impedance adjustment on the modular integrated system and calculating the output efficiency of the modular integrated system under the second impedance adjustment to obtain a second output efficiency; determining an impedance adjustment scheme for the modular integrated system based on the first output efficiency and the second output efficiency; and performing impedance adjustment on the modular integrated system based on the impedance adjustment scheme to obtain an impedance adjustment model.

[0110] Optionally, the impedance network can be constructed using a π-type matching network. The first impedance adjustment of the module integration system using the impedance network and the impedance difference means that there is an impedance difference between the impedance of the first component and the impedance of the second component. For example, if the first impedance is 8 and the second impedance is 7, the second impedance is adjusted from 7 to 8. The first impedance adjustment of the module integration system means adjusting the first impedance from 8 to 7.

[0111] S5. Collect system data of the synchronous condenser, perform clustering processing on the system data to obtain clustered data, analyze the data sensitivity of the clustered data, and determine the control characteristics of the synchronous condenser based on the data sensitivity.

[0112] The embodiments of this invention utilize the system data acquired from the synchronous condenser to monitor and control the operation of the power system, and to help users understand the voltage and phase relationships between power equipment and the system stability. This system data includes voltage, frequency, and magnetic parameters, among others.

[0113] Furthermore, the present invention embodiments, by performing clustering processing on the system data, obtain clustered data that can help users identify potential patterns and structures in the data, revealing the similarities and differences between the data. For example, when there is electrical coupling between a synchronous condenser and its excitation system, the parameters of the two systems will affect each other, making it difficult to perform parameter identification (i.e., determine the specific parameters of each system).

[0114] As an embodiment of the present invention, the step of clustering the system data to obtain clustered data includes: filtering the system data to obtain filtered data, transforming the filtered data to obtain vector data, constructing the vector centroid of the vector data, and calculating the vector distance between the vector data and the vector centroid using the following formula:

[0115]

[0116] Where D represents the vector distance, φ represents the spatial dimension of the vector data, and x i Let y represent the x-coordinate of the centroid of the i-th vector. i The ordinate represents the centroid of the i-th vector;

[0117] Based on the vector distance, the filtered data is clustered to obtain clustered data.

[0118] Optionally, the filtering of the system data to obtain filtered data can be achieved through Gaussian filtering. The construction of the vector centroid of the vector data is achieved by constructing data such as voltage, current, or magnetic field data in power data according to the data categories in the system data. The data clustering of the filtered data is performed based on the vector distance to obtain clustered data, which is divided by vector distance, such as dividing into one class by a distance of 0-1 and another class by a distance of 1-2.

[0119] Furthermore, this embodiment of the invention, through analyzing the data sensitivity of the clustered data, can understand the degree of influence of various parameters on the performance of the power system. Here, data sensitivity refers to a data index used to measure the change in the output trajectory of the power system when parameters change.

[0120] As an embodiment of the present invention, the analysis of the data sensitivity of the clustered data includes: constructing a DC axis and an orthogonal axis of the clustered data; performing DC parameter variation analysis on the DC axis to obtain DC variation analysis results; determining the DC axis sensitivity of the clustered data based on the DC variation analysis results; performing orthogonal parameter variation analysis on the orthogonal axis to obtain orthogonal variation analysis results; determining the orthogonal axis sensitivity of the clustered data based on the orthogonal variation analysis results; and combining the DC axis sensitivity and the orthogonal axis sensitivity to obtain the data sensitivity.

[0121] Wherein, the DC axis refers to the direction of the mechanical magnetic field in the power system, and the orthogonal axis refers to the perpendicular direction of the rotating magnetic field in the power system.

[0122] Optionally, the DC parameter variation analysis of the DC axis to obtain the DC variation analysis result refers to adjusting parameters such as inductance, resistance, and torque constant in the power system and observing the changes in the response speed and stability of the power system. The orthogonal parameter variation analysis of the orthogonal axis to obtain the orthogonal variation analysis result refers to adjusting parameters such as inductance, resistance, and torque constant in the power system and observing the changes in the magnetic field distribution and magnetic flux magnitude of the power system. The orthogonal axis sensitivity of the clustering data determined based on the orthogonal variation analysis result is determined by analyzing the changes in response speed, stability, magnetic field distribution, and magnetic flux magnitude. If the change is large, it indicates high sensitivity; if the change is small, it indicates low sensitivity.

[0123] Furthermore, by determining the control characteristics of the synchronous condenser based on the data sensitivity, this embodiment of the invention can understand the adjustment method of the synchronous condenser when facing different power conditions. If a large adjustment is required, a high-sensitivity adjustment scheme can be selected; if a small adjustment is required, a low-sensitivity adjustment scheme can be selected.

[0124] S6. After executing the phase adjustment reference of the impedance adjustment model using the control characteristics, an initial digital twin model is obtained. The model accuracy of the initial digital twin model is calculated. When the model accuracy meets the preset model accuracy, a digital twin model is obtained. The synchronous condenser is used to collect real-time power data of the power system to be adjusted, and the digital twin model is used to perform data analysis on the real-time power data to obtain data analysis results. Based on the data analysis results, the synchronous condenser is used to perform phase adjustment on the power system to be adjusted.

[0125] It should be noted that obtaining the initial digital twin model after using the control characteristics to execute the phase adjustment reference of the impedance adjustment model means using the control characteristics as the phase adjustment control strategy of the power system.

[0126] The embodiments of the present invention can analyze the performance of the model on a given dataset by calculating the model accuracy of the initial digital twin model, which is very important for understanding the accuracy of the model and its efficiency in processing specific tasks.

[0127] As an embodiment of the present invention, calculating the model accuracy of the initial digital twin model includes: collecting historical data of the power system to be adjusted, using the historical data to perform model testing on the initial digital twin model to obtain a test digital twin model, and calculating the root mean square error of the test digital twin model using the following formula:

[0128]

[0129] Where RS represents the root mean square error, n represents the amount of historical data, and A i This represents the predicted value of the i-th historical data point. This represents the true value of the i-th historical data point;

[0130] The model accuracy of the initial digital twin model is determined based on the root mean square error.

[0131] It should be noted that when the model accuracy meets the preset model accuracy, the obtained digital twin model means that the root mean square error is no more than one percent, indicating that the model accuracy meets the preset model accuracy.

[0132] Furthermore, in this embodiment of the invention, the real-time power data of the power system to be adjusted is collected by the synchronous condenser, and the real-time power data is analyzed by the digital twin model. The data analysis results can be used to understand the phase adjustment scheme of the power system under specific conditions.

[0133] Based on the data analysis results, using the synchronous condenser to perform phase adjustment on the power system to be adjusted can yield accurate phase adjustment results, thereby helping the power system to output power more stably and efficiently.

[0134] like Figure 2 The diagram shown is a functional block diagram of a synchronous phase-shifting system using a digital twin model according to the present invention.

[0135] The synchronous phase-shifting system 200 using a digital twin model described in this invention can be installed in an electronic device. Depending on the functions implemented, the synchronous phase-shifting system using a digital twin model may include a modular processing module 201, a system simulation module 202, a module integration module 203, an impedance adjustment module 204, a control characteristic analysis module 205, and a phase adjustment module 206. The module described in this invention can also be called a unit, referring to a series of computer program segments that can be executed by the processor of an electronic device and perform a fixed function, stored in the memory of the electronic device.

[0136] In this embodiment of the invention, the functions of each module / unit are as follows:

[0137] The modular processing module 201 is used to deploy synchronous condensers in the pre-constructed power system to be adjusted, and to perform modular processing on the component system of the synchronous condensers to obtain system modules.

[0138] The system simulation module 202 is used to identify the power grid topology of the power system to be adjusted, and to collect the generator data and line parameters of the power system to be adjusted. Based on the power grid topology, the generator data and the line parameters, an initial simulation model of the power system to be adjusted is constructed. Using the initial simulation model, power simulation is performed on the power system to be adjusted to obtain the target simulation model.

[0139] The module integration module 203 is used to integrate the system module and the target simulation model to obtain a module integration system.

[0140] The impedance adjustment module 204 is used to determine the component impedance of the power system to be adjusted based on the power grid topology and the line parameters, to obtain the first component impedance, and to extract the component impedance of the synchronous condenser to obtain the second component impedance. Based on the first component impedance and the second component impedance, the module integrated system is impedance adjusted to obtain an impedance adjustment model.

[0141] The control characteristic analysis module 205 is used to collect system data of the synchronous condenser, perform clustering processing on the system data to obtain clustered data, analyze the data sensitivity of the clustered data, and determine the control characteristics of the synchronous condenser based on the data sensitivity.

[0142] The phase adjustment module 206 is used to obtain an initial digital twin model after executing the phase adjustment reference of the impedance adjustment model using the control characteristics, calculate the model accuracy of the initial digital twin model, obtain a digital twin model when the model accuracy meets the preset model accuracy, collect real-time power data of the power system to be adjusted using the synchronous condenser, perform data analysis on the real-time power data using the digital twin model, obtain data analysis results, and perform phase adjustment on the power system to be adjusted using the synchronous condenser based on the data analysis results.

[0143] In detail, the modules in the scene modeling system 200 for intelligent transportation based on digital twins described in this embodiment of the invention employ the same methods as described above. Figure 1 This method uses the same technical means as the one described above for scene modeling in intelligent transportation based on digital twins, and can produce the same technical effects, so it will not be elaborated here.

[0144] In the several embodiments provided by this invention, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and other division methods may be used in actual implementation.

[0145] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0146] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0147] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0148] Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be embraced within the invention. No appended diagram markings in the claims should be construed as limiting the scope of the claims.

[0149] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0150] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

Claims

1. A method for phase adjustment of a synchronous condenser using a digital twin model, characterized in that, The method includes: The components of the synchronous condenser are modularized to obtain system modules, wherein the synchronous condenser is deployed in a pre-constructed power system to be adjusted; Identify the power grid topology of the power system to be adjusted, and collect generator data and line parameters of the power system to be adjusted; construct an initial simulation model of the power system to be adjusted based on the power grid topology, the generator data, and the line parameters; use the initial simulation model to perform power simulation on the power system to be adjusted to obtain the target simulation model; The system modules and the target simulation model are integrated to obtain a module-integrated system. Based on the power grid topology and the line parameters, the component impedance of the power system to be adjusted is determined to obtain the first component impedance; the component impedance of the synchronous condenser is extracted to obtain the second component impedance; the impedance of the module integration system is adjusted based on the first component impedance and the second component impedance to obtain the impedance adjustment model. The system data of the synchronous condenser is collected, the system data is clustered to obtain clustered data, the data sensitivity of the clustered data is analyzed, and the control characteristics of the synchronous condenser are determined based on the data sensitivity. After executing the phase adjustment reference of the impedance adjustment model using the control characteristics, an initial digital twin model is obtained. The model accuracy of the initial digital twin model is calculated. When the model accuracy meets the preset model accuracy, a digital twin model is obtained. The synchronous condenser is used to collect real-time power data of the power system to be adjusted, and the digital twin model is used to perform data analysis on the real-time power data to obtain data analysis results. Based on the data analysis results, the synchronous condenser is used to perform phase adjustment on the power system to be adjusted.

2. The method according to claim 1, characterized in that, The initial simulation model of the power system to be adjusted, constructed based on the power grid topology, the generator data, and the line parameters, includes: Construct a physical model of the power system to be adjusted; After arranging the components of the physical model using the power grid topology, the operating parameters of the physical model are configured according to the generator data and the line parameters to obtain the initial simulation model.

3. The method according to claim 1, characterized in that, The process of using the initial simulation model to perform power simulation on the power system to be adjusted, and obtaining the target simulation model, includes: Define the operating conditions of the initial simulation model; The initial simulation model is run based on the aforementioned operating conditions to obtain the running model; The operating model was subjected to steady-state testing to obtain steady-state test results; When the steady-state test results meet the preset results, a steady-state operation model is obtained; Based on the test data from the steady-state test, the goodness of fit of the steady-state model is calculated using the following formula: Where α represents the goodness of fit, N represents the amount of test data, and B i This represents the predicted value of the i-th data point in the test data. This represents the true value of the i-th data point in the test data. This represents the mean of the true values ​​of the i-th data point in the test data. When the fitting degree meets the preset fitting degree, the target simulation model is obtained.

4. The method according to claim 3, characterized in that, The steady-state test of the operating model to obtain the steady-state test results includes: Determine the phase angle and signal amplitude of the running nodes in the operating model; Based on the phase angle and the signal amplitude, the voltage value of the operating node is calculated using the following formula: V = |δ|cosθ + E|δ|sinθ Where V represents the voltage value, θ represents the phase angle, E represents an imaginary unit, and |δ| represents the signal amplitude; After comparing the voltage value with the input voltage value of the operating model, the steady-state test is completed.

5. The method according to claim 1, characterized in that, The process of integrating the system modules and the target simulation model to obtain a module-integrated system includes: Identify the component connection points of the target simulation model; The system module and the target simulation model are connected based on the component connection points to obtain a connection module; Data transmission is performed on the connection module to obtain a data transmission module; The data transmission module is calibrated to obtain the module integration system.

6. The method according to claim 1, characterized in that, The impedance adjustment of the module integration system based on the impedance of the first component and the impedance of the second component to obtain an impedance adjustment model includes: Calculate the impedance difference between the impedance of the first component and the impedance of the second component; Construct the impedance network of the module integration system; Using the impedance network and the impedance difference, the module integration system is subjected to a first impedance adjustment, and the output efficiency of the module integration system under the first impedance adjustment is calculated to obtain the first output efficiency. The module integration system is subjected to a second impedance adjustment, and the output efficiency of the module integration system under the second impedance adjustment is calculated to obtain the second output efficiency; Based on the first output efficiency and the second output efficiency, the impedance adjustment scheme of the module integration system is determined; The impedance of the module integration system is adjusted based on the impedance adjustment scheme to obtain an impedance adjustment model.

7. The method according to claim 1, characterized in that, The process of clustering the system data to obtain clustered data includes: The system data is filtered to obtain filtered data; The filtered data is then transformed to obtain vector data; Construct the vector centroids of the vector data; The vector distance between the vector data and the centroid of the vector is calculated using the following formula: Where D represents the vector distance, φ represents the spatial dimension of the vector data, and x i Let y represent the x-coordinate of the centroid of the i-th vector. i The ordinate represents the centroid of the i-th vector; Based on the vector distance, the filtered data is clustered to obtain clustered data.

8. The method according to claim 1, characterized in that, The data sensitivity analysis of the clustered data includes: Construct the DC axis and orthogonal axis of the clustered data; DC parameter variation analysis was performed on the DC axis to obtain the DC variation analysis results; The DC axis sensitivity of the clustering data is determined based on the DC variation analysis results. Orthogonal parameter variation analysis was performed on the orthogonal axes to obtain orthogonal variation analysis results; The orthogonal axis sensitivity of the clustering data is determined based on the orthogonal variation analysis results. The data sensitivity is obtained by combining the DC axis sensitivity and the quadrature axis sensitivity.

9. The method according to claim 1, characterized in that, The calculation of the model accuracy of the initial digital twin model includes: Collect historical data of the power system to be adjusted; The initial digital twin model is tested using the historical data to obtain a test digital twin model; The root mean square error of the test digital twin model is calculated using the following formula: Where RS represents the root mean square error, n represents the amount of historical data, and A i This represents the predicted value of the i-th historical data point. This represents the true value of the i-th historical data point; The model accuracy of the initial digital twin model is determined based on the root mean square error.

10. A synchronous phase-shifting system employing a digital twin model, characterized in that, The system includes: A modular processing module is used to modularize the component system of the synchronous condenser to obtain system modules, wherein the synchronous condenser is deployed in a pre-constructed power system to be adjusted; The system simulation module is used to identify the power grid topology of the power system to be adjusted, and to collect the generator data and line parameters of the power system to be adjusted. Based on the power grid topology, the generator data and the line parameters, an initial simulation model of the power system to be adjusted is constructed. Using the initial simulation model, power simulation is performed on the power system to be adjusted to obtain the target simulation model. A module integration module is used to integrate the system modules and the target simulation model to obtain a module integration system; An impedance adjustment module is used to determine the component impedance of the power system to be adjusted based on the power grid topology and the line parameters, to obtain a first component impedance, and to extract the component impedance of the synchronous condenser to obtain a second component impedance. Based on the first component impedance and the second component impedance, the module integrated system is impedance adjusted to obtain an impedance adjustment model. The control characteristic analysis module is used to collect system data of the synchronous condenser, perform clustering processing on the system data to obtain clustered data, analyze the data sensitivity of the clustered data, and determine the control characteristics of the synchronous condenser based on the data sensitivity. The phase adjustment module is used to obtain an initial digital twin model after executing the phase adjustment reference of the impedance adjustment model using the control characteristics, calculate the model accuracy of the initial digital twin model, obtain a digital twin model when the model accuracy meets the preset model accuracy, collect real-time power data of the power system to be adjusted using the synchronous condenser, perform data analysis on the real-time power data using the digital twin model, obtain data analysis results, and perform phase adjustment on the power system to be adjusted using the synchronous condenser based on the data analysis results.

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