CN117634995A - Modeling method and system of digital twin platform for power grid service - Google Patents
Modeling method and system of digital twin platform for power grid service Download PDFInfo
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Abstract
The embodiment of the invention provides a modeling method and a modeling system of a digital twin platform for power grid service, belonging to the technical field of state prediction of digital twin models. The modeling method comprises the following steps: constructing physical information and data driving states of components of the digital twin platform; dividing the physical information and the data driving state into a plurality of state intervals according to a preset time interval in time sequence to form a corresponding state description matrix; training an initial Markov network by taking the state description matrix of the former state interval as input and the state description matrix of the latter state interval as output; predicting a state description matrix of the next state interval according to the state description matrix of the current state interval by adopting the trained Markov network; and updating the digital twin platform according to the state description matrix. The modeling method and the modeling system can realize the rapid update of the digital twin platform of the power grid system.
Description
Technical Field
The invention relates to the technical field of state prediction of digital twin models, in particular to a modeling method and a system of a digital twin platform facing power grid business.
Background
Digital twin models are one of the approaches for viewing the system. In the grid system, since it includes not only the physical state of the components (electrical devices) but also the data-driven state (electrical parameter variation) of the components, this makes an automated update of the digital twin model computationally intensive. Even with such a huge amount of computation, it is difficult to implement a fast update of the state of the digital twin model by using a processing device with high computing power.
Disclosure of Invention
The embodiment of the invention aims to provide a modeling method and a system for a digital twin platform facing power grid business, and the modeling method and the system can realize quick updating of the digital twin platform of a power grid system.
In order to achieve the above object, an embodiment of the present invention provides a modeling method for a digital twin platform for power grid services, including:
constructing physical information and data driving states of components of the digital twin platform;
dividing the physical information and the data driving state into a plurality of state intervals according to a preset time interval in time sequence to form a corresponding state description matrix;
training an initial Markov network by taking the state description matrix of the former state interval as input and the state description matrix of the latter state interval as output;
predicting a state description matrix of the next state interval according to the state description matrix of the current state interval by adopting the trained Markov network;
and updating the digital twin platform according to the state description matrix.
Optionally, constructing the physical information and data driven states of the components of the digital twin platform includes:
processing the physical information and the data driving state by adopting a fuzzification idea to form a corresponding numerical value descriptor;
and normalizing the numerical value descriptors to obtain corresponding description vectors.
Optionally, the physical information and the data driving state are processed by adopting a blurring idea to form corresponding numerical descriptors, including:
dividing the physical information of the component into a plurality of action states according to the physical information;
encoding the motion state code to form the numerical descriptor.
Optionally, for the physical information, dividing the physical information of the component into a plurality of action states includes:
dividing the physical information into the action states according to a plurality of preset action states to obtain corresponding action state distribution;
judging whether the action state distribution meets independent identical distribution or not;
and under the condition that the action state distribution meets the independent same distribution, determining that the action state division is successful.
Optionally, for the physical information, dividing the physical information of the component into a plurality of action states includes:
and under the condition that the motion state distribution does not meet the independent same distribution, adjusting the angle and displacement interval value corresponding to the motion state, and returning to execute the preset multiple motion states, and dividing the physical information into the motion states to obtain the corresponding motion state distribution.
Optionally, the physical information and the data driving state are processed by adopting a blurring idea to form corresponding numerical descriptors, including:
determining a parameter interval combination of the component in different power states for the data driving state;
and respectively carrying out coding operation on the parameter interval combination to form the numerical value descriptor.
Optionally, determining, for the data driving state, a combination of parameter intervals of the component in different power states includes:
dividing the data driving state into the parameter interval combinations according to a plurality of preset parameter interval combinations to obtain corresponding parameter state distribution;
judging whether the parameter state distribution meets independent identical distribution or not;
and under the condition that the parameter state distribution meets the independent identical distribution, determining that the parameter interval combination division is successful.
Optionally, determining, for the data driving state, a combination of parameter intervals of the component in different power states includes:
and under the condition that the parameter state distribution does not meet the independent same distribution, adjusting the threshold value of the parameter interval combination, and returning to execute the step of dividing the data driving state into the parameter interval combination according to a plurality of preset parameter interval combinations so as to obtain the corresponding parameter state distribution.
In another aspect, the invention also provides a modeling system of a digital twin platform for grid services, the modeling system comprising a processor configured to perform a modeling method as described in any of the above.
Through the technical scheme, the invention provides a modeling method and a modeling system of a digital twin platform facing to power grid service, wherein the modeling method and the modeling system are used for constructing physical information and data driving states of components of the digital twin platform, dividing a state description matrix according to time intervals, training a Markov network through the state description matrix of adjacent time, and predicting the state description matrix of the next time interval by directly combining the state description matrix of the previous time interval through the Markov network. Compared with the mode of updating the digital twin model by calculating a large amount of state parameters in the prior art, the modeling method and the modeling system provided by the invention have the advantages that the prediction is carried out only through the Markov network, so that a large amount of calculation is avoided, and the updating speed and the updating efficiency of the digital twin model are improved.
Additional features and advantages of embodiments of the invention will be set forth in the detailed description which follows.
Drawings
The accompanying drawings are included to provide a further understanding of embodiments of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain, without limitation, the embodiments of the invention. In the drawings:
FIG. 1 is a flow chart of a method of modeling a grid business oriented digital twin platform in accordance with an embodiment of the present invention;
FIG. 2 is a flow chart of a method of partitioning an action state according to one embodiment of the invention;
fig. 3 is a flowchart of a method of dividing parameter interval combinations according to one embodiment of the present invention.
Detailed Description
The following describes the detailed implementation of the embodiments of the present invention with reference to the drawings. It should be understood that the detailed description and specific examples, while indicating and illustrating the invention, are not intended to limit the invention.
FIG. 1 is a flow chart of a modeling method of a digital twin platform for grid services according to an embodiment of the present invention. In this fig. 1, the modeling method may include the steps of:
in step S10, physical information and data driving states of components of the digital twin platform are constructed;
in step S11, the physical information and the data driving state are divided into a plurality of state intervals according to a preset time interval in time sequence, so as to form a corresponding state description matrix;
in step S12, training an initial markov network by taking a state description matrix of a previous state section as an input and a state description matrix of a next state section as an output;
in step S13, predicting a state description matrix of the next state interval according to the state description matrix of the current state interval by using the trained markov network;
in step S14, the digital twin platform is updated according to the state description matrix.
In this modeling method as shown in fig. 1, step S10 may be used to build the physical information and data-driven states of the components of the digital twin platform.
Wherein the physical information may be used to represent the physical state of the electrical devices in the electrical power system. For example, in the case where the electrical device is a circuit breaker, the physical state may be a state for indicating an open/close of the circuit breaker. In the case where the electrical device is a manipulator, the physical state may be used to represent the spatial position of the gripping end of the manipulator. The data-driven state may be used to represent an electrical parameter state of the electrical device. In order to facilitate the identification and prediction of subsequent Markov networks, in one example of the present invention, the fuzzy ideas may be employed to process the physical information and data-driven states to form corresponding numerical descriptors, which are then normalized to obtain corresponding description vectors.
Specifically, for the characteristics of the physical information, the method of forming the corresponding numerical descriptor may be to first divide the physical information of the component into a plurality of action states (for example, a plurality of spatial position range intervals are set, in interval 1, action state 1 is represented, in interval 2, action state 2 is represented, and so on) for the physical information, and then encode the action state codes to form the numerical descriptor.
Further, considering that the preset action state may not meet the division requirements of different power systems on the physical information of the components, the divided intervals may be excessive, so that the matrix numerical value is excessively expressed, or the divided intervals are too small, so that the matrix numerical value expression capability is insufficient. In this embodiment, therefore, the method as shown in fig. 2 may be employed to determine the division of the action state. Specifically, in fig. 2, the method for dividing the action state may include the following steps:
in step S20, according to a plurality of preset action states, dividing the physical information into action states to obtain corresponding action state distribution;
in step S21, it is determined whether the motion state distribution satisfies the independent same distribution;
in step S22, if it is determined that the motion state distribution satisfies the independent same distribution, it is determined that the motion state division is successful;
in step S23, if the motion state distribution does not satisfy the independent same distribution, the angle and the displacement interval value corresponding to the motion state are adjusted, and the execution is returned to the execution to divide the physical information into the motion states according to the preset plurality of motion states, so as to obtain the corresponding motion state distribution, that is, the execution returns to step S20. The specific method for adjusting the angle and displacement interval value corresponding to the operation state may be various forms known to those skilled in the art, such as enlarging or reducing the angle interval and displacement interval.
For the data driving state, the method for forming the corresponding numerical value descriptor may be to first determine the parameter interval combinations of the components in different power states for the data driving state, and then perform encoding operations for the parameter interval combinations to form the numerical value descriptor.
Further, considering that the preset parameter interval combination may not meet the dividing requirement of different power systems on the data driving states of the components, that is, there may be excessive divided intervals, which causes excessive matrix numerical expression, or insufficient divided intervals, which causes insufficient matrix numerical expression capability. In this embodiment, therefore, the partitioning of the parameter interval combinations may be determined using a method as shown in fig. 3. Specifically, in fig. 3, the method for dividing the parameter interval combination may include the following steps:
in step S30, dividing the data driving state into parameter interval combinations according to a plurality of preset parameter interval combinations to obtain corresponding parameter state distribution;
in step S31, it is determined whether the parameter status distribution satisfies the independent same distribution;
in step S32, under the condition that the parameter status distribution satisfies the independent same distribution, determining that the parameter interval combination division is successful;
in step S33, when it is determined that the parameter status distribution does not satisfy the independent same distribution, the threshold value of the parameter interval combination is adjusted, and the step of dividing the data driving status into the parameter interval combinations according to the preset plurality of parameter interval combinations is performed, so as to obtain the corresponding parameter status distribution, that is, the step S30 is performed.
The step S11 is configured to divide the physical information and the data driving state into a plurality of state intervals according to a preset time interval, so as to form a corresponding state description matrix, thereby completing the vectorized expression of the physical information and the data driving state of the component. Step S12 may be used to train the initial markov network by taking the state description matrix of the previous state interval as input and the state description matrix of the next state interval as output, thereby completing the training of the markov network. Finally, through step S13, namely: predicting a state description matrix of a next state interval according to the state description matrix of the current state interval by using the trained Markov network to complete the prediction of the digital twin model, and step S14, namely: and updating the digital twin platform according to the state description matrix to finish the updating operation of the digital twin model.
In another aspect, the invention also provides a modeling system of a digital twin platform for grid services, the modeling system comprising a processor configured to perform a modeling method as described in any of the above. In particular, the modeling method may be a method comprising steps as shown in fig. 1 to 3. Specifically, in this fig. 1, the modeling method may include the steps of:
in step S10, physical information and data driving states of components of the digital twin platform are constructed;
in step S11, the physical information and the data driving state are divided into a plurality of state intervals according to a preset time interval in time sequence, so as to form a corresponding state description matrix;
in step S12, training an initial markov network by taking a state description matrix of a previous state section as an input and a state description matrix of a next state section as an output;
in step S13, predicting a state description matrix of the next state interval according to the state description matrix of the current state interval by using the trained markov network;
in step S14, the digital twin platform is updated according to the state description matrix.
In this modeling method as shown in fig. 1, step S10 may be used to build the physical information and data-driven states of the components of the digital twin platform.
Wherein the physical information may be used to represent the physical state of the electrical devices in the electrical power system. For example, in the case where the electrical device is a circuit breaker, the physical state may be a state for indicating an open/close of the circuit breaker. In the case where the electrical device is a manipulator, the physical state may be used to represent the spatial position of the gripping end of the manipulator. The data-driven state may be used to represent an electrical parameter state of the electrical device. In order to facilitate the identification and prediction of subsequent Markov networks, in one example of the present invention, the fuzzy ideas may be employed to process the physical information and data-driven states to form corresponding numerical descriptors, which are then normalized to obtain corresponding description vectors.
Specifically, for the characteristics of the physical information, the method of forming the corresponding numerical descriptor may be to first divide the physical information of the component into a plurality of action states (for example, a plurality of spatial position range intervals are set, in interval 1, action state 1 is represented, in interval 2, action state 2 is represented, and so on) for the physical information, and then encode the action state codes to form the numerical descriptor.
Further, considering that the preset action state may not meet the division requirements of different power systems on the physical information of the components, the divided intervals may be excessive, so that the matrix numerical value is excessively expressed, or the divided intervals are too small, so that the matrix numerical value expression capability is insufficient. In this embodiment, therefore, the method as shown in fig. 2 may be employed to determine the division of the action state. Specifically, in fig. 2, the method for dividing the action state may include the following steps:
in step S20, according to a plurality of preset action states, dividing the physical information into action states to obtain corresponding action state distribution;
in step S21, it is determined whether the motion state distribution satisfies the independent same distribution;
in step S22, if it is determined that the motion state distribution satisfies the independent same distribution, it is determined that the motion state division is successful;
in step S23, if the motion state distribution does not satisfy the independent same distribution, the angle and the displacement interval value corresponding to the motion state are adjusted, and the execution is returned to the execution to divide the physical information into the motion states according to the preset plurality of motion states, so as to obtain the corresponding motion state distribution, that is, the execution returns to step S20. The specific method for adjusting the angle and displacement interval value corresponding to the operation state may be various forms known to those skilled in the art, such as enlarging or reducing the angle interval and displacement interval.
For the data driving state, the method for forming the corresponding numerical value descriptor may be to first determine the parameter interval combinations of the components in different power states for the data driving state, and then perform encoding operations for the parameter interval combinations to form the numerical value descriptor.
Further, considering that the preset parameter interval combination may not meet the dividing requirement of different power systems on the data driving states of the components, that is, there may be excessive divided intervals, which causes excessive matrix numerical expression, or insufficient divided intervals, which causes insufficient matrix numerical expression capability. In this embodiment, therefore, the partitioning of the parameter interval combinations may be determined using a method as shown in fig. 3. Specifically, in fig. 3, the method for dividing the parameter interval combination may include the following steps:
in step S30, dividing the data driving state into parameter interval combinations according to a plurality of preset parameter interval combinations to obtain corresponding parameter state distribution;
in step S31, it is determined whether the parameter status distribution satisfies the independent same distribution;
in step S32, under the condition that the parameter status distribution satisfies the independent same distribution, determining that the parameter interval combination division is successful;
in step S33, when it is determined that the parameter status distribution does not satisfy the independent same distribution, the threshold value of the parameter interval combination is adjusted, and the step of dividing the data driving status into the parameter interval combinations according to the preset plurality of parameter interval combinations is performed, so as to obtain the corresponding parameter status distribution, that is, the step S30 is performed.
The step S11 is configured to divide the physical information and the data driving state into a plurality of state intervals according to a preset time interval, so as to form a corresponding state description matrix, thereby completing the vectorized expression of the physical information and the data driving state of the component. Step S12 may be used to train the initial markov network by taking the state description matrix of the previous state interval as input and the state description matrix of the next state interval as output, thereby completing the training of the markov network. Finally, through step S13, namely: predicting a state description matrix of a next state interval according to the state description matrix of the current state interval by using the trained Markov network to complete the prediction of the digital twin model, and step S14, namely: and updating the digital twin platform according to the state description matrix to finish the updating operation of the digital twin model.
Through the technical scheme, the invention provides a modeling method and a modeling system of a digital twin platform facing to power grid service, wherein the modeling method and the modeling system are used for constructing physical information and data driving states of components of the digital twin platform, dividing a state description matrix according to time intervals, training a Markov network through the state description matrix of adjacent time, and predicting the state description matrix of the next time interval by directly combining the state description matrix of the previous time interval through the Markov network. Compared with the mode of updating the digital twin model by calculating a large amount of state parameters in the prior art, the modeling method and the modeling system provided by the invention have the advantages that the prediction is carried out only through the Markov network, so that a large amount of calculation is avoided, and the updating speed and the updating efficiency of the digital twin model are improved.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In one typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include volatile memory in a computer-readable medium, random Access Memory (RAM) and/or nonvolatile memory, etc., such as Read Only Memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.
Computer readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises an element.
The foregoing is merely exemplary of the present application and is not intended to limit the present application. Various modifications and changes may be made to the present application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc. which are within the spirit and principles of the present application are intended to be included within the scope of the claims of the present application.
Claims (9)
1. The modeling method of the digital twin platform facing the power grid service is characterized by comprising the following steps of:
constructing physical information and data driving states of components of the digital twin platform;
dividing the physical information and the data driving state into a plurality of state intervals according to a preset time interval in time sequence to form a corresponding state description matrix;
training an initial Markov network by taking the state description matrix of the former state interval as input and the state description matrix of the latter state interval as output;
predicting a state description matrix of the next state interval according to the state description matrix of the current state interval by adopting the trained Markov network;
and updating the digital twin platform according to the state description matrix.
2. A modeling method in accordance with claim 1, wherein constructing the physical information and data driven states of the components of the digital twin platform comprises:
processing the physical information and the data driving state by adopting a fuzzification idea to form a corresponding numerical value descriptor;
and normalizing the numerical value descriptors to obtain corresponding description vectors.
3. A modeling method in accordance with claim 2, wherein processing the physical information and data-driven states using a fuzzy idea to form corresponding numerical descriptors comprises:
dividing the physical information of the component into a plurality of action states according to the physical information;
encoding the motion state code to form the numerical descriptor.
4. A modeling method in accordance with claim 3, wherein dividing the physical information of the component into a plurality of action states for the physical information comprises:
dividing the physical information into the action states according to a plurality of preset action states to obtain corresponding action state distribution;
judging whether the action state distribution meets independent identical distribution or not;
and under the condition that the action state distribution meets the independent same distribution, determining that the action state division is successful.
5. The modeling method of claim 4, wherein dividing the physical information of the component into a plurality of action states for the physical information comprises:
and under the condition that the motion state distribution does not meet the independent same distribution, adjusting the angle and displacement interval value corresponding to the motion state, and returning to execute the preset multiple motion states, and dividing the physical information into the motion states to obtain the corresponding motion state distribution.
6. A modeling method in accordance with claim 2, wherein processing the physical information and data-driven states using a fuzzy idea to form corresponding numerical descriptors comprises:
determining a parameter interval combination of the component in different power states for the data driving state;
and respectively carrying out coding operation on the parameter interval combination to form the numerical value descriptor.
7. Modeling method in accordance with claim 6, characterized in that for the data-driven state, determining a combination of parameter intervals for the component in different power states comprises:
dividing the data driving state into the parameter interval combinations according to a plurality of preset parameter interval combinations to obtain corresponding parameter state distribution;
judging whether the parameter state distribution meets independent identical distribution or not;
and under the condition that the parameter state distribution meets the independent identical distribution, determining that the parameter interval combination division is successful.
8. The modeling method of claim 7, wherein determining a combination of parameter intervals for the component at different power states for the data-driven state comprises:
and under the condition that the parameter state distribution does not meet the independent same distribution, adjusting the threshold value of the parameter interval combination, and returning to execute the step of dividing the data driving state into the parameter interval combination according to a plurality of preset parameter interval combinations so as to obtain the corresponding parameter state distribution.
9. A modeling system of a grid business oriented digital twin platform, characterized in that the modeling system comprises a processor configured to perform the modeling method of any of claims 1 to 8.
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