A method for building an intelligent power protection scenario based on digital twins

Abstract

The present invention belongs to the field of three-dimensional modeling of computer graphics, and in particular relates to a method for building an intelligent power protection scenario based on digital twins. The present invention first comprehensively collects equipment operation, status and power grid topology data. An improved graph neural network is used in combination with multi-scale and time series characteristic modeling to deeply analyze equipment relationships. The key is to construct a power protection scenario based on a digital twin model, taking into account the probability of equipment failure, fault propagation and load fluctuations. Through dynamic simulation of power grid evolution, the impact of the scenario on power grid stability is accurately evaluated. This method can dynamically adjust the scenario according to actual working conditions, effectively improve the scientificity and effectiveness of the power protection scenario construction, ensure the stable operation of the power grid, and provide strong support for the power protection work of the power system.

Description

Technical Field

[0001] The present invention belongs to the field of three-dimensional modeling of computer graphics, and in particular relates to a method for building an intelligent power protection scenario based on digital twins. Background Art

[0002] With rapid socioeconomic development and continuous technological advancement, various industries are increasingly demanding stable and reliable power supply. The importance of intelligent power supply scenario development in the power system sector is becoming increasingly prominent. In current power system operations, traditional power supply solutions rely primarily on manual experience and simple monitoring methods. Faced with increasingly complex grid structures and diverse electricity demands, these traditional approaches are gradually revealing numerous shortcomings. For example, when grid topology changes or emergencies occur, traditional power supply solutions struggle to quickly and accurately adjust strategies, resulting in low power supply efficiency and poor results. Existing power supply scenario development lacks systematicity and intelligence. In many cases, it fails to fully leverage the vast amount of device operating data, device status information, and grid topology data for in-depth analysis and accurate prediction. As a result, power supply scenario development often fails to fully consider the various potential factors impacting grid stability, making it difficult to develop optimal power supply solutions. Furthermore, there is a lack of effective dynamic assessment methods for evaluating the impact of power supply scenarios on grid stability. Traditional assessment methods, mostly based on static models or simple empirical formulas, are unable to track changes in grid operating conditions in real time, making it difficult to accurately predict the grid's dynamic response characteristics under different power supply scenarios, and failing to promptly identify potential stability issues. Furthermore, with the continuous expansion of the grid and the rapid increase in the number of devices, the interactions between devices have become extremely complex, making it difficult for traditional technologies to effectively model and analyze these complex interactions. For example, interactions between devices exhibit multi-scale and temporal characteristics, which existing methods struggle to integrate into power supply scenario construction and stability assessments. Summary of the Invention

[0003] In response to the technical problems existing in the background technology, the present invention proposes a method for building an intelligent power protection scenario based on digital twins, which has a reasonable design, simple method, strong theoretical basis and can be realized.

[0004] In order to achieve the above object, the technical solution adopted by the present invention is: comprising the following steps:

[0005] S1. First, collect the equipment's operating data, equipment status, and grid topology data;

[0006] S2. Secondly, we use improved graph neural networks and topological data for modeling. Taking into account the different scales and timing issues of interactions between devices, we introduce multi-scale and timing characteristics. The final node feature representation formula can be integrated into the following form: in is the final node feature representation, ω l is the weight of the l-th layer feature, For the considered timing characteristics, For the multiple scales considered;

[0007] S3. Finally, based on the digital twin model, the method generates multiple power supply scenarios, simulates the operating conditions of the generated power supply scenarios, and dynamically evaluates the impact of the power supply scenarios on grid stability. The specific implementation of the power supply scenario and operating condition simulation in step S3 is as follows:

[0008] S31. First, the improved dynamic failure probability formula is used to calculate the failure probability of each device, taking into account the device operating status, environmental conditions, and the impact of failure propagation on neighboring devices: The dynamic correction factor ψ i (t) including load fluctuation ΔL i , voltage deviation V i deviation and temperature change ΔT i , correlation factor φ i (t) Respond to the impact of failures in adjacent equipment;

[0009] S32, based on the failure probability P(f i ,t) Generate the initial fault device set: F k (t)={i|rand()<P(f i ,t)}, expand the initial fault set and consider the fault propagation to neighboring devices: Where N(i) is the set of neighboring devices of device i, ξ ij (t) is the impact factor of device j failing due to the propagation of the fault of device i;

[0010] S33, combined with the generated fault set F k ′(t) and load fluctuation, define the power protection scenario: S k (t)={F k ′(t),L t +ΔL t}, where L t Load distribution at time t, ΔL t is the charge fluctuation in the scenario, ensuring that the scenario includes dynamic adjustments for fault and load conditions;

[0011] S34. Finally, a dynamic simulation is performed for each scenario, simulating the evolution of the grid operation status through time series integration: Among them, P i (t) is the active power of node i at time t, Q i(t) is reactive power, V i (t), V j (t) The voltage amplitude between nodes i and j at time t, G ij , B ij are the real and imaginary parts of the admittance between nodes i and j, θ ij (t) is the voltage phase difference between node i and node j, ΔP i (t), ΔQ i (t) is the dynamic power correction term of node i at time t, which dynamically updates the voltage and phase to simulate the evolution of power conservation.

[0012] Preferably, the equipment operation data in step S1 includes voltage, current, power, and load; the equipment status includes equipment working time, temperature, humidity, and vibration; and the grid topology data includes grid topology and switch status data.

[0013] Preferably, the specific steps of modeling in step S2 are:

[0014] S21. First, define the power system graph as G = (V, E), where V is the device node set, E represents the connection relationship between devices, and the characteristics of node i are expressed as The adjacency matrix of the graph is A, and the time step is t;

[0015] S22. In the multi-scale graph convolution layer, the adjacency matrix A of different scales is used. l To deal with the interaction between power devices, for each scale l, the update formula of node i is: Among them, Nl(i) represents the neighbor set of node i in the adjacency relationship of the lth layer, is the dynamic attention coefficient calculated by the graph attention mechanism, W l (l) is the weight matrix of the lth layer, is the bias term, σ is the ReLU function;

[0016] S23. Combined with time series modeling to capture the dynamic behavior of power equipment and networks, based on the features of graph convolution output, LSTM is introduced to model time dependency. The time series modeling formula of node i at time step t is: in is the state of node i at the previous time step, is the input feature of node i at the current time step;

[0017] S24. Finally, the final node feature representation is obtained by combining multi-scale and temporal characteristics:

[0018] Preferably, the dynamic attention coefficient calculated by the graph attention mechanism in step S22 is The calculation formula is: in is the dynamic attention coefficient between node i and node j at layer l, and a is the learned attention weight vector.

[0019] As a preference, the impact factor of the fault in step S32 is Among them, P(f j ,t) is the dynamic failure probability of j at time t, d ij is the distance between devices i and j.

[0020] Preferably, the formula for dynamically updating the voltage and phase in step S34 is: in Represents the dynamic change rate of phase and voltage, and α and β are the update step sizes.

[0021] Compared with existing technologies, the advantages and positive effects of the present invention lie in the fact that the introduction of multi-scale and time-series characteristics in the modeling process can accurately handle complex relationships between equipment, which is difficult to achieve with traditional technologies. In terms of fault probability calculation, the improved formula that integrates multiple factors makes the prediction more accurate. The present invention realizes the dynamic construction and evaluation of power supply scenarios based on digital twin models, which can be adjusted in real time according to working conditions. Traditional technologies are mostly static and lack dynamic adaptability. The present invention effectively improves the scientific nature of power supply scenario construction and the ability to ensure grid stability. DETAILED DESCRIPTION

[0022] In order to more clearly understand the above-mentioned objects, features and advantages of the present invention, the present invention is further described below in conjunction with the embodiments. It should be noted that, in the absence of conflict, the embodiments of the present application and the features therein can be combined with each other.

[0023] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways than those described herein. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0024] Embodiment, the present invention addresses the problem that when constructing existing power protection scenarios, it is often impossible to fully consider the impact of various potential factors on the stability of the power grid, overcomes the defects of existing modeling technologies, and conducts reasonable power protection scenario construction and dynamic power grid stability evaluation. In the method for building an intelligent power protection scenario based on digital twins of the present invention, data collection is first performed to collect equipment operating data, equipment status, and power grid topology data. The equipment operating data includes voltage, current, power, and load; the equipment status includes the working time, temperature, humidity, and vibration of the equipment; and the power grid topology data includes power grid topology and switch status data, so as to fully understand the structure and connection relationship of the power grid. By collecting these multi-type and all-round data, a data foundation is provided for subsequent precise modeling and accurate analysis.

[0025] In order to effectively handle the complex interactions between devices and overcome the defects of traditional methods that are difficult to consider multi-scale and time series characteristics, this paper uses improved graph neural networks and topological data for modeling. Taking into account the different scales and time series problems of the interactions between devices, multi-scale and time series characteristics are introduced. The specific implementation is as follows: First, the power system graph is defined as G = (V, E), where V is the device node set, E represents the connection relationship between devices, and the characteristics of node i are represented as The adjacency matrix of the graph is A, and the time step is t; in the multi-scale graph convolution layer, the adjacency matrix A of different scales is used. l To deal with the interaction between power devices, for each scale l, the update formula of node i is: Among them, Nl(i) represents the neighbor set of node i in the adjacency relationship of the lth layer, is the dynamic attention coefficient calculated by the graph attention mechanism, W l (l) is the weight matrix of the lth layer, is the bias term, and σ is the ReLU function. Combined with time series modeling to capture the dynamic behavior of power equipment and networks, based on the features of graph convolution output, LSTM is introduced to model time dependency. The time series modeling formula of node i at time step t is: in is the state of node i at the previous time step, x i (t) is the input feature of node i at the current time step; finally, the final node feature representation is obtained by combining multi-scale and time series characteristics: Existing technologies often ignore multi-scale and temporal characteristics when dealing with device interactions, resulting in an inaccurate understanding of device relationships. However, the solution presented in this paper meticulously depicts the complex interactions between devices, providing a more realistic model foundation for power conservation scenarios.

[0026] To accurately calculate the probability of equipment failure and improve the single and incomplete nature of traditional failure probability calculation methods, this invention comprehensively considers temperature changes, load fluctuations, and the impact of fault propagation from neighboring equipment. This allows for timely detection of significantly increased equipment failure risks and for early preparation for power protection. First, the improved dynamic failure probability formula is used to calculate the failure probability of each device, taking into account the device's operating status, environmental conditions, and the impact of fault propagation from neighboring equipment: The dynamic correction factor ψ i (t) including load fluctuation ΔL i , voltage deviation V i deviation and temperature change ΔT i , correlation factor φ i (t) reflects the impact of failure of adjacent equipment; based on the failure probability P(f i ,t) Generate the initial fault device set: F k (t)={i|rand()<P(f i ,t)}, expand the initial fault set and consider the fault propagation to neighboring devices: Where N(i) is the set of neighboring devices of device i, ξ ij (t) is the impact factor of device j’s failure due to the propagation of the failure of device i Among them, P(f j ,t) is the dynamic failure probability of j at time t, d ij is the distance between devices i and j; the generated fault set F k ′(t) and load fluctuation, define the power protection scenario: S k (t)={F k ′(t),L t +ΔL t}, where L t Load distribution at time t, ΔL t The charge fluctuations in the scenario ensure that the scenario includes dynamic adjustments to fault and load conditions. Finally, a dynamic simulation is performed for each scenario, simulating the evolution of the grid operating state through time series integration: Among them, P i (t) is the active power of node i at time t, Q i (t) is reactive power, V i (t), V j (t) The voltage amplitude between nodes i and j at time t, where in Represents the dynamic change rate of phase and voltage, α, β are the update step size, G ij , B ij are the real and imaginary parts of the admittance between nodes i and j, θij (t) is the voltage phase difference between node i and node j, ΔP i (t), ΔQ i (t) is the dynamic power correction term for node i at time t, dynamically updating the voltage and phase to simulate the evolution of power supply protection. This invention addresses the data, model, and dynamic deficiencies in existing power supply protection scenarios, enabling more efficient, accurate, and dynamic power supply protection scenario construction, ensuring the stable operation of the power system under various operating conditions and meeting the strict requirements for power supply reliability.

[0027] The above description is merely a preferred embodiment of the present invention and does not constitute any other form of limitation to the present invention. Any person skilled in the art may utilize the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes for application in other fields. However, any simple modification, equivalent change, and modification of the above embodiments made in accordance with the technical essence of the present invention without departing from the technical solution of the present invention shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. A method for building an intelligent power conservation scenario based on digital twins, characterized in that: The following steps are involved: S1. First, collect the equipment's operating data, equipment status, and grid topology data; S2. Secondly, we use improved graph neural networks and topological data for modeling. Taking into account the different scales and timing issues of interactions between devices, we introduce multi-scale and timing characteristics. The final node feature representation formula can be integrated into the following form: in is the final node feature representation, ω l is the weight of the l-th layer feature, For the considered timing characteristics, For the multiple scales considered; S3. Finally, based on the digital twin model, the method generates multiple power supply scenarios, simulates the operating conditions of the generated power supply scenarios, and dynamically evaluates the impact of the power supply scenarios on grid stability. The specific implementation of the power supply scenario and operating condition simulation in step S3 is as follows: S31. First, the improved dynamic failure probability formula is used to calculate the failure probability of each device, taking into account the device operating status, environmental conditions, and the impact of failure propagation on neighboring devices: The dynamic correction factor ψ m (t) including load fluctuation ΔL m , voltage deviation and temperature change ΔT m , correlation factor φ m (t) Respond to the impact of failures in adjacent equipment; S32, based on the equipment failure probability P(f m ,t) Generate the initial fault device set: F k (t)={m|rand()<P(f m ,t)}, expand the initial fault set and consider the fault propagation to neighboring devices: Where N(m) is the set of neighboring devices of device m, ξ mn (t) is the impact factor of device n failing due to the propagation of the fault of device m; S33, combined with the generated fault set F k ′(t) and load fluctuation, define the power protection scenario: S k (t)={F k ′(t),L t +ΔL t }, where L t Load distribution at time t, ΔL t is the charge fluctuation in the scenario, ensuring that the scenario includes dynamic adjustments for fault and load conditions; S34. Finally, a dynamic simulation is performed for each scenario, simulating the evolution of the grid operation status through time series integration: Among them, P x (t) is the active power of node x at time t, Q x (t) is reactive power, V x (t), V y (t) The voltage amplitude of node x and node y at time t, G xy , B xy are the real and imaginary parts of the admittance between nodes x and y, θ xy (t) is the voltage phase difference between node x and node y, ΔP x (t), ΔQ x (t) is the dynamic power correction term of node x at time t, which dynamically updates the voltage and phase to simulate the evolution of power conservation; The specific steps of modeling in step S2 are: S21. First, define the power system graph as G = (V, E), where V is the device node set, E represents the connection relationship between devices, and the characteristics of node i are expressed as The adjacency matrix of the graph is A, and the time step is t; S22. In the multi-scale graph convolution layer, the adjacency matrix A of different scales is used. l To deal with the interaction between power devices, for each scale l, the update formula of node i is: Among them, Nl(i) represents the neighbor set of node i in the adjacency relationship of the lth layer, is the dynamic attention coefficient calculated by the graph attention mechanism, W l (l) is the weight matrix of the lth layer, is the bias term, σ is the ReLU function; S23. Combined with time series modeling to capture the dynamic behavior of power equipment and networks, based on the features of graph convolution output, LSTM is introduced to model time dependency. The time series modeling formula of node i at time step t is: in is the state of node i at the previous time step, is the input feature of node i at the current time step; S24. Finally, the final node feature representation is obtained by combining multi-scale and temporal characteristics: Among them, the dynamic attention coefficient calculated by the graph attention mechanism in step S22 is The calculation formula is: in is the dynamic attention coefficient between node i and node j at layer l, and a is the learned attention weight vector The impact factor of the fault in step S32 Among them, P(f n ,t) is the dynamic failure probability of n at time t, d mn is the distance between devices m and n.

2. The method for building an intelligent power conservation scenario based on digital twins according to claim 1 is characterized in that: In step S1, the device operation data includes voltage, current, power, and load; the device status includes the device's working time, temperature, humidity, and vibration; and the grid topology data includes grid topology and switch status data.

3. The method for building an intelligent power conservation scenario based on digital twins according to claim 1 is characterized in that: The formula for dynamically updating the voltage and phase in step S34 is: in Represents the dynamic change rate of phase and voltage, and α and β are the update step sizes.

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