Digital twinning system of transformer substation

Abstract

The invention provides a transformer substation digital twin system, and relates to the technical field of transformer substation operation optimization, and the transformer substation digital twin system comprises an acquisition module which is used for acquiring operation data and physical data of a transformer substation; the construction module is used for constructing a corresponding transformer substation digital twinborn model according to the operation data and the physical data; the generation module is used for generating a corresponding substation operation scheme by using a swarm intelligence optimization algorithm based on the substation digital twin model; the updating module is used for applying the operation scheme of the transformer substation to the transformer substation, collecting real-time operation data and real-time demand information, and updating the digital twin model of the transformer substation according to the real-time operation data and the real-time demand information; safe, reliable and economical operation of the transformer substation can be realized, and the transformer substation can adapt to the change of a real-time operation environment.

Description

Technical Field

[0001] The present invention relates to the technical field of substation operation optimization, and in particular to a substation digital twin system. Background Art

[0002] With the development of power systems and the increasing demand for intelligent power generation, substation operational efficiency, reliability, and flexibility have become increasingly important. Traditional substation operation plan development methods typically rely on manual experience or simple mathematical models, which fail to fully consider the complex operating conditions and real-time changing requirements of substations. In the field of substation operation plan optimization, traditional optimization methods are unable to handle the complexity and uncertainty of substation operations. Summary of the Invention

[0003] In response to the above-mentioned problems, the present invention proposes a substation digital twin system, which solves the technical problem in the existing technology that in the field of substation operation plan optimization, traditional optimization methods are difficult to deal with the complexity and uncertainty in substation operation. It can realize safe, reliable and economical operation of the substation and can adapt to changes in the real-time operating environment.

[0004] An embodiment of the present invention provides a substation digital twin system, including:

[0005] Acquisition module, used to collect operational data and physical data of the substation;

[0006] The construction module is used to build the corresponding substation digital twin model based on operation data and physical data;

[0007] The generation module is used to generate the corresponding substation operation plan based on the substation digital twin model and use the swarm intelligence optimization algorithm;

[0008] The update module is used to apply the substation operation plan to the substation, collect real-time operation data and real-time demand information, and update the substation digital twin model based on the real-time operation data and real-time demand information.

[0009] In some embodiments, the building blocks include:

[0010] The first construction unit is used to construct a geometric model of the substation using 3D modeling software based on the collected physical data, and model the equipment according to the actual size and position;

[0011] The second construction unit is used to add the electrical connection relationship of the equipment to the geometric model, build the topology of the substation, and clarify the electrical connection method and node number between each device;

[0012] A mapping unit is used to map the collected operating data onto the geometric model and assign corresponding electrical characteristics and operating status to each device;

[0013] The third construction unit is used to initialize the equipment status in the geometric model by using the state estimation algorithm to build a digital twin model of the substation.

[0014] In some embodiments, including:

[0015] The weighted least squares state estimation algorithm is used to initialize the device states in the geometric model.

[0016] In some embodiments, the generating module includes:

[0017] A setting unit is used to set optimization objectives and constraints according to the operation requirements and performance indicators of the substation;

[0018] The initialization unit is used to select a suitable swarm intelligence optimization algorithm and initialize the particle swarm. Each particle represents a possible substation operation scheme, and the particle position vector contains the operating parameters of the equipment.

[0019] The calculation unit is used to calculate the fitness value of each particle according to the optimization goal and constraints in each iteration;

[0020] The first updating unit is used to update the velocity and position of the particle;

[0021] The determination unit is used to determine, after multiple iterations, an operation plan that meets the constraint conditions and has the optimal fitness value as the substation operation plan.

[0022] In some embodiments, the first updating unit adopts the following model:

[0023]

[0024] Where, and are the velocity and position of particle i at the kth iteration, ω is the inertia weight, c1 and c2 are learning factors, r1 and r2 are random numbers, and pbest i is the individual optimal position of particle i, and gbest is the global optimal position.

[0025] In some embodiments, the update module includes:

[0026] The application unit is used to apply the generated substation operation plan to the actual substation and adjust the operating parameters of the equipment through the substation control system;

[0027] Collection unit, used to collect substation operation data and real-time demand information in real time;

[0028] The comparative analysis unit is used to compare and analyze real-time operation data and demand information with the digital twin model to identify differences between the model and actual operation conditions;

[0029] The second updating unit is used to update the equipment status and operating parameters in the substation digital twin model using the data assimilation method.

[0030] In some embodiments, the second updating unit includes:

[0031] The Kalman filter algorithm is used to update the status of the substation digital twin model:

[0032]

[0033] In the formula, θ k is the updated state vector, is the predicted state vector, K k is the Kalman gain, z k is the real-time observation vector, H k is the observation matrix, P k is the updated error covariance matrix, is the forecast error covariance matrix, and I is the identity matrix.

[0034] Compared with the prior art, the present invention has the following beneficial effects:

[0035] The acquisition module is used to collect the operating data and physical data of the substation; the construction module is used to build the corresponding substation digital twin model based on the operating data and physical data; the generation module is used to generate the corresponding substation operation plan based on the substation digital twin model using the swarm intelligence optimization algorithm; the update module is used to apply the substation operation plan to the substation, collect real-time operating data and real-time demand information, and update the substation digital twin model based on the real-time operating data and real-time demand information; it can realize the safe, reliable and economical operation of the substation and can adapt to changes in the real-time operating environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] The embodiments of the present invention are further described below with reference to the accompanying drawings:

[0037] Figure 1 A schematic structural diagram of a substation digital twin system provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0038] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limiting the present invention. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0039] In the following description, reference is made to “some embodiments”, which describes a subset of all possible embodiments, but it will be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0040] If similar descriptions of "first\second\third" appear in the application documents, the following explanation is added. In the following description, the terms "first\second\third" are only used to distinguish similar objects and do not represent a specific order of the objects. It can be understood that "first\second\third" can be interchanged with the specific order or sequence where permitted, so that the embodiments of the present invention described herein can be implemented in an order other than that illustrated or described herein.

[0041] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which the present invention pertains. The terms used herein are for the purpose of describing embodiments of the present invention only and are not intended to limit the present invention.

[0042] The embodiment of the present invention provides a substation digital twin system, Figure 1 A schematic diagram of the structure of a substation digital twin system provided by an embodiment of the present invention is shown in FIG. Figure 1 Shown, including:

[0043] Acquisition module, used to collect operational data and physical data of the substation;

[0044] In an embodiment of the present invention, the acquisition module is used to collect the operating data and physical data of the substation. The operating data includes the voltage amplitude, current amplitude, active power, reactive power, equipment operating temperature, etc. of each node. These data are collected in real time by sensors installed in the substation, including voltage transformers, current transformers, power sensors, temperature sensors, etc. The physical data includes information such as the physical size, installation location, and connection relationship of the equipment, which is obtained through on-site measurement and geographic information system (GIS) technology. It provides accurate basic data for subsequent model construction and optimization, ensures the authenticity of the substation digital twin model, and enables it to accurately reflect the actual operating status of the substation.

[0045] The construction module is used to build the corresponding substation digital twin model based on operation data and physical data;

[0046] In some embodiments, the building blocks include:

[0047] The first construction unit is used to construct a geometric model of the substation using 3D modeling software based on the collected physical data, and model the equipment according to the actual size and position;

[0048] The second construction unit is used to add the electrical connection relationship of the equipment to the geometric model, build the topology of the substation, and clarify the electrical connection method and node number between each device;

[0049] A mapping unit is used to map the collected operating data onto the geometric model and assign corresponding electrical characteristics and operating status to each device;

[0050] The third construction unit is used to initialize the equipment status in the geometric model by using the state estimation algorithm to build a digital twin model of the substation.

[0051] In some embodiments, including:

[0052] The weighted least squares state estimation algorithm is used to initialize the device states in the geometric model.

[0053] In an embodiment of the present invention, the construction module uses 3D modeling software to construct a geometric model of the substation based on physical data, and models the equipment according to its actual size and installation location. Then, the electrical connection relationship of the equipment is added to the model, the topology of the substation is constructed, and the electrical connection method and node numbering between each device are clarified. Next, the operating data is mapped to the physical model, and each device is assigned corresponding electrical characteristics and operating status. In this way, the digital twin model of the substation not only has a geometric form, but also has real operating characteristics. Finally, the model is initialized using a state estimation algorithm to improve the initial accuracy of the model and provide a reliable starting point for the subsequent optimization algorithm.

[0054] The generation module is used to generate the corresponding substation operation plan based on the substation digital twin model and use the swarm intelligence optimization algorithm;

[0055] In some embodiments, the generating module includes:

[0056] A setting unit is used to set optimization objectives and constraints according to the operation requirements and performance indicators of the substation;

[0057] The initialization unit is used to select a suitable swarm intelligence optimization algorithm and initialize the particle swarm. Each particle represents a possible substation operation scheme, and the particle position vector contains the operating parameters of the equipment.

[0058] The calculation unit is used to calculate the fitness value of each particle according to the optimization goal and constraints in each iteration;

[0059] The first updating unit is used to update the velocity and position of the particle;

[0060] The determination unit is used to determine, after multiple iterations, an operation plan that meets the constraint conditions and has the optimal fitness value as the substation operation plan.

[0061] In some embodiments, the first updating unit adopts the following model:

[0062]

[0063] Where, and are the velocity and position of particle i at the kth iteration, ω is the inertia weight, c1 and c2 are learning factors, r1 and r2 are random numbers, and pbest i is the individual optimal position of particle i, and gbest is the global optimal position.

[0064] In this embodiment of the present invention, a swarm intelligence optimization algorithm is used to quickly find the optimal operating solution that meets the constraints. The particle swarm optimization algorithm has the advantages of strong global search capabilities and adaptability to complex optimization problems, which can improve the operating efficiency and economic benefits of substations.

[0065] The update module is used to apply the substation operation plan to the substation, collect real-time operation data and real-time demand information, and update the substation digital twin model based on the real-time operation data and real-time demand information.

[0066] In some embodiments, the update module includes:

[0067] The application unit is used to apply the generated substation operation plan to the actual substation and adjust the operating parameters of the equipment through the substation control system;

[0068] Collection unit, used to collect substation operation data and real-time demand information in real time;

[0069] The comparative analysis unit is used to compare and analyze real-time operation data and demand information with the digital twin model to identify differences between the model and actual operation conditions;

[0070] The second updating unit is used to update the equipment status and operating parameters in the substation digital twin model using the data assimilation method.

[0071] In some embodiments, the second updating unit includes:

[0072] The Kalman filter algorithm is used to update the status of the substation digital twin model:

[0073]

[0074] In the formula, θ k is the updated state vector, is the predicted state vector, K k is the Kalman gain, z k is the real-time observation vector, H k is the observation matrix, P k is the updated error covariance matrix, is the forecast error covariance matrix, and I is the identity matrix.

[0075] In this embodiment of the present invention, the substation digital twin model is updated promptly based on real-time operating data and demand information, ensuring it remains consistent with actual operating conditions. By utilizing the Kalman filter algorithm, the model's accuracy and reliability are improved, enabling continuous optimization and thus enhancing the substation's intelligent operation.

[0076] To summarize, the acquisition module is used to collect the operating data and physical data of the substation; the construction module is used to build the corresponding substation digital twin model based on the operating data and physical data; the generation module is used to generate the corresponding substation operation plan based on the substation digital twin model using the swarm intelligence optimization algorithm; the update module is used to apply the substation operation plan to the substation, collect real-time operating data and real-time demand information, and update the substation digital twin model based on the real-time operating data and real-time demand information; it can realize the safe, reliable and economical operation of the substation and can adapt to changes in the real-time operating environment.

[0077] It should be understood that "one embodiment" or "an embodiment" mentioned throughout the specification means that the specific features, structures or characteristics related to the embodiment are included in at least one embodiment of the present invention. Therefore, "in one embodiment" or "in an embodiment" appearing throughout the specification does not necessarily refer to the same embodiment. In addition, these specific features, structures or characteristics can be combined in one or more embodiments in any suitable manner. It should be understood that in various embodiments of the present invention, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention. The serial numbers of the above-mentioned embodiments of the present invention are for description only and do not represent the advantages and disadvantages of the embodiments.

[0078] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, object, or apparatus comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, object, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not preclude the presence of other identical elements in the process, method, object, or apparatus comprising the element.

[0079] The above description is merely an embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A substation digital twin system, characterized in that: include: Acquisition module, used to collect operational data and physical data of the substation; The construction module is used to build the corresponding substation digital twin model based on operation data and physical data; The generation module is used to generate the corresponding substation operation plan based on the substation digital twin model and use the swarm intelligence optimization algorithm; The update module is used to apply the substation operation plan to the substation, collect real-time operation data and real-time demand information, and update the substation digital twin model based on the real-time operation data and real-time demand information.

2. A substation digital twin system according to claim 1, characterized in that: The building blocks include: The first construction unit is used to construct a geometric model of the substation using 3D modeling software based on the collected physical data, and model the equipment according to the actual size and position; The second construction unit is used to add the electrical connection relationship of the equipment to the geometric model, build the topology of the substation, and clarify the electrical connection method and node number between each device; A mapping unit is used to map the collected operating data onto the geometric model and assign corresponding electrical characteristics and operating status to each device; The third construction unit is used to initialize the equipment status in the geometric model by using the state estimation algorithm to build a digital twin model of the substation.

3. A substation digital twin system according to claim 2, characterized in that: include: The weighted least squares state estimation algorithm is used to initialize the device states in the geometric model.

4. A substation digital twin system according to claim 1, characterized in that: The generation module includes: A setting unit is used to set optimization objectives and constraints according to the operation requirements and performance indicators of the substation; The initialization unit is used to select a suitable swarm intelligence optimization algorithm and initialize the particle swarm. Each particle represents a possible substation operation scheme, and the particle position vector contains the operating parameters of the equipment. The calculation unit is used to calculate the fitness value of each particle according to the optimization goal and constraints in each iteration; The first updating unit is used to update the velocity and position of the particle; The determination unit is used to determine, after multiple iterations, an operation plan that meets the constraint conditions and has the optimal fitness value as the substation operation plan.

5. A substation digital twin system according to claim 4, characterized in that: The first updating unit adopts the following model: Where, and are the velocity and position of particle i at the kth iteration, ω is the inertia weight, c1 and c2 are learning factors, r1 and r2 are random numbers, and pbest i is the individual optimal position of particle i, and gbest is the global optimal position.

6. The substation digital twin system according to claim 1, characterized in that: The update module includes: The application unit is used to apply the generated substation operation plan to the actual substation and adjust the operating parameters of the equipment through the substation control system; Collection unit, used to collect substation operation data and real-time demand information in real time; The comparative analysis unit is used to compare and analyze real-time operation data and demand information with the digital twin model to identify differences between the model and actual operation conditions; The second updating unit is used to update the equipment status and operating parameters in the substation digital twin model using the data assimilation method.

7. A substation digital twin system according to claim 6, characterized in that: The second updating unit includes: The Kalman filter algorithm is used to update the status of the substation digital twin model: In the formula, θ k is the updated state vector, is the predicted state vector, K k is the Kalman gain, z k is the real-time observation vector, H k is the observation matrix, P k is the updated error covariance matrix, is the forecast error covariance matrix, and I is the identity matrix.

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