CN112560211B - Method for adjusting a grid structure, associated device and computer program product - Google Patents
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Abstract
The application discloses a method, a device, electronic equipment, a computer readable storage medium and a computer program product for adjusting a power grid structure, and relates to cloud computing and deep learning technologies. One embodiment of the method comprises the following steps: generating an actual graph structure according to each element parameter forming the current power grid structure, wherein a power station, a transformer substation and a load in the power grid structure are used as different nodes in the actual graph structure, a power transmission line is used as a connecting line for connecting each node, and the nodes and the connecting line have corresponding attribute parameters; outputting a target graph structure corresponding to the input actual graph structure by using a preset graph neural network, wherein the graph neural network is obtained by training based on a sample graph structure of a power grid and the corresponding sample target graph structure; and adjusting the current power grid structure to be consistent with the target graph structure. By applying this embodiment, the power system can be kept in a steady state as possible.
Description
Technical Field
The present application relates to the field of artificial intelligence technology, in particular to the field of cloud computing and deep learning technology, and more particularly, to a method, an apparatus, an electronic device, a computer readable storage medium and a computer program product for adjusting a power grid structure.
Background
The power grid, which is composed of power stations, substations, loads and transmission lines, delivers power to states, countries and even continents, which are the props for power distribution, play a central role in economic and social terms by providing reliable power to industry, services and consumers.
The structure of the power grid is complex, so that any link in the complex power grid is abnormal to cause chain reaction, and how to ensure the stability of the power grid structure as much as possible is an important point of attention of the person skilled in the art.
Disclosure of Invention
Embodiments of the present application provide a method, an apparatus, an electronic device, a computer readable storage medium, and a computer program product for adjusting a grid structure.
In a first aspect, an embodiment of the present application proposes a method for adjusting a power grid structure, including: generating an actual graph structure according to each element parameter forming the current power grid structure, wherein a power station, a transformer substation and a load in the power grid structure are used as different nodes in the actual graph structure, a power transmission line is used as a connecting line for connecting each node, and the nodes and the connecting line have corresponding attribute parameters; outputting a target graph structure corresponding to the input actual graph structure by using a preset graph neural network, wherein the graph neural network is obtained by training based on a sample graph structure of a power grid and the corresponding sample target graph structure; and adjusting the current power grid structure to be consistent with the target graph structure.
In a second aspect, an embodiment of the present application proposes an apparatus for adjusting a power grid structure, including: the power grid structure generating unit is configured to generate an actual graph structure according to each element parameter forming the current power grid structure, power stations, transformer substations and loads in the power grid structure are used as different nodes in the actual graph structure, power transmission lines are used as connecting lines for connecting the nodes, and the nodes and the connecting lines have corresponding attribute parameters; the power grid map structure optimizing unit is configured to output a target map structure corresponding to an input actual map structure by using a preset map neural network, wherein the map neural network is obtained by training based on a sample map structure of a power grid and the corresponding sample target map structure; and a grid structure adjustment unit configured to adjust the current grid structure to be consistent with the target graph structure.
In a third aspect, an embodiment of the present application provides an electronic device, including: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to implement a method for adjusting a grid structure as described in any one of the implementations of the first aspect when executed.
In a fourth aspect, embodiments of the present application provide a non-transitory computer-readable storage medium storing computer instructions for enabling a computer to implement a method for adjusting a grid structure as described in any one of the implementations of the first aspect when executed.
In a fifth aspect, embodiments of the present application provide a computer program product comprising a computer program which, when executed by a processor, is capable of implementing a method for adjusting a grid structure as described in any of the implementations of the first aspect.
The method, the device, the electronic equipment, the computer readable storage medium and the computer program product for adjusting the power grid structure provided by the embodiment of the application firstly generate an actual graph structure according to parameters of elements forming the current power grid structure, a power station, a transformer substation and a load in the power grid structure are used as different nodes in the actual graph structure, a power transmission line is used as a connecting line for connecting the nodes, and the nodes and the connecting line have corresponding attribute parameters; then, outputting a target graph structure corresponding to the input actual graph structure by using a preset graph neural network, wherein the graph neural network is obtained by training based on a sample graph structure of a power grid and the corresponding sample target graph structure; and finally, adjusting the current power grid structure to be consistent with the target graph structure.
In the running process of the power grid, the actual graph structure is obtained by carrying out drawing processing on the non-intuitive and complex power grid element parameters, so that complex connection relations are simplified to a certain extent, then an optimized target graph structure is obtained by utilizing a pre-trained graph neural network suitable for processing the graph structure, namely, the graph structure optimization and error correction capability obtained by the aid of a training sample of the graph neural network is achieved, and finally, the power grid structure is adjusted to be consistent with the target graph structure, so that errors are corrected timely and automatically, and the purpose of ensuring continuous and stable running of the power grid as much as possible is achieved.
It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the disclosure, nor is it intended to be used to limit the scope of the disclosure. Other features of the present disclosure will become apparent from the following specification.
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Other features, objects and advantages of the present application will become more apparent upon reading of the detailed description of non-limiting embodiments, made with reference to the following drawings, in which:
FIG. 1 is an exemplary system architecture in which the present application may be applied;
FIG. 2 is a flow chart of a method for adjusting a grid structure according to an embodiment of the present application;
FIG. 3 is a flow chart of another method for adjusting a grid structure provided in an embodiment of the present application;
fig. 4a is an actual diagram structure of a current power grid according to an embodiment of the present application;
fig. 4b is a target graph structure applicable to the current power grid according to the embodiment of the present application;
fig. 5 is a block diagram of an apparatus for adjusting a power grid structure according to an embodiment of the present application;
fig. 6 is a schematic structural diagram of an electronic device suitable for executing the method for adjusting a power grid structure according to an embodiment of the present application.
Detailed Description
The present application is described in further detail below with reference to the drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be noted that, for convenience of description, only the portions related to the present invention are shown in the drawings.
It should be noted that, in the case of no conflict, the embodiments and features in the embodiments may be combined with each other. The present application will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
Fig. 1 illustrates an exemplary system architecture 100 to which embodiments of the methods, apparatuses, electronic devices, computer-readable storage media, and computer program products for adjusting a grid structure of the present application may be applied.
As shown in fig. 1, a system architecture 100 may include terminal devices 101, 102, 103, a network 104, and a server 105. The terminal devices 101, 102, 103 are devices for collecting related parameter data of elements (power station, transformer station, load and power transmission line) forming the power grid, for example, various sensors including current and voltage sensors, and may further integrate components for executing corresponding regulation and control operations on the elements according to the issued operation instructions as integrated functional hardware; the server 105 is configured to perform corresponding data processing according to the received parameters of each element of the power grid; the medium used by the network 104 to provide a communication link between the terminal devices 101, 102, 103 and the server 105 may be embodied in various communication means including wired, wireless communication links, or fiber optic cables.
The server 105 can provide various services through various built-in applications, taking a power grid operation guarantee type application capable of providing a power grid structure real-time adjustment service as an example, the server 105 can realize the following effects when running the power grid operation guarantee type application: firstly, acquiring each element parameter which is acquired by the terminal equipment 101, 102 and 103 and forms the current power grid structure through the network 104, and generating an actual graph structure according to each element parameter: the power station, the transformer substation and the load in the power grid structure are used as different nodes in the actual graph structure, the power transmission line is used as a connecting line for connecting the nodes, and the nodes and the connecting line have corresponding attribute parameters; then, outputting a target graph structure corresponding to the input actual graph structure by using a preset graph neural network, wherein the graph neural network is obtained by training based on a sample graph structure of a power grid and the corresponding sample target graph structure; and finally, adjusting the current power grid structure to be consistent with the target graph structure.
The server 105 may be hardware or software. When the server 105 is hardware, it may be implemented as a distributed server cluster formed by a plurality of servers, or may be implemented as a single server; when the server is software, the server may be implemented as a plurality of software or software modules, or may be implemented as a single software or software module, which is not particularly limited herein. The server 105 may be a server installed in the vicinity of the target regional power grid to be adjusted, or may be an upper-layer adjustment server for adjusting the entire large regional power grid covering a wider region, and installed in a specific region.
It should be understood that the number of terminal devices, networks and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
Referring to fig. 2, fig. 2 is a flowchart of a method for adjusting a power grid structure according to an embodiment of the present application, wherein the flowchart 200 includes the following steps:
step 201: generating an actual graph structure according to each element parameter forming the current power grid structure;
this step aims at generating an actual graph structure from the parameters of the elements that constitute the current grid structure by the execution body of the method for adjusting the grid structure (e.g. the server 105 shown in fig. 1).
The parameters of the elements can be obtained in two ways, one is through a parameter monitoring element, such as a current, voltage, temperature sensor and the like, which is directly arranged on the elements; and secondly, the correlation or fixed correspondence of each element is calculated by theory. In order to obtain accurate parameters as much as possible, the power grid elements of the parameter monitoring elements are conveniently set to obtain the parameters of the corresponding elements by adopting the mode of setting the parameter monitoring elements as much as possible, and the rest of the power grid elements which are inconvenient to set the parameter monitoring elements can be obtained by calculation based on the accurate parameters of the acquired related elements.
The parameterized power grid structure is abstracted into a graph structure in the step, because the main description content in the power grid structure is the trend and the conveying condition of electric energy, namely directional vectors, and therefore, the actual condition can be visually presented as far as possible by abstracting the parameterized power grid structure into the graph structure for describing the elements. And combining the differences of the properties of the power station, the transformer station and the load with the power transmission line, wherein the power station, the transformer station and the load in the power grid structure are respectively used as different nodes in the actual graph structure, the power transmission line is used as a connecting line for connecting the nodes, and the nodes and the connecting line have corresponding attribute parameters. The attribute parameters of the power station include generated power and the like, the attribute parameters of the load include electricity consumption, average electricity consumption and the like, the attribute parameters of the transformer station include the number of power transmission line connection nodes, power transformation and the like, the accessory phase parameters of the power transmission line include maximum current load, actual current load, electricity transportation loss and the like, and besides the basic attribute parameters related to the characteristics of each element, the attribute parameters can also include graph structure parameters related to connection between nodes and connecting lines in a graph structure, such as the number of connected nodes and the like.
Step 202: outputting a target graph structure corresponding to the input actual graph structure by using a preset graph neural network;
on the basis of step 201, this step aims at optimizing the grid structure by the execution subject using the preset graph neural network to the actual graph structure corresponding to the current grid structure.
In order to achieve the purpose, the graph neural network is obtained by training based on a sample graph structure of a power grid and a corresponding sample target graph structure in advance, wherein the sample target graph structure can be a graph structure which is provided after an expert or a professional adjusts aiming at unreasonable parts in the sample graph structure, so that the corresponding relation between the graph structure of any power grid and the optimized target graph structure can be represented, and the target graph structure corresponding to an input actual graph structure is output by means of the trained graph neural network, and the graph neural network minimizes the loss function of the graph structure through a back propagation algorithm at the training moment.
The graph neural network belongs to a kind of neural network, because the graph neural network is specialized for processing the graph on the basis of the conventional neural network, the graph neural network has a better processing effect when processing the input data into the graph.
Step 203: and adjusting the current power grid structure to be consistent with the target graph structure.
On the basis of step 202, this step aims at adjusting the current grid structure to be consistent with the target graph structure by the execution subject, so as to achieve the purpose of optimizing the current grid structure.
It should be appreciated that, since the target graph structure is only an adjusted result, it is not directly provided how to adjust the adjustment operation sequence of the target structure from the actual graph structure step by step, and thus the instruction on how to adjust the current grid structure to be consistent with the target structure by the execution subject may be given by providing some alternatives in advance. Further, in the case where multiple alternatives exist at the same time, the various alternatives may also be evaluated in an appropriate manner to select the most appropriate implementation according to the evaluation result.
In the power grid operation process, the method for adjusting the power grid structure provided by the embodiment of the application carries out picture processing on the non-intuitive and complex power grid element parameters to obtain an actual graph structure, so that complex connection relations are simplified to a certain extent, then the optimized target graph structure is obtained by utilizing the pre-trained graph neural network suitable for processing the graph structure, namely, the graph structure optimization and error correction capability obtained based on training samples by means of the graph neural network is utilized, and finally, the power grid structure is adjusted to be consistent with the target graph structure, so that errors are corrected timely and automatically, and the aim of ensuring continuous and stable operation of the power grid as much as possible is fulfilled.
Referring to fig. 3, fig. 3 is a flowchart of another method for adjusting a power grid structure according to an embodiment of the present application, wherein the flowchart 300 includes the following steps:
step 301: respectively taking a power station, a transformer substation and a load which form the current power grid structure as different vertexes;
step 302: taking a power transmission line in the current power grid structure as an edge for connecting different vertexes;
step 303: constructing an adjacency matrix according to each vertex and each side, and generating an initial graph structure according to the adjacency matrix;
the logical structure of the adjacency matrix is divided into two parts: v and E sets, where V is a vertex and E is an edge. Thus, one-dimensional array is used for storing all vertex data in the graph; the data of the relationship (edge or arc) between vertices is stored in a two-dimensional array, called the adjacency matrix. Therefore, it can be seen that the correspondence between each vertex and each side is recorded in the adjacent matrix, but the attribute information of each vertex and each side is not recorded, and therefore the generated initial graph structure is also lacking in the attribute information of each vertex and each side.
Step 304: respectively adding corresponding attribute parameters for each vertex and each edge in the initial graph structure to generate an actual graph structure;
based on step 303, this step aims at generating an actual graph structure by the execution body attaching corresponding attribute parameters to each vertex and edge in the initial graph structure.
There are various ways to attach corresponding attribute parameters to vertices and edges in the initial graph structure, such as by manual labeling, by recorded correspondence, etc., and a specific implementation of matrix characteristics by means of adjacency matrices is also presented herein:
normalizing all elements forming the current power grid structure and corresponding attribute parameters to obtain an N multiplied by M feature matrix, wherein N is the number of elements, and M is the number of attribute parameters corresponding to each element; and adding corresponding attribute parameters to each vertex and each side in the initial graph structure by utilizing the feature matrix to generate an actual graph structure. The mode conveniently finds the attribute parameters of each vertex and each edge through the feature matrix and the commonalities contained in the initial graph structure constructed based on the adjacent matrix, and further realizes accurate attribute parameter addition.
For step 201 in the flow 200 of the previous embodiment, a specific implementation manner is provided in step 301-step 304, the characteristics that the adjacent matrix can describe the vertex and the edge are specifically adopted, the actual connection states of the power station, the transformer station, the load and the power transmission line in the power grid structure are exactly matched, the attribute information of each element is recorded by combining the feature matrix on the basis, and finally, the actual graph structure which is recorded with the structural relation and the attribute information is generated through the two matrices, so that the finally generated actual graph structure is more visual and simplified on the premise that the important information in the power grid structure is fully reserved.
Step 305: outputting a target graph structure corresponding to the input actual graph structure by using a preset graph neural network;
the present step corresponds to step 202 shown in fig. 2, and the same content is referred to the corresponding parts of the previous embodiment, and will not be described herein.
Furthermore, under the scheme that the actual graph structure is generated according to the feature matrix and the adjacent matrix, in order to enable the graph neural network to better and more accurately process the actual graph structure, the input structure of the graph neural network can be initialized by utilizing the feature matrix and the adjacent matrix, so that the initialized graph neural network can better process the actual graph structure generated based on the feature matrix and the adjacent matrix.
Step 306: comparing the target graph structure with the actual graph structure to obtain a graph structure difference;
step 307: determining at least one corresponding adjustment operation sequence according to the graph structure difference;
the adjustment operation sequence corresponding to the graph structure difference may be preset for different graph structure differences, or may be generated by a preset adjustment operation sequence generation rule, and specifically, the adjustment operation sequence generation rule may be provided with a plurality of judgment rules for which adjustment operation mode the graph structure difference is applicable to, for example, firstly identifying the difference size, then identifying that the difference needs to be implemented through a plurality of adjustment operations, finally identifying the combination of the adjustment operations of a determined number, and the like, which may be flexibly selected according to the actual situation, and is not specifically limited herein.
Step 308: respectively determining the score of each adjustment operation sequence by using a preset scoring function;
in order to make the scoring function accurately evaluate that different adjustment operation sequences are consistent with actual demands in an actual application scene as much as possible, at least one of power transmission loss, a ratio of power generation to power consumption and stability of a power grid structure can be considered for scoring, and certain special factors existing due to special requirements in the actual application scene can be combined, which is not limited in detail herein.
Taking the following three influencing factors of power transmission loss, the ratio of power generation to power consumption and the stability of a power grid structure as examples, a scoring function is specifically given:
loss of power transmission: this is mainly caused by the joule effect in the wire: wherein r is l The resistance of the wire l, y l Is the current flowing through the wire l;
ratio of power generation to power consumption of the power grid:wherein v is p Is the electricity quantity generated by the power station p, v u The power consumption of the load u is that the power consumption of the power grid is smaller as the ratio is closer to 1;
stability of current network structure, i.e. number of time steps n in which current structure can be stably operated without intervention step . The condition of stable operation of the power grid is that each equipment element can normally operate, and the wire load degree does not exceed the highest wire load.
Thus the overall scoring function L loss As shown below, i.e., r, during the steady run time steps loss And E shaped loss Sum of differences:
furthermore, in order to make the scoring function score each adjustment operation sequence as accurately as possible, and consider that the adjustment operation sequence actually corresponds to an adjustment period, which is a continuous adjustment operation, the evaluation process can be performed in a simulation environment which is as consistent as possible with the actual scene, so as to improve the matching degree of the output score and the actual application scene as much as possible.
Step 309: and sequentially executing each adjustment operation in the adjustment operation sequence with the highest score until the last adjustment operation in the sequence is executed.
For step 203 in the flow 200 of the previous embodiment, a specific implementation manner is provided through steps 306-309 in the present embodiment, firstly, the graph structure difference between the actual graph structure and the target graph structure is obtained through comparison, then at least one adjustment operation sequence corresponding to the graph structure difference is determined, then each adjustment operation sequence is accurately scored through a preset scoring function to select a target adjustment operation sequence most suitable for the current actual situation, and finally, the adjustment of the current power grid structure can be completed through executing step by step according to the target adjustment operation sequence. Through the implementation mode, the most suitable adjustment operation sequence can be selected as far as possible, so that the power grid is guaranteed not to have larger problems due to the adjustment operation, and the continuous and stable operation of the power grid is indirectly guaranteed.
It should be understood that there is no causal or dependency between the specific implementation presented in steps 301-304 and the specific implementation presented in steps 306-309, and that the two specific implementations are all one lower implementation presented for the respective upper implementation, and that the two parts may all be combined with the previous embodiment alone to form two separate embodiments, which only exist as preferred embodiments comprising two preferred implementations at the same time.
On the basis of the above embodiment, since the training set of the scoring function is limited and cannot cover all conditions, and the novel problem possibly generated by the complex influencing factors existing in the real world cannot be expected, the composition of the scoring function can be adjusted again according to the influence generated by the abnormality in the execution process in response to the abnormality of any adjusting operation sequence, so that the scoring function can output more accurate scores through continuous learning, adjustment and updating.
For a further understanding, the present application further provides a specific implementation scheme in conjunction with a specific application scenario, please refer to fig. 4a and fig. 4b.
Fig. 4a is a graph structure generated based on the current grid structure, wherein the power stations, substations and loads are all numbered starting from 0, i.e. the same numbers in circles of different patterns actually represent different types of grid elements.
As shown in fig. 4a, if one power line from the substation 0 to the substation 3 fails and the uninterruptible power network structure is changed, a chain reaction will soon occur, which causes the other power lines to exceed the load, thereby creating a hazard.
The cloud server obtains fig. 4b corresponding to fig. 4a by utilizing the pre-trained graph neural network output, as shown in fig. 4b, no transmission line exists between the transformer substation 0 and the transformer substation 3, but the transmission lines for transmitting electric energy from the transformer substation 0 to the transformer substation 2 and from the transformer substation 2 to the transformer substation 3 are replaced by thicker transmission lines, and the thicker transmission lines are used for representing that the thicker transmission lines have stronger electric energy load and transmission capacity compared with the original transmission lines.
The cloud server further selects the following adjustment operation sequences:
1) Enabling a high-load transmission line from the transformer substation 0 to the transformer substation 2;
2) Transferring the electric energy transmitted by the transformer substation 0 to the transformer substation 2 to the high-load power transmission line, and closing the power transmission line with lower original load at the same time;
3) Enabling one high-load power transmission line from the transformer substation 2 to the transformer substation 3;
4) The method comprises the steps of transferring electric energy which is originally transmitted by a transformer substation 2 to a transformer substation 3 through two lower-load power transmission lines to one high-load power transmission line between the two power transmission lines, and closing the original two power transmission lines simultaneously;
5) The other high-load power line of the substation 2 to the substation 3 is enabled and the power delivery amount sharing 35% of the previously enabled high-load power line is set.
The occurrence of chain reaction is avoided through the implementation steps, and the continuous and stable operation of the power grid is ensured.
With further reference to fig. 5, as an implementation of the method shown in the foregoing figures, the present application provides an embodiment of an apparatus for adjusting a power grid structure, where the embodiment of the apparatus corresponds to the embodiment of the method shown in fig. 2, and the apparatus may be specifically applied in various electronic devices.
As shown in fig. 5, the apparatus 500 for adjusting a power grid structure of the present embodiment may include: a grid diagram structure generating unit 501, a grid diagram structure optimizing unit 502 and a grid structure adjusting unit 503. The power grid graph structure generating unit 501 is configured to generate an actual graph structure according to each element parameter forming the current power grid structure, wherein power stations, transformer substations and loads in the power grid structure are used as different nodes in the actual graph structure, power transmission lines are used as connecting lines for connecting the nodes, and the nodes and the connecting lines have corresponding attribute parameters; the power grid graph structure optimization unit 502 is configured to output a target graph structure corresponding to the input actual graph structure by using a preset graph neural network, wherein the graph neural network is obtained by training based on a sample graph structure of a power grid and the corresponding sample target graph structure; the grid structure adjustment unit 503 is configured to adjust the current grid structure to be consistent with the target graph structure.
In this embodiment, in the apparatus 500 for adjusting a power grid structure: the specific processing of the grid pattern structure generating unit 501, the grid pattern structure optimizing unit 502, and the grid pattern structure adjusting unit 503 and the technical effects thereof may refer to the relevant descriptions of steps 201 to 203 in the corresponding embodiment of fig. 2, and are not repeated herein.
In some optional implementations of the present embodiment, the grid graph structure generating unit 501 may include:
a vertex setting subunit configured to regard power stations, substations, and loads constituting the current grid structure as different vertices, respectively;
an edge setting subunit configured to take a power line constituting a current power grid structure as an edge connecting between different vertices;
an initial graph structure generation subunit configured to construct an adjacency matrix from each vertex and each edge, and generate an initial graph structure from the adjacency matrix;
and the actual graph structure generating subunit is configured to attach corresponding attribute parameters to each vertex and side in the initial graph structure respectively to generate the actual graph structure.
In some optional implementations of the present embodiment, the actual graph structure generation subunit may be further configured to:
normalizing all elements forming the current power grid structure and corresponding attribute parameters to obtain an N multiplied by M feature matrix, wherein N is the number of elements, and M is the number of attribute parameters corresponding to each element;
and adding corresponding attribute parameters to each vertex and each side in the initial graph structure by utilizing the feature matrix to generate an actual graph structure.
In some optional implementations of this embodiment, the apparatus 500 for adjusting a power grid structure may further include:
and an initializing unit configured to initialize an input structure of the graph neural network using the feature matrix and the adjacency matrix.
In some optional implementations of the present embodiment, the grid structure adjustment unit 503 may be further configured to:
comparing the target graph structure with the actual graph structure to obtain a graph structure difference;
determining at least one corresponding adjustment operation sequence according to the graph structure difference;
respectively determining the score of each adjustment operation sequence by using a preset scoring function, wherein the scoring function is used for scoring at least one of power transmission loss, the ratio of power generation to power consumption and the stability of a power grid structure;
and sequentially executing each adjustment operation in the adjustment operation sequence with the highest score until the last adjustment operation in the sequence is executed.
In some optional implementations of this embodiment, the apparatus 500 for adjusting a power grid structure may further include:
and a scoring function adjusting unit configured to adjust the scoring function according to an influence generated by an abnormality that occurs in response to the abnormality occurring in the execution of the arbitrary adjustment operation sequence.
The device for adjusting the power grid structure provided by the embodiment of the application processes the non-intuitive and complex power grid element parameters to obtain the actual graph structure, so that the complex connection relationship is simplified to a certain extent, then the optimized target graph structure is obtained by utilizing the pre-trained graph neural network suitable for processing the graph structure, namely, the graph structure optimization and error correction capability obtained by utilizing the graph neural network based on training samples are utilized, and finally the power grid structure is adjusted to be consistent with the target graph structure, so that errors are corrected timely and automatically, and the aim of ensuring continuous and stable operation of the power grid as much as possible is fulfilled.
According to embodiments of the present application, there is also provided an electronic device, a readable storage medium and a computer program product.
Fig. 6 shows a schematic block diagram of an example electronic device 600 that may be used to implement embodiments of the present application. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the application described and/or claimed herein.
As shown in fig. 6, the apparatus 600 includes a computing unit 601 that can perform various appropriate actions and processes according to a computer program stored in a Read Only Memory (ROM) 602 or a computer program loaded from a storage unit 608 into a Random Access Memory (RAM) 603. In the RAM 603, various programs and data required for the operation of the device 600 may also be stored. The computing unit 601, ROM 602, and RAM 603 are connected to each other by a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.
Various components in the device 600 are connected to the I/O interface 605, including: an input unit 606 such as a keyboard, mouse, etc.; an output unit 607 such as various types of displays, speakers, and the like; a storage unit 608, such as a magnetic disk, optical disk, or the like; and a communication unit 609 such as a network card, modem, wireless communication transceiver, etc. The communication unit 609 allows the device 600 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.
The computing unit 601 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of computing unit 601 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 601 performs the various methods and processes described above, such as methods for adjusting the grid structure. For example, in some embodiments, the method for adjusting the grid structure may be implemented as a computer software program tangibly embodied on a machine-readable medium, such as the storage unit 608. In some embodiments, part or all of the computer program may be loaded and/or installed onto the device 600 via the ROM 602 and/or the communication unit 609. When the computer program is loaded into RAM 603 and executed by the computing unit 601, one or more steps of the method for adjusting a grid structure described above may be performed. Alternatively, in other embodiments, the computing unit 601 may be configured to perform the method for adjusting the grid structure by any other suitable means (e.g. by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
Program code for carrying out methods of the present application may be written in any combination of one or more programming languages. These program code may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus such that the program code, when executed by the processor or controller, causes the functions/operations specified in the flowchart and/or block diagram to be implemented. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of this application, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and pointing device (e.g., a mouse or trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), and the internet.
The computer system may include a client and a server. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so as to solve the defects of large management difficulty and weak service expansibility in the traditional physical host and virtual private server (VPS, virtual Private Server) service.
According to the technical scheme provided by the embodiment of the application, the actual graph structure is obtained by performing drawing processing on the non-intuitive and complex power grid element parameters, so that complex connection relations are simplified to a certain extent, then an optimized target graph structure is obtained by utilizing a pre-trained graph neural network suitable for processing the graph structure, namely, the graph structure optimization and error correction capability obtained by means of the graph neural network based on training samples is achieved, and finally, the power grid structure is adjusted to be consistent with the target graph structure, so that errors are corrected timely and automatically, and the purpose of ensuring continuous and stable operation of a power grid as much as possible is achieved.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present application may be performed in parallel, sequentially, or in a different order, provided that the desired results of the technical solutions disclosed in the present application can be achieved, and are not limited herein.
The above embodiments do not limit the scope of the application. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present application are intended to be included within the scope of the present application.
Claims (12)
1. A method for adjusting a grid structure, comprising:
generating an actual graph structure according to each element parameter forming a current power grid structure, wherein a power station, a transformer substation and a load in the power grid structure are used as different nodes in the actual graph structure, a power transmission line is used as a connecting line for connecting the nodes, and the nodes and the connecting line have corresponding attribute parameters;
outputting a target graph structure corresponding to the input actual graph structure by using a preset graph neural network, wherein the graph neural network is obtained by training based on a sample graph structure of a power grid and a corresponding sample target graph structure;
adjusting the current power grid structure to be consistent with the target graph structure;
wherein said adjusting said current grid structure to be consistent with said target graph structure comprises:
comparing the target graph structure with the actual graph structure to obtain a graph structure difference;
determining at least one corresponding adjustment operation sequence according to the graph structure difference;
respectively determining the score of each adjustment operation sequence by using a preset scoring function, wherein the scoring function is used for scoring at least one of power transmission loss, the ratio of power generation to power consumption and the stability of a power grid structure;
and sequentially executing each adjustment operation in the adjustment operation sequence with the highest score until the last adjustment operation in the sequence is executed.
2. The method of claim 1, wherein the generating an actual graph structure from the individual element parameters that make up the current grid structure comprises:
respectively taking a power station, a transformer substation and a load which form the current power grid structure as different vertexes;
taking a power transmission line in the current power grid structure as an edge for connecting different vertexes;
constructing an adjacency matrix according to each vertex and each side, and generating an initial graph structure according to the adjacency matrix;
and respectively adding corresponding attribute parameters to each vertex and each edge in the initial graph structure to generate the actual graph structure.
3. The method of claim 2, wherein the attaching respective attribute parameters for each vertex and edge in the initial graph structure, respectively, generates an actual graph structure, comprising:
normalizing all elements forming the current power grid structure and corresponding attribute parameters to obtain an N multiplied by M feature matrix, wherein N is the number of the elements, and M is the number of the attribute parameters corresponding to each element;
and adding corresponding attribute parameters to each vertex and each side in the initial graph structure by utilizing the feature matrix to generate the actual graph structure.
4. A method according to claim 3, further comprising:
and initializing an input structure of the graph neural network by using the feature matrix and the adjacency matrix.
5. The method of any of claims 1-4, further comprising:
and responding to the abnormal occurrence of any adjusting operation sequence in the execution process, and adjusting the scoring function according to the influence generated by the abnormal occurrence.
6. An apparatus for adjusting a grid structure, comprising:
a power grid graph structure generating unit configured to generate an actual graph structure according to each element parameter constituting a current power grid structure, wherein a power station, a transformer substation and a load in the power grid structure are used as different nodes in the actual graph structure, a power transmission line is used as a connecting line for connecting the nodes, and the nodes and the connecting line have corresponding attribute parameters;
the power grid map structure optimization unit is configured to output a target map structure corresponding to an input actual map structure by using a preset map neural network, wherein the map neural network is obtained by training based on a sample map structure of a power grid and a corresponding sample target map structure;
a grid structure adjustment unit configured to adjust the current grid structure to be consistent with the target graph structure;
the grid structure adjustment unit is further configured to: comparing the target graph structure with the actual graph structure to obtain a graph structure difference; determining at least one corresponding adjustment operation sequence according to the graph structure difference; respectively determining the score of each adjustment operation sequence by using a preset scoring function, wherein the scoring function is used for scoring at least one of power transmission loss, the ratio of power generation to power consumption and the stability of a power grid structure; and sequentially executing each adjustment operation in the adjustment operation sequence with the highest score until the last adjustment operation in the sequence is executed.
7. The apparatus of claim 6, wherein the grid graph structure generation unit comprises:
a vertex setting subunit configured to regard power stations, substations, and loads constituting the current grid structure as different vertices, respectively;
an edge setting subunit configured to take a power line constituting a current power grid structure as an edge connecting between different ones of the vertices;
an initial graph structure generation subunit configured to construct an adjacency matrix from each of the vertices and each of the edges, and generate an initial graph structure from the adjacency matrix;
and the actual graph structure generating subunit is configured to attach corresponding attribute parameters to each vertex and side in the initial graph structure respectively to generate the actual graph structure.
8. The apparatus of claim 7, wherein the actual graph structure generation subunit is further configured to:
normalizing all elements forming the current power grid structure and corresponding attribute parameters to obtain an N multiplied by M feature matrix, wherein N is the number of the elements, and M is the number of the attribute parameters corresponding to each element;
and adding corresponding attribute parameters to each vertex and each side in the initial graph structure by utilizing the feature matrix to generate the actual graph structure.
9. The apparatus of claim 8, further comprising:
an initializing unit configured to initialize an input structure of the graph neural network using the feature matrix and the adjacency matrix.
10. The apparatus of any of claims 6-9, further comprising:
and a scoring function adjusting unit configured to adjust the scoring function according to an influence of an abnormality occurring in response to any of the adjustment operation sequences occurring in the course of execution.
11. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method for adjusting a grid structure of any one of claims 1-5.
12. A non-transitory computer readable storage medium storing computer instructions for causing the computer to perform the method for adjusting a grid structure of any one of claims 1-5.
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