A method and system for generating digital fingerprint of power grid operation mode

Abstract

This invention provides a method and system for generating a digital fingerprint of power grid operation mode. The method, based on physical information such as power grid topology, equipment model parameters, and the operating states of various equipment (loads and power sources), constructs feature vectors using admittance and impedance matrices and performs hash operations to generate the digital fingerprint of power grid operation mode. The digital fingerprint generated by this method and system possesses uniqueness, irreversibility, and collision resistance, and is independent of non-physical information such as specific data storage formats and equipment component naming. It can represent the physical characteristics of power grid topology, equipment model parameters, and the operating states of various equipment (loads and power sources) across platforms and software.

Description

Technical Field

[0001] This invention relates to the field of power grid simulation and calculation technology, and more specifically, to a method and system for generating digital fingerprints of power grid operation modes. Background Technology

[0002] Traditional simulation tools are mainly stand-alone simulation software. In recent years, by combining power system simulation technology with the latest digital and Internet technologies, the research on power grid planning simulation platforms has made significant progress, and new forms of simulation products are increasingly developing towards digital platforms.

[0003] Data describing power grid operation modes includes power grid topology, equipment model parameters, load and power supply operating status information, and other equipment operating status information. Various data formats are used. For example, the PSDB data platform stores power grid topology, equipment model parameters, load and power supply operating status information in a relational database; PSD simulation software records this information using .dat and .swi text files; PSASP simulation software stores information in a database with a completely different data structure than PSDB and generates PSASP data text files for calculation and analysis; other simulation platforms or software also use different data formats. Therefore, the same operating mode is reflected in completely different data formats on different platforms or software. Furthermore, different platforms or software use different naming rules for equipment components, making it impossible to simply establish a one-to-one correspondence between equipment components by name. Within the same platform or software, different orders of equipment components do not affect the essential information of the operating mode, but they are reflected drastically at the data level. There is currently no publicly available method for generating unique characteristic values, i.e., digital fingerprints, for power grid operating modes.

[0004] Message digest algorithms, as a key branch of cryptography, aim to provide unique "fingerprint" information for data, thereby supporting core functions such as data signing and integrity verification. Their irreversible nature also makes them important for encrypting sensitive information. However, message digest algorithms rely on specific data formats and cannot directly reflect the physical characteristics of power grid operation modes, thus they cannot be directly applied to generate digital fingerprints of power grid operation modes. The data formats used for power grid operation modes are diverse; the same operation mode can be represented in completely different formats on different platforms or software. Furthermore, interference from equipment naming and sorting cannot be avoided. Therefore, message digest algorithms cannot be directly used to generate cross-platform, cross-software digital fingerprints of operation modes. Summary of the Invention

[0005] To address the lack of existing methods for generating digital fingerprints of power grid operation modes, and the technical problem that digital fingerprints generated using message digest algorithms cannot directly reflect the physical characteristics of power grid operation modes, this invention provides a method and system for generating digital fingerprints of power grid operation modes.

[0006] According to one aspect of the present invention, the present invention provides a method for generating a digital fingerprint of a power grid operation mode, comprising:

[0007] Obtain the branch impedance modulus, positive sequence parameters and zero sequence parameters of impedance elements in operation, and the operating voltage and equipment injected complex power of the nodes connected to the power injection elements in operation from the power grid operation mode data for which digital fingerprints are to be generated.

[0008] Based on the custom power grid node merging rules, the overall set of power grid nodes for which digital fingerprints are to be generated is determined according to the branch impedance modulus, and the nodes in the overall set of nodes are selected based on the custom filtering rules to determine the representative node set.

[0009] Based on the nodes of the overall node set, positive-sequence admittance matrix and zero-sequence admittance matrix are generated according to the positive-sequence parameter, the zero-sequence parameter, the operating voltage, and the device injected complex power, respectively.

[0010] Invert the positive-sequence admittance matrix and the zero-sequence admittance matrix respectively to generate the corresponding node-oriented positive-sequence impedance matrix and zero-sequence impedance matrix;

[0011] Based on the nodes in the representative node set, the positive-sequence impedance matrix and zero-sequence impedance matrix oriented towards the nodes are reduced in order to generate the positive-sequence impedance matrix and zero-sequence impedance matrix oriented towards the power plant.

[0012] According to the custom feature vector calculation rules, the positive sequence impedance matrix and zero sequence impedance matrix for nodes, and the positive sequence impedance matrix and zero sequence impedance matrix for power plants are calculated respectively to generate the corresponding first positive sequence feature vector and first zero sequence feature vector for nodes, and the first positive sequence feature vector and first zero sequence feature vector for power plants.

[0013] According to the custom sorting rules, the elements in the first positive sequence feature vector and the first zero sequence feature vector for nodes, and the first positive sequence feature vector and the first zero sequence feature vector for plants are sorted to generate the corresponding second positive sequence feature vector and the second zero sequence feature vector for nodes, and the second positive sequence feature vector and the second zero sequence feature vector for plants.

[0014] Hash algorithms are used to perform hash operations on the second positive sequence feature vector and the second zero sequence feature vector for nodes, as well as the second positive sequence feature vector and the second zero sequence feature vector for power plants, to generate corresponding digital fingerprints of the power grid positive sequence operation mode and the power grid zero sequence operation mode for nodes, as well as digital fingerprints of the power grid positive sequence operation mode and the power grid zero sequence operation mode for power plants.

[0015] According to another aspect of the present invention, the present invention provides a system for generating a digital fingerprint of a power grid operation mode, the system comprising:

[0016] The data acquisition module is used to acquire the branch impedance modulus, positive sequence parameters and zero sequence parameters of impedance elements in operation, and operating voltage and equipment injected complex power of the nodes connected to the power injection elements in operation in the power grid operation mode data for which digital fingerprints are to be generated.

[0017] The node set module is used to determine the overall node set of the power grid to be used to generate digital fingerprints based on the branch impedance modulus, according to the custom power grid node merging rules, and to select nodes in the overall node set based on the custom filtering rules to determine the representative node set.

[0018] The first matrix module is used to generate a positive-sequence admittance matrix and a zero-sequence admittance matrix based on the nodes of the overall node set, according to the positive-sequence parameters, the zero-sequence parameters, the operating voltage, and the device injected complex power, respectively.

[0019] The second matrix module is used to invert the positive-sequence admittance matrix and the zero-sequence admittance matrix respectively to generate the corresponding node-oriented positive-sequence impedance matrix and zero-sequence impedance matrix.

[0020] The third matrix module is used to reduce the order of the node-oriented positive-sequence impedance matrix and zero-sequence impedance matrix based on the nodes in the representative node set, and generate the plant-oriented positive-sequence impedance matrix and zero-sequence impedance matrix.

[0021] The first vector module is used to calculate the positive-sequence impedance matrix and zero-sequence impedance matrix for nodes and the positive-sequence impedance matrix and zero-sequence impedance matrix for power plants according to the custom feature vector calculation rules, and generate the corresponding first positive-sequence feature vector and first zero-sequence feature vector for nodes and the first positive-sequence feature vector and first zero-sequence feature vector for power plants.

[0022] The second vector module is used to sort the elements in the first positive sequence feature vector and the first zero sequence feature vector for nodes, and the first positive sequence feature vector and the first zero sequence feature vector for plants and stations, respectively, according to a custom sorting rule, to generate the corresponding second positive sequence feature vector and second zero sequence feature vector for nodes, and the second positive sequence feature vector and second zero sequence feature vector for plants and stations.

[0023] The digital fingerprint module is used to perform hash operations on the second positive sequence feature vector and the second zero sequence feature vector for nodes, and the second positive sequence feature vector and the second zero sequence feature vector for power plants, respectively, to generate corresponding digital fingerprints of the power grid positive sequence operation mode and the power grid zero sequence operation mode for nodes, as well as digital fingerprints of the power grid positive sequence operation mode and the power grid zero sequence operation mode for power plants.

[0024] According to another aspect of the present invention, a computer-readable storage medium is provided, the storage medium storing a computer program that, when executed by a processor, implements the methods described in any of the above aspects of the present invention.

[0025] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising: a processor; a memory for storing executable instructions of the processor; the processor being configured to read the executable instructions from the memory and execute the instructions to implement the method described in any of the preceding aspects of the present invention.

[0026] The method and system for generating digital fingerprints of power grid operation modes according to this invention generate digital fingerprints of power grid operation modes based on physical information such as power grid topology, equipment model parameters, and the operating states of various equipment including loads and power sources. The method utilizes admittance and impedance matrices to construct feature vectors and performs hash operations to generate digital fingerprints of power grid operation modes. For given physical information such as power grid topology, equipment model parameters, and equipment operating states, this digital fingerprint possesses uniqueness, irreversibility, and collision resistance, and is independent of non-physical information such as the specific data storage format and equipment component naming. Its beneficial effects are as follows:

[0027] (1) Signature: Create an unforgeable digital signature for the power grid operation mode data to prove that the data source is reliable and has not been tampered with. For example, in the operation mode calculation and analysis conclusion, digital fingerprint technology can prove the source and authenticity of the operation mode data used.

[0028] (2) Verification: During the data format conversion, transmission, and storage of the operation mode, verify the consistency and integrity of the information. For example, when generating data files used by simulation software such as PSASP and PSD through the PSDB data platform, or during data conversion in other scenarios, regardless of the specific data format, verify that the digital fingerprint of the same method remains unchanged, which can ensure the consistency of the operation mode data.

[0029] (3) Indexing: Based on the physical characteristics of the operating mode, content deduplication, storage optimization, and efficient search are achieved, accelerating sample training. For example, by utilizing the uniqueness of digital fingerprints based on the physical characteristics of the operating mode, redundant storage resources can be effectively reduced in data management platforms and computing analysis platforms; in the sample training of artificial intelligence methods, the machine learning and data mining processes can be accelerated, supporting the application of artificial intelligence technology in the field of operating mode simulation computing.

[0030] (4) Security: Used for desensitizing, encrypting, and verifying operational mode data in simulation analysis tools to ensure its security, confidentiality, and reliability. For example, simulation software such as PSASP and PSD uses plaintext data files, which are difficult to effectively desensitize using existing technologies. In the future, each time the simulation software saves data, it will re-encode all node names and device component names with random codes, encrypt them with digital fingerprints, and independently record the mapping relationship between node names, device component names, and random codes. When the simulation software reopens the operational mode, it will restore the original plaintext by regenerating the digital fingerprint, which can improve data security and ensure that the data has not been tampered with.

[0031] (5) Traceability: After the operation mode data is released, it is tracked throughout the entire chain through the bound digital fingerprint and used for copyright protection. For example, after a set of operation mode data is adjusted in the data management platform or other simulation products, a copyright statement is established by creating a digital fingerprint, and the use of the data is controlled and tracked, which can promote the establishment of the operation mode data ecosystem and support high-quality management of operation mode data assets. Attached Figure Description

[0032] A more complete understanding of exemplary embodiments of the present invention may be obtained by referring to the following drawings:

[0033] Figure 1 A flowchart illustrating a method for generating a digital fingerprint of a power grid operation mode according to a preferred embodiment of the present invention;

[0034] Figure 2 This is a schematic diagram of the structure of a system for generating a digital fingerprint of power grid operation mode according to a preferred embodiment of the present invention;

[0035] Figure 3 This is a schematic diagram of the structure of an electronic device according to a preferred embodiment of the present invention. Detailed Implementation

[0036] Exemplary embodiments of the invention will now be described with reference to the accompanying drawings. However, the invention may be embodied in many different forms and is not limited to the embodiments described herein. These embodiments are provided to fully and completely disclose the invention and to fully convey its scope to those skilled in the art. The terminology used in the exemplary embodiments illustrated in the drawings is not intended to limit the invention. In the drawings, the same units / elements are referred to by the same reference numerals.

[0037] Unless otherwise stated, the terms used herein (including technical terms) have their common meaning as understood by one of ordinary skill in the art. Furthermore, it is understood that terms defined in commonly used dictionaries should be understood to have a meaning consistent with the context of their relevant field, and not to be interpreted as having an idealized or overly formal meaning.

[0038] Exemplary methods

[0039] Figure 1 This is a flowchart illustrating a method for generating a digital fingerprint of power grid operation modes according to a preferred embodiment of the present invention. Figure 1 As shown, the method for generating a digital fingerprint of the power grid operation mode according to this preferred embodiment begins from step 101.

[0040] In step 101, the branch impedance modulus, positive sequence parameters and zero sequence parameters of impedance elements in operation, and the operating voltage and equipment injected complex power of the nodes connected to the power injection elements in operation are obtained from the power grid operation mode data for which digital fingerprints are to be generated.

[0041] In this preferred embodiment, the branch impedance modulus, positive-sequence parameter, and zero-sequence parameter are all basic physical parameters, and voltage and power are also basic electrical quantities for power grid operation, which can be directly obtained in the prior art. This application does not limit the method of obtaining them. The impedance elements include AC lines, transformers, series capacitors, series reactors, parallel capacitors, parallel reactors, etc., and the power injection elements include generators, energy storage devices, loads, DC converters, synchronous condensers, SVCs, and SVGs, etc.

[0042] In step 102, based on the custom power grid node merging rules, the overall set of power grid nodes for which digital fingerprints are to be generated is determined according to the branch impedance modulus, and the nodes in the overall set of nodes are selected based on the custom filtering rules to determine the representative node set.

[0043] Preferably, based on a custom power grid node merging rule, the overall set of power grid nodes for which digital fingerprints are to be generated is determined according to the branch impedance magnitude, and nodes in the overall node set are selected based on a custom filtering rule to determine a representative node set, including:

[0044] Nodes directly connected to branches whose branch impedance moduli satisfy the custom grid node merging rules are merged, and the merged nodes form the overall node set. , wherein the set The number of nodes contained in it is The rule for merging power grid nodes is that the branch impedance modulus is less than a custom modulus threshold.

[0045] The overall node set is filtered based on custom rules. Select nodes from the list to determine the representative node set. The filtering rule is in the set Only nodes at the highest voltage level of the respective power plant are retained, and the set is... The number of nodes contained in it is .

[0046] In step 103, based on the nodes of the overall node set, a positive-sequence admittance matrix and a zero-sequence admittance matrix are generated according to the positive-sequence parameter, the zero-sequence parameter, the operating voltage, and the device injected complex power, respectively.

[0047] Preferably, based on the nodes of the overall node set, a positive-sequence admittance matrix and a zero-sequence admittance matrix are generated according to the positive-sequence parameter, the zero-sequence parameter, the operating voltage, and the injected complex power, respectively, including:

[0048] The order of generation is based on the number of nodes in the overall node set. The positive-order initial admittance matrix and the zero-order initial admittance matrix, wherein the initial value of each element in the positive-order initial admittance matrix and the zero-order initial admittance matrix is ​​0;

[0049] For belonging to set The equivalent admittance of the node connected to the power injection element is calculated based on the operating voltage and the complex power injected into the device. The calculation formula is as follows:

[0050]

[0051] In the formula, , and Sets The Middle The operating voltage of the node connected to each power injection element, the complex power injected into the device, and the equivalent susceptance;

[0052] The elements corresponding to each node in the positive sequence initial admittance matrix are assigned values ​​according to the positive sequence parameters and the equivalent admittance to generate the positive sequence admittance matrix;

[0053] The zero-order admittance matrix is ​​generated by assigning values ​​to the elements corresponding to each node in the zero-order initial admittance matrix according to the zero-order parameters.

[0054] In step 104, the positive-sequence admittance matrix and the zero-sequence admittance matrix are inverted to generate the corresponding node-oriented positive-sequence impedance matrix and zero-sequence impedance matrix.

[0055] In this preferred embodiment, both the positive-order admittance matrix and the zero-order admittance matrix are of order 1. Therefore, the orders of the corresponding node-oriented positive-sequence impedance matrix and zero-sequence impedance matrix obtained by inverting the matrix are also . .

[0056] In step 105, based on the nodes in the representative node set, the positive-sequence impedance matrix and zero-sequence impedance matrix oriented towards the nodes are reduced in order to generate the positive-sequence impedance matrix and zero-sequence impedance matrix oriented towards the power plant.

[0057] In this preferred embodiment, since the representative node set is obtained by retaining the nodes of the highest voltage level in the substation within the overall node set, the order of the node-oriented positive-sequence impedance matrix and zero-sequence impedance matrix is ​​reduced. This means only the rows and columns in the node-oriented positive-sequence impedance matrix and zero-sequence impedance matrix related to the nodes in the representative node set are retained, thus obtaining a result with order [order missing]. The positive-sequence impedance matrix and zero-sequence impedance matrix for power plants.

[0058] In step 106, the positive-sequence impedance matrix and zero-sequence impedance matrix for nodes, and the positive-sequence impedance matrix and zero-sequence impedance matrix for power plants are calculated according to the custom feature vector calculation rules, respectively, to generate the corresponding first positive-sequence feature vector and first zero-sequence feature vector for nodes, and the first positive-sequence feature vector and first zero-sequence feature vector for power plants.

[0059] Preferably, the positive-sequence impedance matrix and zero-sequence impedance matrix for nodes, and the positive-sequence impedance matrix and zero-sequence impedance matrix for power plants are calculated according to the custom feature vector calculation rules to generate the corresponding first positive-sequence feature vector and first zero-sequence feature vector for nodes, and the first positive-sequence feature vector and first zero-sequence feature vector for power plants. The feature vector calculation rules are either row-wise averaging or row-wise summation.

[0060] In step 107, the elements in the first positive sequence feature vector and the first zero sequence feature vector for nodes, and the first positive sequence feature vector and the first zero sequence feature vector for plants are sorted according to the custom sorting rules to generate the corresponding second positive sequence feature vector and the second zero sequence feature vector for nodes, and the second positive sequence feature vector and the second zero sequence feature vector for plants.

[0061] Preferably, the elements in the first positive sequence feature vector and the first zero sequence feature vector for nodes, and the first positive sequence feature vector and the first zero sequence feature vector for plants are sorted according to a custom sorting rule to generate the corresponding second positive sequence feature vector and the second zero sequence feature vector for nodes, and the second positive sequence feature vector and the second zero sequence feature vector for plants. The sorting rule is any one of sorting by element modulus from largest to smallest and from smallest to largest.

[0062] In step 108, a hash algorithm is used to perform hash operations on the second positive sequence feature vector and the second zero sequence feature vector for nodes, and the second positive sequence feature vector and the second zero sequence feature vector for power plants, respectively, to generate corresponding digital fingerprints of the power grid positive sequence operation mode and the power grid zero sequence operation mode for nodes, as well as digital fingerprints of the power grid positive sequence operation mode and the power grid zero sequence operation mode for power plants.

[0063] In this preferred embodiment, the hash algorithm used can be a commonly used algorithm such as SHA1, SHA2, or MD5.

[0064] The preferred embodiment of the method for generating a digital fingerprint of power grid operation mode is based on physical information such as power grid topology, equipment model parameters, and the operating status of various equipment including loads and power sources. It utilizes admittance and impedance matrices to construct feature vectors and performs hash operations to generate the digital fingerprint of power grid operation mode. The digital fingerprint generated by this method is unique, irreversible, and collision-resistant, and is independent of non-physical information such as the specific data storage format and equipment component naming. It can represent the physical characteristics of power grid topology, equipment model parameters, and the operating status of various equipment including loads and power sources across platforms and software.

[0065] Exemplary System

[0066] Figure 2 This is a schematic diagram of the structure of a system for generating a digital fingerprint of power grid operation mode according to a preferred embodiment of the present invention. Figure 2 As shown, the system 200 for generating a digital fingerprint of power grid operation mode according to this preferred embodiment includes:

[0067] The data acquisition module 201 is used to acquire the branch impedance modulus, the positive sequence parameters and zero sequence parameters of the impedance element in operation, and the operating voltage and equipment injection complex power of the node connected to the power injection element in operation in the power grid operation mode data to be used to generate digital fingerprints.

[0068] The node set module 202 is used to determine the overall node set of the power grid to be used to generate digital fingerprints based on the branch impedance modulus, according to the custom power grid node merging rules, and to select nodes in the overall node set based on the custom filtering rules to determine the representative node set.

[0069] The first matrix module 203 is used to generate a positive-sequence admittance matrix and a zero-sequence admittance matrix based on the nodes of the overall node set, according to the positive-sequence parameters, the zero-sequence parameters, the operating voltage, and the device injected complex power, respectively.

[0070] The second matrix module 204 is used to invert the positive-sequence admittance matrix and the zero-sequence admittance matrix respectively to generate the corresponding node-oriented positive-sequence impedance matrix and zero-sequence impedance matrix.

[0071] The third matrix module 205 is used to reduce the order of the node-oriented positive-sequence impedance matrix and zero-sequence impedance matrix based on the nodes in the representative node set, and generate the plant-oriented positive-sequence impedance matrix and zero-sequence impedance matrix.

[0072] The first vector module 206 is used to calculate the positive-sequence impedance matrix and zero-sequence impedance matrix for nodes and the positive-sequence impedance matrix and zero-sequence impedance matrix for power plants according to the custom feature vector calculation rules, and generate the corresponding first positive-sequence feature vector and first zero-sequence feature vector for nodes and the first positive-sequence feature vector and first zero-sequence feature vector for power plants.

[0073] The second vector module 207 is used to sort the elements in the first positive sequence feature vector and the first zero sequence feature vector for nodes, and the first positive sequence feature vector and the first zero sequence feature vector for plants and stations, respectively, according to a custom sorting rule, to generate the corresponding second positive sequence feature vector and the second zero sequence feature vector for nodes, and the second positive sequence feature vector and the second zero sequence feature vector for plants and stations.

[0074] The digital fingerprint module 208 is used to perform hash operations on the second positive sequence feature vector and the second zero sequence feature vector oriented towards nodes, and the second positive sequence feature vector and the second zero sequence feature vector oriented towards power plants, respectively, to generate corresponding digital fingerprints of the power grid positive sequence operation mode and the power grid zero sequence operation mode oriented towards nodes, as well as digital fingerprints of the power grid positive sequence operation mode and the power grid zero sequence operation mode oriented towards power plants.

[0075] Preferably, the node set module 202 determines the overall node set of the power grid to be used to generate the digital fingerprint based on the branch impedance modulus according to a custom power grid node merging rule, and selects nodes from the overall node set based on a custom filtering rule to determine a representative node set, including:

[0076] Nodes directly connected to branches whose branch impedance moduli satisfy the custom grid node merging rules are merged, and the merged nodes form the overall node set. , wherein the set The number of nodes contained in it is The rule for merging power grid nodes is that the branch impedance modulus is less than a custom modulus threshold.

[0077] The overall node set is filtered based on custom rules. Select nodes from the list to determine the representative node set. The filtering rule is in the set Only nodes at the highest voltage level of the respective power plant are retained, and the set is... The number of nodes contained in it is .

[0078] Preferably, the first matrix module 203 generates a positive-sequence admittance matrix and a zero-sequence admittance matrix based on the nodes of the overall node set, according to the positive-sequence parameters, the zero-sequence parameters, the operating voltage, and the injected complex power of the device, respectively, including:

[0079] The order of generation is based on the number of nodes in the overall node set. The positive-order initial admittance matrix and the zero-order initial admittance matrix, wherein the initial value of each element in the positive-order initial admittance matrix and the zero-order initial admittance matrix is ​​0;

[0080] For belonging to set The equivalent admittance of the node connected to the power injection element is calculated based on the operating voltage and the complex power injected into the device. The calculation formula is as follows:

[0081]

[0082] In the formula, , and Sets The Middle The operating voltage of the node connected to each power injection element, the complex power injected into the device, and the equivalent susceptance;

[0083] The elements corresponding to each node in the positive sequence initial admittance matrix are assigned values ​​according to the positive sequence parameters and the equivalent admittance to generate the positive sequence admittance matrix;

[0084] The zero-order admittance matrix is ​​generated by assigning values ​​to the elements corresponding to each node in the zero-order initial admittance matrix according to the zero-order parameters.

[0085] Preferably, the first vector module 206 calculates the positive-sequence impedance matrix and zero-sequence impedance matrix for nodes and the positive-sequence impedance matrix and zero-sequence impedance matrix for power plants according to the custom feature vector calculation rules, and generates the corresponding first positive-sequence feature vector and first zero-sequence feature vector for nodes and the first positive-sequence feature vector and first zero-sequence feature vector for power plants, wherein the feature vector calculation rules are either row-wise averaging or row-wise summation.

[0086] Preferably, the second vector module 207 sorts the elements in the first positive sequence feature vector and the first zero sequence feature vector for nodes, and the first positive sequence feature vector and the first zero sequence feature vector for plants and stations, respectively, according to a custom sorting rule, to generate corresponding second positive sequence feature vector and second zero sequence feature vector for nodes, and second positive sequence feature vector and second zero sequence feature vector for plants and stations, wherein the sorting rule is any one of sorting by element modulus from largest to smallest and sorting by smallest to largest.

[0087] The system for generating digital fingerprints of power grid operation modes described in this preferred embodiment and the method for generating digital fingerprints of power grid operation modes are based on physical information such as power grid topology, equipment model parameters, load and power supply operation status, etc. They use admittance matrix and impedance matrix to construct feature vectors and perform hash operations. The steps for generating digital fingerprints of power grid operation modes are the same, and the technical effects achieved are also the same, so they will not be described again here.

[0088] Exemplary electronic devices

[0089] Figure 3 This is a schematic diagram of the structure of an electronic device according to a preferred embodiment of the present invention. Figure 3 As shown, the electronic device includes one or more processors 301 and memory 302.

[0090] The processor 301 may be a central processing unit (CPU) or other form of processing unit with data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions.

[0091] The memory 302 may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory. The non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 301 may execute the program instructions to implement the methods for generating digital fingerprints of power grid operation modes in the various embodiments disclosed above, and / or other desired functions. In one example, the electronic device may also include an input device 303 and an output device 304, these components being interconnected via a bus system and / or other forms of connection mechanisms (not shown).

[0092] In addition, the input device 303 may also include, for example, a keyboard, a mouse, etc.

[0093] The output device 304 can output various information to the outside. The output device 304 may include, for example, a display, a speaker, a printer, and a communication network and its connected remote output devices, etc.

[0094] Of course, for the sake of simplicity, Figure 3 Only some of the components of the electronic device relevant to this disclosure are shown, omitting components such as buses, input / output interfaces, etc. In addition, the electronic device may include any other suitable components depending on the specific application.

[0095] Exemplary computer program products and computer-readable storage media

[0096] In addition to the methods and apparatus described above, embodiments of this disclosure may also be computer program products comprising computer program instructions that, when executed by a processor, cause the processor to perform the steps of the methods for generating digital fingerprints of power grid operating modes according to various embodiments of this disclosure as described in the "Exemplary Methods" section of this specification.

[0097] The computer program product can be written in any combination of one or more programming languages ​​to perform the operations of the embodiments of this disclosure. The programming languages ​​include object-oriented programming languages ​​such as Java and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on a user's computing device, partially on a user's computing device, as a standalone software package, partially on a user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0098] Furthermore, embodiments of this disclosure may also be computer-readable storage media storing computer program instructions thereon, which, when executed by a processor, cause the processor to perform the steps in the methods for generating digital fingerprints of power grid operation modes according to various embodiments of this disclosure as described in the "Exemplary Methods" section above.

[0099] The computer-readable storage medium may be any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof.

[0100] The basic principles of this disclosure have been described above with reference to specific embodiments. However, it should be noted that the advantages, benefits, and effects mentioned in this disclosure are merely examples and not limitations, and should not be considered as essential features of each embodiment of this disclosure. Furthermore, the specific details disclosed above are for illustrative and facilitative purposes only, and are not limitations. These details do not limit the scope of this disclosure to the necessity of employing the aforementioned specific details for implementation.

[0101] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For system embodiments, since they largely correspond to method embodiments, the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments.

[0102] The block diagrams of devices, apparatuses, devices, and systems disclosed herein are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, apparatuses, devices, and systems can be connected, arranged, and configured in any manner. Words such as “comprising,” “including,” “having,” etc., are open-ended terms meaning “including but not limited to,” and are used interchangeably with them. The terms “or” and “and” as used herein refer to the terms “and / or,” and are used interchangeably with them unless the context clearly indicates otherwise. The term “such as” as used herein refers to the phrase “such as but not limited to,” and is used interchangeably with it.

[0103] The apparatus and methods of this disclosure may be implemented in many ways. For example, they may be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware. The above-described order of steps for the methods is for illustrative purposes only, and the steps of the methods of this disclosure are not limited to the order specifically described above unless otherwise specifically stated. Furthermore, in some embodiments, this disclosure may also be implemented as a program recorded on a recording medium, the program including machine-readable instructions for implementing the methods according to this disclosure. Thus, this disclosure also covers recording media storing programs for performing the methods according to this disclosure.

[0104] It should also be noted that in the apparatus, devices, and methods of this disclosure, the components or steps are decomposable and / or recombinable. Such decomposition and / or recombination should be considered equivalent to the present disclosure. The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use this disclosure. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein can be applied to other aspects without departing from the scope of this disclosure. Therefore, this disclosure is not intended to be limited to the aspects shown herein, but rather to be carried out within the widest scope consistent with the principles and novel features disclosed herein.

[0105] The above description has been given for purposes of illustration and description. Furthermore, this description is not intended to limit the embodiments of this disclosure to the forms disclosed herein. Although numerous exemplary aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations therein.

Claims

1. A method for generating a digital fingerprint of power grid operation mode, characterized in that, The method includes: Obtain the branch impedance modulus, positive sequence parameters and zero sequence parameters of impedance elements in operation, and the operating voltage and equipment injected complex power of the nodes connected to the power injection elements in operation from the power grid operation mode data for which digital fingerprints are to be generated. Based on the custom power grid node merging rules, the overall set of power grid nodes for which digital fingerprints are to be generated is determined according to the branch impedance modulus, and the nodes in the overall set of nodes are selected based on the custom filtering rules to determine the representative node set. Based on the nodes of the overall node set, positive-sequence admittance matrix and zero-sequence admittance matrix are generated according to the positive-sequence parameter, the zero-sequence parameter, the operating voltage, and the device injected complex power, respectively. Invert the positive-sequence admittance matrix and the zero-sequence admittance matrix respectively to generate the corresponding node-oriented positive-sequence impedance matrix and zero-sequence impedance matrix; Based on the nodes in the representative node set, the positive-sequence impedance matrix and zero-sequence impedance matrix oriented towards the nodes are reduced in order to generate the positive-sequence impedance matrix and zero-sequence impedance matrix oriented towards the power plant. According to the custom feature vector calculation rules, the positive sequence impedance matrix and zero sequence impedance matrix for nodes, and the positive sequence impedance matrix and zero sequence impedance matrix for power plants are calculated respectively to generate the corresponding first positive sequence feature vector and first zero sequence feature vector for nodes, and the first positive sequence feature vector and first zero sequence feature vector for power plants. According to the custom sorting rules, the elements in the first positive sequence feature vector and the first zero sequence feature vector for nodes, and the first positive sequence feature vector and the first zero sequence feature vector for plants are sorted to generate the corresponding second positive sequence feature vector and the second zero sequence feature vector for nodes, and the second positive sequence feature vector and the second zero sequence feature vector for plants. Hash algorithms are used to perform hash operations on the second positive sequence feature vector and the second zero sequence feature vector for nodes, as well as the second positive sequence feature vector and the second zero sequence feature vector for power plants, to generate corresponding digital fingerprints of the power grid positive sequence operation mode and the power grid zero sequence operation mode for nodes, as well as digital fingerprints of the power grid positive sequence operation mode and the power grid zero sequence operation mode for power plants.

2. The method according to claim 1, characterized in that, Based on a custom power grid node merging rule, the overall set of power grid nodes for which digital fingerprints are to be generated is determined according to the branch impedance magnitude. Then, based on a custom filtering rule, nodes are selected from this overall set to determine a representative node set, including: Nodes directly connected to branches whose branch impedance moduli satisfy the custom grid node merging rules are merged, and the merged nodes form the overall node set. , wherein the set The number of nodes contained in it is The rule for merging power grid nodes is that the branch impedance modulus is less than a custom modulus threshold. The overall node set is filtered based on custom rules. Select nodes from the list to determine the representative node set. The filtering rule is in the set Only nodes at the highest voltage level of the respective power plant are retained, and the set is... The number of nodes contained in it is .

3. The method according to claim 1, characterized in that, Based on the nodes of the overall node set, positive-sequence admittance matrices and zero-sequence admittance matrices are generated according to the positive-sequence parameters, the zero-sequence parameters, the operating voltage, and the injected complex power, respectively, including: The order of generation is based on the number of nodes in the overall node set. The positive-order initial admittance matrix and the zero-order initial admittance matrix, wherein the initial value of each element in the positive-order initial admittance matrix and the zero-order initial admittance matrix is ​​0; For belonging to set The equivalent admittance of the node connected to the power injection element is calculated based on the operating voltage and the complex power injected into the device. The calculation formula is as follows: In the formula, , and Sets The Middle i The operating voltage of the node connected to each power injection element, the complex power injected into the device, and the equivalent susceptance; The elements corresponding to each node in the positive sequence initial admittance matrix are assigned values ​​according to the positive sequence parameters and the equivalent admittance to generate the positive sequence admittance matrix; The zero-order admittance matrix is ​​generated by assigning values ​​to the elements corresponding to each node in the zero-order initial admittance matrix according to the zero-order parameters.

4. The method according to claim 1, characterized in that, According to the custom feature vector calculation rules, the positive-sequence impedance matrix and zero-sequence impedance matrix for nodes, and the positive-sequence impedance matrix and zero-sequence impedance matrix for power plants are calculated respectively to generate the corresponding first positive-sequence feature vector and first zero-sequence feature vector for nodes, and the first positive-sequence feature vector and first zero-sequence feature vector for power plants. The feature vector calculation rules are either row-wise averaging or row-wise summation.

5. The method according to claim 1, characterized in that, The elements in the first positive sequence feature vector and the first zero sequence feature vector for nodes, and the first positive sequence feature vector and the first zero sequence feature vector for plants are sorted according to a custom sorting rule to generate the corresponding second positive sequence feature vector and second zero sequence feature vector for nodes, and the second positive sequence feature vector and second zero sequence feature vector for plants. The sorting rule can be any one of sorting by element modulus from largest to smallest or from smallest to largest.

6. A system for generating a digital fingerprint of power grid operation mode, characterized in that, The system includes: The data acquisition module is used to acquire the branch impedance modulus, positive sequence parameters and zero sequence parameters of impedance elements in operation, and operating voltage and equipment injected complex power of the nodes connected to the power injection elements in operation in the power grid operation mode data for which digital fingerprints are to be generated. The node set module is used to determine the overall node set of the power grid to be used to generate digital fingerprints based on the branch impedance modulus, according to the custom power grid node merging rules, and to select nodes in the overall node set based on the custom filtering rules to determine the representative node set. The first matrix module is used to generate a positive-sequence admittance matrix and a zero-sequence admittance matrix based on the nodes of the overall node set, according to the positive-sequence parameters, the zero-sequence parameters, the operating voltage, and the device injected complex power, respectively. The second matrix module is used to invert the positive-sequence admittance matrix and the zero-sequence admittance matrix respectively to generate the corresponding node-oriented positive-sequence impedance matrix and zero-sequence impedance matrix. The third matrix module is used to reduce the order of the node-oriented positive-sequence impedance matrix and zero-sequence impedance matrix based on the nodes in the representative node set, and generate the plant-oriented positive-sequence impedance matrix and zero-sequence impedance matrix. The first vector module is used to calculate the positive-sequence impedance matrix and zero-sequence impedance matrix for nodes and the positive-sequence impedance matrix and zero-sequence impedance matrix for power plants according to the custom feature vector calculation rules, and generate the corresponding first positive-sequence feature vector and first zero-sequence feature vector for nodes and the first positive-sequence feature vector and first zero-sequence feature vector for power plants. The second vector module is used to sort the elements in the first positive sequence feature vector and the first zero sequence feature vector for nodes, and the first positive sequence feature vector and the first zero sequence feature vector for plants and stations, respectively, according to a custom sorting rule, to generate the corresponding second positive sequence feature vector and second zero sequence feature vector for nodes, and the second positive sequence feature vector and second zero sequence feature vector for plants and stations. The digital fingerprint module is used to perform hash operations on the second positive sequence feature vector and the second zero sequence feature vector for nodes, and the second positive sequence feature vector and the second zero sequence feature vector for power plants, respectively, to generate corresponding digital fingerprints of the power grid positive sequence operation mode and the power grid zero sequence operation mode for nodes, as well as digital fingerprints of the power grid positive sequence operation mode and the power grid zero sequence operation mode for power plants.

7. The system according to claim 6, characterized in that, The node set module, based on a custom power grid node merging rule, determines the overall node set of the power grid for which digital fingerprints are to be generated according to the branch impedance modulus, and selects nodes from the overall node set based on a custom filtering rule to determine a representative node set, including: Nodes directly connected to branches whose branch impedance moduli satisfy the custom grid node merging rules are merged, and the merged nodes form the overall node set. , wherein the set The number of nodes contained in it is The rule for merging power grid nodes is that the branch impedance modulus is less than a custom modulus threshold. The overall node set is filtered based on custom rules. Select nodes from the list to determine the representative node set. The filtering rule is in the set Only nodes at the highest voltage level of the respective power plant are retained, and the set is... The number of nodes contained in it is .

8. The system according to claim 6, characterized in that, The first matrix module, based on the nodes of the overall node set, generates a positive-sequence admittance matrix and a zero-sequence admittance matrix according to the positive-sequence parameters, the zero-sequence parameters, the operating voltage, and the injected complex power of the device, respectively, including: The order of generation is based on the number of nodes in the overall node set. The positive-order initial admittance matrix and the zero-order initial admittance matrix, wherein the initial value of each element in the positive-order initial admittance matrix and the zero-order initial admittance matrix is ​​0; For belonging to set The equivalent admittance of the node connected to the power injection element is calculated based on the operating voltage and the complex power injected into the device. The calculation formula is as follows: In the formula, , and Sets The Middle i The operating voltage of the node connected to each power injection element, the complex power injected into the device, and the equivalent susceptance; The elements corresponding to each node in the positive sequence initial admittance matrix are assigned values ​​according to the positive sequence parameters and the equivalent admittance to generate the positive sequence admittance matrix; The zero-order admittance matrix is ​​generated by assigning values ​​to the elements corresponding to each node in the zero-order initial admittance matrix according to the zero-order parameters.

9. The system according to claim 6, characterized in that, The first vector module calculates the positive-sequence impedance matrix and zero-sequence impedance matrix for nodes and the positive-sequence impedance matrix and zero-sequence impedance matrix for power plants according to the custom feature vector calculation rules, and generates the corresponding first positive-sequence feature vector and first zero-sequence feature vector for nodes and the first positive-sequence feature vector and first zero-sequence feature vector for power plants. The feature vector calculation rules are either row-wise averaging or row-wise summation.

10. The system according to claim 6, characterized in that, The second vector module sorts the elements in the first positive sequence feature vector and the first zero sequence feature vector for nodes, and the first positive sequence feature vector and the first zero sequence feature vector for plants and stations, respectively, according to a custom sorting rule, to generate the corresponding second positive sequence feature vector and second zero sequence feature vector for nodes, and the second positive sequence feature vector and second zero sequence feature vector for plants and stations. The sorting rule is any one of sorting by element modulus from largest to smallest and from smallest to largest.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps of the method as described in any one of claims 1-5.

12. An electronic device, characterized in that, include: processor; Memory used to store the processor's executable instructions; The processor is configured to read the executable instructions from the memory and execute the instructions to implement the method described in any one of claims 1-5.

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