SCD file decoupling method applying community discovery algorithm in complex network

Abstract

The invention discloses an SCD file decoupling method applying a community discovery algorithm in a complex network, and the method comprises the steps: S1, loading a to-be-analyzed SCD file into a system, carrying out the preprocessing of the to-be-analyzed SCD file, and obtaining the public information of the SCD file; s2, analyzing the SCD file in the step S1 to obtain a device and a connection relationship, and constructing a complex network according to the device and the connection relationship; s3, dividing nodes in the network into a plurality of communities based on modularity optimization by applying a community discovery algorithm, and identifying a key device community; equipment and a virtual loop in the SCD file are divided according to communities, and a plurality of relatively independent subsystems are formed to reduce the checking granularity; and S4, identifying key equipment according to a community discovery result, defining an attribute tag, and outputting the attribute tag in a visual form to serve as to-be-checked data of an SCD checking tool. Through community identification in a complex network, key function modules or subsystems in the transformer substation can be quickly positioned, the invalid checking time is shortened, and the checking efficiency is improved.

Description

Technical Field

[0001] This patent relates to the field of smart grid technology, especially the SCD file verification technology for smart substations. Background Art

[0002] With the rapid development of smart grid technology, smart substations, as a key component of the power grid, have a significant impact on the stable operation of the power system due to their safety and stability. As the core configuration file of the smart substation, the SCD file contains key information such as the equipment configuration and communication relationship of the substation. However, with the expansion of substation scale and the increase of complexity, the traditional manual SCD file verification method has been difficult to meet the requirements of efficiency and accuracy. Therefore, a new type of SCD file verification system has emerged to meet this challenge. In this technical field, how to solve the inaccurate verification results caused by incomplete and unreliable information in the SCD is the main technical problem. Among them, good SCD file decoupling and verification granularity selection are one of the solutions.

[0003] Chinese patent authorization CN 105956245 B relates to a method for decoupling SCD model intervals in a smart substation, the method comprising the following steps: (1) SCD model interval modeling; (2) analysis of association relationships between intervals; (3) physical decoupling of intervals; (4) analysis of the contents of associated interval configuration modifications; (5) merging interval files. The decoupling method of the invention needs to rely on the SSD (System Specification Description) file, but when configuring the engineering example, due to the different requirements of the specifications in different periods, the number of sites that configure the SSD file at this stage is very small, and it is impossible to achieve automatic loop verification of a single SCD, so this method cannot be used as a decoupling method for SCD file verification.

[0004] SCD files are often large in size and contain a lot of node and connection information. Without algorithm optimization, reading, parsing, and verifying these files may consume a lot of ineffective time. In addition, since the loop connection relationship between devices is determined by many factors, if there is no hierarchical area division, there will be multiple possibilities for the connection relationship of this part, which will affect the accuracy of the verification. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present invention provides a SCD file decoupling method for applying a community discovery algorithm in a complex network, comprising the following steps:

[0006] Step S1: construct an SCD file loading module; the SCD file loading module loads the SCD file to be parsed and performs preprocessing, and obtains the public information of the SCD file to further parse the SCD file; wherein the public information includes the head node information and private node information of the SCD file;

[0007] Step S2: further parse the SCD file in step S1 to obtain device information and communication link information between devices, and formalize the device information and communication link information into nodes and edges respectively, so as to construct a complex network;

[0008] Step S3: construct a community identification module. The community identification module applies a community discovery algorithm to divide the nodes in the complex network in step S2 into several communities based on modularity optimization, and identifies key device communities; divide the device information and communication link information in the SCD file according to the community to form several relatively independent subsystems to reduce the granularity of the verification;

[0009] Step S4: Construct an SCD file decoupling output module to decouple and identify the devices in the subsystem obtained by community identification, obtain the information of the corresponding devices according to preset requirements, define the attribute labels of the corresponding devices and output them in a visual form, and call them as the data to be verified by the SCD verification tool.

[0010] Furthermore, step S1 is specifically as follows:

[0011] Step S11: import the SCD file and parse the SCD file based on the SCL language;

[0012] Step S12: obtaining the header node information of the parsed SCD file, the header node information including the SCD version, revision version, SCD name and revision time;

[0013] Step S13: Acquire the private node information of the parsed SCD file, where the private node information includes the whole-station CRC check code.

[0014] Furthermore, step S2 is specifically as follows:

[0015] Step S21: determine the nodes and number them; number each device in the SCD file, and set a unique number for each node;

[0016] Step S21 is specifically as follows:

[0017] Step S211: Parse the SCD file to obtain each device information, intercept the name of the current device information, and obtain the device set, primary type, secondary type and voltage level corresponding to the current device information;

[0018] Step S222: repeat step S211 until all device information types are judged, thereby using the device information as a node of a complex network;

[0019] Step S223: Generate a list based on the voltage level, primary type, secondary type, and set corresponding to each node, and number each node according to the type;

[0020] Step S22: Determine the connection relationship between the nodes in step S21, that is, communication link information;

[0021] Step S221: parse the device information of the SCD file, obtain the corresponding receiving circuit of the current device information, and clean up the invalid connection relationship;

[0022] Step S222: extracting the input terminal path of the current device information;

[0023] Step S223: extracting the output terminal path of the device information on the opposite side;

[0024] Step S224: repeating steps S222 to S223 until the input terminal paths and output terminal paths of all device information are obtained;

[0025] Step S225: constructing a connection relationship based on the input terminal path and the output terminal path parsed in step S224, and then extracting the corresponding device information at both ends, and grouping the device information at both ends together into a set and then aggregating it into a relationship, which is used as an edge of the two device information corresponding nodes;

[0026] Step S23: construct a connectivity matrix to further formalize the connection relationship between nodes;

[0027] Step S23 is specifically as follows:

[0028] Step S231: Create an empty matrix, the number of rows and columns of the empty matrix are equal to the number of nodes; each element of the matrix will indicate whether there is a connection relationship between two nodes;

[0029] Step S232: Fill the empty matrix in step S231: Based on the connection relationship generated in step S225, the corresponding nodes at both ends are taken as a node pair (i, j), i and j respectively represent the numbers corresponding to the nodes at both ends, traverse all node pairs (i, j), if there is a connection relationship between node i and node j, then the element in the i-th row and j-th column in the matrix is ​​set to 1, if there is no connection relationship between node i and node j, then the element in the i-th row and j-th column in the matrix is ​​set to 0;

[0030] Step S233: Aggregate all nodes in the SCD file in the manner of step S232 to generate a complex network.

[0031] Furthermore, the modularity in step S3 is defined as follows:

[0032]

[0033] in, represents the number of edges in the complex network, k i , k j Respectively represent the degree of node i and node j, X ij represents the adjacency matrix; γ represents the scale resolution parameter;

[0034] The above formula is simplified to:

[0035]

[0036] Among them, C represents the community, L c represents the number of edges in community c, k c represents the sum of the node degrees in community c;

[0037] Step S3 is specifically as follows:

[0038] Step S31: Initialize each node in the SCD file as a community;

[0039] Step S32: Divide the community corresponding to each node into adjacent communities, specifically into communities with the largest and positive modularity gain; repeat the above iterative process until the modularity no longer increases the gain;

[0040] Step S32 is specifically as follows:

[0041] Step S321: traverse each node i in order of numbering;

[0042] Step S322: Try to move node i from community A to community B where its neighbor node j is located;

[0043] Step S323: Calculate the change in modularity of the entire complex network after moving node i, expressed as:

[0044]

[0045] Among them, k i,in represents the weight sum of the edges between node i and community B, Σ tot represents the sum of all weights connected to community B;

[0046] Step S324: If ΔZ is maximized, node i is indeed moved to community B corresponding to node j;

[0047] Step S325: If there is no improvement in the modularity of this round of traversal, stop the iteration;

[0048] Step S33: The communities formed by the previous iteration are regarded as nodes, and a new complex network is constructed. The weight of the edge between the nodes is the sum of the weights of all the edges connecting the two communities.

[0049] Step S34: Repeat steps S32 to S33 on the new network until the modularity can no longer be optimized or the difference before and after optimization does not exceed a predetermined threshold.

[0050] Furthermore, step S4 is specifically as follows:

[0051] The results found by the community are used to identify key devices, define attribute labels and output them in a visual form as the data to be verified by the SCD verification tool for its use; specifically:

[0052] Step S41: define community bay type attribute labels according to the primary type, including: main transformer bay, line bay, bus bay, bus coupling bay, section bay, capacitor bay, reactor bay, grounding transformer bay, station-level equipment, circuit breaker bay, and bridge switch bay;

[0053] Step S42: define the main protection in each bay type as the core node of the community, including: main transformer protection, line protection, bus PT protection, bus tie protection, section protection, capacitor protection, reactor protection, grounding transformer protection, station-level equipment, circuit breaker protection and bridge switch bay;

[0054] Step S43: Find the core nodes that exist therein, and label the communities where the core nodes exist with corresponding interval type labels;

[0055] Step S44: the analysis results after the above steps are displayed graphically using a graphical tool;

[0056] Step S45: decoupling is completed.

[0057] The positive and progressive effects of the present invention are:

[0058] Improve verification efficiency: Through community identification in complex networks, key functional modules or subsystems in the substation can be quickly located, reducing invalid verification time and improving verification efficiency.

[0059] Enhanced verification accuracy: Adaptive matching technology can be used to dynamically adjust matching strategies based on the characteristics of the community to ensure the accuracy and pertinence of verification.

[0060] Provide improvement suggestions: Based on the verification results, targeted improvement suggestions or fault diagnosis methods can be provided to help users optimize substation configuration or quickly locate the source of the fault. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1The present invention is a flowchart of the steps of a SCD file decoupling method for applying a community discovery algorithm in a complex network. DETAILED DESCRIPTION

[0062] The following describes the embodiments of the present invention through specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention.

[0063] Reference Figure 1 , a SCD file decoupling method for applying a community discovery algorithm in a complex network, in one example, includes the following steps:

[0064] Step S1: construct an SCD file loading module; the SCD file loading module loads the SCD file to be parsed and performs preprocessing, and obtains the public information of the SCD file to further parse the SCD file; wherein the public information includes the head node information and private node information of the SCD file;

[0065] Step S2: further parse the SCD file in step S1 to obtain device information and communication link information between devices, and formalize the device information and communication link information into nodes and edges respectively, so as to construct a complex network;

[0066] Step S3: construct a community identification module. The community identification module applies a community discovery algorithm to divide the nodes in the complex network in step S2 into several communities based on modularity optimization, and identifies key device communities; divide the device information and communication link information in the SCD file according to the community to form several relatively independent subsystems to reduce the granularity of the verification;

[0067] Step S4: Construct an SCD file decoupling output module to decouple and identify the devices in the subsystem obtained by community identification, obtain the information of the corresponding devices according to preset requirements, define the attribute labels of the corresponding devices and output them in a visual form, and call them as the data to be verified by the SCD verification tool.

[0068] Furthermore, in one example, step S1 is specifically as follows:

[0069] Step S11: import the SCD file and parse the SCD file based on the SCL language;

[0070] Step S12: obtaining the header node information of the parsed SCD file, the header node information including the SCD version, revision version, SCD name and revision time;

[0071] Step S13: Acquire the private node information of the parsed SCD file, where the private node information includes the whole-station CRC check code.

[0072] Furthermore, in one example, step S2 is specifically as follows:

[0073] Step S21: determine the nodes and number them; number each device in the SCD file, and set a unique number for each node;

[0074] Step S21 is specifically as follows:

[0075] Step S211: Parse the SCD file to obtain each device information, intercept the name of the current device information, and obtain the device set, primary type, secondary type and voltage level corresponding to the current device information;

[0076] Step S222: repeat step S211 until all device information types are judged, thereby using the device information as a node of a complex network;

[0077] Step S223: Generate a list based on the voltage level, primary type, secondary type, and set corresponding to each node, and number each node according to the type;

[0078] Step S22: Determine the connection relationship between the nodes in step S21, that is, communication link information;

[0079] Step S221: parse the device information of the SCD file, obtain the corresponding receiving circuit of the current device information, and clean up the invalid connection relationship;

[0080] Step S222: extracting the input terminal path of the current device information;

[0081] Step S223: extracting the output terminal path of the device information on the opposite side;

[0082] Step S224: repeating steps S222 to S223 until the input terminal paths and output terminal paths of all device information are obtained;

[0083] Step S225: constructing a connection relationship based on the input terminal path and the output terminal path parsed in step S224, and then extracting the corresponding device information at both ends, and grouping the device information at both ends together into a set and then aggregating it into a relationship, which is used as an edge of the two device information corresponding nodes;

[0084] Step S23: construct a connectivity matrix to further formalize the connection relationship between nodes;

[0085] Step S23 is specifically as follows:

[0086] Step S231: Create an empty matrix, the number of rows and columns of the empty matrix are equal to the number of nodes; each element of the matrix will indicate whether there is a connection relationship between two nodes;

[0087] Step S232: Fill the empty matrix in step S231: Based on the connection relationship generated in step S225, the corresponding nodes at both ends are taken as a node pair (i, j), i and j respectively represent the numbers corresponding to the nodes at both ends, traverse all node pairs (i, j), if there is a connection relationship between node i and node j, then the element in the i-th row and j-th column in the matrix is ​​set to 1, if there is no connection relationship between node i and node j, then the element in the i-th row and j-th column in the matrix is ​​set to 0;

[0088] Step S233: Aggregate all nodes in the SCD file in the manner of step S232 to generate a complex network.

[0089] Furthermore, in one example, the modularity in step S3 is defined as follows:

[0090]

[0091] in, represents the number of edges in the complex network, k i , k j Respectively represent the degree of node i and node j, X ij represents the adjacency matrix; γ represents the scale resolution parameter;

[0092] The above formula is simplified to:

[0093]

[0094] Among them, C represents the community, L c represents the number of edges in community c, k c represents the sum of the node degrees in community c;

[0095] Step S3 is specifically as follows:

[0096] Step S31: Initialize each node in the SCD file as a community;

[0097] Step S32: Divide the community corresponding to each node into adjacent communities, specifically into communities with the largest and positive modularity gain; repeat the above iterative process until the modularity no longer increases the gain;

[0098] Step S32 is specifically as follows:

[0099] Step S321: traverse each node i in order of numbering;

[0100] Step S322: Try to move node i from community A to community B where its neighbor node j is located;

[0101] Step S323: Calculate the change in modularity of the entire complex network after moving node i, expressed as:

[0102]

[0103] Among them, k i,in represents the weight sum of the edges between node i and community B, Σ tot represents the sum of all weights connected to community B;

[0104] Step S324: If ΔZ is maximized, node i is indeed moved to community B corresponding to node j;

[0105] Step S325: If there is no improvement in the modularity of this round of traversal, stop the iteration;

[0106] Step S33: The communities formed by the previous iteration are regarded as nodes, and a new complex network is constructed. The weight of the edge between the nodes is the sum of the weights of all the edges connecting the two communities.

[0107] Step S34: Repeat steps S32 to S33 on the new network until the modularity can no longer be optimized or the difference before and after optimization does not exceed a predetermined threshold.

[0108] Furthermore, in one example, step S4 is specifically as follows:

[0109] The results found by the community are used to identify key devices, define attribute labels and output them in a visual form as the data to be verified by the SCD verification tool for its use; specifically:

[0110] Step S41: define community bay type attribute labels according to the primary type, including: main transformer bay, line bay, bus bay, bus coupling bay, section bay, capacitor bay, reactor bay, grounding transformer bay, station-level equipment, circuit breaker bay, and bridge switch bay;

[0111] Step S42: define the main protection in each bay type as the core node of the community, including: main transformer protection, line protection, bus PT protection, bus tie protection, section protection, capacitor protection, reactor protection, grounding transformer protection, station-level equipment, circuit breaker protection and bridge switch bay;

[0112] Step S43: Find the core nodes that exist therein, and label the communities where the core nodes exist with corresponding interval type labels;

[0113] Step S44: the analysis results after the above steps are displayed graphically using a graphical tool;

[0114] Step S45: decoupling is completed.

[0115] The present invention is described in detail above in conjunction with the embodiments of the accompanying drawings. A person skilled in the art can make various variations of the present invention according to the above description. Therefore, some details in the embodiments should not be construed as limiting the present invention, and the present invention shall be protected by the scope defined by the attached claims.

Claims

1. A SCD file decoupling method using a community discovery algorithm in a complex network, characterized in that: The following steps are involved: Step S1: construct an SCD file loading module; the SCD file loading module loads the SCD file to be parsed and performs preprocessing, and obtains the public information of the SCD file to further parse the SCD file; wherein the public information includes the head node information and private node information of the SCD file; Step S2: further parse the SCD file in step S1 to obtain device information and communication link information between devices, and formalize the device information and communication link information into nodes and edges respectively, so as to construct a complex network; Step S3: construct a community identification module. The community identification module applies a community discovery algorithm to divide the nodes in the complex network in step S2 into several communities based on modularity optimization, and identifies key device communities; divide the device information and communication link information in the SCD file according to the community to form several relatively independent subsystems to reduce the granularity of the verification; Step S4: Construct an SCD file decoupling output module to decouple and identify the devices in the subsystem obtained by community identification, obtain the information of the corresponding devices according to preset requirements, define the attribute labels of the corresponding devices and output them in a visual form, and call them as the data to be verified by the SCD verification tool.

2. The SCD file decoupling method for applying a community discovery algorithm in a complex network as claimed in claim 1, characterized in that: Step S1 is specifically as follows: Step S11: import the SCD file and parse the SCD file based on the SCL language; Step S12: obtaining the header node information of the parsed SCD file, the header node information including the SCD version, revision version, SCD name and revision time; Step S13: Acquire the private node information of the parsed SCD file, where the private node information includes the whole-station CRC check code.

3. The SCD file decoupling method for applying a community discovery algorithm in a complex network as claimed in claim 1, characterized in that: Step S2 is specifically as follows: Step S21: determine the nodes and number them; number each device in the SCD file, and set a unique number for each node; Step S21 is specifically as follows: Step S211: Parse the SCD file to obtain each device information, intercept the name of the current device information, and obtain the device set, primary type, secondary type and voltage level corresponding to the current device information; Step S222: repeat step S211 until all device information types are judged, thereby using the device information as a node of a complex network; Step S223: Generate a list based on the voltage level, primary type, secondary type, and set corresponding to each node, and number each node according to the type; Step S22: Determine the connection relationship between the nodes in step S21, that is, communication link information; Step S221: parse the device information of the SCD file, obtain the corresponding receiving circuit of the current device information, and clean up the invalid connection relationship; Step S222: extracting the input terminal path of the current device information; Step S223: extracting the output terminal path of the device information on the opposite side; Step S224: repeating steps S222 to S223 until the input terminal paths and output terminal paths of all device information are obtained; Step S225: constructing a connection relationship based on the input terminal path and the output terminal path parsed in step S224, and then extracting the corresponding device information at both ends, and grouping the device information at both ends together into a set and then aggregating it into a relationship, which is used as an edge of the two device information corresponding nodes; Step S23: construct a connectivity matrix to further formalize the connection relationship between nodes; Step S23 is specifically as follows: Step S231: Create an empty matrix, the number of rows and columns of the empty matrix are equal to the number of nodes; each element of the matrix will indicate whether there is a connection relationship between two nodes; Step S232: Fill the empty matrix in step S231: Based on the connection relationship generated in step S225, the corresponding nodes at both ends are taken as a node pair (i, j), i and j respectively represent the numbers corresponding to the nodes at both ends, traverse all node pairs (i, j), if there is a connection relationship between node i and node j, then the element in the i-th row and j-th column in the matrix is ​​set to 1, if there is no connection relationship between node i and node j, then the element in the i-th row and j-th column in the matrix is ​​set to 0; Step S233: Aggregate all nodes in the SCD file in the manner of step S232 to generate a complex network.

4. The SCD file decoupling method for applying a community discovery algorithm in a complex network as claimed in claim 3, characterized in that: The modularity in step S3 is defined as follows: Where m represents the number of edges in the complex network, k i , k j Respectively represent the degree of node i and node j, X ij represents the adjacency matrix; γ represents the scale resolution parameter; The above formula is simplified to: Among them, C represents the community, L c represents the number of edges in community c, k c represents the sum of the node degrees in community c; Step S3 is specifically as follows: Step S31: Initialize each node in the SCD file as a community; Step S32: Divide the community corresponding to each node into adjacent communities, specifically into communities with the largest and positive modularity gain; repeat the above iterative process until the modularity no longer increases the gain; Step S32 is specifically as follows: Step S321: traverse each node i in order of numbering; Step S322: Try to move node i from community A to community B where its neighbor node j is located; Step S323: Calculate the change in modularity of the entire complex network after moving node i, expressed as: Among them, k i,in represents the weight sum of the edges between node i and community B, Σ tot represents the sum of all weights connected to community B; Step S324: If ΔZ is maximized, node i is indeed moved to community B corresponding to node j; Step S325: If there is no improvement in the modularity of this round of traversal, stop the iteration; Step S33: The communities formed by the previous iteration are regarded as nodes, and a new complex network is constructed. The weight of the edge between the nodes is the sum of the weights of all the edges connecting the two communities. Step S34: Repeat steps S32 to S33 on the new network until the modularity can no longer be optimized or the difference before and after optimization does not exceed a predetermined threshold.

5. The SCD file decoupling method for applying a community discovery algorithm in a complex network as claimed in claim 4, characterized in that: Step S4 is specifically as follows: Identify key devices based on the results found by the community, define attribute labels and output them in a visual form as data to be verified by the SCD verification tool for its use; Specifically: Step S41: define community bay type attribute labels according to the primary type, including: main transformer bay, line bay, bus bay, bus coupling bay, section bay, capacitor bay, reactor bay, grounding transformer bay, station-level equipment, circuit breaker bay, and bridge switch bay; Step S42: define the main protection in each bay type as the core node of the community, including: main transformer protection, line protection, bus PT protection, bus tie protection, section protection, capacitor protection, reactor protection, grounding transformer protection, station-level equipment, circuit breaker protection and bridge switch bay; Step S43: Find the core nodes that exist therein, and label the communities where the core nodes exist with corresponding interval type labels; Step S44: the analysis results after the above steps are displayed graphically using a graphical tool; Step S45: decoupling is completed.

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