Substation equipment operation state monitoring method and system based on digital twinning

Abstract

The invention discloses a transformer substation equipment operation state monitoring method and system based on digital twinning, and the method comprises the steps: obtaining transformer substation detection data, analyzing the transformer substation detection data, and screening out feature data; firstly, a 3D digital model of the transformer substation is established according to a real scene object and three-dimensional geometric data of the transformer substation, meanwhile, a feature database of the transformer substation is constructed, then feature matching is conducted on the 3D digital model according to the operation state and real-time parameters of the transformer substation to form a transformer substation digital twinborn body, then operation data in the transformer substation digital twinborn body is analyzed, and the transformer substation digital twinborn body is obtained. Outputting fault feature data; and performing fault analysis on the substation equipment according to the fault feature data. According to the invention, the transformer substation digital twin is established by modeling and simulating the data of the whole transformer substation, so that the operation environment of the transformer substation is simulated, the real-time monitoring of the operation information and data of the transformer substation is realized, and the reliability and real-time performance of fault processing of the transformer substation are greatly improved.

Description

Technical Field

[0001] The present invention relates to the field of substation operation, maintenance and management, and in particular to a substation equipment operating status monitoring method and system based on digital twins. Background Art

[0002] With the continuous development of power systems, the scale and complexity of substations are also increasing, and the safety and reliability requirements for substation operation are becoming higher and higher. As the core of the power system, substations are directly related to the stability and reliability of the system power supply.

[0003] However, traditional substation monitoring and acquisition systems usually use manual inspection methods and rely on manual inspections and inspections of various equipment in the substation to discover and alarm problems with power equipment. This method lacks timeliness, accuracy and reliability, and is prone to problems such as missed detection and misjudgment. In addition, although some common monitoring methods have been adopted during the construction of substations, such as jumper temperature measurement and ultrasonic detection, these methods can often only monitor a single fault situation, and cannot fully cover all possible fault situations in the substation. Substation fault monitoring is facing severe challenges. Summary of the invention

[0004] The purpose of the present invention is to overcome the above-mentioned defects and problems existing in the prior art, and to provide a substation equipment operation status monitoring method and system based on digital twins. By modeling the entities and operation data of the entire substation, a substation digital twin is constructed to simulate the operating environment of the substation, and to realize real-time monitoring of the substation operation information and data, so as to improve the reliability and real-time performance of substation fault handling.

[0005] To achieve the above objectives, the technical solution of the present invention is: a method for monitoring the operating status of substation equipment based on digital twins, comprising:

[0006] Obtain substation detection data, analyze the substation detection data, and filter out characteristic data;

[0007] First, a 3D digital model of the substation is established based on the real scene and 3D geometric data of the substation, and a feature database of the substation is constructed. Then, according to the operating status and real-time parameters of the substation, feature matching is performed on the 3D digital model to form a digital twin of the substation. Then, the operating data in the digital twin of the substation is analyzed to output fault feature data.

[0008] Conduct fault analysis on substation equipment based on fault characteristic data.

[0009] The substation detection data is analyzed to screen out characteristic data, including:

[0010] Normalize the substation detection data to obtain the data matrix Y;

[0011] According to the data matrix Y, the following target loss function is established:

[0012]

[0013] In the formula, h is the target loss function; u represents the mapping matrix of the data matrix Y in the feature space;

[0014] Calculate the covariance matrix C of the data matrix Y:

[0015]

[0016] Where m is the number of data matrices Y;

[0017] Minimize the objective loss function h, and establish the following Lagrangian function based on the Lagrangian multiplier method:

[0018] f(u,λ)=u T Cu-λ(u T u-1);

[0019] In the formula, λ is the penalty factor;

[0020] The penalty factor λ is obtained by finding the extreme value of the Lagrangian function, and the mapping matrix u is obtained using the following formula:

[0021] Cu=λu;

[0022] Perform eigenvector principal component screening on the mapping matrix u to obtain the principal components of the mapping matrix u;

[0023] The principal components of multiple groups of mapping matrices u are convolved, max-pooled, and fully concatenated to obtain feature data.

[0024] The performing eigenvector principal component screening on the mapping matrix u to obtain the principal component of the mapping matrix u includes:

[0025] The mapping matrix u is transformed as follows to obtain a new variable:

[0026]

[0027] In the formula, β=(q 1 ,q 2 , ..., q r ) T is a new variable, u=(u 1 ,u 2 , ..., u s ) T is the mapping matrix, r<s; a rsis an algebraic relation matrix;

[0028] Create the following new variable matrix:

[0029]

[0030] Diagonalize the matrix β and find the characteristic roots;

[0031] The eigenvectors are obtained according to the eigenvalues, and the eigenvalues ​​are arranged into a matrix by row from top to bottom according to the size of the corresponding eigenvalues. The first k rows are taken to form a new matrix P, and the new matrix P is used as the principal component of the mapping matrix u.

[0032] The feature matching of the 3D digital model to form a substation digital twin includes:

[0033] Calculate the expected target value:

[0034]

[0035] Where L(θ) is the expected target value; θ=[θ 1 ,θ 2 ,…,θ m ] is the collected substation electrical data; y is the substation electrical data number;

[0036] Calculate the difference characteristics of the twin output data:

[0037] F dif = abs(L(p)-L(θ));

[0038] In the formula, F dif is the data difference eigenvalue; L(p) represents the target value of the new matrix P;

[0039] Calculate the error function:

[0040]

[0041] In the formula, M is the quantity;

[0042] The expected target value L(θ) and the output data difference characteristics are continuously iterated until the error function is within a reasonable range;

[0043] The expected target value after iteration is used as the data of the 3D digital model to form a digital twin of the substation.

[0044] The performing fault analysis on the substation equipment according to the fault characteristic data includes:

[0045] The substation historical data is matched with the fault type one by one, and the mapping function used is as follows:

[0046] yi =f(x i1 , x i2 ,...x iN );

[0047] In the formula, x i is the i-th device state parameter; N is the number of parameters; f is the mapping from substation state to fault type; y i is the fault type;

[0048] Use the following formula to classify fault information:

[0049]

[0050] In the formula, is the mapping from the original space to the N-dimensional feature space; x i and χ nn are the input and weight vectors respectively; H is the regularization constant; is the slack variable; b nn is the hyperplane threshold;

[0051] Use group search method to update and iterate the classification;

[0052] When the error accuracy is reached, the iteration is stopped and the fault diagnosis type is output.

[0053] The method also includes fault diagnosis:

[0054] According to the fault diagnosis type, the fault information is classified and coded according to the voltage, current and temperature fault types;

[0055] Set failure criteria and thresholds for all failures;

[0056] Establish a fault tree based on fault standards and thresholds, fault characteristic information, and fault diagnosis type, and encode the fault tree address;

[0057] The substation equipment operation data is implanted into the fault tree, and query and analysis are performed to obtain fault diagnosis information.

[0058] The method also includes determining the cause of the fault:

[0059] The following formula is used to screen the fault information. When γ>0, it indicates that the fault information is valid. If γ<0, the fault information is considered to be noise and should be discarded.

[0060]

[0061] Where, γ is the fault information evaluation benchmark; H k (x p ) represents the discretization degree of the nearest k points; e k(x p ) is the local density of the sample; k is the fault information number; dist(x q ) represents x q Distance to the origin;

[0062] Encode and reduce the dimension of the filtered fault information;

[0063] Encode the processed fault information in the fault tree for traversal and comparison;

[0064] The fault type is obtained through the optimization algorithm, and the fault cause is given through the fault tree.

[0065] A substation equipment operation status monitoring system based on digital twin, the system is applied to the above-mentioned method, the system comprising:

[0066] The characteristic data acquisition module is used to acquire the substation detection data, analyze the substation detection data, and filter out the characteristic data;

[0067] The substation digital twin generation module is used to first establish a 3D digital model of the substation based on the real scene and 3D geometric data of the substation, and at the same time build a feature database of the substation. Then, according to the operating status and real-time parameters of the substation, the 3D digital model is feature matched to form a substation digital twin. Then, the operating data in the substation digital twin is analyzed to output fault feature data.

[0068] The substation fault analysis module is used to perform fault analysis on substation equipment based on fault characteristic data.

[0069] A substation equipment operation status monitoring device based on digital twins, comprising a memory and a processor;

[0070] The memory is used to store computer program code and transmit the computer program code to the processor;

[0071] The processor is used to execute the method according to the instructions in the computer program code.

[0072] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method described above is implemented.

[0073] Compared with the prior art, the present invention has the following beneficial effects:

[0074] The present invention provides a substation equipment operating status monitoring method and system based on digital twin. The method constructs a substation digital twin by modeling the entities and operating data of the entire substation to simulate the operating environment of the substation, thereby realizing real-time monitoring of the substation operating information and data, and greatly improving the reliability and real-time performance of substation fault handling. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] Figure 1 It is a flow chart of a method for monitoring the operating status of substation equipment based on digital twins of the present invention.

[0076] Figure 2 It is a structural block diagram of a substation equipment operation status monitoring system based on digital twins of the present invention.

[0077] Figure 3 It is an architecture diagram of a substation equipment operation status monitoring system based on digital twin in an embodiment of the present invention.

[0078] Figure 4 It is a structural block diagram of a substation equipment operation status monitoring device based on digital twins of the present invention. DETAILED DESCRIPTION

[0079] The present invention is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0080] See also Figure 1 , a substation equipment operation status monitoring method based on digital twin, comprising:

[0081] S1. Obtain substation detection data, analyze the substation detection data, and filter out characteristic data;

[0082] S2. First, a 3D digital model of the substation is established based on the real scene and 3D geometric data of the substation, and a feature database of the substation is constructed. Then, according to the operating status and real-time parameters of the substation, the 3D digital model is feature matched to form a digital twin of the substation. Then, the operating data in the digital twin of the substation is analyzed to output fault feature data.

[0083] S3. Perform fault analysis on substation equipment based on fault characteristic data.

[0084] Furthermore, the substation detection data is analyzed to screen out characteristic data, including:

[0085] Normalize the substation detection data to obtain the data matrix Y;

[0086] According to the data matrix Y, the following target loss function is established:

[0087]

[0088] In the formula, h is the target loss function; u represents the mapping matrix of the data matrix Y in the feature space;

[0089] Calculate the covariance matrix C of the data matrix Y:

[0090]

[0091] Where m is the number of data matrices Y;

[0092] Minimize the objective loss function h, and establish the following Lagrangian function based on the Lagrangian multiplier method:

[0093] f(u,λ)=u T Cu-λ(u T u-1);

[0094] In the formula, λ is the penalty factor;

[0095] The penalty factor λ is obtained by finding the extreme value of the Lagrangian function, and the mapping matrix u is obtained using the following formula:

[0096] Cu=λu;

[0097] Perform eigenvector principal component screening on the mapping matrix u to obtain the principal components of the mapping matrix u;

[0098] The principal components of multiple groups of mapping matrices u are convolved, max-pooled, and fully concatenated to obtain feature data.

[0099] Furthermore, the step of performing eigenvector principal component screening on the mapping matrix u to obtain the principal component of the mapping matrix u includes:

[0100] The mapping matrix u is transformed as follows to obtain a new variable:

[0101]

[0102] In the formula, β=(q 1 ,q 2 , ..., q r ) T is a new variable, u=(u 1 ,u 2 , ..., u s ) T is the mapping matrix, r<s; a rs is an algebraic relation matrix;

[0103] Create the following new variable matrix:

[0104]

[0105] Diagonalize the matrix β and find the characteristic roots;

[0106] The eigenvectors are obtained according to the eigenvalues, and the eigenvalues ​​are arranged into a matrix by row from top to bottom according to the size of the corresponding eigenvalues. The first k rows are taken to form a new matrix P, and the new matrix P is used as the principal component of the mapping matrix u.

[0107] Furthermore, the feature matching of the 3D digital model to form a substation digital twin includes:

[0108] Calculate the expected target value:

[0109]

[0110] Where L(θ) is the expected target value; θ=[θ 1 ,θ 2 ,…,θ m ] is the collected substation electrical data; y is the substation electrical data number;

[0111] Calculate the difference characteristics of the twin output data:

[0112] F dif = abs(L(p)-L(θ));

[0113] In the formula, F dif is the data difference eigenvalue; L(p) represents the target value of the new matrix P;

[0114] Calculate the error function:

[0115]

[0116] In the formula, M is the quantity;

[0117] The expected target value L(θ) and the output data difference characteristics are continuously iterated until the error function is within a reasonable range;

[0118] The expected target value after iteration is used as the data of the 3D digital model to form a digital twin of the substation.

[0119] The use of digital twin technology to model and simulate substations, and to simulate the operating environment of substations based on digital twins, can better identify and analyze faults in substations. The use of digital twin technology to provide efficient data processing capabilities can make high-quality predictions and decisions on large amounts of data, ensuring the stability and reliability of substation operation.

[0120] Further, the performing fault analysis on the substation equipment according to the fault characteristic data includes:

[0121] The substation historical data is matched with the fault type one by one, and the mapping function used is as follows:

[0122] y i =f(x i1 , x i2 ,...x iN );

[0123] In the formula, x i is the i-th device state parameter; N is the number of parameters; f is the mapping from substation state to fault type; y i is the fault type;

[0124] Use the following formula to classify fault information:

[0125]

[0126] In the formula, is the mapping from the original space to the N-dimensional feature space; x i and χ nn are the input and weight vectors respectively; H is the regularization constant; is the slack variable; b nn is the hyperplane threshold;

[0127] Use group search method to update and iterate the classification;

[0128] When the error accuracy is reached, the iteration is stopped and the fault diagnosis type is output.

[0129] In another embodiment of the present application, a method for monitoring the operating status of substation equipment based on digital twins also includes fault diagnosis:

[0130] According to the fault diagnosis type, the fault information is classified and coded according to the voltage, current, temperature fault type, etc.;

[0131] Set failure criteria and thresholds for all failures;

[0132] Establish a fault tree based on fault standards and thresholds, fault characteristic information, and fault diagnosis type, and encode the fault tree address;

[0133] The substation equipment operation data is implanted into the fault tree, and query and analysis are performed to obtain fault diagnosis information.

[0134] In another embodiment of the present application, a method for monitoring the operating status of substation equipment based on digital twins further includes determining the cause of the fault:

[0135] The following formula is used to screen the fault information. When γ>0, it indicates that the fault information is valid. If γ<0, the fault information is considered to be noise and should be discarded.

[0136]

[0137] Where, γ is the fault information evaluation benchmark; H k (x p ) represents the discretization degree of the nearest k points; e k (x p ) is the local density of the sample; k is the fault information number; dist(x q ) represents x q Distance from the origin, x q ∈N k (x p );

[0138] Encode and reduce the dimension of the filtered fault information;

[0139] Encode the processed fault information in the fault tree for traversal and comparison;

[0140] The fault type is obtained through the optimization algorithm, and the fault cause is given through the fault tree. According to the cause of the fault, corresponding treatment methods can be formulated to improve the reliability and timeliness of fault handling.

[0141] See also Figure 2 The present invention also provides a substation equipment operation status monitoring system based on digital twins, which is applied to a substation equipment operation status monitoring method based on digital twins, and the system includes:

[0142] The characteristic data acquisition module is used to acquire the substation detection data, analyze the substation detection data, and filter out the characteristic data;

[0143] The substation digital twin generation module is used to first establish a 3D digital model of the substation based on the real scene and 3D geometric data of the substation, and at the same time build a feature database of the substation. Then, according to the operating status and real-time parameters of the substation, the 3D digital model is feature matched to form a substation digital twin. Then, the operating data in the substation digital twin is analyzed to output fault feature data.

[0144] The substation fault analysis module is used to perform fault analysis on substation equipment based on fault characteristic data.

[0145] Specifically, Figure 3As shown in the figure, the substation equipment operation status monitoring system based on digital twins consists of four parts: perception layer, data layer, platform layer and application layer. Among them, the perception layer is based on physical networks and sensors, including environmental perception networks, equipment perception networks, infrared video perception networks, mobile monitoring equipment, etc., to collect data such as the environment and equipment operation of the substation, that is, to obtain substation detection data. The data layer uses big data and artificial intelligence technology data to realize data collection, transmission, management and other functions, and archives and backs up the data for subsequent mining and analysis. The platform layer realizes data mapping, physical entity modeling, equipment simulation modeling, data processing, artificial intelligence analysis, collaborative computing and other functions based on digital twin technology, and provides business interfaces for specific application scenarios. The application layer uses digital twins to realize application functions such as performance analysis, fault diagnosis, trend prediction and data monitoring of substations. When applied, the following steps are included:

[0146] Step 1: Obtain substation detection data through the perception layer;

[0147] Step 2: Analyze the detection data of the perception layer through the data layer to filter out the characteristic data;

[0148] Step 3: Use the platform layer to form a digital twin of the substation, perform collaborative calculations on the characteristic data of the data layer, and perform intelligent analysis and processing;

[0149] Step 4: Based on the platform layer data obtained in step 3, visual application processing is performed through the application layer to realize functions such as operation monitoring and fault analysis of substation equipment.

[0150] The implementation process of the substation digital twin is as follows:

[0151] 1) According to the real scene and 3D geometric data of the substation, a 3D digital model of the substation is established, and a characteristic database of the substation system is constructed;

[0152] 2) According to the substation’s operating status data, real-time parameters, etc., feature matching is performed on the 3D digital model to form a substation digital twin;

[0153] 3) Effectively integrate and intelligently analyze the operating data in the digital twin and provide a data interface for the application layer.

[0154] See also Figure 4 , the present invention also provides a substation equipment operation status monitoring device based on digital twin, including a memory and a processor;

[0155] The memory is used to store computer program code and transmit the computer program code to the processor;

[0156] The processor is used to execute a method for monitoring the operating status of substation equipment based on digital twins according to the instructions in the computer program code.

[0157] The present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, a method for monitoring the operating status of substation equipment based on digital twins is implemented.

[0158] Generally speaking, the computer instructions for implementing the method of the present invention may be carried in any combination of one or more computer-readable storage media. Non-transitory computer-readable storage media may include any computer-readable media except for the signal itself that is temporarily propagating.

[0159] The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EKROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, device, or device.

[0160] Computer program code for performing the operation of the present invention may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages, in particular, Python suitable for neural network computing and platform frameworks based on TensorFlow, PyTorch, etc. may be used. The program code may be executed entirely on the user's computer, partially on the user's computer, as an independent software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer or to an external computer (e.g., using an Internet service provider to connect via the Internet) through any type of network, including a local area network (LAN) or a wide area network (WAN).

[0161] The above-mentioned devices and non-temporary computer-readable storage media can be found in the specific description of a substation equipment operation status monitoring method based on digital twins and its beneficial effects, which will not be repeated here.

[0162] Although the embodiments of the present invention have been shown and described above, it should be understood that the above embodiments are exemplary and should not be construed as limitations on the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. A method for monitoring the operating status of substation equipment based on digital twins, characterized in that: include: Obtain substation detection data, analyze the substation detection data, and filter out characteristic data; First, a 3D digital model of the substation is established based on the real scene and 3D geometric data of the substation, and a feature database of the substation is constructed. Then, according to the operating status and real-time parameters of the substation, feature matching is performed on the 3D digital model to form a digital twin of the substation. Then, the operating data in the digital twin of the substation is analyzed to output fault feature data. Conduct fault analysis on substation equipment based on fault characteristic data.

2. A method for monitoring the operating status of substation equipment based on digital twin according to claim 1, characterized in that: The substation detection data is analyzed to screen out characteristic data, including: Normalize the substation detection data to obtain the data matrix Y; According to the data matrix Y, the following target loss function is established: In the formula, h is the target loss function; u represents the mapping matrix of the data matrix Y in the feature space; Calculate the covariance matrix C of the data matrix Y: Where m is the number of data matrices Y; Minimize the objective loss function h, and establish the following Lagrangian function based on the Lagrangian multiplier method: f(u,λ)=u T Cu-λ(u T in-1); In the formula, λ is the penalty factor; The penalty factor λ is obtained by finding the extreme value of the Lagrangian function, and the mapping matrix u is obtained using the following formula: Cu=λu; Perform eigenvector principal component screening on the mapping matrix u to obtain the principal components of the mapping matrix u; The principal components of multiple groups of mapping matrices u are convolved, max-pooled, and fully concatenated to obtain feature data.

3. A method for monitoring the operating status of substation equipment based on digital twin according to claim 2, characterized in that: The performing eigenvector principal component screening on the mapping matrix u to obtain the principal component of the mapping matrix u includes: The mapping matrix u is transformed as follows to obtain a new variable: Where, β=(q1,q2,...,q r ) T is a new variable, u=(u1,u2,…,u s ) T is the mapping matrix, r<s; a rs is an algebraic relation matrix; Create the following new variable matrix: Diagonalize the matrix β and find the characteristic roots; The eigenvectors are obtained according to the eigenvalues, and the eigenvalues ​​are arranged into a matrix by row from top to bottom according to the size of the corresponding eigenvalues. The first k rows are taken to form a new matrix P, and the new matrix P is used as the principal component of the mapping matrix u.

4. A method for monitoring the operating status of substation equipment based on digital twin according to claim 3, characterized in that: The feature matching of the 3D digital model to form a substation digital twin includes: Calculate the expected target value: Where L(θ) is the expected target value; θ = [θ1, θ2, …, θ m ] is the collected substation electrical data; y is the substation electrical data number; Calculate the difference characteristics of the twin output data: F diff =abs(L(p)-L(θ)); In the formula, F dif is the data difference eigenvalue; L(p) represents the target value of the new matrix P; Calculate the error function: In the formula, M is the quantity; The expected target value L(θ) and the output data difference characteristics are continuously iterated until the error function is within a reasonable range; The expected target value after iteration is used as the data of the 3D digital model to form a digital twin of the substation.

5. A method for monitoring the operating status of substation equipment based on digital twin according to claim 1, characterized in that: The performing fault analysis on the substation equipment according to the fault characteristic data includes: The substation historical data is matched with the fault type one by one, and the mapping function used is as follows: y i =f(x i1 ,x i2 ,...x iN ); In the formula, x i is the i-th device state parameter; N is the number of parameters; f is the mapping from substation state to fault type; y i is the fault type; Use the following formula to classify fault information: In the formula, is the mapping from the original space to the N-dimensional feature space; x i and χ nn are the input and weight vectors respectively; H is the regularization constant; is the slack variable; b nn is the hyperplane threshold; Use group search method to update and iterate the classification; When the error accuracy is reached, the iteration is stopped and the fault diagnosis type is output.

6. A method for monitoring the operating status of substation equipment based on digital twin according to claim 5, characterized in that: The method also includes fault diagnosis: According to the fault diagnosis type, the fault information is classified and coded according to the voltage, current and temperature fault types; Set failure criteria and thresholds for all failures; Establish a fault tree based on fault standards and thresholds, fault characteristic information, and fault diagnosis type, and encode the fault tree address; The substation equipment operation data is implanted into the fault tree, and query and analysis are performed to obtain fault diagnosis information.

7. A method for monitoring the operating status of substation equipment based on digital twin according to claim 5, characterized in that: The method also includes determining the cause of the fault: The following formula is used to screen the fault information. When γ>0, it indicates that the fault information is valid. If γ<0, the fault information is considered to be noise and should be discarded. Where, γ is the fault information evaluation benchmark; H k (x p ) represents the discretization degree of the nearest k points; e k (x p ) is the local density of the sample; k is the fault information number; dist(x q ) represents x q Distance to the origin; Encode and reduce the dimension of the filtered fault information; Encode the processed fault information in the fault tree for traversal and comparison; The fault type is obtained through the optimization algorithm, and the fault cause is given through the fault tree.

8. A substation equipment operation status monitoring system based on digital twin, characterized in that: The system is applied to the method described in any one of claims 1 to 7, and the system comprises: The characteristic data acquisition module is used to acquire the substation detection data, analyze the substation detection data, and filter out the characteristic data; The substation digital twin generation module is used to first establish a 3D digital model of the substation based on the real scene and 3D geometric data of the substation, and at the same time build a feature database of the substation. Then, according to the operating status and real-time parameters of the substation, the 3D digital model is feature matched to form a substation digital twin. Then, the operating data in the substation digital twin is analyzed to output fault feature data. The substation fault analysis module is used to perform fault analysis on substation equipment based on fault characteristic data.

9. A substation equipment operation status monitoring device based on digital twin, characterized in that: including memory and processor; The memory is used to store computer program code and transmit the computer program code to the processor; The processor is configured to execute the method according to any one of claims 1 to 7 according to instructions in the computer program code.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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