A method and device for detecting abnormality of power transmission and transformation equipment

Abstract

This specification provides a method and device for detecting abnormalities in power transmission and transformation equipment. Obtain target state data of the power transmission and transformation equipment to be detected; perform frequency domain conversion processing on each physical field data in the target state data to obtain corresponding frequency domain data; perform corresponding order reduction processing on each physical field data in the target state data according to the frequency domain data corresponding to each physical field data in the target state data to obtain the reduced-order target state data; reconstruct the reduced-order target state data to obtain the reconstructed target state data; determine the type information of the reconstructed target state data by processing the reconstructed target state data using a preset classification prediction model; determine whether the power transmission and transformation equipment to be detected has an abnormality based on the type information. Based on the above method, high-precision order reduction processing of multiple physical field data related to the power transmission and transformation equipment can be quickly achieved, thereby improving the accuracy of abnormality diagnosis of the power transmission and transformation equipment.

Description

Technical Field

[0001] This specification belongs to the field of multi-physics field technology of power equipment, and in particular relates to a method and device for detecting abnormalities in power transmission and transformation equipment. Background Art

[0002] Currently, power equipment, especially power transmission and transformation equipment, as an important part of the power system, usually requires full-order models to simulate and calculate the physical field data of the power transmission and transformation equipment. The calculation results are used to determine whether the power transmission and transformation equipment is abnormal, and the operating status of the power transmission and transformation equipment is determined in combination with the electrical variable measurement results. However, the current full-order model simulation has a large amount of calculation and usually takes days or weeks to complete, resulting in low efficiency and accuracy.

[0003] To address the above issues, no effective solutions have been proposed so far. Summary of the Invention

[0004] This specification provides a method for detecting abnormalities in power transmission and transformation equipment, which can quickly achieve high-precision and lightweight reduction-order processing of multiple physical field data related to the power transmission and transformation equipment, and thus can efficiently and accurately detect and determine whether there are abnormalities in the power transmission and transformation equipment, effectively reducing the amount of calculation and improving the efficiency and accuracy of abnormal operation and maintenance diagnosis of power transmission and transformation equipment.

[0005] This specification provides a method for detecting abnormalities in power transmission and transformation equipment, including:

[0006] Acquire target state data of the power transmission and transformation equipment to be detected; wherein the target state data includes a plurality of physical field data, and the physical field data includes at least one of the following: electric field, magnetic field and temperature field data;

[0007] Performing frequency domain conversion processing on each physical field data in the target state data to obtain corresponding frequency domain data;

[0008] performing corresponding order reduction processing on each physical field data in the target state data according to the frequency domain data corresponding to each physical field data in the target state data to obtain the reduced-order target state data;

[0009] Reconstructing the reduced-order target state data to obtain reconstructed target state data;

[0010] Determining type information of the reconstructed target state data by processing the reconstructed target state data using a preset classification prediction model;

[0011] Determine whether the power transmission and transformation equipment to be detected has an abnormality according to the type information.

[0012] In one embodiment, performing order reduction processing on each physical field data in the target state data according to the frequency domain data corresponding to each physical field in the target state data to obtain the reduced-order target state data includes:

[0013] Determine, for frequency domain data corresponding to each physical field data in the target state data, a cyclostationary spectral density matrix corresponding to each physical field;

[0014] Decomposing the cyclostationary spectral density matrix to obtain a modal matrix corresponding to the cyclostationary spectral density matrix;

[0015] The reduced-order target state data is obtained according to a preset number of dominant modes in the modal matrix.

[0016] In one embodiment, determining the type information of the reconstructed target state data based on the reconstructed target state data according to a preset classification prediction model includes:

[0017] Obtaining a weight coefficient corresponding to each physical field data;

[0018] performing weighted processing on different physical field data in the reconstructed target state data according to the weight coefficient to obtain processed target state data;

[0019] The preset classification prediction model is used to determine type information of the reconstructed target state data based on the processed target state data.

[0020] In one embodiment, performing frequency domain conversion processing on each physical field data in the target state data to obtain frequency domain data corresponding to the target state data includes:

[0021] Determining whether the target state data contains abnormal values;

[0022] When it is determined that the target state data contains abnormal values, the abnormal values ​​in the target state data are assimilated using a preset data assimilation model; wherein the preset data assimilation model is a model constructed according to a data assimilation algorithm;

[0023] Frequency domain conversion processing is performed on each physical field data in the target state data obtained after the assimilation processing to obtain frequency domain data corresponding to the target state data.

[0024] In one embodiment, reconstructing the reduced-order target state data to obtain reconstructed target state data includes:

[0025] According to a preset energy threshold, the modal data corresponding to each physical field in the reduced-order target state data is screened to obtain screened target state data; wherein the preset energy threshold is determined based on the energy proportion of the modal data corresponding to each physical field data in the reconstructed target state data;

[0026] The filtered target state data is reconstructed to obtain reconstructed target state data.

[0027] In one embodiment, performing order reduction processing on each physical field data in the target state data according to the frequency domain data corresponding to each physical field data in the target state data to obtain the reduced-order target state data includes:

[0028] By using a preset reduced-order model, and according to the frequency domain data corresponding to each physical field data in the target state data, each physical field data in the target state data is subjected to order reduction processing to obtain the reduced-order target state data.

[0029] In one embodiment, reconstructing the reduced-order target state data to obtain reconstructed target state data includes:

[0030] The reduced-order target state data is reconstructed using a preset reconstruction model to obtain the reconstructed target state data, wherein the preset reconstruction model is a model constructed according to a preset machine learning algorithm.

[0031] This specification provides an abnormality detection device for power transmission and transformation equipment, including:

[0032] A data acquisition module, configured to acquire target state data of the power transmission and transformation equipment to be detected; wherein the target state data includes a plurality of physical field data, and the physical field data includes at least one of the following: electric field, magnetic field, and temperature field data;

[0033] A frequency domain conversion module is used to perform frequency domain conversion processing on each physical field data in the target state data to obtain corresponding frequency domain data;

[0034] an order reduction processing module, configured to perform corresponding order reduction processing on each physical field data in the target state data according to the frequency domain data corresponding to each physical field data in the target state data, to obtain the reduced-order target state data;

[0035] a reconstruction processing module, configured to perform reconstruction processing on the reduced-order target state data to obtain reconstructed target state data;

[0036] a classification prediction module, configured to determine type information of the reconstructed target state data by processing the reconstructed target state data using a preset classification prediction model;

[0037] The abnormality judgment module is used to determine whether there is an abnormality in the power transmission and transformation equipment to be detected based on the type information.

[0038] This specification also provides an electronic device, including a processor and a memory for storing processor-executable instructions, wherein the processor implements a method for detecting abnormalities in power transmission and transformation equipment when executing the instructions.

[0039] This specification also provides a computer-readable storage medium having computer instructions stored thereon, which, when executed, implement a method for detecting abnormalities in power transmission and transformation equipment.

[0040] A method for detecting anomalies in power transmission and transformation equipment provided in this specification is provided, which obtains target state data of the power transmission and transformation equipment to be detected; wherein the target state data includes multiple physical field data, and the physical field data includes at least one of the following: electric field, magnetic field and temperature field data; each physical field data in the target state data is subjected to frequency domain conversion processing to obtain corresponding frequency domain data; according to the frequency domain data corresponding to each physical field data in the target state data, each physical field data in the target state data is subjected to corresponding order reduction processing to obtain reduced-order target state data; the reduced-order target state data is reconstructed to obtain reconstructed target state data; a preset classification prediction model is used to process the reconstructed target state data to determine type information of the reconstructed target state data; and according to the type information, it is determined whether the power transmission and transformation equipment to be detected has an abnormality. In this way, the physical field data is reduced in order and reconstructed based on the frequency domain data obtained by conversion, so that the reconstructed target state data can remove the noise data in the original physical field data while retaining the valid information in the original physical field data. Furthermore, through the reconstructed target state data, it is possible to quickly and accurately determine whether there is any abnormality in the power transmission and transformation equipment to be tested, thereby improving the efficiency and accuracy of abnormal operation and maintenance diagnosis of power transmission and transformation equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the embodiments of this specification, the following is a brief introduction to the drawings required for use in the embodiments. The drawings described below are only some of the embodiments recorded in this specification. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0042] Figure 1This is a flowchart of a method for detecting abnormalities in power transmission and transformation equipment provided by an embodiment of this specification;

[0043] Figure 2 This is a schematic diagram of the structure of an electronic device provided by an embodiment of this specification;

[0044] Figure 3 This is a schematic diagram of the structure of an abnormality detection device for power transmission and transformation equipment provided by an embodiment of this specification;

[0045] Figure 4 This is a schematic diagram of a data assimilation process for deep learning provided by an embodiment of this specification;

[0046] Figure 5 This is a schematic diagram of a lightweight order reduction algorithm development approach provided by an embodiment of this specification;

[0047] Figure 6 This is a schematic diagram of another lightweight order reduction algorithm development idea provided by an embodiment of this specification;

[0048] Figure 7 is a schematic diagram of an original physical field and a reconstructed physical field after order reduction provided by an embodiment of this specification;

[0049] Figure 8 This is a schematic diagram of a comparison between a true value and a predicted result point cloud provided by an embodiment of this specification;

[0050] Figure 9 This is a schematic diagram of a fourth-order typical mode of magnetic field intensity provided by an embodiment of this specification;

[0051] Figure 10 This is a schematic diagram of an original physical field of magnetic field intensity and a reconstructed physical field after reduction, provided by an embodiment of this specification;

[0052] Figure 11 This is another schematic diagram of the original physical field of magnetic field strength and the reconstructed physical field after reduction, provided by an embodiment of this specification. DETAILED DESCRIPTION

[0053] To help those skilled in the art better understand the technical solutions in this specification, the following will provide a clear and complete description of the technical solutions in the embodiments of this specification, in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of this specification, not all of them. All other embodiments derived by those skilled in the art based on the embodiments in this specification without creative effort shall fall within the scope of protection of this specification.

[0054] See Figure 1 As shown, the embodiment of this specification provides a method for detecting abnormalities in power transmission and transformation equipment, wherein the method is specifically applied to the server side. When implemented, the method may include the following contents:

[0055] S101: Acquire target state data of a power transmission and transformation device to be detected; wherein the target state data includes a plurality of physical field data, and the physical field data includes at least one of the following: electric field, magnetic field, and temperature field data;

[0056] S102: Perform frequency domain conversion processing on each physical field data in the target state data to obtain corresponding frequency domain data;

[0057] S103: performing corresponding order reduction processing on each physical field data in the target state data according to the frequency domain data corresponding to each physical field data in the target state data to obtain the reduced-order target state data;

[0058] S104: reconstructing the reduced-order target state data to obtain reconstructed target state data;

[0059] S105: Determine type information of the reconstructed target state data by processing the reconstructed target state data using a preset classification prediction model;

[0060] S106: Determine whether the power transmission and transformation equipment to be detected has any abnormality according to the type information.

[0061] The above-mentioned power transmission and transformation equipment may be equipment such as converter valves, transformers, and circuit breakers. Specifically, the control transformer may be a part of the above-mentioned power transmission and transformation equipment for stabilizing and regulating voltage.

[0062] Specifically, taking the physical field data as electric field data and the power transmission and transformation equipment as a converter valve as an example, the above-mentioned target state data may include electric variable data such as electric field strength, voltage and current. The electric variable data may include voltage, current, resistance, capacitance, inductance, etc.

[0063] In addition, taking the physical field data as magnetic field data as an example, the above-mentioned target state data may include magnetic field data such as magnetic induction intensity, magnetic field intensity, magnetic flux and magnetic flux density; taking the physical field data as temperature field data as an example, the above-mentioned target state data may include temperature field data such as heat flux, thermal melt and thermal conductivity.

[0064] In some embodiments, the frequency domain conversion processing is performed on each physical field data in the target state data to obtain corresponding frequency domain data. When specifically implemented, it may include:

[0065] Using a preset frequency domain conversion algorithm, each physical field data in the target state data is subjected to frequency domain conversion processing to obtain corresponding frequency domain data.

[0066] The preset frequency domain conversion algorithm may be a Fourier transform algorithm, a wavelet transform algorithm, or the like, which is used to convert time domain data into frequency domain data.

[0067] Specifically, taking the Fourier transform algorithm as an example, the time domain signal corresponding to each physical field data in the target state data is converted into a frequency domain signal, and then the corresponding frequency domain data is obtained. The converted frequency domain data contains an amplitude spectrum and a phase spectrum, which are used to display the intensity and phase information of each frequency component. For example, the high-frequency component of the electric field data signal can reflect the electromagnetic interference problem.

[0068] In some embodiments, taking the physical field data as electric field data as an example, since the electric field intensity in the electric field data focuses on the spatial distribution and instantaneous changes, the voltage in the electric field data focuses on the amplitude and phase characteristics, and the current in the electric field data focuses on the load characteristics and periodic changes, each physical field data in the target state data is subjected to corresponding order reduction processing.

[0069] In some embodiments, the reconstructing the reduced-order target state data to obtain reconstructed target state data may include:

[0070] S1: extracting modal data whose modal data energy ratio is higher than a preset energy ratio from the frequency domain data corresponding to each physical field data in the target state data as the main modal data;

[0071] S2: extracting modal coefficients from the main modal data to obtain the modal coefficients of the main modal data;

[0072] S3: Reconstruct the main modal data using a preset reconstruction algorithm and the modal coefficients to obtain reconstructed target state data.

[0073] Among them, the above-mentioned preset classification prediction model can be a classification model constructed based on a preset machine learning algorithm.

[0074] For example, taking the preset classification prediction model as a classification model built based on the random forest algorithm, historical sample data can be obtained to train the preset classification prediction model to obtain the trained preset classification prediction model. Since the multi-decision tree structure of the random forest algorithm can provide the final classification result through the voting mechanism, the high accuracy and robustness of the trained preset classification prediction model can be ensured.

[0075] The above-mentioned type information may include a normal type, an abnormal type, or a specific fault type. The abnormal type may refer to any insulation failure risk of the power transmission and transformation equipment, and the specific fault type may be, for example, a fault caused by overheating, short circuit, harmonic interference, or electromagnetic interference.

[0076] For example, taking the physical field data as electric field data, since the electric field strength is an important indicator for monitoring and maintaining electrical equipment, the change amplitude of the electric field strength in the physical field data can be used as the main factor, while the change amplitude of the voltage and current can be used as an auxiliary factor to comprehensively determine the type information of the reconstructed target state data.

[0077] Specifically, if the change amplitude of the electric field strength is greater than the first preset threshold, the type information may be an abnormal type or a fault type; if the change amplitude of the electric field strength is not greater than the first preset threshold, and the change amplitude of the voltage or current is greater than the second preset threshold, the type information is a specific fault type; if the change amplitude of the electric field strength is not greater than the first preset threshold, and the change amplitudes of the voltage and current are not greater than the second preset threshold, the type information is a normal type.

[0078] In some embodiments, after determining whether the power transmission and transformation equipment to be detected has an abnormality according to the type information, the method may further include:

[0079] When it is determined that the power transmission and transformation equipment to be detected has an abnormality, an alarm of the type information is sent to relevant operation and maintenance personnel based on the type information.

[0080] This allows maintenance personnel to take different exception handling measures based on the type of information. Furthermore, alarms can include relevant physical field data, monitoring time, device number, and the severity of the anomaly, helping maintenance personnel quickly locate the problem. Clear alarm information allows maintenance personnel to prioritize the most urgent faults, ensuring the normal operation of the equipment while also contributing to energy conservation and environmental protection.

[0081] Furthermore, after determining whether the power transmission and transformation equipment to be inspected has an abnormality based on the type information, the method may further include: determining the cause of the abnormality; querying a preset abnormality handling strategy library based on the abnormality cause to determine a preset abnormality handling strategy corresponding to the abnormality cause as a target handling strategy; and providing the target handling strategy to the operation and maintenance personnel. The preset abnormality handling strategy library includes multiple preset abnormality handling strategies, each of which corresponds to at least one abnormality cause. The multiple preset abnormality handling strategies are pre-acquired and determined through clustering based on a large number of historical abnormality handling records.

[0082] Based on the above embodiment, the physical field data is reduced in order and reconstructed according to the frequency domain data obtained by conversion, so that the reconstructed target state data can remove the noise data in the original physical field data while retaining the valid information in the original physical field data. Furthermore, through the reconstructed target state data, it is possible to quickly and accurately determine whether the power transmission and transformation equipment to be tested has any abnormalities, thereby improving the efficiency and accuracy of abnormal operation and maintenance diagnosis of power transmission and transformation equipment.

[0083] In some embodiments, the method may further include the following steps: performing order reduction processing on each physical field data in the target state data according to the frequency domain data corresponding to each physical field in the target state data to obtain the reduced-order target state data.

[0084] S1: determining, for the frequency domain data corresponding to each physical field data in the target state data, a cyclostationary spectral density matrix corresponding to each physical field;

[0085] S2: Decomposing the cyclostationary spectral density matrix to obtain a modal matrix corresponding to the cyclostationary spectral density matrix;

[0086] S3: Obtaining the reduced-order target state data according to a preset number of dominant modes in the modal matrix.

[0087] The cyclostationary spectral density matrix can be a mathematical tool for describing random processes, particularly when analyzing time series data. It combines the concepts of cyclostationarity and spectral density to more effectively process and analyze multivariate random signals with periodic characteristics. Furthermore, for time series data of multiple physical field variables (such as electric, temperature, and pressure fields), the cyclostationary spectral density matrix can provide a global perspective, revealing the correlations and dynamic relationships between variables.

[0088] In some embodiments, the decomposing the cyclostationary spectral density matrix to obtain a modal matrix corresponding to the cyclostationary spectral density matrix may include:

[0089] Decomposing the cyclostationary spectral density matrix by using eigenvalue decomposition to obtain multiple modal data with different energy proportions;

[0090] According to the modal data of the plurality of different energy proportions, a modal matrix corresponding to the cyclostationary spectral density matrix is ​​constructed.

[0091] The dominant mode may be obtained by sorting the modal data according to the energy proportion of the modal data, and selecting the dominant mode based on the sorted modal data.

[0092] In some embodiments, the method of determining the type information of the reconstructed target state data based on the reconstructed target state data according to a preset classification prediction model may further include the following when implemented:

[0093] S1: Obtaining a weight coefficient corresponding to each physical field data;

[0094] S2: performing weighted processing on different physical field data in the reconstructed target state data according to the weight coefficient to obtain processed target state data;

[0095] S3: Determine type information of the reconstructed target state data based on the processed target state data using the preset classification prediction model.

[0096] The weight coefficients can be the contribution of the importance index of each physical field data, which can be obtained by the following method:

[0097] S1: using a principal component analysis method, performing standardization processing on the physical field data to obtain standardized data;

[0098] S2: calculating the covariance matrix and eigenvalue decomposition of the data obtained by the standardization process, and selecting principal components to obtain the contribution of the importance index of each physical field data;

[0099] S3: Determine the weight coefficient of the importance index of each physical field data according to the contribution of the importance index of each physical field data.

[0100] In addition, the weights of each physical field can be manually set according to the role and influence of each physical field in the system. For example, since the electric field strength may have a greater impact on power transmission and transformation equipment, it can be given a higher weight coefficient.

[0101] In some embodiments, the frequency domain conversion processing is performed on each physical field data in the target state data to obtain frequency domain data corresponding to the target state data. When the method is specifically implemented, the method may further include the following contents:

[0102] S1: Determine whether the target state data contains abnormal values;

[0103] S2: When it is determined that the target state data contains an outlier, assimilate the outlier in the target state data using a preset data assimilation model; wherein the preset data assimilation model is a model constructed according to a data assimilation algorithm;

[0104] S3: Perform frequency domain conversion processing on each physical field data in the target state data obtained after the assimilation processing to obtain frequency domain data corresponding to the target state data.

[0105] Based on the above embodiments, the quality of multiple physical field data and the accuracy of the power transmission and transformation equipment monitoring can be improved, and the stable operation of the system and the reliability of the prediction can be guaranteed.

[0106] In some embodiments, the method of reconstructing the reduced-order target state data to obtain reconstructed target state data may further include the following steps when implemented:

[0107] S1: Filtering the modal data corresponding to each physical field in the reduced-order target state data according to a preset energy threshold to obtain filtered target state data; wherein the preset energy threshold is determined according to the energy proportion of the modal data corresponding to each physical field data in the reconstructed target state data;

[0108] S2: Reconstructing the filtered target state data to obtain reconstructed target state data.

[0109] The energy threshold can be used to filter out dominant modes. The higher the energy threshold, the more modal information is retained. For example, a 90% energy threshold retains the primary modal information while reducing noise. A 95% energy threshold allows for high-precision reconstruction. A 99% energy threshold retains as much information as possible, making it suitable for precise analysis but resulting in poor dimensionality reduction.

[0110] Specifically, the modal data corresponding to each physical field in the reduced-order target state data is screened, and taking an energy threshold of 90% as an example, the modal data with an energy accumulation ratio greater than or equal to 90% is screened out.

[0111] Based on the above embodiment, not only the amount of data is reduced, but also the key features of the system are retained. When combined with the random forest model, the reduced-order data can greatly improve the monitoring accuracy, realize predictive maintenance and accurate positioning of equipment failures, thereby improving the stability and operation efficiency of the system.

[0112] In some embodiments, the method may further include the following steps: performing order reduction processing on each physical field data in the target state data according to the frequency domain data corresponding to each physical field data in the target state data to obtain the reduced-order target state data.

[0113] By using a preset reduced-order model, and according to the frequency domain data corresponding to each physical field data in the target state data, each physical field data in the target state data is subjected to order reduction processing to obtain the reduced-order target state data.

[0114] The aforementioned preset reduced-order model can be a mathematical or computational model that reduces the dimensionality of a system or data while retaining key features or primary dynamic behaviors. Its core goal is to reduce computational complexity without losing important information, thereby making tasks such as analysis, prediction, and control more efficient. For example, the preset reduced-order model can include models constructed using algorithms such as principal component analysis, dynamic mode decomposition, cyclostationary spectral orthogonal decomposition, balanced truncation reduction, and kernel principal component analysis.

[0115] Specifically, taking kernel principal component analysis as an example, a suitable kernel function is selected to map the frequency domain data corresponding to each physical field data in the target state data to a high-dimensional feature space, the kernel matrix is ​​centrally processed, the principal components are determined, and the selected eigenvectors are used to map the frequency domain data corresponding to each physical field data in the target state data to a low-dimensional space. Each physical field data in the target state data is reduced in order to obtain the reduced-order target state data.

[0116] In some embodiments, the method of reconstructing the reduced-order target state data to obtain reconstructed target state data may further include the following steps when implemented:

[0117] The reduced-order target state data is reconstructed using a preset reconstruction model to obtain the reconstructed target state data, wherein the preset reconstruction model is a model constructed according to a preset machine learning algorithm.

[0118] As can be seen from the above, an embodiment of this specification provides a method for detecting abnormalities in power transmission and transformation equipment, which obtains target state data of the power transmission and transformation equipment to be detected; wherein the target state data includes multiple physical field data, and the physical field data includes at least one of the following: electric field, magnetic field and temperature field data; each physical field data in the target state data is subjected to frequency domain conversion processing to obtain corresponding frequency domain data; according to the frequency domain data corresponding to each physical field data in the target state data, each physical field data in the target state data is subjected to corresponding order reduction processing to obtain reduced-order target state data; the reduced-order target state data is reconstructed to obtain reconstructed target state data; a preset classification prediction model is used to process the reconstructed target state data to determine the type information of the reconstructed target state data; based on the type information, it is determined whether the power transmission and transformation equipment to be detected has an abnormality. In this way, the physical field data is reduced in order and reconstructed based on the frequency domain data obtained by conversion, so that the reconstructed target state data can remove the noise data in the original physical field data while retaining the valid information in the original physical field data. Furthermore, through the reconstructed target state data, it is possible to quickly and accurately determine whether there is any abnormality in the power transmission and transformation equipment to be tested, thereby improving the efficiency and accuracy of abnormal operation and maintenance diagnosis of power transmission and transformation equipment.

[0119] See Figure 2 As shown, an embodiment of this specification also provides a specific electronic device, wherein the electronic device includes a network communication port 201, a processor 202 and a memory 203, and the above structures are connected through internal cables so that each structure can perform specific data interaction.

[0120] The network communication port 201 can be used to obtain target state data of the power transmission and transformation equipment to be detected; wherein the target state data includes multiple physical field data, and the physical field data includes at least one of the following: electric field, magnetic field and temperature field data.

[0121] The processor 202 can be specifically used to perform frequency domain conversion processing on each physical field data in the target state data to obtain corresponding frequency domain data; perform corresponding order reduction processing on each physical field data in the target state data according to the frequency domain data corresponding to each physical field data in the target state data to obtain reduced-order target state data; reconstruct the reduced-order target state data to obtain reconstructed target state data; use a preset classification prediction model to process the reconstructed target state data to determine type information of the reconstructed target state data; and determine whether there is an abnormality in the power transmission and transformation equipment to be detected based on the type information.

[0122] The memory 203 may be specifically used to store corresponding instruction programs.

[0123] Based on the above method, the relevant structural performance of electronic equipment can be effectively utilized, the data processing speed of electronic equipment can be improved, and the abnormality detection method of power transmission and transformation equipment can be efficiently implemented.

[0124] In this embodiment, the network communication port 201 can be a virtual port that is bound to different communication protocols, thereby being capable of sending or receiving different data. For example, the network communication port can be a port responsible for web data communication, a port responsible for FTP data communication, or a port responsible for email data communication. Furthermore, the network communication port can also be a physical communication interface or communication chip. For example, it can be a wireless mobile network communication chip, such as GSM or CDMA; it can also be a Wi-Fi chip; or it can be a Bluetooth chip.

[0125] In this embodiment, the processor 202 can be implemented in any appropriate manner. For example, the processor can take the form of a microprocessor or a processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro)processor, logic gates, switches, an application-specific integrated circuit (ASIC), a programmable logic controller, an embedded microcontroller, etc. This specification is not intended to limit this.

[0126] In this embodiment, the memory 203 may include multiple levels. In a digital system, anything that can store binary data can be a memory. In an integrated circuit, a circuit with a storage function that has no physical form is also called a memory, such as RAM, FIFO, etc. In a system, a storage device with a physical form is also called a memory, such as a memory stick, TF card, etc.

[0127] An embodiment of the present specification also provides a computer-readable storage medium based on the above-mentioned method for detecting anomalies in power transmission and transformation equipment, wherein the computer-readable storage medium stores computer program instructions, which, when executed, implement the following: obtaining target state data of the power transmission and transformation equipment to be detected; wherein the target state data includes multiple physical field data, and the physical field data includes at least one of the following: electric field, magnetic field and temperature field data; performing frequency domain conversion processing on each physical field data in the target state data to obtain corresponding frequency domain data; performing corresponding order reduction processing on each physical field data in the target state data according to the frequency domain data corresponding to each physical field data in the target state data to obtain reduced-order target state data; reconstructing the reduced-order target state data to obtain reconstructed target state data; determining type information of the reconstructed target state data by processing the reconstructed target state data using a preset classification prediction model; and determining whether the power transmission and transformation equipment to be detected has an abnormality according to the type information.

[0128] In this embodiment, the storage medium includes, but is not limited to, random access memory (RAM), read-only memory (ROM), cache, hard disk drive (HDD), or memory card. The memory can be used to store computer program instructions. The network communication unit can be an interface configured in accordance with a standard specified by a communication protocol and used for network connection and communication.

[0129] In this embodiment, the functions and effects specifically implemented by the program instructions stored in the computer-readable storage medium can be explained in comparison with other implementations and will not be repeated here.

[0130] See Figure 3 At the software level, the embodiments of this specification also provide an abnormality detection device for power transmission and transformation equipment, which may specifically include the following structural modules:

[0131] The data acquisition module 301 is used to acquire target state data of the power transmission and transformation equipment to be detected; wherein the target state data includes multiple physical field data, and the physical field data includes at least one of the following: electric field, magnetic field and temperature field data;

[0132] The frequency domain conversion module 302 is used to perform frequency domain conversion processing on each physical field data in the target state data to obtain corresponding frequency domain data;

[0133] The order reduction processing module 303 is used to perform corresponding order reduction processing on each physical field data in the target state data according to the frequency domain data corresponding to each physical field data in the target state data to obtain the reduced-order target state data;

[0134] A reconstruction processing module 304 is used to reconstruct the reduced-order target state data to obtain reconstructed target state data;

[0135] A classification prediction module 305 is configured to determine type information of the reconstructed target state data by processing the reconstructed target state data using a preset classification prediction model;

[0136] The abnormality determination module 306 is configured to determine whether the power transmission and transformation equipment to be detected has an abnormality according to the type information.

[0137] In some embodiments, the above-mentioned order reduction processing module 303, when specifically implemented, determines the cyclostationary spectral density matrix corresponding to each physical field data in the target state data for the frequency domain data corresponding to the physical field data; decomposes the cyclostationary spectral density matrix to obtain the modal matrix corresponding to the cyclostationary spectral density matrix; and obtains the reduced-order target state data based on a preset number of dominant modes in the modal matrix.

[0138] In some embodiments, the above-mentioned classification prediction module 305, when implemented, obtains a weight coefficient corresponding to each of the physical field data; performs weighted processing on different physical field data in the reconstructed target state data according to the weight coefficient to obtain processed target state data; and uses the preset classification prediction model to determine the type information of the reconstructed target state data based on the processed target state data.

[0139] In some embodiments, the frequency domain conversion module 302 is specifically implemented to determine whether the target state data contains abnormal values; when it is determined that the target state data contains abnormal values, the abnormal values ​​in the target state data are assimilated using a preset data assimilation model; wherein the preset data assimilation model is a model constructed according to a data assimilation algorithm; and each physical field data in the target state data obtained after the assimilation processing is subjected to frequency domain conversion processing to obtain frequency domain data corresponding to the target state data.

[0140] In some embodiments, the above-mentioned reconstruction processing module 304, when specifically implemented, performs screening processing on the modal data corresponding to each physical field in the reduced-order target state data according to a preset energy threshold to obtain the screened target state data; wherein the preset energy threshold is determined based on the energy proportion of the modal data corresponding to each physical field data in the reconstructed target state data; and the screened target state data is reconstructed to obtain the reconstructed target state data.

[0141] In some embodiments, the above-mentioned order reduction processing module 303, when implemented, can also use a preset order reduction model to perform order reduction processing on each physical field data in the target state data according to the frequency domain data corresponding to each physical field data in the target state data to obtain the reduced-order target state data.

[0142] In some embodiments, the above-mentioned reconstruction processing module 304, when implemented, can also utilize a preset reconstruction model to reconstruct the reduced-order target state data to obtain the reconstructed target state data, wherein the preset reconstruction model is a model constructed according to a preset machine learning algorithm.

[0143] It should be noted that the units, devices or modules described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. For the convenience of description, the above devices are described in terms of functions and are divided into various modules and described separately. Of course, when implementing this specification, the functions of each module can be implemented in the same or multiple software and / or hardware, or the module that implements the same function can be implemented by a combination of multiple sub-modules or sub-units. The device embodiments described above are merely schematic. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0144] As can be seen from the above, based on the abnormality detection device for power transmission and transformation equipment provided in the embodiment of this specification, the relationship between the pore throat diameter and the crude oil content in the first layer obtained by laser confocal microscopy, and the relationship between the transverse relaxation time and the nuclear magnetic resonance signal intensity in the first layer obtained by nuclear magnetic resonance testing, the target relationship between the transverse relaxation time and the pore throat diameter can be determined, and then the distribution information of the pore throat diameter in the second layer can be determined based on the target relationship, the transverse relaxation time in the second layer and the nuclear magnetic resonance signal intensity, so as to improve the accuracy of the pore size characterization in the oil reservoir without destroying the core, so as to improve the development effect of the oil and gas reservoir.

[0145] In a specific scenario example, the power transmission and transformation equipment anomaly detection method provided in this specification can be applied to quickly implement high-precision and lightweight order reduction processing of multiple physical field data related to the power transmission and transformation equipment. This allows for efficient and accurate detection and judgment of abnormalities in the power transmission and transformation equipment, effectively reducing the amount of computation and improving the efficiency and accuracy of abnormal operation and maintenance diagnosis of the power transmission and transformation equipment. The specific implementation process may include the following.

[0146] See Figure 4 As shown in the figure, anomaly processing of multi-physics field data. Specifically, the multi-physics field data observed in real time is monitored for anomaly data. After the abnormal data is detected, it is fed back to the monitoring system module for inspection. Once confirmed as abnormal data, deep learning data assimilation is performed. During the dynamic operation process, the "abnormal data value" is integrated with the new observation data, and the normal value of the abnormal data is assimilated by inferring the existing data. The processed data is then provided to the digital twin reduction algorithm. Based on the feedback of the algorithm, the data assimilation algorithm can be re-optimized until the data obtained by the reduction algorithm no longer detects abnormal data signals.

[0147] See Figure 5 As shown in Figure 1, S1: Circularly Symmetric Power Orthogonal Decomposition (CSPOD) combined with the random forest algorithm with outlier detection.

[0148] Specifically, by performing the orthogonal decomposition of the cyclostationary spectrum and adjusting the preset energy threshold, modal data with different energy proportions are obtained, thereby fully retaining information and improving the accuracy of modal reconstruction.

[0149] The cyclostationary spectral orthogonal decomposition is a purely data-driven reduced-order prediction model. It has no specific model type requirements, requires minimal computation time, and requires no prior model training. It also offers high accuracy and rapid response. By adjusting the energy threshold, the reduced-order model can be used to approximate the full-order data using only a small number of low-order modes, depending on the energy contribution.

[0150] Furthermore, the random forest algorithm with abnormal data detection can use data assimilation capabilities to judge and assimilate abnormal data, ensuring the reliability and accuracy of data as much as possible and reducing the defects of sparse observation (sampling) data. Figure 6 As shown, the input and output physical parameters (i.e., the reconstructed target state data) are selected, the training, test, and validation sets are selected, the model is accurately evaluated, and parameter and sample optimization is performed if it fails. If it passes, the reduced-order model is stored and output.

[0151] S2: Multi-physics field variable coupling weighted algorithm.

[0152] In cases where multiple physical field variables are coupled, the weight coefficients of the individual physical field data are considered, and the coupling terms are appropriately weighted during the calculation process. This multi-physics field variable coupling weighting algorithm helps improve the accuracy and stability of the calculation.

[0153] S3: Build a preset classification prediction model.

[0154] Specifically, by inputting the reconstructed target state data of the sample, an efficient and accurate preset classification prediction model is trained. The model can process a large amount of data in a short time and output results that are similar to or even more accurate than traditional methods.

[0155] S4: Performance evaluation and optimization.

[0156] Based on actual application requirements, while ensuring the accuracy of modal reduction and multi-physics data coupling weighted algorithms, further optimize computing speed and memory usage. This can be achieved through the use of parallel computing technology, specific hardware acceleration, or algorithm optimization.

[0157] S5: Verification and application.

[0158] By comparing and validating the results with traditional methods, the accuracy and efficiency of the calculations were evaluated. Furthermore, the model training phase was compared with real-world data to verify its generalization and stability. Finally, this standardized process was documented with detailed instructions and package management to ensure reusability and continuous optimization. This facilitated communication and collaboration among users, improving the accuracy and efficiency of the entire process.

[0159] Based on the above embodiments, the computational cost and memory cost can be greatly reduced by modal order reduction processing under the conditions of stability, accuracy and efficiency, and the accuracy of the computational results can be effectively guaranteed. In addition, it is possible to efficiently and accurately detect and judge whether there is an abnormality in the power transmission and transformation equipment, effectively reducing the amount of calculation and improving the efficiency and accuracy of abnormal operation and maintenance diagnosis of the power transmission and transformation equipment. In addition, the application of this order reduction method in the field of power transmission and transformation equipment can bring the following potential benefits: based on the reduced order model algorithm and the multi-physics field fast response model, the computational efficiency is significantly improved compared with the full-order model; a reduced order model is constructed to achieve a high-precision, lightweight reduced order simulation model with second-level calculation, and to achieve fast and accurate prediction of the physical field; a reduced order model with versatility and flexibility is achieved, which can use different types of multi-physics field models and can be adjusted and optimized according to the characteristics of the specific physical problem; the model can obtain results in a short time in engineering scenarios such as real-time control and decision support, so that the system can quickly respond to and adapt to different operational requirements.

[0160] The advantages of the algorithm are illustrated using two special application scenarios as examples. The GIS bus model (Geographic Information System, GIS) is steady-state multi-physics field data under a fixed current, while the converter valve model is unsteady-state multi-physics field data obtained from current pulses.

[0161] GIS busbar model

[0162] 1) See Figure 7 As shown in Figure 1, the comparison between the original physical field and the reconstructed physical field after reduction is shown. The results of the comparison between the original data and the reconstructed data are shown in Table 1:

[0163]

[0164] Taking the GIS busbar model as an example, it is found that a high accuracy can be achieved within seconds.

[0165] 2) See Figure 8 As shown, the point cloud comparison of the true value and predicted results of random forest with abnormal data detection,

[0166] Among them, the prediction error and prediction time under different input parameters are shown in Table 2:

[0167]

[0168] Converter valve model (unsteady current pulse data)

[0169] 1) See Figure 9 As shown in Figure 2, the cyclostationary spectral orthogonal decomposition (CSPOD) is used to analyze the fourth-order typical mode of magnetic field intensity; see Figure 10As shown in Table 3, the comparison of the original physical field of magnetic field intensity and the reconstructed physical field after reduction is shown in Table 3:

[0170]

[0171] Taking the converter valve as an example, even for non-steady-state data, the accuracy is still guaranteed to be around 4%.

[0172] 2) See Figure 11 As shown, the original physical field of the magnetic field intensity of the random forest with abnormal data detection (left) is compared with the reconstructed physical field after reduction (right). Through quantitative comparison, the average error that meets the project indicator requirements is less than 5% (compared with the original simulation result data), and the estimated average error is 2.68%.

[0173] Although this specification provides method operation steps as described in the embodiments or flowcharts, more or fewer operation steps may be included based on conventional or non-creative means. The order of steps listed in the embodiments is only one way of executing the steps among many, and does not represent the only execution order. When the device or client product is actually executed, it can be executed in sequence or in parallel according to the method shown in the embodiments or the drawings (for example, in a parallel processor or multi-threaded processing environment, or even a distributed data processing environment). The terms "comprise", "include" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, product or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, product or device. Without further restrictions, it is not excluded that there are other identical or equivalent elements in the process, method, product or device including the elements. Words such as first, second, etc. are used to indicate names and do not indicate any particular order.

[0174] Those skilled in the art will also appreciate that, in addition to implementing the controller in pure computer-readable program code, it is entirely possible to implement the same functionality by logically programming the method steps in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, embedded microcontrollers, and the like. Therefore, such a controller can be considered a hardware component, and the devices included therein for implementing various functions can also be considered structures within the hardware component. Alternatively, the devices for implementing various functions can be considered both software modules implementing the method and structures within the hardware component.

[0175] As can be seen from the above description of the embodiments, those skilled in the art will clearly understand that this specification can be implemented using software plus a necessary general-purpose hardware platform. Based on this understanding, the technical solution of this specification can essentially be embodied in the form of a software product. This computer software product can be stored in a storage medium such as ROM / RAM, a magnetic disk, or an optical disk, and includes a number of instructions for enabling a computer device (such as a personal computer, mobile terminal, server, or network device) to execute the methods described in various embodiments or portions of the embodiments of this specification.

[0176] Although the present specification is described through embodiments, those skilled in the art will appreciate that there are many modifications and variations to the present specification without departing from the spirit of the present specification. It is intended that the appended claims include these modifications and variations without departing from the spirit of the present specification.

Claims

1. A method for detecting abnormality of power transmission and transformation equipment, characterized in that: include: Acquire target state data of the power transmission and transformation equipment to be detected; wherein the target state data includes a plurality of physical field data, and the physical field data includes at least one of the following: electric field, magnetic field and temperature field data; Performing frequency domain conversion processing on each physical field data in the target state data to obtain corresponding frequency domain data; For the frequency domain data corresponding to each physical field data in the target state data, determining a cyclostationary spectral density matrix corresponding to each physical field; Decomposing the cyclostationary spectral density matrix by eigenvalue decomposition to obtain multiple modal data with different energy proportions; Constructing a modal matrix corresponding to a cyclostationary spectral density matrix according to the modal data of the plurality of different energy proportions; Obtaining reduced-order target state data according to a preset number of dominant modes in the modal matrix; Reconstructing the reduced-order target state data to obtain reconstructed target state data; Determine type information of the reconstructed target state data by processing the reconstructed target state data using a preset classification prediction model; It is determined whether the power transmission and transformation equipment to be detected has any abnormality according to the type information.

2. The method according to claim 1, characterized in that The method of using a preset classification prediction model to process the reconstructed target state data to determine type information of the reconstructed target state data includes: Obtaining a weight coefficient corresponding to each of the physical field data; According to the weight coefficient, weighted processing is performed on different physical field data in the reconstructed target state data to obtain processed target state data; The preset classification prediction model is used to determine type information of the reconstructed target state data according to the processed target state data.

3. The method according to claim 2, characterized in that The performing frequency domain conversion processing on each physical field data in the target state data to obtain corresponding frequency domain data includes: Determining whether the target state data contains abnormal values; When it is determined that the target state data contains abnormal values, the abnormal values ​​in the target state data are assimilated using a preset data assimilation model; wherein the preset data assimilation model is a model constructed according to a data assimilation algorithm; Each physical field data in the target state data obtained after assimilation is converted into a frequency domain to obtain the corresponding frequency domain data.

4. The method according to claim 3, characterized in that The reconstructing the reduced-order target state data to obtain reconstructed target state data includes: According to a preset energy threshold, the modal data corresponding to each physical field in the reduced-order target state data is screened to obtain the screened target state data; wherein the preset energy threshold is determined according to the energy proportion of the modal data corresponding to each physical field data in the reconstructed target state data; The filtered target state data is reconstructed to obtain reconstructed target state data.

5. The method according to claim 1, characterized in that The step of performing order reduction processing on each physical field data in the target state data according to the frequency domain data corresponding to each physical field data in the target state data to obtain the reduced-order target state data comprises: By using a preset reduced-order model, each physical field data in the target state data is subjected to reduced-order processing according to the frequency domain data corresponding to each physical field data in the target state data, so as to obtain the reduced-order target state data.

6. The method according to claim 5, characterized in that The reconstructing the reduced-order target state data to obtain reconstructed target state data includes: The reduced-order target state data is reconstructed using a preset reconstruction model to obtain the reconstructed target state data, wherein the preset reconstruction model is a model constructed according to a preset machine learning algorithm.

7. An abnormality detection device for power transmission and transformation equipment, characterized in that: include: A data acquisition module, used to acquire target state data of the power transmission and transformation equipment to be detected; wherein the target state data includes a plurality of physical field data, and the physical field data includes at least one of the following: electric field, magnetic field and temperature field data; A frequency domain conversion module is used to perform frequency domain conversion processing on each physical field data in the target state data to obtain corresponding frequency domain data; A density matrix determination module, used for determining the cyclostationary spectral density matrix corresponding to each physical field in the target state data according to the frequency domain data corresponding to each physical field data; A modal data determination module is used to decompose the cyclostationary spectral density matrix by using eigenvalue decomposition to obtain multiple modal data with different energy proportions; A model matrix determination module, used to construct a modal matrix corresponding to a cyclostationary spectral density matrix according to the modal data of the plurality of different energy proportions; A state data determination module, used to obtain reduced-order target state data according to a preset number of dominant modes in the modal matrix; A reconstruction processing module, used for reconstructing the reduced-order target state data to obtain reconstructed target state data; A classification prediction module, used to determine type information of the reconstructed target state data by processing the reconstructed target state data using a preset classification prediction model; The abnormality judgment module is used to determine whether there is an abnormality in the power transmission and transformation equipment to be detected according to the type information.

8. An electronic device, characterized in that: It comprises a processor and a memory for storing instructions executable by the processor, and when the processor executes the instructions, the steps of the abnormality detection method for power transmission and transformation equipment described in any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium, characterized in that: Computer instructions are stored thereon, and when the instructions are executed by a processor, the steps of the abnormality detection method for power transmission and transformation equipment described in any one of claims 1 to 6 are implemented.

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