Power system fault severity assessment method based on optical CT measurement

Abstract

The invention discloses a power system fault severity assessment method based on optical CT measurement, and relates to the technical field of power systems, and the method comprises the following steps: S1, constructing an optical CT measurement model; s2, performing multi-scale decomposition and feature extraction; s3, positioning a space-time coupling fault; s4, reconstructing the dynamic network topology; s5, analyzing fault influence propagation; s6, predicting the health state of the electric power element; and S7, establishing a fault severity quantitative index. According to the power system fault severity assessment method based on optical CT measurement, by introducing wavelet transform, multi-scale decomposition, a space-time coupling algorithm and a comprehensive severity quantitative index system, the accuracy of fault positioning and the precision of severity assessment are greatly improved; the limitation of the traditional technology in fault signal capture and propagation path analysis is overcome, and the dynamic change of the power system fault can be comprehensively tracked in real time.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to a method for evaluating the severity of power system faults based on optical CT measurement. Background Art

[0002] With the complexity of modern power systems and the development of high-voltage transmission and distribution technologies, fault detection and severity assessment have become key issues in ensuring the stable operation of power grids. Traditional power system fault detection methods, such as those based on current transformers and voltage transformers, are often limited by their own physical characteristics and cannot provide real-time and accurate fault location and severity analysis. Especially when multiple fault types (such as instantaneous overcurrent, continuous overload, line grounding, etc.) coexist, traditional methods are difficult to fully capture fault characteristics, resulting in delayed response strategies and reduced system stability.

[0003] The power system fault assessment method based on optical CT (optical computed tomography) measurement provides new possibilities for power system fault analysis with its non-invasive, real-time and high-precision measurement capabilities. By constructing an optical CT measurement model and combining complex electromagnetic field distribution information with the dynamic characteristics of the power system, it is possible to accurately capture internal faults in the power system. However, when applying optical CT measurement technology, existing technologies are often limited to data acquisition and basic signal processing, and lack in-depth mining of multi-scale decomposition, spatiotemporal coupling analysis, and severity quantification indicators.

[0004] In existing technologies, the multi-scale decomposition and feature extraction processes mostly rely on fixed-frequency Fourier transforms, which are difficult to adapt to the complex changes of fault signals in the time and frequency domains. In addition, traditional fault location methods lack a comprehensive consideration of the spatiotemporal dynamic characteristics of power systems, resulting in inaccurate fault propagation path analysis.

[0005] In terms of fault severity assessment, previous quantitative models mostly focused on the analysis of a single parameter and lacked the integration and optimization of multi-dimensional data (such as the amplitude of electromagnetic signal changes, the health status of power components, the speed of fault propagation, etc.). Summary of the invention

[0006] In view of the deficiencies of the prior art, the present invention provides a method for evaluating the severity of power system faults based on optical CT measurement to solve the problems raised in the above background technology.

[0007] To achieve the above object, the present invention provides the following technical solution: a method for evaluating the severity of power system faults based on optical CT measurement, comprising the following steps: S1, constructing optical CT measurement model; By establishing an optical CT measurement model to obtain the electromagnetic field distribution in the power system, optical sensors are used to measure the spatial distribution characteristics of electromagnetic signals in real time without affecting the normal operation of the power grid. By installing optical CT sensor arrays at key nodes in the power system, multi-dimensional power signal data is captured. The signal data includes key information such as voltage waveform, current density, and electromagnetic wave propagation path. S2, multi-scale decomposition and feature extraction; After obtaining the original optical CT measurement signal, multi-scale decomposition is performed to extract the electromagnetic signal characteristics in different frequency bands. The wavelet transform technology is used to decompose the complex power signal into data at multiple scale levels, corresponding to high-frequency and low-frequency components, to capture the unique characteristics of different fault types in the time and frequency domains. S3, time-space coupled fault location; Based on the spatiotemporal coupling algorithm model, the electromagnetic field data obtained by optical CT is combined with the time series to accurately locate the fault point, track the development path of the fault in the power system in real time, and analyze its propagation speed and diffusion range. In this process, the model combines the topological information of the power system, dynamically associates the data of each node with the adjacent nodes, and identifies the direction and range of possible fault spread; S4, dynamic network topology reconstruction; After a fault occurs, the network topology model of the power system is updated in real time to reconstruct the fault node and the node relationships around it. Based on the measurement results of optical CT, the electromagnetic coupling relationship within the system is reconfigured to identify the critical paths and components that may be affected by the fault. S5. Fault impact propagation analysis; Conduct impact propagation analysis on captured electromagnetic signals, calculate the transmission path and speed of faults in the network, evaluate their potential threats to other nodes and regions, identify critical points of fault expansion and high-risk areas, and provide reference for subsequent risk assessment and emergency response; S6. Prediction of health status of power components; Use machine learning algorithms to predict the health status of key components in the power system, and combine historical trend analysis of optical CT measurement data to evaluate the failure probability of components under different fault conditions; S7. Establishment of quantitative indicators of fault severity; A quantitative indicator system for fault severity is established. Based on the multi-dimensional parameters of the electromagnetic signal change amplitude, power component health status, and fault impact propagation speed obtained by optical CT measurement, the parameters are standardized and assigned different weights to calculate a comprehensive severity score. The scoring system includes different types of fault scenarios, including instantaneous overcurrent, continuous overload, and line grounding, providing a quantitative basis for risk level determination.

[0008] To further optimize the technical solution, in step S1, the constructed optical CT measurement model includes an electromagnetic field distribution equation and a signal reconstruction equation; The electromagnetic field distribution equation is as follows: ; in, For the spatial position and time The electromagnetic field distribution on is the response function of the optical CT sensor, which depends on the wavelength and sensor azimuth ; is the intensity function of the source electromagnetic signal, describing the current or voltage density in the power system over time. and spatial location Distribution of is the wave vector of the electromagnetic wave, describing the propagation direction; is the distance vector between the sensor and the source signal; is the angular frequency, which represents the frequency of the electromagnetic wave; is the integration area, covering all measurement points in the power system; The signal reconstruction equation is as follows: ; in, is the reconstructed three-dimensional electromagnetic field intensity image; is the weight coefficient, which represents the contribution of different optical CT sensors in the measurement process; For the Sensors at location and time The measurement results on .

[0009] To further optimize the technical solution, the optical CT measurement model includes the following specific processes when used: Real-time data acquisition: The optical CT sensor array installed at the key nodes of the power system is used to acquire electromagnetic field data in real time. The data is calculated by the electromagnetic field distribution equation in the model. Processing is performed to capture the preliminary distribution of fault signals in the power system; Fault signal reconstruction: Input the acquired data into the signal reconstruction equation ,Through the weight adjustment mechanism, the three-dimensional distribution image of the electromagnetic field when the fault occurs is reconstructed, and the fault point and the affected area are quickly identified; Propagation path analysis: based on wave vector and the distance vector information, track the propagation path of fault signals, and predict their potential impact on other parts of the power system; Adaptive response strategy generation: adjust the weight coefficients in the model based on real-time updated optical CT measurement data , generate the optimal fault response strategy, dynamically adjust the load distribution and the action of protection equipment in the power system, and reduce the impact of faults on the system.

[0010] To further optimize the technical solution, in step S2, a multi-scale decomposition model is constructed based on wavelet transform technology, and time-frequency analysis technology is combined to achieve decomposition and feature extraction of signals in different frequency bands; The model introduces nonlinear scale adjustment and frequency weighting mechanism to improve the recognition accuracy and robustness of fault signal characteristics; The multi-scale decomposition model includes a multi-scale wavelet decomposition formula, an adaptive frequency weighting function, and a feature extraction formula.

[0011] To further optimize the technical solution, in the multi-scale decomposition model: The multi-scale wavelet decomposition formula is as follows: ; in, For scale and frequency The time-frequency coefficient under represents the energy distribution of the signal at different scales and frequency bands; is the original optical CT measurement signal; For scale and center frequency The mother wavelet function controls the fineness of signal decomposition; Represents the conjugate complex number of the mother wavelet, which is used to capture the amplitude and phase information of the signal in the time-frequency domain; The adaptive frequency weighting function is as follows: ; in, It is a frequency weighting function, which is used to adjust the importance of signals in different frequency bands; is the scale weight factor, which indicates the signal priority at different scales; It is a frequency adjustment parameter, which is used to control the attenuation speed of signals in different frequency bands; is the target frequency, representing the fault signal frequency region of interest; The feature extraction formula is as follows: ; in, is the final extracted feature vector, representing the comprehensive characteristics of the signal at different scales and frequencies; and are discrete series of scale and frequency, respectively; It represents the absolute energy value of a signal at a specific scale and frequency.

[0012] To further optimize the technical solution, the multi-scale decomposition model, when used, includes the following specific processes: Signal decomposition and preprocessing: After obtaining the original optical CT measurement signal After that, it is input into the multi-scale wavelet decomposition formula and decomposed into multiple scale and frequency components, each component corresponds to high-frequency and low-frequency components respectively; Adaptive weighting adjustment: Use adaptive frequency weighting function to adjust each component. Depending on the fault type, the function automatically increases or decreases the weight of certain frequency bands to highlight the signal features that are most useful for fault identification. Comprehensive feature extraction: Finally, the signal energy at different scales and frequencies is summarized using the feature extraction formula to form a comprehensive feature vector, which is input into the fault classifier or machine learning model for further analysis and assessment of the severity of the fault.

[0013] To further optimize the technical solution, in step S3, the spatiotemporal coupling algorithm model uses the electromagnetic field data obtained by the optical CT measurement model and combines it with time series analysis to dynamically associate the topological structure of the power system with the fault characteristics. The model captures the propagation path, speed and impact range of the fault in the power system based on the spatiotemporal correlation coefficient and the dynamic adjacency matrix. The time-space coupling algorithm model includes a time-space correlation calculation formula, a dynamic adjacency matrix formula, and a fault propagation path optimization formula.

[0014] To further optimize this technical solution, in the space-time coupling algorithm model: The calculation formula of spatiotemporal correlation is as follows: ; in, For in time and time difference Next, and The spatiotemporal correlation coefficient between nodes; For Node In time The electromagnetic field strength; For Node The average value of the electromagnetic field over a period of time; is the time delay, which is used to capture the time effect of the fault signal propagating in the system; The dynamic adjacency matrix formula is as follows: ; in, is a dynamic adjacency matrix element used to represent a node and nodes Between time The connection status; is the spatiotemporal correlation threshold, which is used to determine whether there is a fault correlation between two nodes; For Node and nodes The distance between the power grids; The maximum allowed distance limits the spread of the fault impact range; The fault propagation path optimization formula is as follows: ; in, is the optimal fault propagation path; is the set of all possible fault propagation paths; is the spatiotemporal correlation coefficient between nodes on the path; It is the connection status between nodes, ensuring that the nodes on the path are related to each other.

[0015] To further optimize the technical solution, the spatiotemporal coupling algorithm model includes the following specific processes when used: Real-time signal correlation analysis: After the optical CT acquires electromagnetic field data, the time-space correlation formula is first used to calculate the correlation between each node. According to the signal propagation characteristics of the power system, the time-space relationship of the nodes adjacent to the fault point is identified to form a preliminary judgment on the fault impact area; Dynamic network construction: Based on the calculated spatiotemporal correlation coefficient and system topology information, a dynamic adjacency matrix is ​​generated. Each element in the matrix represents the connection status between nodes at a specific moment. By setting thresholds and distance limits, irrelevant nodes are filtered out to form a simplified network structure that focuses on fault propagation. Fault propagation path tracing: Use the fault propagation path optimization formula to find the main propagation path of the fault in the system. The optimal path is used to analyze how the fault signal spreads within the system and help predict the impact that the fault may have on other key devices or areas.

[0016] To further optimize the technical solution, in step S7, when constructing the fault severity quantitative index system, the following process is included: Data collection and standardization: First, collect the raw data of electromagnetic signal changes, power component health status, fault propagation speed and duration from the optical CT measurement model, and normalize each indicator to eliminate the dimensional differences between different dimensions; Weight setting and adjustment: According to the importance of different indicators in fault assessment, the weight of each indicator is set. The weight is adjusted according to historical data and expert experience to ensure the adaptability of the model to different types of faults. For example, if the propagation speed of a certain type of fault has the greatest impact on system safety, the weight of the corresponding speed indicator should be appropriately increased. Comprehensive score calculation: The standardized indicators are combined according to the preset weights and substituted into the fault severity score formula to calculate the final fault severity score. The higher the score, the more harmful the fault is to the power system and requires higher priority to be handled.

[0017] Compared with the prior art, the present invention provides a method for evaluating the severity of power system faults based on optical CT measurement, which has the following beneficial effects: This power system fault severity assessment method based on optical CT measurement has greatly improved the accuracy of fault location and severity assessment by introducing wavelet transform, multi-scale decomposition, time-space coupling algorithm and comprehensive severity quantification index system. It overcomes the limitations of traditional technologies in fault signal capture and propagation path analysis, and can track the dynamic changes of power system faults in real time and comprehensively. Not only can the potential threat of the fault be identified at the early stage, but also a quantitative basis can be provided through comprehensive scoring to help operation and maintenance personnel formulate response strategies more quickly and reasonably, thereby effectively improving the stability and reliability of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 A schematic flow chart of a method for evaluating the severity of a power system fault based on optical CT measurement proposed by the present invention; Figure 2A schematic diagram of the composition and flow of an optical CT measurement model in a method for evaluating the severity of a power system fault based on optical CT measurement proposed by the present invention; Figure 3 A schematic diagram of the composition and flow of a multi-scale decomposition model in a method for evaluating the severity of power system faults based on optical CT measurement proposed by the present invention; Figure 4 A schematic diagram of the composition and flow of a time-space coupling algorithm model in a method for evaluating the severity of power system faults based on optical CT measurement proposed by the present invention; Figure 5 A schematic diagram of the construction process of a fault severity quantification index system in a power system fault severity assessment method based on optical CT measurement proposed by the present invention; Figure 6 A diagram of a comprehensive scoring formula for fault severity in a method for evaluating the fault severity of a power system based on optical CT measurement proposed by the present invention; Figure 7 A schematic diagram of the weights of fault severity parameters in a method for evaluating the fault severity of a power system based on optical CT measurement proposed by the present invention. DETAILED DESCRIPTION

[0019] The following will be combined with the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] Example: See also Figure 1 , a method for evaluating the severity of power system faults based on optical CT measurement, characterized in that it comprises the following steps: S1. Constructing optical CT measurement model By establishing an optical CT measurement model to obtain the electromagnetic field distribution in the power system, optical sensors are used to measure the spatial distribution characteristics of electromagnetic signals in real time without affecting the normal operation of the power grid. By installing optical CT sensor arrays at key nodes in the power system, multi-dimensional power signal data is captured. The signal data includes key information such as voltage waveform, current density, and electromagnetic wave propagation path.

[0021] In this embodiment, if Figure 2 As shown, in step S1, the constructed optical CT measurement model includes an electromagnetic field distribution equation and a signal reconstruction equation; The electromagnetic field distribution equation is as follows: ; in, For the spatial position and time The electromagnetic field distribution on is the response function of the optical CT sensor, which depends on the wavelength and sensor azimuth ; is the intensity function of the source electromagnetic signal, describing the current or voltage density in the power system over time. and spatial location Distribution of is the wave vector of the electromagnetic wave, describing the propagation direction; is the distance vector between the sensor and the source signal; is the angular frequency, which represents the frequency of the electromagnetic wave; is the integration area, covering all measurement points in the power system.

[0022] Introduced the optical CT sensor response function , which enables the model to adjust the measurement sensitivity according to the wavelength and azimuth of the sensor. The model can more accurately capture the characteristics of electromagnetic signals in different frequency bands, especially when a fault occurs, the electromagnetic signals in certain frequency bands of the power system change more significantly. By adjusting the response function of the sensor, abnormal signals can be identified more quickly in complex power environments.

[0023] Using Wave Vector and the distance vector To describe the propagation path of electromagnetic waves, which is not available in traditional CT models. This improvement makes it possible to track the propagation direction and speed of fault signals, thereby more accurately locating the fault source based on time-space coupling. This approach can help identify how electromagnetic fields spread from the fault point in complex topologies and help understand its impact on surrounding nodes.

[0024] The signal reconstruction equation is as follows: ; in, is the reconstructed three-dimensional electromagnetic field intensity image; is the weight coefficient, which represents the contribution of different optical CT sensors in the measurement process; For the Sensors at location and time The measurement results on .

[0025] Introduced weight coefficient , which is dynamically adjusted based on the performance of each optical CT sensor in different fault scenarios. The importance of sensor data can be adaptively adjusted based on the current power system status. For example, when a fault occurs in a certain area, the sensor closest to the area will automatically increase its weight, thereby improving the accuracy of signal reconstruction and fault location.

[0026] Furthermore, when the optical CT measurement model is used, the following specific process is included: Real-time data acquisition: The optical CT sensor array installed at the key nodes of the power system is used to acquire electromagnetic field data in real time. The data is calculated by the electromagnetic field distribution equation in the model. Processing is performed to capture the preliminary distribution of fault signals in the power system; Fault signal reconstruction: Input the acquired data into the signal reconstruction equation ,Through the weight adjustment mechanism, the three-dimensional distribution image of the electromagnetic field when the fault occurs is reconstructed, and the fault point and the affected area are quickly identified; Propagation path analysis: based on wave vector and the distance vector information, track the propagation path of fault signals, and predict their potential impact on other parts of the power system; Adaptive response strategy generation: adjust the weight coefficients in the model based on real-time updated optical CT measurement data , generate the optimal fault response strategy, dynamically adjust the load distribution and the action of protection equipment in the power system, and reduce the impact of faults on the system.

[0027] S2. Multi-scale decomposition and feature extraction After obtaining the original optical CT measurement signal, multi-scale decomposition is performed to extract the electromagnetic signal characteristics in different frequency bands. The wavelet transform technology is used to decompose the complex power signal into data at multiple scale levels, corresponding to high-frequency and low-frequency components, to capture the unique characteristics of different fault types in the time and frequency domains.

[0028] In this embodiment, in step S2, a multi-scale decomposition model is constructed based on wavelet transform technology, and time-frequency analysis technology is combined to achieve decomposition and feature extraction of signals in different frequency bands; The model introduces nonlinear scale adjustment and frequency weighting mechanism to improve the recognition accuracy and robustness of fault signal characteristics; like Figure 3As shown, the multi-scale decomposition model includes a multi-scale wavelet decomposition formula, an adaptive frequency weighting function, and a feature extraction formula.

[0029] Furthermore, in the multi-scale decomposition model: The multi-scale wavelet decomposition formula is as follows: ; in, For scale and frequency The time-frequency coefficient under represents the energy distribution of the signal at different scales and frequency bands; is the original optical CT measurement signal; For scale and center frequency The mother wavelet function controls the fineness of signal decomposition; Represents the conjugate complex number of the mother wavelet, which is used to capture the amplitude and phase information of the signal in the time-frequency domain.

[0030] The adaptive frequency weighting function is as follows: ; in, It is a frequency weighting function, which is used to adjust the importance of signals in different frequency bands; is the scale weight factor, which indicates the signal priority at different scales; It is a frequency adjustment parameter, which is used to control the attenuation speed of signals in different frequency bands; is the target frequency, representing the fault signal frequency region of interest.

[0031] Scale weight factor It can be adaptively adjusted according to the specific characteristics of the optical CT measurement data. This means that for different types of faults (such as transient overcurrent, continuous harmonic distortion, etc.), the model will prioritize the appropriate scale level for analysis.

[0032] Through adaptive frequency weighting function Dynamically adjust the signal energy in different frequency bands, automatically select important frequency areas according to the characteristics of the fault, and ignore those irrelevant or highly interfering signals. For example, when a certain type of fault occurs, the change of high-frequency components in the power system is more obvious, and the model will automatically weight these high-frequency signals to make the extraction of fault characteristics more accurate.

[0033] The feature extraction formula is as follows: ; in, is the final extracted feature vector, representing the comprehensive characteristics of the signal at different scales and frequencies; and are discrete series of scale and frequency, respectively; It represents the absolute energy value of a signal at a specific scale and frequency.

[0034] By weighting and summarizing the energy values ​​at different scales and frequencies, a high-dimensional feature vector is formed. This feature vector has a high degree of discrimination in fault analysis because it considers the changing characteristics of the signal in both the time domain and the frequency domain. Traditional wavelet decomposition models are often limited to single frequency band or time window analysis, while this comprehensive analysis in the time and frequency domains allows us to comprehensively evaluate the evolution of fault signals.

[0035] When the multi-scale decomposition model is used, it includes the following specific processes: Signal decomposition and preprocessing: After obtaining the original optical CT measurement signal After that, it is input into the multi-scale wavelet decomposition formula and decomposed into multiple scale and frequency components, each component corresponds to high-frequency and low-frequency components respectively; Adaptive weighting adjustment: Use adaptive frequency weighting function to adjust each component. Depending on the fault type, the function automatically increases or decreases the weight of certain frequency bands to highlight the signal features that are most useful for fault identification. Comprehensive feature extraction: Finally, the signal energy at different scales and frequencies is summarized using the feature extraction formula to form a comprehensive feature vector, which is input into the fault classifier or machine learning model for further analysis and assessment of the severity of the fault.

[0036] S3. Time-space coupling fault location Based on the time-space coupling algorithm model, the electromagnetic field data obtained by optical CT is combined with the time series to accurately locate the fault point, track the development path of the fault in the power system in real time, and analyze its propagation speed and diffusion range. In this process, the model combines the topological information of the power system, dynamically associates the data of each node with the adjacent nodes, and identifies the direction and range of possible fault spread.

[0037] In the present embodiment, in step S3, the spatiotemporal coupling algorithm model uses the electromagnetic field data obtained by the optical CT measurement model and combines it with time series analysis to dynamically associate the topological structure of the power system with the fault characteristics. The model captures the propagation path, speed and impact range of the fault in the power system based on the spatiotemporal correlation coefficient and the dynamic adjacency matrix. like Figure 4 As shown, the time-space coupling algorithm model includes a time-space correlation calculation formula, a dynamic adjacency matrix formula, and a fault propagation path optimization formula.

[0038] Furthermore, in the space-time coupling algorithm model: The calculation formula of spatiotemporal correlation is as follows: ; in, For in time and time difference Next, and The spatiotemporal correlation coefficient between nodes; For Node In time The electromagnetic field strength; For Node The average value of the electromagnetic field over a period of time; is the time delay, which is used to capture the time effect of the fault signal propagating in the system.

[0039] Calculate the correlation degree of fault signals between different nodes, taking into account not only the instantaneous value of the electromagnetic field signal, but also the time delay of signal propagation , thus accurately capturing the dynamic propagation process of fault signals in the network. This spatiotemporal correlation analysis can help identify how fault signals gradually affect other nodes in the power system, thereby providing accurate fault location information.

[0040] The dynamic adjacency matrix formula is as follows: ; in, is a dynamic adjacency matrix element used to represent a node and nodes Between time The connection status; is the spatiotemporal correlation threshold, which is used to determine whether there is a fault correlation between two nodes; For Node and nodes The distance between the power grids; The maximum allowed distance limits the propagation of the fault impact range.

[0041] Dynamically adjust the connection status between nodes based on spatiotemporal correlation and network distance. By setting the spatiotemporal correlation threshold and distance restrictions ,The model can effectively filter out those irrelevant or distant nodes, ensuring that only those areas that are actually affected by the fault are analyzed.,This adaptive adjacency matrix design makes the model more accurate and efficient in fault analysis in complex power systems.

[0042] The fault propagation path optimization formula is as follows: ; in, is the optimal fault propagation path; is the set of all possible fault propagation paths; is the spatiotemporal correlation coefficient between nodes on the path; It is the connection status between nodes, ensuring that the nodes on the path are related to each other.

[0043] Find the optimal path for fault signal propagation in the power system. This optimization process identifies the main propagation direction and impact range of the fault by maximizing the spatiotemporal correlation and connection status between nodes on the path. Compared with traditional static analysis methods, this path optimization mechanism can track the propagation of fault signals in real time and dynamically, helping operation and maintenance personnel to take countermeasures faster and more accurately.

[0044] When the spatiotemporal coupling algorithm model is used, it includes the following specific processes: Real-time signal correlation analysis: After the optical CT acquires electromagnetic field data, the time-space correlation formula is first used to calculate the correlation between each node. According to the signal propagation characteristics of the power system, the time-space relationship of the nodes adjacent to the fault point is identified to form a preliminary judgment on the fault impact area; Dynamic network construction: Based on the calculated spatiotemporal correlation coefficient and system topology information, a dynamic adjacency matrix is ​​generated. Each element in the matrix represents the connection status between nodes at a specific moment. By setting thresholds and distance limits, irrelevant nodes are filtered out to form a simplified network structure that focuses on fault propagation. Fault propagation path tracing: Use the fault propagation path optimization formula to find the main propagation path of the fault in the system. The optimal path is used to analyze how the fault signal spreads within the system and help predict the impact that the fault may have on other key devices or areas.

[0045] S4. Dynamic network topology reconstruction After a fault occurs, the network topology model of the power system is updated in real time to reconstruct the faulty node and the node relationships around it. Based on the measurement results of optical CT, the electromagnetic coupling relationship within the system is reconfigured to identify the critical paths and components that may be affected by the fault.

[0046] In this embodiment, this dynamic reconstruction process can quickly reflect changes in system status, provide timely and accurate data support for severity assessment, and enable the system to more efficiently analyze the potential impact of multiple faults on the entire network when faced with them.

[0047] S5. Fault Impact Propagation Analysis The impact propagation analysis of the captured electromagnetic signals is carried out. By calculating the transmission path and speed of the fault in the network, the potential threat to other nodes and areas is evaluated, and the critical points and high-risk areas of fault expansion are identified, providing a reference for subsequent risk assessment and emergency response.

[0048] S6. Prediction of health status of power components Machine learning algorithms are used to predict the health status of key components in the power system, and historical trend analysis of optical CT measurement data is combined to evaluate the failure probability of components under different fault conditions.

[0049] In this embodiment, the stress level of the key component during the fault period is predicted to determine whether it is likely to fail in the short term, which is used to guide the formulation of the maintenance plan and has a direct impact on the final assessment of the severity of the fault.

[0050] S7. Establishment of quantitative indicators of fault severity like Figure 5-7 As shown in the figure, a quantitative index system for fault severity is established. Based on the multi-dimensional parameters of the electromagnetic signal change amplitude, power component health status, and fault impact propagation speed obtained by optical CT measurement, the parameters are standardized and assigned different weights to calculate a comprehensive severity score. The scoring system includes different types of fault scenarios, including instantaneous overcurrent, continuous overload, and line grounding, providing a quantitative basis for risk level determination.

[0051] In this embodiment, the fault severity quantitative index system is constructed by including the following processes: Data collection and standardization: First, collect the raw data of electromagnetic signal changes, power component health status, fault propagation speed and duration from the optical CT measurement model, and normalize each indicator to eliminate the dimensional differences between different dimensions; Weight setting and adjustment: According to the importance of different indicators in fault assessment, the weight of each indicator is set. The weight is adjusted according to historical data and expert experience to ensure the adaptability of the model to different types of faults. For example, if the propagation speed of a certain type of fault has the greatest impact on system safety, the weight of the corresponding speed indicator should be appropriately increased. Comprehensive score calculation: The standardized indicators are combined according to the preset weights and substituted into the fault severity score formula to calculate the final fault severity score. The higher the score, the more harmful the fault is to the power system and requires higher priority to be handled.

[0052] The beneficial effects of the present invention are: This power system fault severity assessment method based on optical CT measurement has greatly improved the accuracy of fault location and severity assessment by introducing wavelet transform, multi-scale decomposition, time-space coupling algorithm and comprehensive severity quantification index system. It overcomes the limitations of traditional technologies in fault signal capture and propagation path analysis, and can track the dynamic changes of power system faults in real time and comprehensively. Not only can the potential threat of the fault be identified at the early stage, but also a quantitative basis can be provided through comprehensive scoring to help operation and maintenance personnel formulate response strategies more quickly and reasonably, thereby effectively improving the stability and reliability of the power grid.

[0053] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0054] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for evaluating the severity of power system faults based on optical CT measurement, characterized in that: The following steps are involved: S1, constructing optical CT measurement model; By establishing an optical CT measurement model to obtain the electromagnetic field distribution in the power system, optical sensors are used to measure the spatial distribution characteristics of electromagnetic signals in real time without affecting the normal operation of the power grid. By installing optical CT sensor arrays at key nodes in the power system, multi-dimensional power signal data is captured. The signal data includes key information such as voltage waveform, current density, and electromagnetic wave propagation path. S2, multi-scale decomposition and feature extraction; After obtaining the original optical CT measurement signal, multi-scale decomposition is performed to extract the electromagnetic signal characteristics in different frequency bands. The wavelet transform technology is used to decompose the complex power signal into data at multiple scale levels, corresponding to high-frequency and low-frequency components, to capture the unique characteristics of different fault types in the time and frequency domains. S3, time-space coupled fault location; Based on the spatiotemporal coupling algorithm model, the electromagnetic field data obtained by optical CT is combined with the time series to accurately locate the fault point, track the development path of the fault in the power system in real time, and analyze its propagation speed and diffusion range. In this process, the model combines the topological information of the power system, dynamically associates the data of each node with the adjacent nodes, and identifies the direction and range of possible fault spread; S4, dynamic network topology reconstruction; After a fault occurs, the network topology model of the power system is updated in real time to reconstruct the fault node and the node relationships around it. Based on the measurement results of optical CT, the electromagnetic coupling relationship within the system is reconfigured to identify the critical paths and components that may be affected by the fault. S5. Fault impact propagation analysis; Conduct impact propagation analysis on captured electromagnetic signals, calculate the transmission path and speed of faults in the network, evaluate their potential threats to other nodes and regions, identify critical points of fault expansion and high-risk areas, and provide reference for subsequent risk assessment and emergency response; S6. Prediction of health status of power components; Use machine learning algorithms to predict the health status of key components in the power system, and combine historical trend analysis of optical CT measurement data to evaluate the failure probability of components under different fault conditions; S7. Establishment of quantitative indicators of fault severity; A quantitative indicator system for fault severity is established. Based on the multi-dimensional parameters of the electromagnetic signal change amplitude, power component health status, and fault impact propagation speed obtained by optical CT measurement, the parameters are standardized and assigned different weights to calculate a comprehensive severity score. The scoring system includes different types of fault scenarios, including instantaneous overcurrent, continuous overload, and line grounding, providing a quantitative basis for risk level determination.

2. The method for evaluating the severity of power system faults based on optical CT measurement according to claim 1, characterized in that: In the step S1, the constructed optical CT measurement model includes an electromagnetic field distribution equation and a signal reconstruction equation; The electromagnetic field distribution equation is as follows: ； in, For the spatial position and time The electromagnetic field distribution on is the response function of the optical CT sensor, which depends on the wavelength and sensor azimuth ; is the intensity function of the source electromagnetic signal, describing the current or voltage density in the power system over time. and spatial location Distribution of is the wave vector of the electromagnetic wave, describing the propagation direction; is the distance vector between the sensor and the source signal; is the angular frequency, which represents the frequency of the electromagnetic wave; is the integration area, covering all measurement points in the power system; The signal reconstruction equation is as follows: ； in, is the reconstructed three-dimensional electromagnetic field intensity image; is the weight coefficient, which represents the contribution of different optical CT sensors in the measurement process; For the Sensors at location and time The measurement results on .

3. The method for evaluating the severity of power system faults based on optical CT measurement according to claim 2, characterized in that: When the optical CT measurement model is used, the following specific processes are included: Real-time data acquisition: Using the optical CT sensor array installed at the key nodes of the power system, the electromagnetic field data is acquired in real time. The data is calculated by the electromagnetic field distribution equation in the model. Processing is performed to capture the preliminary distribution of fault signals in the power system; Fault signal reconstruction: Input the acquired data into the signal reconstruction equation ,Through the weight adjustment mechanism, the three-dimensional distribution image of the electromagnetic field when the fault occurs is reconstructed, and the fault point and the affected area are quickly identified; Propagation path analysis: based on wave vector and the distance vector information, track the propagation path of fault signals, and predict their potential impact on other parts of the power system; Adaptive response strategy generation: adjust the weight coefficients in the model based on real-time updated optical CT measurement data , generate the optimal fault response strategy, dynamically adjust the load distribution and the action of protection equipment in the power system, and reduce the impact of faults on the system.

4. The method for evaluating the severity of power system faults based on optical CT measurement according to claim 1, characterized in that: In step S2, a multi-scale decomposition model is constructed based on wavelet transform technology, and time-frequency analysis technology is combined to achieve decomposition and feature extraction of signals in different frequency bands; The model introduces nonlinear scale adjustment and frequency weighting mechanism to improve the recognition accuracy and robustness of fault signal characteristics; The multi-scale decomposition model includes a multi-scale wavelet decomposition formula, an adaptive frequency weighting function, and a feature extraction formula.

5. The method for evaluating the severity of power system faults based on optical CT measurement according to claim 4, characterized in that: In the multi-scale decomposition model: The multi-scale wavelet decomposition formula is as follows: ； in, For scale and frequency The time-frequency coefficient under represents the energy distribution of the signal at different scales and frequency bands; is the original optical CT measurement signal; For scale and center frequency The mother wavelet function controls the fineness of signal decomposition; Represents the conjugate complex number of the mother wavelet, which is used to capture the amplitude and phase information of the signal in the time-frequency domain; The adaptive frequency weighting function is as follows: ； in, It is a frequency weighting function, which is used to adjust the importance of signals in different frequency bands; is the scale weight factor, which indicates the signal priority at different scales; It is a frequency adjustment parameter, which is used to control the attenuation speed of signals in different frequency bands; is the target frequency, representing the fault signal frequency region of interest; The feature extraction formula is as follows: ； in, is the final extracted feature vector, representing the comprehensive characteristics of the signal at different scales and frequencies; and are discrete series of scale and frequency, respectively; It represents the absolute energy value of a signal at a specific scale and frequency.

6. The method for evaluating the severity of power system faults based on optical CT measurement according to claim 4, characterized in that: When the multi-scale decomposition model is used, it includes the following specific processes: Signal decomposition and preprocessing: After obtaining the original optical CT measurement signal After that, it is input into the multi-scale wavelet decomposition formula and decomposed into multiple scale and frequency components, each component corresponds to high-frequency and low-frequency components respectively; Adaptive weighting adjustment: Use adaptive frequency weighting function to adjust each component. Depending on the fault type, the function automatically increases or decreases the weight of certain frequency bands to highlight the signal features that are most useful for fault identification. Comprehensive feature extraction: Finally, the signal energy at different scales and frequencies is summarized using the feature extraction formula to form a comprehensive feature vector, which is input into the fault classifier or machine learning model for further analysis and assessment of the severity of the fault.

7. The method for evaluating the severity of power system faults based on optical CT measurement according to claim 1, characterized in that: In step S3, the spatiotemporal coupling algorithm model uses the electromagnetic field data obtained by the optical CT measurement model and combines it with time series analysis to dynamically associate the topological structure of the power system with the fault characteristics. The model captures the propagation path, speed and impact range of the fault in the power system based on the spatiotemporal correlation coefficient and the dynamic adjacency matrix. The time-space coupling algorithm model includes a time-space correlation calculation formula, a dynamic adjacency matrix formula, and a fault propagation path optimization formula.

8. The method for evaluating the severity of power system faults based on optical CT measurement according to claim 7, characterized in that: In the space-time coupling algorithm model: The calculation formula of spatiotemporal correlation is as follows: ； in, For in time and time difference Next, and The spatiotemporal correlation coefficient between nodes; For Node In time The electromagnetic field strength; For Node The average value of the electromagnetic field over a period of time; is the time delay, which is used to capture the time effect of the fault signal propagating in the system; The dynamic adjacency matrix formula is as follows: ； in, is a dynamic adjacency matrix element used to represent a node and nodes Between time The connection status; is the spatiotemporal correlation threshold, which is used to determine whether there is a fault correlation between two nodes; For Node and nodes The distance between the power grids; The maximum allowed distance limits the spread of the fault impact range; The fault propagation path optimization formula is as follows: ； in, is the optimal fault propagation path; is the set of all possible fault propagation paths; is the spatiotemporal correlation coefficient between nodes on the path; It is the connection status between nodes, ensuring that the nodes on the path are related to each other.

9. The method for evaluating the severity of power system faults based on optical CT measurement according to claim 8, characterized in that: When the spatiotemporal coupling algorithm model is used, it includes the following specific processes: Real-time signal correlation analysis: After the optical CT acquires electromagnetic field data, the time-space correlation formula is first used to calculate the correlation between each node. According to the signal propagation characteristics of the power system, the time-space relationship of the nodes adjacent to the fault point is identified to form a preliminary judgment on the fault impact area; Dynamic network construction: Based on the calculated spatiotemporal correlation coefficient and system topology information, a dynamic adjacency matrix is ​​generated. Each element in the matrix represents the connection status between nodes at a specific moment. By setting thresholds and distance limits, irrelevant nodes are filtered out to form a simplified network structure that focuses on fault propagation. Fault propagation path tracing: Use the fault propagation path optimization formula to find the main propagation path of the fault in the system. The optimal path is used to analyze how the fault signal spreads within the system and help predict the impact that the fault may have on other key devices or areas.

10. The method for evaluating the severity of power system faults based on optical CT measurement according to claim 1, characterized in that: In step S7, when constructing the fault severity quantitative index system, the following process is included: Data collection and standardization: First, collect the raw data of electromagnetic signal changes, power component health status, fault propagation speed and duration from the optical CT measurement model, and normalize each indicator to eliminate the dimensional differences between different dimensions; Weight setting and adjustment: According to the importance of different indicators in fault assessment, the weight of each indicator is set. The weight is adjusted according to historical data and expert experience to ensure the adaptability of the model to different types of faults. For example, if the propagation speed of a certain type of fault has the greatest impact on system safety, the weight of the corresponding speed indicator should be appropriately increased. Comprehensive score calculation: The standardized indicators are combined according to the preset weights and substituted into the fault severity score formula to calculate the final fault severity score. The higher the score, the more harmful the fault is to the power system and requires higher priority to be handled.

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