Panoramic intelligent sensing method for state of power distribution network

Abstract

The invention discloses a power distribution network state panoramic intelligent sensing method, relates to the technical field of power distribution network monitoring management, and is used for solving the problem of poor power distribution network abnormity monitoring. According to the method, multi-scale decomposition and baseline construction of the signals are realized through accurate acquisition and preprocessing of the node signals of the power distribution network, steady-state and abnormal dynamic characteristics of the power distribution network are effectively extracted, a variational mode decomposition technology is used, and a low-frequency mode is screened through a center frequency to construct a baseline, so that the reliability of the power distribution network is improved. Constructing a robust residual error and an abnormal energy index by combining the difference between a baseline and an original signal, accurately determining an abnormal event, switching a power distribution network node to an ultrahigh frequency sampling mode after abnormal triggering, extracting a local disturbance characteristic by using time domain difference, and adaptively adjusting a filtering parameter according to a local edge ratio through robust filtering noise reduction, so as to achieve the purpose of reducing noise. Therefore, the abnormal edge information is fully reserved, the real-time and high-precision perception of the abnormal state of the power distribution network is realized, and the response speed and accuracy of the panoramic perception of the power distribution network are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network monitoring and management, and more specifically, to a method for panoramic intelligent perception of distribution network status. Background Art

[0002] In recent years, with the acceleration of urbanization and the increasing popularity of new energy access, the stable operation and intelligent monitoring of the distribution network, as the terminal key link of the power system, are particularly important. The distribution network is responsible for distributing the electric energy in the high-voltage power grid to each terminal node user. Its operating environment is affected by multiple factors such as load fluctuations, equipment aging, and external interference. It is prone to problems such as voltage fluctuations, short-term load mutations and local failures.

[0003] The shortcomings of existing technologies: It is difficult to capture short-term and small dynamic changes during the distribution network monitoring process, which makes it difficult to identify transient anomalies and subtle disturbances in a timely manner, thereby affecting the accurate detection of local abnormal events. At the same time, the signals of each distribution node are often interfered by noise during transmission, and there is a problem of data discontinuity. While achieving signal smoothing, the existing processing solutions often sacrifice the integrity of key abnormal edge information, which not only reduces the sensitivity of fault warning, but also restricts the accurate response of the intelligent dispatching system to the actual operating status, thereby affecting the safe and stable operation of the distribution network. Summary of the invention

[0004] In order to overcome the above-mentioned defects of the prior art, there is a solution as follows to solve the problem of poor abnormal monitoring of the distribution network in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions: A method for panoramic intelligent perception of distribution network status includes the following steps: The distribution network node signals are collected and processed to obtain the original signals, and the original signals are decomposed into multiple scales using variational mode decomposition. The low-frequency modes are screened out through the center frequency and used to reconstruct the baseline. According to the difference between the reconstructed baseline signal and the original signal, a robust residual and anomaly energy index are constructed to determine the abnormal event; After an abnormal event is triggered, the sampling mode is switched to an ultra-high frequency sampling mode to collect data on the abnormal event; The ultra-high frequency sampled data is differentiated in the time domain to extract the local disturbance characteristics, and the noise is reduced through robust filtering. The parameters of the robust filtering are adjusted according to the local edge ratio, and the adjusted collected signals are used for perception analysis of the distribution network.

[0006] In a preferred embodiment, the distribution network node signals are collected and processed to obtain original signals, and the original signals are decomposed into multiple scales using variational mode decomposition. The specific steps are as follows: Collect distribution network node signals, including voltage signals, current signals, power signals and auxiliary signals; All collected signals are corrected and normalized according to a unified timestamp to obtain the original signal; The normalized original signal is subjected to multi-scale signal decomposition to obtain the mode and the corresponding center frequency set.

[0007] In a preferred embodiment, the low-frequency mode is screened out by the center frequency and used to reconstruct the baseline. The specific steps include: Compare the center frequency with the spectrum threshold, filter out the low-frequency mode, and determine the low-frequency mode index set; The low-frequency modes obtained by decomposing the multi-scale signal are subjected to baseline reconstruction operation and the baseline signal is output.

[0008] In a preferred embodiment, according to the difference between the reconstructed baseline signal and the original signal, a robust residual and anomaly energy index are constructed to determine an abnormal event. The specific steps are as follows: Based on the deviation between the baseline and the original signal, a residual signal is defined; Robust residual and anomaly index functions are constructed according to the Huber loss function, and the anomaly energy index is constructed using the local time window integration method. The calculated anomaly energy index is used as the basis for real-time anomaly judgment in the real-time monitoring link to determine abnormal events.

[0009] In a preferred embodiment, after an abnormal event is triggered, the sampling mode is switched to an ultra-high frequency sampling mode to collect data on the abnormal event. The specific steps are as follows: After an abnormal event is triggered, the abnormal energy index calculated in real time is compared with the preset abnormal energy threshold: When the abnormal energy index is greater than the preset abnormal energy threshold, it is marked as an abnormal event and the abnormal triggering time is recorded and the sampling time window is determined; The ultra-high frequency sampling mode is used to continuously sample data within the sampling time window where the abnormal event is determined.

[0010] In a preferred embodiment, the ultra-high frequency sampled data is time-differentiated to extract local disturbance characteristics, and noise reduction is performed through robust filtering. The parameters of the robust filtering are adjusted according to the local edge ratio, and the adjusted collected signals are subjected to perception analysis of the distribution network, including the following steps: Use time-domain difference operation to analyze the local changes of the continuously sampled data, and determine the local disturbance measure based on the difference between adjacent sampling points; Robust filtering is performed on the local disturbance measure determined by ultra-high frequency sampling and the original signal data to determine a defiltered signal; Define local edge indicators, perform comparative verification of local high-frequency components, determine the differential amplitude ratio between the original signal data and the filtered signal in the local area, and adjust the robust filtering parameters according to the differential amplitude ratio; The adjusted collected signals are used for perception and analysis of the distribution network status.

[0011] The technical effects and advantages of the panoramic intelligent perception method of the distribution network status of the present invention are as follows: The present invention realizes multi-scale decomposition and baseline construction of signals through precise collection and preprocessing of distribution network node signals, effectively extracts steady-state and abnormal dynamic characteristics of distribution network, uses variational mode decomposition technology, selects low-frequency modes through center frequency to construct baseline, and then constructs robust residual and abnormal energy indicators based on the difference between baseline and original signal to accurately determine abnormal events. After abnormality is triggered, distribution network nodes switch to ultra-high frequency sampling mode, extract local disturbance characteristics by time domain difference, and reduce noise through robust filtering. Filter parameters are adaptively adjusted according to local edge ratio, so as to fully retain abnormal edge information, thereby realizing real-time and high-precision perception of abnormal state of distribution network, providing reliable data support for fault warning and intelligent scheduling, and significantly improving the response speed and accuracy of panoramic perception of distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 The present invention is a flow chart of a method for panoramic intelligent perception of distribution network status. DETAILED DESCRIPTION

[0013] The following will be combined with the drawings in 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.

[0014] In order to achieve the above objectives, Figure 1 A schematic diagram of a method for panoramic intelligent perception of distribution network status is provided in the present invention, which specifically includes the following steps: The distribution network node signals are collected and processed to obtain the original signals, and the original signals are decomposed into multiple scales using variational mode decomposition. The low-frequency modes are screened out through the center frequency and used to reconstruct the baseline. According to the difference between the reconstructed baseline signal and the original signal, a robust residual and anomaly energy index are constructed to determine the abnormal event; After an abnormal event is triggered, the sampling mode is switched to an ultra-high frequency sampling mode to collect data on the abnormal event; The ultra-high frequency sampled data is differentiated in the time domain to extract the local disturbance characteristics, and the noise is reduced through robust filtering. The parameters of the robust filtering are adjusted according to the local edge ratio, and the adjusted collected signals are used for perception analysis of the distribution network.

[0015] In the panoramic intelligent perception of distribution networks, the collected original signals (such as voltage, current, power, etc.) contain both low-frequency components that describe the steady-state operation of the distribution network and high-frequency fluctuations that may indicate sudden abnormalities. Therefore, the original signals are often mixed with different frequency components. The low-frequency part reflects the stable operation trend of the distribution network, while the high-frequency part may contain fault precursors or transient disturbances. The variational mode decomposition (VMD) method can be used to adaptively decompose the mixed signal into a series of modal components. Each mode has the characteristic of local concentration in the spectrum, thus providing a basis for subsequent baseline construction. Step 1: Perform multi-scale baseline modeling and real-time residual calculation. The specific steps are as follows: Use high-precision sensors installed at key nodes (such as transformers, distribution cabinets, branch nodes, etc.) to collect distribution network node signals, including voltage signals, current signals, active / reactive power signals, and other auxiliary signals (such as frequency, temperature, etc., depending on the specific application scenario); It should be noted that the distribution network node signal includes data sets related to the distribution network, including but not limited to numerical data, character data, etc. For example, the outdoor ambient temperature of node No. 1 is 27 degrees Celsius, where node No. 1 is character data and 27 degrees Celsius is numerical data.

[0016] Correct all collected signals according to a unified timestamp to eliminate transmission delays and device clock deviations, so that signals from different collection points can be compared at the same time, and use interpolation or timestamp correction algorithms to resample the data to a unified time base; Perform preliminary filtering on the collected signals to remove noise and abnormal values ​​caused by sensor errors, transient interference or occasional failures. At the same time, perform amplitude correction and normalization on the signals according to the equipment calibration information to eliminate differences between sensors and possible DC bias. The normalized original signal is used as the input of multi-scale signal decomposition.

[0017] Perform constrained optimization of the original signal f(t), that is, decompose f(t) into K intrinsic modal components and their center frequencies; the optimization is constructed as follows: , where f(t) represents the original signal to be decomposed, reflecting the real-time status data of the distribution network; represents the kth modal component, the goal is to capture a certain frequency range of the signal; K is the preset number of modes, and its value is based on the understanding of the complexity of the signal (for example, determined by prior analysis or experiment); is the center frequency of the kth mode, which is used to down-convert the mode to the low-frequency region to facilitate bandwidth evaluation; operator Represents the derivative with respect to time, reflecting the instantaneous change of the mode in the time domain; the expression is the Hilbert transform kernel, which is constructed using the convolution operation (symbol *) The analytical signal has positive spectrum information; the exponential factor It is used to shift the spectrum of the modal signal so that the center frequency of the mode moves to zero frequency, thus facilitating bandwidth measurement; Indicates that on a given interval The norm calculates the sum of the squares of the bandwidth energy of the signal; the objective of this optimization problem is to accumulate the bandwidth energy of all modes.

[0018] The objective of the optimization problem is to accumulate the bandwidth energy of all modes, which means minimizing this objective so that each mode The high-frequency energy after spectrum shifting is as low as possible, thus ensuring that its spectrum is compact, that is, has a narrow bandwidth; This optimization aims to ensure that each mode has a high concentration in the spectrum while reconstructing the original signal without distortion; The output is a set of sets, including the modal And the corresponding center frequency , where the modes with lower center frequencies usually carry the steady-state components of the signal, while the high-frequency modes may contain abnormal information; Output Mode It will serve as the original input for subsequent baseline reconstruction; It should be noted that the parameter K represents the preset number of decomposition layers, and its value must be determined based on the spectral complexity of the actual signal. It is used to distinguish low-frequency from high-frequency modes, that is, to classify low-frequency modes as part of the baseline component based on the spectral threshold.

[0019] Baseline reconstruction and multi-scale baseline construction are performed. That is, after obtaining the decomposition results of each mode, the baseline signal reflecting the stable operation trend of the distribution network is reconstructed as a reference for anomaly detection. This process ensures that the constructed baseline can smoothly reflect the global dynamic trend and eliminate short-term disturbance interference through adaptive screening and reconstruction of low-frequency modes. The specific steps are as follows: Determine the low-frequency mode index set according to the size of each mode center frequency The low frequency mode needs to satisfy ,in is the preset frequency threshold; Define the baseline reconstruction operation R: , where R(⋅) represents the reconstruction operation based on the spectrum selection criterion, which requires to preserve the dynamic trend of the system while ensuring smoothness, and L is the low-frequency mode index set, which is determined by the threshold setting of the center frequency of each mode; The low-frequency mode obtained by VMD decomposition is subjected to baseline reconstruction operation to output the baseline signal , this signal serves as the reference input for subsequent residual calculations.

[0020] According to the baseline signal and the original signal The deviation between them is used to define the residual signal to reflect the transient abnormal characteristics, and a robust metric is introduced in the residual calculation to reduce the interference of extreme deviations on the judgment. The robust residual is directly defined as: ; At the same time, in order to avoid extreme values ​​from having too great an impact on subsequent judgments, the Huber loss function is used to construct a robust residual measure. The Huber loss function is specifically defined as for: ,in, is a switching parameter used to determine the extent to which quadratic penalty and linear penalty are used. The switching parameter is set according to the actual system noise level and abnormal amplitude characteristics. Represents the residual signal between the original signal and the baseline signal; Construct an exception indicator function at the same time , defined as the derivative of the Huber loss function: , sgn(⋅) is a sign function used to maintain abnormal direction information; The robust residual obtained by calculation is used as a real-time abnormality criterion.

[0021] The abnormal measurement criteria construction and data transmission are as follows: In order to enable the panoramic perception of the distribution network to respond to the transient anomalies of the distribution network in a timely manner, the robust residual at time t needs to be converted into the abnormal energy index A(t), so as to achieve the effect of the cumulative abnormal effect in the comprehensive local time window; Based on abnormal indicator function The output of is used to construct an abnormality metric index, so as to determine the occurrence of abnormal events in panoramic perception. This index is used to guide the activation of the ultra-high frequency sampling mode. Construct anomaly energy index using local time window integration method: ,in, is the time window width; The calculated abnormal energy index is output as an abnormal trigger signal in the real-time monitoring link. Once the abnormal energy index exceeds the predetermined threshold, the subsequent ultra-high frequency sampling and abnormal capture process is activated.

[0022] In summary, through the above steps, the original distribution network signal is decomposed by VMD to obtain multiple modes. The baseline signal reflecting the steady-state operation trend is adaptively constructed through the low-frequency mode set, and then the residual is constructed based on the difference between the baseline and the original signal, and the Huber loss function is used to design a robust anomaly indicator. Finally, the abnormal energy indicator is constructed through local time integration, which can be used to indicate abnormal events.

[0023] After constructing the abnormal energy index, the abnormal event is triggered in time, and higher-resolution abnormal data is obtained by dynamically adjusting the sampling period.

[0024] Step 2: Switch between event triggering and ultra-high frequency sampling. The abnormal energy index reflects the cumulative effect of the signal deviation from the baseline in the local time window. However, since the conventional sampling period may not meet the requirements of capturing instantaneous abnormal details in terms of sampling density, a dynamic sampling switching mechanism needs to be designed: when the abnormal energy is detected to exceed the preset threshold, it is immediately switched from the conventional mode to the ultra-high frequency mode. A certain sampling time window is set before and after the abnormality occurs, so as to fully record the abnormal characteristics and their evolution process. The specific steps are as follows: After an abnormal event is triggered, the abnormal energy index calculated in real time is compared with the preset abnormal energy threshold. ,when , that is, to calibrate abnormal events and record the abnormal triggering moment of abnormal events as the triggering benchmark for subsequent high-frequency sampling; At the same time, the time window is determined and the event time is calibrated. Then, set the capture window to: ,in, Reserve sampling time before an abnormality occurs, to capture precursors or changing trends before an event occurs; It is the continuation sampling time after the abnormality occurs, which is used to record the recovery process after the abnormality; After determining the event triggering time and sampling time window, use the ultra-high frequency sampling mode for the time interval, and within the time interval, the sampling interval can be adjusted to: , where is the total number of samples expected to be collected within the abnormal collection window, It is an ultra-high frequency sampling period, which is required to be much smaller than the original sampling period in order to capture transient anomalies.

[0025] During the ultra-high frequency sampling period, due to the rapid increase in data volume, it is necessary to use a high-speed cache mechanism to store the collected data in real time. After the abnormal event is over, it is restored to the normal sampling mode to avoid overload, thereby realizing dynamic switching and management of the sampling state; Within the sampling window, the ultra-high frequency sampling data sequence is defined to satisfy: ; The collected data is stored in the cache area. After the abnormal collection period ends, that is, at time When the sampling mode is set to true, the system automatically issues a command to switch back to normal sampling mode.

[0026] It should be noted that the preset abnormal energy threshold is determined based on historical data, equipment characteristics and engineering requirements to distinguish normal fluctuations from true abnormal events.

[0027] Step 3: Preprocess and filter the high-frequency data. The signal sequence obtained by ultra-high frequency sampling can fully record the transient details of abnormal events, but it will also introduce high-frequency noise and random interference. In order to make the abnormal features contained in the signal available for subsequent processing, the ultra-high frequency sampled data is preprocessed and filtered. The specific steps are as follows: The local variation analysis of the continuous sampling data is performed using the differential operation. The local disturbance measure is constructed based on the difference between adjacent sampling points. The time domain differential operation is defined as: , where Indicates at time The ultra-high frequency sampling data at Indicates the ultra-high frequency sampling period; Indicates the increment between adjacent samples, reflecting the local signal mutation; The result of the operation Perform robust filtering on the original signal data, solve the filtered signal z(t), and construct the following optimization problem: , where z(t) represents the signal obtained after filtering; is the first-order derivative of z(t), and its absolute integral is the total variation of the signal, which is used to reflect the smoothness of the signal; is the regularization parameter, balancing the data fidelity term and the smooth regularization term; For a robust loss function, choose, for example, Huber loss or truncated quadratic function to reduce the impact of extreme errors on the filtering results; Indicates the time interval where the ultra-high frequency sampling data is located, corresponding to the abnormal collection window determined in step 2; After robust filtering, the defiltered signal z(t) can retain the edge information of abnormal events. To this end, an edge preservation correction step is introduced to prevent excessive smoothing of abnormal details in the filtering process by comparing and verifying the local high-frequency components. The local edge index E(t) is defined as the differential amplitude ratio between the original signal data and the filtered signal z(t) in the local area: ,in, is the ultra-high frequency sampling period, is the ultra-high frequency sampling data at time t, is a constant term greater than 1; When the differential amplitude ratio is close to 1, it means that the filtering does not lose the abnormal edge features; if it is significantly lower than 1, it indicates that the filtering process may be over-smoothed and the filtering parameters need to be adjusted.

[0028] The differential amplitude ratio is used as feedback information to provide an adaptive adjustment basis for the filtering algorithm, and is also used for subsequent anomaly detection, fault diagnosis, etc., to ensure that the panoramic intelligent perception of the entire distribution network status can capture accurate, continuous and high-quality abnormal features in real-time monitoring, and use the adjusted signal for panoramic perception analysis of the distribution network status to realize the identification and judgment of abnormal events.

[0029] In summary, the local disturbance measure is constructed by time domain difference to provide a basis for noise modeling. Subsequently, a robust total variation regularized filtering model is constructed to solve the optimization problem to obtain the filtered signal, thereby suppressing high-frequency noise while retaining the abnormal edge. The filtering result is then verified for consistency and edge retention correction is performed by calculating the local edge index. The feedback information is used to dynamically adjust the filtering parameters to ensure that abnormal transient information (such as sudden edge and rapid fluctuation) is accurately retained, providing a reliable basis for subsequent fault diagnosis and making the monitoring and early warning functions of the entire distribution network more accurate and efficient.

[0030] It should be noted that the threshold information in this embodiment is pre-set by professionals and will not be explained in detail here. Some parameter English letters in the embodiments have the same situation, but different meanings are explained when used, which will not be explained one by one here.

[0031] The present invention realizes multi-scale decomposition and baseline construction of signals through precise collection and preprocessing of distribution network node signals, effectively extracts steady-state and abnormal dynamic characteristics of distribution network, uses variational mode decomposition technology, selects low-frequency modes through center frequency to construct baseline, and then constructs robust residual and abnormal energy indicators based on the difference between baseline and original signal to accurately determine abnormal events. After abnormality is triggered, distribution network nodes switch to ultra-high frequency sampling mode, extract local disturbance characteristics by time domain difference, and reduce noise through robust filtering. Filter parameters are adaptively adjusted according to local edge ratio, so as to fully retain abnormal edge information, thereby realizing real-time and high-precision perception of abnormal state of distribution network, providing reliable data support for fault warning and intelligent scheduling, and significantly improving the response speed and accuracy of panoramic perception of distribution network.

[0032] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0033] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product.

[0034] Those of ordinary skill in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0035] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0036] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0037] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for panoramic intelligent perception of distribution network status, characterized by: The steps include: The distribution network node signals are collected and processed to obtain the original signals, and the original signals are decomposed into multiple scales using variational mode decomposition. The low-frequency modes are screened out through the center frequency and used to reconstruct the baseline. According to the difference between the reconstructed baseline signal and the original signal, a robust residual and anomaly energy index are constructed to determine the abnormal event; After an abnormal event is triggered, the sampling mode is switched to an ultra-high frequency sampling mode to collect data on the abnormal event; The ultra-high frequency sampled data is differentiated in the time domain to extract the local disturbance characteristics, and the noise is reduced through robust filtering. The parameters of the robust filtering are adjusted according to the local edge ratio, and the adjusted collected signals are used for perception analysis of the distribution network.

2. A method for panoramic intelligent perception of distribution network status according to claim 1, characterized in that: The distribution network node signals are collected and processed to obtain the original signals, and the original signals are decomposed into multiple scales using variational mode decomposition. The specific steps are as follows: Collect distribution network node signals, including voltage signals, current signals, power signals and auxiliary signals; All collected signals are corrected and normalized according to a unified timestamp to obtain the original signal; The normalized original signal is subjected to multi-scale signal decomposition to obtain the mode and the corresponding center frequency set.

3. A method for panoramic intelligent perception of distribution network status according to claim 2, characterized in that: The low-frequency modes are filtered out through the center frequency and used to reconstruct the baseline. The specific steps include: Compare the center frequency with the spectrum threshold, filter out the low-frequency mode, and determine the low-frequency mode index set; The low-frequency modes obtained by decomposing the multi-scale signal are subjected to baseline reconstruction operation and the baseline signal is output.

4. A method for panoramic intelligent perception of distribution network status according to claim 3, characterized in that: According to the difference between the reconstructed baseline signal and the original signal, the robust residual and abnormal energy index are constructed to determine the abnormal event. The specific steps are as follows: Based on the deviation between the baseline and the original signal, a residual signal is defined; Robust residual and anomaly index functions are constructed according to the Huber loss function, and the anomaly energy index is constructed using the local time window integration method. The calculated anomaly energy index is used as the basis for real-time anomaly judgment in the real-time monitoring link to determine abnormal events.

5. A method for panoramic intelligent perception of distribution network status according to claim 4, characterized in that: After an abnormal event is triggered, the sampling mode is switched to the ultra-high frequency sampling mode to collect data for the abnormal event. The specific steps are as follows: After an abnormal event is triggered, the abnormal energy index calculated in real time is compared with the preset abnormal energy threshold: When the abnormal energy index is greater than the preset abnormal energy threshold, it is marked as an abnormal event and the abnormal triggering time is recorded and the sampling time window is determined; The ultra-high frequency sampling mode is used to continuously sample data within the sampling time window where the abnormal event is determined.

6. A method for panoramic intelligent perception of distribution network status according to claim 5, characterized in that: The ultra-high frequency sampled data is differentiated in the time domain to extract the local disturbance characteristics, and the noise is reduced through robust filtering. The parameters of the robust filtering are adjusted according to the local edge ratio, and the adjusted collected signals are used for perception analysis of the distribution network, including the following steps: Use time-domain difference operation to analyze the local changes of the continuously sampled data, and determine the local disturbance measure based on the difference between adjacent sampling points; Robust filtering is performed on the local disturbance measure determined by ultra-high frequency sampling and the original signal data to determine a defiltered signal; Define local edge indicators, perform comparative verification of local high-frequency components, determine the differential amplitude ratio between the original signal data and the filtered signal in the local area, and adjust the robust filtering parameters according to the differential amplitude ratio; The adjusted collected signals are used for perception and analysis of the distribution network status.

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