Distributed DTU station terminal system based on linear interpolation method

Abstract

The invention discloses a distributed DTU station terminal system based on a linear interpolation method. The distributed DTU station terminal system comprises a plurality of interval units and a common unit. The interval unit collects a bus voltage sampling sequence and a feeder current sampling sequence which carry original sampling time labels and sends the bus voltage sampling sequence and the feeder current sampling sequence to the common unit; the common unit comprises an event judgment module, a time difference calculation module and a data synchronization reconstruction module; the event judgment module identifies local switching event moments corresponding to capacitor switching in each interval unit; the time difference calculation module calculates the time alignment offset of each interval unit according to the moment of the local switching event; and the data synchronization reconstruction module performs translation processing on the original sampling moment label of each interval unit by using the time alignment offset, and performs linear interpolation supplementation when any interval unit lacks data at a certain moment position of the total sampling point set. According to the method, the problem of conjoint analysis distortion caused by inconsistent time axes of distributed multi-interval sampling data and splicing defects is solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of power systems and power distribution, more particularly, to a distributed DTU station terminal system based on linear interpolation method. BACKGROUND

[0002] In the power distribution automation system, the DTU station terminal is an important node connecting the primary distribution equipment and the dispatching / master station, commonly used for sampling, uploading and event recording of electrical quantities such as bus voltage and each feedback line current in the station, and cooperating to complete functions such as alarm, wave recording, fault positioning and linkage control. With the increase of station scale and the number of access circuits, a distributed architecture gradually appears in engineering, that is, an independent sampling and processing unit is configured for each interval, and a public unit is used to gather multi-interval data and communicate externally. Under this architecture, whether the cross-interval data can be aligned on the same time axis directly affects the event review, fault wave splicing, and the reliability of the joint judgment of multi-source data by protection and automation algorithms.

[0003] In the prior art, there are many improvement schemes for the distributed station terminal and the power distribution automation system from the aspects of system reliable operation and communication coordination. For example, the patent document "Parameter pushing method of distributed intelligent power distribution terminal system" (publication number CN110336380A) proposes a distributed terminal parameter issuing, initialization synchronization and rollback mechanism composed of a management unit and a front-end unit to solve the parameter synchronization difficulty problem when the online state of the front-end unit is unknown. Another patent document "Power distribution network adaptive differential protection self-healing method based on wireless communication" (publication number CN113452000A) proposes to synchronize multi-terminal data in the wireless communication scenario by combining global time synchronization and interpolation synchronization, which is used for data consistency guarantee of differential protection and self-healing control.

[0004] However, there is still a more subtle and difficult pain point in the engineering landing of the station terminal: the distributed interval units often sample independently and carry local time labels. Even if the sampling period is consistent, the time axis will still be misaligned due to local clock drift, startup phase difference, communication buffering and message queuing, resulting in millisecond-level or even larger misalignment of the same physical event records in different intervals. This misalignment cannot be seen within a single interval, but once the bus voltage and multi-feedback line current are correlated and analyzed under the same time reference, waveform peak matching, transient sequence reversal, and false phase difference after splicing will occur, which will affect the reliability of switching event, disturbance source positioning, and abnormal waveform review. SUMMARY

[0005] The technical problem to be solved by the present application is to provide a distributed DTU station terminal system based on linear interpolation method to solve the problems mentioned in the background.

[0006] To achieve the above object, the present application adopts the following technical solutions: A distributed DTU station terminal system based on linear interpolation method, comprising a plurality of interval units and a common unit; The interval unit is configured to collect bus voltage sampling sequence and feeder current sampling sequence carrying original sampling time labels and send them to the common unit; The common unit includes an event determination module, a time difference calculation module, and a data synchronization reconstruction module; The event determination module is configured to identify the local switching event time of each interval unit based on the capacitor switching feature in the bus voltage sampling sequence; The time difference calculation module is configured to calculate the time alignment offset of each interval unit relative to the reference time of the whole station according to the local switching event time; The data synchronization reconstruction module is configured to shift the original sampling time label of each interval unit using the time alignment offset to obtain the aligned sampling point position of each interval unit, and combine the aligned sampling point positions of all interval units to construct a total set of sampling points; The data synchronization reconstruction module is also configured to, for any one interval unit, if it lacks data at a certain time position in the total set of sampling points, use the original sampling data of the interval unit to perform linear interpolation to fill in the missing data.

[0007] Preferably, the common unit is preloaded with a switching feature frequency band library, which stores target frequency bands determined based on historical capacitor switching waveforms; the event determination module is configured to filter the bus voltage sampling sequence for the target frequency band to extract an energy envelope sequence, and mark the time corresponding to the sampling point with the largest value in the energy envelope sequence as a rough event time.

[0008] Preferably, the event determination module is also configured to intercept the energy envelope sequence near the rough event time as a feature fingerprint segment, and generate a set of fine-tuning candidate offsets.

[0009] Preferably, the time difference calculation module is configured to designate one of the interval units as a reference interval unit, set its feature fingerprint segment as a reference waveform, and set the feature fingerprint segments of the remaining interval units as test waveforms; the time difference calculation module determines the fine alignment deviation of each interval unit by shifting and matching the test waveforms relative to the reference waveform.

[0010] Preferably, the specific manner of the translation matching is: after the to-be-tested waveform is respectively applied to each fine adjustment candidate offset, the waveform coincidence degree of the translated to-be-tested waveform and the reference waveform is calculated, and the fine adjustment candidate offset corresponding to the optimal waveform coincidence degree is determined as the fine alignment deviation.

[0011] Preferably, the waveform coincidence degree is obtained in the following manner: curve fitting processing is respectively performed on the reference waveform and the to-be-tested waveform to construct continuous reference characteristic curves and to-be-tested characteristic curves; the definite integral of the absolute value of the difference between the reference characteristic curves and the translated to-be-tested characteristic curves in the coincident time region is calculated, wherein the optimal waveform coincidence degree means that the calculation result of the definite integral is minimum.

[0012] Preferably, the local switching event time is obtained by adding the coarse event time and the fine alignment deviation; and the time difference calculation module calculates the time alignment offset in the following manner: calculating the difference between the local switching event time of the to-be-synchronized unit and the total station reference time.

[0013] Preferably, the time difference calculation module determines the total station reference time in the following manner: directly setting the local switching event time of the reference interval unit as the total station reference time.

[0014] Preferably, the reference interval unit is determined in the following manner: calculating the pairwise optimal waveform coincidence degree between the characteristic fingerprint segments of each interval unit of the total station, obtaining the optimal waveform coincidence degree sum of each interval unit and all other interval units; and determining the interval unit with the maximum optimal waveform coincidence degree sum as the reference interval unit.

[0015] Preferably, the linear interpolation completion includes: finding the nearest two original sampling points before and after the missing data time as interpolation reference points; calculating the time distance between the missing data time and the previous interpolation reference point, and determining the ratio of the time distance to the fixed sampling period as a weight coefficient; calculating the amplitude difference of the two interpolation reference points, multiplying the amplitude difference by the weight coefficient to obtain an amplitude adjustment amount, and adding the amplitude adjustment amount to the amplitude of the previous interpolation reference point to obtain the sampling value at the missing data time. The fixed sampling period is the time difference between the nearest two original sampling points before and after the missing data time.

[0016] The advantages of the present application over the prior art are that the scheme makes full use of the strong synchronism and strong characteristic law of capacitor switching events on the station busbar side. Capacitor switching usually occurs at the busbar or a position very close to the busbar, and the busbar voltage transient fluctuation caused by the switching will quickly spread along the electrical connection within the station, so that multiple intervals will observe the same type of disturbance almost simultaneously in their respective busbar voltage sampling sequences. The disturbance not only appears at a concentrated time, but also has stable event fingerprint characteristics in terms of frequency band distribution, energy envelope shape, etc., which is obviously different from daily load variation, power frequency background and general noise, and is therefore more suitable as a natural anchor point for station alignment. Compared with the way of relying on external timing or relying on manual experience to select points, using the characteristic of switching fluctuation for event detection can obtain more stable event time recognition results under the conditions of noise, amplitude difference and inconsistent measurement link, thereby providing a reliable basis for high-precision time alignment.

[0017] On this basis, the system identifies the local switching event time from the busbar voltage sampling sequences sent by each interval through the common unit, and calculates the time alignment offset of each interval relative to the station reference time according to the local switching event time. The original sampling time labels of each interval are shifted, the multi-interval sampling point positions are projected onto the same time axis, and the aligned sampling point positions are combined to construct a total set of sampling points. Since the alignment offset is directly derived from the common observation of the same physical event in multiple intervals, the aligned busbar voltage and feeder current sequences can achieve higher consistency in key positions such as transient start, peak and decay, significantly reducing the problems of false phase difference, incorrect sequence judgment, and waveform mismatch after cross-interval splicing, and improving the reliability of event review, wave recording splicing and multi-source correlation analysis.

[0018] To simultaneously consider engineering efficiency and alignment accuracy, the system adopts a two-stage strategy of "coarse positioning + fine micro-alignment". First, the pre-set switching characteristic frequency band is used to filter the busbar voltage sequence and extract the energy envelope, and the sampling point with the most significant energy in the whole sequence is quickly locked as the coarse event time, avoiding high-overhead point-by-point fine matching on long-time data, thereby ensuring processing efficiency and real-time performance. Subsequently, only the characteristic fingerprint segment of the short window near the coarse event time is intercepted, and a group of fine adjustment candidate offsets is generated. The relative reference waveform is shifted and matched in a small range, and the fine alignment deviation is determined by waveform coincidence degree optimization. This way concentrates computing resources on the position where the event is most likely to occur and the local segment that needs fine alignment the most, achieving high-precision alignment under controllable computational load, so that the system can quickly complete station alignment and improve the coincidence degree of multi-interval waveforms at the millisecond level or even finer granularity.

[0019] In actual station operation, capacitor switching belongs to the routine operation event of voltage and reactive power management, which can be triggered by automatic voltage control strategy or executed by operation and maintenance in voltage regulation, load fluctuation or power factor adjustment, so it is not a small probability fault, but a natural event that occurs periodically in the process of normal operation of power grid. Since the event occurs usually near the bus and has common visibility to multiple intervals, its transient fluctuation is highly synchronized in time and has stable characteristics in form, which makes it naturally suitable as an anchor point for full-station time alignment. Using this natural event for alignment, the alignment basis can be automatically extracted from existing operation data without introducing artificial additional time service operation and relying on additional time service equipment modification, so as to realize lower-cost and easier-to-deploy multi-interval high-precision synchronization alignment.

[0020] In addition, the scheme further enhances the usability in data reconstruction after alignment. By constructing the total set of sampling points and identifying the missing data at certain time positions of each interval, and then using linear interpolation to fill in the missing points, it can avoid the breakpoints on the merged time axis, which leads to the failure of subsequent algorithm calculation or misjudgment, and the interpolation process based on adjacent sampling points and fixed sampling period forms an interpretable and reproducible repair result, which is convenient for engineering test, traceability and consistency verification. The determination of the reference interval unit introduces the selection mode of the full-station two-by-two optimal waveform coincidence degree summary, which can automatically select a more representative reference source when the signal-to-noise ratio and link conditions of multiple intervals are inconsistent, reduce the instability caused by manual designation, and further improve the accuracy and consistency of the full-station alignment result. BRIEF DESCRIPTION OF DRAWINGS

[0021] Figure 1 is the overall physical architecture schematic diagram of the distributed DTU station terminal system provided by the embodiment of the present application, which shows the topology structure of connecting multiple interval units to the public unit through the communication network; Figure 2 is the overall logic flowchart of the system for data processing in the embodiment of the present application, which covers the whole process from data acquisition, event identification to data interpolation and filling; Figure 3 is the principle schematic diagram of extracting the rough event time based on capacitor switching characteristics in the embodiment of the present application, which shows the process from original waveform filtering to energy envelope extraction; Figure 4 is the schematic diagram of the feature fingerprint segment interception method in the embodiment of the present application, which shows the process of determining the window on the energy envelope sequence and extracting the feature data; Figure 5 is the principle schematic diagram of determining the fine alignment deviation based on waveform translation matching in the embodiment of the present application, which shows the process of translation comparison between the to-be-tested waveform and the reference waveform; Figure 6is a schematic diagram of the forming process of the total set of sampling points of the application; Figure 7 is a schematic diagram of the geometric principle of the linear interpolation method for filling in missing data in the embodiment of the application, which shows the mathematical logic of calculating the sampling value at the target time using the front and rear reference points and their weights. DETAILED DESCRIPTION

[0022] The specific embodiments of the application will be described below with reference to the accompanying drawings.

[0023] As shown in Figure 1 , the distributed DTU station terminal system provided by the application includes multiple interval units and a common unit. Each interval unit is deployed at a corresponding feeder interval or a measurement and control interval, and is used to collect electrical quantities on site and send them to the common unit through a communication network. The purpose of adopting this distributed form is to complete on-site sampling close to primary equipment, reduce interference caused by long-distance analog quantity lead lines, and at the same time, to complete data alignment, reconstruction and external services at the station level by the common unit, so as to balance the convenience of field arrangement and the processing ability of station consistency.

[0024] In one embodiment, the interval unit at least has a voltage sampling channel and a current sampling channel, and its collection objects include a bus voltage sampling sequence and a feeder current sampling sequence carrying original sampling time labels. The original sampling time label is used to describe the sampling time of each sampling point under the local clock of the interval unit. This label can be generated by an internal timer of the interval unit, or can be obtained by jointly converting sampling interrupt counting and a fixed sampling period. Since the local clocks of the interval units exist independently, even if the sampling periods are consistent, the time axes between different intervals may not be consistent due to start phase difference, clock drift, cache queuing and communication jitter, so it is necessary to send the original sampling time label together with the sampling data, so that subsequent unified correction can be performed on it at the common unit side.

[0025] As shown in Figure 2 , the common unit includes an event judgment module, a time difference calculation module and a data synchronization reconstruction module. The event judgment module is used to identify the capacitor switching event from the bus voltage sampling sequence sent by each interval unit and obtain the local switching event time. The reason for selecting the capacitor switching as the alignment anchor point is that the capacitor switching belongs to the normal operation event in the operation of the station, which is commonly used for reactive power compensation and voltage support, and its occurrence position is usually close to the bus and can present highly synchronized transient disturbance in the bus voltage sampling sequence of multiple intervals, so the multi-interval alignment can be completed directly using the naturally occurring events in the operation process without increasing artificial additional timing operation and without relying on additional time-setting hardware modification.

[0026] As shown in Figure 3As shown, in a further embodiment, the common unit has a pre-set switching characteristic frequency band library, which stores target frequency bands determined based on historical capacitor switching waveforms. The target frequency band is set to highlight the difference between switching disturbances and the power frequency background and random noise, making event identification more stable. The target frequency band can be extracted as follows: during the closing or opening operation of the station's switching device, transient segments of the bus voltage are extracted from at least 2 to 20 historical waveform recordings. Spectral analysis is performed on each segment, and the frequency band where the energy peak is located is statistically analyzed. Then, the intersection of multiple statistical results or the frequency band with an occurrence frequency higher than a threshold is taken as the target frequency band. The occurrence frequency threshold can be a ratio between 0.6 and 0.9 to ensure that the target frequency band includes the common components of most switching events without excessively introducing frequency bands unrelated to switching. If there are many types of capacitor banks in the station, sub-frequency band entries can also be established for different groups, and the corresponding entry can be selected according to the current switching object during operation. To accommodate different sampling rates, the target frequency band in the frequency band library can be represented by normalized frequency or by Hz and converted to discrete filter parameters according to the sampling rate.

[0027] In the implementation of the event determination module, the bus voltage sampling sequence can be filtered for the target frequency band to extract the energy envelope sequence, and the time corresponding to the sampling point with the largest value in the energy envelope sequence is marked as the coarse event time. The filter can be implemented using a finite impulse response filter or an infinite impulse response filter, and the filter order can be selected based on real-time requirements and filter sharpness, with a commonly used range of 16 to 256. The energy envelope sequence can be obtained by taking the absolute value of the filtered sequence and performing a moving mean square or moving integral. The sliding window length can be taken as 1 to 20 sampling periods to avoid misjudgment caused by occasional spikes, while maintaining sensitivity to switching transients. The coarse event time, as the result of the first step of localization, reduces the search problem across the entire sequence to a local fine alignment problem, thereby ensuring overall processing efficiency.

[0028] like Figure 4As shown in the further embodiment, the event determination module will also intercept the energy envelope sequence near the rough event time as a feature fingerprint segment, and generate a set of fine-tuning candidate offsets. The interception window of the feature fingerprint segment can be set as a symmetric window or an asymmetric window around the rough event time. The symmetric window is suitable for scenarios where the shapes before and after the switching transient are relatively balanced, and the asymmetric window is suitable for scenarios where the rising edge of the switching transient is more critical. The window length can take the number of sampling points corresponding to 0.5 cycles to 5 cycles to ensure that the main energy change process of the switching disturbance is included, while avoiding the introduction of other running disturbances by a too long window. The setting of the fine-tuning candidate offset is used to realize a limited search for fine alignment, and its value range can cover the expected interval time offset, for example, when the sampling period is 1 ms, the fine-tuning candidate offset can be set to an integer millisecond set from -20 ms to +20 ms, or an integer sampling point set from -10 sampling points to +10 sampling points. If the field communication jitter is larger, it can also be expanded to -100 ms to +100 ms, but the increase of the candidate set will increase the matching calculation amount, so it can be selected in combination with the station size and real-time requirements.

[0029] As shown in the further embodiment, the time difference calculation module will designate one of the interval units as a reference interval unit, set its feature fingerprint segment as a reference waveform, and set the feature fingerprint segments of the remaining interval units as test waveforms. The fine alignment deviation of each interval unit is determined by performing translational matching of the test waveforms relative to the reference waveform. The purpose of this design is to utilize the synchronization of the switching disturbance in multiple intervals to convert the originally incomparable local time label alignment problem into a waveform alignment problem, and to deduce the time deviation from the alignment result. Figure 5 During translational matching, after applying each fine-tuning candidate offset to the test waveform, the waveform coincidence degree of the translated test waveform and the reference waveform is calculated, and the fine-tuning candidate offset corresponding to the optimal waveform coincidence degree is determined as the fine alignment deviation. This limited candidate set search method avoids the instability brought by continuous domain optimization, and also facilitates deterministic calculation and time overhead estimation on embedded public units.

[0030] In one embodiment, the waveform coincidence degree is obtained by performing curve fitting processing on the reference waveform and the test waveform to construct continuous reference feature curves and test feature curves, and then calculating the definite integral of the absolute value of the difference between the reference feature curves and the translated test feature curves in the coincident time region. In some embodiments, the waveform coincidence degree can be represented by the negative value of the definite integral, so the minimum definite integral calculation result corresponds to the optimal waveform coincidence degree. As a convenient understanding limit case, if two waveforms are completely coincident, the difference between them is 0, and the corresponding definite integral is 0, so the waveform coincidence degree is high.

[0031]

[0032] ​The purpose of curve fitting is to weaken the sawtooth effect caused by sampling point dispersion, so that the coincidence evaluation pays more attention to the overall consistency of the shape rather than the single point error. Curve fitting can be achieved by polynomial fitting, spline fitting or piecewise linear fitting. To ensure real-time performance and numerical stability, the polynomial order can be 2 to 5, the spline fitting can be quadratic or cubic spline, and the piecewise linear fitting can directly connect the sampling points to form a continuous curve. Definite integral can be realized by numerical integration, for example, using trapezoidal integration method to accumulate the absolute value of the difference multiplied by the time step point by point in the coincidence time region. The coincidence time region is determined by the common time coverage interval of the reference waveform and the shifted waveform to be measured, avoiding the evaluation bias caused by boundary missing.

[0033] In further embodiments, the local switching event time is obtained by adding the coarse event time and the fine alignment deviation. The reason for doing so is that the coarse event time provides a quick estimate of the event position, and the fine alignment deviation makes small corrections to the coarse positioning result, so that the final event time is both stable and has higher accuracy. The time difference calculation module calculates the time alignment offset by calculating the difference between the local switching event time of the unit to be synchronized and the reference time of the total station. The determination of the reference time of the total station can be achieved by directly setting the local switching event time of the reference interval unit as the reference time of the total station, which can avoid introducing additional external time source, reduce the deployment threshold, and make the total station alignment start at the observation time of the same physical event in a reference interval.

[0034] In further embodiments, the reference interval unit is not fixedly specified, but is automatically determined by the optimal waveform coincidence degree between the feature fingerprint segments of each interval unit of the total station. The specific method is to perform the above fine adjustment candidate offset search on the feature fingerprint segments of any two intervals to obtain the optimal waveform coincidence degree between them, and sum the optimal waveform coincidence degrees between the same interval and all other intervals. The interval unit with the maximum sum result is determined as the reference interval unit. The purpose of this design is to preferentially select the interval with the strongest consistency with other intervals as the reference source, thereby reducing the influence of individual interval noise, abnormal measurement link or transient response distortion, and making the total station alignment result more robust.

[0035] The data synchronization reconstruction module translates the original sampling time labels of each interval unit by the time alignment offset, thereby obtaining the aligned sampling point positions of each interval unit, and combines the aligned sampling point positions of multiple interval units to construct a total set of sampling points. During the translation, the time label of each original sampling point can be directly added or subtracted by the corresponding time alignment offset, so that the sampling point is mapped to the unified time axis of the total station; when the original sampling time label is expressed by a sampling point index, the time alignment offset can be first converted into a sampling point offset, and then the sampling point index is translated. The total set of sampling points is formed by the sampling point positions of each interval unit on the unified time axis, which can be sorted in ascending order of time and de-duplicated to form a consistent set of time positions of the total station, which is used for subsequent splicing and comparison of multi-interval data under the same time coordinate.

[0036] As shown in Figure 6 It should be noted that the total set of sampling points is the union of the sampling point positions of multiple interval units on the unified time axis, and it is not required that each interval unit naturally exists a sampling point at each time position in the union. Due to the discrete differences in the sampling starting point, clock bias correction and landing position after translation of different interval units, it is common that a sampling point exists at a time position in a certain interval unit, while the corresponding sampling point does not exist at the time position in another interval unit. In order to facilitate the alignment and comparison of multi-interval data under the same time coordinate, the data synchronization reconstruction module will use the linear interpolation of the adjacent original sampling data of any interval unit to fill in the missing sampling value when it lacks the corresponding sampling value at a time position in the total set of sampling points, thereby generating a sampling value for comparison at the time position.

[0037] As shown in Figure 7As shown, the implementation mode of linear interpolation padding is to find the two nearest original sampling points before and after the missing data time as interpolation reference points in the original sampling data of the interval unit, calculate the time distance between the missing data time and the previous interpolation reference point, and determine the ratio of the time distance to the fixed sampling period as the weight coefficient, then calculate the amplitude difference of the two interpolation reference points, multiply the amplitude difference by the weight coefficient to obtain the amplitude adjustment amount, and add the amplitude adjustment amount to the amplitude of the previous interpolation reference point to obtain the sampling value at the missing data time. The fixed sampling period is given by the interval unit sampling configuration, and the common value range is 0.25ms to 5ms. In order to avoid the deviation of the interpolation result from the true waveform when the span of adjacent sampling points is too large, the maximum span of interpolation can be limited in an embodiment, for example, when the time span between the current and the next interpolation reference point exceeds 5 sampling periods, no interpolation result is generated and it is marked as unpadable. Through the above mode, the multi-interval data can have consistent data form that can be directly compared at the unified time position corresponding to the total set of sampling points without changing the original sampling mechanism of each interval unit.

[0038] Through the above embodiments, the application realizes high-precision alignment of multi-interval sampling data without relying on manual additional timing operation, and through the total set of sampling points after alignment, the continuity and availability of the total station data on the unified time axis are ensured through linear interpolation padding, thereby providing a stable and reliable data basis for station-level event review, wave recording splicing and multi-source joint analysis.

[0039] The above is only a preferred specific embodiment of the application, but the protection scope of the application is not limited thereto, any person skilled in the art can make equivalent replacement or change according to the technical solution and inventive concept of the application within the technical range disclosed by the application, which should be covered within the protection scope of the application.

Claims

1. A distributed DTU station terminal system based on linear interpolation, characterized in that, It includes multiple interval units and one common unit; The interval unit is configured to acquire bus voltage sampling sequences and feeder current sampling sequences carrying original sampling time tags and send them to the common unit; The common unit includes an event determination module, a time difference calculation module, and a data synchronization and reconstruction module; The event determination module is configured to identify the local switching event time of each interval unit based on the capacitor switching characteristics in the bus voltage sampling sequence. The time difference calculation module is configured to calculate the time alignment offset of each interval unit relative to the global reference time based on the local switching event time. The data synchronization and reconstruction module is configured to use the time alignment offset to shift the original sampling time label of each interval unit to obtain the aligned sampling point position of each interval unit, and merge the aligned sampling point positions of all interval units to construct a total set of sampling points. The data synchronization and reconstruction module is also configured to perform linear interpolation to fill in missing data at a certain time position of any interval unit if the missing data is at a certain time position of the total set of sampling points.

2. The system according to claim 1, characterized in that, The common unit is pre-set with a switching characteristic frequency band library, which stores target frequency bands determined based on historical capacitor switching waveforms; the event determination module is configured to perform filtering processing on the bus voltage sampling sequence for the target frequency band to extract the energy envelope sequence, and mark the time corresponding to the sampling point with the largest value in the energy envelope sequence as the coarse event time.

3. The system according to claim 2, characterized in that, The event determination module is also configured to extract the energy envelope sequence near the coarse event time as a feature fingerprint fragment and generate a set of fine-tuning candidate offsets.

4. The system according to claim 3, characterized in that, The time difference calculation module is configured to designate one of the interval units as the reference interval unit, set its feature fingerprint segment as the reference waveform, and set the feature fingerprint segments of the remaining interval units as the waveforms to be tested; the time difference calculation module determines the fine alignment deviation of each interval unit by translating and matching the waveform to be tested relative to the reference waveform.

5. The system according to claim 4, characterized in that, The specific method of translation matching is as follows: after applying each of the fine-tuning candidate offsets to the waveform to be tested, the waveform overlap between the translated waveform to be tested and the reference waveform is calculated, and the fine-tuning candidate offset corresponding to the optimal waveform overlap is determined as the fine alignment deviation.

6. The system according to claim 5, characterized in that, The waveform overlap is obtained by performing curve fitting on the reference waveform and the waveform to be tested respectively to construct a continuous reference feature curve and a waveform to be tested feature curve; calculating the definite integral of the absolute value of the difference between the reference feature curve and the translated waveform to be tested in the overlapping time region, wherein the optimal waveform overlap means that the calculation result of the definite integral is minimized.

7. The system according to claim 4, characterized in that, The local switching event time is obtained by adding the coarse event time and the fine alignment deviation; the time difference calculation module calculates the time alignment offset by calculating the difference between the local switching event time of the unit to be synchronized and the global reference time.

8. The system according to claim 4, characterized in that, The time difference calculation module determines the station's reference time by directly setting the local switching event time of the reference interval unit as the station's reference time.

9. The system according to claim 6, characterized in that, The method for determining the reference interval unit is as follows: calculate the pairwise optimal waveform overlap between the characteristic fingerprint segments of each interval unit in the entire station, and obtain the sum of the optimal waveform overlap of each interval unit with all other interval units; the interval unit with the largest sum of the optimal waveform overlap is determined as the reference interval unit.

10. The system according to claim 1, characterized in that, The linear interpolation completion includes: finding the two closest original sampling points before and after the missing data time as interpolation reference points; calculating the time distance between the missing data time and the previous interpolation reference point, and determining the ratio of the time distance to a fixed sampling period as a weighting coefficient; calculating the amplitude difference between the two interpolation reference points, multiplying the amplitude difference by the weighting coefficient to obtain the amplitude adjustment amount, and adding the amplitude adjustment amount to the amplitude of the previous interpolation reference point to obtain the sampling value at the missing data time.

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