Traveling wave characteristic-based distribution feeder fault intelligent research and judgment method and device

Abstract

The invention discloses an intelligent research and judgment method and device for a distribution feeder fault based on traveling wave characteristics. In the operation process of the power distribution feeder line, forming time sequence original sampling data based on a unified time service reference, determining a traveling wave disturbance event and generating candidate traveling wave data; constructing a traveling wave time sequence consistency criterion based on the corresponding relationship between the traveling wave head first arrival time and the unified time service reference, and screening to obtain an effective traveling wave feature set; performing correlation analysis on the effective traveling wave feature set and historical operation feature data of the power distribution feeder to form a target traveling wave feature set; according to the time difference relation between the first arrival time of the wave head of the traveling wave in the target traveling wave feature set, the traveling wave propagation distance is calculated to determine the fault occurrence position, meanwhile, the polarity direction of the traveling wave and the amplitude variation of the traveling wave are integrated, the fault type and grounding characteristics are researched and judged, and a fault research and judgment result is output. According to the invention, the accuracy and reliability of power distribution feeder fault positioning and type identification can be effectively improved.

Description

Technical Field

[0001] This invention relates to the field of power distribution feeder fault technology, and in particular to a method and device for intelligent fault assessment of power distribution feeders based on traveling wave characteristics. Background Technology

[0002] In existing power distribution network operation and maintenance practices, distribution feeder fault assessment technology mainly revolves around the changing characteristics of electrical quantities such as current and voltage. Fault detection and location are usually achieved through steady-state quantity analysis, transient quantity analysis, or a combination of both. Some technical solutions introduce traveling wave signals as the analysis object. By collecting and processing the electromagnetic traveling wave that propagates along the line at the moment of fault occurrence, information related to the fault location is obtained. Related systems mostly rely on time synchronization devices to synchronize the data at monitoring points and combine them with line parameters to calculate the fault distance. This technology has been applied to some extent in the fields of power distribution automation and fault location.

[0003] However, existing fault assessment technologies for distribution feeders based on traveling waves still have problems in practical applications, such as difficulty in effectively distinguishing traveling wave disturbance events from non-fault transient disturbances, difficulty in accurately ensuring the consistency of traveling wave characteristic timing, insufficient utilization of historical operating characteristics, and limited ability to conduct collaborative assessment of multi-dimensional traveling wave characteristics. These problems affect the accuracy of fault location and the reliability of fault type identification in complex operating environments.

[0004] Therefore, it is necessary to propose a new technical solution to further improve the accuracy and engineering applicability of power distribution feeder fault diagnosis. Summary of the Invention

[0005] This application provides a method and device for intelligent fault assessment of power distribution feeders based on traveling wave characteristics, so as to improve the accuracy and reliability of fault location and type identification of power distribution feeders.

[0006] This application provides an intelligent fault assessment method for distribution feeders based on traveling wave characteristics, including: During the operation of the power distribution feeder, the voltage and current signals at at least one monitoring point of the power distribution feeder are synchronously collected, and time-series raw sampling data are generated based on a unified timing reference. When a transient disturbance exceeding a preset threshold appears in the raw sampling data, the traveling wave disturbance event is identified and candidate traveling wave data is formed. For candidate traveling wave data, the first arrival time of the traveling wave wavehead, the change in traveling wave amplitude, and the polarity direction of the traveling wave are extracted. Based on the correspondence between the first arrival time of the traveling wave wavehead and the unified timing reference, a traveling wave timing consistency criterion is constructed, and a set of effective traveling wave features is obtained by filtering accordingly. The effective traveling wave feature set is correlated with the historical operating feature data of the distribution feeder. Based on the historical distribution characteristics of the traveling wave amplitude change and the traveling wave polarity direction, the traveling wave features corresponding to non-fault disturbances are eliminated to form the target traveling wave feature set. Based on the time difference between the first arrival times of the traveling wave front in the target traveling wave feature set, and combined with the line parameters of the distribution feeder, the traveling wave propagation distance is calculated and the fault location is determined. Based on determining the location of the fault, the fault type and grounding characteristics of the distribution feeder are analyzed by comprehensively considering the polarity direction and amplitude variation of the traveling wave in the target traveling wave characteristic set, and the fault analysis results are output.

[0007] The beneficial effects of this application mainly include: (1) By synchronously acquiring the voltage and current signals of the distribution feeder under a unified time reference and triggering the identification of traveling wave disturbance events by transient disturbances, traveling wave information can be captured in a timely manner at the initial stage of a fault, effectively avoiding the distortion of traveling wave characteristics caused by the mixing of data with different time scales, and improving the timing accuracy and reliability of traveling wave analysis from the source. (2) By extracting the first arrival time of the traveling wave wavehead, the change in traveling wave amplitude, and the polarity direction of the traveling wave, and constructing a traveling wave timing consistency criterion based on the correspondence between the first arrival time of the traveling wave wavehead and the unified time reference, the candidate traveling wave data can be screened, which can effectively suppress the pseudo traveling wave characteristics introduced by factors such as measurement noise and external disturbances, and improve the effectiveness and stability of the traveling wave feature set. (3) By correlating the effective traveling wave feature set with the historical operating feature data of the distribution feeder, and combining the historical distribution features of the traveling wave amplitude change and the traveling wave polarity direction to eliminate the traveling wave features corresponding to non-fault disturbances, the historical operating information can be fully utilized to distinguish fault traveling waves from non-fault transient disturbances, significantly reducing the probability of misjudgment and improving the accuracy of fault identification. (4) By calculating the traveling wave propagation distance based on the time difference relationship between the first arrival time of the traveling wave wavehead in the target traveling wave feature set to determine the location of the fault, and further integrating the traveling wave polarity direction and the traveling wave amplitude change to judge the fault type and grounding characteristics, the coordinated output of fault location and fault attribute identification is realized, which is conducive to improving the completeness of the fault judgment results of the distribution feeder and its engineering practical value. Attached Figure Description

[0008] Figure 1 This is a flowchart of a method for intelligent fault assessment of power distribution feeders based on traveling wave characteristics, provided in the first embodiment of this application.

[0009] Figure 2 This is a schematic diagram of a power distribution feeder fault intelligent judgment device based on traveling wave characteristics provided in the second embodiment of this application. Detailed Implementation

[0010] Many specific details are set forth in the following description to provide a full understanding of this application. However, this application can be implemented in many other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of this application; therefore, this application is not limited to the specific embodiments disclosed below.

[0011] The first embodiment of this application provides an intelligent fault assessment method for distribution feeders based on traveling wave characteristics. Please refer to... Figure 1 This figure is a flowchart of the first embodiment of this application. The following is in conjunction with... Figure 1 The first embodiment of this application provides a detailed description of an intelligent fault assessment method for power distribution feeders based on traveling wave characteristics.

[0012] Step S101: During the operation of the power distribution feeder, the voltage and current signals at at least one monitoring point of the power distribution feeder are synchronously acquired, and time-series raw sampling data is generated based on a unified timing reference. When a transient disturbance exceeding a preset threshold appears in the raw sampling data, the traveling wave disturbance event is determined and candidate traveling wave data is formed.

[0013] In this invention, step S101 is the starting point of the entire intelligent fault assessment process for distribution feeders based on traveling wave characteristics. Its function is to reliably and completely acquire the original basic data that can reflect the transient electromagnetic behavior of the distribution feeder without relying on human intervention, and to initially identify traveling wave disturbance events that may be related to the fault at the data level, so as to provide reliable data input for subsequent traveling wave feature extraction and fault assessment.

[0014] Specifically, when the power distribution feeder is in normal operation, the system first synchronously acquires voltage and current signals at at least one monitoring point on the feeder. These monitoring points can be measuring devices located at the beginning, end, intermediate nodes, branch nodes, or critical load nodes of the feeder, and their locations can be configured according to the line length, topology, and operational requirements. Synchronous acquisition of voltage and current signals means sampling the voltage and current quantities at the same monitoring point in parallel under the same time reference, avoiding phase deviations between the voltage and current traveling wave characteristics caused by inconsistent sampling times. To achieve this synchronous acquisition, a high-precision analog-to-digital converter is typically configured within the monitoring point, and the hardware or underlying driver layer ensures that the voltage and current channels are sampled at the same sampling time.

[0015] During synchronous data acquisition, all sampling activities are time-stamped based on a unified time reference. The unified time reference refers to a unified time reference source provided for the entire power distribution feeder monitoring system. This time reference source can originate from a satellite time synchronization system, a network time synchronization system, or a highly stable local clock calibration mechanism. Its core purpose is to ensure that the sampling data from different monitoring points are timestamped on the same time scale. Through this unified time reference, the voltage and current sampling values ​​generated by each sampling point are assigned a clear and comparable time identifier, thus forming time-series raw sampling data arranged strictly in chronological order. The time-series raw sampling data mentioned here refers to the raw discrete voltage and current sampling sequences that have not undergone traveling wave feature extraction, filtering, or discrimination processing. It retains both steady-state operating information and the instantaneous changes during transient disturbances.

[0016] During the continuous generation of the time-series raw sampling data, the system monitors the sampling data in real-time or near real-time to identify the presence of transient disturbances. Transient disturbances refer to sudden changes in voltage or current amplitude, rate of change, or waveform shape that occur within a very short time relative to the normal steady-state operation of the distribution feeder. These disturbances are typically associated with events such as short circuits, grounding, open circuits, and external impacts, but can also originate from non-fault factors, such as lightning strikes, electromagnetic interference, or load surges. To quantify transient disturbances, this step pre-sets at least one preset threshold. This threshold can be defined based on the magnitude of voltage or current change, the slope of change, the difference between adjacent sampling points, or a combination thereof. For example, in one implementation, the current change between two adjacent sampling points can be compared to their normal operating statistical average. When this change exceeds a certain multiple of the upper limit of the change under normal operating conditions, a transient disturbance is determined to have occurred.

[0017] When the system detects a transient disturbance in the raw sampled data exceeding the preset threshold, it considers a traveling wave disturbance event to have occurred near that time. The traveling wave disturbance event referred to here is the physical process of an electromagnetic traveling wave propagating along the line due to an electrical anomaly at a certain location in the distribution feeder, as reflected in the voltage and current signals at the monitoring point. For ease of subsequent analysis, the system does not directly process all the raw sampled data. Instead, it extracts a data segment from the raw sampled data around the detected transient disturbance time, containing a certain time window before and after the disturbance, forming candidate traveling wave data. This data segment contains continuous discrete sampled values ​​of the voltage or current signal of a specific phase at the corresponding monitoring point. The length of this time window can be set according to the sampling frequency and line length. For example, under microsecond or millisecond sampling conditions, a time range that can completely cover the traveling wave front and its main propagation characteristics can be selected, thus ensuring that the candidate traveling wave data contains both the traveling wave initiation characteristics and necessary background information.

[0018] Through the above process, step S101 ultimately achieves the synchronous acquisition of voltage and current signals at the monitoring point of the power distribution feeder under a unified time reference, the time-series organization of the original sampling data, and the automatic identification of transient disturbances exceeding the preset threshold. The data related to the disturbance are then organized into candidate traveling wave data, laying a clear, reliable, and repeatable foundation for the accurate extraction of traveling wave characteristics and fault assessment in subsequent steps.

[0019] Step S102: For candidate traveling wave data, extract the first arrival time of the traveling wave wavehead, the change in traveling wave amplitude, and the polarity direction of the traveling wave. Based on the correspondence between the first arrival time of the traveling wave wavehead and the unified timing reference, construct a traveling wave timing consistency criterion, and select an effective set of traveling wave features accordingly.

[0020] In this invention, step S102 is used to further analyze the physical features and screen the credibility of the candidate traveling wave data formed in step S101. Its core objective is to extract key features that can stably reflect the essential characteristics of traveling wave propagation from the candidate traveling wave data containing noise, background disturbances and various transient components, and to eliminate data that does not have the physical meaning of traveling waves or whose timing is unreliable through strict temporal consistency constraints, thereby forming an effective set of traveling wave features that can be used for subsequent fault location and judgment.

[0021] In practice, for each set of candidate traveling wave data, traveling wave feature extraction is first performed on the voltage and current signals respectively. The candidate traveling wave data typically exhibits a sudden change in voltage or current signal within a very short time; its essence is the electromagnetic abrupt change caused by the arrival of the traveling wave front at the monitoring point. The "first arrival time of the traveling wave front" refers to the moment when the traveling wave first arrives at the monitoring point during its propagation and produces a identifiable abrupt change in the voltage or current signal. To accurately determine this time point, first-order or higher-order differential processing can be performed on the candidate traveling wave data, or a short-time energy surge detection method can be used to locate the start of the abrupt change by comparing the change amplitude between adjacent sampling points. For example, in one implementation, a first-order differential operation can be performed on the current sampling sequence. When the differential value first continuously exceeds several times the background noise level, the corresponding sampling time is identified as the first arrival time of the traveling wave front. This time point is then combined with the timestamp recorded in the sampling data to form a traveling wave front first arrival time identifier accurate to the sampling period level.

[0022] While determining the arrival time of the traveling wave front, it is also necessary to extract the change in traveling wave amplitude. The change in traveling wave amplitude refers to the degree of amplitude change exhibited by the voltage or current signal within a very short time window before and after the arrival of the traveling wave front, reflecting the strength of the traveling wave energy and the fault excitation characteristics. Specifically, the calculation can be performed with the arrival time of the traveling wave front as the center, selecting a short period before arrival as a reference interval, calculating the average value or steady-state reference value of the signal within this interval, and then selecting a short period immediately following arrival as the traveling wave's effective period, calculating the peak value or average value of the signal within this interval. The difference between the two is the change in traveling wave amplitude. For example, if the steady-state average value of the current signal before the arrival of the traveling wave is I0, and the maximum instantaneous value occurring shortly after arrival is I1, then the change in current amplitude corresponding to this traveling wave can be expressed as the difference between I1 and I0 or its absolute value, thus achieving a quantitative characterization of the traveling wave intensity.

[0023] In addition, the polarity of the traveling wave must be determined simultaneously. The polarity of a traveling wave refers to the direction of change of the voltage or current signal relative to its original steady-state value when the wavefront reaches the monitoring point. It typically manifests as a positive surge or a negative drop in the signal. This polarity can be determined by comparing the sign of the signal change at the moment the wavefront arrives. For example, if the current signal shows a significant positive jump at the moment of arrival, the polarity of the traveling wave can be determined to be positive; conversely, if it shows a negative jump, it is determined to be negative. This polarity information is of significant reference value in subsequent judgments of fault type and grounding characteristics.

[0024] After extracting the first arrival time of the traveling wave front, the change in traveling wave amplitude, and the polarity direction of the traveling wave, this step further introduces a traveling wave timing consistency criterion to constrain the reliability of the extracted traveling wave features. The traveling wave timing consistency criterion refers to determining whether a traveling wave feature conforms to the physical timing law of traveling wave propagation along a distribution feeder, based on the correspondence between the first arrival time of the traveling wave front and a unified time reference. Specifically, since the sampling time of each monitoring point is based on the same time reference, theoretically, the first arrival time of the wave front of the same traveling wave event at different monitoring points should satisfy the sequential relationship of propagation along the line, and the arrival time difference between adjacent monitoring points should fall within a reasonable time range jointly defined by the line length and propagation speed. Therefore, when constructing the traveling wave timing consistency criterion, the first arrival time of a candidate traveling wave front can be compared with the unified time reference and correlated with the arrival times of other candidate traveling wave features in the same event. If the arrival time is significantly earlier or later than the physically achievable time interval, the traveling wave feature is considered to lack timing consistency.

[0025] By using the above criteria, spurious features caused by measurement noise, local interference, timing errors, or non-traveling wave transients can be effectively eliminated. For example, if the arrival time of a candidate traveling wave at a distant monitoring point is earlier than that at a nearby monitoring point, and this time difference exceeds the allowable propagation time range of the line, it can be determined that this feature does not conform to the basic physical laws of traveling wave propagation and should be eliminated. Constrained by this timing consistency criterion, only traveling wave features with reasonable time relationships and clear physical meanings under a unified timing reference are retained, ultimately forming a valid set of traveling wave features.

[0026] The effective traveling wave feature set refers to the set of traveling wave feature parameters obtained by extracting the first arrival time of the traveling wave wavehead, the change in traveling wave amplitude, and the polarity direction of the traveling wave from the candidate traveling wave data, and filtering them after passing through the traveling wave time series consistency criterion. These parameters are used to characterize the propagation characteristics of the same traveling wave disturbance event at different monitoring points. For example, during a transient traveling wave disturbance in a power distribution feeder, the first, second, and third monitoring points located at different positions on the feeder detect corresponding traveling wave disturbance events. After alignment with a unified timing reference, the arrival time of the traveling wave front at the first monitoring point is determined to be T1, the change in traveling wave amplitude is A1, and the polarity of the traveling wave is positive; the arrival time of the traveling wave front at the second monitoring point is T2, the change in traveling wave amplitude is A2, and the polarity of the traveling wave is positive; and the arrival time of the traveling wave front at the third monitoring point is T3, the change in traveling wave amplitude is A3, and the polarity of the traveling wave is negative. T1, T2, and T3 satisfy the propagation timing relationship of the traveling wave in the feeder and pass the traveling wave timing consistency criterion. Therefore, the arrival time of the traveling wave front, the change in traveling wave amplitude, and the polarity of the traveling wave corresponding to each of the above monitoring points together constitute the effective traveling wave feature set for this traveling wave disturbance event.

[0027] Through the detailed processing described above, step S102 not only achieves the accurate extraction of key physical features from candidate traveling wave data, but also systematically screens the reliability of traveling wave features by introducing strict traveling wave timing consistency criteria. This ensures that the set of traveling wave features entering the subsequent analysis stage has clear physical meaning and good consistency in terms of time, amplitude, and polarity, providing a solid data foundation for subsequent fault location and fault nature assessment.

[0028] Furthermore, for the candidate traveling wave data, the first arrival time of the traveling wave wavefront, the change in traveling wave amplitude, and the polarity direction of the traveling wave are extracted. Based on the correspondence between the first arrival time of the traveling wave wavefront and the unified timing reference, a traveling wave timing consistency criterion is constructed, and a valid set of traveling wave features is obtained by filtering accordingly, including: In the candidate traveling wave data, the traveling wave disturbance time corresponding to each monitoring point is time-aligned based on a unified time reference. The absolute time marker of the first arrival of the traveling wave wavehead at each monitoring point is determined, and an initial arrival time sequence reflecting the arrival order of the same traveling wave event at different monitoring points is constructed accordingly. Based on the initial arrival time sequence and the known spatial order relationship of each monitoring point in the power distribution feeder, the propagation direction of the traveling wave in the power distribution feeder is determined, and under the constraint of the propagation direction, a directed time difference sequence is constructed for the first arrival time difference of the traveling wave front between each monitoring point. For a directed time difference sequence, combined with the line parameters of the distribution feeder, it is determined whether the arrival time difference between adjacent monitoring points meets the minimum and maximum propagation time constraints of the traveling wave in the propagation direction, thereby forming the travel wave time realizability determination result. Only when the wave time feasibility determination result is valid, the corresponding wave amplitude change and wave polarity direction are further subjected to joint consistency verification with the directed time difference sequence to confirm the physical consistency between the wave amplitude change direction and the propagation direction. Wave features that pass the joint consistency verification are included in the set of valid wave features, while candidate wave features that fail the joint consistency verification are eliminated.

[0029] The purpose of this embodiment is to select traveling wave characteristics that truly reflect the physical process of power distribution feeder faults from candidate traveling wave data through strict time consistency and physical realizability constraints, so as to ensure that the data basis for subsequent fault location and judgment has clear, reliable and verifiable physical meaning.

[0030] Specifically, after candidate traveling wave data is generated during the operation of the power distribution feeder, the first step is to perform time alignment processing on the traveling wave disturbance times corresponding to each monitoring point under a unified time reference. The unified time reference here refers to a time source that provides a unified time reference for all monitoring points, such as satellite time synchronization or a high-precision network time synchronization system, ensuring that the timestamps obtained by different monitoring points during sampling are within the same time coordinate system. Time alignment processing refers to mapping traveling wave disturbance segments from different monitoring points onto the same time axis based on the timestamps recorded in each candidate traveling wave data, thereby eliminating time inconsistencies caused by equipment clock deviations or communication delays. After time alignment is completed, the absolute time marker of the first arrival of the traveling wave front at each monitoring point is determined. The first arrival time of the traveling wave front refers to the moment when the traveling wave first arrives at a monitoring point during propagation and causes a significant abrupt change in the voltage or current signal; this moment corresponds to a specific time value under the unified time reference. By arranging the absolute time markers of each monitoring point in chronological order, an initial arrival time sequence reflecting the arrival order of the same traveling wave event at different monitoring points is constructed. This sequence fully describes the propagation process of the traveling wave in the time dimension.

[0031] After obtaining the initial arrival time series, the propagation direction of the traveling wave in the distribution feeder is determined by further considering the known spatial order of the monitoring points along the feeder. The spatial order refers to the physical arrangement of the monitoring points along the feeder, for example, arranging them in ascending order of line mileage, with the feeder starting point as a reference. By comparing the initial arrival time series with the spatial order, it can be determined whether the traveling wave propagates from the start point to the end point or from the end point to the start point along the feeder, thus determining the propagation direction. After clarifying the propagation direction, the time difference of the first arrival of the traveling wave front between each monitoring point is calculated using this propagation direction as a constraint, and a directed time difference sequence is constructed according to the order of the propagation directions. The directed time difference sequence refers to the set of time differences between adjacent monitoring points arranged in the order of the actual propagation path of the traveling wave, given the known propagation direction. This time difference has a clear positive physical meaning and is used to characterize the propagation time of the traveling wave between adjacent monitoring points.

[0032] After obtaining the directed time difference sequence, it is combined with the line parameters of the distribution feeder to determine the physical feasibility of the traveling wave propagation process. The line parameters referred to here are a set of known parameters that reflect the propagation characteristics of the traveling wave in the distribution feeder, including at least the physical distance between adjacent monitoring points and the propagation speed of the traveling wave in the feeder. The propagation speed of the traveling wave can be calculated based on the inductance and capacitance parameters of the line, or it can be stored as a fixed parameter after calibration through historical tests. For example, in a certain distribution feeder, if the physical distance between two adjacent monitoring points is 500 meters, and the propagation speed of the traveling wave is calibrated to 200 meters per microsecond, then the minimum propagation time constraint between these two monitoring points can be determined as 2.5 microseconds. The maximum propagation time constraint is used to eliminate abnormal time differences that clearly do not conform to the propagation characteristics of the traveling wave; its value can be determined comprehensively based on the line parameters, sampling accuracy, and allowable error range. During implementation, for each adjacent time difference in the directed time difference sequence, it is determined whether it simultaneously satisfies the minimum propagable time constraint and the maximum propagable time constraint. When all adjacent time differences satisfy the constraint conditions, the time realizability determination result of the traveling wave is established; if any time difference does not satisfy the constraint conditions, it is determined that the candidate traveling wave does not have physical realizability.

[0033] Only when the wave travel time feasibility determination result is valid is the corresponding wave travel amplitude change and wave travel polarity direction further introduced into the joint consistency verification process. The wave travel amplitude change referred to here is the degree of amplitude change of the voltage or current signal within a short time window before and after the arrival of the wave front; its magnitude reflects the wave travel energy and fault excitation intensity. The wave travel polarity direction refers to the direction of signal change relative to the steady-state value at the instant the wave front arrives; it can manifest as a positive surge or a negative drop. Joint consistency verification involves comprehensively comparing the wave travel amplitude change and wave travel polarity direction with the propagation direction corresponding to the directed time difference sequence to confirm that the amplitude change trend and polarity change direction exhibited by the wave travel during propagation are physically consistent with the propagation direction. For example, when it is determined that the wave travels in the positive direction along the feeder, monitoring points closer to the fault point usually exhibit more significant amplitude changes, and their polarity change direction should match the expected change direction of that phase under the phase sequence relationship.

[0034] When a candidate traveling wave feature passes both the time feasibility test and the joint consistency check, it is considered consistent in terms of temporal relationship, spatial propagation relationship, and the physical meaning of amplitude and polarity, and is included in the set of valid traveling wave features. Conversely, candidate traveling wave features that fail the joint consistency check are deemed to lack a reliable physical interpretation basis and are eliminated. Through the above complete, coherent process with clearly defined implementation details, this invention ensures that the set of valid traveling wave features contains only feature data that conforms to the physical laws of traveling wave propagation, thereby providing a clear, reliable, and directly implementable data foundation for subsequent fault location calculation and fault type assessment.

[0035] The traveling wave feature in this invention refers to the characteristic information corresponding to the sudden change in voltage or current signal caused by the traveling wave propagating in the feeder and at the monitoring point when a transient electromagnetic disturbance occurs in the power distribution feeder. This information can characterize the propagation time relationship, energy change characteristics and directional attributes of the traveling wave. It includes at least the first arrival time of the traveling wave front, the amount of change in the traveling wave amplitude and the polarity direction of the traveling wave.

[0036] Step S103: Perform correlation analysis between the effective traveling wave feature set and the historical operating feature data of the distribution feeder. Based on the historical distribution characteristics of the traveling wave amplitude change and the traveling wave polarity direction, eliminate the traveling wave features corresponding to non-fault disturbances to form the target traveling wave feature set.

[0037] In this invention, step S103 aims to further introduce historical operating characteristic data of the distribution feeder based on the effective traveling wave characteristic set already obtained in step S102. This involves background constraints and statistical screening of the traveling wave characteristics to distinguish between traveling wave characteristics caused by actual faults and those caused by non-fault disturbances, ultimately forming a target traveling wave characteristic set with clear physical meaning and high correlation to the fault. The core idea of ​​this step is not solely based on the instantaneous characteristics of a single traveling wave event, but rather on comparative analysis of the long-term distribution patterns of traveling wave characteristics during historical operation, making the judgment results more consistent with the actual operating characteristics of the distribution feeder.

[0038] Specifically, the historical operational characteristic data refers to the set of traveling wave-related characteristic data collected and stored by similar monitoring devices during the long-term operation of distribution feeders, assuming no known faults or manual confirmation of normal operation. This historical operational characteristic data includes at least the statistical distribution information of traveling wave amplitude variation and traveling wave polarity direction under different operating conditions. The historical distribution of traveling wave amplitude variation reflects the common range of traveling wave amplitude under non-fault conditions such as load fluctuations, switching operations, lightning induction, and electromagnetic interference, while the historical distribution of traveling wave polarity direction reflects the probability characteristics of polarity occurrence under normal operation or non-fault disturbance conditions. The aforementioned historical operational characteristic data can be categorized and stored by line, season, operating mode, or load level to improve the targeting of subsequent correlation analysis.

[0039] In step S103, each traveling wave feature in the current set of valid traveling wave features is first matched and associated with the historical operating feature data of the corresponding distribution feeder. This association is not a simple comparison of a single threshold, but rather a similarity judgment based on statistical distribution. For example, for a certain valid traveling wave feature, its amplitude change can be compared with the distribution range of non-fault traveling wave amplitude changes recorded in the historical operating feature data. If this amplitude change appears frequently within the statistical range under normal operating conditions over a long period, then this feature is more likely to originate from non-fault disturbances; conversely, if the amplitude significantly deviates from the high-probability range of the historical non-fault distribution, it is more likely to be related to a real fault.

[0040] To enable those skilled in the art to clearly implement this method, historical distribution characteristics can be quantitatively described statistically. For example, over a historical operating period, the amplitude variations of traveling waves collected under non-fault conditions are statistically analyzed, their average and standard deviation are calculated, and a distribution interval centered on this average is constructed. When the amplitude variation of a certain effective traveling wave characteristic falls within this distribution interval, and its frequency is highly consistent with historical statistical results, it can be determined to conform to the characteristics of a non-fault disturbance; conversely, when its amplitude variation exceeds the upper limit of this distribution interval by several times the standard deviation, it can be considered a significant anomaly. Similarly, the polarity direction of traveling waves can also be determined by statistically analyzing the proportion of positive and negative polarities under non-fault conditions. When the current traveling wave polarity is consistent with the high-frequency polarity pattern under historical non-fault conditions, it is highly likely to be a non-fault disturbance.

[0041] During the correlation analysis, the system does not make judgments based solely on a single-dimensional feature, but rather comprehensively considers the joint distribution characteristics of the traveling wave amplitude variation and the traveling wave polarity direction. In other words, a traveling wave feature is only identified as a non-fault disturbance and removed if it simultaneously conforms to the historical non-fault distribution characteristics in both amplitude and polarity dimensions; conversely, if at least one dimension exhibits a significant deviation from the historical non-fault distribution, the traveling wave feature will be retained. This multi-dimensional joint analysis effectively avoids erroneous removals caused by accidental fluctuations of a single feature, thereby improving the reliability of the target traveling wave feature set.

[0042] After the above correlation analysis and screening process, all traveling wave features determined to originate from non-fault disturbances will be removed from the effective traveling wave feature set, and the remaining traveling wave features constitute the target traveling wave feature set. Each traveling wave feature in this target traveling wave feature set exhibits significant anomalies in terms of traveling wave amplitude variation, traveling wave polarity direction, and their statistical relationship with historical operating background. Furthermore, it better matches the physical response characteristics of a real fault in a distribution feeder, thus providing a highly reliable data foundation for subsequent steps of fault location based on traveling wave propagation time difference and fault type determination based on polarity and amplitude features.

[0043] The target traveling wave feature set refers to the set of traveling wave feature parameters retained after statistical correlation and deviation judgment of each traveling wave feature in the effective traveling wave feature set, based on the historical operating characteristic data of the distribution feeder as a comparison background. This set removes traveling wave features that conform to the distribution characteristics of historical non-fault disturbances and is used to characterize the propagation characteristics of traveling waves related to actual faults. Each traveling wave feature in the target traveling wave feature set is still composed of the first arrival time of the traveling wave front, the change in traveling wave amplitude, and the polarity direction. However, if at least one of the amplitude or polarity characteristics of this traveling wave feature does not meet the statistical distribution constraints of historical non-fault disturbances, it is determined to have fault indication significance.

[0044] For example, during the operation of a power distribution feeder, the aforementioned processing yields a set of effective traveling wave characteristics for the same traveling wave disturbance event, including traveling wave characteristics from three monitoring points: the traveling wave amplitude change corresponding to the first monitoring point is A1, with a positive polarity; the traveling wave amplitude change corresponding to the second monitoring point is A2, with a positive polarity; and the traveling wave amplitude change corresponding to the third monitoring point is A3, with a negative polarity.

[0045] After performing correlation analysis between the above effective traveling wave characteristics and the historical traveling wave characteristic sample set formed by the power distribution feeder under the confirmed fault-free operation state, it was found that the traveling wave amplitude change and polarity direction corresponding to the first and second monitoring points both fall within the high probability distribution range of historical non-fault disturbances, while the traveling wave amplitude change corresponding to the third monitoring point significantly exceeds the historical amplitude distribution range, and its polarity direction has an extremely low probability of appearing under the historical non-fault operation background.

[0046] In this case, the traveling wave features corresponding to the first and second monitoring points are determined to be traveling wave features corresponding to non-fault disturbances and are removed, while the traveling wave features corresponding to the third monitoring point are retained and included in the target traveling wave feature set as an effective input basis for subsequent fault location and fault type judgment.

[0047] Furthermore, the step involves correlating the effective traveling wave feature set with historical operating characteristic data of the distribution feeder, and based on the historical distribution characteristics of the traveling wave amplitude variation and polarity direction, eliminating traveling wave features corresponding to non-fault disturbances to form a target traveling wave feature set, including: During the historical period when the distribution feeder is in a confirmed fault-free operation state, based on the sampling configuration and unified timing reference consistent with the current monitoring point, the amplitude change and polarity direction of the traveling wave corresponding to the historical traveling wave events are continuously extracted to construct a historical traveling wave feature sample set for characterizing the non-fault operation background. Based on the historical traveling wave feature sample set, statistical modeling is performed on the change in traveling wave amplitude and the direction of traveling wave polarity, respectively, to generate the historical distribution interval of the change in traveling wave amplitude and the historical probability distribution of the direction of traveling wave polarity, and the historical distribution interval and the historical probability distribution of the direction of traveling wave polarity are used as background feature constraints for non-fault disturbances. For each traveling wave feature in the effective traveling wave feature set, the corresponding traveling wave amplitude change is compared with the historical distribution interval to determine whether the traveling wave amplitude change falls into the high probability distribution interval of non-fault disturbance. At the same time, the polarity direction of the traveling wave feature is matched with the historical occurrence probability distribution to form the amplitude deviation judgment result and polarity deviation judgment result for the traveling wave feature. Based on the joint judgment relationship between the amplitude deviation judgment result and the polarity deviation judgment result, it is determined whether the traveling wave feature simultaneously satisfies the condition that both the amplitude feature and the polarity feature conform to the historical non-fault disturbance distribution characteristics. Traveling wave features that simultaneously satisfy the condition are judged as traveling wave features corresponding to non-fault disturbances and are eliminated. Traveling wave features that do not simultaneously satisfy the condition are retained and included in the target traveling wave feature set.

[0048] In this embodiment, the effective traveling wave feature set is further identified and purified. The core purpose is to distinguish the traveling wave features caused by real faults from those caused by non-fault disturbances by introducing the statistical background formed by the long-term operation of the distribution feeder, thereby ensuring that the traveling wave features entering the subsequent fault location and analysis stage have a clear fault orientation.

[0049] Specifically, it is first necessary to clarify the source and meaning of "historical operational characteristic data." In this invention, historical operational characteristic data refers to traveling wave-related characteristic data collected and stored by sampling devices configured identically to the current monitoring point, under a unified timing reference, during a historical period when the distribution feeder was confirmed to be in a fault-free state through operation records, maintenance logs, or manual verification. "Confirmed fault-free operation" means that within the corresponding time period, the distribution feeder did not experience any known electrical faults such as short circuits, grounding, or open circuits, and its operational status was confirmed normal by the dispatching system or manual inspection. To ensure statistical comparability between historical and current data, the sampling frequency, sampling channels, trigger thresholds, and timing methods used during the historical time period should be consistent with those used at the current monitoring point.

[0050] Within this historical time period, the system extracts the amplitude change and polarity direction of the traveling wave from the detected traveling wave events according to the same traveling wave detection and feature extraction rules as the current method. The amplitude change refers to the degree of amplitude change of the voltage or current signal within a preset time window before and after the arrival of the traveling wave front; the polarity direction refers to the direction of change of the signal relative to its steady-state value at the instant the traveling wave front arrives. By repeating the above extraction process on a large number of historical traveling wave events, a historical traveling wave feature sample set can be constructed. This sample set is used to characterize various traveling wave disturbance characteristics that may occur in the distribution feeder under non-fault operating conditions, such as traveling wave responses caused by load fluctuations, switching operations, lightning induction, or external electromagnetic interference.

[0051] After obtaining the historical traveling wave characteristic sample set, the system performs statistical modeling on the variation in traveling wave amplitude and the polarity direction of the traveling wave. Statistical modeling is not simply recording the maximum or minimum values, but rather obtaining representative distribution characteristics through statistical analysis of a large amount of characteristic data in the historical sample set. For the variation in traveling wave amplitude, its value range, concentration interval, or high-frequency occurrence interval in the historical samples can be statistically analyzed, thereby generating a historical distribution interval for the variation in traveling wave amplitude. This distribution interval reflects the range in which traveling wave amplitude typically occurs under non-fault disturbance conditions. For the polarity direction of the traveling wave, the frequency or proportion of positive and negative polarities in the historical samples can be statistically analyzed, thereby obtaining the historical probability distribution of the occurrence of the traveling wave polarity direction, which reflects the probability of different polarities occurring under non-fault operating conditions. The aforementioned historical distribution interval and historical probability distribution together constitute the background characteristic constraints for non-fault disturbances.

[0052] After completing the historical background feature modeling, each traveling wave feature in the effective traveling wave feature set is compared and analyzed against the aforementioned background feature constraints. Specifically, the amplitude change corresponding to the feature is first compared with the historical distribution range of the amplitude change to determine whether the amplitude change falls within the high-probability distribution range of non-fault disturbances. The "high-probability distribution range" refers to the amplitude range that appears frequently in historical samples and represents typical non-fault disturbance characteristics. For example, if the amplitude changes of non-fault traveling waves in historical samples are mostly concentrated between 5 A and 15 A, then this range can be considered a high-probability distribution range of non-fault disturbances. When the amplitude change of a current traveling wave feature falls within this range, it can be preliminarily determined that it is closer to a non-fault disturbance in terms of amplitude.

[0053] Simultaneously, the polarity direction of the traveling wave characteristic is matched with the historical probability distribution of traveling wave polarity directions to determine whether the polarity direction belongs to a polarity type with a high probability of occurrence under non-fault operating conditions. For example, if in historical samples, the probability of a positive polarity traveling wave of a certain phase occurring under non-fault conditions is significantly higher than that of a negative polarity, then when the current traveling wave characteristic exhibits this high-probability polarity, it can be considered that it conforms to the non-fault disturbance characteristics in the polarity dimension. Through the above two judgments, the amplitude deviation judgment result and polarity deviation judgment result for this traveling wave characteristic are respectively formed.

[0054] After obtaining the amplitude deviation and polarity deviation judgment results, the system further performs a final screening of the traveling wave feature based on the joint judgment relationship between the two. The joint judgment relationship means that a traveling wave feature is only judged as a traveling wave feature corresponding to a non-fault disturbance if it conforms to the historical non-fault disturbance distribution characteristics in both the amplitude and polarity dimensions. In other words, if the amplitude change of a traveling wave feature falls within the high-probability non-fault distribution range, and its polarity direction also belongs to the high-probability polarity under non-fault conditions, then the traveling wave feature is considered to simultaneously meet the condition that both its amplitude and polarity characteristics conform to the historical non-fault disturbance distribution characteristics and should be eliminated. Conversely, if the traveling wave feature exhibits a significant deviation from the historical non-fault distribution in either dimension—for example, its amplitude is significantly greater than the historical non-fault range or its polarity direction belongs to a low-probability polarity type—then the traveling wave feature is considered to have potential fault indication significance and should be retained.

[0055] Through the aforementioned step-by-step, joint screening process, the system removes traveling wave features that simultaneously satisfy both the amplitude and polarity characteristics of non-fault disturbances from the effective traveling wave feature set, while retaining and incorporating traveling wave features that do not simultaneously meet these conditions into the target traveling wave feature set. The resulting target traveling wave feature set, statistically speaking, has effectively removed the influence of non-fault operating backgrounds. The traveling wave features it contains exhibit characteristics significantly different from historical non-fault disturbances in both amplitude and polarity, thus providing a more reliable, clear, and feasible data foundation for subsequent fault location based on traveling wave propagation time difference and for fault type and grounding characteristic assessment based on combinations of traveling wave features.

[0056] Furthermore, based on the historical traveling wave feature sample set, statistical modeling is performed on the variation of traveling wave amplitude and the polarity direction of traveling wave, respectively, to generate the historical distribution interval of the variation of traveling wave amplitude and the historical probability distribution of the occurrence of traveling wave polarity direction. The historical distribution interval and the historical probability distribution are then used as background feature constraints for non-fault disturbances, including: After obtaining the historical traveling wave feature sample set, the historical traveling wave feature samples are checked for consistency and grouped according to the monitoring point number, phase identifier and sampling configuration parameters. The historical samples with sampling frequency, trigger threshold and signal channel configuration that are completely consistent with the current operating state are retained. Based on the grouping results, an effective historical sample subset for subsequent modeling is formed to eliminate the systematic bias of statistical results caused by different sampling conditions. Based on a subset of valid historical samples, the amplitude variation of traveling waves is adaptively divided into amplitude intervals. According to the actual range and density of amplitude variation in historical samples, the continuous amplitude interval is divided into multiple non-overlapping amplitude sub-intervals. The frequency of historical samples appearing in each amplitude sub-interval is counted, thereby constructing an amplitude frequency distribution structure that reflects the distribution pattern of traveling wave amplitude variation under non-fault operation background. After completing the construction of the amplitude frequency distribution structure, the amplitude sub-intervals that appear more frequently than a preset proportion threshold in the historical samples are identified based on the amplitude frequency distribution structure, and the set of amplitude sub-intervals is determined as the historical distribution interval of the traveling wave amplitude change. The historical distribution interval is used to characterize the typical range of values ​​of the traveling wave amplitude change under non-fault disturbance conditions, and serves as a direct reference for subsequent amplitude deviation determination. While processing the amplitude variation of the traveling wave in parallel, polarity statistical analysis is performed on the polarity direction of the traveling wave in the effective historical sample subset. The number of times positive and negative polarities appear in the historical samples are counted respectively, and the occurrence probability of each polarity direction is calculated in combination with the total number of historical samples, thereby forming the historical occurrence probability distribution of the polarity direction of the traveling wave. The historical occurrence probability distribution is used to characterize the occurrence tendency of different polarity directions under non-fault operation background. After obtaining the historical distribution range of the amplitude change of the traveling wave and the historical probability distribution of the polarity direction of the traveling wave, the two are jointly encapsulated to form a background feature constraint set for characterizing the non-fault disturbance background. The background feature constraint set is used as the basic input for subsequent deviation judgment and screening of the current effective traveling wave features, so that the subsequent traveling wave feature judgment process is always limited by a clear historical statistical background.

[0057] In this invention, based on the previously constructed historical traveling wave feature sample set, a systematic model is performed on the non-fault operation background. The core purpose is to transform the "traveling wave features that may occur under fault-free conditions in history" into background feature constraints with clear statistical significance and direct callable form, thereby providing a stable, reproducible and physically reasonable reference benchmark for subsequent deviation judgment and screening of current traveling wave features.

[0058] In practical implementation, after obtaining the historical traveling wave characteristic sample set, the comparability of the historical samples must first be strictly controlled. The historical traveling wave characteristic sample set referred to here is a set of samples of traveling wave amplitude changes and traveling wave polarity directions obtained during a historical period when the distribution feeder is confirmed to be in a fault-free operating state, according to the same traveling wave detection and feature extraction rules as the current operating state. Since the traveling wave response characteristics may differ significantly under different monitoring points, different phases, and different sampling configurations, directly performing statistical modeling on all historical samples can easily introduce systematic bias. Therefore, this invention performs consistency verification and grouping processing on the historical traveling wave characteristic samples according to the monitoring point number, phase identifier, and sampling configuration parameters. Consistency verification refers to checking whether the sampling frequency, trigger threshold, and signal channel configuration corresponding to each historical sample are completely consistent with the current operating state. Only when these parameters are consistent is the historical sample considered statistically comparable to the current sample. Through this verification process, historical samples that meet the consistency conditions are retained, and a valid subset of historical samples is formed based on the grouping results, thereby eliminating the systematic bias caused by differences in sampling conditions to the statistical results.

[0059] After obtaining a subset of valid historical samples, adaptive amplitude interval division is performed on the traveling wave amplitude variation. The traveling wave amplitude variation referred to here is the degree of change in voltage or current signal amplitude within a preset time window before and after the traveling wave front reaches the monitoring point; it is a continuous numerical characteristic. Adaptive amplitude interval division means that instead of pre-fixing the boundaries of the amplitude interval, the continuous amplitude range is dynamically divided based on the actual range of amplitude variation in the historical samples and the density of sample distribution. Specifically, the minimum and maximum values ​​of the traveling wave amplitude variation in the valid historical sample subset are first counted to determine the overall amplitude range. Then, based on the clustering of samples in different amplitude segments, this overall amplitude range is divided into multiple non-overlapping amplitude sub-intervals, ensuring that the number of historical samples in each sub-interval reflects the frequency of occurrence of that amplitude interval under non-fault operating conditions. Subsequently, the frequency of historical samples in each amplitude sub-interval is counted, thereby constructing an amplitude frequency distribution structure that reflects the distribution pattern of traveling wave amplitude variation under non-fault operating conditions. This frequency distribution structure essentially depicts the relative frequency of different amplitude variation ranges under fault-free conditions.

[0060] After constructing the amplitude frequency distribution structure, the system further identifies frequently occurring amplitude sub-intervals in historical samples based on this structure. The phrase "frequency exceeding a preset threshold" refers to a sub-interval where the proportion of historical samples within a given amplitude sub-interval exceeds a predetermined threshold. This threshold can be set based on engineering experience or statistical stability requirements. For example, if a sub-interval contains more than 60% of the total samples, it can be considered to represent a typical amplitude variation range under non-fault disturbance conditions. Through this method, the set of amplitude sub-intervals satisfying the frequency condition is determined as the historical distribution range of traveling wave amplitude variation. This historical distribution range characterizes the range of values ​​where traveling wave amplitude variation most frequently occurs under non-fault disturbance conditions and serves as an important reference for determining whether the current traveling wave amplitude deviates from the non-fault background in subsequent processing. For instance, if the historical distribution range is 10 Å to 25 Å, then when the amplitude variation of a current traveling wave characteristic is significantly greater than the upper limit of this range, it can be preliminarily considered to have anomalies at the amplitude level.

[0061] While processing the amplitude variation of the traveling wave in parallel, a polarity statistical analysis is performed on the polarity direction of the traveling wave in the effective historical sample subset. The polarity direction of the traveling wave refers to the direction of change of the voltage or current signal relative to its steady-state value at the instant the traveling wave front reaches the monitoring point; it is typically positive or negative and is a discrete characteristic. During the statistical process, the frequency of positive and negative polarities in the historical samples is counted separately, and combined with the total number of historical samples, the probability of occurrence for each polarity direction is calculated, thus forming the historical probability distribution of the traveling wave polarity direction. This probability distribution is used to characterize the tendency of different polarities to occur under non-fault operating conditions. For example, if the probability of positive polarity is significantly higher than that of negative polarity in the historical samples, then positive polarity can be considered more consistent with the typical behavior of non-fault disturbances.

[0062] After obtaining the historical distribution range of the traveling wave amplitude variation and the historical probability distribution of the traveling wave polarity direction, these two are jointly encapsulated to form a set of background feature constraints for characterizing non-fault disturbance backgrounds. This joint encapsulation refers to storing and retrieving the historical distribution range of the amplitude dimension and the historical probability distribution of the polarity dimension as a whole background model. This ensures that subsequent determinations of currently valid traveling wave features are simultaneously constrained by the historical statistical background of both amplitude and polarity features. In practical applications, this set of background feature constraints serves as the basic input for subsequent amplitude and polarity deviation determinations, ensuring that the selection process for traveling wave features is always based on a clear, stable, and well-understood historical statistical background, thereby avoiding unreasonable influences from single disturbances or accidental fluctuations on fault assessment results.

[0063] Step S104: Based on the time difference between the first arrival times of the traveling wave front in the target traveling wave feature set, and combined with the line parameters of the distribution feeder, calculate the traveling wave propagation distance and determine the location of the fault.

[0064] In this invention, step S104 serves to convert time information into spatial location information based on the target traveling wave feature set obtained in the preceding steps, utilizing the deterministic physical laws governing the propagation of traveling waves in distribution feeders. This enables quantitative determination of the fault location. This step is a crucial transition from traveling wave features to spatial location of specific fault points. Its implementation must strictly rely on time measurement results under unified timing conditions and the actual line parameters of the distribution feeders to ensure that the location results have clear physical meaning and are feasible for engineering implementation.

[0065] Specifically, the target traveling wave feature set contains at least multiple monitoring points with their corresponding first arrival times of the traveling wave wavefronts. These arrival times are all absolute time stamps obtained under a unified time reference, thus the first arrival times of the traveling wave wavefronts between different monitoring points have a directly comparable time difference relationship. The so-called time difference relationship between the first arrival times of the traveling wave wavefronts refers to the difference between the arrival times of the wavefronts detected at different monitoring points for the same traveling wave disturbance event. This time difference reflects the propagation time that the traveling wave takes to travel from one location to another in the power distribution feeder.

[0066] During implementation, the system first pairs the first arrival times of the wavefronts of each traveling wave feature in the target traveling wave feature set with the corresponding monitoring point locations, forming at least one set of valid time difference data. For two-point location scenarios, two monitoring points located at different positions on the distribution feeder can be selected, such as a monitoring point at the beginning of the feeder and a monitoring point in the middle or end of the feeder, and the difference in the first arrival times of the traveling wavefronts between the two can be calculated. For multi-point monitoring scenarios, multiple sets of time difference relationships can be constructed, and the stability of the location results can be improved through consistency verification or weighting. The sign and magnitude of this time difference are directly related to the spatial location of the fault point relative to each monitoring point.

[0067] After obtaining the first arrival time difference of the traveling wave front, it is necessary to combine this with the line parameters of the distribution feeder to calculate the propagation distance. The line parameters referred to here are a set of known parameters that reflect the propagation characteristics of the traveling wave in the distribution feeder, including at least the traveling wave propagation speed and the physical location coordinates of each monitoring point in the feeder or the line mileage information. The traveling wave propagation speed refers to the speed at which the traveling wave propagates in the conductor of the distribution feeder and its surrounding medium. It is usually determined by parameters such as inductance and capacitance per unit length of the line. In engineering applications, it can be calculated through line design parameters or predetermined through historical experiments or calibration methods and stored as a known constant in the system.

[0068] In specific calculations, the propagation distance of a traveling wave is determined based on the relationship between its propagation time and speed. For the case between two monitoring points, if we assume the first arrival time of the wavefront at monitoring point A is t1, and the first arrival time at monitoring point B is t2, then the time difference Δt can be expressed as the difference between t2 and t1. If the propagation speed of the traveling wave in the distribution feeder is known to be v, then the distance the wave travels during this time can be calculated by multiplying the propagation speed by the time difference; that is, the propagation distance equals the product of v and Δt. By comparing this propagation distance with the known line distance between the monitoring points, the specific location of the fault point between the two monitoring points can be deduced.

[0069] To facilitate understanding and implementation by those skilled in the art, the above calculation process is illustrated below with an example. For instance, suppose a power distribution feeder has two monitoring points, with a line length of one kilometer between them, and the traveling wave propagation speed is pre-calibrated to 200 meters per microsecond. When a traveling wave disturbance event occurs, the monitoring point near the beginning of the feeder detects the first arrival time of the traveling wave front at a certain absolute time point, while the downstream monitoring point detects the first arrival time of the wave front three microseconds later than the former. Therefore, the propagation distance of the traveling wave between the two monitoring points can be calculated as 200 meters per microsecond multiplied by three microseconds, which equals 600 meters. This indicates that the fault point is located between the two monitoring points, approximately 600 meters from the beginning monitoring point.

[0070] When the system is configured with multiple monitoring points, multiple sets of propagation distance calculation results can be cross-validated. For example, by comparing whether the fault locations obtained under different combinations of monitoring points are consistent, or by combining multiple calculation results using the principle of minimum deviation, the accuracy and robustness of the fault location results can be further improved. Regardless of the specific implementation method used, the ultimate goal is to convert time-dimensional information into spatial-dimensional information by combining the time difference between the first arrival times of the traveling wave front with the known line parameters of the distribution feeder, thus clearly indicating the location of the fault in the distribution feeder.

[0071] Through the above processing, step S104 can achieve quantitative calculation and clear location of the fault location of the distribution feeder without relying on human experience, providing clear, reliable and physically based spatial constraints for subsequent fault type and grounding characteristic analysis based on the fault location.

[0072] Step S105: Based on determining the location of the fault, the fault type and grounding characteristics of the distribution feeder are analyzed by comprehensively considering the polarity direction and amplitude change of the traveling wave in the target traveling wave characteristic set, and the fault analysis result is output.

[0073] In this invention, step S105 is a key step to further identify and classify the nature of the fault itself based on the clear spatial location of the fault. Its purpose is to transform the target traveling wave characteristics obtained in the previous steps from the spatial location result of "where it happened" to the qualitative and semi-quantitative judgment result of "what type of fault happened", thereby providing a direct basis for fault handling, maintenance decision-making and operation restoration of power distribution feeders.

[0074] Specifically, after the fault location is determined in step S104, the system first introduces this fault location as a known constraint into the analysis process. The fault location refers to the physical location of the fault point within the distribution feeder or its line mileage coordinates. This information clarifies the direction of traveling wave propagation, i.e., whether the traveling wave propagates unidirectionally from the fault point to the monitoring point or exhibits bidirectional propagation characteristics. Based on this, step S105 focuses on using the confirmed reliable traveling wave polarity direction and amplitude variation from the target traveling wave feature set to comprehensively analyze the fault type and grounding characteristics of the distribution feeder.

[0075] The fault types mentioned here refer to the changes in electrical connection status exhibited by distribution feeders when abnormalities occur, such as phase-to-phase short circuits, single-phase grounding, two-phase grounding, or open circuits. Different types of faults will exhibit different combinations of voltage and current traveling wave characteristics during the traveling wave propagation process. The polarity direction of the traveling wave plays an important role in this judgment process, reflecting the direction of change of voltage or current relative to the original steady-state value when the traveling wave front reaches the monitoring point. Because different fault types cause different equivalent voltage or current source characteristics at the fault point, the traveling waves they generate have distinguishable patterns in propagation direction and polarity. For example, in some grounding faults, the current traveling wave may exhibit a clear single polarity characteristic, while in phase-to-phase short circuit faults, it may be accompanied by symmetrical or antisymmetrical changes in the polarity of different phase traveling waves.

[0076] The variation in traveling wave amplitude reflects the difference in fault excitation intensity and fault electrical characteristics. This variation in traveling wave amplitude, obtained in step S102 by quantifying the signal changes before and after the arrival of the traveling wave front, is closely related to factors such as fault impedance, grounding resistance, and fault phase. In step S105, the system uses the variation in traveling wave amplitude from the target traveling wave characteristic set as one of the criteria for judgment. By comparing it with pre-established amplitude variation patterns or empirical intervals, it determines whether the fault is a low-resistance or high-resistance fault, thus providing a basis for grounding characteristic analysis.

[0077] Grounding characteristics refer to the electrical characteristics describing whether a fault involves ground conduction and the degree of ground conduction. They are typically categorized as direct grounding, grounding through a resistor, or ungrounded. In this step, the assessment of grounding characteristics does not rely on a single traveling wave characteristic, but rather on a combined judgment of the traveling wave polarity direction and amplitude variation. For example, when the traveling wave polarity direction shows a consistent change at multiple monitoring points, and the amplitude variation is relatively large, it is usually identified as a low-resistance grounding fault. Conversely, when the amplitude variation is significantly lower than the amplitude range of a typical short-circuit fault, and the polarity characteristic is not obvious or exhibits attenuation characteristics, it is more likely to correspond to a high-resistance grounding or non-metallic grounding fault.

[0078] The determination of fault type and grounding characteristics can be achieved through rule matching or discrimination logic. For example, a combined discrimination rule of traveling wave polarity direction and traveling wave amplitude change can be established in advance. When the target traveling wave characteristics corresponding to a certain fault event simultaneously meet a certain rule condition in terms of polarity consistency, amplitude range, and propagation direction relationship with the fault location, the corresponding fault type and grounding characteristic results are output. For example, if, after the fault location is determined, the monitoring point near the fault point detects a sudden increase in positive polarity of the current traveling wave, and the side farther from the fault point detects an opposite polarity change, and the amplitude change of the traveling wave is significantly higher than the historical high-resistance disturbance range, then the fault can be judged as a typical phase-to-phase short-circuit fault; if the amplitude change of the traveling wave is significantly smaller, but still shows the same characteristics as the ground loop in polarity direction, then it can be further judged as a high-resistance grounding fault.

[0079] After completing the comprehensive analysis of the fault type and grounding characteristics, the system outputs the results in a clear, structured information format. This output includes at least the fault location, fault type, and grounding characteristic description, and may further include auxiliary information such as traveling wave amplitude and polarity characteristics to facilitate understanding and use by the dispatching system or maintenance personnel. Through this process, step S105 achieves a complete mapping from traveling wave characteristics to fault characteristics, enabling the entire intelligent fault analysis method for distribution feeders to not only accurately locate the fault but also make clear, feasible, and engineering-significant judgments on the electrical attributes of the fault itself.

[0080] Furthermore, based on determining the location of the fault, the method of comprehensively analyzing the polarity direction and amplitude variation of the traveling wave in the target traveling wave characteristic set to determine the fault type and grounding characteristics of the distribution feeder and outputting the fault assessment result includes: After determining the location of the fault, based on the spatial order relationship between the location of the fault and each monitoring point in the power distribution feeder, the relative orientation of each monitoring point relative to the fault point is determined and a monitoring point orientation set is generated. The monitoring point orientation set is used to at least identify whether each monitoring point is located upstream or downstream of the fault point. The monitoring point orientation set is then bound to the target traveling wave feature set to form an orientation traveling wave feature set with spatial orientation identification. Based on the azimuth traveling wave feature set, the sets of traveling wave amplitude changes corresponding to the upstream monitoring point and the downstream monitoring point of the fault point are statistically analyzed respectively. The representative amplitude quantities of the upstream and downstream sides are calculated and amplitude asymmetry discrimination quantities are formed. The representative amplitude quantity is the maximum or mean value of the corresponding side traveling wave amplitude change set, and the amplitude asymmetry discrimination quantity is used to characterize the amplitude difference when the fault excitation propagates on both sides of the fault point. Based on the obtained amplitude asymmetry discrimination quantity, azimuth consistency analysis is performed on the polarity direction of the traveling wave in the azimuth traveling wave feature set to form upstream polarity consistency results and downstream polarity consistency results respectively. Based on the upstream polarity consistency results, downstream polarity consistency results and amplitude asymmetry discrimination quantity, fault type discrimination labels are determined. The fault type discrimination labels include at least a grounding indication label for distinguishing between grounding faults and non-grounding faults and a phase indication label for distinguishing between phase-to-phase faults and single-phase faults. After determining the fault type discrimination label, the amplitude asymmetry discrimination quantity and the ground indication label are jointly mapped to generate the ground characteristic discrimination result. The ground characteristic discrimination result includes at least one of low resistance ground characteristic and high resistance ground characteristic. The fault type discrimination label and the ground characteristic discrimination result are combined and encapsulated to output a fault judgment result containing the fault location, fault type and ground characteristics.

[0081] In this embodiment, based on the fact that the location of the fault has been determined by calculating the time difference of traveling wave propagation, the electrical attributes of the fault are further qualitatively and hierarchically assessed. The purpose is to transform the spatial location result of "where the fault occurred" into an engineering-usable conclusion of "what type of fault occurred and what grounding characteristics the fault has", thereby providing a direct basis for the protection action, operation and maintenance and fault recovery of the power distribution feeder.

[0082] Specifically, after determining the location of the fault, the first step is to use the relationship between the fault location and the physical arrangement of each monitoring point in the power distribution feeder to determine the relative orientation of each monitoring point relative to the fault point. The relative orientation here refers to dividing the power distribution feeder along its power transmission direction into an upstream side and a downstream side, with the fault point as a reference. Monitoring points located on the power supply side are identified as upstream monitoring points, and those on the load side are identified as downstream monitoring points. This generates a set of monitoring point orientations, where each monitoring point is explicitly assigned an "upstream" or "downstream" orientation. Subsequently, this set of monitoring point orientations is bound to the target traveling wave feature set formed in the previous steps. This ensures that each traveling wave feature not only includes electrical characteristics such as the amplitude variation and polarity direction of the traveling wave, but also carries the spatial orientation information of its corresponding monitoring point, thus forming a set of directional traveling wave features with spatial orientation identifiers.

[0083] After obtaining the azimuth traveling wave feature set, the system performs statistical analysis on the amplitude changes of the traveling waves on the upstream and downstream sides of the fault point. Specifically, the amplitude changes of the traveling waves belonging to the upstream monitoring points in the azimuth traveling wave feature set are extracted to form an upstream traveling wave amplitude change set; simultaneously, the amplitude changes of the traveling waves belonging to the downstream monitoring points are extracted to form a downstream traveling wave amplitude change set. Subsequently, representative amplitude quantities are calculated for both sets. These representative amplitude quantities are statistical measures used to summarize the overall level of the traveling wave amplitude on the same side. They can be selected as the maximum value in the set of amplitude changes on that side, or as the arithmetic mean of the set; the specific selection method can be set according to engineering requirements. After obtaining the upstream and downstream representative amplitude quantities, they are compared to form an amplitude asymmetry discrimination quantity. This amplitude asymmetry discriminant is used to characterize the amplitude difference of fault excitation propagating on both sides of the fault point. Its physical meaning is that real faults typically generate stronger traveling wave excitation near the fault point, and the attenuation degree differs between the two sides during propagation, resulting in a significant asymmetry in the amplitude of the traveling wave on the upstream and downstream sides. For example, if the representative amplitude on the upstream side is 60 A and the representative amplitude on the downstream side is 20 A, then the fault can be considered to exhibit significant asymmetry in amplitude.

[0084] After obtaining the amplitude asymmetry discrimination value, the system further performs azimuth consistency analysis on the polarity direction of the traveling wave in the azimuth feature set. The polarity direction referred to here is the direction of change of the voltage or current signal relative to its steady-state value when the traveling wave front reaches the monitoring point; it can be positive or negative. Azimuth consistency analysis refers to determining whether the polarity directions of the traveling waves corresponding to multiple monitoring points exhibit a consistent or highly concentrated trend within the same spatial azimuth. For example, upstream of the fault point, if the traveling wave polarity directions detected by multiple monitoring points are all of the same polarity, the upstream polarity can be considered to have high consistency; if the polarity directions are dispersed or contradictory, the consistency is low. Through the above analysis, upstream polarity consistency results and downstream polarity consistency results are generated, which are used to describe the stability of the polarity change as the traveling wave propagates on both sides of the fault point.

[0085] After obtaining the amplitude asymmetry discrimination value, the upstream polarity consistency result, and the downstream polarity consistency result, the system comprehensively judges the above information to determine the fault type discrimination label. The fault type discrimination label mentioned here is a set of results used to classify and identify the electrical attributes of the fault, and it includes at least a grounding indication label and a phase indication label. The grounding indication label is used to distinguish whether the fault involves grounding, such as a grounding fault or a non-grounding fault; the phase indication label is used to distinguish whether the fault occurs in a single phase or between multiple phases, such as a single-phase fault or a phase-to-phase fault. By combining the magnitude of the amplitude asymmetry discrimination value and the performance of the upstream and downstream polarity consistency, a judgment logic for the above labels can be formed. For example, when the amplitude asymmetry discrimination value is large and the polarity consistency on one side is obvious, it usually indicates a grounding or phase-to-phase fault; while when the amplitude asymmetry is not obvious and the polarity consistency is weak, it is more likely to correspond to a non-grounding or atypical fault.

[0086] After determining the fault type discrimination label, the system further maps the amplitude asymmetry discrimination value with the grounding indication label to generate a grounding characteristic discrimination result. The grounding characteristic referred to here is the electrical characteristic description of the conductivity between the fault point and the ground, which includes at least two cases: low-resistance grounding characteristics and high-resistance grounding characteristics. Low-resistance grounding characteristics typically correspond to cases with large traveling wave amplitudes and significant amplitude asymmetry, while high-resistance grounding characteristics correspond to cases with relatively small traveling wave amplitudes but still possessing certain asymmetric characteristics. By mapping the amplitude asymmetry discrimination value with the grounding indication label, a reasonable judgment of the fault's grounding characteristics can be made without directly measuring the grounding resistance.

[0087] Finally, the system combines and encapsulates the fault type identification label with the grounding characteristic identification result to form a complete fault assessment result output. This output result includes at least the fault location, fault type, and grounding characteristic description, and can serve as a direct basis for subsequent handling by the power distribution automation system or maintenance personnel.

[0088] Furthermore, based on the obtained amplitude asymmetry discrimination quantity, azimuth consistency analysis is performed on the polarity direction of the traveling wave in the azimuth traveling wave feature set to form upstream polarity consistency results and downstream polarity consistency results. Based on the upstream polarity consistency results, downstream polarity consistency results, and amplitude asymmetry discrimination quantity, a fault type discrimination label is determined. The fault type discrimination label includes at least a grounding indication label for distinguishing between grounding faults and non-grounding faults, and a phase indication label for distinguishing between phase-to-phase faults and single-phase faults. After obtaining the amplitude asymmetry discrimination value, the azimuth feature set of the azimuth is further divided according to the azimuth set of the monitoring points. The polarity direction sequence of the traveling wave corresponding to the upstream side of the fault point and the polarity direction sequence of the traveling wave corresponding to the downstream side of the fault point are extracted respectively. The polarity direction sequence of the traveling wave is a polarity value sequence obtained by arranging the first arrival time of the traveling wave front in chronological order, which is used to reflect the polarity change process of the traveling wave in the same azimuth side. Based on the upstream and downstream traveling wave polarity direction sequences, the frequency of positive and negative polarities in each sequence is counted, and the dominant polarity ratio is calculated. The dominant polarity ratio is the proportion of the polarity that appears most frequently in the corresponding sequence, thus forming the upstream dominant polarity ratio result and the downstream dominant polarity ratio result. The dominant polarity ratio result is used to quantitatively describe the consistency of the traveling wave polarity direction within the same azimuth side. After obtaining the upstream dominant polarity ratio and the downstream dominant polarity ratio, they are compared with a preset consistency threshold to determine whether the traveling wave polarity direction in the corresponding azimuth side meets the polarity consistency condition. Based on this, upstream polarity consistency results and downstream polarity consistency results are generated. The polarity consistency results are used to indicate at least whether the traveling wave polarity direction in the corresponding azimuth side exhibits stable and consistent change characteristics. After generating the upstream polarity consistency results and the downstream polarity consistency results, the polarity consistency results and the amplitude asymmetry discrimination quantity are jointly analyzed. Based on the magnitude of the amplitude asymmetry discrimination quantity and the combination relationship of the upstream and downstream polarity consistency results, a fault type discrimination label is determined. Specifically, when the amplitude asymmetry discrimination quantity is significant and at least one side polarity consistency result is established, a grounding indication label for grounding faults is generated. When the amplitude asymmetry discrimination quantity is small and neither the upstream nor downstream polarity consistency results are established, a grounding indication label for non-grounding faults is generated. Based on this, and combined with the correspondence of the dominant polarities of different orientation sides, a phase indication label for distinguishing between phase-to-phase faults and single-phase faults is generated.

[0089] This embodiment, based on the already obtained amplitude asymmetry discrimination quantity, further introduces the consistency analysis of traveling wave polarity in the spatial orientation dimension to jointly interpret the "energy difference characteristics" and "polarity evolution characteristics" of the traveling wave, thereby achieving a refined judgment of the fault type of the distribution feeder. The core idea of ​​this step is not to judge a single point or a single feature, but to construct a fault type discrimination logic with clear physical meaning and engineering feasibility by using the overall consistency characteristics of traveling wave polarity behavior from multiple monitoring points and multiple orientations.

[0090] Specifically, after obtaining the amplitude asymmetry discrimination value, the azimuth wave characteristic set needs to be further subdivided based on the monitoring point azimuth set already formed in the previous steps. The monitoring point azimuth set referred to here is the set of monitoring points explicitly identified as being located upstream or downstream of the fault point, based on the spatial order of each monitoring point in the distribution feeder, given that the fault location has been determined. Using this azimuth set, the original azimuth wave characteristic set is grouped, and the traveling wave polarity direction sequences belonging to the upstream side and the downstream side of the fault point are extracted separately. The traveling wave polarity direction sequence refers to the sequence formed by arranging the traveling wave polarity directions corresponding to multiple monitoring points within the same azimuth side according to the chronological order of the first arrival time of the traveling wave front. This sequence reflects the overall trend of the polarity change of the traveling wave over time as it propagates along that azimuth side.

[0091] After obtaining the upstream and downstream traveling wave polarity direction sequences, polarity statistical analysis is performed on both sequences. Specifically, the frequency of positive and negative polarities is counted for each sequence. Positive and negative polarities refer to the directional attribute of the voltage or current signal exhibiting a sudden rise or fall relative to its steady-state value at the instant the traveling wave front reaches the monitoring point. After statistical analysis, the dominant polarity percentage is calculated. This dominant polarity percentage refers to the proportion of the polarity direction that appears most frequently in the corresponding traveling wave polarity direction sequence. For example, if the upstream traveling wave polarity direction sequence contains five polarity values, with positive polarity appearing four times and negative polarity appearing once, then the dominant polarity on the upstream side is positive, and its dominant polarity percentage is five-quarters. This percentage transforms the qualitative description of "whether the polarities are consistent" into a quantifiable and comparable numerical result, thus providing a clear basis for subsequent judgments.

[0092] After obtaining the dominant polarity percentages on the upstream and downstream sides, these percentages are compared with preset consistency thresholds to determine whether the traveling wave polarity direction in the corresponding azimuth meets the polarity consistency condition. The consistency threshold mentioned here refers to a proportional threshold used to determine whether polarity has a stable and consistent characteristic; its value can be set based on practical engineering experience, for example, 70% or 80%. When the dominant polarity percentage on a certain azimuth is greater than or equal to the consistency threshold, the traveling wave polarity direction in that azimuth is considered to exhibit a stable and consistent change characteristic, thus generating a polarity consistency result for that azimuth as "valid"; conversely, a polarity consistency result is generated as "invalid". Through the above comparison process, upstream and downstream polarity consistency results can be generated respectively, used to characterize whether the polarity evolution of the traveling wave is consistent when propagating in different spatial azimuths.

[0093] After generating upstream and downstream polarity consistency results, these two results are jointly analyzed with the previously obtained amplitude asymmetry discriminant to determine the fault type label. This joint analysis refers to simultaneously considering both the amplitude difference characteristics and polarity consistency characteristics of the traveling wave propagating on both sides of space, thereby comprehensively judging the electrical properties of the fault. The amplitude asymmetry discriminant describes whether there is a significant difference in energy distribution when the fault excitation propagates on both sides of the fault point; a larger value usually indicates a significant energy concentration phenomenon near the fault point. The polarity consistency result reflects whether the electromagnetic change direction of the traveling wave during propagation is stable and uniform.

[0094] In specific discrimination, when the amplitude asymmetry discrimination value reaches a preset significant level, and the polarity consistency result on at least one side (upstream or downstream) is valid, the fault can be considered to have obvious ground conduction characteristics, thus generating a grounding indication tag for a grounding fault. Conversely, when the amplitude asymmetry discrimination value is small, and the polarity consistency results on both the upstream and downstream sides are invalid, the traveling wave energy distribution and polarity evolution can be considered not to exhibit typical grounding fault characteristics, thus generating a grounding indication tag for a non-grounding fault. Based on this, further combining the correspondence of the dominant polarities on different azimuth sides—for example, whether the dominant polarities on the upstream and downstream sides are the same or have reversed—generates phase-specific indication tags to distinguish between phase-to-phase faults and single-phase faults. In this way, a refined distinction of fault types can be achieved solely based on the traveling wave amplitude and polarity characteristics without directly measuring the fault current or grounding resistance.

[0095] In the above embodiments, a method for intelligent fault assessment of distribution feeders based on traveling wave characteristics is provided. Correspondingly, this application also provides a device for intelligent fault assessment of distribution feeders based on traveling wave characteristics. Please refer to... Figure 2 This is a schematic diagram of an embodiment of an intelligent fault assessment device for distribution feeders based on traveling wave characteristics according to this application. Since this embodiment, i.e., the second embodiment, is basically similar to the method embodiment, it is described simply; relevant details can be found in the description of the method embodiment. The system embodiment described below is merely illustrative.

[0096] The second embodiment of this application provides an intelligent fault assessment device for distribution feeders based on traveling wave characteristics, comprising: The acquisition unit 201 is used to synchronously acquire voltage and current signals at at least one monitoring point of the power distribution feeder during operation, and generate time-series raw sampling data based on a unified timing reference. When a transient disturbance exceeding a preset threshold appears in the raw sampling data, the traveling wave disturbance event is identified and candidate traveling wave data is formed. Construction unit 202 is used to extract the first arrival time of the traveling wave wavehead, the change in traveling wave amplitude, and the polarity direction of the traveling wave for candidate traveling wave data. Based on the correspondence between the first arrival time of the traveling wave wavehead and the unified timing reference, a traveling wave timing consistency criterion is constructed, and a set of effective traveling wave features is obtained by filtering accordingly. Analysis unit 203 is used to perform correlation analysis between the effective traveling wave feature set and the historical operating feature data of the distribution feeder. Based on the historical distribution characteristics of the traveling wave amplitude change and the traveling wave polarity direction, it eliminates the traveling wave features corresponding to non-fault disturbances to form the target traveling wave feature set. The calculation unit 204 is used to calculate the propagation distance of the traveling wave and determine the location of the fault based on the time difference between the first arrival times of the traveling wave wavefront in the target traveling wave feature set and the line parameters of the distribution feeder. The analysis unit 205 is used to analyze the fault type and grounding characteristics of the distribution feeder based on the determined location of the fault, and output the fault analysis result by comprehensively analyzing the polarity direction and amplitude change of the traveling wave in the target traveling wave feature set.

[0097] Although this application discloses preferred embodiments as described above, it is not intended to limit this application. Any person skilled in the art can make possible changes and modifications without departing from the spirit and scope of this application. Therefore, the scope of protection of this application should be determined by the scope defined in the claims of this application.

Claims

1. A method for intelligent fault assessment of distribution feeders based on traveling wave characteristics, characterized in that, include: During the operation of the power distribution feeder, the voltage and current signals at at least one monitoring point of the power distribution feeder are synchronously collected, and time-series raw sampling data are generated based on a unified timing reference. When a transient disturbance exceeding a preset threshold appears in the raw sampling data, the traveling wave disturbance event is identified and candidate traveling wave data is formed. For candidate traveling wave data, the first arrival time of the traveling wave wavehead, the change in traveling wave amplitude, and the polarity direction of the traveling wave are extracted. Based on the correspondence between the first arrival time of the traveling wave wavehead and the unified timing reference, a traveling wave timing consistency criterion is constructed, and a set of effective traveling wave features is obtained by filtering accordingly. The effective traveling wave feature set is correlated with the historical operating feature data of the distribution feeder. Based on the historical distribution characteristics of the traveling wave amplitude change and the traveling wave polarity direction, the traveling wave features corresponding to non-fault disturbances are eliminated to form the target traveling wave feature set. Based on the time difference between the first arrival times of the traveling wave front in the target traveling wave feature set, and combined with the line parameters of the distribution feeder, the traveling wave propagation distance is calculated and the fault location is determined. Based on determining the location of the fault, the fault type and grounding characteristics of the distribution feeder are analyzed by comprehensively considering the polarity direction and amplitude variation of the traveling wave in the target traveling wave characteristic set, and the fault analysis results are output.

2. The intelligent fault assessment method for distribution feeders based on traveling wave characteristics according to claim 1, characterized in that, For candidate traveling wave data, the first arrival time of the traveling wave wavehead, the change in traveling wave amplitude, and the polarity direction of the traveling wave are extracted. Based on the correspondence between the first arrival time of the traveling wave wavehead and the unified timing reference, a traveling wave timing consistency criterion is constructed, and a set of effective traveling wave features is obtained by filtering accordingly, including: In the candidate traveling wave data, the traveling wave disturbance time corresponding to each monitoring point is time-aligned based on a unified time reference. The absolute time marker of the first arrival of the traveling wave wavehead at each monitoring point is determined, and an initial arrival time sequence reflecting the arrival order of the same traveling wave event at different monitoring points is constructed accordingly. Based on the initial arrival time sequence and the known spatial order relationship of each monitoring point in the power distribution feeder, the propagation direction of the traveling wave in the power distribution feeder is determined, and under the constraint of the propagation direction, a directed time difference sequence is constructed for the first arrival time difference of the traveling wave front between each monitoring point. For a directed time difference sequence, combined with the line parameters of the distribution feeder, it is determined whether the arrival time difference between adjacent monitoring points meets the minimum and maximum propagation time constraints of the traveling wave in the propagation direction, thereby forming the travel wave time realizability determination result. Only when the wave time feasibility determination result is valid, the corresponding wave amplitude change and wave polarity direction are further subjected to joint consistency verification with the directed time difference sequence to confirm the physical consistency between the wave amplitude change direction and the propagation direction. Wave features that pass the joint consistency verification are included in the set of valid wave features, while candidate wave features that fail the joint consistency verification are eliminated.

3. The intelligent fault assessment method for distribution feeders based on traveling wave characteristics according to claim 1, characterized in that, The process involves correlating the effective traveling wave feature set with historical operating characteristic data of the distribution feeder. Based on the historical distribution characteristics of the traveling wave amplitude variation and polarity direction, traveling wave features corresponding to non-fault disturbances are eliminated to form a target traveling wave feature set, including: During the historical period when the distribution feeder is in a confirmed fault-free operation state, based on the sampling configuration and unified timing reference consistent with the current monitoring point, the amplitude change and polarity direction of the traveling wave corresponding to the historical traveling wave events are continuously extracted to construct a historical traveling wave feature sample set for characterizing the non-fault operation background. Based on the historical traveling wave feature sample set, statistical modeling is performed on the change in traveling wave amplitude and the direction of traveling wave polarity, respectively, to generate the historical distribution interval of the change in traveling wave amplitude and the historical probability distribution of the direction of traveling wave polarity, and the historical distribution interval and the historical probability distribution of the direction of traveling wave polarity are used as background feature constraints for non-fault disturbances. For each traveling wave feature in the effective traveling wave feature set, the corresponding traveling wave amplitude change is compared with the historical distribution interval to determine whether the traveling wave amplitude change falls into the high probability distribution interval of non-fault disturbance. At the same time, the polarity direction of the traveling wave feature is matched with the historical occurrence probability distribution to form the amplitude deviation judgment result and polarity deviation judgment result for the traveling wave feature. Based on the joint judgment relationship between the amplitude deviation judgment result and the polarity deviation judgment result, it is determined whether the traveling wave feature simultaneously satisfies the condition that both the amplitude feature and the polarity feature conform to the historical non-fault disturbance distribution characteristics. Traveling wave features that simultaneously satisfy the condition are judged as traveling wave features corresponding to non-fault disturbances and are eliminated. Traveling wave features that do not simultaneously satisfy the condition are retained and included in the target traveling wave feature set.

4. The intelligent fault assessment method for distribution feeders based on traveling wave characteristics according to claim 1, characterized in that, Based on determining the location of the fault, the fault type and grounding characteristics of the distribution feeder are analyzed by comprehensively considering the polarity direction and amplitude variation of the traveling wave in the target traveling wave characteristic set, and the fault analysis results are output, including: After determining the location of the fault, based on the spatial order relationship between the location of the fault and each monitoring point in the power distribution feeder, the relative orientation of each monitoring point relative to the fault point is determined and a monitoring point orientation set is generated. The monitoring point orientation set is used to at least identify whether each monitoring point is located upstream or downstream of the fault point. The monitoring point orientation set is then bound to the target traveling wave feature set to form an orientation traveling wave feature set with spatial orientation identification. Based on the azimuth traveling wave feature set, the sets of traveling wave amplitude changes corresponding to the upstream monitoring point and the downstream monitoring point of the fault point are statistically analyzed respectively. The representative amplitude quantities of the upstream and downstream sides are calculated and amplitude asymmetry discrimination quantities are formed. The representative amplitude quantity is the maximum or mean value of the corresponding side traveling wave amplitude change set, and the amplitude asymmetry discrimination quantity is used to characterize the amplitude difference when the fault excitation propagates on both sides of the fault point. Based on the obtained amplitude asymmetry discrimination quantity, azimuth consistency analysis is performed on the polarity direction of the traveling wave in the azimuth traveling wave feature set to form upstream polarity consistency results and downstream polarity consistency results respectively. Based on the upstream polarity consistency results, downstream polarity consistency results and amplitude asymmetry discrimination quantity, fault type discrimination labels are determined. The fault type discrimination labels include at least a grounding indication label for distinguishing between grounding faults and non-grounding faults and a phase indication label for distinguishing between phase-to-phase faults and single-phase faults. After determining the fault type discrimination label, the amplitude asymmetry discrimination quantity and the ground indication label are jointly mapped to generate the ground characteristic discrimination result. The ground characteristic discrimination result includes at least one of low resistance ground characteristic and high resistance ground characteristic. The fault type discrimination label and the ground characteristic discrimination result are combined and encapsulated to output a fault judgment result containing the fault location, fault type and ground characteristics.

5. The intelligent fault assessment method for distribution feeders based on traveling wave characteristics according to claim 2, characterized in that, Based on the initial arrival time sequence and the known spatial order relationship of each monitoring point in the distribution feeder, the propagation direction of the traveling wave in the distribution feeder is determined, and under the constraint of the propagation direction, a directed time difference sequence is constructed for the first arrival time difference of the traveling wavefront between each monitoring point, including: After obtaining the initial arrival time sequence corresponding to the same traveling wave event, the initial arrival time sequence is mapped one-to-one with the physical spatial order of each monitoring point along the power distribution feeder to construct a set of correspondences between time order and spatial order. In this set of correspondences, monitoring point pairs whose time order and spatial order are consistent are identified to form at least one candidate propagation direction hypothesis. The candidate propagation direction hypothesis is used to describe the path relationship of the traveling wave that may propagate in the forward or reverse direction along the power distribution feeder. For each candidate propagation direction assumption, under the constraint of the candidate propagation direction, according to the spatial order of the traveling wave propagation path, the first arrival time of the traveling wave wavefront at adjacent monitoring points is calculated by difference, generating a candidate directed time difference sequence that corresponds one-to-one with the candidate propagation direction. Each time difference in the candidate directed time difference sequence has a clear propagation start point monitoring point and propagation end point monitoring point identifier. Based on the candidate directed time difference sequence, combined with the physical distance parameters between adjacent monitoring points in the power distribution feeder and the traveling wave propagation speed parameters, the direction consistency of each time difference in the candidate directed time difference sequence is checked, and it is determined whether the candidate directed time difference sequence satisfies the monotonicity constraint that the time difference increases or decreases step by step along the same propagation direction as a whole, thereby forming a time difference structure consistency judgment result for the candidate propagation direction. Among the time difference structure consistency determination results corresponding to multiple candidate propagation directions, the propagation direction that satisfies the time difference structure consistency determination result is selected as the final traveling wave propagation direction. Under the constraint of this final propagation direction, the corresponding candidate directed time difference sequence is confirmed as a valid directed time difference sequence, which serves as the basic input data for subsequent traveling wave time realizability determination and traveling wave feature consistency verification.

6. The intelligent fault assessment method for distribution feeders based on traveling wave characteristics according to claim 3, characterized in that, Based on the historical traveling wave feature sample set, statistical modeling is performed on the variation of traveling wave amplitude and the polarity direction of traveling wave, respectively, to generate the historical distribution interval of the variation of traveling wave amplitude and the historical occurrence probability distribution of the polarity direction of traveling wave. The historical distribution interval and the historical occurrence probability distribution are used as background feature constraints for non-fault disturbances, including: After obtaining the historical traveling wave feature sample set, the historical traveling wave feature samples are checked for consistency and grouped according to the monitoring point number, phase identifier and sampling configuration parameters. The historical samples with sampling frequency, trigger threshold and signal channel configuration that are completely consistent with the current operating state are retained. Based on the grouping results, an effective historical sample subset for subsequent modeling is formed to eliminate the systematic bias of statistical results caused by different sampling conditions. Based on a subset of valid historical samples, the amplitude variation of traveling waves is adaptively divided into amplitude intervals. According to the actual range and density of amplitude variation in historical samples, the continuous amplitude interval is divided into multiple non-overlapping amplitude sub-intervals. The frequency of historical samples appearing in each amplitude sub-interval is counted, thereby constructing an amplitude frequency distribution structure that reflects the distribution pattern of traveling wave amplitude variation under non-fault operation background. After completing the construction of the amplitude frequency distribution structure, the amplitude sub-intervals that appear more frequently than a preset proportion threshold in the historical samples are identified based on the amplitude frequency distribution structure, and the set of amplitude sub-intervals is determined as the historical distribution interval of the traveling wave amplitude change. The historical distribution interval is used to characterize the typical range of values ​​of the traveling wave amplitude change under non-fault disturbance conditions, and serves as a direct reference for subsequent amplitude deviation determination. While processing the amplitude variation of the traveling wave in parallel, polarity statistical analysis is performed on the polarity direction of the traveling wave in the effective historical sample subset. The number of times positive and negative polarities appear in the historical samples are counted respectively, and the occurrence probability of each polarity direction is calculated in combination with the total number of historical samples, thereby forming the historical occurrence probability distribution of the polarity direction of the traveling wave. The historical occurrence probability distribution is used to characterize the occurrence tendency of different polarity directions under non-fault operation background. After obtaining the historical distribution range of the amplitude change of the traveling wave and the historical probability distribution of the polarity direction of the traveling wave, the two are jointly encapsulated to form a background feature constraint set for characterizing the non-fault disturbance background. The background feature constraint set is used as the basic input for subsequent deviation judgment and screening of the current effective traveling wave features, so that the subsequent traveling wave feature judgment process is always limited by a clear historical statistical background.

7. The intelligent fault assessment method for distribution feeders based on traveling wave characteristics according to claim 4, characterized in that, Based on the obtained amplitude asymmetry discrimination quantity, azimuth consistency analysis is performed on the polarity direction of the traveling wave in the azimuth traveling wave feature set to generate upstream polarity consistency results and downstream polarity consistency results. Based on the upstream polarity consistency results, downstream polarity consistency results, and amplitude asymmetry discrimination quantity, a fault type discrimination label is determined. The fault type discrimination label includes at least a grounding indication label for distinguishing between grounding faults and non-grounding faults, and a phase indication label for distinguishing between phase-to-phase faults and single-phase faults. After obtaining the amplitude asymmetry discrimination value, the azimuth feature set of the azimuth is further divided according to the azimuth set of the monitoring points. The polarity direction sequence of the traveling wave corresponding to the upstream side of the fault point and the polarity direction sequence of the traveling wave corresponding to the downstream side of the fault point are extracted respectively. The polarity direction sequence of the traveling wave is a polarity value sequence obtained by arranging the first arrival time of the traveling wave front in chronological order, which is used to reflect the polarity change process of the traveling wave in the same azimuth side. Based on the upstream and downstream traveling wave polarity direction sequences, the frequency of positive and negative polarities in each sequence is counted, and the dominant polarity ratio is calculated. The dominant polarity ratio is the proportion of the polarity that appears most frequently in the corresponding sequence, thus forming the upstream dominant polarity ratio result and the downstream dominant polarity ratio result. The dominant polarity ratio result is used to quantitatively describe the consistency of the traveling wave polarity direction within the same azimuth side. After obtaining the upstream dominant polarity ratio and the downstream dominant polarity ratio, they are compared with a preset consistency threshold to determine whether the traveling wave polarity direction in the corresponding azimuth side meets the polarity consistency condition. Based on this, upstream polarity consistency results and downstream polarity consistency results are generated. The polarity consistency results are used to indicate at least whether the traveling wave polarity direction in the corresponding azimuth side exhibits stable and consistent change characteristics. After generating the upstream polarity consistency results and the downstream polarity consistency results, the polarity consistency results and the amplitude asymmetry discrimination quantity are jointly analyzed. Based on the magnitude of the amplitude asymmetry discrimination quantity and the combination relationship of the upstream and downstream polarity consistency results, a fault type discrimination label is determined. Specifically, when the amplitude asymmetry discrimination quantity is significant and at least one side polarity consistency result is established, a grounding indication label for grounding faults is generated. When the amplitude asymmetry discrimination quantity is small and neither the upstream nor downstream polarity consistency results are established, a grounding indication label for non-grounding faults is generated. Based on this, and combined with the correspondence of the dominant polarities of different orientation sides, a phase indication label for distinguishing between phase-to-phase faults and single-phase faults is generated.

8. A smart fault detection device for distribution feeders based on traveling wave characteristics, characterized in that, include: The acquisition unit is used to synchronously acquire voltage and current signals at at least one monitoring point of the power distribution feeder during operation, and generate time-series raw sampling data based on a unified timing reference. When a transient disturbance exceeding a preset threshold appears in the raw sampling data, the traveling wave disturbance event is identified and candidate traveling wave data is formed. The construction unit is used to extract the first arrival time of the traveling wave front, the change in traveling wave amplitude, and the polarity direction of the traveling wave for candidate traveling wave data. Based on the correspondence between the first arrival time of the traveling wave front and the unified timing reference, a traveling wave timing consistency criterion is constructed, and a set of effective traveling wave features is obtained by filtering accordingly. The analysis unit is used to perform correlation analysis between the effective traveling wave feature set and the historical operating feature data of the distribution feeder. Based on the historical distribution characteristics of the traveling wave amplitude change and the traveling wave polarity direction, it eliminates the traveling wave features corresponding to non-fault disturbances to form the target traveling wave feature set. The calculation unit is used to calculate the propagation distance of the traveling wave and determine the location of the fault based on the time difference between the first arrival times of the traveling wave wavefront in the target traveling wave feature set and the line parameters of the distribution feeder. The analysis unit is used to analyze the fault type and grounding characteristics of the distribution feeder based on the determined location of the fault, and to output the fault analysis result by comprehensively considering the polarity direction and amplitude change of the traveling wave in the target traveling wave feature set.

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