Intelligent fusion terminal voltage sag event capture recording method

Abstract

The application relates to the technical field of power operation monitoring, and discloses a kind of intelligent fusion terminal voltage sag event capture record method, comprising: based on intelligent fusion terminal continuously sampling the electrical operating parameter of low voltage side of distribution area, and constructs electrical parameter sequence;In each statistical cycle, set observation window, and identify the abnormal observation window in each statistical cycle based on the electrical parameter sequence;Voltage change stability hypothesis test is carried out to abnormal observation window, and the voltage sag event is preliminarily screened according to the hypothesis test result;Voltage sag event is confirmed based on the synchronous change characteristics of electrical operating parameter, and the time range corresponding to voltage sag event is recorded.The application realizes the accurate identification and reliable record of voltage sag event, improves the accuracy and anti-interference ability of sag detection.

Description

Technical Field

[0001] This application relates to the technical field of power operation monitoring, specifically to a method for capturing and recording voltage dip events in an intelligent fusion terminal. Background Technology

[0002] In low-voltage distribution transformer areas, voltage sag events are characterized by short duration, rapid amplitude changes, and susceptibility to being masked by statistical averaging processes. This is especially true in scenarios involving distributed power supply integration, centralized charging pile operation, and frequent single-phase impact loads, where sag phenomena exhibit high frequency and irregularity. Existing converged terminals generally employ periodic sampling and minute-level statistical methods based on the effective voltage value to monitor voltage quality. While this method meets the needs of conventional voltage qualification rate statistics and long-term operational analysis, it is prone to problems such as untriggered events, missing records, or misjudgments when facing short-term, localized, and non-continuous voltage sag events. Furthermore, relying solely on the voltage value itself for sag determination makes it difficult to distinguish between real grid disturbances and anomalies caused by non-electrical factors such as measurement noise and communication jitter, affecting the reliability of event records and the value of subsequent operation and maintenance analysis.

[0003] For example, Chinese Patent CN118091305B discloses a method and system for identifying voltage sags originating from the same source. The identification method includes the following steps: acquiring voltage sag monitoring data recorded by a power grid voltage sag monitoring device; based on the propagation law of voltage sag waveforms between multi-stage transformers, using the Hausdorff distance algorithm to measure the waveform similarity between multiple sets of voltage sag monitoring data, and using the effective value absolute difference method to calculate the duration of the sag; deriving the formula for the transmission of phase jump values ​​through the transformer, using linear interpolation to accurately calculate the phase jump values ​​during the sag process, and applying Euclidean distance to calculate the similarity of phase jump values ​​of multiple sag waveforms respectively; obtaining voltage sag data feature indicators, including waveform similarity, duration, and three-dimensional features of phase jump, forming voltage sag data points, which together constitute a common source identification clustering matrix; based on the color statistics of data points in the point cloud image, counting the number of clusters in the clustering results, and outputting the number of common source data groups of sags and the clustering results. This technical solution has the problem mentioned in the background of this application: it is difficult to distinguish between real power grid disturbances and anomalies caused by non-electrical factors such as measurement noise and communication jitter.

[0004] The information disclosed in this background section is intended only to enhance the understanding of the overall background of this application and should not be construed as an admission or in any way implying that the information constitutes prior art known to those skilled in the art. Summary of the Invention

[0005] The technical problem to be solved by this application is to overcome the defects of the prior art and provide a method for capturing and recording voltage sag events in an intelligent fusion terminal, so as to achieve accurate identification and reliable recording of voltage sag events and improve the accuracy and anti-interference capability of sag detection.

[0006] To solve the above-mentioned technical problems, this application provides the following technical solution:

[0007] A method for capturing and recording voltage sag events in an intelligent fusion terminal includes the following steps:

[0008] Based on the intelligent fusion terminal, the electrical operating parameters of the low-voltage side of the distribution substation are continuously sampled and an electrical parameter sequence is constructed;

[0009] An observation window is set within each statistical period, and abnormal observation windows within each statistical period are identified based on the electrical parameter sequence.

[0010] Hypothesis testing is performed on the voltage change stability within the abnormal observation window, and voltage sag events are initially screened based on the hypothesis test results.

[0011] Voltage sag events are confirmed based on the synchronous change characteristics of electrical operating parameters, and the time range corresponding to the voltage sag events is recorded.

[0012] As a preferred embodiment of the intelligent fusion terminal voltage sag event capture and recording method described in this application, the electrical operating parameters include: the effective values ​​of three-phase voltage, the effective values ​​of three-phase current, and the system frequency; the electrical parameter sequence includes the time sequence of each electrical operating parameter.

[0013] The method for setting the observation window within any statistical period is as follows:

[0014] Set a sliding window of length N; N is a positive integer; based on the sliding window, extract observation windows on the time series of the three-phase voltage RMS values ​​respectively; for any phase voltage RMS value, slide the sliding window on the corresponding time series, and each slide extracts an observation window within the statistical period.

[0015] As a preferred embodiment of the intelligent fusion terminal voltage sag event capture and recording method described in this application, the method for identifying abnormal observation windows within any statistical period is as follows:

[0016] A voltage change sequence is constructed for each observation window; the voltage change sequence of any observation window is a time series of voltage change intensity, and the time contained in the voltage change sequence corresponds one-to-one with the observation window; for any phase voltage effective value, the voltage change intensity at any time is the absolute value of the difference between the voltage effective value at the corresponding time and the voltage effective value at the adjacent previous time.

[0017] Set reference variation intensity for the effective value of each of the three phase voltages;

[0018] Calculate the mean value of the voltage change intensity at each time step in each voltage change sequence, and use it as the change intensity of the observation window corresponding to each voltage change sequence;

[0019] For any observation window, if the intensity of the change is greater than p times the corresponding reference intensity of the change, and there are at least n consecutive abnormal moments in the corresponding voltage change sequence, then the observation window is an abnormal observation window within the statistical period; n is a positive integer.

[0020] As a preferred embodiment of the intelligent fusion terminal voltage sag event capture and recording method described in this application, the method for setting a reference change intensity for any phase voltage effective value is as follows: calculate the average value of the voltage change intensity at each moment within the statistical period, and use it as the reference change intensity for the corresponding phase voltage effective value;

[0021] The method for determining whether any moment in a voltage change sequence is an abnormal moment is as follows: if the voltage change intensity at any moment in the voltage change sequence is greater than q times the corresponding reference change intensity, then the corresponding moment is an abnormal moment.

[0022] As a preferred embodiment of the intelligent fusion terminal voltage sag event capture and recording method described in this application, wherein: for any abnormal observation window, the null hypothesis of the hypothesis test is: within the statistical period, the voltage change intensity of the voltage effective value corresponding to the abnormal observation window follows the same stable distribution.

[0023] The method for hypothesis testing of voltage stability for any abnormal observation window is as follows:

[0024] The median of the voltage change intensity of the voltage effective value corresponding to the abnormal observation window within the statistical period is extracted as the distribution location parameter;

[0025] The median absolute deviation of the voltage change intensity of the voltage effective value corresponding to the abnormal observation window within the statistical period is calculated, and the median absolute deviation is converted into a distribution scale parameter by standardization coefficient conversion.

[0026] The normalized deviation of the abnormal observation window is calculated based on the intensity of change of the abnormal observation window and the distribution location parameter and distribution scale parameter.

[0027] Set the significance level and calculate the rejection region threshold based on the significance level;

[0028] If the standardized deviation of the abnormal observation window is greater than or equal to the rejection region threshold, the null hypothesis is rejected; otherwise, the null hypothesis is not rejected.

[0029] As a preferred embodiment of the intelligent fusion terminal voltage sag event capture and recording method described in this application, the calculation of the standardized deviation of the abnormal observation window specifically includes: calculating the difference between the change intensity of the abnormal observation window and the distribution location parameter, and dividing the difference by the distribution scale parameter to obtain the standardized deviation of the abnormal observation window;

[0030] The initial screening of voltage sag events based on hypothesis testing results includes: if any abnormal observation window rejects the null hypothesis, then a potential voltage sag event exists within the corresponding abnormal observation window; otherwise, no potential voltage sag event exists within the corresponding abnormal observation window.

[0031] As a preferred embodiment of the intelligent fusion terminal voltage sag event capture and recording method described in this application, wherein: the recording of the time range corresponding to the voltage sag event includes: recording the time range covered by the abnormal observation window corresponding to any voltage sag event as the time range corresponding to the voltage sag event;

[0032] Voltage sag events are confirmed based on the synchronous change characteristics of electrical operating parameters, including confirming voltage sags within each abnormal observation window with potential voltage sag events. The method for confirming voltage sags within any abnormal observation window with potential voltage sag events includes:

[0033] Based on the time series of the three-phase current RMS values, the RMS values ​​of the three-phase current corresponding to the abnormal observation window are extracted respectively; if the RMS value of any phase current corresponding to the abnormal observation window shows synchronous change characteristics, it is confirmed that there is a voltage sag event in the abnormal observation window.

[0034] As a preferred embodiment of the intelligent fusion terminal voltage sag event capture and recording method described in this application, the method for determining whether the effective value of any phase current corresponding to the abnormal observation window exhibits synchronous change characteristics is as follows:

[0035] The mean value of the effective current at each moment corresponding to the abnormal observation window is calculated as the current observation value; the mean value of the effective current at each moment within the statistical period is calculated as the current reference value; the difference between the current observation value and the current reference value is calculated and its absolute value is taken as the rate of change of the effective current value; the ratio of the rate of change to the current reference value is calculated as the relative rate of change of the effective current value; if the relative rate of change is greater than a preset rate of change threshold, the effective current value shows synchronous change characteristics.

[0036] As a preferred embodiment of the intelligent fusion terminal voltage sag event capture and recording method described in this application, the method for confirming a voltage sag in any abnormal observation window with a potential voltage sag event further includes: if the three-phase current RMS value corresponding to the abnormal observation window shows an aggravated imbalance, then the existence of a voltage sag event in the abnormal observation window is confirmed; the method for determining whether the three-phase current RMS value corresponding to the abnormal observation window shows an aggravated imbalance is as follows:

[0037] The mean of the current observations of the three-phase current effective values ​​is calculated as the local current mean of the abnormal observation window; the difference between the maximum value of the current observations of the three-phase current effective values ​​and the local current mean is calculated and divided by the local current mean as the abnormal imbalance degree of the abnormal observation window.

[0038] The mean of the three-phase current effective values ​​and the current reference values ​​are calculated as the statistical current mean; the difference between the maximum value of the three-phase current effective values ​​and the statistical current mean is divided by the statistical current mean and used as the reference unbalance.

[0039] If the difference between the abnormal imbalance and the reference imbalance is greater than the preset imbalance increment threshold, the three-phase current effective value corresponding to the abnormal observation window will show an aggravated imbalance.

[0040] As a preferred embodiment of the intelligent fusion terminal voltage sag event capture and recording method described in this application, the method for confirming a voltage sag in any abnormal observation window with a potential voltage sag event further includes: based on the time series of the system frequency, extracting the sampled value of the system frequency corresponding to the abnormal observation window; if the system frequency corresponding to the abnormal observation window shows synchronous change characteristics, then confirming that a voltage sag event exists in the abnormal observation window; the method for determining whether the system frequency corresponding to the abnormal observation window shows synchronous change characteristics is as follows:

[0041] The difference between the maximum and minimum values ​​of the system frequency corresponding to the abnormal observation window is calculated as the frequency change amplitude of the abnormal observation window; if the frequency change amplitude is greater than the preset frequency change threshold, the system frequency corresponding to the abnormal observation window shows synchronous change characteristics.

[0042] Compared with the prior art, the beneficial effects achieved by this application are as follows:

[0043] This application can identify abnormal states where statistical consistency is disrupted on a short timescale when the voltage statistics are still within the acceptable range, thereby improving the ability to detect voltage sag events. By introducing current and frequency change characteristics that are time-related to voltage changes as supporting evidence, it effectively suppresses misjudgments caused by measurement noise and improves the authenticity and engineering usability of voltage sag event records. Attached Figure Description

[0044] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein:

[0045] Figure 1 A flowchart of the method for capturing and recording voltage sag events in a smart fusion terminal provided in this application;

[0046] Figure 2 The flowchart of the method for hypothesis testing of voltage stability in abnormal observation windows provided in this application is shown. Detailed Implementation

[0047] The technical solution of this application will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments and specific features in the embodiments are detailed descriptions of the technical solution of this application, rather than limitations thereof. In the absence of conflict, the embodiments and technical features in the embodiments can be combined with each other.

[0048] This embodiment describes a method for capturing and recording voltage sag events in an intelligent fusion terminal, referring to... Figure 1 The method includes the following steps:

[0049] Based on the intelligent fusion terminal, the electrical operating parameters of the low-voltage side of the distribution substation are continuously sampled and an electrical parameter sequence is constructed;

[0050] The intelligent fusion terminal is an edge computing terminal device on the low-voltage side of the distribution transformer area. It has the ability to continuously collect parameters such as voltage, current, power and frequency, as well as local data processing capabilities. It can perform statistical analysis locally, confirm and record the identified voltage sag events in time, and support the power quality monitoring and operation and maintenance of the transformer area.

[0051] The electrical operating parameters include the effective values ​​of three-phase voltage, the effective values ​​of three-phase current, and the system frequency; the electrical parameter sequence includes the time series of each electrical operating parameter.

[0052] The effective values ​​of the three-phase voltages include the effective values ​​of phases A, B, and C, reflecting the voltage amplitude levels of the distribution substation in the corresponding phases. The corresponding time series is used to characterize the changes in the three-phase voltages over time. The effective values ​​of the three-phase currents include the effective values ​​of phases A, B, and C, reflecting the load current magnitude of the distribution substation in the corresponding phases. The corresponding time series is used to characterize the changes in the load current of each phase and can be used to help determine whether voltage anomalies are accompanied by load surges, single-phase inrushes, or fault current characteristics. The system frequency is the frequency of the current output from the low-voltage side of the distribution substation and is a basic indicator characterizing the power quality on the low-voltage side.

[0053] Optionally, the effective values ​​of three-phase voltage, effective values ​​of three-phase current, and system frequency are continuously sampled synchronously with a sampling period of 1 second, and time series of each of the above electrical operating parameters are constructed and updated in real time, which serve as the electrical parameter series for subsequent analysis.

[0054] An observation window is set within each statistical period, and abnormal observation windows within each statistical period are identified based on the electrical parameter sequence.

[0055] The statistical period is a standard statistical period for voltage quality, for example, a period length of 1 minute. Within each statistical period, the intelligent fusion terminal calculates the average voltage value based on continuously collected effective voltage values, which is then used as the statistical value of the voltage. This value is subsequently used in existing statistical functions such as voltage qualification rate and voltage deviation detection, and serves as standardized statistical data transmitted externally, meeting the main station system's statistical requirements for voltage operating status. Voltage sag events are characterized by a duration shorter than the statistical period. Their occurrence time accounts for a small proportion within the statistical period, and their impact on the average voltage value within the statistical period is usually insufficient to change the significance level of the statistical value. Therefore, they are difficult to identify in conventional statistics.

[0056] The method for setting the observation window within any statistical period is as follows:

[0057] Set a sliding window of length N; N is a positive integer; based on the sliding window, extract observation windows on the time series of the three-phase voltage RMS values ​​respectively; for any phase voltage RMS value, slide the sliding window on the corresponding time series, and each slide extracts an observation window within the statistical period.

[0058] Optionally, N is set to 10, with the unit being seconds (s). This means that any observation window contains the effective voltage value within 10 seconds, which ensures sufficient coverage of short-term voltage dips while balancing the real-time performance and computational overhead of sliding detection. The sliding window step size can be set to 10 seconds.

[0059] The method for identifying anomalous observation windows within any statistical period is as follows:

[0060] A voltage change sequence is constructed for each observation window; the voltage change sequence of any observation window is a time series of voltage change intensity, and the time contained in the voltage change sequence corresponds one-to-one with the observation window; for any phase voltage effective value, the voltage change intensity at any time is the absolute value of the difference between the voltage effective value at the corresponding time and the voltage effective value at the adjacent previous time.

[0061] Reference change intensity is set for the effective values ​​of the three-phase voltages respectively; the method for setting the reference change intensity for the effective value of any phase voltage is as follows: calculate the average value of the voltage change intensity at each moment within the statistical period, and use it as the reference change intensity for the corresponding phase voltage effective value;

[0062] Calculate the mean value of the voltage change intensity at each time step in each voltage change sequence, and use it as the change intensity of the observation window corresponding to each voltage change sequence;

[0063] For any observation window, if the intensity of the change is greater than p times the corresponding reference intensity of the change, and there are at least n consecutive abnormal moments in the corresponding voltage change sequence, then the observation window is an abnormal observation window within the statistical period; n is a positive integer. For example, for any observation window on the time series of the effective value of phase A voltage, the corresponding reference intensity of the change is the reference intensity of the effective value of phase A voltage.

[0064] The method for determining whether any moment in a voltage change sequence is an abnormal moment is as follows: if the voltage change intensity at any moment in the voltage change sequence is greater than q times the corresponding reference change intensity, then the corresponding moment is an abnormal moment.

[0065] The reference change intensity of any phase voltage RMS value is its representative change level within the statistical period, reflecting the overall voltage fluctuation amplitude under normal operating conditions during that statistical period, i.e., the stable change baseline of the voltage RMS value. When the change intensity of the voltage change sequence is too high, and there are multiple consecutive abnormal moments, the voltage RMS value within the observation window is abnormal and the abnormal change is continuous, posing a high risk of voltage sag. p is a preset first abnormality coefficient, and q is a preset second abnormality coefficient, both of which can be set by those skilled in the art based on actual needs; optionally, p is set to 2 for anomaly judgment based on the mean observation window, with a relatively low value; q is set to 2.5 for anomaly judgment at a single moment, with a relatively high value; n is set to 4 to constrain the continuity of the anomaly, thereby suppressing misjudgments caused by occasional fluctuations.

[0066] Hypothesis testing is performed on the voltage change stability within the abnormal observation window, and voltage sag events are initially screened based on the hypothesis test results.

[0067] For any abnormal observation window, the null hypothesis of the hypothesis test is: within the statistical period, the voltage change intensity of the effective voltage value corresponding to the abnormal observation window follows the same stable distribution.

[0068] In this embodiment, if the null hypothesis is true, the voltage change intensity within the abnormal observation window also follows the stable distribution. In this case, the null hypothesis is not rejected, indicating that the fluctuation of the effective voltage value within the abnormal observation window is a normal fluctuation and not a potential voltage sag event. If the null hypothesis is rejected, the change in the effective voltage value within the abnormal observation window deviates significantly from the overall situation within the statistical period, indicating a potential voltage sag event within the abnormal observation window. Accordingly, the alternative hypothesis is: the voltage change intensity within the abnormal observation window does not follow the stable distribution of voltage change intensity within the statistical period.

[0069] Reference Figure 2 The method for hypothesis testing of voltage stability for any abnormal observation window is as follows:

[0070] The median of the voltage change intensity of the voltage effective value corresponding to the abnormal observation window within the statistical period is extracted as the distribution location parameter;

[0071] The median absolute deviation of the voltage change intensity of the voltage effective value corresponding to the abnormal observation window within the statistical period is calculated, and the median absolute deviation is converted into a distribution scale parameter by standardization coefficient conversion.

[0072] Specifically, firstly, the difference between the voltage change intensity and the distribution location parameter at each moment within the statistical period is calculated, and the absolute value is taken as the absolute deviation at each moment; then, the median of the absolute deviations at each moment within the statistical period is taken to obtain the median absolute deviation; finally, the median absolute deviation is multiplied by a conversion factor to obtain the distribution scale parameter. The conversion factor is 1.4826, which is used to convert the median absolute deviation to a scale with the same dimensions as the standard deviation, making the converted distribution scale parameter more robust than the standard deviation.

[0073] The normalized deviation of the abnormal observation window is calculated based on the intensity of change of the abnormal observation window and the distribution location parameter and distribution scale parameter.

[0074] In this embodiment, the distribution location parameter is the representative level of the voltage change intensity within the statistical period, and the distribution scale parameter is the dispersion of the voltage change intensity within the statistical period. Both describe the overall change distribution within the statistical period. The change intensity of the abnormal observation window describes the local change intensity of the voltage change intensity. If the standardization deviation is small, the local change intensity and the overall change distribution follow a unified stable distribution, that is, the abnormal voltage change within the abnormal observation window belongs to normal fluctuation.

[0075] The calculation of the standardized deviation of the abnormal observation window specifically includes: calculating the difference between the change intensity of the abnormal observation window and the distribution location parameter, and dividing the difference by the distribution scale parameter to obtain the standardized deviation of the abnormal observation window.

[0076] Set the significance level and calculate the rejection region threshold based on the significance level;

[0077] If the standardized deviation of the abnormal observation window is greater than or equal to the rejection region threshold, the null hypothesis is rejected; otherwise, the null hypothesis is not rejected.

[0078] Optionally, the significance level is set to 0.05. Treating the voltage change intensity as an approximate standard normal statistic, the one-sided rejection region threshold of 1.645 can be calculated by looking up a table or based on the quantile function of the approximate standard normal distribution. In a standard normal distribution, the probability of a standardized deviation greater than or equal to 1.645 is only 0.05, meaning the probability of the null hypothesis being true is less than 0.05.

[0079] The initial screening of voltage sag events based on hypothesis testing results includes: if any abnormal observation window rejects the null hypothesis, then a potential voltage sag event exists within the corresponding abnormal observation window; otherwise, no potential voltage sag event exists within the corresponding abnormal observation window.

[0080] This application limits subsequent calculations to the period of abnormal voltage performance, and then performs a hypothesis test on the stability of voltage changes within the statistical period of the abnormal observation window. This allows for a statistically significant determination of whether these local anomalies have deviated from the overall stable distribution of the period, thus making the assessment of voltage sag risk timely and statistically consistent.

[0081] Voltage sag events are confirmed based on the synchronous change characteristics of electrical operating parameters, and the time range corresponding to the voltage sag events is recorded.

[0082] The recording of the time range corresponding to the voltage sag event includes: recording the time range covered by the abnormal observation window corresponding to any voltage sag event as the time range corresponding to the voltage sag event.

[0083] Voltage sag events are confirmed based on the synchronous change characteristics of electrical operating parameters, including confirming voltage sags within each abnormal observation window with potential voltage sag events. The method for confirming voltage sags within any abnormal observation window with potential voltage sag events includes:

[0084] Based on the time series of three-phase current RMS values, the RMS values ​​of the three-phase currents corresponding to the abnormal observation windows are extracted respectively. If the RMS value of any phase current corresponding to the abnormal observation window shows synchronous change characteristics, then a voltage sag event is confirmed to exist in the abnormal observation window. The method for determining whether the RMS value of any phase current corresponding to the abnormal observation window shows synchronous change characteristics is as follows:

[0085] The mean value of the effective current at each moment corresponding to the abnormal observation window is calculated as the current observation value; the mean value of the effective current at each moment within the statistical period is calculated as the current reference value; the difference between the current observation value and the current reference value is calculated and its absolute value is taken as the rate of change of the effective current value; the ratio of the rate of change to the current reference value is calculated as the relative rate of change of the effective current value; if the relative rate of change is greater than a preset rate of change threshold, the effective current value shows synchronous change characteristics.

[0086] Those skilled in the art can set the specific value of the rate of change threshold based on actual needs. For example, for any phase current effective value, the rate of change threshold can be set to 0.3 to distinguish between normal fluctuations and voltage dips caused by load, faults, etc., so as to suppress small fluctuations in the effective value of the current while ensuring effective response to its significant changes, thus balancing stability and sensitivity.

[0087] The method for confirming a voltage sag in any abnormal observation window with a potential voltage sag event further includes: if the three-phase current RMS value corresponding to the abnormal observation window shows an increased imbalance, then the existence of a voltage sag event in the abnormal observation window is confirmed; the method for determining whether the three-phase current RMS value corresponding to the abnormal observation window shows an increased imbalance is as follows:

[0088] The mean of the current observations of the three-phase current effective values ​​is calculated as the local current mean of the abnormal observation window; the difference between the maximum value of the current observations of the three-phase current effective values ​​and the local current mean is calculated and divided by the local current mean as the abnormal imbalance degree of the abnormal observation window.

[0089] The mean of the three-phase current effective values ​​and the current reference values ​​are calculated as the statistical current mean; the difference between the maximum value of the three-phase current effective values ​​and the statistical current mean is divided by the statistical current mean and used as the reference unbalance.

[0090] If the difference between the abnormal imbalance and the reference imbalance is greater than the preset imbalance increment threshold, the three-phase current effective value corresponding to the abnormal observation window will show an aggravated imbalance.

[0091] Those skilled in the art can set specific values ​​for the unbalance increment threshold based on actual needs, such as 0.1, to identify voltage sags caused by single-phase impacts or asymmetrical loads. When the difference between the abnormal unbalance and the reference unbalance is greater than 0.1, it indicates that the unbalance of the three-phase current has exceeded the normal fluctuation range and is a significant change, which can be used as a basis for anomaly judgment. When at least one phase current RMS value shows synchronous change characteristics, or the three-phase current RMS value shows an aggravated unbalance, it indicates that the voltage anomaly is accompanied by load mutation, single-phase impact, or fault current characteristics. The voltage anomaly is not an isolated measurement fluctuation, but is accompanied by changes in the actual current state, enhancing the authenticity of voltage sag events.

[0092] The method for confirming a voltage sag in any abnormal observation window with a potential voltage sag event further includes: extracting sampled values ​​of the system frequency corresponding to the abnormal observation window based on the system frequency time series; if the system frequency corresponding to the abnormal observation window shows synchronous change characteristics, then the existence of a voltage sag event in the abnormal observation window is confirmed; the method for determining whether the system frequency corresponding to the abnormal observation window shows synchronous change characteristics is as follows:

[0093] The difference between the maximum and minimum values ​​of the system frequency corresponding to the abnormal observation window is calculated as the frequency change amplitude of the abnormal observation window; if the frequency change amplitude is greater than the preset frequency change threshold, the system frequency corresponding to the abnormal observation window shows synchronous change characteristics.

[0094] Those skilled in the art can set specific values ​​for the frequency change threshold based on actual needs, such as 0.10Hz, to exclude normal measurement jitter; when the frequency change amplitude exceeds this value, it indicates that it has deviated from the normal operating range. If the frequency change amplitude is abnormally high within the abnormal observation window, its change is time-synchronized with the voltage anomaly, ruling out voltage anomalies caused by measurement noise or sampling disturbances.

[0095] Misjudgments of voltage sags often arise from instantaneous sampling glitches, communication jitter, and transient malfunctions of measurement chips. This application introduces a synchronous response relationship between current and frequency to voltage anomalies, enabling secondary confirmation of the authenticity of voltage sags and effectively distinguishing between real power grid disturbances and measurement noise.

[0096] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0097] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of protection of this application, and these forms are all within the protection scope of this application.

Claims

1. A method for capturing and recording voltage sag events in an intelligent fusion terminal, characterized in that: Includes the following steps: Based on the intelligent fusion terminal, the electrical operating parameters of the low-voltage side of the distribution substation are continuously sampled and an electrical parameter sequence is constructed; An observation window is set within each statistical period, and abnormal observation windows within each statistical period are identified based on the electrical parameter sequence. Hypothesis testing is performed on the voltage change stability within the abnormal observation window, and voltage sag events are initially screened based on the hypothesis test results. Voltage sag events are confirmed based on the synchronous change characteristics of electrical operating parameters, and the time range corresponding to the voltage sag events is recorded.

2. The method for capturing and recording voltage sag events in a smart fusion terminal as described in claim 1, characterized in that: The electrical operating parameters include the effective values ​​of three-phase voltage, the effective values ​​of three-phase current, and the system frequency; the electrical parameter sequence includes the time sequence of each electrical operating parameter. The method for setting the observation window within any statistical period is as follows: Set a sliding window of length N; N is a positive integer; based on the sliding window, extract observation windows on the time series of the three-phase voltage RMS values ​​respectively; for any phase voltage RMS value, slide the sliding window on the corresponding time series, and each slide extracts an observation window within the statistical period.

3. The method for capturing and recording voltage sag events in a smart fusion terminal as described in claim 2, characterized in that: The method for identifying anomalous observation windows within any statistical period is as follows: A voltage change sequence is constructed for each observation window; the voltage change sequence of any observation window is a time series of voltage change intensity, and the time contained in the voltage change sequence corresponds one-to-one with the observation window; for any phase voltage effective value, the voltage change intensity at any time is the absolute value of the difference between the voltage effective value at the corresponding time and the voltage effective value at the adjacent previous time. Set reference variation intensity for the effective value of each of the three phase voltages; Calculate the mean value of the voltage change intensity at each time step in each voltage change sequence, and use it as the change intensity of the observation window corresponding to each voltage change sequence; For any observation window, if the intensity of the change is greater than p times the corresponding reference intensity of the change, and there are at least n consecutive abnormal moments in the corresponding voltage change sequence, then the observation window is an abnormal observation window within the statistical period; n is a positive integer.

4. The method for capturing and recording voltage sag events in a smart fusion terminal as described in claim 3, characterized in that: The method for setting a reference change intensity for any phase voltage effective value is as follows: calculate the average value of the voltage change intensity at each moment within the statistical period, and use it as the reference change intensity for the corresponding phase voltage effective value; The method for determining whether any moment in a voltage change sequence is an abnormal moment is as follows: if the voltage change intensity at any moment in the voltage change sequence is greater than q times the corresponding reference change intensity, then the corresponding moment is an abnormal moment.

5. The method for capturing and recording voltage sag events in a smart fusion terminal as described in claim 4, characterized in that: For any abnormal observation window, the null hypothesis of the hypothesis test is: within the statistical period, the voltage change intensity of the effective voltage value corresponding to the abnormal observation window follows the same stable distribution. The method for hypothesis testing of voltage stability for any abnormal observation window is as follows: The median of the voltage change intensity of the voltage effective value corresponding to the abnormal observation window within the statistical period is extracted as the distribution location parameter; The median absolute deviation of the voltage change intensity of the voltage effective value corresponding to the abnormal observation window within the statistical period is calculated, and the median absolute deviation is converted into a distribution scale parameter by standardization coefficient conversion. The normalized deviation of the abnormal observation window is calculated based on the intensity of change of the abnormal observation window and the distribution location parameter and distribution scale parameter. Set the significance level and calculate the rejection region threshold based on the significance level; If the standardized deviation of the abnormal observation window is greater than or equal to the rejection region threshold, the null hypothesis is rejected; otherwise, the null hypothesis is not rejected.

6. The method for capturing and recording voltage sag events in a smart fusion terminal as described in claim 5, characterized in that: The calculation of the standardized deviation of the anomaly observation window specifically includes: calculating the difference between the change intensity of the anomaly observation window and the distribution location parameter, and dividing the difference by the distribution scale parameter to obtain the standardized deviation of the anomaly observation window; The initial screening of voltage sag events based on hypothesis testing results includes: if any abnormal observation window rejects the null hypothesis, then a potential voltage sag event exists within the corresponding abnormal observation window; otherwise, no potential voltage sag event exists within the corresponding abnormal observation window.

7. The method for capturing and recording voltage sag events in a smart fusion terminal as described in claim 6, characterized in that: The recording of the time range corresponding to the voltage sag event includes: recording the time range covered by the abnormal observation window corresponding to any voltage sag event as the time range corresponding to the voltage sag event; Voltage sag events are confirmed based on the synchronous change characteristics of electrical operating parameters, including confirming voltage sags within each abnormal observation window with potential voltage sag events. The method for confirming voltage sags within any abnormal observation window with potential voltage sag events includes: Based on the time series of the three-phase current RMS values, the RMS values ​​of the three-phase current corresponding to the abnormal observation window are extracted respectively; if the RMS value of any phase current corresponding to the abnormal observation window shows synchronous change characteristics, it is confirmed that there is a voltage sag event in the abnormal observation window.

8. The method for capturing and recording voltage sag events in a smart fusion terminal as described in claim 7, characterized in that: The method for determining whether the effective value of any phase current corresponding to an abnormal observation window exhibits synchronous change characteristics is as follows: The mean value of the effective current at each moment corresponding to the abnormal observation window is calculated as the current observation value; the mean value of the effective current at each moment within the statistical period is calculated as the current reference value. The difference between the observed current value and the reference current value is calculated and the absolute value is taken as the rate of change of the effective current value; The ratio of the rate of change to the current reference value is calculated as the relative rate of change of the effective current value; if the relative rate of change is greater than a preset rate of change threshold, the effective current value exhibits synchronous change characteristics.

9. The method for capturing and recording voltage sag events in a smart fusion terminal as described in claim 8, characterized in that: The method for confirming a voltage sag in any abnormal observation window with a potential voltage sag event further includes: if the three-phase current RMS value corresponding to the abnormal observation window shows an increased imbalance, then the existence of a voltage sag event in the abnormal observation window is confirmed; the method for determining whether the three-phase current RMS value corresponding to the abnormal observation window shows an increased imbalance is as follows: The mean of the current observations of the three-phase current effective values ​​is calculated as the local current mean of the abnormal observation window; the difference between the maximum value of the current observations of the three-phase current effective values ​​and the local current mean is calculated and divided by the local current mean as the abnormal imbalance degree of the abnormal observation window. The mean of the three-phase current effective values ​​and the current reference values ​​are calculated as the statistical current mean; the difference between the maximum value of the three-phase current effective values ​​and the statistical current mean is divided by the statistical current mean and used as the reference unbalance. If the difference between the abnormal imbalance and the reference imbalance is greater than the preset imbalance increment threshold, the three-phase current effective value corresponding to the abnormal observation window will show an aggravated imbalance.

10. The method for capturing and recording voltage sag events in a smart fusion terminal as described in claim 9, characterized in that: The method for confirming a voltage sag in any abnormal observation window with a potential voltage sag event further includes: extracting sampled values ​​of the system frequency corresponding to the abnormal observation window based on the system frequency time series; if the system frequency corresponding to the abnormal observation window shows synchronous change characteristics, then the existence of a voltage sag event in the abnormal observation window is confirmed; the method for determining whether the system frequency corresponding to the abnormal observation window shows synchronous change characteristics is as follows: The difference between the maximum and minimum values ​​of the system frequency corresponding to the abnormal observation window is calculated as the frequency change amplitude of the abnormal observation window; if the frequency change amplitude is greater than the preset frequency change threshold, the system frequency corresponding to the abnormal observation window shows synchronous change characteristics.

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