High-precision voltage fluctuation real-time monitoring method and system based on electric energy meter

Abstract

The invention relates to the technical field of power system monitoring, and discloses a high-precision voltage fluctuation real-time monitoring method and system based on an electric energy meter. The method comprises the following steps: collecting voltage waveform data and carrying out denoising and zero point detection; calculating an interval between adjacent zero points, and identifying a phase deviation event; analyzing a frequency component to determine a phase change trend, and classifying offset events; establishing a state association relationship; triggering a response signal; regulating and controlling the priority; and verifying the waveform stability and generating a recovery log. According to the invention, high-precision real-time monitoring and intelligent regulation and control of voltage phase deviation are realized, and the stability and power supply quality of a power system under new energy access are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power system monitoring, and in particular to a high-precision voltage fluctuation real-time monitoring method and system based on an electric energy meter. BACKGROUND

[0002] With large-scale access of intermittent new energy such as wind power and photovoltaic power, and the increasing complexity of user-side loads, voltage fluctuation problems in the power grid have become increasingly prominent. In particular, voltage phase shift has become a key factor affecting power supply reliability. Phase shift not only leads to reduced efficiency and shortened life of electrical equipment, but also may cause system-level faults such as power grid resonance and protection misoperation.

[0003] At present, traditional voltage fluctuation monitoring methods mainly rely on amplitude monitoring and simple zero-crossing detection technology. In a typical prior art scheme, a filter is used to filter the input alternating current signal, and then a comparator is used to convert the filtered signal into a square wave output, and a deviation voltage correction circuit and a control circuit are used to work together. The control circuit calculates the phase difference between the square wave outputs of the comparator and generates a control signal to drive the deviation voltage correction circuit to correct the deviation voltage at the base of the comparator, thereby avoiding phase detection errors caused by the "dead zone" of the comparator.

[0004] However, the prior art scheme mainly relies on local compensation of the hardware circuit, lacks software algorithm analysis and real-time tracking capability for dynamic changes in the phase angle under complex power grid environments, and only corrects the dead zone of the comparator without considering the influence of power grid noise, harmonic interference and waveform distortion on the accuracy of zero-crossing detection, resulting in reduced monitoring accuracy under nonlinear load or new energy access scenarios. In summary, the prior art results in low accuracy of voltage fluctuation real-time monitoring. SUMMARY

[0005] The present application provides a high-precision voltage fluctuation real-time monitoring method and system based on an electric energy meter to solve the problem of low accuracy of voltage fluctuation real-time monitoring in the prior art.

[0006] In a first aspect, to solve the above technical problems, the present application provides a high-precision voltage fluctuation real-time monitoring method based on an electric energy meter, comprising: collecting voltage waveform data, denoising the voltage waveform data and extracting zero point positions to determine zero-crossing detection results; According to the zero-crossing detection results, the interval time between adjacent zero-crossings is calculated, and based on the deviation of the interval time from the preset standard period, potential phase shift events are identified, and a shift event list is generated; analyzing frequency components corresponding to events in the list of phase shift events, determining a phase angle change value and a change trend, and classifying the phase shift events based on the change trend to obtain a classified shift type; According to the classified shift type, the associated state parameters are obtained, and the state association relationship between the shift type and the state parameters is established; Based on the state association relationship, a high correlation parameter group is screened out, and when the phase shift amplitude of the high correlation parameter group exceeds a preset warning threshold, a real-time response signal is triggered to generate a response signal sequence; According to the response signal sequence, the priority of the control operation is adjusted to generate an operation suggestion queue; Verify the waveform sequence change after executing the operation suggestion queue, if the interval time after the change meets the preset standard period, mark it as a stable recovery event, and generate a recovery event log.

[0007] In an optional implementation, the voltage waveform data is collected, the voltage waveform data is denoised and the zero point position is extracted, and the zero point detection result is determined, including: Denoise the voltage waveform data to obtain a denoised waveform sequence; Extract the zero point position from the denoised waveform sequence to obtain a zero point position set; Calculate the similarity between the denoised waveform sequence and the pre-established reference sine wave, combine the zero point position set to determine the accurate time of the waveform crossing the zero axis, and obtain the zero point detection sequence as the zero point detection result.

[0008] In an optional implementation, the interval time between adjacent zero points is calculated according to the zero point detection result, and based on the deviation of the interval time from the preset standard period, a potential phase shift event is identified, and a shift event list is generated, including: According to the zero point detection result, the time stamps of adjacent zero points are obtained, and the time difference between each pair of adjacent zero points is calculated to generate a zero point interval set; If the interval time in the zero point interval set deviates from the preset standard period by more than a preset deviation threshold, the interval time is marked as a potential phase shift event; Summarize all the marked potential phase shift events to generate the shift event list.

[0009] In an optional implementation, the frequency components corresponding to events in the list of phase shift events are analyzed, the phase angle change value and the change trend are determined, and the phase shift events are classified based on the change trend to obtain a classified shift type, including: According to the offset event list, the starting time and offset amplitude data of each event are obtained, and the frequency components corresponding to each event are calculated by Fourier transform to generate a frequency component set; According to the frequency component set, the frequency spectrum in the time window before and after the event occurrence is compared, and the phase difference is calculated to obtain a phase angle change value set; The change trend is extracted from the phase angle change value set; If the matching degree of the change trend and the preset typical load switching characteristic mode exceeds the preset signal threshold, the corresponding event is classified as an offset caused by load switching to obtain a preliminary offset type set; According to the preliminary offset type set, the starting time and the offset amplitude data are linked, and the classified offset type is obtained through an event marker linking operation.

[0010] In an optional implementation, according to the classified offset type, the associated state parameters are obtained, and a state association relationship between the offset type and the state parameters is established, including: According to the classified offset type, the event timestamp and the classification marker are extracted, the event timestamp is time-aligned with the current load data and new energy access data through a time sequence matching operation, and a preliminary time sequence association set is obtained; The correlation coefficient between the current load data and the new energy access data and the offset type is calculated, parameters with a correlation coefficient greater than a preset correlation threshold are screened and retained to form a high-correlation parameter set; The parameters in the high-correlation parameter set are clustered and grouped according to time sequence characteristics to obtain a parameter grouping set; Based on the matching relationship between the parameter grouping set and the offset type, an association analysis operation is performed between the state parameters and the offset type to obtain the state association relationship.

[0011] In an optional implementation, based on the state association relationship, a high-correlation parameter group is screened out, and when the phase offset amplitude of the high-correlation parameter group exceeds a preset warning threshold, a real-time response signal is triggered to generate a response signal sequence, including: The phase offset amplitude data and the time sequence characteristics in the state association relationship are obtained; The phase offset amplitude data is compared with the preset warning threshold, and when the warning threshold is exceeded, a real-time response signal is generated to form a real-time response signal set; The signals in the real-time response signal set are time-sequentially sorted according to the timestamp, matched and aligned with the time sequence characteristics to generate the response signal sequence.

[0012] In an alternative embodiment, the adjusting the priority of the regulation operation according to the response signal sequence, and generating an operation suggestion queue comprises: According to the response signal sequence, the fluctuation characteristic information in the new energy access data is obtained; The fluctuation records with an amplitude exceeding a preset fluctuation amplitude threshold in the fluctuation characteristic information are extracted, and a candidate fluctuation event set is generated; The time identifier of the candidate fluctuation event set is time-matched with the starting time of the offset event list, and when the time deviation is less than a preset time matching threshold, it is determined that the time matching is successful; Based on the severity parameters of each event in the matching successful associated event set, the priority weight of the regulation operation is recalculated; In an alternative embodiment, the verifying the waveform sequence change after the operation suggestion queue is executed, and if the interval time after the change meets the preset standard period, it is marked as a stable recovery event, and a recovery event log is generated, comprising: The real-time voltage waveform after the regulation operation is executed is collected, and the waveform sequence to be analyzed is extracted; The similarity between the waveform sequence and a preset reference waveform is calculated, and when the similarity value exceeds a preset similarity threshold, it is marked as a candidate stable recovery event; The time interval between the candidate stable recovery event and the adjacent event is calculated, and an actual period sequence is generated; The actual period sequence is compared with the standard period, and when the period deviation is less than a preset tolerance threshold, it is confirmed as a stable recovery event and a recovery event log is generated.

[0013] In a second aspect, the present application provides a high-precision voltage fluctuation real-time monitoring system based on an electric energy meter, comprising: A waveform collection and preprocessing module collects voltage waveform data, denoises the voltage waveform data, extracts the zero point position, and determines the zero point detection result; A phase offset identification module calculates the interval time between adjacent zero points according to the zero point detection result, and identifies a potential phase offset event based on the deviation of the interval time from a preset standard period, and generates an offset event list; An event classification and analysis module analyzes the frequency components corresponding to the events in the offset event list, determines the phase angle change value and change trend, and classifies the phase offset events based on the change trend to obtain the classified offset type; A state association establishing module obtains the associated state parameters according to the classified offset type, and establishes a state association relationship between the offset type and the state parameters; The response signal generation module filters out a high-correlation parameter group based on the state correlation relationship, and triggers a real-time response signal to generate a response signal sequence when a phase shift amplitude of the high-correlation parameter group exceeds a warning threshold. The operation queue optimization module adjusts the priority of the control operation according to the response signal sequence to generate an operation suggestion queue. The stable verification feedback module verifies the waveform sequence change after the operation suggestion queue is executed, and if the interval time after the change meets a preset standard period, marks a stable recovery event and generates a recovery event log.

[0014] In a third aspect, the present application further provides an electronic device, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor implements the high-precision voltage fluctuation real-time monitoring method based on an electric energy meter according to any one of the above embodiments when executing the computer program.

[0015] In a fourth aspect, the present application further provides a computer readable storage medium, comprising a stored computer program, wherein the computer readable storage medium controls the device where the computer readable storage medium is located to execute the high-precision voltage fluctuation real-time monitoring method based on an electric energy meter according to any one of the above embodiments when the computer program is running.

[0016] Compared with the prior art, the present application has the following beneficial effects: (1) The present application effectively retains the phase characteristics of the original waveform, reduces noise interference, and improves the accuracy of zero point detection by using the wavelet transform algorithm to denoise the voltage waveform data. (2) The present application accurately identifies the zero-crossing moment by comparing with the reference sinusoidal wave through the cross-correlation algorithm, further improving the accuracy of phase shift detection. (3) The present application can effectively distinguish between load switching and phase shift caused by faults by combining Fourier transform analysis of frequency components and phase angle change trend, improving the reliability of event classification. (4) The present application realizes intelligent identification and response of phase shift events by establishing state correlation relationship and filtering high-correlation parameter group, improving the real-time performance and adaptability of the system. BRIEF DESCRIPTION OF DRAWINGS

[0017] Figure 1 is a flowchart of a high-precision voltage fluctuation real-time monitoring method based on an electric energy meter provided by the first embodiment of the present application; Figure 2 is a structural diagram of a high-precision voltage fluctuation real-time monitoring system based on an electric energy meter provided by the second embodiment of the present application. DETAILED DESCRIPTION

[0018] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative effort belong to the scope of protection of the present application.

[0019] With reference to Figure 1 The first embodiment of the present application provides a high-precision voltage fluctuation real-time monitoring method based on an electric energy meter, comprising the following steps: S101, collecting voltage waveform data, performing denoising processing on the voltage waveform data and extracting zero point positions to determine zero point detection results; S102, calculating interval times between adjacent zero points according to the zero point detection results, and identifying potential phase offset events based on deviations of the interval times from a preset standard period to generate an offset event list; S103, analyzing frequency components corresponding to events in the offset event list to determine phase angle change values and change trends, and classifying the phase offset events based on the change trends to obtain classified offset types; S104, obtaining associated state parameters according to the classified offset types, and establishing a state association relationship between the offset types and the state parameters; S105, screening out a high-correlation parameter group based on the state association relationship, and triggering a real-time response signal to generate a response signal sequence when a phase offset amplitude of the high-correlation parameter group exceeds a preset warning threshold; S106, adjusting priorities of control operations according to the response signal sequence to generate an operation suggestion queue; S107, verifying waveform sequence changes after executing the operation suggestion queue, and if the changed interval times conform to the preset standard period, marking as a stable recovery event and generating a recovery event log.

[0020] In step S101, voltage waveform data is collected, denoising processing is performed on the voltage waveform data, and zero point positions are extracted to determine zero point detection results, comprising: Step S1011, performing denoising processing on the voltage waveform data to obtain a denoised waveform sequence; Step S1012, extracting zero point positions from the denoised waveform sequence to obtain a zero point position set; Step S1013, calculating the similarity between the denoised waveform sequence and a pre-established reference sinusoidal wave, combining the zero point position set to determine accurate time when the waveform crosses the zero axis, and obtaining a zero point detection sequence as the zero point detection results.

[0021] In step S1011, the voltage waveform data is denoised to obtain a denoised waveform sequence.

[0022] It should be noted that the power grid voltage waveform data is obtained by a collection device (such as a high-precision voltage sensor), and the sampling rate is preferably 10 kHz to capture the waveform details; it is worth noting that the front end of the metering chip of the electric energy meter needs to sample the voltage through a voltage dividing resistor or a voltage transformer, which can be understood as an implementation of the high-precision voltage sensor. The original voltage waveform data is denoised using a wavelet transform algorithm, and a Daubechies wavelet basis (such as db4) is preferably used because it has good adaptability to non-stationary signals in power systems.

[0023] In a specific embodiment, in a specific implementation, 3 to 5 layers of decomposition are usually performed, a threshold is generated using an unbiased risk estimation (rigrsure) or heuristic threshold (heursure) rule, and a soft threshold function is used to process the detail coefficients of each layer. The above parameter settings are suitable for a sampling rate environment of 6.4 kHz to 12.8 kHz to ensure that the high-frequency noise is effectively separated while the fundamental frequency and low-order harmonic components are retained.

[0024] In step S1012, zero point positions are extracted from the denoised waveform sequence to obtain a set of zero point positions.

[0025] It should be noted that the threshold judgment method is used to extract the zero point positions from the first waveform sequence. The preset zero point judgment threshold range is set in per unit or percentage form based on the system rated voltage (Un) to adapt to different voltage levels. Preferably, the threshold range is set to -0.002Un to +0.002Un (i.e. ±0.2%Un). For example, for a system with a rated voltage of 220V, the threshold range is about -0.44V to +0.44V; for a 10kV system, the threshold range is about -20V to +20V. When the voltage value in the sequence falls within the threshold range, it is determined as a zero point position.

[0026] The threshold range can be dynamically adjusted according to the device measurement accuracy, background noise level and system operating state of the actual application scenario to achieve the optimal balance between detection sensitivity and anti-interference ability. The principle of threshold setting is to filter out as much false zero-crossing signals caused by noise as possible while ensuring that no real zero-crossing points are missed.

[0027] In step S1013, the similarity between the denoised waveform sequence and the pre-established reference sinusoidal wave is calculated, and the accurate time of the waveform crossing the zero axis is determined in combination with the set of zero point positions to obtain a zero point detection sequence as the zero point detection result.

[0028] It should be noted that the reference sine wave data is dynamically generated: when the system is initialized or a step change in grid frequency is detected, the reconstruction process of the reference waveform is automatically triggered. This process selects a stable voltage waveform under the current state (such as a frequency deviation of less than 0.05 Hz within 1 second), accurately extracts its fundamental frequency, amplitude and initial phase through Fast Fourier Transform (FFT), and generates a ideal sine wave synchronized with the current grid actual frequency as the reference waveform in real time. The cross-correlation algorithm is used to calculate the similarity of the first waveform sequence and the reference sine wave.

[0029] In a specific embodiment, by sliding window comparison, the correlation coefficient is calculated, and the time point corresponding to the peak value of the correlation coefficient is the accurate time when the waveform crosses the zero axis. For example, if the correlation coefficient is maximum at t=0.0102s, it is determined that the time when the zero point crosses is the zero point detection sequence. This method effectively corrects the zero point offset caused by noise or equipment deviation, and improves the accuracy and reliability of phase detection.

[0030] In step S102, according to the zero point detection result, the interval time between adjacent zero points is calculated, and based on the deviation of the interval time from the preset standard period, a potential phase offset event is identified, and an offset event list is generated, including: Step S1021, according to the zero point detection result, the time stamp of adjacent zero points is obtained, and the time difference between each pair of adjacent zero points is calculated to generate a zero point interval set; Step S1022, if the interval time in the zero point interval set deviates from the preset standard period by more than a preset deviation threshold, the interval time is marked as a potential phase offset event; Step S1023, all marked potential phase offset events are summarized to generate the offset event list.

[0031] In step S1021, according to the zero point detection result, the time stamp of adjacent zero points is obtained, and the time difference between each pair of adjacent zero points is calculated to generate a zero point interval set.

[0032] It should be noted that based on the zero point detection sequence obtained in step S101, the time stamp of adjacent zero points is extracted, the time difference between each pair of continuous zero points is calculated, and a zero point interval set is generated.

[0033] In a specific embodiment, in a grid with a rated frequency of 50Hz, the standard half cycle is 10ms. Assuming that the time stamp unit from the zero point detection sequence is: 0.0000, 0.0100, 0.0201, 0.0299, 0.0402, the zero point interval set is calculated as: 0.0100, 0.0101, 0.0098, 0.0103 seconds.

[0034] In step S1022, if the interval time in the set of zero-point intervals deviates from the preset standard period by more than a preset deviation threshold, the interval time is marked as a potential phase shift event.

[0035] It should be noted that the standard period (T_std) in the present application is a preset value, which is calculated based on the rated frequency (f_std) of the power grid. The preset standard period is set based on the rated frequency of the power grid, and for a 50Hz power grid, the standard half period is 10ms. The preset deviation threshold must ensure that false alarms are not triggered within the normal fluctuation range of the power grid frequency (such as ±0.5Hz as specified in GB / T 15945-2008). Therefore, the deviation threshold should be greater than the period change amount corresponding to a ±0.5Hz frequency deviation (about ±400μs).

[0036] Taking into account the monitoring accuracy and anti-interference, the deviation threshold (ΔT_threshold) is preferably set to 300μs to 600μs (i.e. 0.0003 seconds to 0.0006 seconds). This range can effectively filter out period fluctuations caused by normal frequency regulation, and can also sensitively detect real phase shift events caused by load switching, faults, etc.

[0037] In step S1023, all the marked potential phase shift events are summarized to generate the shift event list.

[0038] It should be noted that all the marked potential phase shift events are summarized to generate a structured shift event list.

[0039] In one specific embodiment, all the marked potential phase shift events are summarized in chronological order and stored in a unified structured data list or array structure to form a complete shift event list. Each event entry in the list contains the following field information: the timestamp of the event occurrence, the measured interval time, the absolute value of the deviation from the standard period, and other key data.

[0040] For example, in a programming implementation, an array or list object can be created, and each identified potential phase shift event can be added to the collection in chronological order as an element. Each event element contains specific values of the above-mentioned fields, thereby constructing a structured shift event list, providing a complete and ordered data basis for subsequent frequency analysis and event classification.

[0041] In step S103, the frequency components corresponding to the events in the shift event list are analyzed to determine the phase angle change value and change trend, and the phase shift events are classified based on the change trend to obtain the classified shift type, including: Step S1031, according to the offset event list, the starting time and the offset amplitude data of each event are obtained, and the frequency component corresponding to each event is calculated by Fourier transform to generate a frequency component set; Step S1032, according to the frequency component set, the frequency spectrum in the time window before and after the event occurs is compared, and the phase difference is calculated to obtain a phase angle change value set; Step S1033, the change trend is extracted from the phase angle change value set; Step S1034, if the matching degree of the change trend and the preset typical load switching characteristic mode exceeds the preset signal threshold, the corresponding event is classified as an offset caused by load switching, and a preliminary offset type set is obtained; Step S1035, according to the preliminary offset type set, the starting time and the offset amplitude data are linked, and the classified offset type is obtained by event marker linking operation.

[0042] In step S1031, according to the offset event list, the starting time and the offset amplitude data of each event are obtained, and the frequency component corresponding to each event is calculated by Fourier transform to generate a frequency component set.

[0043] It should be noted that this step extracts the frequency spectrum characteristics of the offset event by frequency domain analysis. In specific implementation, first, the starting time and the offset amplitude data of the event are obtained from the offset event list, then the voltage waveform data in a specific time window before and after the event occurs is selected, the fast Fourier transform algorithm is used to convert the time domain signal into frequency domain representation, and the amplitude and phase information of the main frequency component is extracted.

[0044] In one specific embodiment, taking a certain substation monitoring system as an example, for a phase offset event with a starting time of 10.05 seconds and an offset amplitude of 0.003 seconds, the voltage waveform data in a 0.1 second time window (9.95 seconds to 10.15 seconds) before and after the event occurs is selected. Through 1024-point fast Fourier transform processing, the sampling rate is 10 kHz, and the frequency component data corresponding to the event is obtained: at the fundamental frequency of 50 Hz, the phase before the event occurs is 178.2 degrees, and the amplitude is 219.8 V; the phase after the event occurs is 183.5 degrees, and the amplitude is 218.3 V. At the same time, the amplitude and phase information of the second harmonic (100 Hz) and the third harmonic (150 Hz) is extracted to form a complete frequency component set.

[0045] In step S1032, according to the frequency component set, the frequency spectrum in the time window before and after the event occurs is compared, and the phase difference is calculated to obtain a phase angle change value set.

[0046] It should be noted that the step quantifies the phase angle change by comparing the spectrum characteristics before and after the event. In specific implementation, based on the data in the frequency component set, the phase difference values of each frequency component before and after the event are calculated, and the phase change of the fundamental frequency is focused on, while the influence of harmonic components is considered.

[0047] In a specific embodiment, taking the aforementioned offset event as an example, the phase change of the 50Hz fundamental frequency is calculated: the phase before the event is 178.2 degrees, and the phase after the event is 183.5 degrees, with a phase angle change of +5.3 degrees. At the same time, the 2nd harmonic phase change is +2.1 degrees, and the 3rd harmonic phase change is -1.8 degrees. These phase angle change values are classified and stored according to the event number and frequency component to form a phase angle change value set.

[0048] In step S1033, the change trend is extracted from the phase angle change value set.

[0049] In a specific embodiment, based on the phase angle change value set of multiple offset events, the 50Hz fundamental phase changes are arranged in chronological order: event 1 (10.05 seconds) +5.3 degrees, event 2 (15.30 seconds) +7.8 degrees, and event 3 (21.45 seconds) +9.2 degrees. Through linear regression analysis, it is found that the phase angle change presents an increasing trend, with a change rate of about 0.5 degrees / minute, and the change mode is positive step jump.

[0050] In step S1034, if the matching degree of the change trend with the preset typical load switching characteristic mode exceeds the preset signal threshold, the corresponding event is classified as an offset caused by load switching, and a preliminary offset type set is obtained.

[0051] It should be noted that a typical load switching characteristic mode library is constructed: the mode library is derived from the accumulation and learning of historical data. In the early stage of system deployment, a basic mode library composed of known event data under simulation and typical working conditions (such as high-power motor start-stop, transformer switching, capacitor group switching, etc.) is preset. After the system is running, the manually confirmed or verified offset events with clear classification and their corresponding frequency components and phase angle change trend characteristics are automatically stored in the historical database, and new characteristic modes are generated by periodically mining historical characteristics through clustering algorithms (such as K-Means) to dynamically expand and optimize the initial mode library. The confidence threshold for mode matching is set to 85%, which is based on the 95th percentile of the matching degree data of all verified correctly matched events in the initial training phase (such as 3 consecutive months) of the system as the setting benchmark.

[0052] In one specific embodiment, the trend of the changes of the aforementioned three events is matched with a typical load switching feature mode, and the matching degree reaches 92%, exceeding the confidence threshold of 85%. The phase jump amplitude and duration are highly consistent with the load switching feature, so the three events are classified as the offset caused by load switching, forming a preliminary offset type set containing information such as event number, classification result, and matching degree.

[0053] In step S1035, according to the preliminary offset type set, the starting time and the offset amplitude data are linked, and the classified offset type is obtained through an event marker linking operation.

[0054] In one specific embodiment, the related data of the three load switching events are linked: event 1 (starting time 10.05 seconds, offset amplitude 0.003 seconds, classification: load switching); event 2 (starting time 15.30 seconds, offset amplitude 0.005 seconds, classification: load switching); event 3 (starting time 21.45 seconds, offset amplitude 0.007 seconds, classification: load switching). The data linking operation is realized by assigning a unique event identifier to each event and establishing an event feature index table. Specifically, the system creates a structured data table, each record in the table containing fields such as event identifier, timestamp, offset amplitude, event type, and matching degree, and establishes an association between the event identifier and multi-source data such as original waveform data, frequency analysis results, and associated parameters. This index linking method based on unique identifiers forms a complete classified offset type record, providing multi-dimensional data support for subsequent correlation analysis and state evaluation.

[0055] In step S104, according to the classified offset type, the associated state parameters are obtained, and the state association relationship between the offset type and the state parameters is established, including: Step S1041, according to the classified offset type, the event timestamp and classification marker are extracted, the event timestamp is time-aligned with the current load data and new energy access data through a time sequence matching operation, and a preliminary time sequence association set is obtained; Step S1042, the correlation coefficient between the current load data and the new energy access data and the offset type is calculated, parameters with a correlation coefficient greater than a preset correlation threshold are filtered and retained, and a high correlation parameter set is formed; Step S1043, the parameters in the high correlation parameter set are clustered and grouped according to time sequence features, and a parameter grouping set is obtained; Step S1044, based on the matching relationship between the parameter grouping set and the offset type, an association analysis operation between the state parameters and the offset type is performed, and the state association relationship is obtained.

[0056] In step S1041, the event timestamp and classification label are extracted according to the classified offset type, and the event timestamp is time-aligned with the current load data and the new energy access data through a time sequence matching operation to obtain a preliminary time sequence correlation set.

[0057] It should be noted that the step establishes the correlation between the offset event and the grid operation parameters through a time sequence matching operation. In specific implementation, first, the event timestamp and classification label are extracted from the classified offset type, and then a cubic spline interpolation algorithm is used to unify the monitoring data of different sampling rates to the same time reference, and the time alignment tolerance threshold is set to 0.01 seconds to ensure the time synchronization accuracy of the data.

[0058] In a specific embodiment, taking a certain substation monitoring system as an example, the time stamps of three load switching events extracted from the classification results are 10.05 seconds, 15.30 seconds and 21.45 seconds. Through the time sequence matching operation, the corresponding operation parameters at each time are obtained: at 10.05 seconds, the current load value is 152.3 amperes, and the photovoltaic power station access power is 85.6 kilowatts; at 15.30 seconds, the current load value is 158.7 amperes, and the photovoltaic access power is 82.1 kilowatts; at 21.45 seconds, the current load value is 165.2 amperes, and the photovoltaic access power is 78.9 kilowatts. These matching results are arranged in time sequence to form a preliminary time sequence correlation set containing time stamp, event type, electrical parameter and other multi-dimensional data.

[0059] In step S1042, the correlation coefficient between the current load data and the new energy access data and the offset type is calculated, and parameters with a correlation coefficient greater than a preset correlation threshold are screened and retained to form a high correlation parameter set.

[0060] It should be noted that the step screens the operation parameters highly correlated with the offset event through correlation coefficient analysis. The Pearson correlation coefficient calculation method is used, and the preset correlation threshold is usually set to 0.5-0.7. The threshold is determined based on the statistical significance level a=0.01 to ensure that the selected parameters have statistically significant correlation. The significance level is verified by the following process: first, a null hypothesis that the overall correlation coefficient is 0 is established, and then the Pearson correlation coefficient r and its corresponding p value are calculated based on the sample data. When the calculated p value is less than 0.01, the null hypothesis can be rejected at a confidence level of 99%, and it is considered that the correlation has statistical significance. This significance test ensures that the selected parameter correlation is not caused by random accidental factors, but has statistically significant correlation.

[0061] In a specific embodiment, based on 20 continuous sampling point data in the preliminary time sequence correlation set, the correlation coefficient of the current load value and the load switching event is 0.92, and the correlation coefficient of the new energy access power and the load switching event is -0.87. The absolute values of the correlation coefficients of the two parameters are greater than the preset threshold 0.8, so the two parameters are retained to form a high correlation parameter set containing parameter name, correlation coefficient, significance level and other information.

[0062] In step S1043, the parameters in the high correlation parameter set are clustered and grouped according to the time sequence characteristics to obtain a parameter grouping set.

[0063] It should be noted that this step clusters and groups the high correlation parameters according to the time sequence characteristics by cluster analysis. The K-means clustering algorithm is used, the number of clusters is set to 2, and the elbow rule is used to determine the optimal number of clusters. The feature vector includes the time sequence data of the current load value and the new energy access power, and the Euclidean distance is used as the similarity measure.

[0064] In a specific embodiment, after iterative calculation, the parameters are divided into two clusters with obvious characteristics: cluster 1 contains samples with high current load (150-170 amperes) and low new energy access power (75-85 kilowatts), and all load switching events are located in this cluster; cluster 2 contains samples with low current load (130-150 amperes) and high new energy access power (85-95 kilowatts), and does not contain any offset events. Finally, a parameter grouping set with clear characteristic distinction is formed.

[0065] In step S1044, based on the matching relationship between the parameter grouping set and the offset type, an association analysis operation between the state parameters and the offset type is performed to obtain the state association relationship.

[0066] It should be noted that the step reveals the internal relationship between the state parameters and the deviation types by establishing a quantitative correlation model. The logistic regression algorithm is used to establish the quantitative correlation model. The data set needs to include sufficient classified deviation event samples, the total sample size is not less than 10 times the number of model independent variables, and the sample size of each deviation type is not less than 100. Each sample includes various state parameters aligned at the time of event occurrence and the corresponding deviation type label. After obtaining the historical data set that meets the sample size requirement, the samples are divided into training set and test set in the ratio of 7:3, the SMOTE technology is used to process the class imbalance problem in the training set, and the L2 regularization is introduced to prevent overfitting. The high correlation state parameters selected in the foregoing are used as independent variables, and the deviation type is used as dependent variable, the model parameters are solved by maximum likelihood estimation method, and the weight coefficients and probability calculation formula of each parameter are obtained. After the model training is completed, the state correlation relationship including weight coefficient, probability calculation formula and decision boundary is output, which provides reliable quantitative judgment basis for real-time monitoring system.

[0067] In a specific embodiment, a logistic regression model is established based on the clustering grouping result. The analysis result shows that when the current load value is greater than 150 amperes and the new energy access power is lower than 85 kilowatts, the probability of load switching event reaching 89.7%. After testing set verification, the accuracy of the model is 91.5%, the recall rate is 88.7%, and the F1 score is 90.1%. Finally, the state correlation relationship including weight coefficient, probability calculation formula and decision boundary is obtained, which provides reliable quantitative judgment basis for real-time monitoring system.

[0068] In step S105, based on the state correlation relationship, a high correlation parameter group is screened out, and when the phase deviation amplitude of the high correlation parameter group exceeds a preset warning threshold, a real-time response signal is triggered to generate a response signal sequence, including: Step S1051, obtaining the phase deviation amplitude data and timing characteristics in the state correlation relationship; Step S1052, comparing the phase deviation amplitude data with the preset warning threshold, and generating a real-time response signal when exceeding the warning threshold to form a real-time response signal set; Step S1053, after time stamping the signals in the real-time response signal set in time sequence, matching and aligning with the timing characteristics, generating the response signal sequence.

[0069] In step S1051, the phase deviation amplitude data and timing characteristics in the state correlation relationship are obtained.

[0070] It should be noted that this step extracts the key phase shift feature parameters from the established state association relationship. In specific implementation, the state association relationship library is accessed through the database query interface to extract the phase shift amplitude data and its corresponding time sequence features stored therein, including the time of occurrence, duration, change rate and other time sequence attributes.

[0071] In a specific embodiment, taking a certain power distribution network monitoring system as an example, three phase shift event details are extracted from the state association relationship library: event A phase shift amplitude is 0.05 degrees, the occurrence time is 2.5 seconds, and the change rate is 0.02 degrees / second; event B phase shift amplitude is 0.08 degrees, the occurrence time is 3.2 seconds, and the change rate is 0.025 degrees / second; event C phase shift amplitude is 0.12 degrees, the occurrence time is 4.1 seconds, and the change rate is 0.029 degrees / second. The time stamp and duration of each event are also extracted.

[0072] In step S1052, the phase shift amplitude data is compared with the preset warning threshold, and a real-time response signal is generated when the warning threshold is exceeded, forming a real-time response signal set.

[0073] It should be noted that this step realizes real-time alarm of abnormal events through threshold comparison mechanism. The preset warning threshold is determined based on power grid safe operation standard and historical statistical data, and a multi-level threshold system is set, including a warning threshold (0.05 degrees), an alarm threshold (0.08 degrees) and an emergency threshold (0.12 degrees), which correspond to different response levels respectively.

[0074] In a specific embodiment, the extracted phase shift amplitude data is compared with the multi-level warning threshold: event A amplitude 0.05 degrees reaches the warning threshold, generating a yellow warning signal; event B amplitude 0.08 degrees reaches the alarm threshold, generating an orange alarm signal; event C amplitude 0.12 degrees exceeds the emergency threshold, generating a red emergency signal. These real-time response signals contain event number, alarm level, time stamp and other information, forming a real-time response signal set.

[0075] In step S1053, the signals in the real-time response signal set are time-sequentially sorted according to the time stamp, and then matched and aligned with the time sequence features, generating the response signal sequence.

[0076] It should be noted that this step generates a standardized response signal sequence through time sequence sorting and feature matching. A fast sorting algorithm based on time stamp is used to arrange the response signals in time sequence, and then a time window matching algorithm is used to associate and align the response signals with the corresponding time sequence features.

[0077] In one specific embodiment, the aforementioned three response signals are sorted by time stamp: event A (10:05:02), event B (10:05:05), and event C (10:05:08). Then, they are matched and aligned with the time sequence characteristics: event A matches the 2.5-second duration characteristic, event B matches the 3.2-second duration characteristic, and event C matches the 4.1-second duration characteristic. Finally, a response signal sequence containing time sequence information, alarm level, and duration characteristics is generated, providing complete input basis for subsequent regulation operations.

[0078] In step S106, according to the response signal sequence, the priority of the regulation operation is adjusted, and an operation suggestion queue is generated, including: Step S1061, according to the response signal sequence, obtaining the fluctuation characteristic information in the new energy access data; Step S1062, extracting the fluctuation records with an amplitude exceeding a preset fluctuation amplitude threshold from the fluctuation characteristic information, and generating a candidate fluctuation event set; Step S1063, time sequence matching the time identifier of the candidate fluctuation event set with the starting time of the offset event list, when the time sequence deviation is less than a preset time matching threshold, it is determined that the time sequence matching is successful; Step S1064, based on the severity parameters of each event in the matched associated event set, the priority weight of the regulation operation is recalculated; Step S1065, according to the priority weight, the regulation operation is sorted, and the operation suggestion queue is generated.

[0079] In step S1061, according to the response signal sequence, obtaining the fluctuation characteristic information in the new energy access data.

[0080] It should be noted that this step analyzes the output characteristics of new energy power generation equipment and extracts fluctuation characteristics related to voltage phase offset. Based on the time identifier of the response signal sequence, the new energy output data of the corresponding period is obtained from the SCADA system, and the wavelet packet decomposition algorithm is used to extract the fluctuation characteristic parameters, including fluctuation amplitude, change rate and duration, etc.

[0081] In one specific embodiment, for a certain wind farm access node, according to the event time window recorded by the response signal sequence, the output power data of the wind turbine in this period is obtained. Through 3-layer wavelet packet decomposition, the fluctuation component in the 0.5-5Hz frequency band is extracted, and the fluctuation amplitude of a single 2.5MW unit is calculated to be 12.5% of the rated power, the change rate is 85kW / s, and the duration is 3.2 seconds. These characteristic parameters reflect the correlation characteristics of new energy output fluctuation and voltage phase offset.

[0082] In step S1062, the fluctuation records with an amplitude exceeding a preset fluctuation amplitude threshold in the fluctuation feature information are extracted to generate a candidate fluctuation event set.

[0083] It should be noted that the present step identifies significant new energy fluctuation events through a multi-level threshold screening mechanism. The preset fluctuation amplitude threshold is set based on the technical characteristics of the new energy station and the safe operation requirements of the power grid, combined with the rated capacity of a specific station (such as 50 MW) and the inertia constant of the power grid, and taking into account the requirements of industry standards (such as GB / T 19963-2011). Finally, the first-level fluctuation amplitude threshold is set to 8% of the rated output, and the second-level threshold is set to 12%. The dual-threshold setting is verified by real-time digital simulation, which can realize hierarchical early warning of fluctuation events, and balance the monitoring sensitivity and system anti-interference ability.

[0084] In a specific embodiment, the first-level threshold is set to 8% of the rated output, and the second-level threshold is set to 12%. Three out-of-limit fluctuation events are detected: event 1 with a fluctuation amplitude of 9.2% and a duration of 2.8 seconds; event 2 with a fluctuation amplitude of 13.5% and a duration of 3.5 seconds; and event 3 with a fluctuation amplitude of 15.8% and a duration of 4.2 seconds. These events are classified according to severity to generate a candidate fluctuation event set containing timestamps, fluctuation features, and danger levels.

[0085] In step S1063, the time identifiers of the candidate fluctuation event set are time-matched with the start time of the offset event list. When the time deviation is less than a preset time matching threshold, it is determined that the time matching is successful.

[0086] It should be noted that the present step uses dynamic time warping algorithm for time matching analysis to solve the time alignment problem between different sampling rate data. The preset time matching threshold is determined based on the following technical requirements: first, consider the time scale of the power system transient process (typical fault clearance time 80-120 ms, oscillation period 0.1-2 s), combined with the measurement system accuracy (synchronous phasor measurement device PMU accuracy is ±1% total error, data frame period 10-20 ms), and through the formula to calculate the reference value (where is the transient process characteristic time, is the minimum resolution of the measurement system). Finally, the time matching threshold is determined to be 20-100 ms, which covers more than 90% of the time sequence characteristics of the transient process and meets the measurement accuracy requirements.

[0087] In a specific embodiment, the DTW algorithm is used to calculate the time series similarity of candidate fluctuation events and phase shift events. The matching threshold is set to 0.8, and the time tolerance is 0.1 second. The matching results show that the similarity of fluctuation event 1 and shift event A is 0.92, with a time deviation of 0.3 seconds; the similarity of event 2 and shift event B is 0.87, with a time deviation of 0.4 seconds; the similarity of event 3 and shift event C is 0.94, with a time deviation of 0.2 seconds. The similarity of all matching pairs exceeds the threshold, and the time deviation is less than the tolerance, so the time sequence matching is successful.

[0088] In step S1064, the priority weight of the control operation is recalculated based on the severity parameters of each event in the set of matching events.

[0089] In a specific embodiment, the analytic hierarchy process is used to calculate the comprehensive weight of each event according to the severity parameters of the matching events, including the shift amount (0.05-0.12 degrees), the fluctuation amplitude (9.2-15.8%), and the duration (2.8-4.2 seconds). The severity parameters are obtained through multi-source data fusion, where the shift amount is derived from the real-time monitoring value of the phase angle measurement unit (PMU), the fluctuation amplitude is calculated according to the new energy power fluctuation rate formula , and the duration is determined by the difference between the start and end timestamps of the event. The specific process of the analytic hierarchy process includes: first, establishing a judgment matrix based on the influence degree of the shift amount, the fluctuation amplitude, and the duration on system stability; then calculating the weight of each index (in the example, the weights of the shift amount, the fluctuation amplitude, and the duration are 0.4, 0.35, and 0.25, respectively) through the eigenvalue method; next, performing consistency check to ensure that the consistency ratio CR value of the judgment matrix is less than 0.1; finally, normalizing each event parameter value and weighting the index weight to obtain the weights of events A, B, and C, which are 0.25, 0.35, and 0.40, respectively.

[0090] In step S1065, the control operations are sorted according to the priority weight, and the operation suggestion queue is generated.

[0091] In an embodiment, based on the weight allocation result, the control operations such as voltage adjustment (including adjusting the transformer tap, switching the capacitor reactor) and power control (such as adjusting the active / reactive output of the new energy station) are prioritized to generate an optimized operation queue that prioritizes high-weight events, thereby ensuring the stable operation of the system. It should be noted that the optimized operation queue is a suggestion queue for priority operations of the power grid control, i.e., the operation suggestion queue, which is uploaded to the power grid dispatching system as a reference for control decision-making, and the final execution right still resides in the power grid dispatching system.

[0092] In step S107, the waveform sequence change after the operation suggestion queue is verified, and if the interval time after the change meets the preset standard period, it is marked as a stable recovery event, and a recovery event log is generated, including: Step S1071, collecting real-time voltage waveforms after performing the control operation, and extracting the waveform sequence to be analyzed; Step S1072, calculating the similarity between the waveform sequence and the preset reference waveform, and when the similarity value exceeds the preset similarity threshold, marking it as a candidate stable recovery event; Step S1073, calculating the time interval of the candidate stable recovery event and the adjacent event to generate an actual period sequence; Step S1074, comparing the actual period sequence with the standard period, and when the period deviation is less than the preset tolerance threshold, confirming it as a stable recovery event and generating a recovery event log.

[0093] In step S1071, real-time voltage waveforms after performing the control operation are collected, and the waveform sequence to be analyzed is extracted.

[0094] It should be noted that this step obtains the voltage waveform data after the control by a high-precision sampling device, and extracts the waveform sequence in the key analysis period. Anti-aliasing filter and synchronous sampling technology are used to ensure the accuracy and timing consistency of waveform collection. According to the Nyquist sampling theorem, the sampling rate needs to be higher than 4kHz to accurately analyze the 50Hz fundamental wave and 40th harmonic (2kHz) component; at the same time, the requirements of IEEE C37.118.1 standard for synchronous phasor measurement are referred to, and considering the engineering margin, the sampling rate is finally determined to be not less than 10kHz, ensuring that 200 data points are collected per power frequency cycle, meeting the technical requirement of phase measurement accuracy better than ±0.01°.

[0095] In one specific embodiment, the power quality monitoring device installed at the 10kV bus of a certain substation collects real-time voltage waveforms at a sampling rate of 12.8kHz after performing voltage adjustment operation. The waveform data in the 2-second time window after the completion of the control operation is extracted, containing 10 power frequency cycle waveform sequences, the waveform amplitude range is-325V to +325V, the number of sampling points is 25600, providing data basis for subsequent stability analysis.

[0096] In step S1072, the similarity between the waveform sequence and the preset reference waveform is calculated, and when the similarity value exceeds the preset similarity threshold, it is marked as a candidate stable recovery event.

[0097] It should be noted that the similarity between the actual waveform and the ideal reference waveform is calculated by the dynamic time warping algorithm. After nonlinear alignment of the two time series on the time axis, the path cumulative distance is calculated and normalized to obtain; the reference waveform is a standard 50Hz sine wave, and the similarity threshold is determined based on historical stable operation data statistical analysis, usually set to 0.85, in historical data, the similarity of normal operation waveform is usually concentrated in 0.9 to 1.0, and the similarity of abnormal waveform is generally lower than 0.7, 0.85 is an intermediate value, which can effectively distinguish normal and abnormal waveforms to ensure identification accuracy.

[0098] In a specific embodiment, the collected waveform sequence is calculated with the DTW similarity of the standard 50Hz reference waveform. The similarity values of the three time periods are calculated: 0.92, 0.88, 0.95, all of which exceed the similarity threshold of 0.85. These time periods are marked as candidate stable recovery events, and their start time, duration and similarity value are recorded to form a candidate event list.

[0099] In step S1073, the time interval of the candidate stable recovery event and the adjacent event is calculated to generate an actual cycle sequence.

[0100] It should be noted that the period characteristics of the candidate event are calculated by the zero-crossing detection algorithm. The improved zero interpolation method is used to improve the period measurement accuracy, and the period tolerance threshold is set to ±0.5% of the standard period to meet the requirements of power grid frequency stability.

[0101] In a specific embodiment, the marked candidate stable recovery event is analyzed by zero-crossing point, and the time interval of adjacent zero-crossing points is calculated. The time interval of 10 consecutive periods is measured as: 20.01ms, 19.98ms, 20.03ms, 19.99ms, 20.02ms, 20.00ms, 19.97ms, 20.04ms, 19.98ms, 20.01ms, to generate an actual cycle sequence. All cycle values are within the ±0.5% tolerance range of the standard 50Hz cycle 20ms.

[0102] In step S1074, the actual cycle sequence is compared with the standard cycle, and when the cycle deviation is less than the preset tolerance threshold, it is confirmed as a stable recovery event and a recovery event log is generated.

[0103] It should be noted that the stability of the cycle sequence is evaluated by the statistical process control method. The preset tolerance threshold is determined based on the power grid frequency deviation standard and the measurement system accuracy, and the control limit is set by the 3σ principle to ensure the reliability of the judgment.

[0104] In a specific embodiment, the statistical characteristics of the actual cycle sequence are calculated: mean value 20.003 ms, standard deviation 0.023 ms. The deviation of all cycle data from the standard cycle 20 ms is less than the tolerance threshold of 0.05 ms. As a stable recovery event, a recovery event log containing the event number, recovery time, duration, cycle deviation statistics and waveform quality is generated, providing a basis for system performance evaluation.

[0105] In summary, the application discloses a high-precision voltage fluctuation real-time monitoring method and system based on an electric energy meter, which comprises: collecting voltage waveform data and performing denoising and zero point detection; calculating the interval between adjacent zero points to identify phase shift events; analyzing frequency components to determine phase change trends and classify shift events; establishing a state correlation relationship; triggering a response signal; adjusting the priority of regulation and control; verifying waveform stability and generating a recovery log. The application realizes high-precision real-time monitoring and intelligent regulation and control of voltage phase shift through wavelet transform denoising, cross-correlation zero point detection, Fourier transform frequency domain analysis and other technical means, effectively improving the stability and power supply quality of the power system under new energy access.

[0106] Reference Figure 2 The second embodiment of the application provides a high-precision voltage fluctuation real-time monitoring system based on an electric energy meter, comprising: a waveform collection and preprocessing module for collecting voltage waveform data, performing denoising processing on the voltage waveform data and extracting zero point positions to determine zero point detection results; a phase shift identification module for calculating the interval time between adjacent zero points according to the zero point detection results, and identifying potential phase shift events based on the deviation of the interval time from a preset standard cycle to generate a shift event list; an event classification and analysis module for analyzing the frequency components corresponding to the events in the shift event list, determining the phase angle change value and change trend, and classifying the phase shift events based on the change trend to obtain the classified shift types; a state correlation establishment module for obtaining associated state parameters according to the classified shift types, and establishing a state correlation relationship between the shift types and the state parameters; a response signal generation module for screening out a high-correlation parameter group based on the state correlation relationship, and triggering a real-time response signal and generating a response signal sequence when the phase shift amplitude of the high-correlation parameter group exceeds an alarm threshold; an operation queue optimization module for adjusting the priority of the regulation and control operations according to the response signal sequence to generate an operation suggestion queue; a stability verification feedback module for verifying the change of the waveform sequence after executing the operation suggestion queue, and marking as a stable recovery event and generating a recovery event log if the interval time after the change meets the preset standard cycle.

[0107] It should be noted that the high-precision voltage fluctuation real-time monitoring system based on an electric energy meter provided by the embodiments of the present application is used to execute all process steps of the high-precision voltage fluctuation real-time monitoring method based on an electric energy meter provided by the above embodiments, and the working principles and beneficial effects of the two are one-to-one correspondence, thus not being described again.

[0108] The embodiments of the present application further provide an electronic device. The electronic device comprises a processor, a memory, and a computer program, such as a voltage fluctuation monitoring program, stored in the memory and executable on the processor. The processor implements the steps in each of the above method embodiments when executing the computer program, such as the step S101 shown in the above embodiment. Figure 1 Alternatively, the processor implements the functions of each module in each of the above system embodiments when executing the computer program, such as the waveform acquisition and preprocessing module.

[0109] For example, the computer program can be divided into a plurality of modules, which are stored in the memory and executed by the processor to complete the present application. The plurality of modules can be a series of computer program instruction segments capable of completing a specific function, which are used to describe the execution process of the computer program in the electronic device.

[0110] The electronic device can be a desktop computer, a notebook computer, a palm computer, a smart tablet, and the like. The electronic device can include, but is not limited to, a processor and a memory. Those skilled in the art can understand that the above components are only examples of the electronic device and do not constitute a limitation on the electronic device, and the electronic device can include more or fewer components than the above, or combine certain components or different components, such as the electronic device can also include an input / output device, a network access device, a bus, and the like.

[0111] The processor can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, and the like. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor and the like, and the processor is the control center of the electronic device, which connects all parts of the electronic device through various interfaces and lines.

[0112] The memory can be used to store the computer program and / or modules, and the processor realizes various functions of the electronic device by running or executing the computer program and / or modules stored in the memory, and calling data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application program required by a function (such as a sound playing function, an image playing function, etc.), and the like; and the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), and the like. In addition, the memory can include a high-speed random access memory, and can also include a nonvolatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, at least one disk storage device, a flash memory device, or other volatile solid-state memory devices.

[0113] The modules integrated in the electronic device can be stored in a computer readable storage medium if they are realized in the form of software function units and sold or used as independent products. Based on this understanding, all or part of the processes in the above-mentioned embodiment methods can also be completed by a computer program instructing related hardware, and the computer program can be stored in a computer readable storage medium. The computer program can implement the steps of each method embodiment when executed by a processor. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or some intermediate forms, etc. The computer readable medium can include any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc. It should be noted that the contents included in the computer readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction, for example, in some jurisdictions, according to legislation and patent practice, the computer readable medium does not include electrical carrier signals and telecommunication signals.

[0114] It should be noted that the system embodiments described above are merely illustrative, wherein the units described as separate components can or can not be physically separated, and the components displayed as units can or can not be physical units, i.e. can be located in one place or can be distributed to multiple network units. Part or all of the modules can be selected to achieve the purpose of the embodiment according to actual needs. In addition, the connection relationship between the modules in the system embodiment provided by the present application indicates that there is a communication connection between them, which can be implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement it without creative labor.

[0115] The above specific embodiments further illustrate the purpose, technical scheme and beneficial effects of the present application. It should be understood that the above description is only a specific embodiment of the present application and is not intended to limit the protection scope of the present application. It is particularly pointed out that any modification, equivalent replacement, improvement, etc. made by those skilled in the art within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A high-precision real-time monitoring method for voltage fluctuations based on an electricity meter, characterized in that, include: Collect voltage waveform data, perform noise reduction processing on the voltage waveform data and extract the zero-point position to determine the zero-point detection result; Based on the zero-point detection results, the interval time between adjacent zero points is calculated, and based on the deviation between the interval time and the preset standard period, potential phase shift events are identified, and a list of shift events is generated. Analyze the frequency components corresponding to the events in the offset event list, determine the phase angle change value and trend, and classify the phase offset events based on the trend to obtain the classified offset type; Based on the classified offset type, obtain the associated state parameters and establish the state association relationship between the offset type and the state parameters; Based on the state correlation, highly correlated parameter groups are selected, and when the phase offset amplitude of the highly correlated parameter groups exceeds a preset warning threshold, a real-time response signal is triggered to generate a response signal sequence. Based on the response signal sequence, the priority of the control operation is adjusted, and an operation suggestion queue is generated; Verify the waveform sequence changes after executing the operation suggestion queue. If the interval after the change meets the preset standard period, it is marked as a stable recovery event, and a recovery event log is generated.

2. The high-precision real-time voltage fluctuation monitoring method based on an electricity meter according to claim 1, characterized in that, The process of acquiring voltage waveform data, denoising the voltage waveform data, extracting the zero-point position, and determining the zero-point detection result includes: The voltage waveform data is denoised to obtain a denoised waveform sequence; Zero-point positions are extracted from the denoised waveform sequence to obtain a set of zero-point positions; The similarity between the denoised waveform sequence and the pre-established reference sine wave is calculated, and the precise moment when the waveform crosses the zero axis is determined by combining the zero position set, so as to obtain the zero detection sequence as the zero detection result.

3. The high-precision real-time voltage fluctuation monitoring method based on an electricity meter according to claim 1, characterized in that, The step involves calculating the interval time between adjacent zero points based on the zero-point detection results, and identifying potential phase shift events based on the deviation between the interval time and a preset standard period, generating a list of shift events, including: Based on the zero-point detection results, the timestamps of adjacent zero points are obtained, and the time difference between each pair of adjacent zero points is calculated to generate a set of zero-point intervals; If the interval time in the zero-point interval set deviates from the preset standard period by more than a preset deviation threshold, then the interval time is marked as a potential phase shift event. Summarize all the marked potential phase offset events to generate a list of offset events.

4. The high-precision real-time voltage fluctuation monitoring method based on an electricity meter according to claim 1, characterized in that, The process involves analyzing the frequency components corresponding to the events in the offset event list, determining the phase angle change value and trend, and classifying the phase offset events based on the trend to obtain the classified offset types, including: Based on the list of offset events, the start time and offset magnitude data of each event are obtained, and the frequency components corresponding to each event are calculated by Fourier transform to generate a set of frequency components. Based on the set of frequency components, compare the frequency spectra within the time window before and after the event, calculate the phase difference, and obtain the set of phase angle change values. Extract the trend of change from the set of phase angle change values; If the degree of matching between the changing trend and the preset typical load switching characteristic pattern exceeds the preset threshold, the corresponding event is classified as an offset caused by load switching, and a preliminary offset type set is obtained. Based on the initial set of offset types, the starting time and the offset magnitude data are linked, and the classified offset types are obtained through event tagging linking operations.

5. The high-precision real-time voltage fluctuation monitoring method based on an electricity meter according to claim 1, characterized in that, The step of obtaining associated state parameters based on the classified offset types and establishing state associations between offset types and state parameters includes: Based on the classified offset type, the event timestamp and classification tag are extracted. The event timestamp is then time-aligned with the current load data and new energy access data through a time-series matching operation to obtain a preliminary time-series association set. Calculate the correlation coefficient between the current load data and the new energy access data and the offset type, filter and retain parameters with correlation coefficients greater than a preset correlation threshold, and form a set of highly correlated parameters; The parameters in the highly correlated parameter set are clustered and grouped according to their time-series characteristics to obtain a parameter group set; Based on the matching relationship between the parameter group set and the offset type, an association analysis operation is performed between the state parameters and the offset type to obtain the state association relationship.

6. The high-precision real-time voltage fluctuation monitoring method based on an electricity meter according to claim 1, characterized in that, Based on the state correlation, a highly correlated parameter group is selected, and when the phase shift amplitude of the highly correlated parameter group exceeds a preset warning threshold, a real-time response signal is triggered, generating a response signal sequence, including: Obtain the phase offset amplitude data and timing features from the state association relationship; The phase offset amplitude data is compared with a preset warning threshold. When the warning threshold is exceeded, a real-time response signal is generated, forming a set of real-time response signals. After sorting the signals in the real-time response signal set according to their timestamps, they are matched and aligned with the time sequence features to generate the response signal sequence.

7. The high-precision real-time voltage fluctuation monitoring method based on an electricity meter according to claim 4, characterized in that, The step of adjusting the priority of the control operation and generating an operation suggestion queue based on the response signal sequence includes: Based on the response signal sequence, obtain the fluctuation characteristic information in the new energy access data; Extract fluctuation records whose amplitude exceeds a preset fluctuation amplitude threshold from the fluctuation feature information to generate a candidate fluctuation event set; The time identifiers of the candidate fluctuation event set are matched with the start times of the offset event list. When the time deviation is less than a preset time matching threshold, the time matching is considered successful. Based on the severity parameters of each event in the successfully matched set of related events, the priority weight of the control operation is recalculated; The control operations are sorted according to the priority weights to generate the operation suggestion queue.

8. The high-precision real-time voltage fluctuation monitoring method based on an electricity meter according to claim 1, characterized in that, The waveform sequence changes after the verification of the operation suggestion queue are marked as stable recovery events if the interval after the changes meets the preset standard period, and a recovery event log is generated, including: Collect the real-time voltage waveform after the control operation is performed, and extract the waveform sequence to be analyzed; Calculate the similarity between the waveform sequence and the preset reference waveform. When the similarity value exceeds the preset similarity threshold, it is marked as a candidate stable recovery event. Calculate the time interval between the candidate stable recovery event and its adjacent events to generate the actual periodic sequence; The actual periodic sequence is compared with the standard period. When the periodic deviation is less than a preset tolerance threshold, it is confirmed as a stable recovery event and a recovery event log is generated.

9. A high-precision real-time voltage fluctuation monitoring system based on an electricity meter, characterized in that, include: The waveform acquisition and preprocessing module acquires voltage waveform data, performs noise reduction on the voltage waveform data, extracts the zero-point position, and determines the zero-point detection result. The phase shift identification module calculates the interval time between adjacent zero points based on the zero point detection results, and identifies potential phase shift events based on the deviation between the interval time and a preset standard period, and generates a list of shift events. The event classification and analysis module analyzes the frequency components corresponding to the events in the offset event list, determines the phase angle change value and trend, and classifies the phase offset events based on the trend to obtain the classified offset type. The state association establishment module obtains the associated state parameters based on the classified offset type and establishes the state association relationship between the offset type and the state parameters. The response signal generation module, based on the state correlation, filters out highly correlated parameter groups, and triggers a real-time response signal to generate a response signal sequence when the phase offset amplitude of the highly correlated parameter groups exceeds the warning threshold. The operation queue optimization module adjusts the priority of the control operations based on the response signal sequence and generates an operation suggestion queue. The stability verification feedback module verifies the waveform sequence changes after executing the operation suggestion queue. If the interval after the change meets the preset standard period, it is marked as a stable recovery event and a recovery event log is generated.

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