Voltage sag detection method and device based on single-phase inverter amplitude rapid tracking

Abstract

The invention relates to the technical field of voltage sag detection, and discloses a voltage sag detection method and device based on rapid amplitude tracking of a single-phase inverter, and the method comprises the steps: obtaining an output voltage signal of the single-phase inverter, and carrying out the frequency domain analysis and preliminary filtering of the output voltage signal; performing dynamic tracking processing according to the filtered voltage amplitude sequence, and determining a candidate voltage sag event; performing multi-scale signal decomposition on the candidate event, and identifying and eliminating transient interference matched with a preset load abrupt change feature so as to confirm a real voltage sag event; after the real event is compared with the database, the event duration and recovery time of the real event are calculated; and finally, correlation logic calculation is carried out to generate a voltage sag event report. According to the method, transient characteristics of events are deeply analyzed through a signal decomposition technology, transient interference such as real voltage sag and load abrupt change is effectively distinguished, the problem that in the prior art, detection reliability and accuracy are not high is solved, and accurate recognition of tiny voltage sag is achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of voltage sag detection, and in particular to a voltage sag detection method and device based on single-phase inverter amplitude fast tracking. BACKGROUND

[0002] At present, voltage sag detection technology is a key research field for ensuring power grid stability and safe operation of equipment, and its importance lies in effectively improving system reliability. This technology is widely used in various single-phase inverter containing scenarios, such as household photovoltaic power generation systems, uninterruptible power supplies (UPS), new energy vehicle charging piles, and various precision electronic equipment. In these applications, timely and accurate capture of voltage amplitude changes can effectively avoid cascading failures. The core technical difficulty of voltage sag detection lies in the real-time of amplitude tracking and the accuracy of interference signal identification, especially in the complex electromagnetic environment of inverter operation, load mutation or switching noise is easy to interfere with signal purity.

[0003] In one prior art, the voltage sag detection method often relies on a single signal processing path to determine the occurrence of a sag event by setting a fixed amplitude threshold. After preliminary processing of the collected voltage signal, these methods directly compare the amplitudes, which is simple to implement but performs poorly in complex environments. The main defects of the prior art are the slow response to real-time dynamic signals, and the inability to effectively distinguish between real voltage fluctuations and transient disturbances caused by load mutations such as motor starting, which can easily misjudge external noise as valid events. For example, when a motor in a factory automation line starts and produces high-frequency interference that is superimposed on the voltage signal, the detection system may amplify this short-term noise into a false voltage sag alarm, causing unnecessary equipment downtime and production interruptions.

[0004] In summary, the prior art has the problem that detection reliability and accuracy cannot meet actual needs. SUMMARY

[0005] The present application provides a voltage sag detection method and device based on single-phase inverter amplitude fast tracking to solve the problem of detection reliability and accuracy not meeting actual needs.

[0006] In a first aspect, to solve the above technical problems, the present application provides a voltage sag detection method based on single-phase inverter amplitude fast tracking, comprising: Obtaining an output voltage signal of a single-phase inverter and performing frequency domain analysis and preliminary filtering to obtain a preliminary filtered voltage amplitude sequence; According to the preliminary filtered voltage amplitude sequence, performing dynamic tracking processing and determining candidate voltage sag events to obtain an event candidate list; According to the event duration sequence and the recovery time sequence, correlation logic calculation is performed to generate a final voltage sag event report. According to the real voltage sag event confirmation result, comparison is made with a pre-established voltage sag event database, and if the similarity degree of comparison is higher than a preset similarity threshold, it is determined as a repeated mode event, and an event duration sequence is calculated and obtained; According to the event duration sequence, recovery boundary matching is performed, and if the matching is successful, the recovery time is confirmed, and sequence fusion is performed to obtain a recovery time sequence; According to the event duration sequence and the recovery time sequence, correlation logic calculation is performed to generate a final voltage sag event report.

[0007] Preferably, the output voltage signal of the single-phase inverter is obtained, and frequency domain analysis and preliminary filtering are performed to obtain a preliminary filtered voltage amplitude sequence, including: The output voltage signal of the single-phase inverter is obtained; The output voltage signal is digitized to obtain a digitized voltage signal sequence; According to the digitized voltage signal sequence, frequency domain analysis is performed to obtain a frequency domain feature spectrum; From the frequency domain feature spectrum, high-frequency noise components are extracted, and if the high-frequency noise components exceed a preset noise threshold, the high-frequency noise components are filtered out to obtain a preliminary filtered voltage amplitude sequence.

[0008] Preferably, the preliminary filtered voltage amplitude sequence is subjected to dynamic tracking processing and determination of candidate voltage sag events to obtain an event candidate list, including: According to the preliminary filtered voltage amplitude sequence, state estimation filtering processing is performed to obtain a smoothed amplitude tracking curve; According to the smoothed amplitude tracking curve, voltage changes are analyzed and calculated in a continuous time window to obtain amplitude drop amplitudes of each time window; If the amplitude drop amplitudes of each time window are greater than a preset drop percentage, the events corresponding to each time window are determined as candidate voltage sag events to obtain an event candidate list.

[0009] Preferably, the preliminary filtered voltage amplitude sequence is subjected to dynamic tracking processing and determination of candidate voltage sag events to obtain an event candidate list, including: According to each candidate event in the event candidate list, multi-scale decomposition is performed to obtain multi-scale components of each candidate event; According to the multi-scale components of each candidate event, transient mode feature extraction is performed to obtain transient mode features of each candidate event; The transient mode feature of each candidate event is matched with a preset load mutation feature, if not matched, the amplitude drop information is fused and the event boundary details are determined to obtain a real voltage sag event confirmation result.

[0010] Preferably, according to the real voltage sag event confirmation result, a pre-established voltage sag event database is compared, if the similarity degree of comparison is higher than a preset similarity threshold, it is determined as a repeated mode event, and an event duration sequence is calculated and obtained, including: According to the real voltage sag event confirmation result, a feature vector is extracted and compared with a preset voltage sag event database to obtain a comparison similarity score; If the comparison similarity score is higher than a preset similarity threshold, it is determined as a repeated mode event, and the corresponding event boundary details are obtained to obtain the event boundary details; According to the event boundary details, time stamp correlation analysis and event duration calculation are performed to obtain an event duration sequence.

[0011] Preferably, according to the event duration sequence, recovery boundary matching is performed, if matching is successful, the recovery time is confirmed, and sequence fusion is performed to obtain a recovery time sequence, including: According to the event duration sequence, event recovery stage positioning is performed, and recovery boundary features are extracted to obtain a recovery boundary sequence; According to the recovery boundary features, a pre-established recovery boundary database is compared, if the similarity degree of comparison is higher than a preset recovery threshold, the matching is successful, and an event boundary detail sequence is obtained; According to the event boundary detail sequence, time stamp fusion and correlation calculation are performed to obtain a recovery time sequence.

[0012] Preferably, according to the event duration sequence and the recovery time sequence, correlation logic calculation is performed to generate a final voltage sag event report, including: According to the event duration sequence and the recovery time sequence, matching verification and attribute fusion are performed with the smoothed amplitude tracking curve to obtain an optimized detection result sequence; According to the optimized detection result sequence, multi-scale component decomposition is performed to obtain a refined version of the event boundary detail sequence; According to the refined version of the event boundary detail sequence, refined event duration and refined recovery time are calculated, and multi-scale analysis is performed to quantify the stability of the recovery process to obtain system reliability evaluation details; According to the refined event duration, the refined recovery time, and the system reliability evaluation details, the final voltage sag event report is generated.

[0013] In a second aspect, the present application provides a voltage sag detection device based on single-phase inverter amplitude fast tracking, comprising: A signal acquisition and filtering module is configured to acquire an output voltage signal of a single-phase inverter, and perform frequency domain analysis and preliminary filtering to obtain a preliminary filtered voltage amplitude sequence. A candidate event determination module is configured to perform dynamic tracking processing and determine a candidate voltage sag event according to the preliminary filtered voltage amplitude sequence, and obtain an event candidate list. A real event confirmation module is configured to perform signal decomposition and event boundary detail fusion on each candidate event in the event candidate list, and obtain a real voltage sag event confirmation result. A duration calculation module is configured to compare the real voltage sag event confirmation result with a pre-established voltage sag event database, and if the similarity is higher than a preset similarity threshold, determine it as a repeated mode event, and calculate and obtain an event duration sequence. A recovery time calculation module is configured to perform recovery boundary matching according to the event duration sequence, confirm the recovery time if the matching is successful, and perform sequence fusion to obtain a recovery time sequence. A report generation module is configured to perform associated logic calculation according to the event duration sequence and the recovery time sequence, and generate a final voltage sag event report.

[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 voltage sag detection method based on single-phase inverter amplitude fast tracking according to any one of the above aspects 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 voltage sag detection method based on single-phase inverter amplitude fast tracking according to any one of the above aspects when the computer program is running.

[0016] Compared with the prior art, the present application has the following beneficial effects: (1) The present application filters out high-frequency noise by first performing frequency domain analysis on the collected voltage signal, and then performs multi-scale signal decomposition on the candidate sag event, extracts its transient mode features and matches them with the preset load mutation features. This method not only focuses on the amplitude change of the signal, but also deeply analyzes the structured features of the event. By identifying and excluding transient interference events with load mutation features, the present application can effectively distinguish real voltage sags from transient noise. Therefore, this method significantly reduces the false positives and false negatives caused by interference, greatly improving the reliability and accuracy of voltage sag detection.

[0017] (2) The present application generates a smooth amplitude tracking curve that can reflect the real-time voltage change by using a state estimation filtering algorithm to dynamically track the voltage amplitude sequence after preliminary filtering. Compared with the traditional static threshold judgment method, this dynamic tracking curve can more sensitively capture the instantaneous fluctuations of the voltage. By analyzing the drop amplitude of the smooth curve within a continuous time window to determine the candidate event, the present application realizes fast and continuous tracking of the voltage amplitude. Therefore, this method improves the real-time response capability and sensitivity of the detection system, and can timely discover voltage sag events including micro sags.

[0018] (3) The present application ensures the accuracy of the output results through multi-stage post-processing of verification, boundary refinement and final calculation of the confirmed real events. This process first matches the preliminary detection results with the smooth curve for verification, then uses multi-scale decomposition to accurately position the event boundaries, and finally recalculates the parameters of the event based on the most accurate boundary data and performs reliability evaluation. Therefore, this method not only detects sag events, but also provides high-precision event reports containing accurate start and end times, duration and recovery characteristics, providing reliable data support for subsequent power grid fault diagnosis and system maintenance. BRIEF DESCRIPTION OF DRAWINGS

[0019] Figure 1 is the voltage sag detection method flowchart provided by the first embodiment of the present application based on single-phase inverter amplitude fast tracking; Figure 2 is the voltage sag detection device structure diagram provided by the second embodiment of the present application based on single-phase inverter amplitude fast tracking. DETAILED DESCRIPTION

[0020] The technical solutions in the embodiments of the present application will be described clearly and completely 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, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0021] Referring to Figure 1 The first embodiment of the present application provides a voltage sag detection method based on single-phase inverter amplitude fast tracking, comprising the following steps: S11, obtaining an output voltage signal of a single-phase inverter, and performing frequency domain analysis and preliminary filtering to obtain a preliminary filtered voltage amplitude sequence; S12, performing dynamic tracking processing and determining a candidate voltage sag event according to the preliminary filtered voltage amplitude sequence to obtain an event candidate list; S13, performing signal decomposition and event boundary detail fusion on each candidate event in the event candidate list to obtain a real voltage sag event confirmation result; S14, comparing the real voltage sag event confirmation result with a pre-established voltage sag event database, and if the similarity is higher than a preset similarity threshold, determining it as a repeated mode event, and calculating and obtaining an event duration sequence; S15, performing recovery boundary matching according to the event duration sequence, confirming the recovery time if the matching is successful, and performing sequence fusion to obtain a recovery time sequence; S16, performing associated logic calculation according to the event duration sequence and the recovery time sequence to generate a final voltage sag event report.

[0022] In step S11, the output voltage signal of the single-phase inverter is obtained, and frequency domain analysis and preliminary filtering are performed to obtain a preliminary filtered voltage amplitude sequence, including: Collecting an output voltage signal from a single-phase inverter; Digitizing the output voltage signal to obtain a digitized voltage signal sequence; Performing frequency domain analysis on the digitized voltage signal sequence to obtain a frequency domain feature spectrum; Extracting a high-frequency noise component from the frequency domain feature spectrum, and if the high-frequency noise component exceeds a preset noise threshold, filtering out the high-frequency noise component to obtain a preliminary filtered voltage amplitude sequence.

[0023] First, the output voltage signal of the single-phase inverter is acquired, and frequency domain analysis and preliminary filtering are performed to obtain the specific implementation of the preliminary filtered voltage amplitude sequence: periodic sampling is performed through a high-speed analog-to-digital converter connected to the output end of the single-phase inverter to collect the instantaneous analog signal of the output voltage. The sampling frequency in this case needs to follow the Nyquist sampling theorem, which states that in order to reconstruct the original signal from the sampling points without distortion, the sampling frequency must be at least twice the highest frequency component in the signal to avoid signal distortion due to frequency aliasing. Therefore, in this embodiment, based on general knowledge and experimental statistics of power grid harmonics, the sampling frequency is set to 10 kHz, thereby converting the continuous analog voltage signal into a discrete digital voltage signal sequence containing specific quantized amplitude and timestamp information.

[0024] It should be noted that the specific values of the parameters (such as sampling frequency, noise coefficient, drop percentage, similarity threshold, etc.) in the present application are recommended values or typical values determined based on general knowledge in the technical field, industry standards (such as IEC 61000-4-30), and a large number of preliminary experimental verifications. Those skilled in the art can fully understand that the above parameters can be adjusted adaptively according to the actual application scenario, the specific model of the single-phase inverter, the power grid environment, and the required balance between detection accuracy and real-time performance.

[0025] For example, the sampling frequency needs to be at least 2 times the highest frequency component of the signal to be analyzed. Considering that the 50th harmonic (2.5 kHz) is usually of interest, selecting 10 kHz provides sufficient margin. The noise coefficient of 0.02 means that the high-frequency noise energy threshold is set to 2% of the fundamental energy amplitude, which strikes a practical balance between effectively filtering out abnormal noise and retaining normal harmonic components. The mathematical nature of all formulas is consistent with the general definition in the related field, and those skilled in the art can directly understand them as the corresponding mathematical formulas for clearer expression.

[0026] Next, according to the digitized voltage signal sequence, a fast Fourier transform is applied to map the time domain signal to the frequency domain. The specific operation process is as follows: the digitized voltage signal sequence is taken as a finite-length input vector, and the original time domain signal is decomposed into a linear combination of a series of sine and cosine wave components of different frequencies. The output of this transformation is a set of complex numbers, where the modulus and phase angle of each complex number represent the amplitude and phase of the corresponding frequency component, respectively. By extracting the amplitude corresponding to each frequency point, the time domain signal is mapped to the frequency domain to obtain a frequency domain characteristic spectrum that can intuitively reflect the energy distribution of the signal at each frequency point. This spectrum is a data structure with frequency as the horizontal coordinate and amplitude as the vertical coordinate, clearly showing the amplitude information of the fundamental frequency (such as 50 Hz) and its harmonics (such as 150 Hz, 250 Hz, etc.).

[0027] Subsequently, a high-frequency noise component is extracted from the frequency-domain characteristic spectrum, specifically, by calculating the sum of squares of amplitudes of each frequency point in a specified high-frequency band (e.g., all frequency points higher than 500 Hz) to obtain a total high-frequency noise amplitude, and comparing the total high-frequency noise amplitude with a preset noise threshold. The preset noise threshold is determined based on statistical analysis of a large amount of single-phase inverter operation data under different loads and electromagnetic environments, and combined with the industry standard for harmonic distortion of power systems, and is set as a specific percentage of the fundamental amplitude. The calculation method is: the noise threshold is equal to the square of the amplitude at the fundamental frequency multiplied by a preset noise coefficient. The noise coefficient is set according to two aspects: first, it is an empirical value obtained by statistical analysis of a large amount of single-phase inverter operation data under different loads and electromagnetic environments, ensuring the universality of the threshold; second, it is combined with the industry standard for harmonic distortion of power systems, which usually limits the harmonic content generated by grid-connected equipment within a certain range. Therefore, in this embodiment, the coefficient is set to 0.0004, which can balance between effectively filtering abnormal noise and avoiding excessive filtering of harmonics within the standard allowed range.

[0028] If the judgment result is that the high-frequency noise energy exceeds the noise threshold, a digital low-pass filter is started to filter the original digitized voltage signal sequence. The specific filtering operation process is: first, a set of filter coefficients is determined according to a preset cutoff frequency (e.g., 200 Hz) and filter order; then, the digitized voltage signal sequence is convolved with the filter coefficients, i.e., for each sampling point, the current value and a number of historical values are weighted and summed with the corresponding filter coefficients to calculate a new output sampling point. By performing the convolution operation on each point of the entire sequence, a new time sequence is generated, in which the frequency components higher than the cutoff frequency are effectively attenuated. If the judgment result is that the threshold is not exceeded, no filtering operation is performed, and the original sequence is directly used as the output. Finally, whether filtered or not, a preliminary filtered voltage amplitude sequence with high-frequency noise effectively suppressed is obtained, providing a high-quality data basis for subsequent dynamic tracking.

[0029] In step S12, dynamic tracking processing and determination of candidate voltage sag events are performed based on the preliminary filtered voltage amplitude sequence to obtain an event candidate list, including: State estimation filtering processing is performed based on the preliminary filtered voltage amplitude sequence to obtain a smoothed amplitude tracking curve; According to the smoothed amplitude tracking curve, voltage changes are analyzed and calculated in consecutive time windows to obtain the amplitude drop amplitude of each time window; If the amplitude drop of each time window is greater than a preset drop percentage, the event corresponding to each time window is determined as a candidate voltage sag event, and an event candidate list is obtained.

[0030] First, according to the preliminary filtered voltage amplitude sequence, state estimation filtering processing is performed thereon, in this embodiment, the processing adopts Kalman filtering algorithm, which is a recursive optimal estimation algorithm, and the process first performs parameter and state initialization: the state vector of the system is defined as a two-dimensional vector containing the current voltage amplitude and its rate of change; the state vector is initialized, and usually the amplitude of the first sampling point is used as the initial amplitude, and the rate of change is set to 0; at the same time, based on a large amount of statistical analysis on the characteristics of the historical output signals of the inverter, the process noise covariance matrix (representing the uncertainty of the voltage change model) and the measurement noise covariance (representing the noise level of the measurement process) are preset.

[0031] After initialization, the prediction stage is entered, and the next time state is predicted through the state transition equation, which describes the physical model of the evolution of the voltage amplitude and the rate of change with time, and the specific operation is: the state prediction formula is: the predicted state at time k is equal to the state transition matrix multiplied by the posteriori state estimation at time k-1. At the same time, the covariance at this time is predicted, and the specific operation is: the predicted covariance at time k is equal to the state transition matrix multiplied by the posteriori covariance estimation at time k-1, and then multiplied by the transpose of the state transition matrix, and finally added to the process noise covariance matrix.

[0032] After the prediction is completed, the update stage is entered, first, the Kalman gain is calculated, which is used to weigh the credibility of the predicted value and the measured value, and the specific calculation process is: the Kalman gain is equal to the predicted covariance at time k multiplied by the transpose of the observation matrix, and then multiplied by the inverse of the matrix of (the observation matrix H multiplied by the predicted covariance at time k, and then multiplied by the transpose of H, and finally added to the measurement noise covariance R). Wherein, the observation matrix is used to map the state vector to the measurement space, and its value here is [1, 0]. Subsequently, the state prediction is corrected according to the gain to obtain the optimal estimation at the current time, and the specific correction operation is: the posteriori state estimation at time k is equal to the predicted state at time k, plus the Kalman gain multiplied by (the actual observation value at time k minus the observation matrix H multiplied by the predicted state at time k).

[0033] The covariance is updated to reflect the uncertainty of this estimation, and the specific correction rule is: the posteriori covariance estimation at time k is equal to the unit matrix minus the Kalman gain multiplied by the observation matrix, and finally multiplied by the predicted covariance at time k. By recursively performing the above prediction and update operations on each sampling point in the preliminary filtered voltage amplitude sequence, the voltage amplitude component in the final output posteriori state estimation is connected, and a smooth amplitude tracking curve that has been optimally estimated and whose noise has been further suppressed is obtained.

[0034] Then, according to the smoothed amplitude tracking curve, the voltage variation is analyzed and calculated in a continuous time window by using a sliding window method. The specific analysis process is as follows: first, a fixed size time window is set, and the window size is set according to the typical duration of voltage sag event defined by the power system and the requirement for real-time response, for example, 20 milliseconds. Second, the sliding step of the window is set, which is usually set as a sampling point period to achieve the most detailed point-by-point analysis. When the analysis starts, the time window is placed at the starting position of the smoothed amplitude tracking curve, and the data in the window is analyzed and calculated.

[0035] After one calculation is completed, the time window is slid forward by one step along the time axis, and the analysis and calculation of the new window data are repeated. The process is executed in a loop until the entire amplitude tracking curve is analyzed. In each time window, the amplitude drop of each time window is calculated by the following formula: the amplitude drop is equal to the result of (the reference voltage minus the minimum amplitude in the window) divided by the reference voltage, and multiplied by one hundred percent. The reference voltage is the average value of the voltage effective value (RMS) in a long time before the event occurs, which is determined according to the industry standard, to represent the normal voltage level.

[0036] Finally, the amplitude drop of each time window calculated is compared with a preset drop percentage, which is determined based on the definition of voltage sag event by the International Electrotechnical Commission (IEC) and related power quality standards (i.e. the voltage effective value drops below 90% of the rated value), which is set to 10% in this embodiment. If the judgment result is that the amplitude drop of any time window is greater than the preset drop percentage, the starting time, duration, minimum amplitude and other information of the time window are recorded as a candidate voltage sag event, and stored in the event candidate list; if the judgment result is not greater than, it is considered that the voltage in the window is normal. After the analysis of the entire amplitude tracking curve is completed, the final event candidate list will be used for subsequent interference discrimination processing.

[0037] In step S13, for each candidate event in the event candidate list, signal decomposition and event boundary detail fusion are performed to obtain a real voltage sag event confirmation result, including: According to each candidate event in the event candidate list, multi-scale decomposition is performed to obtain multi-scale components of each candidate event; According to the multi-scale components of each candidate event, transient mode feature extraction is performed to obtain transient mode features of each candidate event; The transient mode feature of each candidate event is matched with a preset load mutation feature, if not matched, the amplitude drop information is fused and the event boundary details are determined to obtain a real voltage sag event confirmation result.

[0038] Firstly, according to each candidate event in the event candidate list, the corresponding original digitized voltage signal sequence in the event time window is subjected to signal decomposition, i.e. multi-scale decomposition, in this embodiment, the decomposition is realized by using discrete wavelet transform, and the process is as follows: a Daubechies4 wavelet basis which is excellent in transient signal analysis based on experimental comparison is selected, and an orthogonal mirror filter bank is constructed according to the Mallat algorithm to decompose the signal step by step. Specifically, in the first layer decomposition, the signal passes through a high-pass filter and a low-pass filter at the same time, and the first layer high-frequency detail component (D1) and the low-frequency approximation component (A1) are obtained; then, the low-frequency approximation component (A1) of the first layer is taken as the input and is sent into the same filter bank again for second layer decomposition to obtain D2 and A2. The process is iteratively executed for a preset decomposition layer number, and the original signal is decomposed into a series of low-frequency approximation components and high-frequency detail components of different frequency scales, wherein the high-frequency detail component can accurately capture the transient mutation characteristics of the signal, and finally the multi-scale components of each candidate event are obtained.

[0039] Then, according to the multi-scale components of each candidate event, the high-frequency detail components are subjected to transient mode feature extraction, which is a parameter vector for quantifying the transient process characteristics, and the specific extraction operation process is as follows: for the high-frequency detail component coefficient sequence of each decomposition level, firstly, the maximum value of the absolute value of the coefficient is found by traversing the sequence, which is taken as a feature representing the transient energy intensity; secondly, the energy entropy is calculated, and the process is as follows: first, the energy of each coefficient, i.e. the square of the coefficient, is calculated, then the energy proportion of each coefficient is obtained by dividing the total energy, and finally the energy entropy is calculated by the following formula: energy entropy equals to the sum of the negative energy proportion multiplied by the logarithm of the energy proportion with 2 as the base, which is used to represent the complexity of the transient signal. By calculating these parameters, the transient mode feature vector which can describe the transient behavior of each candidate event is obtained.

[0040] Subsequently, according to the transient mode feature of each candidate event, it is matched with a preset load mutation feature template library, which is constructed by collecting and decomposing a large number of known voltage waveforms generated by starting or cutting off large inductive loads (such as motors), and statistically analyzing the distribution range and typical value of the transient mode feature. The matching process is realized by calculating the Euclidean distance between the feature vector of the candidate event and each feature template in the template library, and the specific calculation process follows the definition of Euclidean distance.

[0041] If the judgment result is that the feature vector of a certain candidate event has a Euclidean distance less than a preset matching threshold from any template in the template library, it is determined that the event matches the load mutation feature, and the event is identified as an interference event and is removed from the list. The matching threshold is determined based on statistical analysis of the Euclidean distance distribution of a large number of known matching samples (real interference) and non-matching samples (real sag), and is a value that optimally balances the false negative rate and the false positive rate.

[0042] If the judgment result is that the feature vector of a certain candidate event has a Euclidean distance not less than the matching threshold from any template in the template library, it is determined that the event does not match the load mutation feature, and the event boundary details determination and fusion process is started. The specific process is as follows: taking the timestamp of the candidate event as the center, on the smooth amplitude tracking curve generated in step S12, an edge positioning method based on first and second derivative analysis of the signal is used to accurately position the starting point of voltage drop and the ending point of recovery to the stable state.

[0043] The specific positioning operation process is as follows: the smooth amplitude tracking curve is regarded as a one-dimensional signal. First, the gradient sequence of the signal is obtained by calculating the first derivative of the signal, and the starting point of voltage drop is identified as the position where a significant negative peak appears in the gradient sequence. Second, the ending point of voltage recovery to the stable state is identified as the position where the second derivative appears zero crossing and the first derivative tends to be stable, and the information such as amplitude drop depth, duration, etc. during the two time points is fused to form an event record containing complete boundary and amplitude information. Finally, all the event record sets that have passed the mismatch judgment and successfully fused the boundary details constitute the real voltage sag event confirmation result, which excludes the pseudo-sag caused by load mutation and ensures the accuracy of subsequent analysis. The real voltage sag event confirmation result is a data structure containing the real sag events and their accurate boundary details (such as starting point, ending point, amplitude depth, etc.). In subsequent step S14, the corresponding event boundary details are extracted from the result.

[0044] In step S14, according to the real voltage sag event confirmation result, the voltage sag event database established in advance is compared, and if the similarity is higher than a preset similarity threshold, it is determined as a repetitive pattern event, and the event duration sequence is calculated and obtained, including: According to the real voltage sag event confirmation result, the feature vector is extracted, and compared with the preset voltage sag event database to obtain the similarity score; If the similarity score is higher than a preset similarity threshold, it is determined as a repetitive pattern event, and the corresponding event boundary details are obtained. According to the event boundary details, timestamp correlation analysis and event duration calculation are performed to obtain an event duration sequence.

[0045] First, according to the real voltage sag event confirmation result, a feature vector is extracted for each confirmed real event, which is a multi-dimensional array for quantitatively describing the core characteristics of the event, including parameters such as amplitude drop depth, waveform distortion rate during the sag, and transient mode characteristics determined in step S13. At the same time, a pre-set voltage sag event database is called, which is constructed by storing a large number of feature templates of typical voltage sag events caused by specific causes (such as specific types of load switching, grid side faults, etc.) through long-term monitoring and historical data accumulation. Subsequently, the cosine similarity algorithm is used to compare the feature vector of the current event with each feature template in the database one by one, and the specific calculation process is as follows: the cosine similarity is equal to the dot product of the current event feature vector and the template feature vector divided by the product of the modules of the two vectors. This calculation will generate an alignment similarity score between 0 and 1 for each template.

[0046] Then, the obtained alignment similarity score is compared with a pre-set similarity threshold, which is determined by selecting a balance point that maximizes the correct recognition rate while minimizing the false recognition rate through statistical methods such as receiver operating characteristic (ROC) curve analysis on a large number of known matching and non-matching events, and is set to 0.9 in this embodiment. If the judgment result is that the alignment similarity score is higher than the pre-set similarity threshold, the real event is determined as a repetitive mode event corresponding to the template, and the event boundary details determined for the event in step S13 are directly obtained; if the judgment result is not higher than, the event is considered to be a new mode event, and no subsequent processing is performed or it is marked for manual analysis.

[0047] Finally, according to the event boundary details of the repetitive mode event, timestamp correlation analysis and event duration calculation are performed, and the specific calculation process is as follows: the event start timestamp and event end timestamp recorded in the event boundary details are extracted, and the following Chinese calculation formula is used for calculation: event duration equals event end timestamp minus event start timestamp. For example, if the start timestamp is 10:00:03.100 and the end timestamp is 10:00:03.300, the calculated duration is 200 milliseconds. All the calculated durations of the events determined as repetitive mode events are collected, and finally the event duration sequence is obtained.

[0048] In step S15, according to the event duration sequence, recovery boundary matching is performed, and if the matching is successful, the recovery time is confirmed, and sequence fusion is performed to obtain a recovery time sequence, including: According to the event duration sequence, an event recovery stage is located, and a recovery boundary feature is extracted, to obtain a recovery boundary sequence; According to the recovery boundary feature, a pre-established recovery boundary database is compared, if the comparison similarity is higher than a preset recovery threshold, the matching is successful, and an event boundary detail sequence is obtained. According to the event boundary detail sequence, a timestamp fusion and correlation calculation are performed, to obtain a recovery time sequence.

[0049] First, according to the timestamp information of each event recorded in the event duration sequence, the event recovery stage immediately after the end of the sag is located in the smooth amplitude tracking curve generated in step S12, and the waveform of the stage is analyzed to extract its recovery boundary feature, and the specific analysis and extraction process is: for the voltage amplitude sequence of the recovery stage, the voltage recovery slope is calculated by linear regression fitting or taking the two-point line of the recovery start and end points, and the specific calculation formula is: the voltage recovery slope is equal to (the amplitude at the recovery end minus the amplitude at the recovery start) divided by (the recovery end minus the recovery start).

[0050] At the same time, the transient fluctuation amplitude in the recovery process is calculated, the process is to first determine an ideal recovery trend line, and then calculate the root mean square value of the difference between the actual amplitude of each sampling point in the recovery stage and the amplitude of the corresponding point of the trend line. The recovery boundary feature is a vector for quantitatively describing the dynamic characteristics of the recovery process, which is composed of the voltage recovery slope and the transient fluctuation amplitude calculated above, and the recovery boundary sequence of each event is obtained by calculating these parameters.

[0051] Then, according to the recovery boundary feature, it is compared with a pre-established recovery boundary database, the database is constructed in a similar way to the voltage sag event database and the load mutation feature template library, and the feature template library is established by extracting features (such as the statistical quantity of the recovery slope and the fluctuation amplitude) from the known voltage recovery waveform fragments in the historical data with clear causes. The feature template also contains feature vectors such as recovery slope and transient fluctuation amplitude, and those skilled in the art can continuously expand and optimize the database according to actual monitoring data. The comparison process uses cosine similarity algorithm to calculate the similarity between the recovery boundary feature vector of the current event and each template feature vector in the database, and the specific calculation process follows the definition of cosine similarity.

[0052] If the judgment result is that the calculated similarity score is higher than a preset recovery threshold (the threshold is set based on ROC curve analysis and other statistical methods to control the false matching rate while ensuring high recognition rate), it is considered that the matching is successful, and the event boundary detail sequence determined for the event in step S13 is obtained.

[0053] If the judgment result is not higher than the threshold value, the recovery mode is considered as an unknown mode. In a preferred embodiment, manual verification calculation is introduced at this time, allowing professionals to verify and confirm on the corresponding interface, thereby obtaining the recovery time sequence and the event boundary detail sequence determined for the event in step S13. At the same time, a self-learning mechanism is introduced to update the recovery boundary database. Once a new recovery mode with a clear cause is manually confirmed, its feature vector and recovery time data can be added to the recovery boundary database as a new template. In another embodiment, to simplify the calculation process, the unknown mode event can be ignored and not processed subsequently.

[0054] Finally, according to the obtained event boundary detail sequence, timestamp fusion and correlation calculation are performed. The specific process is as follows: the recovery start timestamp (i.e. the moment when the voltage amplitude reenters and stabilizes in the normal range) and the recovery end timestamp (i.e. the moment when the voltage amplitude reenters and stabilizes in the normal range) are extracted from the event boundary detail sequence, and the following formula is used for calculation: recovery time equals recovery end timestamp minus recovery start timestamp. The recovery times calculated for each event are collected, and finally the recovery time sequence is obtained.

[0055] In step S16, according to the event duration sequence and the recovery time sequence, correlation logic calculation is performed to generate a final voltage sag event report, including: According to the event duration sequence and the recovery time sequence, matching verification and attribute fusion are performed with the smoothed amplitude tracking curve to obtain an optimized detection result sequence; According to the optimized detection result sequence, multi-scale component decomposition is performed to obtain a refined version of the event boundary detail sequence; According to the refined version of the event boundary detail sequence, refined event duration and refined recovery time are calculated, and multi-scale analysis is performed to quantify the stability of the recovery process, thereby obtaining system reliability evaluation details; According to the refined event duration, the refined recovery time, and the system reliability evaluation details, the final voltage sag event report is generated.

[0056] Firstly, according to the event duration sequence and the recovery time sequence, a matching verification and attribute fusion is performed with the smoothed amplitude tracking curve generated in step S12, and the specific process is as follows: the corresponding waveform segment is located on the smoothed curve by using the start and end time stamps in the time sequence, and the amplitude drop feature of the segment is verified to be consistent with the initial judgment, and after the verification is passed, the specific fusion operation is as follows: a new data structure is created, the time length parameters (such as the preliminary calculated duration and recovery time) in the original time sequence are taken as the basic fields, and then the detailed physical attributes (such as the average amplitude during the temporary drop and the minimum amplitude) extracted from the waveform segment are supplemented as new fields to the data structure, so as to form an optimized detection result sequence with more complete information.

[0057] Then, according to the optimized detection result sequence, a multi-scale component decomposition is performed on the event boundary to further improve the boundary accuracy, and the specific application and correction process is as follows: first, the discrete wavelet transform described in step S13 is performed on the signal segment to obtain high-frequency detail component coefficients. Secondly, the instantaneous energy of the coefficient sequence is calculated, that is, the square of each coefficient value. Then, the accurate time point at which the energy value first appears a sharp jump and exceeds the preset multiple (for example, 3 times of the standard deviation) of the background noise energy in the instantaneous energy sequence is detected, and the time point is the energy mutation point. Finally, the time stamp corresponding to the energy mutation point is taken as a new and more accurate boundary time stamp, and the original boundary is updated and corrected, so as to obtain a refined version of the event boundary detail sequence with a single sampling point level of accuracy.

[0058] Then, according to the refined version of the event boundary detail sequence, the event duration and recovery time are recalculated (based on the more accurate boundary, the optimized calculation is performed on the basis of the already calculated "event duration sequence" and "recovery time sequence"), that is, the updated and more accurate start and end time stamps are used for subtraction operation, and the smoothness quantification of the recovery process is performed, and the specific multi-scale analysis process is as follows: the waveform of the recovery stage defined by the refined boundary is input as an independent signal segment, and the discrete wavelet transform described in step S13 is performed on it to obtain the high-frequency detail component coefficients of the recovery process at different scales.

[0059] Subsequently, the smoothness of the recovery process is quantified by calculating the energy variance of the high-frequency detail component coefficients, and the smaller the energy variance is, the more stable the recovery process is, and the higher the reliability is. The quantification result is the system reliability evaluation detail.

[0060] Finally, according to the recalculated event duration, recovery time and system reliability evaluation details, all these high-precision parameters are integrated according to a preset data structure, and the specific integration operation process is: a preset data structure template containing multiple fields is called, the structure of the template is set according to the general standard in the power quality monitoring field and the data demand of the power grid operation and maintenance personnel for fault analysis, and the template defines the format of the final report.

[0061] Then, each of the high-precision parameters obtained in the foregoing steps is assigned to the corresponding preset field in the data structure template one by one, and the specific assignment operation is a direct key-value mapping process, and the rule is: each calculated or evaluated parameter value (for example, the recovery duration value is '150 ms') is accurately filled into the field (for example, the field named'recovery_duration_ms') corresponding in semantics in the template as 'value', and the mapping rule defines the unique correspondence between each parameter and the template field; after completing the assignment of all fields, a structured and complete information event record is formed, that is, the final voltage sag event report, which is the final output of the entire detection process.

[0062] In summary, the present application solves the problem of low detection reliability and accuracy in the prior art by deeply analyzing the transient characteristics of the event through signal decomposition technology, effectively distinguishing real voltage sag from load mutation and other transient disturbances, and achieving accurate identification of small voltage sag.

[0063] Referring to Figure 2 The second embodiment of the present application provides a voltage sag detection device based on single-phase inverter amplitude fast tracking, comprising: A signal acquisition and filtering module is configured to acquire the output voltage signal of the single-phase inverter, and perform frequency domain analysis and preliminary filtering to obtain a voltage amplitude sequence after preliminary filtering; A candidate event determination module is configured to perform dynamic tracking processing and determine a candidate voltage sag event according to the voltage amplitude sequence after preliminary filtering, and obtain an event candidate list; A real event confirmation module is configured to perform signal decomposition and event boundary detail fusion on each candidate event in the event candidate list to obtain a real voltage sag event confirmation result; A duration calculation module is configured to compare the real voltage sag event confirmation result with a pre-established voltage sag event database, and if the similarity is higher than a preset similarity threshold, it is determined as a repeated mode event, and an event duration sequence is calculated and obtained. The recovery time calculation module is configured to perform recovery boundary matching according to the event duration sequence, confirm the recovery time if the matching is successful, and perform sequence fusion to obtain a recovery time sequence. The report generation module is configured to perform correlation logic calculation according to the event duration sequence and the recovery time sequence, and generate a final voltage sag event report.

[0064] It should be noted that the voltage sag detection device based on single-phase inverter amplitude fast tracking provided by the embodiment of the present application is used to execute all process steps of the voltage sag detection method based on single-phase inverter amplitude fast tracking of the above-mentioned embodiment, and the working principles and advantages of the two are one-to-one corresponding, thus not being described in detail.

[0065] The embodiment of the present application further provides an electronic device. The electronic device comprises a processor, a memory, and a computer program stored in the memory and executable on the processor, for example, a voltage sag detection program based on single-phase inverter amplitude fast tracking. The processor implements the steps in the above-mentioned various voltage sag detection methods based on single-phase inverter amplitude fast tracking when executing the computer program, for example Figure 1 The processor implements the functions of the modules / units in the above-mentioned various devices when executing the computer program, for example, a signal acquisition and filtering module.

[0066] For example, the computer program can be divided into one or more modules / units, which are stored in the memory and executed by the processor to complete the present application. The one or more modules / units 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.

[0067] 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, a memory. Those skilled in the art can understand that the above-mentioned components are only examples of the electronic device, and do not constitute a limitation on the electronic device, and can include more or fewer components than the above-mentioned, or combine certain components, or different components, for example, the electronic device can also include an input / output device, a network access device, a bus and the like.

[0068] The processor can be a central processing unit (CPU), and can also be other general-purpose processors, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor. The processor is a control center of the electronic device, and connects various parts of the electronic device through various interfaces and lines.

[0069] 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.), etc.; 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.), etc. In addition, the memory can include a high-speed random access memory, and can also include a nonvolatile memory, for example, 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 device.

[0070] The modules / units integrated in the electronic device, if realized in the form of software function units and sold or used as independent products, can be stored in a computer readable storage medium. Based on such 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, 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. that can carry the computer program code. 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.

[0071] It should be noted that the above-described device embodiments are only schematic, and the units described as separate components can or can not be physically separated, and the components shown as units can or can not be physical units, that is, they can be located in one place, or distributed on multiple network units. Part or all of the modules can be selected to achieve the purpose of the embodiment scheme according to actual needs. In addition, the connection relationship between the modules in the device embodiment provided by the present application indicates that there is a communication connection between them, which can be realized as one or more communication buses or signal lines. Those skilled in the art can understand and implement it without creative labor.

[0072] The above-described specific embodiments further illustrate the purpose, technical solutions, and beneficial effects of the present application. It should be understood that the above-described specific embodiments are only examples of the present application and are not intended to limit the protection scope of the present application. In particular, any modifications, equivalent replacements, improvements, 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 voltage sag detection method based on fast amplitude tracking of a single-phase inverter, characterized in that, include: The output voltage signal of the single-phase inverter is acquired, and frequency domain analysis and preliminary filtering are performed to obtain the voltage amplitude sequence after preliminary filtering. Based on the pre-filtered voltage amplitude sequence, dynamic tracking processing is performed and candidate voltage sag events are determined to obtain a candidate event list; For each candidate event in the event candidate list, signal decomposition and event boundary detail fusion are performed to obtain the actual voltage sag event confirmation result; Based on the confirmation results of the actual voltage sag events, a comparison is made with the pre-established voltage sag event database. If the similarity is higher than the preset similarity threshold, it is determined to be a recurring pattern event, and the event duration sequence is calculated and obtained. Based on the event duration sequence, recovery boundary matching is performed. If the matching is successful, the recovery time is confirmed, and the sequence is fused to obtain the recovery time sequence. Based on the event duration sequence and the recovery time sequence, correlation logic calculations are performed to generate a final voltage sag event report.

2. The voltage sag detection method based on fast amplitude tracking of a single-phase inverter according to claim 1, characterized in that, The process of acquiring the output voltage signal of the single-phase inverter, performing frequency domain analysis and preliminary filtering to obtain the preliminarily filtered voltage amplitude sequence includes: The output voltage signal is acquired from the single-phase inverter; The output voltage signal is digitized to obtain a digitized voltage signal sequence; Based on the digitized voltage signal sequence, frequency domain analysis is performed to obtain a frequency domain characteristic spectrum. High-frequency noise components are extracted from the frequency domain feature spectrum. If the high-frequency noise components exceed a preset noise threshold, the high-frequency noise components are filtered out to obtain a pre-filtered voltage amplitude sequence.

3. The voltage sag detection method based on fast amplitude tracking of a single-phase inverter according to claim 1, characterized in that, The process involves dynamic tracking and determining candidate voltage sag events based on the pre-filtered voltage amplitude sequence, resulting in a candidate event list, including: Based on the pre-filtered voltage amplitude sequence, state estimation filtering is performed to obtain a smooth amplitude tracking curve; Based on the smooth amplitude tracking curve, the voltage change is analyzed and calculated within a continuous time window to obtain the amplitude decrease magnitude of each time window. If the magnitude of the voltage drop in each time window is greater than a preset percentage decrease, then the event corresponding to each time window is determined as a candidate voltage sag event, and a candidate event list is obtained.

4. The voltage sag detection method based on fast amplitude tracking of a single-phase inverter according to claim 1, characterized in that, The process of performing signal decomposition and event boundary detail fusion on each candidate event in the event candidate list to obtain the confirmation result of the actual voltage sag event includes: Based on each candidate event in the event candidate list, perform multi-scale decomposition to obtain the multi-scale components of each candidate event; Based on the multi-scale components of each candidate event, transient mode features are extracted to obtain the transient mode features of each candidate event; The transient mode features of each candidate event are matched with preset load mutation features. If they do not match, the amplitude drop information and event boundary details are fused to obtain the actual voltage sag event confirmation result.

5. The voltage sag detection method based on fast amplitude tracking of a single-phase inverter according to claim 1, characterized in that, The process involves comparing the confirmed results of the actual voltage sag events with a pre-established voltage sag event database. If the similarity score is higher than a preset similarity threshold, the event is determined to be a recurring pattern event. The event duration sequence is then calculated and obtained, including: Based on the confirmation results of the actual voltage sag events, feature vectors are extracted and compared with a preset voltage sag event database to obtain a comparison similarity score. If the comparison similarity score is higher than the preset similarity threshold, it is determined to be a repetitive pattern event, and the corresponding event boundary details are obtained. Based on the event boundary details, timestamp correlation analysis and event duration calculation are performed to obtain the event duration sequence.

6. The voltage sag detection method based on fast amplitude tracking of a single-phase inverter according to claim 1, characterized in that, The step of performing recovery boundary matching based on the event duration sequence, confirming the recovery time if a match is successful, and performing sequence fusion to obtain the recovery time sequence includes: Based on the event duration sequence, the event recovery phase is located, and recovery boundary features are extracted to obtain the recovery boundary sequence; Based on the recovery boundary features, a comparison is made with a pre-established recovery boundary database. If the similarity is higher than a preset recovery threshold, the match is successful, and the event boundary detail sequence is obtained. Based on the event boundary detail sequence, timestamp fusion and correlation calculations are performed to obtain the recovered time series.

7. The voltage sag detection method based on fast amplitude tracking of a single-phase inverter according to claim 3, characterized in that, The step of performing correlation logic calculations based on the event duration sequence and the recovery time sequence to generate a final voltage sag event report includes: Based on the event duration sequence and the recovery time sequence, the optimized detection result sequence is obtained by matching and verifying with the smooth amplitude tracking curve and performing attribute fusion. Based on the optimized detection result sequence, multi-scale component decomposition is performed to obtain a refined version of the event boundary detail sequence; Based on the refined event boundary detail sequence, the refined event duration and refined recovery time are calculated, and multi-scale analysis is performed to quantify the stationarity of the recovery process, thereby obtaining the system reliability assessment details. The final voltage sag event report is generated based on the refined event duration, the refined recovery time, and the system reliability assessment details.

8. A voltage sag detection device based on fast amplitude tracking of a single-phase inverter, characterized in that, include: The signal acquisition and filtering module is used to acquire the output voltage signal of the single-phase inverter, perform frequency domain analysis and preliminary filtering, and obtain the voltage amplitude sequence after preliminary filtering. The candidate event determination module is used to perform dynamic tracking processing and determine candidate voltage sag events based on the pre-filtered voltage amplitude sequence, and obtain an event candidate list. The real event confirmation module is used to perform signal decomposition and event boundary detail fusion on each candidate event in the event candidate list to obtain the real voltage sag event confirmation result; The duration calculation module is used to compare the actual voltage sag event confirmation result with a pre-established voltage sag event database. If the similarity is higher than a preset similarity threshold, it is determined to be a recurring pattern event, and the event duration sequence is calculated and obtained. The recovery time calculation module is used to perform recovery boundary matching based on the event duration sequence. If the matching is successful, the recovery time is confirmed, and the sequence is fused to obtain the recovery time sequence. The report generation module is used to perform correlation logic calculations based on the event duration sequence and the recovery time sequence to generate a final voltage sag event report.

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