A power system power quality disturbance identification method and system

Abstract

The present application belongs to the field of power system power quality disturbance identification technology, and specifically discloses a power system power quality disturbance identification method and system, which comprises: acquiring three-phase voltage data of a power system, and constructing a single-cycle combined waveform; calculating skewness value indexes based on the three-phase voltage data and the single-cycle combined waveform data respectively, and identifying single-phase or three-phase imbalance disturbance based on the skewness value indexes; calculating variance indexes of three-phase voltage data of consecutive N cycles, and identifying harmonic disturbance based on the variance indexes; obtaining an analysis window of voltage sag data based on phase voltage effective value, and calculating kurtosis indexes and skewness indexes in each time window by using a preset length sliding time window to identify voltage sag disturbance. The present application uses variance, skewness and kurtosis indexes to represent different characteristics of three-phase voltage waveform, and realizes accurate identification of power system harmonic disturbance, imbalance disturbance and voltage sag disturbance.

Description

Technical Field

[0001] This invention relates to the field of power system disturbance identification technology, and in particular to a method and system for identifying power quality disturbances in power systems. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] With the large-scale integration of nonlinear loads such as new energy sources into the power system, power quality disturbances are becoming increasingly severe, and are gradually exhibiting new characteristics such as strong randomness, strong coupling, and strong fluctuations. In severe cases, this can lead to power equipment outages, precision instrument failures, medical equipment interruptions, and restrictions on residents' lives. Therefore, accurate and reliable quantitative characterization and type identification of power quality disturbances are helpful in ensuring the reliable monitoring and stable operation of the power system.

[0004] Power quality disturbances mainly include harmonic disturbances, single-phase or three-phase imbalances, and voltage sags. Currently, most characterization indicators for these types of power quality disturbances are based on phasor parameters of waveform data, such as RMS value, phase angle, and peak value. Furthermore, there are numerous characterization indicators for different disturbance types. For example, indicators for harmonics include total harmonic distortion and harmonic content; indicators for imbalance include negative-sequence imbalance and zero-sequence imbalance; and indicators for sags include sag depth, duration, and residual voltage.

[0005] It is evident that there are numerous and multi-dimensional characterization indicators for different types of disturbances. Furthermore, because it is impossible to comprehensively characterize the degree of distortion of power quality disturbances using a single type of indicator, and it is impossible to determine the type of disturbance based on its specificity, the disturbance monitoring and analysis process faces significant challenges in terms of data dimensionality. The key reason for these problems is that most indicators are derived solely from variations of phasor data of waveforms. Due to its inherent mathematical simplification, phasor data has very limited ability to quantify the comprehensive characteristics of waveforms when faced with severe distortion or rapid dynamic changes. Summary of the Invention

[0006] To address the aforementioned issues, this invention proposes a method and system for identifying power quality disturbances in power systems. It characterizes the harmonic distortion features of voltage waveforms using a variance index, the asymmetry features of voltage waveforms using a skewness index, and the abrupt changes in voltage waveforms using a kurtosis index. By combining the characteristics of these indices, a characterization index is constructed that can fully quantify disturbance characteristics and specifically determine disturbance types, thereby achieving accurate quantification and identification of power quality disturbances such as harmonic disturbances, single-phase or three-phase imbalance disturbances, and voltage sag disturbances in power systems.

[0007] In some implementations, the following technical solutions are adopted: A method for identifying power quality disturbances in a power system includes: Acquire three-phase voltage data of the power system and construct a single-cycle combined waveform; Considering the voltage asymmetry corresponding to the unbalanced disturbance, the skewness index is calculated based on the three-phase voltage data and the single-cycle combined waveform data, respectively. The single-phase or three-phase unbalanced disturbance is identified based on the skewness index. Considering the harmonic distortion characteristics corresponding to harmonic disturbances, the variance index of three-phase voltage data for N consecutive cycles is calculated, and the harmonic disturbances are identified based on the variance index. Considering the abrupt change and asymmetric characteristics corresponding to voltage sag disturbances, an analysis window for voltage sag data is obtained based on the effective value of phase voltage. Using a sliding time window of preset length, kurtosis and skewness indices are calculated in each time window to identify voltage sag disturbances.

[0008] As a further solution, the skewness index is calculated based on the three-phase voltage data, specifically: The system collects the instantaneous value data of half a power frequency cycle of a phase voltage in the power system, and after a preset time interval, collects the instantaneous value data of half a power frequency cycle of another adjacent phase voltage. The instantaneous voltage values ​​acquired in the two consecutive acquisitions are merged; Calculate the skewness values ​​of the combined voltage data of AC, CB, and BA in sequence according to the above process.

[0009] As a further approach, single-phase unbalance disturbances are identified based on the skewness values ​​of the three combined voltage values, specifically: When the skewness values ​​of the three combined voltage values ​​show a pattern of one being positive, one being negative, and one approaching zero, it is determined that there is a single-phase unbalance disturbance. Furthermore, the phase commonly included in the two sets of data with positive and negative skewness values ​​is the phase in which the single-phase voltage imbalance occurs.

[0010] As a further approach, a single-cycle combined waveform is constructed, specifically as follows: A reference phase is selected, and the single-cycle combined waveform uses the positive half-cycle data of the reference phase voltage as the first half-cycle data, and the deviation phase half-cycle data starting from the ideal zero-crossing point of the deviation phase voltage as the second half-cycle data.

[0011] As a further approach, the three-phase imbalance disturbance is identified based on the skewness index of the single-cycle combined waveform, specifically as follows: Calculate the mean of the first-order origin moments of the single-cycle combined waveform, and calculate the third-order central moments of the single-cycle combined waveform based on the mean of the first-order origin moments; standardize the third-order central moments to obtain the skewness index of the combined waveform. If the combined waveform skewness index is zero, it indicates a three-phase balanced operating condition; if the combined waveform skewness index is not zero, it indicates the presence of a three-phase unbalanced disturbance. The magnitude of the combined waveform skewness index is used to characterize the severity of the three-phase imbalance.

[0012] As a further approach, the variance index of three-phase voltage data over N consecutive cycles is calculated, and harmonic disturbances are identified based on the variance index, specifically as follows: According to a sliding time window of a preset length, the variance index of three-phase voltage data for N consecutive cycles is sampled and calculated. Based on the variance index, the center symmetry error value and range value of the data are calculated respectively. The central symmetry error value and the range value are compared with preset error thresholds respectively: If the central symmetry error value is less than or equal to the set error threshold δ1, and the range value is greater than the set error threshold δ2, then it is determined that there is an interharmonic disturbance. If the central symmetry error value is less than or equal to the set error threshold δ1, and the range value is less than the set error threshold δ2, then it is determined that there is no interharmonic disturbance.

[0013] As a further approach, the center symmetry error and range of the data are calculated based on the variance index, specifically as follows: For a series of variance indexes spanning N consecutive periods, perform first-to-last symmetric pairing and calculate the average difference between the first and second halves of the variance indexes as the central symmetry error value. The maximum and minimum values ​​are selected from the variance index sequence of N consecutive periods, and the difference between the maximum and minimum values ​​is taken as the range value.

[0014] As a further option, it also includes: According to a sliding time window of preset length, the product of the sampled value of the three-phase voltage data and the time series value in a single cycle is sampled and calculated to form a new sequence with time sequence marking; Calculate the skewness index of the new sequence and compare the skewness index with a set error threshold δ3: if the skewness index is greater than the set error threshold δ3, it is determined that there is an even harmonic; otherwise, there is no even harmonic.

[0015] As a further option, the kurtosis index is the transient impact slope within each time window; Calculate each discrete time within the analysis window k The corresponding data is normalized to obtain a data normalized kurtosis sequence, and the maximum rate of change of the normalized kurtosis sequence is used as the transient impact slope.

[0016] As a further option, the skewness index is the cumulative amount of temporary sag asymmetric accumulation within each time window; Calculate each discrete time step k The corresponding third-order cumulant estimate is obtained by performing discrete summation and normalization on the third-order cumulant estimate calculated within the analysis window to obtain the temporary asymmetric cumulant.

[0017] As a further step, voltage sag disturbances are identified, specifically as follows: The asymmetric cumulative amount of voltage sag and the transient impulse slope calculated from the three-phase voltage signals are used to construct a feature vector. The feature vector is then input into a pre-trained voltage sag identification model to identify the type of voltage sag disturbance.

[0018] In other embodiments, the following technical solutions are adopted: A power system power quality disturbance identification system, comprising: The data acquisition module is configured to acquire three-phase voltage data of the power system and construct a single-cycle combined waveform. The unbalanced disturbance identification module is configured to consider the voltage asymmetry corresponding to the unbalanced disturbance, calculate the skewness index based on three-phase voltage data and single-cycle combined waveform data respectively, and identify single-phase or three-phase unbalanced disturbances based on the skewness index. The harmonic disturbance identification module is configured to consider the harmonic distortion characteristics corresponding to the harmonic disturbance, calculate the variance index of three-phase voltage data for N consecutive cycles, and identify the harmonic disturbance based on the variance index. The voltage sag disturbance identification module is configured to consider the abrupt change characteristics and asymmetric characteristics corresponding to the voltage sag disturbance, obtain the analysis window of the voltage sag data based on the effective value of the phase voltage, and use a sliding time window of preset length to calculate the kurtosis index and skewness index in each time window to identify the voltage sag disturbance.

[0019] In other embodiments, the following technical solutions are adopted: A terminal device includes a processor and a memory, wherein the processor is used to implement instructions; and the memory is used to store multiple instructions adapted to be loaded and executed by the processor to identify the power quality disturbances in the power system described above.

[0020] In other embodiments, the following technical solutions are adopted: A computer-readable storage medium storing a plurality of instructions adapted for loading and execution by a processor of a terminal device of the above-described power system power quality disturbance identification method.

[0021] Compared with the prior art, the beneficial effects of the present invention are: The three-phase voltage waveform of a power system contains the most primitive characteristics of various disturbances. This invention fully explores the disturbance characteristics from the perspective of waveform statistics. It uses the variance index to characterize the harmonic distortion characteristics of the voltage waveform, the skewness index to characterize the asymmetry characteristics of the voltage waveform, and the kurtosis index to characterize the abrupt change characteristics of the voltage waveform, thereby improving the accuracy of characterization and realizing lightweight monitoring of multiple types of disturbances.

[0022] This invention considers the voltage asymmetry corresponding to unbalanced disturbances and identifies single-phase or three-phase unbalanced disturbances by calculating the skewness index of the three-phase voltage waveform. It also considers the harmonic distortion characteristics corresponding to harmonic disturbances and identifies them by calculating the variance index of the three-phase voltage waveform. Furthermore, considering the asymmetry of even-order harmonics, it calculates the skewness index of the three-phase voltage data sequence with time-series markers within a single period to identify even-order harmonics. Finally, considering the abrupt change characteristics and asymmetric characteristics corresponding to voltage sag disturbances, it calculates kurtosis and skewness indices within the analysis window of the voltage sag data to accurately identify voltage sag disturbances.

[0023] The power quality comprehensive identification method of the present invention has good feasibility in practical engineering applications. It does not require complex preprocessing operations such as data alignment, which can effectively reduce the complexity and cost of engineering implementation and facilitate deployment and application on engineering sites.

[0024] Other features and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0025] Figure 1 This is a flowchart of the power system power quality disturbance identification method in an embodiment of the present invention; Figure 2 This is a schematic diagram of the instantaneous three-phase voltage data waveform in an embodiment of the present invention; Figure 3 This is the three-phase voltage waveform in an embodiment of the present invention where the phase angle of phase C lags behind that of the three-phase voltage in equilibrium. Figure 4 This is the three-phase voltage waveform in an embodiment of the present invention where the C-phase voltage amplitude is higher than the standard value; Figure 5 This is a three-phase unbalanced waveform in an embodiment of the present invention where the phase angle of phase C voltage lags and the amplitude is higher than the standard value; Figure 6 This is a combined waveform and its probability distribution histogram when only the phase angle is unbalanced in an embodiment of the present invention; Figure 7 This is a combined waveform and its probability distribution histogram when only the amplitude is unbalanced in an embodiment of the present invention; Figure 8 This is a combined waveform and its probability distribution histogram when both amplitude and phase angle are unbalanced in an embodiment of the present invention; Figure 9 This is a schematic diagram of the even-order harmonic disturbance judgment process in an embodiment of the present invention; Figure 10 This is a schematic diagram of the analysis window for voltage sag data in an embodiment of the present invention; Figure 11 This is a schematic diagram of the voltage sag disturbance type identification process in an embodiment of the present invention. Detailed Implementation

[0026] It should be noted that the following detailed description is illustrative and intended to provide further explanation of the invention. Unless otherwise specified, all technical and scientific terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0027] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0028] Example 1 In one or more embodiments, a method for identifying power quality disturbances in a power system is disclosed, combined with... Figure 1 Specifically, it includes the following processes: S101: Acquire three-phase voltage data of the power system and construct a single-cycle combined waveform.

[0029] In this embodiment, the power signal monitored by the measuring equipment is collected in real time, and the phase with the voltage amplitude closest to the rated value or the phase closest to the standard phase is selected as the reference phase; the three-phase voltage data starting from the zero crossing point of the reference phase is obtained, and subsequent power quality disturbance identification is performed.

[0030] Assume the voltage expression in the measured three-phase AC power system is as follows: ; in, , , These are the instantaneous voltages of phases A, B, and C, respectively. 、 、 These are the root mean square values ​​of the three-phase voltages; , , These are the initial phases of the three-phase voltages; ω The system angular frequency is 50Hz.

[0031] In this embodiment, the zero-crossing point of the deviation phase voltage from the positive half-cycle to the negative half-cycle under balanced operating conditions is defined as the ideal zero-crossing point, and the corresponding zero-crossing point under unbalanced operating conditions is defined as the actual zero-crossing point.

[0032] The positive half-cycle of the reference phase voltage is used as the first half-cycle data of the combined waveform, and the half-cycle of the deviation phase, starting from the ideal zero crossing point of the deviation phase, is used as the second half-cycle of the combined waveform, thus forming a complete single-cycle combined waveform.

[0033] As an example, we acquire three-phase voltage data for one cycle, starting from the zero-crossing point of the reference phase. Using phase A voltage as the reference phase and phase C voltage as the deviation phase, we construct a combined waveform. The expression: ; in, Indicates the instantaneous value of the combined waveform; This represents the ideal zero-crossing point of the bias phase voltage. When phase C is the bias phase, T represents the voltage waveform period. This represents the instantaneous voltage value of phase A (reference phase). This represents the instantaneous voltage value of phase C (deviation phase).

[0034] S102: Considering the voltage asymmetry corresponding to the unbalanced disturbance, calculate the skewness index based on the three-phase voltage data and the single-cycle combined waveform data respectively, and identify the single-phase or three-phase unbalanced disturbance based on the skewness index.

[0035] Specifically, the voltages of two adjacent phases are collected sequentially at preset time intervals and then combined to obtain three sets of combined voltage values. Based on the skewness values ​​of the three sets of combined voltage values, it is determined whether there is a single-phase unbalanced disturbance. The skewness index of the single-cycle combined waveform is calculated to determine whether there is a three-phase unbalanced disturbance.

[0036] In this embodiment, a skewness index of higher-order statistics is introduced to determine whether the system has single-phase or three-phase unbalance disturbances. The skewness index has significant advantages in describing the probability distribution characteristics of signal waveforms and highlighting asymmetric information. It is sensitive to characteristics such as amplitude distortion and phase shift of signals and is not easily affected by Gaussian noise. Compared with traditional time-domain and frequency-domain analysis methods, it is more suitable for extracting single-phase or three-phase unbalance characteristics and identifying their types.

[0037] For a three-phase power system with a frequency of 50Hz, combined with Figure 2The given three-phase voltage waveforms, V1, V2, and V3 represent the voltages of phases A, B, and C, respectively. The phase A voltage needs to be taken as its instantaneous value between 0 and 180 degrees, meaning the instantaneous value of phase A is always positive. If phase C is selected as the other phase, then the instantaneous value of phase C is taken as its instantaneous value between (0 + 60 degrees) and (180 + 60 degrees), meaning the instantaneous value of phase C is always negative. Then, the instantaneous values ​​of phase A and phase C are combined sequentially for subsequent skewness calculations. Here, the phase C voltage is delayed by 60 degrees relative to the phase A voltage, corresponding to a time interval of one-sixth of a power frequency cycle.

[0038] from Figure 2 As can be seen, if the first selected data is the instantaneous voltage value of phase A which is positive, then a delay of one-sixth of the time interval is required before collecting the data with the instantaneous voltage value of phase C which is negative; if the first selected data is the instantaneous voltage value of phase C which is positive, then a delay of one-sixth of the time interval is required before collecting the data with the instantaneous voltage value of phase B which is negative; if the first selected data is the instantaneous voltage value of phase B which is positive, then a delay of one-sixth of the time interval is required before collecting the data with the instantaneous voltage value of phase A which is negative.

[0039] Therefore, in this embodiment, the voltage phases acquired in the two acquisitions need to follow the selection order of ACBA to ensure that the starting time of the second voltage acquisition is only one-sixth of the power frequency cycle later than the starting time of the first voltage acquisition.

[0040] The specific process for determining single-phase unbalanced disturbances in this embodiment is as follows: S1021: Based on the acquired instantaneous three-phase voltage data, collect the instantaneous voltage data of one phase voltage for half a power frequency cycle in the system. Then, at a time interval of one-sixth of a power frequency cycle, collect the instantaneous voltage data of another adjacent phase voltage for half a power frequency cycle. The voltage phases collected in the two consecutive collections must strictly follow the ACBA selection order, that is: if the first collection is the A phase voltage, then the C phase voltage is collected after a one-sixth cycle interval; if the first collection is the C phase voltage, then the B phase voltage is collected after a one-sixth cycle interval; if the first collection is the B phase voltage, then the A phase voltage is collected after a one-sixth cycle interval. The instantaneous phase voltage data is selected during the collection process.

[0041] S1022: After the acquisition is completed, the instantaneous values ​​of the two phase voltages acquired in the previous two acquisitions are merged, that is, the instantaneous values ​​of the two phase voltages are combined together and merged into a column of data. The specific number of data in this column depends on the sampling frequency. For example, when the sampling frequency is 100kHz, this column of data has 2000 data points, and when the sampling frequency is 30kHz, this column of sampled data has 600 data points.

[0042] Then calculate the skewness value of the merged data, using the following formula: ; in, The first half represents the instantaneous voltage value of a certain phase. The latter half represents the instantaneous voltage value of another phase. express The mean, E [·] represents the expectation operator, and the subscript here is... i It is one of AC, CB, or BA, indicating which two phase voltages constitute the voltage.

[0043] Following the above process, the merging and skewness calculation of the three voltage groups AC, CB, and BA are completed sequentially to obtain the skewness calculation results of the merged voltage data.

[0044] S1023: When the calculated skewness value of the three sets of combined voltage data shows a positive value, a negative value, and a value close to zero, it can be determined that there is a single-phase unbalance disturbance; in this embodiment, it is set that when the calculated skewness value is less than or equal to 0.0001, it is considered to be close to zero.

[0045] Among them, the two-phase voltages corresponding to the combination with a skewness value approaching zero are in normal operating condition; the phases jointly contained by the two combinations with positive and negative skewness values ​​are the phases where single-phase voltage imbalance occurs; for example: assuming that the data skewness value after merging phases AC is positive, the data skewness value after merging phases BA is negative, and the data skewness value after merging phases CB approaches zero, it can be determined that the voltages of phases B and C are in normal operating condition, and phase A has an imbalance disturbance.

[0046] As a further implementation method, the specific process for determining three-phase unbalanced disturbances in this embodiment is as follows: The uneven distribution of three-phase loads in the distribution network causes changes in the voltage amplitude or phase of a certain phase. When the load connected to a certain phase differs significantly from the loads of the other two phases, it can lead to phase angle imbalance, amplitude imbalance, or both amplitude and phase angle imbalance in the voltage of that phase.

[0047] Taking phase A voltage as the reference phase and phase C voltage as the deviation phase as an example, Figure 3 The waveform of the three-phase voltage with phase angle lags behind that of the C-phase voltage under equilibrium conditions is shown. Figure 4 The three-phase voltage waveforms are shown when the amplitude of phase C voltage is higher than the standard value. Figure 5 The three-phase unbalanced waveform is shown, where the phase angle of the C-phase voltage lags and the amplitude is higher than the standard value.

[0048] Based on the method of constructing the single-cycle combined waveform in step S101, Figures 6-8The single-cycle combined waveforms corresponding to the three unbalanced operating conditions are given respectively. The positive half-cycle is composed of the positive half-cycle of the reference phase voltage, while the negative half-cycle is composed of the half-cycle waveform of the deviation phase voltage starting from the ideal zero crossing point.

[0049] Specifically, Figure 6 The combined waveform and its probability distribution histogram are given when only the phase angle is unbalanced. Since the actual zero-crossing point of the deviation phase does not coincide with the ideal zero-crossing point, there is a special interval in the second half of the combined waveform. The instantaneous value of the waveform in this interval is positive rather than negative. When the phase angle shifts positively, the special interval appears near π, and vice versa. Furthermore, since there is no amplitude imbalance, the amplitude of the second half of the combined waveform remains consistent with that of the balanced condition, only the phase or time corresponding to the amplitude is different. Figure 7 The combined waveform and its probability distribution histogram are given when only the amplitude is unbalanced. The actual zero-crossing point of the deviation phase coincides with the ideal zero-crossing point, that is, there is no zero-crossing deviation, and the combined waveform does not have the special interval in the phase angle imbalance. However, due to the positive or negative deviation of the amplitude of the deviation phase, the combined waveform still has non-central symmetry, but the phase or time corresponding to the amplitude is consistent with the balanced condition. Figure 8 The combined waveform and its probability distribution histogram when both amplitude and phase angle are unbalanced are given. The special interval and amplitude deviation characteristics coexist in the negative half-cycle of the combined waveform. At this time, the non-central symmetry of the combined waveform is more obvious.

[0050] Based on this, this embodiment introduces the third-order central moment to describe the asymmetry of the probability density distribution and standardizes it to obtain the skewness index.

[0051] Specifically, the asymmetry of the probability density distribution of a single-cycle combined waveform is described using the third-order central moment, and its expression is as follows: ; in, The third central moment of the combined waveform, E [·] represents the expectation operator. Represents a single-cycle combined waveform. , representing the mean of a single-cycle combined waveform.

[0052] Then, the third-order central moments are standardized, and the standardized third-order central moments are defined as the skewness index of the combined waveform. Its expression is as follows: ; in, , representing the variance of the second-order central moment of a single-cycle combined waveform.

[0053] In this embodiment, the skewness index The key to its significant advantage in representing asymmetry lies in the odd power function in its expression. Its sign depends on the deviation. The symbol.

[0054] Finally, based on the calculated skewness index of the combined waveform The system assesses three-phase imbalance disturbances; the specific assessment logic is as follows: when At this time, the three-phase balanced operating condition is reached, and the probability distribution of the combined waveform with respect to its first-order moment about the origin is... μ RT The distribution is perfectly symmetrical, and the deviations in the distribution of the mean on the left and right sides, as well as the results of the power function calculation, cancel each other out.

[0055] when When a three-phase unbalanced disturbance is detected, the probability distribution of the combined waveform is asymmetrical, with the right side of the probability distribution being denser and the positive deviation being greater than the negative deviation.

[0056] when At the same time, it is also determined that there is a three-phase imbalance disturbance. At this time, the probability distribution is more concentrated on the left side, and the negative deviation is greater than the positive deviation.

[0057] and, The greater the deviation of the calculation result from the ideal value of zero, the stronger the degree of three-phase asymmetry.

[0058] This embodiment calculates the skewness index of a single-cycle combined waveform, which can determine whether an imbalance disturbance exists and quantitatively characterize its severity.

[0059] To address the shortcomings of traditional national standard methods for calculating unbalance, which require cumbersome calculations involving fundamental component extraction and symmetrical component decomposition, and which only quantitatively evaluate the severity of unbalance but cannot identify specific types, this embodiment analyzes the waveform characteristics of three-phase unbalance. It utilizes the asymmetry of combined waveforms to describe the unbalance and uses skewness indices to quantify the severity of this unbalance. This eliminates the need for extraction and decomposition of various components, significantly simplifying the calculation process for three-phase unbalance identification and greatly reducing computational complexity.

[0060] S103: Considering the harmonic distortion characteristics corresponding to harmonic disturbances, calculate the variance index of three-phase voltage data for N consecutive cycles, and identify harmonic disturbances based on the variance index.

[0061] Specifically, according to a preset sliding time window, the variance index of three-phase voltage data for N consecutive cycles is sampled and calculated. Based on the variance index, the central symmetry error value and range value of the data are calculated respectively. The central symmetry error value and range value are compared with preset error thresholds to determine whether there is interharmonic disturbance.

[0062] In this embodiment, the severity of interharmonics is quantified based on the variance index; where interharmonics refer to components whose frequency is not an integer multiple of the fundamental frequency of the power system (such as 50Hz), and any fluctuating, impulsive, or rapidly changing load may become a source of interharmonics.

[0063] Specifically, a sliding window step size of T / 2 is selected, and the variance index of single-cycle three-phase voltage data for 2K consecutive cycles is calculated. VAR i : N is the number of sampling points per cycle, T is the voltage waveform period, which can be 2π; K is an integer, which can be 10.

[0064] The central symmetry error λ and the range R of this data set are obtained as follows: ; ; in, Let i be the variance index of this set of data. This is the 2K+1-ith variance index of this data set.

[0065] In this set of data, the unique characteristic of interharmonics is their centrally symmetrical distribution and unequal data. Therefore, the central symmetry error value can be used to determine whether the data set exhibits a centrally symmetrical distribution, and the data range can be used to determine the overall distribution trend of the data set. This embodiment constructs an interharmonic criterion based on these two indicators, as follows: ; That is: if the central symmetry error value is less than or equal to the set error threshold δ1, it means that the data set is centrally symmetric, eliminating the interference of power quality problems other than harmonic problems; and if the range value is greater than the set error threshold δ2, it means that the data set has a certain degree of fluctuation, which is consistent with the interharmonic characteristics, and it is judged that there is an interharmonic disturbance. If the central symmetry error value is less than or equal to the set error threshold δ1, and the range value is less than the set error threshold δ2, then it is determined that there is no interharmonic disturbance.

[0066] If the central symmetry error value is greater than the set error threshold δ1, it is impossible to determine whether there is interharmonic disturbance.

[0067] As a further implementation, if interharmonic disturbances exist, the interharmonic active power within each time window is calculated, and the frequency with the largest interharmonic active power amplitude is taken as the dominant frequency of the interharmonic disturbance.

[0068] Specifically, the active power of each interharmonic... Specifically: ; in, , These are the interharmonic voltage and interharmonic current corresponding to the monitoring point, respectively; The interharmonic power factor angle, , h ∈ H ; The voltage phase angle for interharmonics. The phase angle of the interharmonic current. h Indicates the first h Secondary harmonics H It is the set of all interharmonics.

[0069] By analyzing the interharmonic active power within each time window, the frequency with the largest interharmonic active power amplitude is selected as the dominant frequency of the interharmonic disturbance. This dominant frequency directly corresponds to the physical characteristics, oscillation mode, and resonance risk of the disturbance source, thereby identifying and locating the interharmonic disturbance source equipment through the dominant frequency.

[0070] As a further implementation, in addition to identifying and locating interharmonic disturbances, this embodiment also achieves the identification of even-order harmonics; specifically, in conjunction with Figure 9 Introducing the skewness index, the specific method for identifying even harmonics is as follows: (1) For single-cycle three-phase voltage waveform data, the time series of each sampled value within a single cycle is set as (t1, t2, …, t…). n The voltage sampling values ​​are (x1, x2, …, x). n ); (2) Calculate the voltage sample values ​​(x1, x2, …, x n The new sequence (y1, y2, …, y) obtained by multiplying it with its corresponding time series is (y1, y2, …, y). n This yields waveform data with timing markers, calculated as follows: ; (3) Calculate the skewness value S of the waveform data with time stamps: ; in, This represents the number of voltage samples within a single cycle.

[0071] (4) Compare the calculated skewness index S with the set error threshold δ3: If the skewness index is greater than the set error threshold δ3, it is determined that there is an even harmonic; otherwise, there is no even harmonic.

[0072] Specifically, even harmonics have half-wave asymmetry compared to odd harmonics. The skewness index S can calculate the degree of half-wave symmetry. If it is an odd harmonic, the calculated value should be close to 0 after considering the error, while it is not equal to 0 for an even harmonic. Therefore, the set error threshold δ3 is a threshold close to 0. If the skewness index S is greater than the set error threshold δ3, it indicates that an even harmonic exists.

[0073] It should be noted that the error thresholds δ1, δ2, and δ3 are all set based on field experience and historical data.

[0074] Through the above-described determination process, without the need for complex transformations such as time-frequency conversion and mode decomposition, the existence of interharmonic disturbances or even-order harmonic disturbances in the power system can be determined solely by using the variance and skewness indices of the voltage time-domain waveform. Furthermore, the interharmonic source can be accurately located. This achieves lightweight computation while avoiding the problem of spectrum leakage, providing accurate characteristic basis for the subsequent development of targeted suppression measures.

[0075] S104: Considering the abrupt change characteristics and asymmetric characteristics corresponding to voltage sag disturbances, an analysis window for voltage sag data is obtained based on the effective value of the phase voltage. Using a sliding time window of preset length, kurtosis and skewness indices are calculated in each time window to identify voltage sag disturbances.

[0076] Specifically, this embodiment integrates high-order statistical features and support vector machines to effectively identify voltage sag sources. It can effectively distinguish various sag disturbance sources such as short-circuit faults, induction motor starting, and transformer commissioning. Figure 11 The specific implementation process is as follows: (1) Based on the acquired three-phase voltage waveform data, construct a sequence of three-phase discrete voltage sampling values. u ( n )=[ u a ( n ) u b ( n ) u c ( n )],in, u a ( n ), u b ( n )and u c ( n ) are discrete voltage sample value sequences for phases a, b, and c, respectively.

[0077] Construct a sliding window with a length of T / 2 sampling periods, and calculate the effective value (i.e., root mean square value of voltage) of each phase voltage in real time within each sliding window. U rms ( k The calculation formula is: ; in, k This is the starting point number of the sliding window; i The sampling point number within the current calculation window; N h This represents the number of sampling points for half a power frequency cycle. This represents the voltage sample value at the i-th sampling point.

[0078] The root mean square value of each phase voltage is calculated in real time as the effective value of the phase voltage. An analysis window for voltage sag data is obtained based on the effective value of the phase voltage, and the data in the analysis window is preprocessed.

[0079] When the effective value of any phase voltage is monitored U rms ( k When the voltage drops to 0.1 pu~0.9 pu of the nominal voltage and lasts for more than 0.5 power frequency cycles, the sag recording trigger mechanism is immediately activated. Data including the power frequency cycle before the sag initiation point, the duration of the sag, and the power frequency cycle after the sag ends are extracted to form a complete sag data analysis window, such as... Figure 10 As shown, the start time of the window is defined as... t s The end time is defined as t e .

[0080] The specific process of preprocessing the data in the analysis window is as follows: To eliminate the influence of different voltage levels in the power grid on the feature extraction results, the original sequence of the truncated analysis window is normalized to obtain the normalized sequence. ,Right now: ; in, U ref Using the reference voltage, the original voltage sequence of the analysis window is taken. u ( n The rated voltage of the power system to which it belongs.

[0081] To remove the interference of the DC component present in the power grid on the extraction of higher-order statistical features, the mean of the normalized sequence is removed. For sequences of length [missing information], [missing information]. N Standardized sequence Its zero-mean output sequence x( n )for: ; in, This represents the value of the i-th sampling point in the per-unit sequence.

[0082] (2) Based on a sliding time window of a preset length, calculate the preprocessed data sequence and calculate the kurtosis and skewness indices in each time window.

[0083] In this embodiment, the kurtosis index is the transient impact slope, and the skewness index is the transient descent asymmetric cumulative amount.

[0084] Specifically, a sliding window with a length of one power frequency cycle is used to process the preprocessed sequence. x ( n ) to perform calculations.

[0085] The specific process for calculating the transient asymmetric cumulant is as follows: calculate the cumulant at each discrete time step. k The corresponding third-order cumulant estimate is obtained by performing discrete summation and normalization on the third-order cumulant estimate calculated within the analysis window to obtain the temporary asymmetric cumulant.

[0086] Specifically, because the third-order cumulant is sensitive to signal asymmetry, it can extract the waveform distortion differences of each phase before and after the sag, which can be used to distinguish various asymmetric sag sources. In this embodiment, the calculation is performed at each discrete time step. k The corresponding third-order cumulant estimate is: ; in, N 0 represents the number of sampling points within the sliding window. This represents the value of the i-th sampling point in the preprocessed sequence.

[0087] For time t s to t e The process between them, for the third-order cumulant sequence C 3x ( k Discrete summation and normalization are performed to obtain the transient asymmetric cumulant. Specifically: ; Where, Δ t The discrete sampling interval, i.e., the sampling frequency, represents the signal acquisition system. f s The reciprocal of; σ For sequence x ( nThe standard deviation during the temporary descent, , This is the arithmetic mean of the sequence.

[0088] The calculation process for the transient impact slope is as follows: calculate the slope at each discrete moment within the analysis window. k The corresponding data are normalized to obtain a data normalized kurtosis sequence, and the maximum rate of change of the normalized kurtosis sequence is used as the transient impact slope index.

[0089] Specifically, because a short-circuit fault is accompanied by a sudden change in system impedance, its voltage waveform changes rapidly. However, the starting process of an induction motor is affected by mechanical inertia and electromagnetic processes, resulting in a relatively gradual change in waveform distortion. To quantify the difference in the rate of change of this statistical characteristic over time, this embodiment first calculates the normalized fourth-order cumulant sequence during the sag process. K ( k ),Right now: ; Where E[·] is the mathematical expectation operator, which is calculated in discrete systems by the arithmetic mean of the sampling points within the window; x ( k ) represents the sampled voltage value at discrete times.

[0090] Define this fourth-order cumulant sequence K ( k The maximum rate of change is the transient impact slope. : ; Where, Δ t It represents the discrete sampling interval of the signal acquisition system, that is, the reciprocal of the sampling frequency; N 0 represents the number of sampling points within the sliding window; H This is an adjustable parameter used to adjust the time scale of TIS calculations, and can be set according to actual needs.

[0091] (3) Use the transient sag asymmetric cumulative amount and transient impact slope index obtained by calculating the three-phase voltage signals respectively to construct the feature vector.

[0092] Specifically, the two characteristic indices mentioned above are calculated for the three-phase voltage signals, resulting in six characteristic values; these characteristic values ​​are then used to construct a six-dimensional feature vector. F =[SAI A SAI B SAI C TIS A TIS B TIS C ] TThe subscripts A, B, and C represent the characteristic indices calculated for phases A, B, and C, respectively, and the normalization formula is used to map each component of the characteristic vector to the interval [0,1].

[0093] (4) Input the constructed feature vector into the pre-trained voltage sag identification model to identify the voltage sag disturbance type.

[0094] Voltage sag identification models can be constructed based on the Support Vector Machine (SVM) algorithm. The parameters of the SVM can be optimized using algorithms such as the Grey Wolf Optimization Algorithm, the Snake Optimization Algorithm, or the Artificial Bee Colony Algorithm. C , g ) to be optimized, C As a penalty factor, g These are kernel parameters, which ensure that the identification model can escape local extrema and maintain global classification performance.

[0095] Before model deployment, this embodiment uses fault waveform data from the power system historical database for offline training. For each type of typical voltage sag event with labeled attributes, including single-phase ground fault, two-phase ground fault, two-phase short circuit, three-phase short circuit, induction motor start-up, transformer commissioning, etc., a standard model library is constructed. The voltage sag identification model is trained using data from the standard model library.

[0096] Finally, the voltage sag identification model will output the corresponding voltage sag disturbance type, thus completing the determination of the cause of the sag.

[0097] The above process effectively overcomes the limitations of traditional analysis methods in capturing nonlinear and non-stationary signal features by constructing high-order statistical feature vectors that include asymmetric cumulative exponents and transient impact slopes. It significantly enhances the discriminability of different transient sources in the feature space and overcomes the problems of vague physical meaning and low distinguishability of traditional feature vectors.

[0098] In summary, the above implementation process of this embodiment, from the perspective of waveform statistics, utilizes the mathematical properties of higher-order statistics to naturally suppress Gaussian noise. It characterizes the harmonic distortion characteristics of the voltage waveform through the variance index, the asymmetry characteristics of the voltage waveform through the skewness index, and the abrupt change characteristics of the voltage waveform through the kurtosis index. Thus, it achieves accurate identification of different power quality disturbances such as unbalance disturbances, harmonic disturbances, and voltage sag disturbances. It overcomes the drawbacks of traditional time-frequency analysis methods such as wavelet transform, which are sensitive to noise and require cumbersome preprocessing. It can maintain high feature extraction purity even in noisy power grid environments, ensuring the accuracy and reliability of the identification results for different disturbances.

[0099] The method described in this embodiment has good feasibility in practical engineering applications. It does not require complex preprocessing operations such as data alignment, which can effectively reduce the complexity and cost of engineering implementation and facilitate deployment and application on the engineering site.

[0100] Example 2 In one or more embodiments, a power system power quality disturbance identification system is disclosed, specifically including: The data acquisition module is configured to acquire three-phase voltage data of the power system and construct a single-cycle combined waveform. The unbalanced disturbance identification module is configured to consider the voltage asymmetry corresponding to the unbalanced disturbance, calculate the skewness index based on three-phase voltage data and single-cycle combined waveform data respectively, and identify single-phase or three-phase unbalanced disturbances based on the skewness index. The harmonic disturbance identification module is configured to consider the harmonic distortion characteristics corresponding to the harmonic disturbance, calculate the variance index of three-phase voltage data for N consecutive cycles, and identify the harmonic disturbance based on the variance index. The voltage sag disturbance identification module is configured to consider the abrupt change characteristics and asymmetric characteristics corresponding to the voltage sag disturbance, obtain the analysis window of the voltage sag data based on the effective value of the phase voltage, and use a sliding time window of preset length to calculate the kurtosis index and skewness index in each time window to identify the voltage sag disturbance.

[0101] As a further embodiment, the harmonic disturbance identification module is also configured to: According to a preset sliding time window, the sampled values ​​of three-phase voltage data and the time series values ​​within a single cycle are sampled and calculated to form a new sequence with time sequence markings; the skewness index of the new sequence is calculated and compared with a set error threshold δ3: if the skewness index is greater than the set error threshold δ3, it is determined that even harmonics exist; otherwise, even harmonics do not exist.

[0102] It should be noted that the specific implementation methods of the above modules are exactly the same as those in Example 1, and will not be described in detail again.

[0103] Example 3 In one or more embodiments, a terminal device is disclosed, comprising a processor and a memory, wherein the processor is used to implement instructions; and the memory is used to store multiple instructions adapted to be loaded by the processor and executed by the power system power quality disturbance identification method described in Embodiment 1.

[0104] It should be understood that in this embodiment, the processor can be a central processing unit (CPU), or it can be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor.

[0105] Memory may include read-only memory and random access memory, and provides instructions and data to the processor. A portion of memory may also include non-volatile random access memory. For example, memory may also store information about the device type.

[0106] In the implementation process, each step of the above method can be completed by the integrated logic circuits in the processor hardware or by instructions in software form.

[0107] Example 4 In one or more embodiments, a computer-readable storage medium is disclosed, wherein a plurality of instructions are stored, the instructions being adapted to be loaded by a processor of a terminal device and executed by the power system power quality disturbance identification method described in Embodiment 1.

[0108] While the specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims

1. A method for identifying power quality disturbances in a power system, characterized in that, include: To acquire three-phase voltage data of the power system and construct a single-cycle combined waveform, specifically: a reference phase is selected, and the single-cycle combined waveform uses the positive half-cycle data of the reference phase voltage as the first half-cycle data and the deviation phase half-cycle data starting from the ideal zero-crossing point of the deviation phase voltage as the second half-cycle data. Considering the voltage asymmetry corresponding to the unbalanced disturbance, a skewness index is calculated based on three-phase voltage data and single-cycle combined waveform data, respectively. The skewness index is used to identify single-phase or three-phase unbalanced disturbances. Specifically, calculating the skewness index based on three-phase voltage data involves: collecting the instantaneous value data of half a power frequency cycle of a phase voltage in the power system; after a preset time interval, collecting the instantaneous value data of half a power frequency cycle of another adjacent phase voltage; merging the two sets of instantaneous voltage values; and sequentially calculating the skewness values ​​of the three combined voltage data sets AC, CB, and BA according to the above process. When the skewness values ​​of the three combined voltage values ​​show a characteristic of one being positive, one being negative, and one approaching zero, it is determined that a single-phase unbalanced disturbance exists. Furthermore, the phase commonly included in the two sets of data with positive and negative skewness values ​​is the phase where the single-phase voltage imbalance occurs. Considering the harmonic distortion characteristics corresponding to harmonic disturbances, the variance index of three-phase voltage data for N consecutive cycles is calculated, and the harmonic disturbances are identified based on the variance index. Considering the abrupt change and asymmetric characteristics corresponding to voltage sag disturbances, an analysis window for voltage sag data is obtained based on the effective value of phase voltage. Using a sliding time window of preset length, kurtosis and skewness indices are calculated in each time window to identify voltage sag disturbances.

2. The power system power quality disturbance identification method as described in claim 1, characterized in that, Based on the skewness index of the single-cycle combined waveform, the three-phase imbalance disturbance is identified, specifically as follows: Calculate the mean of the first-order origin moments of the single-cycle combined waveform, and calculate the third-order central moments of the single-cycle combined waveform based on the mean of the first-order origin moments; standardize the third-order central moments to obtain the skewness index of the combined waveform. If the combined waveform skewness index is zero, it indicates a three-phase balanced operating condition; if the combined waveform skewness index is not zero, it indicates the presence of a three-phase unbalanced disturbance. The magnitude of the combined waveform skewness index is used to characterize the severity of the three-phase imbalance.

3. The power system power quality disturbance identification method as described in claim 1, characterized in that, Calculate the variance index of three-phase voltage data for N consecutive periods, and identify harmonic disturbances based on the variance index. Specifically: According to a sliding time window of a preset length, the variance index of three-phase voltage data for N consecutive cycles is sampled and calculated. Based on the variance index, the center symmetry error value and range value of the data are calculated respectively. The central symmetry error value and the range value are compared with preset error thresholds respectively: If the central symmetry error value is less than or equal to the set error threshold δ1, and the range value is greater than the set error threshold δ2, then it is determined that there is an interharmonic disturbance. If the central symmetry error value is less than or equal to the set error threshold δ1, and the range value is less than the set error threshold δ2, then it is determined that there is no interharmonic disturbance.

4. The power system power quality disturbance identification method as described in claim 3, characterized in that, Based on the variance index, the central symmetry error and range of the data are calculated respectively, specifically as follows: For a series of variance indexes spanning N consecutive periods, perform first-to-last symmetric pairing and calculate the average difference between the first and second halves of the variance indexes as the central symmetry error value. The maximum and minimum values ​​are selected from the variance index sequence of N consecutive periods, and the difference between the maximum and minimum values ​​is taken as the range value.

5. The power system power quality disturbance identification method as described in claim 3, characterized in that, Also includes: According to a sliding time window of preset length, the sampled values ​​of three-phase voltage data within a single cycle are sampled and the product of the time series values ​​is calculated to form a new sequence with time sequence markings; The skewness index of the new sequence is calculated and compared with a set error threshold δ3: if the skewness index is greater than the set error threshold δ3, it is determined that there is an even harmonic; otherwise, there is no even harmonic.

6. The power system power quality disturbance identification method as described in claim 1, characterized in that, The kurtosis-related metrics are the transient impact slopes within each time window; Calculate each discrete time within the analysis window k The corresponding data is normalized to obtain a data normalized kurtosis sequence, and the maximum rate of change of the normalized kurtosis sequence is used as the transient impact slope.

7. The power system power quality disturbance identification method as described in claim 1, characterized in that, The skewness-type indicators are the cumulative amount of temporary descent asymmetric within each time window; Calculate each discrete time step k The corresponding third-order cumulant estimate is obtained by performing discrete summation and normalization on the third-order cumulant estimate calculated within the analysis window to obtain the temporary asymmetric cumulant.

8. The power system power quality disturbance identification method as described in claim 1, characterized in that, Identifying voltage sag disturbances specifically involves: The asymmetric cumulative amount of voltage sag and the transient impulse slope calculated from the three-phase voltage signals are used to construct a feature vector. The feature vector is then input into a pre-trained voltage sag identification model to identify the type of voltage sag disturbance.

9. A system for implementing the power system power quality disturbance identification method of claim 1, characterized in that, include: The data acquisition module is configured to acquire three-phase voltage data of the power system and construct a single-cycle combined waveform. The unbalanced disturbance identification module is configured to consider the voltage asymmetry corresponding to the unbalanced disturbance, calculate the skewness index based on three-phase voltage data and single-cycle combined waveform data respectively, and identify single-phase or three-phase unbalanced disturbances based on the skewness index. The harmonic disturbance identification module is configured to consider the harmonic distortion characteristics corresponding to the harmonic disturbance, calculate the variance index of three-phase voltage data for N consecutive cycles, and identify the harmonic disturbance based on the variance index. The voltage sag disturbance identification module is configured to consider the abrupt change characteristics and asymmetric characteristics corresponding to the voltage sag disturbance, obtain the analysis window of the voltage sag data based on the effective value of the phase voltage, and use a sliding time window of preset length to calculate the kurtosis index and skewness index in each time window to identify the voltage sag disturbance.

10. The system as described in claim 9, characterized in that, The harmonic disturbance identification module is also configured to: According to a preset sliding time window, the sampled values ​​of three-phase voltage data and the time series values ​​within a single cycle are sampled and calculated to form a new sequence with time sequence markings; the skewness index of the new sequence is calculated and compared with a set error threshold δ3: if the skewness index is greater than the set error threshold δ3, it is determined that even harmonics exist; otherwise, even harmonics do not exist.

11. A terminal device comprising a processor and a memory, the processor for implementing instructions; the memory for storing multiple instructions, characterized in that, The instructions are adapted to be loaded by a processor and executed as the power system power quality disturbance identification method according to any one of claims 1-8.

12. A computer-readable storage medium storing a plurality of instructions, characterized in that, The instructions are adapted to be loaded by the processor of the terminal device and executed by the power system power quality disturbance identification method according to any one of claims 1-8.

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