Method for performing power quality sag source positioning analysis based on four-scale wavelet decomposition

Abstract

The invention discloses a power quality sag source positioning analysis method based on four-scale wavelet decomposition, and the method comprises the steps: S1, monitoring each voltage vector value, and obtaining basic data when a voltage sag fault occurs; s2, calculating the basic data by using a four-scale wavelet energy entropy algorithm to obtain a signal value of each wavelet energy entropy; s3, determining the distance between each monitoring point and the voltage sag fault, and positioning the monitoring point closest to the voltage sag fault; and S4, establishing a transient disturbance distribution network model for verification. According to the method, the wavelet energy entropy is applied to voltage sag source positioning, the voltage change of a monitoring point during a fault is taken as a measured value, and the variable quantity is quantitatively displayed through the wavelet energy entropy, so that the distances from different monitoring points to the sag source are quickly determined; and the accuracy and the effect of positioning are verified by utilizing model verification and grey-scale map comparison, and finally accurate positioning of the sag source and the monitoring point is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of power quality analysis, and in particular to a method for locating and analyzing power quality sag sources based on four-scale wavelet decomposition. Background Art

[0002] As modern industrial equipment tends to be more integrated and sophisticated, especially high-tech industrial equipment with digitalization and information technology as the core, it is very sensitive to changes in power supply voltage. In the power system, the contradiction between the unavoidable voltage sag events and the increasingly sensitive power-consuming equipment is becoming increasingly prominent.

[0003] For electricity users, the use of appropriate control devices can effectively alleviate the impact of voltage sag on sensitive equipment; for power grids, establishing a reliable and sensitive voltage sag source identification and source location system is of great practical significance.

[0004] At present, there are two methods for locating the source of voltage sag. One of them is: based on multiple transient signals from a large number of monitoring points in the power grid, using wavelet transform and network analysis to provide a comprehensive intelligent analysis result of the transient power quality disturbance, providing support for the assessment and management of transient power quality disturbance problems; the other method is: real-time collection of voltage data from each power quality monitoring point, when a voltage sag occurs, determining the fault phase according to the sag depth, and then using three-scale decomposition of the fault phase to calculate the wavelet energy entropy of each power quality monitoring point, and finally determining the monitoring point corresponding to the maximum point of the wavelet energy entropy as the fault point from the high and low frequencies.

[0005] However, these measures currently adopted contain at least the following three problems: 1) A large amount of data related to electrical parameters is required to process and determine the location of the voltage sag source; when less data related to electrical parameters is provided, the positioning method may not be implemented or serious positioning errors may occur; 2) When using wavelet transform, there are often disadvantages such as frequency aliasing and result translation. The lack of deeper processing will also lead to insufficient positioning accuracy and the positioning process is easily affected by interference signals; 3) When performing operations related to wavelet transform, the scale decomposition is often two-scale or three-scale. The scales of these decompositions are insufficient, and the corresponding high-frequency and low-frequency frequency parameters are also insufficient, making it impossible to simultaneously present detailed information in multiple directions of horizontal, vertical and diagonal lines. Summary of the invention

[0006] In view of the above problems, the purpose of the present invention is to propose a method for locating and analyzing the source of power quality sag based on four-scale wavelet decomposition, and apply wavelet energy entropy to the location of the voltage sag source. This method takes the change in voltage amplitude at the monitoring point during a fault as the measurement value, quantifies the size of the change through wavelet energy entropy, and then quickly determines the distance between different monitoring points and the sag source, and locates the position of the sag source; at the same time, model verification and grayscale image comparison are used to verify the accuracy and effect of positioning, and finally achieve accurate positioning estimation of the sag source and the monitoring point.

[0007] This is achieved through the following technical solutions: A method for locating and analyzing power quality sag sources based on four-scale wavelet decomposition comprises the following steps: S1, real-time monitoring of the voltage vector value of each monitoring point, when a voltage sag fault occurs, using the data of each voltage vector value after transient changes and gradually transitioning to a stable state as basic data for positioning analysis; S2, calculating the basic data in step S1 using a four-scale wavelet energy entropy algorithm to obtain a signal value of each wavelet energy entropy corresponding to each voltage vector value; S3, determining the distance of each corresponding monitoring point from the voltage sag fault based on the signal value of each wavelet energy entropy in step S2, and locating the monitoring point closest to the voltage sag fault.

[0008] The present invention calculates the wavelet energy entropy of each voltage vector value at four scales, and can quickly obtain the distance between different monitoring points and the fault location according to the signal value of the wavelet energy entropy, thereby accurately locating the actual location of the fault.

[0009] Preferably, in step S2, the calculation using the four-scale wavelet energy entropy algorithm includes: selecting a wavelet function, four-scale decomposition, downsampling and wavelet reconstruction; wherein the wavelet function is selected based on the basic data; the four-scale decomposition includes decomposing each voltage vector value in the basic data into four components of different frequencies, including a low-frequency approximate component and three high-frequency detail components; downsampling includes selecting samples in each component; and wavelet reconstruction includes reconstructing each corresponding selected sample by inverse operation under each component. Using the four-scale wavelet energy entropy algorithm, detailed data related to transient changes can be obtained from each detail component, thereby obtaining a signal value that can accurately reflect the distance from the fault location.

[0010] Preferably, after determining the distance of each corresponding monitoring point from the voltage sag fault in step S3, grayscale verification is continued; grayscale verification includes: grayscale processing the signal value of each wavelet energy entropy to obtain a grayscale image, and judging whether the grayscale image represents the location of the voltage sag fault. If so, the grayscale verification ends; if not, the step of calculating the basic data in step S1 using the four-scale wavelet energy entropy algorithm in step S2 is returned again, and the cycle is repeated until the grayscale verification ends. The grayscale image is used for verification, and the verification result is very intuitive and effective.

[0011] Preferably, the following steps are also included: S4, building a transient disturbance distribution network model based on each monitoring point in step S1, and setting different numbers for each node of the transient disturbance distribution network model; setting up a fault module for each node in turn according to the order of different numbers and recording the voltage drop value of each node in turn; using a four-scale wavelet energy entropy algorithm to calculate each voltage drop value in turn to obtain a calculation result of each node; storing each voltage drop value and the corresponding number of each node in a matrix form and performing grayscale processing to obtain a drop grayscale map; then performing grayscale processing on the calculation result of each node at the corresponding scale to obtain a calculated grayscale map; comparing the drop grayscale map with the calculated grayscale map to determine whether the clarity of the calculated grayscale map is due to the drop grayscale map. If so, end the operation; if not, return to the process of using a four-scale wavelet energy entropy algorithm to calculate each voltage drop value in turn, and loop until the operation ends. Establishing a transient disturbance distribution network model for verification also ensures the accuracy and effectiveness of positioning. When the verification results do not meet the requirements, a cyclic verification can be performed to avoid positioning problems caused by errors in a certain link.

[0012] Preferably, the transient disturbance distribution network model is an IEEE multi-node model in MATLAB or in Simulink, including multiple power sources, multiple transformers, multiple buses, and multiple transmission lines. The IEEE multi-node model is widely applicable to scenarios in the power system, has high compatibility, and is also convenient for fault analysis.

[0013] Preferably, the fault module is a short-circuit fault module, which is used to generate a short-circuit fault signal at each node, and each short-circuit fault signal includes each corresponding voltage drop value. Among various types of faults in the power system, the probability of occurrence of short-circuit fault is the highest, so using the short-circuit fault module as the fault module for verification improves practicality.

[0014] Compared with the prior art, the present invention has the following beneficial effects: The technical solution of the present invention applies wavelet energy entropy to the location of voltage sag sources. The method uses the change in voltage amplitude at the monitoring point during a fault as a measurement value, quantifies the size of the change through wavelet energy entropy, and then quickly determines the distance between different monitoring points and the sag source, and locates the position of the sag source; at the same time, model verification and grayscale image comparison are used to verify the accuracy and effect of positioning, and ultimately achieve accurate positioning estimation of the sag source and the monitoring point. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a flow chart of a method for locating and analyzing power quality sag sources based on four-scale wavelet decomposition; Figure 2 It is a schematic diagram of an IEEE14 node model; Figure 3 It is a voltage waveform diagram of each node when node 7 is short-circuited and grounded with low resistance in an IEEE14-node model; Figure 4 It is a waveform diagram of the voltage effective value of each node when node 7 is short-circuited and grounded with low resistance in an IEEE14 node model; Figure 5 It is a grayscale image formed based on the voltage drop value when node 7 is short-circuited and grounded with low resistance in an IEEE14 node model; Figure 6 It is a grayscale image measured based on wavelet energy entropy when node 7 is short-circuited and grounded with low resistance in an IEEE14 node model; Figure 7 It is a voltage waveform diagram of each node when node 7 is short-circuited and connected to high-resistance ground in an IEEE14-node model; Figure 8 It is a waveform diagram of the voltage effective value of each node when node 7 is short-circuited and grounded with high resistance in an IEEE14 node model; Fig. 9 It is a grayscale image formed based on the voltage drop value when node 7 is short-circuited and grounded with high resistance in an IEEE14 node model; Fig.10 It is a grayscale image measured based on wavelet energy entropy when node 7 is short-circuited and grounded with high resistance in an IEEE14 node model; Fig.11 The waveform diagram of the effective voltage value of each monitoring point when a voltage sag fault occurs at four monitoring points in an area. DETAILED DESCRIPTION

[0016] The technical solutions in the embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0017] like Figure 1As shown, it is a flow chart of a method for locating and analyzing the source of power quality sag based on four-scale wavelet decomposition. After a voltage sag fault occurs, the collected signal is calculated using wavelet energy entropy, which can effectively calculate the signal value reflecting the distance between the monitoring point and the fault location, thereby completing the positioning.

[0018] A method for locating and analyzing power quality sag sources based on four-scale wavelet decomposition specifically comprises the following steps: S1. Real-time monitoring of the voltage vector value of each monitoring point. When a voltage sag fault occurs, the data of each voltage vector value after transient changes and gradually transitioning to a stable state is used as the basic data for positioning analysis. When a voltage sag fault occurs, the voltage signal, that is, the voltage vector value, will inevitably change. This change includes transient classification changes such as fundamental amplitude drop and high-order harmonic transformation. For monitoring points at different distances from the fault point, these transient classification changes are also different; therefore, the wavelet energy entropy algorithm can be used to process each voltage vector value in the future. These different transient classification changes will be presented through the results of the wavelet energy entropy algorithm. The closer to the sag source, the larger the corresponding wavelet energy entropy result, and the farther from the sag source, the smaller the corresponding wavelet energy entropy result.

[0019] S2. Use the four-scale wavelet energy entropy algorithm to calculate the basic data in step S1 to obtain the signal value of each wavelet energy entropy corresponding to each voltage vector value. The calculation using the four-scale wavelet energy entropy algorithm includes: selecting a wavelet function, four-scale decomposition, downsampling and wavelet reconstruction; wherein the wavelet function is selected based on the basic data; the four-scale decomposition includes decomposing each voltage vector value in the basic data into four components of different frequencies, including a low-frequency approximate component and three high-frequency detail components; downsampling includes selecting samples in each component; and wavelet reconstruction includes reconstructing each corresponding selected sample by inverse operation under each component. Using the four-scale wavelet energy entropy algorithm, detailed data related to transient changes can be obtained from each detail component, thereby obtaining a signal value that can accurately reflect the distance from the fault location.

[0020] Specifically, four-scale decomposition involves decomposing the signal into four different frequency components, which correspond to different frequency characteristics of the signal. The following are the basic steps of four-scale decomposition using orthogonal wavelets: 1) Select a wavelet function: First, you need to select a wavelet function suitable for analysis based on the application scenario, such as Daubechies wavelet function, db4 wavelet function, and Morlet wavelet function.

[0021] 2) Four-scale decomposition: Through four-scale transformation, each original voltage vector value is decomposed into sub-signals of different frequencies. In the case of four-scale decomposition, this means decomposing the signal into four different frequency components, which correspond to the approximate components of the whole and the detail components of the details, specifically one low-frequency approximate component and three high-frequency detail components. The three high-frequency detail components are used to characterize the detail information of the signal in the horizontal, vertical and diagonal directions.

[0022] 3) Downsampling: At each scale, downsampling, also known as subsampling or interlaced sampling, is used to reduce the redundancy of the data while maintaining the main features of each sub-signal. Downsampling is achieved by taking a sample every other number of samples, which helps reduce the dimensionality of the data while retaining key information.

[0023] 4) Wavelet reconstruction: Through the inverse operation, namely wavelet reconstruction, a signal closely related to the original voltage vector value signal can be reconstructed from each of these decomposed frequency components to restore the resolution of the original voltage vector value signal.

[0024] It should be noted that wavelet energy entropy is a combination of wavelet transform and information entropy. Wavelet transform is a transform analysis method different from Fourier transform. It inherits and develops the idea of ​​short-time Fourier transform for localizing signals, while making the window size vary with frequency. Wavelet transform has the characteristics of multi-resolution analysis and is an ideal tool for signal analysis and processing. Its working principle is to gradually refine the signal function at multiple scales through a telescopic window to achieve time subdivision at high frequencies and frequency subdivision at low frequencies, and can automatically adapt to the requirements of time-frequency signal analysis. Since the wavelet transform analysis method has good time-frequency localization characteristics, it can handle weak or sudden signal very well, and is suitable for transient power quality analysis including voltage sag. The definition of information entropy is as follows: for a system, all possible states are summarized into a sample set, let X = {X1, X2, …, Xn}, n is the total number of states, and the probability of each state is used to represent the uncertainty of the system, that is, Pi represents the probability P(Xi) of event Xi in sample set X. The probability of an event is between certain occurrence and certain non-occurrence. If an event is certain to occur, the probability is 1, and if an event is certain not to occur, the probability is 0. Then 0≤≤1, , then the information entropy H(X) of X is defined as: , where when pi=0, let pi log a pi=0, the logarithmic base a is generally taken as a=e in information entropy. So the formula can be expressed as: . Information entropy H(X) reflects the uncertainty of an event. When the probability of each event is the same, the entropy value is the maximum; and when an event is certain to occur, the entropy value is the minimum value 0. Information entropy and wavelet transform constitute wavelet entropy. The result of wavelet transformation operation is further processed by Claude Chanlon information entropy to improve the accuracy of data analysis results. According to different types of signals, wavelet entropy can construct various types of wavelet entropy algorithms.

[0025] The following will introduce how wavelet changes are combined with Claude-Shannong information entropy to form wavelet energy entropy and the calculation method. Let E=E1,E2, …,Em, be the wavelet energy spectrum of the signal at m scales, then a division of the signal energy E can be formed in the scale domain. According to the characteristics of orthogonal wavelet transform, the energy E in the signal within a sliding window should be equal to the sum of the energy Ej of each scale component, that is, E = ∑j(J) = 1 Ej. Let pj = Ej / E, then ∑jpj=1, so the Claude-Shannong wavelet energy entropy Wee corresponding to the time period corresponding to the sliding data window is defined as: , where, when pj=0, let pj ln pj=0. Wavelet energy entropy reflects the energy distribution characteristics of the signal in a certain sliding time window. With the energy distribution characteristics of the signal in the sliding window, as the sliding window slides, it will reflect the energy change of the signal entropy on the time axis. Therefore, wavelet energy entropy can simultaneously characterize the characteristics of the signal in the time domain and frequency domain. The basic principles and characteristics of wavelet energy entropy used for feature extraction and characterization of the complexity of system information. Wavelet energy entropy can reflect the energy distribution of transient signals such as voltage sampled by the power system in the frequency domain and time domain. When a fault occurs in power equipment or the power grid, causing a temporary voltage drop, the voltage signal collected at each monitoring point will contain a drop in the fundamental amplitude and transient component changes such as high-order harmonics. The magnitude of these changes can be quantified and displayed by wavelet energy entropy.

[0026] S3. According to the signal value of each wavelet energy entropy in step S2, determine the distance between each corresponding monitoring point and the voltage sag fault, and locate the monitoring point closest to the voltage sag fault.

[0027] After determining the distance between each corresponding monitoring point and the voltage sag fault, grayscale verification can be continued, and the intuitive expression of the grayscale image can be used to determine whether the previous steps are wrong. Grayscale verification includes: grayscale processing the signal value of each wavelet energy entropy to obtain a grayscale image, and judging whether the grayscale image represents the location of the voltage sag fault. If so, the grayscale verification ends; if not, return to step S2 to calculate the basic data in step S1 using the four-scale wavelet energy entropy algorithm, and repeat until the grayscale verification ends. The verification result is very intuitive and effective when using the grayscale image for verification.

[0028] In order to verify the effectiveness and accuracy of the wavelet energy entropy algorithm, new steps can be continued to be used for modeling and simulation to complete the verification. Step S4: Based on each monitoring point in step S1, a transient disturbance distribution network model is built, and different numbers are set for each node of the transient disturbance distribution network model; in the order of different numbers, a fault module is set up for each node in turn and the voltage drop value of each node is recorded in turn; each voltage drop value is calculated in turn using the four-scale wavelet energy entropy algorithm to obtain the calculation result of each node; each voltage drop value and the corresponding number of each node are stored in a matrix form and grayscale processed to obtain a drop grayscale map; the calculation result of each node is grayscale processed on the corresponding scale to obtain a calculated grayscale map; the drop grayscale map is compared with the calculated grayscale map to determine whether the clarity of the calculated grayscale map is due to the drop grayscale map. If so, the operation is terminated. If not, the process of calculating each voltage drop value in turn using the four-scale wavelet energy entropy algorithm is returned to the process, and the cycle is repeated until the operation is terminated. Establishing a transient disturbance distribution network model for verification also ensures the accuracy and effectiveness of positioning. When the verification results do not meet the requirements, a cyclic verification can be performed to avoid positioning problems caused by errors in a certain link.

[0029] like Figure 2 As shown in the figure, it is a schematic diagram of an IEEE14-node model, which is a transient disturbance distribution network model that can be built in MATLAB or Simulink. The 14 nodes include 5 power sources G1-G5, 6 transformers, 14 busbars and 15 transmission lines. The IEEE14-node model is widely applicable to power systems, has high compatibility, and is also convenient for fault analysis. Since the probability of short-circuit faults in power systems is the highest, the fault module is set as a short-circuit fault module below, which is used to generate a short-circuit fault signal at each node, and each short-circuit fault signal includes the corresponding voltage drop value.

[0030] In the simulation verification, in order to reflect the effectiveness of wavelet energy entropy in locating the source of voltage sag, low impedance 0.1Ω and high impedance 50Ω fault modules are placed on 14 nodes respectively, and 14 nodes are set as short-circuit nodes respectively. The characteristic data of 14 nodes, i.e., voltage drop values, are recorded in each simulation, and wavelet energy entropy is used to extract the characteristics of the voltage drop values. In order to more conveniently observe the metric values ​​of each node. The voltage drop value is represented in grayscale and a 14 by 14 grid is formed. Then, the locating effect of the sag source is judged according to the diagonal elements of the grid.

[0031] The selection of wavelet basis function is the key to the application of wavelet energy entropy. When selecting the wavelet basis, it is particularly important to consider the tight support of the time and frequency domains. The db4 wavelet has the characteristics of orthogonality, tight time and frequency support, and high regularity. Therefore, the db4 wavelet is recommended for use in power systems. Compared with other wavelets, the db4 wavelet has the shortest time window and better time resolution. Therefore, the db4 orthogonal wavelet is used to decompose the signal into four scales. When low-impedance 0.1Ω fault modules are placed at the 14 nodes, it is set at 0.03s, and a three-phase short circuit occurs at the fault point node 7. Figure 3 As shown in FIG. 1 , it is a voltage waveform diagram of each node when node 7 is short-circuited and grounded with low resistance in an IEEE 14-node model. faulty7 indicates that node 7 is faulty, and measured1-measured14 represent 14 nodes in sequence. Figure 4 As shown, it is a waveform diagram of the effective value of the voltage of each node when node 7 is short-circuited and grounded with low resistance in an IEEE14-node model.

[0032] When a low-impedance short circuit occurs at node 7, it can be seen from the voltage waveforms and effective values ​​of each node in the system that the voltage amplitude of node 7 is almost 0, while the voltages of the remaining nodes drop to varying degrees. The 14 nodes are short-circuited in turn, and the voltage amplitudes of the 14 nodes are recorded when each node is short-circuited, forming a 14×14 voltage drop value matrix, as shown in Table 4.1 below: Table 4.1: Matrix table of voltage drop values ​​when low impedance short circuit is grounded

[0033] The matrix is ​​processed in grayscale. The larger the voltage value, the more obvious the color performance. Similarly, the signal value measured by wavelet energy entropy is processed in grayscale at each scale. The processing results are shown in Figures 5 and 6 respectively. Figure 5 It is a grayscale image formed based on the voltage drop value when node 7 is short-circuited and grounded with low resistance in the IEEE14 node model. Figure 6 This is a grayscale image measured by wavelet energy entropy when node 7 is short-circuited and grounded with low resistance in the IEEE14 node model. The clearer the diagonal line, the more obvious the change of the corresponding transient component. It can be seen that the grayscale image measured by wavelet energy entropy has a much better display effect than the grayscale image formed by the voltage drop value.

[0034] When high impedance 50Ω fault modules are placed at 14 nodes, and a three-phase short circuit occurs at fault node 7 at 0.03s, the voltage amplitudes and effective values ​​of the 14 nodes are as follows: Figure 7 and Figure 8 as shown; Figure 7 As shown in FIG. 1 , it is a voltage waveform diagram of each node when node 7 is short-circuited and grounded with high resistance in an IEEE14 node model; Figure 8 As shown, it is a waveform diagram of the effective value of the voltage of each node when node 7 is short-circuited and grounded with high resistance in an IEEE14-node model.

[0035] When node 7 is short-circuited to ground with high impedance, it can be seen from the voltage waveforms and effective values ​​of each node in the system that the voltage of each node in the system drops to varying degrees, and the amplitude is higher than that of node 7 with low impedance grounding. The 14 nodes are short-circuited in turn, and the voltage amplitude and effective value of each node are recorded when each node is short-circuited, and a 14×14 voltage drop value matrix is ​​formed, as shown in Table 4.2 below.

[0036] Table 4.2: Matrix table of voltage drop values ​​when high impedance short circuit is grounded

[0037] In the above table, the columns are fault points, and the rows are the voltage amplitudes detected by the nodes at the corresponding fault points. It can be seen that when the node itself is short-circuited, the node amplitude is the lowest and the diagonal element is the smallest. A grayscale processing is performed on the matrix, and the processing results are as follows: Fig. 9 and Fig.10 As shown, Fig. 9 As shown in FIG. 1 , it is a grayscale image formed based on the voltage drop value when node 7 is short-circuited and grounded with high resistance in the IEEE14 node model; Fig.10 As shown in Figure 10, it is a grayscale image measured based on wavelet energy entropy when node 7 is short-circuited and grounded with high resistance in the IEEE14 node model. Fig.11 From the grayscale image, it can be seen that the clearer the elements on the diagonal of the grid, the better the transient source resolution effect of the feature. When the system is grounded with high impedance, the current at the fault point is very small and the transient component changes are no longer obvious. Fig.10 The voltage sag value shown in FIG11 can no longer well characterize the voltage variation at each node, but FIG11 can still well identify the location of the sag source.

[0038] Based on the above two sets of simulation experiments, when a three-phase short-circuit fault occurs at node 7, the voltage signals of the remaining nodes will drop to varying degrees. The voltage drop value and wavelet energy entropy can better characterize the location of the sag source in the case of a low-resistance 0.1Ω grounding fault. In the case of a high-resistance grounding, due to the reduction of the short-circuit current, the sag feature is very weak. Only the calculation using the four-scale wavelet energy entropy still has a good effect, which verifies the correctness and effectiveness of the calculation using the four-scale wavelet energy entropy.

[0039] In addition, taking a temporary sag event in a certain area as an example, in this temporary sag event, the voltage waveforms of the four monitoring points monitored by the four monitoring devices are as follows: Fig.11 As shown, Fig.11It is a waveform diagram of the voltage RMS value of each monitoring point when a voltage sag fault occurs at four monitoring points in an area. The horizontal axis is the number of sampling points / times, and the vertical axis is the voltage RMS / v. Ua_rms, Ub_rms and Uc_rms represent each phase of the three-phase electricity.

[0040] according to Fig.11 From the waveform in , it can be seen that the voltage at monitoring point 3 is the lowest, which is the closest to the sag source. In order to adapt the proposed fault location model to the monitoring device, the wavelet energy entropy algorithm is written using the Python algorithm, and the relevant data of the four groups of monitoring devices are substituted into the three-phase power fault location model. The four-scale wavelet energy entropy algorithm is used for calculation, and the location results are as shown in Table 4.3: Table 4.3: Wavelet energy entropy output results in the three-phase power fault location model

[0041] As shown in Table 4.3, wavelet energy entropy can obtain relatively accurate fault location results, which is helpful for the division of voltage sag responsibilities between power suppliers and users, and provides reliable data for accident safety investigations; it can also obtain the voltage sag impact domain, providing a basis for site selection for sensitive customers; at the same time, it can be applied to both a small amount of electrical parameter signals from four monitoring points and a large amount of electrical parameter signals from fourteen monitoring points.

[0042] In summary, the present invention applies wavelet energy entropy to the location of voltage sag sources. This method uses the change in voltage amplitude at the monitoring point during a fault as the measurement value, quantifies the size of the change through wavelet energy entropy, and then quickly determines the distance between different monitoring points and the sag source, and locates the position of the sag source; at the same time, model verification and grayscale image comparison are used to verify the accuracy and effect of positioning, and ultimately achieve accurate positioning estimation of the sag source and the monitoring point, which is significantly advanced.

[0043] The above embodiments are only for illustrating the technical idea of ​​the present invention, and cannot be used to limit the protection scope of the present invention. Any changes made on the basis of the technical solution in accordance with the technical idea proposed by the present invention shall fall within the protection scope of the present invention.

Claims

1. A method for locating and analyzing power quality sag sources based on four-scale wavelet decomposition, characterized in that: The steps include: S1. Real-time monitoring of the voltage vector value of each monitoring point. When a voltage sag fault occurs, the data of each voltage vector value after transient changes and gradually transitioning to a stable state is used as the basic data for positioning analysis; S2, using a four-scale wavelet energy entropy algorithm to calculate the basic data in step S1, and obtain a signal value of each wavelet energy entropy corresponding to each voltage vector value; S3. According to the signal value of each wavelet energy entropy in step S2, determine the distance between each corresponding monitoring point and the voltage sag fault, and locate the monitoring point closest to the voltage sag fault.

2. A method for locating and analyzing a temporary drop in power quality according to claim 1, characterized in that: In step S2, the calculation using the four-scale wavelet energy entropy algorithm includes: selecting a wavelet function, four-scale decomposition, downsampling and wavelet reconstruction; Among them, the wavelet function is selected based on the basic data; the four-scale decomposition includes decomposing each voltage vector value in the basic data into four components of different frequencies, including a low-frequency approximate component and three high-frequency detail components; downsampling includes selecting samples in each component; and wavelet reconstruction includes reconstructing each corresponding selected sample by performing an inverse operation under each component.

3. The method for locating and analyzing a temporary drop in power quality according to claim 1, characterized in that: After determining the distance between each corresponding monitoring point and the voltage sag fault in step S3, continue with grayscale verification; Grayscale verification includes: grayscale processing the signal value of each wavelet energy entropy to obtain a grayscale image, and judging whether the grayscale image represents the location of the voltage sag fault. If so, the grayscale verification ends; if not, returning to step S2 to calculate the basic data in step S1 using the four-scale wavelet energy entropy algorithm, and repeating the cycle until the grayscale verification ends.

4. The method for locating and analyzing a temporary drop in power quality according to claim 1, characterized in that: The following steps are also included: S4, building a transient disturbance distribution network model based on each monitoring point in step S1, setting a different number for each node of the transient disturbance distribution network model; setting up a fault module for each node in turn according to the order of different numbers and recording the voltage drop value of each node in turn; The four-scale wavelet energy entropy algorithm is used to calculate each voltage drop value in turn to obtain the calculation result of each node; Each voltage drop value and the number of each corresponding node are stored in a matrix form and grayscale processed to obtain a drop grayscale map; Then the calculation result of each node is grayed out at the corresponding scale to obtain a calculation grayscale map; The drop grayscale image is compared with the calculated grayscale image to determine whether the clarity of the calculated grayscale image is due to the drop grayscale image. If so, the operation is terminated. If not, the process of calculating each voltage drop value in turn using the four-scale wavelet energy entropy algorithm is returned to the process and the cycle is repeated until the operation is terminated.

5. The method for locating and analyzing a temporary drop in power quality according to claim 4, characterized in that: The transient disturbance distribution network model is an IEEE multi-node model in MATLAB or in Simulink, including multiple power sources, multiple transformers, multiple buses and multiple transmission lines.

6. A method for locating and analyzing a temporary drop in power quality according to claim 4, characterized in that: The fault module is a short-circuit fault module, which is used to generate a short-circuit fault signal at each node, and each short-circuit fault signal includes each corresponding voltage drop value.

Images