Fault monitoring method, system and equipment for small power generation and distribution system

Abstract

The invention relates to the technical field of power grid monitoring, in particular to a fault monitoring method, system and equipment for a small power generation and distribution system. According to the method, fluctuation characteristics of monitoring signals are analyzed, fluctuation time periods are determined, fluctuation trends are obtained through the lengths of the fluctuation time periods and voltage instability, and short-term fluctuation time periods and long-term fluctuation time periods are obtained through classification. And analyzing each time point one by one, and further screening out a superposed short-term fluctuation time period in the long-term fluctuation time periods through the voltage value difference between the continuous time points. And for the superposed short-term fluctuation time period, the continuation range is adjusted through the fluctuation trend characteristics, and a monitoring signal with accurate characteristic information is obtained. According to the method, the superposed short-term fluctuation time period is effectively extracted, wavelet transform is used for reconstruction, the monitoring signal with accurate feature information is obtained, the power grid monitoring effect can be improved, and subsequent power control over the micro-grid is facilitated.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power grid monitoring, in particular to a small-scale power generation and distribution system fault monitoring method, system and equipment. BACKGROUND

[0002] In the operation of the power grid, voltage stability is an important factor to ensure the safe, reliable and economic operation of the power system. Small-scale power generation and distribution systems, which are usually not connected to the main power grid, are small-scale power generation and distribution systems that operate independently in the environment of large-scale power grid safety monitoring, and are more important for the fault monitoring of micro-grid stable power supply and off-grid operation. Small-scale power generation and distribution system fault monitoring mainly monitors current, voltage, load and other data, among which the stability monitoring of voltage and frequency is particularly important. By monitoring the voltage fluctuation of the power grid in real time and effectively controlling the voltage fluctuation through intelligent adjustment measures, the stable operation of the power grid is ensured.

[0003] In view of the poor frequency stability of off-grid micro-grid, the PMU device can be used to monitor the power grid voltage and obtain the fluctuation trend of voltage data instability in different time periods. Generally, such fluctuation trend is divided into short-term fluctuation and long-term fluctuation. However, for long-term fluctuation, short-term fluctuation may be superimposed on the long-term fluctuation data segment. If this superimposed state cannot be decomposed and further processed to extract features, the monitoring efficiency will be affected, and the power adjustment of the micro-grid will be affected. SUMMARY

[0004] In order to solve the technical problem that the prior art cannot extract superimposed short-term fluctuation in long-term fluctuation trend, thereby affecting the monitoring efficiency and unable to effectively adjust the power of micro-grid, the purpose of the present application is to provide a small-scale power generation and distribution system fault monitoring method, system and equipment, and the technical solution adopted is as follows: The present application provides a small-scale power generation and distribution system fault monitoring method, which comprises: obtaining the monitoring signal of the synchronous phasor measurement unit in the micro-grid; determining the voltage fluctuation degree of each sampling time according to the voltage amplitude data and voltage phase angle data of each sampling time; classifying the sampling time according to the voltage fluctuation degree to obtain a plurality of time periods; obtaining the voltage instability degree in each time period according to the voltage fluctuation degree distribution characteristics in each time period, and screening out the fluctuation time period; obtaining the fluctuation trend characteristics of the fluctuation time period based on the length of the fluctuation time period and the voltage instability degree, classifying the fluctuation time period, and obtaining the short-term fluctuation time period and the long-term fluctuation time period; In the long-term fluctuation time period, each time point is traversed, and a superimposed short-term fluctuation time period in the long-term fluctuation time period is screened according to the difference between voltage values between continuous time points; the monitoring signal in the superimposed short-term fluctuation time period after wavelet transform is symmetrically extended, and in the symmetric extension process, the extension range is adjusted according to the fluctuation trend characteristics of the superimposed short-term fluctuation time period, to obtain a reconstructed monitoring signal.

[0005] Further, the method for obtaining the voltage fluctuation degree comprises: For each time point, a difference degree of a voltage amplitude and a preset rated voltage value is obtained, a voltage phase angle difference between the current time point and a previous time point is obtained, and a frequency change rate at each time point is obtained; and the voltage fluctuation degree is obtained according to the difference degree, the voltage phase angle difference and the frequency change rate.

[0006] Further, the method for dividing the time period comprises: The sampling time points are clustered based on the voltage fluctuation degree by using an ISODATA algorithm, and continuous sampling time points of the same kind constitute a fluctuation time period.

[0007] Further, the method for obtaining the voltage instability degree comprises: A voltage fluctuation degree difference between adjacent sampling time points in the fluctuation time period is obtained, and a voltage fluctuation degree range in the fluctuation time period is obtained; and the voltage instability degree is obtained according to the average voltage fluctuation degree difference and the voltage fluctuation degree range.

[0008] Further, the method for obtaining the fluctuation trend characteristics comprises: A data deviation between the voltage instability degree and the average voltage instability degree of the fluctuation time period is obtained, and a ratio of the data deviation to the length of the fluctuation time period is taken as the fluctuation trend.

[0009] Further, the method for obtaining the superimposed short-term fluctuation time period comprises: The traversal starts from a first time point of the long-term fluctuation time period as a traversal point, the position point with the largest voltage value difference between adjacent time points is taken as a first division point, the position point with the largest voltage value difference in the time period after the first division point is found again, a second division point is obtained, and the time period between the first division point and the second division point is taken as a superimposed short-term fluctuation time period.

[0010] Further, the method for adjusting the extension range according to the fluctuation trend characteristics of the superimposed short-term fluctuation time period and obtaining the reconstructed monitoring signal comprises: The length of the preset wavelet filter is multiplied by the normalized fluctuation trend, and then is added to an integer 1 and is rounded up to obtain an adjusted extension range; the filter length of each layer in the multi-layer decomposition extension process is adjusted according to the extension range, and the reconstructed monitoring signal is obtained through multi-layer decomposition extension.

[0011] Further, the obtaining of the reconstructed monitoring signal further comprises: At the power grid adjustment time, the reconstructed monitoring signal is analyzed by the static reactive power compensator and the power grid is adjusted.

[0012] The application further provides a small-scale power generation and distribution system fault monitoring system, including a memory, a processor and a computer program stored in the memory and executable on the processor, and the processor implements the steps of any one of the small-scale power generation and distribution system fault monitoring methods.

[0013] The application further provides a small-scale power generation and distribution system fault monitoring device, and the device includes: A micro-grid initial monitoring signal acquisition module is configured to acquire a monitoring signal of a synchronous phasor measurement unit in a micro-grid. A fluctuation time period division module is configured to determine a voltage fluctuation degree of each sampling time according to voltage amplitude data and voltage phase angle data of each sampling time, classify the sampling time according to the voltage fluctuation degree, and obtain a plurality of time periods; and obtain a voltage instability degree in each time period according to a voltage fluctuation degree distribution feature in each time period, and screen out a fluctuation time period. A fluctuation time period classification module is configured to obtain a fluctuation trend feature based on the length of the fluctuation time period and the voltage instability degree, classify the fluctuation time period, obtain a short-term fluctuation time period and a long-term fluctuation time period. A monitoring signal reconstruction module is configured to traverse each time point in the long-term fluctuation time period, screen out a superimposed short-term fluctuation time period in the long-term fluctuation time period according to the difference between voltage values of continuous time points, perform symmetric extension on the monitoring signal in the superimposed short-term fluctuation time period after wavelet transform, adjust an extension range in the symmetric extension process according to the fluctuation trend feature of the superimposed short-term fluctuation time period, and obtain a reconstructed monitoring signal.

[0014] The application has the following beneficial effects: The application firstly analyzes fluctuation characteristics of the monitoring signal, divides a plurality of time periods according to voltage fluctuation degrees of each sampling time, analyzes each time period, determines fluctuation time periods, and obtains fluctuation trends and classifies short-term fluctuation time periods and long-term fluctuation time periods by using lengths of the fluctuation time periods and voltage instability. For the long-term fluctuation time periods, the application analyzes each time point one by one, and further filters superimposed short-term fluctuation time periods in the long-term fluctuation time periods by using voltage value differences between continuous time points. For the superimposed short-term fluctuation time periods, the application considers that superimposition characteristics are easy to cause boundary effects, and direct decomposition and reconstruction will cause data shift after decomposition and cause distortion, therefore, the application adjusts extension range by using fluctuation trend characteristics, can make the superimposed short-term fluctuation time periods more reasonably contain signal boundaries of short-time mutations in the wavelet transform process, avoid mutations in the mutation region due to short-time mutations, and avoid the situation that the symmetric extension appears to be 0, and then obtain monitoring signals with accurate characteristic information, can improve the power grid monitoring effect, and is convenient for subsequent power control of the micro grid. BRIEF DESCRIPTION OF DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, and the advantages thereof, below, a brief introduction will be given to the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained without creative labor based on these drawings.

[0016] Figure 1 A flow chart of a small-scale power generation and distribution system fault monitoring method provided by an embodiment of the present application; Figure 2 A comparison diagram of short-term fluctuation and long-term fluctuation provided by an embodiment of the present application; Figure 3 A diagram of superimposed short-term fluctuation in long-term fluctuation provided by an embodiment of the present application. DETAILED DESCRIPTION

[0017] In order to further illustrate the technical means and effects taken by the present application to achieve the predetermined invention purpose, below, the specific implementation, structure, features and effects of the small-scale power generation and distribution system fault monitoring method, system and device according to the present application are described in detail in combination with the drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0018] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0019] The application provides a small power generation and distribution system fault monitoring method, system and device.

[0020] Please refer to Figure 1 , which shows a small power generation and distribution system fault monitoring method flowchart provided by an embodiment of the application, and the method comprises the following steps. Step S1: obtaining a monitoring signal of a synchronous phasor measurement unit in a micro-grid.

[0021] The synchronous phasor measurement unit (PMU) is an advanced measurement device for a power system, and the PMU describes a power signal by measuring a phasor of voltage and current, and can provide high-precision real-time data of a power grid state. The PMU provides high-frequency and high-precision real-time data acquisition, and usually samples 30 to 60 times per second.

[0022] It should be noted that, because the essential purpose of obtaining the monitoring signal in the embodiment of the application is to adjust the voltage fluctuation of the micro-grid, the embodiment of the application obtains monitoring data before a time range at a power grid adjustment time, and then determines an adjustment strategy of the power grid. The embodiment of the application selects data of half an hour before the power grid adjustment time for analysis.

[0023] Step S2: determining a voltage fluctuation degree of each sampling time according to voltage amplitude data and voltage phase angle data at each sampling time; classifying the sampling time according to the voltage fluctuation degree, obtaining a plurality of time periods; obtaining voltage instability in each time period according to a voltage fluctuation degree distribution characteristic in each time period, and screening out a fluctuation time period; obtaining a fluctuation trend characteristic based on a length of the fluctuation time period and the voltage instability, classifying the fluctuation time period, and obtaining a short-term fluctuation time period and a long-term fluctuation time period.

[0024] Data fluctuation in the power grid voltage mainly includes two types: short-term fluctuation and long-term trend. Please refer to Figure 2 , which shows a comparison diagram of short-term fluctuation and long-term fluctuation provided by an embodiment of the application. In Figure 2In the embodiment of the present application, the trend section corresponding to the serial number 1 is the short-term fluctuation, and the trend section corresponding to the serial number 2 is the long-term fluctuation. The short-term fluctuation mainly refers to the change of the grid voltage in a short time (usually between a few seconds), and common short-term fluctuations include voltage flicker, voltage drop (i.e. instantaneous voltage drop), etc., which form a relatively short but relatively severe voltage change. The long-term trend refers to the continuous fluctuation, deviation or drop of the grid voltage level of the power system in a long time (usually a few minutes, a few hours or even longer), which forms a relatively long and relatively moderate change. Because the time span of the long-term trend is relatively long, the short-term fluctuation may be superimposed in the long-term trend, so the embodiment of the present application needs to divide the entire time period according to the electrical data fluctuation shown in the monitoring signal, filter out the short-term fluctuation time period and the long-term fluctuation time period, and then more carefully divide the long-term fluctuation time period.

[0025] For the monitoring signal in the monitoring period, the voltage amplitude directly reflects the real-time characteristics of the voltage, and the voltage phase angle can reflect the voltage load characteristics. Therefore, for each sampling time, the voltage fluctuation degree of each sampling time can be determined by using the size of the voltage amplitude data and the change of the voltage phase angle data. That is, the more abnormal the voltage amplitude is, and the larger the voltage phase angle data is, the more likely the grid at the sampling time is to produce obvious fluctuation, and the greater the voltage fluctuation degree is.

[0026] Preferably, in the embodiment of the present application, the method for obtaining the voltage fluctuation degree comprises: For each time, the difference degree between the voltage amplitude and the preset rated voltage value is obtained, the voltage phase angle difference between the previous time is obtained, and the frequency change rate at each time is obtained; and the voltage fluctuation degree is obtained according to the difference degree, the voltage phase angle difference and the frequency change rate.

[0027] As an example, in the embodiment of the present application, the method for obtaining the difference degree is that the ratio of the voltage amplitude to the preset rated voltage value is subtracted by a positive integer 1, and the absolute value of the difference value is taken as the difference degree. That is, the closer the ratio is to 1, the more similar the voltage amplitude at the sampling time is to the rated voltage value, and the smaller the difference degree is. The voltage phase angle difference is the absolute value of the difference between the two voltage phase angles. The voltage fluctuation degree is the product of the difference degree, the voltage phase angle difference and the frequency change rate.

[0028] It should be noted that the rated voltage value can be set according to the specific voltage requirement of the microgrid, and the embodiment of the present application does not limit and elaborate.

[0029] After the voltage fluctuation degree of each sampling time is determined, the sampling times can be classified, and then a plurality of time periods are divided in the whole monitoring period, that is, the sampling times in each time period have the same voltage fluctuation degree trend. There are time periods with obvious fluctuations and time periods with relatively stable fluctuations in the obtained time periods. Therefore, the voltage instability degree of each time period can be further obtained according to the distribution characteristics of the voltage fluctuation degree in the time period, that is, the more irregular the voltage fluctuation degree distribution is in a time period, the more obvious the difference is, and the more discrete the distribution is, which indicates that the time period is the irregular fluctuation of unstable voltage. Therefore, the fluctuation time period can be further screened according to the voltage instability degree. Based on the length of the fluctuation time period and the voltage instability degree in the time period, the fluctuation trend characteristics can be further determined, and then the short-term fluctuation time period and the long-term fluctuation time period are classified. That is, the smaller the length of the fluctuation time period is and the more significant the higher voltage instability degree is, the more likely the time period is the short-term fluctuation time period, and then the short-term fluctuation time period is determined, and the other fluctuation time periods are long-term fluctuation time periods.

[0030] Preferably, in the embodiment of the present application, the division method of the interval includes: The ISODATA algorithm is used to cluster the sampling times based on the voltage fluctuation degree, and the continuous sampling times of the same kind constitute a fluctuation time period. It should be noted that the ISODATA algorithm is a technical means familiar to those skilled in the art, the clustering distance can adopt the voltage fluctuation degree difference, and the specific content will not be repeated, and those skilled in the art can also use the DBSCAN clustering algorithm or the threshold segmentation algorithm and other classification methods to realize it.

[0031] Preferably, in the embodiment of the present application, the method for obtaining the voltage instability degree includes: The voltage fluctuation degree difference between adjacent sampling times in the fluctuation time period is obtained, the voltage fluctuation degree range in the fluctuation time period is obtained, and the voltage instability degree is obtained according to the average voltage fluctuation degree difference and the voltage fluctuation degree range.

[0032] In the embodiment of the present application, the voltage fluctuation degree difference is the absolute value of the difference between two voltage fluctuation degrees. The product of the average voltage fluctuation degree difference and the voltage fluctuation degree range is taken as the voltage instability degree, that is, the greater the average voltage fluctuation degree difference indicates that the voltage instability degree is more unstable in the time period, the more irregular the voltage change is in the time period, and the greater the voltage instability degree is; the greater the range indicates that the voltage fluctuation degree range in the time period is larger, and the greater the voltage instability degree is.

[0033] In the embodiment of the present application, after the voltage instability degree is normalized, the first threshold is set to 0.68, and if the voltage instability degree is greater than the first threshold, the time period is judged as a fluctuation time period.

[0034] Preferably, in the embodiment of the present application, the method for obtaining the fluctuation trend characteristic comprises: obtaining a data deviation between the voltage instability of the fluctuation time period and the average voltage instability, and taking the ratio of the data deviation to the length of the fluctuation time period as the fluctuation trend. That is, the greater the data deviation and the shorter the length of the fluctuation time period, the greater the fluctuation trend, which indicates that the fluctuation time period is more likely to be a short-term fluctuation time period.

[0035] It should be noted that the embodiment of the present application normalizes the fluctuation trend, sets the second threshold value to 0.8, and regards the fluctuation time period with a fluctuation trend greater than the second threshold value as a short-term fluctuation time period. After determining the short-term fluctuation time period, the other fluctuation time periods are long-term fluctuation time periods.

[0036] Step S3: In the long-term fluctuation time period, each time point is traversed, and a superimposed short-term fluctuation time period in the long-term fluctuation time period is screened out according to the difference between voltage values of consecutive time points. The monitoring signal in the superimposed short-term fluctuation time period after wavelet transform is symmetrically extended, and in the process of symmetric extension, the extension range is adjusted according to the fluctuation trend characteristic of the superimposed short-term fluctuation time period, to obtain a reconstructed monitoring signal.

[0037] Referring to Figure 3 , a schematic diagram of superimposed short-term fluctuation in long-term fluctuation is shown, Figure 3 the position in the middle circle and the superimposed fluctuation characteristic in the long-term fluctuation. Because the time span of the long-term fluctuation is relatively large, the short-term change of the local position cannot be effectively screened out in the step S2 fluctuation time period division, and needs to be analyzed continuously.

[0038] Generally, the effect after superposition is that there is short-term fluctuation in the long-term trend, and the two have differences in fluctuation amplitude and fluctuation frequency, so the voltage fluctuation degree difference at different times in the long-term trend data segment can be compared to screen the superimposed time. Because the fluctuation superposition position is accompanied by the mutation of the fluctuation characteristic, for the voltage value of any sampling time in a data segment of a long-term trend, the greater the difference in voltage amplitude before and after it, the more likely it is that the time is a fluctuation superposition time. Therefore, in the long-term fluctuation time period, each time point is traversed, and a superimposed short-term fluctuation time period in the long-term fluctuation time period is screened out according to the difference between voltage values of consecutive time points.

[0039] Preferably, in the embodiment of the present application, the method for obtaining the fluctuation trend characteristic comprises: The first time point in the long-term fluctuation period is taken as a traversal point, and a position point with the largest voltage value difference between adjacent time points is taken as a first segmentation point.

[0040] Because the superimposed short-term fluctuation period is a superimposed feature, the voltage real fluctuation information is distorted due to superposition, therefore, the embodiment of the application adopts a wavelet transform processing method to perform targeted feature enhancement on the monitoring signal of the superimposed short-term fluctuation period. For the long-term fluctuation, the transformation at the boundary is slow and is less affected by the symmetric extension, but for the short-term fluctuation superimposed in the long-term fluctuation, the short-term mutation at the boundary will cause the symmetric amplitude in the symmetric extension process, affect the reconstruction effect of the wavelet transform, and cause signal distortion. Therefore, when performing wavelet transform on the superimposed short-term fluctuation period, the extension range is adjusted according to the fluctuation trend feature of the superimposed short-term fluctuation period in the symmetric extension process, and then the symmetric extension is performed to obtain the reconstructed monitoring signal. By reasonably containing the signal boundary in the superimposed short-term fluctuation period, the extension length is better increased, and the situation that the mutation region is 0 due to short-time mutation, the symmetric extension is 0, and then the reconstructed signal is distorted can be avoided.

[0041] Preferably, in the embodiment of the application, adjusting the extension range according to the fluctuation trend feature of the superimposed short-term fluctuation period to obtain the reconstructed monitoring signal comprises: The length of the preset wavelet filter is multiplied by the normalized fluctuation trend, added to 1, and then rounded up to obtain an adjusted extension range. The filter length of each layer in the multi-layer decomposition extension process is adjusted according to the extension range, and the reconstructed monitoring signal is obtained through the multi-layer decomposition extension. Wherein the filter length adjustment formula of each layer is: ; wherein is the filter length of the jth layer, j is the number of layers, is the extension range.

[0042] The embodiment of the application can avoid the boundary effect caused by the superimposed short-term fluctuation period through the reconstruction of the monitoring signal by the wavelet transform, and thus improve the quality of the monitoring signal and reduce signal data distortion.

[0043] Preferably, in the embodiment of the present application, after obtaining the reconstructed monitoring signal, it further comprises: at the power grid adjustment time, analyzing the reconstructed monitoring signal by the static var compensator and adjusting the power grid. The static var compensator (SVC) is an advanced power electronic device used to adjust the voltage of the power grid, stabilize the system frequency and improve the power factor. It suppresses and relieves short-term voltage fluctuations by quickly adjusting the injection or absorption of reactive power. The SVC mainly includes: thyristor controlled reactor (TCR): by adjusting the current flowing through the reactor to control the absorption of reactive power. Thyristor switched capacitor (TSC): by switching in or out the capacitor bank to control the injection of reactive power. If the voltage drops, the SVC will quickly provide reactive power support to the system by switching in the TSC to boost the voltage. If the voltage rises, the SVC will increase the absorption of reactive power by increasing the current of the TCR to reduce the voltage. The voltage is compensated by the static synchronous compensator or off-grid fault warning through the wavelet transform reconstructed signal and the decomposition signal characteristics. The main features of short-term sudden failure are: the power on the main grid side drops to 0 in the case of islanding event; the phase angle jumps and the current increases suddenly, and the impedance decreases in the case of short-circuit fault. Through the grid adjustment, the voltage state of the micro-grid in off-grid state can be stably operated, at the same time, the grid stability of the micro-grid in off-grid independent operation is combined with the grid-connected situation of the smart grid to improve the optimization ability and emergency dispatching ability of the urban distribution network.

[0044] In summary, the embodiment of the present application analyzes the fluctuation characteristics of the monitoring signal, determines the fluctuation time period, obtains the fluctuation trend by using the length of the fluctuation time period and the voltage instability, and classifies to obtain the short-term fluctuation time period and the long-term fluctuation time period. Each time point is analyzed one by one, and the voltage value difference between the continuous time points can further filter out the superimposed short-term fluctuation time period in the long-term fluctuation time period. For the superimposed short-term fluctuation time period, the extension range is adjusted by the fluctuation trend characteristics to obtain the monitoring signal with accurate characteristic information. The present application can improve the grid monitoring effect and facilitate subsequent power control of the micro-grid by effectively extracting the superimposed short-term fluctuation time period and reconstructing by wavelet transform to obtain the monitoring signal with accurate characteristic information.

[0045] Based on the same inventive concept, the present application also provides a small-scale power distribution system fault monitoring system, which comprises a memory, a processor and a computer program stored in the memory and executable on the processor, and the processor implements the steps of any one of the small-scale power distribution system fault monitoring methods when executing the computer program.

[0046] Based on the same inventive concept, the present application also provides a small-scale power distribution system fault monitoring device, which comprises: A micro-grid initial monitoring signal acquisition module is configured to acquire a monitoring signal of a synchronous phasor measurement unit in a micro-grid. The fluctuation time period division module is configured to determine the voltage fluctuation degree at each sampling time according to the voltage amplitude data and the voltage phase angle data at each sampling time, classify the sampling times according to the voltage fluctuation degree, and obtain a plurality of time periods; and obtain the voltage instability in each time period according to the voltage fluctuation degree distribution characteristics in each time period, and screen out the fluctuation time period; The fluctuation time period classification module is configured to obtain the fluctuation trend characteristics of the fluctuation time period based on the length of the fluctuation time period and the voltage instability, classify the fluctuation time period according to the fluctuation trend characteristics, and obtain the short-term fluctuation time period and the long-term fluctuation time period. The monitoring signal reconstruction module is configured to traverse each time point in the long-term fluctuation time period, screen out the superimposed short-term fluctuation time period in the long-term fluctuation time period according to the difference between the voltage values of the continuous time points, perform symmetric extension on the monitoring signal in the superimposed short-term fluctuation time period after the wavelet transform, and adjust the extension range in the symmetric extension process according to the fluctuation trend characteristics of the superimposed short-term fluctuation time period, to obtain the reconstructed monitoring signal.

[0047] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description, and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multi-task processing and parallel processing are also possible or can be advantageous.

[0048] Each of the embodiments in the specification is described in a progressive manner, and the same and similar parts between the embodiments can be referred to each other. Each embodiment mainly describes the difference from other embodiments.

Claims

1. A method for monitoring faults in a small power generation and distribution system, characterized in that: The method comprises: Acquire monitoring signals of synchronized phasor measurement units in microgrids; Determine the voltage fluctuation degree at each sampling moment based on the voltage amplitude data and voltage phase angle data at each sampling moment; classify the sampling moments according to the voltage fluctuation degree to obtain multiple time periods; obtain the voltage instability in each time period based on the voltage fluctuation degree distribution characteristics within each time period, and filter out the fluctuation time periods; obtain the fluctuation trend characteristics based on the length of the fluctuation time period and the voltage instability, and classify the fluctuation time periods into short-term fluctuation time periods and long-term fluctuation time periods; In the long-term fluctuation time period, each time point is traversed, and the superimposed short-term fluctuation time period in the long-term fluctuation time period is screened out according to the difference in voltage values ​​between consecutive time points; the monitoring signal in the superimposed short-term fluctuation time period after wavelet transformation is symmetrically extended, and in the process of symmetrical extension, the extension range is adjusted according to the fluctuation trend characteristics of the superimposed short-term fluctuation time period to obtain the reconstructed monitoring signal.

2. A method for monitoring faults in a small power generation and distribution system according to claim 1, characterized in that: The method for obtaining the voltage fluctuation degree includes: For each moment, the degree of difference between the voltage amplitude and the preset rated voltage value is obtained, the voltage phase angle difference between the voltage amplitude and the previous moment is obtained, and the frequency change rate at each moment is obtained; the voltage fluctuation degree is obtained based on the degree of difference, the voltage phase angle difference and the frequency change rate.

3. A method for monitoring faults in a small power generation and distribution system according to claim 1, characterized in that: The time period division method includes: The ISODATA algorithm is used to cluster the sampling moments based on the voltage fluctuation degree, and consecutive sampling moments of the same type constitute a fluctuation time period.

4. A method for monitoring faults in a small power generation and distribution system according to claim 1, characterized in that: The method for obtaining the voltage instability includes: The voltage fluctuation degree difference between adjacent sampling moments in the fluctuation time period is obtained, and the voltage fluctuation degree extreme difference in the fluctuation time period is obtained; and the voltage instability is obtained according to the average voltage fluctuation degree difference and the voltage fluctuation degree extreme difference.

5. A method for monitoring faults in a small power generation and distribution system according to claim 1, characterized in that: The method for obtaining the fluctuation trend characteristics includes: A data deviation between the voltage instability during the fluctuation period and the average voltage instability is obtained, and a ratio of the data deviation to the length of the fluctuation period is used as the fluctuation trend.

6. A method for monitoring faults in a small power generation and distribution system according to claim 1, characterized in that: The method for obtaining the superimposed short-term fluctuation time period includes: The traversal starts from the first moment of the long-term fluctuation time period as the traversal point, and the position point with the largest voltage value difference between adjacent moment points is taken as the first split point. The time period after the first split point is used to find the position point with the largest voltage value difference between adjacent moment points to obtain the second split point. The time period between the first split point and the second split point is used as the superimposed short-term fluctuation time period.

7. A method for monitoring faults in a small power generation and distribution system according to claim 1, characterized in that: The adjusting of the extension range according to the fluctuation trend characteristics of the superimposed short-term fluctuation period to obtain the reconstructed monitoring signal includes: After multiplying the length of the preset wavelet filter by the normalized fluctuation trend, adding it to the positive integer 1 and rounding it up, the adjusted extension range is obtained; according to the extension range, the filter length of each layer in the multi-layer decomposition and extension process is adjusted, and the reconstructed monitoring signal is obtained through multi-layer decomposition and extension.

8. A method for monitoring faults in a small power generation and distribution system according to claim 7, characterized in that: After obtaining the reconstructed monitoring signal, the method further includes: At the time of grid adjustment, the reconstructed monitoring signal is analyzed by the static VAR compensator and grid adjustment is performed.

9. A small power generation and distribution system fault monitoring system, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the small power generation and distribution system fault monitoring method as described in any one of claims 1 to 8 are implemented.

10. A small power generation and distribution system fault monitoring device, characterized in that: The device comprises: A microgrid initial monitoring signal acquisition module is used to obtain the monitoring signal of the synchronized phasor measurement unit in the microgrid; The fluctuation time period classification module is used to determine the voltage fluctuation degree at each sampling moment based on the voltage amplitude data and voltage phase angle data at each sampling moment; classify the sampling moments according to the voltage fluctuation degree to obtain multiple time periods; obtain the voltage instability in each time period based on the voltage fluctuation degree distribution characteristics within each time period, and filter out the fluctuation time periods; A fluctuation time period classification module is used to obtain fluctuation trend characteristics based on the length of the fluctuation time period and the voltage instability, and classify the fluctuation time period to obtain short-term fluctuation time period and long-term fluctuation time period; The monitoring signal reconstruction module is used to traverse each time point in the long-term fluctuation time period, and screen out the superimposed short-term fluctuation time period in the long-term fluctuation time period based on the difference in voltage values ​​between consecutive time points; perform symmetrical extension on the monitoring signal in the superimposed short-term fluctuation time period after wavelet transformation, and in the symmetrical extension process, adjust the extension range according to the fluctuation trend characteristics of the superimposed short-term fluctuation time period to obtain the reconstructed monitoring signal.

Images