A method, system and equipment for fault monitoring in a small power generation and distribution system

Abstract

The present application relates to the technical field of power grid monitoring, in particular to a small power generation and distribution system fault monitoring method, system and equipment. The method analyzes the fluctuation characteristics of the monitoring signal, determines the fluctuation time period, and 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 power grid monitoring effect and facilitate the subsequent power control of the micro-grid by effectively extracting the superimposed short-term fluctuation time period and reconstructing it by wavelet transform to obtain the monitoring signal with accurate characteristic information.

Description

Technical Field

[0001] This invention relates to the field of power grid monitoring technology, specifically to a method, system, and equipment for fault monitoring in a small power generation and distribution system. Background Technology

[0002] In power grid operation, voltage stability is a crucial factor in ensuring the safe, reliable, and economical operation of the power system. Small-scale power generation and distribution systems, typically not connected to the main grid, operate independently under large-scale grid security monitoring. Ensuring stable power supply to these off-grid microgrids and monitoring faults during their off-grid operation is even more critical. Fault monitoring in small-scale power generation and distribution systems primarily relies on data such as current, voltage, and load. Among these, voltage and frequency stability monitoring is particularly important. By monitoring grid voltage fluctuations in real time and effectively controlling these fluctuations through intelligent adjustment measures, the stable operation of the power grid can be ensured.

[0003] To address the issue of poor frequency stability in off-grid microgrids, current methods include monitoring grid voltage using PMU devices to obtain the fluctuation trends of voltage data instability at different times. These fluctuation trends are generally categorized into short-term and long-term fluctuations. However, for long-term fluctuations, short-term fluctuations may overlap with long-term fluctuation data segments. If this overlap cannot be decomposed and further processed to extract features, it will affect monitoring efficiency and the power adjustment of the microgrid. Summary of the Invention

[0004] To address the technical problem that existing technologies cannot extract superimposed short-term fluctuations from long-term trends, thus affecting monitoring efficiency and hindering effective microgrid power regulation, the present invention aims to provide a fault monitoring method, system, and equipment for small-scale power generation and distribution systems. The specific technical solution adopted is as follows:

[0005] This invention proposes a fault monitoring method for small-scale power generation and distribution systems, the method comprising:

[0006] Acquire monitoring signals from the synchronous phasor measurement unit in the microgrid;

[0007] The voltage fluctuation level at each sampling moment is determined based on the voltage amplitude and voltage phase angle data at each sampling moment; the sampling moments are classified according to the voltage fluctuation level to obtain multiple time periods; the voltage instability in each time period is obtained based on the voltage fluctuation level distribution characteristics within each time period, and the fluctuation time periods are filtered out; based on the length of the fluctuation time periods and the voltage instability, the fluctuation trend characteristics are obtained to classify the fluctuation time periods, and short-term fluctuation time periods and long-term fluctuation time periods are obtained.

[0008] In the long-term fluctuation period, each time point is traversed, and the superimposed short-term fluctuation period in the long-term fluctuation period is selected according to the difference in voltage values ​​between consecutive time points; the monitoring signal in the superimposed short-term fluctuation period after wavelet transform is symmetrically extended. During the symmetrical extension process, the extension range is adjusted according to the fluctuation trend characteristics of the superimposed short-term fluctuation period to obtain the reconstructed monitoring signal.

[0009] Furthermore, the method for obtaining the degree of voltage fluctuation includes:

[0010] For each moment, the degree of difference between the voltage amplitude and the preset rated voltage value is obtained, the voltage phase angle difference with the previous moment is obtained, and the frequency change rate at each moment is obtained; the degree of voltage fluctuation is obtained based on the degree of difference, the voltage phase angle difference, and the frequency change rate.

[0011] Furthermore, the method for dividing the time period includes:

[0012] Using the ISODATA algorithm, sampling times are clustered based on the degree of voltage fluctuation, and consecutive sampling times of the same type constitute a fluctuation time period.

[0013] Furthermore, the method for obtaining the voltage instability includes:

[0014] The difference in voltage fluctuation between adjacent sampling times within a fluctuation period is obtained, and the voltage fluctuation range within the fluctuation period is obtained; the voltage instability is obtained based on the difference in average voltage fluctuation and the voltage fluctuation range.

[0015] Furthermore, the method for obtaining the fluctuation trend characteristics includes:

[0016] The data deviation between the voltage instability during the fluctuation period and the average voltage instability is obtained, and the ratio of the data deviation to the length of the fluctuation period is taken as the fluctuation trend.

[0017] Furthermore, the method for obtaining the superimposed short-term fluctuation time period includes:

[0018] Starting from the first point in the long-term fluctuation period, the traversal begins. The point with the largest voltage difference between adjacent points is taken as the first dividing point. The time period after the first dividing point is then used to find the point with the largest voltage difference between adjacent points to obtain the second dividing point. The time period between the first dividing point and the second dividing point is taken as the superimposed short-term fluctuation period.

[0019] Furthermore, the step of adjusting the extension range based on the fluctuation trend characteristics of the superimposed short-term fluctuation period to obtain the reconstructed monitoring signal includes:

[0020] Multiply the length of the preset wavelet filter by the normalized fluctuation trend, add it to the positive integer 1, and round up to obtain the adjusted extension range; adjust the filter length of each layer in the multi-layer decomposition extension process according to the extension range, and obtain the reconstructed monitoring signal through multi-layer decomposition extension.

[0021] Furthermore, after obtaining the reconstructed monitoring signal, the process further includes:

[0022] During grid adjustment, the reconstructed monitoring signals are analyzed and the grid is regulated using a static var compensator.

[0023] The present invention also proposes a fault monitoring system for a small power generation and distribution system, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of any one of the fault monitoring methods for a small power generation and distribution system.

[0024] The present invention also proposes a small-scale power generation and distribution system fault monitoring device, the device comprising:

[0025] The microgrid initial monitoring signal acquisition module is used to acquire the monitoring signals of the synchronous phasor measurement unit in the microgrid;

[0026] The fluctuation time period segmentation module is used to determine the voltage fluctuation degree at each sampling time based on the voltage amplitude data and voltage phase angle data at each sampling time; classify the sampling times 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.

[0027] The fluctuation time period classification module is used to classify the fluctuation time periods based on the length of the fluctuation time period and the voltage instability, thereby obtaining short-term fluctuation time periods and long-term fluctuation time periods.

[0028] The monitoring signal reconstruction module is used to traverse each time point in the long-term fluctuation period and filter out the superimposed short-term fluctuation period in the long-term fluctuation period based on the difference in voltage values ​​between consecutive time points; and to perform symmetrical extension on the monitoring signal in the superimposed short-term fluctuation period after wavelet transform. During the symmetrical extension process, the extension range is adjusted according to the fluctuation trend characteristics of the superimposed short-term fluctuation period to obtain the reconstructed monitoring signal.

[0029] The present invention has the following beneficial effects:

[0030] This invention first analyzes the fluctuation characteristics of the monitoring signal. Based on the voltage fluctuation level at each sampling moment, it first divides the signal into multiple time periods. Then, it analyzes each time period to determine the fluctuation time period. Using the length of the fluctuation time period and the voltage instability, the fluctuation trend can be obtained and classified into short-term fluctuation time periods and long-term fluctuation time periods. For long-term fluctuation time periods, this invention analyzes each time point individually. By analyzing the voltage value differences between consecutive time points, it can further filter out superimposed short-term fluctuation time periods within the long-term fluctuation time periods. For superimposed short-term fluctuation time periods, this invention considers that superposition features can easily cause boundary effects. Direct decomposition and reconstruction can lead to data shifting and distortion after decomposition. Therefore, this invention adjusts the extension range by using fluctuation trend features. This allows the superimposed short-term fluctuation time periods to more reasonably encompass the signal boundaries of short-term abrupt changes during wavelet transform, avoiding the abrupt change region becoming 0 due to short-term abrupt changes, and the situation of symmetrical 0 after symmetrical extension. This results in a monitoring signal with accurate feature information, which can improve the power grid monitoring effect and facilitate subsequent power control of the microgrid. Attached Figure Description

[0031] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0032] Figure 1 A flowchart of a fault monitoring method for a small power generation and distribution system provided in one embodiment of the present invention;

[0033] Figure 2 This is a schematic diagram comparing short-term and long-term fluctuations as provided in one embodiment of the present invention.

[0034] Figure 3 This is a schematic diagram illustrating the superposition of short-term fluctuations within a long-term fluctuation, as provided in one embodiment of the present invention. Detailed Implementation

[0035] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a small-scale power generation and distribution system fault monitoring method, system, and device proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0036] 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 invention pertains.

[0037] The following description, in conjunction with the accompanying drawings, details the specific scheme of a fault monitoring method, system, and equipment for a small power generation and distribution system provided by the present invention.

[0038] Please see Figure 1 The diagram illustrates a flowchart of a fault monitoring method for a small power generation and distribution system according to an embodiment of the present invention. The method includes:

[0039] Step S1: Acquire the monitoring signal of the synchronous phasor measurement unit in the microgrid.

[0040] A synchronous phasor measurement unit (PMU) is an advanced measurement device used in power systems. The PMU describes power signals by measuring the phasors of voltage and current, providing high-precision real-time data on the power grid status. The PMU offers high-frequency, high-precision real-time data acquisition, typically sampling 30 to 60 times per second.

[0041] It should be noted that, since the essential purpose of obtaining the monitoring signal in this embodiment of the invention is to adjust the voltage fluctuations of the microgrid, this embodiment of the invention acquires and analyzes a segment of monitoring data prior to the grid adjustment time to determine the grid adjustment strategy. This embodiment of the invention selects data from the half-hour preceding the grid adjustment time for analysis.

[0042] Step S2: Determine the voltage fluctuation level at each sampling time based on the voltage amplitude and voltage phase angle data at each sampling time; classify the sampling times according to the voltage fluctuation level to obtain multiple time periods; obtain the voltage instability in each time period based on the voltage fluctuation level distribution characteristics within each time period, and filter out the fluctuation time periods; classify the fluctuation time periods based on the fluctuation trend characteristics obtained from the fluctuation time periods and the voltage instability to obtain short-term fluctuation time periods and long-term fluctuation time periods.

[0043] Voltage fluctuations in the power grid are mainly categorized into two types: short-term fluctuations and long-term trends. Please refer to [link / reference]. Figure 2 This illustrates a comparative diagram of short-term and long-term fluctuations provided by an embodiment of the present invention. Figure 2In the diagram, trend segment 1 corresponds to short-term fluctuations, and trend segment 2 corresponds to long-term fluctuations. Short-term fluctuations mainly refer to changes in grid voltage within a short period (usually within a few seconds). Common short-term fluctuations include voltage flicker and voltage dips (i.e., instantaneous voltage drops), forming short-term but relatively drastic voltage changes. Long-term trends refer to the continuous fluctuations, deviations, or decreases in grid voltage levels over a longer period (usually minutes, hours, or even longer), forming changes that are relatively gradual over a longer period. Because long-term trends have a long time span, short-term fluctuations may be superimposed on them. Therefore, this embodiment of the invention needs to divide the entire time period based on the electrical data fluctuations reflected in the monitoring signals, filter out short-term fluctuation time periods and long-term fluctuation time periods, and then further subdivide the long-term fluctuation time periods.

[0044] For monitoring signals within a monitoring period, voltage amplitude directly reflects the real-time characteristics of the voltage, while voltage phase angle reflects the voltage load characteristics. Therefore, for each sampling moment, the magnitude of the voltage amplitude data and the change in the voltage phase angle data can be used to determine the degree of voltage fluctuation at each sampling moment. That is, the more abnormal the voltage amplitude and the larger the voltage phase angle data, the more likely the power grid is to experience significant fluctuations at that sampling moment, and the greater the degree of voltage fluctuation.

[0045] Preferably, in this embodiment of the invention, the method for obtaining the degree of voltage fluctuation includes:

[0046] For each moment, the degree of difference between the voltage amplitude and the preset rated voltage value is obtained, the voltage phase angle difference with the previous moment is obtained, and the frequency change rate at each moment is obtained; the degree of voltage fluctuation is obtained based on the degree of difference, the voltage phase angle difference, and the frequency change rate.

[0047] As an example, in this embodiment of the invention, the method for obtaining the degree of difference is as follows: subtract a positive integer 1 from the ratio of the voltage amplitude to the preset rated voltage value, and use the absolute value of the difference as the degree of difference. 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 degree of difference. The voltage phase angle difference is the absolute value of the difference between two voltage phase angles. The voltage fluctuation degree is the product of the degree of difference, the voltage phase angle difference, and the frequency change rate.

[0048] It should be noted that the rated voltage value can be set according to the specific voltage requirements of the microgrid, and this embodiment of the invention does not limit or elaborate on it.

[0049] After determining the voltage fluctuation level at each sampling moment, the sampling moments can be classified, thus dividing the entire monitoring period into multiple time periods. Within each time period, the sampling moments exhibit the same trend in voltage fluctuation. Among the obtained time periods, there are periods of significant fluctuation as well as relatively stable periods. Therefore, the voltage instability of each time period can be obtained based on the distribution characteristics of voltage fluctuation within that time period. Specifically, the more irregular the distribution of voltage fluctuation within a time period, the more obvious the differences, and the more discrete the distribution, the more likely that the period is characterized by irregular fluctuations caused by unstable voltage. Therefore, fluctuation time periods can be further screened based on voltage instability. Based on the length of the fluctuation time periods and the voltage instability within those periods, the fluctuation trend characteristics can be further determined, thus classifying short-term and long-term fluctuation time periods. That is, the shorter the length of the fluctuation time period and the more significantly high the voltage instability, the more likely that period is a short-term fluctuation period. Therefore, after identifying short-term fluctuation time periods, the remaining fluctuation time periods are considered long-term fluctuation time periods.

[0050] Preferably, in this embodiment of the invention, the method for dividing segments includes:

[0051] Using the ISODATA algorithm, sampling times are clustered based on the degree of voltage fluctuation, with consecutive sampling times of the same category forming a fluctuation time period. It should be noted that the ISODATA algorithm is a technique well-known to those skilled in the art. The clustering distance can be based on the difference in voltage fluctuation degree; specific details will not be elaborated further. Those skilled in the art can also implement this using classification methods such as the DBSCAN clustering algorithm or threshold segmentation algorithms.

[0052] Preferably, in this embodiment of the invention, the method for obtaining voltage instability includes:

[0053] The difference in voltage fluctuation between adjacent sampling times within a fluctuation period is obtained, and the voltage fluctuation range within the fluctuation period is obtained; the voltage instability is obtained based on the difference in average voltage fluctuation and the voltage fluctuation range.

[0054] In this embodiment of the invention, the voltage fluctuation difference is the absolute value of the difference between two voltage fluctuations. The product of the average voltage fluctuation difference and the voltage fluctuation range is taken as the voltage instability. That is, the larger the average voltage fluctuation difference, the more unstable the voltage instability is within a time period, and the more irregular the voltage change is within that time period, and the greater the voltage instability. The larger the range, the larger the range of voltage fluctuation within a time period, and the greater the voltage instability.

[0055] In this embodiment of the invention, after normalizing the voltage instability, a first threshold is set to 0.68. If the voltage instability is greater than the first threshold, the time period is determined to be a fluctuating time period.

[0056] Preferably, in this embodiment of the invention, the method for obtaining fluctuation trend characteristics includes:

[0057] The data deviation between the voltage instability during the fluctuation period and the average voltage instability is obtained, and the ratio of the data deviation to the length of the fluctuation period is taken as the fluctuation trend. That is, the larger the data deviation and the shorter the fluctuation period, the stronger the fluctuation trend, indicating that the fluctuation period is more likely to be a short-term fluctuation period.

[0058] It should be noted that in this embodiment of the invention, the fluctuation trend is normalized, the second threshold is set to 0.8, and the fluctuation period when the fluctuation trend is greater than the second threshold is taken as the short-term fluctuation period. After the short-term fluctuation period is determined, the other fluctuation periods are the long-term fluctuation periods.

[0059] Step S3: In the long-term fluctuation period, traverse each time point and filter out the superimposed short-term fluctuation period in the long-term fluctuation period according to the difference in voltage values ​​between consecutive time points; perform symmetrical extension on the monitoring signal in the superimposed short-term fluctuation period after wavelet transform. During the symmetrical extension process, adjust the extension range according to the fluctuation trend characteristics of the superimposed short-term fluctuation period to obtain the reconstructed monitoring signal.

[0060] Please see Figure 3 It illustrates a schematic diagram of long-term fluctuations superimposed with short-term fluctuations according to an embodiment of the present invention. Figure 3 The position in the middle circle represents the superimposed fluctuation characteristics in the long-term fluctuation. Because the time span of the long-term fluctuation is relatively small, the short-term changes in the local position will be averaged over the entire time span when the fluctuation time period is divided in step S2. Therefore, it is impossible to effectively filter out the superimposed short-term changes. It is necessary to continue to analyze the long-term fluctuation time period.

[0061] Generally, the effect of superposition is manifested as short-term fluctuations within a long-term trend, with differences in fluctuation amplitude and frequency. Therefore, the superposition time can be selected by comparing the differences in voltage fluctuation at different times within a long-term trend data segment. Since the position of fluctuation superposition is accompanied by abrupt changes in fluctuation characteristics, for any sampling time in a data segment of a long-term trend, the greater the difference in voltage amplitude before and after that time, the more likely that this time is a time of fluctuation superposition. Therefore, in the embodiment of this invention, within the long-term fluctuation period, each time point is traversed, and the superposition of short-term fluctuation time periods within the long-term fluctuation period is selected based on the differences in voltage values ​​between consecutive time points.

[0062] Preferably, in this embodiment of the invention, the method for obtaining the superimposed short-term fluctuation time period includes:

[0063] The process begins by traversing the time period starting from the first point in the long-term fluctuation period. The point with the largest voltage difference between adjacent points is designated as the first dividing point. The time period following the first dividing point is then used to find the second dividing point, where the largest voltage difference between adjacent points is found again. The time period between the first and second dividing points is considered the superimposed short-term fluctuation period. In other words, the first dividing point marks the start of the superimposed short-term fluctuation period, and the second dividing point marks its end.

[0064] Because superimposed short-term fluctuation periods constitute a superimposed feature, and this superposition can distort the true voltage fluctuation information, this embodiment of the invention employs wavelet transform to specifically enhance the features of the monitoring signal within these superimposed short-term fluctuation periods. For long-term fluctuations, the boundary transformation is slow and less affected by symmetrical extension. However, for short-term fluctuations superimposed on long-term fluctuations, abrupt changes at the boundary can lead to symmetrical amplitudes during symmetrical extension, affecting the wavelet transform reconstruction effect and causing signal distortion. Therefore, when performing wavelet transform on superimposed short-term fluctuation periods, the extension range is adjusted according to the fluctuation trend characteristics of the superimposed short-term fluctuation periods during the symmetrical extension process, thereby obtaining the reconstructed monitoring signal. By reasonably encompassing the signal boundaries within the superimposed short-term fluctuation periods and better increasing the extension length, the situation where abrupt changes to 0 in the abrupt region, resulting in symmetrical 0 after symmetrical extension, and thus causing signal distortion in the reconstructed signal, can be avoided.

[0065] Preferably, in this embodiment of the invention, adjusting the extension range according to the fluctuation trend characteristics of the superimposed short-term fluctuation period to obtain the reconstructed monitoring signal includes:

[0066] Multiply the preset wavelet filter length by the normalized fluctuation trend, add the result to a positive integer 1, and round up to obtain the adjusted extension range. Based on this extension range, adjust the filter length of each layer in the multi-layer decomposition extension process to obtain the reconstructed monitoring signal. The formula for adjusting the filter length of each layer is as follows:

[0067] ;in Let J be the filter length of the j-th layer, where j is the layer number. To extend the scope.

[0068] The embodiments of the present invention reconstruct the monitoring signal through wavelet transform, which can avoid the boundary effect caused by superimposed short-term fluctuation time periods, thereby improving the quality of the monitoring signal and reducing signal data distortion.

[0069] Preferably, in this embodiment of the invention, after obtaining the reconstructed monitoring signal, the method further includes: analyzing the reconstructed monitoring signal and performing grid regulation via a static var compensator (SVC) during grid adjustment. A static var compensator (SVC) is an advanced power electronic device used to regulate grid voltage, stabilize system frequency, and improve power factor. It suppresses and mitigates short-term voltage fluctuations by rapidly adjusting the injection or absorption of reactive power. An SVC mainly includes: a thyristor-controlled reactor (TCR): which controls reactive power absorption by adjusting the current flowing through the reactor; and a thyristor-switched capacitor (TSC): which controls reactive power injection by switching capacitor banks in or out. If the voltage drops, the SVC will quickly provide reactive power support to the system by switching the TSC to boost the voltage. If the voltage rises, the SVC will increase the current of the TCR to increase reactive power absorption and lower the voltage. By utilizing the characteristics of the reconstructed and decomposed signals obtained through wavelet transform, static synchronization compensation of voltage can be performed, or off-grid fault early warning can be provided. The main characteristics of short-term abrupt faults include: a sudden drop in power to zero on the main grid side during islanding events; and a sudden increase in phase angle current and a decrease in impedance during short-circuit faults. Grid regulation can achieve stable voltage operation of microgrids in off-grid conditions. Simultaneously, grid stability under independent off-grid operation of microgrids, combined with smart grid integration, improves the optimization and emergency dispatch capabilities of urban distribution networks.

[0070] In summary, this invention analyzes the fluctuation characteristics of monitoring signals, determines fluctuation time periods, and uses the length of these fluctuation time periods and voltage instability to obtain fluctuation trends and classify them into short-term and long-term fluctuation time periods. Each time point is analyzed individually, and the voltage value differences between consecutive time points allow for further filtering of superimposed short-term fluctuation time periods within long-term fluctuation time periods. For superimposed short-term fluctuation time periods, the extension range is adjusted based on fluctuation trend characteristics to obtain a monitoring signal with accurate feature information. This invention, by effectively extracting superimposed short-term fluctuation time periods and reconstructing them using wavelet transform, obtains a monitoring signal with accurate feature information, improving the power grid monitoring effect and facilitating subsequent power control of microgrids.

[0071] Based on the same inventive concept, the present invention also proposes 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. When the processor executes the computer program, it implements any of the steps of the small-scale power generation and distribution system fault monitoring method described above.

[0072] Based on the same inventive concept, the present invention also proposes a small-scale power generation and distribution system fault monitoring device, the device comprising:

[0073] The microgrid initial monitoring signal acquisition module is used to acquire the monitoring signals of the synchronous phasor measurement unit in the microgrid;

[0074] The fluctuation time period segmentation module is used to determine the voltage fluctuation degree at each sampling time based on the voltage amplitude data and voltage phase angle data at each sampling time; classify the sampling times 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.

[0075] The fluctuation time period classification module is used to classify the fluctuation time periods based on the length of the fluctuation time period and the voltage instability, thereby obtaining short-term fluctuation time periods and long-term fluctuation time periods.

[0076] The monitoring signal reconstruction module is used to traverse each time point in the long-term fluctuation period and filter out the superimposed short-term fluctuation period in the long-term fluctuation period based on the difference in voltage values ​​between consecutive time points; and to perform symmetrical extension on the monitoring signal in the superimposed short-term fluctuation period after wavelet transform. During the symmetrical extension process, the extension range is adjusted according to the fluctuation trend characteristics of the superimposed short-term fluctuation period to obtain the reconstructed monitoring signal.

[0077] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0078] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A small-scale power generation and distribution system fault monitoring method characterized by, The method comprises: Obtaining monitoring signals of a synchronous phasor measurement unit in a microgrid; According to the voltage amplitude data and the voltage phase angle data at each sampling time, the voltage fluctuation degree at each sampling time is determined; the sampling times are classified according to the voltage fluctuation degree, and a plurality of time periods are obtained; according to the voltage fluctuation degree distribution characteristics in each time period, the voltage instability in each time period is obtained, and a fluctuation time period is screened out; based on the length of the fluctuation time period and the voltage instability, a fluctuation trend feature is obtained, the fluctuation time period is classified, and a short-term fluctuation time period and a long-term fluctuation time period are obtained; 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 between the voltage values of the continuous time points; the monitoring signals in the superimposed short-term fluctuation time period after wavelet transform are symmetrically extended, and in the symmetric extension process, the extension range is adjusted according to the fluctuation trend feature of the superimposed short-term fluctuation time period, and the reconstructed monitoring signals are obtained; The method for obtaining the superimposed short-term fluctuation time period comprises: Starting from the 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 between adjacent time points 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; The method for obtaining the reconstructed monitoring signals according to the fluctuation trend feature of the superimposed short-term fluctuation time period comprises: The length of the preset wavelet filter is multiplied by the normalized fluctuation trend, added by 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 signals are obtained through the multi-layer decomposition extension.

2. The method of claim 1, wherein, The method for obtaining the voltage fluctuation degree comprises: For each time, the difference degree of 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; the voltage fluctuation degree is obtained according to the difference degree, the voltage phase angle difference and the frequency change rate.

3. The method of claim 1, wherein, The method for dividing the time period comprises: Using the ISODATA algorithm, the sampling times are clustered based on the voltage fluctuation degree, and the continuous sampling times of the same kind constitute a fluctuation time period.

4. The method of claim 1, wherein, The method for obtaining the voltage instability comprises: The voltage fluctuation degree difference between adjacent sampling times in the fluctuation time period is obtained, and the voltage fluctuation degree range in the fluctuation time period is obtained; the voltage instability is obtained according to the average voltage fluctuation degree difference and the voltage fluctuation degree range.

5. The method of claim 1, wherein, The method for obtaining the fluctuation trend feature comprises: The data deviation between the voltage instability of the fluctuation time period and the average voltage instability is obtained, and the ratio of the data deviation to the length of the fluctuation time period is taken as the fluctuation trend.

6. The method of claim 1, wherein, After obtaining the reconstructed monitoring signals, the following steps are further included: At the grid adjustment time, the reconstructed monitoring signal is analyzed by the static reactive compensator to adjust the grid.

7. A small-scale 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, wherein, The processor implements the steps of the small-scale power generation and distribution system fault monitoring method according to any one of claims 1-6 when executing the computer program.

8. A small-sized power generation and distribution system failure monitoring device characterized by comprising: The device 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. 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 at each sampling time, classify the sampling time according to the voltage fluctuation degree, and obtain a plurality of time periods; and obtain voltage instability 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 a length of the fluctuation time period and the voltage instability, classify the fluctuation time period according to the fluctuation trend feature, and 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 a 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 during the symmetric extension according to the fluctuation trend feature of the superimposed short-term fluctuation time period, and obtain a reconstructed monitoring signal. The monitoring signal reconstruction module is further configured to multiply a length of a preset wavelet filter by a normalized fluctuation trend, add 1 to an integer, and take an upper integer to obtain an adjusted extension range; adjust a filter length of each layer in a multi-layer decomposition extension process according to the extension range, and obtain the reconstructed monitoring signal through the multi-layer decomposition extension. The monitoring signal reconstruction module is further configured to start traversing from a first time point in the long-term fluctuation time period as a traversal point, take a position point with a largest voltage value difference between adjacent time points as a first segmentation point, find a position point with a largest voltage value difference between adjacent time points in a time period after the first segmentation point as a second segmentation point, and obtain a time period between the first segmentation point and the second segmentation point as the superimposed short-term fluctuation time period.

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