CN115168780A - Voltage sag homologous identification method and device - Google Patents
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Abstract
The application discloses a voltage sag homologous identification method and device. Wherein, the method comprises the following steps: receiving at least two groups of three-phase voltage sag data collected by a monitoring device in a preset time period; determining one-phase data corresponding to each group of three-phase voltage sag data in at least two groups of three-phase voltage sag data; determining an amplitude distribution function based on one-phase data, and determining similar thresholds of the amplitude distribution functions corresponding to at least two groups of three-phase voltage sag numbers; and comparing the similar threshold value with the maximum mean value difference to obtain a comparison result, and determining whether the at least two groups of three-phase voltage sag data are homologous according to the comparison result. The method and the device solve the technical problems that the calculation amount is huge, the calculation efficiency is low, the real-time application cannot be realized and the cluster number is difficult to determine in a voltage sag identification mode in the correlation technique.
Description
Technical Field
The application relates to the field of voltage sag identification, in particular to a voltage sag homologous identification method and device.
Background
The voltage sag is a phenomenon of power quality disturbance caused by short-circuit faults, large motor starting and the like, and causes huge economic loss to users in various industries. For example, one report shows that the economic loss due to voltage sags in the united states is estimated to be $ 260 billion/year. In order to evaluate the influence of voltage sag and carry out subsequent treatment work, a power grid company installs a large number of power quality monitoring devices in the system, so that intelligent perception of voltage sag is realized. However, as the number of monitoring devices is increasing, the redundancy problem of the monitoring data is also highlighted. For example, a short-circuit fault may trigger multiple monitoring devices to record voltage sag waveforms. The redundant data can influence the correctness of the sag source positioning result, the inversion accuracy of the voltage sag propagation path of the digital twin of the power distribution network and the like. The accurate identification of voltage sag homologous data has important engineering significance.
At present, the research on voltage sag homologous identification is still in the stage of starting. In the related technology, voltage sag waveform similarity measured by Wasserstein distance is taken as a characteristic, and DBSCAN clustering is adopted for homologous clustering, but the Wasserstein distance used by the method is large in calculation amount and low in calculation efficiency, field online application is not facilitated, data points which cannot be classified are regarded as noise, and the condition that some disturbance events only trigger a small number of monitoring devices is ignored. In addition, a mode is provided, which is to extract three-dimensional characteristics of duration, waveform similarity and comprehensive tilt factor, improve the DBSCAN algorithm for homologous clustering and achieve good identification effect; however, the setting of the clustering parameters depends on information such as power grid topology and the like, and the waveform data needs to be subjected to 13 kinds of transformation, so that the calculation amount is large. And a power supply sag identification mode is adopted, the dimension reduction processing can be carried out on the characteristics through multi-dimensional scale analysis, and clustering is carried out through an OPTIC algorithm, but the number of clustering clusters is artificially set, and the method has certain subjectivity.
In view of the above problems, no effective solution has been proposed.
Disclosure of Invention
The embodiment of the application provides a voltage sag homologous identification method and device, and aims to at least solve the technical problems that the voltage sag identification mode in the related technology has huge calculation amount, low calculation efficiency, real-time application incapability and difficulty in determining the number of clustering clusters.
According to an aspect of an embodiment of the present application, there is provided a voltage sag homologous identification method, including: receiving at least two groups of three-phase voltage sag data collected by a monitoring device in a preset time period; determining one-phase data corresponding to each group of three-phase voltage sag data in at least two groups of three-phase voltage sag data; determining an amplitude distribution function based on one-phase data, and determining similar thresholds of the amplitude distribution functions corresponding to at least two groups of three-phase voltage sag numbers; and comparing the similar threshold value with the maximum mean value difference to obtain a comparison result, and determining whether the at least two groups of three-phase voltage sag data are homologous according to the comparison result.
Optionally, determining one-phase data corresponding to each set of three-phase voltage sag data in at least two sets of three-phase voltage sag data includes: carrying out space vector transformation on the at least two groups of three-phase voltage sag data, and calculating the root mean square value of the module value of the space vector corresponding to the at least two groups of three-phase voltage sag data; and determining that the root mean square value of the module value of the space vector corresponding to each of the at least two groups of three-phase voltage sag data is one phase of data corresponding to each group of three-phase voltage sag data in the at least two groups of three-phase voltage sag data.
Optionally, converting the one-phase data into a magnitude distribution function comprises: calculating the maximum value and the minimum value in the root mean square values, and determining a plurality of groups of amplitude intervals according to the maximum value and the minimum value; and determining an amplitude distribution function according to the plurality of groups of amplitude intervals.
Optionally, determining the amplitude distribution function according to a plurality of sets of amplitude intervals includes: determining the number of sampling points corresponding to each amplitude interval in the multiple groups of amplitude intervals; determining the amplitude ratio of each amplitude interval according to the ratio of the number of sampling points to the total number of sampling points; and carrying out weighting processing on the amplitude ratio to obtain an amplitude distribution function.
Optionally, determining whether the at least two sets of three-phase voltage sag data are homologous according to the comparison result includes: and determining at least two groups of three-phase voltage sag data to be homologous under the condition that the comparison result indicates that the maximum mean difference is smaller than the similar threshold value.
Optionally, in a case that the comparison result indicates that the maximum mean difference is greater than the similarity threshold, it is determined that at least two groups of three-phase voltage sag data are different sources.
Optionally, the method further comprises: under the condition that any two groups of three-phase voltage sag data are determined to be homologous, constructing a homologous incidence matrix of any two groups of three-phase voltage sag data; generating a topological graph according to the homologous incidence matrix; and judging the number of the connected graphs according to the topological graph, wherein the number of the connected graphs is the number of homologous sag events, and sag events belonging to the same connected graph are homologous events.
According to another aspect of the embodiments of the present application, there is also provided a voltage sag homologous identification apparatus, including: the receiving module is used for receiving at least two groups of three-phase voltage sag data collected by the monitoring device in a preset time period; the first determining module is used for determining one-phase data corresponding to each group of three-phase voltage sag data in at least two groups of three-phase voltage sag data; the second determining module is used for determining an amplitude distribution function based on the one-phase data and determining similar thresholds of the amplitude distribution functions corresponding to the at least two groups of three-phase voltage sag numbers; and the third determining module is used for comparing the similar threshold with the maximum mean value difference to obtain a comparison result, and determining whether the at least two groups of three-phase voltage sag data are homologous according to the comparison result.
According to another aspect of the embodiments of the present application, there is also provided a non-volatile storage medium, which includes a stored program, wherein the program controls a device in which the non-volatile storage medium is located to execute any one of the voltage transient homology identification methods when the program is running.
According to another aspect of the embodiments of the present application, there is also provided a processor, configured to execute a program, where the program executes any one of the voltage transient homology identification methods.
In the embodiment of the application, whether voltage sag is homologous or not is determined by adopting a mode of space vector and maximum mean difference, and at least two groups of three-phase voltage sag data collected by a monitoring device in a preset time period are received; determining one-phase data corresponding to each group of three-phase voltage sag data in at least two groups of three-phase voltage sag data; determining an amplitude distribution function based on one-phase data, and determining similar thresholds of the amplitude distribution functions corresponding to at least two groups of three-phase voltage sag numbers; the similar threshold value is compared with the maximum mean value difference to obtain a comparison result, whether at least two groups of three-phase voltage sag data are homologous or not is determined according to the comparison result, the purpose of eliminating the influence of a transformer on the voltage sag propagation through space vector transformation is achieved, multiple transformation on sag waveforms is not needed, the similarity between different sag data is calculated through the maximum mean value difference, and whether the two sag data are homologous or not is directly judged, so that the problem that huge calculated amount and the number of clustering clusters are difficult to select due to the use of a clustering method is solved, the technical effects of reducing the calculated amount and improving the calculation efficiency are achieved, and the technical problems that the calculated amount is huge, the calculation efficiency is low, real-time application cannot be achieved and the number of clustering clusters is difficult to determine due to the sag identification mode in the related technology are solved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
FIG. 1 is a schematic flow chart diagram illustrating an alternative voltage sag homologous identification method according to an embodiment of the present disclosure;
fig. 2 is a schematic overall flowchart of an alternative voltage sag homologous detection method based on a difference between a space vector and a maximum mean value according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of an alternative voltage sag source identification apparatus according to an embodiment of the present application.
Detailed Description
In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only partial embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
It should be noted that the terms "first," "second," and the like in the description and claims of this application and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the application described herein are capable of operation in sequences other than those illustrated or described herein. Moreover, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
In accordance with an embodiment of the present application, there is provided an embodiment of a voltage sag homology identification method, where the steps illustrated in the flowchart of the drawings may be performed in a computer system, such as a set of computer-executable instructions, and where a logical order is illustrated in the flowchart, in some cases the steps illustrated or described may be performed in an order different than here.
Fig. 1 is a voltage sag homologous identification method according to an embodiment of the present application, as shown in fig. 1, the method includes the following steps:
step S102, receiving at least two groups of three-phase voltage sag data collected by a monitoring device in a preset time period;
step S104, determining one-phase data corresponding to each group of three-phase voltage sag data in at least two groups of three-phase voltage sag data;
step S106, determining an amplitude distribution function based on one-phase data, and determining a similar threshold value of the amplitude distribution function corresponding to at least two groups of three-phase voltage sag numbers;
and S108, comparing the similar threshold value with the maximum mean value difference to obtain a comparison result, and determining whether the at least two groups of three-phase voltage sag data are homologous according to the comparison result.
In the voltage sag homologous identification method, at least two groups of three-phase voltage sag data collected by a monitoring device in a preset time period are received; determining one-phase data corresponding to each group of three-phase voltage sag data in at least two groups of three-phase voltage sag data; determining an amplitude distribution function based on one-phase data, and determining similar thresholds of the amplitude distribution functions corresponding to at least two groups of three-phase voltage sag numbers; the similar threshold value is compared with the maximum mean value difference to obtain a comparison result, whether at least two groups of three-phase voltage sag data are homologous or not is determined according to the comparison result, the purpose of eliminating the influence of a transformer on the voltage sag propagation through space vector transformation is achieved, multiple transformation on sag waveforms is not needed, the similarity between different sag data is calculated through the maximum mean value difference, and whether the two sag data are homologous or not is directly judged, so that the problem that huge calculated amount and the number of clustering clusters are difficult to select due to the use of a clustering method is solved, the technical effects of reducing the calculated amount and improving the calculation efficiency are achieved, and the technical problems that the calculated amount is huge, the calculation efficiency is low, real-time application cannot be achieved and the number of clustering clusters is difficult to determine due to the sag identification mode in the related technology are solved.
In some embodiments of the present application, determining one-phase data corresponding to each set of three-phase voltage sag data in the at least two sets of three-phase voltage sag data may be implemented by performing space vector transformation on the at least two sets of three-phase voltage sag data, and calculating a root mean square value of a module value of a space vector corresponding to each of the at least two sets of three-phase voltage sag data; and determining that the root mean square value of the module value of the space vector corresponding to each of the at least two groups of three-phase voltage sag data is one phase of data corresponding to each group of three-phase voltage sag data in the at least two groups of three-phase voltage sag data.
In some optional embodiments of the present application, converting the one-phase data into an amplitude distribution function includes: calculating the maximum value and the minimum value in the root mean square values, and determining a plurality of groups of amplitude intervals according to the maximum value and the minimum value; and determining an amplitude distribution function according to the plurality of groups of amplitude intervals.
In some optional embodiments of the present application, the amplitude distribution function may be determined according to multiple groups of amplitude intervals, specifically, the number of sampling points corresponding to each amplitude interval in the multiple groups of amplitude intervals is determined; determining the amplitude ratio of each amplitude interval according to the ratio of the number of sampling points to the total number of sampling points; and carrying out weighting processing on the amplitude ratio to obtain an amplitude distribution function.
In some embodiments of the present application, it may be determined whether the at least two sets of three-phase voltage sag data are homologous according to the comparison result, and specifically, it is determined that the at least two sets of three-phase voltage sag data are homologous when the comparison result indicates that the maximum mean difference is smaller than the similar threshold.
It should be noted that, in the case that the comparison result indicates that the maximum mean difference is greater than the similar threshold, at least two groups of different sources of the three-phase voltage sag data are determined.
In some optional embodiments of the present application, in a case that it is determined that any two groups of three-phase voltage sag data are homologous, a homologous association matrix of any two groups of three-phase voltage sag data is constructed; generating a topological graph according to the homologous incidence matrix; judging the number of connected graphs according to the topological graph, wherein the number of connected graphs is the number of homologous sag events, and sag events belonging to the same connected graph are homologous events.
To facilitate a better understanding of the embodiments related to the present application, a specific exemplary embodiment will now be described, illustrating the embodiments related to the present application:
fig. 2 is a schematic overall flow chart of a voltage sag homologous detection method based on a difference between a space vector and a maximum mean value according to an embodiment of the present application, as shown in fig. 2: the method comprises the following steps: and performing space vector transformation on sag data recorded by all voltage sag monitoring devices, converting the space vector data into amplitude distribution functions, starting double-sample detection, calculating the maximum mean difference and the detection threshold of any two amplitude distribution functions, outputting a detection result, and judging the homologous sag number and the homologous data through a connected graph.
Specifically, step 1: and space vector transformation, namely transforming the three-phase voltage sag data recorded by the monitoring device into one-phase data through the space vector.
The connection modes of the transformer windings are different, and after voltage sag is transmitted by the transformer, amplitude and phase of a three-phase sag waveform can be changed, so that homologous identification can not be directly carried out according to original sag waveform data, and therefore the waveform data needs to be converted. Space vector transformation is a linear transformation method for synthesizing real number domain three-phase waveforms into one-phase complex number domain waveforms, and the influence of a transformer on voltage sag propagation can be eliminated after a modulus value is obtained for the complex number domain waveforms.
The space vector transformation process in the invention mainly comprises the following three substeps:
step 11: the three-phase voltage sag monitoring data obtained from the voltage sag monitoring device are respectively v a ，v b ，v c 。
Step 12: the space vector is established by three-phase waveforms: x (t) =2/3[v a (t)+αv b (t)+α 2 v c (t)]In the formula, α = e j2π/3
Step 13: preprocessing the space vector, calculating the root mean square value of the norm value of the space vector, i.e.
Step 2: and converting the data after the space vector transformation into an amplitude distribution function by using a discretization method.
Because the data size of X (t) is related to the voltage sag duration, that is, the data size is generally large, and different sag data have different data sizes, the problem of large calculation amount exists if the sag data similarity is directly calculated by using X (t), which is not favorable for real-time application. Therefore, the discretization method is adopted to convert X (t) into an amplitude distribution function, and the data volume is greatly reduced.
And (3) calculating the maximum value and the minimum value of the data obtained in the step (2), and dividing 20 amplitude intervals according to the maximum value and the minimum value. And counting the number of amplitude points of each interval, and dividing the number by the total number to obtain the amplitude ratio of the interval. And carrying out weighting processing on the amplitude ratio to obtain an amplitude distribution function.
The step 2 specifically comprises the following steps:
step 21: calculating the maximum value Xmax and the minimum value Xmin of X (t), and uniformly dividing 20 amplitude intervals with the step length of h = (XmaxXmin)/20 according to the maximum value and the minimum value;
step 22: counting the number of sampling points in each interval, and dividing the number by the total number of the sampling points to obtain the amplitude ratio of the interval:
in the formula: d is a radical of all Expressed as [ kstart, kend ]]The total number of sampling points x in between represents the amplitude interval [ Vmin + (x-1) h Vmin + xh]And q (x) is the sampling point number ratio of the voltage sag effective value in the subinterval x.
Step 23: after converting X (t) into amplitude ratio, because the sag section of the rectangular sag amplitude is much smaller than the transition section, in order to ensure the balance between the sag section and the transition section, the amplitude ratio is weighted to obtain an amplitude distribution function;
Q(x)＝α x q(x),x＝1,2,...,20；
in the formula, alpha x Is a weighting coefficient, and
and step 3: and calculating the similarity of the two voltage sag data by using a maximum mean difference method, and judging whether the two voltage sag data are homologous according to a self-adaptive threshold value.
It should be noted that the Maximum Mean Difference (MMD) is a distance that measures the Mean of two distributions in the regenerative Hilbert Space (RKHS), and is a nuclear learning method. The formula for MMD is defined as follows:
where phi () is a non-linear mapping function and H represents the mapping to the regenerated hilbert space. The items are spread out to be expanded,
wherein phi (x) i )φ(x i ') by Gaussian kernel function
And (3) calculating, namely:
the maximum mean difference can be used in a two-sample test (two-sample test) problem to determine whether the two distributions p and q are the same. The basic assumptions are: if for all functions f that have the distribution generated sample space as input, two distributions can be considered to be the same distribution if the mean of their corresponding images over f for a sufficient number of samples generated by the two distributions is equal. For a two-sample detection problem, p and q are the same given a null hypothesis, and p and q are not equal alternative hypotheses. By comparing the test statistic MMD value with a given threshold, if MMD is greater than the threshold, the null hypothesis is rejected, i.e. the two distributions are different. If the MMD is less than a certain threshold, the null hypothesis is accepted. Because of the limited number of samples used in the calculation of MMD, there are two types of errors that can occur: the first type of error occurs at zero and is rejected by the error; i.e. both distributions are originally the same but are judged to be the same. Conversely, a second type of error occurs when the null hypothesis is erroneously accepted.
The step 3 specifically comprises the following steps:
step 31: calculating the maximum mean difference between the magnitude distribution functions QS and QT from the two sag data;
in the formula
Step 32: a similarity threshold between the magnitude distribution functions QS and QT from the two dip data is calculated. The invention adopts a Bootstrap resampling method to obtain the detection threshold.
Step 33: and comparing the maximum mean difference with a similar threshold, if the maximum mean difference is smaller than the threshold, considering that the two sag data are homologous, and otherwise, judging that the two sag data are different.
Step 34: and (3) judging whether the two sag data are homologous by applying the method to all the sag data monitored in a period of time, and if the two sag data are homologous, recording the corresponding element of the homologous incidence matrix W as 1, otherwise, recording the element as 0.
Step 35: and judging the number of connected subgraphs according to the topological graph generated by the homologous incidence matrix. The number of the connected subgraphs is the number of the sag sources, and sag data belonging to the same connected subgraph are homologous data.
Fig. 3 is a voltage sag co-source identification apparatus according to an embodiment of the present application, as shown in fig. 3, the apparatus includes:
the receiving module 40 is configured to receive at least two sets of three-phase voltage sag data acquired by the monitoring device within a predetermined time period;
the first determining module 42 is configured to determine one phase data corresponding to each set of three-phase voltage sag data in the at least two sets of three-phase voltage sag data;
the second determining module 44 is configured to determine an amplitude distribution function based on the one-phase data, and determine a similarity threshold of the amplitude distribution function corresponding to the at least two sets of three-phase voltage sag numbers;
and a third determining module 46, configured to compare the similar threshold with the maximum mean difference to obtain a comparison result, and determine whether the at least two sets of three-phase voltage sag data are homologous according to the comparison result.
In the voltage sag homologous identification device, a receiving module 40 is used for receiving at least two groups of three-phase voltage sag data collected by a monitoring device in a preset time period; the first determining module 42 is configured to determine one phase data corresponding to each set of three-phase voltage sag data in at least two sets of three-phase voltage sag data; the second determining module 44 is configured to determine an amplitude distribution function based on the one-phase data, and determine a similarity threshold of the amplitude distribution function corresponding to the at least two sets of three-phase voltage sag numbers; the third determining module 46 is configured to compare the similar threshold with the maximum mean difference to obtain a comparison result, determine whether at least two sets of three-phase voltage sag data are homologous to each other according to the comparison result, and achieve the purpose of eliminating the influence of the transformer on the voltage sag propagation through space vector transformation, so that multiple transformations are not required to be performed on sag waveforms, and calculate the similarity between different sag data through the maximum mean difference, thereby directly determining whether two sag data are homologous to each other, thereby avoiding the problem that a huge calculation amount and the number of clusters are difficult to select due to the use of a clustering method, achieving the technical effects of reducing the calculation amount and improving the calculation efficiency, and further solving the technical problems that the calculation amount is huge, the calculation efficiency is low, the real-time application is impossible, and the number of clusters is difficult to determine due to the voltage sag identification method in the related art.
According to another aspect of the embodiments of the present application, there is also provided a non-volatile storage medium, which includes a stored program, wherein the program controls a device in which the non-volatile storage medium is located to execute any one of the voltage transient homology identification methods when the program is running.
According to another aspect of the embodiments of the present application, there is also provided a processor, configured to execute a program, where the program executes any one of the voltage transient homology identification methods.
Specifically, the storage medium is used for storing program instructions for executing the following functions, and the following functions are realized:
receiving at least two groups of three-phase voltage sag data collected by a monitoring device in a preset time period; determining one-phase data corresponding to each group of three-phase voltage sag data in at least two groups of three-phase voltage sag data; determining an amplitude distribution function based on one-phase data, and determining similar thresholds of the amplitude distribution functions corresponding to at least two groups of three-phase voltage sag numbers; and comparing the similar threshold value with the maximum mean value difference to obtain a comparison result, and determining whether the at least two groups of three-phase voltage sag data are homologous according to the comparison result.
Specifically, the processor is configured to call a program instruction in the memory, and implement the following functions:
receiving at least two groups of three-phase voltage sag data collected by a monitoring device in a preset time period; determining one-phase data corresponding to each group of three-phase voltage sag data in at least two groups of three-phase voltage sag data; determining an amplitude distribution function based on one-phase data, and determining similar thresholds of the amplitude distribution functions corresponding to at least two groups of three-phase voltage sag numbers; and comparing the similar threshold value with the maximum mean value difference to obtain a comparison result, and determining whether the at least two groups of three-phase voltage sag data are homologous according to the comparison result.
In the embodiment of the application, whether voltage sag is homologous or not is determined by adopting a mode of space vector and maximum mean difference, and at least two groups of three-phase voltage sag data collected by a monitoring device in a preset time period are received; determining one-phase data corresponding to each group of three-phase voltage sag data in at least two groups of three-phase voltage sag data; determining an amplitude distribution function based on one-phase data, and determining similar thresholds of the amplitude distribution functions corresponding to at least two groups of three-phase voltage sag numbers; the similar threshold value is compared with the maximum mean value difference to obtain a comparison result, whether at least two groups of three-phase voltage sag data are homologous or not is determined according to the comparison result, the purpose of eliminating the influence of a transformer on the voltage sag propagation through space vector transformation is achieved, multiple transformation on sag waveforms is not needed, the similarity between different sag data is calculated through the maximum mean value difference, and whether the two sag data are homologous or not is directly judged, so that the problem that huge calculated amount and the number of clustering clusters are difficult to select due to the use of a clustering method is solved, the technical effects of reducing the calculated amount and improving the calculation efficiency are achieved, and the technical problems that the calculated amount is huge, the calculation efficiency is low, real-time application cannot be achieved and the number of clustering clusters is difficult to determine due to the sag identification mode in the related technology are solved.
The above-mentioned serial numbers of the embodiments of the present application are merely for description and do not represent the merits of the embodiments.
In the above embodiments of the present application, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the embodiments provided in the present application, it should be understood that the disclosed technology can be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units may be a logical division, and in actual implementation, there may be another division, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, units or modules, and may be in an electrical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one position, or may be distributed on a plurality of units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be substantially implemented or contributed to by the prior art, or all or part of the technical solution may be embodied in a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.
The foregoing is only a preferred embodiment of the present application and it should be noted that those skilled in the art can make several improvements and modifications without departing from the principle of the present application, and these improvements and modifications should also be considered as the protection scope of the present application.
Claims (10)
1. A voltage sag homologous identification method is characterized by comprising the following steps:
receiving at least two groups of three-phase voltage sag data collected by a monitoring device in a preset time period;
determining one-phase data corresponding to each group of three-phase voltage sag data in the at least two groups of three-phase voltage sag data;
determining an amplitude distribution function based on the one-phase data, and determining a similar threshold value of the amplitude distribution function corresponding to the at least two groups of three-phase voltage sag numbers;
and comparing the similar threshold with the maximum mean difference to obtain a comparison result, and determining whether the at least two groups of three-phase voltage sag data are homologous according to the comparison result.
2. The method of claim 1, wherein determining a phase of data corresponding to each of the at least two sets of three-phase voltage sag data comprises:
performing space vector transformation on the at least two groups of three-phase voltage sag data, and calculating the root-mean-square value of the modulus value of the space vector corresponding to each of the at least two groups of three-phase voltage sag data;
and determining the root mean square value of the module value of the space vector corresponding to each of the at least two groups of three-phase voltage sag data as one-phase data corresponding to each group of three-phase voltage sag data in the at least two groups of three-phase voltage sag data.
3. The method of claim 2, wherein converting the one-phase data to a magnitude distribution function comprises:
calculating the maximum value and the minimum value in the root mean square values, and determining a plurality of groups of amplitude intervals according to the maximum value and the minimum value;
and determining the amplitude distribution function according to the plurality of groups of amplitude intervals.
4. The method of claim 3, wherein determining the magnitude distribution function from the plurality of sets of magnitude intervals comprises:
determining the number of sampling points corresponding to each amplitude interval in the multiple groups of amplitude intervals;
determining the amplitude ratio of each amplitude interval according to the ratio of the number of the sampling points to the total number of the sampling points;
and carrying out weighting processing on the amplitude ratio to obtain the amplitude distribution function.
5. The method of claim 1, wherein determining whether the at least two sets of three-phase voltage sag data are homologous based on the comparison comprises:
and determining that the at least two groups of three-phase voltage sag data are homologous if the comparison result indicates that the maximum mean difference is smaller than the similarity threshold.
6. The method of claim 5, wherein if the comparison result indicates that the maximum mean difference is greater than the similarity threshold, then determining that the at least two sets of three-phase voltage sag data are different sources.
7. The method of claim 5, further comprising:
under the condition that any two groups of three-phase voltage sag data are determined to be homologous, constructing a homologous incidence matrix of any two groups of three-phase voltage sag data;
generating a topological graph according to the homologous incidence matrix;
and judging the number of the connected graphs according to the topological graph, wherein the number of the connected graphs is the number of homologous sag events, and sag events belonging to the same connected graph are homologous events.
8. A voltage sag homology identification device, comprising:
the receiving module is used for receiving at least two groups of three-phase voltage sag data collected by the monitoring device in a preset time period;
the first determining module is used for determining one-phase data corresponding to each group of three-phase voltage sag data in the at least two groups of three-phase voltage sag data;
the second determining module is used for determining an amplitude distribution function based on the one-phase data and determining similar thresholds of the amplitude distribution function corresponding to the at least two groups of three-phase voltage sag numbers;
and the third determining module is used for comparing the similarity threshold with the maximum mean difference to obtain a comparison result, and determining whether the at least two groups of three-phase voltage sag data are homologous according to the comparison result.
9. A non-volatile storage medium, comprising a stored program, wherein the program, when executed, controls a device in which the non-volatile storage medium is located to execute the voltage transient homology identification method according to any one of claims 1 to 7.
10. A processor configured to execute a program, wherein the program is configured to execute the voltage transient homology identification method according to any one of claims 1 to 7 when the program is executed.
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