CN117745123A - Multi-dimensional feature-based transformer substation voltage sag comprehensive evaluation method and device - Google Patents
Multi-dimensional feature-based transformer substation voltage sag comprehensive evaluation method and device Download PDFInfo
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Abstract
The invention discloses a transformer substation voltage sag comprehensive evaluation method and device based on multidimensional features, wherein the evaluation method comprises the following steps: acquiring original sampling signals of all monitoring nodes of the transformer substation, filtering harmonic waves and noise interference, and calculating a voltage half-wave effective value; detecting the boundary of a transition section of the voltage sag by adopting an automatic segmentation method, and extracting a frequency domain characteristic value of the transition section as an evaluation index; taking an average sag depth index and an energy index ASEI as voltage sag time domain indexes, combining a transition section frequency domain index to establish an attribute set, and triggering time difference optimization index data according to a voltage sag event; and calculating the weight of each index data by adopting an entropy weight method, calculating the association degree of each scheme and an ideal scheme according to a gray association analysis method, obtaining an optimal scheme ordering result by comparing the relative association degree of each scheme, and judging the severity of each node voltage sag.
Description
Technical Field
The invention relates to a method and a device for comprehensively evaluating voltage sag of a transformer substation based on multidimensional features, and belongs to the technical field of electric energy quality analysis.
Background
Voltage sag is a common power quality problem. In recent years, with the wide application of sensitive devices such as microelectronics, power electronics, CPUs and the like, and the massive access of distributed power supplies, intelligent high-end devices and the like to power grids, the problems of user equipment damage, shutdown and the like caused by voltage sag become more serious. In some areas, customer complaints caused by voltage sag account for more than 80% of the overall power quality problem, resulting in significant economic losses. The traditional system side voltage sag evaluation index mainly adopts statistical indexes such as characteristics of sag amplitude, duration, occurrence frequency and the like. However, as a large number of new consumers are connected to the grid, it is difficult for these indicators to accurately assess sag severity. This is because these basic feature amounts reflect only the overall features in the statistical sense of the dip event, ignoring the dip information in the frequency domain.
A typical voltage sag event can be divided into four phases: a pre-event segment, a transition segment, an event duration segment, and a voltage recovery segment. In these phases, the transition has a significant feature, namely that the amplitude and phase of the voltage change rapidly. By extracting the frequency domain index of the transition section, more detailed voltage sag characteristic information can be obtained. By combining the time domain indexes, the voltage sag problem of the transformer substation can be analyzed and evaluated more accurately, and a basis is provided for further improving the electric energy quality and the power supply reliability.
Disclosure of Invention
The invention aims to: the invention aims to provide a transformer substation voltage sag comprehensive evaluation method and device based on multidimensional features, which solve the problem that the traditional system side voltage sag evaluation index is difficult to accurately evaluate the severity of sag.
The technical scheme is as follows: the invention discloses a voltage sag comprehensive evaluation method based on multidimensional characteristics, which comprises the following steps of:
(1) Acquiring original sampling signals of all monitoring nodes of the transformer substation, filtering harmonic waves and noise interference, and calculating a voltage half-wave effective value;
(2) Detecting the boundary of a transition section of the voltage sag by adopting an automatic segmentation method, and extracting a frequency domain characteristic value of the transition section as an evaluation index;
(3) Taking an average sag depth index and an energy index ASEI as voltage sag time domain indexes, combining a transition section frequency domain index to establish an attribute set, and triggering time difference optimization index data according to a voltage sag event;
(4) And calculating the weight of each index data by adopting an entropy weight method, calculating the association degree of each scheme and an ideal scheme according to a gray association analysis method, obtaining an optimal scheme ordering result by comparing the relative association degree of each scheme, and judging the severity of each node voltage sag.
Further, in the step (1), a digital low-pass filter is adopted to filter harmonic waves and noise interference in the original signal, and data are preprocessed.
Further, the step (2) of detecting the boundary of the voltage sag transition section by adopting an automatic segmentation method comprises the following steps:
(21) Judging the starting time of a voltage sag transition section, and taking the effective value of the previous voltage half-wave as the starting time of the first transition section of the voltage sag when the effective value of the voltage half-wave is lower than a set threshold value; when the effective value of the voltage half-wave is higher than a set threshold value, taking the effective value of the next voltage half-wave as the ending time of the second transition section of the voltage sag;
(22) Determining a section of interval comprising the starting time of a first transition section and the ending time of a second transition section, performing pairwise difference on the effective values of the half-waves of the voltages in the section to obtain a sequence, and performing pairwise difference on elements in the sequence to obtain the sequence;
(23) And extracting a maximum point from the sequence, summing the maximum points, taking an average value, extracting the maximum point larger than the average value, and determining the ending time of the first transition section and the starting time of the second transition section according to the abscissa of the maximum point so as to determine the boundary of the transition section.
Further, the step (2) of extracting the frequency domain characteristic value of the transition section is to perform FFT analysis on sampling points of the transition section, wherein the power frequency component is used as a waveform main component, the more serious the voltage sag is, the more serious the corresponding frequency spectrum leakage is, the amplitude of the power frequency component of the transition section is extracted, and the content of the power frequency component is used as a characteristic value index of the frequency domain.
Further, the calculation formula of the average sag depth index in the step (3) is as follows:
in U RMSi The voltage half-wave effective value is the minimum value of the voltage half-wave effective value when the voltage sag occurs for the ith time of the node, and N is the number of times of voltage sag occurs for the node; optimizing an average sag depth index according to the event-triggered time difference, and calculating maximum voltage sag depth indexes of a plurality of events when the interval time delta T of adjacent voltage sag events is smaller than a set value T and the plurality of events occurring at similar moments are counted as one event;
the average sag energy index calculation formula is as follows:
in U i,k Is the effective value of the kth voltage half-wave of the duration in the ith event of the node, U n For voltage rating, f 0 Is the power frequency; optimizing an average sag energy index according to the event-triggered time difference, and calculating the sag energy sum of a plurality of voltage sag events according to the sag energy sum of the voltage sag events when a plurality of events occurring at similar moments are counted as one event when the interval time delta T of adjacent voltage sag events is smaller than a set value T;
the frequency domain index calculation formula is as follows:
wherein H is i Is the fundamental component amplitude of the transition section in the ith event of the node, U n Is a voltage rating; according to the time difference frequency domain index triggered by the event, when the interval time delta T of the adjacent voltage sag events is smaller than the set value T, a plurality of events occurring at similar moments are counted as one event, at the moment, the event with the largest voltage sag depth is taken as a target event, and the frequency domain characteristic value of the target event is taken to participate in index calculation.
Further, the step (4) calculates the weight of the evaluation index by adopting an entropy weight method, and m monitoring nodes in the transformer substation construct an evaluation matrix shown in the following formula according to the calculation results of the average sag depth index, the average sag energy index and the frequency domain index of each node in the attribute set:
wherein x is ij An ith attribute value representing a jth monitoring node;
the 3 evaluation indexes in the attribute set are all cost-type indexes which are smaller and better; the unit and the order of magnitude of indexes with different properties are inconsistent, in order to enhance the comparability among the indexes, eliminate the influence of the properties, the dimension and the order of the indexes on the evaluation result, obtain a better evaluation result, normalize the original data of the indexes, and normalize the indexes by using a polar error normalization method, wherein the polar error normalization formula is as follows:
establishing a co-trend matrix Y as follows 3×m ：
Wherein y is ij I.e. x ij Is a normalized value of (2);
according to the evaluation matrix, the method for calculating the entropy of each index information comprises the following steps:
wherein p is ij The calculation formula is as follows:
wherein p is ij The probability that the ith index of the jth node affects the evaluation result is represented; when p is ij When=0, let p ij lnp ij ＝0；
The calculation mode of each attribute index weight is defined as follows:
wherein w is i The upper is the weight of the ith index, and the upper meets the following requirements
Further, in the step (4), the voltage sag severity of m monitoring nodes is estimated by adopting a gray correlation analysis method, and a scheme set is constructed firstly:
F＝{f 1 ,f 2 L,f m }
wherein f j (j=1, 2, l, m) means that the voltage sag severity of the j-th node is evaluated based on the index data;
when all the indexes reach the optimal values in each scheme, the method is called positive ideal solution; otherwise, the solution is the negative ideal solution. The evaluation scheme mainly aims at the voltage sag condition of each monitoring node, and the more serious sag is the worse the evaluation rule is. Thus, a positive ideal solution corresponds to the least severe cases of sag, while a negative ideal solution corresponds to the most severe cases of sag. First according to the co-trend matrix Y 3×m Construction of a positive ideal solution v for an evaluation object + And negative ideal solution v - ：
i＝1,2,3；j＝1,2,L,m
Correlation of the j-th scheme with positive and negative ideal solutions:
ρ∈(0,1),j＝1,2,L m
wherein ρ is a resolution coefficient, the default value is 0.5, and w is an index weight.
The relative relevance of scheme j is:
from the above, R can be seen as j ∈(0,1)，R j The larger will indicate that the scheme is closer to the ideal one, the less severe the voltage sag representing the corresponding node.
The invention relates to a transformer substation voltage sag comprehensive evaluation device based on multidimensional characteristics, which comprises:
the sampling module is used for carrying out omnibearing real-time collection and storage on various electric energy quality data;
the signal filtering module is used for filtering harmonic waves and noise interference in the signals according to the digital low-pass filter principle;
the index calculation module is used for detecting the boundary of the transition section of the voltage sag by adopting an automatic segmentation method, extracting the frequency domain characteristics of the transition section, calculating the average sag depth index and the energy index ASEI, and completing index data optimization;
the voltage sag evaluation module is used for evaluating the severity of the voltage sag according to the time-frequency domain characteristics of the voltage sag, calculating the weight of index data by adopting an entropy weight method, calculating the association degree of each scheme and an ideal scheme by adopting a gray association analysis method, and comparing the relative association degree of each scheme to obtain an optimal scheme ordering result, namely the node voltage sag severity ordering result.
A computer device of the present invention includes one or more processors, memory, and one or more programs; the one or more programs are stored in the memory and executed by the one or more processors; the program, when executed by the processor, realizes the steps of the voltage sag comprehensive evaluation method based on the multidimensional features.
The invention relates to a computer readable storage medium, wherein a computer program is stored on the storage medium, and the computer program realizes the steps of the voltage sag comprehensive evaluation method based on the multidimensional characteristics when being executed.
The beneficial effects are that: compared with the prior art, the invention has the following remarkable advantages: (1) Compared with the traditional voltage sag evaluation index which only focuses on sag amplitude and duration, the method provided by the invention effectively analyzes the sag waveform and provides more comprehensive and accurate data for evaluating sag events; (2) According to the event-triggered time difference optimization evaluation index calculation method, the calculated amount is reduced; (3) The weight of each index is calculated by adopting an entropy weight method, the severity of the voltage sag is estimated by utilizing a gray correlation analysis method, and the voltage sag problem of the transformer substation is estimated more accurately.
Drawings
FIG. 1 is a schematic flow chart of the present invention;
FIG. 2 is a graph of a monitor node voltage sag event in an embodiment of the present invention;
FIG. 3 is a graph of the monitored node voltage half-wave effective value in an embodiment of the present invention;
fig. 4 is a graph of a monitoring node y "sample point in an embodiment of the invention.
Detailed Description
The technical scheme of the invention is further described below with reference to the accompanying drawings. Referring to fig. 1 to 4, this embodiment describes a multi-dimensional feature-based transformer substation voltage sag comprehensive evaluation method, a 35kV transformer substation system simulation model is built, the rated voltage of a secondary side line is 57.74V, the fundamental frequency of a power grid is rated frequency 50Hz, the sampling frequency of a monitoring terminal is 25.6kHz, the sampling time is 1s, and the fft analyzes 5120 point data within 10 cycles.
When a voltage sag event occurs to a transformer substation monitoring node, the monitoring device sends the sampling point to a transformer substation voltage sag comprehensive evaluation device based on multidimensional features, and therefore the device completes node voltage sag data aggregation and evaluation. Referring to FIG. 2, the voltage dip transition period is 40ms, the duration period is 200ms, and the voltage dip depth is 80% U n . Referring to fig. 3, it can be known from the half-wave effective value sampling points that AB and CD are voltage sag transition sections and BC is a continuous section. Referring to FIG. 4, maximum points in the extraction interval are extracted, the average value of the maximum values is 2.5, the maximum point larger than the average value is E, F points, the corresponding abscissa is 3 and 23, and the half-wave effective value is lower than 90% U at 38 points according to the half-wave effective value sampling points n At this time, it is determined that the point 37 is the first passTransition start point, first transition end point = first transition start point + E point abscissa +1, i.e. equal to 41; the second transition start point = first transition start point + F point abscissa, i.e. equal to 60; according to the half-wave effective value sampling point, the half-wave effective value is higher than 92% U at 63 points n (2％U n Hysteresis) at which point it is determined that point 64 is the second transition end point.
The frequency domain characteristic value of the transition section can effectively reflect the severity of the voltage sag. FFT analysis is carried out on voltage sag data at different transition periods, and the content of basic components is shown in Table 1:
TABLE 1 essential component content at different transition times
FFT analysis was performed on voltage dip data at different dip depths, with the basic component content shown in Table 2:
TABLE 2 essential component content at different voltage sag depths
And 67 groups of voltage sag event data of 6 monitoring nodes monitored in 2019 in the energy quality monitoring system of the 35kV transformer substation are selected as study objects to analyze and calculate.
Table 3 shows the calculation results of 3 evaluation indexes of 6 monitoring nodes:
table 3 calculation results of each monitoring node index
Construction of an evaluation matrix X 3×6 For matrix X 3×6 Standardized to obtain matrix Y 3×6 And calculating the weight of each evaluation index by adopting an entropy weight method to obtain 0.3357,0.3396,0.3248. According to matrix Y 3×6 Get the right of the schemeThink of v + ＝(0,0,0) T Negative ideal solution v - ＝(1,1,1) T . The degree of association and the relative degree of association of each scheme with positive and negative ideal solutions are calculated as shown in table 4:
TABLE 4 correlation and relative correlation of each monitoring node with the positive and negative ideal solutions
The schemes are ordered according to the relative degree of association in table 4, with a greater relative degree of association indicating that the better the scheme, the less severe the voltage dip at the corresponding monitoring point. The final evaluation result of the voltage sag condition of the 6 monitoring nodes is as follows:
f 1 f f 5 f f 6 f f 4 f f 2 f f 3
where f denotes the priority of the scheme, the scheme before the symbol is better than the scheme after. Each scheme is the voltage sag condition of each monitoring point, each scheme in the evaluation result is sequentially from good to bad in schemes 1, 5, 6, 4, 2 and 3, scheme 1 is the optimal scheme, scheme 3 is the worst scheme, namely the monitoring point 5 is least severely sag, and node 8 is most severely sag.
The embodiment also describes a transformer substation voltage sag comprehensive evaluation device based on multidimensional features, which comprises:
the sampling module is used for carrying out omnibearing real-time collection and storage on various electric energy quality data;
the signal filtering module is used for filtering harmonic waves and noise interference in the signals according to the digital low-pass filter principle;
the index calculation module is used for detecting the boundary of the transition section of the voltage sag by adopting an automatic segmentation method, extracting the frequency domain characteristics of the transition section, calculating the average sag depth index and the energy index ASEI, and completing index data optimization;
the voltage sag evaluation module is used for evaluating the severity of the voltage sag according to the time-frequency domain characteristics of the voltage sag, calculating the weight of index data by adopting an entropy weight method, calculating the association degree of each scheme and an ideal scheme by adopting a gray association analysis method, and comparing the relative association degree of each scheme to obtain an optimal scheme ordering result, namely the node voltage sag severity ordering result.
The embodiment also describes a computer device comprising a processor and a memory, the memory storing computer instructions, the processor being configured to operate according to the computer instructions to perform the multi-dimensional feature based substation voltage sag comprehensive assessment method according to the invention.
The present embodiment also describes a computer readable storage medium having stored thereon a computer program which when executed implements the steps of the multi-dimensional feature based comprehensive assessment method for voltage sag of a substation according to the present invention.
It will be apparent to those skilled in the art that embodiments of the present invention may be provided as a method, apparatus, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow of the flowcharts, and combinations of flows in the flowcharts, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows.
Claims (10)
1. A voltage sag comprehensive evaluation method based on multidimensional features is characterized by comprising the following steps of: the method comprises the following steps:
(1) Acquiring original sampling signals of all monitoring nodes of the transformer substation, filtering harmonic waves and noise interference, and calculating a voltage half-wave effective value;
(2) Detecting the boundary of a transition section of the voltage sag by adopting an automatic segmentation method, and extracting a frequency domain characteristic value of the transition section as an evaluation index;
(3) Taking an average sag depth index and an energy index ASEI as voltage sag time domain indexes, combining a transition section frequency domain index to establish an attribute set, and triggering time difference optimization index data according to a voltage sag event;
(4) And calculating the weight of each index data by adopting an entropy weight method, calculating the association degree of each scheme and an ideal scheme according to a gray association analysis method, obtaining an optimal scheme ordering result by comparing the relative association degree of each scheme, and judging the severity of each node voltage sag.
2. The voltage sag comprehensive evaluation method based on multidimensional features according to claim 1, wherein: and (2) filtering harmonic waves and noise interference in an original signal by adopting a digital low-pass filter in the step (1), and preprocessing data.
3. The voltage sag comprehensive evaluation method based on multidimensional features according to claim 1, wherein: the step (2) of detecting the boundary of the voltage sag transition section by adopting an automatic segmentation method comprises the following steps:
(21) Judging the starting time of a voltage sag transition section, and taking the effective value of the previous voltage half-wave as the starting time of the first transition section of the voltage sag when the effective value of the voltage half-wave is lower than a set threshold value; when the effective value of the voltage half-wave is higher than a set threshold value, taking the effective value of the next voltage half-wave as the ending time of the second transition section of the voltage sag;
(22) Determining a section comprising the starting time of a first transition section and the ending time of a second transition section, performing pairwise difference on the effective values of the half-waves of the voltages in the section to obtain a sequence y ', and performing pairwise difference on elements in the sequence y to obtain a sequence y';
(23) And extracting a maximum point from the sequence y', summing the maximum points, taking an average value, extracting the maximum point which is larger than the average value, and determining the ending time of the first transition section and the starting time of the second transition section according to the abscissa of the maximum point so as to determine the boundary of the transition section.
4. The voltage sag comprehensive evaluation method based on multidimensional features according to claim 1, wherein: and (2) extracting the frequency domain characteristic value of the transition section, namely carrying out FFT analysis on sampling points of the transition section, taking a power frequency component as a waveform main component, extracting the amplitude of the power frequency component of the transition section, and taking the content of the power frequency component as a characteristic value index of the frequency domain.
5. The voltage sag comprehensive evaluation method based on multidimensional features according to claim 1, wherein: the calculation formula of the average sag depth index in the step (3) is as follows:
in U RMSi The minimum value of the effective value of the half-wave voltage when the voltage sag occurs for the ith time of the node, and N is the number of times of voltage sag occurs for the nodeThe method comprises the steps of carrying out a first treatment on the surface of the Optimizing an average sag depth index according to the event-triggered time difference, and calculating maximum voltage sag depth indexes of a plurality of events when the interval time delta T of adjacent voltage sag events is smaller than a set value T and the plurality of events occurring at similar moments are counted as one event;
the average sag energy index calculation formula is as follows:
in U i,k Is the effective value of the kth voltage half-wave of the duration in the ith event of the node, U n For voltage rating, f 0 Is the power frequency; optimizing an average sag energy index according to the event-triggered time difference, and calculating the sag energy sum of a plurality of voltage sag events according to the sag energy sum of the voltage sag events when a plurality of events occurring at similar moments are counted as one event when the interval time delta T of adjacent voltage sag events is smaller than a set value T;
the frequency domain index calculation formula is as follows:
wherein H is i Is the fundamental component amplitude of the transition section in the ith event of the node, U n Is a voltage rating; according to the time difference frequency domain index triggered by the event, when the interval time delta T of the adjacent voltage sag events is smaller than the set value T, a plurality of events occurring at similar moments are counted as one event, at the moment, the event with the largest voltage sag depth is taken as a target event, and the frequency domain characteristic value of the target event is taken to participate in index calculation.
6. The voltage sag comprehensive evaluation method based on multidimensional features according to claim 1, wherein: the step (4) is to calculate the weight of the evaluation index by adopting an entropy weight method, and m monitoring nodes in the transformer substation construct an evaluation matrix shown in the following formula according to the calculation results of the average sag depth index, the average sag energy index and the frequency domain index of each node in the attribute set:
wherein x is ij An ith attribute value representing a jth monitoring node;
the original data of each index is normalized, and the standard deviation normalization method is adopted to normalize each index, wherein the standard deviation normalization formula is as follows:
establishing a co-trend matrix Y as follows 3×m ：
Wherein y is ij I.e. x ij Is a normalized value of (2);
according to the evaluation matrix, the method for calculating the entropy of each index information comprises the following steps:
wherein p is ij The calculation formula is as follows:
wherein p is ij The probability that the ith index of the jth node affects the evaluation result is represented; when p is ij When=0, let p ij lnp ij ＝0；
The calculation mode of each attribute index weight is defined as follows:
wherein w is i The upper is the weight of the ith index, and the upper meets the following requirements
7. The voltage sag comprehensive evaluation method based on multidimensional features according to claim 1, wherein: step (4) adopts grey correlation analysis method to evaluate voltage sag severity of m monitoring nodes, firstly constructs a scheme set:
F＝{f 1 ,f 2 L,f m }
wherein f j (j=1, 2, l, m) means that the voltage sag severity of the j-th node is evaluated based on the index data; according to co-trend matrix Y 3×m Construction of a positive ideal solution v for an evaluation object + And negative ideal solution v - ：
Correlation of the j-th scheme with positive and negative ideal solutions:
ρ∈(0,1),j＝1,2,L m
wherein ρ is a resolution coefficient, a default value is 0.5, and w is an index weight;
the relative relevance of scheme j is:
8. the utility model provides a transformer substation voltage sag comprehensive evaluation device based on multidimensional feature which characterized in that includes:
the sampling module is used for carrying out omnibearing real-time collection and storage on various electric energy quality data;
the signal filtering module is used for filtering harmonic waves and noise interference in the signals according to the digital low-pass filter principle;
the index calculation module is used for detecting the boundary of the transition section of the voltage sag by adopting an automatic segmentation method, extracting the frequency domain characteristics of the transition section, calculating the average sag depth index and the energy index ASEI, and completing index data optimization;
the voltage sag evaluation module is used for evaluating the severity of the voltage sag according to the time-frequency domain characteristics of the voltage sag, calculating the weight of index data by adopting an entropy weight method, calculating the association degree of each scheme and an ideal scheme by adopting a gray association analysis method, and comparing the relative association degree of each scheme to obtain an optimal scheme ordering result, namely the node voltage sag severity ordering result.
9. A computer device, characterized by: including one or more processors, memory, and one or more programs; the one or more programs are stored in the memory and executed by the one or more processors; the program when executed by a processor implements the steps of the voltage sag aggregate assessment method based on multidimensional features as claimed in any one of claims 1-7.
10. A computer-readable storage medium, characterized by: the storage medium has stored thereon a computer program which, when executed by a processor, implements the steps of the voltage sag integrated assessment method based on multi-dimensional features as set forth in any one of claims 1-7.
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